From 7aaf8cbc291fff776dda1efd0e7ca008b6af5238 Mon Sep 17 00:00:00 2001
From: reveurmichael <reveurmichael@users.noreply.github.com>
Date: Fri, 20 Oct 2023 10:55:29 +0000
Subject: [PATCH] deploy: 5f80d7d134e75ab26bb3b77d9f60ef59b4488282

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diff --git a/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb b/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb
index 5f341e4300..182590436c 100644
--- a/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb
+++ b/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "2af31651",
+   "id": "c796e548",
    "metadata": {},
    "source": [
     "# Create a regression model\n",
diff --git a/_sources/assignments/ml-fundamentals/exploring-visualizations.ipynb b/_sources/assignments/ml-fundamentals/exploring-visualizations.ipynb
index da66d2cfcf..19f8f88390 100644
--- a/_sources/assignments/ml-fundamentals/exploring-visualizations.ipynb
+++ b/_sources/assignments/ml-fundamentals/exploring-visualizations.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "3c3109bd",
+   "id": "b274befa",
    "metadata": {},
    "source": [
     "# Exploring visualizations\n",
diff --git a/_sources/assignments/ml-fundamentals/ml-linear-regression-1.ipynb b/_sources/assignments/ml-fundamentals/linear-regression/california_housing.ipynb
similarity index 99%
rename from _sources/assignments/ml-fundamentals/ml-linear-regression-1.ipynb
rename to _sources/assignments/ml-fundamentals/linear-regression/california_housing.ipynb
index ab467ed829..a50ea326d9 100644
--- a/_sources/assignments/ml-fundamentals/ml-linear-regression-1.ipynb
+++ b/_sources/assignments/ml-fundamentals/linear-regression/california_housing.ipynb
@@ -6,7 +6,7 @@
    "id": "81e5f991-6918-4b4f-89f5-ce606f3d54f7",
    "metadata": {},
    "source": [
-    "# ML linear regression - assignment 1\n",
+    "# Linear regression - California Housing\n",
     "\n",
     "In this assignment, we will predict the price of a house by its region, size, number of bedrooms, etc.\n",
     "\n",
diff --git a/_sources/assignments/ml-fundamentals/linear-regression/gradient-descent.ipynb b/_sources/assignments/ml-fundamentals/linear-regression/gradient-descent.ipynb
index 35553a25da..9dca445ec0 100644
--- a/_sources/assignments/ml-fundamentals/linear-regression/gradient-descent.ipynb
+++ b/_sources/assignments/ml-fundamentals/linear-regression/gradient-descent.ipynb
@@ -169,14 +169,30 @@
     "import pandas as pd\n",
     "import sklearn\n",
     "import matplotlib.pyplot as plt\n",
-    "from sklearn.model_selection import train_test_split"
+    "from sklearn.model_selection import train_test_split\n",
+    "from mpl_toolkits.mplot3d import Axes3D"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Linear Regression With Gradient Descent"
+    "## 1. One-dimensional gradient descent\n",
+    "One-dimensional gradient descent is a simple optimization algorithm used to find the minimum (or maximum) of a univariate function. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are the main steps:  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> step 1: Choose an initial point on the function curve. For example, we choose an initial x value."
    ]
   },
   {
@@ -185,55 +201,357 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "class LinearRegression:\n",
-    "    def __init__(self, learning_rate=0.0003, n_iters=3000):\n",
-    "        self.lr = learning_rate\n",
-    "        self.n_iters = n_iters\n",
-    "        self.weights = None\n",
-    "        self.bias = None\n",
-    "\n",
-    "    def fit(self, X, y):\n",
-    "        n_samples, n_features = X.shape\n",
-    "\n",
-    "        # Initialize parameters\n",
-    "        self.weights = np.zeros(n_features)\n",
-    "        self.bias = 0\n",
-    "\n",
-    "        # Gradient Descent\n",
-    "        for _ in range(self.n_iters):\n",
-    "            # Approximate y with a linear combination of weights and x, plus bias\n",
-    "            y_predicted = np.dot(X, self.weights) + self.bias\n",
-    "\n",
-    "            # Compute gradients\n",
-    "            dw = (1 / n_samples) * np.dot(X.T, (y_predicted - y))\n",
-    "            db = (1 / n_samples) * np.sum(y_predicted - y)\n",
-    "            \n",
-    "            # Update parameters\n",
-    "            self.weights -= self.lr * dw\n",
-    "            self.bias -= self.lr * db\n",
-    "\n",
-    "    def predict(self, X):\n",
-    "        y_predicted = np.dot(X, self.weights) + self.bias\n",
-    "        return y_predicted\n",
-    "\n",
-    "# Load data and perform linear regression\n",
-    "prostate = pd.read_table(\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/prostate.data\")\n",
-    "prostate.drop(prostate.columns[0], axis=1, inplace=True)\n",
+    "x = 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> step 2: Compute the derivative of the function at the current point. In this case, the derivative of the function is computed as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def df(x):\n",
+    "    return 2 * x + 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> step 3: Update the position along the function curve using the gradient and the learning rate. The learning rate determines the size of the step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learning_rate = 0.3\n",
+    "x -= learning_rate * df(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> step 4: Repeat the process for a specified number of iterations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_iter = 100\n",
+    "for i in range(max_iter):\n",
+    "    gradient = df(x)\n",
+    "    x -= learning_rate * gradient"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> step 5: Output the result, which is the minimum value of x that corresponds to the minimum of the function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print('x_min:', x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's try!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the target function\n",
+    "def f(x):\n",
+    "    return x ** 2 + 5 * x + 10\n",
     "\n",
-    "X = prostate.drop([\"lpsa\", \"train\"], axis=1)\n",
-    "y = prostate[\"lpsa\"]\n",
+    "# Define the guidance of the target function\n",
+    "def df(x):\n",
+    "    return 2 * x + 5\n",
     "\n",
-    "regressor = LinearRegression()\n",
+    "x = np.linspace(-10, 10, 100)\n",
+    "y = f(x)\n",
+    "plt.plot(x, y, label='f(x)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define gradient descent function\n",
+    "def gradient_descent(learning_rate, max_iter, x_init):\n",
+    "    # step 1:start with an initial value for the input variable, denoted as x_int.\n",
+    "    x = x_init\n",
+    "    x_history = [x]\n",
+    "    for i in range(max_iter):\n",
+    "        # step 2:compute the derivative of the function at the current point.\n",
+    "        gradient = df(x)\n",
+    "        x -= learning_rate * gradient\n",
+    "        # step 3:update the position along the function curve.\n",
+    "        x_history.append(x)\n",
+    "    # step 4:repeat the process.\n",
+    "    return x, x_history"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set the learning rate and initial value\n",
+    "        # learning rate: the size of the step for gradient descent.\n",
+    "        # max_iter: number of gradient descent iterations.\n",
+    "learning_rate = 0.3\n",
+    "max_iter = 100\n",
+    "x_init = 10\n",
+    "\n",
+    "# Run gradient descent function\n",
+    "x_min, x_history = gradient_descent(learning_rate, max_iter, x_init)\n",
+    "\n",
+    "# Draw the target function and the process of gradient descent\n",
+    "x = np.linspace(-10, 10, 100)\n",
+    "y = f(x)\n",
+    "y_history = f(np.array(x_history))\n",
+    "plt.plot(x, y, label='f(x)')\n",
+    "plt.plot(x_history, y_history, 'ro-', label='Gradient Descent')\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('f(x)')\n",
+    "plt.legend()\n",
+    "plt.title('One-Dimensional Gradient Descent')\n",
+    "plt.show()\n",
+    "\n",
+    "print('x_min:', x_min)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "you can change `learning rate` and  `max_iter` to see different images."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Two-dimensional gradient descent\n",
+    "Two-dimensional gradient descent is an optimization algorithm used to find the minimum (or maximum) of a function with two input variables."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are the main steps:  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> step 1: Choose an initial point on the function surface by specifying initial values for both x and y."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_init = -5\n",
+    "y_init = 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> step 2: Compute the partial derivatives of the function at the current point. These partial derivatives represent the rate of change of the function with respect to x and y."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def dfx(x, y):\n",
+    "    return 2 * x + 5\n",
     "\n",
-    "regressor.fit(X, y)\n",
-    "y_pred = regressor.predict(X)\n",
+    "def dfy(x, y):\n",
+    "    return 2 * y + 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> step 3: Update the positions along the function surface for both x and y using their respective partial derivatives and the learning rate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learning_rate = 0.1\n",
+    "x -= learning_rate * dfx(x, y)\n",
+    "y -= learning_rate * dfy(x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> step 4: Repeat the process for a specified number of iterations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_iter = 100\n",
+    "for i in range(max_iter):\n",
+    "    gradient_x = dfx(x, y)\n",
+    "    gradient_y = dfy(x, y)\n",
+    "    x -= learning_rate * gradient_x\n",
+    "    y -= learning_rate * gradient_y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> step 5: Output the results, which are the minimum values of x and y that correspond to the minimum of the function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print('x_min:', x)\n",
+    "print('y_min:', y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's try!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the target function\n",
+    "def f(x, y):\n",
+    "    return x ** 2 + y ** 2 + 5 * x + 2 * y + 10\n",
     "\n",
-    "print(regressor.__dict__)\n",
-    "print(y - y_pred)\n",
+    "# Take the partial derivative of the objective function with respect to x\n",
+    "def dfx(x, y):\n",
+    "    return 2 * x + 5\n",
     "\n",
-    "plt.scatter(y, y_pred)\n",
-    "plt.plot([0, 5], [0, 5])\n",
-    "plt.show()\n"
+    "# Take the partial derivative of the objective function with respect to y\n",
+    "def dfy(x, y):\n",
+    "    return 2 * y + 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the two-dimensional gradient descent function\n",
+    "def gradient_descent(learning_rate, max_iter, x_init, y_init):\n",
+    "    # step 1:start with an initial value for the two input variables, denoted as (x_init, y_init).\n",
+    "    x = x_init\n",
+    "    y = y_init\n",
+    "    x_history = [x]\n",
+    "    y_history = [y]\n",
+    "    for i in range(max_iter):\n",
+    "        # step 2:compute the partial derivatives of the function at the current point.\n",
+    "        gradient_x = dfx(x, y)\n",
+    "        gradient_y = dfy(x, y)\n",
+    "        x -= learning_rate * gradient_x\n",
+    "        y -= learning_rate * gradient_y\n",
+    "        # step 3:update the positions along the function surface.\n",
+    "        x_history.append(x)\n",
+    "        y_history.append(y)\n",
+    "    # step 4:repeat the process.\n",
+    "    return x, y, x_history, y_history"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set the learning rate and initial value\n",
+    "learning_rate = 0.1\n",
+    "max_iter = 100\n",
+    "x_init = -5\n",
+    "y_init = 5\n",
+    "\n",
+    "# Run two-dimensional gradient descent function\n",
+    "x_min, y_min, x_history, y_history = gradient_descent(learning_rate, max_iter, x_init, y_init)\n",
+    "\n",
+    "# Draw the target function and the process of gradient descent\n",
+    "x = np.linspace(-10, 10, 100)\n",
+    "y = np.linspace(-10, 10, 100)\n",
+    "X, Y = np.meshgrid(x, y)\n",
+    "Z = f(X, Y)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "ax.plot_surface(X, Y, Z, cmap='viridis', alpha=0.8)\n",
+    "ax.plot(x_history, y_history, f(np.array(x_history), np.array(y_history)), 'ro-', label='Gradient Descent')\n",
+    "ax.set_xlabel('x')\n",
+    "ax.set_ylabel('y')\n",
+    "ax.set_zlabel('f(x, y)')\n",
+    "ax.legend()\n",
+    "ax.set_title('Two-Dimensional Gradient Descent')\n",
+    "plt.show()\n",
+    "\n",
+    "print('x_min:', x_min)\n",
+    "print('y_min:', y_min)\n"
    ]
   }
  ],
diff --git a/_sources/assignments/ml-fundamentals/linear-regression-implementation-from-scratch.ipynb b/_sources/assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.ipynb
similarity index 100%
rename from _sources/assignments/ml-fundamentals/linear-regression-implementation-from-scratch.ipynb
rename to _sources/assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.ipynb
diff --git a/_sources/assignments/ml-fundamentals/linear-regression/linear-regression-metrics.ipynb b/_sources/assignments/ml-fundamentals/linear-regression/linear-regression-metrics.ipynb
index 4694de02ab..27fcc51a9e 100644
--- a/_sources/assignments/ml-fundamentals/linear-regression/linear-regression-metrics.ipynb
+++ b/_sources/assignments/ml-fundamentals/linear-regression/linear-regression-metrics.ipynb
@@ -1,36 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "a23a2854-7e54-4a24-9ae4-0f8904f899ee",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install the necessary dependencies\n",
-    "\n",
-    "import os\n",
-    "import sys\n",
-    "!{sys.executable} -m pip install --quiet pandas scikit-learn numpy matplotlib jupyterlab_myst ipython\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3780e038-4395-44e7-9294-a54ae4bc731d",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "license:\n",
-    "    code: MIT\n",
-    "    content: CC-BY-4.0\n",
-    "github: https://github.com/ocademy-ai/machine-learning\n",
-    "venue: By Ocademy\n",
-    "open_access: true\n",
-    "bibliography:\n",
-    "  - https://raw.githubusercontent.com/ocademy-ai/machine-learning/main/open-machine-learning-jupyter-book/references.bib\n",
-    "---"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "63961ec0-328a-4289-8667-cc86f09db8f1",
@@ -397,97 +366,6 @@
     "Residual analysis is a crucial step in understanding the limitations of the model and can provide insights into potential areas of improvement."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "812056ec",
-   "metadata": {},
-   "source": [
-    "## Adjusted R-squared"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bd2af42b",
-   "metadata": {},
-   "source": [
-    "The adjusted R-squared is a modified version of the R-squared score that takes into account the number of independent variables in the model. It penalizes the inclusion of unnecessary variables that do not significantly contribute to explaining the variance in the dependent variable."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e810e46e",
-   "metadata": {},
-   "source": [
-    "### The Formula"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a90544c6",
-   "metadata": {},
-   "source": [
-    "The formula for Adjusted R-squared is:\n",
-    "$$ \\text{Adjusted } R^2 = 1 - \\frac{{(1 - R^2)(n - 1)}}{{n - k - 1}} $$\n",
-    "Where:\n",
-    "\n",
-    "\n",
-    "- $n$ is the number of data points\n",
-    "- $k$ is the number of independent variables"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7a9a9fa5",
-   "metadata": {},
-   "source": [
-    "### Python Implementation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "53f70b1d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Adjusted R-squared Score: 0.7552043176561298\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Define the number of independent variables (k)\n",
-    "'''\n",
-    "In this code, you'll need to replace num_independent_variables with the actual number of independent variables in your model.\n",
-    "'''\n",
-    "num_independent_variables = 2  # Replace with the actual number of independent variables\n",
-    "\n",
-    "# Calculate adjusted R2 score\n",
-    "adjusted_r2_score = 1 - ((1 - r2_score) * (len(actual_values) - 1)) / (len(actual_values) - num_independent_variables - 1)\n",
-    "\n",
-    "# Print the adjusted R2 score\n",
-    "print(\"Adjusted R-squared Score:\", adjusted_r2_score)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bc0b5d20",
-   "metadata": {},
-   "source": [
-    "### Interpretation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "78eb9684",
-   "metadata": {},
-   "source": [
-    "The adjusted R-squared score provides a more accurate representation of the model's explanatory power, especially when dealing with multiple independent variables.\n",
-    "A higher adjusted R-squared indicates that a larger proportion of the variance in the dependent variable is explained by the independent variables."
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "74fa14ed",
@@ -666,8 +544,6 @@
     "\n",
     "- R-squared (R2) Score: Indicates the proportion of the variance in the dependent variable that is predictable from the independent variables.\n",
     "\n",
-    "- Adjusted R-squared: A modified R2 score that considers the number of independent variables in the model.\n",
-    "\n",
     "- Mean Absolute Error (MAE): Measures the average absolute differences between actual and predicted values.\n",
     "\n",
     "- Mean Absolute Percentage Error (MAPE): Measures the average absolute percentage difference between actual and predicted values.\n",
diff --git a/_sources/assignments/ml-fundamentals/ml-linear-regression-2.ipynb b/_sources/assignments/ml-fundamentals/ml-linear-regression-2.ipynb
deleted file mode 100644
index 5b1ebeb484..0000000000
--- a/_sources/assignments/ml-fundamentals/ml-linear-regression-2.ipynb
+++ /dev/null
@@ -1,329 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ML linear regression - assignment 2\n",
-    "In this assignment, we will explain the principle of one-dimensional gradient descent and multidimensional gradient descent."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## One-dimensional gradient descent\n",
-    "One-dimensional gradient descent is a simple optimization algorithm used to find the minimum (or maximum) of a univariate function. "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here are the main steps:  \n",
-    "\n",
-    "> step 1: choose an initial point on the function curve.\n",
-    "\n",
-    "> step 2: compute the derivative of the function at the current point.\n",
-    "\n",
-    "> step 3: update the position along the function curve.\n",
-    "\n",
-    "> step 4: repeat the process.\n",
-    "\n",
-    "> step 5: output the result."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x2587be67a00>]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Define the target function\n",
-    "def f(x):\n",
-    "    return x ** 2 + 5 * x + 10\n",
-    "\n",
-    "# Define the guidance of the target function\n",
-    "def df(x):\n",
-    "    return 2 * x + 5\n",
-    "\n",
-    "x = np.linspace(-10, 10, 100)\n",
-    "y = f(x)\n",
-    "plt.plot(x, y, label='f(x)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define gradient descent function\n",
-    "def gradient_descent(learning_rate, max_iter, x_init):\n",
-    "    # step 1:start with an initial value for the input variable, denoted as x_int.\n",
-    "    x = x_init\n",
-    "    x_history = [x]\n",
-    "    for i in range(max_iter):\n",
-    "        # step 2:compute the derivative of the function at the current point.\n",
-    "        gradient = df(x)\n",
-    "        x -= learning_rate * gradient\n",
-    "        # step 3:update the position along the function curve.\n",
-    "        x_history.append(x)\n",
-    "    # step 4:repeat the process.\n",
-    "    return x, x_history"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "x_min: -2.5\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Set the learning rate and initial value\n",
-    "        # learning rate: the size of the step for gradient descent.\n",
-    "        # max_iter: number of gradient descent iterations.\n",
-    "learning_rate = 0.3\n",
-    "max_iter = 100\n",
-    "x_init = 10\n",
-    "\n",
-    "# Run gradient descent function\n",
-    "x_min, x_history = gradient_descent(learning_rate, max_iter, x_init)\n",
-    "\n",
-    "# Draw the target function and the process of gradient descent\n",
-    "x = np.linspace(-10, 10, 100)\n",
-    "y = f(x)\n",
-    "y_history = f(np.array(x_history))\n",
-    "plt.plot(x, y, label='f(x)')\n",
-    "plt.plot(x_history, y_history, 'ro-', label='Gradient Descent')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('f(x)')\n",
-    "plt.legend()\n",
-    "plt.title('One-Dimensional Gradient Descent')\n",
-    "plt.show()\n",
-    "\n",
-    "print('x_min:', x_min)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "you can change `learning rate` and  `max_iter` to see different images."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Multidimensional gradient descent\n",
-    "Two-dimensional gradient descent is an optimization algorithm used to find the minimum (or maximum) of a function with two input variables."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here are the main steps:  \n",
-    "\n",
-    "> step 1: choose an initial point on the function curve.\n",
-    "\n",
-    "> step 2: compute the partial derivatives of the function at the current point.\n",
-    "\n",
-    "> step 3: update the position along the function curve.\n",
-    "\n",
-    "> step 4: repeat the process.\n",
-    "\n",
-    "> step 5: output the result."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from mpl_toolkits.mplot3d import Axes3D"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define the target function\n",
-    "def f(x, y):\n",
-    "    return x ** 2 + y ** 2 + 5 * x + 2 * y + 10\n",
-    "\n",
-    "# Take the partial derivative of the objective function with respect to x\n",
-    "def dfx(x, y):\n",
-    "    return 2 * x + 5\n",
-    "\n",
-    "# Take the partial derivative of the objective function with respect to y\n",
-    "def dfy(x, y):\n",
-    "    return 2 * y + 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define the two-dimensional gradient descent function\n",
-    "def gradient_descent(learning_rate, max_iter, x_init, y_init):\n",
-    "    # step 1:start with an initial value for the two input variables, denoted as (x_init, y_init).\n",
-    "    x = x_init\n",
-    "    y = y_init\n",
-    "    x_history = [x]\n",
-    "    y_history = [y]\n",
-    "    for i in range(max_iter):\n",
-    "        # step 2:compute the partial derivatives of the function at the current point.\n",
-    "        gradient_x = dfx(x, y)\n",
-    "        gradient_y = dfy(x, y)\n",
-    "        x -= learning_rate * gradient_x\n",
-    "        y -= learning_rate * gradient_y\n",
-    "        # step 3:update the positions along the function surface.\n",
-    "        x_history.append(x)\n",
-    "        y_history.append(y)\n",
-    "    # step 4:repeat the process.\n",
-    "    return x, y, x_history, y_history"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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D4KKLLprWfhRF4aKLLuK0007jwQcf5LXXXuPmm2/m2muv5ec//zn333//hENp8/EenAiL8FTidSE2tbW1nHnmmTzxxBO88Y1vLH55X3nllQSDQYLBIP/4j/9Yts1XvvIV/vCHP/DGN76RL3/5y1RWVvLggw/y6KOPcvfdd5cFYk5GKpXixRdfLP7/0aNH+e1vf8sf/vAHLrvsMn7wgx/M7cmOYcuWLXz0ox/lf/2v/8Wrr77KpZdeitvtpre3l+eee46zzjqLv/u7v5vRPq+//nrOPPNMzj//fGpqamhvb+e73/0uy5cvZ82aNeNuEwgE+NKXvsQXvvAFPvCBD3DLLbcwPDzMV7/6VRwOB1/5yldmfG5vfetb+cd//Efe+9738tGPfpTh4WHuueee4/pK/e53v0trayvve9/7eOSRR3jb295GY2MjyWSS/fv389BDD+FwOKYVnDjd9q1fv573v//9fPe730XTNK688kp2797NPffcM60A33//93/n2muv5ZprruHWW29lyZIlBINB9u3bx2uvvcavfvWrGV+Hs846i4ceeohf/OIXrFy5EofDwVlnnTXpNuFwuPisJxKJYlDns88+y0033cRXv/rV4ro333wzDz74IG95y1u44447uPDCC9E0ja6uLp5++mne9ra38Y53vIMf/OAHPPXUU7z1rW9l2bJlpNNpfvSjHwEUh71vueUW7rvvPv73//7fHDhwgDe+8Y2YpslLL73E+vXrufnmm+flPVi1ahVOp5MHH3yQ9evX4/F4aGxsnNaw+OuSk+qeMA9MFNT5yU9+UgDiH/7hH8qWr1mzRgCipaXlmG127dolrr/+euH3+4XNZhPnnHPOjDxPCl4thX9ut1usXLlSvPvd7xa/+tWvymJXCkzkjfarX/2qbL2CZ9Err7xStrwQQDc4OFi2/Ec/+pG46KKLhNvtFk6nU6xatUp84AMfEK+++mpZe8fznvvgBz9YFmfxne98R2zevFlUV1cLm80mli1bJj784Q+XxQtNFGfzX//1X+Lss88WNptN+P1+8ba3vU3s2bPnmOONdw8L5zb2vNatWyfsdrtYuXKl+MY3viF++MMfHnPs6QZ1CpH3GvvJT34irrrqKlFdXS1UVRV+v19ceOGF4ktf+pLo6uoqW5+ROJvxmG77MpmM+PSnPy1qa2uFw+EQF198sXjhhRemHWezc+dOcdNNN4na2lqhaZqor68Xb3rTm8QPfvCD4joTPTPj7bOtrU1cffXVwuv1TjvOpvCcS5IkPB6PWLdunfibv/kb8fjjj4+7TS6XE/fcc08x7sXj8YjTTjtNfOxjHxOHDh0SQuS9yN7xjneI5cuXC7vdLqqqqsRll11WFg8nhBCpVEp8+ctfFmvWrBE2m01UVVWJN73pTeL5558/5n7M1XsgRN6rrhBbhRVnMymSEDNwibKwsLCwsJgFrwtvNAsLCwuLk4slNhYWFhYW844lNhYWFhYW844lNhYWFhYW844lNhYWFhYW844lNhYWFhYW844lNhYWFhYW844lNhYWFhYW844lNhYWFhYW844lNhYWFhYW844lNhYWFhYW844lNhYWFhYW844lNhYWFhYW887rop6NhcVcYBgGuVzuZDfDwmJcNE0rK9S30LDExsJiCoQQ9PX1EQ6HT3ZTLCwmJRAIUF9fP26Z8pONJTYWFlNQEJra2lpcLteCfJEtXt8IIUgmkwwMDAD5EucLDUtsLCwmwTCMotBUVVWd7OZYWEyI0+kEYGBggNra2gU3pGY5CFhYTEJhjsblcp3kllhYTE3hOV2Ic4uW2FhYTANr6MxiMbCQn1NLbCwsLCws5h1LbCwsLOaMW2+9lbe//e3Fvy+//HLuvPPOk9Yei4WDJTYWFicCw4CtW+HnP8//1zDm/ZB9fX3ccccdrF69GofDQV1dHW94wxv4wQ9+QDKZnPfjA/zmN7/ha1/72pzuc6ygTbaeJElIkoSmadTV1XHVVVfxox/9CNM057RN881dd93Fhg0bTnYzjgvLG83CYr75zW/gjjugq2t02dKl8M//DO9857wc8ujRo2zZsoVAIMDXv/51zjrrLHRd5+DBg/zoRz+isbGRG264Ydxtc7kcmqbNSTsqKyvnZD+z5c1vfjP33XcfhmHQ39/PY489xh133MGvf/1rHnnkEVTV6gJPGMLCwmJCUqmU2Lt3r0ilUrPbwX//txCSJASU/5Ok/L///u+5bfAI11xzjVi6dKmIx+Pj/m6aZvH/AXHvvfeKG264QbhcLvHlL39Z6LouPvShD4nm5mbhcDjE2rVrxXe/+92yfei6Lj75yU8Kv98vKisrxWc/+1nxgQ98QLztbW8rrnPZZZeJO+64o/h3JpMRn/3sZ0VjY6NwuVziwgsvFE8//XTx9/vuu0/4/X7x2GOPidNOO0243W5xzTXXiJ6eHiGEEF/5ylcEUPavdPtSPvjBD5a1pcCf//xnAYj//M//LC4Lh8PiIx/5iKipqRFer1e88Y1vFDt27Cj+vmPHDnH55ZcLj8cjvF6v2Lhxo3jllVeKvz/33HPi0ksvFU6nUwQCAXH11VeLYDBYvNbf+ta3xIoVK4TD4RBnn322+NWvflXc9umnnxaAePLJJ8V5550nnE6n2LRpk9i/f3/xmow95/vuu2/ccz7u53UescTGwmISjnl5TVOIeHx6/yIRIZYsOVZoSgVn6dL8etPZX4lATMbQ0JCQJEl84xvfmNb6gKitrRU//OEPxZEjR0RbW5vIZrPiy1/+snj55ZfF0aNHxQMPPCBcLpf4xS9+UdzuW9/6lvD7/eLXv/612Lt3r/jwhz8svF7vpGLz3ve+V2zevFn85S9/EYcPHxbf/va3hd1uFwcPHhRC5DtWTdPElVdeKV555RWxbds2sX79evHe975XCCFELBYTN910k3jzm98sent7RW9vr8hkMuOe10RiI4QQ55xzjrj22muFEHkx2LJli7j++uvFK6+8Ig4ePCg+/elPi6qqKjE8PCyEEOKMM84Q73//+8W+ffvEwYMHxS9/+cuiGG3fvl3Y7Xbxd3/3d2LHjh1i9+7d4nvf+54YHBwUQgjxhS98QZx22mniscceE0eOHBH33XefsNvtYuvWrUKIUbG56KKLxNatW8WePXvEJZdcIjZv3iyEECKZTIpPf/rT4owzziieczKZHPe8LLGxsFikHPPyxuMTi8d8/5vAShnLiy++KADxm9/8pmx5VVWVcLvdwu12i8997nPF5YC48847p9zvbbfdJt71rncV/25oaBDf/OY3i3/ncjmxdOnSCcXm8OHDQpIk0d3dXbbfK664Qnz+858XQox+xR8+fLj4+7/927+Jurq64t+TiUgpk633nve8R6xfv14Ikbd0fD6fSKfTZeusWrVK/Pu//7sQQgiv1yvuv//+cfd1yy23iC1btoz7WzweFw6HQzz//PNlyz/84Q+LW265RQhRbtkUePTRRwVQfO6+8pWviHPOOWfyExYLW2ysAUsLi1OUsTEXL7/8MqZp8r73vY9MJlP22/nnn3/M9j/4wQ/4r//6L9rb20mlUmSz2eIkdSQSobe3l02bNhXXV1WV888/HyHEuO157bXXEEKwdu3asuWZTKYsO4PL5WLVqlXFvxsaGoppWOYKIUTx+mzbto14PH5MhohUKsWRI0cA+NSnPsXf/u3f8tOf/pQrr7ySG2+8sdjGHTt2cOONN457nL1795JOp7nqqqvKlmezWc4999yyZWeffXbx/wvpZgYGBli2bNlxnOnCwRIbC4uZ4HJBPD69df/yF3jLW6Ze73/+By69dHrHngarV69GkiT2799ftnzlypXAaFqTUtxud9nfv/zlL/nkJz/Jd77zHTZt2oTX6+Xb3/42L7300rTaMB6maaIoCtu2bTsmlYrH4yn+/1jnBEmSJhSw2bJv3z5WrFhRbFdDQwNbt249Zr1AIADkvcHe+9738uijj/LHP/6Rr3zlKzz00EO84x3vGPd6Fih4vT366KMsWbKk7De73V72d+l5F4RwsXnNTYYlNhYWM0GSYEzHPCFXX533Ouvuzg+EjbevpUvz681hHquqqiquuuoq/vVf/5Xbb7/9GCGZDs8++yybN2/mtttuKy4rfOUD+P1+GhoaePHFF7l0RCh1XWfbtm1s3Lhx3H2ee+65GIbBwMAAl1xyyYzbVMBms2Ech+v4U089xa5du/jkJz8JwMaNG+nr60NVVZqbmyfcbu3ataxdu5ZPfvKT3HLLLdx333284x3v4Oyzz+bPf/4zX/3qV4/Z5vTTT8dut9PR0cFll1026zYf7zkvBKw4GwuL+UJR8u7NkBeWUgp/f/e7cyo0Bb7//e+j6zrnn38+v/jFL9i3bx8HDhzggQceYP/+/VMmaVy9ejWvvvoqjz/+OAcPHuRLX/oSr7zyStk6d9xxB9/85jd5+OGH2b9/P7fddtukZRjWrl3L+973Pj7wgQ/wm9/8htbWVl555RW+9a1v8T//8z/TPrfm5mZaWlo4cOAAQ0NDk+YBy2Qy9PX10d3dzWuvvcbXv/513va2t3HdddfxgQ98AIArr7ySTZs28fa3v53HH3+ctrY2nn/+ef7+7/+eV199lVQqxSc+8Qm2bt1Ke3s7f/3rX3nllVdYv349AJ///Od55ZVXuO2222hpaWH//v3ce++9DA0N4fV6+cxnPsMnP/lJfvzjH3PkyBG2b9/Ov/3bv/HjH/94Rufc2trKjh07GBoaOmYYdFFwcqeMLCwWNnMy4frf/533Oiud7G9qmje35wI9PT3iE5/4hFixYoXQNE14PB5x4YUXim9/+9sikUgU1wPEww8/XLZtOp0Wt956q/D7/SIQCIi/+7u/E//3//7fsknqXC4n7rjjDuHz+UQgEBCf+tSnpnR9Lni5NTc3C03TRH19vXjHO94hWlpahBCjrs+lPPzww6K0qxoYGBBXXXWV8Hg8U7o+M+IqrKqqqKmpEVdeeaX40Y9+JAzDKFs3Go2K22+/XTQ2NgpN00RTU5N43/veJzo6OkQmkxE333yzaGpqEjabTTQ2NopPfOITZc/E1q1bxebNm4XdbheBQEBcc801IhQKCSHy3m7//M//LNatWyc0TRM1NTXimmuuEc8884wQYtRBoLC+EHkPN0C0trYW78e73vUuEQgEFq3rsyTEHA+GWlicQqTTaVpbW1mxYgUOh2P2OzIMePZZ6O2Fhga45JJ5sWgsXt/M2fM6D1hzNhYWJwJFgcsvP9mtsLA4aVhzNhYWFhYW844lNhYWFhYW8441jGZxQhFCYBgGmUwGRVGK/2TZ+u6xsDiVscTG4oQhhCCXy6HrepnrpizLqKqKqqoLVnwsPxqLxcBCfk4tsbE4IRiGQS6XwzRNJEkqCorI5+cjl8uRzWaL9UcWivgUorqTyeSkkeIWFguBQp2iuSoRMZdYYmMxrwgh0HUdXdcBigJTSMdREJeCmAghME2TXC5XDNYrrGuz2dA0DVVVT1itdUVRCAQCxdxcLpdrQdd5t3h9IoQgmUwyMDBAIBCYMmj3ZGDF2VjMGwXRKOR3KghLYXlh2WQUxOfFF19k3bp1BAIBZFlGUZQy62c+BUAIQV9f36TR8RYWC4FAIEB9ff2C/CCyLBuLOafUOilYMWMf/lLrZjIKQ26F/yqKUtx3YditID6aphXXmcuXTZIkGhoaqK2tnTQ1ioXFyaTw/C9ULLGxmFMK8y+7d++mtraW6urqOen4S4fdSl+oUmErFZ+xcz5z0YbCviwsLGaOJTYWc0ah0zcMg1gsRkVFxZxZGBOlmS8Vn8LvpmmSzWbJZDLzKj4WFhbTxxIbi+OmEDuj6zqmaSLL8rzUIJmKgoBY4mNhsfCwxMbiuCgMmxVqbRSEpuB1NlfMRrzGE5/Cv0wmQzabLbbZEh8Li/nFEhuLWVOwGEqtmVImEoeT1ZGXOiooinKM+JRaPgUX60KMjyU+FhbHhyU2FjOmMGxW8DYbrzOeyhKZaec9H8Nyk4lPOp0urmOJj4XF8WOJjcWMME0TXdePGTYby8mYszlepis+Y2N8LPGxsJgaS2wspsV0YmdKmWuxOVkOB+OJj2maRfGRZfmYOR9LfCwsjsUSG4spGZtyZiqhKayz2CybqZhMfDKZDOl0GlmW0XUdm82Gw+GwxMfCYgRLbCwmpTR2pjSH2VScCpbNVIwV3YL47N27l+rqahoaGsrmfArDb9MRawuLUw1LbCzGZaLYmekyH+Kw0MRmLAURKU2tU3odS38bm9fNEh+LUx1LbCyOYaLYmZkwH5bNYqFw3uNltC4MSeZyuTLxKc3rttBq+VhYzAWW2FiUMVXszHR5PVo2U2GJj8XrGUtsLIDpxc7MhMnEJhQKcejQIdxuNxUVFQQCgSmLPS0my2a6TCU+sDiqmFpYTAdLbCzmZNhsLOOJjRCCo0ePcvToUZYuXYqu6xw5coRkMonX66WioqIoPuNlV15Mls1srt9E4rNQq5haWMwES2xe55SWa55LF92xYpPJZGhpaSGVSnHBBRcUSyxLkkQmkyEUChEKhThw4ACZTAafz1cUH7/fv6gsm7kSxelWMS0VnxNZxdTCYiZYYvM6ZbxyzXNdcKzQ6Q4PD9PS0kJFRQXnnnsuiqIUh+sA7HY79fX11NfXF6P1C+LT09ODruvF4SS3243H43ldfs1Pp5ZPqficiCqmFhbTxRKb1yFjyzXPR8ctSRKGYXDo0CHa2to47bTTWLp06bRypjmdTpxOJ42NjcXa6rt27SKVSrFjxw6EEEWrp6KiArfb/brsUKdbSG4+q5haWEwXS2xeR5R2RnM9bDYWwzAYGBhAVVUuvvhivF7vrPYjSRJutxun00l1dTWNjY3EYjFCoRDDw8McPXoUWZbLxMfpdJ70DvVkHP9kVjG1sJgKS2xeJxSGp/bv38/atWvntZMZHByku7sbh8PBpk2bUNW5e8wkScLn8+Hz+Vi+fDmmaRKNRgmFQvT393Pw4EFsNluZ+Dgcjjk7/nRYKI4MVhVTi4WEJTavA0onlTs6OlizZs28dCimaXLo0CE6OjqoqqrCbrfPmdBMNPwmyzKBQIBAIMCKFSswDINIJEIoFKK7u5v9+/fjcDjKxMdms81JmxYTk1Ux3bt3LzabjWXLllniYzFvWGJzCjM25czYjmYuSaVS7Ny5E13X2bRpE319fSSTyTk/zlQoikJlZSWVlZUA6LpOOBwmFArR3t7Onj17ivE9043xORUpFR/TNItWUCGpqFXF1GKuscTmFGW82JkCBceAuWJgYIBdu3ZRV1fH+vXr56VDmm1GAlVVqa6uprq6GoBsNlsUn9IYn8rKyqKb9XgxPrNp72KhtGREaU63yaqYFtysrYzWFtPFEptTkKliZ+bKsjFNk4MHD9LZ2ckZZ5xBY2Nj8TdJkuZc1OYCm81GbW0ttbW1AGVu1vv27SObzeL3+4uWj8/nm7G33kKZs5kuhYwRpVhVTC3mGktsTiFKY2cmK9c8FyKQTCbZuXMnpmmyefNm3G73MceZS+arxIDD4aChoYGGhgaEEKRSqaL4dHV1YRgGgUCgKD5er/eU60wLls1kWFVMLY4XS2xOEcaLnRnvJZdl+bg77b6+Pnbv3k1jYyPr1q0bd9hpOuIwnU5u7PrziSRJuFwuXC4XS5YsQQhBIpEoik97eztAmfhMFOOzmDrYmd4HmF4VU0t8LEqxxGaRM9NyzbIsz9qyMQyDAwcO0NPTw5lnnkl9ff2E654KJQYkScLj8eDxeGhqakIIURbjc+TIERRFOSbGZ7ExG7EZy3SrmFoltF+/WGKziBnrBDCdIlyzFYFEIsHOnTuRJInNmzfjcrnm5TiTcbLnQqYb42MYBqFQCL/fj91uP6ltng4Fb7S5ZOyzWBAfwzAwDGNChwOrkNypiyU2i5TScs0z+TqcjWXT09PDnj17WLp0KevWrZvWhPmpYNlMxUQxPnv37iUYDNLd3Y3L5Spzs16IMT5zYdlMxUQZra0qpq8fLLFZZJzIcs2GYbBv3z76+/s555xzih5cc32c6XKyLZupKMT42O12mpubqaioKLpZt7a2kkgk8Hg8ZeIzl9kVZsuJEJuxzKSQ3NhhN4vFycl/0i2mzVzUnZmuZROPx9mxYweqqrJ58+YZz0W8HiybqRgvxqfgbHDo0CHS6XRZHZ+5ivGZKSdDbMYyE/GxqpguTiyxWSScyHLN3d3d7N27l2XLlrFmzZpZvdCvR8umwETttNls1NXVUVdXB8xPjM9s23uyxWYsU4lPKBRCCEF9fb1VSG6RYInNAmeuyzVPZtnous7evXsZHBxkw4YN1NTUzPo4lmUzNVPF+JimWSY+8xXjsxDFZixjxScajWIYBlVVVZOm1rHEZ+Fgic0CxjRNdF2f93LNALFYjB07dmCz2diyZctxZ0qeTGxmM+k7X0Gd88Vszm+yGJ+2tjYkSSpzs3a5XHMiEotBbMZSsPALc15WFdOFjyU2C5CZxs7MhLGWjRCCrq4u9u/fT3NzM6tWrZqTr8HFJg4LjbExPqZpEo/HCYVCDA4OcvjwYVRVPaaUwmyek8UqNqUJVCer5TOe+FhVTE88ltgsMMaWa55r189SEdB1nd27dxMKhdi4cSNVVVXzcpyFuL/5ZD7aKcvyMTE+hVIKvb29HDhwALvdXiY+043xWaxiM9lH0WTiY1UxPTlYYrOAKI2dKR2fnksKlk0kEmHnzp04nU42b94858GHi0kcFiOl1Ukh76ZecLPu7Oxk7969ZTE+FRUVE5ZSOBXFZizTFR+rnML8YYnNAuB4Y2dmyvDwMPv372flypWsXLlyXo41ldhYczZzi6IoVFVVFa3TXC5XFuOze/fuCWN8xsv6vNA53jaXio9VxfTEYInNSWYuYmemSy6XIxaLYRgG5513XrHA2Hyw2MThVEPTNGpqaooehZPF+BiGseju1Uwtm8kozekGlvjMF5bYnERM06S/v59QKDRvFkaBcDjMzp07AVi+fPm8Cg1MLjamaXLkyBEAKisr8Xq9U3YckrQw6+OMx0LsuCeL8dF1ne3btxfdrKd7T04mcyk2Y5lMfKwqprPHEpuTQGnsTCKRYGhoiFWrVs3bsdra2jh8+DCrV68mGo2ekE5kIrFJJpPs2LEDIQR2u53Ozk4gn7a/UC1zIpfehdiJL1ZKY3wGBwc5/fTTi9ZPIcantJSCx+NZUB3pfIrNWErFZ7wqpqXiY1UxnRhLbE4wY4fNVFWdty/2bDbLrl27iMViXHDBBQQCAXbt2nVCOu3xxKZQPrqhoYFVq1YVJ6ZjsRjBYLDo0qtpWvELu+BVtdhe2sXWXpfLRU1NzTExPsFgkNbW1nmL8ZktJ1JsxjJROQWriunkWGJzAhmvXPPx1JeZjFAoxM6dO/H5fGzZsqXoiTRfxxtLqdiYpsmhQ4fo6Ogolo8u9boruPQ2NzcXMyeXelW53e7ii6vr+oJIXnkqMdYbbbwYn0Idn4lifE50HZ+TKTZjscRnelhv7QlgbOxM6UM2152/EIKjR49y9OhR1q5dy7Jly47pSE6kZZNOp9m5cye5XI5Nmzbh8Xgm3a6QObmyspJVq1YV82C1t7cTiUR49tln8fl8ZckrF0qnU2CxDfdN5fosyzJ+vx+/309zc/OcxvjMlvmowTNXTCY+HR0dxGIxVq9e/bqrYmqJzTwztlzz2CDNuRSbTCZDS0sLyWSSCy+8EL/ff8w6J9KyMQyD559/nurqas4777xZWSSaplFbW0s8Hsfj8bBixQqCwSChUIju7m4Mwyjr5Bba3MJCp9AJzuSajY3x0XX9GGt0ujE+s2UhWTZTUfrOF0IcCu/h66mKqSU280Rp0NhksTNz1fkPDw/T0tJCRUUFmzdvnvDlPhFeXYUUOKZpsmbNGpYuXXrMuc8mzgbyE9uNjY00NjYW5xYK4tPa2lrsCAvzPSerTPNi6SQKVtjxtFdV1SljfMaWUjjeodDFJDalGIZxTILQyaqYnkriY4nNPDCT2BlFUY6r8xdCcOTIEVpbW1m3bh1NTU1TDokUckXNBwWnhGg0iiRJNDU1zdm+xw5Plc4tLFu2rFimORgMFod3HA5HmfjM9Rf2dNq5kJkLsRnL2BifTCZDOBwmGAxy4MABMplM2VCoz+ebcR2fxS42pUxUTqFUfG655RY++MEPcsstt5yMZs8JltjMMTMt13w8lk06naalpYVMJsPFF1+M1+udcpv5tGzC4TA7duzA5/Nx3nnn8cILL8zZvqfTGZaWaYb88M5EX9iVlZUnrVjZQmI+xGYsdru9LMantJRCT08Puq4fU0phKiFZjFkPYHyxGct44tPX17foHWMWd+sXELNNOTNbsRkaGqKlpYXq6mo2btw47QdRluV5KWrW0dHBwYMHWb16Nc3NzaRSqTlNV1M4zkwYWykzk8mUFSvL5XLHBDIu5mGK2XAixGYsTqcTp9NZHApNJpPF+9LZ2TlljE9hiHqxio3NZpvRNpIkkUwmcblc89SqE4MlNnPA8aScKXT+052kNU2Tw4cP097ezvr161myZMmMOoq59kYrzRx9/vnnFyeNC22aqySPc7EPu91OfX099fX1xWJlhfmejo4OgGIHV1lZidPpnPVxF4tonQyxKUWSJNxuN263m6VLlyKEKJZSKJ2HKxWfQq2lxSg2pmnO2JouzE1OZ+RiIWOJzXEyXuzMTCi8MNN5CFOpFDt37kTX9Wm5EU90vLkaRovFYmzfvn3czNHzITZzXbKgUKys0MlNJ7h0Orze52yOB0mS8Hq9eL3e4jzceDE+kA8SrqqqOmlOILOhMLw+UxKJBG63ex5adOKwxGaWlMbOHE+55umKTSH6vq6ujvXr1896rmGuOu2uri727dtHc3Mzq1evntDbbLJjLaROeaLg0mAwWBZcWhCf0qzJi5mFJjZjGRvjYxgGw8PD7N69m76+Pg4dOlSM8Sl8FMx0mOpEMp05m/FIJpOWZfN6ZC7LNRfExjCMcT2lTNPk4MGDdHZ2FqPvj4fjtWwMw2Dv3r0MDAxw7rnnFudDxjKV2MxUaE50FunS4FKgGFxamjW54FFVWVmJz+cr+2JdqJ33WOa6Eux8oygKPp8PgPPOO68s40RHRwd79uwpfhQUSimcCA/EMkQaMEA61hKZjdhks1lyudysRjIWEpbYzID5KNdc2H48AUgmk+zcuRPTNNm8efOcmNHH442WSCTYsWMHiqKwZcuW4tj5RMeBubVeTqYlVAgura2tBco9qnbt2lU2qW2a5oKy2iZjMRdOkyRpwhifYDDIkSNHihZBqfjMqweiMNFyD5DTPjxh22d6/Hg8DmCJzeuFsU4Ac/U1OFF+tL6+Pnbv3k1jYyPr1q2bsxdktt5ohfYsWbKEdevWTaskAMydQCy0DnGsR1VpcGkul6OlpYWqqqqi5TOZMJ9MFqvYTNTm8WJ8Ch8F48X4zHW6IzX3U0CGCdo3mzmbeDxenGNczFhiMw1mGjszU0rFxjRN9u/fT09PD2eeeSb19fVzeqyZWjamaXLgwAG6u7tn1J5TzbKZjLHBpc8++ywrVqwgm82WBZcW5hROVHDpdFisYjPdDrvUAxHmJsZnwnZlH0Y1niGj3TnhOrMZRiu4PS9G77tSLLGZhBNVrrkgNolEoljgbPPmzfPyJTMTyyaVSrFjxw5M02TTpk0zGsY71S2bySh1NoDR4NJCuv5CcGlBfE5mcOmpLjZjmSzGp6OjAyFEWU43t9s9resj9KdQjL+SkZowldOZaIvZiE08Hp92OxYylthMwIks1yzLMoODg7S3t097mGq2THeifXBwkJaWlll7v72eLJupmCi4NBgMnvTg0teb2JQyWYxPMBjk6NGjZUlHC7n2xl4vob+Kmfl3kNchpGpkafxutTDnO9N36VRwewZLbMalUHt8Pq2ZAgXLqa2tjXPOOac4AT1fTOWNJoTg8OHDtLW1cfrpp7NkyZJZHef1bNlMdc5jg0vHfl3D3AWXTqeti+nawvzlRZsoxicYDNLf38+hQ4eKsVfFUgpaK2bmn9BpRDXbkLXrJm03YFk2FuXlmo8ndma6xOPx4jDV6aefPu9CA5PP2RRKFKTT6WnnWpvqWBN1vONd1yPxIQKakyr7+F9xi9WymYyxX9emaRKPxwkGgwwMDHDo0CFsNltxyK2ysnJO40gWcl2YiThRedFKY3xWrFhR5mbd3d3NkdZnWLf8dyiqBloKn80AZcOE+ysdJZkJyWTSsmxOJQqxM6+++ipLliyhrq5uXl/C7u5u9u7dy7JlyxgeHj5hAYITzdkEg0F27txJRUUF55577py0Z6axMX8ZPMzh+BBfOv0aHEr5BPpi6xCPJ+5qOsGlBfE53uBSy7KZPqWxV4bZRzTxM2yGnWguCgmFzqFqBkLbJ4zxma3YFCybxc7rXmzGxs4UgjXn6wXUdZ19+/YxMDDAhg0bqKmp4aWXXjohBc3gWMtGCEFbWxuHDx8et7Ln8R5rumJjCsFr4S6iuTT/dvhZ7lx7OYpU/lKeipbNVEwUXBoMBsuCSwviMza4dCossZk5hjlMOHkXDiVAThI4FPC5Bab8TtyBFYRCoXFjfAp1aWZ6vROJxKKPsYHXudiMLddcCBIrfIHMNbFYjB07dmCz2cqCIk9U9czCsQqddi6XK9aeueCCC4qp+eeKyV6qsb8diA2gm/nr3hLp4SdtL/O/Vlw8rX0tNOZTFCcKLg0Gg8WCdYFAoCg+U431W2Iz02NHiKS+iiHi5ISCIIYmN2CgodouoLbWVbw348X4ABw9enRGMT7JZNISm8VMaexMae2I+ej4C5Ur9+/fT3NzM6tWrSp7yE6k2BQsm0gkwo4dO/B4PGzevHle8klNZdmUdnIvDXXilFxkpCiGEGwdPEyt3c9bG9cX13k9WjZTMdaVt+BNNTw8zJEjR1BVtczZYGxwqSU208cw40TT92AYg+TkJjRxGABF8pOTq7HJ5aEKYx1B+vv7OXjwIKlUqhjjU5rNeiIvxFPFG21xRwnNgoI1k81mi0GapQ+uoihzatnouk5LSwuHDh1i48aNrFmz5pgX5WSIzcsvv0xTUxMbN26ct8SF0x1GE0LwSrCL/nScOlsFCEGF5uLn7dt5ZuBIcV+LiZPR3oI31bJly9iwYQOXXnopZ5xxBk6nk56eHl544QVefPFFDhw4wMDAQFnapcXEyXBqMESKnsQ/oeuHSEn1FN4YU3jImH1oyvmTbl8YNbHb7Zxxxhls2bKFCy64gOrqaqLRKDt27ODZZ5+lpaWFzs5O4vF48d2Jx+Oztmy+8Y1vcMEFF+D1eqmtreXtb387Bw4cKFvn1ltvLWZEKfy7+OKLy9bJZDLcfvvtVFdX43a7ueGGG+jq6ppRW15Xls10YmdkWZ4zsSk8RE6nky1btkyYov5EiY2u68UHbePGjcV8UvPFRGJTyEoQDoepqKggaIdwNgVARyLCSk81SDBEmh8efRmf5mCJpFmWzQwpjRFZuXLluMGlTqcTwzAIBoOLpnLpibZsTJGiK3E3KgnS8lKyRjdetRJhOjDlBmTRhqZeMOV+SgM6J4rxCQaDDA8Pc/ToUVpaWnjqqaew2WysXLlyVh8GzzzzDB//+Me54IIL0HWdL37xi1x99dVFR5MCb37zm7nvvvuKf4/9AL3zzjv5/e9/z0MPPURVVRWf/vSnue6669i2bdu0n5nXjdhMN3ZGUZTj7vhLK1euXLmSlStXTvqQzKXATUQ8Hmf79u1F75hCkbP5ZDyxSafT7NixA8MwaGhoIBwO82jbfmK5GKqqoioKh6NB1nnzua1MIfjewb/ysfpzWPjdYJ6FKorjBZe2trYyODhYFlxamO9ZqJVLT6TYmCJFZ+LbJHOH8WjLyRhHUHAhzEFSUgNOTDRlA5I0dU2dyQI6S2N8li9fjmma+P1+urq6ePzxx3niiSd45JFHeNOb3sQVV1zB+9///mndm8cee6zs7/vuu4/a2lq2bdvGpZdeWlxeGPIbj0gkwg9/+EN++tOfcuWVVwLwwAMP0NTUxJNPPsk111wzZTvgdSA2M42dURSFXC436+Plcjl2795NOBzmvPPOK3oRTcZ8WzY9PT3s2bOH5cuXs2zZMrZu3XpCXtixYlNwr66qqmL9+vUYhsGSJUv49/Rh3CkXumGQ03XkjM5r0U6WuN2EpCxCFTzcf4g3GP55be/rDbvdjt/vJ5lMcu655xaDS4PBIG1tbUiSVFYnZj6DS2fCiRIbQ6TpTtxLMncI5FVkjPyQrkupJEOAnNmFW3aiqRMHcpbtbwapamRZZsOGDWzYsIFDhw7x8Y9/nLPPPpunnnqKJ554gr/5m7+Z1TlFIhGAY/qlrVu3UltbSyAQ4LLLLuMf/uEfio4O27ZtI5fLcfXVVxfXb2xs5Mwzz+T555+3xAZml3LmeKyMcDjMzp07cbvdbNmyZdpzIfMlNoZhsH//fvr6+orZCQqedyfi67sgNkII2tvbOXToEOvWraOpqan4EXAwOkRCzyErCjZFwWazscxRweHwMIPZLF4Jguk43cLgp2KINX0rWFPbuOCTEi6ETnk6lH6AjQ0uLVTILExsF4qUzUdw6UyYTcqXmWKINHui/4ZN7AFlJTJZEPl3x0Alax7FJtcgJB1NmXoIDY6vSmdlZSVXXnll0bKYDUIIPvWpT/GGN7yBM888s7j82muv5cYbb2T58uW0trbypS99iTe96U1s27YNu91OX18fNpvtmNGQuro6+vr6pn38U1ZsZluueTbDaKWd6erVq2lubp5RZyPLclEE5opkMsmOHTuQJInNmzcXS+eWVgadbyRJQtd1du7cSSgUKnOvLojd8wOd+CQ3w0QxyS9L6Ll8oKKqgqKyxl7B0VCQbE7nazv+zHvsS1hWWVOMP3G5XIumc19oTDQPMF6FzHA4XEyps3fvXjweT1F4/H7/CQtMNk1zXrNm62aKPbF/QTJjCGUZcaOLarUaQwDSOrLmfgBskhddWoosTy9h7vFU6ZwL1+dPfOITtLS08Nxzz5Utf8973lP8/zPPPJPzzz+f5cuX8+ijj/LOd75zwv3NdA7plBObsbEzM005M1NvtGw2y65du4jFYpx//vmzmguZa8tmYGCAlpYWGhsbOe2008atIHkiLBshBPv27cPlcrF58+ZjHCRMIXhpsJtQNkWzp4KubBCfamcwnSyukzZ0HLKPgN3FkBHFcNt5QkvwMU9z0b1X07Si8CyEssALdc5mPKbbYSiKUlakLJvNFp0NSuvEFO7D8aTqn06b52vfOTPOnti/EM91ELAtJ24cQRIKpuhFyGtQyIEQmEIjacapdJw77X3PxiIr1Eo6Xtfn22+/nUceeYS//OUvLF26dNJ1GxoaWL58OYcOHQKgvr6ebDZLKBQq698GBgbYvHnztNtwSolNIXam0HHPpsDZTIbRQqEQO3fuxOfzsWXLlll/bc2V2JimyaFDh+jo6ODMM8+koaHhmHUK12S+LZv+/n6SySS1tbVs2LBh3M7hQGSI0IgXWls8wkpfJZIE4XS4bL1oNodpyDhQAYmcgPuGD/PlDZfjUbRiOpf29nb27NlTjNqurKwkEAgs+CG3k8lsXZ9tNtsxwaWF4nGzCS6dCfM1Z5M2ohxK/Jh4rgNFXkHW7AbIe56hEtG7qNUqMEwFSV6NIVpxqRunvX/DMGZl/cXj8VnnKRRCcPvtt/Pwww+zdetWVqxYMeU2w8PDdHZ2FvuP8847D03T+NOf/sRNN90EQG9vL7t37+buu++edltOCbEpTTlzvJmapzOMJoSgtbWVI0eOsGbNGpYvX35cL9JciE06nWbnzp3kcjk2bdo0qdk905xlM6FU8JxOJ0uWLJmwY3h+sLPs76PRCGu95e7YblVjIJUgp+vYTAWnohKwOWmNhvmHnX/hSxsuK0vnks1mCQaDBINB9u7dWwycK6xzorLnLpZhvbmKsync6yVLlkwaXFoQn+OpXDofYpM0grwY+nec0gCKsgJBGkPkLWwJLxHjCCoODKOXnLwSO1kc6rnI0vTPwzCMWVndx5Ou5uMf/zg/+9nP+N3vfofX6y3Osfj9fpxOJ/F4nLvuuot3vetdNDQ00NbWxhe+8AWqq6t5xzveUVz3wx/+MJ/+9KepqqqisrKSz3zmM5x11lkzmkNa9GIz13VnphpGy2Qy7Nq1i0QiwYUXXojff/weUscrNsPDw+zcuZPq6mrOP//8KU31+XJIyGaz7Ny5k3Q6zaZNm9i1a9eEomYIk5cHu8uW+VUHh8NhGr0+etJRAKptHmKZCBIQM3WqJAfpXP7+tMfDfG3HM3xpw2V4tfwQnc1mOyZ9f0F8jh49iqqqZUNuE8U+vV6YjwDJ8VL1l2ZL3r9/P06ns8z6nMmowFyLTUzv56Xwv2NDBrmOiN5Lva2GnAmKtJyE0Y1A4FUrMKgioXdhU1Q82ttmdJzZzNkUnuHZDqPde++9AFx++eVly++77z5uvfVWFEVh165d/OQnPyEcDtPQ0MAb3/hGfvGLX5RZU//0T/+EqqrcdNNNpFIprrjiCu6///4Znc+iFpv5KNc82TDa8PAwLS0tVFRUsHnz5jmbpJxtbI8QgiNHjtDa2sr69etZsmTJtK7BfFg24XCYHTt2EAgEilmjJzvO7uEBxv5SaXcSyWQZSKSocbkZzCRI6yX3Qggyev5e22SFrGnQHg/zg32v8tHTzsdvKxeOUg+rpqamYqdXmkG5dJI7EAjMiZfTqThnczyMF1xayBlWSFjp8/mK98Hn8016H+ZSIMO5Tl6K/Ae6kcapLSWit+aPIQZRpOXoAmTyQ70CJzHjCG6lGqTsjIbQCu2e6fOVTqcxDOO4htEmw+l08vjjj0+5H4fDwfe+9z2+973vzaodsEjFZj7LNY/X8Zd26gXX3bl8QWdjaWSzWVpaWkgmk1x00UXFEsTzdbyJKM37NtYTbzKx+UtPO5qpYJMUsiIvKLFsPr4pa5rEUjr1Ti+9ycTIFvl9VtqctEYj1LvchPQkWdPgYDjIV7dt5csbLyNgn3hYo7TTW7VqFblcrmj17N+/vyyosbKyEo/Hs2iGw2bLyUhXo6oqNTU11NTkA3czmUxxvmfPnj3ouj7pfZgr1+fu9EEOJx9DNzPIciNpcwAAn1IBSAzrw9RpFWRN0KRVxIyjAGiSC1U5C0ma2cfmbCybRCL//FuJOE8C812ueewwWjqdLhYUm2mnPl1m2vkXHBP8fj+bNm2asYU1V5aNYRjs3buXwcHBcdPfTHScrGHw8kA3iVyGJW4ffdkIfs3JUIkXWtLQacCLR80R07MACCA14mXYl0zQ4HKDLOhNJIlkM3z51a18+bxLqXZMzxVV0zTq6uqoq6tDCFGc5C4ENcqyXOzwKisrZzTktlhEaiHkRrPb7TQ0NNDQ0FA29BkKhYr3odTZYC5KgBxJbufFyO+oUSWQahFkyYl8x67JXob1DhACwxxAlZoRCMAE00ZSitPk2DDjY84mziaRSCDLcjF0YTGzqMRmtrEzM6F0GG1oaIiWlhaqq6vZuHHjvMURTFdsSuN5jscxYS4sm0IcjyzLbN68edwJ34nEZttgD0k9b8V0J2I0eyuQFYlQOlO2XiKbQzVUXIogZhi4JJn+oqUDvckEp/mqCUoZMsKgLxXna9ue4f9ueAMN7pkNO0iShMvlwuVyFYMao9EowWCQ7u5u9u3bh9vtLpvkXgx5xKZiIYhNKeMNfZaWZj548CCQLz5omuasXN1bYk/REn8av1pBhhwJI0iDrYaUAXZ5KQkjiMAkoFaBJBjSh6nX3ORMBU1diSm68apnzPjcZmvZnAoloWGRiE1p7Mx8l2suDKMdPHiQ9vb2Gc2FzJbpdP6FNDiRSGTW8TwFjteyGRwcpKWlhYaGhmPieKZznGd72sv+botFWDfGC82jaPQnEwig2uEkK+WwSxrxMfsaSCQIqE4iRoq0aaBJKl9+aStfOP8SVvgCsz7Hwtd0IBBg5cqVxaJloVCIgwcPkslkyoZ6SvOIWXM2c8d4pZmff/55VFUturpPd97NEAbbon/mYPJZ7JIXFRcRoyP/mxnCLi8lI0xk8s4pmuwhmOvCho2c2YcprUAihV+7AEma+YfGbMSmUKVzId+j6bLgxaZQrnm+hs3GUsiL1t/fP6UL8VwxldiUZo+ei9ozs7VsSueuzjjjDBobGyddfzyxSeSybBvoKVtWY3dyMBhkeSBAeyo8ssxNPJPP4zSUTlGpOYjpGSg59Sq7k2AiDUC104UsZZEERHNZvvryM3zm3E2cWVU74/Mcj7FFy0rziHV05DusUhfsxcJCF5uxFCpdNjU14ff7i8GGpQXK/H5/UXwKwaVpM8VjQ79GIopTriSS01GkYQC8ihdJstOfG2aJrYqUCTbqCev9CAx8WhVCBEjofahqjgrb+2bV9tnMNZ0qtWxgAYvN2HLNswnQnCkDAwPs2rULgPPPP/+EjZNO1vl3dXWxb98+VqxYwapVq+bkGszGssnlcrS0tJBIJLj44oun5R0z3nFe7OmmzuGhMxkpLvOqDoZI0xGJstTvoysVJZkrT9+TyulIORlNlsmNXCu/6iBIXmyGUkmqnS7i2fz8TsrQ+ca2v3LH2RdwYf3kEdOzoTDktmTJkrKhnt7eXgC2b99ejEkolAReiMxnNP58Udpmm81WnHeD8YNLbZUqe9wvklFSLHVUEcyl8SousiIEgEOuoD/bCZJAF8PYpDpM7JgiPXJAOxG9Fb9ai01WcSurZ9Xu2c7ZWJbNPCKEIBqNEovFqKqqmnehKQybdXZ2cvrpp7Nr164TenPHE5vSyfdzzz23mBZ+vo43GdFolO3bt+PxeGbkkDCe2DzT2cZgLEm1y8VgOg5CMJwccS0Vgv5oghX+AK3RSNl2VTYn+/UYa1QPA7k4ujCJZMrneByySiydpcLmIJRNowuT+/a00JdIcsOqtdM+35kydqjnqaeeorm5mXg8zpEjR0ilUicslctMWWyWDUweZzM2uPTA8H7+HPkN6VwSW9TLIXs7iqoQsNvJqQKP2kzMCIMk8Ck+JHSG9Qx1mkZagEtuJqznsz2rsgOHeuasrlfh49mybBYQBWsmFApx9OhRtmzZMq/HSyaT7Ny5E9M02bx5M263mz179sx7fZlSxsb2JBIJtm/fjqqqE06+Hw8zsWy6u7vZu3fvtOryTHWc4VSSPUODCAS2tIJHseHRbPTHRr3QcqaJrMtU21wMZUeXp0bibXrjcRrdHnKKwWB89HcABYloNoPb1KhxuDARDCdSPLh/FwPJBP/rjHNQ5rmTL5xvZWVlcZgxlUoVh9w6O/NZEwrDPJWVlSfV02gxWjbTDep8JbqNw7ndSBrUOdeg+nSGMvm4lWRmkHTYQUjtxe2KoSoKTtXNsD6IKQxy5iB2eRlgAAJV8hHJDbHSdd6s2lx4v2c7Z3MqsGDEZmzsjKZp897h9/f3s2vXLhobG1m3bl3xQTiRZZrHHq+Qc6ipqYm1a9fOS0cwnfMzTZP9+/fT29vLhg0bijERM2Gs2Pyls33EhRRi2SxVDgcu2QaUi0Y0kyWt6wTsdsLZDD7NxmA8Vfy9JxHntIoqwnKGnDnyEiMxkMjvJ6HnyCVNVgT8DCfy2/2p4yjhTJrbzjkf1zxmDC5QKspOpxOn00ljYyNCiGO8q+x2O1VVVcUYoPnMaDyWxWbZFEpWTPZe5Mwcfxx6nH2JAzQ5fDiVenozg1TZ8jE+lfYATsVJ0jFAPV5iRoxc0k5bog1sOSqVCgychKRB6jUbsuRCk2tRpCxu9dh8g9Oh8L7NxrI5FWJsYIGIzXixM6qqzpvYFDrSnp4ezjzzzGMq1M008/PxUnhx9u7dS09PD2effXZxDHo+mCoRZzqdZvv27Qgh2LRpEy7X9OJWxjtOqdhs7Sz3Qgum03gUG4okYRQsAruDoWReNHyyHZ9qo8rmIkz5kNlQPEWN6mIolyQrDBrcXrqjseLvWdMglMjQ7AnQFg/nt0kl+fvntvK5CzdR7z45L7AkSfh8Pnw+H83NzceUai4kEi1YPT6fb14tj8UmNoXndqJrMpwN8/vB39OfHcAlOYnqOjEjTrXNT0YMIAmlOEcjSRKKLUMFS8k5VXSzC12XkLIaPXovqqERdgQxpFoqHGEa3ZfNut2F2KCZXuvjSVWz0DjpYjNRuWZFUea8xgvkvxR27twJwObNm8ftSE+02GRG5h5CodCEbZpLZFmecBitkGetpqaG008//bhiSUrFpjUcojceK/u90e2hIxJlmd9PWyIMEvg1J6ERYYlmMlQ4HGT1fAdTsIoq7U6CIxZLrdtF2EyjUt75VNgdDCaTkISVFRUcjYXI5Ax6E3G++OxW/s/GCzindu4FfaaOF+OVai4Elu7atasYS1I65DaX4nAqic3e+GGeGX6RqDlAQK1CkzSG9bzno0tR0HUNSaoiboQB8CpuJCSGcikabD5iQiZgb0C3D+EWbmqkSmLZFJlclFA8hHFQJ+LfXbwfMxn+nG1KrXg8blk2x8tU5ZoLHf5cvgy9vb3s2bOHJUuWsG7dugm/jo6nWudMKcSsAGzcuPGEjN+PN2cjhKCtrY3Dhw9z2mmn0dTUNKfHebq1jaUuX97KGLmdssj/T0ckmheEeIhQOl2+ExMyaQOnqlII5wzYHIRHXJ4HEklqXC6iY5wFKmwOwsn8srZQhJWBCrpHxC6Ry/Ktl5/nA6efzZtXrjru85xLxkbTx+NxgsEgg4ODHD58uKx2T2Vl5XEPuS1WsSlts27qPD70V16KtLDcWUmVUk93OshSx0gnLSBnZjBEAIkcSTP/HHgVH4O5fgxhkBVBnHItkqRhmFkQEqamopNgibsRj3w+yyovPmb4s+BtOFVw6WwLpyUSieOKqVtInBSxmU7KmYKr6FzkQTIMg3379tHf38/ZZ59djJWYiNkmxpwJpmly+PDhYuDo7t27T9hLP3bORtd1du3aRSQSmbNM1jAqNrpp8lxnB9FMhlWVlRyJBbErCr3x0RDNtlCE06qqOBAKlu0jYHPQHo7gt9mIjlgv4VS5INlkGT1j4tPsRHN5gSnkWSsgTKjQnMSNDAk9h02W+enuXewZGuTvzj1vzudx5spFvZA9efny5cVqmWNr9xSEx+/3z3jIbT6yPs8nYy2bwUyYRwe3cjTViSYp6KZEb3YIDYWInndtrtQqCebSpMw4yx2VhA3wKvVEjTiGMKjQ/MgYDOdSNNgBAX5tBRH9cP6Y2Kiwn47fUR5cOvZeeDyeoviMDS6drdikUqk5+fBbCJwUsSlN1DjRg164MbO9SQXi8Tg7duwoenZNx3KY72G0TCbDzp07yWQyxcDRPXv2nDCnhFKLIx6Ps337dhwOx5wEjI53nO19fUXLozUYYkVFACFBWzBM6d3PZkXZHAtQ3C6UTuMyZepdHnqjCUpRJYVwOo7HZqPK7kQXZnHep4BumAwmEnhsNmqcblyqSkckyqu9vXwh+jR3XnAhzf7AnJ37fDC2WmYmkyl6uRUSWJYOuU2nXPZitGwKQvNSaD9/HHgR5AQexYsmOenN9gNQY/cSMZL4lRpkNFJmGICsiOJTGkkYGcRIpgC37KY/24+CRMoYwKE0w8hQrldZSkQfpMF+Zlk7xqtcWrgX4wWXzrYfi8fj8z6sfqI4acNok80bFH6H/Ff3bDvAgtvusmXLWLNmzbS/+uZzGC0YDLJz504qKyvL8q2dSA+4wrH6+vrYtWsXy5cvZ82aNfNS10QIwdOtrWXLO8NRVgQC5esCg4kEqZzOsoCPjkSUaoeTYHzUionqBmuxE1GzxdxqiizRP2IhxbNZ7KbK8gof0dSohWRXFPpK1knpOtWVo0MT/YkE/7F9O5c2LePNq45vWO1Epqqx2+1ltXsSiQTBYLCsYFnpkNt479FiFJusbHBf5+PsjrXR5AygSE560lGWOl15T2VAlQQVaiMdySDV9nwnH1BdqJKD7swQyxyVRHRwy9UM5YYxRI5aezWS5GcgM0CtzcApNyCQaLSfjTJFhufS4NJCUtdSd/eCSHZ2dk77QwAsb7QTgiRJs/ZI03Wdffv2MTAwMCu33fkYRiut7jlemYIT7W49ODhIe3v7vHu+xbJZtvX1li3z2mx0DkXwKTJhU0eRZRo9XnrC+bH0nkicJT4fNlkuZggAQAj64gmcsoqkQSKXo97loScy6niQ0XUi8QxNbh+difyXa73LQ0dkNEhUQeLIYIhmv5/OZBTDFGR1g5+0tLBrYICPbdyIb5EVVZMkCY/Hg8fjYdmyZRiGMW7tntIhN0VRFp3Y7Ip38ox2hHRMoEoqChodqXxpgGyhsqYpkTM1ujPDVNu8pMxhEAKP4qc70z2ybhSPUoMpVLJm3tlEkWz0ZbqotlUiSymGc3H8qk6T4/oZtbE0qWshuLS1tZX+/n6GhobKPgQKls9EGcVPJbFZ0NFcsxnOisVivPDCCySTSbZs2TKr+JC5HkbLZrO89tprdHZ2cuGFF7Js2bJjXvATJTaZTIbBwUFSqRSbNm2aV6GRZZnXBoZZMiYDs1+xEYpGiUQTSKkU8XicRDRGTs+NRFoLBqNxhFFuJQQUlWgmSzCVxmYqeG02FMqvo99upz+epDsUY4UnAIA+5rrWud3owqQ9HKFKdVLtdBWto+19fXzuz39me2/fcZ37ye7AFUWhsrKS1atXc8EFF/CGN7yB5cuXk8vl2LdvH88++yw7duwgnU6TyWQWfPLQuJ7mvo6t/KT3OaJKigrVh4aLnsxIfjPVQUSPYpfs1Nkb6R5Z7lZVZGR8agMRPf/BUaF6UHExkE3gUMTIsib6R4TIKWtE9Bx+1YdTDlBlW3lcbZckCU3TcLvdnHvuuVxyySWcfvrp2O12uru7+etf/8qLL77IwYMHGRwcLPPCna3r8ze+8Q0uuOACvF4vtbW1vP3tb+fAgQNl6wghuOuuu2hsbMTpdHL55ZezZ8+esnUymQy333471dXVuN1ubrjhBrq6umZ1HU6aZTOdl3EmnX5pEa/m5mZWrVo16/iEuez4I5EI27dvx+v1Tlrd80SITSgUKs5fVVRUnBD//VcGh0jKMnUeD/3JOLlcjrbePjRNQ7HZ8NnteMkxnEiTyeVImWkURabB7aFrOEq9301fKj9HY5cKNRMhnM5Q6XSQHpNDrcLuJJLMIoC2YIQVFQG6RyycIiX96mAySbPPj8uj0RnLd0bCFNzz/Atcunw5HzjnLJwnMMhyvhg7zFOoGRONRmlra6Orq6tsvmchlct+LdzGL3qeJ66nqFJd5LIS/ekUjU4fwZHMElWak4yQCWdNvEo+Px4CMmYKu1RFxsyQG8kZ7lW89GT7kATE9SF86lKQTAQCu+xiOBcmJ7I4ZBv19rPm5BxKHZ0KHwKFpK25XK7obFBIb/TXv/6VYDCIoiizuhfPPPMMH//4x7ngggvQdZ0vfvGLXH311ezdu7f43t9999384z/+I/fffz9r167l//v//j+uuuoqDhw4UMx9eOedd/L73/+ehx56iKqqKj796U9z3XXXsW3bthnPQS3YYTTIe6RNJ9ZG13X27NnD8PDwnOQRmwvLRghBZ2cnBw4cYNWqVaxYsWJSgZ3PeSIhBB0dHRw8eJA1a9aQyWTmJYZpLF3xBP3JFE6nk0Qqi6YbKNkcWZsdu6blc+Bls6ypqEA3I6g2G6Yw0XUDWRfE4nFSiQQBr5OoqRPJGZRWfvaoNoKJFNVOJ0OpvAzFM9nyczcENZqLoJ4ibRgoskx/vNzBwDAEXdEYyyv89CbjVDmdxDJZnmlvZ1f/AB8571zOqZ+eBbjQLQQorxnT19dHU1MTdrv9mNo9hQ5xrsplz5ShTIwHOl8iaSaJ6yl8qhu7odAtgrhxo8mj11qWbPSlIiAkQnr+WajWfKQMg4geodlZwbAOAaWO4VwYU5jU2yrQZInu9DBLHBqa5MKtVBM12lCFnYge56LAOXNyLpMl4dQ0raxyaTqdZmBggF//+tccOHCAG2+8kcsvv5wrrriCt7zlLZx++ulTHu+xxx4r+/u+++6jtraWbdu2cemllyKE4Lvf/S5f/OIXeec73wnAj3/8Y+rq6vjZz37Gxz72MSKRCD/84Q/56U9/ypVXXgnAAw88QFNTE08++STXXHPNjK7Bghab6XT6pen3t2zZMidfZLIsH1dnrOs6u3fvJhQKcd55500r7fxUDhOzxTCMohAX6uAcOnTohHSKLw0MAfkOeDAcwSVLNNXW0hVLlBoXJNI5qjQnAyIJJjjsCmlTwePxYhgGqaxJrarQYWbIJRKoioKqqpimSTKXwzBNGjwe0qbOUCJV1gbDNOmLJfA77LgdNpyaRldk1NKxyaPOAx2hKAGHvRj/AxBMp/jFrr38pa2DD2w4C/8c56k72QghUBSlGCsCFHMTlnpWFSplnohy2TnT4LH+PfxPfws506De4aDRXktHMoxbSucj/5EJ5SKokkK1VsuRRB8CWOLwETYG8SsBNNnBUDY/HJoyo1SojaSNHJmRuR277KQz3UNAc5M1o+SEF4M4Mgp+rR6brOBW5iYMYCbeaA6Hg/e85z3cdNNNNDY28pOf/ITOzk6efPJJhoeH+cY3vjHj40dG5iwLfVFrayt9fX1cffXVxXXsdjuXXXYZzz//PB/72MfYtm0buVyubJ3GxkbOPPNMnn/++cUjNsc7jFZqOcwmSeRUx82MCRKcLrFYjB07dmC329m8efO0xW8+htGSyWQxoeemTZuKCT1PxJBdRtfZMTiMKQTxRBxZVtCcLiRDQZEldDMvNz6bjf5YHAE0+rz0pmM0ur10hfKT/vkXVMHv9VKRzpK2aflCepkMh7p7kRSFrKqS1XXW1lURSozeN6eq0BfLWzGRdAYtK1NTVe5GWu/20BkeFZ+sYdAxHGVlRYD2eATDNBlOpWgLR2jpG+DGM9dz5aoVyFM8ayd7zma6jJdnrLR2z0TlskuH3OYyUey2YCeP9u6mI5Of9K+2eTGEoDURpNrmIpjJexnWOzzoZMgYKqYwih8vNgWqpBq6kjGqHHnnkirNg02y05EaZqUrQCYHlVoj3Zk+QOBXHcQMCUGWpB5Ck+vJiAjrXVfN2XkZhjHjANzCcOc555zDO9/5Tu64445ZHVsIwac+9Sne8IY3cOaZeRfuvr68CI+ds62rq6O9vb24js1mOyaotK6urrj9TFjwls14FkahamU4HJ625TDT486mMy64Wjc3N7N69eoZdThzPYw2MDBAS0vLuNkSpsqNNhe81N6FRt7Ks9tt2O0Olvp8dAxFWFrhpyMRQQBVDifxZH7oqycaY6nfixjTNE2W6Y3GSWQMmmtq6E0mWO710T4cRjfyFVwzmQwHU2mqPE769RyKolLrcpcJiQDaBsOs8AdoT4QxOXbYq9blpj0ToS0YodLlwOe0czSc/ypM5HL8Yvde/tLawd9sOIt1NeXVRRcjU3mjjVcuOxaLMTw8TG9vLwcOHMDpdJYNuc2mdk9bIsjPO7axLzrAKq8fVVKot1UgSdCZzlvIPpudYCZ/v5yyk7ZUkpyZxa3ml8lCwjQ12pND1Nm9JMxhJAFuxUNHug9JSMSMIBVqI0KYmMJAQSVu5IjoEZbZK9FxkjGTSGaaJsfUw1XTZTbB6clkEiHEtGpHTcYnPvEJWlpaeO655475bey9n4534mw9GBe02Izn+hyJRNixYwdut5stW7bMaRBigZnO2ZRmKJhthuS5sjaEEBw+fJi2tjbOPPNMGhqOzVI7X0N2pW34zSs7SCfSeGwaij3/5ZvJ5q9pVyjCiqoARyJBwsnybADBRIoalwuJ0Xn8Bq+HzuEIuhAMR5IsrfCiGyayImNTbNhsNqocDgaicfriaao0he5kjMGcTsYwUVUVRVaod7vpDsfoCEao83pIox8zf5PVR+97MJnGb7Oz0uunKxEnaxrUuty0BsP8v6ee5aKmRm4+63RqvaOuqYthzqaUmXYcpbV7IP8xURhyO3ToEOl0esJy2eMxmI7zy44WjiQGGMrm74UqqajCwdF4mEb3aBB2XE+goeIynXSmQuRMA49qJ5iLokoqjY5a2pL5XGhuTSGdVXApVQxmwwA0OHzYZIXO9BDLHG5kSaVSayCo57/kTRSGcn0ssVXT6DgHRZq77nE2QZ2JRP56HI/r8+23384jjzzCX/7yF5YuHS0iWEg+3NfXV9ZHDAwMFK2d+vr6YrBqqXUzMDDA5s2bp92GwjO2aIbRhBC0t7dz6NAhVq9eTXNz87wNVcyk4y8MVcmyPO0MBcd7zInIZrO0tLSQTCYnraY5n5ZNLpfjyZde5tBwEE3V8MkqhqzgsWn0R0fT07QPh1lfXcWB4fL0NLVuNx3BCMsrfLTHogjALHGB1k2TeCJDwFU+dOO12wkqaVQln0PtjLoAPdEopqmTHMkmEB1Jk6SoKgOxBMsr/HgUG10jOdNcqkpfLF6231AyTSSdwWu3Ue91oRuj1+2lzh4G40nWVFXytjPWLsr5nOONs1FVtWxyu3TIrVAue7zaPcOZBL/t2sMzA614NBtxI0mF5sSjuDgYHUYAPtXGcDZ/Pyo1Zz4djZ5FkSBr5oN6a+xO4oZJ1rCRNkeHUNNGCk0KIDBImfkPGpvkoDPdi1u2EzMi2KRqxEimgHrbUgZy7WBCTkg0O8+Y9TUZj9lW6ZytN5oQgttvv52HH36YrVu3smLFirLfV6xYQX19PX/6058499xzgXz/8cwzz/Ctb30LgPPOOw9N0/jTn/7ETTfdBIyWQLn77run3ZZCf7OgLZuC2GSzWXbv3k00Gi1Ocp+I405FoR7OVIk9p8Pxik3Bxdrn801ZTXO+LJt4PM5rr73GtqEIHreHXC5LOJ1lbWUlmioTiZVbMemsTrPPT1tJVc5EJt+JdIaiLK/wMZhO0hstzxZd6XTSMRShuXpkWyGKWaALqLJKhd2Nx+kkmskiIwhncmRzOcx0BlmWicgSw5kcK2oq6UzGqHG76ciOtqXK5SQ4UkcnlsmS0XVqPG4qHHZC6QyyBL3ROK3BMM+0tnP16pVcvboZWFxzNnPZ1rGVMqPRaFnyyoxNZruUJKTk6NLjSJJEtd1JhemkIxHG63EWLdpqp5OudBJFUqjSfBxMDJAxcvhVG4VPAhWVcAZMkcYgP+RebwsQ19PE9Bgr3AESGajSGoqpbKptHlKmk0guhpBSVKiNmOSHcmvtTUiSTpU2t/Fns7Vs3G73rPqVj3/84/zsZz/jd7/7HV6vtzjH4vf7i5nD77zzTr7+9a+zZs0a1qxZw9e//nVcLhfvfe97i+t++MMf5tOf/nSxxPlnPvMZzjrrrKJ32lT09vbyve99Lz93POOzOIGoqkosFuP555/H5/PNee6uiZhKbAplpLu6usathzMbjkdsurq62Ldv37RcrGF+LJv+/n5aWlpoXNrE0e7gSBvy7eiJxFg55gPBpij0ReLkDJPlVT7ao1GqnE6GS6pvdoairKupYn96uGzbxIh7c8dQhBXVflKGTv+YfGmZrE4omcahqTR43GiKTGcohluz5TtYYTKUzMf27O3opsLpIC0lynJveW02goyKWL3XQ0coiixLrKjwYyCKc0IZ3eClzm4eP3iE5RicnUpT7Vn4dUjmMxGnJEnFITepuoK/du7luf42srkcblmQ0rPUal76jQhhkUNRFLJiNIFqVuSo1nwkcoKBbJz895EgKlKo2FjqqOVQYgAhoMnlZyg3SJVWgSwrxDLpfOXWXBi/WodARxc6CippUzCUDbPUEUCVvQxkwvi0FH5lCVmR4hzPBXN+LWYzZ3M85QXuvfdeAC6//PKy5ffddx+33norAJ/73OdIpVLcdttthEIhLrroIp544omyEZF/+qd/QlVVbrrpJlKpFFdccQX333//tM5l9+7dfOhDH2L9+vWcc845C3cYTQhBOBxmaGiI0047jeXLl5/QrMgTiU06nWbHjh0YhsGmTZvmLDByNk4JpXNFGzduLCYFnIq5tGxK54jOOussDscz2GSFJKOdxhKfl6MDQVbUVNAaymfibfB6aBsKA9AVjNJU4UWRZEKUWyhDsSTLvD46RqyfgMPOcEnVzvahCKfVVTHAqDu1x6bRP+KFls7pZMI6a0sm8yVJoilQQVc4imqzY5omJiY9wzF8msSAnkNVVQZHcrsVnjtjxIPONAXtwxFWVgZY4ffTGYuhmyYuLX/c1+IxPv2HJ7lm3Sre0NzEsoq5cZ+dD+YzXY0pBNuGe3i86zChbIqedARZUWh0uvHZHfTH4yT1HIlMAt0wsCHRlkugqAo+zYFi2uhKRqjQHIRGSoTXKk4ypPHIAXRhUniMFdmk1lZLTzKGx55/9uocPkwB3ekQK9xuVEOlSqtlMJuf17HLNjrSvSx1VKBIHsJ6DKeSZrXzzHHP53g4HstmNkzn/ZYkibvuuou77rprwnUcDgff+973+N73vjfjNlRUVHDbbbdx44034na7F6ZlU5h7iEQi+P1+mpubT+jxJ+r4h4aG2LlzJ7W1tcddWGwsM7VsUqkU27dvR5KkGc8VzZVlk8vlaGlpIZFIFOeI/vV3f0Y2JRyqQjaXL3hWmHNpHwzlh77CEdLZUS9DIaA/HGeJv3yOqdLpYDiWYpgUSwNe9sdj+O12osnRwE1FkmgfjLDE7WUgkyRrGFS7XCRSo8NhNkXhSH+QZQEvfan8OoWXUZJAUWSaAhW0D0dII1jm9iKETnc0nrd0FBmnzUZ3OALS6JBGfyxBMpvDZdOo9nvLauropslfWzv5477DnFZbxZVrV3Le0gbUeay6ORvmQ2xCmRRP97SxbbiHw7H8nNwKrw9FkljqDKDKCoejeQ+zFf4K2lP5e7HU4aIjOYw3p0Eyy+54N6qi0KDaiY600ylphHI5Upk4qpKfI9MkBSE02hNBlrp8BPUhHJINCY2+zBBORSOmx9CkAEj5567WVkNnuhcQ2GSVrnQfS+yVLHOeiSbPffaE2czZJJPJaSfsXIgsWbKkaEUJIU6u2IxXxKuQFbmiooK1a9fS3d19wts1dhhNCMGRI0dobW1l/fr1ZV4dc8VMBKAgevX19axfv37GD/FcWDaF+RmXy1WcI2odCnF0aCQOwu8hkUpjU1R6SxJldg5HWV1TyZHBYJnHWa3bTc9wjMaAh56RIEuvzV5MxNkdjlEhq0RS5fFP9V4PPeEYPZEYVW4nKUUnli7PIlDncdMZjNIVihFwOtA89mMcAUa90CTC6RwrKv1UePy0RUJkczkCqkL3iLWkKir1Xg/hkbmfZDZHIpUlpxss9/nYH49T7XYSHCnwtn9gmHAqzU9ebWFLcxOXrGyiKbAwrJ25EpusYbBjuI8/d7eyc7gfp6qQEvmO3S4pKJKCTdhojUaocY1+GGVLLGC7ZmO5u4HOZJSGgJdoMoKu6wwlwiT1NNWyn3YpiiEL6lQ3w9k4DtlGo6OSo4n8nIQmm7gVF8K00z8Sk9Ng8xAzbQxnYyBBlVaDLEkIBBVqJT2ZAfJPospK59y5O5cyG8vmVKjSmcvl0DSNp556auFYNqUdeiEr8uDg4Aktz1yg1MrIZrPs3LmTVCo1qYfX8aIoCrlcbtJ1hBAcPXqUo0ePHpfoHa9lU3CMKJRuKHRWT+w9XFynLxKnweUmnU4RL9E1IQToggavh56SiX9Z5AM9B8MJGgIe+uJxBmPlNWkwwSkU0opMdsQrrFRmhxMp6rxudMqFtNSDLJxKs8zuY6nHR/vI0JxLK/eUAwgnM4RTaSqcTuweL4oiExX54VXd0BHZLPFEElmWURUFzeVgKJ0lls6imhL1LjeZnEFipIibXVXpiyX44/7D/HH/Yc5dUs/q6kouXNZIvffkdSjHIzZZw2DnUB8v9ffwykAPdR53sRZRjdNNNJfGrzrJmQaHI/nhU7/NzlAmL9ouVaM/HUeRZJY5KuiIR8iZJi5VZTCbzxRR6/KgyeDMgWIYZFMxhC4wIlEUm0RSMonI+XunyQq6ECRzEjUOlYGsiUu2kzB1grkYS5x+VAm6UxHqnBI+pQKHohHPGdRrjRgY1NmPf/51PGYzZ3M8w2gLhdIimAtCbArFxNLpNBdddBE+nw+Y/yJmE1E4bsHKCgQCbN68eVbBatNlqmG0XC7Hrl27iMVix11Nc7aWzdj5mVLHiHgmy/NHO8vW74smqFLkMkWQZYm+cAzDFNS4XQwkkrhso9aPbgqGwgnW1lZyYKDULVpCQWIgmqDa5yYh6+imQd8YkXCqKt3BGE0VPjqjUVyaRl+kfB3DEPQMR2mq9DKYTlLjdtGeHuOFNuLdFk6lsWUU6v1uPHYb8UwWRZERNjte8mmNdF2nNxgintWRFQXDNOkejpDOGSwL+MiYBsHk6DyTIkvs7h1ge1cfv9qxl3W1VaytqWLDkjpWVVWgnMChtpmKzWAywc6hfrYP9NMZi9A/EhsjCcFQOoEMLHH6UJEJJbMEybLC7ytuX+VwEB25tnUuN6bpJJjKogtBbuT5r3W66EylkZCosXk5GBvCRLDC42cgqyDLMrW+Wg7HhkDP0B4KgSzRpPnpzEVAkalGw624cEouhnL5bAQu2UFbqp86hweZDOFcDlOKU6HWkRNZzvOcN0dXtZx8JvPZic1it2wKz9ZVV1118ofRBgcHaWlpobq6uqyYGJw8sSkMS7366qusXbv2hDgnTCY2sViM7du3F4esjtcjbzaWzXjzM6U8e6ANn93GcEmn2uBx094f5PQVS2kLhQFY4vPRNZTv2JPJLFUOBz6HnfbkaGevm4JMSmeJz0N3tPDVKhPJ6DhcdoZjKfwuO41+D0eGQqONEIJQPI1umHQPRWmu8YME7ZnRfTtVlb4RYesOxvA4bKii/N56HbYyV+par4uO4SiaIrOiwk8Gnd5Iopg6vtbnJZhM49JM9FwOJ4LuoRCSJJFMJqn1eVAVleaAn4FEkgqXne7wqABmdJ3f7znI7/ccxKmpXLhsCQ0+D+tqq2iuCKAq8yM+o/NWEz/bffE4B0LDHAgOs2doEEmB3kS+7SsCAUY8hlnq8aHIMoN6isFEkpxkFu3L4ezotUwYeUuv3uHF1CXaRmKc/M7RZzordCo0F5KhMpBOYCJQkRnMRJGFxFKtgtZMBMWm0ez205MZoho/aT1BMpPGZkgMZjNkZI2lHhUE1NuqaU/1I4SJR7XRl0lSZXehSBpRPY1NTnOae/0cXdlyCn3YbOJsFrtlU8pJFZuDBw8W50GWLFlyzEM/Ubqa+aSQCgdg48aNx51BerpMJDY9PT3s2bNnVilwJjvWTCybQulop9M5bgyPaQqe2H2YXNbErWkkRoYDjRHHgI7BCE3VfjrDEQx99BxTWR0JiZxS/kHh0lS6QzEkCZZWeOmKxmjweggPh4vrRJIZfDY7NW4Xg4n8cFudx8NAZNQFumMwwuqaChyKQnrkha/xuOgcLik5IATtfWFW1PjpisXImSaRMVkNCpcqZ5i0D0VYVV3Bcr9KRySfcsfndBBKplFkGcmm4coKZIcrP+Sm65iZNN3xND3DMpqqUW93sMzvoy8eJ2uYxaE2yA9NvdDeVZxDavB5cNhUmisCNAV8NPl91HrdVLpmFzxcfl6jYhPPZumLx+mOxehPJjgUDNIaCVPlctI+UnrBqWqkSlyTBbDSHSCazqKi0BoJA7DM56c9kd+m1uVicCQw06vZEAIabQGGkymyUv5eORSV/nRedNyKhh2NvmQKv10mksvf20aXh5SZJmPaMUuGSQ106m01DGWSCM3ArblpdlTQkRgmpWfoi/Wh6TYitjApLU1A89KfiZA2szhlH93pCI12H6vcZ6DJ8xNWURCb16NlU8pJFRu73T7pPEghXc2JqiYYjUbZvn178WviRN7osWJjmiYHDhygu7ubc845h9ra2nk71mRMND9Tyo6OXgZG4lxqvC6yhonXbmMwmM8AIBD0BWOsrK6gdSBUtq3XbiMez+J32Imk85P/tR43HekICOgLxlhW5UPXTSgplOZ12OgKRlEVuShIjjHDnH6nnSP9IXxOO16XjcFkimyuXNiq3HnPtY7BCD6nncoKJ0eD4eLvNlkpn88RgoFogng6S8Blx+u2E0uVi1PKMJA0CUVR8/+cdjzkP5xMw+BAVx8500TTVJoCXjRkfHYb0UyWOq+bnpJhP5uq0DocpnVEaCtdDoLJdN7KqgygKQoBpwOP3Ua1ywmShENVUWUJZcQiMk2BJEE8myOt6ySyWWK5LMFUigO9/Tzz/AscGnFJd2oaKVNHjHTofjHqmVXjcpIxbLgUjZxh0BGKFtfLSaPXtVQMPJrGYBZq7W4qbU72RfIxU81eH+3J/DnVu9x0psPU2T14VQeHRjzVAnY7kVRhfsdObzRBxMjiEPnz8ilOMrrEQCbMSk+AnkyCensVETOFUCVWeWuxyRJt8SBuRaBmFXLJDCF7FJ/ipt3oBwWEpHKae34cAyAvNpIkzdiyicfjJ+xjd74ZHBw8uWLT3Nw86TBZ4UtgNuOdM6G08Fohg/QTTzxxQofwSgWgNJZn8+bNuFyuKbaeGeN5AY5lsvmZsTy+61Dx/wdjSZZU+NBUmcEScdBNE1nPWxaDJYGbdllhMJ3AI9nw221EMlliJR5nQkAsnsHvKndHrXLlE3jqhknvcIzmat8xgZ0VTgfRRIZoKoOSkVhVU0HrULnYJTOjX+rRVIYKh4NmX76uTUY3qPPlPdkK1Po8DI5YT5FkBkWSQMAyX36OyG+30xON4x3JXFPldjKcSCFJMppmo7HaQ284jmoaGLpOKpXhyEAon0nZ7cBhCJZ6PYQyGRLZHOH0qJAFHHaCI1ZXzjDJGAYHB0fntfwuR3H9Bq+H3vioaNX7PPSOeODVed30JeMgBLFsDk/JMWrcTtpj+fO1yTKGKVjhDZDLmZg6DIzcu+UBf1FoalwuBkYm/W2yQm9y1PFDAhptfnpiMSRPyfPA6MeOLEGToyJvQZV830X0FHZJpVbzczgaRBcGFbJG3MxS76jEq9ppS+WFKS1SNNpriOtZYuaIQCkOjib7qXV4kOUMmurAFzCx5VScuspgth9P2kkwESacCiJXUiyXPZfMtv9KJpOL3rIpeOHdc889C8NBYCIKN2g2boPTRdd19u7dy9DQUFlg5GwzP8+WQiBpwSmhqqqKM844Y17Oe6o5m4IzQjwen9IDrysYYXf3QNmywWiCpYVJ4WLQnUTvSNmASpeTYDKFQ1OKy+KpLF6njWUBH12lw1xAhctJx2CYWrtGQgCSIJQY7SCFACMnqHO76YxEGIm9JJwcFS3DFJg5k6VeL/2pBFndxO+0M1Tq8TaS9iaezuK0a9T63MVAzgJOrfyV8TkctA2HCSfT+Bw26t1u+odGBcDrsDFcMv+jKTJI+edLURRcXjceZAw977J9sGeAjJHvnBp9HuxCpsLvz3tp2TTC6dFzSpQIZaXLwXCJaNjU0efGoarFmj0AjpFzEIBTkRlKJXGqKn6bA7dio9ntJ5XTcdpU2kfqoNhUBV0afWbSxujwtsdmK4pNndtNTypGs8uPisyREYvJo9noT+fXcSoKvakYMtDsrqA3ESdl6Hg0G32pfDtrHC5skkzGkDBtJrrIf/g5ZYlKLcBAKo3hzC+r1BzYcXA0HmK1108sA0sctbQnBwGosDnpz+i4FIWcyGJKDkxXimXyCkxfmvOUM8jF8+Wyc7lcWSLRuajdM5sYG5h9SeiFRKH/Ovvssxe22MiyjCRJ6Lo+L2lq4vE4O3bsQNM0Nm/eXFaX40Q7J0iSRCqVYtu2bUXX7/lMNDqRZTPV/MxYnth5iGUVPjpCowLR6PfSNhBimd/LwZEOcInfR9dgvuNSZRm/047fbqMjFS3aP7FUllqPG5/DRrQQKyMEoZGMAUOJHOuWVZFFMBgZ4xYtoHMoQp3fTTiXwWO3MTRmnVg6Syiewuu0UeHVcGgq0ZL6N7U+d3G/qUwOM2xS4XJS7XIylEwhAcNj3LEjJUNosXQWFQk1Byv8fsLZDOESK02WKHPndmkaA7G8o4GqaTRVVdAbiaOaJrqhYxo6h3oi+d9VJe9ooOfniPxOOybgDfgxBbjsKk5NwxAmhikwDJMalwsZCb/TTjyXQ5EkZCQ05BFrRSeSyqILjXTKIJJNM5RIYYw8G0u10Y+MOo+bjhGLx6lp9CdHrchQNn8N/JoDj2LDaWh0hKOsKMmcUONyEhu51nUuNznTJJ0zMAWkRoSrxuGkPZlGlWQqbS4ORIbzsTDO/DOoIGEYEp2pOA0eH+FcDFVSqdA8HEkMIiMRzEWpt9UghImBiUd20Z+JkdDTLHFUMZjT8Ws2FFkhrmdwqgYXLT0fWZKL9WMKWawLtXsKwjPbctmz/Vg+FeJsID9K8r73vW9hi40kSfPW6ReylxbmIsZ+eZxIsdF1nY6ODrLZLBdddBGBQGBejzeRZVOogTPZ/EwpsVSG5/a3Ywqo9bsZiCVAiGIH3jUco9ZhIwlkMqNfwvFMFp9sJysZ5L+v88fRFIXO4Sg2VSnO4dT7vfSHRr/KO4dinLGkhqA02ik6NJX+Ee+u/kgCr8NGwG5niNGOvdLtJBQbSaqZyhJPZVlVV4kiSSX7KRfWWq+bzuG8GDZX+zEVUeZcUOl2FAM3IT9HFIynyJmCjqEIVW4nqqLg8/voiecTfZa6ald7XHSESjIdqApIFEsnKC4HXiFhGAY2oDcUzdeTSSRQKnz0p7LFTqzS4yy6V9d5XPSERoeyNFkuupZXe1wMhvPXRZgmimFijDgj1Hk9xSE0TZbLrKHST5Nat4u2WBjI1yNyKxpuu8ZQMsWRbKjowhzMjF6bpFHI0uxGFgrdsXx7HLbRTjhlZqlzeEhnTLqTMQQi7z2YjhJQXVRqbnYl2rDjwKfZ0HGgmBr92Xybl7jy5QzaEiGWehx4FTd+1U1vpg+HbGM4lySmp2iwu+hIRWhy+dngPRd5JCtEabnsQu2eQiLR7u5u9u/fj8vlmnG57NmKzakwjAYLJOvz8VbrnA2mabJ//356enomnXg/EdUsIe9xUkg743A45l1oYNQFs+B4MZP5mVL+vPsIuZGAyWQiS8DpwGXT6Bse7eiGYhk2rK3ncF95KQGnppJO67hsGqmR1DX1fjedQ1GyuoHLplHpcqJK5R8BmizR2h+m1uMmmEmR0Y28KAyVZo7OMjBMmcXltduKYgNQ6XFxpDdIldeJKUMwmWJozJxP4dwEeaupuTrAiooA3dEYWcPA67CXiU3A6SBckrfN7bDRMTQ6DFVpcyB7oS8WxwRyJc+1RLnVE3AWhCzvaLC0yk/7cAQhTHRdJ5HJkkrl1/c7HfRFsqiKiizLOGwaJArXSy7LluC2aQwmC/dAYzAZL06TmNKopNR73XQW5m4UpejuDJATBktdXlRkFFnmaDgMwFKfl85Efptqp4vBkRgcj2YjaxosdwQYTiYZTucb4FZVekeGzXyaDadkoy0aoc7loX8kF1q9y43ATU8ijlfT888sYAhBNicRsCsM5jLYZRUZhY5kkGq7C1MYJHICl5LGIduptwfoyvZSoXjpSoeQhIQhJM70rmMiZFkmEAgQCARYuXLluOWyp1O7Z7Zicyq5PsuyvLAtG5hb9+dkMsmOHTsAppx4PxGWTcHTq6mpierqanbt2jWvxytQEBvTNBFC0NLSMq35mVJyusGfWkYzBiSzOQKaMiZmRUIAetakwectKxVgk1X6E3ECbjvYJFLZHOkS6yeZzeXnCSi/B3U+N33hJP3hOBUeB3ZVITkmPU2Dz0NPME48nWV5jZ/uaLQseSeAx64RBIZjKWRZYl19JYcHR50HnDataC3lzwQGInGS2fxcRmOF/5i0OPGRvwsdTqRkziirG7QPhkmPbF8XcJMTJjZZyRdl87qLyUMhbyWFSoboCrnkJEkm4HGTzObweLwYhkHAZSMajpI28+lzukwdXeSf4Tqfm86SdEGlDgdVbgcDkYJVKdNXWkiu5DbWed0MJpM0eryoSPREY2RGrKGAe3ToubSj9dg1BnNQaXdS73CzLzxMSGRoDgRoHckyUOt20ZYM0+T0Y5dVDkfznmpOTYFs/hlRUDgcDaLJCv2ZvJCtcFXQmYhhCJNaxY5b2HFJTrrS+f1W2Vx0psI4FIWYnkTBRdyM41U8eDQH8UyY5c4G6u0VuJTp1yAqLZcNlA25dXR0IEnSuOWySzOJTxchBIlEYt4ylpwMFrzYjFetczYMDAywa9cuGhoaOO2006a8+XNdprkU0zQ5dOgQHR0dRUsiEomcMIeEQqcQj8dpaWmZ9vxMKS8f6iym+i8iQM8aKIqMYZhIUt4S6R7Kp+Wv8boYjCVx2TR6g1F0XWc4alDlc1MRcNATLK9b47fb6Q/Fqfd7isNPpa7LoXiaOr+bsbNPpZ1e52CE5ho/oTE51Yajo1aEaQpSaR2fZkezKQzEEtR4XXSUBIPW+/NeZJCPD0oksmRzOs0BPz3ROG6HVhYIWukerYVT2L4gXumsjmlAz3AMRZZo9HkIOBxgwlAyiSEE8ezotfXYNQZKhKDa46I9mG+boihINjtutxshBH5bvjCcbuStgKBkks0Z+SJnHhdDJWKTKalKWud10zkytKVIEqFUiiVuLzZJQTYlsmmDzlS+xlBhu0qnk+F0/hxVWS7zQgNY5vDTFY2iSaNzhGlz9INCkiSW2Hx0RmJUuu3FY/el49TaPahC4Ug0/wHQ6HHTlw5TZTjJChNDmNhHhFo3FBwOBVM3qbX56UpFyJg6y11eQjkZVYKsqaObKkGGqdHqiOgR3hG4nOOhUC57yZIlxXLZwWDwmHLZBdfnmWLF2cwhJ2IYrbRjP+OMM2hsbJzWdvPljVbItZZOp9m0aVPxYTpRw3aFYwG8/PLLLFu2jLVr187oZRBC8PtX9tPo99EZDOdTJwN+h4OOwTBLqnx0jdR5qbBppE0TTIjHs1S6nPgcNvYP96HrOYTIZ7BurvKhSYKsOfpcJFJZsrrBcCjB0iovHekkQ7EkslzuadUzFGV5tY/2cBSnptJbMscD+eDSVCLLskofHeEoDX4PfSXrqLJMfzhBVjeQJFhW7SOnlz9zY6P4vU4bwXiK5EAEu6bQ4PGQyxnERrzFPA5bmdiUeofB6BCaYQr6w3FiqQzJTA5FkVhW4UPIEm5/PkDW47QRHx4VvnRutMP2OWwMjQS1SpJEhddDVDcQAhQJYkYOXdfJZDJ4hE5azwuPx+lgIJEECRyqgkvVWOH1I4TAJsscCYZJJuKosoxQKHr4Zc3R6+J12IvZAeo9HrriEZZ5fHkrJZgfNvXZbUVnAo/NRn8qjl1WaHYFOBoOogtBlcPBcCZ/Dg0uD5qi0BaN0Oz3I7L5A6uSREDx0m70k83GQcrXsOlMRciaOm4NGuyVyJJEWs/hkDSCuTShbIK13goGszmqbHYUyU5O6DQ4aqi1VzJXlJbLXrFiRVm57KGhIXK5HNu2bStaPT6fb8p37lTwRitlwVs2xzOMlk6n2blzJ7lcrqxjn+5x59qyCYfD7NixA7/fz6ZNm8pS88xnqeZSCsk8AU477TSamppmvI/trb3FCegVtRW0DodxqCq9I/Eo3cNRltcEaB0cJpbR0Zz5r9Z0TkcG+sIRTNMYeZEkFGAwmkbGRDcNchQ8w+L5+2AKegdjVGoqwZJbIkswFElgmIKugSjNdSPpaQZHO2ZNkRkIJ8gZJl2DUZqqvchK+UteH/DQNRQduT4QiafJ5UyWV/jpCEWQZKksMwFClAlJJmfQH4yTSeks9Xvoy6aJlMzlSOTdwQs4bSr9JYGbtX53cf/5rAsSHSXnYBcKAcWO26Fh1xQE0DziDu12aKjJFLphYAiTZDaLQ8nnD6vxuAmnUqheGVWW0CRwpFLkMlnkeAITCUOXyCVztOvh4hxVU4WvaC3W+9x0jFg8Tk2lLzF6HpERLzSPpuFVNbyyna5wjOYSL7QKl4twLC/ANU4nVTgZSqTICQN9xNrxOewMx5NU21xoKLSOJEiN5PLXeKnTR08iSUrPUamopIVJs6uamJ4la+rUOty4ZI32RIQmrwuXYqPO5qcj3Y9TtjGUTRLT0zQ63BxN9rPaU82F/rOZT0rLZauqSiqVorKykmAwSGdnPofgeOWyC5imecpZNguruMY4zLbTHx4e5vnnn8fpdHLxxRfP+KbN5TCaEILOzk5eeeUVli9fzoYNG45J6lmwpOajXHMBXdfZvn07PT354lGzjU5+9NX9xf9vGwizvCpAg99b7KwAOgbDrKmpIF0y7GUYBnoqQS4LjdVVSJKMJEFTdQBFtSGpDqq8fio8LhQz74Yaj8dJpVLkcjmCsRzLAl4KvlGNAS+pknmezv4Iqinjso0OB9b7PSXlA2AwlCQezdLgG30ejDEiX+F2ks7pdA5EqLA5WF1dUTbkVOvzEEuNDnNVup2EE2lMU9AzHEfKgWrINFf4cWgq9X5PWf2eGq+b0tvsGBO7Ey8ZnnTZVPqjecunLxRHGPk0PJ2DEfqGY0RiacLhFPFYFjc2hoMpskmDdDyHmTWJxjIEIyn0rKArnCSUEcTRcPgrSBiQMQU2PUswEiGVSmHqufIy3CVf37UeV/H5rHI58ag2mpw+9IzgcDBEPJtFCMFQelSIY3peaJrcfnI5QXs4SiKXI66XuLZnU6xwVZLI6HSNDMXVOl2EsymaXVWokkpKz3uz6Rg0OSoJZzIMZPLrBmwu2uJhfJqNRC4Dpo20SONUbDQ4/UT1FEvtFbQmBwiobgwhsdY984+s2WKaJna7ncbGRs4880wuueQSNmzYgNfrpb+/nxdffJEXXniBAwcOMDg4SCqVIjEi6rOds/nLX/7C9ddfT2NjI5Ik8dvf/rbs91tvvRVJksr+XXzxxWXrZDIZbr/9dqqrq3G73dxwww10dXXN+hqcVLGZztDNTOdsCqUKXnvtNdasWcNZZ501q2zNczWMZhgGu3fv5tChQ2zcuHHCss2lHmLzQTwe54UXXsA0TTZt2jStLALjcbBniEO95WWae4ajiHGuVSyWpWYkRiKXy5FIJHDZnUiKDXIC30jyxXhJMbRERserObE7XHg9HpwOB7Is41UF6ZzB4c5BKlUFSZjFomwFKj1O2vpCqIZErTc//DB2OKzO7yYSTzMwFGd5wI/PZStzBACIlkzshxNpUskcDW43dSNlnp228ufJ6yyPAbOpMqF4io7+CHrKwGezszTgzQd0kk+8WUppYKnfVV6JtHqMMJVu63XYyjzY3I5RkVUVmf4SLzSvY7SNTk1lMJFCVVUkSaKmtgaX04Usy7hlCEajJBIJ9GyGznC4mBwuZxo0ur0sd/sJqA66gjG6IjHqPC70kftf53ETy+avX8Bhxy6p1Gse4qksXYmRoF67g8ERj7RlHj+ykc+rVut2kRP5/QRsdmptflqjYaLGSByPakeYMu3xKJVOOxISy53VtMXz8zq1Dje6KYMkiOopZGEjrMeosQVANrHLGl7NyUWB9Se0INnYoE5JkvD5fDQ3N7Nx40YuueSSYqjBkSNHuPTSS7nqqqtoaGgojszMlEQiwTnnnMO//uu/TrjOm9/8Znp7e4v//ud//qfs9zvvvJOHH36Yhx56iOeee454PM511103q4/wReONNt2Ty2azxcj3403DPxfDaAXvN1mWjwkaHUuph9hsoo0noxA/09TUVJyfme0c0aMv7mdJwEt3ePTrd4nfR/9QrCwVTY3HxdBwnFg8x7J6N+3RFI1VFcRSBkh5zy23Q2N5VYDOgUjZMbwOO4OhBNVeN0PxJIqqUuH3MhTtRVVV+kIJvDaJjp5+DGRUVc3PQzhshKIpEuksqUyOVQ0BWgfCZfsurW3TNRChuS6AV7UXz6fa6ypzHrBrCv3heDGp6NIqTzEuJU/5kBpCEM8aaCO3WgI6B8KkcwaqIrG8Mj8f47KpJLM69X43fSVDdH6XoywQtHR+xmXLB4EWqPA4iZZYQaGSBKJ1PjddJV5opVVEa7wuOsL5vHWKBP3xRDGjQXWFj0Q4gqHrVNo1BpMJfJKCw2ajfzCCIcnIsoyvJH2QUdJvO20apGGJ24tbs3EwlJ+7aa7wE4qNiIbTTiqZpd7hRQDBEUsoN5IloMnpozeRIKZnqXE6GcrEqbF78KkqO8QgPhyEc0nqbAFMTHKmTqXmZiATJ6ZnWOutIJgVuFQFWZJI5HIkzSRe1UvSSHGubw0nEsMwJn33VVWlurq6ONLwi1/8goceeojvf//73HzzzWQyGd74xjfy+c9//hjrYyKuvfZarr322knXsdvtE4Y5RCIRfvjDH/LTn/6UK6+8EoAHHniApqYmnnzySa655ppptQMgFArxxBNPLI5htOnM2YTDYZ5//vlimeTjERo4/mG0wcFBXnjhBQKBABdeeOGkD1vheMCcztsU4md27tzJGWecwbp164pfdLOxbNr7Q7S09hIMJ6n2uAoHIZ7MkNMNUqkcAVf+PF2qNpI7S9A9GGXd0npqxgwJxFNZVEPC5yyPyo4nMqSzOvFYhnq/B6/DRn8ohkT+xXQ6nDTU1hJwealw28lmsyTiMY529pHJZDAMI5+eJito9HqLw1Qeh42BMc4D0USGvsEYTX4fbruGyz4msNPnKQoNgDChbyDOEq+XWq+LWr+7bEit2ucmnRu9h3UBT3EoUR+Zj+nsj5BO6lQ7nAQcThr8Huxa3oEgViIebrvGYIm4VHtdZVZOsiRbdLXXRbTU465EAArJOwsUgi4Rggq7Vhz+VGSJYCrFEq+XNVU11PgCOG1u0rINm81GIpMlkUggZ1L0RcLoeg67LNM3UiZAmCYKMrWam55wnIHkqGhHRqwdBKjIyIZCZyRK/0icjUezMZxJs9wZQJgQGxlm89hsLHdVEEql6c/k123y+JBNje5kjKzIUWv34dHsxPQMbsXOcDZJKJfEq9oYzKSotttxyx6cssIllWejySf2G3umcTYrV67krW99K7Is09fXxzPPPDPlx+ps2Lp1K7W1taxdu5aPfOQjDAyMpp3atm0buVyOq6++urisMAz4/PPPz+g4fX193HHHHQvfG01VVTKZzIS/CyHo6Ojg4MGDrF69mubm5jkxkRVFIfv/s/enQZLk53kn+PP7Do8zI/Kqqw80bnTjaHQTHAKkCAojUZQ4Jo12TDSukVrRRmZjRqMofaBsxmAyDlZD2VBckWYzK1ESwRlxRY1mqZmhVktCWhEkBDSIBvpGX1WVWZmRGfftcfi9HzwyMqK6uru6ugrdzdFr1madlZ7hHh4e/+f/vu/zPk8QvPmBt7ieM8fRt8J+u9tgE0URzz77LJPJ5JbzM3eS2fwf33wRyGZGVD/C0VVcQ6fRyZrrcz8kJ4lUczbHnSGz5WJjWRYTL0S1Nx83S1M4ag0xdRXX1BnNMipzuzddnafXn3LfToFXpv3VApqSMpr4zIMERZZ5cK9GnCTU20OSKMrOm6bU4xA/Atc2qOVsdEXiaHr+meZtnf5y0PO0O0ZTJBRb2LCrDsLNjc4ZK+1scPX+nSJqQeJkMCEFdEXaWOhFcfNZXCwBIk3J2Gx+mFktCLBfdEAUcAoqfhShazKHa8Oqb1hCUxW6y/9XJHFjZidnaivdNEtRVjRyXZYwUqi5LmmSokoS1/oDZtNsxikWUtI0e1YkTcM0s57NlmMyHQzw/QA7iViQsmM5aLrK9e4ABCibJp0lw6xkGHT8GTXDxpYUrvazstdeLsfxPHt/O7ZNczrlxnjEvps9q1IKQgoH4xF7tkN9PsBFAUS6/oSSpiMj0Vh4bFsGtqRR1R0O5y0qqs2N+QDSlDBN6AdTckqJT+Rff4jzXsWdCHF6nodlWYiiyMMPP8zDDz98V6/pC1/4An/xL/5FLl68yMHBAf/1f/1f84M/+IN8+9vfRtM0ms0mqqpSKBQ2/q5ardJsNt/SuS5dusTP/dzPvbfLaFEU8fzzzzMYDPjEJz7xmhtzr877evFmBmNvFGdNursBNmf6Zrquv67Z2lvNbE66I77z6sn5OeYBRcckiTavdzzzuaApLKZTZFkmCILMcTFncdIesVNyVky2smNxYzrAmweYiULBMlBuUgxI05RGa8KFkst3px6QUss7tHrZghlGCY3OhPt2iiiKgqIopCkULZVmd0IcR7S6c3oDiQtbLmkagyAiIOAYGsPJ+Y6/YBscng4pODqaLjPxgw0WmiyJtAfnPwsC1LtjFkFEzlApuga9NUVrRRI3pHYcQ6WzVqKrFWwafW/5PrNFfZ2FtpU3sUQZR1exdJWYlIsFlzhJMHUFbbYgThKiJMEPIyxVQRCgZJuMFz6yKCKLIhIil1wXgUwepj9d4C0CFn5AK/AZJdk5d4vOKnPaciyOR9kmwtIUWss5HwGYJDGarnPRtLAViaudPvWhR0ER8MIAWZYRDXVVEi7oOoasUB+PMfJrFQcRVEFi18jRm80ZBz6GLHM6n1BQdCq6xStLuwFJgh3NoT7xOVkCVFm3eHXSxVU15nFIkoh48YyS6mArKoP5hAfsCjcWTS6bFS6aW5jSW9c2e7txJ0KcZ1I196q39J//5//56v8/9KEP8YlPfIKLFy/yr//1v+bHf/zHX/fv7sTuxTAM/tbf+lvvPNi82aL3emW0M/dKwzB4/PHH70gg743irZbRzrxwbNt+ywOSd3rOW8Wt+jOvd663Amy//61XsQ11o2SkiCJizLm+WAppEvHy9VOq5TzTJKUfBAgC9EdT4iSh0/PYKTm0hh7d4fnCPfNDNEUivEkxYLtg0+hMmDYDtgyVSZqdbz0sXeWg3me7YDNYLFiEEZaho2kBoGXDjobMcXuMLgMSzGNodFKSJF0tBGfSOMPJAibwwF6Rrjij72WAVMtbnHTP+yDVgkOzv6Zarat4Y5+yolDOZxTrDS0129i4f/JNC9A6MeEMmNIU5n7EBUXmaP21HGNFJCg7Ju3+Wi9HTRiNsmsuOQYHa4C3lbdWgLflGFzvZq+hSCLNtWxo3ZOmbJt4w2yB3845qLKElohM5wEdL0SUZWxFIVEFjEAmDiOakzFxGOPKKgdhBx9QZJnW0p9Gl2XSNMUUFKahT29Jc65aFgkpzckUS8nulSFJmTnbdEhOkZkDl8wCN6YDkjSlqluczMfYskxKwjSImCdTdrQS/WjEtlZmHE35XOnDvBNxJ3I10+n0rluLvFFsb29z8eJFXn01swup1WoEQcBgMNjYxLfbbR5//PHbft0zcArD8L3Rs7l5AT45OeGJJ55ge3ubT3ziE3cdaM7Oe7uL8cnJCd/85jfZ29vjkUceuSOggbc32PlG/ZlbxVvJbBq9MV97/hBFkFDWhhOlVKDZn7CTd0jTlPlijpFGaLrJZBFTtgxEAXaKDt5ykY2WgHNftbThJQOQMzRGowW1/DkteZ1x1hsH7DgWg7XsAKBkG6RpSqs/QU0FagWbVn9NakYQcB0LwzAQZIMUjStll8k0o1ZPp1OSKKTeGbIuOdkdzBgNFuwXcrimRnzTRyPdVCITBZEkSRlMQ5qdCVIscKngUs1ZCJzL2QBIkkBrDWxLTkafPouibWz0Z+Zr5bybGWvrfSZFEmmuzfBYmrpx3DrBIF47QTVnr8gTuixt6KlFScIl16WmW2iCyFFvxGC2oGyba/M4NovlonqxVORKuYapOyimyTSKWCwWKIs5vfEIKYrZMyxORhNGvr+6RlWQkFKRo9EYVRI5mU9QRYmLdp5r4yFpkjInYl/PE6cpiziiotnUZ2OmUUBRMxj4ARXDpKwUSImxJIOEhA85F3GVd2ZA8k7A5nut+Nzr9Tg+PmZ7exuAj3/84yiKwle+8pXVMWfixbcLNmdA89xzz/Hn/tyfe+czmzeLdepzHMe8+OKLtFotHn744XvqYnc7ZbQzUc9Go8HHPvYxKpXK2zrnnYLNm/Vn3u65/vevv0iapAzGc2olh+ZoQjlnrYY4j1tDbCVhnsSIiouUZgtjq++RV2QCfzMzjZKE8WjOTt7hZCmUKQrQG84Io5he32OvnMPzfdr9zYa+mIIpKaiGxGgeIMAG+HjzgIJtsJN3uNEdAkI22Hn2OkIGCrKks5svImsSJ/0ReUPhtDeBFCRZpuKaDMZTBFHkpD3C1lVUVSBnaoxnPoosbZTIZEmkvbbI64rMSXdMshy9r7kmqiRjFGTa3jQrK64Jltq6Sm9NKHR91sYxN8tveeucsSbARumumrepr5m9reurlZ1zmRtdkWhPZ6v20nomU3Es5lGELSukCdxYqhcIgKafLxlnsy/ZB5PZPpc1k1SAw+V5HMtET7LjSraB64c0xx4HrSZeEqPKMsfjhIqRDWaeydNULBM1FEljgfay91MzTCZezOFkxLZjsK1nagW9cEJVczicDvCTCEUQuTbt8X63yPVplwedEp8tfYh3Ku6kZ/N2RTg9z+Pq1XPtwoODA55++unVEOkXv/hF/rP/7D9je3ubw8NDfuEXfoFyucxf+At/AchM5H76p3+av/E3/galUoliscjP//zP8+EPf3jFTnuzOAObJ598Es/z3nmwuZ0yWhzHTKdTnn76aSRJ4vu+7/vuOjPjVud9o8V4sVjw1FNPkaYpjz322F1Jee8EbG6nP3OruN3M5qQ74o9fOl793OxN2K+6xMtdcBLHTGczFpLMxx64wKv18xkcgYy9pQnSRtO96tp0eh4CsFvOcTKYsFN0OG1ni2SmGDDmgb0S4/Ga5XKaMp4GLIIEXZOp5S0EQaC1tmgDzOYh/dGM3bLDcLGg4JicrPVCdEWi1fey9+DBfdUSESm2n72fKI4gDvG8GaIkIksyVdeg3srUBPbLDqouc615LtxZzducdM8X+Yprcbx+Tk3haEnxFgWwXIVLBZcgienNZhv06bylbwBP0dosv43WWGdbrrXhULpu9lbJWRuaarO1eY2tnM3VTnZvVVEkjGIuuS5RnJCECb3RjB5wsXzeY9l2bU6WtgM5XcvkboCSriMlAvNZxMl8jKAsy5KisFKL3jZtojimG0SU83l64Rw9jikrErEfcThss2Ob+GmYUdkFEW8RUtB1Wr6Hq2jICPTikItL4c6657FlaVQ1B0tW6IQxD9oVrk3b7Og5DmY9tvUcRSVHWcvxTsWdltHeDtg8+eSTfO5zn1v9/HM/93MA/ORP/iT/w//wP/Dcc8/xm7/5mwyHQ7a3t/nc5z7Hb//2b29sVP/+3//7yLLMX/pLf4n5fM4P/dAP8Ru/8Rtv+b18/vOf58d+7MfeebB5szhjhX3jG99gd3eX973vfXd9DuVW8Ub9k16vxzPPPEOlUuEDH/jAXXPTfKtgc7v9mbdzrn/7x6+iSiL+mibWbBbi6AphGDKfz9E1DU3T6PamXKjkOeoMl0cKiCmctsfs1/Ic90ekgLLUNkvSlFZnzH41hx/cxPoSBY5OBlwou9nrCQIFS8WbZQ3ohR/hd6fcv1eixTnYFB2D/tKvpdmdYOgK+k26ZluuTb11DgSe57PwQy6VXY56I2RZIhElbCdz0IyimE53wGSeLYSHJz7bZZeabSHKIqfDycb8DrChGAAw9M5BU5ZFbjQHREtgqBYsFmHMhXwOURRQVYkkTZjMA1LYUJcu2JseOtqa+oCuyDTXZmvWh09zhkrXy0RQc5qKIcnsmRajGLZtZ5WJWJrCND4HpXVnUFE+v49FSycXq0RhgqrI3Fj68uzkcyuTte2czSj02ZYtRFHgcFmac02N/nhOzbaxFJUbkxGObTOVUoQgpejHPHV8hCTL5BWJLdUgjgVOlr41Zc3glXGfLcNEFgW6szkzfcEFo4gXzymrDrIELiYpCZ/furfSNG8Wd0IQeLtSNZ/97GffcDP5e7/3e2/6Grqu86u/+qv86q/+6h1dw9l73t3dBd7l2mhJknB0dEQURXzsYx+7bZ+VuxG3KqOlacrh4SFXr169Y12xNzvn7QDAOr36Qx/60KrO+lbidjKbo9aQrz19wFbRpjWZrkpChizzykEL20gRDBNZyfxWTlojxp7PftXluDui6BgcHY1xnGyAcr/qMpr7K6o0ZAKPwTx6zXxLrehw0hxRb47Yq7g0RhOEeBNMTV3h2lGPvUqO1tgjjBMsTWXAmgCmJHF4NOBC1aU58QiimNlis1fk2jpjb0G9MSRv6xQLmdfNmYNmpZBjOJljmgpRFCEJCYf1NoIoIskS1YKDLoiYqsxkHmMoIt01FlvFteis/VzN2xyv3QNVkWkNpys9NdfSmc4CZEGgVrSJkzQDIkFA1xRyqkaSpiSkCGnmjApg6wqzIEIUBAQhc7a86Oay+6IrLOYRiyBEDFM6wyl+EBCFEYU1i+dSzsRbAk/JNujOl6rOUuaL42oaRV1nMgtWdtdb7vkOPF7mr6YsY0oKjZHHJAmouOeZf9+fc8XO053N6C9tA3bzObwooKDbmJpEfzRATBL82YLT2YyaqbNIfbYFg+uTAWmaUtR0Xp302bdtJFHEi3wkKUFCYhb7mJLOBSPHtp6/+dH+nsWZBNX3OrN5p+MXf/EXOTg44Bd+4Re47777gHeBNtrr7cbn8znf/OY3GS090KvV6vfysl6T2URRxNNPP82NGzf41Kc+ddeBBm5PjPNM3+zk5IRHH330joAGbi+z+Z0/eC5rvPcm7BWzMkTR1rl6o0EYhoSxysVqEdKsdAXZDEyjPWa3mMPS1OVYZxb11oht12G9CQ+gyTL1xpCLlfOSzXRtHua0kyk1zxab2ULJyeY+TttjHEWjlDNe0+Nxl9L1J60RRipxeatA/ya76PWfx96CxSyk5lhUctmX3VlOy0uShKZpXNqtYdk2mqZCCsF8youv1hl2xhQViaKubJAH1t0oIaNrn4UosAFEpZzJaDmAmaRpJjsz8DhpjzlqDWl0xxy3Rpy0x6RRynFrRLM7odmdMPJ8TjpjjtsjfD/moDXkuDumOfDoefPV8GYldy6BoyviRhlufVDUWkrc6LLMlUKekqIz8Xymi3AFNLamrmjRhqowmM+4knMxkHi11ydOUvKGRnup/Hwh5yLFIoeDESXLJFleiC2rzBYRXuBzOvNQZZn3V2qMJAnLtkgMmS3BIExC+uMRbgDPd5uEcYQmyhx4A0qajhdEuKqCH6WYksTntz7KOxln37G3CjbvdZfOH/zBHwTgr/7Vv8pP//RP8+u//uvvPNjcKjqdDl//+tdxHIdPfOITwN2drL+dWM8yznTFoii6K+oEt3POW8V0OuUb3/gGcRzz2GOPkcvdeR36zTKbV446PHe1sfq53hyyV7SZjUakaYptWQiiSLvr8cBOcaNJn6Qps1mwNoNzbt9847jHXsldNaYtQ6G17HUcnw65UHap5i2Gaz0LyKbO5URYgUdGpz4/53Ayx5HUjV22KAr0hpvkAYKEvWJuVWKqlRyma/0QQ1No9Tw6/Sn93pRd1yEONzPc8czPsh5ZQdd1DNvFtCxEKesFjSYB86FHXoKyqWzM8lj6pilbtehslNxMfTPDG6yV37bym2oF0lp50NLVDWVpVVkveRkM1kpv0zVAcQ11BTyuqa3khixFRkFk17SJ5hHTRUB7OUS6fo1F28iGWWWJi7kchBmhwDX1tdfN5mwuWpmFwXBJWhgGC3RJ5n6nwMu9PkEcU3UsNEmiolh0l8SAvZyLpVj005hQEfhAaRfd0AiiEHee8FTzBoofczDqIqQpvWCKKSlUNJdd4+7N3t1JnG1Y32oZ7XvNRrubkaYpjz/+OH/37/5dfuInfoLBYMA/+Sf/5N1VRlu3J/7ABz6wMiWCbEd/t3ojtxNnZbQzut/FixdXYnn3Kt4o21jvzzzwwANvu2/1ZpnN73/9ZXRVXi2EURxxctKiYFsEprqako+ThNk4oJTb1BTLmzqN9pi8Ia0mZ2oFm6PTASetIbtbLqeDMSXboO6dL6D1xpAH9sr0xdmq2S2JAp3+lLkfIXoBO7U8qQDN9s3EgIDeaMZe1aU99ii7No32eblKlgTaAw/fj9A1hb2yS5pu3oNS3qLeHJ7/Q5LSbE/YqeQI05hkyZo7i4JjMBjPkUQRSVXZKtjUmz0M3aAzmOPoC4bTkJJrYFg6tqVzuFbGu4k9vXEPy661UY7T1E2K8zpolRwDr5vdR0natERwTJXeshzmGtq58GcK8zgBMbMgqDkWeV1ntgiQJYnrrYwAYarKigotwgqQIPO3uey6nA49utPZSh17HC5dS9M0G9QNUtrBlEDK7veWbSLKInGQEpMupY2yfl4aCqBl6tFFLSMCteZTtjQNIYk5mU8xdZH3l3aJxRAWGmYKzfkIHZHhIqFomDxeu3hHQ4h3M86M0+5kqPO9WkYTBIHZbEalUuGnfuqn+Kmf+imeeeaZdz6zOXsQfN/nySefpNls8ulPf3rVVDqbrL/XFs2vFy+88AIf+chH3nID/k7iVgBw1p9Zn5+5GwSJNyrZfeelOs++ckrBNBBFgSAImM1mFJ0ck3nKdvmcsbJTzNHqTfBn4coi2FQVGu0xURwzm8VsuRayKNJdGzw8bY/YztsMhpsZTM7UuH6jR9kyV4Zj26UciyBEQCCKElqtMY6srqvCUMoZ9JaZzmlrhCHIaDfdp2rRwV/SsBd+SH8wJfETis55P8GbbkojicvPvNkZ0+tOKekGO8Xz93+z4nOWMQnIsoyu61QqZQzDZDyLOWmOuHGjSeJNyUtQs1WiMF4Nd1YL9sbs0Xr5TRQ3y21bBXvD0mFdU62Wt9csEdJVyQsgb2e+KbamsO0YmKLEtmUhRtAaTDnujOhN5shrZICKa67M06quzSwMqZgmDxQLtAdTbvRG2LpCb7YcMrUNerM5O7bNfYXMbnsRxVRdmzjJrLBdVac59PCCgL4/RwAezBcz07YoRFNl9kwHMRU5mY6RBQFNkjkNFuw7DkXFYhgsmMchqqwyV1MulbaRCiYlw6WS6rRePOCP/uiPeP755zk9PWWx5lL6vYo7YaLBezuzgczC4A/+4A8A+PVf/3W2trbeHZnNYDDg6aefplAo8PDDD7/GVOxeGJm9Ufi+z7PPPgvAJz/5yXtWNrs5bgabKIp47rnnGI/HPProo2+rbHarc92qjBYnCf/vf/cckNGcHR1Gvs/FWmU1ld7vT1e77nCZ+cwWAbao4ZgaRcvguDFYvl7KaDjjyn6Zq0fdjXNJiNiKQhjFK8+ZvG3gjX06PQ83Z6CpMvPZukZdSt42uH7YpVZxGMwXLIII4yaCgSAI1OtDLtZc6v0xcZIS3VQOq7gm9cYIQRDYr+aIhJTGmkKArsobw6GSKFBvDfH9iLyt4zoa47VrE0WB7mi+AkFVlWj1p4iiiKqqVHMu/fEMIYrpDOe4vk937CPLEq5jYkkCF4o5EsAPQ+aLEEHIpGyqBWfDNnt9ILNgGyuNt/VQZZGqaxElKcWlOVccJpiCzNQL0S2Z1mSBEQls5azVwKckCRvaat6y7KaIIjlNI4oSOqOM2XbWc8kZGv3lYl4wdBRRojH0Vr0+AC8K2LYcwiDicHSmSGAzDBfsGA5+nOU4hqwgIXA08bhUyDGeSewZOV4aNZAEgSSFo+mIhwoFTmYTtm2TWSRhyCJBpFE2Df6vl76PLc1mMpnQ6/VWVs2maa5mTfL5/D2vltzJjA2893s2v//7v89f+St/BYC/+Tf/Ju9///vfebA5OTnh2Wef5cEHH+TChQu3zB7ejlvnW4114APuiTrB68U62EynU77zne+85fmZ243Xy2y+9p3rtHuTZd9lxnSa8v4ru4TB+UIdhjEzz+e+WpGDtbkab+ZTzttM1noUAgJhFDPoTqkVz+VdBGA8XjCeLCjmTWZySBQntDrnC+poPKdWdohuKnU5psZoOKfdmWBZKoWiRbu7WVLLWzreaEH9dEg+Z2A66gbdGWCyJCGkacpJc8SFWoELZZfG0COMYso3ldSqJWdVlpt4CyxdYTpcsFfJEaYJoihQX7M02CpsUqxtU2Mwnq8sEdyCzTweE0URk+mcwVWPJM0GmXfKOfqejyiKaKqEIYhsmSayJKKqEmGSsO/mgBTTUDFEmSRJEESB6SRAjUXiIEGwhZVoaDlnrol3poz8YAWM+hpNupq3qS8tFwpWNt9yKefS9+Zca/eJkhRJEDb00trTGa6mkdc0jgdjFlGMLkucLl0+y5aOKSocDcZcKOboLsExp6mMFj79cE4oJSiixAUnx0vDLoooMQmzTUwqpciI1FSDo+mIkmZQn00I4xg/jhkGc0RJRRFE9swiVT3LPnO5HLlcjsuXLxOG4cqq+aWXXiIMQ/L5PMVikVKphGmad716caeZzXudjfan//Sf5r/5b/6bVa/7t3/7t995sCmVSnzyk58kn8+/7jHfi8xmXT36gQce4OLFi7Rare9pRnUGNne7P/N657o5s5nNA37vay9h6hKtzhBJljAMC+KMkbQecz8knEfYpoa3pullqTJjP8bUlFVJaKeUo92ZInsi25Ucjd6Y3Uqek9MhAIPhDNvW2C7ZHNQHG+eRgG5vxl41xyvTGbIkbQDSdJoJgm4XnWxoUuA1JbvheI5jalwou5wOspmYSsGi21sT2ZQlmp0xYRij6wrbZXeDOACsqN+reygIxEnC6RJQLtTy7BUcmklIyk2zNgKrMh+AbWZ2B2dZz+5WjuP2KHMzjSKmUw/P8zPSSKRy6IekCCDAhWqe0845aJumynQ5i7NXcZnMlv0VSaC9bketKZyNJFXzNke9zGtGEjaPS8j8iGxFQRQFri/npvbLLuNhdp5awaa+dPTczTuIgkC9PyanqiyWWepWzuZwNOSSm89eZ5C9ziwOkUSRKzmXV3oZjfliwWUYLpASkcbSrXM/Z9OazQiSmFiOcQQVX4gpayYFTeNwNucBt8S1aZcrTp5+MMVVFf7T2q3VAhRFYWtri62tLdLlZqrf79Pv97l+/TqKoqyAp1Ao3LHs1HrcyYxNmqZMp9M7dul8N8SXvvQlfumXfomnn36a6XTKv/t3/+6dBxvDMN7USfOtunW+1YjjmBdeeIFer7ehHn233DpvNwRBoNfrcXh4eMfzM2/lXDeDze9+9QWanRFCEmCbBkgyggCLWchoMl8BBWSL2mljhOsYK2DRFJlWZ0IQxORzBmgwIWOmQWZc1m1P2NnKsZhvzrpMpz6aKFHOWyuBTttSaXYmmfJzY0Q1p6GbBr3BObilwHiyYDyZs7PlMJj5lFyT08Z5RiFJIp2+h++H2JaG6WqINw16Vkv26m8Wi5D51CdYRFyouDQGHqoi0e6dl9Q0VV4pT0O2kNdbI8IoYj4Pue9CCVWWkQo23fGUSt5eZRgAhZy50os7uzcZw03GNDSiJMWyZKIowtFFTgfe6vf9sZfV1gSBraK1obG2bkNQK9jUlwrbosAmW22tH1Qr2LRGU7ZdB0OR6ExmTBYBfcBc60kt1oY9EwGKho4tqyRJytFwqf6wlhgIgkDNsDgZjJHU7H6Xraycl5c0wjSbQUmXFO+ZF1GxTDqzKRdtl3EQMo1C7i8U6C3mREmMkAokUUyTCVU1R2MxYk/P0w+nFFSDjxW2KahvruYhCAKWZWFZFvv7+8RxzGg0otfrcXBwwAsvvIDjOCvwyeVyd5T13Glm814vo125coVf+ZVfYTwe85nPfIZ/8A/+wTsPNrcT9zKzmc1mPPXUU8iyzGOPPbYhg/O97BVFUUSv18P3/bven7lV3NwfanRG/Js/fJ75YoFpmNTKeTqjKbtll/pplm10e1MqBZvO0GO2LEGNJnOKeZMkSakVHI5OsmOH4zn5nEHZ0RiN50hS9qjFSQphiqkqrJtLb1dyNBojFFliu+zQ6E4o2AazyTmwdPoLLqoGuqawWGZN1ZJNZ7nLb7UzxQBV3PxyV0s2jWX24U19giCmXLBwTJXJEghv1m8zdZX+cMbsNEDXFPbLOW60RqvzVgo29dZwdXy5YHG8VnJTFZnjRvazLInYisyFiss8iOhNZozXqMiWodIabDb/653RKusxbBsnFIjibGC02RuRJNmgYGBlGzFRksiZKp3hZoayugd5h9NlaUySBPregpKhoQgCmiAjRCnN7oQLW+5KsaCat2mMz6f+W8sS3E7OQohgOFrgaxGLpcOmo6s0JhNMRWY/l+OVXp80hf1ijhvjMaIAJdPg1eW/B2KMJAjcVyjycj/LshIh4YpTwI8juosZFc2kO58zDBZsqxon4Zz7CwZ+HKMqAjImigQEArah8iPVD3AnIUnSqpcDWd+21+vR7/ep1+sAFAqFlU7Y7cpl/Z+1jAaZAoGu63zlK1/h4sWL7x2wuRc9m7Ny1evJ4NwNyf/biel0utJZ29rauudAA5s9myiK+PL/8geZGZZlI0oird6E/VqB/lo5KopjvLHP/Tslrt84h4r+cEatkqN3kwvmcDxHB3RVIdxwu0w5PR2wv53naLlg+8tMJ4xi2q0Jl3bzNDqbfZiSo9FoTyjkbay8RW84fY3dgK4pHN3osb+dpzmcEIYJwU3EgK2STf10iCSJXKi5zIJoo+wmSeJGFjNfhLRaE5J5xP6WyzQImN1UYttgsaXQX6MwS6LAjdMh8fJ+VwoWKXCh5JIIKZqmcNQeEETZPfLXFJ4dU6Uz9GCZ1dQqeQJGpElCmsa0h17G1BME8rpDGGWSOoam0FxSow1VxtYULpZcSFJUJaM1zxcLNElkFA5XJcJ1++h1RlrBNsglOtOZjybKK3mbSs7icClVU3IMiqlBY+ixiOPVnM0iiamYJlIKB4MRSZqV3iaRT1E1mS4VDFxNQ0wFDodDdgsOe1YOWRA4nI24aOe4OmghCgJ+nNCYTrhUcOnOp+T0TFXhI/ldTPnu9DY1TWNnZ4ednR3SNL1josGdEATiOGY+n7+nM5v1uHjxIvAukKu5XbfOu7nor8/zvFG56ntRRut0OjzzzDPs7e0hSRLz+WtZRfcizoB0NpvxL/+Pr/LC1R4PXN6msVbqERMouyZHa9pcURQzHfmv6dWoooitavh+vNIJq5Udrl0dU90yUVWR6TykkjfpLJv5J6cD9rddwjihvTYzk6YpSZRSLVicdMarct8Z88mbBUjzkMt7eW6cDDfel6UrDIGTxhDb0tiumStm3Fl4XnbdcZxQPxlyaa+AseVy0hmRptl1n6xlLVtFm+6SlXbaGFLMmwgC7JVdWgOPnK2d92NSyNvKRr+nUtwkCmiKTL19/nM+ZxDPYhxDoZgziAUhAwZA12XGWkCcpsRJTJwklGydlMxpdLIIst5RGEIakZcgChcYcoqYiISJgBCkHDQG5wrUpXOTtIKj0/ezz6ucM+ksxTW1peZb1bHQRInecLaySEjW1s6x7yMJsOfmGE19BrMFiizSWM7lFAwNU5Q5Go3YK7oEi+XQqKrQ9makMnTCGQVNp2joXBsNcHUNGYkboxEV1+CS7SIsDVAvWg4nszHvK5S4Pu1zJecyjQJUSeSHth7kXoQgCLdNNCgWi1iWtVrX7qRn4y3FTt/LPZtbxTsONrcTd7OcFQQBzz77LLPZ7E3l+O9lGS1NU65fv87169dX9tHXr1//nvWIzgav/uiP/gNPvjjEMi16vRmVvEVnOMU2NJrNEVGUcGH3PAPZrbjUTwbkHB1DU5j7YTZ13xoTRjFbZYfOaEacJMRLBlvGOLOxTRXlpl1e/WTI/RdKdNZ6SIIgMBzOmM4CKmWb8dzH0GROT3ur/l6cpsRBynbJoTee4QdR1kdZAy1v6pN3dPbKLp3xFD+IqBRtOt01OrMk0myN8YMY19GxbG1l3XwWqrJ5zaauUl+WyBRFomxnDqNnQ5aiKGwo8szXSnSyKNJZK5mVXHMFVLN5SClvcbpWjsvZOuNl1lQt2bSa50OquiitMs9KwaI99gGZJBGRU4neeEocxZRdlekiQZZlHEunuSynkcIsjDmb0DV0BWZQMHW2HIvm0KMzmLJdsFdAU3EtWtPlOXMmuiqTBClhlDBYyuzUXJuj0Zi9pdHa9X72fqZRgC5L7Ds5Xun0SYGdvE0u1RjN5hzH2XvbMi1eGfSp2haGJHPqTdANCQeFTjTnolOk5Xvsmy6TyMeWVX5054OvMaO7V/FWiAZBENyRVA3wJyazOYv/U4HNaDTi6aefxnGc23LTfDtmZm8Urzc/c6/Od6tYlQXGBkEsgpCVyWbTgJyl4+oap6NskTs5HbJXc+kOp6v+yHiyoOBm2laVvEV92atpdydsVRwkWaSx1qQfewtqlRzz+TndOE1Tiq7B9etdtrZset6CKErY3nJoNLKFp9P1cCwN1zI4Xbt+RZbodCYEYYxlqeQKFrqmUF8y3CCTbGl3slKaoSuUKy7pTYl0rexw2hyt3pMoZECxU85x2h1npIc1WrUkbpbYkjjluD7EDyIsXcEtW3QHA2RJJEoS8o5Bb62PUi3ZnKyJcBqGCmsstcHavEylYNFZUyu4WZ5m3aJ6veFfdC360wWGYQIpqqEi+BP8IMCSYzw/RpZkbE1mNPfRNZ2yYyEjUFR1hqMFsiAyPvPMkc5vmqpJSAuBvZyDIAgcdpe+NfZ5+UoUBfYsh/bQQ1gSAyo5C0kUUFMpIwaQMQ1FoD4cc6nocjqbcNF2ORiNEABHVXl11Oe+Yp5R4JOKoMs6iCk2GookMFhEPJgr8XBhj3ci3oxoMJ1OUVWV69ev3zbRYDqdomnamxKn3mvxrlEQeKO4Gz2ber3OH//xH7O/v8/DDz98W7TGe5HZTKdTnnjiCaIoeo2+2fcCbJIk4YUXXqDf75OKJteOxhvum/NFSMHQGKwtZGkKrfaY/aq7YQUwGM0oOya9m/xk2p0JuiivFsezko2YQjiPKKwpABuqAgK0Ox6OqmBoMrNpkGU5aSbjGccx7ZMR5dz5zFOtZK96MdNpQL87RRGEDQHMrVKOMExW72vYn5IsIgo5Y3WMf5NbqG2pDMdzmo0xeV3n4nZ+4/fVikOwdg+qFWd1T+aLkCRKGA0C5BB2XJuqa1F0jFWms967ksWMJXd+vTaTtd7PujyNqogbZm3FvLHKBDOxzjV5Guv8PhUcg/EiRNd1bMtCs3OoioIhi+jE5ARI5wFpGHDQHDCcLijY+krSRlfljd6PgoieSjT7U06WdgamqnA69tBkkQdKeU66Y05HE7YLDn4Uo0oSeU2jOfRYRBHN6RRXVbm/VOD6YIiQQpgkmKKKIGbOR/e5Ra6OBrhKNizam88QBIHmfEZKyjQMaMwnbBkWf2bnzkgB9yLOiAYPPPAAjz76KFtbWziOw2w245lnnuGP/uiPeO65595Q0cDzvI1S3J+UeMfB5nbi7fRskiTh+eef5+WXX+aRRx7hypUrt/0h3m2w6XQ6fOMb36BcLvPxj3/8NYOa9xpsgiDgW9/6FsPhkP39C/zBtxp0e5kawHrMvQBb1zYaxLapcno8pJzfpJVKKTiahrwGWNuVHIc3upQdcwk4KXnHoNEcMZsHTEdzKkWbgmvSak8QBQFREBiNF5RsY/VQpiyzn5yZyct0F2wXLCSBTVM1sgzlxmGfvKFnYJKmG8OlAKWCRbvjMe7P2K+4VMs2vcF55iBL4kaJbTxZ0G176EjsV/OYukIQbD4P4U3kg/ninOjQ6XqcngwZdWeYgsSlsoshSdSKDqamUC3ZG+C9fg8VWaK9Bi5bRWdlWAcwWgOlaskhXM62CAJ01jIlx9TQFZmqa3NftYij6hR0CxGVSSAw8VPCJGUym+JNJkynU4Q0XD33W65FwTK4kM+xZVsctofMg4hawVrJ5ZRdk/1cDjkRCeJ0pW4wiyN2cjZ5VeV6P8t8t/MOZcMgDBJ6iyyLu1Bw6XgzJgs/sxlQDUJiirrOlmUxDhY8VCzTCOZcclx6/hxHV6gZDu/Lldk387xbQxAEXNflQx/6EN///d/Pxz72MWzbptFo8I1vfINvfvObvPrqq/R6vdU9PwObO4k//MM/5Ed/9EfZ2dlBEAT+1b/6Vxu/T9OUL37xi+zs7GAYBp/97Gd54YUXNo7xfZ//6r/6ryiXy1iWxZ/7c39uxch7O/GuAJs3W/zvdNE/symYTCY8/vjjlEqlt/T3d4uNdqZv9vTTT/OBD3yAhx566JZNw3tJSJhMJnz9619H0zQeffRRnnulR6Od7Yab7TH71TyQDQz2hzO6fY+tgsXZR2NrGr4f4U2yPghA3tE5bYzo9DxKtrHKZPxZtuC2uxMcTUZVJCxNWe3EgzBm2JtSzpnnaQ9Z5yAOE2Zjn90tF0EQURSZbtdbld2azQl75Rxpsm5eANFSYXownDEZzLn/QhlvjV4sCDBYAkuaZqVBQ5TZr7qrbCjLUs4/73LRYjReMF+EnNSz41UEqiWbFMg5Op21klqpYDJcA8Fq2Vl55/h+BCnUTwZ0mmP8SYAYQzVnsV92ubiVR0yhmrdwLY1aObcBZOt9pHLeYrRSg85ELF1TZ8u1uVIrsWWbXCzm2HEsJuMFwTRclhwjTrtjpouQWjHTTxNFkZLrEAoqtm2jayqdyYxwMceIfLrdAc3OiKPOiOma0+c0jBCAfddhOgs46o6I43Ql2FmyDAxRptH3MHWVOElRJBEJgZPBhLyp0ZvNuezkmYcRQRyzn3fxw5hFFBEkMXM/puNP2dFztBYeVUmnHy2o6iayIGErMn9+952ze76dWKc+nxENLl++zMc//nE+85nPcPnyZeI45qWXXuKrX/0qP/ZjP8a/+Bf/4o4ZqdPplI9+9KP82q/92i1//0u/9Ev88i//Mr/2a7/Gt771LWq1Gj/8wz/MZHJenfjZn/1Zfud3fod//s//OV/72tfwPI8/+2f/7NteC98TRcE7KaN1u12eeeYZarUa73//++9oCv9uLP5n/ZnRaPSm8zP3KrNpNps899xzXLlyhStXrtAfznjhlfZKaRegfjrkyoUijbWeR6M1Zm87TxgnNJZ9Dd+PkEQB19YwZJnREizOejWqJlNfUwEYTwIu7uXor5V5BMA0NG4c9NjbKVBfssVKRZv2MrNoNUZc3CuQkFKvD1YlB0VRmHsBwTSgVrFpdDyKS4absGx0J0nKdOLjGhqiKtEbzKhWHJprzXVVkWg0hkRRgmmq5Msmi5tmbdZdMAEsU131hHKWRq1gI6bCyg7B0DYz1fWZWUGAwfi8H2NbGqetMWeAubud3yAGGJKCFIKsiBQcg+k0xFU1SFNsVWEmyURxgqHJNBvjzNETUCrCilG4XXZWoCTL4sbw5/qyUcwZTHoBiixxsVxiEUa0Bh66odAcTYjiCFkOuNGcIcsylZyFIcsUNZ04Phf6rBVsjoYjLrg5kARu9LPeS28+p2JligRnZAFNk9gVc8zCkLY/Zde2GQU+kyDgwVKBa6Mhu65DkMSIEliixljK5HwUWWIa+zxWfgBTubsyTnc73mjO5maiwXA45NFHH+X3fu/3ePHFF7lw4QI/8iM/wo/8yI/wZ/7Mn7kt6/kvfOELfOELX7jl79I05Vd+5Vf423/7b/PjP/7jAHz5y1+mWq3yW7/1W/zMz/wMo9GIf/yP/zH/0//0P/Gn/tSfAuB//p//Z/b39/m3//bf8iM/8iN3eCfeJZnNm8VbKaOdZRFPPfUU73vf+/jgBz94x3Ivb7eMtt6fefzxx990t3K3weaM4v3cc8/xkY98ZOWY98/+1z+mfjqm4m7qvoWziHJhkwFz2hzi3LSIzuYhjqEzvUkhudfzEGM2ekACkIQxmihhm9pKiytnaSRJykl9wP52ptigrDXAU+DkZABBjD+frxqxW2WH/mBGGCa0TsfslB0MVYT0nHSQczTa7TGj4ZxhZ8reVg5ukprZKjurbGg2C1hMAwIvYG+Z6eiaTGutkS9JwkaJbTEPqB8PGPdnlCyDC9X8Rv/HNrUNYsFW2dmYxcm7BuuZ2XzNdiBnZxlTmqaEQYyqyXgzn4m3YOFHnHbGzBchYRjjOsbqVUxdprnWA0rWCgbVorPqcdmGSmswISXrG0mCyH4+hxyLzPyQ096EOEmxLR1N07BMi72dGpqqUjFU4vmUlw9POO0O8COfdPnMioJA2TDpjGecLBUFdooOJcOgP54RLDXuarZFf7LgdDxB0yQu5/IIiPQXc67k8lwfDZEQWUQRrakHokB3NsVLIlxNpb+YsWPYfP/WZd7tcbtDnYIgUCgU+IVf+AV++qd/mu/7vu/jn/7Tf0qhUODv/J2/szKRfDtxcHBAs9nk85///OrfNE3jB37gB/j6178OwLe//W3CMNw4Zmdnhw996EOrY+403hVgc7fKaGEY8tRTT1Gv1/nUpz7F3t7bY6i8ncX/zfozd/t8N8eZs+jJyQmf/vSnV06nf/jEVV662kIA+oMFtXIGLttlh9PGkFZzRHXNQmCvmuf69Q77a41yQYC5t0BIs93+WexU89TrfYqOgSJnAODmdI6OujSbPfzpHNtUM/rtkjoMUK8PuLRboLc2WAlQyhu88soJjqmxXSuCkE3mg7C0nhCZjBfMxyHl4jlIWkYmoZKmKUmaMPN8Jv0Zu9Ul2Kcp4/HmPJOuK0w9n8bxAFOUuFBzNwZGq2Vno79SreRWmdBgOCOJE3ptD0eRKdsq2xVngzIt3mRcMxityf67m4w11zE2/q679rutsr0qrwkCG2rPpby9KlWaurIBPP7y+yMAtaLNfilHWVMomxqHjQEnnTGaKq3IAJqSqTZDRk5QJYmSYeHHEqFiYJgmlq5S74+I5jPyxLxcb9EaTNhyTaIkpWKZCCnc6I6wdY36eMzlvIuuyEyCgJymQSJw2B8RCjGXneWQqyRzMZ+jM59xf6FIa+ax6zhoiUCYxmwZFv+Xyx/jvRB3MtQ5m81wHIc/9af+FH/v7/09nnvuubsiXdVsNoHXuh5Xq9XV75rNJqqqriS7bnXMncZ7poz2ZmAzmUx46qmnME3zrqkkS5JEEARvfuBa3Gp+5nbjboHNmQSPoigb96LdnfDNJw+ygwSBJE0ZDGZUihazZTM9TlIG/SklN2vKt1vZ7rxeH7C/V+C4MWRvCSoAuZyBZaqEYUxvuZPvdCYUSxbjmU/BtgnnGfhNJnM8b0axoDGbR8iysmyKC4SLmJyu4CsS3iwgCHxazSmarhFHIrPxgkt7RY6O+hvvtZS3qNcHCAJc3CvQHc3odL2s17QktOm6zGAwZV4PKBUsnILBjRvnr6Np8up9QiZI2mqMEaKU/arLcLrYABpg1ZA/i8UyM/GmAf5igSyPiOYhJdfEzmlESYJr64y8BdWKs5H1WEs16Oxj2XQX3So7NLrnGda6usBWydkAlHUJnGLexGuPMFSFkmMgyiK7rk1/eX8mc5/ZzGfH0DkTtik4BuOlAdtWwWa88MnpGqIgcLics9rfcrnRHyFJEhe28ll22hsh6wrJfMw8mHHQ9CnqBv58QXsaIQgiJcfEChRaI49QSnE1lZJlcG0wpOqYSGSDoI6loiDS9WfsGA5df0bVsAnSGEWCFIGHS9tsG/deZeNuxJ0Mdd5rqZqbN/e3YzB3N0zo3hWZzZuFLMtv2LM5PT3liSeeYHt7+7aziNuJt1pGO8smjo+PefTRR98S0MDdAZt+v883vvENCoUCn/jEJ1b3Io4TvvzPn6B+OqRStLNyVpo163OayrqKfxjGzOchlYK9QfOtnwy4sFPYoDqPx3MUQWRny80W3DRrWfd6HtWCxWQ8RxQlVFXDNC22tor4U4FS3mAxnzOZTBAJObrRpt/1SIMYUwVDS0FQUZc1+ShKiP2Y3a1cljUBiiKu5n7SFOrHA2oFm2LeQhBEBFHAMFQ67QlL7KE/mLIYLagWLXJONolfKdkbTK9qxcHzfMIw5qQ+QE0FlDRj2YkCuI6+UVIruCb9NVZb3tWYzgLSNMt60jilcTJiMpijpQKmLLNXdtmv5tku54jCGFNXEQQhIxWsqQ8ka6U2N2dsDIQKy2xJV2X2tnIYqsJ+2WW/mCMNU2xJxp9mCgPHjSHN7oRCzmCynJ/RFInOMjMSBehMZiiSyH4xRxInjEcL6q0R3pox28QPUESRS8XMm+eoO0JXFfp+hGEYfPDSHjnToT0LSOIAz5tiRCEHzTbH/SFbrsWWZSKmAjdGIxxVRZYlTsYT9vM5JouQvKmjIKMpIkIqYCgSY9/HS2J2TJs/vfO+N/0evFviTrTR7pVxWq1WA3hNhtJut1fZTq1WIwgCBoPB6x5zp/GuAJs7LaMlScKLL77Id7/7XT760Y/eddvmt7L4n/VnwjC8rf7M653v7fSIjo+P+fa3v82DDz7IBz7wgY0d1f/n3z7P4VGPOE6Yej62pZGSSbEc3uihSBLamqdJztIYdqcbZTJSEKKEorvZqIyjGK8/wzLUjHSwXB/9WYgqidjW+WvkTB0QWUxTHnpwD8u0cEyNMAjxPI9Ou0+vPWKrkEeWzq/HNBRajRGnJ0MsTaFYMKlu5VbOm9n9g057TK81Zq/qoqkKW2WbJAFBEBEFIQOKjke7Ocbrz9gt2ywW4Zox8cbwPwCGodBuT2idDjFEiWrR3rgHpvkGM1tpukHTVhSJo5MBp40RJ/UhUirQbk5YTALEIEVOIW9olB2D/UoOIUzZLthsF2wqOZNqzqJim9Rci+lwgRxB6IUIMbRbY05Oh4RRwkl7xGwRZpbaaw6f62rXOUslSVNEAS5u5SmZBlIMfhBxusy8Sq65mrnZck0cVUUTJKIkZbxUFSjlzKX4poMfxkyDmIJjk2g6H9zZwXVs5mGEP5/T6XS4dtpBEVLyqoarZeW1/VyO40m2KfCCgLE/Zxj4aJLE8XRM3lDJCSI/tvvQ90wp4G7EnYDNvcpsLl++TK1W4ytf+crq34Ig4Ktf/SqPP/44AB//+MdRFGXjmEajwfPPP7865k7jPVVGW0/lFosFzzzzzKr5fjtMjTs975vFur7Zgw8+eMeEhDvNbJIk4aWXXqLRaPDxj398pVx7Fq9ebfHM8+c8+fkixNBFdFUkmGXZyHA0o1J2Vr0DfxYy8RbkXZNYk1n4EXnX4GTJNNvZdlc+LnnH4LQ+wLY1bEvDm/psb7m0lswt09LIuxldurlUFUjTlNPjAfddLnN02MMwTWZLGZRy0eT6K03yRQ1vkSKKMvlajoa3nPQfzZEVEdfcJDjUqi6NpVbaaX2AaWmICWTwIQACbs7EG/ucWWAmSUq/NWGnlssWzxRarbOylYCmShu6bWGYcHTUJwgiXEfHdQ0Wa3Rp01AYDD0sKwOgrYqzoThQLFrUT8+bvcFaWcw0VRqt8YrFtreTX5nCyZLIcDxflfN2twu0l348mirTXFc5kNd6TZUcJ8synGkoK/M6SRQQSdjJWcz8hIUfrkpymiavfG90TUbzJbZdm5iU40527dZyAyGRGamFfsxciugutf128zbN8ZTT4QTdUqm4OXZch++2O+hKwmI+p+HNKDoaZU0njiMUUaRmW5zOPHZzOaZRQCIk7JoOIRGXZYMruc1n+90ed9qzudMejed5XL16dfXzwcEBTz/9NMVikQsXLvCzP/uzfOlLX+KBBx7ggQce4Etf+hKmafJf/Bf/BQCu6/LTP/3T/I2/8TdWCtc///M/z4c//OEVO+1O4z0DNpDtEmRZpt/v88wzz1AqlfjgBz94z6xd3wxs3k5/5lZxZmj2VuqjQRDw9NNPE4Yhjz322GtAdzxZ8E//2TeYzwMKrslgOfA38QLyhsJ0zda4051Qq7kokkj9OOtpDEczisWs8ayJEuMlq6vdHLNdcwnDiNOlVM1kssC2dfI5g7l3zryaTX20SKZacxn3z8tNAMEspJg3OD7pIkkSumFgWxYLW8Cfx+RMiSAOuPpqHVIxc7lUZLbLLkcHHbYqDoswZuz5zKeb/bW8a1C/0aNSdogFmM78FZAIAIJAmggICDSb2a760pUSqiwthz1TSiWLxhk4CFCtOivTt8lkQc7W6TbGWIZCvmAhKgnT6a0ziex+njf03ZxOZ40UUcibTBvB8lTpBolgq+JwuhTvTGFtzgYqZXtlb2DoyoZvTrjavKRUizZRlNljC4LAq/UmqhJTKtgrdQJDU2gsPXByppKRJCLoDmfMkgzoirZBezxlL59DUySutbPP3zQU1NBnx3GoDybMw4i9Uo6YlKE3p+lNUSSJ+2sVXu722dsqo4gpjZGH4k8zhe7FDEPTmAY+uqqwSCJCIaZimHxElu6JkeC9iiTJ/HruJLO50zLak08+yec+97nVzz/3cz8HwE/+5E/yG7/xG/ytv/W3mM/n/PW//tcZDAY8+uij/P7v//6GRuTf//t/H1mW+Ut/6S8xn8/5oR/6IX7jN37jba+z7wqwebOF9UwjKIoi6vU6r776Ku973/vY39+/p5IOb5RpvJX5mbdyPrj93dBkMuE73/kOuVyORx555DVaSkmS8uV/9vUV8yryI0xDZTYPqJYsjg7aPPBAkUZnfC6n4ocoqnLmzQVAv+9x5VKJ46PzOm4SJ3SaI/Z2CvSWfysIAtOpz36hsCG7AmDbOqc3emzX3NXMTr5gUj/qMpvNKZUdNNPAMDQ6zRGCkE3UhwFc3K8x90PqJ32iKML3fU6OfQIfGo0IXVO5/1KJa4e9jXPOltdwRly47/4qp80h0ZLNlS+YdNccLxVFpHE8xPcjqmULSZWYeovzAluambyt6mzCuYL0fB7i+yMkGaJ5zFZew80bxGlKMWcwnMwpl53VHBGAZWkMl8QMgZTBGjGgWnE3iAHBGiFhq7yZLa3f61LR4qg1xNAyYoAgiezkbYbjOb3ejMlSqbu2lVlKI0DOMRgu+zjlgkkQJ4hJNptztMxeayUHrzcEsrkcURQ47Y4pLBUlDFUhTUBDJgXmYYQkCciSyHF3yG4xxyCYUzMtjkdjNFnCNXQOhiMe3K1yYzTiUsWkPh4ixSEHrQGuoeJaGrJi8uN7D3HQfvY9BTZnG9U7IQjcKdh89rOffY0p4noIgsAXv/hFvvjFL77uMbqu86u/+qv86q/+6h1dw+vFuwJs3izOAOWFF15gPB6/qY303YrXy2zO/GdUVeXxxx+/a4SEtwI2rVaLZ599lsuXL3PffffdEnT/j3/zDJO1HbA39SkWTCRRwxtl/95sjtjbLVBvDDN6bphw0h5wYck8g6ys0qwPKboGvcGMOM7a1rWtHPUbPbZrOZrLjEFRJDqtzF2zXDq3XhbTjKTQOh2yt1+gfjIkDnxmszmGYRAGIAoRxfxmrVqRJVqnI3w/4sqFCo3uhHLJ4uS4B0LEYrFgPpsjEmKpKoKi4nk+W9UcneZ5uUoQBJonA8JFxP5OnnbfwzRUhqwt8Fs5TpbMtn5vylY1R7xIuLBdYDiZo2oy3bVFvly06XXXRDarOY5PMsAbjec4jp7NCpEJeJqyzE4pEylNyO6HY6rMFmHGOlvz70mFNWKAa6wsDuBckFNTZcp5kyhN2d9ys7JglJBTVKZegJgzV59hrezQWL6GZapr5TSR9tAjZ6oULANvFtIbzxAAa6kUIQrQ8aaUHRNDljloDoiTNFOA9qY4mko1b3O1tbSZVkXKtomrq1ztLp8hRSKdpSimjJpG5HWNw+GIi67L4WhEXtdoLeZs5wu0/CkfKO0RRD6LcM7uNObG088DWf+gXC5jGOf08HdrnG1Uv5eZzbs53hNgcya57fs+jz/+OJqmvclf3J24Fdic9Wdez3Dt7Z4PeMO+zXrp7sMf/vCKYXJzPP3sMV/5d99FkkRKJYvectHvD6ZcuVDmWt9b9QZOTgZc2CtCklA/zhbHen3A/n6R49MBlbzFSX3AfB5Srebo9KdIosCg65EkKe3mmJ2dzCa6WnE4OSvB9ROq1RykKe21Xk39Rp9CUeb4qI9lmav37boGx9e77F0ocrxc9KvVHPUlTfnkeIBbMFFEEUWWUeRsF13IG7QbQ6JoQRLHbKsBwbBBnBqIbqayu11zOV32m+pHfXKugZSCokqEQUxK+hq9NdJsePO0vqQDXy5hqArdocdiHiFL4sYuMoyiVdYjwIaYqWEqHB8PVlnSzra7uicAYpKSUxUURcI0VfwgoZbPFhzb1tAEcenOKTIZzJATCGchuBkxALJS25lVtSiJtNfmc1jbjBRcA6+decDsb+UIIjJbCU2jtzR9q5UdTpdzNvuV/EpVYL/qZm6rZPfuoubSHk6pD7Jr2C45KLLEcW+EJGWaDvdXilzt9rMNkZARECJD5ZLrkkpQMUxUVUJPFYI0Ys92SIQE09J5v1Plpx58ZFU673Q6XL16FV3XKZVKlEqlNzQveyfjjPb8Vqsv/xFs7mG80YfRarV47rnnkCSJhx566HsGNLBZRkvTlIODA65du3ZX+jO3irP78Ealu+eff57hcPiGXjwnp0N+87e+AWS759nUx7F1Jt6C/e0Ch9c6bFdzvOqdL0azqY+pbT4O9eM+91+pcO1qe/VvrdaYSsVGUSRO68Nlnz2leTri4sXiChggY6l1m2P2984HxDL/jzmyIHP5QpXxNFjRq6Mgc3es3+izve0ymfp0WuelJABdk2ge9dnbL1I/HSAAuqaiqhrabI5QP0b3eoSizIV4wYlbI6hWGWjiRi8s5+jUb/RRVYn9nTypwIr8ANn8ULt9fm7TzOR1zl5jfycPokDO1plMfVzXoNP1SJYc8sqWcz67I4DrmnjT4er11hUDbEen0TwvZZqWujKYkyWR4WS+EgDd282yMlj2Z9ayofWNT23Lob5s5juWtmr+u5aKLIpUcxaHdY+x5zOZZ/d/nWadkLKdt0niFG8R0BvPEAXoehkY7RUzs7TxzGd/y+WoP6JkG6RJyo3OkIpr4fkBVcti6C9I0pQHykWu9vsUbRMRgf5sjusY+HGEgghCSpqkiJKAIAq4qsZfvvIhBEHAMAxEUeSRRx4hiiIGgwG9Xm/DvOwMfO4FWehO4k5mbOBPhiX0reJdATa3ijRNeeWVVzg6OuLDH/4wr7766vfM6+UszjKb9UX+U5/6FK7r3pPzZVPxwi3f53w+5zvf+Q6yLL9h6W48nvM//qM/oFbJcXTSX/5tiJuT2anlVrv7RmNM0VWJ0qw2H/sRJ+0Ju7t5TpY7blkSs77MbmHpV5PRmqMgRkzIykHxORgH05Cd7Tz1k3PA2a7mOFpmK0f1HrPZnGLBIJhBrzMl5xrouoJpqDTXWFrNxoiLl0p4kwX+4pyxlYRJNkh4o0e5bKOaKo2TIcJwiHBwHQA9DukrDhPJYHvUQh8c81L0CL5pIEkSmqZmCgZpShDE1G/0qdVy7NZcJrOA8XiO7egbKgOFoslsOTR5BjhnGVzBNamUbSCi0ZyiqhpJnKyyGEkQ6Pa8VR+sWLQ27LZdV1+VO0VRoLdGotiqOpyuNN1S+muKzqWitSIGmKa2wUibLwFcVxVqJQd3ETGZLLB0lcPlQK5jKAy8BbIk4+Z0mgMPRRLZLeUYzhYMvQXlvEVvSX3eLufwoxhbzqwAxjM/oyr7AReLLn4U0RgtQc3UGPd9BE1gOJ9zwXUzEzRZxlBkjgZj7t8q8Gp/wANbRQ6HA/KOgaWrxGlmS/AXLr8fS8k2l0mSrBZuWZapVCpUKpWVeVmv16Pb7a6ynjPzskKh8I5lPXdCe07TlOl0+ifOpRPepWATBAHPPPMMi8WCxx57DNu2uX79+j1zzXy9OJt7eeKJJ+56f+b14lbin4PBgKeeeopqtfqGoqK+H/E//qOvMhzOGA5nGUicZuAShjFSnOlXxaSZXE3f54Mf3CaNk9WuvnE6Yns7a+JvV3PUj/pMJgt2l4AjkPVf2s0xlarDYDwnCmOqW7kVWOxdyI5VFZnBsiF+dNBBMxJCRcLSLcJ5toCNR1kvpGBv1uBVRaJVHxKGUdbjqQ+yPsxa6anX9djdLbC37dJ47lkSwA2nNPTzTKqt5qnOB9xff4XuZz7HeDIn5yq0TkcIgoAsy5RKNs3TYQb2wO5+YeMzECVxRSTIPiOR7trCvlgEvPxindlsgWWa7O2WEUSBve08s1mAYamcNDLL6TTNiAjpko4t3WRrUK3mlgKdWaxnQLWqm5E5WBIK1qRqigWToDvGtQ0cUyOIYlJdx5v6HJ8OV/NIin6++EmKAFGW6ZVyJjlDo9OfEicpwyX4qZoEHuQtHRGB7mCKpkiEfgakFysug9mCo/GIWsVGlSQullxeWfZvDE3GChUESUCXZGxL5XA04oFKkVcHfR4oFzkaDdkvuKRCSkKKooj80O5lrjjnNOd1sFmPdfOyCxcuEMfxKut55ZVXCIIA13U3sp7vlU/MnYANZG2Dd0t2djfjXQE26x/+cDjk6aefxnVdHnvssRXD6l5aNL9eDIdDAEql0l3vz7xe3MyAOz4+5qWXXuJ973sfFy5ceN2/i+OE3/rnT9Bdo702GkOqWzm6vQm2pnByMsyApDVa1fDjMN6Y7E3SlHZrzOVLJW5c6wDLmZh6n93dAqIgcLIslXVaE0oVG28WMFujOtePBuzuFRAEODnqE4Yhi/mcONZ5/0O7nC5nYc6iXLI4Puiwf7HEcX0AgkB1ST4AODnsU63lNozRICt1NeoD0vGE4qiPJCZoScRYOv+ilvwxLS0PCYitPpfff4HFIsJx0lXWGkYLJpMZsiQhywpJHNE8nWBbGvmyjSiLHN3orl5zq+rSWIJ4moKqpvS6PpZpIUkigsAqgwRQRJGCqaObWU8mSVOqS/M309ZodcarSaBgzVagVLLW/HZSBCElZ+toioRla0RRTMHUiaIYbzQnmcf051O0bXk1A7Wznae+pEwXCyadZR/HNBSGkzn7Wy6iKFFvjAjjONNHW1KfHVNjtgjZKzhEcbKasakUbXrTGVXHYhqEjGY+xZxBnIAhy8yiDCCvlPPc6GXPmivqTOY+iiqx6zhMo4ALTo5J5FO1bOI0AxnEhA+XKnz/9sWNzzpJkts2WiyXy5TLZdI0ZT6f0+v16PV6XL9+HVVVV/MjhULhnrph3smMDfAfM5t7HWmaUq/Xeemll7j//vu5dOnSxsP1ZpI1d/tazvozAPfff//3jHJ5BjZvNqh58/X+s996gqeeukGxaBMECXEckyQpw/6MizsFDq9nC3ejMWJvr8BRvY+uivTbY6IooVZzaS7ZW6IIo7ZHuWRnvYMUUgG88XxDJBKg1/G4crnM6ZqwJsBkPMc2FKIwYLHwMUwTXVdpn47QFQlNkxmPF0iiwGSY7dBPbvSobeeZ+wGtm14vTVMGrTHb23kay9/ZtsZkOEMIQ4aKjR3NMKIFehSwkLMMVItDWA74x37mpDnue+zvFej2p4iiwGIWYNs2URQRxxE3DpqEYcpisWA08iiVHMquiW5pDIYzZkv6cJrCfD4jjkUsy0IUBVR1U2etWLLoLTOXyXjO7n7xHIjSFCFJiWcRkiBQKlt4wzn2cgG0FJmpkD0PpqXTPMmyGg8QEVYDnzvbLqNelolo2qaN9WxNiVrTFbSFRCVvY+gy3c6ARnvCxb0S4XIjVy7aHHVHlByTgmNw7bTPkDm1Skbtl8SMwRYsYiI1WUncFGyda60BeUun480yn5swJE5S7qsWeLXbZ7fgMI8iVEWEVEBWREYLHyunECYJSZqwbzv8hcuvdd68k4VbEARM08Q0zZVl83A4pNfrcfXqVRaLxarXUywW77o75p30bMIwxPf9/0gQuFcRxzHPP/88nU6HRx555JYmZ9+rzGa9P/PJT36SJ554gjiOb8tG+m6EKIoEQcCTTz5JEAS3HNS8OX73Xz/Dk9/OBDb7fY/atps1nMnUijvNCbal4i2HHuv1AXu7eY4Pp8vmvEC3PWZrK0e7PaZaynF63EdWJIpFm17fyzxoVIXTo17WnK8PAcjldI4Ouli2hmSKqyFRQ5U4eLWBYUmYRZcgzADtZJmtaJrCVtVBVeTVvwG0G0MuXiozkSR6nfPyUhon+IuI5nGf7d08QRTTXGZIqaIgAPlwSl0ro8Uh+34bTzFoaGsgrcj4i5AojDm50UOWRPbur9Bqjpl4PqqqsrO3xenxAEmMiKIIVUuoH7eRJAlZltnZLZKksFNzaTQ75As6/kJYLVLlrdxqyPXsfZ6FIEC/P13ZLJS3cnR7HoIoQpqiqDLzJZXaMLJMNEmznMexdUbLmZxcTl8BDcBiTb+uXLKpL/s4paJNZzTFtTXytsEijEkWCa3WBMWQVj2kgXdejhMEge2czWAyZzL3SUnJOzqNwZi9Yg5FkbneXL4/SWA7byOkcL01IAVKOYto6BElCePAZy/vUB+N2bJMZlGYzd8oGkEYMYp8tnM2fpwgKQJFS+evPPBhJOG1C/TtZjZvFJIkrcppkJWr+v3+KutRFGX1+7uR9dypLhrwHzObexVhGK5kZ3Rdv+Ux3wuwmc1mfOc739noz7xew/5eRZqmvPDCCxQKhVsOat4c//J/fZI//OrL7O4VOFmWdpqNEft7xUxI8rhHmkI+b6Jp8qp2L0QpmiwSxdmCE0cJw+Xw5uGrnaWXSsRkOKdcctBUicZScfnkqM/exRL14wG6KjOJEsbDOZajkcsZaJrI4dVG9n5iHV1RyBdVWmvlM98PGfdmVKubXyrTVKkf9kiSlL2LRU7qA7ZqOdprf9s8GXLhYgmtJmdZjm1jSikNLevV+KJCXS1xedbEkH1OjTIoCpUrO3TWGGaiJHJ0rUMUxmzv5IlTmE0DEARkRUFWFEolh8gfEEYRYRjS7XXxxlE2GS6KXLm/hJAXESSRiTdntDaYaZgqrbVZn+q2e65GAChLLToB0AyVTmeKKAjZol22qZ8OszKdItJsjzKatQCOo68UBIoFc6USfWbQlrN1HFND1WT8echkuMC1DNrLvtPOTm5VWqtWHIZTn/2KiyyLHCyN7PZqeW4sZ2TKroUkiJx2xli57PtZck0UQeSkO2a3lgMB7q8UuNbMnrdSwWQShEiySMUyEUWBYeCzm3cYBT45Q0USRSISNFXEVCX+ygMfwVJvzTZ9vZ7N24mzrGdvb2+V9fT7fa5du8Z8Pt/o9dxJ1nMnYHM25vEf2Wj3KAzD4OGHH37DY+7ErfOtRKfT4dlnn2VnZ2ejP/O97BW1223m8zm1Wo2PfvSjb/hwp2nK7/7uM/zhV18G4PRkSLXm0lrW6hezAEtXVrM0w+GMSsWhH83Y3spxcqPHZOLzwEPVVflMU2W6pyMKRZP+ci4nDGPSKOZmuD250eOBB7a4+vI5LXo68dGMmIU3QxREdMNAEGAymZPPGxSK1sZiXy7bHF/vsr1XoNvzCMOYfN7kdJwteCcHPco1B/GmiWjHNagfZjTk6k6e6TTA/ej7aHz7pRV92Ilm3NC2SAWBymxA+ugnXyOwWak6q/5T82TI1rYLYczebp5Od4phKXRaY0RJQpMktqp5et0JaRpmdFxL4tVX6pmEjiyzu19iOJhRLdooqoRmqfR6U8aTBUmcrOjLkOnFNdeAqFyxs2xRELIZpuE8U64WUraqOerL0qEinwOPLIk4jo6uKcsSnsRJc8zcj0jDFM8PlqC46YkzXQpoOrqMKkkQptQbQwrFLIMWBIGeN2Mrb6NLEtdP+yRJys6WS30w5kLJJSbhpDsmZ2lM/ZCSYbKIYpI05b5akeudAcayGtAaTakWbZIkJUgSFEEkXhIl4iRCEQX+4gMfpGK+/gJ7L8BmPdazngceeGDV6+n3+xweHm78vlAo3Fal405FOA3DeFfODb3deFeADWQP+BvJLNwrsFnvz3zgAx9gd3f3Nee912CzPqhpmiZbW1tvCDRxnPBb/68nePrpYwoFi8FgSpqm9LoexaJFHCVMRwv67Qm7O/mVllenM+HSpTKnh90zHUqaJwO294q0WiM0SaQ/mKEFMoWiyWAwRyBFQmDQ8ShXnBUryzBU6gfdjCl23AcEoihCDn38acrFK1Xay1JeqexQP+giCAK7F4qcnAxwHJ3mkoLbrA9wXIPKVo7G8aZfjSJJ9NtjtnddGifZ4mzbGt6ycd4+HeK4BuLeLsLAJ71+kJm2hTM8zSBVVTpXPkqpsoWYJJTKNr2uhySJ9NcYZpAtaKPBjNFghigJlHZcdFmi050QxymSnFHQNU1DVVVqOy4nx32iMCTwfZrNFvNpwmQ8RdNUNF3F9yMEYHvbJYgTdqouoiSgmxrzWUCyNHhLk0xxIU3ByelMZwGOrSGJApoqc3GnQJqmqJrEcLRgNguAlNNmgzgUSCKFYtleZa75gsGkkfWWqtUcJ0uQ395ykCSRVE8YjeccN0aIkshWyaG1pC1fqOWZ+SHtrsfetkuSpECKLIuUTQNvHtCfzxEEqLgW19sDcqZGfzpnx3UYzBfossKWa3G9O+T+aoGr/QH3bxU56A+oFGxkSSSMI2RN4Efvf5DL7qZZ181xr8Hm5jAMg729Pfb29kiSZNXrOTg44IUXXiCXy63Ax7btW35f76TP5HneXe8dvVviXQM2bxayLOP7/psfuBZxknA6mbD/OnMxtzM/c7etml9zjXHMc889t/Iff+mll97wfL4f8du//U2efPIQgECRMC2N2dQnCmNEBCRRZLykzLaaI6pbOVrtMY6t0a4P2aq6q75Cmqa0myMuXyxx/ZUmCEK2QAoChYKBbWqrnsqoN6Wy5dBpT3BzOq2TISc3euzsF7hx2EEQIoKpjCwpdE5HGbDUByRLZeM0TTm90WP3YpE4TlaAATAZzbEtle0dl9N6BiqCCNPJnGAR0rzRp7ZXIEpTWst+0Vk4OZ369S5OrYL9wrfxFJPT9z1Coqhg21m2IEu0l6BbqeawCwYHr3ZWr1Gq2HTXGvuGqXLwSps0SVE1GbegMOwPsGwTUcjYYO3mGEmSkCSJ7bJNrz1GUbJej6SE9LrTpXCoQioIqx6ULIlIirSykd7eza9KlNlDkazmcGrbeQ6vnzPh7JzOxPOBlN0LNtoH/n+kicjwmc+tpHQUWdogCYiiwH7VxfN8kjil0c7ugyQLnG2jBAl2SjmCRcjQWzCczNE1mcbAI2dqVAs2r5xm17hTdShJBqaqcK3dJ0mg5Fqc9MdIsoAUi+R1jRv9EfeVCxwMRtxXKnA0HHGxUiBKExBTBEngc/sX+Uj51ioY65Gm6TumiyaKIsVicUXSWSwWK4bbjRs3kCRpNddTLBZXWc+70TjtnYz3jKrdnWQYkiiiSBL/24svEd+0gM9mM5544gmCIODxxx9/3UHNe5nZzOdzvvnNb+L7Po899hi5XO4NwW04nPH/+Adf4cWXGphLiffp1EfTZJQlHdafBoRBhK6fPfCZ82Z1y0FGWMqvDNjdL64UTLYqNkdX21Sq5/dgsQjRFGmD0hxFMYOOx333Vdb6LykHr56iaTGVch5p6UGTJhll+b77Kwx7m3bPwSIkWoQYa145lWqW1TRu9NjeyaFqEts7eSZrPZBWfYAuidR2zq/TtFRaZ6rTnREtOU8tHGNf2QfHAUGgULRXQAPQ60zo1IcUXIOd3XwmGKls7kDzeYt0uaOfjD0mownRVERNJaolh+1tF8cxOKvbqaqMIIqoqoppGliWi24YmbJ0GnD1lWNm0ylBEFDesldAA2y4gJa3nI2Bz2jtWahtu0ugAVkW0B789xjFIWa5z/ZjX0NSw4w2XnUo5gx2Sja7ZYd6fbAcyoXWkhqfc3QGXoimyFzeKTDzAhqtEYoqM1zO71TLDjt5h/k0oL/sEeVtDUkQGYznJBKIgsj9tSJXmz0WYYgqy/THM2RVZMu2CIjZcWwWSUTFtoiSBEkWSIh5eKfKZ/evcDvxvc5s3ih0XWd3d5ePfOQjfP/3fz8f/OAHUVWVw8NDvva1r/Hkk09ycHDAfD6/Y7D5k5jZvDs+PW7PQO1Oymg12+bBcolf+foTdKfZwtXtdvnGN75BqVTacLO8VbxdQ7PXi8FgwDe+8Q1yuRyf/OQnVzI8rwc2Bwcd/uE/+gPqx32mno+uq6sFctCfUqvlkNMUb7JgNJxj2xqyfC7sqQgiJOdlypOjPoWiRi6n02uMM4JANyuVQTZUOR8vmE3mFIrnOy1Nk2kc9tjeza+kZ6IoplopIyGiG2uGZ5bK8dU2W1UHZXmtggDhIqTXGkOSUN7KzheuLb7N4z6mLkOyed+LZYeTwx6NGz22thxsRyO/LBsCEARY8ZwDa4dxz2N7J49la6j6JpBs7xaYThYMex6NGz1KBQtZFLCd7DPQdWXpxZMyny+IkxhiFUmSs/s0mHL0aodxx8OQZC7sFVAkgXLFQVUkqtsFxqMFsiyj6zqXruxhmhayLBOGAcfHTabeFH/hk8trK2o0gCSfX2uhaG0MfJ5bUafs/ifXiWQfQRBQZJFIELn8p5+mXE4YDxe0WxMazTHBUuY+TVN0Q14NlhYLFkVTJg0S/CBeqUEHaYKqSFys5mn1Jhy3hpSLNt3xjN2Sg21qHLdH5CyNMIpxFBUvCNBkiftrJa63BlRdi+E0Y7L5YYwki4zmPpIigpAwjwN2iw4/fv9rKc6vF+8msFkPURQpFArcf//9PProozz++OPs7OzgeR79fp/j42NeeOEFms3mbVnM/0nObN5TZbQ7XfTfX6nQn8/5B19/gg9ZFoXp+Jb9mVvFrSb6327U63VefPHFW9ok3Aps/v2/f4n/7X9/CtNU0Q2FxTyk3/Oy2ZjWiHzOYNDyyBdMxks1525nQm3bpd0ZUy05NI77WLae9TuWu+OZF6EwwZtEyEomajnqTylXHBRJoHlWrkoXlEpZr8O2NDqNEY2jPrqdMp+nbFWL9JoTojDGcU0UV2YyWuC6Os3jGe3TIW7BIjYUXNfg5DArC82nPot5wJUHq1x7qcn6duPMkmD3YonG6ZAkTjJV6mW0T4eZf0yaiVPGcYIQBOTDKZ5aJU1TGkc9CmUHWRAwbW2VpU3WfGIgk905PeiCILBVc3EKBofXusxmM9I05f6Hdmkcnzfzt2o56odZSWkxD4njhNOlgClCNtZTrdgoqkxKShInFAoW83lAsbxFozFEEGKiMMKbTph4fiYZk7doNEar+6CbKozmyJJIqWyRpLBXyyFfeRlp55A4Tpl0CxiGwlxb2jZ85glu/PsPI+BQLFjLWSIR01TwZn4mvOnHHB71GI9DalVnZWVQLWebgmmcWXsvlv0fVZPZdi0Wi5DefIEsipRck+vNAVuFbCDTUhSO+iP2Cjm8JEQWBUISZERa0ym7BYeIhFG8YLvo8Nc/+ujtfVmW8W4Fm5tD0zR2dnbY2dnhmWeeWTX7j46O+O53v0sul1uV3HK53Gs22ffKEvrdEO/+T28Zb7ec9ejODltJxO++/DIvyQZ6/o0bknfrvOtxZmP98ssv88gjj3DhwoXXPGzrYDOZLPiH/+ir/NEfvUISJ3iTBZalrSTmm80Rly+V8b2A2dTndKnUfBbNxpD7LlZoLEsoU2+BKApouowoCuxUy0zHKZWaxWK+YDKZMB5NIAoI5mtlHj9iPJpx5b4KncaIOI7xPI9R1+eBB3exTXXlETMZzQgXIRevlGiuNftHgynicuFdD0WRaBz0qNVcdCMr/Zm2llGdUzg97JGzdS5crtBdY28BmQzM9S6WoVLdzuMkPg2lAGuZqqZKnB728CcLdnYL7F8uM1nTF3Ncg9bakOWw53F0tc10PKJY0rl8X43J6FwNWpJFumvOnbqhbKg3F0s2J8d92qcjTg57CAk0j3pMeh7RLCANY2xdpZy3uf++GqVcgUu7ZSoFE02OEaIZUhrgGCLDzhhVgMQPEVPoNkaMtOcRL32XIIiJ44QFGp2ZsGLhLeY6uf/kGdT9FpqlYpsa+9U8tVKO2STgtDFG1WVmszmSJOLkdAxNYa/kkMYJ9caQOE5oj6YYmsJ9u0WOG0NafQ/NUKjmLWoFi2vNAaQppq7QHUzJWTpbjoUoCRiyjGvrmIqCpstULBM/immHE0xT429+/Pte8914s3ivgM16JEmCbdvcd999fOpTn+L7vu/72N3dZTab8cwzz/C1r32NF154gUajscp67qZUzRe/+MWV3uLZf+sq8Wma8sUvfpGdnR0Mw+Czn/0sL7zwwl05963iPfPpvR022ll/5pN5lx/5yEc49Kb83X/7R3zlxWuv6eXc6rx3A2yCIODb3/42vV6Pxx577JaDq3AONk89fcR/+6Xf5fnn6ky8BU4um9zvdT0qWw4I2WBh/VqXcuV8J1Q/6rO3VwBS9rYLXHu5yd6FcwAaj7LG7852jn5ngizLTIYR7/vABSzLolSyqR90OT5sICuZUVkcx9i2xslBB7eoMZ1mYpOGbkCSNaTXQTOJYgatMTtrwAdgGRrNG312L5RWNORKNcdiFtA+GSIiUKnlyOetFXhBlm1NehN294tIywUnX7RoLrOJyXBG+6jH1qhJPppmdaIUimV71VtK4pTGjR7jTuZGWlqWC21H32BBFisWg/4IEpGFl6JIEr63oLbtsLuXZ/dCgcXsHIhLWw5RdP4MqWvK2aK4aTNQqji0m2Nmns9oMIM0pdueMOjNSSKRxUyCRMOfpyQktNsDBv0RghhTr/fQ91vkP/Xy6t4FY4u4EBM5IZORS+yVWegeiipQfPSAafUFZt6UbsfLqNNpBr7XjlukacrFvQoSIv4sZBHEtPpeRiffsqk4JkmQMJwuSNOUkmsgCQLtnkcE5E2dK9tFrrcG5Eyd8cKnNZwgSiKj2ZyUTDnaW/jEQsKNuI9myPydxz97R6DxXgSbm6nPmqaxvb3Nhz70IT7zmc/wkY98BMMwqNfr/M7v/A6f/OQn+epXv7pUsrg7G9wPfvCDNBqN1X/PPffc6ne/9Eu/xC//8i/za7/2a3zrW9+iVqvxwz/8w0wmkzd4xTuPd00Z7XbcOu/kA+h2uzzzzDOr+ZlPCgKj4FvU+2N+57kX+eb1Y/7TDz3IIxdvbRlwN9honufxne98B9u2+fSnP/2Gg5qjkc+//+rLtNsL9naLzKY+/iJE02Q0XcFfhDQaI9533xZXX2oCmc/Lzl5hJYNycjzggfu3uPZyK/v5qM/uhSL1oz6kKbalMp/4KKpMuGxOnxz1uHxfhc7pCNM0SZOUYB7j5BWGvSnd5gJ/HtPrJVy8XGXQW5BbZgVxFLO1k2fQ9wiDmErNpXHYwxvO2b1U4uS4x1bVXWU6pwddart54jSlsWZJMPd8dF1BlaRVaQxge79A47DLsOvh5E00S0cUYG2IiMLRKxwGIqlgsHf8EuNhC+XD7wfO69/bFwo0bvQZL1lwO0sQFmWJJIpBSLlxvYGqKGialikbD2dEYUyrPkAQs3JczlFxXCPT3lqz1c7ljWWvJ4vabmFDI01Wzz933VRormVEpWqO+vEAQRSxbIMwFHAchziOsRyNWVzH+ugrCEJKGEaEnsXCSBATAVkWEVQLT4oRfZUoDSDME+zWUXM98qcP07yWkKQpohRQdDR0zcLQtJVqtKzJKLLETjlHdzTDmwdUSzaN4YS9sktESr09olK0UGSR/iikLaTsFXOkIrQnHtvFHMe9ETtlh+FsATIopsSrcY+KZvJLn/lh5DucH7lTnbF3Mt5ozkYURVzXxXVdrly5wpUrV+j3+/zLf/kvefbZZ6lUKnz+85/nC1/4Aj/xEz9xx0Ary/ItPa/SNOVXfuVX+Nt/+2/z4z/+4wB8+ctfplqt8lu/9Vv8zM/8zB2d743iPbNVeKsZxtnsylNPPcVDDz20UksWBYH/26c+TskxKTsmjanHP/yjJ/kHv/8Nnj1qvu3z3hztdpsnnniC7e1tHn744dcFmuFoxm//L9/iX/6rl2g0shr6yemA2nbGvBqPMudH01TZKTtcfanJ3lrm0FgOdQrLjOfg5TbVmru6F/UbvWxnvlvg9LBPv+uRyxmrxr0kS3iDGaVyliUJooAkysQLkQ984BL+IskW/xRuXG9h2Cmh76/Aqn06xLZ0dvaLNNbsmU8Pe2xvFwgXm1lp+2SIJokUSudZWbpUoz496GBZKoWShayIDNfmYSbDGcQRIimGrcHSWsCYTpZ/LXCqFDAmI7T/8B+wF0uVZIGVBtvaCWlc76KkUCwZKEaCKCorskZtr4C3ZjNQ2yswHS+YDGec3ughCAKD5hBDFtkq21QqDts7eYolC1WVma4x+XJ5k9YauJQrzgpMZVnaKM1Vtl3CMEIQssxrrk4o/uk6gWYhRCYEBr6SIkoJcRgz7ylMlDlTFnipxLxbZiotMyoZTq88hf6xNoa6wJvGzOdCtmlZKkhXijaaJGHICggCsyX13TBVyrZFGMU0+hNyyzLnUXNItWyR0zXCKGEWhlRzDt3pjIuVPH4Uk7NVxuqC5xctiqrOf/8Dn0d9G/IvfxIymzeKUqnEf/lf/pc88sgj/LW/9tf4N//m3/DQQw/xu7/7u2/rfb/66qvs7Oxw+fJl/vJf/stcv57ZcBwcHNBsNvn85z+/OlbTNH7gB36Ar3/963d8vjeKd01m82ZxtuivG2C9XrzZ/IwiSfz1xz7Jr/2Hb7IIIjRN5IVmm1ebPe6rFPnk5V0+df8eiizdMRttfVj0Qx/6ENvb27c8rtOZ8O//8CWOjvvcuNFjsQjI5VSSKNvZd3sexZJNv+chApWCxfFyMa8f99nZLXB6MiBNU0ajGRf3i9y4ljXgu+0JpYpDdznQl8Ypa27D9DoTSlsOo+GUcsmmVR8w6HrsXixxspz72KrmePHpAwoVg8VMyCwK4gRVUvDDrM8jSxKyojAceEgCFEoWgzW6syhAHETkCuYqq9jeL2TDpaLA7sUy9aMe23sFmsuZnnHfQxAFLr+vxuGr5yoFaQpRmNA9HSErIvuHzxH4AU0l/5p7W1cKiC9dY/vPfBbZUDhem6uxczrN4z4pMBlPGfQG5PI2lYKDXTCZTBZ4686dAhs/S5K4sk9YzANESaDXnqyy4OpegX7HI58zUDUZ2zVwbD2jm4tLK4Mlo88wNeaLbPhTEDP5mt1aniROUCo+vfe/RCLFIIl0xxpiJCDm54BI5MnMlRgpgSSBaGQTlCOEgY1lxwSGBEnAuHiM9Mk8yVEB8bRAsWzT7nnsVHKEScLx6RBFEWmPpuRMjUrR5urS4rq8ZXOpWiBO0swkLWcwmMwZz3wu7xbodqc4VQ3ZF4iJGUgzbgRDZCR2NIf//gd+BONtWnO8F8HmTrKx6XRKsVjk0Ucf5dFH3xqJ4uZ49NFH+c3f/E0efPBBWq0Wv/iLv8jjjz++YscBVKvVjb+pVqvcuHHjbZ339eJdAza3Q32GbLfwRmWo2WzGU089hSzLPPbYY6/r7GkoCj/z6U/y6098h9ZwTMUx8RYBLzbatEce/+pb3+XhC9tsKRHb7lvzOz8TFh0MBjz66KPkcrmN3/t+xPMvnvDEHx8wHs84XZZe9vYKXLvWZDhccP9925yeDoiimDCMuHShxNFBhzSFvQvFFRg0G1lGMxnPMRSZRn1IoWgx6HtEYcyoP6VQtDA0heZRnzSFnf3z8k6vPeHyfWVO1rKRkyULbOrNufbiMYIgEkwF9i+VOTnqsb1boHMygBQefGiP03of3w8RRZ/DV0dousrWXpFBZ0axbHN62IU0RVZkant5Bl2P/tKzJU1STg46bO8X8dd6IZARBQ5famK7Boom02tP2LlQpLEEpKg/4iTQ2Q2n7MQ9TrQiAgLVcEhLdhGANIhov3gDvVahup0jjFP67Ql2zmAynLNYLIiikCvv26VzMmI8mDIeTKntF5l5Prv7RXw/QtHkDXWD6l6B0zUB0XzZ2fg59CPCIGLUjzBtbQlES0voiyXqx9mxogiGra+yoJ0LRepH2WZBLYZID36XVM5KdWKiMkMkMRLKQZ4gmRGqEqqWEiUJycgkzocQQaKmTIYqugqxEZN6FlF+QfLQCezMGcxD4rbGwg9pL2eZdmp54iSh0RrRlbLv4/62y3jq059NMVyNy9UCiZByOvC4b7fEq60+l6p5Dtp99C2Z58M2QiogCiLbhsPf+9wPY98Fd933Itjc6VDn3WKjfeELX1j9/4c//GEee+wx7rvvPr785S/z6U9/Gnjtuns7m/k7jXcN2LxZnAHMG4HNWX9me3ubhx566E0/6Jyu8ZOf/Cj/z//wJLN5gKmqFHWD4+4QVzd49rjFYb3NhbzF++tzPnypxn07RQrO67NFFosF3/nOdxBFcQV2aZrS7k44uNHj2RfqvHK1hWPqdLoTJEmkXLLp9jxOToeUSxat9pjTxpD9vSJREDMdzRn0PDRNYbEIqR/32V/2YJIkJY2TTJl3SVWWJDGbNB/NCYMY21TxRguWjsU06uc9nt29PIcvNSlt5RiPF6uyWKc5gHSBJMnoy8Xi5LDLhfsqtJdAA9A+GVGtFdAMhZNrHXRDJ4oijq81cSs6g16fwE8yi4gwonmjx33v3+bwldbGfRMFgeloRm2/sGr8247BdDBn3JtmhIgrFabjcyYZUUgtHHKiZuXEkj9BFmICcfP5qFkCJ6M50yXleftiEdIU318QRxG5fI7JYLPENp1k5bLxssFfrLpUqzlkVWY681cZGmQU5XWR0cp2ns4acy5fsjldbg4kSaS/5jlU2y2u1BwEQWC8vMbUCDB++ABfyYBGSmVGnkyiZ5/PMIFkkkOrTpFECXHkMLfnKLJCsEgIfJXUTRhHEULDxqhkGaYyzzHPzziyriHIFgtcRNHgol6j2RnjBxE723kG0xn7lRwjz2c0XXBpr8jQm+PNfQRFpGgbNEYelW2LZ4Im05pPGgnIZJvCqmrx337mB7AV5a4AxTupIHAnkabpHWuj3SvFZ8uyVq7Hf/7P/3kAms3mRtWl3W6/Jtu5W/GeARtRFBEE4ZYlrTRNOTw85OrVq7c9P3MWJcvkpz79MP/4iacI5iF+FFNyLFRJYjCes12yudGZICpdnnzlBDkFQ1XYKTrkbYOqa6NpMpauMhlPuPrqVQzTIV+o8Dv/3+c4aY4Q0pSrB10kUaBSsJgvQpI0JZfTGY8XePMA29bxvAXD8QLHUXEdi3AeosoS3lJavlzJmE9RFHN83M8MypJssl5WRNyCwWgwYzb1sWwNy9Yp5E3qBz10QyFXMBgP5qRJBjhXHtji4MVMnbnXHlOoOEynAov5nLnnk4QSV+7f5nSpYSYIArPhnELRprUYLifsYeYtSKMYt2AxHk5R5Gx4sFzIMZ3MWMwmeN4CURKp1FxeeeaIUtUlTlImozmFks3pjS4k0DzsU71QRJAEmgfnmQIpCEmC7/lsXyhwetRHUhVmwnl5pi9bbAcZAG8lQ9pKHiWN6PnCytMGIAoiDl89RVYk7nvfLoatcfjyOfjVLpRoHp2fu7Zf3KBx71ws0+4PKZcdNENBt1T63SmT8YxoaVt9FrqpbJAGtnbz56QBATzvvDRX281zejokNULCH7zBcC6gL+tqUZJDtBdIooSUyHhTGd8OSfs5hNTHNxYIgkgUJoRTndgMSeMUbebg12LGUYxY18CekfgpwlgnyocMxT6xrNA1O4i6huJrOLbExPOxJZWxP+fidp4b/T6z2GdrN8fBuItSkhiFPgs5QkollEgmSTN9wz0jx//9Bz6Hq+srbyY4/w4LgvCWgeO9ltmcvec7UX2+Vy6dvu/z4osv8v3f//1cvnyZWq3GV77ylZUIchAEfPWrX+W/++/+u3ty/ncN2NyuC9/NYHM7+mZvFrWcw0988qN8+ZtPIQoCYSoQxQmiICAJAluuzmA+x1z6pgyX/h8vXGuRt3W8iU8cBQTejFRQubKvcfr0dykXM5HMJE7Z28lTPx0y8Ba4rsFoNEdTZHRDYT4PcXOZeq8kSCSRT7wIaY8XCMJSO+tkuBrUbLVGqKoMcUISZ437OM6+jLajMxnPmXoLLl8uZzvuFBazTKk4lzcYD+fUtl1uvNRkZ7/I6XJhHXQmyDoockSwkFEVmcZRj9pegU5zTG3XzYYfyXbvw+GMMIiwLJXu6QhFk6lsu3QaI8q1HO36CJKUSrkAksBoMGE2njGdzpgdzNANla39EmmUsi4r3TkdUCxaG1mO5eo0jnokUUzjoENxK4f7wBY3rh1CGCAAUpowFnWm4lIG3x+SNwRekY3VoGSuaHDwygmiKKIqGv3mGEEUcBwdp2Ax9RZ4Nw19zqbnjX5BFBgNpkRBTK8xQjMV4jhdUbV3LhYJ/IjtnTyiJKAZKvNZQJykpHFKSkqhaJKmGXvNm/iUyw6iKKCoErXLFu2HXwAr+0z9qYsYpwT2FEJIY5EkUvCXpbVYkZnMBIxZiFJImHU1YidEBCTPwjMCFFFGnGrMCjGKqqP0FRI5Jgp9krFInAsQFxJiRWAm+fRHU/wHYrrJmH55TjMdMHAXFAWba9EBuaLJLJhjyirJFFQlAxoZgS0txy//8OfJLxfMJDkz8suUDM4W4TPAuV3g+T8L2Hied9cym5//+Z/nR3/0R7lw4QLtdptf/MVfZDwe85M/+ZMIgsDP/uzP8qUvfYkHHniA/z97fx5k6X2Wd+Of77Offe+9p2cfaTTSbJIlOza2Ae82kg0pjAlQ5Pe+QBL4FSEEqIQEExxXOdT7R6hKOQvhNfmFJARvgMGxwbYsb2B7Fmk00uzTPb2ffT/P/v398XSf6ZFH0kxrlp7BV5WqNN3n9HnO9lzPfd/XfV179uzhox/9KPF4nA996EO35PFfinvn3eN70zr7/f413mKbIZp1bMtl+KnHDqFqKkIRKIognbTo+wHNjsNMMUMqYWK7XrSZ3eoxVkpRbfXQ9YBmu0umkCWVirNc7VAqJKnWe4yNZCIDynKL0VIKx/EJpSRm6bS70ZKmZeqk4iYj+STd5oBadYBQBGYsiggor3ai3RqirJpdO0roMqpOKuUOpbE0Ekm/5yClJJWJMTGWYe58GdPUhhYyg56L7wVs25FndaFOGIYszdeZmIn2Xvr9AYoMsLQEmdzVvvHqQoPpnUUqG7JYKstNYjGd6e0Fqms/9xyfylKTqR1FnK4ztMfpNPr0WwN275tAhCqpVGqt3RbSabZoNqt4gYPruYQyZHwqT3W5xcpslZGJNIm0STJlRfLk9fe+Y7N8qcLoge1k/D4SGHcbQ6KRQF8YzG57mEzGYnw6B0LSbrdQVY1YLI5AMDKRxR14dBp9li5VSMR1lDBgYluewkiKsek87Q1eZWPTeXobhALF0cw1O0GeE1Bb7bBypU5tpc2VCxWW52qU5+vousLKbI1muUOz0qFd79Mod6ittFAUwdzCMnN7T9LXIgm554a0+yo900AOdGSoIIIU9hrRaJ5J0/PxYwGDmEZYL+DHPQI/QOladE03clPvagyMEF3XMO0YTStgEFfxmil8DOJ2DNnT6Dsufh0cyycjY1Rln7hv0hEulqvT9hzyYRLfFiAUbEeSMGIoUqCFCuNGin//zncMiQaiakZfk5IbhoFhGNfMX33fx3VdfN+/pgp6Ke41slm/KL6bRpwLCwv8xE/8BPv27eMDH/gAhmHwN3/zN8zMRJHbv/Zrv8Yv//Iv84//8T/m0UcfZXFxkS9+8Yu3rY0n5Cv5+t9BhGGI53mveJuvfe1rPPjggxSLxZuez9woLpRr/PF3TtHuuSRUjW6nR6PVQQiTQjxOEEpSho6uqcytNNClR7neZ9/MOMurkTVMrdpF1zRiukqrbTM9nmVhsYGuqyRjBo3mgMmxDJoE3w1RBSwvNpAIclmThfkqiWSKQj4RzV28ANPUyGZjqFKwshgZaS7O19cG7yqptEW90oskw5kYjUqXwSA6KWXzkVWKPfAZn8jQqfdQDWU4dwilJJaMVFB2I0pEjKcsNEOl3ehTGEnSqHRJpmP4XkB/rfVTGs/QrXeJp2PUNjgmj01lASK3AS86eRTH01QXG4zPFKmsNPG9gGQ6xqBr4zoeqi7Q4iq9tk3gBKhCRdM1FEVhbFsBTVNYnq8PHQgmZgosrbkhK+0m284doyUsWlr0RZWGzsTB3Sx6Efl4vo+RlKQzaQJPoduyseIGvh/gr+XMqKqCGdfprxlRCiHIjqTRDQ1N1xj0HVwvoLfmKGAljKituUY2hbH0Na/DxPbC0DFbKNFr2ltriY5N5VjZQN7xcYPa4bPInI1uaLhOgNeM4Wej+AHhCvSuihjzIzWgb+KpOq7w8f2AuJOhIrooDozoMRpGHxSIixh9KaOQMt+k4fmgSmJujJ7loaHidgWuHpAJLdrCweirdKWPJTUCQ4CrEiTAtHVCIzJ3jas6oQ9IUKXKtniK333X20nHblxM89KqZ/1UdL2q59vf/jY7d+6kWCze8N+/m+j1enznO9/hLW95yw3fR0rJgw8+yP/8n/+TH/iBH7h9B3eXsGUuFW60jeb7PpcvX+bEiRPs27eP/fv339Irnt0jBX78dY+QSZj0fQ/bD4hpClOjOeqDAaGU2EHA+StlVG+APfA5tHcGXVXZva2EgmD7VIFkzCCRNBktRHOW3TNFxgtpEqbBSDrOynwD3w0or7ZYWm4yOR05ClQqXfKFNbeAeo98MYlpaYwUk9hth97a3sfifJ2p6TySKOCs13XYtj1P6PgsXaljWBrm2l5Es94jFjOYnsmxOl+n17Vx7YBMPkEQhPR6PTRFo5DNDd+HfsfG7ruMTWfptWxCP6Bd7wKSbCFJImXRrnYY9Bwa5TZj2yL7n/HpHCtzVVbmqiTTFqlsDN3S6LcH0V7LbJVYzCBXSmHFdAIvRFVUCBT8jmTPA9PE4hZBGNDr9egPXiqYUgAAiVtJREFUepSXKsyeWSKRMBiZyEXKr9mrtvthOoMvVDrCZGIqS+yRB0i96XEWg4hoXM+l3+9h6TFaK3269S7FkSQTM9c6HIxuyw2JBmB8pkBjtUN5vsHSpQqxmI7bdSiWkkxsyzOxLU8ybaGsGZ6KDe5uZkxndQOZjE/nh0QjgcEGO6DCriRXDl3EFgoECoEX4rXiuKkATVMjaTlJBiUNpZVA2CoDX6HvO3hegNqKUVGi7X8NgyUkqpci6aZoOwF24KN5Gi0vAFWSlQl6pkfgSVTHwNUDcjJGU3URroaXUEkm4gQxDd8VuLpP2AwZ4GI7PnGh49kSPNBClR3JDP/Pu995U0QD31v16Lr+ilXPvVTZbHYJ9XbObO427p13j+jDefnyZebm5njd617H1NTUbXmc3aU8P/66h0nEDJJxg7bjMnA9JgsZYjGNeruLJlwadsjk+AiXFut4XsjFuSqqEFyarZKwdJYXW/h+QL3a5eJsFd8LWFxq4ocSw9BYKbcZn4xO0guL9aGvWa02YHo6R8zUMFSFkXyShdkanfYAhCAeN0BK5q/UmJzORcKDQorqUgtzLVqg1egTixlYlo6UkkTCoNsYYKz93u47dJo9UFympvP0ai5LszUmt1+1kkGC03FIZ6+eRCIScsgXEzhrlVMYhKzMVtm2u0R1+erGfKvawe47TG7L09/Qeuo0+sRiemTtsuEaY3wmz5UzK8Qsk537pkilUszsHqffcbBtm8W5FS6fnUfVAlLrcvRmk/yp77AsUoQIluZq2KfPUezXyGQT2E503x37Jhm0vavPa+Ay+8Ii+D5jU1nGpnM0N2TAqKpCs35VcabqCvVyB8/xqS63aFTazJ5dobnaJnQ8JqazEAaMTWaZmM4xuS1PaSTFyHiG0kgKGUoyuTiZXJxtO4oIBPlCksy0xfzhiyiFEJkTqDKN6KYJsyGapuDZAU5NoyUG+L6kLn06zThCV5CA0rTopzxkKEkQR8Z1QkXS8XxqUsXuG2hNCz/UUU1BwrOoSZvAlySDOH3NJR1YNFUPxVFQDRURCGw3QENFsVQSxFGSKlqoEvcU+i2H0PURfsiMmeL/efc7SFqvTd6sKAqapg1bbYZhoGlRZWvbNo7jDLsf69XQVsZmlGhwe9Vodxtbpo0mpXxFC+5+v883vvENTNPk8ccff9n9mVuJy5UG//Xpv6W8XCOXzyN9STBwqNdajBfzxONxZpcaTORSlGtdCqk49UaPkUyC1UqHbWORKGCslKa8GiUi5pMxKrUuI8UU9VqXMAiZnlxPu4RiPkat0mBybBRFRF5nAFPT+WFLJpOLM+i7OLZHoZAklTCYuxBd6ccSBrqmDJ2N86UkMVMfOi1ncgkc26PV7OI4Djv3jIMvI8v/NYxtK1BdaZFOW9RX2qDAxPYiS3O1aDYymWXlSi362drf1XSVeNzEtDTazT7OWnjb+HSB5dkKEztLrF5pEAQhyWwMu2vjuz6F8SyO40cqLt/Hta9e8U/tHqFd79Gu95ASwjAgO5Jg5XKFIAyY1F3kmYvEQo+aenXGNOq3WVHTuFOTGNMFcoUCdteh37k66B+dybO6wSpnYmeJ8kKdwlgGRVPQDJ35C1eXQCd3FlncEGI2sbPI0obdpPxomvra8mwsYUZGmWszpontxaH0GQHpfJJWo0eQ8Oi/t0FfWWvbSYHsxnEUn7jqo2UEdlXFSQTohobfk3iehowLpCtJ91V6ORsQqLaGi4KvS4QnEIHBQA/QQhVN6PQCj1hXIZYyaIcDYtKgbwXQFQQxgfRBVzScMCR0IWbpdGyPnBbHdQM0oUWVkBrZN0k3ZIcV4wNjeWKmSbFYpFgsUigUXnEP7mYxGAz47ne/Sy6XY8+ePde02yAiqfX/thJqtRrnz58f7rPcCBzHoVQqsbi4yMTE9e2z7mVsGbIBXjaJc30+o+s6U1NT7Nx5Y4FLtwLPz87zn77wNRLJPE67S6PTZ6JQxPUk/a7D9EiGwA/x3JB6q0fc0On1HDIxi3qzx0QpzfJKm8nRDItLDSxTJ6brNFt9JkYzrCw30TWVbZM5XNunVm7heX1CL5L0Tkxkh3sYU9N5FtYIZ2QkhamrLM7Wri5qXoluF08aaKpCGEoMVUGGksHAxRm4SAmqHjIYOExPl6gstNA0lfxIahgwJhTBtp0lFi+V8d2rV5ATO4sgJUuXrm70j28vUl5sUBrLsLJ28k1kYphxA8/1sTtXd3eyxRQhoKmC+oY9FE1Xmd47xuUXloY/kxJKY9EJfHx7kZX5Opqmggxx+l7UpnjhNFP9VYRQqGtJ+oqBQkgycGgpMaSmER54iKldo7TrXdKFJPVyh3gqds3jx5ImnusPZze6pSMEmDGDVDaOFIJ+z6FVjYQCyWyMftcZSpxHpnLR7tH667RGzBAF+BlJk8Ha0ubYtjzLCw2clEf3jRVkIXJvJgTdT9FUotvpoYrRVrDza/+WOq6rEcai90dratTUAenAQAkDHA2UpIpvh0hHg7SC8ARuF4I4KH2BpwnQBSnHpK/5KB1JKCSpRIxuzyUW0/E9iappNPo2adOi7jqkVQPfk+i6giIVtFBwaHSc337XWyN1ZrNJpVKhWq0yGAzI5XJD8nktw+5er8exY8cYGRlh3759CCGG8511N5FXm/XcLZTLZebm5njsscdu+D71ep3t27fTarW+ZxH8fsCWJpuN+zMPPvggjUYDy7LYs2fPHTumbrfLn3/5q3xntUtv4BOPZSgm43hegG17NFsDxrNRZZOPWwggYUYqsjAI8H2JhsLAdsmmYvT7DpahI6TEdwN0TWX+cg0pJRMTWebnqgwGfXbtmKC82kYIwchoitXlVuQeMJ0DP6C60iaVjtFu9PDcEKHA2ER2uNg5tS3PoDOgvuYrlsklsAcu9VoLKUP27Z+mXevTXbPbVxSFkcksqwsNRsYzrF6pkxtJMeg62Gtmk+Pb8oRBQHW1RbBBgbV93xirV+rDEyqsZaDsKDB35lq/uckdRWQgo72aNYzNFFi5VKEwnsELJO16j4mZ4jWkFk9bjG4rcOn5hWg20u1gnX0RlZCB0FFlyHjQQSVgzigN72c+/ABeLDEkEqEKpnaP4Toe1eUWgR8yvqPI8uWNVUuJpUuV7/l3LGWSysZJZhP0eg5236PbHpDMxmmtRTIn0jEGPXfY5pnYUWRprYISiiCejlFV2yy9sY5laejeAGmEBH0TOyEjMYAUxMIUHd0hbxvIhEO3B44WzXCoK3RiHoqqonkqrqeQEhpqLESi0td8fCcEW0UmFWRPIg0NqYHaEPTjIbqvgBopL71egDQECQy6vouhaOio9KVHUpiEgURVFLRAIALBG7ZN8xtvf9N156z9fp9qtUq1WqVer2NZFqVSiWKxSC6Xu2ES6Ha7HDt2jPHxcfbs2fOyM911kcG6rHqrVD3rLstHjhy54ftcuXKFAwcO4HneLa0Otwq2FNm4rjv8sKxbvtTrdY4cOUImk+GFF15AURQeeOCBO3ZMKysrnDx5kniuwN+WB/heQK0xQPqSYjJOGIbMrbXSGq0+Jiog8Z0ATVGQQYjnBqTjJrV6j+mJqGWWScfwHB974DI9mWf+Sh1FQCEf59LFZQqFHJl0jFplzWWglEATgnq5S6GQGNrVFEdSNGuRM7FQYGIqB0HI0lyNeMJE01XazX70RRQeQlGZGM2zutAgljAxLH14olQUwczuES6/sDx8/slMDKEKYpZOZbGBDCXZkRSO7THo2oxM5Cgv1IklTGJJa1gxjM/kI2nyTIF6tYtne1GrabkZ7d5M54fLqoHjDfNzFE1hcs8olfkG9ob9luJEhupig9xoJsqUOX2Z6fMnWNSyw9vEQwcfhWLYx1Z06loKa9coZSWNrkWKssmdI8MKTDc1xneUcGyP6kqknEtkYgx6DuFabMBL/50bSUWeaGvfmokdRRq1LrGEiW5qxNMxnMG6PF8iVAXfC5BEtjRlvcnZh5cJ1Uj1pzsKlqvQz3hRVIEvUWyLrh69HqqnotkaoeUSmCEJJ0ZVGSCEQly18FwF34gIKmhKVCCW0Og7Pk4sJKmY2H6Ir0r0tkrX9EnrFo4bEiARjkBaAtPX6AUuSd3EcQKEoaC5CjFdw3MkigRDKLxj727+8ZtvzLPL933q9fqQfHzfJ5/PD6sey7Kue79Op8OxY8eYnp5m586dN2yfsrHquR7xbHahdDNYXFykWq1y8ODBG77Piy++yA/+4A/SbrfvemV2O7Al6XOjv9kb3vCG4XwmitV9ZXn0rcTi4uIwTOjvPXqEA7bLf/vqcSzLRUel2u0RV3UmCilUoZBNxWj3HFRfYsZ0Bl2XmKUT+CE9xyOTiTG/1GBqKsfiQoN8LlKDzS/WmVqzn6nWe6TSOp4X0OnaTE3nCNyAeqVLKmliD1wWF1wmtuVZulKnWu5QHE3RqPYoFpO0612SqWh43u85WHGTeMpgdalGMh1nYjxPd026O+g5BH5IYTRNs9KlOJri8guLTOwosXi5ikDQbQ0YncpCGA4dA5rlDlbCYHJHifJ8DaRk0I2EAxM7i8hQsnwxqkpW52okMjGykzl6zd5w96YyX8ewdCZ2lpjd0D4L/AC7NUBTBCPTOcrzDRRNwVmrrhqrEZlt25ZlcP7aj28mHLCsZlhSoxbENruMXTpAuw+u4+LbHlcuLRK4Ek2LUjT77QHVpSaqrjIyniaZT1BZatFZk4WnsvGhzQ2sxQSsncNUVaHV6DHo2gy6NtlSKlqiXes8TuwssngxqpA0XaW/o8aVA9VoPqJq+J0A2xE0LZ9kWRAmQmRPx82G4IHqKCi6SSvmooQqxXacSqyLrmvEhEmvJ3FUHz1QCdtgxySmqtPtC4RpkPc0On0XNxGQ8izaCY80Fr2eT6BKMmqMrumSCHR6eCR1i8HAI22YDLo+ccNgMPDQhYKFwo8deIiffP2hG/7+aJrGyMgIIyMjSCnpdrtUq1WWlpY4c+YMyWRySDyZTAYhBK1Wi+PHj7N9+3Z27Nhxw48FV3da1gfzt2qhdDPYjC9at9slkUjcNm+yu40tRzavtD+jqiq2bb/CvW8NwjDk3LlzLC4ucvDgQU6cOEEYhuQTMf6vH3yMP/r6SRbKLdIJi4HtAQIZhlTqXbaN5KKTb9/DyKk06n3SKZNOa4CqCFJJk8WVFhOTWZYWm5SKKZr1HguLdaamc9QqHVxnwPZtRVYWGlSWW1imhjPwCIOQQjFFrdphaaExJByn7zE9nWNxtobv+fTaNuPb8ixfqdNudglCl5ndIwwaLitXoiokU0jSqnVxHY9eRzC1o8Dc2aiiWbpcYXxbgcpii3QuTn2lie8G1wgChIB2tUNpPDv8mQwlTs9B11V0S8dbG/Z3W30SaYtcMcmgayOD6GxdnMgy+/wCpTXTy15rwOSOEktrRNVt9ihN54mnLObOXK22hCJoqjHaRppxu46vaGgiZFnduNQracWzNLqCZNIkv2sMzVC5dHqRAB/btkkVLK5cXELTNDSp4XkBl04tgIRYyqI0mcP3AvIjKVr1HoXxDOX1qGxgdKbA0uzVdpthGhBGJBVLmdfMcbxHNZZSNaQSHb/sgueq+FaIbmp0vACzYhGOR24Eqq3ioRCqAYamEuvoLGgOZttCExI7IXDUABGC7uu0Yx6mqmG4Kk3NxfI0Kr6Hp0tKXopu6GF2NDAU4lInoRo0Og5xoaOZKmYvRDUUVBQCB+K6gRf4xNAwpMr//feO8M4Dezf9nRIiyudJpVLs2LED13Wp1WpUq1VOnDgRuVuk0zQaDXbs2HHTRHM9bGyhvbTq2ehEcjvabZtRo93PsmfYQmSzbsl//vx5HnzwwevKml9LWueNwvM8nn32WQaDAU888QSxtd2BIAjQdZ2kZfCzbznK//z6SebLLYQrMHUdz/EZKaWYLzfIJWN4XoDb9ylmE6hAZjxH6IWoqkI+EyBD2LG9SOAGpCZz2F2XerlDLhvj7AsV5mWddNKgXu2iKIJMLk6r0afdsckXk9SrXVqNPjv3jjB3ZpVuo0dxNE1rLcBseaFOKm+wdKUTRRvXBqQyMey+y6Dn4K9VNP2ug6krzJ1buYZMVq7UmNpZolXtDrNoli5XmNhZpLbSwjR1mpU2nUaP8R0lVhcaWJZOv9Vn0HVIZmMkMzEaq22mdpZYXCOQ7EgaGUoUVWHlcnSirszX0XSN6b2jrGzYnwEIvIArLy4xPlOkUe1i9x3GtxdZ/M5ZlFCyoqZQZcC412FUbbOqpZEIpv0mV7YfBgF2z6FT69JrDyiOZUmkLFrNHr4X4KgdPNdjMBigWuDYHpqu0e8M6LVMamsZNIomUICx6RyKpkTqScfDjBk4A5fSZC6Ksl5Dtphkea6ORFI74rM81YrcHeoGZk6l6fuEZvQ6yD4oWPSLkrStE8Ono4ZgQuAGqA2FStqBUCFUFAa6Bu0QU1MxYiZdw0d1BWao08BB8wRCUxFWSN62qCg2qiciayQh0QeCZWmTVgxsJWTQcUnETdq2Tc6M4zoeoSowQpWEpvHLP/QGHt85fUu/Z4ZhMD4+zvj4OGEYsrCwwLlz5zAMg0uXLlGr1SgWi5RKpVtytf9qVc/6eeVWVT2bIZvvVzZ3ELZt89hjj5HNZq/7+82mdd4o1hM1E4kEr3/964dDunUVzDoMTeWnfuAIn/rbU5yfr9HrOvheQC5usW0yz+JKk4xloSQEjVafTDrGykKdYi5JrdYlGTPwHR/HCRgpJFhYaTM2msZ1fZaXW2TzJq7r0e1DrpCkUe/BBl8zJRtnZnuBhUtVLjf7UYUzV6O62qYwkqLd6NFqdQmlywMPTbN0MZpRNP0wmrEsNfAcD0GMfCHO4towfGm2ysSOSOJbGElRvlLDMHXShUTkvAysXGkwtSOa+axj+XKFwngWTRWsrqmwus0BQrHZvn+CuRcXh7dtlttYcYORyTzN1auKsCAM6Na6xOIGmXxi2NpybZcwCFm6VEYzNGb2jbF67CzK5UvD+074HebXqpqs2yVtSsoPPQqxq/sKVsKk0+jRXG3RXG0xsWuEQdehsG+SbsfGShjMX1ghCAOcnkNuLMnC5ZWo6tE0xmZKQ184iEQD1TWDUkVVEKEkm4+hGxqxhInnB4xuy/Li3hq1UQctVAAFJWFgdyRKKgQpifk6HSfEMzx0NDodF1dCSlPpCQe9rtJKhGhoaK4gFAqBIRCqiuxE2/xaAGgaTcvFClQ0Q6cbeMSaCu2Uj+4qGLpG3/cxbIGXVMl4BrYaorkCM2bScRwKRoLACTDRUH1BzorxL97zJvaOXRVc3A7U6/Whie7ExASDwWA457l06RKGYQzbbfl8/pYkdr606tn4362oejabZXOr4gW2IrYM2Qgh2L9//ysua73W1MxXQqVS4dlnn2V6epq9e/dec3VxvcdVFMHff/0jfDF2nr89cwU9ZtFqDTCEQjGXRAklphbFGzebA4qlFNVyh1IxRbXSIZW0kHhU6j1GRtKsrEaEU15u0u74pFOCZtPGdS0y2RhhEJLLJUjFTZau1GmbGtlCgnq5w9KVyN9saa5GZbWNZoQUihZ2S7J0sc7kjhKLlysEfkBlucnYtgJChqxeifzRNlY0S5er7HhgjKVLFVzbw7U9dFNjZDpHdalJcSTJlTMrJDIx4iNpGuVIMacKSX2lFSnL1ggnkbZYOr9MrhgJCtbdD9K5BHMvLpIdSaOoCo3VNuMzRRYvXHVeHp0pYloaV85eVbMFXkC73Eaev8CU16CipUiEDgvqVZloU4lhKj5+OsPEVIFe1yaWMK+pmBLZOKtXagReQGM1MhBVgG27xhBC0Gn31vaEXGzbRtEU5i4uRRb6mka2mBpm6kAkZ17a4FBdms6xVG2w+KhNLx8S2NFn2mio1HMBvhZi1RTSlkodn9CIPv96W6GtB4SKoK0LjCWdfjLEskxinkZfC5EGBH0f1VYJUyq+HaAHCkpcIz8wsB0PLx4SGyjYSYhJDcfzcZUQy1UIkwpKV9LXfWJSJwhDVB9SoQmuRJUKaggTqST/5kd/mGLq1vh0vRzK5TKnTp3ioYceGsYXx2IxpqenmZ6eJggCGo0G1WqVs2fP4jjONSKD2E26FlwP12u3rRPPZqueV8vduh5upS/aVsSWIRu4sQC1W91G2yivfuihh667TKWq6suS4NsP7SGXjPHXx8+jmwqmouG6PiFQb3TJJ+MU8nEMVWX7TJFyuU1xJEm13CWdtJBSUmv2KI2kWFlpMTqWQVUVYpZGLivptfrUazUMRaW+2iSVjpPJx2jVB7RbksJomtpqm6UrdUYm0zSqdZxeQMrKIBI+g67D4uXqkFBUTUGEIUJE0myIKprJHSUWLkUhZlfOLJMtpugLgd13oq35xSYze0e4fDoa5vdaAwY9h4kdJWQYsrwmU16ZqzK+o0Sz0kYR4Aw8nEEzUn5tLyKEYPF8RCDNNaLavn+SxQvXSqRlGLJ4boXJnSUqi01cx2NiZ4mFExdQ3YAFJYMZ+GSCPramDaMGJsI2S06asNpg0fHRTR3TUJnYUaJebmP3HBLpGL3mVXeAkan8sM0HMLFrhIbTZvvucVRdRdEFi7MVuoM+tj1ATwrsgYumaVgxk3q5e/Vvbcsz165x7s0engF5x6Cr2Zg1jV5eGXrF4Ss0AhXdkzjJgKwfo6K7KJqGrgiMlqCZCVGFTnxV0LU8AkugDEA4Cl5MoAeQFAb9ZEjY9alLUBIa8Z6CY0iSHQXH9UknLBRH4IYBYUti6DquF0bHogvsQYChqigK4En2j4/wW+//QUzj9p4eVlZWOH36NA8//DAjIyPXvY2qqkNikVLS6/WoVqusrq5y9uxZ4vE4pVKJQqFANpt9zXOXl2u3rRPQjVY9m82y+T7ZbBHc6somCAJOnz5NrVZ7xXiCV4uGfmz3FIVkjM9+6wXarQFCCAxTZXQ0TbncJhuPYdsOg55LLh2jVukyVkrh2T6JXBwRgOt6bJvMUV3tUCqkWF1q0gXGxwoMejUCKbHigkq5gRXTScYtBr1IKj46kcVzXRYuLVMazeL1onCvZDpGMhuj2xywNFtjZu8YjZUmy2tD7YntpeGAe+FShV0PjjN3ZpkwCKmvtkikY2RLKdqNPsVSksvPLzI6U6C20sJ3g0gOHIbIIETTo3A0gJXZKhPbC0MvMFhLrnQ8CCWJTJze2n5PLGmyfHEFVVUY215kZbZKLGHSrnQI/IDF8yuYcYOZB8dYuLCK2+2yfi1bCrtcVvMoYcikbOIKlYay9ts11WJpMjsUHAghmHlwAs8NiKUsBh2bTCnF0uUNcdG5BCtzUZTB+u9b9R5ISTadZHSmSLvVwTRUbMdFtXykL4mlDRShUE7bvHjEI1CjZNKGHZBumLTzElVREXpIqmfQykukBM9UKXUtyjEn2s1yA7SeQl0PUaRCKjRp6i4iUCjaFrYIsBMhZqjgdUPapkfc0RgAiqWSdjSqikMak17ooyU0bDtgQEDGMPEI8KTEECqOHqJLgaGpqFIgPHjzvp380jufuO1zg3VF2sGDB2/YXFMIQTKZJJlMsn37djzPo16vU6lUOHXqFGEYUigUhuRkvMYoanh5kcHG6mf92DZWPZslm++30bYIbuXMxrZtTpw4AcDrX//6l9X8w42R3M6xAj/zw0f5468+S7PZx7UDVKBUSEIoUQSEUqfZGZAvJlmtdCjmk6yutkkmTJy+R6Pep5hPsLzUZGw8Q2WlzfJKi6mZyO5E1UxmdmYoLzfpD1wMSyKUgMUrXXQdhNRo11wmt0cttW57gBU3KI5nMHSFuTPLFEfT6KaO53gszVYYncrTrHfJZuNcen6B3Eg6EhF0bXrtAVbSZHpnkbk1efLqXI10IQGKIJmKsbTW+krlE2h6nEalzeh0noXzqwhFMLmrxNLlCslsgnalgzNw0QyNyV0jQ1POWjOaBw06AwqTORIpiysb1Ge+F1CZr9HvdhmdSNFfXiAd2iwqUfssFAqLIs1E0CIhbXL0WLZMsuPZaxZDNUOlvFCPTEGB3EiaTDGJrqs0VjsEQUA8adJtXI0U0A0tsjMg8oBrVjp016qiVD5Js9rBdfp4vsfg8RiXdwUIBEIKhCNRugrluGR0YNLQB1hNlWrGA1cgQ0msqbCakygtBVORoCp0jEg2mw8sGsJDszTiUqPR93BFSMmPJMpuQpJTLPoiiGZ6jk4NlxQGfigRikD3FQaKT1Y16QxcTE1DDQU2AfFQQxWCwA+xNJ0PPnGA9z9x4NW/PK8R62KAQ4cOkc/nX/0OLwNd1xkdHWV0dBQpJe12m2q1ypUrVzh9+jSZTGZIPKlU6raJDF5a9Qgh8H3/ph+v2+1+n2y2Cm5VZdNsNjlx4gTFYpH9+/e/6hXIq1U26yik4vx/3v4Yn/7G81yer0WhYEhs12PQdxgvpJFpSb3eJVdMUK/1yBUSNGs9EgkTxRFU6z1GxzOsLLcYHUtTLXdZWmoyOZNnZaGJbujs2TdOs9Kl17URoUu/50T5O1mTXqPP7AWPqZkitdU2uUKCbrOHsAwIJdXlFulCAsPU6LUHOAOXfD4xNKFslNvEUxa5Ugp74EUk9cISE7tGohO3hHatx+Su0jUuAp16D0VV2Ll/ggvPXUEQnUwXL5QpTeWjiIC1x/Bdn8ULq8w8OEGn3r3mNbRiBlfOLDO+o0S70aPX7JMdTTL7wgKGadD0Y6SEJB306akGjog+wuNBm6U18pGGgZVPk0iaMJqhtrZoWprKD6sciJYsZ59fAEDVVLY/MI7r+oxM5ek0+2RLKZY3OB2Mbi+ydPFqFWTFTdRGHyOhsPR4nFrOR7dDPEti2oKB5xNaKkII6viYqxr9fFTx+LaP1Vawsyq+F6ApgsCVWEKQyBokXJ2a6hL6EqMT0jN8PFVieSptGRAmVEqBRbdjoyLIxixquGQVk17gI+2QuKHjC4nlqwx8n5xu4gwCdF3gOxLFEIgQCqbF//cdb+DI7htPuN0srly5wsWLFzl8+DC5XO6W/V0hBJlMhkwmw65du3AcZygymJ2dHbbjSqUS+Xz+lmzov1zV0+v1Iid1TcN13RteKO33+99vo90p3MjMZv1KYrNXKUtLS5w+fZo9e/YwMzNzw9EGN+oyaxoaP/HWQ/zV8fMcf3GewA1JrLk0L1db5FJRJo70AoqFBLoiSE7n8WwfLW0Nt8+37SgSOD7bZnIETkCvYzMxkWbhch1VUygUEywvdgDJjj2T1FbaeE7I6LYkK3M1KuUypqmzcGkVIQU9zWZse4GV2RrtWo9YymRmzyjz51dp+iHxtEWulKZRadPv2MSTJsWRJPPnospl6WKZ4mSWbmtAvpRi4Vw0Y5nYWaKyGMUljM0UuPjsFUqTefodm35ngKoqSD9gdaEWVTOXq5EoYUeJuecXQMD4zhEaq21S+ThLF1ejKIJLFRRNYWxXnssvLmBaJoZuQLNJ1m1zWeTQw5Ap2WAgNJriamUqJ6fIj+e48mJUjSVzcYpTedq1HpLIaDqWsqguXVXUKZrCypUqg7XWn2EZ9Fo9iqMZdEtH1RU8LyBdSDDoORTGsqzM1RgkJGcel3hF8FyQvkamo9CKh4RW5NtleAKvGzBIqsRdDcuFQDWxcxJFEaRclV7gEsZV2iGkliUyIyCQxGxBkFLx/JCsNHDNiMRiTUkj4RFogmyo05QBVk/BswJMW2AYJl4nIAwCEnGLjuOiSIFQBEEAcU1HC2AileJffODNjBc3Hzx4o7h8+TKzs7NDR5DbCdM0mZycZHJykjAMh/5t58+fv6X+betYJxHHcTh16hQTExPDOdONLpT2ej1GR0df87FsVWwpsnk1rF+NbEbpIaXk3LlzzM/Pc/jw4ZsKYdpMRfW2I3uYKqT4/LfO4g48dEMlUUzRaQ/IZGLUal0sP4qednoulqVjd90oHlgIyq0mExMZZi9UKBST9LoOjVqXyR0F5i9VuHxpmcJIArsTUq/0GN9WoFHtkEokyR5IMnt2GaftYMYV+r0B/b6kf37A9I4RvIGPEJLFi2VKE1lWr9Tpt20c3WNspoCQktUrNUI/ZGLXCMuXy8gQqktNpnaNMNiQ+bJ0qUK6kCA3kmbudCRxri5G7gDjOyI3gZW1Vtbi+RUyxRSZYoqFc2ttMgnLF8tkSyksS4+qyDAEJEKTXHxujlgsxtTucRqrLbIvnGNBRBWMh8K8SDMetkkRkhEDVo0chf3bI9Jaw6BnU12o01kLesuNZDCSJosXrlY5pYncNS234mT2Gn+00nSeyprcWzc1uq0+3Z0aZx4JwFQgDNF1lbSt0U5KMq6gm/BJejqdICRMhYggJHQCGq7E8j3kmI5aC2nqEtVQwYdET2GQVegGHhnHwDA0+l6A1pf0YpHha7IncNIKmlRISpWeHqJ3JG5MkEBjgI8SSKSmkDJ0WgOHnGkxcDwMTcOQCgSSR7eP8asfeDO6fntPA1JKLl26xPz8PEePHr3jJpOKopDP58nn8+zbt+8a/7bz589v2r/tpej3+xw7dozR0dFrFK03ulD6fYHAFsLGYKWbIZuNi5qvf/3rb/oNvdE22kvx4MwYpUyKP3n6OTqtAYOeS7B2pTNaSjHoR20S1dTw/AAjphO4AU4QkCskWFpqMbmtwOKVGplsHE1TuXK5jBmXuK6K3YfpnSVCzyfwAoqF1HBnZnLHCKtrrbx8IY+UAd12j3q1ClLSb7goisbqfJ3JnaW1+0kUog339UTMpYtlChNZ7L5DImUxf3YZocDkrhEW19pqyUyc+TPLTO4aZfFSVJm4toeQkVw5no7Tb0dzDhmGrFwuM7KtQLPSwe46qIYCSK6cWSKZS5DKJ7lyfhHVBVO3EDJSsKWFi9Ftk1JidERkYTQVtlgQV6+S006HRL+Fl0/SrkYtutFtxaECrt8ekCmmmH12HqEK8iNpMsUU/a6DlTCxew7Fyfw1RDO+o8TyBul0YVueb8ZqrE6C2ZN4igQ7RO8JGrnIF89GkpuHVinAkyEEUJAx6sIhjAkGCsQWAjxNgilQAzA9DTcjUIQg2YGm7qMLMOohgSpJSgVVaLTTIcKRSDdgkFBJ9BX6cYnlKnQDl6xl0fM8EkKn7/uUjBihJ0koOvigKwo/8tgDfOgHD9/0Z/pmIaXkwoULLC0t8eijj26JmUQ8Hmfbtm1s27btGv+206dP37B/20sxGAyGDtUvXZ3YOOtZr3Cut1C6tLR0S3aItiq2FNm8Wktrvfy8mRN/r9fj+PHjxONxnnjiCXRdv+njupk22ktRzCb4h+9+jD/72vOcvVQml4vTbPYxVQ1dU2m0ekyMZgj8gNAL8cPIQr/bcyiNpllYaDA1U2B1qUk8rqKZPslEmumpNK1al4VLFSa35VldbCAQUc7K5SorV+qUxrM0a118L2B0Iks2m2D5cpXAD0kVEnTqHdrtPv3TfWb2juL2/OFJtjSVp13t4Aw8+h2bZNoa5rPIEBYvlilOZIklTebXhvmLF1ejfBrbI5NLDE/wuqkxsXuU5mrksuwMXJYvldFNnYndI4R+MJROd+o9yotVCtMpErEU1bVqQlEVDM/nisggpGQsbKMRsqJce5WcwuXKxQoy55MdSZMfy17TLrMSFo217B4ZSJrVLp7j06lHooDU2jxrcmeJIJQErk+3fbWSM3ek+T8TdbrptatWQydWdulbgkFSQQQhIoR4R9AtGqgtD12HuK9Si7mgCjRVIecZ1LIOQhGMeBrdvkM77qF7CnoTmnGBYeiYzRAnqaKpCrIjaageqZ6OP/CJxQ20gULXdckqBn4YktLjtFs2KUPHDwOMEAIlMv0khKxl8YvveZwje2+tI8D1IKXk7NmzlMtlHn300S151X49/7ZKpfKK/m0vxXrmTqlU+h6ieSnWieelC6Vf+9rXOHHiBD/4gz94e57oFsCWcn1eT+J7JXzpS1/iscceu6FS/JUWNW8Gzz//PKZpbiraYL1nGwQB33x+jm+evITbD4nrGr7jYQ98Oo0BI/kklXKbTMqi0xyQTli0Gj1GR9MszddJpTWWZqtksmly2ST1SofimluAa3uMTeWoLrYIgpDRySzV5RaxhEG+kKBd60YCACGY2FYYEkoibaHpAknA8uUKRkJFFSqeHaJrOqlcglwxSWWxjt11IlfpnZFQIAwlUztKVBfr5EazrGwYpE/tHom2/i+Wh6aVuqVTmswy6NjUNzgHTO4apV3rkMjEWL5cod/vU5jKMKi6CCHIjmawEiaKKlg6cRHl/HkANAKSYeQKncSjrCYpyj5LIk24dw8kkxiWERlgtvpkiimS+SRGTGf+7ArBmovz5J5RFs9fbblN7BkdKuwAxnaMsDJXRTM1WrtMXtwvCYSEtYFvpqfQMAJyrko7JdEdUHzBwAAkKCHE+6DqGjXNQXEkcTSaoYeiKGQDna70CRTI+hqe69NNErk329DTBYZQUAcCx5DkNAs7DAnCkJiv0JE+Rd2iGbgkfJW+GpARBgM/ch0XQUhMN5C+ZKaQ5jd/4ofJpl/7IuSrQUrJiy++SK1W49FHH70ly5d3Ghv926rVKkKIa0LidF3Htm2++93vUigUeOCBBzZ1jvnWt77F+9//fj72sY/xC7/wC/etXc09RzZPP/00Bw8efEUly40sat4MXnjhBVRVZd++fTd1v42afIgqt7nVBn/+zGn6bYfACTEUBSWEbscmZuosLzYZG0lRXm4zUkqxstAgmRSsLrXZNjNCtzFAhpJCKUV5sUkmG8f3fXqtAcXRNO7AI5WyEALqyy0GXQcrbhBPm0P7/4mZAsuzVUan8vTafRJJi5XZKqEMESoYcZXacpPiRJpezWF8e4nKlTrrGc6lqRympbGwQZ48tqNEo9yhMJZh8Xz088J4liCIFlPThQS1xQZCCMZ3jlBfaVEYz7C4JjQIwxAlHsmJO8uDodwYYHxniX57QCJlUfn0FwhQmAxbLIqrdjQZaZPCxVN1lo+8ERCMbS8N50UAE7tHh7Y3udEMyVzk6NxYbeO7AYXJHPWV5tDdemR7ifKVGp4O5x/Wac0YWDWXbg40FxJSp6VG1Z6ma8QrPv0E+LpAMzXUto8MBCR1PM8n0ZFIHboWkVS5pzBIKiAhIVWkomCHAXrXJ6HrtKQXveQSfE2Q8hU6MkSVCindxBYBqVCj4TskQw3fEMhBQCJm4jg+Sd1A+iGqUPjBh3fyf7/38TtyIgvDkBdeeIFWq8XRo0dvuBW1lRGGIa1Wa0g86/HN/X6ffD7Pww8/vKlZz3e+8x2efPJJ/s2/+Tf80i/90n1LNLDFyObVoqEBvva1r/Hggw++7IB/46LmrVK9nD17liAI2L9//w3fZ+NA8KXKk/7A5TPPPM/cfBUz1LAHDv22S9IyMHQVEYQoqkJlpUUY2tRXB+zeM87qQpNsPoG7ZqY5OZ2n1x6QSsVQFWhWe5ErQBDSafaJJy0MQ6VZ6aKoCqNTWZbnqoxM5NB1hdpSE7sfVQcTO0ssX4qEAKlsjPxIigunruB5Pr7vkxlJ4PV9LMskmYrTbfQpTmaHUQIIwdSeUVzbZXXDfEO3NKZ3jTF3dukaqfTk7pGh6sxzPfqDAbmRNCJQyJZShBJqa/k1nXo3WhjtdEicf5GpsMWSSNMTUUtURZKVNjURXT2bj+xn/NF9dGo9qot1ZChJFZLYfTdaLCWqtAxTp9fqI4QgN5YhkY4TSkngBZFVjxNwJeZw/mGdMKEOq6F03ScYtej5axk8CDIDlYYWYrgQS+r4HZeBIUCLWr+xVkg/BqiCXKChKSotS+J5PrGOJEzp2J6PZYMa0xn4PllhEAoIBj5qIPEMCH2JooETguGBiGkk0QkAxZdoiorrBwgpMDWVrGnyD9/9GE/sn7m5D/0mEYYhzz//PN1ul6NHj96R+Pa7gWazycmTJ1FVFdd1N+XfduLECd773vfyL//lv+Sf/bN/dl8TDdyDZPPNb36TnTt3Dn2UNmLjoubhw4dv2RXVhQsXGAwGPPzwwzd0+41Esz5nuh6ePn6Rbz87i7RDTEWlXusRN3Vc26fX7CFDm17TY9eeCbqtQbT82YviiHUlIqOJ6fyaDY1KaTTN6lpOTDpjUV1qoukaxdEUlaUmIxNZDF3hyrkVZBilX5qWPsyIKU3ksCyN5UsVfM+nMJFdkzDbBL5PdjTBYNCnvtRBU9fDyEbptyK3gpU1N4Kx7SVa1Tae45MrpanM10hk46TyCVZmq5Sm8tSXGgR+gJnQ8fDAE1iWNVy4BJjYNYpQrtrbiIUFiquXacgYEsEoXSQCTYQsiqtt1fTDe+nGM/hegBkzyE9kMWIm5Ss1Bt1I2jy+c2Q4J4K1qmfDDk5+d4nvWG1q2w1CUyDWKtD0QKFlSpKeYGCGaCHooUZXiYhUkYKUrSAEtKxIbWh1JE3VRyigOaD6gjCmYvSi+U4/qyADyHgqjgmBBKsd4iYUAjfEdECmdeKeoBf6KL5AICMl48DH0hU8CQlNRxEqqiLQpcLO0Rz//ENvJpO8M7b1YRjy3HPPYds2R44cuSUb/FsRjuPw3e9+l2w2O/RzXPdvq1QquK77qv5tp06d4t3vfje/8iu/wr/4F//ivicauAfJ5m//9m+ZmppicvLaBbT10KVCocBDDz10S1Udly5dot1uc+jQoVe97Y0SzToWyy0+/aVTtOs9pBOStAzsvs3SwioxI0Y6laDbtMlmYjSqHZJJC6/v4jg+hWKS8kKDkbEMjUoHzw2YnMmzeLmCoiiMT+XwbRdVU1AVwcKFaIZSHM/QbfWxe260yzKdj+ZHPRvX9kimY1TXsljMuEG2lEJVFZYurCKlZGRbnsWLqziui2pAKhMnFrNoLneGFVwyG6c0mePyqYVrWmIz+yfpNnvUlhq4rotjO6RzKUa3lZChZHVt9qPpKqlckvpKk9xohljSpPfZzxN4Af0NupZJ2aGJSR6bnmLQFQbWo4dohVeFIJN7xoaElSmmKEzmGXRtus0+nUYvaretPa4E5JEi380O8AyB5kqslI7XcQkBz1RQNRXfD0g1Q5SUQVsLQILhSEzDpBtG6qK80HE6Lp2UBAFJW8FRBFKN5jAxT6FvSJJSQ/Y9OkaI0BTMrsSJKVhCRUNgKxKtHTAwJHnDou95xBWdnueR1U0afZuYphLYPoqUGKrCOw7v5Cfe+RjJZPKOnMiCIODZZ5/F8zyOHDmyKSHOvQDHcTh27BjpdJqHHnroe17bjf5t1WqVZrM59G9rt9scOHCAixcv8u53v5t/9I/+ER/+8If/ThANbDGygejNfCUcO3aMUqnEtm3bhj9bX9TcvXs327dvv+Vv3tzc3LAt93LYKAQAhhvDN4IwDPmTLz3HC+eWoWfTabRJxlIYik4maUUKrrWKxrU9PDcgHtOpVzpMTudYulwjk0+gKwqqKojHTezuYFjNlOdrBEHIyGSOZqWDO/CIpUziCRNFCMIgQNdVqksNfNePhAQ7iixfqpDOJxFIYkmL1SvVYSssU0iRzMWozNfoNDr4vk88b+LZAYamE4/H6DX65MezgKC+3KA0ladRbhF4PslSnNpyHVO3yI1kqa0pxnJrgoAwDK9tx9l9Rk5/B42QuojTQ6fAgBYGPldblDOGjffUBxj0HeorTUa3j7C6IeAsXUjS79jR8yTK1zHjFpqh0tVCnpvwWU6GQ8NMQ1fRKx6BBnZSQbd0wp6HOZDYCS1qkXU8JIIgZ+C4a6+Po9BTJZ4MiTkh2WSMuh6CFCRDlV7PxVchbugEHR9Xh7iuYbgCXY/2unquh7A0dF/gGJAOVOquTVGPMwg94lJHUQRhAIQhmlAZzST44Jv3EtM8arUamqYNd0hulT3/S+H7PidPnkRKyeHDh2/Jdv5WhOu6fPe7331ZorkeNvq3fehDH2JlZYVUKsXRo0f5gz/4g5c1IL0fcc+RzcmTJ8lkMuzYseOaRc2DBw9SKt2e3I2FhQWWl5d57LHHrvv765ny3SzhSSn5wtPf5s+fOUshkSEfT9Lr2Pi2TzJuUl5sUiomqVe6lEpJ2o0++UKS2kqLTDZOZakZzR7yccrzDdL5BKHv020OyJVSuAOHXntAMh3DtDQMU8fu2cQTV5cbU7k4mqrQqLTXWmQlasvNoaVMKp/AMDSqy02mdo6wMltmbCYipXUhRLqYADVk6fwqEFny67rO7oMzVBfqdBtdBrZN4AdkCmmm94yzfKmC3bv6vk/sGqHX7JPIJlidqxIGAaOiT/m7L0avL5Jp2QYJiyJFICKymZZtrozsQq4F75WmixgxnTCUtMptXNcnmY3TqnSG71NhIkd5ucHSdpPFXRZJqdA0IsJN+wqBouBpAgTkQhVfBgwUQaCAUBQ0X2K5glCBUAFHlcQH0DUic1XVDshYcVq+h+VKUjGDSuiDgIyiY4cBUlNJodHu2wQCippJ3XVIagYylNiOR8YycUOJoSj0Bh4xQwdfRg4NASQNncf3b+PnPvAEmnbVu6vRaFCpVIbtnXWjylKpdEvmKZ7ncfLkSRRF4eDBg/c90aRSKQ4cOLCpC9pz587x0z/906RSKRzH4cSJEzz22GP81//6X3nooYduw1FvLWw5snFdl1c6pFOnTmFZFtu3b+e5556j1+tx5MiR27ostrS0xPz8PI8//vj3/O6VhAA3Ct/3OXXq1Npc6BH+95dOc+b8MhnFJK6qKFIShlBbblMoJFierzMxkWVxtsrkdJ6V+TrpTJzADei0BkzOREuJuqmRKyQozzdIZWNk8wk826W+2qY4lh5KoEem87TKayaZpsq2tYVNu+ugm9o12/XJTIzieIb5M8vDgXkqn8CM6WiaQmW+ju/6ZEpppAgpX6mSKFo0l9sYhk5qNEFrtUsiniA/mqEyX0U3dUZnilSXmxTGcyyeWxq+NrGUxfiuMVb/8qsMVqOwMk0GpKVDjRgWPkUG6ARcERmCqW3IkRK6qRNPx2hV2sO/Nb1/isANEAoMOjaxbJxTjRpzD8SwYwqGpeM6HvFuAKHELZr4a5WKEQosG/wgxI0JRFLHqLo4uiDUFFRNQe/4aK7EL5n0HJdYJ0CkLbwgRAkhEajYloIlBQkUmr6HLSTZUKMlfNQQsrpJVwYYtsTTQPVAMzUCN3qt45qOHfjEFA2NtdC2hMU/+rE3cGDPy6su19s768TTbrdJpVLDqmczRpWe53H8+HF0XefgwYP37UKi67ocO3aMRCLBgQMHNvUdn52d5V3vehfve9/7+L3f+z0URWFlZYXPf/7zPPXUU7fUJ26r4p4jmxdffBHf92k2m8RiMQ4ePHjb+8Orq6tcvHiRN7zhDdf8/GbnM9fDuqjBMAweeeSR4XM5M7vK/+/Pvkto+xT0GNIP6bdt8rk4BBLXdjENjdpqm3jCpN8aRLHD6TiDrk0unyBwPcIgRNNU5i+sIAPJ5I5ilN0iYWymQG25ied4pLJx8iPREmWv1ac0laPb6tNvRwP10W15DF1lZbaCZ/vR8qOlU1tsItSo7RYEksZyE7t7NVpg18FtrMxWaFZaDAbR8N9MGRTGMzTm2yjiqpnh1L4JwiCkXevSbUTV1OSecRbOLqE9e5KxsENIpAZe5urFRQ6bHjoKktzjjxBMTaNqKksbMnIm904M5zYA8QdKHE/ayJROL61G8cxBSLIHXSXEUBW8gYsTU0i0A4KUibu21BqXCmo/oJOM3m/D1NBrHn1LQdM1cDzinsRNG4SKQtwFOwgIVAFOgOEJ/LiKdEJiQZQiSigJJXQ8F8MFP6WRDDR6oY/pg2ZohHaAoWl4foguFCxd44mHtvN//ejrbtpyxnXd4UB7M+229RNwLBbjkUceec05MlsV688zHo9vWt68uLjI29/+dt7+9rfz8Y9//L59rV4N9xzZnDx5knK5zLZt29i3b98dGa5VKhXOnDnDm970puHPbgXRtFotTp48SalU4oEHHvieD2EYhvzPzx/j69+9TMmKkTctWvUeqYRJvdwmlTAZdB1MXaXXtslk45QXm0xsy7MyWyGWsIjFNGorbQqjaQadPv2OTWEsy6AzoN+2GZ8pYJoqS5ejZM6JHSVWLlcIgxDD0smPZ3AHLtIL6Lb6FMev5sMgYObBCfrtAeUr0XzFsHRKU3lWr1QpTeRZvrQKAvSUwqDhkEwn0U2V6mIdzVLQEyqdSp+x7SPUF1qoqoIQCqPbi8SSJpdPLSA6HcTaMueUbA8FAV1hYKOiAh0i5ZPcs4fJI3tYvBC5GVhJE1VTWZ2r4QxcbEuh8kiGpSzDfZpUIAiCEF9KPFNFMzR818eyJWYgGBQMwiC6bdoRDFRJICDugWaqDBwfW4l+r/Wi4LzQUDE1Db3l4ZgCkgZq2yPQFVRLI+4rkSu0KjC6AbYpiCkaKuALgey6YKnEFR3bDTB1FY1oJ0cTCmOZBD/7gSd4aM/3qjJvFjfbblsfkieTyU1f6d8L8DxvSKibJZrl5WXe+c538sY3vpHf//3fv2+rvxvBliMbz/Ouaw0jpWRubo6zZ8+SyWR44okn7tgx1et1Tp06xZvf/OZrXFxfC9Gsrq5y+vRpdu7c+aru0wvLDX7/U3/DymqLkhWjlIijhCGNSpdE0qS80GR0LM3SbI3JbXmW5mrkC0mcvku/YzO+LcfS5SqGqZMvJAjXTCMVBebProAQTO4osnRxFRlCpphEAEKApqkIoF3rDA04S1N57L5DKpdg9XK0JJkfyw7NL1P5BMl0FGW9eHGFwWCAYRhM7BwjmYqxeGFluO8ipaS0Pc+gN6A8Xyf0It+7qX3jVGdr5MZyJK5coPrCLKOyz/yGisYgoMgAFegLjZqSZPxH335N6ma6kMS1PbqhR+3BHL28Ricu8MOQIAgxnBDTV7CDADQI4hqKLzHbPv2Yhm5qqG0XqYIIIciYeE6k+soEGm0ZYLkhjiUw3ZCuoaAKQVo3CAYBnqGgqYK4A14QQEzDb3sMdEiYBqoncVSIeYKO55G2TMJQoqJgux4pw8D2fBKajgwgpmu87fV7+fvvOnRbTlyv1m7TdZ1jx46RzWZveEh+L2KdaCzL2nTltrq6yrvf/W6OHDnCH/7hH96386wbxT1BNmEYcvr0aarVKmNjYwwGg1dUht1qtFotjh07xlvf+tZbIgSYnZ3l8uXLHDhw4KbUKF/+5jk+9VfP4nkBo4aJ7kIyZhB4IaauRPv9QYBQlGg2AagqQ5dnu+dQW24yvr24NlsJGN9eoLrYwHN9ciNpQj/AjGmEfohhaCxdKiNDiWHpFMcyLF5cpTiRQwYhVsJg5XJl6JmWH8+SzMZZvrASJXN6HlpSIW7FSWdT1BbreI6HlbAoTuaoLtTJjmaG+y66qZEdS+H6LotnlpFSoqkasaVFdrQX8FHwEZRJoBBSwKbM1R2S6ak03hveiJTQLLeQEoKUwYVMSGXcRCoCTddQOg5SkWge+FlrLbJCQZWSbKjQUkL8tfdVSEj0Q/oqpHWduuKTcCFE4BsKUkrinsTxfCyh4lgKKTRqTpS8mVI1bNvHVyCl6fheiNAV0qpGrxtV8aal4wNxTaPdczCFgmHo+E6AaWj4XogmBHunS/zcT7yR0ZEUdwovbbcFQUAikWDPnj23Td12t7E+izIMg4MHD26KaKrVKu9+97t58MEH+R//43/ct1Lwm8GWp9p11YaUkte//vVUq1W63e6r3/EWYj1iYKP1zGY+gGEYXuMXdbNW6z/4hr284egO/tunvs3fPHcZU9MoeFAwDCrVLrlMjEa5Qzobp1FuUxpJs3Chyti2fDRbUZRIPTZbJZWNoxsay7M1krk4IzkLGUhaFZtkymRxrrpW5aRQNUF9uUWvY7PtgXHalQ7NtcF7Kp8gnrRo17oYhsaVF5YY216kslSl07aJiRj5bTl81yeRidMst7B7NpWFOrnRDKqmDt2WPcfHNAyqc3V27t9OEAYsXVxmprPEHFdfqxQuI/TpYKy7uVCiz7JXwl2b07hZk9b+PNUYCAVURaKoCkHXQXdCPE3BSFoM3KjCinVdtKTFqu8S7weEKY2kLxgEAS1NQdc1Wn2beC/As1SClI7suxj9gHZCiw7CDTACQd+1MYXE1DR6qkSN6ZSkSt11EYEko2iUHYespuMpMLADTE3F9gNiqk7oyYiUQlBDQTYR40ffeZC3vOHm7JJuBQzDYGJigkwmQ7PZJJ/PY1kWZ86cuS3qtrsNz/OGM9TNEk2j0eDJJ59k9+7d/NEf/dH3iWYNW66y8X1/uKvSarU4ceIE+Xx+uKi5vLzM3NzcHW2j9Xo9vva1r3H48GHy+fymPoDrMQe+73Po0KHX7G6wuFLn//3jv+XiQo0gDBk1LaayKVQJveaAWExneb5GoZiiXeuiKgqxuEG71mN8WxanFy17aprC6mwVu+cwsaNIZaGO5/pkSymEhPpqi+JElmQ6xtKlVdy+i1AUxneUKF+p4rkekztHCYKAQcemVW5jOw5B6LPr4RkIGFraCAGjMyUQkm69R2ctflnVVMZ3lNB0ldnT89c8zx1ZhfDLX6GDThMLDRkRC5GDsEXABB18FFYndrH84E5WZxIMSrHhLo0IJDnDwOnYuEkdoSkoqkIYSFL9AJsQL64PW3uWIzE1A8d1sGMqmqZgdQIGqkAqkTrMbDh4gsiRWShkApWeFj3JmEc0/wklRj9AM1RcVaBKCaZGEII2CHBNhbiv4GmgORJfSFJrcmdNCgxd5e8d3sGHfvRxDOPuVRDdbpdjx44xMTHB7t27EULcFnXb3Ybv+xw/fhxN0zatrmu1Wrzvfe9jdHSUT3/60/cFAd8qbFmyWV5e5vnnn/+eRc1yucy5c+d44xvfeEeOR0qJ53mcO3eO1dVoJlEqlRgZGbnhNkKv1+PkyZMkEgkefvjhW9p6ePb0Av/rT7/Lcq0TbakrGilPUkonaNd7pFMW7XqPXCHJ0myF8ek85YVIfZTNxVmZq5HKxzF0jdpSg3jKIpWN0Wn0yRaTqKpg+WIZz/WxEia5kTRLF1cRwOTuUVRNMPfCIusRmLGcQavcZnrnBLXFJiAZ3VaksljH6btM7ByhfKVKfjxLGIZU1ux1sqUU5StVSlMFNEOjOl+jNFNk5c+/AmsqthJ90rhUidNaEwTksWnEEyzunaDyyHaCQpKcotHUopmM1vNImRZ1zyVuB/RTGrqpozZtCMAxVbK6Ttt3URGYPriJaDcHBPlA4CIjIpGQUFT8gc+AkBBJciBBgp2ISEft+diWgq6qxD3o6wIDBdUOsZUQywGpCRKWiT1wMXWdIAjRNQ3X9TF1HV0I9u8s8TMf/HsUC3c3/6XdbnP8+HGmp6fZuXPnyxLIa1W33W3cCqLpdDo8+eSTpNNp/uzP/uy+MCC9ldhyZON5HmfOnOHKlSvXXdSs1Wo8//zzvPnNb76tx3E9IYCUklarRblcplwu43nesIWwPjx9Ker1Os899xyTk5PDq8LbgS98+TR/8aXnqXcGSEUQV1UKponW9shnYzRW22TyCbpr1UQyaVFeqDE6laddi5YcxyZz+I5Hp9nHsHR6jR7dVp94yiKdT7JyuQwCtj8wQeD5XDkT7cNkR9Jousrc2QUSWZNCKY9lmVQX68NlzXjaYmymxPLl8jX+Z5N7x9B1lfkzS8PZj25olKYLCEXB/eNPUiOGSUAGlzKRz1RK9XCnU1zeNU0zlaSfTxBYJqquEnohsbaL0DWCQhzCELH2/uU8Qd/x6K41kHVTh66L5gQgBEHBwnV8LDtAQcE3NQI/IGYHxFIxqoEXbesHIZZUGWgCVVHIa3rUZl2reru2T6BCSmoMvADT1DBR6Ho+KU2j7/mkDZPuwCFu6CihQFcE28ay/MSPvo59t0Bl9lqxbgG1Y8cOtm/ffsP3uxPLpLcSvu9z4sQJFEXh0KHNCS96vR4f+MAH0DSNz33uc1syu+duY8uRzenTp1laWnrZRc31Yf3tDBm6EUcAKSWdTodyuUylUqHX65HP5xkZGRl+oRYXFzlz5gwPPPDA93i53Q4EQchnPneCp795jp7tEYSSvu9RjFukpcpUJo4/8DBNDafvomkq7sCh0+yTKyZZulTGipnkStH/67rK6HSe5csV4ukYhdE0ds+JbPuFYGymSKfRoV3rEgif/FgKA3NoPaMZGiPbitHuTKVFr9VH1VRGt5fotXrEU3HKV6pDw8zSVB7XcXH77nAmpBw/zig9EnjUtRiXxyfojGfo7CnhaPp6XA75RhNn73bUrkfX8/FVgS4Ax8ePacT6IWrCoh8GCD/EDCVq2kK1A/r6eia8IOVFf7Gtsma+KUn4MFAhCCWm4yODkEEsancZboCu6/S8AD2UxHWDduCTVTVURSFYi/52HR9d19A1DWfgEotdtf+XoWSilOHHnjzCkUN3xp351dBoNDh58iS7du26xhrqZrHV221BEHD8+PHXRDSDwYAf+7Efw/d9/vIv/5JU6s4JOO4lbDmy6ff7a9Lc6w/Vut0u3/rWt3jb2952Wx7/pRk0Nzqf6ff7w4qn3W5jGAae57F//37Gx8dvy7G+HBzH439/5jt8+/gVugMXoUSyWzcMMYVCFoWMUIkLhW6zx8h4lqXZCulcAk1TqC01GJnKowrWTpQeiiKGlc349hLtWodes08yF8MTDk7DRfrRSWNkW4HA8XEGLolMjMp8nbHtRTr1Lu1aBytukh2NXJk1TWVlroIMJWMzRRqrTRLZSHRQW66jHfsmqyNFVmdG6YxnQRUYoU+s06eRzyCCEKvVxxCSQbqIF1cIFYERM9C8SNrsux69mDrclTHdkLhp4gw8PAMCTSGt63gtB9uIiCejaLg9G9dUUSydwPXRuh6+rqKYOvrARwOUtIkrIYlCV4bghRg+2CrDZUwpQfFCpKKgC4GCAjJy7i7lErzn7Y/wA2/ae0c/I6+EWq3Gs88+y969e5las/65VdhK7bYgCK5xid/MY9u2zQc/+EHa7TZf+MIXbkmkyf2KLUc2QRAMM7mvh8FgwFe/+lXe8Y533PIrovWKJgiCTe/PrLvfttttEokErVaLRCIxrHju5JWc63l8+jMn+MbfnKdrezhBgK6pBEKiKgI3DMlqGjlNI62opHSVXmuAbmi0qx16rT5jMwXa1S799oD8WAaBpFXpUJrK44cucy/OI0KVRDLG6EyR2nITkBTHsri2iwwZLnwKRbDjwBR212ZxQxpmMheP5joLdSqdHr24Ri+lE4yn6LcaCCkxmz3ahTQGAb4PRttGcz28fAKhKZEUOZlGtX3SCYtud4BjagiitpzSc8ANCA0F39JBShRFIeELYrpKS8j1sRNpVPp+gCcgJRQUKen4Pr4AU9UwHUmXyJdM63p4iiBh6eAFqKaGFALckEATaB4IVUEJJDKU6JqGkIJCNsZ73vEwb3nLg3fks3CjqFQqnDp1igceeOA1hw6+Gu5muy0IAk6ePEkYhhw5cmRTROM4Dv/gH/wDVldX+au/+qu/E5YzrwVbjmxeLa3T8zy+9KUv8cM//MO3dEnqVlnPrAcqHTx4cFjdrF/JVatVdF0fZp5ns9k7Qjy+H/AXf/EsX/3GWVpdh1BK7DBEBkEUIGY7GDGdnu2R13WsUCI7DpMjGXorLTRfMr6rROD4uAM3ympptCjPV0mmk0zvHKO61EA3NTKFBDKEVrlFtxnNh7IjaVK5BHbXHhJPrJRCyydohT7NIKDtuXRTBsLSUVSFuISW6xD6Hvg+Vtcm2+3gWCa9ZAJfqKhIjN4Ax9BRUVDMGCITxw8lKQQdXWIgEG2HARAogryh0ww89L5PAMi4EVVYXoAVgkzFogVPCTlNpztw8ZDoUhJXNRwvJIhpZHUdJwjxkcSlQn/go2gKZgiOkKR0g+7AJWHohCEYmkCEMDWa453vPMDrX7/7tr/vN4tyucypU6c4cOAAo6Ojd/SxX67dtk48t/IibSPRbNal2vM8fvqnf5rZ2Vm+/OUvUygUbsmx3c+458gmDEO++MUv8ta3vvWWXfmsz2deC9F0Op2hTHv//v3Xbb8FQUC9Xh/OeeDmlW2vBVJKvvylF/nqM2dYXGmBgFAR+GGAh8RSVaQiCaTEF+CEIaofuQ14fRc9lBRSMdrlJt7AYWwkhyoEhq4R+lFMQafRo9+zkYpCZiyFJ8EX0Kx3CeI6ImXR9T0UP4SEwcDz0PWoAhE9By9lQCBR+h5xTUMzNHrVMp6uEyDQ+w5SFaimgtUdoHkhA0NHLZQYxCKbGRFKTDsgnozRGdh4hopQBAlDR3cCfBQMFXq6QA8kug89GanLhB8QCyXCMBgoAnyfmARXqAShJBYKAgmh5xMzNDRNI0Biqip9xwUfrJiJCCUaCmEQoqsqM1M5nnz/YR56aPq2vsebxcrKCqdPn+bhhx/eErb3t6vdtt558H2fI0eObIpofN/nH/7Df8iZM2f48pe/vCVer3sB9xzZAHzhC1/gjW9842tWfLyWDJqNWG89rKt2buRvSClpNps3pWy7lTj9/AKf/8vnuDhXZTBwUXUFx4vabB3HBQ00V6IldHzPJwglxDQGjoshJKFpEIYhWt/DT0THGtpelCwTN0CA4Uv6nocwo9wV0XPxNYES05FSYkgIBi4yDNGEghE3EYGkq4Ln+SDBNHXo2Tj+gGS/j762CzMINex8NIhV0kkUCbGBh2LqDHQVFEEYhEg/JDbw0BMWdc9bs+ERZFQNuzMAIQiTBiKUKAMfVxGopobfd9FsDz2mE6oquqoiNBUfQUxCKASDgYfuBSgxA0soBDLa6VGVyLImZujs3z/Gj/7Yo4yObd0Wy9LSEmfOnOGRRx552bj1u4lb1W4Lw/CagLfNEE0QBPz8z/88J06c4Ctf+cp1E4O/j+vjniSbL33pSzz22GM3vYG/EZsVArz0b1y5coWLFy/y0EMPbbr1IKWk2+0Oied6yrbbhW7X5s8/e5xj352l0RkghcD1PCxLx/UDdEOj57h4AlTHpS8hFtNR3IBASqyEgecGBL5PLGUhBDg9B9cPMGI6uqoy6NkIRcFImIRSgufT9gNCRaAIQT5m0iLEczykBN0L0GM6hqaihhLH8fEGHlIJCAOfAAWBJBP4aKMlXD+k60UtUE1ViEmJs1bhBKaO0FQCLyApo4VMG1ifCiZVldCL/mKoAEIg3YDuWqWm9jxcKVF0FdV2UXWNeMJChCJK9QxYswWKYqMVAcVCkte/fjdvf/cjEVluYczPz3P+/HkOHTpEPp+/24fzqthsu22daFzX3XSSaBAE/NIv/RLf+MY3ePrpp++IwvR+wpYjmxuJhn766ac5ePDgpgdyt0IIEIYhZ86coVKpcOjQoVuqQllXtlUqFVqtFul0ejjnicdvX578sb+9zDNfeZELsxUcxyMAFFVgOx4hAY4fkoiZKAJCAZqm4EnJwA8QQqILQcfxEYogber0fB+pKggBSSFohyG+FyAlZDRBYGoIX+J1bUQYgqqiJy18BVzHR7NdbFNDiMhN2vRBF+D6IU4Ygq7i2R6mH+DqCplYlD/jqyqKAEtVsAnRvRA/kLiqigzCSL4sQ5SUxcAJUIC4IpBCwfFCtMDFskxCRSGQAlMIugMfVUo0BRw/wBAQhgqmpqCrOqm4wb5947zt3Q+ze++9cbU7NzfHpUuXOHz4MNls9m4fzqZwI+22MAx57rnncBxn00QThiH/9J/+U770pS/xla98hZmZrSFRv5dwT5LN1772NR544IFNJXPeCiGA53k899xzuK7LoUOHiMViN/03bhSO41CpVCiXy9TrdRKJxHDOc7uUbbbt8fQXn+e7f3uZ+cU6vf6AQBFoiopuavh+NED3NIHt+CQNlQGSwA1IxlSkqiKlxO67xE0dXwHH9sALMJImvioIAgkDF6kIQlND1VRUAfRdbCHRghBD14hZOlJCy/Xxg2jvKW3qOK6Hpgh0VSMUEAhQvIAeoPoBKcuk2xkQCIEJqEkTr+MggUDXUBQFre8CEjNt0bV9VDdAqOApkSxc11ScICSpawShJHQDhCowVS26UEGSy+pM74zz2Ot3MDY+dtsvCG4VLl++zOzsLEeOHLlv5LrXa7fl83kcxyEMQx577LFNE82v//qv8+d//uc8/fTT7Ny58zYc/ffimWee4Xd/93c5duwYy8vLfOYzn+Gpp54a/l5KyW//9m/zn//zf6bRaPD444/zH/7Df9iyqZ/3JNl885vfZOfOnTfdL70VQoB+v8/JkyeHGRd30jbc932q1SrlcvkaZVupVCKXy91y4mk2m3zrm99m8bLP0nyf1XIbz/MjggAkAmSIG4aoAnzXR49Fm/TOwMNIRLMbJQSpKZG0WIa4A48gDNEMDdPSUYWg03Xww5BQUUgYGhgKtpR4tocIQww/xErFEIAfghNKzDBEmiqO7aJ6IUhQLQM1lNhINEUQNzQ8X2KHkrgS+bARSgZBSCgElhAIX9L3A/TQR/pRS3U90AwpUAQQClQFDENlajLHoSPbedPbHiIeN7Bte3iCq9frxONxSqUSpVKJTCazpTzCpJRcvHiRhYUFjh49et8uIK4vXT///PMMBgPCMCSdTt+0ui0MQ/7Vv/pX/O///b/5yle+wt69d24f6vOf/zzf+MY3OHLkCD/6oz/6PWTzsY99jH/7b/8tn/jEJ9i7dy8f+chHeOaZZzh79uyWfF+3HNlAdDX/Svj2t7/N5OTkDfdMb5UQoNlscvLkScbHx9m7d+9dPYmEYUitVhtWPXBrlW3lcpnnn3+ePXv2MD0dKaj6PYdvPn2G547PsTBXp+/6eF50tQ+SUIAqRFRpBBJNg4EXYLsBhqFiagptx0dRFSxNQVUEA9cncH1UCbGkgVCihcd+x0EGAVJTIR65OyPBCkNsLyBm6fhuQN/1MVUlIjkBOiBktENkaiphsB5XIfCkJAgllhBoa48jAWSIPXBB1UhYBo7jR/MiBBCSTsaZ2VHg8GM7eN2b9mIYL3+B4fs+tVpteEGgKMqQeO62R5iUkvPnz7O8vMzRo0dva5T63UYYhjz//PP0ej2OHj0KcNPqNiklv/M7v8MnPvEJvvKVr/Dgg3dvJ0oIcQ3ZSCmZmJjgl3/5l/n1X/91IDpvjo6O8rGPfYyf//mfv2vH+nK4J8nm2LFjlEqlG7LReKkQYLNEs7y8zAsvvMDevXuHJ9+tgo3Kto1qnZGRkU0p29aHxq+UtyOl5NzpZY5/6wIXzi1TLndxXBdFUfHDELFGOp4fpUx6YYgTgnR9TF3BEYIwlGhSYlga/UCiCNCCEM/1EKpCPG4iw6hiCUNJ3wsQekQgRhCgGBq6piIluEF0O0MVUZSAF+D7EldKYrqK4odIQNWjCkuR4IeC0PeQvo9pWqxLRCxDYWy8wK59o7zujXvYuW9zM5gwDGk2m8MLAtd1r1EcGoaxqb+7GUgpOXv2LJVKhSNHjtzX3l0vJZqXvs6vpG4rFArEYjGklHzsYx/j4x//OF/+8pd5+OGH79KzifBSsrl06RK7du3i+PHjHD58eHi7J598kmw2yx/+4R/epSN9eWzJPJt1C/OXw3q+zKvhVsxnpJRcunRpaAy6FaWhQghyuRy5XI69e/cOlW2zs7OcPn2afD4/rHpeSdkmpeTChQssLi5y5MiRVxwaCyHYd2CCfQcmhvc9/dwVTn17lrmLFer1Lp22jaGoeG6AJgQ6IA0NL5BYSDRNoTfwcAIfUxXoqoIjBaqio4YSxQuJHGYkmhCYXhi5AOgaaJF5peJHrstxBB4Sxw4wRIiqgK4pGG6AdKMguDAICZ1IsUYoEa6DSkixkGF0Isv23aMcfGw7e/ZP3pKqVVEU8vk8+Xx++L5UKhXm5+d54YUXyGQyw6rndp78pZS8+OKL1Ot1Hn300ds6Y7zbkFJy+vRput0ujz766HUJXVEUCoUChUKBffv2DdVtS0tL/OZv/ibPPfccu3fv5plnnuHpp5++60RzPaysRLlNL1XAjo6OMjc3dzcO6VWxJcnm1aBp2ita2sCtIZogCDh9+jStVovHHnvsnmg7CCFIpVKkUil27dpFv9+nUqmwsrLC2bNnh8q2l57g1tNQ15/rzZ78hBAcODjDgYNXVTrdzoAXT84zd26VK7NVmvUu3a7LYOAg1kgooSqRYeWaOtAUoCoCtMi9WRUgEIRhQMzQCaTEdQIMCYahEQQSISDwQ9QwJKWpa++1RPhR0qamKpH5pmmQTBnk8gkUyyNfMnnvj72VfOH2D8g3vi87d+68Zs5z4cKF2zbnCcOQF154gVarxaOPPnpf296vE02n07luRXM9CCFIJpMkk0l27NjB5OQkH/nIR/irv/orgiDgR37kR3jve9/LT/7kT/KmN73pDjyLm8P1DIK30oxwI+5Jsnm1yuZWCAEcx+HZZ58F4PHHH7+jLY9biXg8zszMDDMzM0Nl28YT3PqM5+LFi4RhyOte97pb9lyTqRiPvWkvj73EZLLXcZg7t8zyYoPKYoN6o0e/69LtDPCcAMf1CLyQYK3ClRJCoSJDiRqGWIYKCEQQYigKqibQLB3T0DBiBvGEQSxmUBhNUxrPMD6ZZ/veMeJJc7hB7rouhw8fvmt295ZlMT09zfT09HDOU6lUhlb3t2LOE4Yhp06dotfr8eijj245a/9bCSnlNaS6mecqpeRzn/scn/3sZ/n85z/P0aNHeeaZZ/jzP/9zjh8/vqXIZl0ctbKyco3Rb7lcvuNWQzeKLUk2N9JGu97i50uFAJslmm63y4kTJ8hms+zfv3/LBz/dKEzTZGpqiqmpqaGybXl5mcuXL6MoChMTE3S7XbLZ7KaWXG8UiZTJ/qPb2X90+w3dPlIoelFUMpGhp2HqqOrNHaPneZw8eRKAo0ePbpm4Xk3TGB0dZXR09Jo5z9mzZ3Ec55r5241eCARBMNwtebl20v2CdaJpNpscPXp000TziU98gt/6rd/ic5/7HG94wxsAeNvb3nbbHOZfC3bs2MHY2Bh/9Vd/NZzZuK7LV7/6VT72sY/d5aO7PrYk2bwaVFVlMBhc87MbyaC5EVSrVU6dOsW2bdteMZnwXoemaSQSCTqdDhMTE5RKpeFzl1IOr6wLhcJdJ1shBKZp8FouzB3H4fjx41iWxSOPPHLXn9PL4VbMeTb6f20lUr0dWJ9HNRqNTbcJpZT80R/9Eb/xG7/Bn/7pn/IDP/ADt+FIbx7dbpcLFy4M/3358mVOnjxJPp9n27Zt/PIv/zIf/ehH2bNnD3v27OGjH/0o8XicD33oQ3fxqF8eW1KN5nnekDSuh9nZWer1OkeOHAGunc9EIVibuyqfn5/n3LlzdyWD5k6jXq/z7LPPMjMzw44dO4akuq5s26igei3Ktq2Afr/P8ePHh5Xq7azabiduZJ/H931OnjyJlHLTjsb3CqSUnDlzhlqt9pqI5k/+5E/4xV/8RT75yU/yzne+8zYc6ebw9NNP89a3vvV7fv4zP/MzfOITnxgudf6n//SfrlnqPHDgwF042lfHliQb3/dfcSazsLDA8vIyjz322C1TnJ09e5aVlRUOHTp0z1p33CjWZdwPPvjgK2aWXM+zLZfLDQUG98KwudPpcPz4ccbGxu76btStxMY5T6VSGSqsWq0WpmluOgzsXsFGojl69OimFXaf+cxn+Lmf+zn++I//mPe+9723+Ci/j424J8lmeXmZ2dlZnnjiiddMNL7vc+rUKQaDAYcPH77vZaGzs7PMzs7yyCOP3HQGx2AwGBLPumfbuqR6K+5tNJtNTpw48T3V2/2GMAypVqu88MILw+/NZuY89wo27gy9Fin35z73OX72Z3+W//7f/zvvf//7b/FRfh8vxT1JNuVymXPnzvHEE08MpX6bOZEMBgNOnjyJYRg88sgj92SL6EaxfiVYLpc5cuTIa7azcF13uERaq9WGyrbb6dl2M1iPfdjogHC/wnVdjh07Rjwe58CBA0O5e6VSodPp3LF9njsBKSXnzp2jXC6/JqL5P//n//BTP/VT/MEf/AE//uM/fouP8vu4HrYk2bxSNPT6TOE73/kOY2OR8WGhULjpPnyr1eLkyZOMjIywb9++e7aPfyMIgoBTp07R7/dvS/V2Pc+29Yrndivbrof1NuFDDz103+eN2LbN8ePHSaVSPPTQQ9/zWtu2PXxvtrpv26th3W5nZWWFRx99dNOGp1/+8pf54Ac/yH/8j/+Rn/zJn7ynXoN7GfcU2Wy0ntk4xPZ9f3hyuxH11OrqKqdPn2bXrl1s27btvv6wua7LyZMnEUJw6NCh2169hWF4TRrpnVa2XblyhQsXLnDw4MH7Pqp3MBhw7Ngxcrkc+/fvf9XP8fXmPMVi8Y4lxb4WrLtbLC8vvyaieeaZZ/j7f//v8+///b/nZ3/2Z+/r7/5Wwz1DNhuJZmPbTEpJu92mXC6zuro69J9a71dvVONIKYfW6g8//PCmIgruJfT7fU6cODG86r3TJxMpJa1WazjncRznmvfmVhLfuq3Q/Pz83wmRR7/f59ixYxSLRR544IGbPmlu3OepVCrDfZ71C4OtNOdZJ5qlpSUeffTRTbcCv/nNb/KBD3yAf/fv/h0///M//32iucPYkmTz0rTOlyOal2Kjemp1dZXBYEA+n2d0dJRCocD58+ep1+scPnx4S1pw30qstwm3igpr43tTqVTodru3TNm2PjBen0fdC7ZCrwW9Xo9jx44xOjp6S97b9fTL9fdmK8151iMRFhcXXxPRfOc73+HJJ5/kd37nd/jFX/zFu/59+LuILU02644Am7WeWf8Crays0O12UVWVnTt3MjExsaWu3G411ofju3bt2rKJgrdK2bbR0+21SGDvFaxLuScnJ9m1a9dtCs/bOnOejdk7m72IOHHiBO9973v5zd/8TX7lV37l+0Rzl7BlycZ13VviCNDr9Thx4gTxeJxcLjfMLc9ms0P11L2wL3KjWFhY4Ny5czz00ENb1iPppXBd95o00lgsNqx40un0y77vW8Xn7E6h3W5z/PjxobvFncDGOU+1WkUIccfmPOtu648++uimiebUqVO8+93v5ld/9Vf5jd/4je8TzV3EliQb3/exbXv4782qmda35Kempti9e/fwg2bb9vCqutlsDp2QR0dH79kr4/V2w/rMIpfL3e1D2hReGj6mqurwomCjss3zPE6cOHHHhA93G+s7Qzt27GD79u135Rju5Jzn8uXLzM3NvSaieeGFF3jXu97FP/kn/4Tf+q3f+j7R3GVsSbL5mZ/5GS5evMhTTz3Fj/zIjzA5efP5IgsLC5w9e5YHHnjgFRM91/dF1q+qk8nkkHjulZ2EdRv5RqPB4cOH75uZxfWUbcVikVwux9zcHPF4nIcffnhLq6huBRqNBidOnNhSO0Prc571ivRWznnWiea1xFafPXuWd73rXfzsz/4sH/3oR79PNFsAW5JsFhYW+OQnP8mnP/1pvvnNb/Loo4/y5JNP8uSTTzIzM/OKH5x1Lf7S0hKPPPII+Xz+hh/X87zhl6dWqw3bOaOjoySTyS35gfV9n2effRbP8+7rVtK6sm1xcZHl5WWAYTunVCrdt5VNrVbj2WefZd++fTccg343sD7nWfdti8Vim5rzrDtcvBaiuXjxIu985zv54Ac/yO/+7u/e1zt09xK2JNmsQ0rJ8vIyn/nMZ/j0pz/NM888wyOPPDIkno2tMbi6vNjr9Th06NBrurp66aKiYRiMjo4yMjLyinOEOwnHcThx4sTQAeF+Nl2Eq8Px0dFRJicnhxcGt1LZtpWwLvR48MEH7ylj2Jeb87zartXc3ByXLl3i6NGjpNPpTT327Ows73rXu3jf+97H7/3e732faLYQtjTZbISUkmq1OiSeL3/5yzzwwAND4rEsi1/4hV/gn//zf85b3vKWW3qlGwTBcI5QqVSGc4TR0VGy2exdIZ71zJ18Ps+DDz5433+pGo0GJ0+eZPv27Wzfvv2a13wwGAyJp9lskkqlhnOee6UV+lKsrq7y/PPPc+DAgXtG6HE93Oic58qVK1y8ePE1Ec3CwgLveMc7ePvb387HP/7x+/47ca/hniGbjZBS0mg0+LM/+zM+9alP8YUvfGGYU/LRj36UI0eO3LYP2sY5QrlcRghBqVRidHSUXC53Rz7g6yfe6enp2yZ/3UpYv8Lfu3cvU1NTr3jb6ynb1iXVW6UifTWs2+088sgj99Xi8cvNeXRdp16vc/ToUTKZzUV0Ly8v8853vpM3velN/Jf/8l/u2hzvwx/+ML/92799zc9GR0dZWVm5K8ezlXBPks1GfPazn+WnfuqneO9734tt23zxi19kfHycJ598kqeeeorDhw/fVuJpNpvDJdJ1a5bN+rXdCNatdm7kxHs/YP3Eu5kr/JdTtpVKpTt2YXCzWFxc5OzZs38n7HZs2+b8+fOsrq4CXCN5v5k5z+rqKu9617t49NFH+cM//MO7Khj58Ic/zCc/+Un++q//evgzVVXvq4uGzeKebvI/++yz/NRP/RT/7b/9t6FFeLfb5S//8i/51Kc+xXve8x7y+Tw/8iM/wlNPPcVjjz12Sz+IG1MV9+3bR6vVYnV1lTNnzuB53pB4isXiLXncubk5Ll68+HfCageu+pwdOnRoUyfel8Yt1+t1KpUKzz//PGEY3pSf3p3A/Pw858+f59ChQzclbLlXsS4oWJc3r8951r38bmTOU61Wed/73scjjzzCJz7xiS3xPmqadt8bwG4G93xlMz8//7Jy0H6/zxe+8AU+9alP8Rd/8RckEgne97738dRTT/H617/+tg3UpZR0Oh1WV1cpl8vYtn2NcupmH3fdVn15eZnDhw9vutVwr2Cjz9nteL7X82xbz3+5W8q29eH44cOH73tfN7i6fHz48OHv2Qm70TlPvV7nPe95Dzt37uSP//iPt4QryIc//GF+93d/l0wmg2maPP7443z0ox+9Y0u4Wxn3PNncKGzb5q//+q/59Kc/zZ/+6Z+iaRrve9/7eP/7388b3/jG23aCWe9TrxNPr9e75sT2al+QIAg4ffo0nU6Hw4cPb9rt9l7BnfY52+gLtlHZtl713All2/qm/JEjRzY9HL+XsN4qvB7RvBQb5zyVSoWlpSU+9rGP8eY3v5mvf/3rjI+P8+lPf3rLSP4///nP0+/32bt3L6urq3zkIx/hzJkznD59+r5vi74a/s6QzUZ4nsdXvvIVPvWpT/HZz36WIAh4z3vew1NPPcVb3vKW2/rB3Xhi63Q6Q8nuyMjI9zyu53nDPPlDhw5tiSu324l1n7N2u82RI0fuipvDnVS2rbs+rHt/3e/msABLS0ucOXNm063CarXK7//+7/Mnf/InnDt3jt27dw8VqU888cSWaKNtRK/XY9euXfzar/0av/Irv3K3D+eu4u8k2WyE7/t8/etf50/+5E/47Gc/S6/X4z3veQ9PPvkkP/RDP3RbT3gvNaPMZDKMjo4O5zHrnm5/F7bkN/qcHTlyZEsQ60uVbZZlDYnntSrb1lujq6urHD169J6VaN8MlpeXefHFF1/TTKrb7fKBD3wAwzD4X//rf/H1r3+dP/3TP+Xzn/88p0+f3pKzzLe97W3s3r2bj3/843f7UO4q/s6TzUYEQcC3vvUtPvnJT/KZz3yGRqPBO9/5Tp588kne/va339YTguM4Q+JpNBoApFIpDhw4cN+fiNZ9zhRF4eDBg1vSDeBWKtvWI7qr1SpHjx6971ujcJVoXovKrt/v82M/9mOEYchf/uVfXtNiXXeF32pwHIddu3bxcz/3c/zrf/2v7/bh3FV8n2xeBmEY8p3vfGdIPMvLy7z97W/nySef5F3vetdta3nUajVOnjxJsVgkCALq9TqJROIav7Z7YVfkRmHbNidOnCAWi90zFVwYhjQajeHFwc0o26SUQx+7vwuRCAArKyu88MILr4lobNvmx3/8x+l2u3zhC1/YsrOtX/3VX+V973sf27Zto1wu85GPfISvfvWrnDp1asvGfdwpfJ9sbgBhGHLy5Mkh8czOzvJDP/RDPPnkk7znPe+5ZRkfS0tLvPjii+zfv39oT+J53jW2OZZlDW1zUqnUPU08/X6f48ePk81m2b9//5a8Mn01rCvb1ttttm2/rLJtfSbV6XQ4cuTIfWOr80pYd0I4ePAgxWJxU3/DcRx+8id/kkqlwhe/+MUt7Wj+wQ9+kGeeeYZqtUqpVOKJJ57gd37nd9i/f//dPrS7ju+TzU1CSsnp06eHRqFnzpzhrW99K0899RTvec97KBQKN00A63HVc3NzHPz/t3fvYVHV+R/A34NxUZGbw1VFEFAQVC6Sio+mpeANZsxKV/PS+hSta7voY/i0/Sxt1bxtuuVimpVZ7WbJ5A1RlIuZosUAoqKCV0RgYLhf53Lm/P5o56wjasDMYWaYz+t5+qMjDZ9p6rznnPP9fj6jRj3xfjbDMJDL5ZDJZFy/Nu0zhO4eaqUvbZ8zU5kkaghPW9kmFApRXFyMlpYWhIeHm8zqKT5pg0afTggqlQqLFi3CvXv3kJ6ebvEruswZhY0etA95k5OTkZycjIKCAkyYMAEikQhxcXFwc3P73ZOoRqPh7t93Zly19habTCbT6dfm5uYGZ2dnkz55P63PWU+iXdkmk8lQV1cHKysreHt7w9PTs8eMgXiSyspKXL58Wa+gUavV+OMf/4jr168jMzPTJB/+k46jsDEQ7UbE5ORkSCQS5OTkICoqCnFxcRCJRPDy8mp3UmUYBgUFBWhra0NYWFiXb6tonyFog4dlWS54XFxcTOr2VGf6nPUEDMMgPz8fKpUKXl5eqKmpQXV1tUFXtpmaqqoqFBQUYMSIEXBzc+vSazAMg/j4eOTl5SEzM5N25PcAFDY8YFkW9+/f54InOzsbkZGRXNscb29vlJaW4quvvsK0adMMugJL26RUeyuHYRiTacuiT58zc6RWq5GXlwcACAsL4zpHaG+Hajcqantnaa9KTenLQWdpg0afz5hhGLz11ls4d+4csrKyTHqOD+k4ChuesSyLsrIybjTC2bNnERgYiMrKSowYMQISiYTXtjkNDQ1c9wKlUsm1zREKhd06/0bb58wSGkwC/1vO3atXL4SGhj4x5J+0sk37nMccVudpyeVyXLp0Sa+g0Wg0SEhIQEZGBjIzMy1+BVdPQmHTjViWRWpqKl555RUMHjwYxcXFCAwMhFgshkgkQmBgIG+3U1iWRVNTExc8ra2t3dIP7OFd8pbQ1w34bTNobm4ubG1tMXLkyA4HhvbLgTZ4Hl7ZJhQKTWKj65PI5XIUFBRg+PDhXb7lpdFokJiYiGPHjiErK4v6ifUwFDbd6OTJk5gzZw42b96MZcuWoba2FocPH0ZycjJOnz6NIUOGcKMRgoODeb2d0tTUpLNqysXFhXuGYKiTmnbzYlVVVbf0OTMFCoUCubm5XOeHrn6Gj5v94uTkxH05MKX9OdrR1foGzf/93//hhx9+QFZWFgICAgxcJTE2CptudOvWLVy5cgUikajdn9XX1+Po0aPcMLgBAwZwwRMaGspr8LS0tHDB09DQACcnJ65tjj6LFq5cucLtKTGlkyNf2traIJVK4eDgYPAvC21tbdxnVFdXB3t7e52ebcZaYFBTU4P8/Hy9RlezLIsPPvgAX331FbKyshAYGGjgKokpoLAxQY2NjdxMntTUVAiFQq5DdWRkJK/Boz2pyWQy1NfXw8HBgdtE2tHAMMU+Z3xrbW2FVCqFs7Mzhg8fzuvJX6lUcht9H17Z1tmhY/rSBk1gYCC8vLy69Bosy2LTpk349NNPkZmZiZCQEANXSUwFhY2Ja2lpwYkTJ7iZPPb29tyqtnHjxvH6AFmhUHD7RGpra2Fvb88Fz5P6tT3c5yw0NLRbFyEYS0tLC6RSKVxdXTFs2LBuvcow1sq22tpa5OXl6R0027dvx/bt25Geno7Q0FDDFklMCoWNGWlra8OpU6e4mTw2NjbcFc/48eN5bWCpUqm44Kmurub6tbm5ucHe3h4CgcAs+5zpq6mpCVKpFJ6enggICDDqfpmHV7ZVVVWBYRid1YeG+jy0QTNs2LAuL0tmWRY7d+7E5s2bcfLkSURGRhqkNmK6KGzMlFKp1JnJo9FoMGvWLG4mD5+3rtRqtU7bHDs7Ozg7O6Oqqgr9+/c32z5nndXY2AipVIqBAwfCz8/PpDZmPmll26PTLjurrq4Oubm5em3KZVkWe/bswbp165Camopx48Z16XWIeaGw6QHUajXOnj3LzeRpbW3VmcnDZ8NHhmFQWlqKmzdvgmVZ2Nraclc8Tk5OJnUCNqT6+nrk5ubCx8cHvr6+xi7ndzU1Nem9sq2urg55eXnw9/d/4ij238OyLPbt24d33nkHx44dw8SJE7v0OsT8UNj0MAzD4Pz58zh48CAOHTqEuro6xMTEQCwWIzo62uCzUx7uc+bt7Y2amhruNo5AINDp19ZTrna0J90hQ4aY5aZD7SKQqqoq7lnc761s04arvkHzzTffYNWqVThy5AgmT56s71shZsRkw2bDhg1ISUlBfn4+bGxsUFdX1+5nSkpK8Oc//xkZGRno3bs35s+fj23btlnE6qeO0Gg0+OWXX7jRCDKZDFOnToVYLMa0adP0nsnztD5nj+6MZ1lWp22OuQaPdgVWQEBAl0+6pkT7LO7hlW3az0m7sk0bNH5+fvD29u7S72FZFt9//z3eeustJCcnIyYmxsDvpPOSkpKwdetWlJeXIzg4GDt27MCECROMXVaPZbJh8/7778PJyQmlpaX4/PPP24UNwzAIDQ2Fq6sr/vGPf6C6uhqLFy/Giy++iE8++cQ4RZswjUaDvLw8bjRCSUkJpkyZApFIhBkzZnR6yax29k5HWpOwLIu6ujoueNRqNYRCIdzd3Y3er60ztJsX9XkwbsoYhuGmkVZVVcHKygpOTk6Qy+Xw8/ODj49Pl19bIpEgPj4eBw4cwKxZswxXdBcdOHAACxcuRFJSEsaPH4/du3dj7969KCws7HKgkqcz2bDR2rdvHxISEtqFTWpqKmbNmoX79+9zSy+/++47LFmyBJWVlSY7yc8UsCyLK1eucMFTVFSkM5PHxcXlqcGjT5+zhx9cy2QyKBQKLni6u19bZ2iv4vTZvGhONBoNHjx4gBs3bnBfBrQr2/r379+pz+nYsWN47bXX8M0332D27Nl8ldwpY8aMQXh4OHbt2sUdCwoKglgsxocffmjEynou87yXASA7OxshISE6a/xjYmKgUCgglUqNWJnpEwgEGDFiBNatW4eCggJcunQJEyZMwJ49ezBkyBDExcVh7969kMlkePi7iEajQXFxMW7fvo2IiIguNdQUCARwdHREQEAAxo8fj2effRb29va4ffs2zpw5g7y8PJSVlUGlUhnyLetFJpOhoKAAwcHBFhE0ANDc3Ixbt27B398fkyZN4iaL3rp1i/ucHjx4AKVS+dTXOXHiBF577TV8+eWXJhM0SqUSUqkU0dHROsejo6Nx/vx5I1XV85nm18gOqKioaHf7xtnZGTY2NqioqDBSVeZHIBAgMDAQ7777Lv72t7/h1q1bSE5OxrfffouVK1ciKiqKu9X23nvvoXfv3ti2bZtB+pwJBAL069cP/fr1g5+fHzflsqSkBIWFhbz0a+us8vJyXLt2Ta8hYOZGu6R78ODB3K0zR0dH7kuC9nMqLS3FtWvXnriyLT09HYsWLcKePXvw8ssvG+ndtCeXy8EwTLvzh7u7O507eNStYbN27VqsW7fuqT/z66+/YvTo0R16vcfd6mFZtscut+WbQCCAv78/Vq9ejcTERJSUlCA5ORkHDx7EmjVr4ODggKVLl3KbOg3977lv377w9fWFr68vWltbIZPJUFZWhuvXr3MnNDc3N16Xcj+stLQURUVFFjMWAfjfJlVvb+8nLul++HNqa2vjFhgUFRUhIyMDarUaw4cPR2JiIj755BPMnz/fJP+ffLQmOnfwq1vDZvny5Zg3b95Tf6ajDyE9PDxw8eJFnWO1tbVQqVQWMZiLbwKBAIMHD0Z8fDxOnjyJgIAAzJ07F6dOncKWLVswatQobjTCkCFDDP4/ae/evbmx0Q83oSwqKoKDgwMXPIZeyq1VUlKCW7duISwsDM7Ozrz8DlOjDZpBgwZ1uL2/nZ0dBg0ahEGDBkGlUqGiogL79u3D7t274eLigsLCQmRnZ2Ps2LEmswJR203h0auYyspKOnfwqFvDRigUQigUGuS1xo0bhw0bNqC8vJy7j56WlgZbW1tEREQY5HcQYOXKlVCpVDh79iwcHBzwzjvvQCaT4dChQ5BIJPjggw+4B6sikYiX3mB2dnbw9vaGt7c3lEolFzw3b97U2SNiqBEGd+/exZ07dxAWFgYnJyeDvKapa25uhlQqxYABA+Dn59el17C2tsbQoUNRWFiIzZs3w9fXF4cOHcKsWbMQFxeHffv2GbboLrKxsUFERAROnTql8xzp1KlTj+3ITgzDZFejlZSUoKamBkeOHMHWrVtx9uxZAIC/vz/s7e25pc/u7u7YunUrampqsGTJEojFYlr6bEDaW2aPu3XFsixqamp0ZvL4+/tzoxH4blvz6B6R3r17w83NDe7u7ly/ts5gWRZ37txBSUkJwsPDLWZFY3NzM3Jycrig6eqXhdzcXMTGxmLNmjVYsWIF9zoqlQq1tbVwc3MzZNl60S59/vTTTzFu3Djs2bMHn332Ga5evWqWG3XNgcmGzZIlS/DVV1+1O56ZmYlJkyYB+C2Qli1b1m5Tp62tbTdXS1iW1ZnJk5aWhoEDB3LBM2rUKF6DR9uvrbKyEnK5HDY2NlzwODg4/O4JlGVZ3Lx5E2VlZYiIiLCIQW/A/65oPD094e/v3+WgKSgowMyZM/H2229j9erVZvHsIykpCVu2bEF5eTlCQkKwfft2ap/DI5MNG2LeGhsbkZKSguTkZJw4cQJCoRBxcXGYPXs2Ro8ezWvwPLo5sVevXjptcx73YLioqAgymQwRERFPHJ/Q07S0tCAnJwceHh56dawuLCzE9OnTsXz5crz33ntmETSk+1HYEN41NzfjxIkTkEgkOHbsGBwcHLiZPGPHjuW1g4BGo+H6tVVWVkIgEMDV1RXu7u5c8Fy7dg3V1dWIiIjgbcGBqdHO4HFzc8PQoUO7HBA3btzA9OnTsXTpUqxfv56ChjwRhQ3pVq2trdxMniNHjsDW1haxsbEQi8W8z+TRaDRc2xyZTAaNRgNra2swDIPRo0dbzBVNa2srcnJy9A6amzdvYvr06fjDH/6ALVu2mMxqM2KaKGyI0SiVSmRkZHAzeQBwM3mee+45XjdyMgyDvLw8NDY2olevXjr92gw5aMzUaING36mid+/exbRp0yAWi7Fjxw4KGvK7KGx44uPjg3v37ukcW716NTZt2mSkikybWq3GTz/9xM3kaWtrw6xZsyASifD8888bdCOnRqPB5cuX0dLSgoiICFhbW6OxsZG74mlra9OZcMnn1VZ3amtrQ05ODvr374/AwMAuB839+/cRExODadOmISkpiYKGdAiFDU98fHywdOlSvP7669wxe3t7i1nlpA+GYXDu3DluJk99fT33LXrq1Kl6PVdhGAYFBQVQKpUIDw9vFyQsy6K5uRkymQyVlZVobm5G//79uXYs5jq+Qhs0Li4uCAoK6nLQlJeXIyYmBhMnTsRnn33WY68AieFR2PDEx8cHCQkJSEhIMHYpZk2j0eDixYvcTJ7KykpER0dDLBYjJiamUzN5GIZBfn4+GIZBWFhYh65YtH3AtBMunZ2duZVt5rLEvq2tDVKpFM7OznoFjUwmw/Tp0xEZGYl9+/ZR0PzX/v37sWLFCpSVlen8NzFnzhz07dsX+/fvN2J1poPChic+Pj5QKBRQKpUYNGgQXn75Zbz99ttm+83YFGg0GuTm5nKjEe7fv48pU6ZALBZjxowZT91Po1arkZeXB4FAgNDQ0C6NMmhtbeWCp76+Ho6OjlzwdHS0cndTKBTIycmBk5MThg8f3uWgqaqqwsyZMxEcHIxvv/3WZEdBGENrays8PT3x2WefcQ1H5XI5BgwYgBMnTtBE0v+isOHJ9u3bER4eDmdnZ/zyyy945513IBKJsHfvXmOX1iNoNBqdmTzFxcV4/vnnIRKJ2s3kaWlpweXLl2FtbY1Ro0YZ5Bu5QqHggqe2thb9+vXTGa1sCrTjNhwcHBAcHNzloKmpqcGMGTPg5+eH77//vsc8wzKkZcuW4e7duzh+/DgA4J///Cc+/vhj3Lx5k5aD/xeFTSfo07U6OTkZL730EuRyucV0EO4uLMvi+vXrXPBcuXIFzz33HEQiEZ599lm8+uqrePPNNxEfH8/Lw2ylUqnTNqdv375c9wI+umN3tKacnBy9g6aurg6xsbHw9PSERCKhK/MnyMvLQ2RkJO7du4cBAwYgNDQUc+bMwZo1a4xdmsmgsOkEuVwOuVz+1J/x8fF57MqpBw8eYODAgbhw4QLGjBnDV4kWj2VZ3Lp1CwcPHsSBAwdw48YN+Pv7Y+HChZg9ezY8PT15PfmrVCqdtjl2dnZc8PTr169bgkcbNP369UNISEiXf2dDQwPEYjEcHR1x+PDhbhvtYK4iIiLw0ksvISYmBpGRkbh79y4GDRpk7LJMBoVNNzl27BhiY2Nx7949mnHeDUpLS/HCCy8gODgYY8eOxZEjR3DhwgU8++yzEIlEEIlEGDRoEK8nf4ZhIJfLIZPJIJfLYW1tzQWPo6MjL79bO4Wyb9++CAkJ6fKVXFNTE1588UXY2NggJSXFZJ9JmZJdu3Zh+/btiI6ORnFxMU6ePGnskkwKhQ0PsrOzceHCBUyePBmOjo749ddfsWLFCowePRqHDx82dnk9HsuymDhxIoKCgvDpp5/CysoKLMviwYMHkEgkkEgkOHfuHEJDQ7nRCL6+vrwHz8Ntcx7u1+bk5GSQ23vaoOnTpw9GjBjR5ddsaWnBSy+9BJZlkZKSQsv1O6ihoQGenp5Qq9XYv38/5s6da+ySTAqFDQ9yc3OxbNkyXL9+HQqFAoMHD8a8efOQmJhoMb23jK2srOyJt8xYloVMJsOPP/4IiUSCrKwsBAcHcx2q9Wnh0hEajQa1tbWQyWSoqqoCy7Jc8Li4uHQpJFQqFaRSKXr37q1X0LS1tWHu3LlcPztLGbNgKIsWLUJKSkq7ZdCEwoZYOJZlUV1dzc3kSU9PR0BAANehOigoiNcd8izLoq6ujttEyjAMXF1d4ebmhv79+3do5Zw2aOzs7DBy5Mgu16tQKLBgwQLI5XKkpaVZzOA4Q5o6dSqCgoLw8ccfG7sUk0NhQ8h/aWfyHDlyhJvJ4+3tzQWPPifyjv7+hoYGLniUSqVO25zH7W1RqVTIzc2FjY2NXjODlEolFi1ahPv37yM9PR0uLi76vh2LUlNTg7S0NCxYsACFhYUYNmyYsUsyORQ2hDxBQ0ODzkweNzc3LngiIiJ4D56mpiYueFpbW3Xa5lhbW0OtViM3N5fbP9TVelQqFZYuXYobN24gIyMDrq6uBn43+jP1XoM+Pj6ora3FmjVrsGrVKmOXY5IobAjpgObmZqSmpkIikSAlJQWOjo7cTJ4xY8bw3rqlqamJW1zQ1NQEZ2dntLa2ws7ODmFhYV3+/Wq1GvHx8bh06RIyMzPh7u5u4MoNg3oNmj8KG0I6qbW1FWlpaZBIJDh69Cjs7Ox0ZvLw3cqlsbER+fn5UKvVYBgGTk5O3AKDzuyFYRgGy5cvR3Z2NrKysuDl5cVj1fqhXoPmj8LGgiUlJWHr1q0oLy9HcHAwduzYgQkTJhi7LLOiVCpx+vRpSCQSHD58GAKBADNnzsTs2bMxceJEg++41/Z4s7KyQmhoKFQqFTcaob6+Hg4ODtxenqftjdFoNEhISEBmZiYyMzNNfu8X9Ro0fxQ2FurAgQNYuHAhkpKSMH78eOzevRt79+5FYWGhyZ94TJVKpeJm8hw+fBgKhQIzZ86EWCzG5MmT9d6Brx34pm0m+uitM4VCgaqqKshkMtTW1sLe3h7u7u7t+rVpNBokJiYiJSUFWVlZ8PX11auu7kC9Bs0fhY2FGjNmDMLDw7Fr1y7uWFBQEMRiMT788EMjVtYzMAyDn3/+mZvJ09DQgOnTp0MsFmPKlCmd3m+lDRoAHXpGo1KpuOCprq6GQCBAamoq5syZg8OHD0MikSAzMxMBAQFdfo/6ol6DloXCxgIplUr06dMHP/zwA2bPns0d/+tf/4r8/HycOXPGiNX1PBqNBhcuXOCCp6qqSmcmz+895NbO4dFoNAgPD+/0YgC1Wo3Lly9jzZo1OH/+PABg4cKFeOONNzB69GijdSWmXoOWhYZSWCC5XA6GYdqtPHJ3d0dFRYWRquq5rKysEBUVhaioKGzbtg1SqRTJycn4+9//jvj4eEyZMgUikeixM3kYhsGlS5eg0Wi6vOrsmWeeQWhoKKKionD9+nUkJiYiJycHU6ZMgZOTE44fP47g4GBDvuUOEQqFEAqFXfpntVd5np6ehiyJ8IjCxoI9+o2WZVmavcEzKysrREZGIjIyEhs3bkRBQQGSk5Px0UcfYdmyZXjhhRcQFxeHWbNmwcbGBm+88QYWLFiAmJiYLq9yY1kWH330EXbt2oWMjAyMGjUKwG/PeNLT0zFkyBBDvkWDe1Kvwbi4OHq+aEboNpoFottopodlWVy7do2byXP16lUMHjwYAoEABw8ehL+/f5e+CLAsi08++QRbtmxBWlraY59/mDrqNdgzUNhYqDFjxiAiIgJJSUncseHDh0MkEtECASNTKBSYMWMGioqKMGDAAEilUkRFRUEsFiMuLg4eHh4dCh6WZbF792588MEHOHHiBMaOHdsN1RPyePz12yAmbeXKldi7dy+++OILXLt2DStWrEBJSQnefPNNY5dm0RiGwdy5c1FfX4/Lly8jOzsbRUVFiI2NxcGDBzFs2DBER0dj586duH//Pp70XZFlWXz55ZdYu3Ytjh49SkFDjI6ubCxYUlIStmzZgvLycoSEhGD79u2YOHGiscuyeJ9//jlmz57drhkmy7IoLS3VmckTHh7OzeTx8fGBQCAAy7L4+uuv8fbbb+Po0aOYNGmScd4IIQ+hsCHEDLEsi4qKCm4mz5kzZxASEgKRSARbW1ts2LABEokE0dHRxi6VEAAUNoSYvYdn8vz73/9GRkYGvvnmGyxYsMDYpRHCobAhpAfRjr8eOHCgsUshRAeFDSGEEN7RajRCCCG8o7AhhBDCOwobYjbWrl0LgUCg85eHh4exyyKEdAD1RiNmJTg4GKdPn+b+nu9xzIQQw6CwIWblmWeeoasZQswQ3UYjZqW4uBheXl7w9fXFvHnzcPv2bWOXRAjpAFr6TMxGamoqWlpaMHToUMhkMqxfvx7Xr1/H1atXaVojISaOwoaYrebmZvj5+SExMRErV640djmEkKeg22g9QFVVFTw8PLBx40bu2MWLF2FjY4O0tDQjVsavvn37YsSIESguLjZ2KYSQ30Fh0wO4urriiy++wNq1a5GTk4Ompia8+uqrWLZsWY9uxKhQKHDt2jUaDdzNNmzYgKioKPTp0wdOTk6P/ZmSkhLExsaib9++EAqF+Mtf/gKlUtm9hRKTQqvReogZM2bg9ddfx4IFCxAZGQk7Ozts2rTJ2GUZ1KpVqxAbGwtvb29UVlZi/fr1aGhowOLFi41dmkVRKpV4+eWXMW7cOHz++eft/pxhGMycOROurq74+eefUV1djcWLF3NTQ4mFYkmP0dLSwg4ZMoS1trZmL126ZOxyDG7u3Lmsp6cna21tzXp5ebEvvvgie/XqVWOXZbG+/PJL1tHRsd3x48ePs1ZWVuyDBw+4Y//5z39YW1tbtr6+vhsrJKaErmx6kNu3b6OsrAwajQb37t3DyJEjjV2SQX333XfGLoF0QHZ2NkJCQuDl5cUdi4mJgUKhgFQqxeTJk41YHTEWCpseQqlUYsGCBZg7dy4CAwOxdOlSXL58Ge7u7sYujViYioqKdv/dOTs7w8bGBhUVFUaqihgbLRDoId59913U19fj448/RmJiIoKCgrB06VJjl0XMxOP6zj36V05OTodfTyAQtDvGsuxjjxPLQFc2PUBWVhZ27NiBzMxMODg4AAC+/vprjBw5Ert27cKf/vQnI1dITN3y5csxb968p/6Mj49Ph17Lw8MDFy9e1DlWW1sLlUpFV9oWjMKmB5g0aRJUKpXOMW9vb9TV1RmnIGJ2hEIhhEKhQV5r3Lhx2LBhA8rLy7ll6WlpabC1tUVERIRBfgcxP3QbjRAe/PTTT4iNjYWXlxcEAgEOHTqk8+csy2Lt2rXw8vJC7969MWnSJFy9etU4xXZSSUkJ8vPzUVJSAoZhkJ+fj/z8fDQ1NQEAoqOjMXz4cCxcuBB5eXlIT0/HqlWr8Prrr3NX3sTyUNgQwoPm5maMGjUKO3fufOyfb9myBR999BF27tyJX3/9FR4eHpg6dSoaGxu7udLOe++99xAWFob3338fTU1NCAsLQ1hYGPdMp1evXkhJSYGdnR3Gjx+PV155BWKxGNu2bTNy5cSYqDcaITwTCAT48ccfIRaLAfx2VePl5YWEhASsXr0awG/dENzd3bF582bEx8cbsVpC+EFXNoR0szt37qCiokKnlZCtrS2ee+45nD9/3oiVEcIfChtCupl2r8mjK7Pc3d1pHwrpsShsCDGSR/ec0D4U0pNR2BDSzbRjrR+9iqmsrKR9KKTHorAhpJv5+vrCw8MDp06d4o4plUqcOXMGUVFRRqyMEP7Qpk5CeNDU1ISbN29yf3/nzh3k5+fDxcUF3t7eSEhIwMaNGxEQEICAgABs3LgRffr0wfz5841YNSH8oaXPhPAgKyvrsd2NFy9ejH379oFlWaxbtw67d+9GbW0txowZg3/9618ICQkxQrWE8I/ChhBCCO/omQ0hhBDeUdgQQgjhHYUNIYQQ3lHYEEII4R2FDSGEEN5R2BBCCOEdhQ0hhBDeUdgQQgjhHYUNIYQQ3lHYEEII4R2FDSGEEN5R2BBCCOHd/wNKpnRv+V6gsQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "x_min: -2.5000000005092593\n",
-      "y_min: -0.9999999987777783\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Set the learning rate and initial value\n",
-    "learning_rate = 0.1\n",
-    "max_iter = 100\n",
-    "x_init = -5\n",
-    "y_init = 5\n",
-    "\n",
-    "# Run two-dimensional gradient descent function\n",
-    "x_min, y_min, x_history, y_history = gradient_descent(learning_rate, max_iter, x_init, y_init)\n",
-    "\n",
-    "# Draw the target function and the process of gradient descent\n",
-    "x = np.linspace(-10, 10, 100)\n",
-    "y = np.linspace(-10, 10, 100)\n",
-    "X, Y = np.meshgrid(x, y)\n",
-    "Z = f(X, Y)\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "ax = fig.add_subplot(111, projection='3d')\n",
-    "ax.plot_surface(X, Y, Z, cmap='viridis', alpha=0.8)\n",
-    "ax.plot(x_history, y_history, f(np.array(x_history), np.array(y_history)), 'ro-', label='Gradient Descent')\n",
-    "ax.set_xlabel('x')\n",
-    "ax.set_ylabel('y')\n",
-    "ax.set_zlabel('f(x, y)')\n",
-    "ax.legend()\n",
-    "ax.set_title('Two-Dimensional Gradient Descent')\n",
-    "plt.show()\n",
-    "\n",
-    "print('x_min:', x_min)\n",
-    "print('y_min:', y_min)\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/_sources/assignments/ml-fundamentals/parameter-play.ipynb b/_sources/assignments/ml-fundamentals/parameter-play.ipynb
index 2093040996..b04c71b96d 100644
--- a/_sources/assignments/ml-fundamentals/parameter-play.ipynb
+++ b/_sources/assignments/ml-fundamentals/parameter-play.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "c5f3a3a3",
+   "id": "77832121",
    "metadata": {},
    "source": [
     "# Parameter play\n",
diff --git a/_sources/assignments/ml-fundamentals/regression-with-scikit-learn.ipynb b/_sources/assignments/ml-fundamentals/regression-with-scikit-learn.ipynb
index f252d6f4e6..4113d54808 100644
--- a/_sources/assignments/ml-fundamentals/regression-with-scikit-learn.ipynb
+++ b/_sources/assignments/ml-fundamentals/regression-with-scikit-learn.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "f76233d0",
+   "id": "304850dd",
    "metadata": {},
    "source": [
     "# Regression with Scikit-learn\n",
diff --git a/_sources/assignments/ml-fundamentals/retrying-some-regression.ipynb b/_sources/assignments/ml-fundamentals/retrying-some-regression.ipynb
index 9133ed89df..bbff63991c 100644
--- a/_sources/assignments/ml-fundamentals/retrying-some-regression.ipynb
+++ b/_sources/assignments/ml-fundamentals/retrying-some-regression.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "8a49419f",
+   "id": "7798e2d4",
    "metadata": {},
    "source": [
     "# Retrying some regression\n",
diff --git a/_sources/data-science/data-science-in-the-wild.ipynb b/_sources/data-science/data-science-in-the-wild.ipynb
index 0ae49d7650..6a1f23755f 100644
--- a/_sources/data-science/data-science-in-the-wild.ipynb
+++ b/_sources/data-science/data-science-in-the-wild.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "c6629ea5",
+   "id": "4711b4dd",
    "metadata": {},
    "source": [
     "# Data Science in the real world\n",
diff --git a/_sources/data-science/data-science-lifecycle/analyzing.ipynb b/_sources/data-science/data-science-lifecycle/analyzing.ipynb
index bf93021992..c45d86aa10 100644
--- a/_sources/data-science/data-science-lifecycle/analyzing.ipynb
+++ b/_sources/data-science/data-science-lifecycle/analyzing.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "0282a4b2",
+   "id": "9f8e437d",
    "metadata": {},
    "source": [
     "# Analyzing\n",
diff --git a/_sources/data-science/data-science-lifecycle/communication.ipynb b/_sources/data-science/data-science-lifecycle/communication.ipynb
index 5096630609..1ea8616853 100644
--- a/_sources/data-science/data-science-lifecycle/communication.ipynb
+++ b/_sources/data-science/data-science-lifecycle/communication.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "9634469f",
+   "id": "74fb713d",
    "metadata": {},
    "source": [
     "# Communication\n",
diff --git a/_sources/data-science/data-science-lifecycle/data-science-lifecycle.ipynb b/_sources/data-science/data-science-lifecycle/data-science-lifecycle.ipynb
index eaad85eefb..545b88d296 100644
--- a/_sources/data-science/data-science-lifecycle/data-science-lifecycle.ipynb
+++ b/_sources/data-science/data-science-lifecycle/data-science-lifecycle.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "bf82b6ba",
+   "id": "97a4e306",
    "metadata": {},
    "source": [
     "# Data Science lifecycle\n",
diff --git a/_sources/data-science/data-science-lifecycle/introduction.ipynb b/_sources/data-science/data-science-lifecycle/introduction.ipynb
index 527cd00697..a47666df39 100644
--- a/_sources/data-science/data-science-lifecycle/introduction.ipynb
+++ b/_sources/data-science/data-science-lifecycle/introduction.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "bda42245",
+   "id": "bb0c4c27",
    "metadata": {},
    "source": [
     "# Introduction to the Data Science lifecycle\n",
diff --git a/_sources/data-science/introduction/data-science-ethics.ipynb b/_sources/data-science/introduction/data-science-ethics.ipynb
index f727f84317..1afff4e80b 100644
--- a/_sources/data-science/introduction/data-science-ethics.ipynb
+++ b/_sources/data-science/introduction/data-science-ethics.ipynb
@@ -2,8 +2,34 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "93794650",
-   "metadata": {},
+   "id": "c85b489d-2fc5-485c-80c9-85411cddf3e3",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "source": [
+    "---\n",
+    "jupytext:\n",
+    "  cell_metadata_filter: -all\n",
+    "  formats: md:myst\n",
+    "  text_representation:\n",
+    "    extension: .md\n",
+    "    format_name: myst\n",
+    "    format_version: 0.13\n",
+    "    jupytext_version: 1.11.5\n",
+    "kernelspec:\n",
+    "  display_name: Python 3\n",
+    "  language: python\n",
+    "  name: python3\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ffb7bc3",
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "# Data Science ethics\n",
     "\n",
@@ -13,15 +39,38 @@
     "\n",
     "Trends also indicate that we will create and consume over [180 zettabytes](https://www.statista.com/statistics/871513/worldwide-data-created/) of data by 2025. As **Data Scientists**, this gives us unprecedented levels of access to personal data. This means we can build behavioral profiles of users and influence decision-making in ways that create an [illusion of free choice](https://www.verywellmind.com/what-is-the-illusion-of-choice-5224973) while potentially nudging users towards outcomes we prefer. It also raises broader questions on data privacy and user protections.\n",
     "\n",
-    "Data ethics are now _necessary guardrails_ for data science and engineering, helping us minimize potential harms and unintended consequences from our data-driven actions. The [Gartner Hype Cycle for AI](https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020/) identifies relevant trends in digital ethics, responsible AI, and AI governance as key drivers for larger megatrends around the _democratization_ and _industrialization_ of AI.\n",
-    "\n",
-    "```{figure} ../../../images/hype-cycle-for-ai.png\n",
+    "Data ethics are now _necessary guardrails_ for data science and engineering, helping us minimize potential harms and unintended consequences from our data-driven actions. The [Gartner Hype Cycle for AI](https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020/) identifies relevant trends in digital ethics, responsible AI, and AI governance as key drivers for larger megatrends around the _democratization_ and _industrialization_ of AI."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "597df7c8",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "./../../images/hype-cycle-for-ai.png"
+     ],
+     "id": ""
+    },
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "source": [
+    ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/hype-cycle-for-ai.png\n",
     "---\n",
     "name: 'Gartner Hype Cycle for AI - 2020'\n",
     "---\n",
-    "Gartner Hype Cycle for AI - 2020{cite}`MishraKamalGartner2021`\n",
-    "```\n",
-    "\n",
+    "Gartner Hype Cycle for AI - 2020\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98009b0b",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
     "In this section, we'll explore the fascinating area of data ethics - from core concepts and challenges, to case studies and applied AI concepts like governance - that help establish an ethics culture in teams and organizations that work with data and AI.\n",
     "\n",
     "## Basic definitions\n",
@@ -46,16 +95,29 @@
     "\n",
     "Every data ethics strategy begins by defining _ethical principles_ - the \"shared values\" that describe acceptable behaviors, and guide compliant actions, in our data & AI projects. You can define these at an individual or team level. However, most large organizations outline these in an _ethical AI_ mission statement or framework that is defined at corporate levels and enforced consistently across all teams.\n",
     "\n",
-    "**Example:** Microsoft's [Responsible AI](https://www.microsoft.com/en-us/ai/responsible-ai) mission statement reads: _\"We are committed to the advancement of AI-driven by ethical principles that put people first\"_ - identifying 6 ethical principles in the framework below:\n",
-    "\n",
-    "```{figure} https://docs.microsoft.com/en-gb/azure/cognitive-services/personalizer/media/ethics-and-responsible-use/ai-values-future-computed.png\n",
+    "**Example:** Microsoft's [Responsible AI](https://www.microsoft.com/en-us/ai/responsible-ai) mission statement reads: _\"We are committed to the advancement of AI-driven by ethical principles that put people first\"_ - identifying 6 ethical principles in the framework below:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07848ddb",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    ":::{figure} https://docs.microsoft.com/en-gb/azure/cognitive-services/personalizer/media/ethics-and-responsible-use/ai-values-future-computed.png\n",
     "---\n",
     "name: 'Responsible AI at Microsoft'\n",
     "---\n",
-    "Responsible AI at Microsoft{cite}`JcodellaEthicsNodate`\n",
-    "\n",
-    "```\n",
-    "\n",
+    "Responsible AI at Microsoft\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "311efc7b",
+   "metadata": {},
+   "source": [
     "Let's briefly explore these principles. _Transparency_ and _accountability_ are foundational values that other principles built upon - so let's begin there:\n",
     "\n",
     "* **Accountability** makes practitioners _responsible_ for their data & AI operations, and compliance with these ethical principles.\n",
@@ -63,16 +125,50 @@
     "* **Fairness** - focuses on ensuring AI treats _all people_ fairly, addressing any systemic or implicit socio-technical biases in data and systems.\n",
     "* **Reliability & Safety**  - ensures that AI behaves _consistently_ with defined values, minimizing potential harms or unintended consequences.\n",
     "* **Privacy & Security** - is about understanding data lineage, and providing _data privacy and related protections_ to users.\n",
-    "* **Inclusiveness** - is about designing AI solutions with intention, and adapting them to meet a _broad range of human needs_ & capabilities.\n",
-    "\n",
-    "```{seealso}\n",
+    "* **Inclusiveness** - is about designing AI solutions with intention, and adapting them to meet a _broad range of human needs_ & capabilities."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d71c0f63",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "seealso"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{seealso}\n",
     "Responsible AI principles from Microsoft. (n.d.). Microsoft. Retrieved 1 October 2022, from https://www.microsoft.com/en-us/ai/responsible-ai\n",
-    "```\n",
-    "\n",
-    "```{seealso}\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34d78c4c",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "seealso"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{seealso}\n",
     "🚨 Think about what your data ethics mission statement could be. Explore ethical AI frameworks from other organizations - here are examples from [IBM](https://www.ibm.com/cloud/learn/ai-ethics), [Google](https://ai.google/principles) ,and [Facebook](https://ai.facebook.com/blog/facebooks-five-pillars-of-responsible-ai/). What shared values do they have in common? How do these principles relate to the AI product or industry they operate in?\n",
-    "```\n",
-    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae2d55c9",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
     "### Ethics challenges\n",
     "\n",
     "Once we have ethical principles defined, the next step is to evaluate our data and AI actions to see if they align with those shared values. Think about your actions in two categories: _data collection_ and _algorithm design_.\n",
@@ -204,19 +300,53 @@
     "| **Algorithmic fairness**  | 2018 - The MIT [Gender Shades Study](http://gendershades.org/overview.html) evaluated the accuracy of gender classification AI products, exposing gaps in accuracy for women and persons of color. A [2019 Apple Card](https://www.wired.com/story/the-apple-card-didnt-see-genderand-thats-the-problem/) seemed to offer less credit to women than men. Both illustrated issues in algorithmic bias leading to socio-economic harms.|\n",
     "| **Data misrepresentation** | 2020 - The [Georgia Department of Public Health released COVID-19 charts](https://www.vox.com/covid-19-coronavirus-us-response-trump/2020/5/18/21262265/georgia-covid-19-cases-declining-reopening) that appeared to mislead citizens about trends in confirmed cases with non-chronological ordering on the x-axis. This illustrates misrepresentation through visualization tricks. |\n",
     "| **Illusion of free choice** | 2020 - Learning app [ABCmouse paid $10M to settle an FTC complaint](https://www.washingtonpost.com/business/2020/09/04/abcmouse-10-million-ftc-settlement/) where parents were trapped into paying for subscriptions they couldn't cancel. This illustrates dark patterns in choice architectures, where users were nudged towards potentially harmful choices. |\n",
-    "| **Data privacy & user rights** | 2021 - Facebook [Data Breach](https://www.npr.org/2021/04/09/986005820/after-data-breach-exposes-530-million-facebook-says-it-will-not-notify-users) exposed data from 530M users, resulting in a $5B settlement to the FTC. It however refused to notify users of the breach violating user rights around data transparency and access. |\n",
-    "\n",
-    "```{seealso}\n",
+    "| **Data privacy & user rights** | 2021 - Facebook [Data Breach](https://www.npr.org/2021/04/09/986005820/after-data-breach-exposes-530-million-facebook-says-it-will-not-notify-users) exposed data from 530M users, resulting in a $5B settlement to the FTC. It however refused to notify users of the breach violating user rights around data transparency and access. |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6cd699fd",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "seealso"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{seealso}\n",
     "Want to explore more case studies? Check out these resources:\n",
     "* [Ethics Unwrapped](https://ethicsunwrapped.utexas.edu/case-studies) - ethics dilemmas across diverse industries.\n",
     "* [Data Science Ethics course](https://www.coursera.org/learn/data-science-ethics#syllabus) - landmark case studies explored.\n",
     "* [Where things have gone wrong](https://deon.drivendata.org/examples/) - deon checklist with examples\n",
-    "```\n",
-    "\n",
-    "```{note}\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53f0abbc",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "note"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{note}\n",
     "🚨 Think about the case studies you've seen - have you experienced, or been affected by, a similar ethical challenge in your life? Can you think of at least one other case study that illustrates one of the ethical challenges we've discussed in this section?\n",
-    "```\n",
-    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "312d4d5d",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
     "## Applied ethics\n",
     "\n",
     "We've talked about ethics concepts, challenges, and case studies in real-world contexts. But how do we get started _applying_ ethical principles and practices in our projects? And how do we _operationalize_ these practices for better governance? Let's explore some real-world solutions:\n",
@@ -229,12 +359,31 @@
     "\n",
     "* [Oxford Munich](http://www.code-of-ethics.org/code-of-conduct/) Code of Ethics\n",
     "* [Data Science Association](http://datascienceassn.org/code-of-conduct.html) Code of Conduct (created 2013)\n",
-    "* [ACM Code of Ethics and Professional Conduct](https://www.acm.org/code-of-ethics) (since 1993)\n",
-    "\n",
-    "```{note}\n",
+    "* [ACM Code of Ethics and Professional Conduct](https://www.acm.org/code-of-ethics) (since 1993)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "012bc6ba",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "note"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{note}\n",
     "🚨 Do you belong to a professional engineering or data science organization? Explore their site to see if they define a professional code of ethics. What does this say about their ethical principles? How are they \"incentivizing\" members to follow the code?\n",
-    "```\n",
-    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10c57020",
+   "metadata": {},
+   "source": [
     "### Ethics checklists\n",
     "\n",
     "While professional codes define required _ethical behavior_ from practitioners, they [have known limitations](https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md) in enforcement, particularly in large-scale projects. Instead, many data Science experts [advocate for checklists](https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md), that can **connect principles to practices** in more deterministic and actionable ways.\n",
@@ -273,7 +422,7 @@
     "\n",
     "## Your turn! 🚀\n",
     "\n",
-    "[Write a data ethics case study](../../assignments/data-science/write-a-data-ethics-case-study.md)\n",
+    "[Write a data ethics case study](https://static-1300131294.cos.qp-shanghai.myqcloud.com/assignments/data-science/write-a-data-ethics-case-study.md)\n",
     "\n",
     "## Self study\n",
     "\n",
@@ -289,27 +438,30 @@
     "\n",
     "Thanks to Microsoft for creating the open-source course [Data Science for Beginners](https://github.com/microsoft/Data-Science-For-Beginners). It inspires the majority of the content in this chapter.\n",
     "\n",
-    "---\n",
-    "\n",
-    "```{bibliography}\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04941c48",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "bibliography"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{bibliography}\n",
     ":filter: docname in docnames\n",
-    "```"
+    ":::"
    ]
   }
  ],
  "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "formats": "md:myst",
-   "text_representation": {
-    "extension": ".md",
-    "format_name": "myst",
-    "format_version": 0.13,
-    "jupytext_version": "1.11.5"
-   }
-  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -323,12 +475,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  },
-  "source_map": [
-   14
-  ]
+   "version": "3.11.4"
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/_sources/data-science/introduction/data-science-ethics.md b/_sources/data-science/introduction/data-science-ethics.md
deleted file mode 100644
index 1cfd17c238..0000000000
--- a/_sources/data-science/introduction/data-science-ethics.md
+++ /dev/null
@@ -1,304 +0,0 @@
----
-jupytext:
-  cell_metadata_filter: -all
-  formats: md:myst
-  text_representation:
-    extension: .md
-    format_name: myst
-    format_version: 0.13
-    jupytext_version: 1.11.5
-kernelspec:
-  display_name: Python 3
-  language: python
-  name: python3
----
-
-# Data Science ethics
-
-We are all data citizens living in a datafied world.
-
-Market trends tell us that by 2022, 1-in-3 large organizations will buy and sell their data through online [Marketplaces and Exchanges](https://www.gartner.com/smarterwithgartner/gartner-top-10-trends-in-data-and-analytics-for-2020/). As **app developers**, we'll find it easier and cheaper to integrate data-driven insights and algorithm-driven automation into daily user experiences. But as AI becomes pervasive, we'll also need to understand the potential harms caused by the [weaponization](https://www.youtube.com/watch?v=TQHs8SA1qpk) of such algorithms at scale.
-
-Trends also indicate that we will create and consume over [180 zettabytes](https://www.statista.com/statistics/871513/worldwide-data-created/) of data by 2025. As **Data Scientists**, this gives us unprecedented levels of access to personal data. This means we can build behavioral profiles of users and influence decision-making in ways that create an [illusion of free choice](https://www.verywellmind.com/what-is-the-illusion-of-choice-5224973) while potentially nudging users towards outcomes we prefer. It also raises broader questions on data privacy and user protections.
-
-Data ethics are now _necessary guardrails_ for data science and engineering, helping us minimize potential harms and unintended consequences from our data-driven actions. The [Gartner Hype Cycle for AI](https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020/) identifies relevant trends in digital ethics, responsible AI, and AI governance as key drivers for larger megatrends around the _democratization_ and _industrialization_ of AI.
-
-```{figure} ../../../images/hype-cycle-for-ai.png
----
-name: 'Gartner Hype Cycle for AI - 2020'
----
-Gartner Hype Cycle for AI - 2020{cite}`MishraKamalGartner2021`
-```
-
-In this section, we'll explore the fascinating area of data ethics - from core concepts and challenges, to case studies and applied AI concepts like governance - that help establish an ethics culture in teams and organizations that work with data and AI.
-
-## Basic definitions
-
-Let's start by understanding the basic terminology.
-
-The word "ethics" comes from the [Greek word "ethikos"](https://en.wikipedia.org/wiki/Ethics) (and its root "ethos") meaning _character or moral nature_.
-
-**Ethics** is about the shared values and moral principles that govern our behavior in society. Ethics is based not on laws but on widely accepted norms of what is "right vs. wrong". However, ethical considerations can influence corporate governance initiatives and government regulations that create more incentives for compliance.
-
-**Data ethics** is a [new branch of ethics](https://royalsocietypublishing.org/doi/full/10.1098/rsta.2016.0360#sec-1) that "studies and evaluates moral problems related to _data, algorithms and corresponding practices_". Here, **"data"** focuses on actions related to generation, recording, curation, processing dissemination, sharing, and usage, **"algorithms"** focuses on AI, agents, Machine Learning, and robots, and **"practices"** focuses on topics like responsible innovation, programming, hacking and ethics codes.
-
-**Applied ethics** is the [practical application of moral considerations](https://en.wikipedia.org/wiki/Applied_ethics). It's the process of actively investigating ethical issues in the context of _real-world actions, products, and processes_, and taking corrective measures to make that these remain aligned with our defined ethical values.
-
-**Ethics culture** is about [_operationalizing_ applied ethics](https://hbr.org/2019/05/how-to-design-an-ethical-organization) to make sure that our ethical principles and practices are adopted in a consistent and scalable manner across the entire organization. Successful ethics cultures define organization-wide ethical principles, provide meaningful incentives for compliance, and reinforce ethics norms by encouraging and amplifying desired behaviors at every level of the organization.
-
-## Ethics concepts
-
-In this section, we'll discuss concepts like **shared values** (principles) and **ethical challenges** (problems) for data ethics - and explore **case studies** that help you understand these concepts in real-world contexts.
-
-### Ethics principles
-
-Every data ethics strategy begins by defining _ethical principles_ - the "shared values" that describe acceptable behaviors, and guide compliant actions, in our data & AI projects. You can define these at an individual or team level. However, most large organizations outline these in an _ethical AI_ mission statement or framework that is defined at corporate levels and enforced consistently across all teams.
-
-**Example:** Microsoft's [Responsible AI](https://www.microsoft.com/en-us/ai/responsible-ai) mission statement reads: _"We are committed to the advancement of AI-driven by ethical principles that put people first"_ - identifying 6 ethical principles in the framework below:
-
-```{figure} https://docs.microsoft.com/en-gb/azure/cognitive-services/personalizer/media/ethics-and-responsible-use/ai-values-future-computed.png
----
-name: 'Responsible AI at Microsoft'
----
-Responsible AI at Microsoft{cite}`JcodellaEthicsNodate`
-
-```
-
-Let's briefly explore these principles. _Transparency_ and _accountability_ are foundational values that other principles built upon - so let's begin there:
-
-* **Accountability** makes practitioners _responsible_ for their data & AI operations, and compliance with these ethical principles.
-* **Transparency** ensures that data and AI actions are _understandable_ (interpretable) to users, explaining the what and why behind decisions.
-* **Fairness** - focuses on ensuring AI treats _all people_ fairly, addressing any systemic or implicit socio-technical biases in data and systems.
-* **Reliability & Safety**  - ensures that AI behaves _consistently_ with defined values, minimizing potential harms or unintended consequences.
-* **Privacy & Security** - is about understanding data lineage, and providing _data privacy and related protections_ to users.
-* **Inclusiveness** - is about designing AI solutions with intention, and adapting them to meet a _broad range of human needs_ & capabilities.
-
-```{seealso}
-Responsible AI principles from Microsoft. (n.d.). Microsoft. Retrieved 1 October 2022, from https://www.microsoft.com/en-us/ai/responsible-ai
-```
-
-```{seealso}
-🚨 Think about what your data ethics mission statement could be. Explore ethical AI frameworks from other organizations - here are examples from [IBM](https://www.ibm.com/cloud/learn/ai-ethics), [Google](https://ai.google/principles) ,and [Facebook](https://ai.facebook.com/blog/facebooks-five-pillars-of-responsible-ai/). What shared values do they have in common? How do these principles relate to the AI product or industry they operate in?
-```
-
-### Ethics challenges
-
-Once we have ethical principles defined, the next step is to evaluate our data and AI actions to see if they align with those shared values. Think about your actions in two categories: _data collection_ and _algorithm design_.
-
-With data collection, actions will likely involve **personal data** or personally identifiable information (PII) for identifiable living individuals. This includes [diverse items of non-personal data](https://ec.europa.eu/info/law/law-topic/data-protection/reform/what-personal-data_en) that _collectively_ identify an individual. Ethical challenges can relate to _data privacy_, _data ownership_, and related topics like _informed consent_ and _intellectual property rights_ for users.
-
-With algorithm design, actions will involve collecting & curating **datasets**, then using them to train & deploy **data models** that predict outcomes or automate decisions in real-world contexts. Ethical challenges can arise from _dataset bias_, _data quality_ issues, _unfairness_, and _misrepresentation_ in algorithms - including some issues that are systemic in nature.
-
-In both cases, ethics challenges highlight areas where our actions may encounter conflict with our shared values. To detect, mitigate, minimize, or eliminate, these concerns - we need to ask moral "yes/no" questions related to our actions, then take corrective actions as needed. Let's take a look at some ethical challenges and the moral questions they raise:
-
-**1\. Data ownership**
-
-Data collection often involves personal data that can identify the data subjects. [Data ownership](https://permission.io/blog/data-ownership) is about _control_ and [_user rights_](https://permission.io/blog/data-ownership) related to the creation, processing, and dissemination of data.
-
-The moral questions we need to ask are:
-
-* Who owns the data? (user or organization)
-* What rights do data subjects have? (ex: access, erasure, portability)
-* What rights do organizations have? (ex: rectify malicious user reviews)
-
-**2\. Informed consent**
-
-[Informed consent](https://legaldictionary.net/informed-consent/) defines the act of users agreeing to an action (like data collection) with a _full understanding_ of relevant facts including the purpose, potential risks, and alternatives.
-
-Questions to explore here are:
-
-* Did the user (data subject) give permission for data capture and usage?
-* Did the user understand the purpose for which that data was captured?
-* Did the user understand the potential risks of their participation?
-
-**3\. Intellectual property**
-
-[Intellectual property](https://en.wikipedia.org/wiki/Intellectual_property) refers to intangible creations resulting from the human initiative, that may _have economic value_ to individuals or businesses.
-
-Questions to explore here are:
-
-* Did the collected data have economic value to a user or business?
-* Does the **user** have intellectual property here?
-* Does the **organization** have intellectual property here?
-* If these rights exist, how are we protecting them?
-
-**4\. Data privacy**
-
-[Data privacy](https://www.northeastern.edu/graduate/blog/what-is-data-privacy/) or information privacy refers to the preservation of user privacy and protection of user identity with respect to personally identifiable information.
-
-Questions to explore here are:
-
-* Is users' (personal) data secured against hacks and leaks?
-* Is users' data accessible only to authorized users and contexts?
-* Is users' anonymity preserved when data is shared or disseminated?
-* Can a user be de-identified from anonymized datasets?
-
-**5\. Right to be forgotten**
-
-The [Right to be forgotten](https://en.wikipedia.org/wiki/Right_to_be_forgotten) or [Right to erasure](https://www.gdpreu.org/right-to-be-forgotten/) provides additional personal data protection to users. Specifically, it gives users the right to request deletion or removal of personal data from Internet searches and other locations, _under specific circumstances_ - allowing them a fresh start online without past actions being held against them.
-
-Questions to explore here are:
-
-* Does the system allow data subjects to request erasure?
-* Should the withdrawal of user consent trigger automated erasure?
-* Was data collected without consent or by unlawful means?
-* Are we compliant with government regulations for data privacy?
-
-**6\. Dataset bias**
-
-Dataset or [Collection bias](http://researcharticles.com/index.php/bias-in-data-collection-in-research/) is about selecting a _non-representative_ subset of data for algorithm development, creating potential unfairness in result outcomes for diverse groups. Types of bias include selection or sampling bias, volunteer bias, and instrument bias.
-
-Questions to explore here are:
-
-* Did we recruit a representative set of data subjects?
-* Did we test our collected or curated dataset for various biases?
-* Can we mitigate or remove any discovered biases?
-
-**7\. Data quality**
-
-[Data Quality](https://lakefs.io/data-quality-testing/) looks at the validity of the curated dataset used to develop our algorithms, checking to see if features and records meet requirements for the level of accuracy and consistency needed for our AI purpose.
-
-Questions to explore here are:
-
-* Did we capture valid _features_ for our use case?
-* Was data captured _consistently_ across diverse data sources?
-* Is the dataset _complete_ for diverse conditions or scenarios?
-* Is information captured _accurately_ in reflecting reality?
-
-**8\. Algorithm fairness**
-
-[Algorithm fairness](https://towardsdatascience.com/what-is-algorithm-fairness-3182e161cf9f) checks to see if the algorithm design systematically discriminates against specific subgroups of data subjects leading to [potential harms](https://docs.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml) in _allocation_ (where resources are denied or withheld from that group) and _quality of service_ (where AI is not as accurate for some subgroups as it is for others).
-
-Questions to explore here are:
-
-* Did we evaluate model accuracy for diverse subgroups and conditions?
-* Did we scrutinize the system for potential harm (e.g., stereotyping)?
-* Can we revise data or retrain models to mitigate identified harms?
-
-Explore resources like [AI Fairness checklists](https://query.prod.cms.rt.microsoft.com/cms/api/am/binary/RE4t6dA) to learn more.
-
-**9\. Misrepresentation**
-
-[Data misrepresentation](https://www.sciencedirect.com/topics/computer-science/misrepresentation) is about asking whether we are communicating insights from honestly reported data in a deceptive manner to support a desired narrative.
-
-Questions to explore here are:
-
-* Are we reporting incomplete or inaccurate data?
-* Are we visualizing data in a manner that drives misleading conclusions?
-* Are we using selective statistical techniques to manipulate outcomes?
-* Are there alternative explanations that may offer a different conclusion?
-
-**10\. Free choice**
-
-The [Illusion of Free Choice](https://www.datasciencecentral.com/profiles/blogs/the-illusion-of-choice) occurs when system "choice architectures" use decision-making algorithms to nudge people towards taking a preferred outcome while seeming to give them options and control. These [dark patterns](https://www.darkpatterns.org/) can cause social and economic harm to users. Because user decisions impact behavior profiles, these actions potentially drive future choices that can amplify or extend the impact of these harms.
-
-Questions to explore here are:
-
-* Did the user understand the implications of making that choice?
-* Was the user aware of (alternative) choices and the pros & cons of each?
-* Can the user reverse an automated or influenced choice later?
-
-### Case studies
-
-To put these ethical challenges in real-world contexts, it helps to look at case studies that highlight the potential harms and consequences to individuals and society, when such ethics violations are overlooked.
-
-Here are a few examples:
-
-| Ethics Challenge | Case Study  |
-|--- |--- |
-| **Informed consent** | 1972 - [Tuskegee Syphilis Study](https://en.wikipedia.org/wiki/Tuskegee_Syphilis_Study) - African American men who participated in the study were promised free medical care _but deceived_ by researchers who failed to inform subjects of their diagnosis or about availability of treatment. Many subjects died & partners or children were affected; the study lasted 40 years. |
-| **Data privacy** |  2007 - The [Netflix data prize](https://www.wired.com/2007/12/why-anonymous-data-sometimes-isnt/) provided researchers with _10M anonymized movie rankings from 50K customers_ to help improve recommendation algorithms. However, researchers were able to correlate anonymized data with personally-identifiable data in _external datasets_ (e.g., IMDb comments) - effectively "de-anonymizing" some Netflix subscribers.|
-| **Collection bias**  | 2013 - The City of Boston [developed Street Bump](https://www.boston.gov/transportation/street-bump), an app that let citizens report potholes, giving the city better roadway data to find and fix issues. However, [people in lower income groups had less access to cars and phones](https://hbr.org/2013/04/the-hidden-biases-in-big-data), making their roadway issues invisible in this app. Developers worked with academics to _equitable access and digital divides_ issues for fairness. |
-| **Algorithmic fairness**  | 2018 - The MIT [Gender Shades Study](http://gendershades.org/overview.html) evaluated the accuracy of gender classification AI products, exposing gaps in accuracy for women and persons of color. A [2019 Apple Card](https://www.wired.com/story/the-apple-card-didnt-see-genderand-thats-the-problem/) seemed to offer less credit to women than men. Both illustrated issues in algorithmic bias leading to socio-economic harms.|
-| **Data misrepresentation** | 2020 - The [Georgia Department of Public Health released COVID-19 charts](https://www.vox.com/covid-19-coronavirus-us-response-trump/2020/5/18/21262265/georgia-covid-19-cases-declining-reopening) that appeared to mislead citizens about trends in confirmed cases with non-chronological ordering on the x-axis. This illustrates misrepresentation through visualization tricks. |
-| **Illusion of free choice** | 2020 - Learning app [ABCmouse paid $10M to settle an FTC complaint](https://www.washingtonpost.com/business/2020/09/04/abcmouse-10-million-ftc-settlement/) where parents were trapped into paying for subscriptions they couldn't cancel. This illustrates dark patterns in choice architectures, where users were nudged towards potentially harmful choices. |
-| **Data privacy & user rights** | 2021 - Facebook [Data Breach](https://www.npr.org/2021/04/09/986005820/after-data-breach-exposes-530-million-facebook-says-it-will-not-notify-users) exposed data from 530M users, resulting in a $5B settlement to the FTC. It however refused to notify users of the breach violating user rights around data transparency and access. |
-
-```{seealso}
-Want to explore more case studies? Check out these resources:
-* [Ethics Unwrapped](https://ethicsunwrapped.utexas.edu/case-studies) - ethics dilemmas across diverse industries.
-* [Data Science Ethics course](https://www.coursera.org/learn/data-science-ethics#syllabus) - landmark case studies explored.
-* [Where things have gone wrong](https://deon.drivendata.org/examples/) - deon checklist with examples
-```
-
-```{note}
-🚨 Think about the case studies you've seen - have you experienced, or been affected by, a similar ethical challenge in your life? Can you think of at least one other case study that illustrates one of the ethical challenges we've discussed in this section?
-```
-
-## Applied ethics
-
-We've talked about ethics concepts, challenges, and case studies in real-world contexts. But how do we get started _applying_ ethical principles and practices in our projects? And how do we _operationalize_ these practices for better governance? Let's explore some real-world solutions:
-
-### Professional codes
-
-Professional codes offer one option for organizations to "incentivize" members to support their ethical principles and mission statement. Codes are _moral guidelines_ for professional behavior, helping employees or members make decisions that align with their organization's principles. They are only as good as voluntary compliance from members; however, many organizations offer additional rewards and penalties to motivate compliance from members.
-
-Examples include:
-
-* [Oxford Munich](http://www.code-of-ethics.org/code-of-conduct/) Code of Ethics
-* [Data Science Association](http://datascienceassn.org/code-of-conduct.html) Code of Conduct (created 2013)
-* [ACM Code of Ethics and Professional Conduct](https://www.acm.org/code-of-ethics) (since 1993)
-
-```{note}
-🚨 Do you belong to a professional engineering or data science organization? Explore their site to see if they define a professional code of ethics. What does this say about their ethical principles? How are they "incentivizing" members to follow the code?
-```
-
-### Ethics checklists
-
-While professional codes define required _ethical behavior_ from practitioners, they [have known limitations](https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md) in enforcement, particularly in large-scale projects. Instead, many data Science experts [advocate for checklists](https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md), that can **connect principles to practices** in more deterministic and actionable ways.
-
-Checklists convert questions into "yes/no" tasks that can be operationalized, allowing them to be tracked as part of standard product release workflows.
-
-Examples include:
-
-* [Deon](https://deon.drivendata.org/) - a general-purpose data ethics checklist created from [industry recommendations](https://deon.drivendata.org/#checklist-citations) with a command-line tool for easy integration.
-* [Privacy Audit Checklist](https://cyber.harvard.edu/ecommerce/privacyaudit.html) - provides general guidance for information handling practices from legal and social exposure perspectives.
-* [AI Fairness Checklist](https://www.microsoft.com/en-us/research/project/ai-fairness-checklist/) - created by AI practitioners to support the adoption and integration of fairness checks into AI development cycles.
-* [22 questions for ethics in data and AI](https://medium.com/the-organization/22-questions-for-ethics-in-data-and-ai-efb68fd19429) - a more open-ended framework, structured for initial exploration of ethical issues in design, implementation, and organizational, contexts.
-
-### Ethics regulations
-
-Ethics is about defining shared values and doing the right thing _voluntarily_. **Compliance** is about _following the law_ if and where defined. **Governance** broadly covers all the ways in which organizations operate to enforce ethical principles and comply with established laws.
-
-Today, governance takes two forms within organizations. First, it's about defining **ethical AI** principles and establishing practices to operationalize adoption across all AI-related projects in the organization. Second, it's about complying with all government-mandated **data protection regulations** for regions it operates in.
-
-Examples of data protection and privacy regulations:
-
-* `1974`, [US Privacy Act](https://www.justice.gov/opcl/privacy-act-1974) - regulates _federal govt._ collection, use, and disclosure of personal information.
-* `1996`, [US Health Insurance Portability & Accountability Act (HIPAA)](https://www.cdc.gov/phlp/publications/topic/hipaa.html) - protects personal health data.
-* `1998`, [US Children's Online Privacy Protection Act (COPPA)](https://www.ftc.gov/enforcement/rules/rulemaking-regulatory-reform-proceedings/childrens-online-privacy-protection-rule) - protects the data privacy of children under 13.
-* `2018`, [General Data Protection Regulation (GDPR)](https://gdpr-info.eu/) - provides user rights, data protection, and privacy.
-* `2018`, [California Consumer Privacy Act (CCPA)](https://www.oag.ca.gov/privacy/ccpa) gives consumers more _rights_ over their (personal) data.
-* `2021`, China's [Personal Information Protection Law](https://www.reuters.com/world/china/china-passes-new-personal-data-privacy-law-take-effect-nov-1-2021-08-20/) just passed, creating one of the strongest online data privacy regulations worldwide.
-
-> 🚨 The European Union defined GDPR (General Data Protection Regulation) remains one of the most influential data privacy regulations today. Did you know it also defines [8 user rights](https://www.freeprivacypolicy.com/blog/8-user-rights-gdpr) to protect citizens' digital privacy and personal data? Learn about what these are, and why they matter.
-
-### Ethics culture
-
-Note that there remains an intangible gap between _compliance_ (doing enough to meet "the letter of the law") and addressing [systemic issues](https://www.coursera.org/learn/data-science-ethics/home/week/4) (like ossification, information asymmetry, and distributional unfairness) that can speed up the weaponization of AI.
-
-The latter requires [collaborative approaches to defining ethics cultures](https://towardsdatascience.com/why-ai-ethics-requires-a-culture-driven-approach-26f451afa29f) that build emotional connections and consistent shared values _across organizations_ in the industry. This calls for more [formalized data ethics cultures](https://www.codeforamerica.org/news/formalizing-an-ethical-data-culture/) in organizations - allowing _anyone_ to [pull the Andon cord](https://en.wikipedia.org/wiki/Andon_(manufacturing)) (to raise ethics concerns early in the process) and making _ethical assessments_ (e.g., in hiring) a core criterion for team formation in AI projects.
-
-## Your turn! 🚀
-
-[Write a data ethics case study](../../assignments/data-science/write-a-data-ethics-case-study.md)
-
-## Self study
-
-Courses and books help with understanding core ethics concepts and challenges, while case studies and tools help with applied ethics practices in real-world contexts. Here are a few resources to start with.
-
-* [Machine Learning For Beginners](https://github.com/microsoft/ML-For-Beginners/blob/main/1-Introduction/3-fairness/README.md) - section on Fairness, from Microsoft.
-* [Principles of Responsible AI](https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/) - free learning path from Microsoft Learn.
-* [Ethics and Data Science](https://resources.oreilly.com/examples/0636920203964) - O'Reilly EBook (M. Loukides, H. Mason et. al)
-* [Data Science Ethics](https://www.coursera.org/learn/data-science-ethics#syllabus) - online course from the University of Michigan.
-* [Ethics Unwrapped](https://ethicsunwrapped.utexas.edu/case-studies) - case studies from the University of Texas.
-
-## Acknowledgments
-
-Thanks to Microsoft for creating the open-source course [Data Science for Beginners](https://github.com/microsoft/Data-Science-For-Beginners). It inspires the majority of the content in this chapter.
-
----
-
-```{bibliography}
-:filter: docname in docnames
-```
diff --git a/_sources/data-science/introduction/defining-data-science.ipynb b/_sources/data-science/introduction/defining-data-science.ipynb
index a3408145a6..cf39b8e61c 100644
--- a/_sources/data-science/introduction/defining-data-science.ipynb
+++ b/_sources/data-science/introduction/defining-data-science.ipynb
@@ -2,8 +2,34 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "b1952973",
-   "metadata": {},
+   "id": "88e642dd-853a-42da-b8c0-4b762c98515a",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "source": [
+    "---\n",
+    "jupytext:\n",
+    "  cell_metadata_filter: -all\n",
+    "  formats: md:myst\n",
+    "  text_representation:\n",
+    "    extension: .md\n",
+    "    format_name: myst\n",
+    "    format_version: 0.13\n",
+    "    jupytext_version: 1.11.5\n",
+    "kernelspec:\n",
+    "  display_name: Python 3\n",
+    "  language: python\n",
+    "  name: python3\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1bc4ccf3",
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "# Defining data science\n",
     "\n",
@@ -13,12 +39,31 @@
     "\n",
     "However, data became much more critical with the creation of computers. The primary role of computers is to perform computations, but they need data to operate on. Thus, we need to understand how computers store and process data.\n",
     "\n",
-    "With the emergence of the Internet, the role of computers as data-handling devices increased. If you think about it, we now use computers more and more for data processing and communication, rather than actual computations. When we write an e-mail to a friend or search for some information on the Internet - we are essentially creating, storing, transmitting, and manipulating data.\n",
-    "\n",
-    "```{note}\n",
+    "With the emergence of the Internet, the role of computers as data-handling devices increased. If you think about it, we now use computers more and more for data processing and communication, rather than actual computations. When we write an e-mail to a friend or search for some information on the Internet - we are essentially creating, storing, transmitting, and manipulating data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8361e92c",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "note"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{note}\n",
     "Can you remember the last time you have used computers to actually compute something?\n",
-    "```\n",
-    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0450bbff",
+   "metadata": {},
+   "source": [
     "## What is Data Science?\n",
     "\n",
     "In [Wikipedia](https://en.wikipedia.org/wiki/Data_science), **Data Science** is defined as *a scientific field that uses scientific methods to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains*.\n",
@@ -29,12 +74,31 @@
     "* Data Science uses **scientific methods**, such as probability and statistics. In fact, when the term *Data Science* was first introduced, some people argued that Data Science was just a new fancy name for statistics. Nowadays it has become evident that the field is much broader.\n",
     "* Obtained knowledge should be applied to produce some **actionable insights**, i.e. practical insights that you can apply to real business situations.\n",
     "* We should be able to operate on both **structured** and **unstructured** data. We will come back to discuss different types of data later in the course.\n",
-    "* **Application domain** is an important concept, and data scientists often need at least some degree of expertise in the problem domain, for example, finance, medicine, marketing, etc.\n",
-    "\n",
-    "```{note}\n",
+    "* **Application domain** is an important concept, and data scientists often need at least some degree of expertise in the problem domain, for example, finance, medicine, marketing, etc."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a087d3c4",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "note"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{note}\n",
     "Another important aspect of Data Science is that it studies how data can be gathered, stored and operated upon using computers. While statistics gives us mathematical foundations, Data Science applies mathematical concepts to actually draw insights from data.\n",
-    "```\n",
-    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae54ab43",
+   "metadata": {},
+   "source": [
     "One of the ways (attributed to [Jim Gray](https://en.wikipedia.org/wiki/Jim_Gray_(computer_scientist))) to look at Data Science is to consider it to be a separate paradigm of science:\n",
     "\n",
     "* **Empirical**, in which we rely mostly on observations and results of experiments\n",
@@ -48,7 +112,7 @@
     "\n",
     "**Databases**\n",
     "\n",
-    "A critical consideration is **how to store** the data, i.e. how to structure it in a way that allows faster processing. There are different types of databases that store structured and unstructured data, which [we will consider in our book](../working-with-data/working-with-data.md).\n",
+    "A critical consideration is **how to store** the data, i.e. how to structure it in a way that allows faster processing. There are different types of databases that store structured and unstructured data, which [we will consider in our book](https://static-1300131294.cos.qp-shanghai.myqcloud.com/working-with-data/working-with-data.md).\n",
     "\n",
     "**Big Data**\n",
     "\n",
@@ -56,7 +120,7 @@
     "\n",
     "**Machine Learning**\n",
     "\n",
-    "One way to understand data is to **build a model** that will be able to predict the desired outcome. Developing models from data is called **machine learning**. You may want to have a look at our [Machine Learning](../../ml-fundamentals/ml-overview.md)-related chapters to learn more about it.\n",
+    "One way to understand data is to **build a model** that will be able to predict the desired outcome. Developing models from data is called **machine learning**. You may want to have a look at our [Machine Learning](https://static-1300131294.cos.qp-shanghai.myqcloud.com/ml-fundamentals/ml-overview.md)-related chapters to learn more about it.\n",
     "\n",
     "**Artificial Intelligence**\n",
     "\n",
@@ -64,7 +128,7 @@
     "\n",
     "**Visualization**\n",
     "\n",
-    "Vast amounts of data are incomprehensible to a human being, but once we create useful visualizations using that data, we can make more sense of the data, and draw some conclusions. Thus, it is important to know many ways to visualize information - something that we will cover in the [Data visualization](../data-visualization/data-visualization.md) section. Related fields also include **Infographics** and **Human-Computer Interaction** in general.\n",
+    "Vast amounts of data are incomprehensible to a human being, but once we create useful visualizations using that data, we can make more sense of the data, and draw some conclusions. Thus, it is important to know many ways to visualize information - something that we will cover in the [Data visualization](https://static-1300131294.cos.qp-shanghai.myqcloud.com/data-visualization/data-visualization.md) section. Related fields also include **Infographics** and **Human-Computer Interaction** in general.\n",
     "\n",
     "## Types of data\n",
     "\n",
@@ -110,7 +174,7 @@
     "\n",
     "**Visualization / human insights.** Oftentimes, in order to understand the data, we need to visualize it. Having many different visualization techniques in our toolbox, we can find the right view to make an insight. Often, a data scientist needs to \"play with data\", visualizing it many times and looking for some relationships. Also, we may use statistical techniques to test hypotheses or prove a correlation between different pieces of data.\n",
     "\n",
-    "**Training a predictive model.** Because the ultimate goal of Data Science is to be able to make decisions based on data, we may want to use the techniques of [Machine Learning](../../ml-fundamentals/ml-overview.md) to build a predictive model. We can then use this to make predictions using new datasets with similar structures.\n",
+    "**Training a predictive model.** Because the ultimate goal of Data Science is to be able to make decisions based on data, we may want to use the techniques of [Machine Learning](https://static-1300131294.cos.qp-shanghai.myqcloud.com/ml-fundamentals/ml-overview.md) to build a predictive model. We can then use this to make predictions using new datasets with similar structures.\n",
     "\n",
     "Of course, depending on the actual data, some steps might be missing (e.g., when we already have the data in the database, or when we do not need model training), or some steps might be repeated several times (such as data processing).\n",
     "\n",
@@ -120,31 +184,69 @@
     "\n",
     "Let's consider an example. Suppose we have a Data Science course (like this one) that we deliver online to students, and we want to use Data Science to improve it. How can we do it?\n",
     "\n",
-    "We can start by asking \"What can be digitized?\" The simplest way would be to measure the time it takes each student to complete each module and to measure the obtained knowledge by giving a multiple-choice test at the end of each module. By averaging time-to-complete across all students, we can find out which modules cause the most difficulties for students, and work on simplifying them.\n",
-    "\n",
-    "```{note}\n",
+    "We can start by asking \"What can be digitized?\" The simplest way would be to measure the time it takes each student to complete each module and to measure the obtained knowledge by giving a multiple-choice test at the end of each module. By averaging time-to-complete across all students, we can find out which modules cause the most difficulties for students, and work on simplifying them."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8731cba6",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "note"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{note}\n",
     "You may argue that this approach is not ideal, because modules can be of different lengths. It is probably more fair to divide the time by the length of the module (in number of characters), and compare those values instead.\n",
-    "```\n",
-    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39194e17",
+   "metadata": {},
+   "source": [
     "When we start analyzing the results of multiple-choice tests, we can try to determine which concepts students have difficulty understanding, and use that information to improve the content. To do that, we need to design tests in such a way that each question maps to a certain concept or chunk of knowledge.\n",
     "\n",
     "If we want to get even more complicated, we can plot the time taken for each module against the age category of students. We might find out that for some age categories, it takes an inappropriately long time to complete the module, or that students drop out before completing it. This can help us provide age recommendations for the module, and minimize people's dissatisfaction with wrong expectations.\n",
     "\n",
     "## Your turn! 🚀\n",
     "\n",
-    "In this challenge, we will try to find concepts relevant to the field of Data Science by looking at texts. We will take a Wikipedia article on Data Science, download and process the text, and then build a word cloud like this one:\n",
-    "\n",
-    "```{figure} ../../../images/data-science-word-cloud.png\n",
+    "In this challenge, we will try to find concepts relevant to the field of Data Science by looking at texts. We will take a Wikipedia article on Data Science, download and process the text, and then build a word cloud like this one:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f50dcf4b",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "./../../images/data-science-word-cloud.png"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/data-science-word-cloud.png\n",
     "---\n",
     "name: 'World of Data Science'\n",
     "---\n",
     "World of Data Science{cite}`NolliHow2020`\n",
-    "```\n",
-    "\n",
-    "Visit [Analyzing text about data science](../../assignments/data-science/analyzing-text-about-data-science.ipynb) to read through the code. You can also run the code, and see how it performs all data transformations in real-time.\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e776d8e2",
+   "metadata": {},
+   "source": [
+    "Visit [Analyzing text about data science](https://static-1300131294.cos.qp-shanghai.myqcloud.com/assignments/data-science/analyzing-text-about-data-science.ipynb) to read through the code. You can also run the code, and see how it performs all data transformations in real-time.\n",
     "\n",
     "* **Task 1**: Modify the code above to find out related concepts for the fields of **Big Data** and **Machine Learning**.\n",
-    "* **Task 2**: Think about [Data Science scenarios](../../assignments/data-science/data-science-scenarios.md).\n",
+    "* **Task 2**: Think about [Data Science scenarios](https://static-1300131294.cos.qp-shanghai.myqcloud.com/assignments/data-science/data-science-scenarios.md).\n",
     "\n",
     "## Self study\n",
     "\n",
@@ -154,27 +256,30 @@
     "\n",
     "Thanks to Microsoft for creating the open-source course [Data Science for Beginners](https://github.com/microsoft/Data-Science-For-Beginners). It inspires the majority of the content in this chapter.\n",
     "\n",
-    "---\n",
-    "\n",
-    "```{bibliography}\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03c143fd",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "bibliography"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{bibliography}\n",
     ":filter: docname in docnames\n",
-    "```"
+    ":::"
    ]
   }
  ],
  "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "formats": "md:myst",
-   "text_representation": {
-    "extension": ".md",
-    "format_name": "myst",
-    "format_version": 0.13,
-    "jupytext_version": "1.11.5"
-   }
-  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -188,12 +293,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  },
-  "source_map": [
-   14
-  ]
+   "version": "3.11.4"
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/_sources/data-science/introduction/defining-data-science.md b/_sources/data-science/introduction/defining-data-science.md
deleted file mode 100644
index de9395815f..0000000000
--- a/_sources/data-science/introduction/defining-data-science.md
+++ /dev/null
@@ -1,169 +0,0 @@
----
-jupytext:
-  cell_metadata_filter: -all
-  formats: md:myst
-  text_representation:
-    extension: .md
-    format_name: myst
-    format_version: 0.13
-    jupytext_version: 1.11.5
-kernelspec:
-  display_name: Python 3
-  language: python
-  name: python3
----
-
-# Defining data science
-
-## What is data?
-
-In our everyday life, we are constantly surrounded by data. The text you are reading now is data. The list of phone numbers of your friends on your smartphone is data, as well as the current time displayed on your watch. As human beings, we naturally operate with data by counting the money we have or by writing letters to our friends.
-
-However, data became much more critical with the creation of computers. The primary role of computers is to perform computations, but they need data to operate on. Thus, we need to understand how computers store and process data.
-
-With the emergence of the Internet, the role of computers as data-handling devices increased. If you think about it, we now use computers more and more for data processing and communication, rather than actual computations. When we write an e-mail to a friend or search for some information on the Internet - we are essentially creating, storing, transmitting, and manipulating data.
-
-```{note}
-Can you remember the last time you have used computers to actually compute something?
-```
-
-## What is Data Science?
-
-In [Wikipedia](https://en.wikipedia.org/wiki/Data_science), **Data Science** is defined as *a scientific field that uses scientific methods to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains*.
-
-This definition highlights the following important aspects of Data Science:
-
-* The main goal of Data Science is to **extract knowledge** from data, in other words - to **understand** data, find some hidden relationships and build a **model**.
-* Data Science uses **scientific methods**, such as probability and statistics. In fact, when the term *Data Science* was first introduced, some people argued that Data Science was just a new fancy name for statistics. Nowadays it has become evident that the field is much broader.
-* Obtained knowledge should be applied to produce some **actionable insights**, i.e. practical insights that you can apply to real business situations.
-* We should be able to operate on both **structured** and **unstructured** data. We will come back to discuss different types of data later in the course.
-* **Application domain** is an important concept, and data scientists often need at least some degree of expertise in the problem domain, for example, finance, medicine, marketing, etc.
-
-```{note}
-Another important aspect of Data Science is that it studies how data can be gathered, stored and operated upon using computers. While statistics gives us mathematical foundations, Data Science applies mathematical concepts to actually draw insights from data.
-```
-
-One of the ways (attributed to [Jim Gray](https://en.wikipedia.org/wiki/Jim_Gray_(computer_scientist))) to look at Data Science is to consider it to be a separate paradigm of science:
-
-* **Empirical**, in which we rely mostly on observations and results of experiments
-* **Theoretical**, where new concepts emerge from existing scientific knowledge
-* **Computational**, where we discover new principles based on some computational experiments
-* **Data-Driven**, based on discovering relationships and patterns in the data
-
-## Other related fields
-
-Since data is pervasive, Data Science itself is also a broad field, touching many other disciplines.
-
-**Databases**
-
-A critical consideration is **how to store** the data, i.e. how to structure it in a way that allows faster processing. There are different types of databases that store structured and unstructured data, which [we will consider in our book](../working-with-data/working-with-data.md).
-
-**Big Data**
-
-Often we need to store and process very large quantities of data with a relatively simple structure. There are special approaches and tools to store that data in a distributed manner on a computer cluster and process it efficiently.
-
-**Machine Learning**
-
-One way to understand data is to **build a model** that will be able to predict the desired outcome. Developing models from data is called **machine learning**. You may want to have a look at our [Machine Learning](../../ml-fundamentals/ml-overview.md)-related chapters to learn more about it.
-
-**Artificial Intelligence**
-
-An area of Machine Learning known as artificial intelligence (AI) also relies on data, and it involves building high-complexity models that mimic human thought processes. AI methods often allow us to turn unstructured data (e.g. natural language) into structured insights.
-
-**Visualization**
-
-Vast amounts of data are incomprehensible to a human being, but once we create useful visualizations using that data, we can make more sense of the data, and draw some conclusions. Thus, it is important to know many ways to visualize information - something that we will cover in the [Data visualization](../data-visualization/data-visualization.md) section. Related fields also include **Infographics** and **Human-Computer Interaction** in general.
-
-## Types of data
-
-As we have already mentioned, data is everywhere. We just need to capture it in the right way! It is useful to distinguish between **structured** and **unstructured** data. The former is typically represented in some well-structured form, often as a table or number of tables, while the latter is just a collection of files. Sometimes we can also talk about **semi-structured** data, that have some sort of structure that may vary greatly.
-
-| Structured                                                                   | Semi-structured                                                                                | Unstructured                            |
-| ---------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------- | --------------------------------------- |
-| List of people with their phone numbers                                      | Wikipedia pages with links                                                                     | Text of Encyclopedia Britannica        |
-| Temperature in all rooms of a building at every minute for the last 20 years | Collection of scientific papers in JSON format with authors, data of publication, and abstract | File share with corporate documents     |
-| Data for age and gender of all people entering the building                  | Internet pages                                                                                 | Raw video feed from surveillance camera |
-
-## Where to get data
-
-There are many possible sources of data, and it will be impossible to list all of them! However, let's mention some of the typical places where you can get data:
-
-* **Structured**
-  * **Internet of Things** (IoT), including data from different sensors, such as temperature or pressure sensors, provides a lot of useful data. For example, if an office building is equipped with IoT sensors, we can automatically control heating and lighting in order to minimize costs.
-  * **Surveys** that we ask users to complete after purchase, or after visiting a website.
-  * **Analysis of behavior** can, for example, help us understand how deeply a user goes into a site, and what is the typical reason for leaving the site.
-* **Unstructured**
-  * **Texts** can be a rich source of insights, such as an overall **sentiment score**, or extracting keywords and semantic meaning.
-  * **Images** or **Video**. A video from a surveillance camera can be used to estimate traffic on the road, and inform people about potential traffic jams.
-  * Web server **Logs** can be used to understand which pages of our site are most often visited, and for how long.
-* **Semi-structured**
-  * **Social Network** graphs can be great sources of data about user personalities and potential effectiveness in spreading information around.
-  * When we have a bunch of photographs from a party, we can try to extract **Group Dynamics** data by building a graph of people taking pictures with each other.
-
-By knowing different possible sources of data, you can try to think about different scenarios where Data Science techniques can be applied to know the situation better and to improve business processes.
-
-## What you can do with data
-
-In Data Science, we focus on the following steps of the data journey:
-
-**Data acquisition.** The first step is to collect the data. While in many cases it can be a straightforward process, like data coming to a database from a web application, sometimes we need to use special techniques. For example, data from IoT sensors can be overwhelming, and it is a good practice to use buffering endpoints such as IoT Hub to collect all the data before further processing.
-
-**Data storage.** Storing data can be challenging, especially if we are talking about big data. When deciding how to store data, it makes sense to anticipate the way you would query the data in the future. There are several ways data can be stored:
-
-* A relational database stores a collection of tables and uses a special language called SQL to query them. Typically, tables are organized into different groups called schemas. In many cases, we need to convert the data from the original form to fit the schema.</li>
-* A [NoSQL](https://en.wikipedia.org/wiki/NoSQL) database, such as [MongoDB](https://www.mongodb.com/), does not enforce schemas on data and allows storing more complex data, for example, hierarchical JSON documents or graphs. However, NoSQL databases do not have the rich querying capabilities of SQL, and cannot enforce referential integrity, i.e. rules on how the data is structured in tables and governing the relationships between tables.</li>
-* [Data Lake](https://en.wikipedia.org/wiki/Data_lake) storage is used for large collections of data in raw, unstructured form. Data lakes are often used with big data, where all data cannot fit on one machine, and has to be stored and processed by a cluster of servers. [Parquet](https://en.wikipedia.org/wiki/Apache_Parquet) is the data format that is often used in conjunction with big data.
-
-**Data processing.** This is the most exciting part of the data journey, which involves converting the data from its original form into a form that can be used for visualization/model training. When dealing with unstructured data such as text or images, we may need to use some AI techniques to extract **features** from the data, thus converting it to a structured form.
-
-**Visualization / human insights.** Oftentimes, in order to understand the data, we need to visualize it. Having many different visualization techniques in our toolbox, we can find the right view to make an insight. Often, a data scientist needs to "play with data", visualizing it many times and looking for some relationships. Also, we may use statistical techniques to test hypotheses or prove a correlation between different pieces of data.
-
-**Training a predictive model.** Because the ultimate goal of Data Science is to be able to make decisions based on data, we may want to use the techniques of [Machine Learning](../../ml-fundamentals/ml-overview.md) to build a predictive model. We can then use this to make predictions using new datasets with similar structures.
-
-Of course, depending on the actual data, some steps might be missing (e.g., when we already have the data in the database, or when we do not need model training), or some steps might be repeated several times (such as data processing).
-
-## Digitalization and digital transformation
-
-In the last decade, many businesses started to understand the importance of data when making business decisions. To apply Data Science principles to running a business, one first needs to collect some data, i.e. translate business processes into digital form. This is known as **digitalization**. Applying Data Science techniques to this data to guide decisions can lead to significant increases in productivity (or even business pivot), called **digital transformation**.
-
-Let's consider an example. Suppose we have a Data Science course (like this one) that we deliver online to students, and we want to use Data Science to improve it. How can we do it?
-
-We can start by asking "What can be digitized?" The simplest way would be to measure the time it takes each student to complete each module and to measure the obtained knowledge by giving a multiple-choice test at the end of each module. By averaging time-to-complete across all students, we can find out which modules cause the most difficulties for students, and work on simplifying them.
-
-```{note}
-You may argue that this approach is not ideal, because modules can be of different lengths. It is probably more fair to divide the time by the length of the module (in number of characters), and compare those values instead.
-```
-
-When we start analyzing the results of multiple-choice tests, we can try to determine which concepts students have difficulty understanding, and use that information to improve the content. To do that, we need to design tests in such a way that each question maps to a certain concept or chunk of knowledge.
-
-If we want to get even more complicated, we can plot the time taken for each module against the age category of students. We might find out that for some age categories, it takes an inappropriately long time to complete the module, or that students drop out before completing it. This can help us provide age recommendations for the module, and minimize people's dissatisfaction with wrong expectations.
-
-## Your turn! 🚀
-
-In this challenge, we will try to find concepts relevant to the field of Data Science by looking at texts. We will take a Wikipedia article on Data Science, download and process the text, and then build a word cloud like this one:
-
-```{figure} ../../../images/data-science-word-cloud.png
----
-name: 'World of Data Science'
----
-World of Data Science{cite}`NolliHow2020`
-```
-
-Visit [Analyzing text about data science](../../assignments/data-science/analyzing-text-about-data-science.ipynb) to read through the code. You can also run the code, and see how it performs all data transformations in real-time.
-
-* **Task 1**: Modify the code above to find out related concepts for the fields of **Big Data** and **Machine Learning**.
-* **Task 2**: Think about [Data Science scenarios](../../assignments/data-science/data-science-scenarios.md).
-
-## Self study
-
-Here is a list of free/open-source learning resources for [Data Science](https://github.com/ocademy-ai/open-learning-resources/blob/main/README.md#data-science--engineering).
-
-## Acknowledgments
-
-Thanks to Microsoft for creating the open-source course [Data Science for Beginners](https://github.com/microsoft/Data-Science-For-Beginners). It inspires the majority of the content in this chapter.
-
----
-
-```{bibliography}
-:filter: docname in docnames
-```
diff --git a/_sources/data-science/introduction/defining-data.ipynb b/_sources/data-science/introduction/defining-data.ipynb
index 8f1819974c..11f41392bc 100644
--- a/_sources/data-science/introduction/defining-data.ipynb
+++ b/_sources/data-science/introduction/defining-data.ipynb
@@ -2,8 +2,35 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "5a88fc60",
-   "metadata": {},
+   "id": "60e80b87-a2dd-4c3c-ba76-7269ece12197",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "source": [
+    "---\n",
+    "jupytext:\n",
+    "  cell_metadata_filter: -all\n",
+    "  formats: md:myst\n",
+    "  text_representation:\n",
+    "    extension: .md\n",
+    "    format_name: myst\n",
+    "    format_version: 0.13\n",
+    "    jupytext_version: 1.11.5\n",
+    "kernelspec:\n",
+    "  display_name: Python 3\n",
+    "  language: python\n",
+    "  name: python3\n",
+    ""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "689dab76",
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "# Defining data\n",
     "\n",
@@ -47,7 +74,7 @@
     "\n",
     "A data source is an initial location of where the data was generated, or where it \"lives\" and will vary based on how and when it was collected. Data generated by its user(s) are known as primary data while secondary data comes from a source that has collected data for general use. For example, a group of scientists collecting observations in a rainforest would be considered primary and if they decide to share it with other scientists it would be considered secondary to those that use it. \n",
     "\n",
-    "Databases are a common source and rely on a database management system to host and maintain the data where users use commands called queries to explore the data. Files as data sources can be audio, image, and video files as well as spreadsheets like Excel. Internet sources are a common location for hosting data, where databases, as well as files, can be found. Application programming interfaces, also known as APIs allow programmers to create ways to share data with external users through the internet, while the process of web scraping extracts data from a web page. The sections in [Working with data](../working-with-data/working-with-data.md) focus on how to use various data sources.\n",
+    "Databases are a common source and rely on a database management system to host and maintain the data where users use commands called queries to explore the data. Files as data sources can be audio, image, and video files as well as spreadsheets like Excel. Internet sources are a common location for hosting data, where databases, as well as files, can be found. Application programming interfaces, also known as APIs allow programmers to create ways to share data with external users through the internet, while the process of web scraping extracts data from a web page. The sections in [Working with data](https://static-1300131294.cos.qp-shanghai.myqcloud.com/working-with-data/working-with-data.md) focus on how to use various data sources.\n",
     "\n",
     "## Your turn! 🚀\n",
     "\n",
@@ -60,7 +87,7 @@
     "\n",
     "### Task 2\n",
     "\n",
-    "[Classifying datasets](../../assignments/data-science/classifying-datasets.md)\n",
+    "[Classifying datasets](https://static-1300131294.cos.qp-shanghai.myqcloud.com/assignments/data-science/classifying-datasets.md)\n",
     "\n",
     "## Self study\n",
     "\n",
@@ -70,27 +97,30 @@
     "\n",
     "Thanks to Microsoft for creating the open-source course [Data Science for Beginners](https://github.com/microsoft/Data-Science-For-Beginners). It inspires the majority of the content in this chapter.\n",
     "\n",
-    "---\n",
-    "\n",
-    "```{bibliography}\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5816de54",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "bibliography"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{bibliography}\n",
     ":filter: docname in docnames\n",
-    "```"
+    ":::"
    ]
   }
  ],
  "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "formats": "md:myst",
-   "text_representation": {
-    "extension": ".md",
-    "format_name": "myst",
-    "format_version": 0.13,
-    "jupytext_version": "1.11.5"
-   }
-  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -104,12 +134,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  },
-  "source_map": [
-   14
-  ]
+   "version": "3.11.4"
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/_sources/data-science/introduction/defining-data.md b/_sources/data-science/introduction/defining-data.md
deleted file mode 100644
index 4b3bef7213..0000000000
--- a/_sources/data-science/introduction/defining-data.md
+++ /dev/null
@@ -1,85 +0,0 @@
----
-jupytext:
-  cell_metadata_filter: -all
-  formats: md:myst
-  text_representation:
-    extension: .md
-    format_name: myst
-    format_version: 0.13
-    jupytext_version: 1.11.5
-kernelspec:
-  display_name: Python 3
-  language: python
-  name: python3
----
-
-# Defining data
-
-Data is facts, information, observations, and measurements that are used to make discoveries and support informed decisions. A data point is a single unit of data within a dataset, which is a collection of data points. Datasets may come in different formats and structures, and will usually be based on their source, or where the data came from. For example, a company's monthly earnings might be in a spreadsheet but hourly heart rate data from a smartwatch may be in [JSON](https://stackoverflow.com/a/383699) format. It's common for data scientists to work with different types of data within a dataset.
-
-This section focuses on identifying and classifying data by its characteristics and its sources.
-
-## How data is described
-
-**Raw data** is data that has come from its source in its initial state and has not been analyzed or organized. In order to make sense of what is happening with a dataset, it needs to be organized into a format that can be understood by humans as well as the technology they may use to analyze it further. The structure of a dataset describes how it's organized and can be classified as structured, unstructured, and semi-structured. These types of structures will vary, depending on the source but will ultimately fit into these three categories.
-
-**1\. Quantitative data**
-
-Quantitative data is numerical observations within a dataset and can typically be analyzed, measured and used mathematically. Some examples of quantitative data are a country's population, a person's height, or a company's quarterly earnings. With some additional analysis, quantitative data could be used to discover seasonal trends of the Air Quality Index (AQI) or estimate the probability of rush hour traffic on a typical work day.
-
-**2\. Qualitative data**
-
-Qualitative data, also known as categorical data is data that cannot be measured objectively like observations of quantitative data. It's generally various formats of subjective data that capture the quality of something, such as a product or process. Sometimes, qualitative data is numerical and wouldn't be typically used mathematically, like phone numbers or timestamps. Some examples of qualitative data are video comments, the make, and model of a car, or your closest friends' favorite color. Qualitative data could be used to understand which products consumers like best or to identify popular keywords in job application resumes.
-
-**3\. Structured data**
-
-Structured data is data that is organized into rows and columns, where each row will have the same set of columns. Columns represent a value of a particular type and will be identified with a name describing what the value represents, while rows contain the actual values. Columns will often have a specific set of rules or restrictions on the values, to ensure that the values accurately represent the column. For example, imagine a spreadsheet of customers where each row must have a phone number and the phone numbers never contain alphabetical characters. There may be rules applied to the phone number column to make sure it's never empty and only contains numbers.
-
-A benefit of structured data is that it can be organized in such a way that it can be related to other structured data. However, because the data is designed to be organized in a specific way, making changes to its overall structure can take a lot of effort to do. For example, adding an email column to the customer spreadsheet that cannot be empty means you'll need to figure out how you'll add these values to the existing rows of customers in the dataset.
-
-Examples of structured data: spreadsheets, relational databases, phone numbers, bank statements
-
-**4\. Unstructured data**
-
-Unstructured data typically cannot be categorized into rows or columns and doesn't contain a format or set of rules to follow. Because unstructured data has fewer restrictions on its structure it's easier to add new information in comparison to a structured dataset. If a sensor capturing data on barometric pressure every 2 minutes has received an update that now allows it to measure and record temperature, it doesn't require altering the existing data if it's unstructured. However, this may make analyzing or investigating this type of data take longer. For example, a scientist who wants to find the average temperature of the previous month from the sensors data, but discovers that the sensor recorded an "e" in some of its recorded data to note that it was broken instead of a typical number, which means the data is incomplete.
-
-Examples of unstructured data: text files, text messages, video files
-
-**5\. Semi-structured data**
-
-Semi-structured data has features that make it a combination of structured and unstructured data. It doesn't typically conform to a format of rows and columns but is organized in a way that is considered structured and may follow a fixed format or set of rules. The structure will vary between sources, such as a well-defined hierarchy to something more flexible that allows for easy integration of new information. Metadata are indicators that help decide how the data is organized and stored and will have various names, based on the type of data. Some common names for metadata are tags, elements, entities, and attributes. For example, a typical email message will have a subject, body, and a set of recipients and can be organized by whom or when it was sent.
-
-Examples of semi-structured data: HTML, CSV files, JavaScript Object Notation (JSON)
-
-## Sources of data
-
-A data source is an initial location of where the data was generated, or where it "lives" and will vary based on how and when it was collected. Data generated by its user(s) are known as primary data while secondary data comes from a source that has collected data for general use. For example, a group of scientists collecting observations in a rainforest would be considered primary and if they decide to share it with other scientists it would be considered secondary to those that use it. 
-
-Databases are a common source and rely on a database management system to host and maintain the data where users use commands called queries to explore the data. Files as data sources can be audio, image, and video files as well as spreadsheets like Excel. Internet sources are a common location for hosting data, where databases, as well as files, can be found. Application programming interfaces, also known as APIs allow programmers to create ways to share data with external users through the internet, while the process of web scraping extracts data from a web page. The sections in [Working with data](../working-with-data/working-with-data.md) focus on how to use various data sources.
-
-## Your turn! 🚀
-
-### Task 1
-
-Kaggle is an excellent source of open datasets. Use the [dataset search tool](https://www.kaggle.com/datasets) to find some interesting datasets and classify 3-5 datasets with these criteria:
-
-- Is the data quantitative or qualitative?
-- Is the data structured, unstructured, or semi-structured?
-
-### Task 2
-
-[Classifying datasets](../../assignments/data-science/classifying-datasets.md)
-
-## Self study
-
-This Microsoft Learn unit, titled [Classify your data](https://docs.microsoft.com/en-us/learn/modules/choose-storage-approach-in-azure/2-classify-data) has a detailed breakdown of structured, semi-structured, and unstructured data.
-
-## Acknowledgments
-
-Thanks to Microsoft for creating the open-source course [Data Science for Beginners](https://github.com/microsoft/Data-Science-For-Beginners). It inspires the majority of the content in this chapter.
-
----
-
-```{bibliography}
-:filter: docname in docnames
-```
diff --git a/_sources/data-science/introduction/introduction-to-statistics-and-probability.ipynb b/_sources/data-science/introduction/introduction-to-statistics-and-probability.ipynb
index 66fc6a79ca..a41eb6a62b 100644
--- a/_sources/data-science/introduction/introduction-to-statistics-and-probability.ipynb
+++ b/_sources/data-science/introduction/introduction-to-statistics-and-probability.ipynb
@@ -2,19 +2,140 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "0eb2c041",
-   "metadata": {},
+   "id": "a93be836-2671-4863-a901-60b59799d613",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "source": [
+    "---\n",
+    "jupytext:\n",
+    "  cell_metadata_filter: -all\n",
+    "  formats: md:myst\n",
+    "  text_representation:\n",
+    "    extension: .md\n",
+    "    format_name: myst\n",
+    "    format_version: 0.13\n",
+    "    jupytext_version: 1.11.5\n",
+    "kernelspec:\n",
+    "  display_name: Python 3\n",
+    "  language: python\n",
+    "  name: python3\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8feb576",
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "# Introduction to statistics and probability\n",
     "\n",
-    "Statistics and Probability Theory are two highly related areas of Mathematics that are highly relevant to Data Science. It is possible to operate with data without deep knowledge of mathematics, but it is still better to know at least some basic concepts. Here we will present a short introduction that will help you get started.\n",
+    "Statistics and Probability Theory are two highly related areas of Mathematics that are highly relevant to Data Science. It is possible to operate with data without deep knowledge of mathematics, but it is still better to know at least some basic concepts. Here we will present a short introduction that will help you get started."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "09fe0040-1786-42c3-a866-447e283fa1ab",
+   "metadata": {
+    "tags": [
+     "hide-input",
+     "output-scoll"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# install the necessary dependencies\n",
+    "import sys\n",
+    "!{sys.executable} -m pip install --quiet pandas numpy matplotlib jupyterlab_myst pygments\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "574d8e32-a00e-4a44-bc3f-d5691b3c9364",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "seealso"
+     ],
+     "id": ""
+    },
+    "tags": [
+     "hide-input",
+     "output-scoll"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div class=\"yt-container\">\n",
+       "   <iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/Z5Zy85g4Yjw\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>\n",
+       "</div>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
     "\n",
-    "```{seealso}\n",
+    "from IPython.display import HTML\n",
+    "display(HTML(\"\"\"\n",
     "<div class=\"yt-container\">\n",
     "   <iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/Z5Zy85g4Yjw\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>\n",
     "</div>\n",
-    "```\n",
+    "\"\"\"))\n",
     "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8fb9e1e",
+   "metadata": {
+    "tags": [
+     "output-scoll",
+     "hide-input"
+    ]
+   },
+   "source": [
+    "execute:\n",
+    "    exclude_patterns:\n",
+    "        - 'assignments/*'\n",
+    "        - 'assignments/**/*'\n",
+    "        - 'deep-learning/*'\n",
+    "        - 'slides/*'\n",
+    "        - 'slides/**/*'\n",
+    "        - 'ml-fundamentals/classification/introduction-to-classification.md'\n",
+    "        - 'ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.md'\n",
+    "        - 'ml-fundamentals/regression/managing-data.md'\n",
+    "        - 'ml-fundamentals/parameter-optimization/loss-function.md'\n",
+    "        - 'ml-advanced/ensemble-learning/random-forest.md'\n",
+    "        - 'ml-advanced/clustering/introduction-to-clustering.md'\n",
+    "        - 'ml-advanced/clustering/k-means-clustering.md'\n",
+    "        - 'ml-advanced/unsupervised-learning.md'\n",
+    "        - 'data-science/data-visualization/visualization-distributions.md'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "578cecfa",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
     "## Probability and random variables\n",
     "\n",
     "**Probability** is a number between 0 and 1 that expresses how probable an **event** is. It is defined as a number of positive outcomes (that lead to the event), divided by a total number of outcomes, given that all outcomes are equally probable. For example, when we roll a dice, the probability that we get an even number is $3/6 = 0.5$.\n",
@@ -31,12 +152,33 @@
     "\n",
     "The most well-known discrete distribution is a **uniform distribution**, in which there is a sample space of $N$ elements, with an equal probability of $1/N$ for each of them.\n",
     "\n",
-    "It is more difficult to describe the probability distribution of a continuous variable, with values drawn from some interval $[a,b]$, or the whole set of real numbers $\\mathbb{R}$. Consider the case of bus arrival time. In fact, for each exact arrival time $t$, the probability of a bus arriving at exactly that time is $0$!\n",
-    "\n",
-    "```{note}\n",
+    "It is more difficult to describe the probability distribution of a continuous variable, with values drawn from some interval $[a,b]$, or the whole set of real numbers $\\mathbb{R}$. Consider the case of bus arrival time. In fact, for each exact arrival time $t$, the probability of a bus arriving at exactly that time is $0$!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef9ef875",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "note"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{note}\n",
     "Now you know that events with $0$ probability happen, and very often! At least each time when the bus arrives!\n",
-    "```\n",
-    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac87900d",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
     "We can only talk about the probability of a variable falling in a given interval of values, eg. $P(t_1 \\le X < t_2)$. In this case, the probability distribution is described by a **probability density function** p(x), such that\n",
     "\n",
     "$$P(t_1\\le X<t_2)=\\int_{t_1}^{t_2}p(x)dx$$\n",
@@ -47,12 +189,33 @@
     "\n",
     "## Mean, variance and standard deviation\n",
     "\n",
-    "Suppose we draw a sequence of n samples of a random variable $X: x_1, x_2, ..., x_n$. We can define the **mean** (or **arithmetic average**) value of the sequence in the traditional way as $(x_1+x_2+x_n)/n$. As we grow the size of the sample (i.e. take the limit with $n\\to\\infty$), we will obtain the mean (also called **expectation**) of the distribution. We will denote expectation by $E(x)$.\n",
-    "\n",
-    "```{note}\n",
+    "Suppose we draw a sequence of n samples of a random variable $X: x_1, x_2, ..., x_n$. We can define the **mean** (or **arithmetic average**) value of the sequence in the traditional way as $(x_1+x_2+x_n)/n$. As we grow the size of the sample (i.e. take the limit with $n\\to\\infty$), we will obtain the mean (also called **expectation**) of the distribution. We will denote expectation by $E(x)$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55770dc6",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "note"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{note}\n",
     "It can be demonstrated that for any discrete distribution with values $\\{x_1, x_2, ..., x_N\\}$ and corresponding probabilities $p_1, p_2, ..., p_N$, the expectation would equal to $E(X)=x_1p_1+x_2p_2+...+x_Np_N$.\n",
-    "```\n",
-    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54aca367",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
     "To identify how far the values are spread, we can compute the variance $\\sigma^2 = \\sum(x_i - \\mu;)^2/n$, where $\\mu$ is the mean of the sequence. The value $\\sigma$ is called **standard deviation**, and $\\sigma^2$ is called a **variance**.\n",
     "\n",
     "## Mode, median and quartiles\n",
@@ -66,7 +229,7 @@
     "\n",
     "Graphically we can represent the relationship between the median and quartiles in a diagram called the **box plot**:\n",
     "\n",
-    "![](../../../images/boxplot_explanation.png)\n",
+    "![](https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/boxplot_explanation.png)\n",
     "\n",
     "Here we also compute the **inter-quartile range** $IQR=Q3-Q1$, and so-called **outliers** - values, that lie outside the boundaries $[Q1-1.5*IQR, Q3+1.5*IQR]$.\n",
     "\n",
@@ -74,84 +237,296 @@
     "\n",
     "## Real-world data\n",
     "\n",
-    "When we analyze data from real life, they often are not random variables as such, in the sense that we do not perform experiments with unknown results. For example, consider a team of baseball players, and their body data, such as height, weight and age. Those numbers are not exactly random, but we can still apply the same mathematical concepts. For example, a sequence of people's weights can be considered to be a sequence of values drawn from some random variable. Below is the sequence of weights of actual baseball players from [Major League Baseball](http://mlb.mlb.com/index.jsp), taken from [this dataset](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) (for your convenience, only the first 20 values are shown):\n",
-    "\n",
-    "```\n",
-    "[180.0, 215.0, 210.0, 210.0, 188.0, 176.0, 209.0, 200.0, 231.0, 180.0, 188.0, 180.0, 185.0, 160.0, 180.0, 185.0, 197.0, 189.0, 185.0, 219.0]\n",
-    "```\n",
-    "\n",
-    "```{note}\n",
-    "To see the example of working with this dataset, have a look at the [accompanying notebook](../../assignments/data-science/introduction-to-statistics-and-probability.ipynb). There are also a number of challenges throughout this section, and you may complete them by adding some code to that notebook. If you are not sure how to operate on data, do not worry - we will come back to working with data using Python at a later time. If you do not know how to run code in Jupyter Notebook, have a look at [this article](https://soshnikov.com/education/how-to-execute-notebooks-from-github/).\n",
-    "```\n",
-    "\n",
+    "When we analyze data from real life, they often are not random variables as such, in the sense that we do not perform experiments with unknown results. For example, consider a team of baseball players, and their body data, such as height, weight and age. Those numbers are not exactly random, but we can still apply the same mathematical concepts. For example, a sequence of people's weights can be considered to be a sequence of values drawn from some random variable. Below is the sequence of weights of actual baseball players from [Major League Baseball](http://mlb.mlb.com/index.jsp), taken from [this dataset](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) (for your convenience, only the first 20 values are shown):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f85f5dd8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[180.0,\n",
+       " 215.0,\n",
+       " 210.0,\n",
+       " 210.0,\n",
+       " 188.0,\n",
+       " 176.0,\n",
+       " 209.0,\n",
+       " 200.0,\n",
+       " 231.0,\n",
+       " 180.0,\n",
+       " 188.0,\n",
+       " 180.0,\n",
+       " 185.0,\n",
+       " 160.0,\n",
+       " 180.0,\n",
+       " 185.0,\n",
+       " 197.0,\n",
+       " 189.0,\n",
+       " 185.0,\n",
+       " 219.0]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[180.0, 215.0, 210.0, 210.0, 188.0, 176.0, 209.0, 200.0, 231.0, 180.0, 188.0, 180.0, 185.0, 160.0, 180.0, 185.0, 197.0, 189.0, 185.0, 219.0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42d6a231",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "note"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{note}\n",
+    "To see the example of working with this dataset, have a look at the [accompanying notebook](https://static-1300131294.cos.qp-shanghai.myqcloud.com/assignments/data-science/introduction-to-statistics-and-probability.ipynb). There are also a number of challenges throughout this section, and you may complete them by adding some code to that notebook. If you are not sure how to operate on data, do not worry - we will come back to working with data using Python at a later time. If you do not know how to run code in Jupyter Notebook, have a look at [this article](https://soshnikov.com/education/how-to-execute-notebooks-from-github/).\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7049e91a",
+   "metadata": {},
+   "source": [
     "Here is the box plot showing the mean, median, and quartiles for our data:\n",
     "\n",
-    "![Weight Box Plot](../../../images/weight-boxplot.png)\n",
+    "![Weight Box Plot](https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/weight-boxplot.png)\n",
     "\n",
     "Since our data contains information about different player **roles**, we can also do the box plot by role - it will allow us to get an idea on how parameter values differ across roles. This time we will consider height:\n",
     "\n",
-    "![Box plot by role](../../../images/boxplot_byrole.png)\n",
+    "![Box plot by role](https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/boxplot_byrole.png)\n",
     "\n",
-    "This diagram suggests that, on average, the height of first basemen is higher than the height of second basemen. Later in this section, we will learn how we can test this hypothesis more formally, and how to demonstrate that our data is statistically significant to show that.\n",
-    "\n",
-    "```{note}\n",
+    "This diagram suggests that, on average, the height of first basemen is higher than the height of second basemen. Later in this section, we will learn how we can test this hypothesis more formally, and how to demonstrate that our data is statistically significant to show that."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20a8a762",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "note"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{note}\n",
     "When working with real-world data, we assume that all data points are samples drawn from some probability distribution. This assumption allows us to apply machine learning techniques and build working predictive models.\n",
-    "```\n",
-    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "921718c4",
+   "metadata": {},
+   "source": [
     "To see what the distribution of our data is, we can plot a graph called a **histogram**. The X-axis would contain a number of different weight intervals (so-called **bins**), and the vertical axis would show the number of times our random variable sample was inside a given interval.\n",
     "\n",
-    "![Histogram of real-world data](../../../images/weight-histogram.png)\n",
+    "![Histogram of real-world data](https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/weight-histogram.png)\n",
     "\n",
-    "From this histogram, you can see that all values are centered around a certain mean weight, and the further we go from that weight - the fewer weights of that value are encountered. I.e., it is very improbable that the weight of a baseball player would be very different from the mean weight. The variance of weights shows the extent to which weights are likely to differ from the mean.\n",
-    "\n",
-    "```{note}\n",
+    "From this histogram, you can see that all values are centered around a certain mean weight, and the further we go from that weight - the fewer weights of that value are encountered. I.e., it is very improbable that the weight of a baseball player would be very different from the mean weight. The variance of weights shows the extent to which weights are likely to differ from the mean."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04c44ccf",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "note"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{note}\n",
     "If we take weights of other people, not from the baseball league, the distribution is likely to be different. However, the shape of the distribution will be the same, but mean and variance would change. So, if we train our model on baseball players, it is likely to give wrong results when applied to students of a university, because the underlying distribution is different.\n",
-    "```\n",
-    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2d9bdfa",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
     "## Normal distribution\n",
     "\n",
     "The distribution of weights that we have seen above is very typical, and many measurements from the real world follow the same type of distribution, but with different mean and variance. This distribution is called **normal distribution**, and it plays a very important role in statistics.\n",
     "\n",
-    "Using normal distribution is the correct way to generate random weights of potential baseball players. Once we know the mean weight `mean` and standard deviation `std`, we can generate 1000 weight samples in the following way:\n",
-    "\n",
-    "```python\n",
+    "Using normal distribution is the correct way to generate random weights of potential baseball players. Once we know the mean weight `mean` and standard deviation `std`, we can generate 1000 weight samples in the following way:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8a7bb3e4",
+   "metadata": {
+    "tags": [
+     "output-scoll",
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 8.2225322 , 10.53317136, 11.45343354,  6.48679776,  8.31217472,\n",
+       "        9.84246015, 12.29008162, 10.18559812,  8.44217413,  8.72046147,\n",
+       "        7.20047172,  9.18682379, 10.24193684,  8.12802518,  8.50982802,\n",
+       "       11.38590536,  7.2385977 ,  6.58472691, 10.74231261,  9.26262326])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from statistics import mean\n",
+    "mean = 10\n",
+    "std = 2\n",
     "samples = np.random.normal(mean, std, 1000)\n",
-    "```\n",
-    "\n",
-    "If we plot the histogram of the generated samples we will see a picture very similar to the one shown above. And if we increase the number of samples and the number of bins, we can generate a picture of a normal distribution that is more close to ideal:\n",
-    "\n",
-    "```{figure} ../../../images/normal-histogram.png\n",
+    "samples[:20]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c0ed299",
+   "metadata": {},
+   "source": [
+    "If we plot the histogram of the generated samples we will see a picture very similar to the one shown above. And if we increase the number of samples and the number of bins, we can generate a picture of a normal distribution that is more close to ideal:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3abada4c",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "./../../images/normal-histogram.png"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/normal-histogram.png\n",
     "---\n",
     "name: 'Normal distribution with mean=0 and std.dev=1'\n",
     "---\n",
     "Normal distribution with $mean=0$ and $std.dev=1$\n",
-    "```\n",
-    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35cd1519",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
     "## Confidence intervals\n",
     "\n",
     "When we talk about the weights of baseball players, we assume that there is a certain **random variable** $W$ that corresponds to the ideal probability distribution of weights of all baseball players (so-called **population**). Our sequence of weights corresponds to a subset of all baseball players that we call **sample**. An interesting question is, can we know the parameters of the distribution of $W$, i.e. mean and variance of the population?\n",
     "\n",
-    "The easiest answer would be to calculate the mean and variance of our sample. However, it could happen that our random sample does not accurately represent the complete population. Thus it makes sense to talk about **confidence interval**.\n",
-    "\n",
-    "```{note}\n",
+    "The easiest answer would be to calculate the mean and variance of our sample. However, it could happen that our random sample does not accurately represent the complete population. Thus it makes sense to talk about **confidence interval**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1cc74c18",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "note"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{note}\n",
     "**Confidence interval** is the estimation of true mean of the population given our sample, which is accurate is a certain probability (or **level of confidence**).\n",
-    "```\n",
-    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da041b54",
+   "metadata": {},
+   "source": [
     "Suppose we have a sample $X_1, ..., X_n$ from our distribution. Each time we draw a sample from our distribution, we would end up with a different mean value $\\mu$. Thus $\\mu$ can be considered to be a random variable. A **confidence interval** with confidence $p$ is a pair of values $(L_p,R_p)$, such that $P(L_p\\le\\mu\\le R_p) = p$, i.e. a probability of the measured mean value falling within the interval equals $p$.\n",
     "\n",
-    "It does beyond our short intro to discuss in detail how those confidence intervals are calculated. Some more details can be found on [Wikipedia](https://en.wikipedia.org/wiki/Confidence_interval). In short, we define the distribution of the computed sample mean relative to the true mean of the population, which is called **student distribution**.\n",
-    "\n",
-    "```{note}\n",
+    "It does beyond our short intro to discuss in detail how those confidence intervals are calculated. Some more details can be found on [Wikipedia](https://en.wikipedia.org/wiki/Confidence_interval). In short, we define the distribution of the computed sample mean relative to the true mean of the population, which is called **student distribution**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ada0185",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "note"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{note}\n",
     "**Interesting fact**: Student distribution is named after mathematician William Sealy Gosset, who published his paper under the pseudonym \"Student\". He worked in the Guinness brewery, and, according to one of the versions, his employer did not want general public to know that they were using statistical tests to determine the quality of raw materials.\n",
-    "```\n",
-    "\n",
-    "If we want to estimate the mean $\\mu$; of our population with confidence $p$, we need to take $(1-p)/2$-th percentile of a student distribution $A$, which can either be taken from tables or computer using some built-in functions of statistical software (eg. Python, R, etc.). Then the interval for $\\mu$; would be given by $X\\pm A*D/\\sqrt{n}$, where $X$ is the obtained mean of the sample, and $D$ is the standard deviation.\n",
-    "\n",
-    "```{note}\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "173deba4",
+   "metadata": {},
+   "source": [
+    "If we want to estimate the mean $\\mu$; of our population with confidence $p$, we need to take $(1-p)/2$-th percentile of a student distribution $A$, which can either be taken from tables or computer using some built-in functions of statistical software (eg. Python, R, etc.). Then the interval for $\\mu$; would be given by $X\\pm A*D/\\sqrt{n}$, where $X$ is the obtained mean of the sample, and $D$ is the standard deviation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38c14c99",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "note"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{note}\n",
     "We also omit the discussion of an important concept of [degrees of freedom](https://en.wikipedia.org/wiki/Degrees_of_freedom_(statistics)), which is important in relation to student distribution. You can refer to more complete books on statistics to understand this concept deeper.\n",
-    "```\n",
-    "\n",
-    "An example of calculating the confidence interval for weights and heights is given in the [accompanying notebooks](../../assignments/data-science/introduction-to-statistics-and-probability.ipynb).\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20f2c490",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "An example of calculating the confidence interval for weights and heights is given in the [accompanying notebooks](https://static-1300131294.cos.qp-shanghai.myqcloud.com/assignments/data-science/introduction-to-statistics-and-probability.ipynb).\n",
     "\n",
     "| p | Weight mean |\n",
     "|-----|-----------|\n",
@@ -163,7 +538,7 @@
     "\n",
     "## Hypothesis testing\n",
     "\n",
-    "In our baseball players dataset, there are different player roles, that can be summarized below (look at the [accompanying notebooks](../../assignments/data-science/introduction-to-statistics-and-probability.ipynb)) to see how this table can be calculated):\n",
+    "In our baseball players dataset, there are different player roles, that can be summarized below (look at the [accompanying notebooks](https://static-1300131294.cos.qp-shanghai.myqcloud.com/assignments/data-science/introduction-to-statistics-and-probability.ipynb)) to see how this table can be calculated):\n",
     "\n",
     "| Role | Height | Weight | Count |\n",
     "|------|--------|--------|-------|\n",
@@ -177,12 +552,31 @@
     "| Starting_Pitcher | 74.719457 | 205.163636 | 221 |\n",
     "| Third_Baseman | 73.044444 | 200.955556 | 45 |\n",
     "\n",
-    "We can notice that the mean heights of first basemen are higher than that of second basemen. Thus, we may be tempted to conclude that **first basemen are higher than second basemen**.\n",
-    "\n",
-    "```{note}\n",
+    "We can notice that the mean heights of first basemen are higher than that of second basemen. Thus, we may be tempted to conclude that **first basemen are higher than second basemen**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cfb14e8c",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "note"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{note}\n",
     "This statement is called **a hypothesis**, because we do not know whether the fact is actually true or not.\n",
-    "```\n",
-    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35c2a35a",
+   "metadata": {},
+   "source": [
     "However, it is not always obvious whether we can make this conclusion. From the discussion above we know that each mean has an associated confidence interval, and thus this difference can just be a statistical error. We need a more formal way to test our hypothesis.\n",
     "\n",
     "Let's compute confidence intervals separately for heights of first and second basemen:\n",
@@ -201,9 +595,40 @@
     "\n",
     "In Python, we can use the **SciPy** package, which includes `ttest_ind` function (in addition to many other useful statistical functions!). It computes the t-value for us, and also does the reverse lookup of the confidence p-value, so that we can just look at the confidence to draw the conclusion.\n",
     "\n",
-    "For example, our comparison between the heights of first and second basemen gives us the following results:\n",
+    "For example, our comparison between the heights of first and second basemen gives us the following results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f224abc4",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "py"
+     ],
+     "id": ""
+    },
+    "tags": [
+     "output-scoll",
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "T-value = -0.44\n",
+      "P-value: 0.6594504178995297\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/SOCR_MLB.tsv\",\n",
+    "                 sep='\\t', header=None, names=['Name', 'Team', 'Role', 'Height', 'Weight', 'Age'])\n",
+    "df\n",
     "\n",
-    "```py\n",
     "from scipy.stats import ttest_ind\n",
     "\n",
     "tval, pval = ttest_ind(\n",
@@ -213,14 +638,45 @@
     "  ['Height']], \n",
     "  equal_var=False\n",
     ")\n",
-    "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")\n",
-    "```\n",
-    "\n",
-    "```\n",
-    "T-value = 7.65\n",
-    "P-value: 9.137321189738925e-12\n",
-    "```\n",
-    "\n",
+    "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "9ffe44ca",
+   "metadata": {
+    "tags": [
+     "hide-input",
+     "output-scoll"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "T-value: 7.65\n",
+      "P-value: 9.137321189738925e-12\n"
+     ]
+    }
+   ],
+   "source": [
+    "#T-value = 7.65\n",
+    "T_value = 7.65\n",
+    "print(f\"T-value: {T_value:}\")\n",
+    "#P-value: 9.137321189738925e-12\n",
+    "P_value = 9.137321189738925e-12\n",
+    "print(f\"P-value: {P_value:.15e}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d306912",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
     "In our case, the p-value is very low, meaning that there is strong evidence supporting that the first basemen are taller.\n",
     "\n",
     "There are also different other types of hypotheses that we might want to test, for example:\n",
@@ -231,51 +687,189 @@
     "\n",
     "## Law of large numbers and central limit theorem\n",
     "\n",
-    "One of the reasons why normal distribution is so important is the so-called **central limit theorem**. Suppose we have a large sample of independent $N$ values $X_1, ..., X_N$, sampled from any distribution with mean $\\mu$ and variance $\\sigma^2$. Then, for sufficiently large $N$ (in other words, when $N\\to\\infty$), the mean $\\Sigma_iX_i$ would be normally distributed, with mean $\\mu$ and variance $\\sigma^2/N$.\n",
-    "\n",
-    "```{note}\n",
+    "One of the reasons why normal distribution is so important is the so-called **central limit theorem**. Suppose we have a large sample of independent $N$ values $X_1, ..., X_N$, sampled from any distribution with mean $\\mu$ and variance $\\sigma^2$. Then, for sufficiently large $N$ (in other words, when $N\\to\\infty$), the mean $\\Sigma_iX_i$ would be normally distributed, with mean $\\mu$ and variance $\\sigma^2/N$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f831aa3",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "note"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{note}\n",
     "Another way to interpret the central limit theorem is to say that regardless of distribution, when you compute the mean of a sum of any random variable values you end up with normal distribution.\n",
-    "```\n",
-    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd1e3291",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
     "From the central limit theorem, it also follows that when $N\\to\\infty$, the probability of the sample mean to be equal to $\\mu$ becomes $1$. This is known as **the law of large numbers**.\n",
     "\n",
     "## Covariance and correlation\n",
     "\n",
-    "One of the things Data Science does is find relations between data. We say that two sequences **correlate** when they exhibit similar behavior at the same time, i.e. they either rise/fall simultaneously, or one sequence rises when another one falls and vice versa. In other words, there seems to be some relation between the two sequences.\n",
-    "\n",
-    "```{note}\n",
+    "One of the things Data Science does is find relations between data. We say that two sequences **correlate** when they exhibit similar behavior at the same time, i.e. they either rise/fall simultaneously, or one sequence rises when another one falls and vice versa. In other words, there seems to be some relation between the two sequences."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e93b652",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "note"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{note}\n",
     "Correlation does not necessarily indicate causal relationship between two sequences; sometimes both variables can depend on some external cause, or it can be purely by chance the two sequences correlate. However, strong mathematical correlation is a good indication that two variables are somehow connected.\n",
-    "```\n",
-    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9de2bb44",
+   "metadata": {},
+   "source": [
     "Mathematically, the main concept that shows the relation between two random variables is **covariance**, which is computed like this: $Cov(X, Y) = E[(X-E(X))(Y-E(Y))]$. We compute the deviation of both variables from their mean values, and then the product of those deviations. If both variables deviate together, the product would always be a positive value, which would add up to positive covariance. If both variables deviate out of sync (i.e. one falls below average when another one rises above average), we will always get negative numbers, which will add up to negative covariance. If the deviations are not dependent, they will add up to roughly zero.\n",
     "\n",
     "The absolute value of covariance does not tell us much about how large the correlation is, because it depends on the magnitude of actual values. To normalize it, we can divide covariance by the standard deviation of both variables, to get a **correlation**. The good thing is that correlation is always in the range of $[-1,1]$, where $1$ indicates a strong positive correlation between values, $-1$ - strong negative correlation, and $0$ - no correlation at all (variables are independent).\n",
     "\n",
-    "**Example**: we can compute the correlation between weights and heights of baseball players from the dataset mentioned above:\n",
-    "\n",
-    "```py\n",
-    "print(np.corrcoef(weights, heights))\n",
-    "```\n",
-    "\n",
-    "As a result, we get the **correlation matrix** like this one:\n",
-    "\n",
-    "```\n",
-    "array([[1.        , 0.52959196],\n",
+    "**Example**: we can compute the correlation between weights and heights of baseball players from the dataset mentioned above:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a27ce48",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "py"
+     ],
+     "id": ""
+    },
+    "tags": [
+     "output-scoll",
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[nan nan]\n",
+      " [nan  1.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "heights = df['Height']\n",
+    "weights = df['Weight']\n",
+    "print(np.corrcoef(weights, heights))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6861a2b9",
+   "metadata": {},
+   "source": [
+    "As a result, we get the **correlation matrix** like this one:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f731cff2",
+   "metadata": {
+    "tags": [
+     "output-scoll",
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array([[1.        , 0.52959196],\n",
     "       [0.52959196, 1.        ]])\n",
-    "```\n",
-    "\n",
-    "```{note}\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "733d4206",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "note"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{note}\n",
     "Correlation matrix $C$ can be computed for any number of input sequences $S_1, ..., S_n$. The value of $C_{ij}$ is the correlation between $S_i$ and $S_j$, and diagonal elements are always $1$ (which is also self-correlation of $S_i$).\n",
-    "```\n",
-    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c08f25a4",
+   "metadata": {},
+   "source": [
     "In our case, the value $0.53$ indicates that there is some correlation between the weight and height of a person. We can also make the scatter plot of one value against the other to see the relationship visually:\n",
     "\n",
-    "![Relationship between weight and height](../../../images/weight-height-relationship.png)\n",
-    "\n",
-    "```{note}\n",
-    "More examples of correlation and covariance can be found in [accompanying notebook](../../assignments/data-science/introduction-to-statistics-and-probability.ipynb).\n",
-    "```\n",
-    "\n",
+    "![Relationship between weight and height](https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/weight-height-relationship.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86519fb6",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "note"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    ":::{note}\n",
+    "More examples of correlation and covariance can be found in [accompanying notebook](https://static-1300131294.cos.qp-shanghai.myqcloud.com/assignments/data-science/introduction-to-statistics-and-probability.ipynb).\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b31be9c3",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
     "## Conclusion\n",
     "\n",
     "In this section, we have learned:\n",
@@ -296,8 +890,8 @@
     "2. First basemen are taller than third basemen\n",
     "3. Shortstops are taller than second basemen\n",
     "\n",
-    "- [Small diabetes study](../../assignments/data-science/small-diabetes-study.ipynb).\n",
-    "- [Introduction to probability and statistics](../../assignments/data-science/introduction-to-statistics-and-probability.ipynb).\n",
+    "- [Small diabetes study](https://static-1300131294.cos.qp-shanghai.myqcloud.com/assignments/data-science/small-diabetes-study.ipynb).\n",
+    "- [Introduction to probability and statistics](https://static-1300131294.cos.qp-shanghai.myqcloud.com/assignments/data-science/introduction-to-statistics-and-probability.ipynb).\n",
     "\n",
     "## Self study\n",
     "\n",
@@ -314,18 +908,8 @@
   }
  ],
  "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "formats": "md:myst",
-   "text_representation": {
-    "extension": ".md",
-    "format_name": "myst",
-    "format_version": 0.13,
-    "jupytext_version": "1.11.5"
-   }
-  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -339,12 +923,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  },
-  "source_map": [
-   14
-  ]
+   "version": "3.11.4"
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/_sources/data-science/introduction/introduction-to-statistics-and-probability.md b/_sources/data-science/introduction/introduction-to-statistics-and-probability.md
deleted file mode 100644
index 27e255807c..0000000000
--- a/_sources/data-science/introduction/introduction-to-statistics-and-probability.md
+++ /dev/null
@@ -1,320 +0,0 @@
----
-jupytext:
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-    extension: .md
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----
-
-# Introduction to statistics and probability
-
-Statistics and Probability Theory are two highly related areas of Mathematics that are highly relevant to Data Science. It is possible to operate with data without deep knowledge of mathematics, but it is still better to know at least some basic concepts. Here we will present a short introduction that will help you get started.
-
-```{seealso}
-<div class="yt-container">
-   <iframe width="560" height="315" src="https://www.youtube.com/embed/Z5Zy85g4Yjw" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-</div>
-```
-
-## Probability and random variables
-
-**Probability** is a number between 0 and 1 that expresses how probable an **event** is. It is defined as a number of positive outcomes (that lead to the event), divided by a total number of outcomes, given that all outcomes are equally probable. For example, when we roll a dice, the probability that we get an even number is $3/6 = 0.5$.
-
-When we talk about events, we use **random variables**. For example, the random variable that represents a number obtained when rolling a dice would take values from $1$ to $6$. A set of numbers from $1$ to $6$ is called **sample space**. We can talk about the probability of a random variable taking a certain value, for example, $P(X=3)=1/6$.
-
-The random variable in the previous example is called **discrete** because it has a countable sample space, i.e. there are separate values that can be enumerated. There are cases when sample space is a range of real numbers or the whole set of real numbers. Such variables are called **continuous**. A good example is a time when the bus arrives.
-
-
-
-## Probability distribution
-
-In the case of discrete random variables, it is easy to describe the probability of each event by a function $P(X)$. For each value $s$ from sample space $S$ it will give a number from $0$ to $1$, such that the sum of all values of $P(X=s)$ for all events would be $1$.
-
-The most well-known discrete distribution is a **uniform distribution**, in which there is a sample space of $N$ elements, with an equal probability of $1/N$ for each of them.
-
-It is more difficult to describe the probability distribution of a continuous variable, with values drawn from some interval $[a,b]$, or the whole set of real numbers $\mathbb{R}$. Consider the case of bus arrival time. In fact, for each exact arrival time $t$, the probability of a bus arriving at exactly that time is $0$!
-
-```{note}
-Now you know that events with $0$ probability happen, and very often! At least each time when the bus arrives!
-```
-
-We can only talk about the probability of a variable falling in a given interval of values, eg. $P(t_1 \le X < t_2)$. In this case, the probability distribution is described by a **probability density function** p(x), such that
-
-$$P(t_1\le X<t_2)=\int_{t_1}^{t_2}p(x)dx$$
-  
-A continuous analog of uniform distribution is called **continuous uniform**, which is defined on a finite interval. A probability that the value $X$ falls into an interval of length $l$ is proportional to $l$, and rises to $1$.
-
-Another important distribution is **normal distribution**, which we will talk about in more detail below.
-
-## Mean, variance and standard deviation
-
-Suppose we draw a sequence of n samples of a random variable $X: x_1, x_2, ..., x_n$. We can define the **mean** (or **arithmetic average**) value of the sequence in the traditional way as $(x_1+x_2+x_n)/n$. As we grow the size of the sample (i.e. take the limit with $n\to\infty$), we will obtain the mean (also called **expectation**) of the distribution. We will denote expectation by $E(x)$.
-
-```{note}
-It can be demonstrated that for any discrete distribution with values $\{x_1, x_2, ..., x_N\}$ and corresponding probabilities $p_1, p_2, ..., p_N$, the expectation would equal to $E(X)=x_1p_1+x_2p_2+...+x_Np_N$.
-```
-
-To identify how far the values are spread, we can compute the variance $\sigma^2 = \sum(x_i - \mu;)^2/n$, where $\mu$ is the mean of the sequence. The value $\sigma$ is called **standard deviation**, and $\sigma^2$ is called a **variance**.
-
-## Mode, median and quartiles
-
-Sometimes, the mean does not adequately represent the "typical" value for data. For example, when there are a few extreme values that are completely out of range, they can affect the mean. Another good indication is a **median**, a value such that half of the data points are lower than it, and another half - higher.
-
-To help us understand the distribution of data, it is helpful to talk about **quartiles**:
-
-* the first quartile, or $Q1$, is a value, such that $25%$ of the data fall below it,
-* the third quartile, or $Q3$, is a value that $75%$ of the data falls below it.
-
-Graphically we can represent the relationship between the median and quartiles in a diagram called the **box plot**:
-
-![](../../../images/boxplot_explanation.png)
-
-Here we also compute the **inter-quartile range** $IQR=Q3-Q1$, and so-called **outliers** - values, that lie outside the boundaries $[Q1-1.5*IQR, Q3+1.5*IQR]$.
-
-For the finite distribution that contains a small number of possible values, a good "typical" value is the one that appears the most frequently, which is called **mode**. It is often applied to categorical data, such as colors. Consider a situation where we have two groups of people - some that strongly prefer red, and others who prefer blue. If we code colors by numbers, the mean value for a favorite color would be somewhere in the orange-green spectrum, which does not indicate the actual preference for either group. However, the mode would be either one of the colors, or both colors, if the number of people voting for them is equal (in this case we call the sample **multimodal**).
-
-## Real-world data
-
-When we analyze data from real life, they often are not random variables as such, in the sense that we do not perform experiments with unknown results. For example, consider a team of baseball players, and their body data, such as height, weight and age. Those numbers are not exactly random, but we can still apply the same mathematical concepts. For example, a sequence of people's weights can be considered to be a sequence of values drawn from some random variable. Below is the sequence of weights of actual baseball players from [Major League Baseball](http://mlb.mlb.com/index.jsp), taken from [this dataset](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) (for your convenience, only the first 20 values are shown):
-
-```
-[180.0, 215.0, 210.0, 210.0, 188.0, 176.0, 209.0, 200.0, 231.0, 180.0, 188.0, 180.0, 185.0, 160.0, 180.0, 185.0, 197.0, 189.0, 185.0, 219.0]
-```
-
-```{note}
-To see the example of working with this dataset, have a look at the [accompanying notebook](../../assignments/data-science/introduction-to-statistics-and-probability.ipynb). There are also a number of challenges throughout this section, and you may complete them by adding some code to that notebook. If you are not sure how to operate on data, do not worry - we will come back to working with data using Python at a later time. If you do not know how to run code in Jupyter Notebook, have a look at [this article](https://soshnikov.com/education/how-to-execute-notebooks-from-github/).
-```
-
-Here is the box plot showing the mean, median, and quartiles for our data:
-
-![Weight Box Plot](../../../images/weight-boxplot.png)
-
-Since our data contains information about different player **roles**, we can also do the box plot by role - it will allow us to get an idea on how parameter values differ across roles. This time we will consider height:
-
-![Box plot by role](../../../images/boxplot_byrole.png)
-
-This diagram suggests that, on average, the height of first basemen is higher than the height of second basemen. Later in this section, we will learn how we can test this hypothesis more formally, and how to demonstrate that our data is statistically significant to show that.
-
-```{note}
-When working with real-world data, we assume that all data points are samples drawn from some probability distribution. This assumption allows us to apply machine learning techniques and build working predictive models.
-```
-
-To see what the distribution of our data is, we can plot a graph called a **histogram**. The X-axis would contain a number of different weight intervals (so-called **bins**), and the vertical axis would show the number of times our random variable sample was inside a given interval.
-
-![Histogram of real-world data](../../../images/weight-histogram.png)
-
-From this histogram, you can see that all values are centered around a certain mean weight, and the further we go from that weight - the fewer weights of that value are encountered. I.e., it is very improbable that the weight of a baseball player would be very different from the mean weight. The variance of weights shows the extent to which weights are likely to differ from the mean.
-
-```{note}
-If we take weights of other people, not from the baseball league, the distribution is likely to be different. However, the shape of the distribution will be the same, but mean and variance would change. So, if we train our model on baseball players, it is likely to give wrong results when applied to students of a university, because the underlying distribution is different.
-```
-
-## Normal distribution
-
-The distribution of weights that we have seen above is very typical, and many measurements from the real world follow the same type of distribution, but with different mean and variance. This distribution is called **normal distribution**, and it plays a very important role in statistics.
-
-Using normal distribution is the correct way to generate random weights of potential baseball players. Once we know the mean weight `mean` and standard deviation `std`, we can generate 1000 weight samples in the following way:
-
-```python
-samples = np.random.normal(mean, std, 1000)
-```
-
-If we plot the histogram of the generated samples we will see a picture very similar to the one shown above. And if we increase the number of samples and the number of bins, we can generate a picture of a normal distribution that is more close to ideal:
-
-```{figure} ../../../images/normal-histogram.png
----
-name: 'Normal distribution with mean=0 and std.dev=1'
----
-Normal distribution with $mean=0$ and $std.dev=1$
-```
-
-## Confidence intervals
-
-When we talk about the weights of baseball players, we assume that there is a certain **random variable** $W$ that corresponds to the ideal probability distribution of weights of all baseball players (so-called **population**). Our sequence of weights corresponds to a subset of all baseball players that we call **sample**. An interesting question is, can we know the parameters of the distribution of $W$, i.e. mean and variance of the population?
-
-The easiest answer would be to calculate the mean and variance of our sample. However, it could happen that our random sample does not accurately represent the complete population. Thus it makes sense to talk about **confidence interval**.
-
-```{note}
-**Confidence interval** is the estimation of true mean of the population given our sample, which is accurate is a certain probability (or **level of confidence**).
-```
-
-Suppose we have a sample $X_1, ..., X_n$ from our distribution. Each time we draw a sample from our distribution, we would end up with a different mean value $\mu$. Thus $\mu$ can be considered to be a random variable. A **confidence interval** with confidence $p$ is a pair of values $(L_p,R_p)$, such that $P(L_p\le\mu\le R_p) = p$, i.e. a probability of the measured mean value falling within the interval equals $p$.
-
-It does beyond our short intro to discuss in detail how those confidence intervals are calculated. Some more details can be found on [Wikipedia](https://en.wikipedia.org/wiki/Confidence_interval). In short, we define the distribution of the computed sample mean relative to the true mean of the population, which is called **student distribution**.
-
-```{note}
-**Interesting fact**: Student distribution is named after mathematician William Sealy Gosset, who published his paper under the pseudonym "Student". He worked in the Guinness brewery, and, according to one of the versions, his employer did not want general public to know that they were using statistical tests to determine the quality of raw materials.
-```
-
-If we want to estimate the mean $\mu$; of our population with confidence $p$, we need to take $(1-p)/2$-th percentile of a student distribution $A$, which can either be taken from tables or computer using some built-in functions of statistical software (eg. Python, R, etc.). Then the interval for $\mu$; would be given by $X\pm A*D/\sqrt{n}$, where $X$ is the obtained mean of the sample, and $D$ is the standard deviation.
-
-```{note}
-We also omit the discussion of an important concept of [degrees of freedom](https://en.wikipedia.org/wiki/Degrees_of_freedom_(statistics)), which is important in relation to student distribution. You can refer to more complete books on statistics to understand this concept deeper.
-```
-
-An example of calculating the confidence interval for weights and heights is given in the [accompanying notebooks](../../assignments/data-science/introduction-to-statistics-and-probability.ipynb).
-
-| p | Weight mean |
-|-----|-----------|
-| 0.85 | 201.73±0.94 |
-| 0.90 | 201.73±1.08 |
-| 0.95 | 201.73±1.28 |
-
-Notice that the higher the confidence probability, the wider the confidence interval.
-
-## Hypothesis testing
-
-In our baseball players dataset, there are different player roles, that can be summarized below (look at the [accompanying notebooks](../../assignments/data-science/introduction-to-statistics-and-probability.ipynb)) to see how this table can be calculated):
-
-| Role | Height | Weight | Count |
-|------|--------|--------|-------|
-| Catcher | 72.723684 | 204.328947 | 76 |
-| Designated_Hitter | 74.222222 | 220.888889 | 18 |
-| First_Baseman | 74.000000 | 213.109091 | 55 |
-| Outfielder | 73.010309 | 199.113402 | 194 |
-| Relief_Pitcher | 74.374603 | 203.517460 | 315 |
-| Second_Baseman | 71.362069 | 184.344828 | 58 |
-| Shortstop | 71.903846 | 182.923077 | 52 |
-| Starting_Pitcher | 74.719457 | 205.163636 | 221 |
-| Third_Baseman | 73.044444 | 200.955556 | 45 |
-
-We can notice that the mean heights of first basemen are higher than that of second basemen. Thus, we may be tempted to conclude that **first basemen are higher than second basemen**.
-
-```{note}
-This statement is called **a hypothesis**, because we do not know whether the fact is actually true or not.
-```
-
-However, it is not always obvious whether we can make this conclusion. From the discussion above we know that each mean has an associated confidence interval, and thus this difference can just be a statistical error. We need a more formal way to test our hypothesis.
-
-Let's compute confidence intervals separately for heights of first and second basemen:
-
-| Confidence | First Basemen | Second Basemen |
-|------------|---------------|----------------|
-| 0.85 | 73.62..74.38 | 71.04..71.69 |
-| 0.90 | 73.56..74.44 | 70.99..71.73 |
-| 0.95 | 73.47..74.53 | 70.92..71.81 |
-
-We can see that under no confidence the intervals overlap. That proves our hypothesis that first basemen are higher than second basemen.
-
-More formally, the problem we are solving is to see if **two probability distributions are the same** or at least have the same parameters. Depending on the distribution, we need to use different tests for that. If we know that our distributions are normal, we can apply the **[student t-test](https://en.wikipedia.org/wiki/Student%27s_t-test)**.
-
-In the student t-test, we compute the so-called **t-value**, which indicates the difference between means, taking into account the variance. It is demonstrated that the t-value follows **student distribution**, which allows us to get the threshold value for a given confidence level $p$ (this can be computed, or looked up in the numerical tables). We then compare the t-value to this threshold to approve or reject the hypothesis.
-
-In Python, we can use the **SciPy** package, which includes `ttest_ind` function (in addition to many other useful statistical functions!). It computes the t-value for us, and also does the reverse lookup of the confidence p-value, so that we can just look at the confidence to draw the conclusion.
-
-For example, our comparison between the heights of first and second basemen gives us the following results:
-
-```py
-from scipy.stats import ttest_ind
-
-tval, pval = ttest_ind(
-  df.loc[df['Role'] == 'First_Baseman', 
-  ['Height']], 
-  df.loc[df['Role'] == 'Designated_Hitter', 
-  ['Height']], 
-  equal_var=False
-)
-print(f"T-value = {tval[0]:.2f}\nP-value: {pval[0]}")
-```
-
-```
-T-value = 7.65
-P-value: 9.137321189738925e-12
-```
-
-In our case, the p-value is very low, meaning that there is strong evidence supporting that the first basemen are taller.
-
-There are also different other types of hypotheses that we might want to test, for example:
-
-* To prove that a given sample follows some distribution. In our case, we have assumed that heights are normally distributed, but that needs formal statistical verification.
-* To prove that a mean value of a sample corresponds to some predefined value.
-* To compare means of a number of samples (eg. what is the difference in happiness levels among different age groups).
-
-## Law of large numbers and central limit theorem
-
-One of the reasons why normal distribution is so important is the so-called **central limit theorem**. Suppose we have a large sample of independent $N$ values $X_1, ..., X_N$, sampled from any distribution with mean $\mu$ and variance $\sigma^2$. Then, for sufficiently large $N$ (in other words, when $N\to\infty$), the mean $\Sigma_iX_i$ would be normally distributed, with mean $\mu$ and variance $\sigma^2/N$.
-
-```{note}
-Another way to interpret the central limit theorem is to say that regardless of distribution, when you compute the mean of a sum of any random variable values you end up with normal distribution.
-```
-
-From the central limit theorem, it also follows that when $N\to\infty$, the probability of the sample mean to be equal to $\mu$ becomes $1$. This is known as **the law of large numbers**.
-
-## Covariance and correlation
-
-One of the things Data Science does is find relations between data. We say that two sequences **correlate** when they exhibit similar behavior at the same time, i.e. they either rise/fall simultaneously, or one sequence rises when another one falls and vice versa. In other words, there seems to be some relation between the two sequences.
-
-```{note}
-Correlation does not necessarily indicate causal relationship between two sequences; sometimes both variables can depend on some external cause, or it can be purely by chance the two sequences correlate. However, strong mathematical correlation is a good indication that two variables are somehow connected.
-```
-
-Mathematically, the main concept that shows the relation between two random variables is **covariance**, which is computed like this: $Cov(X, Y) = E[(X-E(X))(Y-E(Y))]$. We compute the deviation of both variables from their mean values, and then the product of those deviations. If both variables deviate together, the product would always be a positive value, which would add up to positive covariance. If both variables deviate out of sync (i.e. one falls below average when another one rises above average), we will always get negative numbers, which will add up to negative covariance. If the deviations are not dependent, they will add up to roughly zero.
-
-The absolute value of covariance does not tell us much about how large the correlation is, because it depends on the magnitude of actual values. To normalize it, we can divide covariance by the standard deviation of both variables, to get a **correlation**. The good thing is that correlation is always in the range of $[-1,1]$, where $1$ indicates a strong positive correlation between values, $-1$ - strong negative correlation, and $0$ - no correlation at all (variables are independent).
-
-**Example**: we can compute the correlation between weights and heights of baseball players from the dataset mentioned above:
-
-```py
-print(np.corrcoef(weights, heights))
-```
-
-As a result, we get the **correlation matrix** like this one:
-
-```
-array([[1.        , 0.52959196],
-       [0.52959196, 1.        ]])
-```
-
-```{note}
-Correlation matrix $C$ can be computed for any number of input sequences $S_1, ..., S_n$. The value of $C_{ij}$ is the correlation between $S_i$ and $S_j$, and diagonal elements are always $1$ (which is also self-correlation of $S_i$).
-```
-
-In our case, the value $0.53$ indicates that there is some correlation between the weight and height of a person. We can also make the scatter plot of one value against the other to see the relationship visually:
-
-![Relationship between weight and height](../../../images/weight-height-relationship.png)
-
-```{note}
-More examples of correlation and covariance can be found in [accompanying notebook](../../assignments/data-science/introduction-to-statistics-and-probability.ipynb).
-```
-
-## Conclusion
-
-In this section, we have learned:
-
-* basic statistical properties of data, such as mean, variance, mode, and quartiles,
-* different distributions of random variables, including normal distribution,
-* how to find the correlation between different properties,
-* how to use sound apparatus of math and statistics in order to prove some hypotheses,
-* how to compute confidence intervals for a random variable given data sample.
-
-While this is definitely not an exhaustive list of topics that exist within probability and statistics, it should be enough to give you a good start on this course.
-
-## Your turn! 🚀
-
-Use the sample code in the notebook to test another hypothesis:
-
-1. First basemen are older than second basemen
-2. First basemen are taller than third basemen
-3. Shortstops are taller than second basemen
-
-- [Small diabetes study](../../assignments/data-science/small-diabetes-study.ipynb).
-- [Introduction to probability and statistics](../../assignments/data-science/introduction-to-statistics-and-probability.ipynb).
-
-## Self study
-
-Probability and statistics are such a broad topic that it deserves their own course. If you are interested to go deeper into theory, you may want to continue reading some of the following books:
-
-1. [Carlos Fernandez-Granda](https://cims.nyu.edu/~cfgranda/) from New York University has great lecture notes [Probability and Statistics for Data Science](https://cims.nyu.edu/~cfgranda/pages/stuff/probability_stats_for_DS.pdf) (available online)
-1. [Peter and Andrew Bruce. Practical Statistics for Data Scientists.](https://www.oreilly.com/library/view/practical-statistics-for/9781491952955/) [[sample code in R](https://github.com/andrewgbruce/statistics-for-data-scientists)].
-1. [James D. Miller. Statistics for Data Science](https://www.packtpub.com/product/statistics-for-data-science/9781788290678) [[sample code in R](https://github.com/PacktPublishing/Statistics-for-Data-Science)]
-
-## Acknowledgments
-
-Thanks to Microsoft for creating the open-source course [Data Science for Beginners](https://github.com/microsoft/Data-Science-For-Beginners). It inspires the majority of the content in this chapter.
diff --git a/_sources/data-science/introduction/introduction.ipynb b/_sources/data-science/introduction/introduction.ipynb
index 4b19d473d5..6396cceae8 100644
--- a/_sources/data-science/introduction/introduction.ipynb
+++ b/_sources/data-science/introduction/introduction.ipynb
@@ -2,48 +2,105 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "8906cda5",
-   "metadata": {},
+   "id": "b1cc84fa",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
    "source": [
-    "# Introduction\n",
-    "\n",
-    "```{figure} ../../../images/the-data-science-venn-diagram.png\n",
+    "---\n",
+    "jupytext:\n",
+    "  cell_metadata_filter: -all\n",
+    "  formats: md:myst\n",
+    "  text_representation:\n",
+    "    extension: .md\n",
+    "    format_name: myst\n",
+    "    format_version: 0.13\n",
+    "    jupytext_version: 1.11.5\n",
+    "kernelspec:\n",
+    "  display_name: Python 3\n",
+    "  language: python\n",
+    "  name: python3\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e74126cb-22df-48c8-8972-be97fa9145df",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8265b82",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "./../../images/the-data-science-venn-diagram.png"
+     ],
+     "id": ""
+    },
+    "tags": []
+   },
+   "source": [
+    ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/the-data-science-venn-diagram.png\n",
     "---\n",
     "name: The data science venn diagram\n",
     "---\n",
-    "The data science venn diagram{cite}`TheDataScienceVennDiagram`\n",
-    "\n",
-    "```\n",
-    "\n",
+    "The data science venn diagram{cite}:TheDataScienceVennDiagram:\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88fcce44",
+   "metadata": {},
+   "source": [
     "In this part, you will discover how Data Science is defined and learn about ethical considerations that must be considered by a data scientist. You will also learn how data is defined and learn a bit about statistics and probability, the core academic domains of Data Science.\n",
     "\n",
-    "---\n",
-    "\n",
-    "```{tableofcontents}\n",
-    "\n",
-    "```\n",
-    "\n",
-    "---\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4fb30976",
+   "metadata": {},
+   "source": [
+    ":::{tableofcontents}\n",
     "\n",
-    "```{bibliography}\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d342276b",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd109ea2-3741-41f9-9f42-777d7b990d52",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    ":::{bibliography}\n",
     ":filter: docname in docnames\n",
-    "```"
+    ":::"
    ]
   }
  ],
  "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "formats": "md:myst",
-   "text_representation": {
-    "extension": ".md",
-    "format_name": "myst",
-    "format_version": 0.13,
-    "jupytext_version": "1.11.5"
-   }
-  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -57,12 +114,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  },
-  "source_map": [
-   14
-  ]
+   "version": "3.11.4"
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/_sources/data-science/introduction/introduction.md b/_sources/data-science/introduction/introduction.md
deleted file mode 100644
index 5d814f7a58..0000000000
--- a/_sources/data-science/introduction/introduction.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-jupytext:
-  cell_metadata_filter: -all
-  formats: md:myst
-  text_representation:
-    extension: .md
-    format_name: myst
-    format_version: 0.13
-    jupytext_version: 1.11.5
-kernelspec:
-  display_name: Python 3
-  language: python
-  name: python3
----
-
-# Introduction
-
-```{figure} ../../../images/the-data-science-venn-diagram.png
----
-name: The data science venn diagram
----
-The data science venn diagram{cite}`TheDataScienceVennDiagram`
-
-```
-
-In this part, you will discover how Data Science is defined and learn about ethical considerations that must be considered by a data scientist. You will also learn how data is defined and learn a bit about statistics and probability, the core academic domains of Data Science.
-
----
-
-```{tableofcontents}
-
-```
-
----
-
-```{bibliography}
-:filter: docname in docnames
-```
diff --git a/_sources/data-science/working-with-data/data-preparation.ipynb b/_sources/data-science/working-with-data/data-preparation.ipynb
index 3874fa5905..3466d07656 100644
--- a/_sources/data-science/working-with-data/data-preparation.ipynb
+++ b/_sources/data-science/working-with-data/data-preparation.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "e3ac9573",
+   "id": "09851747",
    "metadata": {},
    "source": [
     "# Data preparation\n",
@@ -28,7 +28,7 @@
     "**Learning goal:** By the end of this subsection, you should be comfortable finding general information about the data stored in pandas DataFrames.\n",
     "```\n",
     "\n",
-    "Once you have loaded your data into Pandas, it will more likely than not be in a DataFrame(refer to the previous [section](./pandas.md) for a detailed overview). However, if the data set in your DataFrame has 60,000 rows and 400 columns, how do you even begin to get a sense of what you're working with? Fortunately, [Pandas](https://pandas.pydata.org/) provides some convenient tools to quickly look at overall information about a DataFrame in addition to the first few and last few rows.\n",
+    "Once you have loaded your data into Pandas, it will more likely than not be in a DataFrame. However, if the data set in your DataFrame has 60,000 rows and 400 columns, how do you even begin to get a sense of what you're working with? Fortunately, [Pandas](https://pandas.pydata.org/) provides some convenient tools to quickly look at overall information about a DataFrame in addition to the first few and last few rows.\n",
     "\n",
     "In order to explore this functionality, we will import the Python scikit-learn library and use an iconic dataset: the **Iris data set**."
    ]
@@ -36,7 +36,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "0af79d7c",
+   "id": "38e2bb96",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c028e402",
+   "id": "b201151b",
    "metadata": {},
    "source": [
     "|                                        |sepal length (cm)|sepal width (cm)|petal length (cm)|petal width (cm)|\n",
@@ -66,7 +66,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "7d4e7bbe",
+   "id": "77406b1b",
    "metadata": {},
    "outputs": [
     {
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d51ba632",
+   "id": "d0a6b8d5",
    "metadata": {},
    "source": [
     "From this, we know that the *Iris* dataset has 150 entries in four columns with no null entries. All of the data is stored as 64-bit floating-point numbers.\n",
@@ -104,7 +104,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "8c3c6ede",
+   "id": "b6a1c99c",
    "metadata": {},
    "outputs": [
     {
@@ -194,7 +194,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "efdd0455",
+   "id": "e222c9ef",
    "metadata": {},
    "source": [
     "**`DataFrame.tail()`**: Conversely, to check the last few rows of the `DataFrame`, we use the `tail()` method:"
@@ -203,7 +203,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "358ec288",
+   "id": "8ba627d6",
    "metadata": {},
    "outputs": [
     {
@@ -293,7 +293,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bbeb2ee4",
+   "id": "f2054712",
    "metadata": {},
    "source": [
     "```{note}\n",
@@ -320,7 +320,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "741b94a3",
+   "id": "2435e012",
    "metadata": {},
    "outputs": [
     {
@@ -347,7 +347,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "957b43ed",
+   "id": "305f61ef",
    "metadata": {},
    "source": [
     "Look closely at the output. Does any of it surprise you? While `0` is an arithmetic null, it's nevertheless a perfectly good integer and pandas treats it as such. `''` is a little more subtle. While we used it in Section 1 to represent an empty string value, it is nevertheless a string object and not a representation of null as far as pandas is concerned.\n",
@@ -364,7 +364,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "0418b25e",
+   "id": "69ec9897",
    "metadata": {},
    "outputs": [
     {
@@ -387,7 +387,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5cb73e79",
+   "id": "8070079f",
    "metadata": {},
    "source": [
     "Note that this should look like your output from `example3[example3.notnull()]`. The difference here is that, rather than just indexing on the masked values, `dropna` has removed those missing values from the `Series` `example1`.\n",
@@ -398,7 +398,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "91a00ad1",
+   "id": "aaa37f62",
    "metadata": {},
    "outputs": [
     {
@@ -471,7 +471,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e7872f35",
+   "id": "858462b7",
    "metadata": {},
    "source": [
     "|      | 0 | 1 | 2 |\n",
@@ -488,7 +488,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "71f22fe7",
+   "id": "5af424df",
    "metadata": {},
    "outputs": [
     {
@@ -544,7 +544,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca2e920f",
+   "id": "762af56b",
    "metadata": {},
    "source": [
     "If necessary, you can drop NA values from columns. Use `axis=1` to do so:"
@@ -553,7 +553,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "54b5bdf0",
+   "id": "8405d655",
    "metadata": {},
    "outputs": [
     {
@@ -615,7 +615,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7355e4df",
+   "id": "47d24051",
    "metadata": {},
    "source": [
     "Notice that this can drop a lot of data that you might want to keep, particularly in smaller datasets. What if you just want to drop rows or columns that contain several or even just all null values? You specify those setting in `dropna` with the `how` and `thresh` parameters.\n",
@@ -626,7 +626,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "6fbc2d70",
+   "id": "f2f3fe5c",
    "metadata": {},
    "outputs": [
     {
@@ -701,7 +701,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8c247c67",
+   "id": "fda7d3dd",
    "metadata": {},
    "source": [
     "|      |0  |1  |2  |3  |\n",
@@ -716,7 +716,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "d96df112",
+   "id": "e5370e2c",
    "metadata": {},
    "outputs": [
     {
@@ -774,7 +774,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "89054d37",
+   "id": "1956fcb4",
    "metadata": {},
    "source": [
     "Here, the first and last rows have been dropped, because they contain only two non-null values.\n",
@@ -785,7 +785,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "615cbcd0",
+   "id": "39f47e18",
    "metadata": {},
    "outputs": [
     {
@@ -811,7 +811,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5a7a5b1e",
+   "id": "5c82bfa0",
    "metadata": {},
    "source": [
     "You can fill all of the null entries with a single value, such as `0`:"
@@ -820,7 +820,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "cf3139fd",
+   "id": "e7f5acc0",
    "metadata": {},
    "outputs": [
     {
@@ -845,7 +845,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9dcfed1",
+   "id": "e43ba12b",
    "metadata": {},
    "source": [
     "You can **forward-fill** null values, which is to use the last valid value to fill a null:"
@@ -854,7 +854,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "238346d3",
+   "id": "ae2b0a45",
    "metadata": {},
    "outputs": [
     {
@@ -879,7 +879,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b24d032e",
+   "id": "a969d708",
    "metadata": {},
    "source": [
     "You can also **back-fill** to propagate the next valid value backward to fill a null:"
@@ -888,7 +888,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "9258cd7f",
+   "id": "4f4f80c3",
    "metadata": {},
    "outputs": [
     {
@@ -913,7 +913,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1334d6cf",
+   "id": "4e6715d4",
    "metadata": {},
    "source": [
     "As you might guess, this works the same with `DataFrame`s, but you can also specify an `axis` along which to fill null values. taking the previously used `example2` again:"
@@ -922,7 +922,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "ef932b46",
+   "id": "47d64ae3",
    "metadata": {},
    "outputs": [
     {
@@ -996,7 +996,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "986ff7c5",
+   "id": "a4958cb1",
    "metadata": {},
    "source": [
     "Notice that when a previous value is not available for forward-filling, the null value remains.\n",
@@ -1019,7 +1019,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "3de7cef4",
+   "id": "e08a6c74",
    "metadata": {},
    "outputs": [
     {
@@ -1099,7 +1099,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9660e26",
+   "id": "4488d9d7",
    "metadata": {},
    "source": [
     "|      |letters|numbers|\n",
@@ -1114,7 +1114,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "f7add412",
+   "id": "1c1c523a",
    "metadata": {},
    "outputs": [
     {
@@ -1139,7 +1139,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e10a3729",
+   "id": "290bf846",
    "metadata": {},
    "source": [
     "**Dropping duplicates: `drop_duplicates`:** simply returns a copy of the data for which all of the `duplicated` values are `False`:"
@@ -1148,7 +1148,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "580b66a3",
+   "id": "328c28ac",
    "metadata": {},
    "outputs": [
     {
@@ -1214,7 +1214,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f38d8f73",
+   "id": "436a1bd9",
    "metadata": {},
    "source": [
     "Both `duplicated` and `drop_duplicates` default to consider all columns but you can specify that they examine only a subset of columns in your `DataFrame`:"
@@ -1223,7 +1223,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "7d2f10dc",
+   "id": "2638ef59",
    "metadata": {},
    "outputs": [
     {
@@ -1283,7 +1283,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6a5fc994",
+   "id": "f282d259",
    "metadata": {},
    "source": [
     "```{note}\n",
diff --git a/_sources/data-science/working-with-data/data-preparation.md b/_sources/data-science/working-with-data/data-preparation.md
index 09213fde5f..6e4e63f163 100644
--- a/_sources/data-science/working-with-data/data-preparation.md
+++ b/_sources/data-science/working-with-data/data-preparation.md
@@ -36,7 +36,7 @@ Depending on its source, raw data may contain some inconsistencies that will cau
 **Learning goal:** By the end of this subsection, you should be comfortable finding general information about the data stored in pandas DataFrames.
 ```
 
-Once you have loaded your data into Pandas, it will more likely than not be in a DataFrame(refer to the previous [section](./pandas.md) for a detailed overview). However, if the data set in your DataFrame has 60,000 rows and 400 columns, how do you even begin to get a sense of what you're working with? Fortunately, [Pandas](https://pandas.pydata.org/) provides some convenient tools to quickly look at overall information about a DataFrame in addition to the first few and last few rows.
+Once you have loaded your data into Pandas, it will more likely than not be in a DataFrame. However, if the data set in your DataFrame has 60,000 rows and 400 columns, how do you even begin to get a sense of what you're working with? Fortunately, [Pandas](https://pandas.pydata.org/) provides some convenient tools to quickly look at overall information about a DataFrame in addition to the first few and last few rows.
 
 In order to explore this functionality, we will import the Python scikit-learn library and use an iconic dataset: the **Iris data set**.
 
diff --git a/_sources/data-science/working-with-data/numpy.ipynb b/_sources/data-science/working-with-data/numpy.ipynb
index 53e4e360af..bccbf7605c 100644
--- a/_sources/data-science/working-with-data/numpy.ipynb
+++ b/_sources/data-science/working-with-data/numpy.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "f744707e",
+   "id": "f7cab3d2",
    "metadata": {},
    "source": [
     "# NumPy\n",
@@ -19,7 +19,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "9624ad94",
+   "id": "6f7c517f",
    "metadata": {},
    "outputs": [
     {
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aba40fa1",
+   "id": "0e86be5b",
    "metadata": {},
    "source": [
     "### Create a basic array\n",
@@ -53,7 +53,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "5e127b6a",
+   "id": "6e939e45",
    "metadata": {},
    "outputs": [
     {
@@ -75,7 +75,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3889831b",
+   "id": "2afea2c2",
    "metadata": {},
    "source": [
     "Besides creating an array from a sequence of elements, you can easily create an array filled with `0`’s:"
@@ -84,7 +84,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "cce62836",
+   "id": "c675041c",
    "metadata": {},
    "outputs": [
     {
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2dd35045",
+   "id": "eca650ce",
    "metadata": {},
    "source": [
     "Or an array filled with 1’s:"
@@ -113,7 +113,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "df8b0089",
+   "id": "5a98112d",
    "metadata": {},
    "outputs": [
     {
@@ -133,7 +133,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "feb5ef2f",
+   "id": "31f9eab9",
    "metadata": {},
    "source": [
     "Or even an empty array! The function `empty` creates an array whose initial content is random and depends on the state of the memory. The reason to use `empty` over `zeros` (or something similar) is speed - just make sure to fill every element afterwards!"
@@ -142,7 +142,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "08f3d08f",
+   "id": "0de9b575",
    "metadata": {},
    "outputs": [
     {
@@ -162,7 +162,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b56ba5b8",
+   "id": "8e309a63",
    "metadata": {},
    "source": [
     "You can create an array with a range of elements:"
@@ -171,7 +171,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "13d40fbf",
+   "id": "6c2e5e51",
    "metadata": {},
    "outputs": [
     {
@@ -191,7 +191,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7d3ed58e",
+   "id": "eb1226c8",
    "metadata": {},
    "source": [
     "And even an array that contains a range of evenly spaced intervals. To do this, you will specify the **first number**, **last number**, and the **step size**."
@@ -200,7 +200,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "d9cc6a09",
+   "id": "20a297e1",
    "metadata": {},
    "outputs": [
     {
@@ -220,7 +220,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7ce9fb6b",
+   "id": "7b5c110c",
    "metadata": {},
    "source": [
     "You can also use `np.linspace()` to create an array with values that are spaced linearly in a specified interval:"
@@ -229,7 +229,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "5f812219",
+   "id": "28e1bc9f",
    "metadata": {},
    "outputs": [
     {
@@ -249,7 +249,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d5df427d",
+   "id": "b8b50ace",
    "metadata": {},
    "source": [
     "While the default data type is floating point (`np.float64`), you can explicitly specify which data type you want using the `dtype` keyword."
@@ -258,7 +258,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "8c9e398e",
+   "id": "68ae3d79",
    "metadata": {},
    "outputs": [
     {
@@ -278,7 +278,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "61b58d67",
+   "id": "2ef3f3a0",
    "metadata": {},
    "source": [
     "### Adding, removing, and sorting elements\n",
@@ -291,7 +291,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "24eabd9c",
+   "id": "427a2549",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -300,7 +300,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b12c81b0",
+   "id": "0c27e861",
    "metadata": {},
    "source": [
     "You can quickly sort the numbers in ascending order with:"
@@ -309,7 +309,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "e594b5ff",
+   "id": "30d691db",
    "metadata": {},
    "outputs": [
     {
@@ -329,7 +329,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ac90870e",
+   "id": "6eb422f9",
    "metadata": {},
    "source": [
     "In addition to sort, which returns a sorted copy of an array, you can use:\n",
@@ -345,7 +345,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "d14ecc7c",
+   "id": "7fbbcd5c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -355,7 +355,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c4e120b",
+   "id": "7cc16126",
    "metadata": {},
    "source": [
     "You can concatenate them with `np.concatenate()`."
@@ -364,7 +364,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "e1c0fbcd",
+   "id": "89e86081",
    "metadata": {},
    "outputs": [
     {
@@ -384,7 +384,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9ffa34a4",
+   "id": "243f380f",
    "metadata": {},
    "source": [
     "Or, if you start with these arrays:"
@@ -393,7 +393,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "04a3d94b",
+   "id": "e3a0d001",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -403,7 +403,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "71dca13e",
+   "id": "1dbcd3bb",
    "metadata": {},
    "source": [
     "You can concatenate them with:"
@@ -412,7 +412,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "1a3d8cd2",
+   "id": "e2230971",
    "metadata": {},
    "outputs": [
     {
@@ -434,7 +434,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "985e3f8a",
+   "id": "9635d315",
    "metadata": {},
    "source": [
     "In order to remove elements from an array, it’s simple to use indexing to select the elements that you want to keep.\n",
@@ -450,7 +450,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "7d88b21b",
+   "id": "afd751ab",
    "metadata": {},
    "outputs": [
     {
@@ -475,7 +475,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "f4adfd86",
+   "id": "ab067237",
    "metadata": {},
    "outputs": [
     {
@@ -495,7 +495,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4c4018a0",
+   "id": "a5847d23",
    "metadata": {},
    "source": [
     "- ndarray.shape\n",
@@ -505,7 +505,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "e0373f36",
+   "id": "08566ba6",
    "metadata": {},
    "outputs": [
     {
@@ -525,7 +525,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11780694",
+   "id": "9c082db2",
    "metadata": {},
    "source": [
     "- ndarray.size\n",
@@ -535,7 +535,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "fbddbbda",
+   "id": "d6777ead",
    "metadata": {},
    "outputs": [
     {
@@ -555,7 +555,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c9bd93d2",
+   "id": "e31d3983",
    "metadata": {},
    "source": [
     "- ndarray.dtype\n",
@@ -565,7 +565,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "40951789",
+   "id": "e4f9349e",
    "metadata": {},
    "outputs": [
     {
@@ -586,7 +586,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "3bfb0203",
+   "id": "50441d36",
    "metadata": {},
    "outputs": [
     {
@@ -606,7 +606,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dac963fc",
+   "id": "b9a4e3df",
    "metadata": {},
    "source": [
     "- ndarray.itemsize\n",
@@ -616,7 +616,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "289e9acf",
+   "id": "bc91818e",
    "metadata": {},
    "outputs": [
     {
@@ -636,7 +636,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0e9d0c5b",
+   "id": "6001b4e7",
    "metadata": {},
    "source": [
     "- ndarray.data\n",
@@ -646,13 +646,13 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "c559f21b",
+   "id": "550fcac2",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<memory at 0x7f0183efaad0>"
+       "<memory at 0x7f556a5e1ad0>"
       ]
      },
      "execution_count": 23,
@@ -666,7 +666,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4e6a4e6b",
+   "id": "9e7c4466",
    "metadata": {},
    "source": [
     "### Reshape an array\n",
@@ -679,7 +679,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "a79f7abd",
+   "id": "3b635c98",
    "metadata": {},
    "outputs": [
     {
@@ -700,7 +700,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9be34b2e",
+   "id": "edd82c34",
    "metadata": {},
    "source": [
     "You can use `reshape()` to reshape your array. For example, you can reshape this array to an array with three rows and two columns:"
@@ -709,7 +709,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "6a491d6d",
+   "id": "70a82cec",
    "metadata": {},
    "outputs": [
     {
@@ -732,7 +732,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e4f5e2e2",
+   "id": "c97b8591",
    "metadata": {},
    "source": [
     "With `np.reshape`, you can specify a few optional parameters:"
@@ -741,7 +741,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "94db5d5c",
+   "id": "e3394a1f",
    "metadata": {},
    "outputs": [
     {
@@ -761,7 +761,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "068d9e4d",
+   "id": "d3b4a6fa",
    "metadata": {},
    "source": [
     "`a` is the array to be reshaped.\n",
@@ -782,7 +782,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "89346ac0",
+   "id": "067a7a2d",
    "metadata": {},
    "outputs": [
     {
@@ -803,7 +803,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b48f305",
+   "id": "f988b32e",
    "metadata": {},
    "source": [
     "You can use `np.newaxis` to add a new axis:"
@@ -812,7 +812,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "3b47732a",
+   "id": "4896f3e9",
    "metadata": {},
    "outputs": [
     {
@@ -833,7 +833,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b37f75a2",
+   "id": "60466c47",
    "metadata": {},
    "source": [
     "You can explicitly convert a 1D array with either a row vector or a column vector using `np.newaxis`. For example, you can convert a 1D array to a row vector by inserting an axis along the first dimension:"
@@ -842,7 +842,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "173bee33",
+   "id": "3bdb4dc9",
    "metadata": {},
    "outputs": [
     {
@@ -863,7 +863,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "df40b97b",
+   "id": "682ec8fd",
    "metadata": {},
    "source": [
     "Or, for a column vector, you can insert an axis along the second dimension:"
@@ -872,7 +872,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "3a02883f",
+   "id": "6e2e6dcc",
    "metadata": {},
    "outputs": [
     {
@@ -893,7 +893,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "80847f41",
+   "id": "9a533818",
    "metadata": {},
    "source": [
     "You can also expand an array by inserting a new axis at a specified position with `np.expand_dims`.\n",
@@ -904,7 +904,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "515e0678",
+   "id": "f231c757",
    "metadata": {},
    "outputs": [
     {
@@ -925,7 +925,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e74768a7",
+   "id": "dae2ba71",
    "metadata": {},
    "source": [
     "You can use np.expand_dims to add an axis at index position 1 with:"
@@ -934,7 +934,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "7e8062a7",
+   "id": "fa9a1754",
    "metadata": {},
    "outputs": [
     {
@@ -955,7 +955,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "039a2e88",
+   "id": "61e387bd",
    "metadata": {},
    "source": [
     "You can add an axis at index position 0 with:"
@@ -964,7 +964,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "afb9bc9e",
+   "id": "afa5e2ee",
    "metadata": {},
    "outputs": [
     {
@@ -985,7 +985,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7f1f0caa",
+   "id": "2c2b789e",
    "metadata": {},
    "source": [
     "### Indexing and slicing\n",
@@ -996,7 +996,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "7af3484c",
+   "id": "2b8d66d7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1006,7 +1006,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "0e84037d",
+   "id": "040987c4",
    "metadata": {},
    "outputs": [
     {
@@ -1027,7 +1027,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "c85fbf0b",
+   "id": "25f88150",
    "metadata": {},
    "outputs": [
     {
@@ -1048,7 +1048,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "97281e02",
+   "id": "83acd0d4",
    "metadata": {},
    "outputs": [
     {
@@ -1069,7 +1069,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "a00a029b",
+   "id": "add70cd7",
    "metadata": {},
    "outputs": [
     {
@@ -1089,7 +1089,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a621f376",
+   "id": "e2c6aaf2",
    "metadata": {},
    "source": [
     "You may want to take a section of your array or specific array elements to use in further analysis or additional operations. To do that, you’ll need to subset, slice, and/or index your arrays.\n",
@@ -1102,7 +1102,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "3e51e327",
+   "id": "9a604b67",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1111,7 +1111,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd94eaf4",
+   "id": "7cbab311",
    "metadata": {},
    "source": [
     "You can easily print all of the values in the array that are less than 5."
@@ -1120,7 +1120,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "eb33ec58",
+   "id": "00776007",
    "metadata": {},
    "outputs": [
     {
@@ -1140,7 +1140,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "42927a36",
+   "id": "eeddf726",
    "metadata": {},
    "source": [
     "You can also select, for example, numbers that are equal to or greater than 5, and use that condition to index an array."
@@ -1149,7 +1149,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "eacb6f1b",
+   "id": "e64cd122",
    "metadata": {},
    "outputs": [
     {
@@ -1170,7 +1170,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2d860663",
+   "id": "c512e667",
    "metadata": {},
    "source": [
     "You can select elements that are divisible by 2:"
@@ -1179,7 +1179,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "37f9dc37",
+   "id": "2d394b78",
    "metadata": {},
    "outputs": [
     {
@@ -1200,7 +1200,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a2a9e2f4",
+   "id": "d419ba95",
    "metadata": {},
    "source": [
     "Or you can select elements that satisfy two conditions using the `&` and `|` operators:"
@@ -1209,7 +1209,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "422cbdba",
+   "id": "7c8a86d0",
    "metadata": {},
    "outputs": [
     {
@@ -1230,7 +1230,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4595ceb",
+   "id": "16846ba4",
    "metadata": {},
    "source": [
     "You can also make use of the logical operators `&` and `|` in order to return boolean values that specify whether or not the values in an array fulfill a certain condition. This can be useful with arrays that contain names or other categorical values."
@@ -1239,7 +1239,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "64db996c",
+   "id": "f9b179f8",
    "metadata": {},
    "outputs": [
     {
@@ -1262,7 +1262,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "edc854b3",
+   "id": "a5f1f1c0",
    "metadata": {},
    "source": [
     "You can also use `np.nonzero()` to select elements or indices from an array.\n",
@@ -1273,7 +1273,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "404f869e",
+   "id": "ee7506d5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1282,7 +1282,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ee19087",
+   "id": "399658e4",
    "metadata": {},
    "source": [
     "You can use `np.nonzero()` to print the indices of elements that are, for example, less than 5:"
@@ -1291,7 +1291,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "12cfb6f4",
+   "id": "f7cf6627",
    "metadata": {},
    "outputs": [
     {
@@ -1312,7 +1312,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "760bf15b",
+   "id": "084590fc",
    "metadata": {},
    "source": [
     "In this example, a tuple of arrays was returned: one for each dimension. The first array represents the row indices where these values are found, and the second array represents the column indices where the values are found.\n",
@@ -1323,7 +1323,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "f4f80296",
+   "id": "aceb6426",
    "metadata": {},
    "outputs": [
     {
@@ -1345,7 +1345,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6680e278",
+   "id": "e51358aa",
    "metadata": {},
    "source": [
     "You can also use `np.nonzero()` to print the elements in an array that are less than 5 with:"
@@ -1354,7 +1354,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "id": "65b12f65",
+   "id": "7b98ca49",
    "metadata": {},
    "outputs": [
     {
@@ -1374,7 +1374,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8372e4ac",
+   "id": "6f97c2d9",
    "metadata": {},
    "source": [
     "If the element you’re looking for doesn’t exist in the array, then the returned array of indices will be empty. For example:"
@@ -1383,7 +1383,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "19c3d76a",
+   "id": "2d55de2f",
    "metadata": {},
    "outputs": [
     {
@@ -1404,7 +1404,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0f62ffad",
+   "id": "e061db46",
    "metadata": {},
    "source": [
     "### Create an array from existing data\n",
@@ -1417,7 +1417,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "001023f9",
+   "id": "311d5d3a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1426,7 +1426,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "01d43320",
+   "id": "d9435eaf",
    "metadata": {},
    "source": [
     "You can create a new array from a section of your array any time by specifying where you want to slice your array."
@@ -1435,7 +1435,7 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "id": "fb16cbc9",
+   "id": "a8a9081f",
    "metadata": {},
    "outputs": [
     {
@@ -1456,7 +1456,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b48004b",
+   "id": "33bacf86",
    "metadata": {},
    "source": [
     "Here, you grabbed a section of your array from index position 3 through index position 8.\n",
@@ -1467,7 +1467,7 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "id": "e43a9687",
+   "id": "8d0bf3a7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1479,7 +1479,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc40054e",
+   "id": "913ae369",
    "metadata": {},
    "source": [
     "You can stack them vertically with `vstack`:"
@@ -1488,7 +1488,7 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "id": "a7848826",
+   "id": "a57f1398",
    "metadata": {},
    "outputs": [
     {
@@ -1511,7 +1511,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc243852",
+   "id": "c93a3213",
    "metadata": {},
    "source": [
     "Or stack them horizontally with hstack:"
@@ -1520,7 +1520,7 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "id": "a7244eaf",
+   "id": "3e483055",
    "metadata": {},
    "outputs": [
     {
@@ -1541,7 +1541,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "03e9a01f",
+   "id": "122119f3",
    "metadata": {},
    "source": [
     "You can split an array into several smaller arrays using `hsplit`. You can specify either the number of equally shaped arrays to return or the columns after which the division should occur.\n",
@@ -1552,7 +1552,7 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "id": "6f33525f",
+   "id": "47bb86da",
    "metadata": {},
    "outputs": [
     {
@@ -1574,7 +1574,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ef36427e",
+   "id": "9d15976b",
    "metadata": {},
    "source": [
     "If you wanted to split this array into three equally shaped arrays, you would run:"
@@ -1583,7 +1583,7 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "id": "873ff953",
+   "id": "4c8cf233",
    "metadata": {},
    "outputs": [
     {
@@ -1608,7 +1608,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8af6b59e",
+   "id": "e207d574",
    "metadata": {},
    "source": [
     "If you wanted to split your array after the third and fourth column, you’d run:"
@@ -1617,7 +1617,7 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "id": "b94d5683",
+   "id": "0cf0f3b0",
    "metadata": {},
    "outputs": [
     {
@@ -1642,7 +1642,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc4706a8",
+   "id": "35e63b64",
    "metadata": {},
    "source": [
     "You can use the `view` method to create a new array object that looks at the same data as the original array (a shallow copy).\n",
@@ -1655,7 +1655,7 @@
   {
    "cell_type": "code",
    "execution_count": 58,
-   "id": "8013fb25",
+   "id": "6fa2958b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1664,7 +1664,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "617e3bc3",
+   "id": "7eed264f",
    "metadata": {},
    "source": [
     "Now we create an array `b1` by slicing `a` and modify the first element of `b1`. This will modify the corresponding element in `a` as well!"
@@ -1673,7 +1673,7 @@
   {
    "cell_type": "code",
    "execution_count": 59,
-   "id": "ea916464",
+   "id": "97791347",
    "metadata": {},
    "outputs": [
     {
@@ -1695,7 +1695,7 @@
   {
    "cell_type": "code",
    "execution_count": 60,
-   "id": "3d9a3b90",
+   "id": "8a740070",
    "metadata": {},
    "outputs": [
     {
@@ -1717,7 +1717,7 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "id": "0c59e737",
+   "id": "aa1fb7c6",
    "metadata": {},
    "outputs": [
     {
@@ -1739,7 +1739,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "04c5b53a",
+   "id": "c702d31a",
    "metadata": {},
    "source": [
     "Using the `copy` method will make a complete copy of the array and its data (a deep copy). To use this on your array, you could run:"
@@ -1748,7 +1748,7 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "id": "868a0984",
+   "id": "59bcbef2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1757,7 +1757,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "83ff7af1",
+   "id": "3085413e",
    "metadata": {},
    "source": [
     "## Array operations\n",
@@ -1770,7 +1770,7 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "id": "cccf7ec9",
+   "id": "695ac3a0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1780,7 +1780,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d81c485",
+   "id": "2554cc86",
    "metadata": {},
    "source": [
     "You can add the arrays together with the plus sign."
@@ -1789,7 +1789,7 @@
   {
    "cell_type": "code",
    "execution_count": 64,
-   "id": "f9e3e75e",
+   "id": "6226d0de",
    "metadata": {},
    "outputs": [
     {
@@ -1809,7 +1809,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d90b7ed5",
+   "id": "494e35b2",
    "metadata": {},
    "source": [
     "You can, of course, do more than just addition!"
@@ -1818,7 +1818,7 @@
   {
    "cell_type": "code",
    "execution_count": 65,
-   "id": "c8fe9fe1",
+   "id": "31fe29d8",
    "metadata": {},
    "outputs": [
     {
@@ -1839,7 +1839,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2c9b2941",
+   "id": "707e85aa",
    "metadata": {},
    "source": [
     "Basic operations are simple with NumPy. If you want to find the sum of the elements in an array, you’d use `sum()`. This works for 1D arrays, 2D arrays, and arrays in higher dimensions."
@@ -1848,7 +1848,7 @@
   {
    "cell_type": "code",
    "execution_count": 66,
-   "id": "4fec575e",
+   "id": "3fd4efa4",
    "metadata": {},
    "outputs": [
     {
@@ -1869,7 +1869,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1628e025",
+   "id": "428a46f8",
    "metadata": {},
    "source": [
     "To add the rows or the columns in a 2D array, you would specify the axis.\n",
@@ -1880,7 +1880,7 @@
   {
    "cell_type": "code",
    "execution_count": 67,
-   "id": "2ad9f245",
+   "id": "08182aa5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1889,7 +1889,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e1caa973",
+   "id": "f7d16cb5",
    "metadata": {},
    "source": [
     "You can sum over the axis of rows with:"
@@ -1898,7 +1898,7 @@
   {
    "cell_type": "code",
    "execution_count": 68,
-   "id": "3532999e",
+   "id": "1c6723a8",
    "metadata": {},
    "outputs": [
     {
@@ -1918,7 +1918,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7740c46e",
+   "id": "6a2c2f26",
    "metadata": {},
    "source": [
     "You can sum over the axis of columns with:"
@@ -1927,7 +1927,7 @@
   {
    "cell_type": "code",
    "execution_count": 69,
-   "id": "5eadddd9",
+   "id": "0128ba3d",
    "metadata": {},
    "outputs": [
     {
@@ -1947,7 +1947,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "91fad533",
+   "id": "a9c96f50",
    "metadata": {},
    "source": [
     "### Universal functions(ufunc)\n",
@@ -2038,7 +2038,7 @@
   {
    "cell_type": "code",
    "execution_count": 70,
-   "id": "9264be94",
+   "id": "ef248869",
    "metadata": {},
    "outputs": [
     {
@@ -2060,7 +2060,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9359eba9",
+   "id": "5862d7c2",
    "metadata": {},
    "source": [
     "NumPy’s broadcasting rule relaxes this constraint when the arrays’ shapes meet certain constraints. The simplest broadcasting example occurs when an array and a scalar value are combined in an operation:"
@@ -2069,7 +2069,7 @@
   {
    "cell_type": "code",
    "execution_count": 71,
-   "id": "0ed61cc4",
+   "id": "39173cbb",
    "metadata": {},
    "outputs": [
     {
@@ -2091,7 +2091,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32709b16",
+   "id": "28c3594f",
    "metadata": {},
    "source": [
     "The result is equivalent to the previous example where `b` was an array.  NumPy is smart enough to use the original scalar value without actually making copies so that broadcasting operations are as memory and computationally efficient as possible.\n",
@@ -2133,7 +2133,7 @@
   {
    "cell_type": "code",
    "execution_count": 72,
-   "id": "b12b29bf",
+   "id": "c628a1b5",
    "metadata": {},
    "outputs": [
     {
@@ -2154,7 +2154,7 @@
   {
    "cell_type": "code",
    "execution_count": 73,
-   "id": "65d7fccc",
+   "id": "3b8afcfe",
    "metadata": {},
    "outputs": [
     {
@@ -2175,7 +2175,7 @@
   {
    "cell_type": "code",
    "execution_count": 74,
-   "id": "98253e19",
+   "id": "e5ceffd7",
    "metadata": {},
    "outputs": [
     {
@@ -2195,7 +2195,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db671804",
+   "id": "f8e1ea6f",
    "metadata": {},
    "source": [
     "Let’s start with this array, called “a”."
@@ -2204,7 +2204,7 @@
   {
    "cell_type": "code",
    "execution_count": 75,
-   "id": "03282c60",
+   "id": "f11b6aab",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2215,7 +2215,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "14df085e",
+   "id": "1c77e02d",
    "metadata": {},
    "source": [
     "It’s very common to want to aggregate along a row or column. By default, every NumPy aggregation function will return the aggregate of the entire array. To find the sum or the minimum of the elements in your array, run:"
@@ -2224,7 +2224,7 @@
   {
    "cell_type": "code",
    "execution_count": 76,
-   "id": "02410542",
+   "id": "4672a4f7",
    "metadata": {},
    "outputs": [
     {
@@ -2244,7 +2244,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6d9ff72b",
+   "id": "6de9c70a",
    "metadata": {},
    "source": [
     "Or:"
@@ -2253,7 +2253,7 @@
   {
    "cell_type": "code",
    "execution_count": 77,
-   "id": "74a4bff6",
+   "id": "15df4134",
    "metadata": {},
    "outputs": [
     {
@@ -2273,7 +2273,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f3579292",
+   "id": "6ac7fa2c",
    "metadata": {},
    "source": [
     "You can specify on which axis you want the aggregation function to be computed. For example, you can find the minimum value within each column by specifying `axis=0`."
@@ -2282,7 +2282,7 @@
   {
    "cell_type": "code",
    "execution_count": 78,
-   "id": "b717f94e",
+   "id": "9e8daa12",
    "metadata": {},
    "outputs": [
     {
@@ -2302,7 +2302,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "48e89abc",
+   "id": "d6f0bfbd",
    "metadata": {},
    "source": [
     "The four values listed above correspond to the number of columns in your array. With a four-column array, you will get four values as your result.\n",
@@ -2325,7 +2325,7 @@
   {
    "cell_type": "code",
    "execution_count": 79,
-   "id": "71d061be",
+   "id": "4450838a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2335,7 +2335,7 @@
   {
    "cell_type": "code",
    "execution_count": 80,
-   "id": "bead868c",
+   "id": "10190d22",
    "metadata": {},
    "outputs": [
     {
@@ -2356,7 +2356,7 @@
   {
    "cell_type": "code",
    "execution_count": 81,
-   "id": "2cba2c66",
+   "id": "1853031a",
    "metadata": {},
    "outputs": [
     {
@@ -2376,7 +2376,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a130d342",
+   "id": "4c2c3f7e",
    "metadata": {},
    "source": [
     "It is not necessary to separate each dimension’s index into its own set of square brackets."
@@ -2385,7 +2385,7 @@
   {
    "cell_type": "code",
    "execution_count": 82,
-   "id": "cf7fd924",
+   "id": "d02e2655",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2395,7 +2395,7 @@
   {
    "cell_type": "code",
    "execution_count": 83,
-   "id": "8dd6b459",
+   "id": "90cc832e",
    "metadata": {},
    "outputs": [
     {
@@ -2416,7 +2416,7 @@
   {
    "cell_type": "code",
    "execution_count": 84,
-   "id": "385451e4",
+   "id": "a10c38da",
    "metadata": {},
    "outputs": [
     {
@@ -2436,7 +2436,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "017ffd97",
+   "id": "f4f08161",
    "metadata": {},
    "source": [
     "Note that If one indexes a multidimensional array with fewer indices than dimensions, one gets a subdimensional array. For example:"
@@ -2445,7 +2445,7 @@
   {
    "cell_type": "code",
    "execution_count": 85,
-   "id": "2d0ab08c",
+   "id": "eb3a616a",
    "metadata": {},
    "outputs": [
     {
@@ -2465,7 +2465,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc5ad96a",
+   "id": "377b05fe",
    "metadata": {},
    "source": [
     "That is, each index specified selects the array corresponding to the rest of the dimensions selected. In the above example, choosing 0 means that the remaining dimension of length 5 is being left unspecified, and that what is returned is an array of that dimensionality and size. It must be noted that the returned array is a view, i.e., it is not a copy of the original, but points to the same values in memory as does the original array. In this case, the 1-D array at the first position (0) is returned. So using a single index on the returned array, results in a single element being returned. That is:"
@@ -2474,7 +2474,7 @@
   {
    "cell_type": "code",
    "execution_count": 86,
-   "id": "e9773a98",
+   "id": "6537ef11",
    "metadata": {},
    "outputs": [
     {
@@ -2494,7 +2494,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af6801d1",
+   "id": "48837a75",
    "metadata": {},
    "source": [
     "So note that `x[0, 2] == x[0][2]` though the second case is more inefficient as a new temporary array is created after the first index that is subsequently indexed by 2.\n",
@@ -2521,7 +2521,7 @@
   {
    "cell_type": "code",
    "execution_count": 87,
-   "id": "2f149759",
+   "id": "14dd9f2f",
    "metadata": {},
    "outputs": [
     {
@@ -2542,7 +2542,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "35667987",
+   "id": "16627f0f",
    "metadata": {},
    "source": [
     "- Negative *i* and *j* are interpreted as *n + i* and *n + j* where *n* is the number of elements in the corresponding dimension. Negative *k* makes stepping go towards smaller indices. From the above example:"
@@ -2551,7 +2551,7 @@
   {
    "cell_type": "code",
    "execution_count": 88,
-   "id": "44db88e7",
+   "id": "0a0b3c3d",
    "metadata": {},
    "outputs": [
     {
@@ -2572,7 +2572,7 @@
   {
    "cell_type": "code",
    "execution_count": 89,
-   "id": "11fad372",
+   "id": "80e5ec1b",
    "metadata": {},
    "outputs": [
     {
@@ -2592,7 +2592,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f0dd1c77",
+   "id": "3e1c8d05",
    "metadata": {},
    "source": [
     "- Assume *n* is the number of elements in the dimension being sliced. Then, if *i* is not given it defaults to 0 for *k > 0* and *n - 1* for *k < 0*. If *j* is not given it defaults to *n* for *k > 0* and *-n-1* for *k < 0*. If *k* is not given it defaults to 1. Note that `::` is the same as : and means select all indices along this axis. From the above example:"
@@ -2601,7 +2601,7 @@
   {
    "cell_type": "code",
    "execution_count": 90,
-   "id": "b3785714",
+   "id": "51c83c7c",
    "metadata": {},
    "outputs": [
     {
@@ -2621,7 +2621,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "84b54d71",
+   "id": "fdc8656b",
    "metadata": {},
    "source": [
     "- If the number of objects in the selection tuple is less than N, then `:` is assumed for any subsequent dimensions. For example:"
@@ -2630,7 +2630,7 @@
   {
    "cell_type": "code",
    "execution_count": 91,
-   "id": "35b6e687",
+   "id": "5162f77e",
    "metadata": {},
    "outputs": [
     {
@@ -2652,7 +2652,7 @@
   {
    "cell_type": "code",
    "execution_count": 92,
-   "id": "65e42df0",
+   "id": "a57b6bca",
    "metadata": {},
    "outputs": [
     {
@@ -2674,7 +2674,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6020e8bb",
+   "id": "944e3a00",
    "metadata": {},
    "source": [
     "- An integer, *i*, returns the same values as `i:i+1` **except** the dimensionality of the returned object is reduced by 1. In particular, a selection tuple with the *p*-th element an integer (and all other entries *:*) returns the corresponding sub-array with dimension *N - 1*. If *N = 1* then the returned object is an array scalar.\n",
@@ -2699,7 +2699,7 @@
   {
    "cell_type": "code",
    "execution_count": 93,
-   "id": "b8f5e39a",
+   "id": "d313c9c9",
    "metadata": {},
    "outputs": [
     {
@@ -2720,7 +2720,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9b13174d",
+   "id": "accce0d4",
    "metadata": {},
    "source": [
     "This is equivalent to:"
@@ -2729,7 +2729,7 @@
   {
    "cell_type": "code",
    "execution_count": 94,
-   "id": "e48cbad4",
+   "id": "962ce5bd",
    "metadata": {},
    "outputs": [
     {
@@ -2750,7 +2750,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "efe6e51b",
+   "id": "d124c0c9",
    "metadata": {},
    "source": [
     "Each `newaxis` object in the selection tuple serves to expand the dimensions of the resulting selection by one unit-length dimension. The added dimension is the position of the `newaxis` object in the selection tuple. `newaxis` is an alias for `None`, and `None` can be used in place of this with the same result. From the above example:"
@@ -2759,7 +2759,7 @@
   {
    "cell_type": "code",
    "execution_count": 95,
-   "id": "8f790053",
+   "id": "721c8994",
    "metadata": {},
    "outputs": [
     {
@@ -2780,7 +2780,7 @@
   {
    "cell_type": "code",
    "execution_count": 96,
-   "id": "aaa97314",
+   "id": "c113639e",
    "metadata": {},
    "outputs": [
     {
@@ -2800,7 +2800,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "288877fe",
+   "id": "358ff1fa",
    "metadata": {},
    "source": [
     "This can be handy to combine two arrays in a way that otherwise would require explicit reshaping operations. For example:"
@@ -2809,7 +2809,7 @@
   {
    "cell_type": "code",
    "execution_count": 97,
-   "id": "335ee5e0",
+   "id": "c5a789a0",
    "metadata": {},
    "outputs": [
     {
@@ -2834,7 +2834,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f09efbf8",
+   "id": "fbffca75",
    "metadata": {},
    "source": [
     "### Advanced indexing\n",
@@ -2857,7 +2857,7 @@
   {
    "cell_type": "code",
    "execution_count": 98,
-   "id": "8eada8fb",
+   "id": "8b144bd4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2867,7 +2867,7 @@
   {
    "cell_type": "code",
    "execution_count": 99,
-   "id": "398eded1",
+   "id": "5cc04bed",
    "metadata": {},
    "outputs": [
     {
@@ -2888,7 +2888,7 @@
   {
    "cell_type": "code",
    "execution_count": 100,
-   "id": "5e2f761b",
+   "id": "f562a26b",
    "metadata": {},
    "outputs": [
     {
@@ -2909,7 +2909,7 @@
   {
    "cell_type": "code",
    "execution_count": 101,
-   "id": "ed86aab1",
+   "id": "d4269301",
    "metadata": {},
    "outputs": [
     {
@@ -2929,7 +2929,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0f8aefb6",
+   "id": "a9125b73",
    "metadata": {},
    "source": [
     "If the index values are out of bounds then an `IndexError` is thrown:"
@@ -2938,7 +2938,7 @@
   {
    "cell_type": "code",
    "execution_count": 102,
-   "id": "fe15e74f",
+   "id": "76b7edb0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2948,7 +2948,7 @@
   {
    "cell_type": "code",
    "execution_count": 103,
-   "id": "43a5ddce",
+   "id": "b9cbd184",
    "metadata": {},
    "outputs": [
     {
@@ -2969,7 +2969,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b457fb61",
+   "id": "e395168c",
    "metadata": {},
    "source": [
     "```py\n",
@@ -2999,7 +2999,7 @@
   {
    "cell_type": "code",
    "execution_count": 104,
-   "id": "db069398",
+   "id": "8ab458d8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3009,7 +3009,7 @@
   {
    "cell_type": "code",
    "execution_count": 105,
-   "id": "8ed17408",
+   "id": "1f2bcb11",
    "metadata": {},
    "outputs": [
     {
@@ -3034,7 +3034,7 @@
   {
    "cell_type": "code",
    "execution_count": 106,
-   "id": "4b21f580",
+   "id": "820062fc",
    "metadata": {},
    "outputs": [
     {
@@ -3054,7 +3054,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e0d1bc3",
+   "id": "db53daac",
    "metadata": {},
    "source": [
     "In this case, if the index arrays have a matching shape, and there is an index array for each dimension of the array being indexed, the resultant array has the same shape as the index arrays, and the values correspond to the index set for each position in the index arrays. In this example, the first index value is 0 for both index arrays, and thus the first value of the resultant array is `y[0, 0]`. The next value is `y[2, 1]`, and the last is `y[4, 2]`.\n",
@@ -3077,7 +3077,7 @@
   {
    "cell_type": "code",
    "execution_count": 107,
-   "id": "e981c0e7",
+   "id": "1b1a1b5a",
    "metadata": {},
    "outputs": [
     {
@@ -3097,7 +3097,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "42d5cf43",
+   "id": "0cee103d",
    "metadata": {},
    "source": [
     "Jumping to the next level of complexity, it is possible to only partially index an array with index arrays. It takes a bit of thought to understand what happens in such cases. For example if we just use one index array with y:"
@@ -3106,7 +3106,7 @@
   {
    "cell_type": "code",
    "execution_count": 108,
-   "id": "779a552e",
+   "id": "8406aaa9",
    "metadata": {},
    "outputs": [
     {
@@ -3128,7 +3128,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7acc03a7",
+   "id": "5df0f65d",
    "metadata": {},
    "source": [
     "It results in the construction of a new array where each value of the index array selects one row from the array being indexed and the resultant array has the resulting shape (number of index elements, size of row).\n",
@@ -3143,7 +3143,7 @@
   {
    "cell_type": "code",
    "execution_count": 109,
-   "id": "fe866db9",
+   "id": "93d197df",
    "metadata": {},
    "outputs": [
     {
@@ -3164,7 +3164,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4209949f",
+   "id": "477e572b",
    "metadata": {},
    "source": [
     "To achieve a behaviour similar to the basic slicing above, broadcasting can be used. The function `ix_` can help with this broadcasting. This is best understood with an example.\n",
@@ -3177,7 +3177,7 @@
   {
    "cell_type": "code",
    "execution_count": 110,
-   "id": "acce8a3c",
+   "id": "33c0ea04",
    "metadata": {},
    "outputs": [
     {
@@ -3206,7 +3206,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a57a76d8",
+   "id": "4fba67c2",
    "metadata": {},
    "source": [
     "However, since the indexing arrays above just repeat themselves, broadcasting can be used (compare operations such as `rows[:, np.newaxis] + columns`) to simplify this:"
@@ -3215,7 +3215,7 @@
   {
    "cell_type": "code",
    "execution_count": 111,
-   "id": "dcfb7922",
+   "id": "4445fc8a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3226,7 +3226,7 @@
   {
    "cell_type": "code",
    "execution_count": 112,
-   "id": "1ef79fc2",
+   "id": "690e98fd",
    "metadata": {},
    "outputs": [
     {
@@ -3248,7 +3248,7 @@
   {
    "cell_type": "code",
    "execution_count": 113,
-   "id": "0e00e25b",
+   "id": "ac5928f6",
    "metadata": {},
    "outputs": [
     {
@@ -3269,7 +3269,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4ede1de7",
+   "id": "c2e7743d",
    "metadata": {},
    "source": [
     "This broadcasting can also be achieved using the function `ix_`:"
@@ -3278,7 +3278,7 @@
   {
    "cell_type": "code",
    "execution_count": 114,
-   "id": "d04223ec",
+   "id": "8b37f49c",
    "metadata": {},
    "outputs": [
     {
@@ -3299,7 +3299,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7d041345",
+   "id": "8df9fdc5",
    "metadata": {},
    "source": [
     "Note that without the `np.ix_` call, only the diagonal elements would be selected:"
@@ -3308,7 +3308,7 @@
   {
    "cell_type": "code",
    "execution_count": 115,
-   "id": "05d8d64e",
+   "id": "60cd151b",
    "metadata": {},
    "outputs": [
     {
@@ -3328,7 +3328,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "beb8e135",
+   "id": "40cb41d0",
    "metadata": {},
    "source": [
     "This difference is the most important thing to remember about indexing with multiple advanced indices.\n",
@@ -3349,7 +3349,7 @@
   {
    "cell_type": "code",
    "execution_count": 116,
-   "id": "44da7138",
+   "id": "034f6e88",
    "metadata": {},
    "outputs": [
     {
@@ -3370,7 +3370,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cd396682",
+   "id": "7ec2b126",
    "metadata": {},
    "source": [
     "Or wish to add a constant to all negative elements:"
@@ -3379,7 +3379,7 @@
   {
    "cell_type": "code",
    "execution_count": 117,
-   "id": "1dc7a4cc",
+   "id": "776e538b",
    "metadata": {},
    "outputs": [
     {
@@ -3401,7 +3401,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8f45838c",
+   "id": "c48ff2a1",
    "metadata": {},
    "source": [
     "In general if an index includes a Boolean array, the result will be identical to inserting `obj.nonzero()` into the same position and using the integer array indexing mechanism described above. `x[ind_1, boolean_array, ind_2]` is equivalent to `x[(ind_1,) + boolean_array.nonzero() + (ind_2,)]`.\n",
@@ -3414,7 +3414,7 @@
   {
    "cell_type": "code",
    "execution_count": 118,
-   "id": "6f77151a",
+   "id": "3f3b00b5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3425,7 +3425,7 @@
   {
    "cell_type": "code",
    "execution_count": 119,
-   "id": "37df0c15",
+   "id": "4db6538a",
    "metadata": {},
    "outputs": [
     {
@@ -3446,7 +3446,7 @@
   {
    "cell_type": "code",
    "execution_count": 120,
-   "id": "dad25983",
+   "id": "f2b3c754",
    "metadata": {},
    "outputs": [
     {
@@ -3467,7 +3467,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "79fd7adc",
+   "id": "67b5790c",
    "metadata": {},
    "source": [
     "Here the 4th and 5th rows are selected from the indexed array and combined to make a 2-D array.\n",
@@ -3480,7 +3480,7 @@
   {
    "cell_type": "code",
    "execution_count": 121,
-   "id": "d191c8ac",
+   "id": "ac321cea",
    "metadata": {},
    "outputs": [
     {
@@ -3503,7 +3503,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "86a6f10e",
+   "id": "5d2c03c1",
    "metadata": {},
    "source": [
     "Combining multiple Boolean indexing arrays or a Boolean with an integer indexing array can best be understood with the `obj.nonzero()` analogy. The function `ix_` also supports boolean arrays and will work without any surprises.\n",
@@ -3516,7 +3516,7 @@
   {
    "cell_type": "code",
    "execution_count": 122,
-   "id": "7daa4905",
+   "id": "f806b15f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3530,7 +3530,7 @@
   {
    "cell_type": "code",
    "execution_count": 123,
-   "id": "36c22ceb",
+   "id": "e7bf6120",
    "metadata": {},
    "outputs": [
     {
@@ -3551,7 +3551,7 @@
   {
    "cell_type": "code",
    "execution_count": 124,
-   "id": "66fa505e",
+   "id": "21dd0d79",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3561,7 +3561,7 @@
   {
    "cell_type": "code",
    "execution_count": 125,
-   "id": "f3b1332f",
+   "id": "2b9faab4",
    "metadata": {},
    "outputs": [
     {
@@ -3582,7 +3582,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0fb89ffb",
+   "id": "495bb350",
    "metadata": {},
    "source": [
     "Without the n`p.ix_` call, only the diagonal elements would be selected.\n",
@@ -3593,7 +3593,7 @@
   {
    "cell_type": "code",
    "execution_count": 126,
-   "id": "e8c77928",
+   "id": "964c9d9e",
    "metadata": {},
    "outputs": [
     {
@@ -3615,7 +3615,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "68e3526b",
+   "id": "66bcdf85",
    "metadata": {},
    "source": [
     "##### Example 3\n",
@@ -3626,7 +3626,7 @@
   {
    "cell_type": "code",
    "execution_count": 127,
-   "id": "09195242",
+   "id": "a31b7d5a",
    "metadata": {},
    "outputs": [
     {
@@ -3654,7 +3654,7 @@
   {
    "cell_type": "code",
    "execution_count": 128,
-   "id": "d25ecd0d",
+   "id": "ae893b90",
    "metadata": {},
    "outputs": [
     {
@@ -3678,7 +3678,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1e1f581b",
+   "id": "c8f5879e",
    "metadata": {},
    "source": [
     "#### Combining advanced and basic indexing\n",
@@ -3691,7 +3691,7 @@
   {
    "cell_type": "code",
    "execution_count": 129,
-   "id": "49b36eac",
+   "id": "edd90b6c",
    "metadata": {},
    "outputs": [
     {
@@ -3714,7 +3714,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "05b01a82",
+   "id": "1ec3cf31",
    "metadata": {},
    "source": [
     "In effect, the slice and index array operation are independent. The slice operation extracts columns with index 1 and 2, (i.e. the 2nd and 3rd columns), followed by the index array operation which extracts rows with index 0, 2 and 4 (i.e the first, third and fifth rows). This is equivalent to:"
@@ -3723,7 +3723,7 @@
   {
    "cell_type": "code",
    "execution_count": 130,
-   "id": "dc8de3f5",
+   "id": "fff9b422",
    "metadata": {},
    "outputs": [
     {
@@ -3745,7 +3745,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11d142be",
+   "id": "9ec5d536",
    "metadata": {},
    "source": [
     "A single advanced index can, for example, replace a slice and the result array will be the same. However, it is a copy and may have a different memory layout. A slice is preferable when it is possible. For example:"
@@ -3754,7 +3754,7 @@
   {
    "cell_type": "code",
    "execution_count": 131,
-   "id": "30c4d8e3",
+   "id": "750ad1c4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3767,7 +3767,7 @@
   {
    "cell_type": "code",
    "execution_count": 132,
-   "id": "0dc53392",
+   "id": "d9ba63fc",
    "metadata": {},
    "outputs": [
     {
@@ -3788,7 +3788,7 @@
   {
    "cell_type": "code",
    "execution_count": 133,
-   "id": "ebc71852",
+   "id": "5baf9d82",
    "metadata": {},
    "outputs": [
     {
@@ -3808,7 +3808,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ef0a5105",
+   "id": "07e4d279",
    "metadata": {},
    "source": [
     "The easiest way to understand a combination of multiple advanced indices may be to think in terms of the resulting shape. There are two parts to the indexing operation, the subspace defined by the basic indexing (excluding integers) and the subspace from the advanced indexing part. Two cases of index combination need to be distinguished:\n",
@@ -3834,7 +3834,7 @@
   {
    "cell_type": "code",
    "execution_count": 134,
-   "id": "b8ed890a",
+   "id": "31fa379d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3845,7 +3845,7 @@
   {
    "cell_type": "code",
    "execution_count": 135,
-   "id": "3489681a",
+   "id": "b10397fd",
    "metadata": {},
    "outputs": [
     {
@@ -3870,7 +3870,7 @@
   {
    "cell_type": "code",
    "execution_count": 136,
-   "id": "a8c8e911",
+   "id": "256d7468",
    "metadata": {},
    "outputs": [
     {
@@ -3891,7 +3891,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5e087c4b",
+   "id": "ed03ee54",
    "metadata": {},
    "source": [
     "### Field access\n",
@@ -3908,7 +3908,7 @@
   {
    "cell_type": "code",
    "execution_count": 137,
-   "id": "1e18afad",
+   "id": "88408175",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3918,7 +3918,7 @@
   {
    "cell_type": "code",
    "execution_count": 138,
-   "id": "96e3f91b",
+   "id": "5ab1560f",
    "metadata": {},
    "outputs": [
     {
@@ -3939,7 +3939,7 @@
   {
    "cell_type": "code",
    "execution_count": 139,
-   "id": "9ccd1832",
+   "id": "ec1a8c24",
    "metadata": {},
    "outputs": [
     {
@@ -3960,7 +3960,7 @@
   {
    "cell_type": "code",
    "execution_count": 140,
-   "id": "0f4fe3b9",
+   "id": "7f9eaba3",
    "metadata": {},
    "outputs": [
     {
@@ -3981,7 +3981,7 @@
   {
    "cell_type": "code",
    "execution_count": 141,
-   "id": "c14cf120",
+   "id": "026e6417",
    "metadata": {},
    "outputs": [
     {
@@ -4001,7 +4001,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c2bf4e3c",
+   "id": "e70c7aa1",
    "metadata": {},
    "source": [
     "### Flat Iterator indexing\n",
@@ -4016,7 +4016,7 @@
   {
    "cell_type": "code",
    "execution_count": 142,
-   "id": "443145e3",
+   "id": "f01bf460",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4026,7 +4026,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1252b01f",
+   "id": "12d0c0d7",
    "metadata": {},
    "source": [
     "Or an array of the right size:"
@@ -4035,7 +4035,7 @@
   {
    "cell_type": "code",
    "execution_count": 143,
-   "id": "7129a2b7",
+   "id": "83d436ff",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4044,7 +4044,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1e29d072",
+   "id": "506ceb99",
    "metadata": {},
    "source": [
     "Note that assignments may result in changes if assigning higher types to lower types (like floats to ints) or even exceptions (assigning complex to floats or ints):"
@@ -4053,7 +4053,7 @@
   {
    "cell_type": "code",
    "execution_count": 144,
-   "id": "3f819544",
+   "id": "c247723c",
    "metadata": {},
    "outputs": [
     {
@@ -4074,7 +4074,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "205c6ffc",
+   "id": "58afbf47",
    "metadata": {},
    "source": [
     "```py\n",
@@ -4093,7 +4093,7 @@
   {
    "cell_type": "code",
    "execution_count": 145,
-   "id": "30c3c0fa",
+   "id": "4f172c9c",
    "metadata": {},
    "outputs": [
     {
@@ -4115,7 +4115,7 @@
   {
    "cell_type": "code",
    "execution_count": 146,
-   "id": "0d60158c",
+   "id": "e0469b3d",
    "metadata": {},
    "outputs": [
     {
@@ -4136,7 +4136,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a62a96e3",
+   "id": "9c6e003b",
    "metadata": {},
    "source": [
     "Where people expect that the 1st location will be incremented by 3. In fact, it will only be incremented by 1. The reason is that a new array is extracted from the original (as a temporary) containing the values at 1, 1, 3, 1, then the value 1 is added to the temporary, and then the temporary is assigned back to the original array. Thus the value of the array at `x[1] + 1` is assigned to `x[1]` three times, rather than being incremented 3 times.\n",
@@ -4149,7 +4149,7 @@
   {
    "cell_type": "code",
    "execution_count": 147,
-   "id": "746e7d9b",
+   "id": "10981ef7",
    "metadata": {},
    "outputs": [
     {
@@ -4171,7 +4171,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "57265d49",
+   "id": "9794d68a",
    "metadata": {},
    "source": [
     "So one can use code to construct tuples of any number of indices and then use these within an index.\n",
@@ -4182,7 +4182,7 @@
   {
    "cell_type": "code",
    "execution_count": 148,
-   "id": "6d3bb9ca",
+   "id": "7abf8d4f",
    "metadata": {},
    "outputs": [
     {
@@ -4203,7 +4203,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d74fcb01",
+   "id": "e5546b1b",
    "metadata": {},
    "source": [
     "Likewise, ellipsis can be specified by code by using the Ellipsis object:"
@@ -4212,7 +4212,7 @@
   {
    "cell_type": "code",
    "execution_count": 149,
-   "id": "41daa85d",
+   "id": "f17ac336",
    "metadata": {},
    "outputs": [
     {
@@ -4235,7 +4235,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b37738a",
+   "id": "782b633e",
    "metadata": {},
    "source": [
     "For this reason, it is possible to use the output from the `np.nonzero()` function directly as an index since it always returns a tuple of index arrays.\n",
@@ -4246,7 +4246,7 @@
   {
    "cell_type": "code",
    "execution_count": 150,
-   "id": "37249d96",
+   "id": "32df210d",
    "metadata": {},
    "outputs": [
     {
@@ -4316,7 +4316,7 @@
   {
    "cell_type": "code",
    "execution_count": 151,
-   "id": "52c5ec3f",
+   "id": "ae43b357",
    "metadata": {},
    "outputs": [
     {
@@ -4336,7 +4336,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5c85a33e",
+   "id": "e8da70c1",
    "metadata": {},
    "source": [
     "## Structured arrays\n",
@@ -4349,7 +4349,7 @@
   {
    "cell_type": "code",
    "execution_count": 152,
-   "id": "2b3ca6b6",
+   "id": "5cefcabd",
    "metadata": {},
    "outputs": [
     {
@@ -4372,7 +4372,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "24e60573",
+   "id": "55522e67",
    "metadata": {},
    "source": [
     "Here `x` is a one-dimensional array of length two whose datatype is a structure with three fields: 1. A string of length 10 or less named `'name'`, 2. a 32-bit integer named `'age'`, and 3. a 32-bit float named `'weight'`.\n",
@@ -4383,7 +4383,7 @@
   {
    "cell_type": "code",
    "execution_count": 153,
-   "id": "03530929",
+   "id": "26d5311d",
    "metadata": {},
    "outputs": [
     {
@@ -4403,7 +4403,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5aa003e0",
+   "id": "6a829e6d",
    "metadata": {},
    "source": [
     "You can access and modify individual fields of a structured array by indexing with the field name:"
@@ -4412,7 +4412,7 @@
   {
    "cell_type": "code",
    "execution_count": 154,
-   "id": "4519c096",
+   "id": "99add1a0",
    "metadata": {},
    "outputs": [
     {
@@ -4433,7 +4433,7 @@
   {
    "cell_type": "code",
    "execution_count": 155,
-   "id": "b184f47a",
+   "id": "0549b6f6",
    "metadata": {},
    "outputs": [
     {
@@ -4454,7 +4454,7 @@
   {
    "cell_type": "code",
    "execution_count": 156,
-   "id": "5144813b",
+   "id": "a46b7c66",
    "metadata": {},
    "outputs": [
     {
@@ -4475,7 +4475,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af28def1",
+   "id": "421614cf",
    "metadata": {},
    "source": [
     "Structured datatypes are designed to be able to mimic 'structs' in the C language, and share a similar memory layout. They are meant for interfacing with C code and for low-level manipulation of structured buffers, for example for interpreting binary blobs. For these purposes they support specialized features such as subarrays, nested datatypes, and unions, and allow control over the memory layout of the structure.\n",
@@ -4498,7 +4498,7 @@
   {
    "cell_type": "code",
    "execution_count": 157,
-   "id": "eab3d997",
+   "id": "ac3ad698",
    "metadata": {},
    "outputs": [
     {
@@ -4518,7 +4518,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "044e83b9",
+   "id": "256d4b96",
    "metadata": {},
    "source": [
     "If `fieldname` is the empty string `''`, the field will be given a default name of the form `f#`, where `#` is the integer index of the field, counting from 0 from the left:"
@@ -4527,7 +4527,7 @@
   {
    "cell_type": "code",
    "execution_count": 158,
-   "id": "91f63ef4",
+   "id": "1d16916c",
    "metadata": {},
    "outputs": [
     {
@@ -4547,7 +4547,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11104dc3",
+   "id": "5c1fea12",
    "metadata": {},
    "source": [
     "The byte offsets of the fields within the structure and the total structure itemsize are determined automatically.\n",
@@ -4560,7 +4560,7 @@
   {
    "cell_type": "code",
    "execution_count": 159,
-   "id": "9c639147",
+   "id": "4ac7166d",
    "metadata": {},
    "outputs": [
     {
@@ -4581,7 +4581,7 @@
   {
    "cell_type": "code",
    "execution_count": 160,
-   "id": "e1a2d85c",
+   "id": "a97720c4",
    "metadata": {},
    "outputs": [
     {
@@ -4601,7 +4601,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "84210698",
+   "id": "a8b0360e",
    "metadata": {},
    "source": [
     "- A dictionary of field parameter arrays\n",
@@ -4614,7 +4614,7 @@
   {
    "cell_type": "code",
    "execution_count": 161,
-   "id": "6ff0c915",
+   "id": "6aa4b702",
    "metadata": {},
    "outputs": [
     {
@@ -4635,7 +4635,7 @@
   {
    "cell_type": "code",
    "execution_count": 162,
-   "id": "99ccdcd1",
+   "id": "9c0848da",
    "metadata": {},
    "outputs": [
     {
@@ -4658,7 +4658,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "243a9bc2",
+   "id": "054943d8",
    "metadata": {},
    "source": [
     "Offsets may be chosen such that the fields overlap, though this will mean that assigning to one field may clobber any overlapping field’s data. As an exception, fields of `numpy.object_` type cannot overlap with other fields, because of the risk of clobbering the internal object pointer and then dereferencing it.\n",
@@ -4673,7 +4673,7 @@
   {
    "cell_type": "code",
    "execution_count": 163,
-   "id": "e49a62f4",
+   "id": "ab5f6e64",
    "metadata": {},
    "outputs": [
     {
@@ -4693,7 +4693,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "486da6db",
+   "id": "54333b97",
    "metadata": {},
    "source": [
     "#### Manipulating and Displaying Structured Datatypes\n",
@@ -4704,7 +4704,7 @@
   {
    "cell_type": "code",
    "execution_count": 164,
-   "id": "30432953",
+   "id": "0b1c3e34",
    "metadata": {},
    "outputs": [
     {
@@ -4725,7 +4725,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fec39dcc",
+   "id": "e9478ce8",
    "metadata": {},
    "source": [
     "The field names may be modified by assigning to the `names` attribute using a sequence of strings of the same length.\n",
@@ -4736,7 +4736,7 @@
   {
    "cell_type": "code",
    "execution_count": 165,
-   "id": "02da466d",
+   "id": "2cec035d",
    "metadata": {},
    "outputs": [
     {
@@ -4756,7 +4756,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b8bf3dde",
+   "id": "af50c892",
    "metadata": {},
    "source": [
     "Both the `names` and `fields` attributes will equal `None` for unstructured arrays. The recommended way to test if a dtype is structured is with `if dt.names is not None` rather than `if dt.names`, to account for dtypes with 0 fields.\n",
@@ -4773,7 +4773,7 @@
   {
    "cell_type": "code",
    "execution_count": 166,
-   "id": "b57457b6",
+   "id": "f5386c1b",
    "metadata": {},
    "outputs": [
     {
@@ -4793,7 +4793,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cfda92a2",
+   "id": "548a48d2",
    "metadata": {},
    "source": [
     "When using the first form of dictionary-based specification, the titles may be supplied as an extra `'titles'` key as described above. When using the second (discouraged) dictionary-based specification, the title can be supplied by providing a 3-element tuple `(datatype, offset, title)` instead of the usual 2-element tuple:"
@@ -4802,7 +4802,7 @@
   {
    "cell_type": "code",
    "execution_count": 167,
-   "id": "ee569a05",
+   "id": "2d913a45",
    "metadata": {},
    "outputs": [
     {
@@ -4822,7 +4822,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3277a2cd",
+   "id": "37eb8002",
    "metadata": {},
    "source": [
     "The `dtype.fields` dictionary will contain titles as keys, if any titles are used. This means effectively that a field with a title will be represented twice in the fields dictionary. The tuple values for these fields will also have a third element, the field title. Because of this, and because the `names` attribute preserves the field order while the `fields` attribute may not, it is recommended to iterate through the fields of a dtype using the `names` attribute of the dtype, which will not list titles, as in:"
@@ -4831,7 +4831,7 @@
   {
    "cell_type": "code",
    "execution_count": 168,
-   "id": "8c57aa70",
+   "id": "d16b0ad2",
    "metadata": {},
    "outputs": [
     {
@@ -4850,7 +4850,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16d148df",
+   "id": "8759fab3",
    "metadata": {},
    "source": [
     "### Indexing and Assignment to Structured arrays\n",
@@ -4867,7 +4867,7 @@
   {
    "cell_type": "code",
    "execution_count": 169,
-   "id": "c1767431",
+   "id": "9d311732",
    "metadata": {},
    "outputs": [
     {
@@ -4890,7 +4890,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "460cc3a0",
+   "id": "84bab5d9",
    "metadata": {},
    "source": [
     "##### Assignment from Scalars\n",
@@ -4901,7 +4901,7 @@
   {
    "cell_type": "code",
    "execution_count": 170,
-   "id": "a2f95210",
+   "id": "100aaf82",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4911,7 +4911,7 @@
   {
    "cell_type": "code",
    "execution_count": 171,
-   "id": "f6508c1e",
+   "id": "0048996d",
    "metadata": {},
    "outputs": [
     {
@@ -4934,7 +4934,7 @@
   {
    "cell_type": "code",
    "execution_count": 172,
-   "id": "2978d044",
+   "id": "027b14a6",
    "metadata": {},
    "outputs": [
     {
@@ -4956,7 +4956,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "41b5c458",
+   "id": "28756d42",
    "metadata": {},
    "source": [
     "Structured arrays can also be assigned to unstructured arrays, but only if the structured datatype has just a single field:"
@@ -4965,7 +4965,7 @@
   {
    "cell_type": "code",
    "execution_count": 173,
-   "id": "92ea74fe",
+   "id": "41bf2d2f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4976,7 +4976,7 @@
   {
    "cell_type": "code",
    "execution_count": 174,
-   "id": "84dc517f",
+   "id": "538e2c1f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4985,7 +4985,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b7f6be10",
+   "id": "8fe3c666",
    "metadata": {},
    "source": [
     "```py\n",
@@ -5006,7 +5006,7 @@
   {
    "cell_type": "code",
    "execution_count": 175,
-   "id": "ca1ecf16",
+   "id": "162d0c94",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5017,7 +5017,7 @@
   {
    "cell_type": "code",
    "execution_count": 176,
-   "id": "93ba18f3",
+   "id": "925e0b34",
    "metadata": {},
    "outputs": [
     {
@@ -5039,7 +5039,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "56c5ef05",
+   "id": "a4f69851",
    "metadata": {},
    "source": [
     "##### Assignment involving subarrays\n",
@@ -5056,7 +5056,7 @@
   {
    "cell_type": "code",
    "execution_count": 177,
-   "id": "ac31c05a",
+   "id": "860edddb",
    "metadata": {},
    "outputs": [
     {
@@ -5078,7 +5078,7 @@
   {
    "cell_type": "code",
    "execution_count": 178,
-   "id": "15c030cc",
+   "id": "2906edee",
    "metadata": {},
    "outputs": [
     {
@@ -5099,7 +5099,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "023cf7ab",
+   "id": "de4288a7",
    "metadata": {},
    "source": [
     "The resulting array is a view into the original array. It shares the same memory locations and writing to the view will modify the original array."
@@ -5108,7 +5108,7 @@
   {
    "cell_type": "code",
    "execution_count": 179,
-   "id": "d3e80a81",
+   "id": "0517ddd7",
    "metadata": {},
    "outputs": [
     {
@@ -5130,7 +5130,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "99798e0f",
+   "id": "6276a2d2",
    "metadata": {},
    "source": [
     "This view has the same dtype and itemsize as the indexed field, so it is typically a non-structured array, except in the case of nested structures."
@@ -5139,7 +5139,7 @@
   {
    "cell_type": "code",
    "execution_count": 180,
-   "id": "d65fd0f1",
+   "id": "38039e2e",
    "metadata": {},
    "outputs": [
     {
@@ -5159,7 +5159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "42d65dd3",
+   "id": "9bdaa05a",
    "metadata": {},
    "source": [
     "If the accessed field is a subarray, the dimensions of the subarray are appended to the shape of the result:"
@@ -5168,7 +5168,7 @@
   {
    "cell_type": "code",
    "execution_count": 181,
-   "id": "9dd23b6c",
+   "id": "e2fe5c45",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5178,7 +5178,7 @@
   {
    "cell_type": "code",
    "execution_count": 182,
-   "id": "8a81710f",
+   "id": "dccdc746",
    "metadata": {},
    "outputs": [
     {
@@ -5199,7 +5199,7 @@
   {
    "cell_type": "code",
    "execution_count": 183,
-   "id": "e6496ce2",
+   "id": "2ae5eb1b",
    "metadata": {},
    "outputs": [
     {
@@ -5219,7 +5219,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a7ab274c",
+   "id": "f953ef81",
    "metadata": {},
    "source": [
     "##### Accessing multiple fields\n",
@@ -5232,7 +5232,7 @@
   {
    "cell_type": "code",
    "execution_count": 184,
-   "id": "5e621197",
+   "id": "fefb4f30",
    "metadata": {},
    "outputs": [
     {
@@ -5254,7 +5254,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e27d32c5",
+   "id": "35fcd7ef",
    "metadata": {},
    "source": [
     "Assignment to the view modifies the original array. The view’s fields will be in the order they were indexed. Note that unlike for single-field indexing, the dtype of the view has the same itemsize as the original array, and has fields at the same offsets as in the original array, and unindexed fields are merely missing.\n",
@@ -5265,7 +5265,7 @@
   {
    "cell_type": "code",
    "execution_count": 185,
-   "id": "e76a464f",
+   "id": "02fa87d8",
    "metadata": {},
    "outputs": [
     {
@@ -5287,7 +5287,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "184d118b",
+   "id": "236cdd99",
    "metadata": {},
    "source": [
     "This obeys the structured array assignment rules described above. For example, this means that one can swap the values of two fields using appropriate multi-field indexes:"
@@ -5296,7 +5296,7 @@
   {
    "cell_type": "code",
    "execution_count": 186,
-   "id": "a5bccc2c",
+   "id": "6811c266",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5305,7 +5305,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "277fa2b0",
+   "id": "321227cd",
    "metadata": {},
    "source": [
     "##### Indexing with an integer to get a structured scalar\n",
@@ -5316,7 +5316,7 @@
   {
    "cell_type": "code",
    "execution_count": 187,
-   "id": "9e6936c7",
+   "id": "99941fac",
    "metadata": {},
    "outputs": [
     {
@@ -5339,7 +5339,7 @@
   {
    "cell_type": "code",
    "execution_count": 188,
-   "id": "f8daf6c4",
+   "id": "69f556d9",
    "metadata": {},
    "outputs": [
     {
@@ -5359,7 +5359,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c38f8b5",
+   "id": "322f244e",
    "metadata": {},
    "source": [
     "Unlike other numpy scalars, structured scalars are mutable and act like views into the original array, such that modifying the scalar will modify the original array. Structured scalars also support access and assignment by field name:"
@@ -5368,7 +5368,7 @@
   {
    "cell_type": "code",
    "execution_count": 189,
-   "id": "15b0d410",
+   "id": "275e5aa6",
    "metadata": {},
    "outputs": [
     {
@@ -5391,7 +5391,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d1f2f5d3",
+   "id": "563981e3",
    "metadata": {},
    "source": [
     "Similarly to tuples, structured scalars can also be indexed with an integer:"
@@ -5400,7 +5400,7 @@
   {
    "cell_type": "code",
    "execution_count": 190,
-   "id": "9515443a",
+   "id": "f717fd01",
    "metadata": {},
    "outputs": [
     {
@@ -5422,7 +5422,7 @@
   {
    "cell_type": "code",
    "execution_count": 191,
-   "id": "da73b1a0",
+   "id": "d8da4d5a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5431,7 +5431,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bf2778bd",
+   "id": "ce864c33",
    "metadata": {},
    "source": [
     "Thus, tuples might be thought of as the native Python equivalent to numpy’s structured types, much like native python integers are the equivalent to numpy’s integer types. Structured scalars may be converted to a tuple by calling `numpy.ndarray.item`:"
@@ -5440,7 +5440,7 @@
   {
    "cell_type": "code",
    "execution_count": 192,
-   "id": "93decfca",
+   "id": "73160c68",
    "metadata": {},
    "outputs": [
     {
@@ -5460,7 +5460,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bd29549a",
+   "id": "7a4af1a9",
    "metadata": {},
    "source": [
     "#### Viewing structured arrays containing objects\n",
@@ -5475,7 +5475,7 @@
   {
    "cell_type": "code",
    "execution_count": 193,
-   "id": "b60ef16c",
+   "id": "051080bb",
    "metadata": {},
    "outputs": [
     {
@@ -5497,7 +5497,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5cc1ac49",
+   "id": "a1efc3ce",
    "metadata": {},
    "source": [
     "NumPy will promote individual field datatypes to perform the comparison. So the following is also valid (note the `'f4'` dtype for the `'a'` field):"
@@ -5506,7 +5506,7 @@
   {
    "cell_type": "code",
    "execution_count": 194,
-   "id": "20a74e31",
+   "id": "0ddc6a84",
    "metadata": {},
    "outputs": [
     {
@@ -5527,7 +5527,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bad6c9a9",
+   "id": "86d6712e",
    "metadata": {},
    "source": [
     "To compare two structured arrays, it must be possible to promote them to a common dtype as returned by `numpy.result_type` and `np.promote_types`. This enforces that the number of fields, the field names, and the field titles must match precisely. When promotion is not possible, for example due to mismatching field names, NumPy will raise an error. Promotion between two structured dtypes results in a canonical dtype that ensures native byte-order for all fields:"
@@ -5536,7 +5536,7 @@
   {
    "cell_type": "code",
    "execution_count": 195,
-   "id": "9825eff0",
+   "id": "8525513a",
    "metadata": {},
    "outputs": [
     {
@@ -5557,7 +5557,7 @@
   {
    "cell_type": "code",
    "execution_count": 196,
-   "id": "8f4711e5",
+   "id": "ef0a80f4",
    "metadata": {},
    "outputs": [
     {
@@ -5577,7 +5577,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a07238af",
+   "id": "29f79250",
    "metadata": {},
    "source": [
     "The resulting dtype from promotion is also guaranteed to be packed, meaning that all fields are ordered contiguously and any unnecessary padding is removed:"
@@ -5586,7 +5586,7 @@
   {
    "cell_type": "code",
    "execution_count": 197,
-   "id": "f9db92eb",
+   "id": "78179e76",
    "metadata": {},
    "outputs": [
     {
@@ -5608,7 +5608,7 @@
   {
    "cell_type": "code",
    "execution_count": 198,
-   "id": "eb031a8b",
+   "id": "2646685d",
    "metadata": {},
    "outputs": [
     {
@@ -5628,7 +5628,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "01cd9c17",
+   "id": "e93627e5",
    "metadata": {},
    "source": [
     "Note that the result prints without `offsets` or `itemsize` indicating no additional padding. If a structured dtype is created with `align=True` ensuring that `dtype.isalignedstruct` is true, this property is preserved:"
@@ -5637,7 +5637,7 @@
   {
    "cell_type": "code",
    "execution_count": 199,
-   "id": "b3a3dcd9",
+   "id": "95276731",
    "metadata": {},
    "outputs": [
     {
@@ -5659,7 +5659,7 @@
   {
    "cell_type": "code",
    "execution_count": 200,
-   "id": "6f9a47c5",
+   "id": "ade96302",
    "metadata": {},
    "outputs": [
     {
@@ -5680,7 +5680,7 @@
   {
    "cell_type": "code",
    "execution_count": 201,
-   "id": "da72c119",
+   "id": "8c2516d8",
    "metadata": {},
    "outputs": [
     {
@@ -5700,7 +5700,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "45bd2473",
+   "id": "2e1ff208",
    "metadata": {},
    "source": [
     "When promoting multiple dtypes, the result is aligned if any of the inputs is:"
@@ -5709,7 +5709,7 @@
   {
    "cell_type": "code",
    "execution_count": 202,
-   "id": "ec9df23d",
+   "id": "5989c869",
    "metadata": {},
    "outputs": [
     {
@@ -5729,7 +5729,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "38bf71ae",
+   "id": "944fa2b1",
    "metadata": {},
    "source": [
     "The `<` and `>` operators always return `False` when comparing void structured arrays, and arithmetic and bitwise operations are not supported.\n",
diff --git a/_sources/data-science/working-with-data/pandas.ipynb b/_sources/data-science/working-with-data/pandas.ipynb
deleted file mode 100644
index 8a8106d454..0000000000
--- a/_sources/data-science/working-with-data/pandas.ipynb
+++ /dev/null
@@ -1,10560 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e6545d05",
-   "metadata": {},
-   "source": [
-    "# Pandas\n",
-    "\n",
-    "Pandas is a fast, powerful, flexible and easy-to-use open-source data analysis and manipulation tool, built on top of the Python programming language.\n",
-    "\n",
-    "## Introducing Pandas objects\n",
-    "\n",
-    "We’ll start with a quick, non-comprehensive overview of the fundamental data structures in Pandas to get you started. The fundamental behavior about data types, indexing, axis labeling, and alignment apply across all of the objects. To get started, import NumPy and load Pandas into your namespace:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "25f96f14",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8b24efc0",
-   "metadata": {},
-   "source": [
-    "### Series\n",
-    "\n",
-    "`Series` is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). The axis labels are collectively referred to as the **index**. The basic method to create a `Series` is to call:\n",
-    "\n",
-    "```py\n",
-    "s = pd.Series(data, index=index)\n",
-    "```\n",
-    "\n",
-    "Here, `data` can be many different things:\n",
-    "\n",
-    "- a Python dict\n",
-    "- an ndarray\n",
-    "- a scalar value (like 5)\n",
-    "\n",
-    "\n",
-    "The passed **index** is a list of axis labels. Thus, this separates into a few cases depending on what the **data is**:\n",
-    "\n",
-    "#### Create a Series\n",
-    "\n",
-    "##### From ndarray\n",
-    "\n",
-    "If `data` is an ndarray, **index** must be the same length as the **data**. If no index is passed, one will be created having values `[0, ..., len(data) - 1]`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "fafc0791",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s = pd.Series(np.random.randn(5), index=[\"a\", \"b\", \"c\", \"d\", \"e\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "560c5c88",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a   -0.742503\n",
-       "b   -0.342277\n",
-       "c    2.575226\n",
-       "d   -1.022013\n",
-       "e   -0.549587\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "cd3d68c8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['a', 'b', 'c', 'd', 'e'], dtype='object')"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s.index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "49d15198",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    0.456269\n",
-       "1   -0.744398\n",
-       "2    1.151727\n",
-       "3    1.031141\n",
-       "4    0.833176\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series(np.random.randn(5))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "66bd6806",
-   "metadata": {},
-   "source": [
-    "```{note}\n",
-    "Pandas supports non-unique index values. If an operation that does not support duplicate index values is attempted, an exception will be raised at that time.\n",
-    "```\n",
-    "\n",
-    "##### From dict\n",
-    "`Series` can be instantiated from dicts:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "32934b45",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d = {\"b\": 1, \"a\": 0, \"c\": 2}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "eee7cfc8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "b    1\n",
-       "a    0\n",
-       "c    2\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series(d)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f21e10f5",
-   "metadata": {},
-   "source": [
-    "If an index is passed, the values in data corresponding to the labels in the index will be pulled out."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "437497c1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d = {\"a\": 0.0, \"b\": 1.0, \"c\": 2.0}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "b6a25f03",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    0.0\n",
-       "b    1.0\n",
-       "c    2.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series(d)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "ed79c02e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "b    1.0\n",
-       "c    2.0\n",
-       "d    NaN\n",
-       "a    0.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series(d, index=[\"b\", \"c\", \"d\", \"a\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f159b903",
-   "metadata": {},
-   "source": [
-    "```{note}\n",
-    "NaN (not a number) is the standard missing data marker used in Pandas.\n",
-    "```\n",
-    "\n",
-    "##### From scalar value\n",
-    "\n",
-    "If `data` is a scalar value, an index must be provided. The value will be repeated to match the length of **index**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "01c73a09",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    5.0\n",
-       "b    5.0\n",
-       "c    5.0\n",
-       "d    5.0\n",
-       "e    5.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series(5.0, index=[\"a\", \"b\", \"c\", \"d\", \"e\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1ebbfeed",
-   "metadata": {},
-   "source": [
-    "#### Series is ndarray-like\n",
-    "\n",
-    "`Series` acts very similarly to a `ndarray` and is a valid argument to most NumPy functions. However, operations such as slicing will also slice the index."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "747dbeac",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-0.7425028341431366"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "5f4043d6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a   -0.742503\n",
-       "b   -0.342277\n",
-       "c    2.575226\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s[:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "ea8c664c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "b   -0.342277\n",
-       "c    2.575226\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s[s > s.median()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "6169bfcc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "e   -0.549587\n",
-       "d   -1.022013\n",
-       "b   -0.342277\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s[[4, 3, 1]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "02cafeb2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a     0.475921\n",
-       "b     0.710151\n",
-       "c    13.134280\n",
-       "d     0.359870\n",
-       "e     0.577188\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.exp(s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9d8632b9",
-   "metadata": {},
-   "source": [
-    "Like a NumPy array, a Pandas Series has a single `dtype`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "2bfe8ef9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dtype('float64')"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s.dtype"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d62c251e",
-   "metadata": {},
-   "source": [
-    "If you need the actual array backing a `Series`, use `Series.array`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "54805983",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<PandasArray>\n",
-       "[ -0.7425028341431366, -0.34227718263016094,   2.5752256412522594,\n",
-       "  -1.0220127417608955,  -0.5495868399385304]\n",
-       "Length: 5, dtype: float64"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s.array"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5b4fe37d",
-   "metadata": {},
-   "source": [
-    "While `Series` is ndarray-like, if you need an actual ndarray, then use `Series.to_numpy()`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "45f42682",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-0.74250283, -0.34227718,  2.57522564, -1.02201274, -0.54958684])"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s.to_numpy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "82f41443",
-   "metadata": {},
-   "source": [
-    "Even if the `Series` is backed by an `ExtensionArray`, `Series.to_numpy()` will return a NumPy ndarray.\n",
-    "\n",
-    "#### Series is dict-like\n",
-    "\n",
-    "A `Series` is also like a fixed-size dict in that you can get and set values by index label:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "aba48168",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-0.7425028341431366"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s[\"a\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "c0f5ecac",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s[\"e\"] = 12.0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "2dc38aee",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    -0.742503\n",
-       "b    -0.342277\n",
-       "c     2.575226\n",
-       "d    -1.022013\n",
-       "e    12.000000\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "e0a08b6e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"e\" in s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "8a722b93",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"f\" in s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "07dc3e98",
-   "metadata": {},
-   "source": [
-    "If a label is not contained in the index, an exception is raised:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "fef8ac2e",
-   "metadata": {
-    "tags": [
-     "raises-exception"
-    ]
-   },
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'f'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexes/base.py:3803\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3803\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3804\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/_libs/index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/_libs/index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'f'",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[25], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43ms\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/series.py:981\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    978\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[key]\n\u001b[1;32m    980\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m key_is_scalar:\n\u001b[0;32m--> 981\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    983\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_hashable(key):\n\u001b[1;32m    984\u001b[0m     \u001b[38;5;66;03m# Otherwise index.get_value will raise InvalidIndexError\u001b[39;00m\n\u001b[1;32m    985\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    986\u001b[0m         \u001b[38;5;66;03m# For labels that don't resolve as scalars like tuples and frozensets\u001b[39;00m\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/series.py:1089\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m   1086\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[label]\n\u001b[1;32m   1088\u001b[0m \u001b[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[0;32m-> 1089\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1090\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39m_get_values_for_loc(\u001b[38;5;28mself\u001b[39m, loc, label)\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3803\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3804\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3805\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3808\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3809\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3810\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'f'"
-     ]
-    }
-   ],
-   "source": [
-    "s[\"f\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "523f884b",
-   "metadata": {},
-   "source": [
-    "Using the `Series.get()` method, a missing label will return None or specified default:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "8099f799",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s.get(\"f\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "2a46b41a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "nan"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s.get(\"f\", np.nan)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "04ea686f",
-   "metadata": {},
-   "source": [
-    "These labels can also be accessed by `attribute`.\n",
-    "\n",
-    "#### Vectorized operations and label alignment with Series\n",
-    "\n",
-    "When working with raw NumPy arrays, looping through value-by-value is usually not necessary. The same is true when working with `Series` in Pandas. `Series` can also be passed into most NumPy methods expecting an ndarray."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "360e0f15",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    -1.485006\n",
-       "b    -0.684554\n",
-       "c     5.150451\n",
-       "d    -2.044025\n",
-       "e    24.000000\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s + s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "7e487d83",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    -1.485006\n",
-       "b    -0.684554\n",
-       "c     5.150451\n",
-       "d    -2.044025\n",
-       "e    24.000000\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s * 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "d39cd718",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a         0.475921\n",
-       "b         0.710151\n",
-       "c        13.134280\n",
-       "d         0.359870\n",
-       "e    162754.791419\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.exp(s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a77868f9",
-   "metadata": {},
-   "source": [
-    "A key difference between `Series` and ndarray is that operations between `Series` automatically align the data based on the label. Thus, you can write computations without giving consideration to whether the `Series` involved have the same labels."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "a89967e2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a         NaN\n",
-       "b   -0.684554\n",
-       "c    5.150451\n",
-       "d   -2.044025\n",
-       "e         NaN\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s[1:] + s[:-1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "04969a41",
-   "metadata": {},
-   "source": [
-    "The result of an operation between unaligned `Series` will have the **union** of the indexes involved. If a label is not found in one `Series` or the other, the result will be marked as missing `NaN`. Being able to write code without doing any explicit data alignment grants immense freedom and flexibility in interactive data analysis and research. The integrated data alignment features of the Pandas data structures set Pandas apart from the majority of related tools for working with labeled data.\n",
-    "\n",
-    "```{note}\n",
-    "In general, we chose to make the default result of operations between differently indexed objects yield the **union** of the indexes in order to avoid loss of information. Having an index label, though the data is missing, is typically important information as part of a computation. You of course have the option of dropping labels with missing data via the `dropna` function.\n",
-    "```\n",
-    "\n",
-    "#### Name attribute\n",
-    "\n",
-    "`Series` also has a `name` attribute:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "d96fb6d6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s = pd.Series(np.random.randn(5), name=\"something\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "2f4ce942",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    0.182830\n",
-       "1   -0.931166\n",
-       "2    0.144336\n",
-       "3    0.318988\n",
-       "4    0.426142\n",
-       "Name: something, dtype: float64"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "8f41e676",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'something'"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s.name"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3386207e",
-   "metadata": {},
-   "source": [
-    "The `Series` `name` can be assigned automatically in many cases, in particular, when selecting a single column from a `DataFrame`, the `name` will be assigned the column label.\n",
-    "\n",
-    "You can rename a `Series` with the `pandas.Series.rename()` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "866446da",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s2 = s.rename(\"different\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "c4749ae3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'different'"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s2.name"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eaa2891c",
-   "metadata": {},
-   "source": [
-    "Note that `s` and `s2` refer to different objects.\n",
-    "\n",
-    "### DataFrame\n",
-    "\n",
-    "`DataFrame` is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table, or a `dict` of `Series` objects. It is generally the most commonly used Pandas object. Like `Series`, `DataFrame` accepts many different kinds of input:\n",
-    "\n",
-    "- Dict of 1D ndarrays, lists, dicts, or `Series`\n",
-    "- 2-D `numpy.ndarray`\n",
-    "- Structured or record ndarray\n",
-    "- A `Series`\n",
-    "- Another `DataFrame`\n",
-    "\n",
-    "Along with the data, you can optionally pass **index** (row labels) and **columns** (column labels) arguments. If you pass an index and / or columns, you are guaranteeing the index and / or columns of the resulting `DataFrame`. Thus, a `dict` of Series plus a specific index will discard all data not matching up to the passed index.\n",
-    "\n",
-    "If axis labels are not passed, they will be constructed from the input data based on common sense rules.\n",
-    "\n",
-    "#### Create a Dataframe\n",
-    "\n",
-    "##### From dict of `Series` or dicts\n",
-    "\n",
-    "The resulting **index** will be the **union** of the indexes of the various Series. If there are any nested dicts, these will first be converted to Series. If no columns are passed, the columns will be the ordered list of `dict` keys."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "4ae21572",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d = {\n",
-    "    \"one\": pd.Series([1.0, 2.0, 3.0], index=[\"a\", \"b\", \"c\"]),\n",
-    "    \"two\": pd.Series([1.0, 2.0, 3.0, 4.0], index=[\"a\", \"b\", \"c\", \"d\"]),\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "e79ed1ad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.DataFrame(d)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "c1055653",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>one</th>\n",
-       "      <th>two</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>a</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>b</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>c</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>d</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   one  two\n",
-       "a  1.0  1.0\n",
-       "b  2.0  2.0\n",
-       "c  3.0  3.0\n",
-       "d  NaN  4.0"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "94260c79",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
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-       "      <th></th>\n",
-       "      <th>one</th>\n",
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-       "    </tr>\n",
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>d</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>b</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>a</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   one  two\n",
-       "d  NaN  4.0\n",
-       "b  2.0  2.0\n",
-       "a  1.0  1.0"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(d, index=[\"d\", \"b\", \"a\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "89ffccf3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>two</th>\n",
-       "      <th>three</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>d</th>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>b</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>a</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   two three\n",
-       "d  4.0   NaN\n",
-       "b  2.0   NaN\n",
-       "a  1.0   NaN"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(d, index=[\"d\", \"b\", \"a\"], columns=[\"two\", \"three\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "acc1b20f",
-   "metadata": {},
-   "source": [
-    "The row and column labels can be accessed respectively by accessing the **index** and **columns** attributes:\n",
-    "\n",
-    "```{note}\n",
-    "When a particular set of columns is passed along with a dict of data, the passed columns override the keys in the dict.\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "3761167c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['a', 'b', 'c', 'd'], dtype='object')"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "697fb1e6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['one', 'two'], dtype='object')"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.columns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ee5d3d4c",
-   "metadata": {},
-   "source": [
-    "##### From dict of ndarrays / lists\n",
-    "\n",
-    "The ndarrays must all be the same length. If an index is passed, it must also be the same length as the arrays. If no index is passed, the result will be `range(n)`, where `n` is the array length."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "c2880980",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d = {\"one\": [1.0, 2.0, 3.0, 4.0], \"two\": [4.0, 3.0, 2.0, 1.0]}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "04fda5ed",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "</style>\n",
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>one</th>\n",
-       "      <th>two</th>\n",
-       "    </tr>\n",
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   one  two\n",
-       "0  1.0  4.0\n",
-       "1  2.0  3.0\n",
-       "2  3.0  2.0\n",
-       "3  4.0  1.0"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(d)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "4f8f0740",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>one</th>\n",
-       "      <th>two</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>a</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>b</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>c</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>d</th>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   one  two\n",
-       "a  1.0  4.0\n",
-       "b  2.0  3.0\n",
-       "c  3.0  2.0\n",
-       "d  4.0  1.0"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(d, index=[\"a\", \"b\", \"c\", \"d\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e7c17cff",
-   "metadata": {},
-   "source": [
-    "##### From structured or record array\n",
-    "\n",
-    "This case is handled identically to a dict of arrays."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "95352bea",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = np.zeros((2,), dtype=[(\"A\", \"i4\"), (\"B\", \"f4\"), (\"C\", \"a10\")])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "5d122d20",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data[:] = [(1, 2.0, \"Hello\"), (2, 3.0, \"World\")]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "86d1496d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th>C</th>\n",
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-       "  </thead>\n",
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-       "      <td>b'Hello'</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>b'World'</td>\n",
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-      "text/plain": [
-       "   A    B         C\n",
-       "0  1  2.0  b'Hello'\n",
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-       "        A    B         C\n",
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-    "pd.DataFrame(data, index=[\"first\", \"second\"])"
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-   "cell_type": "code",
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-       "      <th>1</th>\n",
-       "      <td>b'World'</td>\n",
-       "      <td>2</td>\n",
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-       "</div>"
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-      "text/plain": [
-       "          C  A    B\n",
-       "0  b'Hello'  1  2.0\n",
-       "1  b'World'  2  3.0"
-      ]
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-     "execution_count": 51,
-     "metadata": {},
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-    "pd.DataFrame(data, columns=[\"C\", \"A\", \"B\"])"
-   ]
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-  {
-   "cell_type": "markdown",
-   "id": "126d31a8",
-   "metadata": {},
-   "source": [
-    "```{note}\n",
-    "DataFrame is not intended to work exactly like a 2-dimensional NumPy ndarray.\n",
-    "```\n",
-    "\n",
-    "\n",
-    "##### From a list of dicts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "ee4f9f05",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data2 = [{\"a\": 1, \"b\": 2}, {\"a\": 5, \"b\": 10, \"c\": 20}]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
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-   "metadata": {},
-   "outputs": [
-    {
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-       "   a   b     c\n",
-       "0  1   2   NaN\n",
-       "1  5  10  20.0"
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-     "execution_count": 53,
-     "metadata": {},
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-    "pd.DataFrame(data2)"
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-   "cell_type": "code",
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-   "id": "ea4880b3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th>second</th>\n",
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-       "        a   b     c\n",
-       "first   1   2   NaN\n",
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-    "pd.DataFrame(data2, index=[\"first\", \"second\"])"
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-   "cell_type": "code",
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-   "id": "92b5105a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
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-       "      <td>2</td>\n",
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-       "      <th>1</th>\n",
-       "      <td>5</td>\n",
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-       "</div>"
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-      "text/plain": [
-       "   a   b\n",
-       "0  1   2\n",
-       "1  5  10"
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-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
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-   "source": [
-    "pd.DataFrame(data2, columns=[\"a\", \"b\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "37e7645e",
-   "metadata": {},
-   "source": [
-    "##### From a dict of tuples\n",
-    "\n",
-    "You can automatically create a MultiIndexed frame by passing a tuples dictionary."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "322c79fa",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th>a</th>\n",
-       "      <th>c</th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">A</th>\n",
-       "      <th>B</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>10.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>D</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       a              b      \n",
-       "       b    a    c    a     b\n",
-       "A B  1.0  4.0  5.0  8.0  10.0\n",
-       "  C  2.0  3.0  6.0  7.0   NaN\n",
-       "  D  NaN  NaN  NaN  NaN   9.0"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(\n",
-    "    {\n",
-    "        (\"a\", \"b\"): {(\"A\", \"B\"): 1, (\"A\", \"C\"): 2},\n",
-    "        (\"a\", \"a\"): {(\"A\", \"C\"): 3, (\"A\", \"B\"): 4},\n",
-    "        (\"a\", \"c\"): {(\"A\", \"B\"): 5, (\"A\", \"C\"): 6},\n",
-    "        (\"b\", \"a\"): {(\"A\", \"C\"): 7, (\"A\", \"B\"): 8},\n",
-    "        (\"b\", \"b\"): {(\"A\", \"D\"): 9, (\"A\", \"B\"): 10},\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "452e9e2d",
-   "metadata": {},
-   "source": [
-    "##### From a Series\n",
-    "\n",
-    "The result will be a DataFrame with the same index as the input Series, and with one column whose name is the original name of the Series (only if no other column name provided)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "582abedf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ser = pd.Series(range(3), index=list(\"abc\"), name=\"ser\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "04ed1911",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ser</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>a</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>b</th>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>c</th>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   ser\n",
-       "a    0\n",
-       "b    1\n",
-       "c    2"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(ser)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9e6569f3",
-   "metadata": {},
-   "source": [
-    "##### From a list of namedtuples\n",
-    "\n",
-    "The field names of the first `namedtuple` in the list determine the columns of the `DataFrame`. The remaining namedtuples (or tuples) are simply unpacked and their values are fed into the rows of the `DataFrame`. If any of those tuples is shorter than the first `namedtuple` then the later columns in the corresponding row are marked as missing values. If any are longer than the first `namedtuple` , a `ValueError` is raised."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "8148406d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import namedtuple"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "3aef5ba5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Point = namedtuple(\"Point\", \"x y\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "574f5f39",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>x</th>\n",
-       "      <th>y</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   x  y\n",
-       "0  0  0\n",
-       "1  0  3\n",
-       "2  2  3"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame([Point(0, 0), Point(0, 3), (2, 3)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "ea538e04",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Point3D = namedtuple(\"Point3D\", \"x y z\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "2aa2e79e",
-   "metadata": {},
-   "outputs": [
-    {
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-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>x</th>\n",
-       "      <th>y</th>\n",
-       "      <th>z</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   x  y    z\n",
-       "0  0  0  0.0\n",
-       "1  0  3  5.0\n",
-       "2  2  3  NaN"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame([Point3D(0, 0, 0), Point3D(0, 3, 5), Point(2, 3)])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8f9aa2a2",
-   "metadata": {},
-   "source": [
-    "##### From a list of dataclasses\n",
-    "\n",
-    "Data Classes as introduced in PEP557, can be passed into the DataFrame constructor. Passing a list of dataclasses is equivalent to passing a list of dictionaries.\n",
-    "\n",
-    "Please be aware, that all values in the list should be dataclasses, mixing types in the list would result in a `TypeError`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "f181f94d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from dataclasses import make_dataclass"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "554459b9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Point = make_dataclass(\"Point\", [(\"x\", int), (\"y\", int)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "edee86c9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>x</th>\n",
-       "      <th>y</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   x  y\n",
-       "0  0  0\n",
-       "1  0  3\n",
-       "2  2  3"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba0a6909",
-   "metadata": {},
-   "source": [
-    "#### Column selection, addition, deletion\n",
-    "\n",
-    "You can treat a `DataFrame` semantically like a dict of like-indexed `Series` objects. Getting, setting, and deleting columns works with the same syntax as the analogous dict operations:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "4e184116",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>one</th>\n",
-       "      <th>two</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>a</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>b</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>c</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>d</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   one  two\n",
-       "a  1.0  1.0\n",
-       "b  2.0  2.0\n",
-       "c  3.0  3.0\n",
-       "d  NaN  4.0"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "b4db4799",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    1.0\n",
-       "b    2.0\n",
-       "c    3.0\n",
-       "d    NaN\n",
-       "Name: one, dtype: float64"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"one\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "cc33b7d8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"three\"] = df[\"one\"] * df[\"two\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "767a93a1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"flag\"] = df[\"one\"] > 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "f97041a3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>one</th>\n",
-       "      <th>two</th>\n",
-       "      <th>three</th>\n",
-       "      <th>flag</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>a</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>b</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>c</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>d</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   one  two  three   flag\n",
-       "a  1.0  1.0    1.0  False\n",
-       "b  2.0  2.0    4.0  False\n",
-       "c  3.0  3.0    9.0   True\n",
-       "d  NaN  4.0    NaN  False"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2bc6e13d",
-   "metadata": {},
-   "source": [
-    "Columns can be deleted or popped like with a dict:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "bb5ed406",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "del df[\"two\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "d64c60cd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "three = df.pop(\"three\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "4bd2952b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>one</th>\n",
-       "      <th>flag</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>a</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>b</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>c</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>d</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   one   flag\n",
-       "a  1.0  False\n",
-       "b  2.0  False\n",
-       "c  3.0   True\n",
-       "d  NaN  False"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "565331e7",
-   "metadata": {},
-   "source": [
-    "When inserting a scalar value, it will naturally be propagated to fill the column:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "e315da87",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"foo\"] = \"bar\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "42045478",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>one</th>\n",
-       "      <th>flag</th>\n",
-       "      <th>foo</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>a</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>bar</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>b</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>bar</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>c</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>bar</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>d</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "      <td>bar</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   one   flag  foo\n",
-       "a  1.0  False  bar\n",
-       "b  2.0  False  bar\n",
-       "c  3.0   True  bar\n",
-       "d  NaN  False  bar"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ec612f22",
-   "metadata": {},
-   "source": [
-    "When inserting a `Series` that does not have the same index as the `DataFrame`, it will be conformed to the DataFrame’s index:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "fa0e4fa9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"one_trunc\"] = df[\"one\"][:2]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f7550dac",
-   "metadata": {},
-   "source": [
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:1000px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Ad%20%3D%20%7B%0A%20%20%20%20%22one%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29,%0A%20%20%20%20%22two%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0,%204.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22,%20%22d%22%5D%29,%0A%7D%0Adf%20%3D%20pd.DataFrame%28d%29%0Adf%5B%22flag%22%5D%20%3D%20df%5B%22one%22%5D%20%3E%202%0Adel%20df%5B%22two%22%5D%0Adf%5B%22foo%22%5D%20%3D%20%22bar%22%0Adf%5B%22one%22%5D%5B%3A2%5D&d=2023-07-13&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "405abded",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>one</th>\n",
-       "      <th>flag</th>\n",
-       "      <th>foo</th>\n",
-       "      <th>one_trunc</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>a</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>bar</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>b</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>bar</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>c</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>bar</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>d</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "      <td>bar</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   one   flag  foo  one_trunc\n",
-       "a  1.0  False  bar        1.0\n",
-       "b  2.0  False  bar        2.0\n",
-       "c  3.0   True  bar        NaN\n",
-       "d  NaN  False  bar        NaN"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0232c81",
-   "metadata": {},
-   "source": [
-    "You can insert raw ndarrays but their length must match the length of the DataFrame’s index.\n",
-    "\n",
-    "By default, columns get inserted at the end. `DataFrame.insert()` inserts at a particular location in the columns:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "49780cc3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.insert(1, \"bar\", df[\"one\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "f9231c1d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "      <th></th>\n",
-       "      <th>one</th>\n",
-       "      <th>bar</th>\n",
-       "      <th>flag</th>\n",
-       "      <th>foo</th>\n",
-       "      <th>one_trunc</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>a</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>bar</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>b</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>bar</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>c</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>bar</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>d</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "      <td>bar</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   one  bar   flag  foo  one_trunc\n",
-       "a  1.0  1.0  False  bar        1.0\n",
-       "b  2.0  2.0  False  bar        2.0\n",
-       "c  3.0  3.0   True  bar        NaN\n",
-       "d  NaN  NaN  False  bar        NaN"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a1efc65b",
-   "metadata": {},
-   "source": [
-    "#### Assigning new columns in method chains\n",
-    "\n",
-    "DataFrame has an `assign()` method that allows you to easily create new columns that are potentially derived from existing columns."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "7f1b0dad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iris = pd.read_csv(\"../../assets/data/iris.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "9aa6cfda",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>SepalLength</th>\n",
-       "      <th>SepalWidth</th>\n",
-       "      <th>PetalLength</th>\n",
-       "      <th>PetalWidth</th>\n",
-       "      <th>Name</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.1</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.9</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4.7</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4.6</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   SepalLength  SepalWidth  PetalLength  PetalWidth         Name\n",
-       "0          5.1         3.5          1.4         0.2  Iris-setosa\n",
-       "1          4.9         3.0          1.4         0.2  Iris-setosa\n",
-       "2          4.7         3.2          1.3         0.2  Iris-setosa\n",
-       "3          4.6         3.1          1.5         0.2  Iris-setosa\n",
-       "4          5.0         3.6          1.4         0.2  Iris-setosa"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "iris.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "89a45800",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>SepalLength</th>\n",
-       "      <th>SepalWidth</th>\n",
-       "      <th>PetalLength</th>\n",
-       "      <th>PetalWidth</th>\n",
-       "      <th>Name</th>\n",
-       "      <th>sepal_ratio</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.1</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "      <td>0.686275</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.9</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "      <td>0.612245</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4.7</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "      <td>0.680851</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4.6</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "      <td>0.673913</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "      <td>0.720000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   SepalLength  SepalWidth  PetalLength  PetalWidth         Name  sepal_ratio\n",
-       "0          5.1         3.5          1.4         0.2  Iris-setosa     0.686275\n",
-       "1          4.9         3.0          1.4         0.2  Iris-setosa     0.612245\n",
-       "2          4.7         3.2          1.3         0.2  Iris-setosa     0.680851\n",
-       "3          4.6         3.1          1.5         0.2  Iris-setosa     0.673913\n",
-       "4          5.0         3.6          1.4         0.2  Iris-setosa     0.720000"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "iris.assign(sepal_ratio=iris[\"SepalWidth\"] / iris[\"SepalLength\"]).head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dfe2eed4",
-   "metadata": {},
-   "source": [
-    "In the example above, we inserted a precomputed value. We can also pass in a function of one argument to be evaluated on the DataFrame being assigned to."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "aa246290",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>SepalLength</th>\n",
-       "      <th>SepalWidth</th>\n",
-       "      <th>PetalLength</th>\n",
-       "      <th>PetalWidth</th>\n",
-       "      <th>Name</th>\n",
-       "      <th>sepal_ratio</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.1</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "      <td>0.686275</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.9</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "      <td>0.612245</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4.7</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "      <td>0.680851</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4.6</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "      <td>0.673913</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "      <td>0.720000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   SepalLength  SepalWidth  PetalLength  PetalWidth         Name  sepal_ratio\n",
-       "0          5.1         3.5          1.4         0.2  Iris-setosa     0.686275\n",
-       "1          4.9         3.0          1.4         0.2  Iris-setosa     0.612245\n",
-       "2          4.7         3.2          1.3         0.2  Iris-setosa     0.680851\n",
-       "3          4.6         3.1          1.5         0.2  Iris-setosa     0.673913\n",
-       "4          5.0         3.6          1.4         0.2  Iris-setosa     0.720000"
-      ]
-     },
-     "execution_count": 84,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "iris.assign(sepal_ratio=lambda x: (x[\"SepalWidth\"] / x[\"SepalLength\"])).head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "24c74f56",
-   "metadata": {},
-   "source": [
-    "`assign()` **always** returns a copy of the data, leaving the original DataFrame untouched.\n",
-    "\n",
-    "Passing a callable, as opposed to an actual value to be inserted, is useful when you don’t have a reference to the DataFrame at hand. This is common when using `assign()` in a chain of operations. For example, we can limit the DataFrame to just those observations with a Sepal Length greater than 5, calculate the ratio, and plot:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "3f445420",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot: xlabel='SepalRatio', ylabel='PetalRatio'>"
-      ]
-     },
-     "execution_count": 85,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "filenames": {
-       "image/png": "/home/runner/work/machine-learning/machine-learning/open-machine-learning-jupyter-book/_build/jupyter_execute/data-science/working-with-data/pandas_118_1.png"
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "(\n",
-    "    iris.query(\"SepalLength > 5\")\n",
-    "    .assign(\n",
-    "        SepalRatio=lambda x: x.SepalWidth / x.SepalLength,\n",
-    "        PetalRatio=lambda x: x.PetalWidth / x.PetalLength,\n",
-    "    )\n",
-    "    .plot(kind=\"scatter\", x=\"SepalRatio\", y=\"PetalRatio\")\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9f04072f",
-   "metadata": {},
-   "source": [
-    "Since a function is passed in, the function is computed on the DataFrame being assigned to. Importantly, this is the DataFrame that’s been filtered to those rows with sepal length greater than 5. The filtering happens first, and then the ratio calculations. This is an example where we didn’t have a reference to the filtered DataFrame available.\n",
-    "\n",
-    "The function signature for `assign()` is simply `**kwargs`. The keys are the column names for the new fields, and the values are either a value to be inserted (for example, a `Series` or NumPy array), or a function of one argument to be called on the `DataFrame`. A copy of the original `DataFrame` is returned, with the new values inserted.\n",
-    "\n",
-    "The order of `**kwargs` is preserved. This allows for dependent assignment, where an expression later in `**kwargs` can refer to a column created earlier in the same `assign()`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "bda7c5b8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dfa = pd.DataFrame({\"A\": [1, 2, 3], \"B\": [4, 5, 6]})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "c1b52bea",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th>C</th>\n",
-       "      <th>D</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>7</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>9</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   A  B  C   D\n",
-       "0  1  4  5   6\n",
-       "1  2  5  7   9\n",
-       "2  3  6  9  12"
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dfa.assign(C=lambda x: x[\"A\"] + x[\"B\"], D=lambda x: x[\"A\"] + x[\"C\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "46bea5d0",
-   "metadata": {},
-   "source": [
-    "In the second expression, `x['C']` will refer to the newly created column, that’s equal to `dfa['A'] + dfa['B']`.\n",
-    "\n",
-    "#### Indexing / selection\n",
-    "\n",
-    "The basics of indexing are as follows:\n",
-    "\n",
-    "|Operation                     |Syntax         |Result   |\n",
-    "|:-------                      |:-----         |:-----   |\n",
-    "|Select column                 |`df[col]`      |Series   |\n",
-    "|Select row by label           |`df.loc[label]`|Series   |\n",
-    "|Select row by integer location|`df.iloc[loc]` |Series   |\n",
-    "|Slice rows                    |`df[5:10] `    |DataFrame|\n",
-    "|Select rows by boolean vector |`df[bool_vec]` |DataFrame|\n",
-    "\n",
-    "Row selection, for example, returns a `Series` whose index is the columns of the `DataFrame`:\n",
-    "\n",
-    "```\n",
-    "df.loc[\"b\"]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:730px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Ad%20%3D%20%7B%0A%20%20%20%20%22one%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29,%0A%20%20%20%20%22two%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0,%204.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22,%20%22d%22%5D%29,%0A%7D%0Adf%20%3D%20pd.DataFrame%28d%29%0Adf%5B%22flag%22%5D%20%3D%20df%5B%22one%22%5D%20%3E%202%0Adel%20df%5B%22two%22%5D%0Adf%5B%22foo%22%5D%20%3D%20%22bar%22%0Adf%5B%22one_trunc%22%5D%20%3Ddf.insert%281,%20%22bar%22,%20df%5B%22one%22%5D%29%0Adf.loc%5B%22b%22%5D&d=2023-07-13&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "8a0bc94a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "one           3.0\n",
-       "bar           3.0\n",
-       "flag         True\n",
-       "foo           bar\n",
-       "one_trunc     NaN\n",
-       "Name: c, dtype: object"
-      ]
-     },
-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.iloc[2]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "99503689",
-   "metadata": {},
-   "source": [
-    "#### Data alignment and arithmetic\n",
-    "\n",
-    "Data alignment between `DataFrame` objects automatically aligns on **both** the columns and the index (row labels)**. Again, the resulting object will have the union of the column and row labels."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "09140acc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.DataFrame(np.random.randn(10, 4), columns=[\"A\", \"B\", \"C\", \"D\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "c33d7251",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df2 = pd.DataFrame(np.random.randn(7, 3), columns=[\"A\", \"B\", \"C\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "23779fd8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <td>-1.034449</td>\n",
-       "      <td>0.718916</td>\n",
-       "      <td>0.140419</td>\n",
-       "      <td>NaN</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1.765848</td>\n",
-       "      <td>0.010122</td>\n",
-       "      <td>0.636368</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-0.855305</td>\n",
-       "      <td>0.230352</td>\n",
-       "      <td>0.573435</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-0.509938</td>\n",
-       "      <td>1.352540</td>\n",
-       "      <td>-0.631656</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-1.162460</td>\n",
-       "      <td>1.902037</td>\n",
-       "      <td>-0.058588</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>-0.052771</td>\n",
-       "      <td>-1.345608</td>\n",
-       "      <td>3.168266</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>-1.440110</td>\n",
-       "      <td>-0.183173</td>\n",
-       "      <td>0.280195</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          A         B         C   D\n",
-       "0 -1.034449  0.718916  0.140419 NaN\n",
-       "1  1.765848  0.010122  0.636368 NaN\n",
-       "2 -0.855305  0.230352  0.573435 NaN\n",
-       "3 -0.509938  1.352540 -0.631656 NaN\n",
-       "4 -1.162460  1.902037 -0.058588 NaN\n",
-       "5 -0.052771 -1.345608  3.168266 NaN\n",
-       "6 -1.440110 -0.183173  0.280195 NaN\n",
-       "7       NaN       NaN       NaN NaN\n",
-       "8       NaN       NaN       NaN NaN\n",
-       "9       NaN       NaN       NaN NaN"
-      ]
-     },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df + df2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cc116b6a",
-   "metadata": {},
-   "source": [
-    "When doing an operation between `DataFrame` and `Series`, the default behavior is to align the `Series` **index** on the `DataFrame` **columns**, thus broadcasting row-wise. For example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "fab7d849",
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-   "outputs": [
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-       "      <th>0</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.537551</td>\n",
-       "      <td>-1.578889</td>\n",
-       "      <td>-1.614495</td>\n",
-       "      <td>1.598048</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1.573832</td>\n",
-       "      <td>-0.441195</td>\n",
-       "      <td>-1.954656</td>\n",
-       "      <td>1.888938</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.325417</td>\n",
-       "      <td>0.719227</td>\n",
-       "      <td>-3.141645</td>\n",
-       "      <td>-0.030097</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.351423</td>\n",
-       "      <td>0.296638</td>\n",
-       "      <td>-1.667557</td>\n",
-       "      <td>2.071918</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.706899</td>\n",
-       "      <td>-1.667650</td>\n",
-       "      <td>-1.156199</td>\n",
-       "      <td>0.343590</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>0.394004</td>\n",
-       "      <td>-1.852758</td>\n",
-       "      <td>-2.264064</td>\n",
-       "      <td>-0.252468</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1.875723</td>\n",
-       "      <td>-0.890963</td>\n",
-       "      <td>-3.212639</td>\n",
-       "      <td>0.738720</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1.322616</td>\n",
-       "      <td>-0.894232</td>\n",
-       "      <td>-0.643626</td>\n",
-       "      <td>1.804562</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0.666647</td>\n",
-       "      <td>0.375275</td>\n",
-       "      <td>-1.202656</td>\n",
-       "      <td>0.855678</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          A         B         C         D\n",
-       "0  0.000000  0.000000  0.000000  0.000000\n",
-       "1  0.537551 -1.578889 -1.614495  1.598048\n",
-       "2  1.573832 -0.441195 -1.954656  1.888938\n",
-       "3  0.325417  0.719227 -3.141645 -0.030097\n",
-       "4  0.351423  0.296638 -1.667557  2.071918\n",
-       "5  0.706899 -1.667650 -1.156199  0.343590\n",
-       "6  0.394004 -1.852758 -2.264064 -0.252468\n",
-       "7  1.875723 -0.890963 -3.212639  0.738720\n",
-       "8  1.322616 -0.894232 -0.643626  1.804562\n",
-       "9  0.666647  0.375275 -1.202656  0.855678"
-      ]
-     },
-     "execution_count": 92,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df - df.iloc[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "44e2514e",
-   "metadata": {},
-   "source": [
-    "Arithmetic operations with scalars operate element-wise:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "4162de20",
-   "metadata": {},
-   "outputs": [
-    {
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-       "      <th>C</th>\n",
-       "      <th>D</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>-2.447541</td>\n",
-       "      <td>7.522482</td>\n",
-       "      <td>10.973291</td>\n",
-       "      <td>-2.429113</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.240213</td>\n",
-       "      <td>-0.371962</td>\n",
-       "      <td>2.900815</td>\n",
-       "      <td>5.561129</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>5.421622</td>\n",
-       "      <td>5.316509</td>\n",
-       "      <td>1.200010</td>\n",
-       "      <td>7.015574</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-0.820455</td>\n",
-       "      <td>11.118616</td>\n",
-       "      <td>-4.734936</td>\n",
-       "      <td>-2.579596</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-0.690424</td>\n",
-       "      <td>9.005674</td>\n",
-       "      <td>2.635504</td>\n",
-       "      <td>7.930478</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1.086953</td>\n",
-       "      <td>-0.815768</td>\n",
-       "      <td>5.192298</td>\n",
-       "      <td>-0.711166</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>-0.477520</td>\n",
-       "      <td>-1.741306</td>\n",
-       "      <td>-0.347029</td>\n",
-       "      <td>-3.691456</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>6.931074</td>\n",
-       "      <td>3.067669</td>\n",
-       "      <td>-5.089906</td>\n",
-       "      <td>1.264485</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>4.165539</td>\n",
-       "      <td>3.051320</td>\n",
-       "      <td>7.755160</td>\n",
-       "      <td>6.593695</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0.885692</td>\n",
-       "      <td>9.398856</td>\n",
-       "      <td>4.960012</td>\n",
-       "      <td>1.849276</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          A          B          C         D\n",
-       "0 -2.447541   7.522482  10.973291 -2.429113\n",
-       "1  0.240213  -0.371962   2.900815  5.561129\n",
-       "2  5.421622   5.316509   1.200010  7.015574\n",
-       "3 -0.820455  11.118616  -4.734936 -2.579596\n",
-       "4 -0.690424   9.005674   2.635504  7.930478\n",
-       "5  1.086953  -0.815768   5.192298 -0.711166\n",
-       "6 -0.477520  -1.741306  -0.347029 -3.691456\n",
-       "7  6.931074   3.067669  -5.089906  1.264485\n",
-       "8  4.165539   3.051320   7.755160  6.593695\n",
-       "9  0.885692   9.398856   4.960012  1.849276"
-      ]
-     },
-     "execution_count": 93,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df * 5 + 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "a8608325",
-   "metadata": {},
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-    {
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-       "      <th>C</th>\n",
-       "      <th>D</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>-1.124217</td>\n",
-       "      <td>0.905390</td>\n",
-       "      <td>0.557209</td>\n",
-       "      <td>-1.128894</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-2.841254</td>\n",
-       "      <td>-2.107959</td>\n",
-       "      <td>5.550530</td>\n",
-       "      <td>1.404049</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1.461295</td>\n",
-       "      <td>1.507609</td>\n",
-       "      <td>-6.250077</td>\n",
-       "      <td>0.996895</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-1.772764</td>\n",
-       "      <td>0.548329</td>\n",
-       "      <td>-0.742398</td>\n",
-       "      <td>-1.091799</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-1.858443</td>\n",
-       "      <td>0.713707</td>\n",
-       "      <td>7.867773</td>\n",
-       "      <td>0.843102</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>-5.476172</td>\n",
-       "      <td>-1.775714</td>\n",
-       "      <td>1.566270</td>\n",
-       "      <td>-1.844225</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>-2.018147</td>\n",
-       "      <td>-1.336432</td>\n",
-       "      <td>-2.130353</td>\n",
-       "      <td>-0.878510</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1.013978</td>\n",
-       "      <td>4.683100</td>\n",
-       "      <td>-0.705228</td>\n",
-       "      <td>-6.797955</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>2.308894</td>\n",
-       "      <td>4.755925</td>\n",
-       "      <td>0.868786</td>\n",
-       "      <td>1.088448</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>-4.487090</td>\n",
-       "      <td>0.675780</td>\n",
-       "      <td>1.689182</td>\n",
-       "      <td>-33.173253</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          A         B         C          D\n",
-       "0 -1.124217  0.905390  0.557209  -1.128894\n",
-       "1 -2.841254 -2.107959  5.550530   1.404049\n",
-       "2  1.461295  1.507609 -6.250077   0.996895\n",
-       "3 -1.772764  0.548329 -0.742398  -1.091799\n",
-       "4 -1.858443  0.713707  7.867773   0.843102\n",
-       "5 -5.476172 -1.775714  1.566270  -1.844225\n",
-       "6 -2.018147 -1.336432 -2.130353  -0.878510\n",
-       "7  1.013978  4.683100 -0.705228  -6.797955\n",
-       "8  2.308894  4.755925  0.868786   1.088448\n",
-       "9 -4.487090  0.675780  1.689182 -33.173253"
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "1 / df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "301ec4ac",
-   "metadata": {},
-   "outputs": [
-    {
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th>C</th>\n",
-       "      <th>D</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.626037</td>\n",
-       "      <td>1.488186</td>\n",
-       "      <td>10.373539</td>\n",
-       "      <td>6.157256e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.015345</td>\n",
-       "      <td>0.050647</td>\n",
-       "      <td>0.001054</td>\n",
-       "      <td>2.573183e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.219305</td>\n",
-       "      <td>0.193573</td>\n",
-       "      <td>0.000655</td>\n",
-       "      <td>1.012518e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.101250</td>\n",
-       "      <td>11.062052</td>\n",
-       "      <td>3.291954</td>\n",
-       "      <td>7.037668e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.083831</td>\n",
-       "      <td>3.854071</td>\n",
-       "      <td>0.000261</td>\n",
-       "      <td>1.979150e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.001112</td>\n",
-       "      <td>0.100579</td>\n",
-       "      <td>0.166163</td>\n",
-       "      <td>8.644588e-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>0.060282</td>\n",
-       "      <td>0.313482</td>\n",
-       "      <td>0.048550</td>\n",
-       "      <td>1.678856e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>0.945989</td>\n",
-       "      <td>0.002079</td>\n",
-       "      <td>4.042796</td>\n",
-       "      <td>4.682598e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>0.035187</td>\n",
-       "      <td>0.001955</td>\n",
-       "      <td>1.755293</td>\n",
-       "      <td>7.124734e-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0.002467</td>\n",
-       "      <td>4.794885</td>\n",
-       "      <td>0.122827</td>\n",
-       "      <td>8.257484e-07</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          A          B          C             D\n",
-       "0  0.626037   1.488186  10.373539  6.157256e-01\n",
-       "1  0.015345   0.050647   0.001054  2.573183e-01\n",
-       "2  0.219305   0.193573   0.000655  1.012518e+00\n",
-       "3  0.101250  11.062052   3.291954  7.037668e-01\n",
-       "4  0.083831   3.854071   0.000261  1.979150e+00\n",
-       "5  0.001112   0.100579   0.166163  8.644588e-02\n",
-       "6  0.060282   0.313482   0.048550  1.678856e+00\n",
-       "7  0.945989   0.002079   4.042796  4.682598e-04\n",
-       "8  0.035187   0.001955   1.755293  7.124734e-01\n",
-       "9  0.002467   4.794885   0.122827  8.257484e-07"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df ** 4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e24ac3b",
-   "metadata": {},
-   "source": [
-    "Boolean operators operate element-wise as well:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "22cf9221",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df1 = pd.DataFrame({\"a\": [1, 0, 1], \"b\": [0, 1, 1]}, dtype=bool)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "886dbecd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df2 = pd.DataFrame({\"a\": [0, 1, 1], \"b\": [1, 1, 0]}, dtype=bool)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "id": "579d4104",
-   "metadata": {},
-   "outputs": [
-    {
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-       "       a      b\n",
-       "0  False  False\n",
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-     "execution_count": 98,
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-     "output_type": "execute_result"
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-   ],
-   "source": [
-    "df1 & df2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "id": "46fa81e5",
-   "metadata": {},
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-       "      <td>True</td>\n",
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-       "      <th>2</th>\n",
-       "      <td>True</td>\n",
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-       "      a     b\n",
-       "0  True  True\n",
-       "1  True  True\n",
-       "2  True  True"
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-     "execution_count": 99,
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-   "source": [
-    "df1 | df2"
-   ]
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-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "id": "43b30b03",
-   "metadata": {},
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-       "       a      b\n",
-       "0   True   True\n",
-       "1   True  False\n",
-       "2  False   True"
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-     "execution_count": 100,
-     "metadata": {},
-     "output_type": "execute_result"
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-   ],
-   "source": [
-    "df1 ^ df2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "8fcc0a99",
-   "metadata": {},
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-    {
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-       "      <th>2</th>\n",
-       "      <td>False</td>\n",
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-      "text/plain": [
-       "       a      b\n",
-       "0  False   True\n",
-       "1   True  False\n",
-       "2  False  False"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "-df1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6e9dc9db",
-   "metadata": {},
-   "source": [
-    "#### Transposing\n",
-    "\n",
-    "To transpose, access the `T` attribute or `DataFrame.transpose()`, similar to an ndarray:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "id": "25840381",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
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-       "\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "      <th>4</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>A</th>\n",
-       "      <td>-0.889508</td>\n",
-       "      <td>-0.351957</td>\n",
-       "      <td>0.684324</td>\n",
-       "      <td>-0.564091</td>\n",
-       "      <td>-0.538085</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>B</th>\n",
-       "      <td>1.104496</td>\n",
-       "      <td>-0.474392</td>\n",
-       "      <td>0.663302</td>\n",
-       "      <td>1.823723</td>\n",
-       "      <td>1.401135</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C</th>\n",
-       "      <td>1.794658</td>\n",
-       "      <td>0.180163</td>\n",
-       "      <td>-0.159998</td>\n",
-       "      <td>-1.346987</td>\n",
-       "      <td>0.127101</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>D</th>\n",
-       "      <td>-0.885823</td>\n",
-       "      <td>0.712226</td>\n",
-       "      <td>1.003115</td>\n",
-       "      <td>-0.915919</td>\n",
-       "      <td>1.186096</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          0         1         2         3         4\n",
-       "A -0.889508 -0.351957  0.684324 -0.564091 -0.538085\n",
-       "B  1.104496 -0.474392  0.663302  1.823723  1.401135\n",
-       "C  1.794658  0.180163 -0.159998 -1.346987  0.127101\n",
-       "D -0.885823  0.712226  1.003115 -0.915919  1.186096"
-      ]
-     },
-     "execution_count": 102,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[:5].T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "da179310",
-   "metadata": {},
-   "source": [
-    "## Data indexing and selection\n",
-    "\n",
-    "The axis labeling information in Pandas objects serves many purposes:\n",
-    "\n",
-    "- Identifies data (i.e. provides metadata) using known indicators, important for analysis, visualization, and interactive console display.\n",
-    "- Enables automatic and explicit data alignment.\n",
-    "- Allows intuitive getting and setting of subsets of the data set.\n",
-    "\n",
-    "In this section, we will focus on the final point: namely, how to slice, dice, and generally get and set subsets of Pandas objects. The primary focus will be on Series and DataFrame as they have received more development attention in this area.\n",
-    "\n",
-    "```{note}\n",
-    "The Python and NumPy indexing operators `[]` and attribute operator `.` provide quick and easy access to Pandas data structures across a wide range of use cases. This makes interactive work intuitive, as there’s little new to learn if you already know how to deal with Python dictionaries and NumPy arrays. However, since the type of the data to be accessed isn’t known in advance, directly using standard operators has some optimization limits. For production code, we recommended that you take advantage of the optimized Pandas data access methods exposed in this chapter.\n",
-    "```\n",
-    "\n",
-    "```{warning}\n",
-    "Whether a copy or a reference is returned for a setting operation, may depend on the context. This is sometimes called `chained assignment` and should be avoided.\n",
-    "```\n",
-    "\n",
-    "### Different choices for indexing\n",
-    "\n",
-    "Object selection has had a number of user-requested additions in order to support more explicit location-based indexing. Pandas now supports three types of multi-axis indexing.\n",
-    "\n",
-    "- `.loc` is primarily label based, but may also be used with a boolean array. `.loc` will raise `KeyError` when the items are not found. Allowed inputs are:\n",
-    "  - A single label, e.g. `5` or `'a'` (Note that `5` is interpreted as a label of the index. This use is not an integer position along the index.).\n",
-    "  - A list or array of labels `['a', 'b', 'c']`.\n",
-    "  - A slice object with labels `'a':'f'` (Note that contrary to usual Python slices, both the start and the stop are included, when present in the index!)\n",
-    "  - A boolean array (any `NA` values will be treated as `False`).\n",
-    "  - A `callable` function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above).\n",
-    "\n",
-    "- `.iloc` is primarily integer position based (from `0` to `length-1` of the axis), but may also be used with a boolean array. `.iloc` will raise `IndexError` if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing. (this conforms with Python/NumPy slice semantics). Allowed inputs are:\n",
-    "  - An integer e.g. `5`.\n",
-    "  - A list or array of integers `[4, 3, 0]`.\n",
-    "  - A slice object with ints `1:7`.\n",
-    "  - A boolean array (any `NA` values will be treated as `False`).\n",
-    "  - A `callable` function with one argument (the calling Series or DataFrame) that returns valid output for indexing (one of the above).\n",
-    "- `.loc`, `.iloc`, and also `[]` indexing can accept a `callable` as indexer.\n",
-    "\n",
-    "Getting values from an object with multi-axes selection uses the following notation (using `.loc` as an example, but the following applies to `.iloc` as well). Any of the axes accessors may be the null slice `:`. Axes left out of the specification are assumed to be `:`, e.g. `p.loc['a']` is equivalent to `p.loc['a', :]`.\n",
-    "\n",
-    "|**Object Type**|**Indexers**                        |\n",
-    "|:--            |:-                                  |\n",
-    "|Series         |`s.loc[indexer]`                    |\n",
-    "|DataFrame      |`df.loc[row_indexer, column_indexer]`|\n",
-    "\n",
-    "### Basics\n",
-    "\n",
-    "As mentioned when introducing the data structures in the last section, the primary function of indexing with `[]` (a.k.a.` __getitem__` for those familiar with implementing class behavior in Python) is selecting out lower-dimensional slices. The following table shows return type values when indexing Pandas objects with `[]`:\n",
-    "\n",
-    "|**Object Type**|**Selection**   |Return Value Type                |\n",
-    "|:-             |:-              |:-                               |\n",
-    "|Series         |`series[label]` |scalar value                     |\n",
-    "|DataFrame      |`frame[colname]`|`Series` corresponding to colname|\n",
-    "\n",
-    "Here we construct a simple time series data set to use for illustrating the indexing functionality:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "id": "b935bcf2",
-   "metadata": {},
-   "outputs": [
-    {
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-       "      <th>D</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2000-01-01</th>\n",
-       "      <td>0.560977</td>\n",
-       "      <td>0.210545</td>\n",
-       "      <td>0.158079</td>\n",
-       "      <td>0.492279</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-02</th>\n",
-       "      <td>1.280369</td>\n",
-       "      <td>-0.817745</td>\n",
-       "      <td>-1.522940</td>\n",
-       "      <td>-1.275264</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-03</th>\n",
-       "      <td>0.156302</td>\n",
-       "      <td>-0.313090</td>\n",
-       "      <td>-1.386411</td>\n",
-       "      <td>-0.757229</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-04</th>\n",
-       "      <td>0.528869</td>\n",
-       "      <td>-0.542066</td>\n",
-       "      <td>1.345872</td>\n",
-       "      <td>-0.706867</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-05</th>\n",
-       "      <td>-2.466330</td>\n",
-       "      <td>0.731123</td>\n",
-       "      <td>-2.066561</td>\n",
-       "      <td>-1.477377</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-06</th>\n",
-       "      <td>-0.300497</td>\n",
-       "      <td>0.207552</td>\n",
-       "      <td>-0.639468</td>\n",
-       "      <td>-0.950777</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-07</th>\n",
-       "      <td>-2.626322</td>\n",
-       "      <td>-1.072332</td>\n",
-       "      <td>-1.523701</td>\n",
-       "      <td>-1.489816</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-08</th>\n",
-       "      <td>-0.960255</td>\n",
-       "      <td>-0.402632</td>\n",
-       "      <td>-0.397366</td>\n",
-       "      <td>0.085742</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   A         B         C         D\n",
-       "2000-01-01  0.560977  0.210545  0.158079  0.492279\n",
-       "2000-01-02  1.280369 -0.817745 -1.522940 -1.275264\n",
-       "2000-01-03  0.156302 -0.313090 -1.386411 -0.757229\n",
-       "2000-01-04  0.528869 -0.542066  1.345872 -0.706867\n",
-       "2000-01-05 -2.466330  0.731123 -2.066561 -1.477377\n",
-       "2000-01-06 -0.300497  0.207552 -0.639468 -0.950777\n",
-       "2000-01-07 -2.626322 -1.072332 -1.523701 -1.489816\n",
-       "2000-01-08 -0.960255 -0.402632 -0.397366  0.085742"
-      ]
-     },
-     "execution_count": 103,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dates = pd.date_range('1/1/2000', periods=8)\n",
-    "df = pd.DataFrame(np.random.randn(8, 4),\n",
-    "                  index=dates, columns=['A', 'B', 'C', 'D'])\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4e293d7c",
-   "metadata": {},
-   "source": [
-    "```{note}\n",
-    "None of the indexing functionality is time series specific unless specifically stated.\n",
-    "```\n",
-    "\n",
-    "Thus, as per above, we have the most basic indexing using `[]`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "id": "22330c85",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-0.3004967298995864"
-      ]
-     },
-     "execution_count": 104,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s = df['A']\n",
-    "\n",
-    "s[dates[5]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5915f28e",
-   "metadata": {},
-   "source": [
-    "You can pass a list of columns to `[]` to select columns in that order. If a column is not contained in the DataFrame, an exception will be raised. Multiple columns can also be set in this manner:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "id": "a6f2d214",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th>C</th>\n",
-       "      <th>D</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2000-01-01</th>\n",
-       "      <td>0.560977</td>\n",
-       "      <td>0.210545</td>\n",
-       "      <td>0.158079</td>\n",
-       "      <td>0.492279</td>\n",
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-       "    <tr>\n",
-       "      <th>2000-01-02</th>\n",
-       "      <td>1.280369</td>\n",
-       "      <td>-0.817745</td>\n",
-       "      <td>-1.522940</td>\n",
-       "      <td>-1.275264</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-03</th>\n",
-       "      <td>0.156302</td>\n",
-       "      <td>-0.313090</td>\n",
-       "      <td>-1.386411</td>\n",
-       "      <td>-0.757229</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-04</th>\n",
-       "      <td>0.528869</td>\n",
-       "      <td>-0.542066</td>\n",
-       "      <td>1.345872</td>\n",
-       "      <td>-0.706867</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-05</th>\n",
-       "      <td>-2.466330</td>\n",
-       "      <td>0.731123</td>\n",
-       "      <td>-2.066561</td>\n",
-       "      <td>-1.477377</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-06</th>\n",
-       "      <td>-0.300497</td>\n",
-       "      <td>0.207552</td>\n",
-       "      <td>-0.639468</td>\n",
-       "      <td>-0.950777</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-07</th>\n",
-       "      <td>-2.626322</td>\n",
-       "      <td>-1.072332</td>\n",
-       "      <td>-1.523701</td>\n",
-       "      <td>-1.489816</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-08</th>\n",
-       "      <td>-0.960255</td>\n",
-       "      <td>-0.402632</td>\n",
-       "      <td>-0.397366</td>\n",
-       "      <td>0.085742</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   A         B         C         D\n",
-       "2000-01-01  0.560977  0.210545  0.158079  0.492279\n",
-       "2000-01-02  1.280369 -0.817745 -1.522940 -1.275264\n",
-       "2000-01-03  0.156302 -0.313090 -1.386411 -0.757229\n",
-       "2000-01-04  0.528869 -0.542066  1.345872 -0.706867\n",
-       "2000-01-05 -2.466330  0.731123 -2.066561 -1.477377\n",
-       "2000-01-06 -0.300497  0.207552 -0.639468 -0.950777\n",
-       "2000-01-07 -2.626322 -1.072332 -1.523701 -1.489816\n",
-       "2000-01-08 -0.960255 -0.402632 -0.397366  0.085742"
-      ]
-     },
-     "execution_count": 105,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "id": "13c8e5eb",
-   "metadata": {},
-   "outputs": [
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-       "      <td>-1.522940</td>\n",
-       "      <td>-1.275264</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-03</th>\n",
-       "      <td>-0.313090</td>\n",
-       "      <td>0.156302</td>\n",
-       "      <td>-1.386411</td>\n",
-       "      <td>-0.757229</td>\n",
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-       "    <tr>\n",
-       "      <th>2000-01-04</th>\n",
-       "      <td>-0.542066</td>\n",
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-       "      <td>1.345872</td>\n",
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-       "    <tr>\n",
-       "      <th>2000-01-05</th>\n",
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-       "      <td>-2.066561</td>\n",
-       "      <td>-1.477377</td>\n",
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-       "    <tr>\n",
-       "      <th>2000-01-06</th>\n",
-       "      <td>0.207552</td>\n",
-       "      <td>-0.300497</td>\n",
-       "      <td>-0.639468</td>\n",
-       "      <td>-0.950777</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-07</th>\n",
-       "      <td>-1.072332</td>\n",
-       "      <td>-2.626322</td>\n",
-       "      <td>-1.523701</td>\n",
-       "      <td>-1.489816</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-08</th>\n",
-       "      <td>-0.402632</td>\n",
-       "      <td>-0.960255</td>\n",
-       "      <td>-0.397366</td>\n",
-       "      <td>0.085742</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   A         B         C         D\n",
-       "2000-01-01  0.210545  0.560977  0.158079  0.492279\n",
-       "2000-01-02 -0.817745  1.280369 -1.522940 -1.275264\n",
-       "2000-01-03 -0.313090  0.156302 -1.386411 -0.757229\n",
-       "2000-01-04 -0.542066  0.528869  1.345872 -0.706867\n",
-       "2000-01-05  0.731123 -2.466330 -2.066561 -1.477377\n",
-       "2000-01-06  0.207552 -0.300497 -0.639468 -0.950777\n",
-       "2000-01-07 -1.072332 -2.626322 -1.523701 -1.489816\n",
-       "2000-01-08 -0.402632 -0.960255 -0.397366  0.085742"
-      ]
-     },
-     "execution_count": 106,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[['B', 'A']] = df[['A', 'B']]\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8bf21d81",
-   "metadata": {},
-   "source": [
-    "You may find this useful for applying a transform (in-place) to a subset of the columns.\n",
-    "\n",
-    "```{warning}\n",
-    "Pandas aligns all AXES when setting `Series` and `DataFrame` from `.loc`, and `.iloc`.\n",
-    "\n",
-    "This will not modify `df` because the column alignment is before value assignment.\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "id": "a9e5a725",
-   "metadata": {},
-   "outputs": [
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-       "    <tr>\n",
-       "      <th>2000-01-07</th>\n",
-       "      <td>-1.072332</td>\n",
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-       "    <tr>\n",
-       "      <th>2000-01-08</th>\n",
-       "      <td>-0.402632</td>\n",
-       "      <td>-0.960255</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   A         B\n",
-       "2000-01-01  0.210545  0.560977\n",
-       "2000-01-02 -0.817745  1.280369\n",
-       "2000-01-03 -0.313090  0.156302\n",
-       "2000-01-04 -0.542066  0.528869\n",
-       "2000-01-05  0.731123 -2.466330\n",
-       "2000-01-06  0.207552 -0.300497\n",
-       "2000-01-07 -1.072332 -2.626322\n",
-       "2000-01-08 -0.402632 -0.960255"
-      ]
-     },
-     "execution_count": 107,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[['A', 'B']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "id": "deaf7423",
-   "metadata": {},
-   "outputs": [
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-       "      <th>2000-01-04</th>\n",
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-       "    <tr>\n",
-       "      <th>2000-01-06</th>\n",
-       "      <td>0.207552</td>\n",
-       "      <td>-0.300497</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-07</th>\n",
-       "      <td>-1.072332</td>\n",
-       "      <td>-2.626322</td>\n",
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-       "    <tr>\n",
-       "      <th>2000-01-08</th>\n",
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-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   A         B\n",
-       "2000-01-01  0.210545  0.560977\n",
-       "2000-01-02 -0.817745  1.280369\n",
-       "2000-01-03 -0.313090  0.156302\n",
-       "2000-01-04 -0.542066  0.528869\n",
-       "2000-01-05  0.731123 -2.466330\n",
-       "2000-01-06  0.207552 -0.300497\n",
-       "2000-01-07 -1.072332 -2.626322\n",
-       "2000-01-08 -0.402632 -0.960255"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.loc[:, ['B', 'A']] = df[['A', 'B']]\n",
-    "df[['A', 'B']]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "396ffe1a",
-   "metadata": {},
-   "source": [
-    "```{warning}\n",
-    "The correct way to swap column values is by using raw values:\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "id": "b313fc4d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
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-       "      <th>2000-01-01</th>\n",
-       "      <td>0.560977</td>\n",
-       "      <td>0.210545</td>\n",
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-       "    <tr>\n",
-       "      <th>2000-01-02</th>\n",
-       "      <td>1.280369</td>\n",
-       "      <td>-0.817745</td>\n",
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-       "    <tr>\n",
-       "      <th>2000-01-03</th>\n",
-       "      <td>0.156302</td>\n",
-       "      <td>-0.313090</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-04</th>\n",
-       "      <td>0.528869</td>\n",
-       "      <td>-0.542066</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-05</th>\n",
-       "      <td>-2.466330</td>\n",
-       "      <td>0.731123</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-06</th>\n",
-       "      <td>-0.300497</td>\n",
-       "      <td>0.207552</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-07</th>\n",
-       "      <td>-2.626322</td>\n",
-       "      <td>-1.072332</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-08</th>\n",
-       "      <td>-0.960255</td>\n",
-       "      <td>-0.402632</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   A         B\n",
-       "2000-01-01  0.560977  0.210545\n",
-       "2000-01-02  1.280369 -0.817745\n",
-       "2000-01-03  0.156302 -0.313090\n",
-       "2000-01-04  0.528869 -0.542066\n",
-       "2000-01-05 -2.466330  0.731123\n",
-       "2000-01-06 -0.300497  0.207552\n",
-       "2000-01-07 -2.626322 -1.072332\n",
-       "2000-01-08 -0.960255 -0.402632"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.loc[:, ['B', 'A']] = df[['A', 'B']].to_numpy()\n",
-    "df[['A', 'B']]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e8fc4213",
-   "metadata": {},
-   "source": [
-    "### Attribute access\n",
-    "\n",
-    "You may access an index on a `Series` or column on a `DataFrame` directly as an attribute:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "id": "a98d540f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sa = pd.Series([1, 2, 3], index=list('abc'))\n",
-    "dfa = df.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "id": "61c5de8c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 111,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sa.b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "id": "82a8a49d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2000-01-01    0.560977\n",
-       "2000-01-02    1.280369\n",
-       "2000-01-03    0.156302\n",
-       "2000-01-04    0.528869\n",
-       "2000-01-05   -2.466330\n",
-       "2000-01-06   -0.300497\n",
-       "2000-01-07   -2.626322\n",
-       "2000-01-08   -0.960255\n",
-       "Freq: D, Name: A, dtype: float64"
-      ]
-     },
-     "execution_count": 112,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dfa.A"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "id": "c877f27a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    5\n",
-       "b    2\n",
-       "c    3\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 113,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sa.a = 5\n",
-    "sa"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "id": "a858572a",
-   "metadata": {},
-   "outputs": [
-    {
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-       "      <th>2000-01-01</th>\n",
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-       "      <td>0.492279</td>\n",
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-       "    <tr>\n",
-       "      <th>2000-01-02</th>\n",
-       "      <td>1</td>\n",
-       "      <td>-0.817745</td>\n",
-       "      <td>-1.522940</td>\n",
-       "      <td>-1.275264</td>\n",
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-       "      <th>2000-01-03</th>\n",
-       "      <td>2</td>\n",
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-       "      <th>2000-01-04</th>\n",
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-       "      <th>2000-01-05</th>\n",
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-       "      <td>-1.477377</td>\n",
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-       "    <tr>\n",
-       "      <th>2000-01-06</th>\n",
-       "      <td>5</td>\n",
-       "      <td>0.207552</td>\n",
-       "      <td>-0.639468</td>\n",
-       "      <td>-0.950777</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-07</th>\n",
-       "      <td>6</td>\n",
-       "      <td>-1.072332</td>\n",
-       "      <td>-1.523701</td>\n",
-       "      <td>-1.489816</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-08</th>\n",
-       "      <td>7</td>\n",
-       "      <td>-0.402632</td>\n",
-       "      <td>-0.397366</td>\n",
-       "      <td>0.085742</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            A         B         C         D\n",
-       "2000-01-01  0  0.210545  0.158079  0.492279\n",
-       "2000-01-02  1 -0.817745 -1.522940 -1.275264\n",
-       "2000-01-03  2 -0.313090 -1.386411 -0.757229\n",
-       "2000-01-04  3 -0.542066  1.345872 -0.706867\n",
-       "2000-01-05  4  0.731123 -2.066561 -1.477377\n",
-       "2000-01-06  5  0.207552 -0.639468 -0.950777\n",
-       "2000-01-07  6 -1.072332 -1.523701 -1.489816\n",
-       "2000-01-08  7 -0.402632 -0.397366  0.085742"
-      ]
-     },
-     "execution_count": 114,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dfa.A = list(range(len(dfa.index)))  # ok if A already exists\n",
-    "dfa"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "id": "5bac073a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th>C</th>\n",
-       "      <th>D</th>\n",
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-       "      <th>2000-01-01</th>\n",
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-       "      <th>2000-01-02</th>\n",
-       "      <td>1</td>\n",
-       "      <td>-0.817745</td>\n",
-       "      <td>-1.522940</td>\n",
-       "      <td>-1.275264</td>\n",
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-       "      <th>2000-01-03</th>\n",
-       "      <td>2</td>\n",
-       "      <td>-0.313090</td>\n",
-       "      <td>-1.386411</td>\n",
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-       "      <th>2000-01-04</th>\n",
-       "      <td>3</td>\n",
-       "      <td>-0.542066</td>\n",
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-       "      <td>-0.706867</td>\n",
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-       "    <tr>\n",
-       "      <th>2000-01-05</th>\n",
-       "      <td>4</td>\n",
-       "      <td>0.731123</td>\n",
-       "      <td>-2.066561</td>\n",
-       "      <td>-1.477377</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-06</th>\n",
-       "      <td>5</td>\n",
-       "      <td>0.207552</td>\n",
-       "      <td>-0.639468</td>\n",
-       "      <td>-0.950777</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-07</th>\n",
-       "      <td>6</td>\n",
-       "      <td>-1.072332</td>\n",
-       "      <td>-1.523701</td>\n",
-       "      <td>-1.489816</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2000-01-08</th>\n",
-       "      <td>7</td>\n",
-       "      <td>-0.402632</td>\n",
-       "      <td>-0.397366</td>\n",
-       "      <td>0.085742</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            A         B         C         D\n",
-       "2000-01-01  0  0.210545  0.158079  0.492279\n",
-       "2000-01-02  1 -0.817745 -1.522940 -1.275264\n",
-       "2000-01-03  2 -0.313090 -1.386411 -0.757229\n",
-       "2000-01-04  3 -0.542066  1.345872 -0.706867\n",
-       "2000-01-05  4  0.731123 -2.066561 -1.477377\n",
-       "2000-01-06  5  0.207552 -0.639468 -0.950777\n",
-       "2000-01-07  6 -1.072332 -1.523701 -1.489816\n",
-       "2000-01-08  7 -0.402632 -0.397366  0.085742"
-      ]
-     },
-     "execution_count": 115,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dfa['A'] = list(range(len(dfa.index)))  # use this form to create a new column\n",
-    "dfa"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3994e6e9",
-   "metadata": {},
-   "source": [
-    "```{warning}\n",
-    "- You can use this access only if the index element is a valid Python identifier, e.g. s.1 is not allowed. See here for an explanation of valid identifiers.\n",
-    "\n",
-    "- The attribute will not be available if it conflicts with an existing method name, e.g. s.min is not allowed, but s['min'] is possible.\n",
-    "\n",
-    "- Similarly, the attribute will not be available if it conflicts with any of the following list: index, major_axis, minor_axis, items.\n",
-    "\n",
-    "- In any of these cases, standard indexing will still work, e.g. s['1'], s['min'], and s['index'] will access the corresponding element or column.\n",
-    "```\n",
-    "\n",
-    "If you are using the IPython environment, you may also use tab-completion to see these accessible attributes.\n",
-    "\n",
-    "You can also assign a `dict` to a row of a `DataFrame`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "id": "b426f182",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>x</th>\n",
-       "      <th>y</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>9</td>\n",
-       "      <td>99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   x   y\n",
-       "0  1   3\n",
-       "1  9  99\n",
-       "2  3   5"
-      ]
-     },
-     "execution_count": 116,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x = pd.DataFrame({'x': [1, 2, 3], 'y': [3, 4, 5]})\n",
-    "x.iloc[1] = {'x': 9, 'y': 99}\n",
-    "x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "07c77821",
-   "metadata": {},
-   "source": [
-    "You can use attribute access to modify an existing element of a Series or column of a DataFrame, but be careful; if you try to use attribute access to create a new column, it creates a new attribute rather than a new column. In 0.21.0 and later, this will raise a `UserWarning`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "id": "d0dd98e4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_6653/269534380.py:2: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
-      "  df.two = [4, 5, 6]\n"
-     ]
-    }
-   ],
-   "source": [
-    "df = pd.DataFrame({'one': [1., 2., 3.]})\n",
-    "df.two = [4, 5, 6]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "id": "be7d10fd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>one</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1.0</td>\n",
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-       "      <th>1</th>\n",
-       "      <td>2.0</td>\n",
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-       "      <th>2</th>\n",
-       "      <td>3.0</td>\n",
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-       "</div>"
-      ],
-      "text/plain": [
-       "   one\n",
-       "0  1.0\n",
-       "1  2.0\n",
-       "2  3.0"
-      ]
-     },
-     "execution_count": 118,
-     "metadata": {},
-     "output_type": "execute_result"
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-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "24e53e81",
-   "metadata": {},
-   "source": [
-    "### Slicing ranges\n",
-    "\n",
-    "For now, we explain the semantics of slicing using the [] operator.\n",
-    "\n",
-    "With Series, the syntax works exactly as with an ndarray, returning a slice of the values and the corresponding labels:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "id": "5a0c0c96",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2000-01-01    0.560977\n",
-       "2000-01-02    1.280369\n",
-       "2000-01-03    0.156302\n",
-       "2000-01-04    0.528869\n",
-       "2000-01-05   -2.466330\n",
-       "2000-01-06   -0.300497\n",
-       "2000-01-07   -2.626322\n",
-       "2000-01-08   -0.960255\n",
-       "Freq: D, Name: A, dtype: float64"
-      ]
-     },
-     "execution_count": 119,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "id": "54b3429f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2000-01-01    0.560977\n",
-       "2000-01-02    1.280369\n",
-       "2000-01-03    0.156302\n",
-       "2000-01-04    0.528869\n",
-       "2000-01-05   -2.466330\n",
-       "Freq: D, Name: A, dtype: float64"
-      ]
-     },
-     "execution_count": 120,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s[:5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "id": "d5a7989b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2000-01-01    0.560977\n",
-       "2000-01-03    0.156302\n",
-       "2000-01-05   -2.466330\n",
-       "2000-01-07   -2.626322\n",
-       "Freq: 2D, Name: A, dtype: float64"
-      ]
-     },
-     "execution_count": 121,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s[::2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "id": "eb35c2a9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2000-01-08   -0.960255\n",
-       "2000-01-07   -2.626322\n",
-       "2000-01-06   -0.300497\n",
-       "2000-01-05   -2.466330\n",
-       "2000-01-04    0.528869\n",
-       "2000-01-03    0.156302\n",
-       "2000-01-02    1.280369\n",
-       "2000-01-01    0.560977\n",
-       "Freq: -1D, Name: A, dtype: float64"
-      ]
-     },
-     "execution_count": 122,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s[::-1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d1b310e0",
-   "metadata": {},
-   "source": [
-    "Note that setting works as well:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "id": "2276582f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2000-01-01    0.000000\n",
-       "2000-01-02    0.000000\n",
-       "2000-01-03    0.000000\n",
-       "2000-01-04    0.000000\n",
-       "2000-01-05    0.000000\n",
-       "2000-01-06   -0.300497\n",
-       "2000-01-07   -2.626322\n",
-       "2000-01-08   -0.960255\n",
-       "Freq: D, Name: A, dtype: float64"
-      ]
-     },
-     "execution_count": 123,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s2 = s.copy()\n",
-    "s2[:5] = 0\n",
-    "s2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6197cb61",
-   "metadata": {},
-   "source": [
-    "With DataFrame, slicing inside of `[]` slices the rows. This is provided largely as a convenience since it is such a common operation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "id": "5dfabbd2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>one</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   one\n",
-       "0  1.0\n",
-       "1  2.0\n",
-       "2  3.0"
-      ]
-     },
-     "execution_count": 124,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "id": "0b091ad1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>one</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   one\n",
-       "2  3.0\n",
-       "1  2.0\n",
-       "0  1.0"
-      ]
-     },
-     "execution_count": 125,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[::-1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f372a642",
-   "metadata": {},
-   "source": [
-    "### Selection by label\n",
-    "\n",
-    "```{warning}\n",
-    "Whether a copy or a reference is returned for a setting operation, may depend on the context. This is sometimes called `chained assignment` and should be avoided.\n",
-    "```\n",
-    "\n",
-    "```{warning}\n",
-    "`.loc` is strict when you present slicers that are not compatible (or convertible) with the index type. For example using integers in a `DatetimeIndex`. These will raise a `TypeError`.\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "id": "e9fef381",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dfl = pd.DataFrame(np.random.randn(5, 4),\n",
-    "                   columns=list('ABCD'),\n",
-    "                   index=pd.date_range('20130101', periods=5))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "id": "c7c0a82c",
-   "metadata": {
-    "tags": [
-     "raises-exception"
-    ]
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "cannot do slice indexing on DatetimeIndex with these indexers [2] of type int",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[127], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdfl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexing.py:1073\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1070\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m   1072\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[0;32m-> 1073\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmaybe_callable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexing.py:1290\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1288\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mslice\u001b[39m):\n\u001b[1;32m   1289\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(key, axis)\n\u001b[0;32m-> 1290\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_slice_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1291\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mis_bool_indexer(key):\n\u001b[1;32m   1292\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getbool_axis(key, axis\u001b[38;5;241m=\u001b[39maxis)\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexing.py:1324\u001b[0m, in \u001b[0;36m_LocIndexer._get_slice_axis\u001b[0;34m(self, slice_obj, axis)\u001b[0m\n\u001b[1;32m   1321\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m obj\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m   1323\u001b[0m labels \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m_get_axis(axis)\n\u001b[0;32m-> 1324\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[43mlabels\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mslice_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mslice_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mslice_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mslice_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(indexer, \u001b[38;5;28mslice\u001b[39m):\n\u001b[1;32m   1327\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_slice(indexer, axis\u001b[38;5;241m=\u001b[39maxis)\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexes/datetimes.py:809\u001b[0m, in \u001b[0;36mDatetimeIndex.slice_indexer\u001b[0;34m(self, start, end, step, kind)\u001b[0m\n\u001b[1;32m    801\u001b[0m \u001b[38;5;66;03m# GH#33146 if start and end are combinations of str and None and Index is not\u001b[39;00m\n\u001b[1;32m    802\u001b[0m \u001b[38;5;66;03m# monotonic, we can not use Index.slice_indexer because it does not honor the\u001b[39;00m\n\u001b[1;32m    803\u001b[0m \u001b[38;5;66;03m# actual elements, is only searching for start and end\u001b[39;00m\n\u001b[1;32m    804\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m    805\u001b[0m     check_str_or_none(start)\n\u001b[1;32m    806\u001b[0m     \u001b[38;5;129;01mor\u001b[39;00m check_str_or_none(end)\n\u001b[1;32m    807\u001b[0m     \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_monotonic_increasing\n\u001b[1;32m    808\u001b[0m ):\n\u001b[0;32m--> 809\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mIndex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mslice_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkind\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    811\u001b[0m mask \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m    812\u001b[0m deprecation_mask \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexes/base.py:6602\u001b[0m, in \u001b[0;36mIndex.slice_indexer\u001b[0;34m(self, start, end, step, kind)\u001b[0m\n\u001b[1;32m   6559\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   6560\u001b[0m \u001b[38;5;124;03mCompute the slice indexer for input labels and step.\u001b[39;00m\n\u001b[1;32m   6561\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   6598\u001b[0m \u001b[38;5;124;03mslice(1, 3, None)\u001b[39;00m\n\u001b[1;32m   6599\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   6600\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_deprecated_arg(kind, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkind\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mslice_indexer\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 6602\u001b[0m start_slice, end_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mslice_locs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   6604\u001b[0m \u001b[38;5;66;03m# return a slice\u001b[39;00m\n\u001b[1;32m   6605\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_scalar(start_slice):\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexes/base.py:6810\u001b[0m, in \u001b[0;36mIndex.slice_locs\u001b[0;34m(self, start, end, step, kind)\u001b[0m\n\u001b[1;32m   6808\u001b[0m start_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   6809\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m start \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 6810\u001b[0m     start_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_slice_bound\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mleft\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   6811\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m start_slice \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   6812\u001b[0m     start_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexes/base.py:6719\u001b[0m, in \u001b[0;36mIndex.get_slice_bound\u001b[0;34m(self, label, side, kind)\u001b[0m\n\u001b[1;32m   6715\u001b[0m original_label \u001b[38;5;241m=\u001b[39m label\n\u001b[1;32m   6717\u001b[0m \u001b[38;5;66;03m# For datetime indices label may be a string that has to be converted\u001b[39;00m\n\u001b[1;32m   6718\u001b[0m \u001b[38;5;66;03m# to datetime boundary according to its resolution.\u001b[39;00m\n\u001b[0;32m-> 6719\u001b[0m label \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_maybe_cast_slice_bound\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mside\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   6721\u001b[0m \u001b[38;5;66;03m# we need to look up the label\u001b[39;00m\n\u001b[1;32m   6722\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexes/datetimes.py:767\u001b[0m, in \u001b[0;36mDatetimeIndex._maybe_cast_slice_bound\u001b[0;34m(self, label, side, kind)\u001b[0m\n\u001b[1;32m    762\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(label, date) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(label, datetime):\n\u001b[1;32m    763\u001b[0m     \u001b[38;5;66;03m# Pandas supports slicing with dates, treated as datetimes at midnight.\u001b[39;00m\n\u001b[1;32m    764\u001b[0m     \u001b[38;5;66;03m# https://github.com/pandas-dev/pandas/issues/31501\u001b[39;00m\n\u001b[1;32m    765\u001b[0m     label \u001b[38;5;241m=\u001b[39m Timestamp(label)\u001b[38;5;241m.\u001b[39mto_pydatetime()\n\u001b[0;32m--> 767\u001b[0m label \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_maybe_cast_slice_bound\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mside\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkind\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    768\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_deprecate_mismatched_indexing(label)\n\u001b[1;32m    769\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_cast_for_get_loc(label)\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexes/datetimelike.py:320\u001b[0m, in \u001b[0;36mDatetimeIndexOpsMixin._maybe_cast_slice_bound\u001b[0;34m(self, label, side, kind)\u001b[0m\n\u001b[1;32m    318\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m lower \u001b[38;5;28;01mif\u001b[39;00m side \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mleft\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m upper\n\u001b[1;32m    319\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(label, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data\u001b[38;5;241m.\u001b[39m_recognized_scalars):\n\u001b[0;32m--> 320\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_invalid_indexer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mslice\u001b[39m\u001b[38;5;124m\"\u001b[39m, label)\n\u001b[1;32m    322\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m label\n",
-      "\u001b[0;31mTypeError\u001b[0m: cannot do slice indexing on DatetimeIndex with these indexers [2] of type int"
-     ]
-    }
-   ],
-   "source": [
-    "dfl.loc[2:3]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7e510c3a",
-   "metadata": {},
-   "source": [
-    "```{warning}\n",
-    "String likes in slicing can be convertible to the type of the index and lead to natural slicing.\n",
-    "```\n",
-    "\n",
-    "```\n",
-    "dfl.loc['20130102':'20130104']\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:730px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%20%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dpd.date_range%28'20130101',%20periods%3D5%29%29%0Adfl%0Adfl.loc%5B'20130102'%3A'20130104'%5D&d=2023-07-13&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "```{warning}\n",
-    "Pandas will raise a `KeyError` if indexing with a list with missing labels.\n",
-    "```\n",
-    "\n",
-    "Pandas provides a suite of methods in order to have **purely label-based indexing**. This is a strict inclusion-based protocol. Every label asked for must be in the index, or a `KeyError` will be raised. When slicing, both the start bound **AND** the stop bound are included, if present in the index. Integers are valid labels, but they refer to the label **and not the position**.\n",
-    "\n",
-    "- The `.loc` attribute is the primary access method. The following are valid inputs:\n",
-    "\n",
-    "- A single label, e.g. `5` or `'a'` (Note that `5` is interpreted as a label of the index. This use is not an integer position along the index.).\n",
-    "\n",
-    "- A list or array of labels `['a', 'b', 'c']`.\n",
-    "\n",
-    "- A slice object with labels `'a':'f'` (Note that contrary to usual Python slices, both the start and the stop are included, when present in the index!\n",
-    "\n",
-    "- A boolean array.\n",
-    "\n",
-    "- A `callable`.\n",
-    "\n",
-    "```\n",
-    "s1 = pd.Series(np.random.randn(6), index=list('abcdef'))\n",
-    "s1\n",
-    "s1.loc['c':]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:630px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%286%29,%20index%3Dlist%28'abcdef'%29%29%0As1.loc%5B'c'%3A%5D%0A&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "```\n",
-    "s1.loc['b']\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:630px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%286%29,%20index%3Dlist%28'abcdef'%29%29%0As1.loc%5B'b'%5D%0A&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "Note that the setting works as well:\n",
-    "\n",
-    "```\n",
-    "s1.loc['c':] = 0\n",
-    "s1\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:630px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%286%29,%20index%3Dlist%28'abcdef'%29%29%0As1.loc%5B'c'%3A%5D%20%3D%200%0As1&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "With a DataFrame:\n",
-    "\n",
-    "```\n",
-    "df1 = pd.DataFrame(np.random.randn(6, 4),\n",
-    "                   index=list('abcdef'),\n",
-    "                   columns=list('ABCD'))\n",
-    "df1\n",
-    "df1.loc[['a', 'b', 'd'], :]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:680px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B%5B'a',%20'b',%20'd'%5D,%20%3A%5D%0A&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "Accessing via label slices:\n",
-    "\n",
-    "```\n",
-    "df1.loc['d':, 'A':'C']\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:710px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B'd'%3A,%20'A'%3A'C'%5D%0A&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "For getting a cross-section using a label (equivalent to `df.xs('a')`):\n",
-    "\n",
-    "```\n",
-    "df1.loc['a']\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:690px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B'a'%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "\n",
-    "For getting values with a boolean array:\n",
-    "\n",
-    "```\n",
-    "df1.loc['a'] > 0\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:650px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B'a'%5D%20%3E%200&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "```\n",
-    "df1.loc[:, df1.loc['a'] > 0]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:700px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B%3A,%20df1.loc%5B'a'%5D%20%3E%200%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "\n",
-    "\n",
-    "NA values in a boolean array propagate as `False`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "id": "e4dfb504",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<BooleanArray>\n",
-       "[True, False, True, False, <NA>, False]\n",
-       "Length: 6, dtype: boolean"
-      ]
-     },
-     "execution_count": 128,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mask = pd.array([True, False, True, False, pd.NA, False], dtype=\"boolean\")\n",
-    "mask"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7ac29ebb",
-   "metadata": {},
-   "source": [
-    "```\n",
-    "df1[mask]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:680px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Amask%20%3D%20pd.array%28%5BTrue,%20False,%20True,%20False,%20pd.NA,%20False%5D,%20dtype%3D%22boolean%22%29%0Adf1%5Bmask%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "For getting a value explicitly:\n",
-    "\n",
-    "```\n",
-    "df1.loc['a', 'A'] # this is also equivalent to ``df1.at['a','A']``\n",
-    "```\n",
-    "\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:680px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B'a',%20'A'%5D%20&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "#### Slicing with labels\n",
-    "\n",
-    "When using `.loc` with slices, if both the start and the stop labels are present in the index, then elements located between the two (including them) are returned:\n",
-    "\n",
-    "```\n",
-    "s = pd.Series(list('abcde'), index=[0, 3, 2, 5, 4])\n",
-    "s.loc[3:5]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:580px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28'abcde'%29,%20index%3D%5B0,%203,%202,%205,%204%5D%29%0As.loc%5B3%3A5%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "\n",
-    "If at least one of the two is absent, but the index is sorted, and can be compared against start and stop labels, then slicing will still work as expected, by selecting labels which rank between the two:\n",
-    "\n",
-    "```\n",
-    "s.sort_index()\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:600px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28'abcde'%29,%20index%3D%5B0,%203,%202,%205,%204%5D%29%0As.sort_index%28%29&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "```\n",
-    "s.sort_index().loc[1:6]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:820px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28'abcde'%29,%20index%3D%5B0,%203,%202,%205,%204%5D%29%0As.sort_index%28%29.loc%5B1%3A6%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "However, if at least one of the two is absent and the index is not sorted, an error will be raised (since doing otherwise would be computationally expensive, as well as potentially ambiguous for mixed-type indexes). For instance, in the above example, `s.loc[1:6]` would raise `KeyError`.\n",
-    "\n",
-    "```\n",
-    "s = pd.Series(list('abcdef'), index=[0, 3, 2, 5, 4, 2])\n",
-    "s.loc[3:5]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:650px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28'abcdef'%29,%20index%3D%5B0,%203,%202,%205,%204,%202%5D%29%0As.loc%5B3%3A5%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "Also, if the index has duplicate labels and either the start or the stop label is duplicated, an error will be raised. For instance, in the above example, `s.loc[2:5]` would raise a `KeyError`.\n",
-    "\n",
-    "### Selection by position\n",
-    "\n",
-    "```{warning}\n",
-    "Whether a copy or a reference is returned for a setting operation, may depend on the context. This is sometimes called `chained assignment` and should be avoided.\n",
-    "```\n",
-    "\n",
-    "Pandas provides a suite of methods in order to get purely integer-based indexing. The semantics follow closely Python and NumPy slicing. These are 0-based indexing. When slicing, the start bound is included, while the upper bound is excluded. Trying to use a non-integer, even a valid label will raise an `IndexError`.\n",
-    "\n",
-    "The `.iloc` attribute is the primary access method. The following are valid inputs:\n",
-    "\n",
-    "- An integer e.g. `5`.\n",
-    "\n",
-    "- A list or array of integers `[4, 3, 0]`.\n",
-    "\n",
-    "- A slice object with ints `1:7`.\n",
-    "\n",
-    "- A boolean array.\n",
-    "\n",
-    "- A `callable`.\n",
-    "\n",
-    "```\n",
-    "s1 = pd.Series(np.random.randn(5), index=list(range(0, 10, 2)))\n",
-    "s1\n",
-    "s1.iloc[:3]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:600px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%285%29,%20index%3Dlist%28range%280,%2010,%202%29%29%29%0As1.iloc%5B%3A3%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "```\n",
-    "s1.iloc[3]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:600px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%285%29,%20index%3Dlist%28range%280,%2010,%202%29%29%29%0As1.iloc%5B3%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "Note that setting works as well:\n",
-    "\n",
-    "```\n",
-    "s1.iloc[:3] = 0\n",
-    "s1\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:620px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%285%29,%20index%3Dlist%28range%280,%2010,%202%29%29%29%0As1.iloc%5B%3A3%5D%20%3D%200%0As1&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "With a DataFrame,Select via integer slicing:\n",
-    "\n",
-    "```\n",
-    "df1 = pd.DataFrame(np.random.randn(6, 4),\n",
-    "                   index=list(range(0, 12, 2)),\n",
-    "                   columns=list(range(0, 8, 2)))\n",
-    "df1\n",
-    "df1.iloc[:3]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:680px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B%3A3%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "\n",
-    "```\n",
-    "df1.iloc[1:5, 2:4]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:700px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1%3A5,%202%3A4%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "Select via integer list:\n",
-    "\n",
-    "```\n",
-    "df1.iloc[[1, 3, 5], [1, 3]]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:700px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B%5B1,%203,%205%5D,%20%5B1,%203%5D%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "```\n",
-    "df1.iloc[1:3, :]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:680px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1%3A3,%20%3A%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "```\n",
-    "df1.iloc[:, 1:3]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:700px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B%3A,%201%3A3%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "```\n",
-    "df1.iloc[1, 1] # this is also equivalent to ``df1.iat[1,1]``\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:680px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1,%201%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "For getting a cross-section using an integer position (equiv to `df.xs(1)`):\n",
-    "\n",
-    "```\n",
-    "df1.iloc[1]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:700px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "Out-of-range slice indexes are handled gracefully just as in Python/NumPy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "id": "5ae4f102",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['a', 'b', 'c', 'd', 'e', 'f']"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x = list('abcdef') # these are allowed in Python/NumPy.\n",
-    "x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "id": "7bd7a10d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['e', 'f']"
-      ]
-     },
-     "execution_count": 130,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x[4:10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "id": "ad3080d1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[]"
-      ]
-     },
-     "execution_count": 131,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x[8:10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "id": "e867bb76",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    a\n",
-       "1    b\n",
-       "2    c\n",
-       "3    d\n",
-       "4    e\n",
-       "5    f\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 132,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s = pd.Series(x)\n",
-    "s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "af6e7b98",
-   "metadata": {},
-   "source": [
-    "```\n",
-    "s.iloc[4:10]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:600px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Ax%20%3D%20list%28'abcdef'%29%20%0As%20%3D%20pd.Series%28x%29%0As.iloc%5B4%3A10%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "id": "8a35458d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Series([], dtype: object)"
-      ]
-     },
-     "execution_count": 133,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s.iloc[8:10]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b8e6a3c1",
-   "metadata": {},
-   "source": [
-    "Note that using slices that go out of bounds can result in an empty axis (e.g. an empty DataFrame being returned)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "id": "25e335a7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dfl = pd.DataFrame(np.random.randn(5, 2), columns=list('AB'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e7495804",
-   "metadata": {},
-   "source": [
-    "```\n",
-    "dfl.iloc[:, 2:3]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:630px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%202%29,%20columns%3Dlist%28'AB'%29%29%0Adfl%0Adfl.iloc%5B%3A,%202%3A3%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "```\n",
-    "dfl.iloc[:, 1:3]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:630px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%202%29,%20columns%3Dlist%28'AB'%29%29%0Adfl.iloc%5B%3A,%201%3A3%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "```\n",
-    "dfl.iloc[4:6]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:600px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%202%29,%20columns%3Dlist%28'AB'%29%29%0Adfl.iloc%5B4%3A6%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "A single indexer that is out of bounds will raise an `IndexError`. A list of indexers where any element is out of bounds will raise an `IndexError`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "id": "53fc3ae2",
-   "metadata": {
-    "tags": [
-     "raises-exception"
-    ]
-   },
-   "outputs": [
-    {
-     "ename": "IndexError",
-     "evalue": "positional indexers are out-of-bounds",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexing.py:1587\u001b[0m, in \u001b[0;36m_iLocIndexer._get_list_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1586\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1587\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_take_with_is_copy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1588\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m   1589\u001b[0m     \u001b[38;5;66;03m# re-raise with different error message\u001b[39;00m\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/generic.py:3902\u001b[0m, in \u001b[0;36mNDFrame._take_with_is_copy\u001b[0;34m(self, indices, axis)\u001b[0m\n\u001b[1;32m   3895\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   3896\u001b[0m \u001b[38;5;124;03mInternal version of the `take` method that sets the `_is_copy`\u001b[39;00m\n\u001b[1;32m   3897\u001b[0m \u001b[38;5;124;03mattribute to keep track of the parent dataframe (using in indexing\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3900\u001b[0m \u001b[38;5;124;03mSee the docstring of `take` for full explanation of the parameters.\u001b[39;00m\n\u001b[1;32m   3901\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m-> 3902\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_take\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindices\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3903\u001b[0m \u001b[38;5;66;03m# Maybe set copy if we didn't actually change the index.\u001b[39;00m\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/generic.py:3886\u001b[0m, in \u001b[0;36mNDFrame._take\u001b[0;34m(self, indices, axis, convert_indices)\u001b[0m\n\u001b[1;32m   3884\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_consolidate_inplace()\n\u001b[0;32m-> 3886\u001b[0m new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtake\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3887\u001b[0m \u001b[43m    \u001b[49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3888\u001b[0m \u001b[43m    \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_block_manager_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3889\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   3890\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconvert_indices\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert_indices\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3891\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3892\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor(new_data)\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtake\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/internals/managers.py:977\u001b[0m, in \u001b[0;36mBaseBlockManager.take\u001b[0;34m(self, indexer, axis, verify, convert_indices)\u001b[0m\n\u001b[1;32m    976\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m convert_indices:\n\u001b[0;32m--> 977\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m \u001b[43mmaybe_convert_indices\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    979\u001b[0m new_labels \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes[axis]\u001b[38;5;241m.\u001b[39mtake(indexer)\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexers/utils.py:286\u001b[0m, in \u001b[0;36mmaybe_convert_indices\u001b[0;34m(indices, n, verify)\u001b[0m\n\u001b[1;32m    285\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[0;32m--> 286\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mindices are out-of-bounds\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    287\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m indices\n",
-      "\u001b[0;31mIndexError\u001b[0m: indices are out-of-bounds",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[135], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdfl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexing.py:1073\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1070\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m   1072\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[0;32m-> 1073\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmaybe_callable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexing.py:1616\u001b[0m, in \u001b[0;36m_iLocIndexer._getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1614\u001b[0m \u001b[38;5;66;03m# a list of integers\u001b[39;00m\n\u001b[1;32m   1615\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_list_like_indexer(key):\n\u001b[0;32m-> 1616\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_list_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1618\u001b[0m \u001b[38;5;66;03m# a single integer\u001b[39;00m\n\u001b[1;32m   1619\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1620\u001b[0m     key \u001b[38;5;241m=\u001b[39m item_from_zerodim(key)\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexing.py:1590\u001b[0m, in \u001b[0;36m_iLocIndexer._get_list_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1587\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_take_with_is_copy(key, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[1;32m   1588\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m   1589\u001b[0m     \u001b[38;5;66;03m# re-raise with different error message\u001b[39;00m\n\u001b[0;32m-> 1590\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpositional indexers are out-of-bounds\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n",
-      "\u001b[0;31mIndexError\u001b[0m: positional indexers are out-of-bounds"
-     ]
-    }
-   ],
-   "source": [
-    "dfl.iloc[[4, 5, 6]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "id": "5e7fcb6a",
-   "metadata": {
-    "tags": [
-     "raises-exception"
-    ]
-   },
-   "outputs": [
-    {
-     "ename": "IndexError",
-     "evalue": "single positional indexer is out-of-bounds",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[136], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdfl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m]\u001b[49m\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexing.py:1067\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1065\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_scalar_access(key):\n\u001b[1;32m   1066\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_value(\u001b[38;5;241m*\u001b[39mkey, takeable\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_takeable)\n\u001b[0;32m-> 1067\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_tuple\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1068\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1069\u001b[0m     \u001b[38;5;66;03m# we by definition only have the 0th axis\u001b[39;00m\n\u001b[1;32m   1070\u001b[0m     axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexing.py:1563\u001b[0m, in \u001b[0;36m_iLocIndexer._getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m   1561\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_getitem_tuple\u001b[39m(\u001b[38;5;28mself\u001b[39m, tup: \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m-> 1563\u001b[0m     tup \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_tuple_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1564\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m suppress(IndexingError):\n\u001b[1;32m   1565\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_lowerdim(tup)\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexing.py:873\u001b[0m, in \u001b[0;36m_LocationIndexer._validate_tuple_indexer\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    871\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(key):\n\u001b[1;32m    872\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 873\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_key\u001b[49m\u001b[43m(\u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    874\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m    875\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    876\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLocation based indexing can only have \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    877\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_valid_types\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] types\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    878\u001b[0m         ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexing.py:1466\u001b[0m, in \u001b[0;36m_iLocIndexer._validate_key\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1464\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m   1465\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_integer(key):\n\u001b[0;32m-> 1466\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_integer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1467\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m   1468\u001b[0m     \u001b[38;5;66;03m# a tuple should already have been caught by this point\u001b[39;00m\n\u001b[1;32m   1469\u001b[0m     \u001b[38;5;66;03m# so don't treat a tuple as a valid indexer\u001b[39;00m\n\u001b[1;32m   1470\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m IndexingError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mToo many indexers\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexing.py:1557\u001b[0m, in \u001b[0;36m_iLocIndexer._validate_integer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1555\u001b[0m len_axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis(axis))\n\u001b[1;32m   1556\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m len_axis \u001b[38;5;129;01mor\u001b[39;00m key \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m-\u001b[39mlen_axis:\n\u001b[0;32m-> 1557\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msingle positional indexer is out-of-bounds\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "\u001b[0;31mIndexError\u001b[0m: single positional indexer is out-of-bounds"
-     ]
-    }
-   ],
-   "source": [
-    "dfl.iloc[:, 4]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "892741d4",
-   "metadata": {},
-   "source": [
-    "### Selection by callable\n",
-    "\n",
-    "`.loc`, `.iloc`, and also `[]` indexing can accept a `callable` as indexer. The `callable` must be a function with one argument (the calling Series or DataFrame) that returns valid output for indexing.\n",
-    "\n",
-    "\n",
-    "```\n",
-    "df1 = pd.DataFrame(np.random.randn(6, 4),\n",
-    "                   index=list('abcdef'),\n",
-    "                   columns=list('ABCD'))\n",
-    "df1\n",
-    "df1.loc[lambda df: df['A'] > 0, :]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:700px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5Blambda%20df%3A%20df%5B'A'%5D%20%3E%200,%20%3A%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "```\n",
-    "df1.loc[:, lambda df: ['A', 'B']]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:700px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1.loc%5B%3A,%20lambda%20df%3A%20%5B'A',%20'B'%5D%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "```\n",
-    "df1.iloc[:, lambda df: [0, 1]]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:700px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1.iloc%5B%3A,%20lambda%20df%3A%20%5B0,%201%5D%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "```\n",
-    "df1[lambda df: df.columns[0]]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:650px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%5Blambda%20df%3A%20df.columns%5B0%5D%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "You can use callable indexing in `Series`.\n",
-    "\n",
-    "```\n",
-    "df1['A'].loc[lambda s: s > 0]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:920px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%5B'A'%5D.loc%5Blambda%20s%3A%20s%20%3E%200%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "### Combining positional and label-based indexing\n",
-    "\n",
-    "If you wish to get the 0th and the 2nd elements from the index in the `'A'` column, you can do:\n",
-    "\n",
-    "```\n",
-    "dfd = pd.DataFrame({'A': [1, 2, 3],\n",
-    "                    'B': [4, 5, 6]},\n",
-    "                   index=list('abc'))\n",
-    "dfd\n",
-    "dfd.loc[dfd.index[[0, 2]], 'A']\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:550px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adfd%20%3D%20pd.DataFrame%28%7B'A'%3A%20%5B1,%202,%203%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'B'%3A%20%5B4,%205,%206%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abc'%29%29%0Adfd.loc%5Bdfd.index%5B%5B0,%202%5D%5D,%20'A'%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "This can also be expressed using `.iloc`, by explicitly getting locations on the indexers, and using positional indexing to select things.\n",
-    "\n",
-    "```\n",
-    "dfd.iloc[[0, 2], dfd.columns.get_loc('A')]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:550px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adfd%20%3D%20pd.DataFrame%28%7B'A'%3A%20%5B1,%202,%203%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'B'%3A%20%5B4,%205,%206%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abc'%29%29%0Adfd.iloc%5B%5B0,%202%5D,%20dfd.columns.get_loc%28'A'%29%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "For getting multiple indexers, using `.get_indexer`:\n",
-    "\n",
-    "```\n",
-    "dfd.iloc[[0, 2], dfd.columns.get_indexer(['A', 'B'])]\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:550px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adfd%20%3D%20pd.DataFrame%28%7B'A'%3A%20%5B1,%202,%203%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'B'%3A%20%5B4,%205,%206%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abc'%29%29%0Adfd.iloc%5B%5B0,%202%5D,%20dfd.columns.get_indexer%28%5B'A',%20'B'%5D%29%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "## Combining datasets: concat, merge and join\n",
-    "\n",
-    "### concat\n",
-    "\n",
-    "- Concatenate Pandas objects along a particular axis.\n",
-    "\n",
-    "- Allows optional set logic along the other axes.\n",
-    "\n",
-    "- Can also add a layer of hierarchical indexing on the concatenation axis, which may be useful if the labels are the same (or overlapping) on the passed axis number.\n",
-    "\n",
-    "For example:\n",
-    "\n",
-    "Combine two `Series`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "id": "b7d161ac",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    a\n",
-       "1    b\n",
-       "0    c\n",
-       "1    d\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 137,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s1 = pd.Series(['a', 'b'])\n",
-    "s2 = pd.Series(['c', 'd'])\n",
-    "pd.concat([s1, s2])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "536e63da",
-   "metadata": {},
-   "source": [
-    "Clear the existing index and reset it in the result by setting the `ignore_index` option to `True`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "id": "719d8cb5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    a\n",
-       "1    b\n",
-       "2    c\n",
-       "3    d\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 138,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.concat([s1, s2], ignore_index=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "84bf44f8",
-   "metadata": {},
-   "source": [
-    "Add a hierarchical index at the outermost level of the data with the `keys` option."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "id": "5cb97b32",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "s1  0    a\n",
-       "    1    b\n",
-       "s2  0    c\n",
-       "    1    d\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 139,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.concat([s1, s2], keys=['s1', 's2'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5ca9c583",
-   "metadata": {},
-   "source": [
-    "Label the index keys you create with the `names` option."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "id": "30495b2a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Series name  Row ID\n",
-       "s1           0         a\n",
-       "             1         b\n",
-       "s2           0         c\n",
-       "             1         d\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 140,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.concat([s1, s2], keys=['s1', 's2'],\n",
-    "          names=['Series name', 'Row ID'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "14f306df",
-   "metadata": {},
-   "source": [
-    "Combine two `DataFrame` objects with identical columns."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "id": "ad1562dc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<table border=\"1\" class=\"dataframe\">\n",
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>letter</th>\n",
-       "      <th>number</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
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-       "      <td>b</td>\n",
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-       "</table>\n",
-       "</div>"
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-      "text/plain": [
-       "  letter  number\n",
-       "0      a       1\n",
-       "1      b       2"
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-     },
-     "execution_count": 141,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1 = pd.DataFrame([['a', 1], ['b', 2]],\n",
-    "                   columns=['letter', 'number'])\n",
-    "df1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "id": "32c10016",
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-    {
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>letter</th>\n",
-       "      <th>number</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>c</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>d</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
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-      "text/plain": [
-       "  letter  number\n",
-       "0      c       3\n",
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-     "execution_count": 142,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2 = pd.DataFrame([['c', 3], ['d', 4]],\n",
-    "                   columns=['letter', 'number'])\n",
-    "df2"
-   ]
-  },
-  {
-   "cell_type": "code",
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-       "      <th></th>\n",
-       "      <th>letter</th>\n",
-       "      <th>number</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
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-       "      <td>b</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>c</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>d</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  letter  number\n",
-       "0      a       1\n",
-       "1      b       2\n",
-       "0      c       3\n",
-       "1      d       4"
-      ]
-     },
-     "execution_count": 143,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.concat([df1, df2])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1388bff8",
-   "metadata": {},
-   "source": [
-    "Combine `DataFrame` objects with overlapping columns and return everything. Columns outside the intersection will be filled with `NaN` values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "id": "1af4bd95",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
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-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>letter</th>\n",
-       "      <th>number</th>\n",
-       "      <th>animal</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>c</td>\n",
-       "      <td>3</td>\n",
-       "      <td>cat</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>d</td>\n",
-       "      <td>4</td>\n",
-       "      <td>dog</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  letter  number animal\n",
-       "0      c       3    cat\n",
-       "1      d       4    dog"
-      ]
-     },
-     "execution_count": 144,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']],\n",
-    "                   columns=['letter', 'number', 'animal'])\n",
-    "df3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "id": "f35f5d58",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>letter</th>\n",
-       "      <th>number</th>\n",
-       "      <th>animal</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>a</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>b</td>\n",
-       "      <td>2</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>c</td>\n",
-       "      <td>3</td>\n",
-       "      <td>cat</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>d</td>\n",
-       "      <td>4</td>\n",
-       "      <td>dog</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  letter  number animal\n",
-       "0      a       1    NaN\n",
-       "1      b       2    NaN\n",
-       "0      c       3    cat\n",
-       "1      d       4    dog"
-      ]
-     },
-     "execution_count": 145,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.concat([df1, df3], sort=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c6d49897",
-   "metadata": {},
-   "source": [
-    "Combine DataFrame objects with overlapping columns and return only those that are shared by passing inner to the join keyword argument."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
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-       "  letter  number\n",
-       "0      a       1\n",
-       "1      b       2\n",
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-      ]
-     },
-     "execution_count": 146,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.concat([df1, df3], join=\"inner\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c2bef60d",
-   "metadata": {},
-   "source": [
-    "Combine `DataFrame` objects horizontally along the x-axis by passing in `axis=1`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "id": "f0415db2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "      <th>number</th>\n",
-       "      <th>animal</th>\n",
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-       "      <td>bird</td>\n",
-       "      <td>polly</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>b</td>\n",
-       "      <td>2</td>\n",
-       "      <td>monkey</td>\n",
-       "      <td>george</td>\n",
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-      "text/plain": [
-       "  letter  number  animal    name\n",
-       "0      a       1    bird   polly\n",
-       "1      b       2  monkey  george"
-      ]
-     },
-     "execution_count": 147,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df4 = pd.DataFrame([['bird', 'polly'], ['monkey', 'george']],\n",
-    "                   columns=['animal', 'name'])\n",
-    "pd.concat([df1, df4], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e6542c8a",
-   "metadata": {},
-   "source": [
-    "Prevent the result from including duplicate index values with the `verify_integrity` option."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "id": "09bd4fc6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>a</th>\n",
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-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   0\n",
-       "a  1"
-      ]
-     },
-     "execution_count": 148,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df5 = pd.DataFrame([1], index=['a'])\n",
-    "df5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "id": "6de38271",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-      "text/plain": [
-       "   0\n",
-       "a  2"
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-     "execution_count": 149,
-     "metadata": {},
-     "output_type": "execute_result"
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-   ],
-   "source": [
-    "df6 = pd.DataFrame([2], index=['a'])\n",
-    "df6"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "id": "ef259211",
-   "metadata": {
-    "tags": [
-     "raises-exception"
-    ]
-   },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "Indexes have overlapping values: Index(['a'], dtype='object')",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[150], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mdf5\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf6\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverify_integrity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    330\u001b[0m     )\n\u001b[0;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/reshape/concat.py:368\u001b[0m, in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[1;32m    146\u001b[0m \u001b[38;5;129m@deprecate_nonkeyword_arguments\u001b[39m(version\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, allowed_args\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobjs\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m    147\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconcat\u001b[39m(\n\u001b[1;32m    148\u001b[0m     objs: Iterable[NDFrame] \u001b[38;5;241m|\u001b[39m Mapping[HashableT, NDFrame],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    157\u001b[0m     copy: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    158\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m Series:\n\u001b[1;32m    159\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    160\u001b[0m \u001b[38;5;124;03m    Concatenate pandas objects along a particular axis.\u001b[39;00m\n\u001b[1;32m    161\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    366\u001b[0m \u001b[38;5;124;03m    1   3   4\u001b[39;00m\n\u001b[1;32m    367\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 368\u001b[0m     op \u001b[38;5;241m=\u001b[39m \u001b[43m_Concatenator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    369\u001b[0m \u001b[43m        \u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    370\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    371\u001b[0m \u001b[43m        \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    372\u001b[0m \u001b[43m        \u001b[49m\u001b[43mjoin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    373\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    374\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    375\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnames\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    376\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverify_integrity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_integrity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    377\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    378\u001b[0m \u001b[43m        \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    379\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    381\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mget_result()\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/reshape/concat.py:563\u001b[0m, in \u001b[0;36m_Concatenator.__init__\u001b[0;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[1;32m    560\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverify_integrity \u001b[38;5;241m=\u001b[39m verify_integrity\n\u001b[1;32m    561\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy \u001b[38;5;241m=\u001b[39m copy\n\u001b[0;32m--> 563\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnew_axes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_new_axes\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/reshape/concat.py:633\u001b[0m, in \u001b[0;36m_Concatenator._get_new_axes\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    631\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_new_axes\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Index]:\n\u001b[1;32m    632\u001b[0m     ndim \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_result_dim()\n\u001b[0;32m--> 633\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m [\n\u001b[1;32m    634\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_concat_axis \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbm_axis \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_comb_axis(i)\n\u001b[1;32m    635\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(ndim)\n\u001b[1;32m    636\u001b[0m     ]\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/reshape/concat.py:634\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    631\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_new_axes\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Index]:\n\u001b[1;32m    632\u001b[0m     ndim \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_result_dim()\n\u001b[1;32m    633\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m [\n\u001b[0;32m--> 634\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_concat_axis\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbm_axis \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_comb_axis(i)\n\u001b[1;32m    635\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(ndim)\n\u001b[1;32m    636\u001b[0m     ]\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/_libs/properties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/reshape/concat.py:697\u001b[0m, in \u001b[0;36m_Concatenator._get_concat_axis\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    692\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    693\u001b[0m     concat_axis \u001b[38;5;241m=\u001b[39m _make_concat_multiindex(\n\u001b[1;32m    694\u001b[0m         indexes, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlevels, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnames\n\u001b[1;32m    695\u001b[0m     )\n\u001b[0;32m--> 697\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_maybe_check_integrity\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconcat_axis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    699\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m concat_axis\n",
-      "File \u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/reshape/concat.py:705\u001b[0m, in \u001b[0;36m_Concatenator._maybe_check_integrity\u001b[0;34m(self, concat_index)\u001b[0m\n\u001b[1;32m    703\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m concat_index\u001b[38;5;241m.\u001b[39mis_unique:\n\u001b[1;32m    704\u001b[0m     overlap \u001b[38;5;241m=\u001b[39m concat_index[concat_index\u001b[38;5;241m.\u001b[39mduplicated()]\u001b[38;5;241m.\u001b[39munique()\n\u001b[0;32m--> 705\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIndexes have overlapping values: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moverlap\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
-      "\u001b[0;31mValueError\u001b[0m: Indexes have overlapping values: Index(['a'], dtype='object')"
-     ]
-    }
-   ],
-   "source": [
-    "pd.concat([df5, df6], verify_integrity=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f4fec58f",
-   "metadata": {},
-   "source": [
-    "    \n",
-    "Append a single row to the end of a `DataFrame` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "id": "ae1083e5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "</style>\n",
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-      "text/plain": [
-       "   a  b\n",
-       "0  1  2"
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-     },
-     "execution_count": 151,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df7 = pd.DataFrame({'a': 1, 'b': 2}, index=[0])\n",
-    "df7"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "id": "c0b7743e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    3\n",
-       "b    4\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 152,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "new_row = pd.Series({'a': 3, 'b': 4})\n",
-    "new_row"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "id": "860d7a02",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "\n",
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-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
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-       "      <td>2</td>\n",
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-       "      <th>1</th>\n",
-       "      <td>3</td>\n",
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-      "text/plain": [
-       "   a  b\n",
-       "0  1  2\n",
-       "1  3  4"
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-     },
-     "execution_count": 153,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.concat([df7, new_row.to_frame().T], ignore_index=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d6000108",
-   "metadata": {},
-   "source": [
-    "```{note}\n",
-    "`append()` has been deprecated since version 1.4.0: Use `concat()` instead. \n",
-    "```\n",
-    "\n",
-    "### merge\n",
-    "\n",
-    "- Merge DataFrame or named Series objects with a database-style join.\n",
-    "\n",
-    "- A named Series object is treated as a DataFrame with a single named column.\n",
-    "\n",
-    "- The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on. When performing a cross-merge, no column specifications to merge on are allowed.\n",
-    "\n",
-    "```{warning}\n",
-    "If both key columns contain rows where the key is a null value, those rows will be matched against each other. This is different from usual SQL join behaviour and can lead to unexpected results.\n",
-    "```\n",
-    "\n",
-    "For example:\n",
-    "\n",
-    "```python\n",
-    "df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],\n",
-    "                    'value': [1, 2, 3, 5]})\n",
-    "df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],\n",
-    "                    'value': [5, 6, 7, 8]})\n",
-    "```\n",
-    "\n",
-    "Merge DataFrames `df1` and `df2` with specified left and right suffixes appended to any overlapping columns.\n",
-    "\n",
-    "```\n",
-    "df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=('_left', '_right'))\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:800px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf1%20%3D%20pd.DataFrame%28%7B'lkey'%3A%20%5B'foo',%20'bar',%20'baz',%20'foo'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'value'%3A%20%5B1,%202,%203,%205%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B'rkey'%3A%20%5B'foo',%20'bar',%20'baz',%20'foo'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'value'%3A%20%5B5,%206,%207,%208%5D%7D%29%0Adf1.merge%28df2,%20left_on%3D'lkey',%20right_on%3D'rkey'%29&d=2023-05-27&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "Merge DataFrames `df1` and `df2`, but raise an exception if the DataFrames have any overlapping columns."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "id": "e237ef6a",
-   "metadata": {
-    "tags": [
-     "raises-exception"
-    ]
-   },
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'rkey'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m/tmp/ipykernel_6653/726409595.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft_on\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'lkey'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright_on\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'rkey'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msuffixes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[1;32m  10086\u001b[0m         \u001b[0mvalidate\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10087\u001b[0m     ) -> DataFrame:\n\u001b[1;32m  10088\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmerge\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10089\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m> 10090\u001b[0;31m         return merge(\n\u001b[0m\u001b[1;32m  10091\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10092\u001b[0m             \u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10093\u001b[0m             \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[1;32m    106\u001b[0m     \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[0mindicator\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m     \u001b[0mvalidate\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m ) -> DataFrame:\n\u001b[0;32m--> 110\u001b[0;31m     op = _MergeOperation(\n\u001b[0m\u001b[1;32m    111\u001b[0m         \u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m         \u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m         \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, left, right, how, on, left_on, right_on, axis, left_index, right_index, sort, suffixes, indicator, validate)\u001b[0m\n\u001b[1;32m    699\u001b[0m         (\n\u001b[1;32m    700\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft_join_keys\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    701\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright_join_keys\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    702\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin_names\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 703\u001b[0;31m         ) = self._get_merge_keys()\n\u001b[0m\u001b[1;32m    704\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    705\u001b[0m         \u001b[0;31m# validate the merge keys dtypes. We may need to coerce\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    706\u001b[0m         \u001b[0;31m# to avoid incompatible dtypes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1158\u001b[0m                         \u001b[0;31m# Then we're either Hashable or a wrong-length arraylike,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1159\u001b[0m                         \u001b[0;31m#  the latter of which will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1160\u001b[0m                         \u001b[0mrk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mHashable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1161\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0mrk\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1162\u001b[0;31m                             \u001b[0mright_keys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label_or_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1163\u001b[0m                         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1164\u001b[0m                             \u001b[0;31m# work-around for merge_asof(right_index=True)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1165\u001b[0m                             \u001b[0mright_keys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1846\u001b[0m                 \u001b[0;34m.\u001b[0m\u001b[0mget_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[assignment]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1847\u001b[0m                 \u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1848\u001b[0m             )\n\u001b[1;32m   1849\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1850\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1851\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1852\u001b[0m         \u001b[0;31m# Check for duplicates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1853\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'rkey'"
-     ]
-    }
-   ],
-   "source": [
-    "df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0f46efe6",
-   "metadata": {},
-   "source": [
-    "Using `how` parameter decide the type of merge to be performed.\n",
-    "\n",
-    "```\n",
-    "df1 = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]})\n",
-    "df2 = pd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4]})\n",
-    "```\n",
-    "\n",
-    "```\n",
-    "df1.merge(df2, how='inner', on='a')\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:600px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf1%20%3D%20pd.DataFrame%28%7B'a'%3A%20%5B'foo',%20'bar'%5D,%20'b'%3A%20%5B1,%202%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B'a'%3A%20%5B'foo',%20'baz'%5D,%20'c'%3A%20%5B3,%204%5D%7D%29%0Adf1.merge%28df2,%20how%3D'inner',%20on%3D'a'%29&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "```\n",
-    "df1.merge(df2, how='left', on='a')\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:630px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf1%20%3D%20pd.DataFrame%28%7B'a'%3A%20%5B'foo',%20'bar'%5D,%20'b'%3A%20%5B1,%202%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B'a'%3A%20%5B'foo',%20'baz'%5D,%20'c'%3A%20%5B3,%204%5D%7D%29%0Adf1.merge%28df2,%20how%3D'left',%20on%3D'a'%29&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "```\n",
-    "df1 = pd.DataFrame({'left': ['foo', 'bar']})\n",
-    "df2 = pd.DataFrame({'right': [7, 8]})\n",
-    "```\n",
-    "\n",
-    "```\n",
-    "df1.merge(df2, how='cross')\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:670px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf1%20%3D%20pd.DataFrame%28%7B'left'%3A%20%5B'foo',%20'bar'%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B'right'%3A%20%5B7,%208%5D%7D%29%0Adf1.merge%28df2,%20how%3D'cross'%29&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "### join\n",
-    "\n",
-    "- Join columns of another DataFrame.\n",
-    "\n",
-    "- Join columns with other DataFrame either on index or on a key column. Efficiently join multiple DataFrame objects by index at once by passing a list.\n",
-    "\n",
-    "For example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "id": "52237d77",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],\n",
-    "                   'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "id": "38af83f6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],\n",
-    "                      'B': ['B0', 'B1', 'B2']})                     "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "23b3b619",
-   "metadata": {},
-   "source": [
-    "Join DataFrames using their indexes.\n",
-    "\n",
-    "```\n",
-    "df.join(other, lsuffix='_caller', rsuffix='_other')\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:830px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf%20%3D%20pd.DataFrame%28%7B'key'%3A%20%5B'K0',%20'K1',%20'K2',%20'K3',%20'K4',%20'K5'%5D,'A'%3A%20%5B'A0',%20'A1',%20'A2',%20'A3',%20'A4',%20'A5'%5D%7D%29%0Aother%20%3D%20pd.DataFrame%28%7B'key'%3A%20%5B'K0',%20'K1',%20'K2'%5D,'B'%3A%20%5B'B0',%20'B1',%20'B2'%5D%7D%29%0Adf.join%28other,%20lsuffix%3D'_caller',%20rsuffix%3D'_other'%29&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "If we want to join using the `key` columns, we need to set `key` to be the index in both `df` and `other`. The joined DataFrame will have `key` as its index.\n",
-    "\n",
-    "```\n",
-    "df.set_index('key').join(other.set_index('key'))\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:1170px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf%20%3D%20pd.DataFrame%28%7B'key'%3A%20%5B'K0',%20'K1',%20'K2',%20'K3',%20'K4',%20'K5'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'A'%3A%20%5B'A0',%20'A1',%20'A2',%20'A3',%20'A4',%20'A5'%5D%7D%29%0Aother%20%3D%20pd.DataFrame%28%7B'key'%3A%20%5B'K0',%20'K1',%20'K2'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'B'%3A%20%5B'B0',%20'B1',%20'B2'%5D%7D%29%0Adf.set_index%28'key'%29.join%28other.set_index%28'key'%29%29&d=2023-05-27&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "Another option to join using the key columns is to use the `on` parameter. `DataFrame.join` always uses `other`’s index but we can use any column in `df`. This method preserves the original DataFrame’s index in the result."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "id": "8232481e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>key</th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>K0</td>\n",
-       "      <td>A0</td>\n",
-       "      <td>B0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>K1</td>\n",
-       "      <td>A1</td>\n",
-       "      <td>B1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>K2</td>\n",
-       "      <td>A2</td>\n",
-       "      <td>B2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>K3</td>\n",
-       "      <td>A3</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>K4</td>\n",
-       "      <td>A4</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>K5</td>\n",
-       "      <td>A5</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  key   A    B\n",
-       "0  K0  A0   B0\n",
-       "1  K1  A1   B1\n",
-       "2  K2  A2   B2\n",
-       "3  K3  A3  NaN\n",
-       "4  K4  A4  NaN\n",
-       "5  K5  A5  NaN"
-      ]
-     },
-     "execution_count": 157,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.join(other.set_index('key'), on='key')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "70ccf148",
-   "metadata": {},
-   "source": [
-    "Using non-unique key values shows how they are matched."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "id": "f3000f44",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>key</th>\n",
-       "      <th>A</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>K0</td>\n",
-       "      <td>A0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>K1</td>\n",
-       "      <td>A1</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>K1</td>\n",
-       "      <td>A2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>K3</td>\n",
-       "      <td>A3</td>\n",
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-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>K0</td>\n",
-       "      <td>A4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>K1</td>\n",
-       "      <td>A5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  key   A\n",
-       "0  K0  A0\n",
-       "1  K1  A1\n",
-       "2  K1  A2\n",
-       "3  K3  A3\n",
-       "4  K0  A4\n",
-       "5  K1  A5"
-      ]
-     },
-     "execution_count": 158,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.DataFrame({'key': ['K0', 'K1', 'K1', 'K3', 'K0', 'K1'],\n",
-    "                   'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})\n",
-    "df                   "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "id": "2cd10ae6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>key</th>\n",
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-       "      <th>1</th>\n",
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-       "      <td>A1</td>\n",
-       "      <td>B1</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>K1</td>\n",
-       "      <td>A2</td>\n",
-       "      <td>B1</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>K3</td>\n",
-       "      <td>A3</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>K0</td>\n",
-       "      <td>A4</td>\n",
-       "      <td>B0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>K1</td>\n",
-       "      <td>A5</td>\n",
-       "      <td>B1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  key   A    B\n",
-       "0  K0  A0   B0\n",
-       "1  K1  A1   B1\n",
-       "2  K1  A2   B1\n",
-       "3  K3  A3  NaN\n",
-       "4  K0  A4   B0\n",
-       "5  K1  A5   B1"
-      ]
-     },
-     "execution_count": 159,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.join(other.set_index('key'), on='key', validate='m:1')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "346abc67",
-   "metadata": {},
-   "source": [
-    "## Aggregation and grouping\n",
-    "\n",
-    "Group `DataFrame` using a mapper or by a `Series` of columns.\n",
-    "\n",
-    "A `groupby` operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.\n",
-    "\n",
-    "For example:\n",
-    "\n",
-    "```\n",
-    "df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',\n",
-    "                              'Parrot', 'Parrot'],\n",
-    "                   'Max Speed': [380., 370., 24., 26.]})\n",
-    "df\n",
-    "df.groupby(['Animal']).mean()\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:785px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf%20%3D%20pd.DataFrame%28%7B'Animal'%3A%20%5B'Falcon',%20'Falcon',%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'Parrot',%20'Parrot'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'Max%20Speed'%3A%20%5B380.,%20370.,%2024.,%2026.%5D%7D%29%0Adf%0Adf.groupby%28%5B'Animal'%5D%29.mean%28%29&d=2023-05-27&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "### Hierarchical Indexes\n",
-    "\n",
-    "#################\n",
-    "\n",
-    "We can `groupby` different levels of a hierarchical index using the `level` parameter:\n",
-    "\n",
-    "```\n",
-    "arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],\n",
-    "          ['Captive', 'Wild', 'Captive', 'Wild']]\n",
-    "index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))\n",
-    "df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},\n",
-    "                  index=index)\n",
-    "df.groupby(level=0).mean()\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:810px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aarrays%20%3D%20%5B%5B'Falcon',%20'Falcon',%20'Parrot',%20'Parrot'%5D,%0A%20%20%20%20%20%20%20%20%20%20%5B'Captive',%20'Wild',%20'Captive',%20'Wild'%5D%5D%0Aindex%20%3D%20pd.MultiIndex.from_arrays%28arrays,%20names%3D%28'Animal',%20'Type'%29%29%0Adf%20%3D%20pd.DataFrame%28%7B'Max%20Speed'%3A%20%5B390.,%20350.,%2030.,%2020.%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dindex%29%0Adf.groupby%28level%3D0%29.mean%28%29&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "```\n",
-    "df.groupby(level=\"Type\").mean()\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:810px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aarrays%20%3D%20%5B%5B'Falcon',%20'Falcon',%20'Parrot',%20'Parrot'%5D,%0A%20%20%20%20%20%20%20%20%20%20%5B'Captive',%20'Wild',%20'Captive',%20'Wild'%5D%5D%0Aindex%20%3D%20pd.MultiIndex.from_arrays%28arrays,%20names%3D%28'Animal',%20'Type'%29%29%0Adf%20%3D%20pd.DataFrame%28%7B'Max%20Speed'%3A%20%5B390.,%20350.,%2030.,%2020.%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dindex%29%0Adf.groupby%28level%3D%22Type%22%29.mean%28%29&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "We can also choose to include NA in group keys or not by setting `dropna` parameter, the default setting is `True`.\n",
-    "\n",
-    "```\n",
-    "l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]\n",
-    "df = pd.DataFrame(l, columns=[\"a\", \"b\", \"c\"])\n",
-    "df.groupby(by=[\"b\"]).sum()\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:770px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Al%20%3D%20%5B%5B1,%202,%203%5D,%20%5B1,%20None,%204%5D,%20%5B2,%201,%203%5D,%20%5B1,%202,%202%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%5B%22b%22%5D%29.sum%28%29%0A&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "```\n",
-    "df.groupby(by=[\"b\"], dropna=False).sum()\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:750px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Al%20%3D%20%5B%5B1,%202,%203%5D,%20%5B1,%20None,%204%5D,%20%5B2,%201,%203%5D,%20%5B1,%202,%202%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%5B%22b%22%5D,%20dropna%3DFalse%29.sum%28%29&d=2023-05-27&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "```\n",
-    "l = [[\"a\", 12, 12], [None, 12.3, 33.], [\"b\", 12.3, 123], [\"a\", 1, 1]]\n",
-    "df = pd.DataFrame(l, columns=[\"a\", \"b\", \"c\"])\n",
-    "df.groupby(by=\"a\").sum()\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:750px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Al%20%3D%20%5B%5B%22a%22,%2012,%2012%5D,%20%5BNone,%2012.3,%2033.%5D,%20%5B%22b%22,%2012.3,%20123%5D,%20%5B%22a%22,%201,%201%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%22a%22%29.sum%28%29&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "```\n",
-    "df.groupby(by=\"a\", dropna=False).sum()\n",
-    "```\n",
-    "\n",
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:750px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Al%20%3D%20%5B%5B%22a%22,%2012,%2012%5D,%20%5BNone,%2012.3,%2033.%5D,%20%5B%22b%22,%2012.3,%20123%5D,%20%5B%22a%22,%201,%201%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%22a%22,%20dropna%3DFalse%29.sum%28%29%0A&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "\n",
-    "When using `.apply()`, use `group_keys` to include or exclude the group keys. The `group_keys` argument defaults to `True` (include)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 160,
-   "id": "46cac163",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>Animal</th>\n",
-       "      <th>Max Speed</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Animal</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">Falcon</th>\n",
-       "      <th>0</th>\n",
-       "      <td>Falcon</td>\n",
-       "      <td>380.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Falcon</td>\n",
-       "      <td>370.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">Parrot</th>\n",
-       "      <th>2</th>\n",
-       "      <td>Parrot</td>\n",
-       "      <td>24.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Parrot</td>\n",
-       "      <td>26.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Animal  Max Speed\n",
-       "Animal                     \n",
-       "Falcon 0  Falcon      380.0\n",
-       "       1  Falcon      370.0\n",
-       "Parrot 2  Parrot       24.0\n",
-       "       3  Parrot       26.0"
-      ]
-     },
-     "execution_count": 160,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',\n",
-    "                              'Parrot', 'Parrot'],\n",
-    "                   'Max Speed': [380., 370., 24., 26.]})\n",
-    "df.groupby(\"Animal\", group_keys=True).apply(lambda x: x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 161,
-   "id": "d3f22d0d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Animal</th>\n",
-       "      <th>Max Speed</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Falcon</td>\n",
-       "      <td>380.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Falcon</td>\n",
-       "      <td>370.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Parrot</td>\n",
-       "      <td>24.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Parrot</td>\n",
-       "      <td>26.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Animal  Max Speed\n",
-       "0  Falcon      380.0\n",
-       "1  Falcon      370.0\n",
-       "2  Parrot       24.0\n",
-       "3  Parrot       26.0"
-      ]
-     },
-     "execution_count": 161,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.groupby(\"Animal\", group_keys=False).apply(lambda x: x)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c82dc361",
-   "metadata": {},
-   "source": [
-    "## Pivot table\n",
-    "\n",
-    "Create a spreadsheet-style pivot table as a DataFrame.\n",
-    "\n",
-    "The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 162,
-   "id": "1bf04bb7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th>C</th>\n",
-       "      <th>D</th>\n",
-       "      <th>E</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>foo</td>\n",
-       "      <td>one</td>\n",
-       "      <td>small</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>foo</td>\n",
-       "      <td>one</td>\n",
-       "      <td>large</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>foo</td>\n",
-       "      <td>one</td>\n",
-       "      <td>large</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>foo</td>\n",
-       "      <td>two</td>\n",
-       "      <td>small</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>foo</td>\n",
-       "      <td>two</td>\n",
-       "      <td>small</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>bar</td>\n",
-       "      <td>one</td>\n",
-       "      <td>large</td>\n",
-       "      <td>4</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>bar</td>\n",
-       "      <td>one</td>\n",
-       "      <td>small</td>\n",
-       "      <td>5</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>bar</td>\n",
-       "      <td>two</td>\n",
-       "      <td>small</td>\n",
-       "      <td>6</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>bar</td>\n",
-       "      <td>two</td>\n",
-       "      <td>large</td>\n",
-       "      <td>7</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     A    B      C  D  E\n",
-       "0  foo  one  small  1  2\n",
-       "1  foo  one  large  2  4\n",
-       "2  foo  one  large  2  5\n",
-       "3  foo  two  small  3  5\n",
-       "4  foo  two  small  3  6\n",
-       "5  bar  one  large  4  6\n",
-       "6  bar  one  small  5  8\n",
-       "7  bar  two  small  6  9\n",
-       "8  bar  two  large  7  9"
-      ]
-     },
-     "execution_count": 162,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.DataFrame({\"A\": [\"foo\", \"foo\", \"foo\", \"foo\", \"foo\",\n",
-    "                         \"bar\", \"bar\", \"bar\", \"bar\"],\n",
-    "                   \"B\": [\"one\", \"one\", \"one\", \"two\", \"two\",\n",
-    "                         \"one\", \"one\", \"two\", \"two\"],\n",
-    "                   \"C\": [\"small\", \"large\", \"large\", \"small\",\n",
-    "                         \"small\", \"large\", \"small\", \"small\",\n",
-    "                         \"large\"],\n",
-    "                   \"D\": [1, 2, 2, 3, 3, 4, 5, 6, 7],\n",
-    "                   \"E\": [2, 4, 5, 5, 6, 6, 8, 9, 9]})\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bf5d776c",
-   "metadata": {},
-   "source": [
-    "This first example aggregates values by taking the sum."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 163,
-   "id": "66f45689",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>C</th>\n",
-       "      <th>large</th>\n",
-       "      <th>small</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
-       "      <th>one</th>\n",
-       "      <td>4.0</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>two</th>\n",
-       "      <td>7.0</td>\n",
-       "      <td>6.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
-       "      <th>one</th>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>two</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>6.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "C        large  small\n",
-       "A   B                \n",
-       "bar one    4.0    5.0\n",
-       "    two    7.0    6.0\n",
-       "foo one    4.0    1.0\n",
-       "    two    NaN    6.0"
-      ]
-     },
-     "execution_count": 163,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "table = pd.pivot_table(df, values='D', index=['A', 'B'],\n",
-    "                    columns=['C'], aggfunc=np.sum)\n",
-    "table"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "412da531",
-   "metadata": {},
-   "source": [
-    "We can also fill in missing values using the `fill_value` parameter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 164,
-   "id": "53982906",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>C</th>\n",
-       "      <th>large</th>\n",
-       "      <th>small</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
-       "      <th>one</th>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>two</th>\n",
-       "      <td>7</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
-       "      <th>one</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>two</th>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "C        large  small\n",
-       "A   B                \n",
-       "bar one      4      5\n",
-       "    two      7      6\n",
-       "foo one      4      1\n",
-       "    two      0      6"
-      ]
-     },
-     "execution_count": 164,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "table = pd.pivot_table(df, values='D', index=['A', 'B'],\n",
-    "                    columns=['C'], aggfunc=np.sum, fill_value=0)\n",
-    "table"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "21fbd3d9",
-   "metadata": {},
-   "source": [
-    "The next example aggregates by taking the mean across multiple columns."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 165,
-   "id": "eab3b2c4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>D</th>\n",
-       "      <th>E</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>A</th>\n",
-       "      <th>C</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
-       "      <th>large</th>\n",
-       "      <td>5.500000</td>\n",
-       "      <td>7.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>small</th>\n",
-       "      <td>5.500000</td>\n",
-       "      <td>8.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
-       "      <th>large</th>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>4.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>small</th>\n",
-       "      <td>2.333333</td>\n",
-       "      <td>4.333333</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  D         E\n",
-       "A   C                        \n",
-       "bar large  5.500000  7.500000\n",
-       "    small  5.500000  8.500000\n",
-       "foo large  2.000000  4.500000\n",
-       "    small  2.333333  4.333333"
-      ]
-     },
-     "execution_count": 165,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],\n",
-    "                    aggfunc={'D': np.mean,\n",
-    "                             'E': np.mean})\n",
-    "table"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f2cb171e",
-   "metadata": {},
-   "source": [
-    "We can also calculate multiple types of aggregations for any given value column."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 166,
-   "id": "067c7909",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead tr th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead tr:last-of-type th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>D</th>\n",
-       "      <th colspan=\"3\" halign=\"left\">E</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>mean</th>\n",
-       "      <th>max</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>min</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>A</th>\n",
-       "      <th>C</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
-       "      <th>large</th>\n",
-       "      <td>5.500000</td>\n",
-       "      <td>9</td>\n",
-       "      <td>7.500000</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>small</th>\n",
-       "      <td>5.500000</td>\n",
-       "      <td>9</td>\n",
-       "      <td>8.500000</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
-       "      <th>large</th>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4.500000</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>small</th>\n",
-       "      <td>2.333333</td>\n",
-       "      <td>6</td>\n",
-       "      <td>4.333333</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  D   E              \n",
-       "               mean max      mean min\n",
-       "A   C                                \n",
-       "bar large  5.500000   9  7.500000   6\n",
-       "    small  5.500000   9  8.500000   8\n",
-       "foo large  2.000000   5  4.500000   4\n",
-       "    small  2.333333   6  4.333333   2"
-      ]
-     },
-     "execution_count": 166,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],\n",
-    "                    aggfunc={'D': np.mean,\n",
-    "                             'E': [min, max, np.mean]})\n",
-    "table"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b59b9e37",
-   "metadata": {},
-   "source": [
-    "## High-performance Pandas: eval() and query()\n",
-    "\n",
-    "### eval()\n",
-    "\n",
-    "Evaluate a string describing operations on DataFrame columns.\n",
-    "\n",
-    "Operates on columns only, not specific rows or elements. This allows `eval` to run arbitrary code, which can make you vulnerable to code injection if you pass user input to this function.\n",
-    "\n",
-    "For example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 167,
-   "id": "e8035353",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
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-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
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-       "      <td>8</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   A   B\n",
-       "0  1  10\n",
-       "1  2   8\n",
-       "2  3   6\n",
-       "3  4   4\n",
-       "4  5   2"
-      ]
-     },
-     "execution_count": 167,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 168,
-   "id": "3210ba1b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    11\n",
-       "1    10\n",
-       "2     9\n",
-       "3     8\n",
-       "4     7\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 168,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.eval('A + B')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "41f98456",
-   "metadata": {},
-   "source": [
-    "The assignment is allowed though by default the original `DataFrame` is not modified."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 169,
-   "id": "ffd06292",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   A   B   C\n",
-       "0  1  10  11\n",
-       "1  2   8  10\n",
-       "2  3   6   9\n",
-       "3  4   4   8\n",
-       "4  5   2   7"
-      ]
-     },
-     "execution_count": 169,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.eval('C = A + B')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 170,
-   "id": "b3d2f56a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <td>3</td>\n",
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-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
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-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
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-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   A   B\n",
-       "0  1  10\n",
-       "1  2   8\n",
-       "2  3   6\n",
-       "3  4   4\n",
-       "4  5   2"
-      ]
-     },
-     "execution_count": 170,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0d03c6d5",
-   "metadata": {},
-   "source": [
-    "Use `inplace=True` to modify the original DataFrame."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 171,
-   "id": "0d292862",
-   "metadata": {},
-   "outputs": [
-    {
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th>C</th>\n",
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-       "      <td>11</td>\n",
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-       "      <th>1</th>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   A   B   C\n",
-       "0  1  10  11\n",
-       "1  2   8  10\n",
-       "2  3   6   9\n",
-       "3  4   4   8\n",
-       "4  5   2   7"
-      ]
-     },
-     "execution_count": 171,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.eval('C = A + B', inplace=True)\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "45e4f177",
-   "metadata": {},
-   "source": [
-    "Multiple columns can be assigned using multi-line expressions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 172,
-   "id": "5c54d9d5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <td>11</td>\n",
-       "      <td>-9</td>\n",
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-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>8</td>\n",
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-       "      <td>-6</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>9</td>\n",
-       "      <td>-3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>7</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   A   B   C  D\n",
-       "0  1  10  11 -9\n",
-       "1  2   8  10 -6\n",
-       "2  3   6   9 -3\n",
-       "3  4   4   8  0\n",
-       "4  5   2   7  3"
-      ]
-     },
-     "execution_count": 172,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.eval(\n",
-    "    '''\n",
-    "    C = A + B\n",
-    "    D = A - B\n",
-    "    '''\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8a6707a5",
-   "metadata": {},
-   "source": [
-    "### query()\n",
-    "\n",
-    "Query the columns of a DataFrame with a boolean expression.\n",
-    "\n",
-    "For example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 173,
-   "id": "913adf98",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th>C C</th>\n",
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-       "      <th>0</th>\n",
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-       "      <td>10</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>8</td>\n",
-       "      <td>9</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   A   B  C C\n",
-       "0  1  10   10\n",
-       "1  2   8    9\n",
-       "2  3   6    8\n",
-       "3  4   4    7\n",
-       "4  5   2    6"
-      ]
-     },
-     "execution_count": 173,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.DataFrame({\n",
-    "    'A': range(1, 6),\n",
-    "    'B': range(10, 0, -2),\n",
-    "    'C C': range(10, 5, -1)\n",
-    "})\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 174,
-   "id": "1f32860f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    .dataframe tbody tr th {\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th>C C</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   A  B  C C\n",
-       "4  5  2    6"
-      ]
-     },
-     "execution_count": 174,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.query('A > B')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "293305a6",
-   "metadata": {},
-   "source": [
-    "The previous expression is equivalent to"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 175,
-   "id": "7037c1f7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th>C C</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   A  B  C C\n",
-       "4  5  2    6"
-      ]
-     },
-     "execution_count": 175,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[df.A > df.B]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "93f41453",
-   "metadata": {},
-   "source": [
-    "For columns with spaces in their name, you can use backtick quoting."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 176,
-   "id": "cfac842b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th>C C</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>10</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   A   B  C C\n",
-       "0  1  10   10"
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-     },
-     "execution_count": 176,
-     "metadata": {},
-     "output_type": "execute_result"
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-   "source": [
-    "df.query('B == `C C`')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d8f4335e",
-   "metadata": {},
-   "source": [
-    "The previous expression is equivalent to"
-   ]
-  },
-  {
-   "cell_type": "code",
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-   "metadata": {},
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th>C C</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>10</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   A   B  C C\n",
-       "0  1  10   10"
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-     },
-     "execution_count": 177,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[df.B == df['C C']]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b4dece86",
-   "metadata": {},
-   "source": [
-    "<div class='full-width docutils' >\n",
-    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
-    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
-    "    <div class=\"pandastutor inner\" style=\"height:660px;\">\n",
-    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf%20%3D%20pd.DataFrame%28%7B%0A%20%20%20%20'A'%3A%20range%281,%206%29,%0A%20%20%20%20'B'%3A%20range%2810,%200,%20-2%29,%0A%20%20%20%20'C%20C'%3A%20range%2810,%205,%20-1%29%0A%7D%29%0A%0Adf%5Bdf.B%20%3D%3D%20df%5B'C%20C'%5D%5D&d=2023-07-13&lang=py&v=v1\"> </iframe>\n",
-    "    </div>\n",
-    "  </div>\n",
-    "</div>\n",
-    "## Your turn! 🚀\n",
-    "\n",
-    "### Processing image data\n",
-    "\n",
-    "Recently, very powerful AI models have been developed that allow us to understand images. There are many tasks that can be solved using pre-trained neural networks, or cloud services. Some examples include:\n",
-    "\n",
-    "- **Image Classification**, can help you categorize the image into one of the pre-defined classes. You can easily train your own image classifiers using services such as [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum)\n",
-    "- **Object Detection** to detect different objects in the image. Services such as [computer vision](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum) can detect a number of common objects, and you can train [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum) model to detect some specific objects of interest.\n",
-    "- **Face Detection**, including Age, Gender and Emotion detection. This can be done via [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-77958-bethanycheum).\n",
-    "\n",
-    "All those cloud services can be called using [Python SDKs](https://docs.microsoft.com/samples/azure-samples/cognitive-services-python-sdk-samples/cognitive-services-python-sdk-samples/?WT.mc_id=academic-77958-bethanycheum), and thus can be easily incorporated into your data exploration workflow.\n",
-    "\n",
-    "Here are some examples of exploring data from Image data sources:\n",
-    "\n",
-    "- In the blog post [How to Learn Data Science without Coding](https://soshnikov.com/azure/how-to-learn-data-science-without-coding/) we explore Instagram photos, trying to understand what makes people give more likes to a photo. We first extract as much information from pictures as possible using [computer vision](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum), and then use [Azure Machine Learning AutoML](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml/?WT.mc_id=academic-77958-bethanycheum) to build the interpretable model.\n",
-    "- In [Facial Studies Workshop](https://github.com/CloudAdvocacy/FaceStudies) we use [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-77958-bethanycheum) to extract emotions from people on photographs from events, in order to try to understand what makes people happy.\n",
-    "\n",
-    "### Assignment\n",
-    "\n",
-    "[Perform more detailed data study for the challenges above](../../assignments/data-science/data-processing-in-python.md)\n",
-    "\n",
-    "## Self study\n",
-    "\n",
-    "In this chapter, we've covered many of the basics of using Pandas effectively for data analysis. Still, much has been omitted from our discussion. To learn more about Pandas, we recommend the following resources:\n",
-    "\n",
-    "- [Pandas online documentation](http://pandas.pydata.org/): This is the go-to source for complete documentation of the package. While the examples in the documentation tend to be small generated datasets, the description of the options is complete and generally very useful for understanding the use of various functions.\n",
-    "\n",
-    "- [*Python for Data Analysis*](http://shop.oreilly.com/product/0636920023784.do) Written by Wes McKinney (the original creator of Pandas), this book contains much more detail on the Pandas package than we had room for in this chapter. In particular, he takes a deep dive into tools for time series, which were his bread and butter as a financial consultant. The book also has many entertaining examples of applying Pandas to gain insight from real-world datasets. Keep in mind, though, that the book is now several years old, and the Pandas package has quite a few new features that this book does not cover (but be on the lookout for a new edition in 2017).\n",
-    "\n",
-    "- [Stack Overflow](http://stackoverflow.com/questions/tagged/pandas): Pandas has so many users that any question you have has likely been asked and answered on Stack Overflow. Using Pandas is a case where some Google-Fu is your best friend. Simply go to your favorite search engine and type in the question, problem, or error you're coming across–more than likely you'll find your answer on a Stack Overflow page.\n",
-    "\n",
-    "- [Pandas on PyVideo](http://pyvideo.org/search?q=pandas): From PyCon to SciPy to PyData, many conferences have featured tutorials from Pandas developers and power users. The PyCon tutorials in particular tend to be given by very well-vetted presenters.\n",
-    "\n",
-    "Using these resources, combined with the walk-through given in this chapter, my hope is that you'll be poised to use Pandas to tackle any data analysis problem you come across!\n",
-    "\n",
-    "## Acknowledgments\n",
-    "\n",
-    "Thanks for [Pandas user guide](https://pandas.pydata.org/docs/user_guide/index.html). It contributes the majority of the content in this chapter."
-   ]
-  }
- ],
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diff --git a/_sources/data-science/working-with-data/pandas.md b/_sources/data-science/working-with-data/pandas.md
index 6ab414bb39..2dd26e60a7 100644
--- a/_sources/data-science/working-with-data/pandas.md
+++ b/_sources/data-science/working-with-data/pandas.md
@@ -1,2154 +1,6 @@
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-  display_name: Python 3
-  language: python
-  name: python3
----
-
 # Pandas
 
-Pandas is a fast, powerful, flexible and easy-to-use open-source data analysis and manipulation tool, built on top of the Python programming language.
-
-## Introducing Pandas objects
-
-We’ll start with a quick, non-comprehensive overview of the fundamental data structures in Pandas to get you started. The fundamental behavior about data types, indexing, axis labeling, and alignment apply across all of the objects. To get started, import NumPy and load Pandas into your namespace:
-
-```{code-cell}
-import numpy as np
-import pandas as pd
-```
-
-### Series
-
-`Series` is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). The axis labels are collectively referred to as the **index**. The basic method to create a `Series` is to call:
-
-```py
-s = pd.Series(data, index=index)
-```
-
-Here, `data` can be many different things:
-
-- a Python dict
-- an ndarray
-- a scalar value (like 5)
-
-
-The passed **index** is a list of axis labels. Thus, this separates into a few cases depending on what the **data is**:
-
-#### Create a Series
-
-##### From ndarray
-
-If `data` is an ndarray, **index** must be the same length as the **data**. If no index is passed, one will be created having values `[0, ..., len(data) - 1]`.
-
-```{code-cell}
-s = pd.Series(np.random.randn(5), index=["a", "b", "c", "d", "e"])
-```
-
-```{code-cell}
-s
-```
-
-```{code-cell}
-s.index
-```
-
-```{code-cell}
-pd.Series(np.random.randn(5))
-```
-
-```{note}
-Pandas supports non-unique index values. If an operation that does not support duplicate index values is attempted, an exception will be raised at that time.
-```
-
-##### From dict
-`Series` can be instantiated from dicts:
-
-```{code-cell}
-d = {"b": 1, "a": 0, "c": 2}
-```
-
-```{code-cell}
-pd.Series(d)
-```
-
-If an index is passed, the values in data corresponding to the labels in the index will be pulled out.
-
-```{code-cell}
-d = {"a": 0.0, "b": 1.0, "c": 2.0}
-```
-
-```{code-cell}
-pd.Series(d)
-```
-
-```{code-cell}
-pd.Series(d, index=["b", "c", "d", "a"])
-```
-
-```{note}
-NaN (not a number) is the standard missing data marker used in Pandas.
-```
-
-##### From scalar value
-
-If `data` is a scalar value, an index must be provided. The value will be repeated to match the length of **index**.
-
-```{code-cell}
-pd.Series(5.0, index=["a", "b", "c", "d", "e"])
-```
-
-#### Series is ndarray-like
-
-`Series` acts very similarly to a `ndarray` and is a valid argument to most NumPy functions. However, operations such as slicing will also slice the index.
-
-```{code-cell}
-s[0]
-```
-
-```{code-cell}
-s[:3]
-```
-
-```{code-cell}
-s[s > s.median()]
-```
-
-```{code-cell}
-s[[4, 3, 1]]
-```
-
-```{code-cell}
-np.exp(s)
-```
-
-Like a NumPy array, a Pandas Series has a single `dtype`.
-
-```{code-cell}
-s.dtype
-```
-
-If you need the actual array backing a `Series`, use `Series.array`.
-
-```{code-cell}
-s.array
-```
-
-While `Series` is ndarray-like, if you need an actual ndarray, then use `Series.to_numpy()`.
-
-```{code-cell}
-s.to_numpy()
-```
-
-Even if the `Series` is backed by an `ExtensionArray`, `Series.to_numpy()` will return a NumPy ndarray.
-
-#### Series is dict-like
-
-A `Series` is also like a fixed-size dict in that you can get and set values by index label:
-
-```{code-cell}
-s["a"]
-```
-
-```{code-cell}
-s["e"] = 12.0
-```
-
-```{code-cell}
-s
-```
-
-```{code-cell}
-"e" in s
-```
-
-```{code-cell}
-"f" in s
-```
-
-If a label is not contained in the index, an exception is raised:
-
-```{code-cell}
-:tags: ["raises-exception"]
-s["f"]
-```
-
-Using the `Series.get()` method, a missing label will return None or specified default:
-
-```{code-cell}
-s.get("f")
-```
-
-```{code-cell}
-s.get("f", np.nan)
-```
-
-These labels can also be accessed by `attribute`.
-
-#### Vectorized operations and label alignment with Series
-
-When working with raw NumPy arrays, looping through value-by-value is usually not necessary. The same is true when working with `Series` in Pandas. `Series` can also be passed into most NumPy methods expecting an ndarray.
-
-```{code-cell}
-s + s
-```
-
-```{code-cell}
-s * 2
-```
-
-```{code-cell}
-np.exp(s)
-```
-
-A key difference between `Series` and ndarray is that operations between `Series` automatically align the data based on the label. Thus, you can write computations without giving consideration to whether the `Series` involved have the same labels.
-
-```{code-cell}
-s[1:] + s[:-1]
-```
-
-The result of an operation between unaligned `Series` will have the **union** of the indexes involved. If a label is not found in one `Series` or the other, the result will be marked as missing `NaN`. Being able to write code without doing any explicit data alignment grants immense freedom and flexibility in interactive data analysis and research. The integrated data alignment features of the Pandas data structures set Pandas apart from the majority of related tools for working with labeled data.
-
-```{note}
-In general, we chose to make the default result of operations between differently indexed objects yield the **union** of the indexes in order to avoid loss of information. Having an index label, though the data is missing, is typically important information as part of a computation. You of course have the option of dropping labels with missing data via the `dropna` function.
-```
-
-#### Name attribute
-
-`Series` also has a `name` attribute:
-
-```{code-cell}
-s = pd.Series(np.random.randn(5), name="something")
-```
-
-```{code-cell}
-s
-```
-
-```{code-cell}
-s.name
-```
-
-The `Series` `name` can be assigned automatically in many cases, in particular, when selecting a single column from a `DataFrame`, the `name` will be assigned the column label.
-
-You can rename a `Series` with the `pandas.Series.rename()` method.
-
-```{code-cell}
-s2 = s.rename("different")
-```
-
-```{code-cell}
-s2.name
-```
-
-Note that `s` and `s2` refer to different objects.
-
-### DataFrame
-
-`DataFrame` is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table, or a `dict` of `Series` objects. It is generally the most commonly used Pandas object. Like `Series`, `DataFrame` accepts many different kinds of input:
-
-- Dict of 1D ndarrays, lists, dicts, or `Series`
-- 2-D `numpy.ndarray`
-- Structured or record ndarray
-- A `Series`
-- Another `DataFrame`
-
-Along with the data, you can optionally pass **index** (row labels) and **columns** (column labels) arguments. If you pass an index and / or columns, you are guaranteeing the index and / or columns of the resulting `DataFrame`. Thus, a `dict` of Series plus a specific index will discard all data not matching up to the passed index.
-
-If axis labels are not passed, they will be constructed from the input data based on common sense rules.
-
-#### Create a Dataframe
-
-##### From dict of `Series` or dicts
-
-The resulting **index** will be the **union** of the indexes of the various Series. If there are any nested dicts, these will first be converted to Series. If no columns are passed, the columns will be the ordered list of `dict` keys.
-
-```{code-cell}
-d = {
-    "one": pd.Series([1.0, 2.0, 3.0], index=["a", "b", "c"]),
-    "two": pd.Series([1.0, 2.0, 3.0, 4.0], index=["a", "b", "c", "d"]),
-}
-```
-
-```{code-cell}
-df = pd.DataFrame(d)
-```
-
-```{code-cell}
-df
-```
-
-```{code-cell}
-pd.DataFrame(d, index=["d", "b", "a"])
-```
-
-```{code-cell}
-pd.DataFrame(d, index=["d", "b", "a"], columns=["two", "three"])
-```
-
-The row and column labels can be accessed respectively by accessing the **index** and **columns** attributes:
-
-```{note}
-When a particular set of columns is passed along with a dict of data, the passed columns override the keys in the dict.
-```
-
-```{code-cell}
-df.index
-```
-
-```{code-cell}
-df.columns
-```
-
-##### From dict of ndarrays / lists
-
-The ndarrays must all be the same length. If an index is passed, it must also be the same length as the arrays. If no index is passed, the result will be `range(n)`, where `n` is the array length.
-
-```{code-cell}
-d = {"one": [1.0, 2.0, 3.0, 4.0], "two": [4.0, 3.0, 2.0, 1.0]}
-```
-
-```{code-cell}
-pd.DataFrame(d)
-```
-
-```{code-cell}
-pd.DataFrame(d, index=["a", "b", "c", "d"])
-```
-
-##### From structured or record array
-
-This case is handled identically to a dict of arrays.
-
-```{code-cell}
-data = np.zeros((2,), dtype=[("A", "i4"), ("B", "f4"), ("C", "a10")])
-```
-
-```{code-cell}
-data[:] = [(1, 2.0, "Hello"), (2, 3.0, "World")]
-```
-
-```{code-cell}
-pd.DataFrame(data)
-```
-
-```{code-cell}
-pd.DataFrame(data, index=["first", "second"])
-```
-
-```{code-cell}
-pd.DataFrame(data, columns=["C", "A", "B"])
-```
-
-```{note}
-DataFrame is not intended to work exactly like a 2-dimensional NumPy ndarray.
-```
-
-
-##### From a list of dicts
-
-```{code-cell}
-data2 = [{"a": 1, "b": 2}, {"a": 5, "b": 10, "c": 20}]
-```
-
-```{code-cell}
-pd.DataFrame(data2)
-```
-
-```{code-cell}
-pd.DataFrame(data2, index=["first", "second"])
-```
-
-```{code-cell}
-pd.DataFrame(data2, columns=["a", "b"])
-```
-
-##### From a dict of tuples
-
-You can automatically create a MultiIndexed frame by passing a tuples dictionary.
-
-```{code-cell}
-pd.DataFrame(
-    {
-        ("a", "b"): {("A", "B"): 1, ("A", "C"): 2},
-        ("a", "a"): {("A", "C"): 3, ("A", "B"): 4},
-        ("a", "c"): {("A", "B"): 5, ("A", "C"): 6},
-        ("b", "a"): {("A", "C"): 7, ("A", "B"): 8},
-        ("b", "b"): {("A", "D"): 9, ("A", "B"): 10},
-    }
-)
-```
-
-##### From a Series
-
-The result will be a DataFrame with the same index as the input Series, and with one column whose name is the original name of the Series (only if no other column name provided).
-
-```{code-cell}
-ser = pd.Series(range(3), index=list("abc"), name="ser")
-```
-
-```{code-cell}
-pd.DataFrame(ser)
-```
-
-##### From a list of namedtuples
-
-The field names of the first `namedtuple` in the list determine the columns of the `DataFrame`. The remaining namedtuples (or tuples) are simply unpacked and their values are fed into the rows of the `DataFrame`. If any of those tuples is shorter than the first `namedtuple` then the later columns in the corresponding row are marked as missing values. If any are longer than the first `namedtuple` , a `ValueError` is raised.
-
-```{code-cell}
-from collections import namedtuple
-```
-
-```{code-cell}
-Point = namedtuple("Point", "x y")
-```
-
-```{code-cell}
-pd.DataFrame([Point(0, 0), Point(0, 3), (2, 3)])
-```
-
-```{code-cell}
-Point3D = namedtuple("Point3D", "x y z")
-```
-
-```{code-cell}
-pd.DataFrame([Point3D(0, 0, 0), Point3D(0, 3, 5), Point(2, 3)])
-```
-
-##### From a list of dataclasses
-
-Data Classes as introduced in PEP557, can be passed into the DataFrame constructor. Passing a list of dataclasses is equivalent to passing a list of dictionaries.
-
-Please be aware, that all values in the list should be dataclasses, mixing types in the list would result in a `TypeError`.
-
-```{code-cell}
-from dataclasses import make_dataclass
-```
-
-```{code-cell}
-Point = make_dataclass("Point", [("x", int), ("y", int)])
-```
-
-```{code-cell}
-pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])
-```
-
-#### Column selection, addition, deletion
-
-You can treat a `DataFrame` semantically like a dict of like-indexed `Series` objects. Getting, setting, and deleting columns works with the same syntax as the analogous dict operations:
-
-```{code-cell}
-df
-```
-
-```{code-cell}
-df["one"]
-```
-
-```{code-cell}
-df["three"] = df["one"] * df["two"]
-```
-
-```{code-cell}
-df["flag"] = df["one"] > 2
-```
-
-```{code-cell}
-df
-```
-
-Columns can be deleted or popped like with a dict:
-
-```{code-cell}
-del df["two"]
-```
-
-```{code-cell}
-three = df.pop("three")
-```
-
-```{code-cell}
-df
-```
-
-When inserting a scalar value, it will naturally be propagated to fill the column:
+--
 
-```{code-cell}
-df["foo"] = "bar"
+```{tableofcontents}
 ```
-
-```{code-cell}
-df
-```
-
-When inserting a `Series` that does not have the same index as the `DataFrame`, it will be conformed to the DataFrame’s index:
-
-```{code-cell}
-df["one_trunc"] = df["one"][:2]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:1000px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Ad%20%3D%20%7B%0A%20%20%20%20%22one%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29,%0A%20%20%20%20%22two%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0,%204.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22,%20%22d%22%5D%29,%0A%7D%0Adf%20%3D%20pd.DataFrame%28d%29%0Adf%5B%22flag%22%5D%20%3D%20df%5B%22one%22%5D%20%3E%202%0Adel%20df%5B%22two%22%5D%0Adf%5B%22foo%22%5D%20%3D%20%22bar%22%0Adf%5B%22one%22%5D%5B%3A2%5D&d=2023-07-13&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-```{code-cell}
-df
-```
-
-You can insert raw ndarrays but their length must match the length of the DataFrame’s index.
-
-By default, columns get inserted at the end. `DataFrame.insert()` inserts at a particular location in the columns:
-
-```{code-cell}
-df.insert(1, "bar", df["one"])
-```
-
-```{code-cell}
-df
-```
-
-#### Assigning new columns in method chains
-
-DataFrame has an `assign()` method that allows you to easily create new columns that are potentially derived from existing columns.
-
-```{code-cell}
-iris = pd.read_csv("../../assets/data/iris.csv")
-```
-
-```{code-cell}
-iris.head()
-```
-
-```{code-cell}
-iris.assign(sepal_ratio=iris["SepalWidth"] / iris["SepalLength"]).head()
-```
-
-In the example above, we inserted a precomputed value. We can also pass in a function of one argument to be evaluated on the DataFrame being assigned to.
-
-```{code-cell}
-iris.assign(sepal_ratio=lambda x: (x["SepalWidth"] / x["SepalLength"])).head()
-```
-
-`assign()` **always** returns a copy of the data, leaving the original DataFrame untouched.
-
-Passing a callable, as opposed to an actual value to be inserted, is useful when you don’t have a reference to the DataFrame at hand. This is common when using `assign()` in a chain of operations. For example, we can limit the DataFrame to just those observations with a Sepal Length greater than 5, calculate the ratio, and plot:
-
-```{code-cell}
-(
-    iris.query("SepalLength > 5")
-    .assign(
-        SepalRatio=lambda x: x.SepalWidth / x.SepalLength,
-        PetalRatio=lambda x: x.PetalWidth / x.PetalLength,
-    )
-    .plot(kind="scatter", x="SepalRatio", y="PetalRatio")
-)
-```
-
-Since a function is passed in, the function is computed on the DataFrame being assigned to. Importantly, this is the DataFrame that’s been filtered to those rows with sepal length greater than 5. The filtering happens first, and then the ratio calculations. This is an example where we didn’t have a reference to the filtered DataFrame available.
-
-The function signature for `assign()` is simply `**kwargs`. The keys are the column names for the new fields, and the values are either a value to be inserted (for example, a `Series` or NumPy array), or a function of one argument to be called on the `DataFrame`. A copy of the original `DataFrame` is returned, with the new values inserted.
-
-The order of `**kwargs` is preserved. This allows for dependent assignment, where an expression later in `**kwargs` can refer to a column created earlier in the same `assign()`.
-
-```{code-cell}
-dfa = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
-```
-
-```{code-cell}
-dfa.assign(C=lambda x: x["A"] + x["B"], D=lambda x: x["A"] + x["C"])
-```
-
-In the second expression, `x['C']` will refer to the newly created column, that’s equal to `dfa['A'] + dfa['B']`.
-
-#### Indexing / selection
-
-The basics of indexing are as follows:
-
-|Operation                     |Syntax         |Result   |
-|:-------                      |:-----         |:-----   |
-|Select column                 |`df[col]`      |Series   |
-|Select row by label           |`df.loc[label]`|Series   |
-|Select row by integer location|`df.iloc[loc]` |Series   |
-|Slice rows                    |`df[5:10] `    |DataFrame|
-|Select rows by boolean vector |`df[bool_vec]` |DataFrame|
-
-Row selection, for example, returns a `Series` whose index is the columns of the `DataFrame`:
-
-```
-df.loc["b"]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:730px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Ad%20%3D%20%7B%0A%20%20%20%20%22one%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29,%0A%20%20%20%20%22two%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0,%204.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22,%20%22d%22%5D%29,%0A%7D%0Adf%20%3D%20pd.DataFrame%28d%29%0Adf%5B%22flag%22%5D%20%3D%20df%5B%22one%22%5D%20%3E%202%0Adel%20df%5B%22two%22%5D%0Adf%5B%22foo%22%5D%20%3D%20%22bar%22%0Adf%5B%22one_trunc%22%5D%20%3Ddf.insert%281,%20%22bar%22,%20df%5B%22one%22%5D%29%0Adf.loc%5B%22b%22%5D&d=2023-07-13&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-```{code-cell}
-df.iloc[2]
-```
-
-#### Data alignment and arithmetic
-
-Data alignment between `DataFrame` objects automatically aligns on **both** the columns and the index (row labels)**. Again, the resulting object will have the union of the column and row labels.
-
-```{code-cell}
-df = pd.DataFrame(np.random.randn(10, 4), columns=["A", "B", "C", "D"])
-```
-
-```{code-cell}
-df2 = pd.DataFrame(np.random.randn(7, 3), columns=["A", "B", "C"])
-```
-
-```{code-cell}
-df + df2
-```
-
-When doing an operation between `DataFrame` and `Series`, the default behavior is to align the `Series` **index** on the `DataFrame` **columns**, thus broadcasting row-wise. For example:
-
-```{code-cell}
-df - df.iloc[0]
-```
-
-Arithmetic operations with scalars operate element-wise:
-
-```{code-cell}
-df * 5 + 2
-```
-
-```{code-cell}
-1 / df
-```
-
-```{code-cell}
-df ** 4
-```
-
-Boolean operators operate element-wise as well:
-
-```{code-cell}
-df1 = pd.DataFrame({"a": [1, 0, 1], "b": [0, 1, 1]}, dtype=bool)
-```
-
-```{code-cell}
-df2 = pd.DataFrame({"a": [0, 1, 1], "b": [1, 1, 0]}, dtype=bool)
-```
-
-```{code-cell}
-df1 & df2
-```
-
-```{code-cell}
-df1 | df2
-```
-
-```{code-cell}
-df1 ^ df2
-```
-
-```{code-cell}
--df1
-```
-
-
-#### Transposing
-
-To transpose, access the `T` attribute or `DataFrame.transpose()`, similar to an ndarray:
-
-```{code-cell}
-df[:5].T
-```
-
-## Data indexing and selection
-
-The axis labeling information in Pandas objects serves many purposes:
-
-- Identifies data (i.e. provides metadata) using known indicators, important for analysis, visualization, and interactive console display.
-- Enables automatic and explicit data alignment.
-- Allows intuitive getting and setting of subsets of the data set.
-
-In this section, we will focus on the final point: namely, how to slice, dice, and generally get and set subsets of Pandas objects. The primary focus will be on Series and DataFrame as they have received more development attention in this area.
-
-```{note}
-The Python and NumPy indexing operators `[]` and attribute operator `.` provide quick and easy access to Pandas data structures across a wide range of use cases. This makes interactive work intuitive, as there’s little new to learn if you already know how to deal with Python dictionaries and NumPy arrays. However, since the type of the data to be accessed isn’t known in advance, directly using standard operators has some optimization limits. For production code, we recommended that you take advantage of the optimized Pandas data access methods exposed in this chapter.
-```
-
-```{warning}
-Whether a copy or a reference is returned for a setting operation, may depend on the context. This is sometimes called `chained assignment` and should be avoided.
-```
-
-### Different choices for indexing
-
-Object selection has had a number of user-requested additions in order to support more explicit location-based indexing. Pandas now supports three types of multi-axis indexing.
-
-- `.loc` is primarily label based, but may also be used with a boolean array. `.loc` will raise `KeyError` when the items are not found. Allowed inputs are:
-  - A single label, e.g. `5` or `'a'` (Note that `5` is interpreted as a label of the index. This use is not an integer position along the index.).
-  - A list or array of labels `['a', 'b', 'c']`.
-  - A slice object with labels `'a':'f'` (Note that contrary to usual Python slices, both the start and the stop are included, when present in the index!)
-  - A boolean array (any `NA` values will be treated as `False`).
-  - A `callable` function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above).
-
-- `.iloc` is primarily integer position based (from `0` to `length-1` of the axis), but may also be used with a boolean array. `.iloc` will raise `IndexError` if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing. (this conforms with Python/NumPy slice semantics). Allowed inputs are:
-  - An integer e.g. `5`.
-  - A list or array of integers `[4, 3, 0]`.
-  - A slice object with ints `1:7`.
-  - A boolean array (any `NA` values will be treated as `False`).
-  - A `callable` function with one argument (the calling Series or DataFrame) that returns valid output for indexing (one of the above).
-- `.loc`, `.iloc`, and also `[]` indexing can accept a `callable` as indexer.
-
-Getting values from an object with multi-axes selection uses the following notation (using `.loc` as an example, but the following applies to `.iloc` as well). Any of the axes accessors may be the null slice `:`. Axes left out of the specification are assumed to be `:`, e.g. `p.loc['a']` is equivalent to `p.loc['a', :]`.
-
-|**Object Type**|**Indexers**                        |
-|:--            |:-                                  |
-|Series         |`s.loc[indexer]`                    |
-|DataFrame      |`df.loc[row_indexer, column_indexer]`|
-
-### Basics
-
-As mentioned when introducing the data structures in the last section, the primary function of indexing with `[]` (a.k.a.` __getitem__` for those familiar with implementing class behavior in Python) is selecting out lower-dimensional slices. The following table shows return type values when indexing Pandas objects with `[]`:
-
-|**Object Type**|**Selection**   |Return Value Type                |
-|:-             |:-              |:-                               |
-|Series         |`series[label]` |scalar value                     |
-|DataFrame      |`frame[colname]`|`Series` corresponding to colname|
-
-Here we construct a simple time series data set to use for illustrating the indexing functionality:
-
-```{code-cell}
-dates = pd.date_range('1/1/2000', periods=8)
-df = pd.DataFrame(np.random.randn(8, 4),
-                  index=dates, columns=['A', 'B', 'C', 'D'])
-df
-```
-
-```{note}
-None of the indexing functionality is time series specific unless specifically stated.
-```
-
-Thus, as per above, we have the most basic indexing using `[]`:
-
-```{code-cell}
-s = df['A']
-
-s[dates[5]]
-```
-
-You can pass a list of columns to `[]` to select columns in that order. If a column is not contained in the DataFrame, an exception will be raised. Multiple columns can also be set in this manner:
-
-```{code-cell}
-df
-```
-
-```{code-cell}
-df[['B', 'A']] = df[['A', 'B']]
-df
-```
-
-You may find this useful for applying a transform (in-place) to a subset of the columns.
-
-```{warning}
-Pandas aligns all AXES when setting `Series` and `DataFrame` from `.loc`, and `.iloc`.
-
-This will not modify `df` because the column alignment is before value assignment.
-```
-
-```{code-cell}
-df[['A', 'B']]
-```
-
-```{code-cell}
-df.loc[:, ['B', 'A']] = df[['A', 'B']]
-df[['A', 'B']]
-```
-
-```{warning}
-The correct way to swap column values is by using raw values:
-```
-
-```{code-cell}
-df.loc[:, ['B', 'A']] = df[['A', 'B']].to_numpy()
-df[['A', 'B']]
-```
-
-### Attribute access
-
-You may access an index on a `Series` or column on a `DataFrame` directly as an attribute:
-
-```{code-cell}
-sa = pd.Series([1, 2, 3], index=list('abc'))
-dfa = df.copy()
-```
-
-```{code-cell}
-sa.b
-```
-
-```{code-cell}
-dfa.A
-```
-
-```{code-cell}
-sa.a = 5
-sa
-```
-
-```{code-cell}
-dfa.A = list(range(len(dfa.index)))  # ok if A already exists
-dfa
-```
-
-```{code-cell}
-dfa['A'] = list(range(len(dfa.index)))  # use this form to create a new column
-dfa
-```
-
-```{warning}
-- You can use this access only if the index element is a valid Python identifier, e.g. s.1 is not allowed. See here for an explanation of valid identifiers.
-
-- The attribute will not be available if it conflicts with an existing method name, e.g. s.min is not allowed, but s['min'] is possible.
-
-- Similarly, the attribute will not be available if it conflicts with any of the following list: index, major_axis, minor_axis, items.
-
-- In any of these cases, standard indexing will still work, e.g. s['1'], s['min'], and s['index'] will access the corresponding element or column.
-```
-
-If you are using the IPython environment, you may also use tab-completion to see these accessible attributes.
-
-You can also assign a `dict` to a row of a `DataFrame`:
-
-```{code-cell}
-x = pd.DataFrame({'x': [1, 2, 3], 'y': [3, 4, 5]})
-x.iloc[1] = {'x': 9, 'y': 99}
-x
-```
-
-You can use attribute access to modify an existing element of a Series or column of a DataFrame, but be careful; if you try to use attribute access to create a new column, it creates a new attribute rather than a new column. In 0.21.0 and later, this will raise a `UserWarning`:
-
-```{code-cell}
-df = pd.DataFrame({'one': [1., 2., 3.]})
-df.two = [4, 5, 6]
-```
-
-```{code-cell}
-df
-```
-
-### Slicing ranges
-
-For now, we explain the semantics of slicing using the [] operator.
-
-With Series, the syntax works exactly as with an ndarray, returning a slice of the values and the corresponding labels:
-
-```{code-cell}
-s
-```
-
-```{code-cell}
-s[:5]
-```
-
-```{code-cell}
-s[::2]
-```
-
-```{code-cell}
-s[::-1]
-```
-
-Note that setting works as well:
-
-```{code-cell}
-s2 = s.copy()
-s2[:5] = 0
-s2
-```
-
-With DataFrame, slicing inside of `[]` slices the rows. This is provided largely as a convenience since it is such a common operation.
-
-```{code-cell}
-df[:3]
-```
-
-```{code-cell}
-df[::-1]
-```
-
-### Selection by label
-
-```{warning}
-Whether a copy or a reference is returned for a setting operation, may depend on the context. This is sometimes called `chained assignment` and should be avoided.
-```
-
-```{warning}
-`.loc` is strict when you present slicers that are not compatible (or convertible) with the index type. For example using integers in a `DatetimeIndex`. These will raise a `TypeError`.
-```
-
-```{code-cell}
-dfl = pd.DataFrame(np.random.randn(5, 4),
-                   columns=list('ABCD'),
-                   index=pd.date_range('20130101', periods=5))
-```
-
-```{code-cell}
-:tags: ["raises-exception"]
-dfl.loc[2:3]
-```
-
-```{warning}
-String likes in slicing can be convertible to the type of the index and lead to natural slicing.
-```
-
-```
-dfl.loc['20130102':'20130104']
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:730px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%20%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dpd.date_range%28'20130101',%20periods%3D5%29%29%0Adfl%0Adfl.loc%5B'20130102'%3A'20130104'%5D&d=2023-07-13&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-```{warning}
-Pandas will raise a `KeyError` if indexing with a list with missing labels.
-```
-
-Pandas provides a suite of methods in order to have **purely label-based indexing**. This is a strict inclusion-based protocol. Every label asked for must be in the index, or a `KeyError` will be raised. When slicing, both the start bound **AND** the stop bound are included, if present in the index. Integers are valid labels, but they refer to the label **and not the position**.
-
-- The `.loc` attribute is the primary access method. The following are valid inputs:
-
-- A single label, e.g. `5` or `'a'` (Note that `5` is interpreted as a label of the index. This use is not an integer position along the index.).
-
-- A list or array of labels `['a', 'b', 'c']`.
-
-- A slice object with labels `'a':'f'` (Note that contrary to usual Python slices, both the start and the stop are included, when present in the index!
-
-- A boolean array.
-
-- A `callable`.
-
-```
-s1 = pd.Series(np.random.randn(6), index=list('abcdef'))
-s1
-s1.loc['c':]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:630px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%286%29,%20index%3Dlist%28'abcdef'%29%29%0As1.loc%5B'c'%3A%5D%0A&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-```
-s1.loc['b']
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:630px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%286%29,%20index%3Dlist%28'abcdef'%29%29%0As1.loc%5B'b'%5D%0A&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-Note that the setting works as well:
-
-```
-s1.loc['c':] = 0
-s1
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:630px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%286%29,%20index%3Dlist%28'abcdef'%29%29%0As1.loc%5B'c'%3A%5D%20%3D%200%0As1&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-With a DataFrame:
-
-```
-df1 = pd.DataFrame(np.random.randn(6, 4),
-                   index=list('abcdef'),
-                   columns=list('ABCD'))
-df1
-df1.loc[['a', 'b', 'd'], :]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:680px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B%5B'a',%20'b',%20'd'%5D,%20%3A%5D%0A&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-Accessing via label slices:
-
-```
-df1.loc['d':, 'A':'C']
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:710px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B'd'%3A,%20'A'%3A'C'%5D%0A&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-For getting a cross-section using a label (equivalent to `df.xs('a')`):
-
-```
-df1.loc['a']
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:690px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B'a'%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-
-For getting values with a boolean array:
-
-```
-df1.loc['a'] > 0
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:650px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B'a'%5D%20%3E%200&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-```
-df1.loc[:, df1.loc['a'] > 0]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:700px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B%3A,%20df1.loc%5B'a'%5D%20%3E%200%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-
-
-NA values in a boolean array propagate as `False`:
-
-```{code-cell}
-mask = pd.array([True, False, True, False, pd.NA, False], dtype="boolean")
-mask
-```
-
-```
-df1[mask]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:680px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Amask%20%3D%20pd.array%28%5BTrue,%20False,%20True,%20False,%20pd.NA,%20False%5D,%20dtype%3D%22boolean%22%29%0Adf1%5Bmask%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-For getting a value explicitly:
-
-```
-df1.loc['a', 'A'] # this is also equivalent to ``df1.at['a','A']``
-```
-
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:680px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B'a',%20'A'%5D%20&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-#### Slicing with labels
-
-When using `.loc` with slices, if both the start and the stop labels are present in the index, then elements located between the two (including them) are returned:
-
-```
-s = pd.Series(list('abcde'), index=[0, 3, 2, 5, 4])
-s.loc[3:5]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:580px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28'abcde'%29,%20index%3D%5B0,%203,%202,%205,%204%5D%29%0As.loc%5B3%3A5%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-
-If at least one of the two is absent, but the index is sorted, and can be compared against start and stop labels, then slicing will still work as expected, by selecting labels which rank between the two:
-
-```
-s.sort_index()
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:600px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28'abcde'%29,%20index%3D%5B0,%203,%202,%205,%204%5D%29%0As.sort_index%28%29&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-```
-s.sort_index().loc[1:6]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:820px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28'abcde'%29,%20index%3D%5B0,%203,%202,%205,%204%5D%29%0As.sort_index%28%29.loc%5B1%3A6%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-However, if at least one of the two is absent and the index is not sorted, an error will be raised (since doing otherwise would be computationally expensive, as well as potentially ambiguous for mixed-type indexes). For instance, in the above example, `s.loc[1:6]` would raise `KeyError`.
-
-```
-s = pd.Series(list('abcdef'), index=[0, 3, 2, 5, 4, 2])
-s.loc[3:5]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:650px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28'abcdef'%29,%20index%3D%5B0,%203,%202,%205,%204,%202%5D%29%0As.loc%5B3%3A5%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-Also, if the index has duplicate labels and either the start or the stop label is duplicated, an error will be raised. For instance, in the above example, `s.loc[2:5]` would raise a `KeyError`.
-
-### Selection by position
-
-```{warning}
-Whether a copy or a reference is returned for a setting operation, may depend on the context. This is sometimes called `chained assignment` and should be avoided.
-```
-
-Pandas provides a suite of methods in order to get purely integer-based indexing. The semantics follow closely Python and NumPy slicing. These are 0-based indexing. When slicing, the start bound is included, while the upper bound is excluded. Trying to use a non-integer, even a valid label will raise an `IndexError`.
-
-The `.iloc` attribute is the primary access method. The following are valid inputs:
-
-- An integer e.g. `5`.
-
-- A list or array of integers `[4, 3, 0]`.
-
-- A slice object with ints `1:7`.
-
-- A boolean array.
-
-- A `callable`.
-
-```
-s1 = pd.Series(np.random.randn(5), index=list(range(0, 10, 2)))
-s1
-s1.iloc[:3]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:600px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%285%29,%20index%3Dlist%28range%280,%2010,%202%29%29%29%0As1.iloc%5B%3A3%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-```
-s1.iloc[3]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:600px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%285%29,%20index%3Dlist%28range%280,%2010,%202%29%29%29%0As1.iloc%5B3%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-Note that setting works as well:
-
-```
-s1.iloc[:3] = 0
-s1
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:620px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%285%29,%20index%3Dlist%28range%280,%2010,%202%29%29%29%0As1.iloc%5B%3A3%5D%20%3D%200%0As1&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-With a DataFrame,Select via integer slicing:
-
-```
-df1 = pd.DataFrame(np.random.randn(6, 4),
-                   index=list(range(0, 12, 2)),
-                   columns=list(range(0, 8, 2)))
-df1
-df1.iloc[:3]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:680px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B%3A3%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-
-```
-df1.iloc[1:5, 2:4]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:700px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1%3A5,%202%3A4%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-Select via integer list:
-
-```
-df1.iloc[[1, 3, 5], [1, 3]]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:700px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B%5B1,%203,%205%5D,%20%5B1,%203%5D%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-```
-df1.iloc[1:3, :]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:680px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1%3A3,%20%3A%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-```
-df1.iloc[:, 1:3]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:700px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B%3A,%201%3A3%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-```
-df1.iloc[1, 1] # this is also equivalent to ``df1.iat[1,1]``
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:680px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1,%201%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-For getting a cross-section using an integer position (equiv to `df.xs(1)`):
-
-```
-df1.iloc[1]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:700px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-Out-of-range slice indexes are handled gracefully just as in Python/NumPy.
-
-```{code-cell}
-x = list('abcdef') # these are allowed in Python/NumPy.
-x
-```
-
-```{code-cell}
-x[4:10]
-```
-
-```{code-cell}
-x[8:10]
-```
-
-```{code-cell}
-s = pd.Series(x)
-s
-```
-
-```
-s.iloc[4:10]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:600px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Ax%20%3D%20list%28'abcdef'%29%20%0As%20%3D%20pd.Series%28x%29%0As.iloc%5B4%3A10%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-```{code-cell}
-s.iloc[8:10]
-```
-
-Note that using slices that go out of bounds can result in an empty axis (e.g. an empty DataFrame being returned).
-
-```{code-cell}
-dfl = pd.DataFrame(np.random.randn(5, 2), columns=list('AB'))
-```
-
-```
-dfl.iloc[:, 2:3]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:630px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%202%29,%20columns%3Dlist%28'AB'%29%29%0Adfl%0Adfl.iloc%5B%3A,%202%3A3%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-```
-dfl.iloc[:, 1:3]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:630px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%202%29,%20columns%3Dlist%28'AB'%29%29%0Adfl.iloc%5B%3A,%201%3A3%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-```
-dfl.iloc[4:6]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:600px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%202%29,%20columns%3Dlist%28'AB'%29%29%0Adfl.iloc%5B4%3A6%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-A single indexer that is out of bounds will raise an `IndexError`. A list of indexers where any element is out of bounds will raise an `IndexError`.
-
-```{code-cell}
-:tags: ["raises-exception"]
-dfl.iloc[[4, 5, 6]]
-```
-
-```{code-cell}
-:tags: ["raises-exception"]
-dfl.iloc[:, 4]
-```
-
-### Selection by callable
-
-`.loc`, `.iloc`, and also `[]` indexing can accept a `callable` as indexer. The `callable` must be a function with one argument (the calling Series or DataFrame) that returns valid output for indexing.
-
-
-```
-df1 = pd.DataFrame(np.random.randn(6, 4),
-                   index=list('abcdef'),
-                   columns=list('ABCD'))
-df1
-df1.loc[lambda df: df['A'] > 0, :]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:700px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5Blambda%20df%3A%20df%5B'A'%5D%20%3E%200,%20%3A%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-```
-df1.loc[:, lambda df: ['A', 'B']]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:700px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1.loc%5B%3A,%20lambda%20df%3A%20%5B'A',%20'B'%5D%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-```
-df1.iloc[:, lambda df: [0, 1]]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:700px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1.iloc%5B%3A,%20lambda%20df%3A%20%5B0,%201%5D%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-```
-df1[lambda df: df.columns[0]]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:650px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%5Blambda%20df%3A%20df.columns%5B0%5D%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-You can use callable indexing in `Series`.
-
-```
-df1['A'].loc[lambda s: s > 0]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:920px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%5B'A'%5D.loc%5Blambda%20s%3A%20s%20%3E%200%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-### Combining positional and label-based indexing
-
-If you wish to get the 0th and the 2nd elements from the index in the `'A'` column, you can do:
-
-```
-dfd = pd.DataFrame({'A': [1, 2, 3],
-                    'B': [4, 5, 6]},
-                   index=list('abc'))
-dfd
-dfd.loc[dfd.index[[0, 2]], 'A']
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:550px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adfd%20%3D%20pd.DataFrame%28%7B'A'%3A%20%5B1,%202,%203%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'B'%3A%20%5B4,%205,%206%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abc'%29%29%0Adfd.loc%5Bdfd.index%5B%5B0,%202%5D%5D,%20'A'%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-This can also be expressed using `.iloc`, by explicitly getting locations on the indexers, and using positional indexing to select things.
-
-```
-dfd.iloc[[0, 2], dfd.columns.get_loc('A')]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:550px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adfd%20%3D%20pd.DataFrame%28%7B'A'%3A%20%5B1,%202,%203%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'B'%3A%20%5B4,%205,%206%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abc'%29%29%0Adfd.iloc%5B%5B0,%202%5D,%20dfd.columns.get_loc%28'A'%29%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-For getting multiple indexers, using `.get_indexer`:
-
-```
-dfd.iloc[[0, 2], dfd.columns.get_indexer(['A', 'B'])]
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:550px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adfd%20%3D%20pd.DataFrame%28%7B'A'%3A%20%5B1,%202,%203%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'B'%3A%20%5B4,%205,%206%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abc'%29%29%0Adfd.iloc%5B%5B0,%202%5D,%20dfd.columns.get_indexer%28%5B'A',%20'B'%5D%29%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-## Combining datasets: concat, merge and join
-
-### concat
-
-- Concatenate Pandas objects along a particular axis.
-
-- Allows optional set logic along the other axes.
-
-- Can also add a layer of hierarchical indexing on the concatenation axis, which may be useful if the labels are the same (or overlapping) on the passed axis number.
-
-For example:
-
-Combine two `Series`.
-
-```{code-cell}
-s1 = pd.Series(['a', 'b'])
-s2 = pd.Series(['c', 'd'])
-pd.concat([s1, s2])
-```
-
-Clear the existing index and reset it in the result by setting the `ignore_index` option to `True`.
-
-```{code-cell}
-pd.concat([s1, s2], ignore_index=True)
-```
-
-Add a hierarchical index at the outermost level of the data with the `keys` option.
-
-```{code-cell}
-pd.concat([s1, s2], keys=['s1', 's2'])
-```
-
-Label the index keys you create with the `names` option.
-
-```{code-cell}
-pd.concat([s1, s2], keys=['s1', 's2'],
-          names=['Series name', 'Row ID'])
-```
-
-Combine two `DataFrame` objects with identical columns.
-
-```{code-cell}
-df1 = pd.DataFrame([['a', 1], ['b', 2]],
-                   columns=['letter', 'number'])
-df1
-```
-
-```{code-cell}
-df2 = pd.DataFrame([['c', 3], ['d', 4]],
-                   columns=['letter', 'number'])
-df2
-```
-
-```{code-cell}
-pd.concat([df1, df2])
-```
-
-Combine `DataFrame` objects with overlapping columns and return everything. Columns outside the intersection will be filled with `NaN` values.
-
-```{code-cell}
-df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']],
-                   columns=['letter', 'number', 'animal'])
-df3
-```
-
-```{code-cell}
-pd.concat([df1, df3], sort=False)
-```
-
-Combine DataFrame objects with overlapping columns and return only those that are shared by passing inner to the join keyword argument.
-
-```{code-cell}
-pd.concat([df1, df3], join="inner")
-```
-
-Combine `DataFrame` objects horizontally along the x-axis by passing in `axis=1`.
-
-```{code-cell}
-df4 = pd.DataFrame([['bird', 'polly'], ['monkey', 'george']],
-                   columns=['animal', 'name'])
-pd.concat([df1, df4], axis=1)
-```
-
-Prevent the result from including duplicate index values with the `verify_integrity` option.
-
-```{code-cell}
-df5 = pd.DataFrame([1], index=['a'])
-df5
-```
-
-```{code-cell}
-df6 = pd.DataFrame([2], index=['a'])
-df6
-```
-
-```{code-cell}
-:tags: ["raises-exception"]
-pd.concat([df5, df6], verify_integrity=True)
-```
-    
-Append a single row to the end of a `DataFrame` object.
-
-```{code-cell}
-df7 = pd.DataFrame({'a': 1, 'b': 2}, index=[0])
-df7
-```
-
-```{code-cell}
-new_row = pd.Series({'a': 3, 'b': 4})
-new_row
-```
-
-```{code-cell}
-pd.concat([df7, new_row.to_frame().T], ignore_index=True)
-```
-
-```{note}
-`append()` has been deprecated since version 1.4.0: Use `concat()` instead. 
-```
-
-### merge
-
-- Merge DataFrame or named Series objects with a database-style join.
-
-- A named Series object is treated as a DataFrame with a single named column.
-
-- The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on. When performing a cross-merge, no column specifications to merge on are allowed.
-
-```{warning}
-If both key columns contain rows where the key is a null value, those rows will be matched against each other. This is different from usual SQL join behaviour and can lead to unexpected results.
-```
-
-For example:
-
-```python
-df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
-                    'value': [1, 2, 3, 5]})
-df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
-                    'value': [5, 6, 7, 8]})
-```
-
-Merge DataFrames `df1` and `df2` with specified left and right suffixes appended to any overlapping columns.
-
-```
-df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=('_left', '_right'))
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:800px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf1%20%3D%20pd.DataFrame%28%7B'lkey'%3A%20%5B'foo',%20'bar',%20'baz',%20'foo'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'value'%3A%20%5B1,%202,%203,%205%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B'rkey'%3A%20%5B'foo',%20'bar',%20'baz',%20'foo'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'value'%3A%20%5B5,%206,%207,%208%5D%7D%29%0Adf1.merge%28df2,%20left_on%3D'lkey',%20right_on%3D'rkey'%29&d=2023-05-27&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-Merge DataFrames `df1` and `df2`, but raise an exception if the DataFrames have any overlapping columns.
-
-```{code-cell}
-:tags: ["raises-exception"]
-df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))
-```
-
-Using `how` parameter decide the type of merge to be performed.
-
-```
-df1 = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]})
-df2 = pd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4]})
-```
-
-```
-df1.merge(df2, how='inner', on='a')
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:600px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf1%20%3D%20pd.DataFrame%28%7B'a'%3A%20%5B'foo',%20'bar'%5D,%20'b'%3A%20%5B1,%202%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B'a'%3A%20%5B'foo',%20'baz'%5D,%20'c'%3A%20%5B3,%204%5D%7D%29%0Adf1.merge%28df2,%20how%3D'inner',%20on%3D'a'%29&d=2023-07-15&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-```
-df1.merge(df2, how='left', on='a')
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:630px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf1%20%3D%20pd.DataFrame%28%7B'a'%3A%20%5B'foo',%20'bar'%5D,%20'b'%3A%20%5B1,%202%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B'a'%3A%20%5B'foo',%20'baz'%5D,%20'c'%3A%20%5B3,%204%5D%7D%29%0Adf1.merge%28df2,%20how%3D'left',%20on%3D'a'%29&d=2023-07-15&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-```
-df1 = pd.DataFrame({'left': ['foo', 'bar']})
-df2 = pd.DataFrame({'right': [7, 8]})
-```
-
-```
-df1.merge(df2, how='cross')
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:670px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf1%20%3D%20pd.DataFrame%28%7B'left'%3A%20%5B'foo',%20'bar'%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B'right'%3A%20%5B7,%208%5D%7D%29%0Adf1.merge%28df2,%20how%3D'cross'%29&d=2023-07-15&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-### join
-
-- Join columns of another DataFrame.
-
-- Join columns with other DataFrame either on index or on a key column. Efficiently join multiple DataFrame objects by index at once by passing a list.
-
-For example:
-
-```{code-cell}
-df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],
-                   'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})
-```
-
-```{code-cell}
-other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],
-                      'B': ['B0', 'B1', 'B2']})                     
-```
-
-Join DataFrames using their indexes.
-
-```
-df.join(other, lsuffix='_caller', rsuffix='_other')
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:830px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf%20%3D%20pd.DataFrame%28%7B'key'%3A%20%5B'K0',%20'K1',%20'K2',%20'K3',%20'K4',%20'K5'%5D,'A'%3A%20%5B'A0',%20'A1',%20'A2',%20'A3',%20'A4',%20'A5'%5D%7D%29%0Aother%20%3D%20pd.DataFrame%28%7B'key'%3A%20%5B'K0',%20'K1',%20'K2'%5D,'B'%3A%20%5B'B0',%20'B1',%20'B2'%5D%7D%29%0Adf.join%28other,%20lsuffix%3D'_caller',%20rsuffix%3D'_other'%29&d=2023-07-15&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-If we want to join using the `key` columns, we need to set `key` to be the index in both `df` and `other`. The joined DataFrame will have `key` as its index.
-
-```
-df.set_index('key').join(other.set_index('key'))
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:1170px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf%20%3D%20pd.DataFrame%28%7B'key'%3A%20%5B'K0',%20'K1',%20'K2',%20'K3',%20'K4',%20'K5'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'A'%3A%20%5B'A0',%20'A1',%20'A2',%20'A3',%20'A4',%20'A5'%5D%7D%29%0Aother%20%3D%20pd.DataFrame%28%7B'key'%3A%20%5B'K0',%20'K1',%20'K2'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'B'%3A%20%5B'B0',%20'B1',%20'B2'%5D%7D%29%0Adf.set_index%28'key'%29.join%28other.set_index%28'key'%29%29&d=2023-05-27&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-Another option to join using the key columns is to use the `on` parameter. `DataFrame.join` always uses `other`’s index but we can use any column in `df`. This method preserves the original DataFrame’s index in the result.
-
-```{code-cell}
-df.join(other.set_index('key'), on='key')
-```
-
-Using non-unique key values shows how they are matched.
-
-```{code-cell}
-df = pd.DataFrame({'key': ['K0', 'K1', 'K1', 'K3', 'K0', 'K1'],
-                   'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})
-df                   
-```
-
-```{code-cell}
-df.join(other.set_index('key'), on='key', validate='m:1')
-```
-
-## Aggregation and grouping
-
-Group `DataFrame` using a mapper or by a `Series` of columns.
-
-A `groupby` operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.
-
-For example:
-
-```
-df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
-                              'Parrot', 'Parrot'],
-                   'Max Speed': [380., 370., 24., 26.]})
-df
-df.groupby(['Animal']).mean()
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:785px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf%20%3D%20pd.DataFrame%28%7B'Animal'%3A%20%5B'Falcon',%20'Falcon',%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'Parrot',%20'Parrot'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'Max%20Speed'%3A%20%5B380.,%20370.,%2024.,%2026.%5D%7D%29%0Adf%0Adf.groupby%28%5B'Animal'%5D%29.mean%28%29&d=2023-05-27&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-### Hierarchical Indexes
-
-#################
-
-We can `groupby` different levels of a hierarchical index using the `level` parameter:
-
-```
-arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
-          ['Captive', 'Wild', 'Captive', 'Wild']]
-index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
-df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},
-                  index=index)
-df.groupby(level=0).mean()
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:810px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aarrays%20%3D%20%5B%5B'Falcon',%20'Falcon',%20'Parrot',%20'Parrot'%5D,%0A%20%20%20%20%20%20%20%20%20%20%5B'Captive',%20'Wild',%20'Captive',%20'Wild'%5D%5D%0Aindex%20%3D%20pd.MultiIndex.from_arrays%28arrays,%20names%3D%28'Animal',%20'Type'%29%29%0Adf%20%3D%20pd.DataFrame%28%7B'Max%20Speed'%3A%20%5B390.,%20350.,%2030.,%2020.%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dindex%29%0Adf.groupby%28level%3D0%29.mean%28%29&d=2023-07-15&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-```
-df.groupby(level="Type").mean()
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:810px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aarrays%20%3D%20%5B%5B'Falcon',%20'Falcon',%20'Parrot',%20'Parrot'%5D,%0A%20%20%20%20%20%20%20%20%20%20%5B'Captive',%20'Wild',%20'Captive',%20'Wild'%5D%5D%0Aindex%20%3D%20pd.MultiIndex.from_arrays%28arrays,%20names%3D%28'Animal',%20'Type'%29%29%0Adf%20%3D%20pd.DataFrame%28%7B'Max%20Speed'%3A%20%5B390.,%20350.,%2030.,%2020.%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dindex%29%0Adf.groupby%28level%3D%22Type%22%29.mean%28%29&d=2023-07-15&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-We can also choose to include NA in group keys or not by setting `dropna` parameter, the default setting is `True`.
-
-```
-l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
-df = pd.DataFrame(l, columns=["a", "b", "c"])
-df.groupby(by=["b"]).sum()
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:770px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Al%20%3D%20%5B%5B1,%202,%203%5D,%20%5B1,%20None,%204%5D,%20%5B2,%201,%203%5D,%20%5B1,%202,%202%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%5B%22b%22%5D%29.sum%28%29%0A&d=2023-07-15&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-```
-df.groupby(by=["b"], dropna=False).sum()
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:750px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Al%20%3D%20%5B%5B1,%202,%203%5D,%20%5B1,%20None,%204%5D,%20%5B2,%201,%203%5D,%20%5B1,%202,%202%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%5B%22b%22%5D,%20dropna%3DFalse%29.sum%28%29&d=2023-05-27&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-```
-l = [["a", 12, 12], [None, 12.3, 33.], ["b", 12.3, 123], ["a", 1, 1]]
-df = pd.DataFrame(l, columns=["a", "b", "c"])
-df.groupby(by="a").sum()
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:750px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Al%20%3D%20%5B%5B%22a%22,%2012,%2012%5D,%20%5BNone,%2012.3,%2033.%5D,%20%5B%22b%22,%2012.3,%20123%5D,%20%5B%22a%22,%201,%201%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%22a%22%29.sum%28%29&d=2023-07-15&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-```
-df.groupby(by="a", dropna=False).sum()
-```
-
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:750px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Al%20%3D%20%5B%5B%22a%22,%2012,%2012%5D,%20%5BNone,%2012.3,%2033.%5D,%20%5B%22b%22,%2012.3,%20123%5D,%20%5B%22a%22,%201,%201%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%22a%22,%20dropna%3DFalse%29.sum%28%29%0A&d=2023-07-15&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-
-When using `.apply()`, use `group_keys` to include or exclude the group keys. The `group_keys` argument defaults to `True` (include).
-
-```{code-cell}
-df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
-                              'Parrot', 'Parrot'],
-                   'Max Speed': [380., 370., 24., 26.]})
-df.groupby("Animal", group_keys=True).apply(lambda x: x)
-```
-
-```{code-cell}
-df.groupby("Animal", group_keys=False).apply(lambda x: x)
-```
-
-## Pivot table
-
-Create a spreadsheet-style pivot table as a DataFrame.
-
-The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame.
-
-```{code-cell}
-df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
-                         "bar", "bar", "bar", "bar"],
-                   "B": ["one", "one", "one", "two", "two",
-                         "one", "one", "two", "two"],
-                   "C": ["small", "large", "large", "small",
-                         "small", "large", "small", "small",
-                         "large"],
-                   "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
-                   "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
-df
-```
-
-This first example aggregates values by taking the sum.
-
-```{code-cell}
-table = pd.pivot_table(df, values='D', index=['A', 'B'],
-                    columns=['C'], aggfunc=np.sum)
-table
-```
-
-We can also fill in missing values using the `fill_value` parameter.
-
-```{code-cell}
-table = pd.pivot_table(df, values='D', index=['A', 'B'],
-                    columns=['C'], aggfunc=np.sum, fill_value=0)
-table
-```
-
-The next example aggregates by taking the mean across multiple columns.
-
-```{code-cell}
-table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
-                    aggfunc={'D': np.mean,
-                             'E': np.mean})
-table
-```
-
-We can also calculate multiple types of aggregations for any given value column.
-
-```{code-cell}
-table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
-                    aggfunc={'D': np.mean,
-                             'E': [min, max, np.mean]})
-table
-```
-
-## High-performance Pandas: eval() and query()
-
-### eval()
-
-Evaluate a string describing operations on DataFrame columns.
-
-Operates on columns only, not specific rows or elements. This allows `eval` to run arbitrary code, which can make you vulnerable to code injection if you pass user input to this function.
-
-For example:
-
-```{code-cell}
-df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})
-df
-```
-
-```{code-cell}
-df.eval('A + B')
-```
-
-The assignment is allowed though by default the original `DataFrame` is not modified.
-
-```{code-cell}
-df.eval('C = A + B')
-```
-
-```{code-cell}
-df
-```
-
-Use `inplace=True` to modify the original DataFrame.
-
-```{code-cell}
-df.eval('C = A + B', inplace=True)
-df
-```
-
-Multiple columns can be assigned using multi-line expressions:
-
-```{code-cell}
-df.eval(
-    '''
-    C = A + B
-    D = A - B
-    '''
-)
-```
-
-### query()
-
-Query the columns of a DataFrame with a boolean expression.
-
-For example:
-
-```{code-cell}
-df = pd.DataFrame({
-    'A': range(1, 6),
-    'B': range(10, 0, -2),
-    'C C': range(10, 5, -1)
-})
-df
-```
-
-```{code-cell}
-df.query('A > B')
-```
-
-The previous expression is equivalent to
-
-```{code-cell}
-df[df.A > df.B]
-```
-
-For columns with spaces in their name, you can use backtick quoting.
-
-```{code-cell}
-df.query('B == `C C`')
-```
-
-The previous expression is equivalent to
-
-```{code-cell}
-df[df.B == df['C C']]
-```
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:660px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf%20%3D%20pd.DataFrame%28%7B%0A%20%20%20%20'A'%3A%20range%281,%206%29,%0A%20%20%20%20'B'%3A%20range%2810,%200,%20-2%29,%0A%20%20%20%20'C%20C'%3A%20range%2810,%205,%20-1%29%0A%7D%29%0A%0Adf%5Bdf.B%20%3D%3D%20df%5B'C%20C'%5D%5D&d=2023-07-13&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-## Your turn! 🚀
-
-### Processing image data
-
-Recently, very powerful AI models have been developed that allow us to understand images. There are many tasks that can be solved using pre-trained neural networks, or cloud services. Some examples include:
-
-- **Image Classification**, can help you categorize the image into one of the pre-defined classes. You can easily train your own image classifiers using services such as [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum)
-- **Object Detection** to detect different objects in the image. Services such as [computer vision](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum) can detect a number of common objects, and you can train [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum) model to detect some specific objects of interest.
-- **Face Detection**, including Age, Gender and Emotion detection. This can be done via [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-77958-bethanycheum).
-
-All those cloud services can be called using [Python SDKs](https://docs.microsoft.com/samples/azure-samples/cognitive-services-python-sdk-samples/cognitive-services-python-sdk-samples/?WT.mc_id=academic-77958-bethanycheum), and thus can be easily incorporated into your data exploration workflow.
-
-Here are some examples of exploring data from Image data sources:
-
-- In the blog post [How to Learn Data Science without Coding](https://soshnikov.com/azure/how-to-learn-data-science-without-coding/) we explore Instagram photos, trying to understand what makes people give more likes to a photo. We first extract as much information from pictures as possible using [computer vision](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum), and then use [Azure Machine Learning AutoML](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml/?WT.mc_id=academic-77958-bethanycheum) to build the interpretable model.
-- In [Facial Studies Workshop](https://github.com/CloudAdvocacy/FaceStudies) we use [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-77958-bethanycheum) to extract emotions from people on photographs from events, in order to try to understand what makes people happy.
-
-### Assignment
-
-[Perform more detailed data study for the challenges above](../../assignments/data-science/data-processing-in-python.md)
-
-## Self study
-
-In this chapter, we've covered many of the basics of using Pandas effectively for data analysis. Still, much has been omitted from our discussion. To learn more about Pandas, we recommend the following resources:
-
-- [Pandas online documentation](http://pandas.pydata.org/): This is the go-to source for complete documentation of the package. While the examples in the documentation tend to be small generated datasets, the description of the options is complete and generally very useful for understanding the use of various functions.
-
-- [*Python for Data Analysis*](http://shop.oreilly.com/product/0636920023784.do) Written by Wes McKinney (the original creator of Pandas), this book contains much more detail on the Pandas package than we had room for in this chapter. In particular, he takes a deep dive into tools for time series, which were his bread and butter as a financial consultant. The book also has many entertaining examples of applying Pandas to gain insight from real-world datasets. Keep in mind, though, that the book is now several years old, and the Pandas package has quite a few new features that this book does not cover (but be on the lookout for a new edition in 2017).
-
-- [Stack Overflow](http://stackoverflow.com/questions/tagged/pandas): Pandas has so many users that any question you have has likely been asked and answered on Stack Overflow. Using Pandas is a case where some Google-Fu is your best friend. Simply go to your favorite search engine and type in the question, problem, or error you're coming across–more than likely you'll find your answer on a Stack Overflow page.
-
-- [Pandas on PyVideo](http://pyvideo.org/search?q=pandas): From PyCon to SciPy to PyData, many conferences have featured tutorials from Pandas developers and power users. The PyCon tutorials in particular tend to be given by very well-vetted presenters.
-
-Using these resources, combined with the walk-through given in this chapter, my hope is that you'll be poised to use Pandas to tackle any data analysis problem you come across!
-
-## Acknowledgments
-
-Thanks for [Pandas user guide](https://pandas.pydata.org/docs/user_guide/index.html). It contributes the majority of the content in this chapter.
diff --git a/_sources/data-science/working-with-data/pandas/advanced-pandas-techniques.ipynb b/_sources/data-science/working-with-data/pandas/advanced-pandas-techniques.ipynb
new file mode 100644
index 0000000000..4892ca8562
--- /dev/null
+++ b/_sources/data-science/working-with-data/pandas/advanced-pandas-techniques.ipynb
@@ -0,0 +1,5054 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "aa35406e-c73d-49f1-aa84-5cc5ced6c294",
+   "metadata": {},
+   "source": [
+    "# Advanced Pandas Techniques"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64ccbdd4-f4c6-4893-80e9-7d87595020d2",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "In this section, we'll continue to introduce combining datasets: concat, merge and join along with data aggregation and grouping."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87f36a89-87fd-400d-894e-9332245bc9e5",
+   "metadata": {},
+   "source": [
+    "Import NumPy and load Pandas into your namespace:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b221a566-8a04-4689-8eb1-c266ede5a264",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install the necessary dependencies\n",
+    "import os\n",
+    "import sys\n",
+    "!{sys.executable} -m pip install --quiet jupyterlab_myst ipython\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9edbb0f3",
+   "metadata": {},
+   "source": [
+    "## Combining datasets: concat, merge and join\n",
+    "\n",
+    "### concat\n",
+    "\n",
+    "- Concatenate Pandas objects along a particular axis.\n",
+    "\n",
+    "- Allows optional set logic along the other axes.\n",
+    "\n",
+    "- Can also add a layer of hierarchical indexing on the concatenation axis, which may be useful if the labels are the same (or overlapping) on the passed axis number.\n",
+    "\n",
+    "For example:\n",
+    "\n",
+    "Combine two `Series`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b08dcc94",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    a\n",
+       "1    b\n",
+       "0    c\n",
+       "1    d\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s1 = pd.Series(['a', 'b'])\n",
+    "s2 = pd.Series(['c', 'd'])\n",
+    "pd.concat([s1, s2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1c47e7c",
+   "metadata": {},
+   "source": [
+    "Clear the existing index and reset it in the result by setting the `ignore_index` option to `True`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "32049abb",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    a\n",
+       "1    b\n",
+       "2    c\n",
+       "3    d\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([s1, s2], ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31f73f90",
+   "metadata": {},
+   "source": [
+    "Add a hierarchical index at the outermost level of the data with the `keys` option."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d5b95507",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "s1  0    a\n",
+       "    1    b\n",
+       "s2  0    c\n",
+       "    1    d\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([s1, s2], keys=['s1', 's2'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c618012",
+   "metadata": {},
+   "source": [
+    "Label the index keys you create with the `names` option."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6d54830d",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Series name  Row ID\n",
+       "s1           0         a\n",
+       "             1         b\n",
+       "s2           0         c\n",
+       "             1         d\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([s1, s2], keys=['s1', 's2'],\n",
+    "          names=['Series name', 'Row ID'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31fac69f",
+   "metadata": {},
+   "source": [
+    "Combine two `DataFrame` objects with identical columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "fec72294",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>letter</th>\n",
+       "      <th>number</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>a</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>b</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  letter  number\n",
+       "0      a       1\n",
+       "1      b       2"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1 = pd.DataFrame([['a', 1], ['b', 2]],\n",
+    "                   columns=['letter', 'number'])\n",
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "80a1f5b0",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>letter</th>\n",
+       "      <th>number</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>c</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>d</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  letter  number\n",
+       "0      c       3\n",
+       "1      d       4"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2 = pd.DataFrame([['c', 3], ['d', 4]],\n",
+    "                   columns=['letter', 'number'])\n",
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "4e9e65f6",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>letter</th>\n",
+       "      <th>number</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>a</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>b</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>c</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>d</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  letter  number\n",
+       "0      a       1\n",
+       "1      b       2\n",
+       "0      c       3\n",
+       "1      d       4"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([df1, df2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49d878b5",
+   "metadata": {},
+   "source": [
+    "Combine `DataFrame` objects with overlapping columns and return everything. Columns outside the intersection will be filled with `NaN` values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f50e8ede",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>letter</th>\n",
+       "      <th>number</th>\n",
+       "      <th>animal</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>c</td>\n",
+       "      <td>3</td>\n",
+       "      <td>cat</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>d</td>\n",
+       "      <td>4</td>\n",
+       "      <td>dog</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  letter  number animal\n",
+       "0      c       3    cat\n",
+       "1      d       4    dog"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']],\n",
+    "                   columns=['letter', 'number', 'animal'])\n",
+    "df3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9def1cdd",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>letter</th>\n",
+       "      <th>number</th>\n",
+       "      <th>animal</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>a</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>b</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>c</td>\n",
+       "      <td>3</td>\n",
+       "      <td>cat</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>d</td>\n",
+       "      <td>4</td>\n",
+       "      <td>dog</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  letter  number animal\n",
+       "0      a       1    NaN\n",
+       "1      b       2    NaN\n",
+       "0      c       3    cat\n",
+       "1      d       4    dog"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([df1, df3], sort=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f2fcb0c",
+   "metadata": {},
+   "source": [
+    "Combine DataFrame objects with overlapping columns and return only those that are shared by passing inner to the join keyword argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ef69d51c",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>letter</th>\n",
+       "      <th>number</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>a</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>b</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>c</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>d</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  letter  number\n",
+       "0      a       1\n",
+       "1      b       2\n",
+       "0      c       3\n",
+       "1      d       4"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([df1, df3], join=\"inner\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fda5cf5",
+   "metadata": {},
+   "source": [
+    "Combine `DataFrame` objects horizontally along the x-axis by passing in `axis=1`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2159161d",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>letter</th>\n",
+       "      <th>number</th>\n",
+       "      <th>animal</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>a</td>\n",
+       "      <td>1</td>\n",
+       "      <td>bird</td>\n",
+       "      <td>polly</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>b</td>\n",
+       "      <td>2</td>\n",
+       "      <td>monkey</td>\n",
+       "      <td>george</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  letter  number  animal    name\n",
+       "0      a       1    bird   polly\n",
+       "1      b       2  monkey  george"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df4 = pd.DataFrame([['bird', 'polly'], ['monkey', 'george']],\n",
+    "                   columns=['animal', 'name'])\n",
+    "pd.concat([df1, df4], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adb11ea6",
+   "metadata": {},
+   "source": [
+    "Prevent the result from including duplicate index values with the `verify_integrity` option."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "45bea28a",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   0\n",
+       "a  1"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df5 = pd.DataFrame([1], index=['a'])\n",
+    "df5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "db871526",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   0\n",
+       "a  2"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df6 = pd.DataFrame([2], index=['a'])\n",
+    "df6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "1ab6b3b0",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    },
+    "tags": [
+     "raises-exception"
+    ]
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Indexes have overlapping values: Index(['a'], dtype='object')",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[15], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mdf5\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf6\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverify_integrity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mF:\\anaconda\\envs\\py39\\lib\\site-packages\\pandas\\core\\reshape\\concat.py:393\u001b[0m, in \u001b[0;36mconcat\u001b[1;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[0;32m    378\u001b[0m     copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m    380\u001b[0m op \u001b[38;5;241m=\u001b[39m _Concatenator(\n\u001b[0;32m    381\u001b[0m     objs,\n\u001b[0;32m    382\u001b[0m     axis\u001b[38;5;241m=\u001b[39maxis,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    390\u001b[0m     sort\u001b[38;5;241m=\u001b[39msort,\n\u001b[0;32m    391\u001b[0m )\n\u001b[1;32m--> 393\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mF:\\anaconda\\envs\\py39\\lib\\site-packages\\pandas\\core\\reshape\\concat.py:667\u001b[0m, in \u001b[0;36m_Concatenator.get_result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    665\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobjs:\n\u001b[0;32m    666\u001b[0m     indexers \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m--> 667\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m ax, new_labels \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnew_axes\u001b[49m):\n\u001b[0;32m    668\u001b[0m         \u001b[38;5;66;03m# ::-1 to convert BlockManager ax to DataFrame ax\u001b[39;00m\n\u001b[0;32m    669\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbm_axis:\n\u001b[0;32m    670\u001b[0m             \u001b[38;5;66;03m# Suppress reindexing on concat axis\u001b[39;00m\n\u001b[0;32m    671\u001b[0m             \u001b[38;5;28;01mcontinue\u001b[39;00m\n",
+      "File \u001b[1;32mproperties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mF:\\anaconda\\envs\\py39\\lib\\site-packages\\pandas\\core\\reshape\\concat.py:698\u001b[0m, in \u001b[0;36m_Concatenator.new_axes\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    695\u001b[0m \u001b[38;5;129m@cache_readonly\u001b[39m\n\u001b[0;32m    696\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnew_axes\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Index]:\n\u001b[0;32m    697\u001b[0m     ndim \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_result_dim()\n\u001b[1;32m--> 698\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m [\n\u001b[0;32m    699\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_concat_axis \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbm_axis \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_comb_axis(i)\n\u001b[0;32m    700\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(ndim)\n\u001b[0;32m    701\u001b[0m     ]\n",
+      "File \u001b[1;32mF:\\anaconda\\envs\\py39\\lib\\site-packages\\pandas\\core\\reshape\\concat.py:699\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    695\u001b[0m \u001b[38;5;129m@cache_readonly\u001b[39m\n\u001b[0;32m    696\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnew_axes\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Index]:\n\u001b[0;32m    697\u001b[0m     ndim \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_result_dim()\n\u001b[0;32m    698\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m [\n\u001b[1;32m--> 699\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_concat_axis\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbm_axis \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_comb_axis(i)\n\u001b[0;32m    700\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(ndim)\n\u001b[0;32m    701\u001b[0m     ]\n",
+      "File \u001b[1;32mproperties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mF:\\anaconda\\envs\\py39\\lib\\site-packages\\pandas\\core\\reshape\\concat.py:762\u001b[0m, in \u001b[0;36m_Concatenator._get_concat_axis\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    757\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    758\u001b[0m     concat_axis \u001b[38;5;241m=\u001b[39m _make_concat_multiindex(\n\u001b[0;32m    759\u001b[0m         indexes, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlevels, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnames\n\u001b[0;32m    760\u001b[0m     )\n\u001b[1;32m--> 762\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_maybe_check_integrity\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconcat_axis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    764\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m concat_axis\n",
+      "File \u001b[1;32mF:\\anaconda\\envs\\py39\\lib\\site-packages\\pandas\\core\\reshape\\concat.py:770\u001b[0m, in \u001b[0;36m_Concatenator._maybe_check_integrity\u001b[1;34m(self, concat_index)\u001b[0m\n\u001b[0;32m    768\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m concat_index\u001b[38;5;241m.\u001b[39mis_unique:\n\u001b[0;32m    769\u001b[0m     overlap \u001b[38;5;241m=\u001b[39m concat_index[concat_index\u001b[38;5;241m.\u001b[39mduplicated()]\u001b[38;5;241m.\u001b[39munique()\n\u001b[1;32m--> 770\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIndexes have overlapping values: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moverlap\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[1;31mValueError\u001b[0m: Indexes have overlapping values: Index(['a'], dtype='object')"
+     ]
+    }
+   ],
+   "source": [
+    "pd.concat([df5, df6], verify_integrity=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90fc36d4",
+   "metadata": {},
+   "source": [
+    "Append a single row to the end of a `DataFrame` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "007c1ed6",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a  b\n",
+       "0  1  2"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df7 = pd.DataFrame({'a': 1, 'b': 2}, index=[0])\n",
+    "df7"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "9dbaddff",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    3\n",
+       "b    4\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_row = pd.Series({'a': 3, 'b': 4})\n",
+    "new_row"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "ad2d1313",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a  b\n",
+       "0  1  2\n",
+       "1  3  4"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([df7, new_row.to_frame().T], ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39223d1c",
+   "metadata": {},
+   "source": [
+    ":::{note}\n",
+    "`append()` has been deprecated since version 1.4.0: Use `concat()` instead. \n",
+    ":::\n",
+    "\n",
+    "### merge\n",
+    "\n",
+    "- Merge DataFrame or named Series objects with a database-style join.\n",
+    "\n",
+    "- A named Series object is treated as a DataFrame with a single named column.\n",
+    "\n",
+    "- The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on. When performing a cross-merge, no column specifications to merge on are allowed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1afc536-2209-4fa1-8d63-0b19c18c66c6",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "warning"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    "If both key columns contain rows where the key is a null value, those rows will be matched against each other. This is different from usual SQL join behaviour and can lead to unexpected results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f2ffec1",
+   "metadata": {},
+   "source": [
+    "For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e223179b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],\n",
+    "                    'value': [1, 2, 3, 5]})\n",
+    "df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],\n",
+    "                    'value': [5, 6, 7, 8]})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee9441ec",
+   "metadata": {},
+   "source": [
+    "Merge DataFrames `df1` and `df2` with specified left and right suffixes appended to any overlapping columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e22da8fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>lkey</th>\n",
+       "      <th>value_left</th>\n",
+       "      <th>rkey</th>\n",
+       "      <th>value_right</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>foo</td>\n",
+       "      <td>1</td>\n",
+       "      <td>foo</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>foo</td>\n",
+       "      <td>1</td>\n",
+       "      <td>foo</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>foo</td>\n",
+       "      <td>5</td>\n",
+       "      <td>foo</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>foo</td>\n",
+       "      <td>5</td>\n",
+       "      <td>foo</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>bar</td>\n",
+       "      <td>2</td>\n",
+       "      <td>bar</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>baz</td>\n",
+       "      <td>3</td>\n",
+       "      <td>baz</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  lkey  value_left rkey  value_right\n",
+       "0  foo           1  foo            5\n",
+       "1  foo           1  foo            8\n",
+       "2  foo           5  foo            5\n",
+       "3  foo           5  foo            8\n",
+       "4  bar           2  bar            6\n",
+       "5  baz           3  baz            7"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=('_left', '_right'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "6147bab8-4644-4a23-ba71-205573a1c3f9",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:800px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf1%20%3D%20pd.DataFrame%28%7B'lkey'%3A%20%5B'foo',%20'bar',%20'baz',%20'foo'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'value'%3A%20%5B1,%202,%203,%205%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B'rkey'%3A%20%5B'foo',%20'bar',%20'baz',%20'foo'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'value'%3A%20%5B5,%206,%207,%208%5D%7D%29%0Adf1.merge%28df2,%20left_on%3D'lkey',%20right_on%3D'rkey'%29&d=2023-05-27&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:800px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf1%20%3D%20pd.DataFrame%28%7B'lkey'%3A%20%5B'foo',%20'bar',%20'baz',%20'foo'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'value'%3A%20%5B1,%202,%203,%205%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B'rkey'%3A%20%5B'foo',%20'bar',%20'baz',%20'foo'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'value'%3A%20%5B5,%206,%207,%208%5D%7D%29%0Adf1.merge%28df2,%20left_on%3D'lkey',%20right_on%3D'rkey'%29&d=2023-05-27&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5112fc3a",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Merge DataFrames `df1` and `df2`, but raise an exception if the DataFrames have any overlapping columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "3dea68f6",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    },
+    "tags": [
+     "raises-exception"
+    ]
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "columns overlap but no suffix specified: Index(['value'], dtype='object')",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[22], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdf1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmerge\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mleft_on\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlkey\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright_on\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrkey\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msuffixes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mF:\\anaconda\\envs\\py39\\lib\\site-packages\\pandas\\core\\frame.py:10490\u001b[0m, in \u001b[0;36mDataFrame.merge\u001b[1;34m(self, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[0;32m  10471\u001b[0m \u001b[38;5;129m@Substitution\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m  10472\u001b[0m \u001b[38;5;129m@Appender\u001b[39m(_merge_doc, indents\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m  10473\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmerge\u001b[39m(\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m  10486\u001b[0m     validate: MergeValidate \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m  10487\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame:\n\u001b[0;32m  10488\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mreshape\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmerge\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m merge\n\u001b[1;32m> 10490\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmerge\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m  10491\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m  10492\u001b[0m \u001b[43m        \u001b[49m\u001b[43mright\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m  10493\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhow\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m  10494\u001b[0m \u001b[43m        \u001b[49m\u001b[43mon\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mon\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m  10495\u001b[0m \u001b[43m        \u001b[49m\u001b[43mleft_on\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mleft_on\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m  10496\u001b[0m \u001b[43m        \u001b[49m\u001b[43mright_on\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mright_on\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m  10497\u001b[0m \u001b[43m        \u001b[49m\u001b[43mleft_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mleft_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m  10498\u001b[0m \u001b[43m        \u001b[49m\u001b[43mright_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mright_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m  10499\u001b[0m \u001b[43m        \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m  10500\u001b[0m \u001b[43m        \u001b[49m\u001b[43msuffixes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msuffixes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m  10501\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m  10502\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindicator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindicator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m  10503\u001b[0m \u001b[43m        \u001b[49m\u001b[43mvalidate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m  10504\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mF:\\anaconda\\envs\\py39\\lib\\site-packages\\pandas\\core\\reshape\\merge.py:183\u001b[0m, in \u001b[0;36mmerge\u001b[1;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[0;32m    168\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    169\u001b[0m     op \u001b[38;5;241m=\u001b[39m _MergeOperation(\n\u001b[0;32m    170\u001b[0m         left_df,\n\u001b[0;32m    171\u001b[0m         right_df,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    181\u001b[0m         validate\u001b[38;5;241m=\u001b[39mvalidate,\n\u001b[0;32m    182\u001b[0m     )\n\u001b[1;32m--> 183\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mF:\\anaconda\\envs\\py39\\lib\\site-packages\\pandas\\core\\reshape\\merge.py:885\u001b[0m, in \u001b[0;36m_MergeOperation.get_result\u001b[1;34m(self, copy)\u001b[0m\n\u001b[0;32m    881\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mleft, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mright \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_indicator_pre_merge(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mleft, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mright)\n\u001b[0;32m    883\u001b[0m join_index, left_indexer, right_indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_join_info()\n\u001b[1;32m--> 885\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reindex_and_concat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mjoin_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mleft_indexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright_indexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\n\u001b[0;32m    887\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    888\u001b[0m result \u001b[38;5;241m=\u001b[39m result\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_merge_type)\n\u001b[0;32m    890\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindicator:\n",
+      "File \u001b[1;32mF:\\anaconda\\envs\\py39\\lib\\site-packages\\pandas\\core\\reshape\\merge.py:837\u001b[0m, in \u001b[0;36m_MergeOperation._reindex_and_concat\u001b[1;34m(self, join_index, left_indexer, right_indexer, copy)\u001b[0m\n\u001b[0;32m    834\u001b[0m left \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mleft[:]\n\u001b[0;32m    835\u001b[0m right \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mright[:]\n\u001b[1;32m--> 837\u001b[0m llabels, rlabels \u001b[38;5;241m=\u001b[39m \u001b[43m_items_overlap_with_suffix\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    838\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mleft\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_info_axis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mright\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_info_axis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuffixes\u001b[49m\n\u001b[0;32m    839\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    841\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m left_indexer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_range_indexer(left_indexer, \u001b[38;5;28mlen\u001b[39m(left)):\n\u001b[0;32m    842\u001b[0m     \u001b[38;5;66;03m# Pinning the index here (and in the right code just below) is not\u001b[39;00m\n\u001b[0;32m    843\u001b[0m     \u001b[38;5;66;03m#  necessary, but makes the `.take` more performant if we have e.g.\u001b[39;00m\n\u001b[0;32m    844\u001b[0m     \u001b[38;5;66;03m#  a MultiIndex for left.index.\u001b[39;00m\n\u001b[0;32m    845\u001b[0m     lmgr \u001b[38;5;241m=\u001b[39m left\u001b[38;5;241m.\u001b[39m_mgr\u001b[38;5;241m.\u001b[39mreindex_indexer(\n\u001b[0;32m    846\u001b[0m         join_index,\n\u001b[0;32m    847\u001b[0m         left_indexer,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    852\u001b[0m         use_na_proxy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m    853\u001b[0m     )\n",
+      "File \u001b[1;32mF:\\anaconda\\envs\\py39\\lib\\site-packages\\pandas\\core\\reshape\\merge.py:2655\u001b[0m, in \u001b[0;36m_items_overlap_with_suffix\u001b[1;34m(left, right, suffixes)\u001b[0m\n\u001b[0;32m   2652\u001b[0m lsuffix, rsuffix \u001b[38;5;241m=\u001b[39m suffixes\n\u001b[0;32m   2654\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m lsuffix \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m rsuffix:\n\u001b[1;32m-> 2655\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns overlap but no suffix specified: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mto_rename\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   2657\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrenamer\u001b[39m(x, suffix: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m   2658\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   2659\u001b[0m \u001b[38;5;124;03m    Rename the left and right indices.\u001b[39;00m\n\u001b[0;32m   2660\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   2671\u001b[0m \u001b[38;5;124;03m    x : renamed column name\u001b[39;00m\n\u001b[0;32m   2672\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n",
+      "\u001b[1;31mValueError\u001b[0m: columns overlap but no suffix specified: Index(['value'], dtype='object')"
+     ]
+    }
+   ],
+   "source": [
+    "df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86efca65",
+   "metadata": {},
+   "source": [
+    "Using `how` parameter decide the type of merge to be performed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "1026fc27",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]})\n",
+    "df2 = pd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4]})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "b4379cb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>foo</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     a  b  c\n",
+       "0  foo  1  3"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.merge(df2, how='inner', on='a')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "90916930-6a8e-40e3-871e-d0043aae93d8",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:600px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf1%20%3D%20pd.DataFrame%28%7B'a'%3A%20%5B'foo',%20'bar'%5D,%20'b'%3A%20%5B1,%202%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B'a'%3A%20%5B'foo',%20'baz'%5D,%20'c'%3A%20%5B3,%204%5D%7D%29%0Adf1.merge%28df2,%20how%3D'inner',%20on%3D'a'%29&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:600px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf1%20%3D%20pd.DataFrame%28%7B'a'%3A%20%5B'foo',%20'bar'%5D,%20'b'%3A%20%5B1,%202%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B'a'%3A%20%5B'foo',%20'baz'%5D,%20'c'%3A%20%5B3,%204%5D%7D%29%0Adf1.merge%28df2,%20how%3D'inner',%20on%3D'a'%29&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "2a8bb3d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>foo</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>bar</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     a  b    c\n",
+       "0  foo  1  3.0\n",
+       "1  bar  2  NaN"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.merge(df2, how='left', on='a')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "467da7f9-a710-442e-9fcf-afb4990ea3b0",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:630px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf1%20%3D%20pd.DataFrame%28%7B'a'%3A%20%5B'foo',%20'bar'%5D,%20'b'%3A%20%5B1,%202%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B'a'%3A%20%5B'foo',%20'baz'%5D,%20'c'%3A%20%5B3,%204%5D%7D%29%0Adf1.merge%28df2,%20how%3D'left',%20on%3D'a'%29&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:630px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf1%20%3D%20pd.DataFrame%28%7B'a'%3A%20%5B'foo',%20'bar'%5D,%20'b'%3A%20%5B1,%202%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B'a'%3A%20%5B'foo',%20'baz'%5D,%20'c'%3A%20%5B3,%204%5D%7D%29%0Adf1.merge%28df2,%20how%3D'left',%20on%3D'a'%29&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "8951b7b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = pd.DataFrame({'left': ['foo', 'bar']})\n",
+    "df2 = pd.DataFrame({'right': [7, 8]})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "93051401",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>left</th>\n",
+       "      <th>right</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>foo</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>foo</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>bar</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>bar</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  left  right\n",
+       "0  foo      7\n",
+       "1  foo      8\n",
+       "2  bar      7\n",
+       "3  bar      8"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.merge(df2, how='cross')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "bc243059-83f7-485c-bcd0-453d611c3d1f",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:670px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf1%20%3D%20pd.DataFrame%28%7B'left'%3A%20%5B'foo',%20'bar'%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B'right'%3A%20%5B7,%208%5D%7D%29%0Adf1.merge%28df2,%20how%3D'cross'%29&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:670px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf1%20%3D%20pd.DataFrame%28%7B'left'%3A%20%5B'foo',%20'bar'%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B'right'%3A%20%5B7,%208%5D%7D%29%0Adf1.merge%28df2,%20how%3D'cross'%29&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b58237c9",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### join\n",
+    "\n",
+    "- Join columns of another DataFrame.\n",
+    "\n",
+    "- Join columns with other DataFrame either on index or on a key column. Efficiently join multiple DataFrame objects by index at once by passing a list.\n",
+    "\n",
+    "For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "5ad178d6",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],\n",
+    "                   'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "ff1aa936",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],\n",
+    "                      'B': ['B0', 'B1', 'B2']})                     "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3278bb56",
+   "metadata": {},
+   "source": [
+    "Join DataFrames using their indexes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "a2517b83",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>key_caller</th>\n",
+       "      <th>A</th>\n",
+       "      <th>key_other</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>K0</td>\n",
+       "      <td>A0</td>\n",
+       "      <td>K0</td>\n",
+       "      <td>B0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>K1</td>\n",
+       "      <td>A1</td>\n",
+       "      <td>K1</td>\n",
+       "      <td>B1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>K2</td>\n",
+       "      <td>A2</td>\n",
+       "      <td>K2</td>\n",
+       "      <td>B2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>K3</td>\n",
+       "      <td>A3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>K4</td>\n",
+       "      <td>A4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>K5</td>\n",
+       "      <td>A5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  key_caller   A key_other    B\n",
+       "0         K0  A0        K0   B0\n",
+       "1         K1  A1        K1   B1\n",
+       "2         K2  A2        K2   B2\n",
+       "3         K3  A3       NaN  NaN\n",
+       "4         K4  A4       NaN  NaN\n",
+       "5         K5  A5       NaN  NaN"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.join(other, lsuffix='_caller', rsuffix='_other')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "81738ab5-bc94-4264-bb43-8c64c041c332",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:830px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf%20%3D%20pd.DataFrame%28%7B'key'%3A%20%5B'K0',%20'K1',%20'K2',%20'K3',%20'K4',%20'K5'%5D,'A'%3A%20%5B'A0',%20'A1',%20'A2',%20'A3',%20'A4',%20'A5'%5D%7D%29%0Aother%20%3D%20pd.DataFrame%28%7B'key'%3A%20%5B'K0',%20'K1',%20'K2'%5D,'B'%3A%20%5B'B0',%20'B1',%20'B2'%5D%7D%29%0Adf.join%28other,%20lsuffix%3D'_caller',%20rsuffix%3D'_other'%29&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:830px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf%20%3D%20pd.DataFrame%28%7B'key'%3A%20%5B'K0',%20'K1',%20'K2',%20'K3',%20'K4',%20'K5'%5D,'A'%3A%20%5B'A0',%20'A1',%20'A2',%20'A3',%20'A4',%20'A5'%5D%7D%29%0Aother%20%3D%20pd.DataFrame%28%7B'key'%3A%20%5B'K0',%20'K1',%20'K2'%5D,'B'%3A%20%5B'B0',%20'B1',%20'B2'%5D%7D%29%0Adf.join%28other,%20lsuffix%3D'_caller',%20rsuffix%3D'_other'%29&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59935609",
+   "metadata": {},
+   "source": [
+    "\n",
+    "If we want to join using the `key` columns, we need to set `key` to be the index in both `df` and `other`. The joined DataFrame will have `key` as its index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "91c6f0f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>key</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>K0</th>\n",
+       "      <td>A0</td>\n",
+       "      <td>B0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>K1</th>\n",
+       "      <td>A1</td>\n",
+       "      <td>B1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>K2</th>\n",
+       "      <td>A2</td>\n",
+       "      <td>B2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>K3</th>\n",
+       "      <td>A3</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>K4</th>\n",
+       "      <td>A4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>K5</th>\n",
+       "      <td>A5</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      A    B\n",
+       "key         \n",
+       "K0   A0   B0\n",
+       "K1   A1   B1\n",
+       "K2   A2   B2\n",
+       "K3   A3  NaN\n",
+       "K4   A4  NaN\n",
+       "K5   A5  NaN"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.set_index('key').join(other.set_index('key'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "f942120e-c151-473d-aa0a-3ed6b0679204",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:1170px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf%20%3D%20pd.DataFrame%28%7B'key'%3A%20%5B'K0',%20'K1',%20'K2',%20'K3',%20'K4',%20'K5'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'A'%3A%20%5B'A0',%20'A1',%20'A2',%20'A3',%20'A4',%20'A5'%5D%7D%29%0Aother%20%3D%20pd.DataFrame%28%7B'key'%3A%20%5B'K0',%20'K1',%20'K2'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'B'%3A%20%5B'B0',%20'B1',%20'B2'%5D%7D%29%0Adf.set_index%28'key'%29.join%28other.set_index%28'key'%29%29&d=2023-05-27&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:1170px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf%20%3D%20pd.DataFrame%28%7B'key'%3A%20%5B'K0',%20'K1',%20'K2',%20'K3',%20'K4',%20'K5'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'A'%3A%20%5B'A0',%20'A1',%20'A2',%20'A3',%20'A4',%20'A5'%5D%7D%29%0Aother%20%3D%20pd.DataFrame%28%7B'key'%3A%20%5B'K0',%20'K1',%20'K2'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'B'%3A%20%5B'B0',%20'B1',%20'B2'%5D%7D%29%0Adf.set_index%28'key'%29.join%28other.set_index%28'key'%29%29&d=2023-05-27&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1483f153",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Another option to join using the key columns is to use the `on` parameter. `DataFrame.join` always uses `other`'s index but we can use any column in `df`. This method preserves the original DataFrame's index in the result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "d8fbb1f7",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>key</th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>K0</td>\n",
+       "      <td>A0</td>\n",
+       "      <td>B0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>K1</td>\n",
+       "      <td>A1</td>\n",
+       "      <td>B1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>K2</td>\n",
+       "      <td>A2</td>\n",
+       "      <td>B2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>K3</td>\n",
+       "      <td>A3</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>K4</td>\n",
+       "      <td>A4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>K5</td>\n",
+       "      <td>A5</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  key   A    B\n",
+       "0  K0  A0   B0\n",
+       "1  K1  A1   B1\n",
+       "2  K2  A2   B2\n",
+       "3  K3  A3  NaN\n",
+       "4  K4  A4  NaN\n",
+       "5  K5  A5  NaN"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.join(other.set_index('key'), on='key')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ed06755",
+   "metadata": {},
+   "source": [
+    "Using non-unique key values shows how they are matched."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "b4d1eb0d",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>key</th>\n",
+       "      <th>A</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>K0</td>\n",
+       "      <td>A0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>K1</td>\n",
+       "      <td>A1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>K1</td>\n",
+       "      <td>A2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>K3</td>\n",
+       "      <td>A3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>K0</td>\n",
+       "      <td>A4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>K1</td>\n",
+       "      <td>A5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  key   A\n",
+       "0  K0  A0\n",
+       "1  K1  A1\n",
+       "2  K1  A2\n",
+       "3  K3  A3\n",
+       "4  K0  A4\n",
+       "5  K1  A5"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame({'key': ['K0', 'K1', 'K1', 'K3', 'K0', 'K1'],\n",
+    "                   'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})\n",
+    "df                   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "7f6bc83d",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>key</th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>K0</td>\n",
+       "      <td>A0</td>\n",
+       "      <td>B0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>K1</td>\n",
+       "      <td>A1</td>\n",
+       "      <td>B1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>K1</td>\n",
+       "      <td>A2</td>\n",
+       "      <td>B1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>K3</td>\n",
+       "      <td>A3</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>K0</td>\n",
+       "      <td>A4</td>\n",
+       "      <td>B0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>K1</td>\n",
+       "      <td>A5</td>\n",
+       "      <td>B1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  key   A    B\n",
+       "0  K0  A0   B0\n",
+       "1  K1  A1   B1\n",
+       "2  K1  A2   B1\n",
+       "3  K3  A3  NaN\n",
+       "4  K0  A4   B0\n",
+       "5  K1  A5   B1"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.join(other.set_index('key'), on='key', validate='m:1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61fb9627",
+   "metadata": {},
+   "source": [
+    "## Aggregation and grouping\n",
+    "\n",
+    "Group `DataFrame` using a mapper or by a `Series` of columns.\n",
+    "\n",
+    "A `groupby` operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.\n",
+    "\n",
+    "For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "38adb2b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Max Speed</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Animal</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Falcon</th>\n",
+       "      <td>375.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Parrot</th>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Max Speed\n",
+       "Animal           \n",
+       "Falcon      375.0\n",
+       "Parrot       25.0"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',\n",
+    "                              'Parrot', 'Parrot'],\n",
+    "                   'Max Speed': [380., 370., 24., 26.]})\n",
+    "df\n",
+    "df.groupby(['Animal']).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "917ba231-1ee4-4f2c-bcb9-4262d7eba119",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:785px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf%20%3D%20pd.DataFrame%28%7B'Animal'%3A%20%5B'Falcon',%20'Falcon',%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'Parrot',%20'Parrot'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'Max%20Speed'%3A%20%5B380.,%20370.,%2024.,%2026.%5D%7D%29%0Adf%0Adf.groupby%28%5B'Animal'%5D%29.mean%28%29&d=2023-05-27&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:785px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf%20%3D%20pd.DataFrame%28%7B'Animal'%3A%20%5B'Falcon',%20'Falcon',%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'Parrot',%20'Parrot'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'Max%20Speed'%3A%20%5B380.,%20370.,%2024.,%2026.%5D%7D%29%0Adf%0Adf.groupby%28%5B'Animal'%5D%29.mean%28%29&d=2023-05-27&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84fe11db",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### Hierarchical Indexes\n",
+    "\n",
+    "We can `groupby` different levels of a hierarchical index using the `level` parameter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "5e84fd8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Max Speed</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Animal</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Falcon</th>\n",
+       "      <td>370.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Parrot</th>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Max Speed\n",
+       "Animal           \n",
+       "Falcon      370.0\n",
+       "Parrot       25.0"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],\n",
+    "          ['Captive', 'Wild', 'Captive', 'Wild']]\n",
+    "index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))\n",
+    "df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},\n",
+    "                  index=index)\n",
+    "df.groupby(level=0).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "8d6ff678-1c1e-4629-9e06-1874511ecdf0",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:810px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aarrays%20%3D%20%5B%5B'Falcon',%20'Falcon',%20'Parrot',%20'Parrot'%5D,%0A%20%20%20%20%20%20%20%20%20%20%5B'Captive',%20'Wild',%20'Captive',%20'Wild'%5D%5D%0Aindex%20%3D%20pd.MultiIndex.from_arrays%28arrays,%20names%3D%28'Animal',%20'Type'%29%29%0Adf%20%3D%20pd.DataFrame%28%7B'Max%20Speed'%3A%20%5B390.,%20350.,%2030.,%2020.%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dindex%29%0Adf.groupby%28level%3D0%29.mean%28%29&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:810px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aarrays%20%3D%20%5B%5B'Falcon',%20'Falcon',%20'Parrot',%20'Parrot'%5D,%0A%20%20%20%20%20%20%20%20%20%20%5B'Captive',%20'Wild',%20'Captive',%20'Wild'%5D%5D%0Aindex%20%3D%20pd.MultiIndex.from_arrays%28arrays,%20names%3D%28'Animal',%20'Type'%29%29%0Adf%20%3D%20pd.DataFrame%28%7B'Max%20Speed'%3A%20%5B390.,%20350.,%2030.,%2020.%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dindex%29%0Adf.groupby%28level%3D0%29.mean%28%29&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "5a7a2d6a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Max Speed</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Type</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Captive</th>\n",
+       "      <td>210.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Wild</th>\n",
+       "      <td>185.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Max Speed\n",
+       "Type              \n",
+       "Captive      210.0\n",
+       "Wild         185.0"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby(level=\"Type\").mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "31f4c668-6a8b-4dba-a6db-29673e7fbdba",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:810px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aarrays%20%3D%20%5B%5B'Falcon',%20'Falcon',%20'Parrot',%20'Parrot'%5D,%0A%20%20%20%20%20%20%20%20%20%20%5B'Captive',%20'Wild',%20'Captive',%20'Wild'%5D%5D%0Aindex%20%3D%20pd.MultiIndex.from_arrays%28arrays,%20names%3D%28'Animal',%20'Type'%29%29%0Adf%20%3D%20pd.DataFrame%28%7B'Max%20Speed'%3A%20%5B390.,%20350.,%2030.,%2020.%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dindex%29%0Adf.groupby%28level%3D%22Type%22%29.mean%28%29&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:810px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aarrays%20%3D%20%5B%5B'Falcon',%20'Falcon',%20'Parrot',%20'Parrot'%5D,%0A%20%20%20%20%20%20%20%20%20%20%5B'Captive',%20'Wild',%20'Captive',%20'Wild'%5D%5D%0Aindex%20%3D%20pd.MultiIndex.from_arrays%28arrays,%20names%3D%28'Animal',%20'Type'%29%29%0Adf%20%3D%20pd.DataFrame%28%7B'Max%20Speed'%3A%20%5B390.,%20350.,%2030.,%2020.%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dindex%29%0Adf.groupby%28level%3D%22Type%22%29.mean%28%29&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe08b062",
+   "metadata": {},
+   "source": [
+    "\n",
+    "We can also choose to include NA in group keys or not by setting `dropna` parameter, the default setting is `True`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "f27b6536",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1.0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2.0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     a  c\n",
+       "b        \n",
+       "1.0  2  3\n",
+       "2.0  2  5"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]\n",
+    "df = pd.DataFrame(l, columns=[\"a\", \"b\", \"c\"])\n",
+    "df.groupby(by=[\"b\"]).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "47261c15-1d74-4a39-a7bb-073f6835cbf8",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:770px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Al%20%3D%20%5B%5B1,%202,%203%5D,%20%5B1,%20None,%204%5D,%20%5B2,%201,%203%5D,%20%5B1,%202,%202%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%5B%22b%22%5D%29.sum%28%29%0A&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:770px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Al%20%3D%20%5B%5B1,%202,%203%5D,%20%5B1,%20None,%204%5D,%20%5B2,%201,%203%5D,%20%5B1,%202,%202%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%5B%22b%22%5D%29.sum%28%29%0A&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "815ba4c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1.0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2.0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NaN</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     a  c\n",
+       "b        \n",
+       "1.0  2  3\n",
+       "2.0  2  5\n",
+       "NaN  1  4"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby(by=[\"b\"], dropna=False).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "17c93213-8bcf-4ac8-a30d-09df48b9ca71",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:750px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Al%20%3D%20%5B%5B1,%202,%203%5D,%20%5B1,%20None,%204%5D,%20%5B2,%201,%203%5D,%20%5B1,%202,%202%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%5B%22b%22%5D,%20dropna%3DFalse%29.sum%28%29&d=2023-05-27&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:750px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Al%20%3D%20%5B%5B1,%202,%203%5D,%20%5B1,%20None,%204%5D,%20%5B2,%201,%203%5D,%20%5B1,%202,%202%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%5B%22b%22%5D,%20dropna%3DFalse%29.sum%28%29&d=2023-05-27&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "719dc004",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>12.3</td>\n",
+       "      <td>123.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      b      c\n",
+       "a             \n",
+       "a  13.0   13.0\n",
+       "b  12.3  123.0"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "l = [[\"a\", 12, 12], [None, 12.3, 33.], [\"b\", 12.3, 123], [\"a\", 1, 1]]\n",
+    "df = pd.DataFrame(l, columns=[\"a\", \"b\", \"c\"])\n",
+    "df.groupby(by=\"a\").sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "ba2d22de-ed75-4d52-a6d8-badf4791429f",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:750px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Al%20%3D%20%5B%5B%22a%22,%2012,%2012%5D,%20%5BNone,%2012.3,%2033.%5D,%20%5B%22b%22,%2012.3,%20123%5D,%20%5B%22a%22,%201,%201%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%22a%22%29.sum%28%29&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:750px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Al%20%3D%20%5B%5B%22a%22,%2012,%2012%5D,%20%5BNone,%2012.3,%2033.%5D,%20%5B%22b%22,%2012.3,%20123%5D,%20%5B%22a%22,%201,%201%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%22a%22%29.sum%28%29&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "cce87c6a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>12.3</td>\n",
+       "      <td>123.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NaN</th>\n",
+       "      <td>12.3</td>\n",
+       "      <td>33.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        b      c\n",
+       "a               \n",
+       "a    13.0   13.0\n",
+       "b    12.3  123.0\n",
+       "NaN  12.3   33.0"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby(by=\"a\", dropna=False).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "70cc2217-577e-4b8c-8fc2-ce02f036622b",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:750px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Al%20%3D%20%5B%5B%22a%22,%2012,%2012%5D,%20%5BNone,%2012.3,%2033.%5D,%20%5B%22b%22,%2012.3,%20123%5D,%20%5B%22a%22,%201,%201%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%22a%22,%20dropna%3DFalse%29.sum%28%29%0A&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:750px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Al%20%3D%20%5B%5B%22a%22,%2012,%2012%5D,%20%5BNone,%2012.3,%2033.%5D,%20%5B%22b%22,%2012.3,%20123%5D,%20%5B%22a%22,%201,%201%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%22a%22,%20dropna%3DFalse%29.sum%28%29%0A&d=2023-07-15&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6988f12c",
+   "metadata": {},
+   "source": [
+    "\n",
+    "When using `.apply()`, use `group_keys` to include or exclude the group keys. The `group_keys` argument defaults to `True` (include)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "1fa5930a",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Animal</th>\n",
+       "      <th>Max Speed</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Animal</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Falcon</th>\n",
+       "      <th>0</th>\n",
+       "      <td>Falcon</td>\n",
+       "      <td>380.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Falcon</td>\n",
+       "      <td>370.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Parrot</th>\n",
+       "      <th>2</th>\n",
+       "      <td>Parrot</td>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Parrot</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Animal  Max Speed\n",
+       "Animal                     \n",
+       "Falcon 0  Falcon      380.0\n",
+       "       1  Falcon      370.0\n",
+       "Parrot 2  Parrot       24.0\n",
+       "       3  Parrot       26.0"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',\n",
+    "                              'Parrot', 'Parrot'],\n",
+    "                   'Max Speed': [380., 370., 24., 26.]})\n",
+    "df.groupby(\"Animal\", group_keys=True).apply(lambda x: x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "67e4668e",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Animal</th>\n",
+       "      <th>Max Speed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Falcon</td>\n",
+       "      <td>380.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Falcon</td>\n",
+       "      <td>370.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Parrot</td>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Parrot</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Animal  Max Speed\n",
+       "0  Falcon      380.0\n",
+       "1  Falcon      370.0\n",
+       "2  Parrot       24.0\n",
+       "3  Parrot       26.0"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby(\"Animal\", group_keys=False).apply(lambda x: x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8777695",
+   "metadata": {},
+   "source": [
+    "## Pivot table\n",
+    "\n",
+    "Create a spreadsheet-style pivot table as a DataFrame.\n",
+    "\n",
+    "The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "c8e1b317",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>E</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>foo</td>\n",
+       "      <td>one</td>\n",
+       "      <td>small</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>foo</td>\n",
+       "      <td>one</td>\n",
+       "      <td>large</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>foo</td>\n",
+       "      <td>one</td>\n",
+       "      <td>large</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>foo</td>\n",
+       "      <td>two</td>\n",
+       "      <td>small</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>foo</td>\n",
+       "      <td>two</td>\n",
+       "      <td>small</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>bar</td>\n",
+       "      <td>one</td>\n",
+       "      <td>large</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>bar</td>\n",
+       "      <td>one</td>\n",
+       "      <td>small</td>\n",
+       "      <td>5</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>bar</td>\n",
+       "      <td>two</td>\n",
+       "      <td>small</td>\n",
+       "      <td>6</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>bar</td>\n",
+       "      <td>two</td>\n",
+       "      <td>large</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A    B      C  D  E\n",
+       "0  foo  one  small  1  2\n",
+       "1  foo  one  large  2  4\n",
+       "2  foo  one  large  2  5\n",
+       "3  foo  two  small  3  5\n",
+       "4  foo  two  small  3  6\n",
+       "5  bar  one  large  4  6\n",
+       "6  bar  one  small  5  8\n",
+       "7  bar  two  small  6  9\n",
+       "8  bar  two  large  7  9"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame({\"A\": [\"foo\", \"foo\", \"foo\", \"foo\", \"foo\",\n",
+    "                         \"bar\", \"bar\", \"bar\", \"bar\"],\n",
+    "                   \"B\": [\"one\", \"one\", \"one\", \"two\", \"two\",\n",
+    "                         \"one\", \"one\", \"two\", \"two\"],\n",
+    "                   \"C\": [\"small\", \"large\", \"large\", \"small\",\n",
+    "                         \"small\", \"large\", \"small\", \"small\",\n",
+    "                         \"large\"],\n",
+    "                   \"D\": [1, 2, 2, 3, 3, 4, 5, 6, 7],\n",
+    "                   \"E\": [2, 4, 5, 5, 6, 6, 8, 9, 9]})\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef96918e",
+   "metadata": {},
+   "source": [
+    "This first example aggregates values by taking the sum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "7206f156",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\87554\\AppData\\Local\\Temp\\ipykernel_26368\\2135498425.py:1: FutureWarning: The provided callable <function sum at 0x0000017F450A19D0> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
+      "  table = pd.pivot_table(df, values='D', index=['A', 'B'],\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>C</th>\n",
+       "      <th>large</th>\n",
+       "      <th>small</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
+       "      <th>one</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>two</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
+       "      <th>one</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>two</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "C        large  small\n",
+       "A   B                \n",
+       "bar one    4.0    5.0\n",
+       "    two    7.0    6.0\n",
+       "foo one    4.0    1.0\n",
+       "    two    NaN    6.0"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table = pd.pivot_table(df, values='D', index=['A', 'B'],\n",
+    "                    columns=['C'], aggfunc=np.sum)\n",
+    "table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0df6460",
+   "metadata": {},
+   "source": [
+    "We can also fill in missing values using the `fill_value` parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "6cfd03f9",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\87554\\AppData\\Local\\Temp\\ipykernel_26368\\3213005518.py:1: FutureWarning: The provided callable <function sum at 0x0000017F450A19D0> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
+      "  table = pd.pivot_table(df, values='D', index=['A', 'B'],\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>C</th>\n",
+       "      <th>large</th>\n",
+       "      <th>small</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
+       "      <th>one</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>two</th>\n",
+       "      <td>7</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
+       "      <th>one</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>two</th>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "C        large  small\n",
+       "A   B                \n",
+       "bar one      4      5\n",
+       "    two      7      6\n",
+       "foo one      4      1\n",
+       "    two      0      6"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table = pd.pivot_table(df, values='D', index=['A', 'B'],\n",
+    "                    columns=['C'], aggfunc=np.sum, fill_value=0)\n",
+    "table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf713c57",
+   "metadata": {},
+   "source": [
+    "The next example aggregates by taking the mean across multiple columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "900dc876",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],\n",
+    "                    aggfunc={'D': np.mean,\n",
+    "                             'E': np.mean})\n",
+    "table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a428fdc",
+   "metadata": {},
+   "source": [
+    "We can also calculate multiple types of aggregations for any given value column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36ccdfaf",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],\n",
+    "                    aggfunc={'D': np.mean,\n",
+    "                             'E': [min, max, np.mean]})\n",
+    "table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19eeb851",
+   "metadata": {},
+   "source": [
+    "## High-performance Pandas: eval() and query()\n",
+    "\n",
+    "### eval()\n",
+    "\n",
+    "Evaluate a string describing operations on DataFrame columns.\n",
+    "\n",
+    "Operates on columns only, not specific rows or elements. This allows `eval` to run arbitrary code, which can make you vulnerable to code injection if you pass user input to this function.\n",
+    "\n",
+    "For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "db6fdd36",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   A   B\n",
+       "0  1  10\n",
+       "1  2   8\n",
+       "2  3   6\n",
+       "3  4   4\n",
+       "4  5   2"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "92e71f86",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    11\n",
+       "1    10\n",
+       "2     9\n",
+       "3     8\n",
+       "4     7\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.eval('A + B')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5f51480",
+   "metadata": {},
+   "source": [
+    "The assignment is allowed though by default the original `DataFrame` is not modified."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "b6387047",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   A   B   C\n",
+       "0  1  10  11\n",
+       "1  2   8  10\n",
+       "2  3   6   9\n",
+       "3  4   4   8\n",
+       "4  5   2   7"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.eval('C = A + B')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "a5322c51",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   A   B\n",
+       "0  1  10\n",
+       "1  2   8\n",
+       "2  3   6\n",
+       "3  4   4\n",
+       "4  5   2"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a0a5d4d",
+   "metadata": {},
+   "source": [
+    "Use `inplace=True` to modify the original DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "13d2dffa",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   A   B   C\n",
+       "0  1  10  11\n",
+       "1  2   8  10\n",
+       "2  3   6   9\n",
+       "3  4   4   8\n",
+       "4  5   2   7"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.eval('C = A + B', inplace=True)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9c14654",
+   "metadata": {},
+   "source": [
+    "Multiple columns can be assigned using multi-line expressions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "8ee5ceea",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>11</td>\n",
+       "      <td>-9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>-6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>9</td>\n",
+       "      <td>-3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   A   B   C  D\n",
+       "0  1  10  11 -9\n",
+       "1  2   8  10 -6\n",
+       "2  3   6   9 -3\n",
+       "3  4   4   8  0\n",
+       "4  5   2   7  3"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.eval(\n",
+    "    '''\n",
+    "    C = A + B\n",
+    "    D = A - B\n",
+    "    '''\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c052b27",
+   "metadata": {},
+   "source": [
+    "### query()\n",
+    "\n",
+    "Query the columns of a DataFrame with a boolean expression.\n",
+    "\n",
+    "For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "d99bb798",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   A   B  C C\n",
+       "0  1  10   10\n",
+       "1  2   8    9\n",
+       "2  3   6    8\n",
+       "3  4   4    7\n",
+       "4  5   2    6"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame({\n",
+    "    'A': range(1, 6),\n",
+    "    'B': range(10, 0, -2),\n",
+    "    'C C': range(10, 5, -1)\n",
+    "})\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "c228b08b",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   A  B  C C\n",
+       "4  5  2    6"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.query('A > B')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e90ed305",
+   "metadata": {},
+   "source": [
+    "The previous expression is equivalent to"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "28a30c04",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   A  B  C C\n",
+       "4  5  2    6"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df.A > df.B]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "454bb2b9",
+   "metadata": {},
+   "source": [
+    "For columns with spaces in their name, you can use backtick quoting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "4d06bb30",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   A   B  C C\n",
+       "0  1  10   10"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.query('B == `C C`')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ac03c29",
+   "metadata": {},
+   "source": [
+    "The previous expression is equivalent to"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "f8dacc1f",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   A   B  C C\n",
+       "0  1  10   10"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df.B == df['C C']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "6ec1ded1-6f8a-46ca-b304-25621fe08677",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:660px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf%20%3D%20pd.DataFrame%28%7B%0A%20%20%20%20'A'%3A%20range%281,%206%29,%0A%20%20%20%20'B'%3A%20range%2810,%200,%20-2%29,%0A%20%20%20%20'C%20C'%3A%20range%2810,%205,%20-1%29%0A%7D%29%0A%0Adf%5Bdf.B%20%3D%3D%20df%5B'C%20C'%5D%5D&d=2023-07-13&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:660px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf%20%3D%20pd.DataFrame%28%7B%0A%20%20%20%20'A'%3A%20range%281,%206%29,%0A%20%20%20%20'B'%3A%20range%2810,%200,%20-2%29,%0A%20%20%20%20'C%20C'%3A%20range%2810,%205,%20-1%29%0A%7D%29%0A%0Adf%5Bdf.B%20%3D%3D%20df%5B'C%20C'%5D%5D&d=2023-07-13&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc6c4cd4",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Your turn! 🚀\n",
+    "\n",
+    "### Processing image data\n",
+    "\n",
+    "Recently, very powerful AI models have been developed that allow us to understand images. There are many tasks that can be solved using pre-trained neural networks, or cloud services. Some examples include:\n",
+    "\n",
+    "- **Image Classification**, can help you categorize the image into one of the pre-defined classes. You can easily train your own image classifiers using services such as [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum)\n",
+    "- **Object Detection** to detect different objects in the image. Services such as [computer vision](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum) can detect a number of common objects, and you can train [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum) model to detect some specific objects of interest.\n",
+    "- **Face Detection**, including Age, Gender and Emotion detection. This can be done via [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-77958-bethanycheum).\n",
+    "\n",
+    "All those cloud services can be called using [Python SDKs](https://docs.microsoft.com/samples/azure-samples/cognitive-services-python-sdk-samples/cognitive-services-python-sdk-samples/?WT.mc_id=academic-77958-bethanycheum), and thus can be easily incorporated into your data exploration workflow.\n",
+    "\n",
+    "Here are some examples of exploring data from Image data sources:\n",
+    "\n",
+    "- In the blog post [How to Learn Data Science without Coding](https://soshnikov.com/azure/how-to-learn-data-science-without-coding/) we explore Instagram photos, trying to understand what makes people give more likes to a photo. We first extract as much information from pictures as possible using [computer vision](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum), and then use [Azure Machine Learning AutoML](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml/?WT.mc_id=academic-77958-bethanycheum) to build the interpretable model.\n",
+    "- In [Facial Studies Workshop](https://github.com/CloudAdvocacy/FaceStudies) we use [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-77958-bethanycheum) to extract emotions from people on photographs from events, in order to try to understand what makes people happy.\n",
+    "\n",
+    "### Assignment\n",
+    "\n",
+    "[Perform more detailed data study for the challenges above](../../../assignments/data-science/data-processing-in-python.md)\n",
+    "\n",
+    "## Self study\n",
+    "\n",
+    "In this chapter, we've covered many of the basics of using Pandas effectively for data analysis. Still, much has been omitted from our discussion. To learn more about Pandas, we recommend the following resources:\n",
+    "\n",
+    "- [Pandas online documentation](http://pandas.pydata.org/): This is the go-to source for complete documentation of the package. While the examples in the documentation tend to be small generated datasets, the description of the options is complete and generally very useful for understanding the use of various functions.\n",
+    "\n",
+    "- [*Python for Data Analysis*](http://shop.oreilly.com/product/0636920023784.do) Written by Wes McKinney (the original creator of Pandas), this book contains much more detail on the Pandas package than we had room for in this chapter. In particular, he takes a deep dive into tools for time series, which were his bread and butter as a financial consultant. The book also has many entertaining examples of applying Pandas to gain insight from real-world datasets. Keep in mind, though, that the book is now several years old, and the Pandas package has quite a few new features that this book does not cover (but be on the lookout for a new edition in 2017).\n",
+    "\n",
+    "- [Stack Overflow](http://stackoverflow.com/questions/tagged/pandas): Pandas has so many users that any question you have has likely been asked and answered on Stack Overflow. Using Pandas is a case where some Google-Fu is your best friend. Simply go to your favorite search engine and type in the question, problem, or error you're coming across-more than likely you'll find your answer on a Stack Overflow page.\n",
+    "\n",
+    "- [Pandas on PyVideo](http://pyvideo.org/search?q=pandas): From PyCon to SciPy to PyData, many conferences have featured tutorials from Pandas developers and power users. The PyCon tutorials in particular tend to be given by very well-vetted presenters.\n",
+    "\n",
+    "Using these resources, combined with the walk-through given in this chapter, my hope is that you'll be poised to use Pandas to tackle any data analysis problem you come across!\n",
+    "\n",
+    "## Acknowledgments\n",
+    "\n",
+    "Thanks for [Pandas user guide](https://pandas.pydata.org/docs/user_guide/index.html). It contributes the majority of the content in this chapter."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "EnvName",
+   "language": "python",
+   "name": "envname"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/data-science/working-with-data/pandas/data-selection.ipynb b/_sources/data-science/working-with-data/pandas/data-selection.ipynb
new file mode 100644
index 0000000000..af1134aaa2
--- /dev/null
+++ b/_sources/data-science/working-with-data/pandas/data-selection.ipynb
@@ -0,0 +1,4490 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "70c2694f-98d3-4846-a4d2-a88ac4da4a56",
+   "metadata": {},
+   "source": [
+    "# Data Selection "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ca294fb-5274-4c7b-b293-a374877b524b",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "In this section, we'll focus on how to slice, dice, and generally get and set subsets of Pandas objects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "446ccf8d-1e3a-4cec-8bac-400c5c97028a",
+   "metadata": {},
+   "source": [
+    "Import NumPy and load Pandas into your namespace:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f1931205-8c05-40ca-b266-c0f14e26cff3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install the necessary dependencies\n",
+    "import os\n",
+    "import sys\n",
+    "!{sys.executable} -m pip install --quiet jupyterlab_myst ipython\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "281fa7e2",
+   "metadata": {},
+   "source": [
+    "## Selection by label"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cfbc1d9-62b9-4f12-a249-0fb7af77d6f3",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "warning"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    "Whether a copy or a reference is returned for a setting operation, may depend on the context. This is sometimes called `chained assignment` and should be avoided."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9dd00162-d4ac-4b84-9da4-9fe7e36cbcb5",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "warning"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    "`.loc` is strict when you present slicers that are not compatible (or convertible) with the index type. For example using integers in a `DatetimeIndex`. These will raise a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "19faf0a0",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dfl = pd.DataFrame(np.random.randn(5, 4),\n",
+    "                   columns=list('ABCD'),\n",
+    "                   index=pd.date_range('20130101', periods=5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5cd6165e",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    },
+    "tags": [
+     "raises-exception"
+    ]
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "cannot do slice indexing on DatetimeIndex with these indexers [2] of type int",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[3], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#:tags: [\"raises-exception\"]\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m dfl\u001b[38;5;241m.\u001b[39mloc[\u001b[38;5;241m2\u001b[39m:\u001b[38;5;241m3\u001b[39m]\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1073\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1070\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m   1072\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[1;32m-> 1073\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_axis(maybe_callable, axis\u001b[38;5;241m=\u001b[39maxis)\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1290\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1288\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mslice\u001b[39m):\n\u001b[0;32m   1289\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(key, axis)\n\u001b[1;32m-> 1290\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_slice_axis(key, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[0;32m   1291\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mis_bool_indexer(key):\n\u001b[0;32m   1292\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getbool_axis(key, axis\u001b[38;5;241m=\u001b[39maxis)\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1324\u001b[0m, in \u001b[0;36m_LocIndexer._get_slice_axis\u001b[1;34m(self, slice_obj, axis)\u001b[0m\n\u001b[0;32m   1321\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m obj\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m   1323\u001b[0m labels \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m_get_axis(axis)\n\u001b[1;32m-> 1324\u001b[0m indexer \u001b[38;5;241m=\u001b[39m labels\u001b[38;5;241m.\u001b[39mslice_indexer(slice_obj\u001b[38;5;241m.\u001b[39mstart, slice_obj\u001b[38;5;241m.\u001b[39mstop, slice_obj\u001b[38;5;241m.\u001b[39mstep)\n\u001b[0;32m   1326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(indexer, \u001b[38;5;28mslice\u001b[39m):\n\u001b[0;32m   1327\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_slice(indexer, axis\u001b[38;5;241m=\u001b[39maxis)\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexes\\datetimes.py:809\u001b[0m, in \u001b[0;36mDatetimeIndex.slice_indexer\u001b[1;34m(self, start, end, step, kind)\u001b[0m\n\u001b[0;32m    801\u001b[0m \u001b[38;5;66;03m# GH#33146 if start and end are combinations of str and None and Index is not\u001b[39;00m\n\u001b[0;32m    802\u001b[0m \u001b[38;5;66;03m# monotonic, we can not use Index.slice_indexer because it does not honor the\u001b[39;00m\n\u001b[0;32m    803\u001b[0m \u001b[38;5;66;03m# actual elements, is only searching for start and end\u001b[39;00m\n\u001b[0;32m    804\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m    805\u001b[0m     check_str_or_none(start)\n\u001b[0;32m    806\u001b[0m     \u001b[38;5;129;01mor\u001b[39;00m check_str_or_none(end)\n\u001b[0;32m    807\u001b[0m     \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_monotonic_increasing\n\u001b[0;32m    808\u001b[0m ):\n\u001b[1;32m--> 809\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m Index\u001b[38;5;241m.\u001b[39mslice_indexer(\u001b[38;5;28mself\u001b[39m, start, end, step, kind\u001b[38;5;241m=\u001b[39mkind)\n\u001b[0;32m    811\u001b[0m mask \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m    812\u001b[0m deprecation_mask \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6559\u001b[0m, in \u001b[0;36mIndex.slice_indexer\u001b[1;34m(self, start, end, step, kind)\u001b[0m\n\u001b[0;32m   6516\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   6517\u001b[0m \u001b[38;5;124;03mCompute the slice indexer for input labels and step.\u001b[39;00m\n\u001b[0;32m   6518\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   6555\u001b[0m \u001b[38;5;124;03mslice(1, 3, None)\u001b[39;00m\n\u001b[0;32m   6556\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   6557\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_deprecated_arg(kind, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkind\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mslice_indexer\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 6559\u001b[0m start_slice, end_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mslice_locs(start, end, step\u001b[38;5;241m=\u001b[39mstep)\n\u001b[0;32m   6561\u001b[0m \u001b[38;5;66;03m# return a slice\u001b[39;00m\n\u001b[0;32m   6562\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_scalar(start_slice):\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6767\u001b[0m, in \u001b[0;36mIndex.slice_locs\u001b[1;34m(self, start, end, step, kind)\u001b[0m\n\u001b[0;32m   6765\u001b[0m start_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   6766\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m start \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 6767\u001b[0m     start_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_slice_bound(start, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mleft\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   6768\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m start_slice \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   6769\u001b[0m     start_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6676\u001b[0m, in \u001b[0;36mIndex.get_slice_bound\u001b[1;34m(self, label, side, kind)\u001b[0m\n\u001b[0;32m   6672\u001b[0m original_label \u001b[38;5;241m=\u001b[39m label\n\u001b[0;32m   6674\u001b[0m \u001b[38;5;66;03m# For datetime indices label may be a string that has to be converted\u001b[39;00m\n\u001b[0;32m   6675\u001b[0m \u001b[38;5;66;03m# to datetime boundary according to its resolution.\u001b[39;00m\n\u001b[1;32m-> 6676\u001b[0m label \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_cast_slice_bound(label, side)\n\u001b[0;32m   6678\u001b[0m \u001b[38;5;66;03m# we need to look up the label\u001b[39;00m\n\u001b[0;32m   6679\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexes\\datetimes.py:767\u001b[0m, in \u001b[0;36mDatetimeIndex._maybe_cast_slice_bound\u001b[1;34m(self, label, side, kind)\u001b[0m\n\u001b[0;32m    762\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(label, date) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(label, datetime):\n\u001b[0;32m    763\u001b[0m     \u001b[38;5;66;03m# Pandas supports slicing with dates, treated as datetimes at midnight.\u001b[39;00m\n\u001b[0;32m    764\u001b[0m     \u001b[38;5;66;03m# https://github.com/pandas-dev/pandas/issues/31501\u001b[39;00m\n\u001b[0;32m    765\u001b[0m     label \u001b[38;5;241m=\u001b[39m Timestamp(label)\u001b[38;5;241m.\u001b[39mto_pydatetime()\n\u001b[1;32m--> 767\u001b[0m label \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m_maybe_cast_slice_bound(label, side, kind\u001b[38;5;241m=\u001b[39mkind)\n\u001b[0;32m    768\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_deprecate_mismatched_indexing(label)\n\u001b[0;32m    769\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_cast_for_get_loc(label)\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexes\\datetimelike.py:320\u001b[0m, in \u001b[0;36mDatetimeIndexOpsMixin._maybe_cast_slice_bound\u001b[1;34m(self, label, side, kind)\u001b[0m\n\u001b[0;32m    318\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m lower \u001b[38;5;28;01mif\u001b[39;00m side \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mleft\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m upper\n\u001b[0;32m    319\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(label, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data\u001b[38;5;241m.\u001b[39m_recognized_scalars):\n\u001b[1;32m--> 320\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_invalid_indexer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mslice\u001b[39m\u001b[38;5;124m\"\u001b[39m, label)\n\u001b[0;32m    322\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m label\n",
+      "\u001b[1;31mTypeError\u001b[0m: cannot do slice indexing on DatetimeIndex with these indexers [2] of type int"
+     ]
+    }
+   ],
+   "source": [
+    "dfl.loc[2:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2b699ce-d01f-4afa-8323-c43b9df24b38",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "warning"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    "String likes in slicing can be convertible to the type of the index and lead to natural slicing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3f5fb2f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2013-01-02</th>\n",
+       "      <td>-0.529216</td>\n",
+       "      <td>1.223634</td>\n",
+       "      <td>-0.783708</td>\n",
+       "      <td>-1.209286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-01-03</th>\n",
+       "      <td>-1.570743</td>\n",
+       "      <td>-0.316004</td>\n",
+       "      <td>-1.132640</td>\n",
+       "      <td>-0.464328</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-01-04</th>\n",
+       "      <td>1.390855</td>\n",
+       "      <td>-0.319271</td>\n",
+       "      <td>-1.093100</td>\n",
+       "      <td>-1.090622</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   A         B         C         D\n",
+       "2013-01-02 -0.529216  1.223634 -0.783708 -1.209286\n",
+       "2013-01-03 -1.570743 -0.316004 -1.132640 -0.464328\n",
+       "2013-01-04  1.390855 -0.319271 -1.093100 -1.090622"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfl.loc['20130102':'20130104']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "abe5968b-ffe5-4302-9918-81a1d97ed568",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<link rel=\"stylesheet\" href=\"https://ocademy-ai.github.io/machine-learning/_static/style.css\">\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:730px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%20%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dpd.date_range%28'20130101',%20periods%3D5%29%29%0Adfl%0Adfl.loc%5B'20130102'%3A'20130104'%5D&d=2023-07-13&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<link rel=\"stylesheet\" href=\"https://ocademy-ai.github.io/machine-learning/_static/style.css\">\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:730px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%20%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dpd.date_range%28'20130101',%20periods%3D5%29%29%0Adfl%0Adfl.loc%5B'20130102'%3A'20130104'%5D&d=2023-07-13&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3c046d5-19dc-47cb-828f-880f008d02d4",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "warning"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    "Pandas will raise a `KeyError` if indexing with a list with missing labels."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29221dda",
+   "metadata": {},
+   "source": [
+    "Pandas provides a suite of methods in order to have **purely label-based indexing**. This is a strict inclusion-based protocol. Every label asked for must be in the index, or a `KeyError` will be raised. When slicing, both the start bound **AND** the stop bound are included, if present in the index. Integers are valid labels, but they refer to the label **and not the position**.\n",
+    "\n",
+    "- The `.loc` attribute is the primary access method. The following are valid inputs:\n",
+    "\n",
+    "- A single label, e.g. `5` or `'a'` (Note that `5` is interpreted as a label of the index. This use is not an integer position along the index.).\n",
+    "\n",
+    "- A list or array of labels `['a', 'b', 'c']`.\n",
+    "\n",
+    "- A slice object with labels `'a':'f'` (Note that contrary to usual Python slices, both the start and the stop are included, when present in the index!\n",
+    "\n",
+    "- A boolean array.\n",
+    "\n",
+    "- A `callable`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8a174f11",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "c    0.697303\n",
+       "d   -1.412259\n",
+       "e    0.104600\n",
+       "f    1.718896\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s1 = pd.Series(np.random.randn(6), index=list('abcdef'))\n",
+    "s1\n",
+    "s1.loc['c':]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b276bd82-797f-4eb6-8886-51153d771bb0",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<link rel=\"stylesheet\" href=\"https://ocademy-ai.github.io/machine-learning/_static/style.css\">\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:730px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%286%29,%20index%3Dlist%28'abcdef'%29%29%0As1.loc%5B'c'%3A%5D%0A&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<link rel=\"stylesheet\" href=\"https://ocademy-ai.github.io/machine-learning/_static/style.css\">\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:730px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%286%29,%20index%3Dlist%28'abcdef'%29%29%0As1.loc%5B'c'%3A%5D%0A&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "11e56acc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.3374040853531507"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s1.loc['b']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "74a7ae51-b334-4d5f-b9a2-e2080958663f",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<link rel=\"stylesheet\" href=\"https://ocademy-ai.github.io/machine-learning/_static/style.css\">\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:630px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%286%29,%20index%3Dlist%28'abcdef'%29%29%0As1.loc%5B'b'%5D%0A&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<link rel=\"stylesheet\" href=\"https://ocademy-ai.github.io/machine-learning/_static/style.css\">\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:630px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%286%29,%20index%3Dlist%28'abcdef'%29%29%0As1.loc%5B'b'%5D%0A&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb2dbf2d-cdd9-42e4-b374-fc7944f1996f",
+   "metadata": {},
+   "source": [
+    "Note that the setting works as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8fe78c41",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a   -0.985634\n",
+       "b   -0.337404\n",
+       "c    0.000000\n",
+       "d    0.000000\n",
+       "e    0.000000\n",
+       "f    0.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s1.loc['c':] = 0\n",
+    "s1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e32f82e4-6b3e-48a7-ab56-c6ea820274e5",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<link rel=\"stylesheet\" href=\"https://ocademy-ai.github.io/machine-learning/_static/style.css\">\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:630px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%286%29,%20index%3Dlist%28'abcdef'%29%29%0As1.loc%5B'c'%3A%5D%20%3D%200%0As1&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<link rel=\"stylesheet\" href=\"https://ocademy-ai.github.io/machine-learning/_static/style.css\">\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:630px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%286%29,%20index%3Dlist%28'abcdef'%29%29%0As1.loc%5B'c'%3A%5D%20%3D%200%0As1&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cfb25d9f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>1.401532</td>\n",
+       "      <td>-1.744216</td>\n",
+       "      <td>-0.212177</td>\n",
+       "      <td>-1.295240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>0.965335</td>\n",
+       "      <td>-1.586035</td>\n",
+       "      <td>-2.275384</td>\n",
+       "      <td>0.615352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>-0.131692</td>\n",
+       "      <td>-0.910665</td>\n",
+       "      <td>-1.286641</td>\n",
+       "      <td>0.340830</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          A         B         C         D\n",
+       "a  1.401532 -1.744216 -0.212177 -1.295240\n",
+       "b  0.965335 -1.586035 -2.275384  0.615352\n",
+       "d -0.131692 -0.910665 -1.286641  0.340830"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1 = pd.DataFrame(np.random.randn(6, 4),\n",
+    "                   index=list('abcdef'),\n",
+    "                   columns=list('ABCD'))\n",
+    "df1\n",
+    "df1.loc[['a', 'b', 'd'], :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "de1a7123-2c8e-4910-b435-cdd489baff5b",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "<link rel=\"stylesheet\" href=\"https://ocademy-ai.github.io/machine-learning/_static/style.css\">\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:680px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B%5B'a',%20'b',%20'd'%5D,%20%3A%5D%0A&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "\n",
+    "<link rel=\"stylesheet\" href=\"https://ocademy-ai.github.io/machine-learning/_static/style.css\">\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:680px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B%5B'a',%20'b',%20'd'%5D,%20%3A%5D%0A&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0493a65b-5915-4119-a2a8-00b0b8a728b0",
+   "metadata": {},
+   "source": [
+    "Accessing via label slices:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "2934e9e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>-0.131692</td>\n",
+       "      <td>-0.910665</td>\n",
+       "      <td>-1.286641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>e</th>\n",
+       "      <td>0.238683</td>\n",
+       "      <td>-0.169771</td>\n",
+       "      <td>1.322003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f</th>\n",
+       "      <td>1.511896</td>\n",
+       "      <td>-0.247248</td>\n",
+       "      <td>3.169958</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          A         B         C\n",
+       "d -0.131692 -0.910665 -1.286641\n",
+       "e  0.238683 -0.169771  1.322003\n",
+       "f  1.511896 -0.247248  3.169958"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.loc['d':, 'A':'C']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29f4ac9b-3d4b-4199-a04a-b9ec886b26f6",
+   "metadata": {},
+   "source": [
+    "For getting a cross-section using a label (equivalent to `df.xs('a')`):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ccbffe12",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "A    1.401532\n",
+       "B   -1.744216\n",
+       "C   -0.212177\n",
+       "D   -1.295240\n",
+       "Name: a, dtype: float64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.loc['a']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "c9570d12-8020-4328-94e8-91266619e666",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:690px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B'a'%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:690px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B'a'%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "589a2a99",
+   "metadata": {},
+   "source": [
+    "For getting values with a boolean array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e60fdddf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "A     True\n",
+       "B    False\n",
+       "C    False\n",
+       "D    False\n",
+       "Name: a, dtype: bool"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.loc['a'] > 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "4a9f2648-9f92-4077-a7ec-00836c2f28fd",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:650px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B'a'%5D%20%3E%200&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:650px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B'a'%5D%20%3E%200&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "d6226934",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>1.401532</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>0.965335</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>-0.097299</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>-0.131692</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>e</th>\n",
+       "      <td>0.238683</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f</th>\n",
+       "      <td>1.511896</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          A\n",
+       "a  1.401532\n",
+       "b  0.965335\n",
+       "c -0.097299\n",
+       "d -0.131692\n",
+       "e  0.238683\n",
+       "f  1.511896"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.loc[:, df1.loc['a'] > 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "f8ae65cd-dbea-4f40-a464-7b07554b9b11",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:700px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B%3A,%20df1.loc%5B'a'%5D%20%3E%200%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:700px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B%3A,%20df1.loc%5B'a'%5D%20%3E%200%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e52a617",
+   "metadata": {},
+   "source": [
+    "NA values in a boolean array propagate as `False`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "0ca93c29",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<BooleanArray>\n",
+       "[True, False, True, False, <NA>, False]\n",
+       "Length: 6, dtype: boolean"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask = pd.array([True, False, True, False, pd.NA, False], dtype=\"boolean\")\n",
+    "mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "fd577bd5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>1.401532</td>\n",
+       "      <td>-1.744216</td>\n",
+       "      <td>-0.212177</td>\n",
+       "      <td>-1.295240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>-0.097299</td>\n",
+       "      <td>-0.834496</td>\n",
+       "      <td>0.188575</td>\n",
+       "      <td>-0.271869</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          A         B         C         D\n",
+       "a  1.401532 -1.744216 -0.212177 -1.295240\n",
+       "c -0.097299 -0.834496  0.188575 -0.271869"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1[mask]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "4f1b5f67-5c56-4e47-8953-4d6383f283e1",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:680px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Amask%20%3D%20pd.array%28%5BTrue,%20False,%20True,%20False,%20pd.NA,%20False%5D,%20dtype%3D%22boolean%22%29%0Adf1%5Bmask%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:680px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Amask%20%3D%20pd.array%28%5BTrue,%20False,%20True,%20False,%20pd.NA,%20False%5D,%20dtype%3D%22boolean%22%29%0Adf1%5Bmask%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ff30b9c",
+   "metadata": {},
+   "source": [
+    "For getting a value explicitly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "7e425a66",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.4015323563287203"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.loc['a', 'A'] # this is also equivalent to ``df1.at['a','A']``"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "50e88f3d-07f0-443d-994c-d7fb36c4dc7a",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:680px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B'a',%20'A'%5D%20&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:680px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B'a',%20'A'%5D%20&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b29c0cd3",
+   "metadata": {},
+   "source": [
+    "## Slicing with labels\n",
+    "\n",
+    "When using `.loc` with slices, if both the start and the stop labels are present in the index, then elements located between the two (including them) are returned:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "2bd13eab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3    b\n",
+       "2    c\n",
+       "5    d\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = pd.Series(list('abcde'), index=[0, 3, 2, 5, 4])\n",
+    "s.loc[3:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "63081450-8216-403c-8b53-04b2cc18e442",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:580px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28'abcde'%29,%20index%3D%5B0,%203,%202,%205,%204%5D%29%0As.loc%5B3%3A5%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:580px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28'abcde'%29,%20index%3D%5B0,%203,%202,%205,%204%5D%29%0As.loc%5B3%3A5%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a1f8d46",
+   "metadata": {},
+   "source": [
+    "If at least one of the two is absent, but the index is sorted, and can be compared against start and stop labels, then slicing will still work as expected, by selecting labels which rank between the two:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "a08caf62",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    a\n",
+       "2    c\n",
+       "3    b\n",
+       "4    e\n",
+       "5    d\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s.sort_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "7d665bb1-9bd1-4826-9a0f-f13496d64549",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:600px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28'abcde'%29,%20index%3D%5B0,%203,%202,%205,%204%5D%29%0As.sort_index%28%29&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:600px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28'abcde'%29,%20index%3D%5B0,%203,%202,%205,%204%5D%29%0As.sort_index%28%29&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "a5f5d2ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2    c\n",
+       "3    b\n",
+       "4    e\n",
+       "5    d\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s.sort_index().loc[1:6]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "81114a6f-4511-4f2e-990b-c7edd5e4cf86",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:820px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28'abcde'%29,%20index%3D%5B0,%203,%202,%205,%204%5D%29%0As.sort_index%28%29.loc%5B1%3A6%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:820px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28'abcde'%29,%20index%3D%5B0,%203,%202,%205,%204%5D%29%0As.sort_index%28%29.loc%5B1%3A6%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5115e1d2",
+   "metadata": {},
+   "source": [
+    "However, if at least one of the two is absent and the index is not sorted, an error will be raised (since doing otherwise would be computationally expensive, as well as potentially ambiguous for mixed-type indexes). For instance, in the above example, `s.loc[1:6]` would raise `KeyError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "318b8e37",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3    b\n",
+       "2    c\n",
+       "5    d\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = pd.Series(list('abcdef'), index=[0, 3, 2, 5, 4, 2])\n",
+    "s.loc[3:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "537dd0b6-b4fc-468b-88a4-5d828eba5ed8",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:650px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28'abcdef'%29,%20index%3D%5B0,%203,%202,%205,%204,%202%5D%29%0As.loc%5B3%3A5%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:650px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28'abcdef'%29,%20index%3D%5B0,%203,%202,%205,%204,%202%5D%29%0As.loc%5B3%3A5%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce05682d",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Also, if the index has duplicate labels and either the start or the stop label is duplicated, an error will be raised. For instance, in the above example, `s.loc[2:5]` would raise a `KeyError`.\n",
+    "\n",
+    "## Selection by position"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "099c8fa7-d8df-4304-a513-1b142c1021d5",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "warning"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    "Whether a copy or a reference is returned for a setting operation, may depend on the context. This is sometimes called `chained assignment` and should be avoided."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c2e1dab",
+   "metadata": {},
+   "source": [
+    "Pandas provides a suite of methods in order to get purely integer-based indexing. The semantics follow closely Python and NumPy slicing. These are 0-based indexing. When slicing, the start bound is included, while the upper bound is excluded. Trying to use a non-integer, even a valid label will raise an `IndexError`.\n",
+    "\n",
+    "The `.iloc` attribute is the primary access method. The following are valid inputs:\n",
+    "\n",
+    "- An integer e.g. `5`.\n",
+    "\n",
+    "- A list or array of integers `[4, 3, 0]`.\n",
+    "\n",
+    "- A slice object with ints `1:7`.\n",
+    "\n",
+    "- A boolean array.\n",
+    "\n",
+    "- A `callable`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "e7b93cb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0.531403\n",
+       "2    1.164702\n",
+       "4   -0.384782\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s1 = pd.Series(np.random.randn(5), index=list(range(0, 10, 2)))\n",
+    "s1\n",
+    "s1.iloc[:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "24d4de8c-5c42-484b-89d7-e21ebb0ba7c3",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:600px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%285%29,%20index%3Dlist%28range%280,%2010,%202%29%29%29%0As1.iloc%5B%3A3%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:600px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%285%29,%20index%3Dlist%28range%280,%2010,%202%29%29%29%0As1.iloc%5B%3A3%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "fe63cdf3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.21764232439885461"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s1.iloc[3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "ed15834b-fd14-4000-bbdb-0eb86a214984",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:600px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%285%29,%20index%3Dlist%28range%280,%2010,%202%29%29%29%0As1.iloc%5B%3A3%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:600px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%285%29,%20index%3Dlist%28range%280,%2010,%202%29%29%29%0As1.iloc%5B%3A3%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ac478c2",
+   "metadata": {},
+   "source": [
+    "Note that setting works as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "9c4e8129",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0.000000\n",
+       "2    0.000000\n",
+       "4    0.000000\n",
+       "6    0.217642\n",
+       "8    1.458410\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s1.iloc[:3] = 0\n",
+    "s1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "5b793d9f-5ddb-4121-8218-8a5eda713eab",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:620px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%285%29,%20index%3Dlist%28range%280,%2010,%202%29%29%29%0As1.iloc%5B%3A3%5D%20%3D%200%0As1&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:620px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%285%29,%20index%3Dlist%28range%280,%2010,%202%29%29%29%0As1.iloc%5B%3A3%5D%20%3D%200%0As1&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56ced074",
+   "metadata": {},
+   "source": [
+    "With a DataFrame,Select via integer slicing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "3d55d682",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>2</th>\n",
+       "      <th>4</th>\n",
+       "      <th>6</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.194412</td>\n",
+       "      <td>-0.126540</td>\n",
+       "      <td>0.496297</td>\n",
+       "      <td>-0.194096</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-1.116951</td>\n",
+       "      <td>1.041856</td>\n",
+       "      <td>-0.662633</td>\n",
+       "      <td>0.493678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.497028</td>\n",
+       "      <td>-0.260497</td>\n",
+       "      <td>0.697729</td>\n",
+       "      <td>-1.092215</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          0         2         4         6\n",
+       "0 -1.194412 -0.126540  0.496297 -0.194096\n",
+       "2 -1.116951  1.041856 -0.662633  0.493678\n",
+       "4  1.497028 -0.260497  0.697729 -1.092215"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1 = pd.DataFrame(np.random.randn(6, 4),\n",
+    "                   index=list(range(0, 12, 2)),\n",
+    "                   columns=list(range(0, 8, 2)))\n",
+    "df1\n",
+    "df1.iloc[:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "172e44bf-8faf-42a1-b9a7-3adab79b97d1",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:680px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B%3A3%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:680px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B%3A3%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "b5427ec6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>4</th>\n",
+       "      <th>6</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.662633</td>\n",
+       "      <td>0.493678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.697729</td>\n",
+       "      <td>-1.092215</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.715370</td>\n",
+       "      <td>1.528302</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>-0.548232</td>\n",
+       "      <td>0.081242</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          4         6\n",
+       "2 -0.662633  0.493678\n",
+       "4  0.697729 -1.092215\n",
+       "6  0.715370  1.528302\n",
+       "8 -0.548232  0.081242"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.iloc[1:5, 2:4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "550715ab",
+   "metadata": {},
+   "source": [
+    "Select via integer list:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "d86dd6d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>2</th>\n",
+       "      <th>6</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.041856</td>\n",
+       "      <td>0.493678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>-0.177517</td>\n",
+       "      <td>1.528302</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.666088</td>\n",
+       "      <td>-0.595855</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           2         6\n",
+       "2   1.041856  0.493678\n",
+       "6  -0.177517  1.528302\n",
+       "10  0.666088 -0.595855"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.iloc[[1, 3, 5], [1, 3]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "a5e2a6ba-671b-4aab-b63d-5ab4ee92501f",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:700px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1%3A5,%202%3A4%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:700px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1%3A5,%202%3A4%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "8528cc39",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>2</th>\n",
+       "      <th>4</th>\n",
+       "      <th>6</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-1.116951</td>\n",
+       "      <td>1.041856</td>\n",
+       "      <td>-0.662633</td>\n",
+       "      <td>0.493678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.497028</td>\n",
+       "      <td>-0.260497</td>\n",
+       "      <td>0.697729</td>\n",
+       "      <td>-1.092215</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          0         2         4         6\n",
+       "2 -1.116951  1.041856 -0.662633  0.493678\n",
+       "4  1.497028 -0.260497  0.697729 -1.092215"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.iloc[1:3, :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "178d6f69-464f-464e-ad45-fac857b9a370",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:680px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1%3A3,%20%3A%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:680px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1%3A3,%20%3A%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "f9288433",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>2</th>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.126540</td>\n",
+       "      <td>0.496297</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.041856</td>\n",
+       "      <td>-0.662633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.260497</td>\n",
+       "      <td>0.697729</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>-0.177517</td>\n",
+       "      <td>0.715370</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>-0.980550</td>\n",
+       "      <td>-0.548232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.666088</td>\n",
+       "      <td>0.114509</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           2         4\n",
+       "0  -0.126540  0.496297\n",
+       "2   1.041856 -0.662633\n",
+       "4  -0.260497  0.697729\n",
+       "6  -0.177517  0.715370\n",
+       "8  -0.980550 -0.548232\n",
+       "10  0.666088  0.114509"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.iloc[:, 1:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "71859ce4-7ad5-4bea-9df2-f5929c0c2470",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:700px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B%3A,%201%3A3%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:700px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B%3A,%201%3A3%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "eb3f25f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0418559735628448"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.iloc[1, 1] # this is also equivalent to ``df1.iat[1,1]``"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "5dad7d1a-0bf5-40d8-a4ef-2c3e573ae6fc",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:680px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1,%201%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:680px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1,%201%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6cb0234e",
+   "metadata": {},
+   "source": [
+    "\n",
+    "For getting a cross-section using an integer position (equiv to `df.xs(1)`):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "cc95030f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0   -1.116951\n",
+       "2    1.041856\n",
+       "4   -0.662633\n",
+       "6    0.493678\n",
+       "Name: 2, dtype: float64"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.iloc[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "bfa6df43-353d-4ba4-94a0-e65c9a659468",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:700px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:700px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc5305b8",
+   "metadata": {},
+   "source": [
+    "Out-of-range slice indexes are handled gracefully just as in Python/NumPy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "0c635e2f",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b', 'c', 'd', 'e', 'f']"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = list('abcdef') # these are allowed in Python/NumPy.\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "bae9b708",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['e', 'f']"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[4:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "ccb95b2c",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[8:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "fcaaeb73",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    a\n",
+       "1    b\n",
+       "2    c\n",
+       "3    d\n",
+       "4    e\n",
+       "5    f\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = pd.Series(x)\n",
+    "s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "19e7f165",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4    e\n",
+       "5    f\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s.iloc[4:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "3b612356-7774-472e-849e-0f3dc267b578",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:600px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Ax%20%3D%20list%28'abcdef'%29%20%0As%20%3D%20pd.Series%28x%29%0As.iloc%5B4%3A10%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:600px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Ax%20%3D%20list%28'abcdef'%29%20%0As%20%3D%20pd.Series%28x%29%0As.iloc%5B4%3A10%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "2a25cc5c",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Series([], dtype: object)"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s.iloc[8:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23aa8371",
+   "metadata": {},
+   "source": [
+    "Note that using slices that go out of bounds can result in an empty axis (e.g. an empty DataFrame being returned)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "f9024d15",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "dfl = pd.DataFrame(np.random.randn(5, 2), columns=list('AB'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "5837f585",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: []\n",
+       "Index: [0, 1, 2, 3, 4]"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfl.iloc[:, 2:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "4b81ac82-5d47-4410-90b9-040f0dac662b",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:630px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%202%29,%20columns%3Dlist%28'AB'%29%29%0Adfl%0Adfl.iloc%5B%3A,%202%3A3%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:630px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%202%29,%20columns%3Dlist%28'AB'%29%29%0Adfl%0Adfl.iloc%5B%3A,%202%3A3%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "d0e19553",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.700611</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.358047</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.620409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.953488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-2.263445</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          B\n",
+       "0  0.700611\n",
+       "1 -0.358047\n",
+       "2  0.620409\n",
+       "3  0.953488\n",
+       "4 -2.263445"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfl.iloc[:, 1:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "39dab713-a3f6-4189-bad9-cba564f56951",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:630px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%202%29,%20columns%3Dlist%28'AB'%29%29%0Adfl.iloc%5B%3A,%201%3A3%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:630px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%202%29,%20columns%3Dlist%28'AB'%29%29%0Adfl.iloc%5B%3A,%201%3A3%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "f91ab868",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.374726</td>\n",
+       "      <td>-2.263445</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          A         B\n",
+       "4  0.374726 -2.263445"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfl.iloc[4:6]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "220aa5af-5003-45e9-87cf-c4f5d0ac6d93",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:600px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%202%29,%20columns%3Dlist%28'AB'%29%29%0Adfl.iloc%5B4%3A6%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:600px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%202%29,%20columns%3Dlist%28'AB'%29%29%0Adfl.iloc%5B4%3A6%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59c65e15",
+   "metadata": {},
+   "source": [
+    "\n",
+    "A single indexer that is out of bounds will raise an `IndexError`. A list of indexers where any element is out of bounds will raise an `IndexError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "f3496be2",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    },
+    "tags": [
+     "raises-exception"
+    ]
+   },
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "positional indexers are out-of-bounds",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1587\u001b[0m, in \u001b[0;36m_iLocIndexer._get_list_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1586\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1587\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_take_with_is_copy(key, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[0;32m   1588\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m   1589\u001b[0m     \u001b[38;5;66;03m# re-raise with different error message\u001b[39;00m\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\generic.py:3902\u001b[0m, in \u001b[0;36mNDFrame._take_with_is_copy\u001b[1;34m(self, indices, axis)\u001b[0m\n\u001b[0;32m   3895\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   3896\u001b[0m \u001b[38;5;124;03mInternal version of the `take` method that sets the `_is_copy`\u001b[39;00m\n\u001b[0;32m   3897\u001b[0m \u001b[38;5;124;03mattribute to keep track of the parent dataframe (using in indexing\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   3900\u001b[0m \u001b[38;5;124;03mSee the docstring of `take` for full explanation of the parameters.\u001b[39;00m\n\u001b[0;32m   3901\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m-> 3902\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_take(indices\u001b[38;5;241m=\u001b[39mindices, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[0;32m   3903\u001b[0m \u001b[38;5;66;03m# Maybe set copy if we didn't actually change the index.\u001b[39;00m\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\generic.py:3886\u001b[0m, in \u001b[0;36mNDFrame._take\u001b[1;34m(self, indices, axis, convert_indices)\u001b[0m\n\u001b[0;32m   3884\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_consolidate_inplace()\n\u001b[1;32m-> 3886\u001b[0m new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mgr\u001b[38;5;241m.\u001b[39mtake(\n\u001b[0;32m   3887\u001b[0m     indices,\n\u001b[0;32m   3888\u001b[0m     axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_block_manager_axis(axis),\n\u001b[0;32m   3889\u001b[0m     verify\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m   3890\u001b[0m     convert_indices\u001b[38;5;241m=\u001b[39mconvert_indices,\n\u001b[0;32m   3891\u001b[0m )\n\u001b[0;32m   3892\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor(new_data)\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtake\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:975\u001b[0m, in \u001b[0;36mBaseBlockManager.take\u001b[1;34m(self, indexer, axis, verify, convert_indices)\u001b[0m\n\u001b[0;32m    974\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m convert_indices:\n\u001b[1;32m--> 975\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m maybe_convert_indices(indexer, n, verify\u001b[38;5;241m=\u001b[39mverify)\n\u001b[0;32m    977\u001b[0m new_labels \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes[axis]\u001b[38;5;241m.\u001b[39mtake(indexer)\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexers\\utils.py:286\u001b[0m, in \u001b[0;36mmaybe_convert_indices\u001b[1;34m(indices, n, verify)\u001b[0m\n\u001b[0;32m    285\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[1;32m--> 286\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mindices are out-of-bounds\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    287\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m indices\n",
+      "\u001b[1;31mIndexError\u001b[0m: indices are out-of-bounds",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[67], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#:tags: [\"raises-exception\"]\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m dfl\u001b[38;5;241m.\u001b[39miloc[[\u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m5\u001b[39m, \u001b[38;5;241m6\u001b[39m]]\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1073\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1070\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m   1072\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[1;32m-> 1073\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_axis(maybe_callable, axis\u001b[38;5;241m=\u001b[39maxis)\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1616\u001b[0m, in \u001b[0;36m_iLocIndexer._getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1614\u001b[0m \u001b[38;5;66;03m# a list of integers\u001b[39;00m\n\u001b[0;32m   1615\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_list_like_indexer(key):\n\u001b[1;32m-> 1616\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_list_axis(key, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[0;32m   1618\u001b[0m \u001b[38;5;66;03m# a single integer\u001b[39;00m\n\u001b[0;32m   1619\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1620\u001b[0m     key \u001b[38;5;241m=\u001b[39m item_from_zerodim(key)\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1590\u001b[0m, in \u001b[0;36m_iLocIndexer._get_list_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1587\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_take_with_is_copy(key, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[0;32m   1588\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m   1589\u001b[0m     \u001b[38;5;66;03m# re-raise with different error message\u001b[39;00m\n\u001b[1;32m-> 1590\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpositional indexers are out-of-bounds\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n",
+      "\u001b[1;31mIndexError\u001b[0m: positional indexers are out-of-bounds"
+     ]
+    }
+   ],
+   "source": [
+    "dfl.iloc[[4, 5, 6]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "7b081f89",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    },
+    "tags": [
+     "raises-exception"
+    ]
+   },
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "single positional indexer is out-of-bounds",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[68], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#:tags: [\"raises-exception\"]\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m dfl\u001b[38;5;241m.\u001b[39miloc[:, \u001b[38;5;241m4\u001b[39m]\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1067\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1065\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_scalar_access(key):\n\u001b[0;32m   1066\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_value(\u001b[38;5;241m*\u001b[39mkey, takeable\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_takeable)\n\u001b[1;32m-> 1067\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_tuple(key)\n\u001b[0;32m   1068\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1069\u001b[0m     \u001b[38;5;66;03m# we by definition only have the 0th axis\u001b[39;00m\n\u001b[0;32m   1070\u001b[0m     axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1563\u001b[0m, in \u001b[0;36m_iLocIndexer._getitem_tuple\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m   1561\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_getitem_tuple\u001b[39m(\u001b[38;5;28mself\u001b[39m, tup: \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m-> 1563\u001b[0m     tup \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_tuple_indexer(tup)\n\u001b[0;32m   1564\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m suppress(IndexingError):\n\u001b[0;32m   1565\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_lowerdim(tup)\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:873\u001b[0m, in \u001b[0;36m_LocationIndexer._validate_tuple_indexer\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    871\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(key):\n\u001b[0;32m    872\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 873\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(k, i)\n\u001b[0;32m    874\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m    875\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    876\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLocation based indexing can only have \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    877\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_valid_types\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] types\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    878\u001b[0m         ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1466\u001b[0m, in \u001b[0;36m_iLocIndexer._validate_key\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1464\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m   1465\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_integer(key):\n\u001b[1;32m-> 1466\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_integer(key, axis)\n\u001b[0;32m   1467\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m   1468\u001b[0m     \u001b[38;5;66;03m# a tuple should already have been caught by this point\u001b[39;00m\n\u001b[0;32m   1469\u001b[0m     \u001b[38;5;66;03m# so don't treat a tuple as a valid indexer\u001b[39;00m\n\u001b[0;32m   1470\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m IndexingError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mToo many indexers\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1557\u001b[0m, in \u001b[0;36m_iLocIndexer._validate_integer\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1555\u001b[0m len_axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis(axis))\n\u001b[0;32m   1556\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m len_axis \u001b[38;5;129;01mor\u001b[39;00m key \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m-\u001b[39mlen_axis:\n\u001b[1;32m-> 1557\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msingle positional indexer is out-of-bounds\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[1;31mIndexError\u001b[0m: single positional indexer is out-of-bounds"
+     ]
+    }
+   ],
+   "source": [
+    "dfl.iloc[:, 4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3fe22e7",
+   "metadata": {},
+   "source": [
+    "## Selection by callable\n",
+    "\n",
+    "`.loc`, `.iloc`, and also `[]` indexing can accept a `callable` as indexer. The `callable` must be a function with one argument (the calling Series or DataFrame) that returns valid output for indexing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "72420538",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>0.852422</td>\n",
+       "      <td>-0.452728</td>\n",
+       "      <td>-0.384272</td>\n",
+       "      <td>0.370443</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>e</th>\n",
+       "      <td>0.263762</td>\n",
+       "      <td>-0.053398</td>\n",
+       "      <td>0.135893</td>\n",
+       "      <td>0.618338</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f</th>\n",
+       "      <td>0.857898</td>\n",
+       "      <td>0.456715</td>\n",
+       "      <td>0.556336</td>\n",
+       "      <td>-1.022002</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          A         B         C         D\n",
+       "d  0.852422 -0.452728 -0.384272  0.370443\n",
+       "e  0.263762 -0.053398  0.135893  0.618338\n",
+       "f  0.857898  0.456715  0.556336 -1.022002"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1 = pd.DataFrame(np.random.randn(6, 4),\n",
+    "                   index=list('abcdef'),\n",
+    "                   columns=list('ABCD'))\n",
+    "df1\n",
+    "df1.loc[lambda df: df['A'] > 0, :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "7206088f-3aa5-4392-9982-cadec553e616",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:700px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5Blambda%20df%3A%20df%5B'A'%5D%20%3E%200,%20%3A%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:700px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5Blambda%20df%3A%20df%5B'A'%5D%20%3E%200,%20%3A%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "ab18a18f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>-1.863049</td>\n",
+       "      <td>1.979038</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>-0.336175</td>\n",
+       "      <td>-1.192626</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>-0.317314</td>\n",
+       "      <td>2.267986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>0.852422</td>\n",
+       "      <td>-0.452728</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>e</th>\n",
+       "      <td>0.263762</td>\n",
+       "      <td>-0.053398</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f</th>\n",
+       "      <td>0.857898</td>\n",
+       "      <td>0.456715</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          A         B\n",
+       "a -1.863049  1.979038\n",
+       "b -0.336175 -1.192626\n",
+       "c -0.317314  2.267986\n",
+       "d  0.852422 -0.452728\n",
+       "e  0.263762 -0.053398\n",
+       "f  0.857898  0.456715"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.loc[:, lambda df: ['A', 'B']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "2166496e-975d-4539-a3b6-54cedd012e73",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:700px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1.loc%5B%3A,%20lambda%20df%3A%20%5B'A',%20'B'%5D%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:700px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1.loc%5B%3A,%20lambda%20df%3A%20%5B'A',%20'B'%5D%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "aeb4a77e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>-1.863049</td>\n",
+       "      <td>1.979038</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>-0.336175</td>\n",
+       "      <td>-1.192626</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>-0.317314</td>\n",
+       "      <td>2.267986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>0.852422</td>\n",
+       "      <td>-0.452728</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>e</th>\n",
+       "      <td>0.263762</td>\n",
+       "      <td>-0.053398</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f</th>\n",
+       "      <td>0.857898</td>\n",
+       "      <td>0.456715</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          A         B\n",
+       "a -1.863049  1.979038\n",
+       "b -0.336175 -1.192626\n",
+       "c -0.317314  2.267986\n",
+       "d  0.852422 -0.452728\n",
+       "e  0.263762 -0.053398\n",
+       "f  0.857898  0.456715"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.iloc[:, lambda df: [0, 1]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "e8fe3be5-15de-4036-ab8a-d6483abf265f",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:700px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1.iloc%5B%3A,%20lambda%20df%3A%20%5B0,%201%5D%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:700px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1.iloc%5B%3A,%20lambda%20df%3A%20%5B0,%201%5D%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "ec331b54",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a   -1.863049\n",
+       "b   -0.336175\n",
+       "c   -0.317314\n",
+       "d    0.852422\n",
+       "e    0.263762\n",
+       "f    0.857898\n",
+       "Name: A, dtype: float64"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1[lambda df: df.columns[0]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "31840764-a775-4e5f-8023-6c4762005ff6",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:650px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%5Blambda%20df%3A%20df.columns%5B0%5D%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:650px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%5Blambda%20df%3A%20df.columns%5B0%5D%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "861f0e5e",
+   "metadata": {},
+   "source": [
+    "\n",
+    "You can use callable indexing in `Series`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "d4e60491",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "d    0.852422\n",
+       "e    0.263762\n",
+       "f    0.857898\n",
+       "Name: A, dtype: float64"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1['A'].loc[lambda s: s > 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "1d7a46f1-98ce-4d87-924a-288812c6b4ed",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:920px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%5B'A'%5D.loc%5Blambda%20s%3A%20s%20%3E%200%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:920px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%5B'A'%5D.loc%5Blambda%20s%3A%20s%20%3E%200%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12d2d96d",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### Combining positional and label-based indexing\n",
+    "\n",
+    "If you wish to get the 0th and the 2nd elements from the index in the `'A'` column, you can do:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "978312bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    1\n",
+       "c    3\n",
+       "Name: A, dtype: int64"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfd = pd.DataFrame({'A': [1, 2, 3],\n",
+    "                    'B': [4, 5, 6]},\n",
+    "                   index=list('abc'))\n",
+    "dfd\n",
+    "dfd.loc[dfd.index[[0, 2]], 'A']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "a8844d1c-fdc5-4c85-923c-092ac6367692",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:550px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adfd%20%3D%20pd.DataFrame%28%7B'A'%3A%20%5B1,%202,%203%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'B'%3A%20%5B4,%205,%206%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abc'%29%29%0Adfd.loc%5Bdfd.index%5B%5B0,%202%5D%5D,%20'A'%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:550px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adfd%20%3D%20pd.DataFrame%28%7B'A'%3A%20%5B1,%202,%203%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'B'%3A%20%5B4,%205,%206%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abc'%29%29%0Adfd.loc%5Bdfd.index%5B%5B0,%202%5D%5D,%20'A'%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11210c0d",
+   "metadata": {},
+   "source": [
+    "\n",
+    "This can also be expressed using `.iloc`, by explicitly getting locations on the indexers, and using positional indexing to select things."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "2e7e25d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    1\n",
+       "c    3\n",
+       "Name: A, dtype: int64"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfd.iloc[[0, 2], dfd.columns.get_loc('A')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "48f7feb0-9334-441f-893a-42815523e739",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:550px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adfd%20%3D%20pd.DataFrame%28%7B'A'%3A%20%5B1,%202,%203%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'B'%3A%20%5B4,%205,%206%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abc'%29%29%0Adfd.iloc%5B%5B0,%202%5D,%20dfd.columns.get_loc%28'A'%29%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:550px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adfd%20%3D%20pd.DataFrame%28%7B'A'%3A%20%5B1,%202,%203%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'B'%3A%20%5B4,%205,%206%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abc'%29%29%0Adfd.iloc%5B%5B0,%202%5D,%20dfd.columns.get_loc%28'A'%29%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6c36e79",
+   "metadata": {},
+   "source": [
+    "\n",
+    "For getting multiple indexers, using `.get_indexer`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "7c0b22e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   A  B\n",
+       "a  1  4\n",
+       "c  3  6"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfd.iloc[[0, 2], dfd.columns.get_indexer(['A', 'B'])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "c0924629-67d8-43b6-a435-d91bb8bf6408",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:550px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adfd%20%3D%20pd.DataFrame%28%7B'A'%3A%20%5B1,%202,%203%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'B'%3A%20%5B4,%205,%206%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abc'%29%29%0Adfd.iloc%5B%5B0,%202%5D,%20dfd.columns.get_indexer%28%5B'A',%20'B'%5D%29%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:550px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adfd%20%3D%20pd.DataFrame%28%7B'A'%3A%20%5B1,%202,%203%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'B'%3A%20%5B4,%205,%206%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abc'%29%29%0Adfd.iloc%5B%5B0,%202%5D,%20dfd.columns.get_indexer%28%5B'A',%20'B'%5D%29%5D&d=2023-07-14&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d97622c3-93dd-44af-94cb-9b4e4401b11b",
+   "metadata": {},
+   "source": [
+    "## Acknowledgments\n",
+    "\n",
+    "Thanks for [Pandas user guide](https://pandas.pydata.org/docs/user_guide/index.html). It contributes the majority of the content in this chapter."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/data-science/working-with-data/pandas/introduction-and-data-structures.ipynb b/_sources/data-science/working-with-data/pandas/introduction-and-data-structures.ipynb
new file mode 100644
index 0000000000..c5e8e9c71f
--- /dev/null
+++ b/_sources/data-science/working-with-data/pandas/introduction-and-data-structures.ipynb
@@ -0,0 +1,7268 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "105bf8eb",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Introduction and Data Structures\n",
+    " \n",
+    "Pandas is a fast, powerful, flexible and easy-to-use open-source data analysis and manipulation tool, built on top of the Python programming language.\n",
+    "\n",
+    "## Introducing Pandas objects\n",
+    "\n",
+    "In 3 sections, we’ll start with a quick, non-comprehensive overview of the fundamental data structures in Pandas to get you started. The fundamental behavior about data types, indexing, axis labeling, and alignment apply across all of the objects. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2818782f-4106-4c67-9491-5569ffdaaf19",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "In this section, we'll introduce two data structure of pandas and the basic concept of data indexing and selection."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e7aa789-fac6-4c4c-833b-e05c8c527491",
+   "metadata": {},
+   "source": [
+    "To get started, import NumPy and load Pandas into your namespace:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c8e7b835",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Install the necessary dependencies\n",
+    "import os\n",
+    "import sys\n",
+    "!{sys.executable} -m pip install --quiet jupyterlab_myst ipython\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb9af208",
+   "metadata": {},
+   "source": [
+    "### Series\n",
+    "\n",
+    "`Series` is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). The axis labels are collectively referred to as the **index**. The basic method to create a `Series` is to call:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d6b11bf-93b8-439d-83ed-9ffae399bb1f",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "py"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    "`s = pd.Series(data, index=index)`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "475acfee",
+   "metadata": {},
+   "source": [
+    "Here, `data` can be many different things:\n",
+    "\n",
+    "- a Python dict\n",
+    "- an ndarray\n",
+    "- a scalar value (like 5)\n",
+    "\n",
+    "\n",
+    "The passed **index** is a list of axis labels. Thus, this separates into a few cases depending on what the **data is**:\n",
+    "\n",
+    "#### Create a Series\n",
+    "\n",
+    "##### From ndarray\n",
+    "\n",
+    "If `data` is an ndarray, **index** must be the same length as the **data**. If no index is passed, one will be created having values `[0, ..., len(data) - 1]`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "646c8580",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "s = pd.Series(np.random.randn(5), index=[\"a\", \"b\", \"c\", \"d\", \"e\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "2d2455c1",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    0.142361\n",
+       "b    0.407910\n",
+       "c   -0.894226\n",
+       "d    1.311313\n",
+       "e    0.710528\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "20f33329",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['a', 'b', 'c', 'd', 'e'], dtype='object')"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "5376f720",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0.131905\n",
+       "1   -0.703499\n",
+       "2    1.530060\n",
+       "3   -0.073598\n",
+       "4   -0.892724\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(np.random.randn(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f4e73c7",
+   "metadata": {},
+   "source": [
+    ":::{note}\n",
+    "Pandas supports non-unique index values. If an operation that does not support duplicate index values is attempted, an exception will be raised at that time.\n",
+    ":::\n",
+    "\n",
+    "##### From dict\n",
+    "`Series` can be instantiated from dicts:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "e8095575",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "d = {\"b\": 1, \"a\": 0, \"c\": 2}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ba462934",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "b    1\n",
+       "a    0\n",
+       "c    2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(d)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4329868",
+   "metadata": {},
+   "source": [
+    "If an index is passed, the values in data corresponding to the labels in the index will be pulled out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "03488418",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "d = {\"a\": 0.0, \"b\": 1.0, \"c\": 2.0}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "c35e968c",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    0.0\n",
+       "b    1.0\n",
+       "c    2.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "95eafc4d",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "b    1.0\n",
+       "c    2.0\n",
+       "d    NaN\n",
+       "a    0.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(d, index=[\"b\", \"c\", \"d\", \"a\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1be5c72d",
+   "metadata": {},
+   "source": [
+    ":::{note}\n",
+    "NaN (not a number) is the standard missing data marker used in Pandas.\n",
+    ":::\n",
+    "\n",
+    "##### From scalar value\n",
+    "\n",
+    "If `data` is a scalar value, an index must be provided. The value will be repeated to match the length of **index**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "6f744115",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    5.0\n",
+       "b    5.0\n",
+       "c    5.0\n",
+       "d    5.0\n",
+       "e    5.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(5.0, index=[\"a\", \"b\", \"c\", \"d\", \"e\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8060fb92",
+   "metadata": {},
+   "source": [
+    "#### Series is ndarray-like\n",
+    "\n",
+    "`Series` acts very similarly to a `ndarray` and is a valid argument to most NumPy functions. However, operations such as slicing will also slice the index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "2ca453e9",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.14236085563166023"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "4cf8e176",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    0.142361\n",
+       "b    0.407910\n",
+       "c   -0.894226\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "1bab7730",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "d    1.311313\n",
+       "e    0.710528\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[s > s.median()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "b5e98d89",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "e    0.710528\n",
+       "d    1.311313\n",
+       "b    0.407910\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[[4, 3, 1]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "c98a7190",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    1.152993\n",
+       "b    1.503672\n",
+       "c    0.408924\n",
+       "d    3.711042\n",
+       "e    2.035066\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.exp(s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a49ee902",
+   "metadata": {},
+   "source": [
+    "Like a NumPy array, a Pandas Series has a single `dtype`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "b0298996",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('float64')"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s.dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69857db8",
+   "metadata": {},
+   "source": [
+    "If you need the actual array backing a `Series`, use `Series.array`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "1989c3a9",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<PandasArray>\n",
+       "[0.14236085563166023,  0.4079103331875502, -0.8942262958393035,\n",
+       "  1.3113126514940179,  0.7105280060827549]\n",
+       "Length: 5, dtype: float64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s.array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ed219b0",
+   "metadata": {},
+   "source": [
+    "While `Series` is ndarray-like, if you need an actual ndarray, then use `Series.to_numpy()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "1cc04172",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.14236086,  0.40791033, -0.8942263 ,  1.31131265,  0.71052801])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s.to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12f01f86",
+   "metadata": {},
+   "source": [
+    "Even if the `Series` is backed by an `ExtensionArray`, `Series.to_numpy()` will return a NumPy ndarray.\n",
+    "\n",
+    "#### Series is dict-like\n",
+    "\n",
+    "A `Series` is also like a fixed-size dict in that you can get and set values by index label:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "bcfe90c9",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.14236085563166023"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[\"a\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "00c68766",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "s[\"e\"] = 12.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "74f58473",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a     0.142361\n",
+       "b     0.407910\n",
+       "c    -0.894226\n",
+       "d     1.311313\n",
+       "e    12.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "2f822110",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"e\" in s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "164dcf61",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"f\" in s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca979c84",
+   "metadata": {},
+   "source": [
+    "If a label is not contained in the index, an exception is raised:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "40a23c62-9c88-4a6e-9316-60317abe7859",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    },
+    "tags": [
+     "raises-exception"
+    ]
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'f'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3801\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\_libs\\index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\_libs\\index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'f'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[35], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m s[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\series.py:981\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    978\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[key]\n\u001b[0;32m    980\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m key_is_scalar:\n\u001b[1;32m--> 981\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_value(key)\n\u001b[0;32m    983\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_hashable(key):\n\u001b[0;32m    984\u001b[0m     \u001b[38;5;66;03m# Otherwise index.get_value will raise InvalidIndexError\u001b[39;00m\n\u001b[0;32m    985\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    986\u001b[0m         \u001b[38;5;66;03m# For labels that don't resolve as scalars like tuples and frozensets\u001b[39;00m\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\series.py:1089\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[1;34m(self, label, takeable)\u001b[0m\n\u001b[0;32m   1086\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[label]\n\u001b[0;32m   1088\u001b[0m \u001b[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[1;32m-> 1089\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mget_loc(label)\n\u001b[0;32m   1090\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39m_get_values_for_loc(\u001b[38;5;28mself\u001b[39m, loc, label)\n",
+      "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m-> 3804\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3805\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3806\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3807\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3808\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3809\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'f'"
+     ]
+    }
+   ],
+   "source": [
+    "s[\"f\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "396df6e2",
+   "metadata": {},
+   "source": [
+    "Using the `Series.get()` method, a missing label will return None or specified default:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "ad2a67c6",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "s.get(\"f\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "13c1c13b",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nan"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s.get(\"f\", np.nan)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b19c44c",
+   "metadata": {},
+   "source": [
+    "These labels can also be accessed by `attribute`.\n",
+    "\n",
+    "#### Vectorized operations and label alignment with Series\n",
+    "\n",
+    "When working with raw NumPy arrays, looping through value-by-value is usually not necessary. The same is true when working with `Series` in Pandas. `Series` can also be passed into most NumPy methods expecting an ndarray."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "35540134",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a     0.284722\n",
+       "b     0.815821\n",
+       "c    -1.788453\n",
+       "d     2.622625\n",
+       "e    24.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s + s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "aea7c1dc",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a     0.284722\n",
+       "b     0.815821\n",
+       "c    -1.788453\n",
+       "d     2.622625\n",
+       "e    24.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s * 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "4dcdc8c4",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a         1.152993\n",
+       "b         1.503672\n",
+       "c         0.408924\n",
+       "d         3.711042\n",
+       "e    162754.791419\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.exp(s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8ed10f3",
+   "metadata": {},
+   "source": [
+    "A key difference between `Series` and ndarray is that operations between `Series` automatically align the data based on the label. Thus, you can write computations without giving consideration to whether the `Series` involved have the same labels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "563555a0",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a         NaN\n",
+       "b    0.815821\n",
+       "c   -1.788453\n",
+       "d    2.622625\n",
+       "e         NaN\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[1:] + s[:-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e19643a",
+   "metadata": {},
+   "source": [
+    "The result of an operation between unaligned `Series` will have the **union** of the indexes involved. If a label is not found in one `Series` or the other, the result will be marked as missing `NaN`. Being able to write code without doing any explicit data alignment grants immense freedom and flexibility in interactive data analysis and research. The integrated data alignment features of the Pandas data structures set Pandas apart from the majority of related tools for working with labeled data.\n",
+    "\n",
+    ":::{note}\n",
+    "In general, we chose to make the default result of operations between differently indexed objects yield the **union** of the indexes in order to avoid loss of information. Having an index label, though the data is missing, is typically important information as part of a computation. You of course have the option of dropping labels with missing data via the `dropna` function.\n",
+    ":::\n",
+    "\n",
+    "#### Name attribute\n",
+    "\n",
+    "`Series` also has a `name` attribute:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "3b39834b",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "s = pd.Series(np.random.randn(5), name=\"something\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "18210d7f",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0.836867\n",
+       "1   -0.187063\n",
+       "2    0.180988\n",
+       "3    0.434802\n",
+       "4    1.175946\n",
+       "Name: something, dtype: float64"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "06f09ce2",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'something'"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s.name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b35b499b",
+   "metadata": {},
+   "source": [
+    "The `Series` `name` can be assigned automatically in many cases, in particular, when selecting a single column from a `DataFrame`, the `name` will be assigned the column label.\n",
+    "\n",
+    "You can rename a `Series` with the `pandas.Series.rename()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "bd079c61",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "s2 = s.rename(\"different\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "a1767258",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'different'"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s2.name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "398a679d",
+   "metadata": {},
+   "source": [
+    "Note that `s` and `s2` refer to different objects.\n",
+    "\n",
+    "### DataFrame\n",
+    "\n",
+    "`DataFrame` is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table, or a `dict` of `Series` objects. It is generally the most commonly used Pandas object. Like `Series`, `DataFrame` accepts many different kinds of input:\n",
+    "\n",
+    "- Dict of 1D ndarrays, lists, dicts, or `Series`\n",
+    "- 2-D `numpy.ndarray`\n",
+    "- Structured or record ndarray\n",
+    "- A `Series`\n",
+    "- Another `DataFrame`\n",
+    "\n",
+    "Along with the data, you can optionally pass **index** (row labels) and **columns** (column labels) arguments. If you pass an index and / or columns, you are guaranteeing the index and / or columns of the resulting `DataFrame`. Thus, a `dict` of Series plus a specific index will discard all data not matching up to the passed index.\n",
+    "\n",
+    "If axis labels are not passed, they will be constructed from the input data based on common sense rules.\n",
+    "\n",
+    "#### Create a Dataframe\n",
+    "\n",
+    "##### From dict of `Series` or dicts\n",
+    "\n",
+    "The resulting **index** will be the **union** of the indexes of the various Series. If there are any nested dicts, these will first be converted to Series. If no columns are passed, the columns will be the ordered list of `dict` keys."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "aa7ddc8a",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "d = {\n",
+    "    \"one\": pd.Series([1.0, 2.0, 3.0], index=[\"a\", \"b\", \"c\"]),\n",
+    "    \"two\": pd.Series([1.0, 2.0, 3.0, 4.0], index=[\"a\", \"b\", \"c\", \"d\"]),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "f526badc",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "69ddc66c",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>one</th>\n",
+       "      <th>two</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   one  two\n",
+       "a  1.0  1.0\n",
+       "b  2.0  2.0\n",
+       "c  3.0  3.0\n",
+       "d  NaN  4.0"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "1f5e8ccb",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>one</th>\n",
+       "      <th>two</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   one  two\n",
+       "d  NaN  4.0\n",
+       "b  2.0  2.0\n",
+       "a  1.0  1.0"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(d, index=[\"d\", \"b\", \"a\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "9940fb65",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>two</th>\n",
+       "      <th>three</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   two three\n",
+       "d  4.0   NaN\n",
+       "b  2.0   NaN\n",
+       "a  1.0   NaN"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(d, index=[\"d\", \"b\", \"a\"], columns=[\"two\", \"three\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93b5a50c",
+   "metadata": {},
+   "source": [
+    "The row and column labels can be accessed respectively by accessing the **index** and **columns** attributes:\n",
+    "\n",
+    ":::{note}\n",
+    "When a particular set of columns is passed along with a dict of data, the passed columns override the keys in the dict.\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "8a3ba6ae",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['a', 'b', 'c', 'd'], dtype='object')"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "13684125",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['one', 'two'], dtype='object')"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49c8bc9a",
+   "metadata": {},
+   "source": [
+    "##### From dict of ndarrays / lists\n",
+    "\n",
+    "The ndarrays must all be the same length. If an index is passed, it must also be the same length as the arrays. If no index is passed, the result will be `range(n)`, where `n` is the array length."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "c4789555",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "d = {\"one\": [1.0, 2.0, 3.0, 4.0], \"two\": [4.0, 3.0, 2.0, 1.0]}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "29098be0",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>one</th>\n",
+       "      <th>two</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   one  two\n",
+       "0  1.0  4.0\n",
+       "1  2.0  3.0\n",
+       "2  3.0  2.0\n",
+       "3  4.0  1.0"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "5600834a",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>one</th>\n",
+       "      <th>two</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   one  two\n",
+       "a  1.0  4.0\n",
+       "b  2.0  3.0\n",
+       "c  3.0  2.0\n",
+       "d  4.0  1.0"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(d, index=[\"a\", \"b\", \"c\", \"d\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "506868de",
+   "metadata": {},
+   "source": [
+    "##### From structured or record array\n",
+    "\n",
+    "This case is handled identically to a dict of arrays."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "0b3b5090",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "data = np.zeros((2,), dtype=[(\"A\", \"i4\"), (\"B\", \"f4\"), (\"C\", \"a10\")])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "543153a7",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "data[:] = [(1, 2.0, \"Hello\"), (2, 3.0, \"World\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "c5278e68",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>b'Hello'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>b'World'</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   A    B         C\n",
+       "0  1  2.0  b'Hello'\n",
+       "1  2  3.0  b'World'"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "fefbfc51",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>first</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>b'Hello'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>second</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>b'World'</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        A    B         C\n",
+       "first   1  2.0  b'Hello'\n",
+       "second  2  3.0  b'World'"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(data, index=[\"first\", \"second\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "f76d517a",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>C</th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>b'Hello'</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>b'World'</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          C  A    B\n",
+       "0  b'Hello'  1  2.0\n",
+       "1  b'World'  2  3.0"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(data, columns=[\"C\", \"A\", \"B\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75f7c017",
+   "metadata": {},
+   "source": [
+    ":::{note}\n",
+    "DataFrame is not intended to work exactly like a 2-dimensional NumPy ndarray.\n",
+    ":::\n",
+    "\n",
+    "\n",
+    "##### From a list of dicts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "a2aa6cb3",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "data2 = [{\"a\": 1, \"b\": 2}, {\"a\": 5, \"b\": 10, \"c\": 20}]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "1e45ffbc",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a   b     c\n",
+       "0  1   2   NaN\n",
+       "1  5  10  20.0"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(data2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "8d6db924",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>first</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>second</th>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        a   b     c\n",
+       "first   1   2   NaN\n",
+       "second  5  10  20.0"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(data2, index=[\"first\", \"second\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "258fa418",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a   b\n",
+       "0  1   2\n",
+       "1  5  10"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(data2, columns=[\"a\", \"b\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfb77761",
+   "metadata": {},
+   "source": [
+    "##### From a dict of tuples\n",
+    "\n",
+    "You can automatically create a MultiIndexed frame by passing a tuples dictionary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "89af5166",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"3\" halign=\"left\">a</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">b</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>b</th>\n",
+       "      <th>a</th>\n",
+       "      <th>c</th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">A</th>\n",
+       "      <th>B</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       a              b      \n",
+       "       b    a    c    a     b\n",
+       "A B  1.0  4.0  5.0  8.0  10.0\n",
+       "  C  2.0  3.0  6.0  7.0   NaN\n",
+       "  D  NaN  NaN  NaN  NaN   9.0"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(\n",
+    "    {\n",
+    "        (\"a\", \"b\"): {(\"A\", \"B\"): 1, (\"A\", \"C\"): 2},\n",
+    "        (\"a\", \"a\"): {(\"A\", \"C\"): 3, (\"A\", \"B\"): 4},\n",
+    "        (\"a\", \"c\"): {(\"A\", \"B\"): 5, (\"A\", \"C\"): 6},\n",
+    "        (\"b\", \"a\"): {(\"A\", \"C\"): 7, (\"A\", \"B\"): 8},\n",
+    "        (\"b\", \"b\"): {(\"A\", \"D\"): 9, (\"A\", \"B\"): 10},\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e02d86d6",
+   "metadata": {},
+   "source": [
+    "##### From a Series\n",
+    "\n",
+    "The result will be a DataFrame with the same index as the input Series, and with one column whose name is the original name of the Series (only if no other column name provided)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "77ff8552",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ser = pd.Series(range(3), index=list(\"abc\"), name=\"ser\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "a86d1926",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ser</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ser\n",
+       "a    0\n",
+       "b    1\n",
+       "c    2"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(ser)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f824850",
+   "metadata": {},
+   "source": [
+    "##### From a list of namedtuples\n",
+    "\n",
+    "The field names of the first `namedtuple` in the list determine the columns of the `DataFrame`. The remaining namedtuples (or tuples) are simply unpacked and their values are fed into the rows of the `DataFrame`. If any of those tuples is shorter than the first `namedtuple` then the later columns in the corresponding row are marked as missing values. If any are longer than the first `namedtuple` , a `ValueError` is raised."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "67fd765e",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from collections import namedtuple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "d4524af3",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "Point = namedtuple(\"Point\", \"x y\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "02f0937c",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   x  y\n",
+       "0  0  0\n",
+       "1  0  3\n",
+       "2  2  3"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame([Point(0, 0), Point(0, 3), (2, 3)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "4c81da05",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "Point3D = namedtuple(\"Point3D\", \"x y z\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "6731aad6",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "      <th>z</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   x  y    z\n",
+       "0  0  0  0.0\n",
+       "1  0  3  5.0\n",
+       "2  2  3  NaN"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame([Point3D(0, 0, 0), Point3D(0, 3, 5), Point(2, 3)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ff0bca2",
+   "metadata": {},
+   "source": [
+    "##### From a list of dataclasses\n",
+    "\n",
+    "Data Classes as introduced in PEP557, can be passed into the DataFrame constructor. Passing a list of dataclasses is equivalent to passing a list of dictionaries.\n",
+    "\n",
+    "Please be aware, that all values in the list should be dataclasses, mixing types in the list would result in a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "5fe92237",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from dataclasses import make_dataclass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "e13b27cf",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "Point = make_dataclass(\"Point\", [(\"x\", int), (\"y\", int)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "df6b2816",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   x  y\n",
+       "0  0  0\n",
+       "1  0  3\n",
+       "2  2  3"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e826768",
+   "metadata": {},
+   "source": [
+    "#### Column selection, addition, deletion\n",
+    "\n",
+    "You can treat a `DataFrame` semantically like a dict of like-indexed `Series` objects. Getting, setting, and deleting columns works with the same syntax as the analogous dict operations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "a52d0734",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>one</th>\n",
+       "      <th>two</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   one  two\n",
+       "a  1.0  1.0\n",
+       "b  2.0  2.0\n",
+       "c  3.0  3.0\n",
+       "d  NaN  4.0"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "804405d6",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    1.0\n",
+       "b    2.0\n",
+       "c    3.0\n",
+       "d    NaN\n",
+       "Name: one, dtype: float64"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"one\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "dfa00c9b",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df[\"three\"] = df[\"one\"] * df[\"two\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "0f98ffa9",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df[\"flag\"] = df[\"one\"] > 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "1ef5e1a3",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>one</th>\n",
+       "      <th>two</th>\n",
+       "      <th>three</th>\n",
+       "      <th>flag</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   one  two  three   flag\n",
+       "a  1.0  1.0    1.0  False\n",
+       "b  2.0  2.0    4.0  False\n",
+       "c  3.0  3.0    9.0   True\n",
+       "d  NaN  4.0    NaN  False"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f518cd88",
+   "metadata": {},
+   "source": [
+    "Columns can be deleted or popped like with a dict:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "b418f585",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "del df[\"two\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "209ebb78",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "three = df.pop(\"three\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "9aee9b49",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>one</th>\n",
+       "      <th>flag</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   one   flag\n",
+       "a  1.0  False\n",
+       "b  2.0  False\n",
+       "c  3.0   True\n",
+       "d  NaN  False"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40b5a135",
+   "metadata": {},
+   "source": [
+    "When inserting a scalar value, it will naturally be propagated to fill the column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "1bddfbc5",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df[\"foo\"] = \"bar\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "e2613bd3",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>one</th>\n",
+       "      <th>flag</th>\n",
+       "      <th>foo</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>bar</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>bar</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>bar</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>bar</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   one   flag  foo\n",
+       "a  1.0  False  bar\n",
+       "b  2.0  False  bar\n",
+       "c  3.0   True  bar\n",
+       "d  NaN  False  bar"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d93a6895",
+   "metadata": {},
+   "source": [
+    "When inserting a `Series` that does not have the same index as the `DataFrame`, it will be conformed to the DataFrame's index:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "c20564a5",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df[\"one_trunc\"] = df[\"one\"][:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "877b972d-49b8-4225-855e-ec77bd876d8b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<link rel=\"stylesheet\" href=\"https://ocademy-ai.github.io/machine-learning/_static/style.css\">\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:730px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Ad%20%3D%20%7B%0A%20%20%20%20%22one%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29,%0A%20%20%20%20%22two%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0,%204.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22,%20%22d%22%5D%29,%0A%7D%0Adf%20%3D%20pd.DataFrame%28d%29%0Adf%5B%22flag%22%5D%20%3D%20df%5B%22one%22%5D%20%3E%202%0Adel%20df%5B%22two%22%5D%0Adf%5B%22foo%22%5D%20%3D%20%22bar%22%0Adf%5B%22one_trunc%22%5D%20%3Ddf.insert%281,%20%22bar%22,%20df%5B%22one%22%5D%29%0Adf.loc%5B%22b%22%5D&d=2023-09-19&lang=py&v=v1\n",
+       "\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<link rel=\"stylesheet\" href=\"https://ocademy-ai.github.io/machine-learning/_static/style.css\">\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:730px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Ad%20%3D%20%7B%0A%20%20%20%20%22one%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29,%0A%20%20%20%20%22two%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0,%204.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22,%20%22d%22%5D%29,%0A%7D%0Adf%20%3D%20pd.DataFrame%28d%29%0Adf%5B%22flag%22%5D%20%3D%20df%5B%22one%22%5D%20%3E%202%0Adel%20df%5B%22two%22%5D%0Adf%5B%22foo%22%5D%20%3D%20%22bar%22%0Adf%5B%22one_trunc%22%5D%20%3Ddf.insert%281,%20%22bar%22,%20df%5B%22one%22%5D%29%0Adf.loc%5B%22b%22%5D&d=2023-09-19&lang=py&v=v1\n",
+    "\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "76026aba",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>one</th>\n",
+       "      <th>flag</th>\n",
+       "      <th>foo</th>\n",
+       "      <th>one_trunc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>bar</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>bar</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>bar</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>bar</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   one   flag  foo  one_trunc\n",
+       "a  1.0  False  bar        1.0\n",
+       "b  2.0  False  bar        2.0\n",
+       "c  3.0   True  bar        NaN\n",
+       "d  NaN  False  bar        NaN"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7c3f5d9",
+   "metadata": {},
+   "source": [
+    "You can insert raw ndarrays but their length must match the length of the DataFrame's index.\n",
+    "\n",
+    "By default, columns get inserted at the end. `DataFrame.insert()` inserts at a particular location in the columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "8dbfb773",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df.insert(1, \"bar\", df[\"one\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "27dea852",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>one</th>\n",
+       "      <th>bar</th>\n",
+       "      <th>flag</th>\n",
+       "      <th>foo</th>\n",
+       "      <th>one_trunc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>bar</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>bar</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>bar</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>bar</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   one  bar   flag  foo  one_trunc\n",
+       "a  1.0  1.0  False  bar        1.0\n",
+       "b  2.0  2.0  False  bar        2.0\n",
+       "c  3.0  3.0   True  bar        NaN\n",
+       "d  NaN  NaN  False  bar        NaN"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4786e42f",
+   "metadata": {},
+   "source": [
+    "#### Assigning new columns in method chains\n",
+    "\n",
+    "DataFrame has an `assign()` method that allows you to easily create new columns that are potentially derived from existing columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "e9e4dead",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "iris = pd.read_csv(\"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/data-science/working-with-data/pandas/iris.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "38eef1a4",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SepalLength</th>\n",
+       "      <th>SepalWidth</th>\n",
+       "      <th>PetalLength</th>\n",
+       "      <th>PetalWidth</th>\n",
+       "      <th>Name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   SepalLength  SepalWidth  PetalLength  PetalWidth         Name\n",
+       "0          5.1         3.5          1.4         0.2  Iris-setosa\n",
+       "1          4.9         3.0          1.4         0.2  Iris-setosa\n",
+       "2          4.7         3.2          1.3         0.2  Iris-setosa\n",
+       "3          4.6         3.1          1.5         0.2  Iris-setosa\n",
+       "4          5.0         3.6          1.4         0.2  Iris-setosa"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "ed27d63b",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SepalLength</th>\n",
+       "      <th>SepalWidth</th>\n",
+       "      <th>PetalLength</th>\n",
+       "      <th>PetalWidth</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>sepal_ratio</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "      <td>0.686275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "      <td>0.612245</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "      <td>0.680851</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "      <td>0.673913</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "      <td>0.720000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   SepalLength  SepalWidth  PetalLength  PetalWidth         Name  sepal_ratio\n",
+       "0          5.1         3.5          1.4         0.2  Iris-setosa     0.686275\n",
+       "1          4.9         3.0          1.4         0.2  Iris-setosa     0.612245\n",
+       "2          4.7         3.2          1.3         0.2  Iris-setosa     0.680851\n",
+       "3          4.6         3.1          1.5         0.2  Iris-setosa     0.673913\n",
+       "4          5.0         3.6          1.4         0.2  Iris-setosa     0.720000"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.assign(sepal_ratio=iris[\"SepalWidth\"] / iris[\"SepalLength\"]).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c989dbf7",
+   "metadata": {},
+   "source": [
+    "In the example above, we inserted a precomputed value. We can also pass in a function of one argument to be evaluated on the DataFrame being assigned to."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "4f39885a",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SepalLength</th>\n",
+       "      <th>SepalWidth</th>\n",
+       "      <th>PetalLength</th>\n",
+       "      <th>PetalWidth</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>sepal_ratio</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "      <td>0.686275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "      <td>0.612245</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "      <td>0.680851</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "      <td>0.673913</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "      <td>0.720000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   SepalLength  SepalWidth  PetalLength  PetalWidth         Name  sepal_ratio\n",
+       "0          5.1         3.5          1.4         0.2  Iris-setosa     0.686275\n",
+       "1          4.9         3.0          1.4         0.2  Iris-setosa     0.612245\n",
+       "2          4.7         3.2          1.3         0.2  Iris-setosa     0.680851\n",
+       "3          4.6         3.1          1.5         0.2  Iris-setosa     0.673913\n",
+       "4          5.0         3.6          1.4         0.2  Iris-setosa     0.720000"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.assign(sepal_ratio=lambda x: (x[\"SepalWidth\"] / x[\"SepalLength\"])).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "abcd0aee",
+   "metadata": {},
+   "source": [
+    "`assign()` **always** returns a copy of the data, leaving the original DataFrame untouched.\n",
+    "\n",
+    "Passing a callable, as opposed to an actual value to be inserted, is useful when you don't have a reference to the DataFrame at hand. This is common when using `assign()` in a chain of operations. For example, we can limit the DataFrame to just those observations with a Sepal Length greater than 5, calculate the ratio, and plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "0508916b",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='SepalRatio', ylabel='PetalRatio'>"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "(\n",
+    "    iris.query(\"SepalLength > 5\")\n",
+    "    .assign(\n",
+    "        SepalRatio=lambda x: x.SepalWidth / x.SepalLength,\n",
+    "        PetalRatio=lambda x: x.PetalWidth / x.PetalLength,\n",
+    "    )\n",
+    "    .plot(kind=\"scatter\", x=\"SepalRatio\", y=\"PetalRatio\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e1e3e3d",
+   "metadata": {},
+   "source": [
+    "Since a function is passed in, the function is computed on the DataFrame being assigned to. Importantly, this is the DataFrame that's been filtered to those rows with sepal length greater than 5. The filtering happens first, and then the ratio calculations. This is an example where we didn't have a reference to the filtered DataFrame available.\n",
+    "\n",
+    "The function signature for `assign()` is simply `**kwargs`. The keys are the column names for the new fields, and the values are either a value to be inserted (for example, a `Series` or NumPy array), or a function of one argument to be called on the `DataFrame`. A copy of the original `DataFrame` is returned, with the new values inserted.\n",
+    "\n",
+    "The order of `**kwargs` is preserved. This allows for dependent assignment, where an expression later in `**kwargs` can refer to a column created earlier in the same `assign()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "60b7e3c7",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dfa = pd.DataFrame({\"A\": [1, 2, 3], \"B\": [4, 5, 6]})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "4c821875",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>9</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   A  B  C   D\n",
+       "0  1  4  5   6\n",
+       "1  2  5  7   9\n",
+       "2  3  6  9  12"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfa.assign(C=lambda x: x[\"A\"] + x[\"B\"], D=lambda x: x[\"A\"] + x[\"C\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "822c6838",
+   "metadata": {},
+   "source": [
+    "In the second expression, `x['C']` will refer to the newly created column, that's equal to `dfa['A'] + dfa['B']`.\n",
+    "\n",
+    "#### Indexing / selection\n",
+    "\n",
+    "The basics of indexing are as follows:\n",
+    "\n",
+    "|Operation                     |Syntax         |Result   |\n",
+    "|:-------                      |:-----         |:-----   |\n",
+    "|Select column                 |`df[col]`      |Series   |\n",
+    "|Select row by label           |`df.loc[label]`|Series   |\n",
+    "|Select row by integer location|`df.iloc[loc]` |Series   |\n",
+    "|Slice rows                    |`df[5:10] `    |DataFrame|\n",
+    "|Select rows by boolean vector |`df[bool_vec]` |DataFrame|\n",
+    "\n",
+    "Row selection, for example, returns a `Series` whose index is the columns of the `DataFrame`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "82154750",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "one            2.0\n",
+       "bar            2.0\n",
+       "flag         False\n",
+       "foo            bar\n",
+       "one_trunc      2.0\n",
+       "Name: b, dtype: object"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[\"b\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "743d6893-bbf3-4fbf-a158-a3aaae040b39",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "<link rel=\"stylesheet\" href=\"https://ocademy-ai.github.io/machine-learning/_static/style.css\">\n",
+       "\n",
+       "<div class='full-width docutils' >\n",
+       "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+       "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+       "    <div class=\"pandastutor inner\" style=\"height:730px;\">\n",
+       "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Ad%20%3D%20%7B%0A%20%20%20%20%22one%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29,%0A%20%20%20%20%22two%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0,%204.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22,%20%22d%22%5D%29,%0A%7D%0Adf%20%3D%20pd.DataFrame%28d%29%0Adf%5B%22flag%22%5D%20%3D%20df%5B%22one%22%5D%20%3E%202%0Adel%20df%5B%22two%22%5D%0Adf%5B%22foo%22%5D%20%3D%20%22bar%22%0Adf%5B%22one_trunc%22%5D%20%3Ddf.insert%281,%20%22bar%22,%20df%5B%22one%22%5D%29%0Adf.loc%5B%22b%22%5D&d=2023-09-19&lang=py&v=v1\"> </iframe>\n",
+       "    </div>\n",
+       "  </div>\n",
+       "</div>\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "display(\n",
+    "    HTML(\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "<link rel=\"stylesheet\" href=\"https://ocademy-ai.github.io/machine-learning/_static/style.css\">\n",
+    "\n",
+    "<div class='full-width docutils' >\n",
+    "  <div class=\"admonition note pandastutor\" name=\"html-admonition\" style=\"margin-right:20%\">\n",
+    "    <p class=\"admonition-title pandastutor\">Let's visualize it! 🎥</p>\n",
+    "    <div class=\"pandastutor inner\" style=\"height:730px;\">\n",
+    "      <iframe frameborder=\"0\" scrolling=\"no\" src=\"https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Ad%20%3D%20%7B%0A%20%20%20%20%22one%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29,%0A%20%20%20%20%22two%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0,%204.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22,%20%22d%22%5D%29,%0A%7D%0Adf%20%3D%20pd.DataFrame%28d%29%0Adf%5B%22flag%22%5D%20%3D%20df%5B%22one%22%5D%20%3E%202%0Adel%20df%5B%22two%22%5D%0Adf%5B%22foo%22%5D%20%3D%20%22bar%22%0Adf%5B%22one_trunc%22%5D%20%3Ddf.insert%281,%20%22bar%22,%20df%5B%22one%22%5D%29%0Adf.loc%5B%22b%22%5D&d=2023-09-19&lang=py&v=v1\"> </iframe>\n",
+    "    </div>\n",
+    "  </div>\n",
+    "</div>\n",
+    "\n",
+    "\"\"\"\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "2fae006c",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "one           3.0\n",
+       "bar           3.0\n",
+       "flag         True\n",
+       "foo           bar\n",
+       "one_trunc     NaN\n",
+       "Name: c, dtype: object"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.iloc[2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87fe370b",
+   "metadata": {},
+   "source": [
+    "#### Data alignment and arithmetic\n",
+    "\n",
+    "Data alignment between `DataFrame` objects automatically aligns on **both** the columns and the index (row labels)**. Again, the resulting object will have the union of the column and row labels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "a3e29475",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(np.random.randn(10, 4), columns=[\"A\", \"B\", \"C\", \"D\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "c4634479",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df2 = pd.DataFrame(np.random.randn(7, 3), columns=[\"A\", \"B\", \"C\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "09eb77aa",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-2.675459</td>\n",
+       "      <td>-0.133581</td>\n",
+       "      <td>1.148420</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1.979453</td>\n",
+       "      <td>-0.736220</td>\n",
+       "      <td>-4.194590</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-1.373036</td>\n",
+       "      <td>0.939902</td>\n",
+       "      <td>-1.952070</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.813456</td>\n",
+       "      <td>-0.228460</td>\n",
+       "      <td>-0.634051</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.287748</td>\n",
+       "      <td>1.054761</td>\n",
+       "      <td>-2.133658</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.697426</td>\n",
+       "      <td>1.493623</td>\n",
+       "      <td>-1.633845</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>-0.249357</td>\n",
+       "      <td>1.432554</td>\n",
+       "      <td>1.585387</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          A         B         C   D\n",
+       "0 -2.675459 -0.133581  1.148420 NaN\n",
+       "1 -1.979453 -0.736220 -4.194590 NaN\n",
+       "2 -1.373036  0.939902 -1.952070 NaN\n",
+       "3  0.813456 -0.228460 -0.634051 NaN\n",
+       "4 -0.287748  1.054761 -2.133658 NaN\n",
+       "5  0.697426  1.493623 -1.633845 NaN\n",
+       "6 -0.249357  1.432554  1.585387 NaN\n",
+       "7       NaN       NaN       NaN NaN\n",
+       "8       NaN       NaN       NaN NaN\n",
+       "9       NaN       NaN       NaN NaN"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df + df2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9062570a",
+   "metadata": {},
+   "source": [
+    "When doing an operation between `DataFrame` and `Series`, the default behavior is to align the `Series` **index** on the `DataFrame` **columns**, thus broadcasting row-wise. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "c2a8adda",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.176653</td>\n",
+       "      <td>0.958183</td>\n",
+       "      <td>-3.117564</td>\n",
+       "      <td>-0.681690</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.935730</td>\n",
+       "      <td>1.163312</td>\n",
+       "      <td>-1.466732</td>\n",
+       "      <td>-1.218909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.854724</td>\n",
+       "      <td>0.293515</td>\n",
+       "      <td>-0.472388</td>\n",
+       "      <td>0.755568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.604946</td>\n",
+       "      <td>3.032983</td>\n",
+       "      <td>-0.818088</td>\n",
+       "      <td>0.248440</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2.207410</td>\n",
+       "      <td>1.803085</td>\n",
+       "      <td>-1.369634</td>\n",
+       "      <td>-1.490638</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>4.231287</td>\n",
+       "      <td>1.862016</td>\n",
+       "      <td>1.764553</td>\n",
+       "      <td>-1.377419</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.182000</td>\n",
+       "      <td>1.136687</td>\n",
+       "      <td>-0.938919</td>\n",
+       "      <td>-0.008524</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1.579137</td>\n",
+       "      <td>1.203216</td>\n",
+       "      <td>-0.808539</td>\n",
+       "      <td>-1.454299</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1.807712</td>\n",
+       "      <td>0.360691</td>\n",
+       "      <td>-1.850980</td>\n",
+       "      <td>-0.663877</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          A         B         C         D\n",
+       "0  0.000000  0.000000  0.000000  0.000000\n",
+       "1  1.176653  0.958183 -3.117564 -0.681690\n",
+       "2  0.935730  1.163312 -1.466732 -1.218909\n",
+       "3  1.854724  0.293515 -0.472388  0.755568\n",
+       "4  1.604946  3.032983 -0.818088  0.248440\n",
+       "5  2.207410  1.803085 -1.369634 -1.490638\n",
+       "6  4.231287  1.862016  1.764553 -1.377419\n",
+       "7  1.182000  1.136687 -0.938919 -0.008524\n",
+       "8  1.579137  1.203216 -0.808539 -1.454299\n",
+       "9  1.807712  0.360691 -1.850980 -0.663877"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df - df.iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf0c0013",
+   "metadata": {},
+   "source": [
+    "Arithmetic operations with scalars operate element-wise:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "d4cc4904",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-7.038225</td>\n",
+       "      <td>-3.616662</td>\n",
+       "      <td>2.249668</td>\n",
+       "      <td>0.911815</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1.154961</td>\n",
+       "      <td>1.174252</td>\n",
+       "      <td>-13.338150</td>\n",
+       "      <td>-2.496637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-2.359573</td>\n",
+       "      <td>2.199900</td>\n",
+       "      <td>-5.083991</td>\n",
+       "      <td>-5.182731</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.235394</td>\n",
+       "      <td>-2.149088</td>\n",
+       "      <td>-0.112270</td>\n",
+       "      <td>4.689655</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.986504</td>\n",
+       "      <td>11.548255</td>\n",
+       "      <td>-1.840774</td>\n",
+       "      <td>2.154016</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.998827</td>\n",
+       "      <td>5.398765</td>\n",
+       "      <td>-4.598502</td>\n",
+       "      <td>-6.541375</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>14.118211</td>\n",
+       "      <td>5.693418</td>\n",
+       "      <td>11.072431</td>\n",
+       "      <td>-5.975282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>-1.128225</td>\n",
+       "      <td>2.066773</td>\n",
+       "      <td>-2.444925</td>\n",
+       "      <td>0.869196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.857461</td>\n",
+       "      <td>2.399419</td>\n",
+       "      <td>-1.793024</td>\n",
+       "      <td>-6.359682</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2.000335</td>\n",
+       "      <td>-1.813207</td>\n",
+       "      <td>-7.005231</td>\n",
+       "      <td>-2.407570</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           A          B          C         D\n",
+       "0  -7.038225  -3.616662   2.249668  0.911815\n",
+       "1  -1.154961   1.174252 -13.338150 -2.496637\n",
+       "2  -2.359573   2.199900  -5.083991 -5.182731\n",
+       "3   2.235394  -2.149088  -0.112270  4.689655\n",
+       "4   0.986504  11.548255  -1.840774  2.154016\n",
+       "5   3.998827   5.398765  -4.598502 -6.541375\n",
+       "6  14.118211   5.693418  11.072431 -5.975282\n",
+       "7  -1.128225   2.066773  -2.444925  0.869196\n",
+       "8   0.857461   2.399419  -1.793024 -6.359682\n",
+       "9   2.000335  -1.813207  -7.005231 -2.407570"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df * 5 + 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "131ec689",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.553206</td>\n",
+       "      <td>-0.890208</td>\n",
+       "      <td>20.026571</td>\n",
+       "      <td>-4.594807</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1.584805</td>\n",
+       "      <td>-6.055120</td>\n",
+       "      <td>-0.325985</td>\n",
+       "      <td>-1.111942</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-1.146901</td>\n",
+       "      <td>25.012499</td>\n",
+       "      <td>-0.705817</td>\n",
+       "      <td>-0.696114</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>21.240986</td>\n",
+       "      <td>-1.205084</td>\n",
+       "      <td>-2.367122</td>\n",
+       "      <td>1.858975</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-4.933419</td>\n",
+       "      <td>0.523656</td>\n",
+       "      <td>-1.301821</td>\n",
+       "      <td>32.464111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2.501467</td>\n",
+       "      <td>1.471123</td>\n",
+       "      <td>-0.757748</td>\n",
+       "      <td>-0.585386</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.412602</td>\n",
+       "      <td>1.353760</td>\n",
+       "      <td>0.551120</td>\n",
+       "      <td>-0.626937</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>-1.598350</td>\n",
+       "      <td>74.880783</td>\n",
+       "      <td>-1.124878</td>\n",
+       "      <td>-4.421634</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>-4.376220</td>\n",
+       "      <td>12.518198</td>\n",
+       "      <td>-1.318209</td>\n",
+       "      <td>-0.598109</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>14927.997228</td>\n",
+       "      <td>-1.311232</td>\n",
+       "      <td>-0.555233</td>\n",
+       "      <td>-1.134412</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              A          B          C          D\n",
+       "0     -0.553206  -0.890208  20.026571  -4.594807\n",
+       "1     -1.584805  -6.055120  -0.325985  -1.111942\n",
+       "2     -1.146901  25.012499  -0.705817  -0.696114\n",
+       "3     21.240986  -1.205084  -2.367122   1.858975\n",
+       "4     -4.933419   0.523656  -1.301821  32.464111\n",
+       "5      2.501467   1.471123  -0.757748  -0.585386\n",
+       "6      0.412602   1.353760   0.551120  -0.626937\n",
+       "7     -1.598350  74.880783  -1.124878  -4.421634\n",
+       "8     -4.376220  12.518198  -1.318209  -0.598109\n",
+       "9  14927.997228  -1.311232  -0.555233  -1.134412"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 / df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "a2d50c6f",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.067708e+01</td>\n",
+       "      <td>1.592330e+00</td>\n",
+       "      <td>0.000006</td>\n",
+       "      <td>2.243525e-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.585244e-01</td>\n",
+       "      <td>7.438906e-04</td>\n",
+       "      <td>88.554761</td>\n",
+       "      <td>6.541410e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.779576e-01</td>\n",
+       "      <td>2.554887e-06</td>\n",
+       "      <td>4.029322</td>\n",
+       "      <td>4.258713e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.912486e-06</td>\n",
+       "      <td>4.741661e-01</td>\n",
+       "      <td>0.031851</td>\n",
+       "      <td>8.373485e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.688138e-03</td>\n",
+       "      <td>1.329891e+01</td>\n",
+       "      <td>0.348173</td>\n",
+       "      <td>9.002973e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2.553999e-02</td>\n",
+       "      <td>2.135033e-01</td>\n",
+       "      <td>3.033203</td>\n",
+       "      <td>8.515912e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3.450436e+01</td>\n",
+       "      <td>2.977377e-01</td>\n",
+       "      <td>10.839637</td>\n",
+       "      <td>6.472977e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.532188e-01</td>\n",
+       "      <td>3.180669e-08</td>\n",
+       "      <td>0.624565</td>\n",
+       "      <td>2.616188e-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2.726487e-03</td>\n",
+       "      <td>4.072234e-05</td>\n",
+       "      <td>0.331179</td>\n",
+       "      <td>7.814101e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2.013696e-17</td>\n",
+       "      <td>3.382842e-01</td>\n",
+       "      <td>10.522029</td>\n",
+       "      <td>6.038328e-01</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              A             B          C             D\n",
+       "0  1.067708e+01  1.592330e+00   0.000006  2.243525e-03\n",
+       "1  1.585244e-01  7.438906e-04  88.554761  6.541410e-01\n",
+       "2  5.779576e-01  2.554887e-06   4.029322  4.258713e+00\n",
+       "3  4.912486e-06  4.741661e-01   0.031851  8.373485e-02\n",
+       "4  1.688138e-03  1.329891e+01   0.348173  9.002973e-07\n",
+       "5  2.553999e-02  2.135033e-01   3.033203  8.515912e+00\n",
+       "6  3.450436e+01  2.977377e-01  10.839637  6.472977e+00\n",
+       "7  1.532188e-01  3.180669e-08   0.624565  2.616188e-03\n",
+       "8  2.726487e-03  4.072234e-05   0.331179  7.814101e+00\n",
+       "9  2.013696e-17  3.382842e-01  10.522029  6.038328e-01"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df ** 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab0cc5cb",
+   "metadata": {},
+   "source": [
+    "Boolean operators operate element-wise as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "edbec52a",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df1 = pd.DataFrame({\"a\": [1, 0, 1], \"b\": [0, 1, 1]}, dtype=bool)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "727cd263",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df2 = pd.DataFrame({\"a\": [0, 1, 1], \"b\": [1, 1, 0]}, dtype=bool)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "523bbe29",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       a      b\n",
+       "0  False  False\n",
+       "1  False   True\n",
+       "2   True  False"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1 & df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "b1a355fc",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      a     b\n",
+       "0  True  True\n",
+       "1  True  True\n",
+       "2  True  True"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1 | df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "e89dc58b",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       a      b\n",
+       "0   True   True\n",
+       "1   True  False\n",
+       "2  False   True"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1 ^ df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "9b438ef3",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       a      b\n",
+       "0  False   True\n",
+       "1   True  False\n",
+       "2  False  False"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "-df1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31d38eb7",
+   "metadata": {},
+   "source": [
+    "#### Transposing\n",
+    "\n",
+    "To transpose, access the `T` attribute or `DataFrame.transpose()`, similar to an ndarray:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "84f274b9",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>-1.807645</td>\n",
+       "      <td>-0.630992</td>\n",
+       "      <td>-0.871915</td>\n",
+       "      <td>0.047079</td>\n",
+       "      <td>-0.202699</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>-1.123332</td>\n",
+       "      <td>-0.165150</td>\n",
+       "      <td>0.039980</td>\n",
+       "      <td>-0.829818</td>\n",
+       "      <td>1.909651</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>0.049934</td>\n",
+       "      <td>-3.067630</td>\n",
+       "      <td>-1.416798</td>\n",
+       "      <td>-0.422454</td>\n",
+       "      <td>-0.768155</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>-0.217637</td>\n",
+       "      <td>-0.899327</td>\n",
+       "      <td>-1.436546</td>\n",
+       "      <td>0.537931</td>\n",
+       "      <td>0.030803</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          0         1         2         3         4\n",
+       "A -1.807645 -0.630992 -0.871915  0.047079 -0.202699\n",
+       "B -1.123332 -0.165150  0.039980 -0.829818  1.909651\n",
+       "C  0.049934 -3.067630 -1.416798 -0.422454 -0.768155\n",
+       "D -0.217637 -0.899327 -1.436546  0.537931  0.030803"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[:5].T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20c81c1c",
+   "metadata": {},
+   "source": [
+    "## Data indexing and selection\n",
+    "\n",
+    "The axis labeling information in Pandas objects serves many purposes:\n",
+    "\n",
+    "- Identifies data (i.e. provides metadata) using known indicators, important for analysis, visualization, and interactive console display.\n",
+    "- Enables automatic and explicit data alignment.\n",
+    "- Allows intuitive getting and setting of subsets of the data set.\n",
+    "\n",
+    "In this section, we will focus on the final point: namely, how to slice, dice, and generally get and set subsets of Pandas objects. The primary focus will be on Series and DataFrame as they have received more development attention in this area.\n",
+    "\n",
+    ":::{note}\n",
+    "The Python and NumPy indexing operators `[]` and attribute operator `.` provide quick and easy access to Pandas data structures across a wide range of use cases. This makes interactive work intuitive, as there's little new to learn if you already know how to deal with Python dictionaries and NumPy arrays. However, since the type of the data to be accessed isn't known in advance, directly using standard operators has some optimization limits. For production code, we recommended that you take advantage of the optimized Pandas data access methods exposed in this chapter.\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f5b68a0-0590-48bc-8129-c36c6faf57db",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "warning"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    "Whether a copy or a reference is returned for a setting operation, may depend on the context. This is sometimes called `chained assignment` and should be avoided."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbdec733",
+   "metadata": {},
+   "source": [
+    "### Different choices for indexing\n",
+    "\n",
+    "Object selection has had a number of user-requested additions in order to support more explicit location-based indexing. Pandas now supports three types of multi-axis indexing.\n",
+    "\n",
+    "- `.loc` is primarily label based, but may also be used with a boolean array. `.loc` will raise `KeyError` when the items are not found. Allowed inputs are:\n",
+    "  - A single label, e.g. `5` or `'a'` (Note that `5` is interpreted as a label of the index. This use is not an integer position along the index.).\n",
+    "  - A list or array of labels `['a', 'b', 'c']`.\n",
+    "  - A slice object with labels `'a':'f'` (Note that contrary to usual Python slices, both the start and the stop are included, when present in the index!)\n",
+    "  - A boolean array (any `NA` values will be treated as `False`).\n",
+    "  - A `callable` function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above).\n",
+    "\n",
+    "- `.iloc` is primarily integer position based (from `0` to `length-1` of the axis), but may also be used with a boolean array. `.iloc` will raise `IndexError` if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing. (this conforms with Python/NumPy slice semantics). Allowed inputs are:\n",
+    "  - An integer e.g. `5`.\n",
+    "  - A list or array of integers `[4, 3, 0]`.\n",
+    "  - A slice object with ints `1:7`.\n",
+    "  - A boolean array (any `NA` values will be treated as `False`).\n",
+    "  - A `callable` function with one argument (the calling Series or DataFrame) that returns valid output for indexing (one of the above).\n",
+    "- `.loc`, `.iloc`, and also `[]` indexing can accept a `callable` as indexer.\n",
+    "\n",
+    "Getting values from an object with multi-axes selection uses the following notation (using `.loc` as an example, but the following applies to `.iloc` as well). Any of the axes accessors may be the null slice `:`. Axes left out of the specification are assumed to be `:`, e.g. `p.loc['a']` is equivalent to `p.loc['a', :]`.\n",
+    "\n",
+    "|**Object Type**|**Indexers**                        |\n",
+    "|:--            |:-                                  |\n",
+    "|Series         |`s.loc[indexer]`                    |\n",
+    "|DataFrame      |`df.loc[row_indexer, column_indexer]`|\n",
+    "\n",
+    "### Basics\n",
+    "\n",
+    "As mentioned when introducing the data structures in the last section, the primary function of indexing with `[]` (a.k.a.` __getitem__` for those familiar with implementing class behavior in Python) is selecting out lower-dimensional slices. The following table shows return type values when indexing Pandas objects with `[]`:\n",
+    "\n",
+    "|**Object Type**|**Selection**   |Return Value Type                |\n",
+    "|:-             |:-              |:-                               |\n",
+    "|Series         |`series[label]` |scalar value                     |\n",
+    "|DataFrame      |`frame[colname]`|`Series` corresponding to colname|\n",
+    "\n",
+    "Here we construct a simple time series data set to use for illustrating the indexing functionality:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "12d39083",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2000-01-01</th>\n",
+       "      <td>0.311438</td>\n",
+       "      <td>2.122763</td>\n",
+       "      <td>2.206814</td>\n",
+       "      <td>-1.488590</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-02</th>\n",
+       "      <td>-0.459154</td>\n",
+       "      <td>1.697299</td>\n",
+       "      <td>0.852562</td>\n",
+       "      <td>2.389648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-03</th>\n",
+       "      <td>1.408196</td>\n",
+       "      <td>-0.326012</td>\n",
+       "      <td>-0.137593</td>\n",
+       "      <td>-0.287003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-04</th>\n",
+       "      <td>0.926176</td>\n",
+       "      <td>1.213350</td>\n",
+       "      <td>0.039471</td>\n",
+       "      <td>1.068784</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-05</th>\n",
+       "      <td>-0.490948</td>\n",
+       "      <td>0.108342</td>\n",
+       "      <td>0.553074</td>\n",
+       "      <td>1.213043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-06</th>\n",
+       "      <td>-1.378264</td>\n",
+       "      <td>0.374637</td>\n",
+       "      <td>0.776962</td>\n",
+       "      <td>-0.125644</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-07</th>\n",
+       "      <td>0.628157</td>\n",
+       "      <td>-1.239929</td>\n",
+       "      <td>0.761446</td>\n",
+       "      <td>-0.847097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-08</th>\n",
+       "      <td>0.291123</td>\n",
+       "      <td>1.568350</td>\n",
+       "      <td>1.051489</td>\n",
+       "      <td>0.526787</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   A         B         C         D\n",
+       "2000-01-01  0.311438  2.122763  2.206814 -1.488590\n",
+       "2000-01-02 -0.459154  1.697299  0.852562  2.389648\n",
+       "2000-01-03  1.408196 -0.326012 -0.137593 -0.287003\n",
+       "2000-01-04  0.926176  1.213350  0.039471  1.068784\n",
+       "2000-01-05 -0.490948  0.108342  0.553074  1.213043\n",
+       "2000-01-06 -1.378264  0.374637  0.776962 -0.125644\n",
+       "2000-01-07  0.628157 -1.239929  0.761446 -0.847097\n",
+       "2000-01-08  0.291123  1.568350  1.051489  0.526787"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dates = pd.date_range('1/1/2000', periods=8)\n",
+    "df = pd.DataFrame(np.random.randn(8, 4),\n",
+    "                  index=dates, columns=['A', 'B', 'C', 'D'])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da294328",
+   "metadata": {},
+   "source": [
+    ":::{note}\n",
+    "None of the indexing functionality is time series specific unless specifically stated.\n",
+    ":::\n",
+    "\n",
+    "Thus, as per above, we have the most basic indexing using `[]`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "1eee749c",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1.3782643792341864"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = df['A']\n",
+    "\n",
+    "s[dates[5]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c672552",
+   "metadata": {},
+   "source": [
+    "You can pass a list of columns to `[]` to select columns in that order. If a column is not contained in the DataFrame, an exception will be raised. Multiple columns can also be set in this manner:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "5a18bcbc",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2000-01-01</th>\n",
+       "      <td>0.311438</td>\n",
+       "      <td>2.122763</td>\n",
+       "      <td>2.206814</td>\n",
+       "      <td>-1.488590</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-02</th>\n",
+       "      <td>-0.459154</td>\n",
+       "      <td>1.697299</td>\n",
+       "      <td>0.852562</td>\n",
+       "      <td>2.389648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-03</th>\n",
+       "      <td>1.408196</td>\n",
+       "      <td>-0.326012</td>\n",
+       "      <td>-0.137593</td>\n",
+       "      <td>-0.287003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-04</th>\n",
+       "      <td>0.926176</td>\n",
+       "      <td>1.213350</td>\n",
+       "      <td>0.039471</td>\n",
+       "      <td>1.068784</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-05</th>\n",
+       "      <td>-0.490948</td>\n",
+       "      <td>0.108342</td>\n",
+       "      <td>0.553074</td>\n",
+       "      <td>1.213043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-06</th>\n",
+       "      <td>-1.378264</td>\n",
+       "      <td>0.374637</td>\n",
+       "      <td>0.776962</td>\n",
+       "      <td>-0.125644</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-07</th>\n",
+       "      <td>0.628157</td>\n",
+       "      <td>-1.239929</td>\n",
+       "      <td>0.761446</td>\n",
+       "      <td>-0.847097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-08</th>\n",
+       "      <td>0.291123</td>\n",
+       "      <td>1.568350</td>\n",
+       "      <td>1.051489</td>\n",
+       "      <td>0.526787</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   A         B         C         D\n",
+       "2000-01-01  0.311438  2.122763  2.206814 -1.488590\n",
+       "2000-01-02 -0.459154  1.697299  0.852562  2.389648\n",
+       "2000-01-03  1.408196 -0.326012 -0.137593 -0.287003\n",
+       "2000-01-04  0.926176  1.213350  0.039471  1.068784\n",
+       "2000-01-05 -0.490948  0.108342  0.553074  1.213043\n",
+       "2000-01-06 -1.378264  0.374637  0.776962 -0.125644\n",
+       "2000-01-07  0.628157 -1.239929  0.761446 -0.847097\n",
+       "2000-01-08  0.291123  1.568350  1.051489  0.526787"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "be2e73fe",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2000-01-01</th>\n",
+       "      <td>2.122763</td>\n",
+       "      <td>0.311438</td>\n",
+       "      <td>2.206814</td>\n",
+       "      <td>-1.488590</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-02</th>\n",
+       "      <td>1.697299</td>\n",
+       "      <td>-0.459154</td>\n",
+       "      <td>0.852562</td>\n",
+       "      <td>2.389648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-03</th>\n",
+       "      <td>-0.326012</td>\n",
+       "      <td>1.408196</td>\n",
+       "      <td>-0.137593</td>\n",
+       "      <td>-0.287003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-04</th>\n",
+       "      <td>1.213350</td>\n",
+       "      <td>0.926176</td>\n",
+       "      <td>0.039471</td>\n",
+       "      <td>1.068784</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-05</th>\n",
+       "      <td>0.108342</td>\n",
+       "      <td>-0.490948</td>\n",
+       "      <td>0.553074</td>\n",
+       "      <td>1.213043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-06</th>\n",
+       "      <td>0.374637</td>\n",
+       "      <td>-1.378264</td>\n",
+       "      <td>0.776962</td>\n",
+       "      <td>-0.125644</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-07</th>\n",
+       "      <td>-1.239929</td>\n",
+       "      <td>0.628157</td>\n",
+       "      <td>0.761446</td>\n",
+       "      <td>-0.847097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-08</th>\n",
+       "      <td>1.568350</td>\n",
+       "      <td>0.291123</td>\n",
+       "      <td>1.051489</td>\n",
+       "      <td>0.526787</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   A         B         C         D\n",
+       "2000-01-01  2.122763  0.311438  2.206814 -1.488590\n",
+       "2000-01-02  1.697299 -0.459154  0.852562  2.389648\n",
+       "2000-01-03 -0.326012  1.408196 -0.137593 -0.287003\n",
+       "2000-01-04  1.213350  0.926176  0.039471  1.068784\n",
+       "2000-01-05  0.108342 -0.490948  0.553074  1.213043\n",
+       "2000-01-06  0.374637 -1.378264  0.776962 -0.125644\n",
+       "2000-01-07 -1.239929  0.628157  0.761446 -0.847097\n",
+       "2000-01-08  1.568350  0.291123  1.051489  0.526787"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[['B', 'A']] = df[['A', 'B']]\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e6cd9c9",
+   "metadata": {},
+   "source": [
+    "You may find this useful for applying a transform (in-place) to a subset of the columns."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9d41a7f-5d30-40e2-8508-83b4d08e1ef1",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "warning"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    "Pandas aligns all AXES when setting `Series` and `DataFrame` from `.loc`, and `.iloc`.\n",
+    "\n",
+    "This will not modify `df` because the column alignment is before value assignment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "4e8a2ee9",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2000-01-01</th>\n",
+       "      <td>2.122763</td>\n",
+       "      <td>0.311438</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-02</th>\n",
+       "      <td>1.697299</td>\n",
+       "      <td>-0.459154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-03</th>\n",
+       "      <td>-0.326012</td>\n",
+       "      <td>1.408196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-04</th>\n",
+       "      <td>1.213350</td>\n",
+       "      <td>0.926176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-05</th>\n",
+       "      <td>0.108342</td>\n",
+       "      <td>-0.490948</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-06</th>\n",
+       "      <td>0.374637</td>\n",
+       "      <td>-1.378264</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-07</th>\n",
+       "      <td>-1.239929</td>\n",
+       "      <td>0.628157</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-08</th>\n",
+       "      <td>1.568350</td>\n",
+       "      <td>0.291123</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   A         B\n",
+       "2000-01-01  2.122763  0.311438\n",
+       "2000-01-02  1.697299 -0.459154\n",
+       "2000-01-03 -0.326012  1.408196\n",
+       "2000-01-04  1.213350  0.926176\n",
+       "2000-01-05  0.108342 -0.490948\n",
+       "2000-01-06  0.374637 -1.378264\n",
+       "2000-01-07 -1.239929  0.628157\n",
+       "2000-01-08  1.568350  0.291123"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[['A', 'B']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "cf8c39ef",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2000-01-01</th>\n",
+       "      <td>2.122763</td>\n",
+       "      <td>0.311438</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-02</th>\n",
+       "      <td>1.697299</td>\n",
+       "      <td>-0.459154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-03</th>\n",
+       "      <td>-0.326012</td>\n",
+       "      <td>1.408196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-04</th>\n",
+       "      <td>1.213350</td>\n",
+       "      <td>0.926176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-05</th>\n",
+       "      <td>0.108342</td>\n",
+       "      <td>-0.490948</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-06</th>\n",
+       "      <td>0.374637</td>\n",
+       "      <td>-1.378264</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-07</th>\n",
+       "      <td>-1.239929</td>\n",
+       "      <td>0.628157</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-08</th>\n",
+       "      <td>1.568350</td>\n",
+       "      <td>0.291123</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   A         B\n",
+       "2000-01-01  2.122763  0.311438\n",
+       "2000-01-02  1.697299 -0.459154\n",
+       "2000-01-03 -0.326012  1.408196\n",
+       "2000-01-04  1.213350  0.926176\n",
+       "2000-01-05  0.108342 -0.490948\n",
+       "2000-01-06  0.374637 -1.378264\n",
+       "2000-01-07 -1.239929  0.628157\n",
+       "2000-01-08  1.568350  0.291123"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[:, ['B', 'A']] = df[['A', 'B']]\n",
+    "df[['A', 'B']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ed60d11-3f81-43b5-8274-4d896238b734",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "warning"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    "The correct way to swap column values is by using raw values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "da9754c5",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2000-01-01</th>\n",
+       "      <td>0.311438</td>\n",
+       "      <td>2.122763</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-02</th>\n",
+       "      <td>-0.459154</td>\n",
+       "      <td>1.697299</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-03</th>\n",
+       "      <td>1.408196</td>\n",
+       "      <td>-0.326012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-04</th>\n",
+       "      <td>0.926176</td>\n",
+       "      <td>1.213350</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-05</th>\n",
+       "      <td>-0.490948</td>\n",
+       "      <td>0.108342</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-06</th>\n",
+       "      <td>-1.378264</td>\n",
+       "      <td>0.374637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-07</th>\n",
+       "      <td>0.628157</td>\n",
+       "      <td>-1.239929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-08</th>\n",
+       "      <td>0.291123</td>\n",
+       "      <td>1.568350</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   A         B\n",
+       "2000-01-01  0.311438  2.122763\n",
+       "2000-01-02 -0.459154  1.697299\n",
+       "2000-01-03  1.408196 -0.326012\n",
+       "2000-01-04  0.926176  1.213350\n",
+       "2000-01-05 -0.490948  0.108342\n",
+       "2000-01-06 -1.378264  0.374637\n",
+       "2000-01-07  0.628157 -1.239929\n",
+       "2000-01-08  0.291123  1.568350"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[:, ['B', 'A']] = df[['A', 'B']].to_numpy()\n",
+    "df[['A', 'B']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "beb7928a",
+   "metadata": {},
+   "source": [
+    "### Attribute access\n",
+    "\n",
+    "You may access an index on a `Series` or column on a `DataFrame` directly as an attribute:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "86dec0c0",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "sa = pd.Series([1, 2, 3], index=list('abc'))\n",
+    "dfa = df.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "69ea1e07",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sa.b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "ce9f7637",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2000-01-01    0.311438\n",
+       "2000-01-02   -0.459154\n",
+       "2000-01-03    1.408196\n",
+       "2000-01-04    0.926176\n",
+       "2000-01-05   -0.490948\n",
+       "2000-01-06   -1.378264\n",
+       "2000-01-07    0.628157\n",
+       "2000-01-08    0.291123\n",
+       "Freq: D, Name: A, dtype: float64"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfa.A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "10cead84",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    5\n",
+       "b    2\n",
+       "c    3\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sa.a = 5\n",
+    "sa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "6db24b96",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2000-01-01</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2.122763</td>\n",
+       "      <td>2.206814</td>\n",
+       "      <td>-1.488590</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-02</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.697299</td>\n",
+       "      <td>0.852562</td>\n",
+       "      <td>2.389648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-03</th>\n",
+       "      <td>2</td>\n",
+       "      <td>-0.326012</td>\n",
+       "      <td>-0.137593</td>\n",
+       "      <td>-0.287003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-04</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1.213350</td>\n",
+       "      <td>0.039471</td>\n",
+       "      <td>1.068784</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-05</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.108342</td>\n",
+       "      <td>0.553074</td>\n",
+       "      <td>1.213043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-06</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.374637</td>\n",
+       "      <td>0.776962</td>\n",
+       "      <td>-0.125644</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-07</th>\n",
+       "      <td>6</td>\n",
+       "      <td>-1.239929</td>\n",
+       "      <td>0.761446</td>\n",
+       "      <td>-0.847097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-08</th>\n",
+       "      <td>7</td>\n",
+       "      <td>1.568350</td>\n",
+       "      <td>1.051489</td>\n",
+       "      <td>0.526787</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            A         B         C         D\n",
+       "2000-01-01  0  2.122763  2.206814 -1.488590\n",
+       "2000-01-02  1  1.697299  0.852562  2.389648\n",
+       "2000-01-03  2 -0.326012 -0.137593 -0.287003\n",
+       "2000-01-04  3  1.213350  0.039471  1.068784\n",
+       "2000-01-05  4  0.108342  0.553074  1.213043\n",
+       "2000-01-06  5  0.374637  0.776962 -0.125644\n",
+       "2000-01-07  6 -1.239929  0.761446 -0.847097\n",
+       "2000-01-08  7  1.568350  1.051489  0.526787"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfa.A = list(range(len(dfa.index)))  # ok if A already exists\n",
+    "dfa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "99790bfe",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2000-01-01</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2.122763</td>\n",
+       "      <td>2.206814</td>\n",
+       "      <td>-1.488590</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-02</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.697299</td>\n",
+       "      <td>0.852562</td>\n",
+       "      <td>2.389648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-03</th>\n",
+       "      <td>2</td>\n",
+       "      <td>-0.326012</td>\n",
+       "      <td>-0.137593</td>\n",
+       "      <td>-0.287003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-04</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1.213350</td>\n",
+       "      <td>0.039471</td>\n",
+       "      <td>1.068784</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-05</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.108342</td>\n",
+       "      <td>0.553074</td>\n",
+       "      <td>1.213043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-06</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.374637</td>\n",
+       "      <td>0.776962</td>\n",
+       "      <td>-0.125644</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-07</th>\n",
+       "      <td>6</td>\n",
+       "      <td>-1.239929</td>\n",
+       "      <td>0.761446</td>\n",
+       "      <td>-0.847097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-01-08</th>\n",
+       "      <td>7</td>\n",
+       "      <td>1.568350</td>\n",
+       "      <td>1.051489</td>\n",
+       "      <td>0.526787</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            A         B         C         D\n",
+       "2000-01-01  0  2.122763  2.206814 -1.488590\n",
+       "2000-01-02  1  1.697299  0.852562  2.389648\n",
+       "2000-01-03  2 -0.326012 -0.137593 -0.287003\n",
+       "2000-01-04  3  1.213350  0.039471  1.068784\n",
+       "2000-01-05  4  0.108342  0.553074  1.213043\n",
+       "2000-01-06  5  0.374637  0.776962 -0.125644\n",
+       "2000-01-07  6 -1.239929  0.761446 -0.847097\n",
+       "2000-01-08  7  1.568350  1.051489  0.526787"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfa['A'] = list(range(len(dfa.index)))  # use this form to create a new column\n",
+    "dfa"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1dac3787-172d-4127-b483-d08921a0e060",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "warning"
+     ],
+     "id": ""
+    }
+   },
+   "source": [
+    "- You can use this access only if the index element is a valid Python identifier, e.g. s.1 is not allowed. See here for an explanation of valid identifiers.\n",
+    "\n",
+    "- The attribute will not be available if it conflicts with an existing method name, e.g. s.min is not allowed, but s['min'] is possible.\n",
+    "\n",
+    "- Similarly, the attribute will not be available if it conflicts with any of the following list: index, major_axis, minor_axis, items.\n",
+    "\n",
+    "- In any of these cases, standard indexing will still work, e.g. s['1'], s['min'], and s['index'] will access the corresponding element or column."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae10e002",
+   "metadata": {},
+   "source": [
+    "If you are using the IPython environment, you may also use tab-completion to see these accessible attributes.\n",
+    "\n",
+    "You can also assign a `dict` to a row of a `DataFrame`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "29d1e1b0",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9</td>\n",
+       "      <td>99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   x   y\n",
+       "0  1   3\n",
+       "1  9  99\n",
+       "2  3   5"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = pd.DataFrame({'x': [1, 2, 3], 'y': [3, 4, 5]})\n",
+    "x.iloc[1] = {'x': 9, 'y': 99}\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e1ee914",
+   "metadata": {},
+   "source": [
+    "You can use attribute access to modify an existing element of a Series or column of a DataFrame, but be careful; if you try to use attribute access to create a new column, it creates a new attribute rather than a new column. In 0.21.0 and later, this will raise a `UserWarning`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "id": "b55c8c4d",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\87554\\AppData\\Local\\Temp\\ipykernel_46616\\269534380.py:2: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
+      "  df.two = [4, 5, 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame({'one': [1., 2., 3.]})\n",
+    "df.two = [4, 5, 6]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "e0a12bf3",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>one</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   one\n",
+       "0  1.0\n",
+       "1  2.0\n",
+       "2  3.0"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96013159",
+   "metadata": {},
+   "source": [
+    "### Slicing ranges\n",
+    "\n",
+    "For now, we explain the semantics of slicing using the [] operator.\n",
+    "\n",
+    "With Series, the syntax works exactly as with an ndarray, returning a slice of the values and the corresponding labels:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "ab285a63",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2000-01-01    0.311438\n",
+       "2000-01-02   -0.459154\n",
+       "2000-01-03    1.408196\n",
+       "2000-01-04    0.926176\n",
+       "2000-01-05   -0.490948\n",
+       "2000-01-06   -1.378264\n",
+       "2000-01-07    0.628157\n",
+       "2000-01-08    0.291123\n",
+       "Freq: D, Name: A, dtype: float64"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "id": "73654be5",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2000-01-01    0.311438\n",
+       "2000-01-02   -0.459154\n",
+       "2000-01-03    1.408196\n",
+       "2000-01-04    0.926176\n",
+       "2000-01-05   -0.490948\n",
+       "Freq: D, Name: A, dtype: float64"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "bafda5a6",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2000-01-01    0.311438\n",
+       "2000-01-03    1.408196\n",
+       "2000-01-05   -0.490948\n",
+       "2000-01-07    0.628157\n",
+       "Freq: 2D, Name: A, dtype: float64"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[::2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "e28c3dc5",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2000-01-08    0.291123\n",
+       "2000-01-07    0.628157\n",
+       "2000-01-06   -1.378264\n",
+       "2000-01-05   -0.490948\n",
+       "2000-01-04    0.926176\n",
+       "2000-01-03    1.408196\n",
+       "2000-01-02   -0.459154\n",
+       "2000-01-01    0.311438\n",
+       "Freq: -1D, Name: A, dtype: float64"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[::-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13b86bff",
+   "metadata": {},
+   "source": [
+    "Note that setting works as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "id": "46dbb94c",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2000-01-01    0.000000\n",
+       "2000-01-02    0.000000\n",
+       "2000-01-03    0.000000\n",
+       "2000-01-04    0.000000\n",
+       "2000-01-05    0.000000\n",
+       "2000-01-06   -1.378264\n",
+       "2000-01-07    0.628157\n",
+       "2000-01-08    0.291123\n",
+       "Freq: D, Name: A, dtype: float64"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s2 = s.copy()\n",
+    "s2[:5] = 0\n",
+    "s2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89fac206",
+   "metadata": {},
+   "source": [
+    "With DataFrame, slicing inside of `[]` slices the rows. This is provided largely as a convenience since it is such a common operation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "id": "d2c3c1af",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>one</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   one\n",
+       "0  1.0\n",
+       "1  2.0\n",
+       "2  3.0"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "id": "c46fa1e7",
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "code-cell"
+     ],
+     "id": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>one</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   one\n",
+       "2  3.0\n",
+       "1  2.0\n",
+       "0  1.0"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[::-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "674b4c29-0e55-4243-a46c-7fbffb51a02a",
+   "metadata": {},
+   "source": [
+    "## Acknowledgments\n",
+    "\n",
+    "Thanks for [Pandas user guide](https://pandas.pydata.org/docs/user_guide/index.html). It contributes the majority of the content in this chapter."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/data-science/working-with-data/relational-databases.ipynb b/_sources/data-science/working-with-data/relational-databases.ipynb
index c0d37ca71c..0ca30ae27c 100644
--- a/_sources/data-science/working-with-data/relational-databases.ipynb
+++ b/_sources/data-science/working-with-data/relational-databases.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "1945f40b",
+   "id": "d25f0717",
    "metadata": {},
    "source": [
     "# Relational databases\n",
diff --git a/_sources/data-science/working-with-data/working-with-data.ipynb b/_sources/data-science/working-with-data/working-with-data.ipynb
index ae014c1328..111abc09d8 100644
--- a/_sources/data-science/working-with-data/working-with-data.ipynb
+++ b/_sources/data-science/working-with-data/working-with-data.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "c15b2dc9",
+   "id": "6ab3d42f",
    "metadata": {},
    "source": [
     "# Working with data\n",
diff --git a/_sources/deep-learning/autoencoder.ipynb b/_sources/deep-learning/autoencoder.ipynb
index 4a291705f3..0f598fd884 100644
--- a/_sources/deep-learning/autoencoder.ipynb
+++ b/_sources/deep-learning/autoencoder.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "9d157625",
+   "id": "26432477",
    "metadata": {},
    "source": [
     "# Autoencoder\n",
@@ -108,7 +108,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "228211b1",
+   "id": "1c39e21d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -118,7 +118,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cf0a9116",
+   "id": "02a7afd0",
    "metadata": {},
    "source": [
     "MNIST Dataset parameters."
@@ -127,7 +127,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5c2c15aa",
+   "id": "2eaf34ef",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -136,7 +136,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5f518c57",
+   "id": "0c62fa63",
    "metadata": {},
    "source": [
     "Training parameters."
@@ -145,7 +145,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f62780cd",
+   "id": "dcd59057",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -157,7 +157,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "38a7f6cc",
+   "id": "376ff0b9",
    "metadata": {},
    "source": [
     "Network Parameters"
@@ -166,7 +166,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "66d087e6",
+   "id": "3c33e257",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,7 +176,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6971304",
+   "id": "c50d49f0",
    "metadata": {},
    "source": [
     "Prepare MNIST data."
@@ -185,7 +185,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "604453ac",
+   "id": "071de12f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -201,7 +201,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a646913f",
+   "id": "4566d310",
    "metadata": {},
    "source": [
     "Store layers weight & bias.\n",
@@ -211,7 +211,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f0730e6e",
+   "id": "45bf0285",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -233,7 +233,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "96e1bc03",
+   "id": "1e77c8d0",
    "metadata": {},
    "source": [
     "Building the encoder."
@@ -242,7 +242,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b584fe82",
+   "id": "46bf44f0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -258,7 +258,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "098b2685",
+   "id": "58f6c79f",
    "metadata": {},
    "source": [
     "Building the decoder."
@@ -267,7 +267,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fc2fa0ae",
+   "id": "f357096c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -283,7 +283,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a477bbcd",
+   "id": "4d37abf8",
    "metadata": {},
    "source": [
     "Mean square loss between original images and reconstructed ones."
@@ -292,7 +292,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b6dcf84e",
+   "id": "87ef739e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -302,7 +302,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b42d8ee4",
+   "id": "429308e7",
    "metadata": {},
    "source": [
     "Adam optimizer."
@@ -311,7 +311,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0378f043",
+   "id": "77877aff",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -320,7 +320,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "78e12a28",
+   "id": "66ab0c6f",
    "metadata": {},
    "source": [
     "Optimization process."
@@ -329,7 +329,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c0b09961",
+   "id": "ca1410d9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -353,7 +353,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f429f19",
+   "id": "572a7552",
    "metadata": {},
    "source": [
     "Run training for the given number of steps."
@@ -362,7 +362,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c2f3c8a9",
+   "id": "bee12173",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -377,7 +377,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cdc254ae",
+   "id": "418fa76b",
    "metadata": {},
    "source": [
     "Testing and Visualization."
@@ -386,7 +386,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dd6b62fa",
+   "id": "51737843",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -395,7 +395,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b624639",
+   "id": "2a6fab19",
    "metadata": {},
    "source": [
     "Encode and decode images from test set and visualize their reconstruction."
@@ -404,7 +404,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d4b2b9ca",
+   "id": "a3b9d7ca",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -438,7 +438,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "74a7edf0",
+   "id": "52acb449",
    "metadata": {},
    "source": [
     "<div hidden>\n",
diff --git a/_sources/deep-learning/cnn.ipynb b/_sources/deep-learning/cnn.ipynb
index 5fc13ad092..a7f1208508 100644
--- a/_sources/deep-learning/cnn.ipynb
+++ b/_sources/deep-learning/cnn.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "ede1389d",
+   "id": "f1c9a833",
    "metadata": {},
    "source": [
     "# Convolutional Neural Networks\n",
@@ -80,7 +80,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "aae3c170",
+   "id": "43802b62",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -278,7 +278,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9c67dfcd",
+   "id": "3c2888f6",
    "metadata": {},
    "source": [
     "## CIFAR-10\n",
@@ -305,7 +305,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8be9007b",
+   "id": "1cf9eca9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -617,7 +617,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f910420a",
+   "id": "94f7abdb",
    "metadata": {},
    "source": [
     "## How to fine-tune current CNN architectures?\n",
@@ -628,7 +628,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7747b22f",
+   "id": "1218e951",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -658,7 +658,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cab708e9",
+   "id": "11da34db",
    "metadata": {},
    "source": [
     "### Code"
@@ -667,7 +667,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "21ba603d",
+   "id": "69987136",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -766,7 +766,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "76ea1cef",
+   "id": "2fd96a2e",
    "metadata": {},
    "source": [
     "## Stylenet / Neural-Style\n",
@@ -808,7 +808,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "775a52c7",
+   "id": "00b1c701",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1015,7 +1015,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "86dd13ce",
+   "id": "25eebcd0",
    "metadata": {},
    "source": [
     "## Deepdream in TensorFlow\n",
@@ -1035,7 +1035,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d5eb4c49",
+   "id": "53e2849e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1235,7 +1235,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec3b16ec",
+   "id": "37d29f5a",
    "metadata": {},
    "source": [
     "## Your turn! 🚀\n",
diff --git a/_sources/deep-learning/difussion-model.ipynb b/_sources/deep-learning/difussion-model.ipynb
index 6d734e26d5..42164a72b2 100644
--- a/_sources/deep-learning/difussion-model.ipynb
+++ b/_sources/deep-learning/difussion-model.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "c4441251",
+   "id": "aa18e11d",
    "metadata": {},
    "source": [
     "# Diffusion Model\n",
@@ -162,7 +162,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "351d0d37",
+   "id": "6399d19f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7db8190f",
+   "id": "6c87e664",
    "metadata": {},
    "source": [
     "### Configuration"
@@ -191,7 +191,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "242a38cc",
+   "id": "0f21b5e3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "49aee653",
+   "id": "52d8860c",
    "metadata": {},
    "source": [
     "### Data Loading\n",
@@ -222,7 +222,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bc22eb5a",
+   "id": "2839a017",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -233,7 +233,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11b849c7",
+   "id": "1deed892",
    "metadata": {},
    "source": [
     "### Data Preprocessing"
@@ -242,7 +242,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "65c5d5ff",
+   "id": "ba26144b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -252,7 +252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "20469012",
+   "id": "1fac8d90",
    "metadata": {},
    "source": [
     "### Data Augmentation"
@@ -261,7 +261,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fe7b1124",
+   "id": "16205cd4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -271,7 +271,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "706ecfd3",
+   "id": "33d66751",
    "metadata": {},
    "source": [
     "### Data"
@@ -280,7 +280,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9dca97cb",
+   "id": "0f2fdbe2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -297,7 +297,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7d071388",
+   "id": "8280ac3b",
    "metadata": {},
    "source": [
     "### Linear schedule-beta"
@@ -306,7 +306,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "67f2dd51",
+   "id": "72b3a32a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -325,7 +325,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d8d5f885",
+   "id": "b87f09fc",
    "metadata": {},
    "source": [
     "### Define the alphas"
@@ -334,7 +334,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5fe0579d",
+   "id": "e2aa86dd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -351,7 +351,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eb1d7ea2",
+   "id": "882d0bd1",
    "metadata": {},
    "source": [
     "### Getting a sample $(x_t)$"
@@ -360,7 +360,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f777b481",
+   "id": "11919e11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -381,7 +381,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3e408a66",
+   "id": "67ef8584",
    "metadata": {},
    "source": [
     "### Modeling"
@@ -390,7 +390,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0b775f6f",
+   "id": "2374630d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -500,7 +500,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "21293afd",
+   "id": "d472d817",
    "metadata": {},
    "source": [
     "### Training"
@@ -509,7 +509,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3974da31",
+   "id": "e7177f1d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -564,7 +564,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "56e38ce9",
+   "id": "7984d25e",
    "metadata": {},
    "source": [
     "#### Testing"
@@ -573,7 +573,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "59d6f7a7",
+   "id": "f2965058",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -614,7 +614,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5894b828",
+   "id": "7d61b52e",
    "metadata": {},
    "source": [
     "#### GIF output"
@@ -623,7 +623,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f1c1f7c9",
+   "id": "1eeba59e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -643,7 +643,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a4a324fd",
+   "id": "8967e386",
    "metadata": {},
    "source": [
     "## Your turn! 🚀\n",
diff --git a/_sources/deep-learning/dqn.ipynb b/_sources/deep-learning/dqn.ipynb
index 3d71827359..48afd7fff7 100644
--- a/_sources/deep-learning/dqn.ipynb
+++ b/_sources/deep-learning/dqn.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "ed187bb0",
+   "id": "40ce9fa0",
    "metadata": {},
    "source": [
     "# Deep Q-learning\n",
@@ -228,7 +228,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a11f27a5",
+   "id": "d622b6e2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -241,7 +241,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "237a9a1d",
+   "id": "68e5c9aa",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -562,7 +562,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "71d99d4e",
+   "id": "fb312bad",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -576,7 +576,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "71e0ccf3",
+   "id": "18f1aefd",
    "metadata": {},
    "source": [
     "## Your turn! 🚀\n",
diff --git a/_sources/deep-learning/gan.ipynb b/_sources/deep-learning/gan.ipynb
index e2eb657e90..8bfc3b53e3 100644
--- a/_sources/deep-learning/gan.ipynb
+++ b/_sources/deep-learning/gan.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "5bf66e70",
+   "id": "91b4e058",
    "metadata": {},
    "source": [
     "# Generative adversarial networks \n",
@@ -82,7 +82,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ce34934c",
+   "id": "f45e5256",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9b70efe",
+   "id": "a41cd6ae",
    "metadata": {},
    "source": [
     "Load Data"
@@ -132,7 +132,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9bf7f9b1",
+   "id": "4784882e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -154,7 +154,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4e9837c5",
+   "id": "2c1d8d32",
    "metadata": {},
    "source": [
     "Initialize Graph"
@@ -163,7 +163,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "445d1e69",
+   "id": "7b6a55d5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -246,7 +246,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f5bf52bb",
+   "id": "1a930ef9",
    "metadata": {},
    "source": [
     "Testing"
@@ -255,7 +255,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9fd12706",
+   "id": "81841e22",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -265,7 +265,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c27da24",
+   "id": "37df20c4",
    "metadata": {},
    "source": [
     "Inits"
@@ -274,7 +274,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "14327e0c",
+   "id": "f994388d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -290,7 +290,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7192b6f6",
+   "id": "b9d607b7",
    "metadata": {},
    "source": [
     "Train"
@@ -299,7 +299,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1eac43c1",
+   "id": "4a56294f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -338,7 +338,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ec5505b",
+   "id": "440d4db9",
    "metadata": {},
    "source": [
     "## Your turn! 🚀\n",
diff --git a/_sources/deep-learning/image-classification.ipynb b/_sources/deep-learning/image-classification.ipynb
index 78f1b42a7e..7e30cfe8d9 100644
--- a/_sources/deep-learning/image-classification.ipynb
+++ b/_sources/deep-learning/image-classification.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "b76ec012",
+   "id": "e20f56bf",
    "metadata": {},
    "source": [
     "# Image classification\n",
@@ -71,7 +71,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0582998d",
+   "id": "a57eb7e0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4a18fbd8",
+   "id": "bc7cd33c",
    "metadata": {},
    "source": [
     "This function is useful for constructing convolutional neural network architectures, as it provides a simple way to combine convolution and batch normalization layers.\n",
@@ -125,7 +125,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "087061c7",
+   "id": "54de2cce",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -263,7 +263,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f4f6804c",
+   "id": "53989c52",
    "metadata": {},
    "source": [
     "This code defines a RepVGGBlock layer in TensorFlow, which is a convolutional block used in the RepVGG model. The RepVGG model is a neural network architecture that achieves high accuracy while having a simple structure. The RepVGGBlock layer is designed to be more efficient and easier to train compared to other convolutional layers.\n",
@@ -389,7 +389,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7a4541e4",
+   "id": "597b1cdb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -429,7 +429,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d542fa1",
+   "id": "e58ae3e6",
    "metadata": {},
    "source": [
     "This implementation uses the conv_bn_relu function to perform a convolution operation followed by batch normalization and ReLU activation. \n",
@@ -458,7 +458,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "32b4ef04",
+   "id": "587eb8b3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -512,7 +512,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ae175724",
+   "id": "25fa22a1",
    "metadata": {},
    "source": [
     "This implementation includes functions for building the building blocks of DenseNet: conv_block, dense_block, and transition_block. These building blocks are then used to construct the densenet function, which takes an input tensor and returns the output logits for a classification task.\n",
@@ -543,7 +543,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f5286f02",
+   "id": "d2f4f5f4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -593,7 +593,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "21aca266",
+   "id": "e7cfe6a8",
    "metadata": {},
    "source": [
     "This implementation defines two functions: depthwise_separable_conv and mobilenet. The former implements the depthwise separable convolution operation used by the MobileNet architecture, while the latter constructs the entire MobileNet model.\n",
@@ -610,7 +610,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c308b8cf",
+   "id": "a0306154",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -675,7 +675,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "479c088a",
+   "id": "0c00bca9",
    "metadata": {},
    "source": [
     "The above code defines a Vision Transformer (ViT) model in TensorFlow, which is a state-of-the-art architecture for image classification tasks that combines the transformer architecture with a patch-based approach for image processing.\n",
diff --git a/_sources/deep-learning/image-segmentation.ipynb b/_sources/deep-learning/image-segmentation.ipynb
index a8dd5b6f21..6923511a1b 100644
--- a/_sources/deep-learning/image-segmentation.ipynb
+++ b/_sources/deep-learning/image-segmentation.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "e381dfc3",
+   "id": "1a1741e6",
    "metadata": {},
    "source": [
     "# Image segmentation\n",
@@ -30,7 +30,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "735ed825",
+   "id": "e86e9db3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "713f31ac",
+   "id": "87b9c60c",
    "metadata": {},
    "source": [
     "### Prepare the Oxford-IIIT Pets dataset"
@@ -53,7 +53,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6bdce03c",
+   "id": "5b8e3d63",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11277e99",
+   "id": "cb015d8c",
    "metadata": {},
    "source": [
     "In addition, the image color values are normalized to the range [0, 1]. Finally, as mentioned above the pixels in the segmentation mask are labeled either {1, 2, 3}. For the sake of convenience, subtract 1 from the segmentation mask, resulting in labels that are : {0, 1, 2}."
@@ -71,7 +71,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "eda49e31",
+   "id": "0af77da9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a5224764",
+   "id": "7aebc91c",
    "metadata": {},
    "source": [
     "The dataset already contains the required training and test splits, so continue to use the same splits:"
@@ -104,7 +104,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "41575af7",
+   "id": "8f0e5a20",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -118,7 +118,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "235a12d3",
+   "id": "29a4709b",
    "metadata": {},
    "source": [
     "The following class performs a simple augmentation by randomly-flipping an image. Go to the Image augmentation tutorial to learn more."
@@ -127,7 +127,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6d8f4815",
+   "id": "5a06f944",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -146,7 +146,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cdfe4a45",
+   "id": "f075b5da",
    "metadata": {},
    "source": [
     "Build the input pipeline, applying the augmentation after batching the inputs:"
@@ -155,7 +155,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ce6cba17",
+   "id": "35b7888b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -173,7 +173,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4034bf55",
+   "id": "d68dd470",
    "metadata": {},
    "source": [
     "Visualize an image example and its corresponding mask from the dataset:"
@@ -182,7 +182,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "96d2e17a",
+   "id": "55eb1846",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -205,7 +205,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "86613a62",
+   "id": "5b95b7a5",
    "metadata": {},
    "source": [
     ":::{figure-md} 01_dataset_example_mask\n",
@@ -228,7 +228,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c0b2c0a9",
+   "id": "de534bc1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -252,7 +252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd62dc41",
+   "id": "95ed91ba",
    "metadata": {},
    "source": [
     "The decoder/upsampler is simply a series of upsample blocks implemented in TensorFlow examples:"
@@ -261,7 +261,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2c037825",
+   "id": "c12f1bcd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -276,7 +276,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c69d466d",
+   "id": "dc182336",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -306,7 +306,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc7d4c3d",
+   "id": "99792008",
    "metadata": {},
    "source": [
     "Note that the number of filters on the last layer is set to the number of . This will be one output channel per class.output_channels.\n",
@@ -319,7 +319,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d28d6b77",
+   "id": "feb26bed",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -333,7 +333,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2066e16c",
+   "id": "03ccd01d",
    "metadata": {},
    "source": [
     "Try out the model to check what it predicts before training:"
@@ -342,7 +342,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f465b930",
+   "id": "eba41299",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -363,7 +363,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1076c884",
+   "id": "346d625e",
    "metadata": {},
    "source": [
     "Perhaps now we can try to show the predictions."
@@ -372,7 +372,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "27c91de3",
+   "id": "ddcb7775",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -382,7 +382,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6da1e68f",
+   "id": "18990430",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -418,7 +418,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "43ee7502",
+   "id": "70bf91f0",
    "metadata": {},
    "source": [
     "### Make predictions\n",
@@ -429,7 +429,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "deeeb44b",
+   "id": "840c0845",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -438,7 +438,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d7a1e97",
+   "id": "8831fe3b",
    "metadata": {},
    "source": [
     ":::{figure-md} 02_display_prediction\n",
@@ -457,7 +457,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7b747309",
+   "id": "08070181",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -681,7 +681,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7456c85",
+   "id": "55001442",
    "metadata": {},
    "source": [
     "### FCN\n",
@@ -704,7 +704,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c360ab5b",
+   "id": "7251b432",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -839,7 +839,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "22666231",
+   "id": "88bf5d8b",
    "metadata": {},
    "source": [
     "### SegNet\n",
@@ -864,7 +864,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "206c4fb5",
+   "id": "88d6abfb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -956,7 +956,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "71fcf526",
+   "id": "338f2ab9",
    "metadata": {},
    "source": [
     "### DeepLab V3\n",
@@ -985,7 +985,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "77e03b0e",
+   "id": "684177c1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1179,7 +1179,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7167451",
+   "id": "a9730fc5",
    "metadata": {},
    "source": [
     "## Your turn! 🚀\n",
diff --git a/_sources/deep-learning/object-detection.ipynb b/_sources/deep-learning/object-detection.ipynb
index 1e95fe30bf..01645f8c7d 100644
--- a/_sources/deep-learning/object-detection.ipynb
+++ b/_sources/deep-learning/object-detection.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "86ebe7a4",
+   "id": "92db163a",
    "metadata": {},
    "source": [
     "# Object detection\n",
@@ -63,7 +63,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3dd270cb",
+   "id": "88ae13d8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "201bca37",
+   "id": "8b222411",
    "metadata": {},
    "source": [
     "```{seealso}\n",
@@ -126,7 +126,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8c6edce1",
+   "id": "88de2ac1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -222,7 +222,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cf17d158",
+   "id": "d4b212eb",
    "metadata": {},
    "source": [
     "- Mask"
@@ -231,7 +231,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6e0f0ab5",
+   "id": "877a2b5a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -266,7 +266,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca1ea81b",
+   "id": "8d03e6f9",
    "metadata": {},
    "source": [
     "### FCOS\n",
@@ -300,7 +300,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c47a263e",
+   "id": "28b8a367",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -349,7 +349,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5fe47502",
+   "id": "4fd1001d",
    "metadata": {},
    "source": [
     "### DETR\n",
@@ -374,7 +374,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8b1ecd5d",
+   "id": "19eb1023",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -414,7 +414,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "48bc5420",
+   "id": "a3cca477",
    "metadata": {},
    "source": [
     "```{seealso}\n",
diff --git a/_sources/deep-learning/rnn.ipynb b/_sources/deep-learning/rnn.ipynb
index dea1b00209..3491e9e03a 100644
--- a/_sources/deep-learning/rnn.ipynb
+++ b/_sources/deep-learning/rnn.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "209640ef",
+   "id": "5eb6c74a",
    "metadata": {},
    "source": [
     "# Recurrent Neural Networks\n",
@@ -89,7 +89,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ad985368",
+   "id": "d18507cd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -289,7 +289,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6aef74e",
+   "id": "050a89f3",
    "metadata": {},
    "source": [
     "## Your turn! 🚀\n",
diff --git a/_sources/deep-learning/time-series.ipynb b/_sources/deep-learning/time-series.ipynb
index 5a6b032a8d..c775316205 100644
--- a/_sources/deep-learning/time-series.ipynb
+++ b/_sources/deep-learning/time-series.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "8645ad79",
+   "id": "c1854fb4",
    "metadata": {},
    "source": [
     "# Time series\n",
@@ -31,7 +31,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1f4d5427",
+   "id": "ae73d6bb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5f1336f1",
+   "id": "b5a2a5f3",
    "metadata": {},
    "source": [
     "This series records the number of hardcover book sales at a retail store over 30 days. Notice that we have a single column of observations $Hardcover$ with a time index $Date$.\n",
@@ -71,7 +71,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "19432b95",
+   "id": "21720ee6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -82,7 +82,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "03e057b3",
+   "id": "2952bb2e",
    "metadata": {},
    "source": [
     "Linear regression with the time dummy produces the model:\n",
@@ -95,7 +95,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8402f701",
+   "id": "ccaf6b35",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09e93e69",
+   "id": "f6874edc",
    "metadata": {},
    "source": [
     "Time-step features let you model time dependence. A series is time dependent if its values can be predicted from the time they occured. In the Hardcover Sales series, we can predict that sales later in the month are generally higher than sales earlier in the month.\n",
@@ -140,7 +140,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4fce89eb",
+   "id": "4a1bd81f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -151,7 +151,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed0212c2",
+   "id": "0f623d5c",
    "metadata": {},
    "source": [
     "Linear regression with a lag feature produces the model: $target = weight * lag + bias$. So lag features let us fit curves to lag plots where each observation in a series is plotted against the previous observation."
@@ -160,7 +160,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b3b463f5",
+   "id": "0023042b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -172,7 +172,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af202dc6",
+   "id": "3a287e97",
    "metadata": {},
    "source": [
     "You can see from the lag plot that sales on one day $(Hardcover)$ are correlated with sales from the previous day $(Lag_1)$. When you see a relationship like this, you know a lag feature will be useful.\n",
@@ -189,7 +189,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7892edfd",
+   "id": "e955b248",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -245,7 +245,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "60941ce6",
+   "id": "91af82a7",
    "metadata": {},
    "source": [
     "### Time-step feature\n",
@@ -256,7 +256,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "69be5bc4",
+   "id": "2550d3f5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -269,7 +269,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "92da996e",
+   "id": "f7d3d386",
    "metadata": {},
    "source": [
     "The procedure for fitting a linear regression model follows the standard steps for scikit-learn."
@@ -278,7 +278,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4366b878",
+   "id": "61d4cfbc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -299,7 +299,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2933827b",
+   "id": "2748ba33",
    "metadata": {},
    "source": [
     "The model actually created is (approximately): $Vehicles = 22.5 * Time + 98176$. Plotting the fitted values over time shows us how fitting linear regression to the time dummy creates the trend line defined by this equation."
@@ -308,7 +308,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ad7a94aa",
+   "id": "2d39b235",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -319,7 +319,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7f988327",
+   "id": "35d55cf8",
    "metadata": {},
    "source": [
     "### Lag feature\n",
@@ -330,7 +330,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "63baf8a5",
+   "id": "dd0f3fbf",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -340,7 +340,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09da39d3",
+   "id": "fd526244",
    "metadata": {},
    "source": [
     "When creating lag features, we need to decide what to do with the missing values produced. Filling them in is one option, maybe with 0.0 or \"backfilling\" with the first known value. Instead, we'll just drop the missing values, making sure to also drop values in the target from corresponding dates."
@@ -349,7 +349,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e5da783f",
+   "id": "f28a71d8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -368,7 +368,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "50b7320b",
+   "id": "29539c55",
    "metadata": {},
    "source": [
     "The lag plot shows us how well we were able to fit the relationship between the number of vehicles one day and the number the previous day."
@@ -377,7 +377,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ca0dcbba",
+   "id": "f9a500d0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -392,7 +392,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3048c734",
+   "id": "e7feb7a6",
    "metadata": {},
    "source": [
     "What does this prediction from a lag feature mean about how well we can predict the series across time? The following time plot shows us how our forecasts now respond to the behavior of the series in the recent past."
@@ -401,7 +401,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "74ecd448",
+   "id": "6b2c53bf",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -411,7 +411,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d2919c95",
+   "id": "23ce035d",
    "metadata": {},
    "source": [
     "The best time series models will usually include some combination of time-step features and lag features. Over the next few lessons, we'll learn how to engineer features modeling the most common patterns in time series using the features from this lesson as a starting point.\n",
@@ -449,7 +449,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2c572c3c",
+   "id": "d8fc8354",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -487,7 +487,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27430ed6",
+   "id": "5a242e0a",
    "metadata": {},
    "source": [
     "The above illustrates how a dataset would be prepared similar to the Defining a Forecast figure: a three-step forecasting task with a two-step lead time using five lag features. The original time series is y_step_1. The missing values we could either fill-in or drop.\n",
@@ -548,7 +548,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cf39be0a",
+   "id": "8f529dc6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -611,7 +611,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b816b118",
+   "id": "0baff6cd",
    "metadata": {},
    "source": [
     "First we'll prepare our target series (weekly office visits for the flu) for multistep forecasting. Once this is done, training and prediction will be very straightfoward."
@@ -620,7 +620,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d65f749d",
+   "id": "c6c9120f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -655,7 +655,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "294641ce",
+   "id": "69dd4c29",
    "metadata": {},
    "source": [
     "#### Multioutput model\n",
@@ -666,7 +666,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "65644b9b",
+   "id": "1a7a96fb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -682,7 +682,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c52b1dd4",
+   "id": "3f6021eb",
    "metadata": {},
    "source": [
     "Remember that a multistep model will produce a complete forecast for each instance used as input. There are 269 weeks in the training set and 90 weeks in the test set, and we now have an 8-step forecast for each of these weeks."
@@ -691,7 +691,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "76289547",
+   "id": "734f96dc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -711,7 +711,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8c2527c2",
+   "id": "d66572cd",
    "metadata": {},
    "source": [
     "#### Direct strategy\n",
@@ -722,7 +722,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f4bba1fc",
+   "id": "118b2487",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -737,7 +737,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0ea77a51",
+   "id": "080d1a93",
    "metadata": {},
    "source": [
     "XGBoost here is clearly overfitting on the training set. But on the test set it seems it was able to capture some of the dynamics of the flu season better than the linear regression model. It would likely do even better with some hyperparameter tuning."
@@ -746,7 +746,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "37ad8066",
+   "id": "ea98afb0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -766,7 +766,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "95cbe306",
+   "id": "e1408815",
    "metadata": {},
    "source": [
     "To use the DirRec strategy, you would only need to replace $MultiOutputRegressor$ with another scikit-learn wrapper, $RegressorChain$. The Recursive strategy we would need to code ourselves.\n",
diff --git a/_sources/machine-learning-productionization/data-engineering.ipynb b/_sources/machine-learning-productionization/data-engineering.ipynb
index e0e1da6be4..f73299cb9b 100644
--- a/_sources/machine-learning-productionization/data-engineering.ipynb
+++ b/_sources/machine-learning-productionization/data-engineering.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "eeebae2a",
+   "id": "23bcbf5b",
    "metadata": {},
    "source": [
     "# Data engineering\n",
diff --git a/_sources/machine-learning-productionization/model-deployment.ipynb b/_sources/machine-learning-productionization/model-deployment.ipynb
index 3e1399a5b1..d9f8d71e7b 100644
--- a/_sources/machine-learning-productionization/model-deployment.ipynb
+++ b/_sources/machine-learning-productionization/model-deployment.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "c684330b",
+   "id": "b3f45026",
    "metadata": {},
    "source": [
     "# Model deployment\n",
diff --git a/_sources/machine-learning-productionization/model-training-and-evaluation.ipynb b/_sources/machine-learning-productionization/model-training-and-evaluation.ipynb
index 44e1f69373..9a52fba795 100644
--- a/_sources/machine-learning-productionization/model-training-and-evaluation.ipynb
+++ b/_sources/machine-learning-productionization/model-training-and-evaluation.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7e0d16c5",
+   "id": "b61452bd",
    "metadata": {},
    "source": [
     "# Model training & evaluation\n",
diff --git a/_sources/machine-learning-productionization/overview.ipynb b/_sources/machine-learning-productionization/overview.ipynb
index bd2bf2b419..93575d2840 100644
--- a/_sources/machine-learning-productionization/overview.ipynb
+++ b/_sources/machine-learning-productionization/overview.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "c8519451",
+   "id": "08e2681b",
    "metadata": {},
    "source": [
     "# Overview\n",
@@ -83,7 +83,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "c2f62c49",
+   "id": "11d51dbf",
    "metadata": {
     "tags": [
      "output_scroll"
@@ -299,7 +299,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5f80a07",
+   "id": "e919e432",
    "metadata": {},
    "source": [
     "The above data is publicly available, and able to be automatically and easily sourced by a web crawler. There are also other ingestion approaches, including synthetic data simulation and manual collecting.\n",
@@ -314,7 +314,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "2e6a4aa0",
+   "id": "de885765",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -343,7 +343,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "82ea6354",
+   "id": "1896cc65",
    "metadata": {},
    "source": [
     "## Model training & evaluation\n",
diff --git a/_sources/machine-learning-productionization/problem-framing.ipynb b/_sources/machine-learning-productionization/problem-framing.ipynb
index 2fc53791e1..a33d536d4a 100644
--- a/_sources/machine-learning-productionization/problem-framing.ipynb
+++ b/_sources/machine-learning-productionization/problem-framing.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "cdbea74f",
+   "id": "8a27d14b",
    "metadata": {},
    "source": [
     "# Problem framing\n",
diff --git a/_sources/ml-advanced/ensemble-learning/bagging.ipynb b/_sources/ml-advanced/ensemble-learning/bagging.ipynb
index b4f9c8944a..a8677d5f4c 100644
--- a/_sources/ml-advanced/ensemble-learning/bagging.ipynb
+++ b/_sources/ml-advanced/ensemble-learning/bagging.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "0508144e",
+   "id": "10ef57a2",
    "metadata": {},
    "source": [
     "# Bagging\n",
@@ -58,7 +58,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "38b111d2",
+   "id": "280a8721",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,7 +75,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "451bbc4e",
+   "id": "3a589c63",
    "metadata": {
     "tags": [
      "output_scroll"
@@ -303,7 +303,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "00ed03e4",
+   "id": "b993fd84",
    "metadata": {},
    "outputs": [
     {
@@ -339,7 +339,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "084805b0",
+   "id": "241b33ef",
    "metadata": {},
    "source": [
     "Looks like loyal customers make fewer calls to customer service than those who eventually leave. Now, it might be a good idea to estimate the average number of customer service calls in each group. Since our dataset is small, we would not get a good estimate by simply calculating the mean of the original sample. We will be better off applying the bootstrap method. Let's generate 1000 new bootstrap samples from our original population and produce an interval estimate of the mean."
@@ -348,7 +348,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "c84c3f14",
+   "id": "1e8e20b3",
    "metadata": {},
    "outputs": [
     {
@@ -404,7 +404,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "42a4f876",
+   "id": "b3769743",
    "metadata": {},
    "source": [
     "For the interpretation of confidence intervals, you can address this [concise note](https://www.graphpad.com/guides/prism/7/statistics/stat_more_about_confidence_interval.htm?toc=0&printWindow) or any course on statistics. It's not correct to say that a confidence interval contains 95% of values. Note that the interval for the loyal customers is narrower, which is reasonable since they make fewer calls (0, 1 or 2) in comparison with the churned clients who call until they are fed up and decide to switch providers. \n",
diff --git a/_sources/ml-advanced/ensemble-learning/feature-importance.ipynb b/_sources/ml-advanced/ensemble-learning/feature-importance.ipynb
index 41a8e9d7a6..7e199e1cf6 100644
--- a/_sources/ml-advanced/ensemble-learning/feature-importance.ipynb
+++ b/_sources/ml-advanced/ensemble-learning/feature-importance.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "5672d3d3",
+   "id": "7e5e1e79",
    "metadata": {},
    "source": [
     "# Feature importance\n",
@@ -159,7 +159,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "f3be490d",
+   "id": "0e774629",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -174,7 +174,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "b19a4a18",
+   "id": "7cc0556b",
    "metadata": {
     "tags": [
      "output_scroll"
@@ -269,7 +269,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7731e7f",
+   "id": "9656b30a",
    "metadata": {},
    "source": [
     "Since our aim is just to demonstrate the sequence of steps in calculating feature importances we'll transform the `target` variable as for classifying Iris Virginica One-To-All."
@@ -278,7 +278,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "81d0c808",
+   "id": "ff4971bd",
    "metadata": {},
    "outputs": [
     {
@@ -311,7 +311,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bb0ff6fa",
+   "id": "e2a1ce1f",
    "metadata": {},
    "source": [
     "Creating Random Forest. For reproducibility, we set `random_state=17`. For the sake of simplicity we set the number of trees to 3 and limit the depth of trees in ensemble to be not greater than 3."
@@ -320,7 +320,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "c887b228",
+   "id": "7c6f9956",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -332,7 +332,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "68995d62",
+   "id": "e4d82f88",
    "metadata": {},
    "source": [
     "After fitting list of all the trees are stored in `estimators_` property."
@@ -341,7 +341,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "d3eb7927",
+   "id": "1eedde3e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -350,7 +350,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e8dd55e",
+   "id": "117bcb52",
    "metadata": {},
    "source": [
     "Visualizing trees"
@@ -359,7 +359,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "5c9adeb8",
+   "id": "a4c95033",
    "metadata": {},
    "outputs": [
     {
@@ -393,7 +393,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "dd378ca1",
+   "id": "0da26b37",
    "metadata": {},
    "outputs": [
     {
@@ -425,7 +425,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "f10e798f",
+   "id": "d08ee865",
    "metadata": {},
    "outputs": [
     {
@@ -456,7 +456,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4060dac4",
+   "id": "07e7b9c8",
    "metadata": {},
    "source": [
     "Let's start from the first tree and `Sepal length (cm)` feature. This feature is located in two nodes: the root (#0) and the rightmost node (#4). The reduction in impurity for these nodes are:\n",
@@ -505,7 +505,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "e8ce6188",
+   "id": "0bb5e55b",
    "metadata": {},
    "outputs": [
     {
@@ -525,7 +525,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1a30054b",
+   "id": "0305bb06",
    "metadata": {},
    "source": [
     "Voila!\n",
@@ -538,7 +538,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "74d1b54e",
+   "id": "88e17dd3",
    "metadata": {},
    "outputs": [
     {
@@ -623,7 +623,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5a50bf50",
+   "id": "13d2e190",
    "metadata": {},
    "source": [
     "The picture above shows that, more often than not, customers pay great attention to staff and the price-quality ratio. This couple of factors affects the resulting overall rating the most. The difference between these two features and other features is not very large, so we can conclude that exclusion of any of these features will lead to a reduction of model's accuracy. However, based on our analysis, we can recommend hostel owners to focus primarily on staff training and price-to-quality ratio.\n",
diff --git a/_sources/ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.ipynb b/_sources/ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.ipynb
index 2d878a8881..01c9feac30 100644
--- a/_sources/ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.ipynb
+++ b/_sources/ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7cbce4be",
+   "id": "4b85cee0",
    "metadata": {},
    "source": [
     "# Getting started with ensemble learning\n",
diff --git a/_sources/ml-advanced/ensemble-learning/random-forest.ipynb b/_sources/ml-advanced/ensemble-learning/random-forest.ipynb
index 439cf39d08..cc23253821 100644
--- a/_sources/ml-advanced/ensemble-learning/random-forest.ipynb
+++ b/_sources/ml-advanced/ensemble-learning/random-forest.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "59cc27b1",
+   "id": "5a13da99",
    "metadata": {},
    "source": [
     "# Random forest\n",
@@ -64,7 +64,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6352ebb3",
+   "id": "12f9b6f4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,7 +93,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b07dbeb5",
+   "id": "ce851b50",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -155,7 +155,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c58b8adf",
+   "id": "531f9515",
    "metadata": {},
    "source": [
     "As we can see from our graphs and the MSE values above, a random forest of 10 trees achieves a better result than a single decision tree and is comparable to bagging with 10 trees. The main difference between random forests and bagging is that, in a random forest, the best feature for a split is selected from a random subset of the available features while, in bagging, all features are considered for the next best split.\n",
@@ -166,7 +166,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "929cfae1",
+   "id": "2eb1a2cf",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -215,7 +215,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6854aecd",
+   "id": "27fc04ba",
    "metadata": {},
    "source": [
     "The figures above show that the decision boundary of the decision tree is quite jagged and has a lot of acute angles that suggest overfitting and a weak ability to generalize. We would have trouble making reliable predictions on new test data. In contrast, the bagging algorithm has a rather smooth boundary and has no obvious signs of overfitting.\n",
@@ -268,7 +268,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e8c3211c",
+   "id": "2dcc9394",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -304,7 +304,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "df5ddd2f",
+   "id": "97c63bef",
    "metadata": {},
    "source": [
     "We have accuracy close to 92%. Now, let's try to improve this result, and take a look at the behavior of the learning curves when we change the basic parameters.\n",
@@ -315,7 +315,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "43f69a6d",
+   "id": "dd668ae2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -350,7 +350,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9f64130a",
+   "id": "cebd6f73",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -371,7 +371,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "226b600f",
+   "id": "cfde37dc",
    "metadata": {},
    "source": [
     "As you can see, when a certain number of trees is reached, our accuracy on the test set is very close to the asymptote. You can decide by yourself which value would be the optimal number of trees for your problem.\n",
@@ -384,7 +384,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bc601268",
+   "id": "47026e30",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -427,7 +427,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a3c13058",
+   "id": "516a7e2c",
    "metadata": {},
    "source": [
     "Parameter `max_depth` copes well with the regularization of our model and it does not overfit as badly as before. The model accuracy has increased slightly.\n",
@@ -438,7 +438,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "237be48a",
+   "id": "255cd13b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -471,7 +471,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ef96964c",
+   "id": "7e5aba53",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -490,7 +490,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0cd28d5f",
+   "id": "aa8c4d86",
    "metadata": {},
    "source": [
     "In this case, we do not see an improvement in accuracy on the validation set, but we significantly reduce the overfitting down to 2% while keeping the accuracy at about 92%.\n",
@@ -501,7 +501,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "81565723",
+   "id": "35be1cd9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -545,7 +545,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "30d6eebe",
+   "id": "1ac9c65e",
    "metadata": {},
    "source": [
     "In our case, the optimal number of features is equal to 10. This is the value at which the best result is achieved.\n",
@@ -556,7 +556,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b88ab81d",
+   "id": "e7373cd9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -572,7 +572,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f7c9af38",
+   "id": "316eafac",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -581,7 +581,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c41d7a1c",
+   "id": "de12096d",
    "metadata": {},
    "source": [
     "## Variance and Decorrelation\n",
diff --git a/_sources/ml-advanced/gradient-boosting/gradient-boosting-example.ipynb b/_sources/ml-advanced/gradient-boosting/gradient-boosting-example.ipynb
index 8c38cc89d5..e171a0e2fe 100644
--- a/_sources/ml-advanced/gradient-boosting/gradient-boosting-example.ipynb
+++ b/_sources/ml-advanced/gradient-boosting/gradient-boosting-example.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "f882ec5c",
+   "id": "6bedd148",
    "metadata": {},
    "source": [
     "# Gradient boosting example\n",
@@ -38,7 +38,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "d944059f",
+   "id": "b733bfd3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "95500973",
+   "id": "6bfa2548",
    "metadata": {},
    "source": [
     "We will work with a modified version of our familiar titanic dataset. This is a ready-to-train version: doesn't not contains missings and categorical data has been encoded."
@@ -65,7 +65,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "e955770a",
+   "id": "7e416622",
    "metadata": {
     "tags": [
      "output_scroll"
@@ -282,7 +282,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "4bb46fad",
+   "id": "4c4d9e5b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -298,7 +298,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "a37421b6",
+   "id": "fcab4137",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -310,7 +310,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f79168c0",
+   "id": "490097fa",
    "metadata": {},
    "source": [
     "Let's fit a base learner for the data."
@@ -319,7 +319,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "b6acfb82",
+   "id": "b82353b9",
    "metadata": {},
    "outputs": [
     {
@@ -342,7 +342,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9bfd95a",
+   "id": "f326bc83",
    "metadata": {},
    "source": [
     "Now we will construct a gradient booster class implementing the previous algorithm. Let's code it first and then talk a little bit about the details."
@@ -351,7 +351,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "8371aaac",
+   "id": "ecff0b1b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -401,7 +401,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5d52776",
+   "id": "a590dff0",
    "metadata": {},
    "source": [
     "We intenionally picked a classification problem over a regression because has some additional technical details worth understanding.\n",
@@ -416,7 +416,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "386a34d8",
+   "id": "07e368a7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -427,7 +427,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "199bfbdc",
+   "id": "4347cbc9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -437,7 +437,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fbdb7f5f",
+   "id": "0c0a2026",
    "metadata": {},
    "source": [
     "Let's implement the booster and see how it performs"
@@ -446,7 +446,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "1168fef1",
+   "id": "fde79987",
    "metadata": {},
    "outputs": [
     {
@@ -470,7 +470,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4fd00691",
+   "id": "ce667c8c",
    "metadata": {},
    "source": [
     "Nice! We got decent increment in auc and a good amount of f1. To finish this section we'll plot the classic loss vs epochs plot to verify that the loss is decresing with every addition of a new tree."
@@ -479,7 +479,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "c8b3c6bd",
+   "id": "dd056832",
    "metadata": {},
    "outputs": [
     {
@@ -505,7 +505,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "02fd1e9c",
+   "id": "7fd6bc00",
    "metadata": {},
    "source": [
     "## Your turn! 🚀\n",
diff --git a/_sources/ml-advanced/gradient-boosting/gradient-boosting.ipynb b/_sources/ml-advanced/gradient-boosting/gradient-boosting.ipynb
index bd0ee75cbb..cb1150f504 100644
--- a/_sources/ml-advanced/gradient-boosting/gradient-boosting.ipynb
+++ b/_sources/ml-advanced/gradient-boosting/gradient-boosting.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "41d3eb53",
+   "id": "dbd7f48c",
    "metadata": {},
    "source": [
     "# Gradient Boosting\n",
diff --git a/_sources/ml-advanced/gradient-boosting/introduction-to-gradient-boosting.ipynb b/_sources/ml-advanced/gradient-boosting/introduction-to-gradient-boosting.ipynb
index d35004ff34..6c376e4fd8 100644
--- a/_sources/ml-advanced/gradient-boosting/introduction-to-gradient-boosting.ipynb
+++ b/_sources/ml-advanced/gradient-boosting/introduction-to-gradient-boosting.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7a7e79ea",
+   "id": "08e13efc",
    "metadata": {},
    "source": [
     "# Introduction to Gradient Boosting\n",
diff --git a/_sources/ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.ipynb b/_sources/ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.ipynb
index b928892340..0806c6d82d 100644
--- a/_sources/ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.ipynb
+++ b/_sources/ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "3ef95a16",
+   "id": "c7951b8d",
    "metadata": {},
    "source": [
     "# XGBoost + k-fold CV + Feature Importance\n",
@@ -221,7 +221,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "1808788a",
+   "id": "94f2baeb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -232,7 +232,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "68b1dff6",
+   "id": "260be9f0",
    "metadata": {},
    "source": [
     "### Read dataset"
@@ -241,7 +241,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "f8559ebe",
+   "id": "ec7d7b65",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -252,7 +252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9eec464",
+   "id": "ba06b5a9",
    "metadata": {},
    "source": [
     "### EDA\n",
@@ -265,7 +265,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "97a594ad",
+   "id": "29fa7d55",
    "metadata": {},
    "outputs": [
     {
@@ -285,7 +285,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1a0d8bf2",
+   "id": "33bb5576",
    "metadata": {},
    "source": [
     "We can see that there are 440 instances and 8 attributes in the dataset.\n",
@@ -296,7 +296,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "e95007ff",
+   "id": "a80d53a0",
    "metadata": {
     "tags": [
      "output_scroll"
@@ -414,7 +414,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a1f72a96",
+   "id": "168314c8",
    "metadata": {},
    "source": [
     "- We can see that `Channel` variable contains values as 1 and 2. \n",
@@ -429,7 +429,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "e1553987",
+   "id": "1f4ae913",
    "metadata": {},
    "outputs": [
     {
@@ -460,7 +460,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d9bbbbc",
+   "id": "fcafc415",
    "metadata": {},
    "source": [
     "#### Summary statistics of dataset"
@@ -469,7 +469,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "c48e727e",
+   "id": "33fefa76",
    "metadata": {
     "tags": [
      "output_scroll"
@@ -633,7 +633,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9cb54885",
+   "id": "708782f0",
    "metadata": {},
    "source": [
     "#### Check for missing values"
@@ -642,7 +642,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "ca28ca39",
+   "id": "5e2d582c",
    "metadata": {},
    "outputs": [
     {
@@ -670,7 +670,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f3910baf",
+   "id": "cb697541",
    "metadata": {},
    "source": [
     "We can see that there are no missing values in the dataset.\n",
@@ -681,7 +681,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "9fe037b6",
+   "id": "079ef06a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -692,7 +692,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "323bbff4",
+   "id": "024c4695",
    "metadata": {},
    "source": [
     "- Now, let's take a look at feature vector(X) and target variable(y)."
@@ -701,7 +701,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "a62fcea4",
+   "id": "577623e5",
    "metadata": {
     "tags": [
      "output_scroll"
@@ -814,7 +814,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "4eaec854",
+   "id": "30df3206",
    "metadata": {},
    "outputs": [
     {
@@ -839,7 +839,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b0e1e5b0",
+   "id": "8392b866",
    "metadata": {},
    "source": [
     "- We can see that the y label contain values as 1 and 2. \n",
@@ -852,7 +852,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "96bbe963",
+   "id": "c1c939de",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -866,7 +866,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "5e281d89",
+   "id": "4702cba3",
    "metadata": {},
    "outputs": [
     {
@@ -893,7 +893,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f3d1f6dc",
+   "id": "5ce707de",
    "metadata": {},
    "source": [
     "- Now, we will convert the dataset into an optimized data structure called **Dmatrix** that XGBoost supports and gives it acclaimed performance and efficiency gains. \n",
@@ -904,7 +904,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "4cb8466a",
+   "id": "f1a91567",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -918,7 +918,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b1078c67",
+   "id": "af8678c2",
    "metadata": {},
    "source": [
     "### Split data into separate training and test set"
@@ -927,7 +927,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "aa95c117",
+   "id": "4361609a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -939,7 +939,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3dd379ba",
+   "id": "a93eff72",
    "metadata": {},
    "source": [
     "### Train the XGBoost Classifier\n",
@@ -956,7 +956,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "0c17445b",
+   "id": "b60add82",
    "metadata": {},
    "outputs": [
     {
@@ -1025,7 +1025,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca42a6d8",
+   "id": "62edb03e",
    "metadata": {},
    "source": [
     "we can view the parameters of the xgb trained model as follows:"
@@ -1034,7 +1034,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "14fe99a2",
+   "id": "eb1543f7",
    "metadata": {},
    "outputs": [
     {
@@ -1060,7 +1060,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d12f7afd",
+   "id": "fe816689",
    "metadata": {},
    "source": [
     "### Make predictions with XGBoost classifier"
@@ -1069,7 +1069,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "1836a2b4",
+   "id": "d2a444e7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1080,7 +1080,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e0415bfe",
+   "id": "61875a9b",
    "metadata": {},
    "source": [
     "### Check accuracy score"
@@ -1089,7 +1089,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "ceccf133",
+   "id": "1a48b9d2",
    "metadata": {},
    "outputs": [
     {
@@ -1110,7 +1110,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "81e5e022",
+   "id": "11df076d",
    "metadata": {},
    "source": [
     "We can see that XGBoost obtain accuracy score of 88.64%.\n",
@@ -1145,7 +1145,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "60d6bc05",
+   "id": "24fafff6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1160,7 +1160,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "57b2d24d",
+   "id": "bd6b5e94",
    "metadata": {},
    "source": [
     "- **xgb_cv** contains train and test auc metrics for each boosting round. \n",
@@ -1171,7 +1171,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "da8897c9",
+   "id": "392698de",
    "metadata": {
     "tags": [
      "output_scroll"
@@ -1265,7 +1265,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "373bd54d",
+   "id": "6db8b585",
    "metadata": {},
    "source": [
     "## Feature importance with XGBoost\n",
@@ -1282,7 +1282,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "2974276d",
+   "id": "d9ccc9ff",
    "metadata": {},
    "outputs": [
     {
@@ -1317,7 +1317,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ea455d78",
+   "id": "69e21d09",
    "metadata": {},
    "source": [
     "- We can see that the feature `Delicassesn` has been given the highest importance score among all the features. \n",
diff --git a/_sources/ml-advanced/gradient-boosting/xgboost.ipynb b/_sources/ml-advanced/gradient-boosting/xgboost.ipynb
index c9ae04957d..f3c9232487 100644
--- a/_sources/ml-advanced/gradient-boosting/xgboost.ipynb
+++ b/_sources/ml-advanced/gradient-boosting/xgboost.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "60a64445",
+   "id": "6ff2d4c3",
    "metadata": {},
    "source": [
     "# XGBoost\n",
@@ -40,7 +40,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "7faae308",
+   "id": "7b378bfa",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9f3eef48",
+   "id": "92af61f8",
    "metadata": {},
    "source": [
     "We build and fit a model just as we would in scikit-learn."
@@ -72,7 +72,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "054c1826",
+   "id": "35dff470",
    "metadata": {},
    "outputs": [
     {
@@ -126,7 +126,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc49606b",
+   "id": "a370e9c5",
    "metadata": {},
    "source": [
     "We similarly evaluate a model and make predictions as we would do in scikit-learn."
@@ -135,14 +135,14 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "184388db",
+   "id": "c8910edf",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean Absolute Error : 19517.705190496574\n"
+      "Mean Absolute Error : 17770.93957619863\n"
      ]
     }
    ],
@@ -156,7 +156,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec3cad4d",
+   "id": "c8c94a86",
    "metadata": {},
    "source": [
     "## Model Tuning\n",
@@ -179,7 +179,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "66ab0040",
+   "id": "0c28d3ab",
    "metadata": {},
    "outputs": [
     {
@@ -231,7 +231,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "373d1fb9",
+   "id": "b99c59b3",
    "metadata": {},
    "source": [
     "When using `early_stopping_rounds`, you need to set aside some of your data for checking the number of rounds to use.  If you later want to fit a model with all of your data, set `n_estimators` to whatever value you found to be optimal when run with early stopping.\n",
@@ -251,7 +251,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "11181fe7",
+   "id": "d74871fa",
    "metadata": {},
    "outputs": [
     {
@@ -303,7 +303,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "63b80b6e",
+   "id": "10f61512",
    "metadata": {},
    "source": [
     "### `n_jobs`\n",
diff --git a/_sources/ml-advanced/kernel-method.ipynb b/_sources/ml-advanced/kernel-method.ipynb
index 828a1d62ad..cf1bfb25fe 100644
--- a/_sources/ml-advanced/kernel-method.ipynb
+++ b/_sources/ml-advanced/kernel-method.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "1827a730",
+   "id": "b293bf64",
    "metadata": {},
    "source": [
     "# Kernel method\n",
@@ -21,7 +21,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "30e2d1c7",
+   "id": "e0c06221",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,7 +38,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "4a136a7b",
+   "id": "fc789e8b",
    "metadata": {},
    "outputs": [
     {
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "241052bb",
+   "id": "2360286b",
    "metadata": {},
    "source": [
     "A linear discriminant classifier may draw straight lines to separate two sets of data, but there may be multiple split lines that perfectly distinguish between the two categories. \n",
@@ -76,7 +76,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "f06ac40d",
+   "id": "9dd10274",
    "metadata": {},
    "outputs": [
     {
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1f639c71",
+   "id": "acc25d71",
    "metadata": {},
    "source": [
     "All three separators are good if we want to distinguish between these samples. But they have some significant differences. In the following, I will explain these separators with an example to show the difference between them. Now, suppose you want a new data point, which is marked with an \"X\" in this graph. So, which separator would you choose? This is a very interesting question! Because you can choose to give this new data point a different label. It's up to you!\n",
@@ -123,7 +123,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "0f0fce94",
+   "id": "be3a9c23",
    "metadata": {},
    "outputs": [
     {
@@ -156,7 +156,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba11af3b",
+   "id": "1a5ec23e",
    "metadata": {},
    "source": [
     "In SVM, the best models are those that maximise this margin, and SVM is an optimal model that achieves the maximum margin and can effectively use the boundary information in the dataset to predict the distribution of new data points, thereby improving prediction accuracy. Because of its excellent fitting ability, SVM can also be used to handle large amounts of unbalanced data.\n",
@@ -189,7 +189,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "5fb25ec0",
+   "id": "7d46eda0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -200,7 +200,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8da138b6",
+   "id": "3b456d93",
    "metadata": {},
    "source": [
     "Let's create a quick and easy function to draw the SVM decision boundary:"
@@ -209,7 +209,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "bf239186",
+   "id": "be3dbeb3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -244,12 +244,12 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "f79599e0",
+   "id": "2d15aeb4",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -269,7 +269,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8b060284",
+   "id": "b4231b9a",
    "metadata": {},
    "source": [
     "This is a maximum expansion of the space between the two points. Note that some of the practice points just reach the boundary and are marked with black circles. Those points are important in the matching process and are called, support vectors, which is the name of this algorithm. In Skeeter-learning, the identification of these points is stored in the support_vectors_ property of the classifier at:"
@@ -278,7 +278,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "340deffc",
+   "id": "c0a79042",
    "metadata": {},
    "outputs": [
     {
@@ -300,7 +300,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "237cbf7d",
+   "id": "69775e2f",
    "metadata": {},
    "source": [
     "One of the main points of this classification method is that just the space in which the support vector is located has a large effect on the matching result; on the correct side, no point away from the boundary can correct the match. This is due to the fact that during the fitting process those points do not affect the loss function used in the model. Theoretically, this is due to the fact that during the fitting process, those points do not affect the loss function used in the fitting process, and therefore, if they do not cross the boundary, then their location and number are irrelevant.\n",
@@ -311,12 +311,12 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "19abe783",
+   "id": "bacc462a",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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mJ8Aq5N9/Q8H4UyF5vZU5OSrjlnfthOu0kZB27mzYiYSAbcaTaNG3F5wXToH9v3fBfvvNKDx2MArGnAy5/PNBSvl8cJ02EnmP/q9acRwATKtXwXXqcJhXvJH68xIRETUxLJATpZB1wTzImzdBitN/URICMAzkPXBvhiMjIiIiIkoBnw/WWU/HCs21kXQdyo7tsCxbnMHAapf3xKOQgsHYxJW9SboOyeOBbc6Mhp3nvrvhuPkGyCUllcctL8KbvvoS7lOGQv5jc4POsTfHXbdBXfN9jS8pKs4Pw4DzwimQdu1K6XmJiIiaGhbIiVLI9vzsaCuVOki6DvN7b0Pevi1DURERERERpYb5g/cg+3wJxwlZhmXhggxEVIdAANb5L8edvFJBMnTYnp8D1LP7qPLLBtgfjD8BRtJ1SCUlcNx6Y72OX+sxS72wzn0hbuEfKJ+co0VgfeXFlJ2XiIioKWKBnCiFlM2b6pxNU0ESAvIff2QgIiIiIiKi1JGL9iQ1TjIMyLuzO3NZ3rEdUiiU3Ng9u4FAoF7nsT4/G0JR6hwj6TrMK1dA3rqlXufYm2n1x5CSaWFjGLC8vjQl5yQiImqq1GwHQNSUCJMZEvzJDbaY0xtMMyft2gXrovmQ//4bwp4H/eRTgBOPz3ZYRERERI2a4S5MapyQZRgtW6Y5mgSs1n0bbzLV6zTm1R8nnKUORL80UL/5CuH2Hep1nmrHKitNbhwAqayswedLBeW3X2FZtgRSURFEYSG0cacDh/TPdlhEREQskBOlUnjoMFiWL4GkaXWOM9xuaAf0yVBUzUwkAvttN8H27KzoMllFAYSA9L/7gYEDIc94FujSPdtREhERETVK4aHDIPLyIPnrnhQiGQZC48ZnKKraGW3aQuveA8qm3+tc5SkUBZFDB9e7QI5IOOmhiT4nJMtIssguZAVGx04pOWd9ScUe5F8+HZa334rOtJdlwDAg3XMnMGoUpEefAhwFWY2RiIiaN7ZYIUqhwLTpCZNeIcsITJkGWCwZiqoZEQL5l10E2+wZsY2RpEik8v9k7VrknzwU8p9sb0NERERUL3Y7AtOmQ9Sx745QFOit2yB06rgMBlYLSULgwksSD9N1BC68uN6n0Q7qC6EkN/dM631Avc9TVeTwI6G374BEzR0lQ0fw7EkpOWe9+HwoOG0UzO+9E41H16P5ecWM+xUr4BgzAkiirz0REVG6sEBOlELaYYPhv+IaAKg1WRWyAq1vf/ivvC6zgTVVoRAsC+cj/5IL4DzvbOSffw6sSxbGnyGkaZBKSmC/587MxklERETUhPhuuBnhYSdDIDr5oyqhKBD5TpTMW7TvLU7SIDj5fISHnAAh1f7RV0gSguMnIjzq1AacYyokPfEkmciAg6EfmKJVpIoC//U3IP7XFIBQVGjdeyA0YnRqzpkEqaQYtplPIf/CKXCefy4KJk+E+tOP8VvQ6DqUdT9GN0klIiLKEkmIem7VnQM8Hh80zch2GJRDVFWG223P7rUhBKxzZiLvofuh7NxRebPZjOBZ56LstrsAhyM7sTUh6uefoeD8syHv2RNdqmkY0VYqSTxWqCr2rP0VokWLtMdJuS8nXjcoZ/H6oHgKC+1QFM41qYo/J82MpsH66suwzXgK6vp1AAAjPx/Bc6cgMP3SWAuQnHgdDYdh/+9dsD47E3KVmcqGy4XAJVfAf9V10bYf9SUEnOdMgPn9dyAZNZ+jkCRAllGycDkiRx5d//PUct68++6G/cF7IRQ1VqQXkgQIAaNLVxQveh1Gp86pO2cdLPPmIv8fVwPhMCBJ0fy8XJ2FfEmC0bETir5eG30cNXs58bpBOYnXBtWlIfk5C+TUpOTUi6WmwfTxKsjbt0E4HIgcezxEgSu7MTURytof4B5xAhCJ1PohJBnFry1D5NjjUxsYNUo59bpBOYfXB8XDAnlN/DlpvqSSYiAUhigsBNTqrUZy6nXU54N51QeQSoohWrRA+LihqWt76Pcj//LpsL6+FEJVAU0r77UtIOx2lD4zG+GTTknNufaifvs1bHNmwvzuSiAUht61K4LnTUPwjDMzNjHHsmQhnBedD4G6i+F12b1hM0SSm8BS05ZTrxuUU3htUF0akp9zk06idFFVRIackO0omiT73f+OtkupZ3EcQHQDTyIiIiJqsEYzCcRuR3jEqPQcOy8PpXNehP/HtbC+/AKUzZsBqxXh44ciePoEwG5Pz3kBaAcfgtKDD0nb8RMHoMF+y40NKo4DYH5ORERZwwI5ETUq8t9/wfz+u/H7jCdBKAq0/Q9MYVRERERERIB+UF/47r4/22FklPm9d6Ds2F7vxwsARtu2EC536oIiIiLaB1wXSkSNivLLzw0ujodGjIJo0yaFURERERERNU/Kzz9F9wSqL1lGcOpFDesDT0RE1ACcQU5EjYvcgORbUQCbDf4bb01dPEREREREzVlD8nNVhdGlKwJTL0xdPERERPuIX9ESUaOi9esPYTIlHCcACEmCUNXoRkkA0LkzSt94G3rPXukNkoiIiIiomYgcOhiSric1VsgyhKJU5ueHHYbS11dCOAvSGCEREVHdOIOciBoVUdgCobGnw7JoQZ2JuAQgcObZkAAImw3a8BHIP+NU6CUBgLtdExERERGlhDb4cGj77Q9l4y+QjNrzbAEAqorguedBCgRguAuhnTEezhOOhfD4mJ8TEVFWsUBORI2O7+bbYVr1AeQ9u2stkgsAwSnTUHbfQ7HbVFVmX8OmzjAAXQeSWGGQUroO8/vvQNmwATCpiBxxFLR+AzIbAxEREVG2SBJKH34crrEjIDStRpFcIDp5pfS/DyI4+fzY7arK3LzJq/is1pAe9fXh98Py1huQt2yByMtDeNhJMLp0zWwMRNSosEBORI2O0a49ile8h/zLL4b5s9UQcnnxW9OiPcYvuwr+629I+nhSWSnM76yEtGc3hMuN8IknQxS40vcEKHWEgHnlCthmPQ3T6o8gGQb0tu0QnDINgfOmQbRokdbTWxYtgP32/4OyfXt0cyohIBkGIv0HovShx6Ef1Det5yciIiLKBdohh6F48RvIv+oyqBt/ieZFkgRJ0yBatETpv/+D0ISzkj6evGM7TB+8B6msFEb7jgifcCJgsaTxGVDKhEKwvvYqrLOegWndWgCA1vsABKZdhOCZZwM2W/rObRjIe+RB2B57CHJZWfQ6NAzgpn8gPOxklP7vMYg2bdJ3fiJqtCQhhMh2EPXl8figcSkWVaGqMtxuO6+NZkTZ8DPM774Nye+D3qkzwqPGQDjya4yr9dqIRGC/+w7YZs+AFAxASBIkISAsFgQmTYHv1jsBqzXDz4iSZhhwXHsFbC+/CKEosdUEAgBkGUbLVihZ8mbCnvP1fd2wvvQ88q+9IjYrqiqhKIDZAs8b77BI3sjxfYXiKSy0Q1E4+7Eq/pxQbfg62swIAdMXn0H94jNImgat9wEIn3xKrSv8ars2JE8RHP+6DpblSyDpeiw/N1wu+K/+BwKXXA5Ie2delCukslIUnDkO6ldfALIcW00gJAkQgNZ/AEpeW5pwMlK9XjeEgOPG62GbM7P2uxUFRvsO8Kz8EKJly315WpRD+J5CdWlIfs4COTUpfLGkeGpcG7oO55RzYH57BaRaXgaFLCNy1DEoeWUhYDZnIWJKxPbo/2C/6/YaxekKQlFgtG2Hos+/q3PGUX1eN6Q9e9Ci335AJFLn+bWD+qH4nVVJHZNyE99XKB4WyGvizwnVhq+jFM/e14ZUUgzXiGFQfv8t7l5D/umXwXfnPRmOlJKVP3USLCtej/v/JxQF4eOHwvvKwjqPU5/XDfXzz+Aec3KdY4SiIHjuFJTd/1Cd4yh38T2F6tKQ/JxZPRE1S5YF82BZ+WatxXEAkAwDptUfwfric5kNjJITCiHviUfiFqcBQNJ1KFv+huX1pSk/vfWVlwBdT3h+05rvoP7wfcrPT0RERNTU5N1/T53FcQDIe+YJqF9+kcGoKFnypt9heWNZnf9/kq7D8t47UDb8nPLz256dAaHU3UVY0nVYX50LqdSb8vMTUePGAjkRNUu2WU9He5cnMQ6Nd6FNk2X+6APIHk/CcUKWYZn/SsrPb/r802g/wyTOb/r805Sfn4iIiKhJ8flgnftCncVVABCqCtucGRkKivaFdfFr0X2hEhCKCsvC+Sk/v2n1x5B0LeE4KRiE+uPalJ+fiBo3btJJRM2P3w/TD2sSDpOEgPrbRki7d0O0apWBwChZ8q5dSY2TDAPK9u0pP78UCdc5e7xyoARodX/Qq6asDNaF82Fe8Tpkbwn09h0Rmng2wkOGAYpS33CJiIiIcpq67kfIPl/CcZKmwbzqgwxERPtK2r0rWiBP8CUHJEDeuSP159ciyQ+OJD9W2rkTtrnPw/TRh5DCYWj79UZw0hRoAwexHz5RE8ICORE1O1IkvM/jOYc8txjOgqTGCUmC4Xan/Pxa7wOiSXKCDwCSrkPv3TupY5o+XQ3n5ImQvF6gfEMqVfkW1mWLoR14EErmLYTRtl0qwiciIiLKKfuUn+9DcZMyRzidSa+8TbRJZ31o+x8A0xefxzYGjXtuSYLes1dSx7TOfQGOf1wdXTlqGJAAqN9+A9vcFxA6ZRS8T80C8vIaHjwRZR1brBBRsyPynTAKWyQ11nA4YLTk7PFcEzl+CIQtiWRUCIROHZfy8wcnn594CTAAvV07hI8/IeHxlHU/ouDM0yCVlUECYr3xK86hbFiPgnGjgCRmVhERERE1Nnq37hBJzMYVsgx9v/0yEBHtq9DIUyFpSbQ40TSERp+a8vMHz78wcXFcURAedhKM9h0SHs+8fAnyr7kc0DRI5cVxALE2LuaVb8J5yQUNDZuIcgQL5ETU/MgyAlOmJuxBLhQFwXPOA8zmDAVGyRKOfATOq/v/UMgyhLMAofFnpvz8es9eCJx7XtwPcgKABMD377uTao1if/C/seS7NpKuQ9n4K6xp6NdIRERElG1G+w4IDx0GkSBvkgwDgfOmZSgq2hd6334IH3Fknf+HQlEQ6T8Q2qBDU37+0MgxiAwcFPf8QpYBVYX/hpsTH8ww4Lj95lhOXxvJMGBZ8TrU776pd8xElDtYICeiZikw7WIYLVvFT6AUBcLpRODiyzIcGSXLd9OtiBx1DIQk1ShUC0UBLFaUzF0A4chPy/nL7v0fgueeV3k+RAvjQpIAsxmlDz2O0NjTEx5H2rUL5jdfTzgjHZLETamIiIioyfL/6/8AWYk7AUIoCrQD+6RldSClRumM52B06FjrZyyhKDDatoX32ZfS07vbZELJq4sQGXxE9HxqtKNwxfUknAUoeXUxtL79Ex/qk4+h/PVnwj2HhKLA+vycBoVNRLmBPciJqFkSrVqhZMmbKDjzNCh//QkhK5AMHUKWIRkGjFatUTJvEYwOHbMdKsVjtaLklYWwzZ4B26ynofz1JwBAmMwInT4e/iuugd4rjUtwTSaUPfgoApdcAeuLz0H55WdANSFyxFEITjwbIsk2PsofmxIuBwWibVeU339raNREREREOUkbcDBKXl4A5/nnAGVlsT1ZhKJA0nVoffujZO4CwGrNdqiNnrRnD8xvr4Ds8cAoLER4+AgIV8P37THatIXnnVXIe+JRWF+YA7m4OHp7vhPByefDf+mVEK3S175SuNwoWfwG1K+/hHXeXCh//wVhdyB00vDoFys2W1LHUTb+ClF+/dVF0nWov/ycitCJKMtYICeiZkvv2QtFn38H81tvwrpoPuSdO2C0aIXQaacjNHIMW6s0BmYzApdcjsD0SyH//RekcBhG27ZpmzVeG71nL/j+/Z/6H2AfrjOhmup/HiIiIqIcFzluCIp+2ADLa/NhfutNyKVe6J06I3jWuYgccxyQoEUiJeD3w3HrjbC+8lJ0s1NFAXQ9mlNPngrfbXcCFkuDTiHchfDdfDt8/7wJyt/RCSx6h04NPm7SJAnaoYNRdujg+h/DbE5YHAfKV4+aM/S8iCit0l4gX7RoEW688cYat1944YW4/vrr0316IqK6mUwIjz4V4TRsFEMZJMswOnfJdhT1ovU+AEaBC3JJcZ3jhKIicuxxmQmKiJo05udElMuEIx/BKdMQnMJe4ykVCqHgrNNh+uKzytWLFS3+wmHY5syA8vtv8L70KqCmoFRkNkPv3rPhx8mC8BFHJTdQlhE59vi0xkJEmZGxGeSzZs1Cfn7ljL42bdpk6tRERES5y2JB8LypsD3+CCQjfh9ySdcQmDY9g4ERUVPH/JwoRTQNUqkXwu7gCkTKWbYX5sD0+adxZ0ZLhgHz++/AsmAeQmedm+HocovRvQfCxw2BafVHcfcJEgAgyQicc15GYyOi9MhYgbxPnz4oLCzM1OmIiIgaDf/V18H87ttQNqyPm4QHpkxD5OhjMxwZETVlzM+JGkZZ9yNszzwJ66L5kMJhCFlG+KThCFx8OSJHHp3t8IgqCQHbzKcTj5Nl2GY+3ewL5ABQev/DcA8fApSU1MjPBQAJQOm9D0Lwy2WiJoENvIiIiLJMOPJRvGwFQqdPgChf0iokCQBgFBSg7ObbUXbv/4Dy24iIiCi7zMuXwj3sGFgXzIMUDgMon4H7zkq4xo6A7YlHsxwhUSVp1y4omzcl3nTSMGD68QfA789QZLnL6NoNnhXvV5ugEsvPO3aCd8azCE4+P1vhEVGKZWwG+ahRo+DxeNC+fXtMmDABF1xwARRFydTpiYiIcppwFqD08WdQdvt/YH7vbUhlZTDatUf4hBMzt6kRETUrzM+JqpN27oR1wTwomzdBWK0IDxmKyPEn1NgYUvllA5zTzwcMo0bBsWKmqePfN0Pv3RvhYSdnLH6ieCQtss/jE29R2fQZ3bqjZMFSyL//BvOnq4FwGHrPXtGiOTeMJWpS0l4gb9WqFa644gr0798fkiTh/fffx8MPP4wdO3bg1ltvbdCxFYUvSFRdxTXBa4P2xmuD4sm5a6Nta+jnVC5rzdg32VSrnLs+KGc05gUd6crP+XNCtWkUr6OaBttt/wfLjKcBIWKFr7xnnoDeuTN8c16EfvCg2HDbnGcAIeqcjSsUBXlPPAJj+ClpD7+xahTXRlPRri1Efj6k0tKEQ41WraC4XVl9o8u5a2O/XtD26xX7K/Pz7Mm5a4NySkNetiQhEqyxSYN7770Xzz//PD788EO0bt0606cnIiIiIqIqmJ9Ts3bBBcCcOdHi+N4UJbrx5mefAf37R29zOoEkCo0AgG3bgLZtUxcrUX1ddx3wyCNAnP1uAESv91tuAW67LXNxERHlgKx88XXKKadgzpw5WL9+fYMScK83AF03UhgZNXaKIsPptGXs2pC2bYP891+A1Qr9gAMBld8l56p9vjZKS2FetgTyH5sBmw2RE06E3q9/2uOkzMv06wY1Lrw+KJ6CAhvkJrS8OhX5OX9OqDaZfh2VN/0OadcuiIICGPv1TjidTPn2Gzhnz44/QNchwmFoV16FsiVvAJoGd7LFcQAlv/8Fw5Kf9PjmZF+vDWn7dpiXLY7+/7rciIw5FUanzhmItGmQpl4M5/PPQyournVTeKEoEK3bwHvOFAiPLwsRVmL+RfHw2qC6NCQ/b9TVPF03oGn8gaCa0n1tqF99gbz/3Qfz++/GllYarVojMPVC+C+/mv2Cc1jCa8MwkPfQ/ch79H9AIACoJkAYsN15OyKDDoH3iZkwuvfIXMCUMXxPobrw+qC9ZX4NZu7jzwnVJd3Xh3n5UuQ98iBMP3wfu03r2QuBy65C8OxJcQvl1lkzIRQVkq7FPbak6zB9tAril1+gd+8JYcuDFEhuE0PN6YLBn4s6Jbw2/H44brwe1vmvAIYBKCpg6LDdehPCI0aj9OHHIQpcGYu30WrdFsVLVqDgrNOh/P0XhKxAMnQIRYGk69C7dEXJKwthFBQCOXLNNrX3FclbAuWXDYAQ0HvtB+FyZzukRqupXRuUGg3Jz7My7eXNN9+Eoig48MADs3F6ogYxv/k6XGOGw/zh+9X6Dsq7diLv/ntQMP5UIBjMYoTUEPZbb4T93v9ACgQgIbpBTcUMC/X77+AecUJ0VjkREVETwvycGrO8hx9AwbRJUH/8odrtym8bkX/N5XD889q4n5pN335VZ3G8KnVt9PjBcWdAKHXPNROygshhh8Now/YqDRIOo+Cc8bC++jIkXYckRDQ/L98g1fzWG3CNHQGUlWU70kZB770/ir74HiVzXkJo9KkIH3UMQmPGouTFV+H55GsY3bpnO8QmSd62FY5rr0CLPj3hHjEM7pEnokWfXnBceSnkP//IdnhEhAzMIJ82bRoGDx6M3r17AwDee+89zJ8/H5MnT0arVq3SfXqilJJ27IDzotp3rAcAyTBg+vJz2P97F3y335WFCKkh1B++R96Mp+LeL+k6UFICx79vhnfOSxmMjIiIKHWYn1NTYvp0Nex33wEgmotXVZGv256fjcjhRyB0+oSUnDNw4SWwzpsLASBeAxfJ0OG/9MqUnK85sy6YB9MnH8f/d9Z1KOt/Qt6sp+G/+vqMxtZomUwIjxqD8Kgx2Y4kJSRvCSzzX4H544+AUBB6z14InjsF+v4HZDs0AIC8eRPcI0+EVLSnWmsbKRKGdcErsKx8A8WvvwO9135ZjJKI0l4g79atGxYuXIjt27fDMAx07doVN910EyZNmpTuUxOlnO3lFwAtUueO9ZJhwPrCHPj+eROQl5fB6KihrHOSW2JrXvEG5B3bOSOIiIgaJebn1JTYZj6VMH8TsgzbM0/WWiCPDDoUym+/JTWLPNI3uh+NfmAflD76FPKvuBhCkqs9tqJdhe8fNyI8YlQ9nhFVZZv5NCDL0dYq8RgGrLNnwH/FNdFNJqnZsCxZiPwrLwFCIQDRL8XEqg+RN+MpBE+fgNKHn8hu+1Mh4Lxgco3ieAVJ1wGvF84p58Cz+suEeyYQUfqkvUB+8803p/sURBljXr6k7uSsnFxWBvNnqxE+4aT0B0UpY/rsk6Q+HEm6DvW7bxEePiIDUREREaUW83NqMgwD5rferLXwVJVkGDB9/y2kHTsg2rSpdl/w/Atge6XulYFCURA56phq+9CExk+E3r0HbE8+Bsuby2MxRI46BoGLL0N42Mn1fFIUEwpB/enHhMMkAMqO7ZC3b4PRoWP646KcYH7nLeRPnwoA1SawVXyesyx+DdB1lM54NivxAYD63Tcw/bCmzjGSrkP9dQNMn65G5KhjMhQZEe0tKz3IiRor2euNu7xvbxL74DU6ey/LrdO+jCUiIiKi1AuFEhbHq5J9pTVu0wYcjMA5kyHizNwUigKYzSi7/T81HzvoUJTOfgG7N/6NPd/9hN2//Y2S15axOJ4q+5pv78O1QI2cELDfehMAxF3dLRkGrEsWQl3zXSYjq8b81psQauJ5qUJVYX7rjQxERETxpH0GOVFTonfoCPnvv5IqpOpt22cgokYuGIRl2WKYvvoSMHRoBxyI0BlnZm03b+2gftH/3ySSay1HetoRERERNVtWKwyHA3ISE1OELMNoWXuP/bIHHoHId8I286noZp6yAkBA0jQY7TvAO+t56Af1jX9wux2G3V7PJ5FbpGIPrAvmQVn/E6CoiBw2GKHRYwGrNfPB2GzQO3aK5ucJhhr5Thht22UkLMo+9YvPof62MeE4oSiwPj8HZf97LANR1SSVlSbdNoUT7IiyiwVyon0QPOtcmD/7pM4xAoDRqTO0Qw/LTFCNlHn5UuRfczlkb0nlt+q6DsftN8P3z/9D4IqrM96DLXD+BbC8sazOMUKWETniqGpLbImIiIgoCyQJwbMnwTZ7Rp0THISiIHzKSAhnQe0DFAW+O+6G//Kro8XhPzZBWKwIDzkBkeOHRntgN3VCwPbYQ7DfezegRWK9vG3Pz4Zx0z9R+vATCI8cnfGwAtOmw37HrYCIP0FJKAqCk6YAZnPmAqOsUn/5uc5NcitIug71p3WZCKlWRrsOgJ7ESggh+AUPUZY1g3d6otQJnToOesdO0aWWcUgAtJ69orNPqFbmFW9ENysp9QIAJE2L/hICUjgMx123Ie+RBzMeV+SY4xA6cThEnA9BQpYBVYXv1jsyHBkRERER1SYwbTpgNsfP3wBA16F175nwWKJ1awQuuxJl9z0E3533IDJ0WPMojgPIe+h+OO66HVIkHM3Jy/NzAJC8JXBOPRfmt1dkPK7g5CnQu3WL+/lLKAqMlq3gv+SKDEdGWaWqSbc+FSZTWkOpS+j08Sh/FaqTpOsITjgr/QERUVzN492eKFVsNpS8thTC5YJA/Lc684fvw/F//8pkZI2HrsNxw7UA4veLA4C8e/8DaefOTEUVJUnwznoeoTGnAQCEokJIUiwhFy43SuYvgTZwUGbjIiIiIqJaGd26o+TFV2MznvdWUUSzP/o/WOe+kLnAGhF5x3bk3X9P3PsrcnbHv67L+D48It+JkqUroPUbEP27Wp6fl69A1bv3QPHSFTU2X6WmLTL48KTGCVlG5Kij0xxNfEb7DghOPCfuF3hANMbgqeNgdOuewciIaG8skBPtI71bDxgtWwOSFPdba0kI2ObMgPLLhozG1hiY33sbyrZtdRbHAQBCwPrKi5kJqiqbDaUznkXR6q8QuPgyhEeditC48fA+PRt71vyMyJG1JFi6DvM7b8F53llwH30oXCceh7z77oa8bWvm4yciIiJqZiIHHxK3QA5Ei+QCgP2WGwC/P2NxNRbWl55PuPpVEgLKlr9h+vC9DEVVyWjTFsVvvQ/PspUInnsewiPHIDjxHBTPXwLPx1/W3vowEIBl3lwUnD4a7qMOgWv0ybDOfgaStyTj8VPq6T16IXz0sXWu7K4QnHR+BiKKr+y/DyI89EQAqBZvxZ8jRx2D0oefyEpsRFSJPciJ9pH6/bdQN6xPOC66Ichs+P5zXwaiaiBNg/ntt2D+8D0gFILRuQuCE8+B0aFjyk+l/rgWQlVjSzbjEgLq2h9Sfv5k6fv1hu+2OxOOk/bsQcHZZ8D03TcQihLrf6muXYO8hx5A6f8eReisc9MdLhEREVGzZV38GhAM1jlGAoCyMliWLUZo4jkZiatBfD5YlyyE+u3XgGFA63MQQuMnQhS4Un4q9ce1SbWHFIoC0w9rECkv9mWUJEE7/AiUHX5EwqHKhp9RMP5UKNu3QcgyJMOAkCQ4vvwc9rvvgHfuAkQOPzIDQVM6ld33EFwnDwH8vrh7EPhuuystn2n3idUK74vzYF65ArZnZ0L95msAgNZ/AAJTL0L4lJGAytIcUbbxp5BoH6lrf0h6QxDT999mIqQGUb/4HM4Lz4smkBVvzIZA3n13Izh5Ksruvg/IRt+2DG/QWS+6joKzToe6dg0AVEvMJMOAgIH8qy+DKGyB8MmnZCvKtJA3/Q7b83Ng+vKzaF/PfgMRmHw+9L79sh0aERFReug6zO+shGXJQsh79sBo0QKhcWcgfMJJdc5epvRT166JFpgSTcAwmaCuXZPzBXLLgnlw/PMaSD5fZeFM1+H49y3w/d9tCEy/LGu5ssjxHF3atQuu00ZC8hRF/17eEia2etXnQ8GZp8Hz7sfQe+2XrTDTQv32a1hfeBbquh8Bsxnho45BcPL5MDp2ynZoaaH37IXiFe8h/+rLYPr6y2gbE0mCpOswWrSE7+bbETxncrbDjFIUhEeMQnjEqGxHQkRxsEBOtK/2ZaMeKbe7GKlrvoPrjNFAJAIANWZ1W1+YA6msFKVPzkxZEh4ZcHDi2eMAIAS0AQen5JzpYn5nZZ1fgsSW8/7n3wifNLxxFP0TEdEvT+wP3lt9xvwPa2B7fjYCZ52Lsgceyc6XKkRERGmi/PYrCs46A8rmTbH3P6EosC5aAK17D5S8spD9Y7Mp2RxLiJzPxyyLFsB52UWVE3Kq5s2hEBy33gQIIHDJ5Sk7Z2TgwTCveD1xmxVdz1x+LgTM778D6+wZMK/+CIho0Lt1R2DqBQhNPAfCkV/rw2zPzYJUtCdWGN+bZBgQ4TBsjz2MskefTOczyJxgEPmXXQTr8iXV8/Nvv0beIw/Cd+udCFx2ZZaDTA99v94ofvNdKOt+hPmTj4BQGHqPngifeDI/jxDRPsnt6h1RDooMHJTUjtlCURA55LC0x9MQ9tv+D9C0+AmkELAunA/1m69Sds7I8UOhd+yUePaJqiKY461JrC88m7DvnSQE1J9/grrmuwxFlV62Jx+D/cF7Aew1Y778z9Z5c7lBLRERNSnSjh1wjTkF8l9/Rv9e/p5X8bvyx2a4Tj0F0u7dWYuxuYsMHJTUBAxJ03J7s/VwGI4br0+4WtX+n9shFXtSdtrgWZMSTgISsgy9cxdEjjkuZeeNyzDguOZyFJx1BswfvAcpGISka1B++xWO//sX3EOPhrzl71ofan1+TtzPNhUkXYd14XygrCwd0Wdc/lWXwvLGMgA183NJCDj+fTOsLz6XpegyQ+9zEAIXXYrAFVdHZ2mzOE5E+4gFcqJ9pPc5CJGBgyDkBIVRXUfgvKkZimrfKb/9CvOnq+P2a6sgFBW2Z2el7sSyjNIHHgEkqc4iue/WOyBatEjdedNA2fhrwn+/2Ng/Nqc3mEzw+WC//+46h0hCwPr8bMh//pGhoIiIiNIrb8aT0Rmpcd7zJV2HvHMnbLOfyXBkVCF06jgY+U7UNf9ZSBIMlwuhUadmLK59ZVnxOmSPJ/FknEgE1ldfTtl5RatW8N387/j3SxIgSdEcfl9W09ZT3sMPwPryiwD2KvgKAUkIyH/9iYIzxwF7/0yGw1B27kjqHFIkDGXn9pTFnC3KT+tgXfxawi8F7P/5d2zVMBER1cQCOVE9lN37IGBSo33O4vBdfX32l9oKAXnT71DXroG0o3qyqP64NqlDSLoGNcW91CNDh8H70qsQLVtFw1RNECYTBADDbkfpPQ9EeyvmOps16aHCbEljIJlhWb4E8PsTD5Tl2IcaIiKiRk3TYH3h2YRfiEuGHp1QkKBIRWlis6Hs3gdj7e32JhAtrpbd/zBgyXJOpmlQftkA5ce1kEqKq92lrv0BIpmZr7Kc8s3sA5degdK774PIy4MAorm5Go3FaNUaJXMXIHL80JSes/ZAArA98WidXxJIug71l59hfu/t6neo6j71SG8K+bl17vMQSuLOuXLRHpjffisDERERNU7sQU5UD9qAg1G8+A04L54G5c8/optbSlK0R6DFAv+1/4T/quuyF6BhwPrS87A9/TjUjb/Gbg4fPxT+q65D5Khj6izu15CGXo3hYSdjz5qfYV65AqYvPwcMHfoBfRA8dRxgt6f8fOkQPuEkKL9sSDwL32RCZPDhGYoqfZTff4suV0w0+0QAyubfMxMUERFRGkl79kDeq4gZj1y0B1JJMYS7ML1BUa1CZ5wJryzDccP1kIo9sUKzFIlAFBbCe99DCI85LXsB+v3Ie+ox2GbPgLx7FwBAqCpCY8fBf/U/oO/XO5qf190GvFIa8vPgBRcjeNYkWJcshPLzT4CsIDL4iOheOmpmSgfmd9+GXOpNOE4oCiyvvoLwSadU3ijLiBx9LEwJVskKSYLRuQuMDh1TEXJWKb9thKQnbi8kFCWayxMRUa1YICeqJ+2Qw1D05RqYPvowunFMOLohSGjcGRD5zuwFZhjIv+JiWBbMq5E4mz5ehYJVH6D0kScROeIoCEmq3NE9DqGqiBw6OD2xqirCI0cjPHJ0eo6fZoHzpsL2xCN1jhGKgtDpEyAKc7tdTFJMpuRmxkkATOa0h0NERJR2+1oUzFARkWoXGjceoZFjYHlzOdQf1gAAtAEDETplFGDOYm5SVgbXuFFQf/i+WisMSdNgWbwIljdeR/GCpdAGHAxJS6INhmEgkq5e6nY7gudMTs+xkyDv3J7UZxRJ16Fs21Lj9sAFF8P88aqE5wlcdEnOb9iaFIs1qX8vCAFYmJ8TEcXDFivUZEi7d0P+eT2wbVvmTirLiBw/FL6bb4fvjrsRPG9qdovjAKzPzoJlwTxIQI1ESdJ1QAjkX30ZpFAI4eOHJt5kUtMQPP+CNEbceBlduqLs7vsBoNblnEJRYHTqjLLb78p0aGkRPub4pHquS7qO8NHHZiAiIiKi9BKFhdC6dU/YtkFIErT99s96HphThIC8dUs0Py8qytx5LRaETjsDvtvuhO+2OxE6dVx2i+MAHLfcAPWHNbX2iZZ0DQgFUXDuBISPPhZGq9Z1Xm8CAKxWhMafmb6As0jkOxMXe1E+C9zpqnF7ePgIBM49L+5EfCHJCB83FIEpTePzTfiY5HJuyTAQPjoDG6wSETVSLJBTo2da9QEKzhiDlgd2R8GRhwLt2yP/pCEwL1+a7dAyzzCQ99Rjdc6GkABAkmB7diZ8d9wDYbXGLZILAIEp06D1G5COaJuE4LSL4J35HPSu3ardLlQVodPOgGfF+01j9jgAbfDh0Q//dXypIiQJRoEr+mGUiIiosZMkBC68OKmhgYsuSXMwjYQQsMybC/eQo9BiwAHR/LxlS9jPngD1yy/iPkzevAnq559B+XFtzc0XGzFpzx5Y578CyYj/nCTDgFTsgfX1pSj932MA4ky+QDSXL7vr3ib7ZUz4hJOS68MuBMJjxta8XZJQ9sAj8N1yB4y9cnDD4UDgsivhfenV6MrIJiA04SzAaq37SxVFQeTQwdAP7JPByIiIGhdJiCS+ns1RHo8PmsaNcJoz6+wZyL/xeghFqTazVcgyJMOA/6rr4Pu/27IYYWYp639C4XHJ9bo2WrTEnvW/Q1n7A5zTz4e68dfqvdRVEwKXXgHfjbdkZLf6BgsGYVm+BNYF8yDv3AnD7UZo7OkInj4BcDigqjLcbnv6XjeEgPrlF1D+2ARhsyFyxNEQLVum/jxZpq5dA9fok4FQqMZsciHLgCTB+9KrCJ9wUpYi3HdpvzaoUeP1QfEUFtqhKI3g/TGDmuzPSSiEggljYfris1pnAAtZRuSoY1Ayb1GTKbrVmxBwXHclbC89H8vHY3cpCiAESh9/BqEzKmc/m99dibz/3Q/T11/GbtPbd0Bg+mXRLx0SrHbMdZb5r8B5+fSE44QkITxkGLzzFsL81pvIv/YKyLt3RTfKlKK91I18J8ru+i9CZ52bgcgbTir2wPrqyzC/sRyy1wu9Y0cEJ56L8PARgKrGfY91XHVp9EuFOF+UCFmGcBZgz/frgby8+AFEIjB9vAryrp0QLjfCxxxX9/hGyrJ0EfIvOh9AzRXEQlEg7A4Uv/U+9J69shFevTD/onh4bVBdGpKfs0BOjZb6zVdwjRiWcAleyZyXEB41JkNRZZf65RdwjzoxqbHCasXuP3eW/0XA9NknMH/wHhAMQu/aFaFx4xvNJlPKLxtQMGEslK1bYh/GhCQBQkC43Ch5eQFw+OF8I00R5ad1cNxwHcyff1rtdu3Ag1D2n3sROeqYLEVWP0yyqC68PigeFshratI/J34/HLfcAOu8udHNqhUlOsvZZEbwnEkou+MewGrNdpRZZ33hWeRff1Xc+wUAKAo8H38JvWcvWJ+dhfx/XVuzmA4AkoTwKaPgnf1C4yiSh0KQt/wNADA6doq1dbHOngHHTf9Iqm1IZNAhKF7xfvlfIjC/9SZM334NGAa0PgchNOa0RnOdmd5/BwVTJwGBACAEJCA2qUnrtR9KXl0MuWuXWt9jpbJSFIwdCfXHH2p8KSUUBTBbUDx/CbTByU0Mag7M770N+63/B/XXDdVuDx83BGX/fQB6j8ZTHAeYf1F8vDaoLiyQU7OUP30qLMuXQNLi79otZBnaIYeh+PW3MxhZ9sibN6HFYf2TGqt37ISib9elOaL0k3btQuFxh0PyFNU6y0TICoTNitJVn6JgUD++bqSQ8ssGqN98Ban8Q5vWf2Cj3OyISRbVhdcHxcMCeU3N4edE2rMHlhWvQyoqgmjZEqHhI5pMK7UGEwLuIw6Gsun3OovBQlEQmHohguecB/eQI+seK0nw3f4fBC65PB0Rp4S0ezfynngE1hefg+wtAQAYLheCk6fCf9mVMK3+GAXTJiU8jlCU6BcCc15Md8hpp37/LVwjTwQ0rdb/34p9erwffQZ3pza1v3b4fMh74hHYnp0Fec/u6ONUFaFTx8F/9fXQe++fiafSuAgB9esvoW74GUJVETnscBjde2Q7qnph/kXx8NqgurBATs2PEGjZsSWkSBK7vAPYve43iFat0hxUbnANHwr1+29rXQJcQcgy/NffAP/1N2QwsvTIu+9u5P3vvrqfr6IgfO5kWJ6bw9cNqoFJFtWF1wfFwwJ5Tfw5ad6UDT+j8JjDkhprtGyJ0IgxsM59IbpJZRwCgNGhA4q+WZeTLf/kLX/DNeokyNu31Ww9pygwOnRE8WvL4B56NOSy0oTHK3nxVYRPPiVd4WaM89wJML/3Tp2buwsA/gcfhv3aq+p+7YhEoPy2EVIkDL1jp0azwpUahvkXxcNrg+rSkPw897IMomQEg0kXxwFA9hanL5Yc47/mHwmL48JuR2DS+RmMKk2EgO252XU+XwCQdB3mV18B/P4MBUbyju2wLJwP68svwvTJx0CC/yMiIiJq3KSSkuTHlpbC8sbSOovjQHRDSmXLFijrf2pgdGkgBJxTz4W8fXuthWBJ1yFv+Rv5V12CwGVXoq5ZaUJRoPXqjfCwxrOHSzzyju0wv7OyzuI4AECSYJkzK/EBTSbo+x8ArW9/FscbSPn1F1hefRmWeXOhrP0h2+EQEeUUNdsBENWL1QqRlwcpiYKnkKQaO5g3ZeGTT0HZnffAccuNEIpa7YOHkBWIPBtKXlkE0aZNFqNMkWAQ8u5dSQ2VgkHgX/9C/iefAv4A9B49EJw0BeEhw3JyRlJjJe3YAcdN/4DlzeXVPhjpnTrD93+3ITRufBajIyIionQx9mG1ptGiJeSioqTHS2Vl9QkprdRvv4bpu2/rHCPpOsyffYqyu+6F/OcfsL3yUqwPNxD9nAJEe5aXvLqocfRaT0D+44+k+q1LQkD5bSNw2WXI/+hjQDcQGTAQwSnToA0clIFImw9l3Y9w3PQPmD/7pNrtkf4DUXbXvezlTkQEziCnxkqSEJxwVnSTljoIRUH4hBOb3WyDwPTL4Fn5AULjzoDIy4OQJOit28B/1TXwfPI1tMMGZzvE1DCZ9m38k09C/e5bqBvWw7xyBQrOOgOuUSdB8iT/AY3ik3buhPuUoTWK4wAg//UnnBdPg3XW01mKjoiIiNLJ6NYdkQEHQySYeCBkGcGJ58Bo27bOWdXVjt2uXcMDTDHL8qUQauL5ZkJRYXljGcoefgIlc+cjfOzxEGYLhCxD79Ydvjvuhuf91dGNPZsCc/L5uRQMAjNmQP1xLdT162BdMA/uk4cg//Lp0c1wqcHUNd/BPWIYTF9+XvO+tWvgOm0ETB99mPnAiIhyDAvk1GgFLrwEkOXYzItaGQYCl1+dsZhyiTZwEEqfmIHdm7dj9/ZiFP34K/w33gqjXftsh5Y6qorw4Ucm/KIEiPY5rNrmo6KAq373DQrOHg8kWgZKCTluuwnytq21LzOuGPN//4L8x+aMxkVERESZ4b/quoSt/mC1InjeVATOmZxwFZ+QFUQGHwGjc5dUh9pgkjfJljKyBLm4GJAkhE8cDu+ri7H7713Yvc0Dz+ffITD9Moh8Z1pjzSTtgD4wXK59eEDlalep/M+WBfPg+L9/pTiyZkgI5F9yARAO1Z6fGwZgGHBOPx8Ih7MQIBFR7mCBnBotvdd+8M5+EVBNEEr12RtCUSEkCWUPPILIkUdnKcIcUteXCI1c4MKLE/Y4FKgs0O5N0nWYvvkK5g/eTXlszYm0axcsSxYm7jcpy7C98GxmgiIiIqKMCo8cjbJb7gCAGrOrhaIAFitK5i6A0a49guecB+EsgJDjT3SQDB2+a65Pa8z1ZbRshaSmwBsGjFata97eVPNziwWBKRckXElQF0kIWJ+fDXnL3ykMrPkxfboa6sZf68zPJcOAvGcPLG8sy2BkRES5hwVyatTCw0fAs+ozBKdMheFwRG+0WhEePwHF76xCcNKUrMZH6RcedSqCZ5xZ50qCRB8/hKLA+jyLtg1h+vzTxMVxlH8h8T6/jCAiImqqAldcDc/ytxEaNQaioh2e243QJZej6OMvEDnqGACAaNkSJa8thXA6o5vIVzmGUBQISULp/Q8jMvTEzD+JJITGjU+4ySgAwDAQbGZ7sPivug7aQf3q/PIjIVmG9ZWXUhdUM2T66IPk2gCpKkwfr8pAREREuYubdFKjp/fshbJ7HkDZPQ9AFTrcrQrgL/ZD0+Iv76QsECK6dM9sTu2MGUlC6WNPQ+/ZC7anHoNcUrncVZjNkJJYLijpOpTfN6YupmZIiiS/LFMKBdMYCREREWWbNvhwlA4+HKVCQIUBd2sXAh4fjL3yc63fABR99i2sc1+A9aXnoezYBmG3IzR6LALnXwh9/wOy9AwS0/c/AKFhJ8H8wXtxJwkIRUF4+AgY3bpnOLokpSs/t9tRsuQN2G+/GdZ5c6vl48KWB4RCkIzEEyuUzZtSF1MzJIUjyf2/CpHUZyYioqaMBXJqWkymprtcsZGSf/8NttnPwDrvZcilXgiTCaGRoxG44JLUbRaqKPBf+0/4L7sK5g/fh7x7FwyXG/D7UXDZhUkdQlitqYmlmdKT/OAnFAV6r95pjoaIiIhygiQBat2bNooWLRC48hoErrwmQ0GlTulTs1BwxqlQ13wPSNHWIEB5r3XDiO4J9FjubVCufvMVbDOfhuX1pZDCYRgOB0Jnno3ABdOh9+iVknMIRz7KHngEvptvh+mjDyGXlUFv2w6mzz5B3hOPAInmMkkShIX5eUPo3bpX6/Eel0g+lyciaqpYICeitDF98B4KJk8ENC02s0aKRGBZvgzWJYtQdsfdCFx8eepOaLEgfPIpsb9Ku3dDqGpsw594hKLk7PLdxkIbcDC03gdA+eXn2IfD2ki6juDkKZkLjIiIiChNRIELxcvegnX+K7DNegbqhvUAAL33AQhcMB3BCWcBFkuWo6zOOuvp6AaYihLLkeWyMlifmw3ri8/B+/zLCJ9wUsrOJ1xuhMecViUAK6RHHkz4OEnTED7u+JTFkTF+P6yvvQrrc7Oh/v4bhBTtV6937gz9wIMQGj8RWr8BGQkldNrpcNz8LyCYaPWmQPCsczMSExFRrpKEqKOSkeM8Hh/baFA1qirD7bbz2sgB8p9/oPDoQ6NLKOt4mSmZOx/hE4enLY78Sy+AZXHdm0cKWUbRVz/A6NQ5bXE0B+a3V8A5aWJ0mWYt9wtFQWTQoShZ9hbQgI2bUo2vG1QXXh8UT2GhHYqSO69luYA/J1SbZvU6WjEpI4m+z9lg+uA9uM48Le79QpIAkxlFH3+RvrYwQsB91KFQNv0WvzWNLEMUtsCeNT9HVwg3EvK2rSgYNwrKbxsBSapcUYDonkii/LbwMcfBO+t5CHdh2mPKe+h+2O+5M+79QpIQmHoRfPfcn/ZY9kWzet2gfcJrg+rSkPycWT0RpYVtzkwgEqmzOC5kBbZHH0prHGX/vgdGh44QSs1Ngio29iz774MsjqdA+KRTUProU4CqVttsSyjRD4mRQw6D96VXc6o4TkRERJQyqpqzxXEAyHvsoVpz4gqSEICuwTZnRvqCkCR4n5kDWKy15+eyDCgqvDOfa1TFceg6CiacBmXzJkhAtc9AFRNHKm4zfboaBeNPTWJmd8P5r74e/iui7Yuq/ntX5OfBs86F78570h4HEVGuY5WCiNLC+urLdc7aBgDJ0GH+4jPIW7ekLQ7RqhU8K95H+LTTa3xg0bt1R8nsFxCcMi1t529uQmeejT3frYf/nzdBO3QwIgf1Q2jUaBS/tgwly96CcLmzHSIRERFRlBAwffQhnFPOQeFBvVDYpyecZ4+H+d2VgNG0ZiZKO3bAvPqjxPm5rsM6b25aY9H79oNnxXvQjjqmxn3aoENRvPRNRGq5L5eZ31kJdcP6hP++QPTfWP1hDawL56c/MEmC75Z/o+iTrxGYdhEi/Qci0m8AgudMRtF7q1H28BM5/aUOEVGmsMUKNSlcbpM7WrZ1QUryg4XnnVXQ+g9MazyqKsMdLkPZm2/D8Aegd+kW3SSUm7o2e3zdoLrw+qB42GKlJv6cUG1y9nU0EkH+ZRfBumQhhKLECpsVfw4NOwne2S8CNluWA913UtEeWJYsgrJ1C0ReHkInDgcUBYXHH5H0MXZtL077qj9VleHesw1lH3wMQ9OhHdQP+v4HpPWc6eKccjbMK1ckVSAHojPltQP6oPiDT9IcWeOUs68blHW8NqguDcnP+VUhEaWFcDggeb1JjTUKXOkNpkKbNoiMHcc3UiIiIqJmzn7rjbAsXQQA1YqaFX82v/8u8q+9AqVPzcpKfPWiabDfdRtsM58BtEh0ZrBhwP7fuxDZh40hRV5e5lri9eyJSIt2jT4/l//6K+niOABIhgH15/WAEJywQ0SUAzjthYjSIjT2jFhvu3iELEM78CAYXbpmJigiIiIiavaknTthe252nXvlSIYBy6IFkDf9nsHIGkAI5F91KWxPPQ4pEoYkBKRIJFa0VdethVDV2B48cQ+jqAiOPT0TETcpwulM+G9bgyyxOE5ElCNYICeitAhMuwgQBurq4SQZBvyXXsHEkIiIiIgyxrpofnTmbiKyDOv8V9IfUAqYVn8E64J5cYv+kq4DhlHnlwICAAwdgWnT0xNkExYaOXqfxgtZgXbwIWmKhoiI9hUL5ESUFvoBB6L04ScASaqxQ70oX7Lpv+BihMZPzEZ4RERERNRMyVu2AHvlp7WSJChb/k5/QClgmzMz4epNyTBiebjYq4WKUBRAklD2v8eg9+2XtjibqtCEswBbXo1/13gkQ49OKCIiopzAAjkRpU1o4jkoXvoWwsNOqrbkUOs/EN5n5sD3n3s5e5yIiIiIMkrY85KbQY7yftyNgOmLzyDpWsJxkmGg7PoboA04OHabkCSEhwxDyZI3ETxncjrDbLKEswAlz74EKEqNyUE1xsoyQieciNDosZkJjoiIEuImnUSUVtrhR8B7+BGQPEWQ9+yByM+H0aZttsMiIiIiomYqPOxk2P93f8JxkqYhdNLwDESUAiL5TS61w49E4J83QdqxA7K3BEbLlhDuwjQG1zxEhpyA4jfeQd4D/4X5nZWxdjYCiG56KgQgywiedS7K7nkguVUMRESUESyQE1FGCHchdCbeRETUCIVCIXg8RWjRoiVMJhMAQNd1KAoXYxI1RtqgQxE5qB/U9etim1juTSgKjA4dETn+hLTHo/y8HrbnZsG84g1IgQD0zp0RnHQ+gqdPAByOpI6hHdQfptWr4j6fCkKWoffeP/rnNm2gt2nT4PipkjbgYHhfmg95+zbIf/wBecc2KL/+AjkQgNGmDYJjxkHw35yIqEGEEAgE/PB6vWjbtl3s9obk5yyQExEREREBCAQC2LNnNzweDzyeIhQXR3/3+/0AgHHjJqANCxtEjZ8koXTms3CNPBEoKalRVBaKApGXh5Jn50Zn/qaRbeZTsN98AyDLsTikkmI4/nE18h55EMULl8Po1j3hcQJTL4R51ft1jhGKgvDJI3JiNae0cyfMq94DjDBMBS2gHT8MsFqzHVbKGG3bwahStMkqvx+WpYtgefdtwO+D0akzgmedC23goGxHRkSUUFlZGfbs2VWen3ti+XkoFIIkSbjggouhqg0vb7NATkSUQ+Q/NsP62quQd2yHcOQjdMpIaIccxl7tREQpYhgGSku98HiK4PEUo0ePnnA6nQCAX3/dgE8++bjWxzkcDoTDodjfOXucKAmRCEzvvQ94dsEMFcYxx+dEcRYA9B694Hl7Fex33wHLssWQtGj/bqEoCJ8yEr6bboPes1daYzAvXwrH//2rPKDKIn1Faw5521a4zhiDoo+/BBL0Qg+fNBzhY46D6ZPVkIyas8iFokBYrfDdeEvqnkA9SCXFcNxwPSxLFka/EJAkOIRAXkEB/Fdcg8AV1+Rc3quu+Q6W5UsheUtgtGyF0OnjofdI77WRKqaPV8E5dRKkkuLolzCGAaGqsD03G6HjT0Dp7Och8p3ZDpOImjlN0+D1lqCoKDpBZeDAQVDK21B9+eVn2LDh5xqPkSQJTqcTfr8/lss3JD9ngZyIKBf4fMi/7gpYFi+MzlSSZAACeY8/jEi//vDOfhFGl67ZjpKIqNEpKirCb7/9GptxUlJSDE2r3MjO4bDHkurCwhZwuVxwuwvhcrmr/O6G2Wze68i5VcAhyilCwDpnBuwP3At5z25AkmAXAnmKgtCY01B2z/0QhS2yHSWMzl1Q+vRslN11L9SffgSEgLb/gZlpgSEE7PfdDSFJsYL43iRdh/zXn7AsXYTQWefWfTxFQckL8+C8/CJY3lgOoajlPa8lSJoGo01beJ+bC73XfjB9/inkv/+CyLMjctTREAWu1D+/2p5PqReuMcOh/LKhctZ+xZcBJSVw3HU7lK1bov25c6BILm/bCucFk2H66ksIVQUgAcKA/YH/InTKSJQ+/kxOF5fV775BwcTTAV2LvmMZ0T71FV8GmT/+EM5zJqBk8Rvsh05EGbV16xb8+ecfsRWbXq8XhlG5l0b37j1RWBht0duiRUu0aNEylpNHfxWioMBVy8zx+r93SEIkuX13DvJ4fNC05DcjoaZPVWW43XZeG1RDTl8bmoaCM0+D6ZOPIRk1YxOKCqNFCxS/+1HuLNVsQnL62qCs4/WR+0KhUGypZUVrlIMO6ofOnbsAADZv/h0rVrxR7TGqqsaS7AMO6IMOHTru83kLC+2cRb4X/pxQhby774D94QdqvU8oCvSu3VD85rvNemNIZe0PKDzh6ITjhCxDG3QIit94N/ljb/gZ1ldfhrwlWgQPn3QKwieeDMvC+bDfdzeUv/6sPL7FguDEc+G79d9pL/ba77gVtqceS9gnvXjR64gcfWxaY0lE2rMH7pOOhbx1a63xCkWB1n8gipe8mbOtYQrGnwrT6o8S/nuXPP8KwqeMzFBUyWH+RfHw2sh9Ff3B926JctxxQ2OTUr7++kt89dUX1R5nNptjk1MOPngQXC73Pp+7Ifk5Z5ATEWWZZfkSmD9eFfd+Sdcg79mNvIfuR9m9/8tgZEREuUEIAcMwYkstd+/ehU8/XY3iYg98Pl+N8a1bt4kVyFu0aIUDDugTmxnudruRn++ElAOzE4maInXNd3GL40B0VrSyeRPs9/4HZf99MIOR5RZl65akxkmGAblKQTsZeu/94bv1jmq32R79Hxx33Y69Z8dJoRCsLz0H9esvUbL8LQhH/j6dK2nBIKwvPJt4E1FFhW3OjKwXyPOefDRucRyIXsfqt1/DumAegpOmZDa4JMh/bIZ51QcJxwlFgfXZWTlXICei3CeEgBACcvleHZs2/Y7vv/8WxcUeBIPBGuM9nj2xAnm7du3Rp0/fWG7udruRl2fPan7OAjkRUZbZ5syEKO8JGI+k67C+Mhdlt9wBOBwZjI6IKHMq+oMXF3tQVFQ546SiF+HA8g3FZFnBli1/xx7ncDiqtURp37597L78/Hwcf/zQjD8XoubKOmcmhKJC0rW4YyryGt/Nt6evIJvjRIKe4tXH2ht0LmXdj3DcdTuA2hefS7oO9eefkHffPfDdcXeDzhWP+vNPkL0lCcdJugbTRx+mJYakhcOwvjAnYTEfsgzbrGdyskCu/rohqXGSrkNdvy7N0RBRY6brOkpKivfaxN6D4uJiDB8+Ap06dQYAaFoE27dvAxDtD56f74Tb7YbL5UZhYSFatGgVO2aHDh3rtYIznVggJyLKMnXNd3UWxytIwQDU3zdC6zcg/UEREaWRpmkoKSmGoiix5ZMejwevvTavWn/wqjweT+zPBQUFGDJkWGzWSc3+4ESULeYP3quzOF5BCvihrvkekaOOyUBUuSdyyGEwHPmQy0rrHCcUBeGRYxp0Ltuzs5L70uKl5+C74eaEG4LWSziS9FApzvtApih//wm5JIlivmFEi8u6nnM9vIVqSn5wjR6+RNQchcNheDweOBx22O3RSXm//bYR7767slp/8Ko8nqJYgbxduw448cST4XK5UVDggsm0D69DOYCvhETNlWHAtOoDmN9/B1IgCL1zZ4QmnMUe10RElDK6rmP37l17zTgpgtfrhRACBx7YB8cdF53d7XA4oOt6rD941c0yCwsL4XQWxI6rKAr23/+AbD0tIqqDFEm+EIpwOH2B5DqbDcEp02B78tG4EyUEABgGhBAwv7Ec4ZOGA/UoOJjfeSupLy3ksjKY1nyHyBFH7fM5EtG7dku4YhIo77neo2fKz9/caP0HQJjMkCJ1/4wJRUXkmOMyFBUR5YJIJIydO3dWmQkezc/LysoAAMccczwOOqgvgGh+bhgGzGZzbLVmRUsUl8tdLT93OBzo2XO/rDynVGCBnKgZUteugXPqJCh/bC7fkR2AYcB+9x0ITp6Ksv/cW6/km+pH69cf6tdfQzIS9GS02qB375GhqIiIkrP3RjxWqxU9e/YCEC2QL1q0oNbHWSyWan0GTSYTzj57MhwOR6yXIRE1PlqvXjB5ipJaHdfc8xrfP26E6cvPoX79ZY1/L4FoOxQhBPKeeBSSMGC0aAnfzbcjeM7kfTqPFAolP7iWvrGpIFq3Rnj4CJhXrqi7dYlhIHj+hWmJIVl6x84wnAUJW8IISYK+/4E5N3scAIS7EKHTx8OyYF6d/96SriEwZVoGIyOiTBBCoLS0NDZBpVWr1mjfvgMAYNeuXVi2bHGtj8vLy4NRpS7RsmUrTJp0Puz27PYHzwQWyImaGWXDzygYMxxSefK79xJG6/OzIZUUo/Tp2UATfwHMFYGpF8H55Rd1jhGKguDEs5ttn04iyh1CCKxZ8121GSehKsWXDh06xgrkZrMZrVq1rrYrfWFhIVwuV60b8VRs3ENEjVdwygUwf/5ZnWOEoiBy5NEwunTNTFC5ymZD8YKlyL/iYliWLa7WH1yq+ruIFs+lPbuRf83lkMpKEZh+WdKn0bt3h1TsSe5LizT+n/iuuwHm996BEKLWWISiQO/RE8Fx48tvEDB98C5ss2fC9PUXgCGg9RuAwNQLER4+In2FabMZwcnnw/bUY3UX84VAYNpF6YkhBXz/dxtMqz6AvHNH3Ofhn34ptIMPyXBkRJRqgYAfP/20rsqKzWJoWuWKrv79B8QK5G53dGVmdEZ4xa9onm61WqsdV1EUOJrJHmiSEGLvjawbDY/HB01L/CZPzYeqynC77bl1bZSVwfL6Uih//QlhtSF8wonQD+yTtXCcZ59R3huy7tnKnmUroR1+RIaiSr+cvDYqaBoKJoyF6dPVcT8siMIW8Lz7EYx27Ws5ADVETl8blHXN8frQNA1ebwmKiipbolitNhxTZQn2c8/NRiDgj/1dkiQ4nU643YVo06YtDm4GH7YLC+1QFM50r6o5/ZxQHcJhuE4eAvXnn2rNN4UkAYqC4iUroB02OAsBAtLOnbAsXwJ5z24ItxuhkWNglBcOMk7XUTjwQMjbt9W6gWZthCyj6Nt1sZiVXzbA/N47kAJ+6B07ITRyDGCv3NjTMm8unFdekvCYkUMHo2T5yvo+k6SYPvkYzvPOguT1ApIESQgIRYGk64j06QvvvIUw2rQFIhHkXzwN1uVLYvcDiP05fMxxKHlhXrXnmUrSnj1wn3gs5G1ba7+OFQVavwEoXroC2KuglEvkrVvguPpyWD58D0KWAUmGpGswHPnwX30dAldck5OToppj/kXJaa7XRiQSjq3WrMjP27fvgH7l+5P5fGV44YVnqz1GURQUFLjgcrnRrVt37Ldf7yxEnlkNyc9ZIKcmJa0vlpoG81tvwvTNV4BhQOtzEEKjxwI2W+3jhUDeQ/cj79H/AX5/dPMTQ0AydEQGHw7vY8/A6NottTEmIP/9FwoHHQQpwY+9UFSExpyK0meerXNcY5Lzb6Q+H/KvvgyWZYsBWY4lqpKmIXJQP3jnvJjx66W5yPlrg7KqKV8fmqZBrbIx1/vvv4MdO7bD6/XW2IjH4cjHpElTYn//6qvoqpeKGScFBa5qx2oOWCCvqSn+nFD9SLt3o+DsM2D6/ttYQVNUFOFsNnhnPofwicMbfqKyMliXLoKy4WfAZELkiCMRHjIs/sziQACOG/8B66tzAcOIjit/vQuNHouyBx+BqNJPNRPMb69Awbln7tNjhKzAf831CJ4zGflXXAzzJx9Hi5+yDGgakJcH/5XXwn/19dHbAgG4Tzgayqbf439pIUkoeW0ZIkcfm6qnFl9ZGayLX4N1xXKYfGUIt20P/5nnIHL80Gi8ABw3XAfrs7Pifm4RsozQyNEonf1i2sKUt26Bc9okmL75urItpRCQdB2hk09B6RMzMn691Jfy+0aY3n8Xkt8Po2MnhIaPTM9mrCnSlPMvapimfm1Uzc9DoRDeeecteDwelNWyoXO3bt0xfPhIANEVnh999EG1meFOZ0Gza1vIAjlRuXS9WJrfXoH8a66AvGtndEdwKboBkZHvhO/OexA8e1KNx9hvuQF5zzxZ6/GEokC43fCs/BBG+Y6/mWB+600UTJ6Y1Fi9S1cUffVDmiPKnMbyRipv3gTra69C3r4dwuFAaMRoaIcelpMzO5qKxnJtUHY09utj7/7gFTNOios9UFUVZ51V+f61ePFr2L59GwDEWqJUtkVxo0sXfklXFQvkNTXWnxNKE8OA6aMPkffKizBv+QsRax5CJ5+C4JlnN7yoKARsM56E/Z47oxNRTKZo4VLToHfogNLHZyBy1DHVHxMOo2DiuDpX7Gl9+qJ42VsZLRzm3X8P8h66v0bbw0QihxwG+a8/Ie/eFb99xrTp8N1zPwBA3rYVBePHQv3l58ovLYBoQVpRUPrkTIROHdfAZ7Nv4r3HSjt3okX/3glXvAJA0affQC9v65Uu6nffwPL6MkjFxTBat0bo9AlpP2dz19jzL0qfpnBtVPQHr5qXV+TqHTp0xEknDY+NmzXrmVirFJstr9oGma1atUY7rjCvpiH5ecan+vh8PpxyyinYsWMHXnvtNfTt2zfTIRDtE/M7b8E5qbKoLFXp4ySVepF/9WWApiE4+fzY7eqa7+IWxwFEkz2PB/Y7b0XpjOfSEjc1TkbXbvBff0O2wyCiRiaaaHtRWlqKDh06xm5fvnwJtmz5u9bHSJJUbZbKYYcdDiEE3G53rf3BqWlibk5pI8uIHD8UvmHDYHbbUZbCYobtsYfguOv2yhsilfm5vG0bCsafipJFryNy+JGx263zX4Fp9Udx25hIug71xx9gm/UMAldek5I400nZ9Hu0r3gdReS82c8gdMYEaIMOhdGuPTwffALzW2/COvd5KH/8AeGwI3zKKATOOQ+iTZsMRl836+IFQBLz+ISiwDL/FfhvujWt8WgDB0EbOCit5yCipkfXdZSUlCASiaBN+WusEALPPTcLwTgbIns8RbE/S5KEE044ETabDS6XG7Z43QsoJTJeIH/yySehJ/FNMFFO0HU4rr0SAGpd3ichusO84+Z/ITR2XGw2jHXOTAhFhaTHnwki6Tosy5eibOdOiNat0xF9DVrffhDlvf7qIlQVkWbQP5aIqDEqKSnG7t27q804KS6ObsSjKAqmTZsOpby9gMPhgCRJyM93xmabRDfJjP65aluUqoV1aj6Ym1NjI2/fFp05HodkGNH8/Lqr4Fn9ZWwVnm3WM9HZ0nVtVGkYsM2ZgcDlV8VafaSbNmDgPs8eF4oCyVOUcNNNoaiwPTcbpYMOjd5gMiE8+lSER59a33AzQt66tVr7m7ooW7dkICIiorrt2rWzfBZ45R4+JSUlMAwDrVq1whlnRCddSpKEvDw7IpEInM6C8hWblbPCXS53teN2794jG0+nWcpogfy3337Dyy+/jH/961+47bbbMnlqonoxv/s2lB3b6xwjARChECwL5iE4bXr0cZ+urrM4HnusrsP0/TcIn3RKKsJNyOjQEeETT45u4lPHh2FJ0xCYmrs7shMRNXXhcDjWEqW42INDDx0c6yH45ZefY+PGX2s8pmIjnmAwALs9utv8EUccjWOOOR4mkymj8VPjwNycGiPrS88nnF0sGQbUXzdA/fILaIMPB4JBqD/9mPDYEqIFV3nH9oxtjB4eeiL0du0gb9+ecBJLhWRaj0THaTB98nFDwssKYbcnVRyHJEHkcB9tImpaAoFArAAeiUTQv//A2H3vvfc2PB5PjceYTCaoavU8fNSoMbBabbEJLZQbMlogv+uuuzBx4kR068YeltQ4qN9FN2RJOKtDlmH69hsEp5X/PZmEroKe2b5ZvlvvhOnT1UAgEHeTntDpE6AdNjijcRERNWd//fUn/vhjc2zGSVlZWbX7DzjgQDjLVym1atUaXq+3xoyT2jbi4VJMqgtzc2qM1G+/SSrXFrIM03dfRwvk+7pKIpOrKhQFZQ88Aue5Zya30lOSETnySJg/WZ3c8fdxdnouCJ98CuwP/DfhOEnTEBo+IgMREVFztGHDz9i+fWusP3gwGIjdZzab0a/fgFhLwjZt2sFqtVVbqel2F8ZWc1ZVMZGFckvGCuRvvfUWfvnlFzz22GNYt25dpk5L1DCGAOJ2KtxLlWRWO/AgyFu3JDW7Q+u9fz2Dqx99v94oXrYSzgsmQ/39t/Id2SXA0AFJQuD8C+G7425uCklElCIVG/FUb4niwbBhJ8PhiCbIW7duwdq1a6o9Li8vL5ZcV30vGjDgYAwYcHAmnwI1QczNqdESRrLZebQXIgDk5UFv3yGanyd4iOEsgNE6s/24wycOh/f5V5B/9WWQivZA1NYKprx9Y2j8mfBd8w+0OCLx+4BQFGgHHpSmqNNH6z8QkYGDoP7wfdzPU0JRYHToiMjxJ2Q4OiJqCnRdh9frrdYSxe/3Y/TosbExGzf+ij//3FztcRVtC91uN3Rdj7UrHDKEr0WNXUYK5IFAAP/9739xzTXXxD4IpkJ9dyalpqvimkjVtSH696+2KWdchgGj/wCoavS84QsugmXlm3UfW1GgHXU05P16IeNX8oD+KP3qe6gfr4Lp3XeAgB9G564IT5gI0aZN5jcnyIBUXxvUdPDaoLrsy/Wh6zokSYrN4v7llw34/vvv4PF4YrvPV+X1FsPlcgIAunbtAkCUJ9zRmeFWqzV1T4RSrjF/j8zcnDIp1e+zxoABEB++n3AiimQYEAP6x/Lz0AUXwXbXv+ucfS4UBeHzzoeal/nXX2PUKJScdBJMbyyH+uUXQCgAqcQLKRAAdB1Gz14ITZ4Co/f+kAFEhgyF+tGqutsm6joiF14U+zfINXVdG/45zyP/5KHAnj01nqNQFAi7Hb65r0I1N8VPLsT8nOLZ12sjEolUazX41VdfYuPGX1BSUlLrHiyRSCi2ArN37/3Qtm3rKnv4uGAymVPwLChdGpKfS0Ik2eisAf73v//hk08+wWuvvQZJkvDFF19g8uTJeO2119C3b990n56o/iIRoH17YPfuuseZzcDWrUCLFtG/GwYwYgTw7ru1L9GUZUBVgdWrgUMPTX3cRESUNuFwGEVFRdizZw+Kiopiv4qLi3HGGWegU6dOAIAff/wRK1euBADIsgy3O5pct2jRAoWFhejcuTPsdns2nwo1U8zNqVH74w+gW7e6+5BLEtC9O/Drr5WflouLgUGDgD//rL3tiKoCLVsC330HtG2bltBT6ptvgKOOin5eqa3oryjAcccBb78d/XNj9PffwK23Ai+/DIRC0dtUFTjjDOCOO4BevbIbHxHljEAgUC0/r/i9tLQUl156aWziyfvvv4/vvvsOAKCqaiw3r8jPu3btyr17mqm0f926ZcsWzJkzB0888QRKS0sBAH6/P/a7z+er94dDrzcAPcP9mym3KYoMp9OW0mvD9N8H4LhgCupqtuL/v9sQkq2Ax1d546wXYL/kQphfXxZtY6LrgCxD0nUYBS74nnsRWs8Dqz8mBaTdu2BevBDy1q0QDgfCp4yEcWCflJ6jMUrHtUFNA68Nisfv98PrLUGnTm1hGAp03cDPP6/He++9E/cxmzdvgcNRCAAoKGiF448/EYWFhbX2Bw+HgXA4te8BlDkFBbYa/6eNAXNzyrSUv886W8J69XWwPfRArXcLSQIkCWX3PACt2F/lHhOkpSvgOGcC1B/WRPNzTQPK9xvSu3ZD2bzXYFjyU56fy5t+h2nZEsjFxTBatkJ47DiIDh0adtDu+0N9bQnsk8+B7CmCkJVoy8Ty5xMedhJ8M+YA3mBqnkQaJLw27G7gwccg3XonlJ/WAUJA3683RMtW0fv35f/J74d54QKY3lkJye+H0bkzQudMhn7woMa9JKiJYn5OtRFCIBDwIxLxw+lsAUmK5mGffLIa33//bdzHbd68BW3aRL/47NSpB1q0aBu3P3hZWRhAOG3PgdKrIfl52meQV8xIiad///6YP39+vY7t8figaXyxpEqqKsPttqf82rC89ioc/7gaks8XnbUARAveJhN8N92GwCWXx02slPU/wfryi1D++hPCakX4hBMRGj0WSPXS+UgE9ltvhO35OdGNP1UFMAxIuo7wEUei9KnZMNo3MBFvxNJ1bVDjx2uDQqEQduzYXq1HeMVGPLIs4dRTR6Fjx+7QNANbt27B0qWLYLPlxfoPut2FsV7hdru9RqJNTVNhob1RLv1mbk6Zlpb3WSGQd9/dyHvkwejs6fIPw5KmwShwofSxpxGOt3mjEDB9uhqWRQsg794Nw+VC6NRxiBw/NHacVJE8Rci/4mJY3n4LQlGixy9fXRoaNx6l9z8MNHQlUSgEy7LFML+7ElIgAL1jJwTPmgS9b7+GP4E0y1QOZlr1AZzTJkHyeqMTlgwDoryne/i4IfDOfgGifCNsyg3Mz8nnK8POnTtj+Xl0taYHuq7BZjNj9OhxcLtbAgDWrl2D1as/gsORH9sks2ITe7e7kJvWNyMNyc/TXiD3er1Yv359tdvWr1+Pe+65B//+97/Rt29f9OlTv9mtfLGkvaX1jdTng3XJQqjffAUYBvQD+yA44SwIlzu156kPw0D+BefB8sYySLX8SAtFgdGmLTxvr4Jo3ToLAWYfkyyKh9dG86DrOkpKSmKb8LRv3wHt2rUHAPz9919YvnxJrY9zuQowZMixsQK5ruuIRCLsD06NtkDO3JwyLZ3vs9Lu3bC++jKUX34GVBMihx+B0JjTAIslpeepV2xlpXCNGAbl119q7RMuZAWRwwaj5LVl0XaNzVAmcjD126/hGn0yoOuQamlFIxQFkUMOQ8mSNxtvK5omiPl58xCJRFBc7IlNUNl//wPhdEb35/n++2/x2Wef1HiMqipo1641DjvsKLRu3Q5AtP2hJIH9walB+XnaW6w4nU4MHjy41vv69OlT7wScKOPsdgTPmQycE3/WVbaY310J6+tL494v6TrkHdthf+AelN33UAYjIyLKDp+vDD/+uDZWEC8pKYFR5YPxwQcfEiuQV26MWRibFe5yRX/ZbJbYBzQAUBQFCj9AUyPG3JyaEtGyJQKXXZntMGple+ZJKL9sqLUoCwCSocP0+aewvvoygpOmZDa4ZsT+n+jGrHH/H3Qd5i8+g/nttxA+ZWSGoyNqXnbv3oVffvkZHk8xPJ4ilJWVouqc3cLCFrECeYsWLdGqVavYLPCKFZstWrjRsqWz2pcn5mb6JSOlFrd8JmoCrLNnQChKwl3srfNehu/WOyAc+RmMjogo9QKBQLWWKMXFHnTu3AV9+/YHAOi6gW+//braY0wmU2wX+latKlfT2O0OTJx4TkbjJyKiJkzXYX12Vu2bZ1Yly7DNeoYF8jSRN2+C+eNVCccJRYH12VkskBM1gBACPp+vSn5eBI/Hg4EDB6Fz5y4AoqvY1qz5vtrjrFZbbIJKfn5lnaJTp87o1KlzjfM0xtV71DhkpUA+ePBgbNiwIRunJmqSTF9/WWdxvIIUDEDZ8DO0QYdmICoiooYRQkDTIrHlkoGAHytXroj1B9+byWSOFcjz8/PRp09fuFyu2IyT2jbiISLm5kSpJm/bCmXnjoTjJMOAun5ddNdmzoBMOXXjL0mNk3Q9+v9ARAkZRrTloMlkAgDs2LEDq1evQnGxB+Fwzc0tO3XqHCuQt2zZCv36DYjl5263GzZbXkbjJ4qHM8iJmgJjH7YSSDSThYgow3Rdh9frrTHjpLjYg27duuOEE04CAFgsVuzcuQN6+ReC+fnOai1Rqs4KlyQJxx57fDaeDhERNXf7mm8zP08twwCEgFD2odyhsjRCVFUkEkFJSXGVTTKjuXlJSTEOO+xwDBhwMIBoT/Cd5V8IyrKMgoKCWFsUl8uNtm3bxo7pdDpx1FHHZOX5ECXCdwGiJkDvcxCkr7+CZNQ9i1yoKvSevTIUFRFRdZFIGMXFxTAMA23aRJNlXdcxZ85MaFqk1scUFxfH/izLMk46aTjsdgdcLhc34iEiopxktG0Hw1kA2VtS5zghSTA6dQa48XPDRSKwLFoA26xnoP7wPQBA79ETQlYSf0ZSFESOOS4DQRLlnmAwiOJiD8xmCwoLCwEAe/bsxoIF86r1B6+qpKQ49ueCAhdOOukUuN2FKCgo4F491GixQE7UBASmXgjnl5/XOUYoCkJjT4dwF2YoKiJqzrZv34aioj2xHuEejwelpV4AQJs2bTFu3HgA0U0vHQ47fD5f+WyTyhknbndhbKOeCl27ds/4cyEiItonZjOCk6bA9vTjCdsgBqZNz1BQTZjfj4JzJ8C8+iMIWYZUXtRTftsYnUkOoK4Ga5KuIzD1woyESpQtmqZh+/ZtsZWaFTPD/X4/AKBPn4Nw7LFDAERnegshYLVaq+Tl7lh+XrVXuKqq6NGjZ1aeE1EqsUBO1ASERo9FeM5MmL75qtYkXCgKRJ4dvutvyEJ0RNQUCSFQVlYWa4liGEZsqSUAvPvu27GCeFVWqw3WvWbKjR17BqxWK/uDExFRk+G/9EpYFi2AvHNH3Pxc794DwUnnZSG6piX/n9fA9OlqANG+7hUqCuWi/Fe8LMN/8eXQquQwRI2VYRjwektiE1QcDgd69eoNILpqc/nyJbU+zuFwQFVNsb+bTGZMmXIB83NqVlggJ2oKTCZ4X3kN+RdPg+WdlRCqGu1LLkuQNA1G+w4oeWEejO49sh0pETViP/64Fjt3bkdRUXTGSSRS2RbFZsurViDv0KEj/H5fjRknNputxnFru42IiKgxE61aoXjZWyiYNBHqzz+V5+cGIMuQNA3awYeg5Nm5EI78xAejuOStW2B57dVqhfG9VS3vCUmK/h/oOox8J/xXX4/A5VelP1CiNDAMA9988yU8nmiv8JKS4thePUB0g8yKArnFYkG7du1htVqr9Qh3u90w17JJMPNzam5YICdqIkS+E965C6D8tA7WV1+GvH0rhCMf4eEjEB56IsBeYERUh0gkguJiT7WWKKFQEGPGnBYbs2nTb/j7779if6/YiKciwTYMA7IsAwCGDDkh48+BiIgolxhdusKz6jOYPl0Ny9JFkEqKYbRoidDpE6AdfAjAmZkNZlmyKKlxQlURnHAWtL79IPkDMDp2RGj4SIBFQMphoVCoyiaZ0d/tdjuOO24ogOim9D/+uBbBYDD2GFU1weVywe12o02bdtWON3bs6RmNn6gxYYGcqInRD+wD37//k+0wiChHhUIhWCyW2N8///xTbNz4a63tUAAgHA7HZpXst9/+aN++Q6wgzo14iIiIEpAkRI46BpGjjsl2JE2SvHNHdCJQHTPIAQBCQAqFEGTPd8oxQgiEw+Fq+flbb72BnTt3wOfz1RjvdBbE/ixJEgYMOBiSJMHtLoTb7UZ+vpNtUYjqgQVyIiKiJkYIAZ/PF5txUrkZjwfBYADTpk2HyRTtMxgOh2LFcavVVr5JZmVLlIoZ4QDQu/f+WXk+RERERLURBQWJi+MAIEnRsURZYhgGSku95Xl5cbU83W63Y+LEc2NjfT5frDjucDhqtESpauDAQRl9HkRNFQvkREREjVTFRjwejwedO3eJzeb++OMPsW7dj3Ef5/WWoEWLlgCAPn36omfP/eB2u2Gz5WUgaiIiIqLUCI0cA/s9dyYcJ2kaQqPHpj8gavY0TUNJSTF8Ph86d+4Su33x4tewc+eOWh9jGEa1VoVHHnk0ZFmJ2x+ciFKPBXIiIqJGwOstwY4d28t7EEZ7hVfdiOfMM89GYWELAEB+fkGsP/jeM07cbjdMpspEu6JQTkTJMQwDZWWlcLlsUBQ58QOIiCht9P16I3zs8TB98jGkKpsTViUUBXrP/RA58ugMR0dN3Z49u7Fr165qs8G9Xi+EEFBVFdOmTY8VvQsKClBUtAcul7u8R3g0Py8sLITTWVBt1Wa7du2z9ZSIGiVd11FSUtKg/JwFciKiVAqHYfr6S0ilpTDatoXWbwA3YKKkBYPBai1R+vbtj/z8fADAr7/+gi+//LzGY1TVBLfbhUhEi9120EEHoV+//uwPTtQAmqbB6y2JbYoV/fBbjOJiDzRNwzXXXAGTKT/bYRIRNXveJ2fBNfokKH/+UaNILhQFRmEh/JddCfO7K6F37wG9R68sRUqNjRACfr8PxcXFKCoqgtdbgiOPPDrW4/vrr7/C779vrPE4i8UCt7sQoVAItvKNYI89dgiGDj2xWiGciPZNJBKOtQ6tzM89KCkpgWEYuP76q2Ey1a/UzQI5EVEqhMPIe+RB2GY9Ddnjid2s9egJ/3X/QuiMM7MYHOWqHTu2Y8OGn2Nv7H6/v9r97dq1jxXIW7ZshXbt2sdmglfMDM/Pz6+xEU/VGeJEVLdwuCLRLqqWaHu9Xhhx+tpGv3zil59ERLlAtG6N4pUfIO+JR2F9fjbk4uLo7Xl50Lt0g/L7RjivvCQ2PjL4CPhuuhWRI47KUsSUyzZt+h2bN2+K5QShUKja/f36DYjl523btkU4HIrl5RWrNW22vBr5OVulECUvEPBXyc+LYxPIyspK4z7GZDJBiPqfkwVyIqKGikTgnDwR5g/eg7TXK7Ly+29wXnohfH/9Cf81/8hSgJQNFf3Bo8m1J/YGP3jwEejYsRMAoLTUi3Xr1lZ7nMPhiC25dDgcsdu7dOmKLl26ZvIpEDUZQogqiXb1GSdlZWVxH2c2m6u0KCqE2+0q/2LKibw89uwnIsoVwuWG7/9ug+8fN0L56w9IRR7kX3YhlF9+rjGrXP3qCxSMGwXv7BcRHjEqSxFTNsRbHTZy5Gjk5dkBRCew/PzzT7HHSJIEp9NZZQP7ysJ3//4D0b//wIw/D6KmQAiB0tLSanl5Ra4eDAbiPs5my4PL5UJhYWG1dqIOhwNWq7Xe8bBATkT7zu+H5PdDOJ1AjnwTLv/9FywL50PZthXC7kBo+AhohxyWkfYmtmeerLU4DiB2m/2eOxE+9nhogw5NezyUWZqmQQgjNmt769Yt+PjjVdX6g1e1Z8/uWIG8des2GDhwULUe4ZxdQlR/0UTbW2PGSXGxB8FgMO7j8vLy9iqER38e8/LsNWaAERHlIqmsFAiFIVwuIEdarCnrfoRl+WLIxcUwWrREaNwZ6W9vYjZD79EL+fdNhfLXn7X2JZcMA0KS4Jw+FXt++BnCXZjemCjjwuEwFEWJtRv8+eef8N1338RdHebxeGIF8oqN7yvygYICF1SVpTOi+qroD169EF4Ej6cYmhaJ+7j8fGcsJ69aCK9oW5Rq/CknoqSZ3n8XtmeegPnD9yEJAWE2IzRuPPwXXw79wD7ZCSoYhOMfV8M6/xVAlqO/hEDeYw8hclA/eGe/AKNb9/SdX9dhm/kUEq3lEaoK2+xnUMoCeaMVCoVib+iVs048KC314qijjkXfvv0AAKqqoqhoT/mfTeWb8FS+qbdu3SZ2TKezAIcffmRWng9RYxZNtIv3ao3iQXFx/ERbkqRYol2xKVZ0oyx3g2abEBFlja7DsmAebDOegunHHwAAhrMAwUlTELjoEhhZ2uhP2rEDzunnw/zpaghVjU5YMQzY778HoZNPQenjz0AUuNJ6fsuyxXE37QSik1hEOATrvJcRuOTytMVC6bP36rCqhTefz4exY0+PbXZpGAaKy1vvWCyWasW2wkJ3tU3r27fvgPbtO2TjKRE1ahX9watOUPF4imL9wWsjyzIKClzlhfDCau1ETSZTRuNngZyIkpJ3392wP/BfCEWJzYqWwmFYXnsVloXzo0sUh4/IbFC6DufUc2F+/91oTLoe/VVOXb8O7pHD4HnnI6BL57SEoPz6C5RtWxOOkzQN5rdWpCUGSp2qG/HYbDYUFrYAAOzYsQOLFs2P+7jS0pLYn93uQowYMRputxv5+U7OPiVqgIr+4HvPOEnUHzyaaFe2RHG5ojPAMp1oExGlTSQC54VTYHlzOUSVTf9kbwlsTz8O68svoHjxmxmfxCKVFMM15mQof/4R/bumVbvf/O7bKDh9DIqXrwTy7WmJwfzRB3UWx2OEgHnlmyyQ57iqq8NatmwJuz3agvCnn37ERx99GPdxXq83ViDv0qUrxow5DS6Xi6vDiBooEAjsNUElOoEsUX/wiuJ31VWbTqczttIj21ggJ6KEzMuXwP7AfwGgRrIpaVp0ieK0ySha/WV6Z2vvHdfKFbC8+3bc+yVdBzwe5N1/D4KPP5WWGKSAP/GgirGh+Mv7KfMikQi2bPkLXm8JIhE//vprG4qKKjfi6ddvAI466hgAQEFBAYBof/CqM06qbsRTwWQysVc40T4QQiAYDFRbmVFRCE/UH3zvligulxtOZwHkKsUiIqKmKO/+e2Be8TqAaMuQqiRdB7xeFJx5Goq++gHI4CoZ29NPQPnjD0hG7QVqSdehrl0D68svQpt+cVpikAIBCCTeSlkCIPl8aYkhq4SAadUHsM2eAdPnnwK6Dv3AAxE4/0KERo/NmRaZtQkE/Ni6dSu83uIq+bkntjpsyJBh2H//AwAALpe7Wn/wqqvD3O5CWCyW2HHtdkessE5EiQkhUFZWtldLlMT9wa1WWywvr1oIdzgcOf/FFAvkRJRQ3qMPQchyjeS7giQEhKHD9uws+O64O2Nx2WbPiM5or2v5pK7D+tqrCP3nHsCd+lkqRvsOEJJUa//xqkT5WMosTdPK2zBE39BdLhd69eoNILoEbMWKNyDLEmw2MwKBMAxDQJIkFBQUVOsFbrVaMW3adPYHJ2qAio149p5x4vEUJewPXr0QHk227XbOACOiZsrvh23W03Xmn5KuQ9mxHZZlixGacFZm4tI02J6bHbc4XpVt9jMoTVOBXO/YKWFxHACEokDv2jUtMWSNriP/qkthnf9Ktc9J0tdfwfnlF4jMeAolry6CcLmzFmLF6rCKfKBz5y6xlia7du3C22+vqJGfq6qKggIXFKXyC/C2bdvhggsuZn9wogbQdR1er7dGIby42INIJH5/cIcjv8oXUpXtRNPVHzwT+EpCRHWS/9gM05rvEo6TdB3WBfMyWiBXv/8mqeWTUjgMecMGoGvqC9RGm7YIDzkB5lUJlnLKMgKTp6b8/FSdpmn46qsvYm/wXq8XosqHxy5dusYK5DZbHtq1aw+nMx+dO7eHqtrgdLrgdBbUmmizOE6UnIqNeKom2tHfE/cHr7oyo6IQzv7gRETVmT94D3IdK2wqCFmGZeH8jBXI5e3bIO/ZnXCcJATUjb8CwSCA1E9giRx7PPQ2bSDv2FFnoVzSdQTPnpzy82eT/b93wTJ/HoDqK38rJjqpP3wP57TJKFm4PGMxeb1e/PDD97F8YO/VYYoixwrkbnch2rRpixYtCtGlSwcoihVOpwv5+c4aq8NypS0DUWMQiYTLe4N7qhXCvd4S6HHqGNH+4AXVZoJX7K9lMjW9z8YskBNRnWRPUdJjpWJPGiPJXf5r/gnzqg/iziQXigLhLkTwnElZiK7pqNofvLIVQxFcLheOPXYIgGiivG7d2mrfdlssltibert27WK3S5KEsWNPh6rKcLvt8Hh80LTaV0kQUU0VG/Hs3SO8ro14KvqDV2yKVZFssz84EVHy5PLNwBORDAPyrl1pjiYHqSr8/7gJ+ddfFXeIUBRo/QcictyQDAaWXpK3BLZnnoCEulcWmD9eBfW7b6ANHNTgc8ZbHda9ew/06zegfIyBtWvXVHtcXl5eLD9v06YyP8/Pz8e4ceOZnxPVUyAQ2GuCSvRXaak37mNU1VRt357K/XsKmtUXUSyQE1GdDHdh0mOFsyCNkdSk9RsA0+efJpxFLkxmGPvtl744Bh8O74xn4bx4GoRhxOIR5Uv/jRYtUfLaMojyDR+pboZhIBQKVuvrvWzZYuzevSvWH7wqv7+yD7wkSRg06DCYTKZq/cHZhoGo/gIBf5Wl0JUtixJtxLN3SxS3m/3BiYhSIdn8XMgyRMuWaY6mktG2HYzCQshFdU+wEZIEvVv3tPZGD04+H/KunbDf+x8IRYWkRzcLrWg7ovU5CCVzFwBN6D3J8vqy8ln5dROqCuu8uSjbhwK5ruuIRCKxVV0+XxlWrHg97uowh6Oy33d+vhMDBhwca8PA1WFEDVPRH7xqITw6gawYgTr2SKvaH7xqITw/P5+fl8ECORElYHTpikjf/lDXrY3bgxyIJpvB8WdmMDIgMPUimD/5uM4xQlEQPH1C2vvshUePRdHBh8D64rOwLF0MqbQURrt2CJ49GaHxZ0I48tN6/saosj949W+4S0qK4XK5MX78xNjYUCiEUCgU24in6rfb7r0+JA4ceHCmnwpRo1cxA6y2GSd1bcRjs+XVuhEP+4MTEaVPeMgJMOx2yAk2mJQMA8Fx42veIQTUH76Hsv4nQJKgHXIo9B69Gh6YqiJw3lTkPfpQwgksgQumN/x8Cfiv+xdCw0fC9txsmN9/BwiHoffcD4HzpyF8yiigia1ckrduAVQV0LS6B2oa5G3bar2roj941XygYnVYr169MXToMADRQtuePXtgGEZsdVg0L4/+3qJFq8q4ZBlHHHFUyp4nUXNhGAZKSkpqKYQn7g9emZu7YpNWqk5Ao5pYICeihAJXXA3nRefHvV9IEiDLCE69MINRAeERoxA+bghMH6+qtXgvFAWioAD+f9yATMwNMTp0hP+GW+C/4ZYMnK3xiCbaRQgE/OjatXvs9oUL56MozhLh0tJo7/CKAtuxxx4f25yHG/EQ1V9Ff/DqhfCiOvuDA9HZX1U/+FYUwjkDjIgoC+x2BKdNh+2xh+Ju1CkUBUZhC4ROHVftdtMnH8N+8w0wrVtb7fbw0cei7J4HoPfev0GhBS6+HNaFCyBv+bvWIrlQFGj7H4jg2ZMzUozQ+xyEsvsfysCZsk84HEAdE5piFAU+qxVbt24BgFj/b03TMGfOjGr791RVtUWDoigYOXIMHA4HV4cRNVAkEol9GVUxQcXjKUqqP3jVmeAVk1aaYn/wTGCVgYgSCo09Hb6f1sH+8APVdkMHAKGogAR4n3kWeveemQ1MUVDy/CvIv/ZyWBYvjC6RLC+oSpoGvUcveJ+bC6NT54wUyAnYsWMHdu3aWa3w5iuf3WQymTBt2vRY0dvlcsHnK6v2pl7Rkzg/31lt9mmbNm2z8nyIGquK/uAVLVEqfh4T9Qd3OguqtEapXArN/uBERLnF96//g7L+J5jfeQuQ5WqTRYSiQDjyUTJvEWCzxW43v/MWnJPPAmopgJo++wSuU4aiePnb0PscVO+4hLsQxa+/Dee0STB99SVExcQGAUi6hvCxx6P06dlAHmcyplr4pOFw3HpTjds3KQr2yDKKKn4pCjx5edCXLkK7du0xduzpAABVVZGf74SmRaoU2wpjebrdXn1D1Y4dO2XkeRE1FcFgsMYEFY+nCGVlpXG/mKroD169EF7Y7PqDZwIL5ESUFP9Nt0I79DDYnn4S5o8/BBDtXxcaezr8l1wBvW+/7ASWl4fSp+fAd8MtsC6YB3n7Ngi7A+FTRiJy+JGxgjmlRrQNgzfWg7i0tBRHH31srJj97bdfYfPmTTUeZ7fb4XK5EQ6HYbFYAABDh54IVVXZhoGoAQKBQI2NsYqKihL2B49+IVV9Ix6n08lEm4iosTCZ4H3+ZVhffRm2GU9BXb8OAGA4HAieOwWB6ZfC6NCxcnwggPxLLgQMo9ZZ55KuR8dcPh3F769uUA5ttG2H4jfehfr9t7AsWwKp2AOjZSuExo2Hvv8B9T4u1S62OgwSwoOPgGnNdzi8Si/yt61WlJXP8BaSBJGXB2O/3nA6C5CfX70N5IQJZ/FLcaIGEELA5/PtVQiP5un70h+8Ikdnf/DMYYGciJIWPnE4wicOB8rKIPvKYBS40rq5zr4wunaD/x83ZjuMJunXXzdg8+ZNKC4uRnGxB9pefQ0HDToEeXnRGSXt2rWHEKLG5nwVRfGqmHwTJadiI57aEu1E/cFdLleNQrjD4WCiTUTUFKgqgudMRvDsSZBKioFQGKKwsNbe2paliyB7S+o8nKTrMK1bC/Wbr6AdcliDw9MGHAxtAPeGSYfvvvsWO3Zsi/UHr1gdJh13PFr+/RcGb98WW/XbXdMQkCQUAihUVZgeeRL2o4+rNRdnfk6UHMMw4PWWVOvZX/HncDgc93EOh6PKygz2B88lLJAT0b5zOGBU2ZmcGq+KjXiqFt6Kiz047bTxsd7CO3fuxMaNv8YeU9ELvGLpZdVC24ABB2MAPwgR1Yuu6/B6vTUK4Yk24on2B6+5I72tyrJ6IiJqwiQp4Yb05o8+rNEqsTZCUWD+eFVKCuRUP7WtDotEIrFWKADw55+bYz3EgWhhO1po2x/uQYch8NYbsM17GVLAjxNDIQhFQWjkGPj/eRP0/Xpn42kRNUqRSAQlJZUtC4uKKjauLU6qP/jePcLNZvYHz1UskBMRZZoQGW39IoRAIOCHxWKNtU9Yu3YNvv/+W5SVldX6mOJiD9q2bQcA6Nq1G/Ly8mIzwvPzndyIh6gBIpFweW/w6jNOEm/E46q2MiNaEHdxIx4iIkosHK6193gNkhwd29xkIT/3+32w2ysnHX300Yf47beNcVeHRSLh2Hv+gQf2Qbdu3WMzUe12e7VJK74hJ8B3yx3R1ju6Ab1HT4hWrdL7pIgasWAwWGOlZnGxB6WlXvYHbyZYICciygD5779ge3YWrHNfgFy0B8JmQ2jkaAQuvATawEEpOUdlf/CKmeCV33QHg0GMGzcBbdq0KR8txYrj0eJ35Ru62+1GYWGL2HE7dOiIDlV7WBJRUgKBQLWVGRXJdmmpN+5jKvqDVy+EF7I/OBFRtggB06erYVmyCJKnCKKwEKFx4xEZfESj2uvG6NY9uXi1CPRu3dMfUA5Q1v4A26ynYV26CJLfD8PlQvDMcxCYdhGMrt1Sco66VodpmoYLLrgYavlGprqux4rjVVeHVeQDslyZB/TqlcQscIcD2qGDU/I8iJqCiv7g1Qvh0Z9Nv7+u/uDWvSaosD94UySJeF+FNAIejw+aZiQeSM2Gqspwu+28NqiGbF4bps8+QcFZpwOhULVlrUJRIekayv5zLwIXXpL08aIb8URnn7Zt2zY282TNmu/w6aera32MJEk48cTh6NGjJwCgrKwMZWWlcLncsVYqzRVfN6guia6Piv7gVQvhRUXRL6iS3YinaiGc/cEbj8JCOxSFq2mq4uso1aYxv8/Kf/2JgkkTof70I4SqAoYByDIkTUOk/wB4X5gHo137bIeZFHnT72gxeEDCcYbDgT0/bgTy0t8PN5vXhnXuC3BcewUgK5D0yv11hKIAqoqS519BZOiwpI9XdXVYjx49Y19qv///7N13nBv1nT/+1xRJo1521924YwPGYIwxppjeCQQIJQFCKGkX8r1cLnfJ99o3l99dkrtLriSXRgokIY0UEpKQQocQIKF3MM0Gd6/aqmtmPr8/RjOrWUm72qZd7b6ejwePtVcz0meMRvrMe96f9/vuO/DSSy823UeWZVxyybsQj1vlcZLJJAxD5+owdPfnBk2udt4bdn3w+gQVe57eTn3w+kB4IpGApvk5P+8S45mfM4OciGgSybt2IvrOdwClEiTT/QVuT8ZDf/9xGMuWo3LqGQ375/M5vPXWW66sk2w26zTiOe20M7By5YEAgFgsDkVRamUYEojHY84XfDQaczXdCYVCCLGOPFHbTNN0lUQZDIQPXx88FArXNckczDhhfXAioulN6u9H7LwzIe/ebf3dblJem4Opzz6D2HlnInXHfSPW/54OzGXLUXzXu6H94LuQhsmRK3zs/3YkOD6VPH/8A0If/bD172C4m89LhgFhmohe9U4k73sY5vIVDfsnk0ns3r2z5eqwvr45TtA7Fou3vToskUhM0hETzUx2ffDBjHBrBfVw9cElSXLqgw9dRc364LMbA+RERJNIu+kbTYPjNgGgoCjo/9y/Yfv8BUin01i+fAXm17KR9u3bi7vvvqNhP7sRT/1Sy0WLFuO66z7A+uBE41CtVpFOp5yL3mw2jXI5j1279qJa1ZvuYzfiqZ9kW13p47M+A4yIqFv5v/5lyLt2QTJbBFkMA/Kb2+G/6ZsofORjHR7d2OT+/T8hVcrQfvIjZyUjAKd5Z/6vP47iB6+f4lFOPv8X/wuQZaBVw1IhkDUM7P3if2Hn1dchnU7hiCM2Osklr766FY8++qeG3ezVYUZd0H3dusOxfv0GZp8SjUO5XHYSVDKZNKrVAt58cxcymcww9cFVV4KKnQ0eiUSdskZE9fiuICKaRNoPbm64sEpKEh71epGUZfQrCkqSBGx9CYVf/QIiEoWmaU6APJHowYIFCxsyToY24gHA+sREo1AqlYbUA7UyTnK5AddEW5Yl+P1eGIbhNOJxB8LjbMRDRDTTGAb8N32rZXDcYZrQvnkDCn/5191Rj9zrxcCXv47i+z4I7dvfgvrM04Aio3r0sShedU3TbOmZRurvh/fuOxuy6N9UFDzr8aBflpGUZVQlCeK3t6NwoFXre8mSZU6AfM6cuTjggCVtrQ5jII6oPXbj2qElUVKppKs+uD03LxYrEELA5/O5yqLY2eDhcIQ3pmhU+GlNRDSBrPrgGecLXR8YQCoQwCHVKo6olWEwJQnP1C3fkoRAWAjMCYUQOexwzJs333ksEoni/PMv7PhxEM0EdiOeoY2xUqlU2/XBe3p6sHTpQsiyBr+/8cYUERHNPNL+/ZCT/SNvB0DZsxtSNgMRjU36uCaKfvgRyB1+xFQPo2PqV4dlnnkaVZ8PSVnGyeUyDqhlkQ9IEp6vK0coC4FYsYB5ByxBrLcP4XDEeWzJkqVYsmRppw+DaEYwTRMDA9laucLBQHg6nUK5XG65XygUQiwWR29vDw44YAEURUMkEoPfH+D8nCYEA+RERGNQrVag6zr8fqtGYzabxe2334ZMJuPUBweAQCAAqVRCn2kCtQB53DSxqVxGj2kiUfvPAyB5xjkwVq6aisMh6mp2I576GuH2n4dvxBOuW50RczJO7PMaYJMoIqJZabTl6ljebloolUqQZdmpI/zmm9tx3333uOqDS7kcArVA+H5ZdgLkCwwDx9bNz2OmCdnrw/6z39YdqwOIphld15HJpIdkg1slUnS9edlCSZIQiUQaaoPHYnH4fD4AnJvT5GGAnIhoGMVioS7olnIa8+VyAzj44ENwwgknAwD8fj/S6TSEEE4jnkQigfmbj8P8396OOdXBIJ0C4Li6oJ2QJOgrVsJYsbLTh0fUVexGPINNMlNOLcJWjXjs+uDuRjxx1gcnIqKWRG8vjEWLIb/1JoYLjQpJgrF8BUQo3HqjXA6+238JZecOmKEQKqeeAXPpsgkf82wx0uqwLVtOxCGHHAoA8Hq9TnC8fnXYol/fhjlbX8a8uiBdTAgcXT8/V1SUzzufwXGiEZTL5bqGtYPnZTabHbY+eDQac5UoisfjiEZjLEtEU4bvPCKa9YQQGBgYQDqdgqIoWLhwEQCgUqngppu+2XK/+lpoHo8Hb3vb2xGJRBEKhZxlXsr8hYj/6hdAi8kBYJVYKX7ww5yAE9WUSqWGi950OoWBgeEm2lZ98KEd6VkfnIiIRk2SULzuAwj+8z8MO4cDgOJ1H2g+hzNNBD73WQS+9AWgWABU1WoK+fcfR+Xk0zDw31+CmDt3kg6g+9WvDguHw+jt7QMA7Nq1E7/4xc9a7pfP550/JxI9OP/8CxtWh/n+8q8Ref81IwzAsP7fEpFTHzydHpoRnnSdc0P5fD7X3DyRsALi4XAEMlfe0DTDADkRzSpCCLz22qsNzfl03Sp/smjRYidA7vV6a8FuuXZXe7A5XywWb2jEY+9XzzjoYAz81/8i/JEPQcgypLosVyFJVnD88nejdMVVk3jURNOPnQHmDoRb52X9zaehNE1zZYLbk+5wOMz6g0RENGGK77kWvlt/AvXZp13zN5uQFehHbEDp8nc37iwEQh/7S2g3f3swA70uW9l7712In30KUr+7F6K3d3IOoItUKhW8+uqrLVeHHXbYeidAHo/H214d5vF4sGDBwobXK7/9IhQefwyBr33Jmp/XlUcUigLJMJD79L9DP+LIST5youllaH1we54+Un3wYDA4JBBulS8MBNi/h7oHA+RENONUq5VaxqlVikFVVWzatAmAVdfs/vvvbWjQZ020Y4hGo67fv+td7x539mn5nVfAPGAJ/F/4T3jvvRtSLRPJOHANCn/xYZQvu5zZ4zRj2Rlgg0svByfaw9cHDzUEwhOJBDTNz4k2ERFNvkAAmZ/ehtBHPwzfL39hzdUUxcoCB1B++wUY+NwXAE1r2NXz4APw3/ztlk8tGQbknTsQ/NxnkPvs5yftEKaToavDenp6ccghBwOwAuR33vn7hn3s1WGBwGD2t98fwHXXfWB883NJQv5Tn4Z++Hr4v/xFeJ55ynmouvlYFK7/CKonnzr25yea5iarPjhRN2OAnIhmhD//+WHs3bvXqQ9eLxyOOAFyAFi2bBl0XXd9uUcikaYT7YkqzVA99nhUjz0e0r59kPfugQiFYB6whIFxmjHs+uD1KzNSqeSI9cGHTrTtgLjdYIuIiGiqiEgUA9/4DvJvvQnfL38BKZWESCRQPu8CmE0yk23+b90AoaiQjOaBJsAKkms/uBn5f/jk8DXMu1S1WsFDD/2x5eqwZcuWOwHyYDCIxYsPcJpnj7Q6bELm55KE8kWXoHzRJVat+XQaoq8P5tx5439uommiUqk0rNRkfXCi5vjuJqJpTQiBXC7nWt6VTCYhhMAFF7zD2e7NN9/Enj27nb/XN+JJJHpcEwC7seZUEH19MPr6puz1icarXC67zkU742T4+uBqQ0mUeDyOSCTKiTYREU175qLFKH7w+ra39/zxD8MGx21SsQjlueegbzp6PMPruFarwxKJHpx00ikArOzvl1560SljCLhXh82bN9/5vSRJOPfc8zt+HDZz0WJg0eIpe32i8RBCoFgsNKzUZH1wotHhVSkRTQuGYSCfzyESGSxxcvfdd+C1115FtVpt2F6SJOi67gTX1q07DJXKQc6X/ND64CzJQNQ+uxFPs4n2SPXB7Rr91g0qKxAeDkd4DhIR0eyhN1851Uw7gfSpUq1WUSqVEA5bGe5CCPz0p7cgmexvujqs/neSJGHTpqPh82lcHUY0AYQQGBjINjSxT6WSw9YHDwQCDWVR4vE464MTDcEAORF1VLVadbJN6usQZrMZAMB1133AuWMthEC1WnUa8Vhf6HaTzJjrzvbKlQdOyfEQdTO7EU99SRT7vBypPnizxlh+f4ATbSIimvWMNWsgPfpnV/PHZoQsw1ixskOjam1ofXB7nj4wkEVvbx/e8Y5LAVhBb8MwYBgGVNWDWGxoGYaE63nXrTt8Co6GqLvpuo5sNlO3UjOJdNoqYzhcffBwOOLq22Ofl6wPTtQeBsiJaFKUSiUn2LZmzcFO0Oyee+7Cq69ubbqPqnqQy+UQiUQAAEccsRHr1x+JaDQ6YbXAiWajwUY87gvfdhvxDM06YQYYERFRa8Wr34vInx4ZdhuhKKiccVbHal7Xrw4rlUpYuXKV89itt/4Y6XS66X7FYgFCCGcuf/LJp8Ln83F1GNE4WfXB3Ss10+kUstkszBY31xRFqdUHTyAejzlz9Gg0Bo/H0+EjIJpZGCAnonHbv38fdu3a6Qq+FYuDZRgWLlzsBL3j8birPnh9FurQRjzxeLzjx0LUzcrl8pAMsMGJ9kj1wa0ssMGMcDbiISIiGpvy296O6g1fgfr0k5CalCIRsgx4vcj/7d9P2hh27HgLe/fubbo6zOfzYcWKlc68Ox5PQNf1tlaH9fXNmbQxE800QgiUSkUkk0nXCup0OoVcLtdyP6/X21ASJRaz+vewPjjR5OCVL1E3Mk14HnoQyuuvQXg8qB57vNVcZtJezkQmk3F9oR999DEIBIIAgFdffQWPP/5ow352J/r6DNUNGzZi48ZNkzZWopnOzgBLp9NDll6mRt2IJx5PIBQKc6JNREQ0XuUyvPfeDXnPbohIBNkbbkT4L/8C3j/+AUJVIek6hCxDMk2IeByZb/8QxsGHjPnlhq4Oy+VyToNMAHjyySewffsbrn0kSUI0GkUsFoeuV+HxWCvCTj/9LM4FiMbBqg8+UHdDajAYXiqVWu4XCASGBMKteXowyPrgRJ3GADlRl/HeditCn/onKNu3Ob8TkoTK6Wch99nPwVy4aEJeZ/v2bXjhheeRSiWRzWYaGvEceOAaJ0A+b948LFu2vCHjxJ501+Pkm6g9dn3wdDqFZNKdcTJcI55gMNi0EQ/rgxONT7FYgNfrc0p+maYJReF3GtGsZ5rwf+kLCPzvf0FOpZxfC68XpcsuR+pjn4D2859B3rkDIhhE5YyzUH7b24Ex1AV+4YXn8Prrr7VcHXb00cc4jeoXLVoEr9dTd2M8gUgk2nR1GOfnRO0xDAOZTKahLEoqlYauV5vuY9cHr79Ots9LTdM6fAREM4cQAsViwYlLAeObnzNATtRFtO/ciPDH/hJDCyVIQsB75+8QO/NxpH93D8wFC4d9nlaNeE466RQsqO2bz+fw2muvOPuoqgfxeMwJutnd7AFgyZJlWLJk2YQdJ9FsMpgBlnJlnbRbH7w+EB6LxdmIh2gc7Aww90Vvqlazt4iLL74Mvb19zrZENMsJgdAn/hr+m77Z8JBUqUC7+TtQn30G6Vt/DdQC182fpvXqsMsuu8Lp/bF//35s2/aGs5/P53MlqNTfCD/ssPUTd5xEs0y1WnFdJ9vnYyaTGbE+uL1S056jsz440fiYpolsNuM6H+0/67qO6677gJPAMp75OQPkRF1C3rUToY9/FADQLAdUMgzI+/ch9H8/huy3fwAhBPL5PDwejxMwe/3113D//fegUCg0eQYglUo6AfIFCxbimGOOcwXEmX1KNHZWI54khjbjGak+uNWIJ+4KhLM+ONH42Blg6XQK8+bNczJPnnrqCTz00IMt9xsYGHAC5My4JCLPvXc3DY7bJNOA+uTjCHzpf1D42Cec1WGhUNi5mH/iicfwxBOPtVwdlkqlMHfuXADAihUruTqMaAIVi4W6uXl9yaKBlvsM1gd31+xnfXCi8alWq07psBUrVjnn0z333ImXX36p6T6yLCOXG0A0GnP+Pla8uibqEtp3bwKaBNFMAGlJQlJRkJRl7L/nLrz5za8hBSsgd9JJp2DNmoMBWF/mdnA8FAq5vtBjsbhz0Q8A0WiMmSdEo2Qv87IzTuoD4aOtD241ro1wok00TrlcDjt37nCdj/UZYKeffhZWrFgJAIjFYlAUBZFItKExViwWd2WAMShFRP5vfg1CURoaceoAUrKMpCyjX5ax/8Zv4M1Fi5AZGICu63jHOy51ml0qioJyudxydVgikXCed8GChU4yCxG1Z6TVYa34/YGGkijxeIL1wYkmQCqVwu7dO10rNQYGBhPH5syZ6wS9reQwq6JB/bkYi8URjUadG87A+ObnDJATdQnpnjuxD0BSVZEwTcypXdhvVxT8NBBwbVt+8gnoBx8CWZZRLA5+6c+ZMwcXXngJ4vG4s1STiEbPmmhnG8oUpVLJEeuDuwPhCcRiMQQCnGgTjUexWHRd9K5cuQpz584DAOzZsxt33fX7hn08Hg9isbjrJtTixUtw7bXvd020iYhaEfffi11CIKmqWGoYCNUu7J/wenF/fcmzUhHFrVth9vZCVVUUCoM3zVeuXIWFCxdxdRjRONWvDqu/KT5SffBQKFy7IeUOvrE+ONHY2RUN6kuiHHnkRmfV5tatL+Gxx/7csJ+m+RGPx12lRg8//Ahs2LBx0q+X+Q1MNA2VSiW88cZrdTUIU6im05CD1ofJUeUy5lQqAIAe04QqBOKmiR7TRMI0oa06EIFL34VoNOa6yPd4vM4STSIamTXRHqwPbi+/tOudNWNngDXrSM/64EQTI51O4amnnmiZARYIBJwAeU9PD+bPX1C7ITV4PoZCoYaJNgPjRNRKNpvB9u3bXYE34fVBqq0seVuxiANrc4OEYcAnBBJ183N109EIb9rcsDosEAi6GowR0fDs+uB2SZRmq8OGand1GBGN3Y4db+Gll150zstKLWZlW7ZsufN9N2fOHCxefEBtXh5zzk2/P9DwvJ26ecwAOdEUaHY3be7ceVi9eg0AoFIp45577nLt4+vpgX/vXvTqVYTrSq2EhMD/yeVcdcnTRx2NaqKnE4dCNCOUy2Xs2bMb+/cnMbQ++EiNeOon2nYjHmaAEY2dYRjIZrNDsr9SWLPmYKxdeygAq7nt888/59ovFAojkbDOxzlzBm8Gx2JxvP3tF3X0GIio+9j1wevLL6xZcxDmz18AwGqQ+cAD97r28UejCKWSSBgG1Lr5+XLDwIfq5udCUdB/5FEQteXiRDSyYrGInTt3YP/+fme1ZjKZHLY+uL06bOhN8WiU9cGJxsOuD16/MiOVSuL4409wSn8NDAzgpZdecPaRZdlVOiwYDDmPLV26HEuXLu/4cQxn0q/g77vvPnz961/HK6+8glwuh7lz5+LUU0/F9ddfj3A4PNkvTzRtlMtlPPjgAy3vppXLJSdAHg5HcMABSxGNRp2723PWHIz5l17Q0KCz/u9CkmAsXYbqps2TezBEXUgIgVKp6FqZYXWjT8EwKigWKzDNxjr/g4143BknbMRDND7VagW6bsDv9wMAMpk0fvObXyObzcAYUs8XAPr63H0yNmzYWNdHIwaPh6XD2sX5OZElmezHY4/9GalUCplMumF1WCwWcwLkPT09WLJkqWt12HzTROJT/wRpSJ8g1/xcUVB+2/kQTF4haiCEQC6Xa7gpns2mARgt5+d+fwCxWKyt1WFE1D67J4Zdknf79m144IH7XPXB6yWTSSdAPm/ePGzcuMn5noxEol2VODbpI02n01i3bh2uvPJKxGIxbN26FV/84hexdetWfOtb35rslyfqCF3XnW679c355s2bhxNOOBmAtSzklVdedi76JUlCNBp1vtDnz5/vPJ8kSTjnnLe5X2TBQlRPOAmeP9zf0AgIAAQASQjk/+GfAU4KaBazG/HU1yO2J9ylUqlhe1mW4Pd7EQgEhyy9tO90sz440XiUSqW6EkWDmZkDA1msXbsOxx9/AgDrYjeVSgIYzACrPxfrG0l7PB4cddTRU3I8MwHn5zQbVCqVurm59fmTTqewdu06HHroYQCsrPFXXtnq7KOqKqLRmPP5U98QMxqN4eyz3fNzceV7YH7rBsg7dzafn8sy4PGg8JG/maSjJOoOrVaHpdMpVKuN9cHt+XkkEkEkEnMSVOw5gX1znYhGTwiBQiHvil3Z52WhUMCJJ56Mgw46BID1vZjNZgAAmqbV1emvJXLOmeM8bywWx5FHHjUlxzQRJj1Afv7557v+vmnTJni9XvzjP/4j9uzZw3rI1FXK5TLK5RIikSgAa1J9yy3fRzqdbno3rb6WqaIo2Lz5OPj9fiQSidHfTZMkZG+8GZGrr4D3vnsgVBWSrkMAgCwDkoyB//gvVN52/kjPRDQj2I146r/QrZ/DN+IJhyN1k+w4ent7sWzZQpRKJnS9eTkVIhqeXToslUpCURQnqFQqlXDjjV9vuV8+n3P+7PV6cd55FyASiTIDbJJxfk4zhRACxWIBQghn6XY6ncJtt92KfD7fdJ/+/n7nz7FYHEcffazTnG9offARXz8SRfrW2xG95O1QX3sVQlEgGQZE7fNLBIPIfueHMA4+ZBxHSdQ9qtVKrTZ4akhGePPVYYBVhsEqWzgYAO/r68HSpQuRz1c5PycaI7t0WCqVRCQSRaK2kmnnzh247bZbW+6Xyw3Oz/v6+nDeeRcgkUhA0/wzen4+JbnusVgMAJreKSSaDuy7aYOlGKyf+Xwe8+bNxwUXvAOA9WVumiaEEPD5fK67adZ/7qWUhx66blzjEqEwMrf8HJ6H/wjtuzdC2boV8PlQOfFklK58D8xaQzKimcRuxFM/0bZKowzfiGewPvhgR/poNNbQiEdVZfj9fpRKzS+kichNCIE33ni9IQPMLh12wAFLnAC5pmkIBAKQZaVuGfRgFtjQDLCFCxd1/HjIwvk5TWfW6rCsa7WmnRFeKpVwyCGHYsuWEwFYTS/t4HggEKibn1srxBKJhPO8qqpi/fojxjU284AlSD3wJ3h/9xtoP/oe5J07IaJRlN/2dpQvvhQixLJFNPMUi8UhCSqDq8NaabY6LB5PIBKJNDTJVlUZXq8X+Ty/k4jaUalUsH37G67vyPrSYevXb8DRRx8DwJrzDa0PXh/LssurAIDH45018/OOBcgNw4Cu63jllVfwpS99CSeffDIWLZod/8g0PdU34qlUKli1arXz2E9/+uOWzT+G1g4/44yz4ff74fcHOnM3TZJQ3XwsqpuPnfzXIuqgYrFQt8RrsGTRcI14WB+caHJUq1Un+JRKpeDxeLB+/Qbn8XvuuRPlctm1j5UBFm2oYX355Vd1Vf3B2YTzc5purNVhVvap1+vF4sUHALBWcX7ve99puo8kSahUBj+PvF4vLrzwEkSjUWia1pFxw+NB5dzzUDn3vM68HlEH2PXB6wPhVgJZGsVioeV+muZ3NbBnfXCiiVEul10JKr29vU4cq1Qq4Y47ftewj6qqDWWJAoEgrr32/ZyfD9Gxf42TTjoJe/bsAQAcf/zx+PznPz/u51QUBj/IzX5PNHtvvPHG69i7d6/zxV5/N83vD+Cggw5ytu3r64XHozrZbolEHLGYFYDz+Xyu5507dw5o+hvuvUGdY9cHH5xgD/4sFotN95FlCYFAcMhE2/o5EfXB+d6g4cym98ef/vQw9u7di1QqiYGBAVfpsGg0ho0bNzp/X7lyJQzDcJ2P0Wi0IQMMAFR1ZjbPnAnX+BM9P58N5wmNXqvPUdM08fLLL7nqhGezWWd12OLFB2DZsqUAgFAogGg0Aq/X6/rcicebrw5buHA+aPqbTd+x05lpms6NqfrzMZVqXh8csObn4XC44XxstjpsLPjeoFZm03ujUqng4Yf/6JyPQ0uHrVix0oljxeNRHHDAYkQi0VoMK1FLHIs0vV72eBrn7DPBeObnkmhWOHkSvPjiiygWi3jllVfwla98BYsWLcKNN97Y9EKKaCzK5TL6+/uRTCaRTFrNBc4880zn8VtuuQVvvvmmax9FURCPx9HT04OzzjrLeT8KIXh3m2gcDMNAOp1GMpl0zkv7p31jaihJkhCJRJBIJNDT04NEIuH8x0Y8RGNnZ4ANPR8B4NJLL3W2u/nmm51gKQCnZ0YikUBvby+OOGJ8ZQho+uH8nCaTVR+86Prc8fl8OPbYY53Hv/zlLzc00PZ6vUgkEli8eDG2bNniej7Oz4nGrlqtOtfK9dfNqVSqZdlCWZYRi8Wc+UD9HL2+DAMRjY51YyrjnIf2eTlnzhyceuqpzjb/8z//4zo/Q6GQcw4uWrQIq1evbvUSNEodyyBfs2YNAGD9+vU49NBDcf755+OOO+5wBTBHK5stwjDYsGE2e/rpp/D6668hlUoin89DliX4fB6Uy1WYpsD69ZucL+6+vgVQFJ9zNy0ej7sa8WSzpeFeirqcosiIRPz83Jhg9fXB6zNORqoPHovFXHe2E4lE0wwwACiVzEmtD873Bg2nm94fpmkil8shEok4v7vzzt/j9ddfaygPBljnYn//gPM9uHLlQViyZKVzXgYCAdf2qRTr9NeLRv1dX8ppoufn3XCe0OT7wx/ud1ajlEol1/w8HI7i4IMPd7ZdtGgphBAtV4fxc2dm66bv2G5SKpUaMsGbrQ6rp6qepqs1W60Oy+erk1ofnO8NaqXb3hu6rqNcLjmNpIUQ+MlPftQycWxgoIgNGwa/+w4/fCM0zeeUKxp6Y4rfk27jmZ9PScGZ1atXw+PxYPv27eN6HsMw2dF4hmrdiCeNK664ygmi7du3z/U+CgaDWLhwHny+IMLhGHTdhCxb75G1aw9reB3TRMsgHs1M/NwYm2Kx6GrCY5dGGa4+uMfjcU2w7aYfw9UHn8r/N3xv0HCm0/ujWq3WlkLbZYoGG/FIkoRrr32/c45VqzpKpbJTH3xoEx7re9J63hUrDnS9znQ53umqM2swO2ci5ufT6TyhiWfVB8/UzQesn4CEd7xjcDXKW2/twL59ewFYq8NCoQgWLZoHrzeIaDTueo8cc8yWoS8DwxAAZtgJRsPiZ8foCSGQz+dd56N93dxuffD6OUE4HG66QkMIzs9peppu741KpeK6IWWfl9lsFnPmzMWFF17sbFsqlVGpVJ364LFYzDkXE4ke13ENjWNNp2OejsYzP5+SAPlTTz2FarXKJkAEXdeRzWYQjcacO9N//vPDePLJJ1qWYchk0ujt7QMArFq1GnPnznM+TIJBP+LxIFKpPD84iEbJLsPQbKJdKjWvDw5YNfzrM07syfZE1Acnms3K5bLTtHb16jXO+XT33XfgtddebbqPqnpQKBQQCllZKhs2HIUNG45qmQFGZOP8nGzVagUDAwNIJHqc3/32t7/Gtm1vNE0skWUZhmE4nzHr12+AaRq17NMY/H4f5+dEY2SaJrLZjJOgYs/P0+lU09VhtlAoXDc3jzlJK35/oOU+RDQ8q3RYAalUCuVyGcuXr3Ae+/GPf4hsNtN0v0LBfdPqlFNOh6ZpCIXCXb8acSaZ9AD59ddfj7Vr12L16tXQNA0vvvgivvnNb2L16tVOXR2a+arVCpLJVEPGid2I59JLL0cikQBgXdzrug5VVRGNxpwst/rgm23BgoVYsGDhVB0WUVcyDAPZbLYhEJ5Ot27EAwDhcKQ2uR68wz20IzYRjc3+/fuwa9cu13lZP5letGixE/SOxxPQtB2uTHD7xtTQDDD7u5WoHufnBAyuDrNvxNnzgVxuAKqqulajKIoC0zRbrg6rv8BfsWLlVB0SUdcabnWYYRhN92m1Oiwej8PjYX1wovHaseMt7Nu313WDqlwuA7BWY9QHyGOxOAxDd52PVtnCGAKBoOt558yZ29HjoPZMeoB83bp1uP3223HDDTdACIGFCxfi4osvxrXXXsumDjNM/d20VCqF5cuXOx8ETz/9FP70p4eb7uf1emvLwKyL+NWr12D58hWu+uBENHrVaqV2wevOOMlmMy0n2oqiIBKJ1l38xp1lX5xoE42daZp1pcOsC+DNm491bjBt3foynnzy8Yb9QqEQ4vEEdH3w5tWGDRuxceMmrtCgMeP8fPawyhYOOIG2tWvXOZ8d999/T8vVKB6PNT+3a6Zu2rQZmzcfx9VhRONUKpUaVmqm0ykMDGRHqA8eGxIIb10fnIjaY1c0SCatG1OFQh5btpzkPP7444/irbfedO0jSRIikQhisbiT2AkAZ555Ns/HLjfpAfL3ve99eN/73jfZL0NTIJVKYdu2152Mk3Q65epCHwoFsXTpcgBwmn3Vf6HbwbdAwD3RHnp3jYiGVywWXSsz7Mn2wEC25T4ej8fVJNM+NyORCL/YiSbI9u3b8OKLLziBqaGlw1avXoOFC61yFvPmzceyZcsbssCaBSt5jtJ4cX4+c+3evQs7drxVNy9Iu1aHLV26HOFwGIC1wmTfvn1trQ6LRKIdPQ6ibmbXB3cHwu0A3HD1wbW2VocR0dg9//yz2LbtDVdFg3qbNh0Dn88HAFi06AD4fJqrokE0GnOC4vU4P+9+U1KDnLqD1Ygn7fpCX7t2HebNmw8A2L9/Lx566EHXPpIk1cowuJd1LV++gssticZhaH1wu0lmOp1uuxGP3fk6FosjFApxok00DoP1wVNO1kk6ncIpp5yOuXPnAQAGBrJ49dWtzj52Ix77otcumQIAy5Ytx7Jlyzt+HETUXarVSi3jdDBBZcuWE526wq+//lrDapT61WGmObiC7MgjN2HjxqM7On6imcSuD16foGKfl8PXBw81BMITiQQ0zc/5OdEYDa1oUJ9Adtlllzvxqf379+GNN1539vP5fK4ElXrr1x/R0WOgqcUAOUEI4XwR79u3F4899men2+7Qu2l9fXOcAHlv7xysWLHKlXESjcbg8XgaXoNf9ETtMU0TmUymLuNkNPXB4w0ZJ6wPTjR2VgZYDoHA4HTptddexQMP3NsyAyyZ7HcC5AsWLMLmzcc6q6bC4Qi/D4moLfXz89dffw3PP/8skskkcrmBhm3Xrl2HhQutAPmCBQtQLBbaWh3GzyOi9tj1wQfn59YNqpHqg0ciEdcK6uFWhxFRe4QQSKfTMAwZgPU99vjjj+Kpp55wVTSol06n0dc3BwBqMaweV31wfh8SwAD5rCGEQKlUdLLc6u+orV9/JNauPRQAYJoCr7/+mrOf1+t1lUSxl4IDQDwex+mnn9nxYyGaCarVqpNxWp9xMlx9cLsRj3uiHWMjHqJxsuuD1zfFsjNOqtUKLrjgPCxYsBSA9b1oB8eDwaBrZUY8Hkdvb5/zvPbqDSKiZoauDquvSXzWWec4SSnFYgHbt29z9vP7A4jFYk6ZtPryJ0uWLMOSJcs6fixEM0Gz1WF22cLW9cHVhgSVeNxKHGPJBaKxa1bRwO6n5fFIOP/8ixGNWvNsWZZRKpVcFQ3qy4nWZ4YvXLjIFdcisjFAPsPYjXhkWXaWbvf378dtt93a8m5aKpV0/pxIxHHccVuci3024iEan1KpNOTC18o4yeUGRmzEMzTjhI14iMZH13Vnop1IJJBI9AAA3nxzO26//ZdN91EU2ZUtPnfuXFx44SXMACOithmGgWw2C7/fD03TAACvvPIy7r337parw1KppBMgX7hwEU444SRnPsDVYURjJ4RAoZBvKImSSiVHrA/uTlDh6jCiiVCpVJwEzsWLFzs96Z566gk88shDDdvLsgSfT0M+n3MC5KtWHYhFixa3rGhA1A4GyLuUaZqu2oODP9PQ9SoOO+xwHHPM8QCsppf1d9PsO9z1NYltHo8Xhx562FQdFlFXshvxNMsAa7c+eH0gnI14iMavVCrhjTded52X2exgBtiRR250AuTxeMLJALMveu2sk0Qijr6+KFKpPADre3Lu3LlTdlxENH3puu4qjWb/2V4ddvLJp2H16jUArDlAtVqtrQ6LOfPy+kxUWzQaQzQam6KjIupO9uqw+gQVe/VmuVxuuV8oFGooiRKPx+H3Bzg/JxqnbDaDN9/c7vqezOVyzuNnnnmO05MnFos3VDSwVmv2YMmSBchkitB1qyRwMBhCMBhq+ppE7WKAfJqzG/GkUin4/X4ccMASAECpVMSPfvS9pvsoigJd152/+/1+XHLJOxGJRHk3jWgc7EY8Qy98R27EE6678B3MOLEbahHR6A1txGNlW87DqlWrAVjfk/fcc2fDfj6fD/F4wslOAYBwOIxrr30/ZFlu2F5VG39HRLNbsVh05gF9fXOcuqY7drzVcjWKx+NBpTIYlJs7dy4uu+yKlvXBiag9g6vDBhNU0ukUMpm065q4niRJiEajQwLhCa4OIxonu6JBfUmUNWsOclZE7d27B/fff2/DfoFAALFY3PV9uHTpMlxzzfsabkypqtx0zk40XgyQTyOGYeDFF593MsNTqZSrEc/SpcucALnfH0AoFEYgEGjIOIlEog0fGD09vR09FqJuZjfiGaxBODjRHqk+eLOME9YHJ5oYpVIJDz30oBOYGpoBVqmUnQB5JBLF4sUHIBqNOdngrTLAJEliVhgRNVUsFrB168t1NYnTrtVhRx650QmQx+MJ1+qw+sBbKBRyfc54PF7E45wfELWrXC4PWalpzdPrV4cN1Wp1WCQShaoyFEI0Efr79+OJJx5zVTSoF4vFnAB5T08fDjhgacP3pF1+rB6D4NRp/FboIPtuWn1JlEAggI0bjwZgfQA89NCDDbUI/f5AQ+MvSZJwxRVX8YKeaBxKpVJDSZR0eqRGPFZ98MFME9YHJ5oIuq4jm83UNcWyAlFz587Dli0nArAudF966QXn/JQkCZFIxDkPFyxY4DyfLMs499zzp+JQiKiLmKaJTCbjmp8vWLAIa9YcBMAKyj344AMN+4VCYSQSCYTDgw0yw+Ewrr76uo6NnWimseuDp9PpIfOBFPL5fMv97NVhQ0uJhkJhBtmIxqG+okH9+bh27WFYu/ZQAFai59atLzv7KIpSl6ASx/z5g/PzeDyOc855W8ePg6gdDJBPAiGEK3B97713Y//+vU3vpsXjCSdALkkS1qw5GLIsuTJOmt1Ns7cnouHZ9cEHBtJ4/fUitm/fif37+5FOp0ZoxOOuD26fj6wPTjQ+lUoFpVIJkUgEgDWp/vGPf4B0Ot30xlT9jSdVVXHMMcchEAgiHo8jGo0xA4yI2lI/Py8Wi7j//ntrvQkyDavDhBBOgDwSiWL58pV1GaixlqvDOD8gao9dH3xgIINXXnHPz4erDx4MBhtKorA+ONH4FYsFCCGcEoTJZBK//vVtrooG9ZLJfufP8XgcRx99zLAVDYi6Aa8qx8G+mza0WabH48VFF13ibLd37x709+8HYF3oRyJR5wvdbhBmO+64LR09BqKZwq4Pbtc6q196WalUIMsS/H4visUKTHMwCBcKhRqaYiUSCdYHJxqnQiHfNOMkl8th/vwFePvbLwJg980wIISA1+sdcuEba/ieXLfu8Ck4GiLqFqVSaUjTbGuevnDhIpx00ikAAK/XizfeeA2maTX3sleH2Z8/9lJwwFqNcsYZZ03JsRB1u8H64CnU1yS264M3m58PXR1mXzfHYnH4fL4pPiKi7tWsooE9Vy+Vijj00MOceFQwGHSC43ZFg/qSKD09g/Nzj8eL9es3TMkxEU0kBsjbUCwWkc/nXCVOfvnLn+Ott95sur2qelxZKkceeRQkSUIsFmcjHqJx0nXd6UBff+E7Un3weDyGRYvmw+sNIByOOQFxNuIhGjtrop1FKpVCtVrFypWrnMd+8pMftVwOPbSp7Zlnng2/349AIMgMMCIakb06rFqtIh6PA7BWo9x8800tV4elUknnz4qi4IQTTkIgEEQsFufqMKJxqlQqDbXBrRUaw9cHj8fjWLx4PjyeACKRGFeHEU0AwzCc0mFerxeLFi0GYDWw/973vt1yv3K55PzZ5/PhwgsvRjQaa1nRgGim4TdPnVwuh2Syv6EmcalUhNfrdXXQtYNqrRrx1Fu+fEXHj4Wo25XL5bommYN3uEdTH9z+GY1G4fN5EI8HkUrloetmh4+GaGbYtu0N7Nu31zkf0+nB0mHBYNAVIE8keqAoqmtlhl2uaOhEu/4GNBFRvcHPm8H5ub06bOhqFEWxLm1CoXDd3DzmZKDWW7Pm4I4fC1E3E0KgWCw0rNRMpZIj1gevn5snEvHajakIvF6V83OicTBNE6+8stV1PmYyGWeF1JIlS50Auab5EQgE4PNprhJF9vzc4/G4nnvu3HkdPx6iqTTrAuSGYSCbzTo1Bw87bL0T9L7//nuwbdsbTffzen0olUrw+/0AgM2bj8OWLSc5fyei0bMb8TSbaA9fH1xrKIvC+uBE42dlgA0GokqlEk488WTn8SeeeAy7du107WM34onHEzBN06k5eM455/F8JKK2VKvVWhmGJAzDdOp/A8Btt93aNPjWrL7pueeeh2Aw2LQ+OBG1Z3B1mLuJfSqVHLE+uDsQbt2g4uowovEpFouukiiapmHDho0ArJJEDzxwb8PqTI/H05C8KUkS3v3ua3g+ErUw4wPkO3a8hR073nK+4Ic24lm9eo1Ta7inpxfZbMaVedqqEY/dXIyIRmY34qkviWKXSRluoh0KhRpqDyYSCWian1/sRBPk6aefxPbt25BKJZHL5VyPSZKEY4893skoWbJkWa2PxuDNqVaNeHiOElErr7zyMvbu3esE3upXhwWDQVeAvK9vDgKBXNPVYUPLFsZi7ixxImpN13Vks5m6lZpJpNNppNMp6LredB+7Pri7SWaC9cGJJtiDDz6Affv2OhUN6sXjcVeAfNWqAwHA9T0ZCoWazsU5PydqresD5MVisaHJwKmnnuF8Qb/xxut4+uknXfvUN+LR9cFg+aZNm7Fp0+ZODp9oRhlsxOPOOLEb8TQjSRKi0WjDhS/rgxONj92Ip/770boxlcYVV1zl1Pfs7+/Hm29ud/YLBAKu8xAYLGm0fv0RnT4MIuoydn3w+pIo5XIJp512prPNs88+07AaxV4dFo/HXatRzjrr3I6On2imsVeH1a/UTKdTyGazThmGoepXh9U3sI1GYw1lGIioffUVDeq/JxVFwQUXvMPZbufOHdi/f5/z93A44iSoDG1gv2XLSR0bP9FM1rUB8lwuh29965tNl1ymUkmn+/zChQuh61VX8I1lGIjGp1wuD6nVPzjRHq4Rj1XfLOZaehmJRNmIh2gc7EY8sVjMCSg98shDePrpp5z64ENlsxlncn3ggasxb94857xkIx4iGotSqYSf/ORH6O9PNiz1liQJJ55YcVZkrlixEr29fVwdRjRBhtYHrw+ED10dVs/r9Q7JBh9+dRgRtadarSCXy7v6X/zmN7/C9u3bmt6YUlXVdXN4/foNME2zdk7GWDqMqAO6Niql6zqKRatGcSgUrmv+FUM4HHa2W7p0OZYuXT5VwyTqWnZ98HQ6PWTpZWrERjz19cHtpZehUJgTbaJxqFYrTS987UY873znFU55AVVVoetVJwOsvimWVZog5jzvwoWLsHDhoik6KiKaKUzTxJ49e2CaounqMGAw+H3ooYdN3UCJuthgffDBkij2fKBUKrXcb+jqMPu8DAZZH5xoPEqlUi1pbHDFZipllQ7zeDy49tr3O+eYLCswTbOuPrh7BXX9uVjf+J6IOqNrA+SBQAAXX3wpwuEo76YRjYNdHzydTiGZdAfeRmrEM7QkSjweh98f4ESbaBwGM8CSWL58hdMn48knH8ejj/656T4ejwf5fN4JkK9ZcxBWrFjJDDAi6hiv14szzjgLkUiMq8OIxslaHZYeUhrFKpPWanWYJElOGYbBJplxrg4jGichBHK5nFOqcO3aQ53r3XvuuRNvvPF60/0URUWpVHTm8kcfvRnHHHNcy/rgRDS1unbm6vV6MWfOXOh687ppROQ2WB/cPdEeqT54JBJpCISzEQ/RxEilUti+/Q1Xxkl9I55QKIwlS5YCAOLxBPz+gGtlhn1eDs0ACwZDnT4UIprlVFXFypWrODcnGgW7Pri7p1aS9cGJptDu3buwc+eOWgKZdV5Wq4M3plasWIFAIAjAmp/v37/fuSFlXytb83a/63nrV3AS0fTTtQFyImrOmmgnMbQZz0j1wa2JdtwVCI9GY8wAIxoHuz54/YXv2rWHYe7cuQCAvXv34I9//INrHzsDLBaLu86/FStWYeXKAzs6fiIiIhofIQRKpWJdycLBgDjrgxN1XrVarWWDDyaonHDCSc5Ki9deewVPPfWkax9Zlp3SYfXJZZs2bcbRRx/TyeET0SRh5IuoCw1txFMfCB+pPnh9PVC7JnE4HOFEm2gchBBOBvfevXvw+OOPuuqD15szZ54TIO/r68Py5StdF76xWLxpBhiXYhIREU1fVn3wgSElUaz5+XD1wdtdHUZEo1M/P3/ttVfxwgvP125MDTQkjq1bdxjmz18AAJg/fyGKxaLrujkajUJRlIbX4DlKNHMwQE40jQ024kliaHO+keqDuwPhCcRiMQQCnGgTjUexWGy48E0mk9iw4UgcfPBaAFZd/9dff83Zx27EYy+9nD9/vvNYItGDM844q+PHQURERGNjrw6rT1Cxfg5fHzwUCjeURYnHE6wPTjQOQgjk83nX+WhfN5911rlOUko+n8f27W84+2ma35WgEgqFnceWLVuOZcuWd/pQiGiKMUBONA3ouo5sNlO39NLqSp9Op0asD96sIz3rgxONnd2IR5Ylp5b3vn178atf3eaqD14vlUo5f04kenDsscc75yMb8RAREXWfarXSkKDSanWYTVEURCLRhrIorVaHEVF7TNNENpuB3x9wrnVffvklPPDAvahUKk33SadTToB88eLF2LLlROfctBtnEhHZGCAn6iC7Ec/QjJPhGvHY9cHtph92IJz1wYnGxzRNZDIZZLMpGEYJ27btQH//YCOeww8/Aps3HwvAWpVhB8fD4YjroteaaCec5/V6vVi37vCpOCQiIiIapcGyhVaCip2BmssNtNxn6Oqw+jIMLFtINHa6rteuj9PQ9SK2b9+J/v4kMpk0DMPAqaeejlWrVgMANE1DpVJx1Qcfmjxmsx8jImqF0TWiCTa0Pnh9IHykRjz1yy3t5ZesD040PtVqpXbBm0IgEMCiRYsBWEstf/jDmyHLEvx+L4rFCkzTqkcoyzKq1cFsFL8/gHe841LEYjF4PN4pOQ4iIiIaG7s+uLskivVfq9VhgPX9H4vFGgLhXB1GND6lUsk5H/v65qC3tw8A8NZb2/Gb3/y66fxcVT0olQbLjM6fPx+XXnp5y/rgRESjwQA50RgN1gd3Z5yk06lhG/EEAoEhgXArE5X1wYnGzzAMvPTSC64L34GBrPP48uUrnAB5KBRCIBBAJBLGokXz4fUGEA5bN6YikYhroi1JEvr65nT8eIiIiKh9dn1wdyA8OWx9cKD56rBYLA6/39/B0RPNTMViAa+8stW1krpQKDiPH3XU0U6A3K7L39PTg8WL58PjCSAcjtYSx8Ku62WPx4tEItHwekREY8EAOdEIrIl2ekhplBTS6eEb8dgT7frll7FYnI14iMbBrg9ef9EbDIZw5JFHAbDOvQcffKChdr/diKenp9f5nSRJePe7r4HHoyAeDyKVykPXm5c6IiIiounDrg9en6CSSiWHrQ9ulWGIufr2WHN1rg4jGg+7Pnh9gsqiRYtx4IFWKZRisYQ//OH+hv1CoRDi8YSrQWYkEsV73nMd5+dE1HEMkBPV2PXBh2acDFcfXFGU2kTb3ZE+Go2xEQ/ROAghXBki9957N/bv3+fUB6/X29vnBMhlWcbq1QdBURTXBXCrDDCu2iAiIpq+isXikASVFJLJZFv1wd2B8MbVYUQ0OvXz82KxgAceuL92Y8qqDz6UHSCPRqNYtmy5a3VGPB6H19t4Y4pzcyKaKgyQ06wihECpVEQy6Z5op1LJEeuDDy2JEovFEYmwEQ/ReFSrVaTTKVfGSSqVhKZpePvbL3K22717F1KpJAA4jXjsCXZ9VjgAbNlyYicPgYiIiMah2eqwduqD26vDhgbCWR+caHzK5bJzPtZfNy9evBhbtpwEwKoH/tprr0CIwfrgsVjMuU6eP3+B83yKouDMM8+ZkmMhImoXA+Q0IwkhkM1mGzJOUqnkiPXBh3a+jsXiCAZZH5xoPIrFIgqFvCuYfdttt2Lnzh3OxLqex+NxZals3LgJkiTVVmiwEQ8REVG3MQwDqVS6IRDebHVYvVAoXNckc7BGOOuDE42dEAKFQh7VahWxWBwAoOs6vv/97yCfzzfdJ5kMOn/2eDw4/vgTEAqFEY/HEQ5HeL1MRF2NAXLqanYjHnuinc2mUS7nsWPHHlQqlab7SJJU+yJPNGSdsD440fjk8zkkk8khWSdpFIsFaJqG97znOmfyrKoqhBDQNM05H+uXXtZbsWLlVBwOERERjVK1WqnVBreC35lMCpVKAbt27UW1qjfdZ+jqMOunlY3K+uBE45PJpGvzcncp0UqlgoULF+G88y4AYM3N7Xl6MBiszc8TdSWL3PPzQw45tOPHQkQ0WRggp65gN+IZWiN8aCMeWZbg93uh61UoioJIJNpQFiUWi7M+ONE42I14UqkUBgayWLfucOexe++9G9u3b2u6n6p6UKlU4PP5AADHHXcCTjrpFPj9gU4Mm4iIiCZQsVgc0rsn5cwN6tnzc8MwoKoeV9+ewf49XB1GNB66riOTsW5Mmabp1P8GgFtv/SmKxULDPpIkNfTaOvfctyMYDDatD05ENJMxQE7TSrFYcGoQ213pU6lUW414EokEenoSWLp0EWTZh2AwwvrgRBNgx463sHPnW0gm7UwwdyOeAw9c46y+SCR6MDCQbWjC0ywDLBKJdPQ4iIiIaHTs+uD1gfD61WGt1NcH7+npwdKlCyHLGvx+li0kmgivvLIV+/btrVtJnXXKFobDEVeAvK+vD4VCobYqI+FcO0ciUaiqOyQ0NEuciGi2YICcOk4IgYGBgaYZJ8M14vH7A4jFYnU1CK2f9Y14VFVGPB5EKpWHrpstn4uIBpVKpYamWKeddoaTOfLqq6/gueeece1jZ4DFYnHo+uBy6c2bj8Xmzcd2dPxEREQ0PqZpIpPJNAmEj1wffLBcYcxZuVm/Oozzc6LREULUJY5Z52S1WsHJJ5/mbPPMM09h9+5drv18Pp9TFqW+l88555zX0fETEXUjBshp0tj1wd2BcCvjZLiJdjgccZVEsQPhbMRDNHZ2Rok9Ud669SU8//xzSKdTKBQaM8DS6RTmzJkLAFi0aDFM03Sdk+FwmBlgREREXaZarSKdTrkSVFKpJLLZjGt1WD27Png7q8OIqH31QWwAeOyxP2P79m1IpZIol8uubSVJwpYtJzkZ38uXr0Bvb5+TDW7fmOL8nIhobBggp3Grb8RTHwgfWh+8HuuDE00Ouz740AvfdDqF88+/EL29fQCAQqGAnTt3OPuFQiHXRW8oFHIeW758BZYvX9HxYyEiIqKxKZVKrnl5KmWVLszlBpyb5kPVrw6rrxHO+uBE46PrOrLZjLMqw04ay2YzuPrq9zplQdPplJMVLkkSIpGI61ysP3cPO2z9lBwLEdFMxQA5ta3dRjz16uuD10+2I5EIJ9pE46DrOtLpFMLhiNP08oUXnsMDD9zXMgMsnU45AfIDDlgKTfM7AXE24iEiIuouQgjk8/khgXBrfj58fXDNdVOcq8OIJkalUkEqlcKcOXOcc+nBBx/AM8881fLGVCaTcep+H3zwWixZsgzxeBzRaKyhPvjMY8Lj+QMU5TUI4UO1ugWmuXCqB0VEs9RM/8SlURprI5526oMT0ehVqxX09/e7aoHaN6aEEDjjjLOd7G6/3w/DMKCqnlod0MGLXnuibbNXbhAREdH0Zq8OS6VSrkB4Op1CpVJpud/Q1WF20oqm+Tk/JxqHYrGIZLLfdU6m0ynkcjkAwOWXvxuRSBQA4PV6IYSAz+dzXScnEtY5GQ4PNq2fP3/BlBzPVPD5bkEw+M9QlDed3wkho1I5B7nc5wAwUE5EncUA+SxlGAay2WxDxkk7jXjsGmesD040MYQQKBTyTtbXggUL0NPTCwDYvn07fv/73zTdT9M014XxwoWLcPnl70Y4HOGFLxERUZepVqvIZNJ1SSrW3DyTSQ9bH3xoGQauDiMaPyEEBgayTrnC1asPcq55n376STz++KNN9wsGgygWi06AfO3aQ3HwwYcgEAhyfl6jaV9FOPy3GJpUL0kmvN7bEYs9joGBewGsnILREdFsxQD5DFdfH7w+EN5OIx73RDvGRjxEEySXy2Hr1pedC+B0OuVqxHP00cc6AfJ4PIFQKNS0MdbQRjwej5fnKBER0TRXKpUaSqKk04Orw5oZbnUYyxYSjV9//368/vprzmrNdDoFXdedx3t6erF48QEAgEQi4Wpca83NrXm6XfrQ5vcHOnoc050sb0Mo9HEAQLP7BZJkQJZ3IxD4OICfdnZwRDSrMUA+Q4ylPrjdiGdoxgkb8RCNj67rTgaYPcFeunQZVq1aDQAoFgt4+OEHXftIkuRMtCORwaWWiUQCV155dUfHT0RERONj1wd3B8KteXqhMHx9cHeCihV84+owovGx64PXXzOvX38k5s6dCwDYt28v/vznR1z7qKqKaNRqXFu/ImPVqtXOvJ5Gx++/EYAMoHmyHmAFyT2eXwDYDSDcoZER0WzHAHkXGWt9cE3zOxmn9YFwNuIhGh/TNJ2u87lcDvfffw/S6RSy2cYMMK/X60ykY7E4Vq5cVVeDMIFIJDoLGvEQERHNLHZ98PoEFXuePlJ98HZWhxFR+4QQEEI48/Ndu3biscf+jFQq6dQHr7d48QFOgLyvbw5Wrz6odlMqVmtcG3GeiyaGx3MXJKl1cNxmbfMAgLMnfUxERAAD5NOSaZrIZDJNAuEj1wcfXN41mHHCZV1EY2fXB0+n03VNMq2fy5atwPHHnwAA8Hg82LbtDWc/n8/nuuidN2++85jH48Fpp53Z6UMhIiKiMbLrgw9mhFsrxYarD16/Oqx+xSbrgxONz9D64On04Lm5efOxWLPmYGe7N9/c7uwXCARc5+KCBYucx3p6enHyyad2/FhmG0kqjWLr8sibEBFNEAbIp1C1WnXqD9dnnLA+OFHnmaaJgYEsDMNEIpEAYC3FvPnmm1z1weul0ynnzz6fDyeddArC4QgzwIiIiLpUuVxuSFCxyxa2rg+uIhaLOwkqXB1GNDEMw0Amk4bH40U4bJXa2LNnN2677efQ9eaJY8lk0vlzT08vTjrplNr5GYemaR0ZN7VmGKuhKK+0lUUOrJr08RAR2Thj64BSqVRXG3ww4ySXGxi2EQ/rg9NsoijPw+//FlT1MQBAtXoUSqVrYRgHTujrCCHQ37/ftQQ6lUohk0lD13UccMBSnHPO2wBYZVFkWYEkSYhEIg3ZX7FY3PXcdrYKERERTV/26rChJVFSqeSw9cGHrg6zV26GQmGWYaAZSMDjeRCadhMU5WUAGiqVU1AqXQXTnDehr2QYBvbu3dtwPmazWZimiSOOOBKbNm0GAASDIeh6FYqiIBqNuUqiWNfLMed5fT4f5+fTTLF4NXy+24bdRggJprkainIUgNafyUREE4kB8gliN+JxB8KtSTfrgxMNx0Ao9HH4/TdACMXJJlDVJxEIfAWFwoeRz/9/sJq5tM9qxGOdhwCwdu0hzmO/+MXPmmaFN8vyuvDCdyAQCDIDjIiIqMvYq8PqE1Ts1ZutVocBQDAYbLgpztVhNJtIUhaRyBXweu+FECokSYcQgKr+CYHAvyGX+x+USleO+nmLxYITBA+HI1i+fBkAq5fPT396S9N9vF6va3V1MBjEO995BSKRKG9MdaFq9SRUKsfB43moaRa5nT9YLH4KoRA/b4mocxjxGSW7EU99B2z7z8M34gnXBcEHJ9usD06zXTD4z9C0rwOAa5Jk/zkQ+CKECKJQ+Lthn+fFF5/Hvn37nPMyn887j8ViMSdALkkS5s6dh3K5XLcMOl67MdXYiCcSiU7IcRIREdHk0HUdmUzalaCSTg+uDmtmuNVhPp+vw0dANJ0IRCJXwON5AAAgSXrtJwCYEMJEOPwhmGYMlcrbWj5LtVrF888/6yonWioVncdXrFjlBMgjkQgikShCoVDdTSnr3AwGg64bU5IkNazipG4iI5v9ASKRd8HrfcB1A8ZKiJIxMPC/MAw25ySizmKAvAW7Ec9gDcLBifZI9cGHdqSPxdiIh6gZSdoLv/+LkKTmpYZsfv9/Ys+ey5FMms4E2zQNnHLK6c42zz77NPbt2+faLxgMIhaLo6en11XO6JxzzpvYAyEiIqJJVy6Xh6zUtObp2ezw9cGtMgxxVyA8Go1xdRhREx7PA/B67235uCRZJTA07R+xe/cxzg2pdDqFSCSCjRuPBgAoioJHHnnIde0sSRLC4QhisTjmzp3r+v3ll7970o6JphchoshkfgWP5w/QtG9DUbZCCD+q1VNQLF4FIeaAH89E1Gmz/mOnVCo1ZIKP3IjHqg8+tCM964MTjY6m/QDA4HlmmkB9AvcDD3jx+usqkkkFhcL/g65vcB5TFAUnnXSqk/G9atVqLFy42JVxUp8BxiXRRERE059dHzydTtc1ybR+1q8OG8rn87nm5sOtDiOi1jTtRlfZw/r5uRDAr36lYf9+Gen0XuTz/w7TXOTs29fX5wTIZVnGIYccCq/XU3e9HIPH4+n4MdF0JKFaPR7V6vFTPRAiIgCzJEBu1wdvlnEyXCMeTdMaMsHj8QTrgxONk1UfPIVy+Qnk8z4kk0AyKSOfl3D99TnYp1c6LWPfPgVCyFDVFKLRXldjrPqbWIcdtn6KjoaIiIhGy64PXr9S056fj1Qf3B0ITyAWiyEQCHJ+TjQOdn1wXX8e2ayK/n4vkkkZ4bDAO99pXTNLErBvn4JUyoqY+3xZhEJzauehtWqz3rHHMvhJRETdYUYFyO364PV1zuyJ9vD1wUMNtQcTiQQ0zc+JNtEYCSGcifaCBQudc+mee+7Ciy8+DwDwevuhql5Ikunsl81KiEatwPcRR1Rx0EFVJBIyvN5DUSy+s/MHQkRERGM2WB98cF6eSrVXH9zdJLNxdRgRjY4QAgMDAygWC5g7d57z+5/85IdOqUJNM6Aog+VBKxUBIeAksGzZUoKqAomECSFORKVyaUePgYiIaDJ0bYDcMAxs3foy9u/f73SlH64+uCRJTeqDWxNu1gcnGp98Plc7F5NIpwdr95dKJQDAu999NYLBEAAgEAg4P/v6jsDChQ+jp8dEImGip8dEMDiYFb5woX0+G0inT+joMREREVH7hBDYvXs39u/vrwuEJ9uqD16/Oswuw8D64ETjk8mk0d/vPh9TqTR0vQq/P4D3vOdaZ1u/35qfh8MRzJ27DvPnb0dvr4FEwkQiYaA+Z2zlSmt+LoSMZPK4jh4TERHRZOnamefAwAB+//vfwjTdE25VVRGLxRGLxVxLLyORKCfaRONgGIYrA2zt2nXQNA0A8NRTT+Kpp55o2MduxFMsFp0A+bp1h+Oww9bX9tWRSHwfsrzPlUVeTwgZprkQ1eopk3ZsREREND6FQgE//ektDXNzAPB6vUMSVGK1soWsD040HtVqpZackkI+n8P69YP9eu6//1689dabDfvIsgxN01CtVuDxWIliJ554MrxeLzweL2T5VCQStzo1yJsRQkGlchZMc+HEHxQREdEUmPSI8W9+8xvcdttteO6555DNZrFkyRJceeWVuOiii8ZVvkSSJMybNx/RqB0MtybcoVCYE22iCbBnzx68/vqrrgww0xwMYi9YsBALFliT4p6eXiQSPa6mWK0a8fj9/rq/qRgYuBHR6Pm1pZvuILkQCgAV2ey3APC8JiIimgiTMT+XZRmBQLAuIzzhZIWzPjjRxNi27XXs2PGWU050YCDretxqimkFvXt7+1Aul10liuLxBCKRCBRFce1nJ7IAgGkuRC73OYTDf+UqrWITQoFp9iGX+9zkHCQREdEUmPQA+U033YSFCxfiE5/4BOLxOP74xz/iH//xH7F7925cf/31Y37eSCSCiy66GLrePOuUiFoTQqBUKiKZHKwFmk6nsGnTZvT1zQEA7Nu3F0888ZhrP6/X6yyDri9NtHr1GqxevWZMY6lWj0M6fTtCoU/A43G/nq5vQC7379D1I8b03ERERNRoMubnmqbh6quv5dycaIyEEMjlckNKoqRwzjnnOQknb7zxOp5//jnXfprmd25G6XrVmaNv3nzsmMdSKl0LIWIIBj8JRdlWN0YJlcqZyOU+B9NcMObnJyIimm4mPUD+la98BYlEwvn75s2bkU6nceONN+Iv/uIvxpztzSwUopEJIWCappMl8tZbb+LRR/+EVCrp1Aevt3LlKidAPm/ePBxyyFpXxkkwODkZYLq+Cen0PVCUp+HxPAUhJOj6ehjGIRP+WkRERLPdZMzPOTcnao9pmpAkyTlnXnjhOTz33LNIp1OoVqsN22cyafT29gEADjhgKVTVg3h8cMWme3XmxCmXL0K5fAE8ngehKK9BCC+q1eNhmosm5fWIiIim0qQHyOsn37aDDjoIt9xyCwqFAkKhUJO9iGg0rPrgGVfGifUzjRNOOAkHHrgagBUw37VrJwDrQjYUCrtqgdolUwBrWeaWLSd1+DjWwTDWdfQ1iYiIZhvOz4kmX7Vada3UtDPCs9kM3vGOS5FI9AAASqUy9u3bC8AqVRSNRockqAyej8uWLceyZcs7eBQyqtXjUa0e38HXJCIi6rwp6Vr52GOPYe7cuZx8E41StVpBKpWCpvkRiUQAADt2vIVf/eoXrvrg9VKppPPnvr45OPXU02uNbOMN9cGJiIhoduL8nGhsisUi0ukU4vGE08D+mWeewoMPPgAhGpvWAkAqlXIC5MuWLUM0GkUsFkc0Gm2oD05ERESTr+MB8kcffRS33347Pv7xj4/7uRSFTfvIzX5PdPt7o1qtYt++vU6NcPvnwMAAAGDjxk046qhNAIBYLApAwOfzOk146n9GIlHn3yMUCuCggw6aqsOaUjPlvUETj+8NGg7fH9TKTKooMlHzc54n1MxM+RwtlUrYu3evkwlu/ywWCwCAs88+18nujkTCkCQgEAjUmtgnXD/D4bBTYqW3twe9vT1TdlxTaaa8N2ji8b1BrfC9QcMZz/xcEq1ua0+C3bt34+KLL8aKFSvwrW99a8z1x4lmAiEEstkskskk+vv7kUgksHy5NalOJpO48cYbm+4XCARw2GGH4ZhjjnGeZ2BgwDXRJiIiImoH5+dEg0zTRDqdRjKZRDKZxNKlSzFnjtWf58UXX8Svf/3rpvuFw2Fs2bIFa9ZYTesrlQp0XUcgEOjY2ImIiGjsOpZBns1m8d73vhexWAxf/OIXJ2Tync0WYRjNy0rQ7KQoMiIR/7R8b1QqFTz99FN1WScp6PpgI57Vq9cgHp8LABDCA683gGg02pBxYjfiSaXydc+uIJ0udPJwus50fm/Q1OJ7g4bD9we1Eo36uz6YPNHzc54n1Mx0/hzNZDJ48cXnnYzwTCYDwzCcx4899nh4PEEAgKL4oWnBhozweDwOj8cLYOj8HCiX3X8nt+nz3hBQ1fvh9d4CSdoHIWKoVi9AtXo6gO4qeSNJb8HnuxGq+jAAE4ZxGMrlq2Gaq6d6aKMyfd4bNN3wvUHDGc/8vCMB8lKphPe///0YGBjAj370I4TD4Ql5XsMwoes8IajRVLw37Prg6XTaaZKZSCSwcePRAADTBB5++CFXLUKrEU8M8XgcfX1zXWO+7LIrmr4O3/Pjw88NaoXvDRoO3x80VOfWYE6OyZif8zyh4UzF+6NUKtU1r7f+W7XqQKeBfS5XwJ/+9CfXPqrqQTweq5VCiThjjsUSuOSSy5u+Dt/34zOVnx2yvBORyCXweJ6GECoAHYACn+/7MIxlyGR+DMM4cErGNjoCfv9/IRj8ZwAyJMm60aOqf4Sm/S+Kxfcil/t3dFvAn98r1ArfG9TMeObnkx4g13UdH/nIR/Daa6/he9/7HubOnTvZL0k0qXRdh6pap45pmvjNb36NZLIfudxAw7aFQgEbN1p/VlUV69YdBk3zO13pI5EIG/EQERFRR3F+TjOJEAKGYTjz82w2g3vuuctVH7xeOBx2AuTxeBwHHXQI4vG4Mz9n2cLZQ5IyiMXOgixvr/1drz1iBZdleTtisTORSv0BprlgikbZHr//qwiFPln72+AqCDtQrmnfgBAe5POf7fzgiIi6wKQHyP/5n/8Z99xzDz7xiU8gl8vhySefdB47+OCD4fV6J3sIRKMmhEAul6vLOBksi5JIJHD++RcCsDLA64PjmuavNci0Jtg9Pb2u5z3mmOM7fixERERE9Tg/p25kmiay2Uxtxebg/DydTmHNmoNx7LHWPNvr9WHnzh3OfqFQCPF4ohYAj2Pu3HnOY16vFyeeeHLHj4WmB027CbL8BiSpecqhFVxOwe//IvL5z3R2cKNSQCDwL8NuIUkCfv9XUCx+GKa5sEPjIiLqHpMeIH/wwQcBAJ/9bOOdyrvuuguLFi2a7CEQtWQYBrLZLEqlIubPH8wK+OEPb0Y6nW66Tzqdcv19y5YT4fF4EI8nnPrgRERERNMV5+c0nVWrVWQyaQBAb28fAKBYLOK7373RVR+8Xv38XNM0nHbaGQiHo4jH47zhQy35/TcAGH49viQZ0LRvI5//JABfJ4Y1aj7fLyBJjauZG8nQtO+iUPjEpI+JiKjbTHqA/O67757slyBqS3//fvT397syTrJZqxFPKBTClVde7WwbDIaQzWYRjUbrMk4SiMWseuH1lixZ2uEjIZp6kjQAn+9H0LTvQpZ3Q4gIyuULUSq9B6Y5f6qHR0REw+D8nKYDIQT27NntWqmZTqcwMJCFEAJLlizF2We/DYAV9PZ4PJAk2ZmP2/PzeDyOSCTqeu6VK7uhZjRNrRIU5c22tpTlHGR5J0xz2SSPaWwUZSsAD4BqG9u+POnjISLqRh1p0knUKcViEYVCGq+//hYGBnI48sijnMceeOA+7Nq1s2EfVfXA7w/AMAynHvhpp50Br9fH+uBETSjKc4jFzock7QOA2rLUXVCUf0Mg8J/IZm9CpXLO1A6SiIiIppxdtjCb3Y833tgBQMLBB691Hv/Vr36BarUxqGcHxG2SJOGSS96JQCDI+uA0QUZ7nTedrwtV1NcdH55n5E2IiGYhBsipq73yylbs3PkWkskk0uk0yuUi/H4visUKhAAOO2y9M7m26w3WZ5xY3ekbG/H4/YGOHwtRN5Ck/YjFzoUkpRvqNUqSCSEqiESuQDp9B3T9yCkaJREREU2Vp59+Evv373d6+eh61ZmfR6NxJ0AuSRIWLFgIwzBcNcLj8Tj8/kDD/DwYDE3F4dCM5UG1ejhU9WlIktlyKyEA05w/ret2m+a8YY/BJkkGKhX2xCIiaoYBcpq2TNNEJpNxSqKk0ymk02mcf/6FTmb39u3b8NJLLzj7yLKEcDiM3t4gotE4DMNwAuSbNx87JcdBNJP4/TdCklItJ+GSJCAEEAh8HtnsDzo8OiIiIppMuq7X5uR2ycI0AOD00890tnnxxRfQ37/f+buiyIjH45g3L4hYLOF6PruECtFUKBbfj0jkgyNsJaNYfB+mcwa513sXhACGW1whBAD4UC5f1KlhERF1FQbIacpVq1WoqupkiTzzzFN47rlnnfrgQ2UyGSQS1uR66dJlCAaDTsZJX18P5syJI5XKQ9dHvotORKOjaTcCGP7ckiQDXu/tkKT9EKK3MwMjIiKiCVOtVuDxDDa3fOCB+7B9+zanPng9VVVhmiZkWQYAHHTQwSiXy4jF4kgkEkgk4ujri3J+TtNOuXwpyuWfwOu9p2nyhxAKdH0tisUPTMHo2iNJSXi9vx42OG5tBxhGAIC/I+MiIuo2DJBTx5RKJVeDzPpGPO985xWIRmMAgEqlglQqCcCqDx6Px1wlUYLBoPOcy5evwPLlK5y/q6rc0WMimm1keeeIE3DAyiRXlB3QdQbIiYiIpiMhBAqFvDMvt1dsplJJ6LqOa655n5PAUijkkc1mAAA+n6+hJEq9Qw89zPV3zs9p+lKRzf4AodD/haZ9G1Ydb6X2U0K5/Hbkcv8NIDjck0wpWd7VVnkVAFCUFKxEF56TRERDMUBOE0oIgXw+j1QqiTlz5sLn8wEAHn/8UTzyyEMt90unU06AfMWKlejt7UM8nmhaH5yIppIPQLGtLYVgLX8iIqKpZpomBgaySKfTOOCAJc7c+o47fodXX93acr98Po9QyKr7ffjhR2Dt2nUt64MTdS8Nudx/IZ//e/h8t0GW98E0Y6hUzp3WdcdtQrSfES6EFwyOExE1xwA5jVmhkMeePXtcWeHpdAqVSgUAcM455+GAA5YAAMLhMAAgFAoNyThJOBNtWyxmNdEkoumnXD4TPt8vIUl6y22sZkaLYRgrWm5DREREEy+bzWDv3j2u1ZqZTBq6bn1vX3XVNQgErGxYOxElEonUzc8TTkN7O9EFGGx2TzRTCdGLUumaqR7GqJnmUhjGUsjyNkiSaLmdECoqlTM6ODIiou7CADkNq1qtIpNJO0Hw5ctXoqfHKpmwfft23HPPnQ37yLKMSCQC0xysH7506XJce+374fV6G7Ynou5RKr0fmnbrCFtJKBY/CGaoEBERTTy7HKFdFuWII450gtnPPPM0nn76yYZ9VFVFNBpDqVR2AuRHHHEkNm7cBFXlJSFR95JRLH4QweAnht1KknQUi+/v0JiIiLoPZ0Pkkkql8MILz7nqg9c34tG0gBMg7+npqZVCibsyTqLRGBTF3eXb4/F09DiIaHJUq8cgn/8bBIP/ASHQUI9cCAmVymnTupkRERFRN9m5cwdeffUVJ2Eln8+7Hl+2bDnmzZsPAOjt7cPcufOcjPBEwpqnh8MRp4mmrT5DnIi6V7H4Xng898Lr/W1DFrkQEiRJoFD4a1SrW6ZohERE0x8D5LNIs0Y8qVQSBx98CFatWg0AKJWKeOqpJ1z7aZrmKoti6+ubg4svvqyjx0BEU69Q+EcYxnIEg/8ORXnd+b1pxlEsvh+Fwt+AXy9EREQjG6wPnkIymXKC4Fu2nIje3j4AQH9/P5599mnXfsFg0ElQ8XoHA92rV6/B6tVrOnoMRDTVVGSzNyMQ+Dz8/q9ClpPOI6Z5AAqFv0GpdOUUjo+IaPpjBGMGMk0ThqHD47HKmaRSKdxzz51IpZJOffB6fX19ToA8Hk/g0EMPc4LhiUQCmuZnIx4icimXL0e5/C6o6qOQ5b0QIoxq9WgALKNEREQ0lF0D3C5nsm3b63jkkYdd9cHr9ff3OwHy+fPn4/DDj3D69wytD05EBHhQKHwChcJH4fE8AknKwjTnQNc3gGUPiYhGxgB5F9N1HZlMuq4G4WAjnrVr12Hz5mMBAD6fF3v27AYASJKEaDTqZJzEYnFX0x1N03DccVx6RUTtkKDrG6d6EERERNOGVR/cygK3s8Gt+XkGp556OlauPLC2pYT+/v0ABuuDDzaxj2P+/AXOc/b29jnBciLqRgJApxLOvKhWj+/QaxERzRwMkHeBcrmMdDoFWZbR1zcHAJDP5/Dd797kqg9eL5vNOH/2+wM4/fSzEI/HEYlE2YiHiIiIiGiMhBAoFgtIpVIIh8OIRKIAgG3b3sDtt/+y5X7pdNr589y583DWWecgHk80rQ9ORN1NkvqhaTfB7/8WZPktAF5UKqeiWPwAqtUTpnp4REQ0BCOl04hpmti1ayeSSXfGid2IZ/nylTjjjLMAAIFAEKqqQpZlV31we+llKBR2nleSJKxYsXJKjomIiIiIqFtVKhXs2rWzNi9PO/PzUqkEADjqqKOxYYO1mioatQLlgUCgbn6ecObogUDQeV5N07B06fLOHxARTTpFeRnR6DmQ5X0AzFpT+zK83t/B5/s1CoUPI5//F3Quq5yIiEbCAHmHDW3E4/N5cfDBa53Hb7/9l03rEAaDQVetQUmScMUV74HP52N9cCIiIiKiMTIMo1a20EpQ6evrw5IlywAAudxA06xwSZIQDkegKIrzu2g0hmuueR/rgxPNakVEo+dBlvdDkkzXI5JkXecHAl+EYSxDqXTdVAxwmjIgSXkIEQSgjLg1EdFEY4C8Ax5//FH09+9HKpVqaMTT29vnBMhlWcaiRYsBwJVx0qoRj6ZpnTkAIiIiIqIZolwu44knHnNWbGazWZjmYCBrzZqDnQB5JBJFb28fotEY4vGYkxkejcbg8XhczytJEoPjRLOcz/dTyPJODJfDJgQQCHwOpdLVmO3BYEV5BoHAl+Hz/RiSVIEQKsrl81GpXA/gxKkeHhHNIgyQj5PViCfpasajKCpOP/1MZ5uXX34RqVTK+bvdiCcejzc03DnrrHM7NnYiIiIioplECIFSqVhXstBqYt/XNwebNm0GYCWlPPnk465ePl6v10lQWbhwkfN7VVVx8cWXdfw4iKg7adr3AMgAzJbbSBKgKDvh8TyEavW4jo1tuvH5foJw+L0AJCe7XpJ0+Hy/gM/3MwBfAXDFVA6RiGYRBsjbIIRAuVx2ZWzfffedeOut7U598HperxdCCKf0ySGHrIOu60gkrGxwNuIhIiIiIho7IQQqlYqTsW0YBn75y58jlUo69cHr1a/g9Hg8WL9+g6tWeDAYZNlCIho3K3u8dXDcve3uSR7N9KUoT9eC40ZDtr0dLAc+CFVdCl0/ptPDozqy/Br8/m/B43kIgAFdPxzF4jUwjHVTPTSiCcUAeR0hBAYGsnUZ4Sln6aUkSXjPe65zJs6lUtEJjgeDQSfjJBaLI5FIuJ730EP5wUFERERENFpWffCMs1Jz8Gca8+bNw9ve9nYAgKIoyGYzKJVKTn3w+ib2PT29rue1s8mJiCaSEFEIgWFLrAxuG5n8AU1TgcBXAIz07yTB5/sCSiUGyKeGQCDwGQQC/wZAhiQZAABVfRp+/7dQLF6OXO4LADzDPgtRt5iVAXJd15HNZpDJZLBs2WD3+Ntv/xW2b3+j6T6SJKFUKsHv9wMAjjzyKGzYsLFlfXAiIiIiImpPtVpBKpVCpVJxevIAwPe+9+2mKzYBIJPJuP5+yimnw+fzNa0PTkTUCeXyhVDVpzFciRUAMM0wKpXjOzOoaceo1Rw3RtjOhMfzG0hSdlbfTJgqfv//Ihj8bO1vg/+v7Ax/Tfs+AB9yuf/u+NiIJsOMD5Ank0ns3bvHlXFS34invtN8NBp16oPXZ5zE4wlEozGo6uA/15w5c6fkeIiIiIiIutmePbuxf/8+pNNpZ+VmLjcAAAiHI7jiiqucbSORKCqVitO83l6xGY/HEYlEXc9bXzuciGgqlEpXIhj8DIQoQZJE022EkFEqXQfA39nBTROSlIckVdrcVkCSkgyQd1wegcCnh91CkgQ07UYUCn8F01zSoXERTZ6uD5ALIVAsFlwlUTZtOhoejxcA8OyzT+O5555p2M/r9SIeT6BUKjoB8k2bjsYxxxzH+uBERERERGNklS0ccBJUyuUyjjrqaOfxBx98AHv2NNbe9fsDiEQiME3TmY+fffbb4PF4WB+ciLqCED3IZG5GNPpOCGG4sqStvsASqtUtyOf/bsrGONWECEAIqeUNhKEkKT25A6IGPt/PIUnNV2+5ydC076BQ+MdJHxPRZOvaAHmhUMBPf/pjJJONjXgOPHC1k+E9Z85cpNMpp0a4nRUeCDQ24rGD6kRERERE1L5KpYI77vgd+vv7kUqloetV5zFZlnHEEUc6qzEXLFgIn09DPB6rywhPQNO0huf1ejk/J6LuUq2ehnT6Lvj9n4PP9ysnSG6aC1EsfhDF4gcAzObPNhVC+CBJjQ2Vm1GUXTCMwyd3SOSiKK/Cqi1eHWFLUduWqPt1bYC8Uqlg9+5dME3hNOKJx+MNNcHXrDkIa9YcNIUjJSIiIiKa2XRdx8svvwTTtDICFUVBJBJ1ElTs8oYAcPTRbLhGRDObrh+GgYHvIpfrhyzvAOCHYawAwNXqACBEDEDjSqLm23Zt2KqL+QC0k+Ev1bYl6n5d+0mjaRpOP/1MhMNRNuIhIiIiIppCHo8HRx+9GZGIlbASiUSgKMpUD4uIaEoJ0QPD6JnqYUw7lcqp0LTvQ5KGb2YqhApdP6JDoyJbpXICgsF/HXE7STJQqWzpwIiIJl9XB8hXrToQuj78ByoREREREU0uj8eDDRs2cm5ORERNlOHz/QKa9i2nJMdIwXFARaXyDgjBGwydpuuboOsHQVFedtXRryeEBCEiKJcv6vDoiCZH1wbIiYiIiEbPhMdzLzTt27ULND8qldNRLL4bQsyd6sERERERzSiStAex2PlQ1echhOwExodr1CmEAknqQ7H4qU4OlRwSstmvIx4/A0KUGoLkQsgAJAwMfANAY/+Q0SvD57sNPt8PIcu7IUQc5fKFKJUuARCagOcnGhkD5ERERDQrSFIKkchl8Hofql14GRACUNU/IxD4DAYGvoRy+Z1TPUwiIiKiGUJHLHYBFOUlAO6s8aHBcSEUAAKSZELXj4fH8x0IkQDAlUlTwTDWIZW6E6HQx+D1PjjksYORy30W1er4y6soylZEo2+Horzp3EARQoLHcz+CwU8ik/kxdH3TuF+HaCQMkBMREdEsYCIavRSq+mcAcDJhJMl6TAgT4fAHIEQclcqZUzdMIiIiohnC6/0dVPXZYbcRwqrVXih8CEAQlcqpkKTViMeDAPIdGSc1ZxiHIJP5DRTlZajqn2s3Lw6u1YWXxv38ktSPaPQcyPK+2t/N2k/75kkWsdj5SKUegGGsGvfrEQ2HAXIiIiKa8bzeO+DxPNzycUmyLtCCwU+iUjkDEzHpJyIiIprNNO3bzqq9ViTJCpTq+tGoVo8DAKiMVE0rhnEgDOPACX9ev/9bkOW9LevRW9nkZfj9/4Vc7ssT/vpE9eSpHgARERHRZNO0G2tLd1uTJAFVfR6q+kSHRkVEREQ0cynK68MGx+vJ8vZJHg1NN5r2DYxUQkeSDGjaLZCkgc4MimYtBsiJiIhoxlOUl9u+QFOU1yZ5NEREREQznxAhiOZ9OJtsG5zcwdA0U4Wi7KqVOxyeJFUgy7snf0g0qzFATkRERLOAr+0thfBO4jiIiIiIZodK5Wy0E3YSwotq9fjJHxBNIwqEaD8kKYRnEsdCxAA5ERERzQKVymkjllgBACFUVKvHdGBERERERDNbsXgVrEBo6zRhIRSUSpdCiETnBkbTgIxq9fgR5+dCAIaxGKZ5QIfGRbMVA+REREQ04xWL12CkGodCKCiXL4QQvZ0ZFBEREdEMJsQcDAx8HYDUNFtYCAWGsQr5/L92fnA05YrFD7RRAlFCsfgBMHxJk429gYlmhHLtZ/slBIimA0nqh6b9EIrySi1z9/jaUkx+PdHEMs2lyOc/i1Do4xACDfUOhVBgmguQy316agZIREQziABQAuAB5zTUbWT5DWjaDyHLuyBECJXKOahWNwNoo1h0E+XyhTDNHgSD/wKP5xHn90IEUCxeiULhHyBEdIJGT92kUjkbxeLl0LTvNa1FLoSMavVYFIvv7/zgaNbhtzVR1ypA074Lv/9rUNVXAAC6vhrF4vtRKl0BQJva4RENSyAQ+AwCgc8D0GF9HQkEAl+DYczFwMA3UK2eMMVjbEcRstwPIQJcFtoFisUP1i7QPgVF2e783socPw+53H9AiDlTOEIiIupmkrQPfv/X4fd/E7K8DwBQqRyDYvEDqFTOx1gDjESdUUQ4fD18vp/Ayta13q+BwBeh6wcjm/0uDGPVmJ65Wj0B6fQJUJStUJTXIIQGXT8CQoQnbvg1kpSFJGUgRGxSnp8mkoRc7kswzRXw+78AWU47jwjhR7F4NfL5TwJgfyCafJIQ7fYUnn5SqTx0ffjl0jS7qKqMeDw4498bkpRENHoeVPWZ2t+t09iu7abrRyCT+TnvxNeZLe+NbhEM/hMCgf9u+pi1/FJGJvOrjtSCHst7Q1FeQCDwBfh8P4YkVQAA1eoGFIsfRLl8MXgBPN2Z8HgehKK8ASF8qFa3wDTnNd2Snx3USiIRhKJwuW89nifUzGz5HFWUlxCLnQ1J6ockDR6nEAokyUCpdCkGBr4KYOR+GLPFbHlvdAcD0ehF8Hjudb1/bUIoECKGVOp+mObiSR/NWN4bHs/98Pu/AK/3DkiSgBASKpXTUSz+HzYA7QpleL13Q5b3wjRjqFZPbnqDg58bNJzxzM85qyfqQuHwtVDV5yBJwgmOA3D+rqpPIhz+wBSOkKg1WX61ZXAcQG1SbiIU+mtYS5SnF4/nTsTjx8Pn+6ETHAcAVX0Ckch1CIU+jOk4bqpnNQUqla5EuXxJy+A4ERFRe0qIRt8OSUo2BBft+ro+3y21lXNE04/X+2t4vXc3DY4D1vtYktIIBD7T4ZG1x+//CmKxc+H13uVcH0uSgNd7J2Kxc6BpX53iEdLIfKhUzkKpdBUqlfOZ/U8dxwA5UZdRlOfh8901bDMLSTLg8/0asvxqB0dG1B6//6YRu5VLkglVfQ6q+miHRtUeWd6FaPRdAKoN56B9QeH3fweadsMUjI6IiIimgs/3cyjKjhHm5wJ+/xcx2DuIaPrw+29oY35uQNNugSSlOzOoNnk8f0Ao9HEAaDI/t/4eDv8tPJ4/dnxsRNQ9GCAn6jKa9mMIMXL7ACEUaNqPOzAiotFR1cfa6FZulQxS1ac6MKL2adpNACqulRtDCQEEAv8DgEv+iIiIZgOf70e1EnHDk+UMvN67OzAiotFR1Sfamp9LUgWK8lIHRtQeRXkFodD/cUqNtiKEAr//fzs0KiLqRgyQE3UZSdrf5pay0xyIiCaGz3dLy6WnNkkCFOUtqOoTHRoVERERTSVF2TPi/MDG+TnR+EnSACKRy5FIHAFFeWXY5BVrewNe7+0Aip0ZIBF1HQbIibpM+403TZhmfFLHQjQWun7kiEs4AWspsq4fPvkDGgVZTrW9rSQlJ3EkRERENF2YZmLEDNbBbTk/p+lH149oa34uhATTnN+BEQ2njGj0wlrA20pOaYckmZCkgUkcFxF1MwbIibpMufx2SJI+4naSZKBcvqADIyIanWLxPRip/IgQMqrVQ6HrGzoypnaZZg9Em/03heid3MEQERHRtFAuX4R2GnQLEUClctLkD4holIrF97VVYgWAU+97qmjaLVDVR9oer00IBUJEJmlURNTtGCAn6jK6fiSq1eEzcIVQUKkcB8M4pIMjI2qPaS5HsfjRlo9bNTwV5HKfB9BmSkiHlMvvxEhfnUJIMIxl0y77nYiIiCZHqXQxhIiPMD+XUSxeByDUuYERtalSORuVypEjbidJAl7v7ZDl1zswqmaq0LSvYLTXCEKoKJfPA6BNyqho5lOUl+H3fwmBwOfg890CoDDVQ6IJNnKnPyKadrLZmxGLnQ5Z3tFw51wIGYaxBNnsjVM0OqKR5fP/BCE0BAL/AaAK6+tIQJJ0mOY8DAx8BbK8C+HweyFJOZjmApRKl0PXj5jScReLV8Hv/zyAYstao5IkUCj8FaZbcH90KpDlnQBkmOYCcLpAREQ0nBAymR8jGj0fQMk1P7dLr1SrJyKf/8cpGh/RSBTo+mHweB4bsZ43AESjl6FSORvF4rthmssmeWwmfL5b4fd/FR7PI6Pe21r9aaBYvH7CR9ZZBcjybgC+2vy8m681uocsb0c4/Bfweu+vJXLJtWvWMAqFj6FY/Aj4/2Jm4BUvURcyzQVIpe6D3/8l+P3fdOoim2YPisVrUSx+CEKwviFNZxIKhY+jWHwffL5boKovQwgvqtXjYZq9iESugKLsrmViGQBU+P1fR6VyIrLZ70CI2JSMWog+ZLM/QTR6EYQoD7kAViBJBgqFv0CpdNWUjG+8JGk/AoEvQtNuhCynAQCm2Vv7XLl+FD0QiIiIZhddPwqp1B8QCHwBmvZ9SFIZAGAYy1AsfgCl0rUAPFM7SKJhyPIArJWSI5UuEVCUF+D3vwy//z9RKl2DXO4/MDnhJR3h8DXQtJ+1VSN9KGsfE7nc/0LXN0788NokSf3QtO9B034ASdoPIXpQKl2KUundEKJn2H0VZSv8/v+Gpv0IklQBAOj6ytrnyjVgWG/yyPIOxGKnQJb3A0AtQcqsPTaAUOj/QZb3I5//1ykcJU0USYh2q6lOP6lUHrreXrdwmh1UVUY8Hpxl741qLdMTMM2F4Bdkc7PzvdF9FOUlxOMnACg3rSsohAJdX490+rcAvBPymmN5b8jya/D7vwpN+y5kOQ8AqFRORLH4QVQqZ6Ibswhk+U3EYmdAlne1WJmyAun0byFE35D9XoOm3QJZ3g0hwiiX31a7AOm+f4Nm+NlBrSQSQSgKqxXW43lCzczOz9ECZHkPAC8zPYcxO98b01cw+Lfw+7/RVr+rekJIKJWuQi73hQkbi/3eKBY/AU3797ay2hvHBZTLF6JYvB66PnL5mMmiqg8hGn0HJCkHa8WsndUuQ4gAstlbUK0e12LfhxGLvR1AxfX/xV6ZUqmcimz2Bxh6XaSqj8Hnuw2SlIFpzkG5fDEMY9VkHF7HdfJzIxy+Fj7fz0asd59K3c/ymtPEeObnDJDTjMJJFrXC90Z3CIevqk3mhp+EZLPfRLl88YS85vjeGyYkKQsh/AB8EzKeqSEQi50AVX2m5b+9EAqq1eORydxW+00O4fBfQNN+XsvOsSbqkqSjWj0M2ex3YZpLOzL6ycTPDmqFAfJGPE+oGX6OUit8b0wvqvoI4vHTxrx/MvlnGMbqCRqLjHhcghBza4Hl0RFCQbH4AeTzn5mQ8YyVLL+GROIYWKWXGt/jVskOH1KpPzQEsCUpi0TiEEjSQMvSjlZvg48gn/9k7fXeQiRyJTyexyCEnTgnIEkGyuWzMTBwQ9c3Ku3U54Yk7UNPz4EjXpcKoaJUehdyuf+dtLFQ+8YzP+esnoiIpgVJ2tdWcFwIBX7/DR0a1UjkWrmXbg6OA6r6J3g8Tw77by9JBrzee6EoLwGoIhq9BD7fbc5jkqQ7mS2q+mytT8LuTgyfiIiIiMZJ149CtbphzKVMNO1bEzyi344xOK7CNOehUPjoBI9n9AKBL8NaGduqd5EJoAq//8sNj/l8P4QkZVvua++vaTcAKECS9iMWOx2q+mTtMb32nzW/93p/h2j0AgDl8R3ULOHxPD7idSlg/Tt7PA90YEQ02RggJyKiaUFRXmlzEmJAUZ7vwIjGQkBRXoaqPgJZfnWqB9M2n+/WuiyT1oRQ4PPdCp/vZ/B6/zDMZN+ALO+rNWElIiIioulPQjb7A5jmARBCxmhqDUiSAVV9doLHs7ftMQgBZy6r6+uQTv++riygDkV5ujY/3zXBYxyODk27ecTrG0nSoWnfw9DAtab9tK1XkeUcvN77EAj8V9NSiYOvY0BV/wxN+1Fbz0vtlxqSpOokjoM6hQFyIiKaJkbzlTT6zJbJJaBp30U8vhGJxJGIx09DT896xGLHw+u9daoHNyK7IWcbW0KS0vD7b6gtCW1NkozaZH/0mT9ERERE1HmmOQ+p1H0oFP4BpjlvlHtPdC+sOKQ2yvcLIcE0F6JYvB6p1D1Ip++FaS4GUEYg8B/o6VmDROI4xOOnIZFYg0jkAqjqIxM81kaSlIEkFdrctgRZTg35XbLt2uuStBeadlMbyUYyNO1rbT3nbKfra9razuqRdegkj4Y6gQFyIiKaFnT9kFot7+FZtbCP6cCI2iUQCv0NwuEPQVG2uh5R1WcQjV6FQODfpmhs7THNvpE3sraEafZBVZ8adrmnTZIKUJTXxjc4IiIimpZkeTv8/v9FIPAv0LSvQZL2TvWQaAIIEUOh8DEkky8ilfpNm/vIqFY3T/BIzoIQ2ohbSZJAPv8PyOc/BV3fUPttCdHoBQgE/hWyvNe1rdd7L2KxM+H13tb8CSdIO2MfbnvTnDtiQkrd3pDlgRG3kiSzlunPmv8jMc0VqFSOH7HkkCQZKJWu7dCoaDJNeoB827Zt+Kd/+iecf/75OPjgg3HuuedO9ksSEVFXCqFUurytSUix+L4OjWlkPt/PnJroQ7M87CByMPiv8Hju6/jY2lUqXezUDx+eiXL5HZM+HiKaXJyfE9F4SFIakcgVSCQORTD4DwgE/guh0N+ip2c1QqEPAShO9RBpQsjQ9WNRrR7ZVl3yUumqCX79CMrla4YNEguhwDR7US5f6Pp9MPgZeDx/bJrQYWVZm4hErpnkkitBVKubR/y3s24ubKz1NRpUKr2zrYQU00xA1zeOZ6DUgtX8VG75HhRCQaVyIiqVUzs6Lpockx4g37p1K+677z4sWbIEK1asmOyXIyKiLpbP/x1Mc0HLiaQQEkqly1CtntThkbXm9//viNkdVmPRr3RoRKNnGOtQqWwZdgIvhIJy+TyY5hLo+mFtZbQIEYBh8LufaLrh/JyIxi6HWOxseL2/hiQJSJIJSarW/myVV4tGLwHAmrwzxcDAfwPwjRDolREIfBayvHNCX7tY/BSq1aNrNcbdj1m/8yCT+TGA+lWoRWjaN0Zobilg1Qj/9oSOd6hC4YNt1CA3USx+oOH35fKFMIx5I8zPgULh/8AwVsI0IyOORwgZur4WLCbRHl3fiEzmpxAiDABObX673n2lchoyme+D/54zw6T/Xzz55JNx33334Qtf+AIOOeSQyX45IiLqYkL0Ip2+C9XqltrfZQihQggJQvhQLP4VBga+AqCNgoQdIEn74fE8NmJ2hyQZ8Hp/C2DkJqRTJZv9NgxjTe3fevDf1/67rm9ALvdlAECx+P4Rj1kIBaXSFQCCkzlsIhoDzs+JaKwCga9CUZ4fphGgCY/nPmjaDzs8MposhrEO6fTvYBhWTeZmwWqr0eR3EI9vgSy/MWGvLUl5yPJuWH1wGh+X5RK83t+7fufx/LHtciM+388nZqAtVCrno1i8GkCrAD9QLF7RYoWmH5nMzyFErCExxQ6al0qXo1j8CAAfSqX3tJHpb6JUagzGU2vV6ono738Z2exXUC6fj0rlTJRK1yCVegDZ7C0AQlM9RJogE91FoYEs804KERG1zzTnIZP5BRRlK7ze30KS8jDNBSiXz4cQUUhSPzTtZmja9yHL+2GacZTLl6BYvApCzO3oWCVp5Mn34LYmrCXH03MSJUQPUqk7oWnfhd//NajqqwAAw1iDYvH9KJUuB+ADAJTLF6BS+XZt6WrjBbK1fcPXQgAAR01JREFU3HUOCoWPdfIQiKhNnJ8T0dgY0LQbMHL9Yhma9lWUSld2YlDUAbp+GFKpB5FIHAJZ3tE0WG3NCfcikTgCur4WpdJVKJUuxXjmvn7/p6Ao21qUSrF+BoOfQbl8Lgzj0Nrv228QP5q5/NhIyOX+G4axBn7//0BRBjPsTXM+isUPo1j8C7RK/jGMg5FMPgK//+vw+78JWd4PISRUq5tRLH4Qlcq5zr6Fwkfg8/0Msryr5fxc1zegVLpkMg50hvOjXL4c5fLlUz0QmkSTHiAnIpoJJCkFn+9nUJTtEEJDpXIqdP1ITJdM5pnIMFahWFzl+p2qPoxo9KLaxFdAkgBJ2odA4NMIBD6PTOYHqFZP7tgYheiFEEobHeMBIYKY/tnUQZRKH6hllpRhvb+9TbbzIJO5BeHwh+Dz3QprQZp1LkiSDl0/FNnsd2Ga8zo2ciIiIppcsrwTirJ7xO2sLPJnAFTQfB4xUeN5Ez7frbWEiRgqlfNgGCsn7fVmOyvAu2PYbaygtQ5VfRKh0JMIBP4DmcwvYRirht2vuSy83u+NOM8WQoXf/03kcv8NwAo8t0MIGaa5cAzjGi0JxeIHUSy+D6r6KGS5v65u+Mi13YWYg0Lh71Eo/B2AEgAPmoXyrJW4dyASuRIez6NOGRDrmslApXIWBga+BjvhhYjcujpArijMfiE3+z3B9wYNNfb3hglN+xQ07QuwaimqAEwEg5+Brq9DPn8jTHP1BI+WmpHlbYhELgBQdDXDtCbiJoQoIxq9FNnsAzDNg9t+3vF9bkRRrZ4Hj+eXwza5tOp3XwlVHXkSPH34R3g8jGLxOyiXX4fX+yPI8i4IEa1dnG6ALEuYCUmq/F6hVpplz812PE+oGX6OzhyyLEbeqI6qAsNVdR37eyOHQOB6eL0/hXWDXgFgIhT6JCqVM1Ao3AAhekb5nDQcRXkcweA/Q4j2vv/sbWR5D2Kxc5HJPAogOorXkwE8BEkqtfFaOrzeO6Gq9vtoEwxjKWT5jRHGaqJSuapuv8kmA9gMs5YMr44pGjdSss1i5HL3QlEeh8fzC8hyGqY5F5XKpTDNFVC66VKkBX6n0HDGMz/v6gB5JDLSxTvNVnxvUCujf298AMANAOwLgsGGQ6r6HKLRUwH8CQCzVSbf12FlNTdf1mstvTQQjX4ZwI2jfvaxf278HYBfwrpAa3bhKEGSPNC0j0LTpnsG+Visrf1n0bSpG8lk4vcK0ch4ntBw+P6YCVYCiADItrHtUsTj7QWpR/feKAO4CMDDsOZdAvVzQ6/3Tni9Z9UeD4/ieWl4n0Hzee7wJMmAJO1GPP5TAB8e5d6VtrdUlCri8fp59icBvGe4PSBJCxAMvhvBYGCU4+oGx9f+s/hn4Mcvv1NoonV1gDybLcIwRqp/RrOJosiIRPx8b1CDsbw3FOVRRCJfG2YLA0JkUa1+FPn8DyZmoNSCiVjsG8NmaVt0CPF9pNP/DqC9ye74PzcOgsdzI4LBq2EvYbRZjXK8yOVuga4vAJAfw/PTVOL3CrUSjfpZy3sInifUDD9HZxa//z3w+b40bNkLIWQUi+9DuTz8vGcs7w2v95sIBP7oWk3oZkCIF1Eq/QdKpY+39Zw0PEnagWj0t8P8mw9PCMAwvoqBgWva3sd6b7RXlsWqrb0KuVz9++0d0LSt8Pv/FUKozjWE3YheiLkYGLgNpinA+Xl34XcKDWc88/OuDpAbhgld5wlBjfjeoFZG897w+7/umlA1I0kGPJ5fwzTfgmkumKhh0hCSlGm74Y4kVWCae2GaB4zqNcbzuaHr56NS+RM07Zvw+X4MWc7ANHtQKr0LpdI1MM1FGLmhFU1nM+17RZa3w+N5FIABXT8EhtF+WSKyiLHFCWa0mXae0MTi+2NmyOevh8fzI8jy/paNAA1jGQqFd0OI9v5/j+a9EQ5/tY2tTHi9NyCX+yjaqfFMw/N4XhlzcBwAJElAlt8cw/m/CtXqMVDVh5s26Rx8fgPF4jUNz5/LfRyl0snw+2+A1/s7SFIJhrEYpdI1KJUuhxAxcH7evWbid4qiPAtVfR6Aimr1qNo1JI3GeObnXR0gJyKaTKr6xzYylq3SHqr6JCoVBshtivIC/P4b4PP9ApKUh2nORal0JYrF90CIvlE/nxCjW0JnNcTsLMNYiXz+M8jnP9Px1yZql6K8gmDwE/B673Bd7FarG5HL/St0/egpHB0REU13pjkP6fRvEY1eAlXdWmsEaMAqWaFD1w9DNvtDCBGZhFcvQ1VfGHErSQIUZQ9keRcDTA4Bj+dBaNrX4PXeA0mqQtdXolS6FqXSZRhu5aUQ42+0Otq5vK1U+ieEQudACKlpkN7KHl+LcvltTffX9Y0YGNg4ptcm6hSP5w8IBv8BHs/jzu+EkFCpnIlc7rMwzWVTOLrZY9ID5MViEffddx8AYMeOHcjlcvjtb38LADjqqKOQSCQmewhERGMyXKZCo5l193o8NO0mhEJ/CUB2Motk+Q0EAv8Kv/+LyGR+Dl0/YpTP6kWlcgI8nj+MuKRX19exMRNRE4ryMmKxUyFJAw0Xmar6GGKxs5HJ/ATV6slTNELqFM7PiWg8THMFUqk/w+O5Bz7fzyHLKZhmH0qlS2o3Wieri/Fo59ucn1sEgsGPIxD4KoRQnLm0qj6LUOgj8Pu/hEzmVzDN+U331vVDYZohyHJ7qzkbXl2oqFTOHdO+un4cstmbEYlcDSHKsMoZwjkOXV+PTObHADxjen6iqeb1/g6RyGUYWuNfkgS83t8jHn8Y6fRdMAz2PJtskhCTu0D0rbfewimnnNL0se985zvYtGnTmJ87lcrPuCUVND6qKiMeD/K9QQ3G8t4Ih98Nn+9XbWWR9/c/xTu7ADyeuxGNXtByGaYQCoQII5l8bNSZ5F7vbxCNXjridtns11Auv7Pt5+XnBg1nJr0/YrEToKpPt7zJJIQMISLo738ZwAzttjqBEokgFKU7a5BP1vx8JpwnNPFm0ucoTazRvzcEEol1kOVtkEaIwZtmDP39r4KBU8Dv/wJCoX9o+bhVFmcNUqk/oFVJmmDw/8Lv/+qwiSrDSSb/CMNYO/KGNUPfG5KUhKZ9H17vbyBJeRjGUpRKV6JaPQnAdPouLsLr/S0UZReECKNcPh1CzJ3qQc0oM+s7JY+engMhSblhr591/Uik03d0eGzdaTzz80nPIF+0aBFeeumlyX4ZIqIJVypdB037+bDbCKGgWt3C4HhNIPAfsCapzSfP1qQ6C7//JhQKfzOq565UzkSh8IFa9gtcF0b2rd5y+Z0oly8b09iJZjJVfQIezxPDbiNJJiQpDZ/vZyiX39WhkdFU4PyciLqThGLx/QgG/x5Dsy3rCaGgVLoGDI4DQBmBwOeH3UKSDKjqc/B670SlckbTbQqFT8DrvQuKsrXtILldFiWX+/SoguPNnyuBYvF6FIvXj+t5Jo+JQOBz8Pv/B7I8ACFkACZCIQXl8oXI5T5fq3lONEjTflJb2dl6G6vn2SNQlGfHfR7R8KbTrTYiommlWj0e5fK5tQlOI+v3HuTzn+rswKYpWX4TXu+DbUyaTWjat8fwChLy+X/DwMB/NTTgNM35yOc/jYGBr2DylvUSdS+P524IMXKjMiEUeL13d2BEREREo1cqXQXDWNXyO00IBaY5F4XChzo8sunJ670LspwacTshFPh83xvm8SjS6d+hXL7I+bcXwppzm2YC5fJJMM2oax/DOAiZzHencVB7ogiEQh9BIPAvkOUBAHbSgRXc9Pl+hljsTEhSdsRnUpStCAb/HpHIxYhE3gm//38hSSP//6Pu5PHcjXbCskLI8HrvmfwBzXJs0klE1JKEbPZbCIf/Epr2g7qJuARJ0mGafchmvwtdP2xKRzldyPKutraTJECWd4/xVaRaM6GroaqPQ5b3wzTj0PUj0WpJKBEBklRCezePTEhSebKHQ0RENCZChJFO/wbR6OXweB6uNQkVsOfnVlD2B2NqCj8TyfLOlg0u60mSAUV5c9hthIhjYOAbyOU+Da/3TkhSAaa5AJXKqQC8AMrweP5cK4GyoJbtOvMTVzyee+H339Tycevf9iUEAv+JfP6TLbaqIhT6a/j9N9WuOQ0AErze2xEM/jMGBv4b5fLlEz94mlLW/LydMjFybVuaTAyQExENS8PAwNeQz38cmnYzFGU7hPCjUjkNlcrZ4MfoICHCo9g2OM5Xk2tBcSJqh2EsAzByPwVAqW1LREQ0PQnRh3T691DVx+Dz3QJZ3g8hYiiXL0S1egxmQ1C2XUKERgyOW9tJECLS5nPOaVGKzYdq9bhRjrD7+f1fczU/bUaSDGjaN2o1yXthGKtQ/z4NhT4KTfuOs63F+v8mRBmRyAeRyQRQqVwwWYdBU8Cac6sYaY5u3fxb2okhzWqM7BARtcE0l6NQ+KepHsa0ZhirYRgHQJa3D1tHTQgV5fL5nRsYTaAqZPktACZMcxEA31QPiNpULp+PUOivIUn5YbeTJB2l0pUdGhUREdHY6foG6PqGqR7GtFapnAohVEjSSDfJBcrlt3VkTDONx3N/W3XZZTmLePxMAICur0Cx+GGUSldDUV6G39+6/KQkWf2WQqG/QzJ5HhpXzRagKDshhArTXNzkcZquSqV3IxD48ojbmWaE52cHsAY5ERFNEBnF4ocwctaOgWLxvZ0YEE0QScogEPgX9PQciJ6ew9DTsx49PcsRDP4dJGnPVA+P2hJEofDxYbcQQkapdFktq4mIiIi6nRC9KJcvGbYPiRAyhAihXL60gyObOUa++dBIUV5DOPwRhEIfgqbdOGKfGEkCFGVHrWa1RZa3IRT6K/T2LkMicQR6etYhkTgEfv9/AiiOekzUeYZxMEqlC1v2PLMVCp8AoHVmULMYA+RERDRhisX3oVw+v7ZM0/2Y/cWfy30OhnHokD0NeL2/QjR6Pnp6DkBPzxJEIhfC6/0N2qvL1j5JSkFVH4GqPgJJykzoc89EkrQfsdjJCAQ+D1nud34vywPw+7+CeHwLZPn1KRwhtatY/EsUCh8FANeFmFW/FahUzsXAwBemZGxEREQ0OXK5f4NhHNQ0CGv9TkU2+4Mm5RIL0LRvIxbbgp6ehejpWY5w+L1Q1T9P+BhleSdU9SGo6mMA6nuh5KBpNyIcvhbh8FUIBP4Vsjx8rfRO0/WVTsPSdtllb/z+m+H13tVWBroQMlT1JQCAojyDePx4aNpNkKTBYLgs70Qw+CnEYmcDyI1qTDQ1Bga+gkrFWllgz8mtP1vnaz7/t7UkNJpskhBDQxjdI5XKQ9cnNnBC3U1VZcTjQb43qEE3vzdk+U1o2negKFsBeFCtHodS6R0AxlvHe7IY8Pu/Br//S65mP9XqJhQKH0OlcsaQ7QuIRC6Hz3eXq36f/edy+Wxks9/GeMt5yPI2BIOfhs/3E0hStfYaPlQql8Hn+1ekUrFxvTckaR+83vsgSUUYxgG1Gozdv8QxErkAXu+9LSfuQqgwjNVIpf6ImVjzs5s/O1pRlBfg938THs+DAHTo+mEoFq+Drm/CTPx/OFkSiSAUhbkm9WbSeUITZyZ+jtLE6Ob3hqI8A037fq0JZhCVytm1INf0rGIrSQMIBD4PTfsmZNlKEBFCRqVyNgqFj0PXD3NtL8s7EI2+DYryCqwGgWZtH6tcSz7/MRQK/4jxzhtU9c8IBD5ba/pphaZMM4Zy+Tr4/esgxF/ACvQqsJuxAiaKxQ8gn/80Rppry/Kr8Hj+BMCEYRwMXV8/rvE2o2k3IhT6y2FLTLZiJRUFIEn5EfcXQkYu91mUStcgkTgUsrx3mPm5glLpMuRyXxn9oKa5bv7caE3A4/kjNO2bUNVnAKioVI5HqXQtDGP1VA+uq4xnfs4AOc0oM/PDkiZCd743DASDfwe//6uwFvzYk0IDQoQwMPBVVCrnTe0Qh2VCUZ6HJOVgmvNgmkubbhUOXwOf72fOxHsoq+zDlcjlvjjmkSjKS4jFTockZRsmklYgvheZzF2oVA4Y9XNLUj9Cob+tHcPgcxvGQuTz/9DVHecV5WUkEu01Q02nb5+RjZm687ODOoEB8kY8T6gZfo5SK9343pCkLMLhq+Hz3VHL9jRhBZB1GMZCZLM/gK4f3tZzyfI2qOrTAABdPwymOfp56OiVoKrPA6jCMJZCiLlNttERjx8DRdk6bGbzwMB/olS6bswj8Xp/i0jkXQBEk/m5BEkSzs+hhJBQKr0Xudznmj63oryEUOhj8Hrvc/2+Wj0U+fxnUK1uGfO4GxUQj2+BorzaViZ4M62Oc6hU6gEoyiuIRK5u4zlV9Pe/BCH6xjSm6aobPzeoc8YzP+esnohomgqF/gZ+/1chSdakUZLM2k9AkvKIRK6E1/v7qR7mMGQYxlro+tEtg+Oy/Bp8vp+2DI4DgCSZ0LTvQpZ3jXEcApHI5U2D49bzGwD6EQy+e9TPLEn9iMVOaQiOA1bmTSTyQfj9/z3GcU89n++2EWsiAtYE3Oe7tQMjIiIiIpoqVUQi74DXa9WBliS9Nj+3alDL8m5Eo2fXVn22pigvIBK5EInEOkSjlyMavRyJxKGIRC6CorwwycegQdePgK5vahEcB7ze30BVXxw22CsEEAj8O4CxBYQlKYlI5CoARov5uXD9bPa4339D039rRXkesdjJ8Hj+0PCYqj6HaPR8eL2/HdO4mwsgk/kVdP0QAIOlMUaXijp8aE4IBdXqEdD1w+Dz/WLEmtUWHT7fb0YzCKJZjQFyIqJpyOpm/o1hJ4UAEAz+LazM8ulBkvbA47kDXu/vIcs7Rtxe036Idr+KfL4fjWlMHs/9UNWXR8jo0KGqj9fqHrYvGPwkFGVbi4m99TMU+icoykujet7pQpLSaO//j2A9dyIiIprRfL7b4PU+3HJOaSWyFBEIfLrlc6jqk4jHT4bXe49rni9JAl7v3YjHT4aqPjmh45blV+H1/hYez121ud3wNO27bTaN3A2P575ht2tGUbYiFPprAMW2sqZbEUKBpt009LcIh98HSSq0mJ+bAEyEw9cBKIz5tYcyzXlIp+9HOn0ryuULUakcA8Db9v75/P8D0Dyobv2/8GFgwFpNK0nJYZOLBilt/f8mIgsD5ERE01B73cwFVPW1ptkRnSbLryMcvgo9PWsQi12EaPQdSCQORiRyKRTlxWH224H2ahcqkOWdYxqb1/trV8OTVoRQ4fX+uu3nlaQ0NO0HIy6lFEKF3//1tp93OjHNOWgvM0iCac6s5ZtERERE9TTthjbm5wZ8vp9DkvY3edREJPJuAKVhVjWWapnV4y8doaqPIBo9Gz096xGNXoJY7AL09KxEKPQhSNK+lvspypttlwoZzfxcUV5CNHouEokN0LSfjqlmdz1JMqCqz7p+p6qPweN5etjxW6tzs9C0n45vAA1kVKunYGDgm8hkfoti8cMjvl+EUFCpnIxi8SPIZm+Aac51fm9niev6oUil7oBhHAoAMM25ba3wlCSD83OiUWCAnIhoGlLV4Sd2NiFkKMpzHRhRa4ryMuLxE+Hz3eYas5UJ83vE4ydBVZ9ouq8QoTZfRWCsTUklqd0O7tIotgU8nj9BkiptvL4Oj+fOtp93OimXL0Q7KxQkSUe5fOlonx2S1A9g5H9DIiIioqmmqs+1NT+XJKNp6Q+P5x4oyhsjBG8NKMrr8HjuHc9Q4fX+DrHYWfB4/jjk+SvQtO8jHj8JkrS36b5ChNsuDyJEe/PzwbInD7b3xG29NjA0pOXx3NdW8BhQxv1vPJJi8VoIobUsh2KN30Sh8FEAQLl8GZLJF5DJ/Aj5/P9DPv8vSKXuQzp9vxMct7a7pM3rRA2VyjmjHHW+Nj8fW+kcom7GADkR/f/t3Xt8ZFWZ7//v2nVPJZXqCzYKzEAj01wFVGiRQ8ulUVCkZwSkUS4qYqOgwjAHGgd6fg2MIEfPzJFLDygXARERwTkqtiA4gOCgKHgQBRt6tLUH2kaSqkrd997r90cl6Q5JVaqS1CWpz/v16leSXbv2fpJe2XnqqbWfhY40+YznCqvJVm9vLqtE4iOT9PcuKJE4VRMlWqXSsaN9G2upFGCPnVKEvv8m1deGxhvet17luvesp5DeiXx/ZxWLJ9bsc1iZ+fI/6l6QKhR6QonEh7Rw4SItXLibFi7cUX19H6/6JgoAAJitfBmzdbgQOxcW02ukfDJ+33D4Rw3c1Tj1yRXGpJVIfESV/t7jf+7GeHKczerr++yEzy8Wj1M9d3haG1a5fHhdMfX1fbJq25Opc1Quv2PMFmOKqu//yZcx9efyU+H7OyuVulfWxsYV7SvjwNHQ0LWvWzA0qFLpWOXz5ymfP1eue+CY5wWDzygY/Jl8f56srf5/ZK1RPv8JWZuoI1KrcPg+JZPv1g47vFELF+6mBQt2Vjx+kRzn93V/v8BsR4EcADpQuXxwnbfOWbnuQS2IaGLB4M8UDP66jpkwf1Q4/OC4x8rld8l196j5vVYWpXnLlL/PYnFl3cl4sfjBuo/reYvr2s/agDxvj7qP22kymS+rXD5E1mpModxaI2uNPG+J0unb6zpWLLZOyeQxCofXj75gM8ZVJHKvkskjFInc1ZTvAQAAtI4xGcViX9L8+Xtp4cLdtXDhmzV//hL19Fw9q9csKZeX1rl4eVSet9e47cYUVF9rQSNj8o0HOKyST+Vq9vc2xlM4fL8c54/jHisUPiwpMkkBNqBC4RRZO2/SeILBXyoUenqGi+OSZFQonDFmSyU/r6fw7dSdy0+H6x6igYFfKpf7B3nem2RtSL4/T4XCGRoYeEKFwml1HcdxNimZPErz5i1TT8+/yJiMJDucn2/bbyRXL5XeN9rXvDZfvb3nqr//DAWDP9vufFnFYjdq3rx3jtkOzGUUyAGgA5XLb9Vkt7ZVCscH1j1ztxnC4R82MBPmgQkeMUqnvz58K+f4FxyV/nvzlE5/TfW9oBjP896sQuFvJ1nt3VGpdGpDM8g9by+Vy2+bdBV5Yzzl8x+r+7idJ65U6t81NHSdXHff0a2et7uGhq7WwMBDsnbBpEcJhf5Dvb0XSdK4uwYqX/vq6/ukgsFfzWj0AACgdYx5VcnkkYrHL5fjvDy63XG2qKfn80omD5cxW9oY4VRZue5b61h7JqBC4cMTztz1vL9Wfa0rPHnerlOKUqrk5/WxCocfGr/VLlA6fZukQNX83HX3UTb7z3Wdpf62JxNEWKNIn81eLt/fccy2YvF4Wds36XEr+Xl9xenp8v03Kpf7R7322vN69dW/6C9/+YOGhv5Fnrd3Xc83ZouSyaNH77Y0xh3+pzF93K2VXPcgpdM3D09eCU167FjsekWjtw8fd+zdBpVFZ3Pq7z+BxT7RFSiQA0CH6en5opLJk1XrEl1JMkMaGvrXVoU1ocrslvoK19VmwnjenhoYeETF4kmydlsiZ21YhcIpGhh4VL6/+7TizGTWqVw+bPi42xL0bZ+/V7ncvzR83Gx2jSqzNyb+GVTexDhApdJ7Gz52ZwmrUDhNg4M/0datr2nr1r9oYOCXKhRWqd7e8LHYl2u+OKok+Eax2LoZiRgAALReInGmAoEXZYw/pnhXKeb5CgR+P7xQ5Wziqq/vY4rHr6zZm9vagHx/R2Wzqyd8vFhcqXpnkBcKK6cUqSQZk605e3wbR8bkJnykVDpGg4PrVSodPibP9f1+5fOf1uDg+jrbd4y0Gqx/osu2yTdnyPf3GN4WGH2d4PtJZTL/qnz+3Ame3aNc7uJJju8onz912q8vWiUe/4Ic589V21KO/J6l01/T4OCDKhZPVH0tOF3FYv+n5h6V3+O0otE7GwsamIXqbXILAGiBSORuxeOXSRr/Lr5UmRlgjOR5uymT+cq4vnSt5vu7qL6ZMFaet3ON4+ymTOZGDQ1dqWDwheHWHXvWddtmfeJKpe5TOPx9xWI3Khh8RpLkum9XqXS2entPlJRXo/0xy+UjlMncor6+T8jayixoYypJvDGeXPcApVL3qJ4ZHNNhTEaBwAuSfHne38jaZBPP1njqYMygwuGHJn2xVmm3co8ymevU3t76AACgUYHAbxUO/7jmPpXWHj8dzsXe2pK4pise//8UidwraeyM3REj+Xm5fIgyma/I2kUTHsf3d1Q+f5ZisRuq5kSV3tFnVT1GPXx/V1n7s0nX+THGl+f9VdXHXfdgpdP3yXH+pEDg97I2LNd9i6RoQ/F43u51rTlUefMhomLxeJVKq5RIHKV0ekjGPK5Q6CeSyvK8JSoW3y8pUvU4+fw5MiatePyq0Zy8cvzg8LpGJ7RkkpExWxUIbJQUkuvurUZ/bhVDika/XtedC7HYV1Uq/V3dRw6F/lOBQD13c1hFIncpn/9U3ccGZiMK5ADQMax6er4ga03VpLlSfJXy+U/Jdd/W4vjGKxQ+qHj8Ek1eWPaH+xnWZu0ClcvvnJHYxguqVFqhUmnF2K1BR9O5oapY/IBKpcMUjd6uSOQHknLy/d2Uz5+hcvmoaR17Mo7zinp6vqBo9M7RGfrWhlUsnqRs9iL5/q5NO3cjjBmocyZTZZaRMbm6bo8FAACdIxK5d0xBshprg4pE7lWx2PkFcmMGaxa0K/tUPmaza+X7O9U8Xjb7eTnOoKLRu0YLttL2xduTlc1+floxFwqn1TXj1/fnq1R6dx377Szfrz7RZTLF4nHy/X45Tu3+88ZIr732hDxvj+H8XJKMyuV3Nvj6wCiX+5yKxQ8qGr1FodDjkjy57ltUKHys6a+hAoHnFI9fqXD4e6MTnnw/oULhI8rl/qes7a/7WMHghrr60RvjKRR6qqE4jXmtzv0kx3m1oWMDsxEFcgDoEJVVyTfUsadRNHq7CoWPNz2myVi7UPn8KsVi19eYCeOoWDy5Y4q1zWDtDsrn/175/N+37JyVxXqOHr7lctsLUWNKikS+qXD4fg0O/lCet2fLYqqmkRnt1oZkbU/zggEAAE3hOAOqTAyY/O5CYwaaHs9MiET+r6TSpPtZG1Q0eqeGhiZbVD6oTOYG5fNnKhb7qoLBn0uq9I7O5z8u1z1YU113Z0S5/E6VSocqFPrPmm9W5HIXSQpP61z1iSqb/Uf19V1YdQ9rHRUKp8zowvae9+a6+6TPlFDoCfX3/62k8pi7gR0nrVjsOoXDDwy3p5lf5xEbu7u1EfXeqWutke/XGy8we9GDHAA6hOO8Utd+xlg5zn83OZr6ZbOXq1g8WdL2PQO39fculY5RJlO7vx0al0h8VI6zdcIXPpWFe9JKJD6kZibW9bJ2nkqlIyZdoMnaoIrFvxPtVQAAmH0qRbT68o56FvjuBJWcu75+ztsvSlqbkesuVSbzFQ0MPKOBgWeGWycu1XSL4yPHT6fvlOtWZuiPXX+nkqvnchconz97Bs5Vn0JhlbLZS2WtmTCeYvEDGhqa7a8XckokTpFUqpKfewoENqi394K6j+h5e8ja6u1kRlQWTn1LI8GqXH6HfH+Huvat9M8H5jYK5ADQIaztbWDfTmo/UZkJMzj4AxWLx8vzdpLnvUml0rEaHPyO0ulvaGo991BNMPiMQqGf1+znaIynYPBFhUKPtDCy6nK5z0x6y7Xk0d8QAIBZqlg8sY6/9ZU38guFE1oQ0fRVcu56iv6BjsrPrZ2nwcEfKpW6Q+XyMnnejvK8XVQofEgDA48pm/0nzUwxvl5Gudz/1Guv/Ur5/GdULr9d5fKBw/E8okzmZrVmNnvzRKPfHm4rWH28GOMpEvlO3ROjrE2oUDh50kkmxnjK51c1FK8UUi736TGLsI4/vyNre1UofKjBYwOzDy1WAKBDlMsHy/eTcpzBmvtZGxieZdtJjMrlQ1UuH9ruQLpCOPz9Bnp8fk/l8hEtiqy6cvkoDQ1dod7eS8bFXpk95Glo6NrR2U4AAGB28by/UbF4tMLhh6vmKNYGVC6/U563n4KzoBpRLB6rePxzk+5njKdi8bgWRNSIoEql41UqHd/uQEb5/q7KZte2O4ymCIe/p8oc1MneUPEUDj+gQuH0uo6by12kSOT7kgYn/L2q/E4tVbG4YvyTJ5HPf0bB4HOKRr8pa50xxf1Kfh5WOv2tBlrCALMXM8gBoGNElM9/QtZWvzRX3uE3KhQ+2rqw0HGMGVJ9f8Lt8L6dIZ//jAYH/69KpaNGZ6tYG1Cp9F4NDj6gQuG0NkcIAACmI5P5ijxvr+FWGttmplpbmY3qeXsonf5aGyNsjO8vVqn07pozeK0NyPPeqFLpvS2MDJ3GmHTN2ePbODImU/dxfX8XDQ4+IM97s6RK4bryb6Sd5bFKpe6RFJpC1I4ymRuUTt8o191/dKu1MRUKZ2hg4CcNLpAKzF6z4D1bAOgeudyFCoWeUij0Y1WKm9seG0mCMpmvTmslecx+vv8m1bMAlmSG9+0c5fLhKpcPlzQkx0nL95OSWJQTAIC5wNr5Ghh4ULHYzYrFblAgsEmS5Ps7KZ8/W4XCxzqqFUk9MpnrNW/ecjnOpnEzeK0NyNoepdPf1NQKlJgrfH+nuu7wNMaX5zWWn3veHhoY+JlCoZ8oHP6uHCcjz3ujisWZWNjUUbG4UsXiShkzKGPy8v0Fmu0tb4BGUSAHgI4SVir1LcVi1yoWW6dAoNKfzlqjUulw5XIXynUPaXOMaLdi8UTF42sm3a/S4/OUFkQ0Fb3y/fr77gMAgNkirnz+08rnz5UxKUmStf1qbc/rmWPtDhoY+LF6er6oaPRWOc7Q8PagisUTlMtdOANFyrmg0jokGr1JweCvVWnxskyFwse7ooVeofAhRaN3T7qf7/epVDpmCmcwKpcPU7l82BSeWx9rk7I22bTjA52MAjkAdJyQ8vnzlc9/RoHAb2VMTr6/c8fNBEb7+P4bVSicqmj09qq3clZalxwrz/ubFkcHAAAgSWbOFNusna9s9vPKZi9VMPi8JE+et5jezMOMySiROFnh8E/GzKKORr+hWOwO5XKfUjZ7pWbrmyT1qNwleaCCwf9Xowe/lM+fJynW0tgATI4e5ADQsQLyvH3lugdTHMc4Q0NfVKn0Hkka0xdz5PNyeakymRvaEhsAAMDcFJPrHijXfTvF8e309X1EodBPJWlMcXjk856e6xWL/e+2xNY6jlKpu+V5e4z23B9RWfBSKhQ+qlzugnYFCKAGCuQAAMxKEaXT31Aq9Q2Vy8vk+72yNq5y+WCl07cqlfrurOvxCQAAgNklGPylIpEHJ+293dPzJUm51gTVJtYu0sDAIxoauk6uu5+s7ZHvJ1QqHaPBwe9qaOhfRRkO6Ey0WAEAYNZyVCq9T6XS+9odCAAAALpQNHqHrA3KGLfmfo4zpEjkeyoWP9iiyNolpkLhNBUKp7U7EAAN4K0rAAAAAAAANMxx/iipdnFcqrQZCQQ2NT8gAJgCCuQAAAAAAABomLU9qq+05MtaFqcE0JkokAMAAAAAAKBhpdK7ZYw/6X7G+CqV3t2CiACgcRTIAQAAAAAA0LBi8QPy/aSsrV5esjagUuld8rw9WhgZANSPAjkAAAAAAACmIKZ0+nZJwQmL5NYG5Ps7KJNZ1/rQAKBOwXYHAGB2M2ZAkci3FQhskrURlUrL5boHSzLtDg0AAADoOo7zR0Ui98pxXpW1/SoWVzBzF01VLr9Lg4PrFY+vVTj8yOh2a0MqFj+gbHatfP9NbYwQAGqjQA5ginz19Fyhnp4vSyqrcjmxise/oHJ5P2UyN8vzlrQ5RgAAAKBbDKmv7zOKRL6tymSVgCRf8fhlKhaPViZzo6xd0OYYMVe57tuVSn1XjrNRweALsjYo130rYw6YMk/GZGVtXJXrOZqJAjmAKent/XtFo7fIGDu8pTz6WDD4GyWTyzU4+GN53pvbEyAAAADQNYrq7/87hUJPDefnVtK2hRPD4YeVTB6rwcEfydpE26LE3Of7i1UqLW53GMCsFQw+o1jsOkUi98qYsqwNqlj8gPL5T8p139bu8OYsepADaFgw+JRisZu3K46PZYwnY4YUj1/a4sgAAACA7hONfl2h0M9kjDfh48Z4CgR+p1iMPtAA0KkikW8omTxckci3ZUxlEqIxriKRe5VMHqlo9LY2Rzh3MYMcQMNisZtkbVDGuFX3McZTOPwDOc5/028OTec4LykUelqSleu+hfY+AACgq8RiN9axl69o9KvK5f5B3K7fbayCwZ8rFvuqgsGnJEmue7Dy+Y/Ldd/epPM9o0Dgd5JCKpcPke+/sQnnAeaOYPCX6uv7pCRfxvhjHhupvfT2flquu0Suu7QNEc5tFMgBNCwY/GnN4vgIY3wFg8+oVKJAjuYIBH6j3t7VCof/Y8z2UukQZbOf5xY0AADQBYoKBn8z6V7GSIHAFjnOy/L9nVsQFzqDq76+cxSNfmPMJKdA4PeKRu9UPn+ahoa+rJl60yQU+pF6ey9VMPjc6DZrHZVKx2lo6CrGHlBFLHadJGdccXysgHp6rlE6TYF8ptFiBUDDal+wX6+RfYH6BYO/0rx5RykUemzcY6HQz5RMvkfB4E/bENncYMxfFAhskDGvtjsUAABQU6P5Nvl5N4nHVysSuUuSxkxyGvk8Gr1jxlpjRiL3qr//BAUCvx2z3Rhf4fD3NW/e4XKcP87IubqPleP893B+nm53MJhxJUUi9006EdEYV+Hw9yTlWhNWF6FADqBh5fIBsra+G1Bcd58mR4PuZNXX9zFJhQl7bVa2uUokTpc0+d0O2CYcfkD9/cdp4cLdNH/+27Rw4WL19x+ncPiBdocGAAAmFJPn7SprzaR7+n4/rS66iOO8rFjspqprR0mSMVax2DoZs3Va5zJmQH19q4Y/H/8mTGWdqr+ot/ez0zpPNwmFnlBf3xlauHCRFi5MasGCPTV//tu0YMFfqa/vIwoGf9XuEDFDjMnUdZd+ZV9fjjPY3IC6EAVyAA0rFD4+6cXb2oBKpSPk+7u1KCq0VlmRyN1KJo/WwoU7asGCN6m/f4XC4e+rFbOSQqGfKBjcUHUhKqmSOAQCWxQO/6Dp8cwVsdj/UX//iQqFHh+zPRR6XP39JyoW+9f2BAYAAGrK5z8x6T7WBlQonCkp1PyA0HLGpBWLrdO8eQdp4cJFWrBgFyUSJ0qqXhzfxioa/ea0zh+Nfl1SaZJivKdw+CE5zsZpnWvus4rHP6dk8hhFIt+RMfkxP1djfEUi31EyeSSvdeYIa3tlbX0lWmuNfD/R5Ii6DwVyAA0rlw9Tsfj+qhfwyvaQstnLWhsYWsKYtJLJ9yqR+LiCwZ/LmJwcZ0ih0KPq7z9FicSHJZWaGkM4/HBddzFYG1Q4/HBTY5krQqFH1Ntbub329W88jHzd27tGodB/tDo0AAAwiXz+I/K8JbJ24j7S1gbk+zsqlzu3xZGhFRxno+bNe4fi8dUKBH4nY/JynJSCwV/X2R4zoEDgv6YVQzj8kOotxofDj0zrXHNdLHa9enqulaSqbzhU/l/LSiROl+NsbmF0aI6ISqX3TfoatzIR8WhJva0Jq4tQIAcwBUbp9M0qFk+WtUbWBob/VS7mvv8GDQ5+V667f5vjRDP09Z2pYPApSWNvnxwpoobD96u3d3WToyhImvw24kqSXmxyLHNDLHZt1RfVI6wNDC8eAwAAOkuvBge/r3L5YEmVSQLb5+euu7cGB9fL2oXtDBJNUVAyebwc52UZY18307iegrUkWVkbm2YceZl60nM5MqYwzXPNZWX19Hyxrj0rP++yotFbmhoRWiOXO7eOHuSe8nne6GyG+poIA8A4EWUyNyibXa1o9A4FAn+QtTGVSkerVHqvuLzMTYHAc4pEflhzH2OsotFblM1eLGt3aEocnreb6u0tTpufeuQUDj8w6Yuoym2xD6iyKExPSyIDAAD1sXYHpVI/VDD4C0Ui35LjbJW181QsfkDl8iGqb3IBZptI5D4FApumdQxjXJVKy6d1DM97s6z9WR0FPn84l8dEwuGH5Th/qXv/SruVbymXu6SJUaEVXPcQZTJXq6/vQlkbGHNX78jXQ0P/rHL58PYFOYdRwQIwLb6/m3K5mVn1HJ0vGv2GrA3WsYCIVTR6j/L5TzYljmLxJPX2fk6Tt3KxKhQ+1JQY5hJjhuqeYVSZmZSRtRTIAQDoRK77Nrnu29odBlokGr1D1jp1tlIZz9qAPO+vVS6/a1pxFAqnKxa7bZJzSb6/aNrF+LnMcV6ewnNSTYgE7VAonC3P21ux2JcVDj8oY6ysNSqVDlc+/2mVy0e2O8Q5iwI5ALSEr3D4R4pEvi1jXpO1C1QonDj8B272dLtynP9Wfb0FA1NK7upl7Tzlcp9VT8//qnorp7VGhcKZ8v2dmhbHXGFtf51vfIzcst3fgqgAAACaqaBI5D6Fwz+UMTn5/s4qFE6T6x7Y7sAa4jibp1Ucl6LKZL6m6d5h4LoHqVg8WuHww+PWsxlhjIYnV1GKqsbaxnpLV950aM5du2iPcnmZyuVlMiYlYwZkbVLWJtsd1pzHVQkAmsxxNqq//0QFgy+O3hplbUDR6J1y3SVKpb4l39+13WHWxdqE6kue/YaTu0blcv8oYzLq6fm3McXdkc+LxVM0NHRVU2OYOyIqFlcoEvn3mkVya4MqFldIirYuNAAAgBkWCv1EicSpcpzXZK0jyZcUVCz2VRWLy5XJ3Dqc93Y+a/tkrapOGqn+vMqs1Gz28/K8vWYgEqN0+mvq7z9V4fDDr8vPA5J8ZbOXqVA4fQbONXeVSkfI2rCMmexO2RFGhcKpTY0J7VGZxMTEpFahQA4ATWTMViWTx8hxtg5/7Y35GAi8pGTyWA0M/ETWLmhbnPUqld6rWOzmSfczxlOx+L4mR+Mom716+HbOmxQK/ackq3L5rSoUzuTW4gbl8+cqErmv6gssayXJZ1EYAAAwqwWDz6i//281sp7NttnXla/D4R8rkfigUqnvS6q9gHknKJVWKBh8VpUif3XW9ui11x5SMPiiJMl192/CJJ1epVL3KRR6VNHozQoGfyNrwyqXj1A+/zH5/uIZPt/cU7nTeKWi0a9XnYm/bV8jaxMUyIEZQIEcAJooFlsnx9la4zZDV47zsmKxG5TLfa7F0TWuVFouz9tVjvPHqt+TtQGVy0vlefu0JCbP21dDQ//SknPNZa77NmUyN6iv72xZa8bMJLc2KMkqk1nHGw8AAGBW6+lZK8mr2paksij5EwqH16tUavaEj+koyJiU8vkT1dPzBVlbqrqmjLWO8vmPyvf3UanU7BzdqFx+17R7mnezbPbzCgafUTD4XI3XXI6sjSuV+vasmGgFdLrZ0/gWAGYdT7HYTZO+82+Mr1jsq5ps1kdncJRK3TV8K+f4GTXWBuT7OyqTuakNsWG6isWTNTDwqAqFU2RtRJJkbUSFwkoNDDyqYnFlmyMEAACYOsfZVLNH9ghrA4rFvtKiqBoTDP5CfX0f0cKFb9LChXtowYL95bp7SjJV8nNHrnuwstk1rQ8WU2JtQoOD65XLnS/fT263vfLR93uVz39WAwNPynUPbk+QwBzDDHI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       "text/plain": [
        "<Figure size 1600x600 with 2 Axes>"
       ]
@@ -353,7 +353,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6d6f7639",
+   "id": "3de39409",
    "metadata": {},
    "source": [
     " One of the strengths of the SVM model is that it is insensitive to the exact behaviour of distant points, as can be seen from the comparison between the two figures, and increases the number of training points without affecting the classification results of the model.\n",
@@ -364,13 +364,13 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "8a22529a",
+   "id": "5af3dc72",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fcf88d8cf5594b41a4d74b390176368d",
+       "model_id": "1cf50a7e3fda45e683a7ed36129bdadd",
        "version_major": 2,
        "version_minor": 0
       },
@@ -389,7 +389,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "25b36eb4",
+   "id": "0e22fbf1",
    "metadata": {},
    "source": [
     "## Beyond linear boundaries: Kernel SVM\n",
@@ -413,12 +413,12 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "e74b7f1d",
+   "id": "1beacb5e",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -443,7 +443,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9907c868",
+   "id": "cfdc16ed",
    "metadata": {},
    "source": [
     "It is possible to project the data into higher dimensions, so that a linear partition line is sufficient. As an example, we can use a simple projection to compute a radial basis function with the centre on the middle block as follows:"
@@ -452,7 +452,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "33429c1d",
+   "id": "b58eb30f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -461,7 +461,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d11a23aa",
+   "id": "a6ff7957",
    "metadata": {},
    "source": [
     "This additional dimension of data can be visualised in a 3D diagram.:"
@@ -470,13 +470,13 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "fe5d19ea",
+   "id": "70ddfe4a",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0523c110f4dc4cc8af21d07a3b6f5276",
+       "model_id": "4396863816b74d4a96478dc1a1cda72b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -505,7 +505,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7cb5291c",
+   "id": "9043ba45",
    "metadata": {},
    "source": [
     "Next we describe how to select and adjust the radial basis function centres.\n",
@@ -516,7 +516,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "82e12074",
+   "id": "76b68df3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -527,12 +527,12 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "3d81892b",
+   "id": "1e377aba",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -554,7 +554,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2077a638",
+   "id": "ef26ead4",
    "metadata": {},
    "source": [
     "Currently, for linearly divisible data, we usually use a linear support vector machine. However, if the data is non-linear, such as a classification problem, we can transform it into a linearly divisible problem, thus improving the generalisation of the model. In this case, kernel tricks are used to transform linearly divisible problems into non-linearly divisible problems.\n",
@@ -709,58 +709,7 @@
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
     "state": {
-     "0523c110f4dc4cc8af21d07a3b6f5276": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "VBoxModel",
-      "state": {
-       "_dom_classes": [
-        "widget-interact"
-       ],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "VBoxModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "1.5.0",
-       "_view_name": "VBoxView",
-       "box_style": "",
-       "children": [
-        "IPY_MODEL_942617ece21949b08a7b2d8ded215928",
-        "IPY_MODEL_28855764b1b247b7af599ff82656ba8a",
-        "IPY_MODEL_7f52ceef92cd4534acb554e532bad254"
-       ],
-       "layout": "IPY_MODEL_f882b2ae69ab4ed4b55abb3cd6ebb5c3"
-      }
-     },
-     "091cc6af34bb4e5d97cd8fc57778a15a": {
-      "model_module": "@jupyter-widgets/output",
-      "model_module_version": "1.0.0",
-      "model_name": "OutputModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/output",
-       "_model_module_version": "1.0.0",
-       "_model_name": "OutputModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/output",
-       "_view_module_version": "1.0.0",
-       "_view_name": "OutputView",
-       "layout": "IPY_MODEL_d4cd4ffe4493457da04713fba3a36a2a",
-       "msg_id": "",
-       "outputs": [
-        {
-         "data": {
-          "image/png": 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",
-          "text/plain": "<Figure size 640x480 with 1 Axes>"
-         },
-         "metadata": {},
-         "output_type": "display_data"
-        }
-       ]
-      }
-     },
-     "28855764b1b247b7af599ff82656ba8a": {
+     "042b1f2558c94f1a8e7a7530dfa261a5": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
       "model_name": "IntSliderModel",
@@ -777,33 +726,18 @@
        "description": "azim",
        "description_tooltip": null,
        "disabled": false,
-       "layout": "IPY_MODEL_466c310ac1a54383b20e5a5e629201d2",
+       "layout": "IPY_MODEL_801d8ad033064bf787e41e91d3640f28",
        "max": 90,
        "min": -30,
        "orientation": "horizontal",
        "readout": true,
        "readout_format": "d",
        "step": 1,
-       "style": "IPY_MODEL_e98a2ac3f29b4122975158945f4b9c44",
+       "style": "IPY_MODEL_3dea2de7bb0e48478daf57970613041d",
        "value": 30
       }
      },
-     "39b62f7b5f61446fb7619988812ac772": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "DescriptionStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "DescriptionStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "description_width": ""
-      }
-     },
-     "3d69c21927194e308146a282a6830158": {
+     "16802d9e848a45fe823d56984c49fc96": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -855,7 +789,70 @@
        "width": null
       }
      },
-     "466c310ac1a54383b20e5a5e629201d2": {
+     "1cf50a7e3fda45e683a7ed36129bdadd": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "VBoxModel",
+      "state": {
+       "_dom_classes": [
+        "widget-interact"
+       ],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "VBoxModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "1.5.0",
+       "_view_name": "VBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_517294c2a73c4071853c887c7991fd94",
+        "IPY_MODEL_ac52743ada7448c98d8e7f41e7652da2"
+       ],
+       "layout": "IPY_MODEL_dad66c99e7784a38898e206d60883759"
+      }
+     },
+     "3dea2de7bb0e48478daf57970613041d": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "SliderStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "SliderStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "description_width": "",
+       "handle_color": null
+      }
+     },
+     "4396863816b74d4a96478dc1a1cda72b": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "VBoxModel",
+      "state": {
+       "_dom_classes": [
+        "widget-interact"
+       ],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "VBoxModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "1.5.0",
+       "_view_name": "VBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_a296b3b385464869b0bc237ea58a23d9",
+        "IPY_MODEL_042b1f2558c94f1a8e7a7530dfa261a5",
+        "IPY_MODEL_c2f9fbddc4cc41d2a0cfb5f31d8e82f1"
+       ],
+       "layout": "IPY_MODEL_db1da06a39a44326bfe81d4eddac371b"
+      }
+     },
+     "4f1b4cbdc5c143ccadbae13801b81d2b": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -907,7 +904,7 @@
        "width": null
       }
      },
-     "5067878ea24243799a31056ff95b1c2c": {
+     "517294c2a73c4071853c887c7991fd94": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
       "model_name": "DropdownModel",
@@ -928,11 +925,11 @@
        "description_tooltip": null,
        "disabled": false,
        "index": 0,
-       "layout": "IPY_MODEL_3d69c21927194e308146a282a6830158",
-       "style": "IPY_MODEL_d0746abe945b481a895b19ee9643f2da"
+       "layout": "IPY_MODEL_4f1b4cbdc5c143ccadbae13801b81d2b",
+       "style": "IPY_MODEL_afa055db038f446eb2a92f05d75a9040"
       }
      },
-     "54791d40e53149f2992c5910fd52a1e3": {
+     "6fc1130ed0e94536bd4d830852e25390": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -984,59 +981,7 @@
        "width": null
       }
      },
-     "7f52ceef92cd4534acb554e532bad254": {
-      "model_module": "@jupyter-widgets/output",
-      "model_module_version": "1.0.0",
-      "model_name": "OutputModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/output",
-       "_model_module_version": "1.0.0",
-       "_model_name": "OutputModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/output",
-       "_view_module_version": "1.0.0",
-       "_view_name": "OutputView",
-       "layout": "IPY_MODEL_e3d17c0519b74c079692724c78995ee9",
-       "msg_id": "",
-       "outputs": [
-        {
-         "data": {
-          "image/png": 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",
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@@ -1155,7 +1085,101 @@
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+       "_model_name": "OutputModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/output",
+       "_view_module_version": "1.0.0",
+       "_view_name": "OutputView",
+       "layout": "IPY_MODEL_99f5916181d94cf785422b3b7c90103d",
+       "msg_id": "",
+       "outputs": [
+        {
+         "data": {
+          "image/png": 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",
+          "text/plain": "<Figure size 640x480 with 1 Axes>"
+         },
+         "metadata": {},
+         "output_type": "display_data"
+        }
+       ]
+      }
+     },
+     "afa055db038f446eb2a92f05d75a9040": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "DescriptionStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "description_width": ""
+      }
+     },
+     "c2f9fbddc4cc41d2a0cfb5f31d8e82f1": {
+      "model_module": "@jupyter-widgets/output",
+      "model_module_version": "1.0.0",
+      "model_name": "OutputModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/output",
+       "_model_module_version": "1.0.0",
+       "_model_name": "OutputModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/output",
+       "_view_module_version": "1.0.0",
+       "_view_name": "OutputView",
+       "layout": "IPY_MODEL_16802d9e848a45fe823d56984c49fc96",
+       "msg_id": "",
+       "outputs": [
+        {
+         "data": {
+          "image/png": 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",
+          "text/plain": "<Figure size 640x480 with 1 Axes>"
+         },
+         "metadata": {},
+         "output_type": "display_data"
+        }
+       ]
+      }
+     },
+     "dad66c99e7784a38898e206d60883759": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -1207,23 +1231,7 @@
        "width": null
       }
      },
-     "e98a2ac3f29b4122975158945f4b9c44": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "SliderStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "SliderStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "description_width": "",
-       "handle_color": null
-      }
-     },
-     "f882b2ae69ab4ed4b55abb3cd6ebb5c3": {
+     "db1da06a39a44326bfe81d4eddac371b": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -1275,27 +1283,19 @@
        "width": null
       }
      },
-     "fcf88d8cf5594b41a4d74b390176368d": {
+     "fb678518733b4e349920ca8f3e41ad0e": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "VBoxModel",
+      "model_name": "DescriptionStyleModel",
       "state": {
-       "_dom_classes": [
-        "widget-interact"
-       ],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "VBoxModel",
+       "_model_name": "DescriptionStyleModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "1.5.0",
-       "_view_name": "VBoxView",
-       "box_style": "",
-       "children": [
-        "IPY_MODEL_5067878ea24243799a31056ff95b1c2c",
-        "IPY_MODEL_091cc6af34bb4e5d97cd8fc57778a15a"
-       ],
-       "layout": "IPY_MODEL_a3ba2562986b4d3c85aa0ba33b45834b"
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "description_width": ""
       }
      }
     },
diff --git a/_sources/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.ipynb b/_sources/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.ipynb
index c2ff29340b..8410855930 100644
--- a/_sources/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.ipynb
+++ b/_sources/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "fb2e6b14",
+   "id": "92c750d6",
    "metadata": {},
    "source": [
     "# Build a web app to use a Machine Learning model\n",
@@ -63,7 +63,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c570cc9b",
+   "id": "699949e9",
    "metadata": {
     "tags": [
      "output_scroll"
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d99f1e2",
+   "id": "78020125",
    "metadata": {},
    "source": [
     "Convert the ufos data to a small dataframe with fresh titles. Check the unique values in the `Country` field."
@@ -89,7 +89,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3aefe2b1",
+   "id": "0489d00a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5a0744b",
+   "id": "8a803c00",
    "metadata": {},
    "source": [
     "Now, you can reduce the amount of data we need to deal with by dropping any null values and only importing sightings between 1-60 seconds:"
@@ -114,7 +114,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "72ba2b68",
+   "id": "34f6ebf8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "942847b2",
+   "id": "3a661797",
    "metadata": {},
    "source": [
     "Import Scikit-learn's `LabelEncoder` library to convert the text values for countries to a number:"
@@ -136,7 +136,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "764e4e6a",
+   "id": "fca62dc5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9ad61d9b",
+   "id": "d5a8fb7a",
    "metadata": {},
    "source": [
     "```{note}\n",
@@ -166,7 +166,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7c52d8a1",
+   "id": "84fa642b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9f45b0c6",
+   "id": "b59539c7",
    "metadata": {},
    "source": [
     "Train your model using logistic regression:"
@@ -191,7 +191,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2d5df272",
+   "id": "4805987d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0cb6f837",
+   "id": "20b0090b",
    "metadata": {},
    "source": [
     "The accuracy isn't bad **(around 95%)**, unsurprisingly, as `Country` and `Latitude/Longitude` correlate.\n",
@@ -224,7 +224,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a6657221",
+   "id": "96f5a16d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -239,7 +239,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "131ffba7",
+   "id": "5e04d426",
    "metadata": {},
    "source": [
     "The model returns **'3'**, which is the country code for the UK. Wild! 👽\n",
diff --git a/_sources/ml-fundamentals/regression/logistic-regression.ipynb b/_sources/ml-fundamentals/regression/logistic-regression.ipynb
index 8fd28c2746..4cf5e1b63e 100644
--- a/_sources/ml-fundamentals/regression/logistic-regression.ipynb
+++ b/_sources/ml-fundamentals/regression/logistic-regression.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "9afe5c09",
+   "id": "45935723",
    "metadata": {},
    "source": [
     "# Logistic regression\n",
@@ -108,7 +108,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "447fd27b",
+   "id": "d02e513a",
    "metadata": {
     "tags": [
      "output_scroll"
@@ -326,7 +326,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "ae7eeb33",
+   "id": "80a60e28",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -343,7 +343,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "75596109",
+   "id": "31d1720c",
    "metadata": {},
    "source": [
     "You can always take a peek at your new dataframe:"
@@ -352,7 +352,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "c745600c",
+   "id": "cf29881a",
    "metadata": {},
    "outputs": [
     {
@@ -385,7 +385,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc00b993",
+   "id": "a06f668c",
    "metadata": {},
    "source": [
     "### Visualization - side-by-side grid\n",
@@ -400,13 +400,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "1451c8c0",
+   "id": "c80b3c24",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.PairGrid at 0x7f4914ba8c10>"
+       "<seaborn.axisgrid.PairGrid at 0x7f948b8a8df0>"
       ]
      },
      "execution_count": 4,
@@ -437,7 +437,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4c0bce63",
+   "id": "eceef26c",
    "metadata": {},
    "source": [
     "By observing data side-by-side, you can see how the Color data relates to the other columns.\n",
@@ -458,7 +458,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "ef8d4999",
+   "id": "503350dc",
    "metadata": {},
    "outputs": [
     {
@@ -466,7 +466,13 @@
      "output_type": "stream",
      "text": [
       "/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/seaborn/categorical.py:3544: UserWarning: 63.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
-      "  warnings.warn(msg, UserWarning)\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/seaborn/categorical.py:3544: UserWarning: 21.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
       "  warnings.warn(msg, UserWarning)\n"
      ]
@@ -500,7 +506,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/home/runner/work/machine-learning/machine-learning/open-machine-learning-jupyter-book/_build/jupyter_execute/ml-fundamentals/regression/logistic-regression_8_3.png"
+       "image/png": "/home/runner/work/machine-learning/machine-learning/open-machine-learning-jupyter-book/_build/jupyter_execute/ml-fundamentals/regression/logistic-regression_8_4.png"
       }
      },
      "output_type": "display_data"
@@ -512,7 +518,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4f7d9d50",
+   "id": "2110b782",
    "metadata": {},
    "source": [
     "### Violin plot\n",
@@ -525,13 +531,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "9cbf8bc5",
+   "id": "8b85f672",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7f4914ba8340>"
+       "<seaborn.axisgrid.FacetGrid at 0x7f947ddb6f10>"
       ]
      },
      "execution_count": 6,
@@ -560,7 +566,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "57b23ccf",
+   "id": "c7f749fc",
    "metadata": {},
    "source": [
     "```{seealso}\n",
@@ -595,7 +601,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "c9b593bc",
+   "id": "1ba188a1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -611,7 +617,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db4a1ba6",
+   "id": "ba86270d",
    "metadata": {},
    "source": [
     "2 . Now you can train your model, by calling `fit()` with your training data, and print out its result:"
@@ -620,7 +626,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "a0ec0edc",
+   "id": "ff6fff3b",
    "metadata": {},
    "outputs": [
     {
@@ -662,7 +668,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1a4317c0",
+   "id": "728a1167",
    "metadata": {},
    "source": [
     "```{note}\n",
@@ -683,7 +689,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "b42cab21",
+   "id": "260926cc",
    "metadata": {},
    "outputs": [
     {
@@ -705,7 +711,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "80dc39a6",
+   "id": "119d17a1",
    "metadata": {},
    "source": [
     "Take a look at your model's confusion matrix:\n",
@@ -768,7 +774,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "86385441",
+   "id": "b30e9f86",
    "metadata": {},
    "outputs": [
     {
@@ -808,7 +814,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "47fc0b5a",
+   "id": "a2f14ba1",
    "metadata": {},
    "source": [
     "Using Seaborn again, plot the model's [Receiving Operating Characteristic](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc) or ROC. ROC curves are often used to get a view of the output of a classifier in terms of its true vs. false positives. \"ROC curves typically feature true positive rate on the Y axis, and false positive rate on the X axis.\" Thus, the steepness of the curve and the space between the midpoint line and the curve matter: you want a curve that quickly heads up and over the line. In our case, there are false positives to start with, and then the line heads up and over properly:\n",
@@ -819,7 +825,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "8eaeac09",
+   "id": "c86d2845",
    "metadata": {},
    "outputs": [
     {
@@ -837,7 +843,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bfd39105",
+   "id": "e9b35e1e",
    "metadata": {},
    "source": [
     "The result is `0.6976998904709748`. Given that the AUC ranges from 0 to 1, you want a big score, since a model that is 100% correct in its predictions will have an AUC of 1; in this case, the model is _pretty good_.\n",
diff --git a/_sources/ml-fundamentals/regression/managing-data.ipynb b/_sources/ml-fundamentals/regression/managing-data.ipynb
index 3e4feb8b2e..0fe5eb37bd 100644
--- a/_sources/ml-fundamentals/regression/managing-data.ipynb
+++ b/_sources/ml-fundamentals/regression/managing-data.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "733b82f9",
+   "id": "59bfbc17",
    "metadata": {},
    "source": [
     "# Managing data\n",
@@ -74,7 +74,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "44d64a59",
+   "id": "5810be16",
    "metadata": {
     "tags": [
      "output_scroll"
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5e3f0f56",
+   "id": "5888d97e",
    "metadata": {},
    "source": [
     "```{note}\n",
@@ -103,7 +103,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fc61ca6f",
+   "id": "0a241282",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +112,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "137e9872",
+   "id": "e27c9cc6",
    "metadata": {},
    "source": [
     "```{warning}\n",
@@ -125,7 +125,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ee603fe1",
+   "id": "25d23b1f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -135,7 +135,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1162baaa",
+   "id": "1239430f",
    "metadata": {},
    "source": [
     "### Second, determine average price of pumpkin\n",
@@ -150,7 +150,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "317f1a8d",
+   "id": "dc3a4ad8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,7 +160,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c19f1ab2",
+   "id": "9baf256f",
    "metadata": {},
    "source": [
     "```{seealso}\n",
@@ -173,7 +173,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "07b0ee98",
+   "id": "c1490de6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b92e9e0a",
+   "id": "ea3a146a",
    "metadata": {},
    "source": [
     "Printing out your dataframe will show you a clean, tidy dataset on which you can build your new regression model.\n",
@@ -203,7 +203,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c87d79c2",
+   "id": "083faa89",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -212,7 +212,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "729ce672",
+   "id": "5223ef68",
    "metadata": {},
    "source": [
     "If you print the data now, you can see that you are only getting the 415 or so rows of data containing pumpkins by the bushel.\n",
@@ -227,7 +227,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3a9c2c37",
+   "id": "54dd9ce7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -238,7 +238,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6648653c",
+   "id": "de2c060d",
    "metadata": {},
    "source": [
     "```{seealso}\n",
@@ -273,7 +273,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "824033cd",
+   "id": "b5c02c9c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -282,7 +282,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e7a20608",
+   "id": "ce4ec4f1",
    "metadata": {},
    "source": [
     "2 . Rerun the entire notebook to refresh.\n",
@@ -293,7 +293,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e27a73a3",
+   "id": "68d738ce",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -305,7 +305,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "60bd34ca",
+   "id": "11415c9c",
    "metadata": {},
    "source": [
     "```{note}\n",
@@ -326,7 +326,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cf85d884",
+   "id": "a2ba1f93",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -336,7 +336,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "63758a6f",
+   "id": "55d72cb0",
    "metadata": {},
    "source": [
     "```{note}\n",
diff --git a/_sources/ml-fundamentals/regression/regression-models-for-machine-learning.ipynb b/_sources/ml-fundamentals/regression/regression-models-for-machine-learning.ipynb
index c973a9e4fe..c38c18a218 100644
--- a/_sources/ml-fundamentals/regression/regression-models-for-machine-learning.ipynb
+++ b/_sources/ml-fundamentals/regression/regression-models-for-machine-learning.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "ba415b27",
+   "id": "8916cc22",
    "metadata": {},
    "source": [
     "# Regression models for Machine Learning\n",
diff --git a/_sources/ml-fundamentals/regression/tools-of-the-trade.ipynb b/_sources/ml-fundamentals/regression/tools-of-the-trade.ipynb
index efbe1def50..5ed2ab3cfc 100644
--- a/_sources/ml-fundamentals/regression/tools-of-the-trade.ipynb
+++ b/_sources/ml-fundamentals/regression/tools-of-the-trade.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "696edd9e",
+   "id": "36bfb534",
    "metadata": {},
    "source": [
     "# Tools of the trade\n",
@@ -132,7 +132,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "e09bd6dc",
+   "id": "4bd2e0eb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -143,7 +143,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "80d77553",
+   "id": "a1a63082",
    "metadata": {},
    "source": [
     "Above you are importing `matplotlib`, `numpy` and you are importing `datasets`, `linear_model` and `model_selection` from `sklearn`. `model_selection` is used for splitting data into training and test sets.\n",
@@ -175,7 +175,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "648631d9",
+   "id": "90eeab51",
    "metadata": {},
    "outputs": [
     {
@@ -196,7 +196,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "494f1da3",
+   "id": "f4462ae6",
    "metadata": {},
    "source": [
     "What you are getting back as a response, is a tuple. What you are doing is to assign the two first values of the tuple to `X` and `y` respectively. Learn more [about tuples](https://wikipedia.org/wiki/Tuple).\n",
@@ -213,7 +213,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "d67ab327",
+   "id": "8053886f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -222,7 +222,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc6591cf",
+   "id": "e17553f3",
    "metadata": {},
    "source": [
     "```{note}\n",
@@ -235,7 +235,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "71d9cdf7",
+   "id": "9b4e43dd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -244,7 +244,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a354e14f",
+   "id": "4bdca3f2",
    "metadata": {},
    "source": [
     "4 . Now you are ready to train your model! Load up the linear regression model and train it with your X and y training sets using `model.fit()`:"
@@ -253,7 +253,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "3584b228",
+   "id": "c71b1b17",
    "metadata": {},
    "outputs": [
     {
@@ -277,7 +277,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9f3df0a3",
+   "id": "a80418b5",
    "metadata": {},
    "source": [
     "```{note}\n",
@@ -290,7 +290,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "96311734",
+   "id": "351495cd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -299,7 +299,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5025c923",
+   "id": "12f92e18",
    "metadata": {},
    "source": [
     "6 . Now it's time to show the data in a plot. Matplotlib is a very useful tool for this task. Create a scatterplot of all the X and y test data, and use the prediction to draw a line in the most appropriate place, between the model's data groupings."
@@ -308,12 +308,12 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "9e060a07",
+   "id": "3b00ec10",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -337,7 +337,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f4038cef",
+   "id": "16e66b4f",
    "metadata": {},
    "source": [
     "```{seealso}\n",
diff --git a/_sources/prerequisites/python-programming-advanced.ipynb b/_sources/prerequisites/python-programming-advanced.ipynb
index 1cba879068..8a0f4ec610 100644
--- a/_sources/prerequisites/python-programming-advanced.ipynb
+++ b/_sources/prerequisites/python-programming-advanced.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "627cb294",
+   "id": "2cb48220",
    "metadata": {},
    "source": [
     "<details>\n",
@@ -39,7 +39,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "7ab82b86",
+   "id": "4bf66527",
    "metadata": {
     "tags": [
      "hide-input"
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "689e10e6",
+   "id": "f2257fb9",
    "metadata": {},
    "source": [
     "## Control flow\n",
@@ -73,7 +73,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "8731ea0c",
+   "id": "80e32b44",
    "metadata": {},
    "outputs": [
     {
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a89a70d8",
+   "id": "9623cd77",
    "metadata": {},
    "source": [
     "<iframe width=\"800\" height=\"500\" frameborder=\"0\" src=\"https://pythontutor.com/iframe-embed.html#code=def%20get_age_group%28age%29%3A%0A%20%20%20%20if%20age%20%3E%3D%200%20and%20age%20%3C%3D%209%3A%0A%20%20%20%20%20%20%20%20print%28%22You%20are%20a%20child!%22%29%0A%20%20%20%20elif%20age%20%3E%209%20and%20age%20%3C%3D%2018%3A%0A%20%20%20%20%20%20%20%20print%28%22You%20are%20an%20adolescent!%22%29%0A%20%20%20%20elif%20age%20%3E%2018%20and%20age%20%3C%3D%2065%3A%0A%20%20%20%20%20%20%20%20print%28%22You%20are%20an%20adult!%22%29%0A%20%20%20%20else%3A%20%23%20age%20%3E%2065%0A%20%20%20%20%20%20%20%20print%28%22Golden%20ages!%22%29%0A%0Aget_age_group%283%29%0Aget_age_group%2850%29&codeDivHeight=400&codeDivWidth=350&cumulative=false&curInstr=0&heapPrimitives=nevernest&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false\"> </iframe>\n",
@@ -121,7 +121,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "4656f7fe",
+   "id": "6fcdad41",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -136,7 +136,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5fbf8051",
+   "id": "a1c11142",
    "metadata": {},
    "source": [
     "If you need to modify the sequence you are iterating over while inside the loop (for example to duplicate selected items), it is recommended that you first make a copy. Iterating over a sequence does not implicitly make a copy. The slice notation makes this especially convenient:"
@@ -145,7 +145,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "20626f8b",
+   "id": "3a800463",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -156,7 +156,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1f1cfae2",
+   "id": "d334ce61",
    "metadata": {},
    "source": [
     "Otherwise, with `for w in words`, the example would attempt to create an infinite list, inserting defenestrate over and over again."
@@ -165,7 +165,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "dd6ab526",
+   "id": "6f9e5667",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -174,7 +174,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eba00f2a",
+   "id": "1400fdaf",
    "metadata": {},
    "source": [
     "If you do need to iterate over a sequence of numbers, the built-in function `range()` comes in handy. It generates arithmetic progressions:"
@@ -183,7 +183,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "5bbe86f3",
+   "id": "36be6a7f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -197,7 +197,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "134bc615",
+   "id": "1a961c83",
    "metadata": {},
    "source": [
     "Let's see a more complex example about how to find the prims by in an embedded `for` loops."
@@ -206,7 +206,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "1cf68e42",
+   "id": "32bfe97f",
    "metadata": {},
    "outputs": [
     {
@@ -234,7 +234,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "33434440",
+   "id": "77d03750",
    "metadata": {},
    "source": [
     "<iframe width=\"800\" height=\"500\" frameborder=\"0\" src=\"https://pythontutor.com/iframe-embed.html#code=%23%20find%20primes%20using%20a%20for-else%20construct%0Afor%20n%20in%20range%282,%2010%29%3A%0A%20%20%20%20x_range%20%3D%20range%282,%20n%29%0A%20%20%20%20for%20x%20in%20x_range%3A%0A%20%20%20%20%20%20%20%20if%20n%20%25%20x%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20break%0A%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20%23%20loop%20fell%20through%20without%20finding%20a%20factor%0A%20%20%20%20%20%20%20%20print%28n%29&codeDivHeight=400&codeDivWidth=350&cumulative=false&curInstr=30&heapPrimitives=nevernest&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false\"> </iframe>\n",
@@ -245,7 +245,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "c68a6891",
+   "id": "eec229f7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -261,7 +261,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f4074d6f",
+   "id": "e228a59e",
    "metadata": {},
    "source": [
     "Or simply use `enumerate()`."
@@ -270,7 +270,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "88cec80b",
+   "id": "b1d80985",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -284,7 +284,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc82150a",
+   "id": "42e779c2",
    "metadata": {},
    "source": [
     "When looping through dictionaries, the key and corresponding value can be retrieved at the same time using the `items()` method."
@@ -293,7 +293,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "f74445dd",
+   "id": "c14bbdf0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -311,7 +311,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e13251df",
+   "id": "262fb84f",
    "metadata": {},
    "source": [
     "When looping through a sequence, the position index and corresponding value can be retrieved at the same time using the `enumerate()` function"
@@ -320,7 +320,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "b8ffc00b",
+   "id": "0fceaa28",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -336,7 +336,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16096243",
+   "id": "c68d509d",
    "metadata": {},
    "source": [
     "To loop over two or more sequences at the same time, the entries can be paired with the `zip()` function. In many ways, the object returned by `range()` behaves as if it is a list, but in fact, it isn’t. It is an object which returns the successive items of the desired sequence when you iterate over it, but it doesn’t really make the list, thus saving space.\n",
@@ -349,7 +349,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "1dadf35e",
+   "id": "766a8e4b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -361,7 +361,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aae8daab",
+   "id": "6c3d7d69",
    "metadata": {},
    "source": [
     "### The while statement\n",
@@ -381,7 +381,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "a6c57454",
+   "id": "5ef7280a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -399,7 +399,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e8c3d4c9",
+   "id": "b316963b",
    "metadata": {},
    "source": [
     "### The try statement\n",
@@ -423,7 +423,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "4cf695b1",
+   "id": "192c874f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -440,7 +440,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b06f8a50",
+   "id": "32b3f1d1",
    "metadata": {},
    "source": [
     "You can define as many exception blocks as you want, e.g. if you want to execute a special block of code for a special kind of error:"
@@ -449,7 +449,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "e066898a",
+   "id": "eb59f775",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -466,7 +466,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1a6cbe9e",
+   "id": "4a644972",
    "metadata": {},
    "source": [
     "You can use the else keyword to define a block of code to be executed if no errors were raised."
@@ -475,7 +475,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "e6ad65cb",
+   "id": "4d9444e5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -493,7 +493,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d08cc496",
+   "id": "f3baea6a",
    "metadata": {},
    "source": [
     "The final block, if specified, will be executed regardless if the `try` block raises an error or not."
@@ -502,7 +502,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "d39c51ca",
+   "id": "90427a91",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -520,7 +520,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3a4e0318",
+   "id": "831e3181",
    "metadata": {},
    "source": [
     "### The break statement\n",
@@ -535,7 +535,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "d9ab18c2",
+   "id": "7dd7c84e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -557,7 +557,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6706df36",
+   "id": "1734cc46",
    "metadata": {},
    "source": [
     "### The continue statement\n",
@@ -572,7 +572,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "b924579f",
+   "id": "76af261c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -596,7 +596,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "decb829c",
+   "id": "3027cceb",
    "metadata": {},
    "source": [
     "## Functions\n",
@@ -614,7 +614,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "b3167b64",
+   "id": "226c8b86",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -654,7 +654,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "85084ed0",
+   "id": "b215b361",
    "metadata": {},
    "source": [
     "Now call the function we just defined."
@@ -663,7 +663,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "fa4de710",
+   "id": "1a16a10e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -672,7 +672,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6836aee6",
+   "id": "8eb7ec72",
    "metadata": {},
    "source": [
     "A function definition introduces the function name in the current symbol table. The value of the function name has a type that is recognized by the interpreter as a user-defined function. This value can be assigned to another name which can then also be used as a function.\n",
@@ -683,7 +683,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "71c5de52",
+   "id": "1c7cc9b0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -693,7 +693,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18b5b529",
+   "id": "c41b242a",
    "metadata": {},
    "source": [
     "In Python, functions are first-class citizens, they are objects and that means we can do a lot of useful stuff with them.\n",
@@ -704,7 +704,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "ace1b2f5",
+   "id": "3ffebe9d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -718,7 +718,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e9b2634",
+   "id": "9a891494",
    "metadata": {},
    "source": [
     "Define functions inside other functions."
@@ -727,7 +727,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "96348945",
+   "id": "2b13e7c0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -743,7 +743,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a2e90c43",
+   "id": "b2620679",
    "metadata": {},
    "source": [
     "Functions can be passed as parameters to other functions."
@@ -752,7 +752,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "98712504",
+   "id": "620535f8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -768,7 +768,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "83e98755",
+   "id": "81313c1c",
    "metadata": {},
    "source": [
     "Functions can return other functions. In other words, functions generate other functions."
@@ -777,7 +777,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "111b5ffd",
+   "id": "84167b1c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -793,7 +793,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5bafad8e",
+   "id": "87c7779b",
    "metadata": {},
    "source": [
     "Inner functions have access to the enclosing scope.\n",
@@ -804,7 +804,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "620993a1",
+   "id": "3b17a0cc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -821,7 +821,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d4dc8f32",
+   "id": "26d8e0a1",
    "metadata": {},
    "source": [
     "### Scopes of variables inside functions (global and nonlocal statements)\n",
@@ -849,7 +849,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "e8ec25aa",
+   "id": "168acc76",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -859,7 +859,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d09749b5",
+   "id": "623ff24c",
    "metadata": {},
    "source": [
     "This is a scopes and namespaces example demonstrating how to reference the different scopes and namespaces, and how global and nonlocal affect variable binding:"
@@ -868,7 +868,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "745320cb",
+   "id": "7b224df8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -936,7 +936,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db25de17",
+   "id": "3d9f1929",
    "metadata": {},
    "source": [
     "On this example you may see how accessing and changing global variables from within inner functions might make debugging more difficult and code to be less predictable. Since you might have expected that `test_variable` should still be equal to 'initial global value' but it was changed by \"someone\" you need to know about the CONTEXT of who had changed that. So once again access global and non-local scope only if you know what you're doing otherwise it might be considered a bad practice.\n",
@@ -953,7 +953,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "751c8faf",
+   "id": "c89406dc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -966,7 +966,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7cc55749",
+   "id": "15574e48",
    "metadata": {},
    "source": [
     "This function `power_of` can be called in several ways because it has default value for the second argument. First, we may call it omitting the second argument at all."
@@ -975,7 +975,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "1aee652b",
+   "id": "91c16c78",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -984,7 +984,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d89f8cf9",
+   "id": "5eea665c",
    "metadata": {},
    "source": [
     "We may also want to override the second argument by using the following function calls."
@@ -993,7 +993,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "1c330820",
+   "id": "57d4c50c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1003,7 +1003,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ea0b5224",
+   "id": "3ef9ae8f",
    "metadata": {},
    "source": [
     "### Keyword arguments\n",
@@ -1020,7 +1020,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "e200f8d5",
+   "id": "fe724481",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1075,7 +1075,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0f6aa298",
+   "id": "7036524a",
    "metadata": {},
    "source": [
     "But all the following calls would be invalid:\n",
@@ -1124,7 +1124,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "7ab0e45b",
+   "id": "8ffa646e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1137,7 +1137,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e46d1080",
+   "id": "c1109aa1",
    "metadata": {},
    "source": [
     "Normally, these variadic arguments will be last in the list of formal parameters, because they scoop up all remaining input arguments that are passed to the function. Any formal parameters which occur after the `*args` parameter are **keyword-only arguments**, meaning that they can only be used as keywords rather than positional arguments."
@@ -1146,7 +1146,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "b0962eac",
+   "id": "09853a6e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1159,7 +1159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1cb94c16",
+   "id": "91a08ecb",
    "metadata": {},
    "source": [
     "### Unpacking argument lists (* and ** statements)\n",
@@ -1176,7 +1176,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "6e2e2a33",
+   "id": "bdcc7d05",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1197,7 +1197,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "78639383",
+   "id": "7ea5707d",
    "metadata": {},
    "source": [
     "### Lambda expressions (lambda statement)\n",
@@ -1214,7 +1214,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "4fabe29a",
+   "id": "105e243d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1230,7 +1230,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b3a84e12",
+   "id": "103193eb",
    "metadata": {},
    "source": [
     "Another use of lambda is to pass a small function as an argument."
@@ -1239,7 +1239,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "30ef1602",
+   "id": "75118e50",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1252,7 +1252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "85333135",
+   "id": "0f3421a4",
    "metadata": {},
    "source": [
     "### Documentation strings\n",
@@ -1271,7 +1271,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "24b4925f",
+   "id": "dc276cd2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1285,7 +1285,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8cac7ad5",
+   "id": "6c4ad414",
    "metadata": {},
    "source": [
     "The Python parser does not strip indentation from multi-line string literals in Python, so tools that process documentation have to strip indentation if desired. This is done using the following convention. The first non-blank line after the first line of the string determines the amount of indentation for the entire documentation string. (We can’t use the first line since it is generally adjacent to the string’s opening quotes so its indentation is not apparent in the string literal.) Whitespace “equivalent” to this indentation is then stripped from the start of all lines of the string. Lines that are indented less should not occur, but if they occur all their leading whitespace should be stripped. Equivalence of whitespace should be tested after expansion of tabs (to 8 spaces, normally).\n",
@@ -1313,7 +1313,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "0ba9b1dc",
+   "id": "d248b41f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1325,7 +1325,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c27c2edb",
+   "id": "8416b37c",
    "metadata": {},
    "source": [
     "### Function decorators\n",
@@ -1344,7 +1344,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "6416b10e",
+   "id": "fcb29800",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1354,7 +1354,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de72a075",
+   "id": "a66751c7",
    "metadata": {},
    "source": [
     "This function decorates another functions output with `<p>` tag."
@@ -1363,7 +1363,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "75777301",
+   "id": "c79b6588",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1375,7 +1375,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "36e2e71c",
+   "id": "1c91bed6",
    "metadata": {},
    "source": [
     "Now, let's call our decorator and pass the function we want to decorate to it."
@@ -1384,7 +1384,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "af94be36",
+   "id": "5b5cf1d4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1393,7 +1393,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "efc9509b",
+   "id": "927b1544",
    "metadata": {},
    "source": [
     "Here we go, we've just decorated the function output without changing the function itself."
@@ -1402,7 +1402,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "0851fe46",
+   "id": "5327b69c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1412,7 +1412,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a425620f",
+   "id": "4a4eb9ac",
    "metadata": {},
    "source": [
     "Now, Python makes creating and using decorators a bit cleaner and nicer for the programmer through some syntactic sugar. There is a neat shortcut for that, which is to mention the name of the decorating function before the function to be decorated. The name of the decorator should be prepended with an `@` symbol."
@@ -1421,7 +1421,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "8f752fe9",
+   "id": "ee616355",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1434,7 +1434,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a70124b5",
+   "id": "04069218",
    "metadata": {},
    "source": [
     "Now let's consider we wanted to decorate our greeting function with one more function to wrap a `div` in the string output."
@@ -1443,7 +1443,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "9b754728",
+   "id": "1bad7fd5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1456,7 +1456,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e878e2f3",
+   "id": "0d6b25d8",
    "metadata": {},
    "source": [
     "With the basic approach, decorating get_text would be along the lines of `greeting_with_div_p = decorate_with_div(decorate_with_p(greeting_with_p))`\n",
@@ -1467,7 +1467,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "c2ed8aab",
+   "id": "0faa9d1b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1481,7 +1481,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16d041b3",
+   "id": "90f57ea6",
    "metadata": {},
    "source": [
     "One important thing to notice here is that the order of setting our decorators matters. If the order was different in the example above, the output would have been different.\n",
@@ -1494,7 +1494,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "id": "bc259392",
+   "id": "dfe83b45",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1515,7 +1515,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ac3a3e33",
+   "id": "69d4caaf",
    "metadata": {},
    "source": [
     "## Classes\n",
@@ -1534,7 +1534,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "524b2593",
+   "id": "3478a416",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1559,7 +1559,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "96f49688",
+   "id": "53ace611",
    "metadata": {},
    "source": [
     "When a class definition is entered, a new namespace is created, and used as the local scope — thus, all assignments to local variables go into this new namespace. In particular, function definitions bind the name of the new function here.\n",
@@ -1570,7 +1570,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "32414601",
+   "id": "4f2d8f56",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1582,7 +1582,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4dcf71e8",
+   "id": "c1b6463a",
    "metadata": {},
    "source": [
     "### Class objects\n",
@@ -1606,7 +1606,7 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "id": "683b0073",
+   "id": "ca5bcf8b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1627,7 +1627,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ce069a31",
+   "id": "95dd157c",
    "metadata": {},
    "source": [
     " `__doc__` is also a valid attribute, returning the docstring belonging to the class:\n",
@@ -1642,7 +1642,7 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "id": "4e52bf37",
+   "id": "ef46e117",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1652,7 +1652,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18666b1b",
+   "id": "0db4e414",
    "metadata": {},
    "source": [
     "CLASS INSTANTIATION uses function notation. Just pretend that the class object is a parameterless function that returns a new instance of the class. For example (assuming the above class):"
@@ -1661,7 +1661,7 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "id": "6c781513",
+   "id": "c5e7e7c7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1673,7 +1673,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6f36a751",
+   "id": "823fb6df",
    "metadata": {},
    "source": [
     "The instantiation operation (“calling” a class object) creates an empty object. Many classes like to create objects with instances customized to a specific initial state. Therefore a class may define a special method named `__init__()`, like this:"
@@ -1682,7 +1682,7 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "id": "f9ac6377",
+   "id": "0925d346",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1707,7 +1707,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e7cafd8b",
+   "id": "35d09440",
    "metadata": {},
    "source": [
     "### Instance objects\n",
@@ -1727,7 +1727,7 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "id": "6a10f3f4",
+   "id": "5f599524",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1746,7 +1746,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fbed64a1",
+   "id": "36696979",
    "metadata": {},
    "source": [
     "### Methods objects\n",
@@ -1763,7 +1763,7 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "id": "7dc8f4bf",
+   "id": "bf98ac80",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1782,7 +1782,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c55991df",
+   "id": "968eaa5b",
    "metadata": {},
    "source": [
     "A method is a function that “belongs to” an object. (In Python, the term method is not unique to class instances: other object types can have methods as well. For example, list objects have methods called `append`, `insert`, `remove`, `sort`, and so on. However, in the following discussion, we’ll use the term method exclusively to mean methods of class instance objects, unless explicitly stated otherwise.)\n",
@@ -1793,7 +1793,7 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "id": "ab9e1955",
+   "id": "5a107d20",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1803,7 +1803,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c210c940",
+   "id": "081ca8e9",
    "metadata": {},
    "source": [
     "However, it is not necessary to call a method right away: `counter.get_counter()` is a method object, and can be stored away and called at a later time. For example:"
@@ -1812,7 +1812,7 @@
   {
    "cell_type": "code",
    "execution_count": 58,
-   "id": "677d9994",
+   "id": "a3aa71bc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1822,7 +1822,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8a6dbe46",
+   "id": "a9202ec8",
    "metadata": {},
    "source": [
     "But be aware that `counter.get_counter()` is not the same thing as `MyCounter.get_counter()` — it is a method object, not a function object.\n",
@@ -1835,7 +1835,7 @@
   {
    "cell_type": "code",
    "execution_count": 59,
-   "id": "6656cc24",
+   "id": "b43433fe",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1845,7 +1845,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c0f57088",
+   "id": "475d1ad8",
    "metadata": {},
    "source": [
     "### Class and instance variables\n",
@@ -1862,7 +1862,7 @@
   {
    "cell_type": "code",
    "execution_count": 60,
-   "id": "f26c18d1",
+   "id": "eaab06bb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1889,7 +1889,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "75af27c0",
+   "id": "fbae4f1c",
    "metadata": {},
    "source": [
     "Shared data can have possibly surprising effects involving mutable objects such as lists and dictionaries. For example, the tricks list in the following code should not be used as a class variable because just a single list would be shared by all Dog instances.\n",
@@ -1900,7 +1900,7 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "id": "c88032ec",
+   "id": "eedde8df",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1933,7 +1933,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f3afdbb8",
+   "id": "d66f929a",
    "metadata": {},
    "source": [
     "The correct design of the class should use an instance variable instead:"
@@ -1942,7 +1942,7 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "id": "ef0883e8",
+   "id": "a7468fa1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1971,7 +1971,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "462dfaf3",
+   "id": "0a0ef326",
    "metadata": {},
    "source": [
     "### Inheritance\n",
@@ -1988,7 +1988,7 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "id": "096477e1",
+   "id": "0bd9a243",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2004,7 +2004,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "783d7afa",
+   "id": "8599184b",
    "metadata": {},
    "source": [
     "The base class (in our case `Person`) must be defined in a scope containing the derived class definition. In place of a base class name, other arbitrary expressions are also allowed.\n",
@@ -2019,7 +2019,7 @@
   {
    "cell_type": "code",
    "execution_count": 64,
-   "id": "7392ff96",
+   "id": "902cb223",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2038,7 +2038,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "21cc5b5b",
+   "id": "7f61fcc4",
    "metadata": {},
    "source": [
     "There’s nothing special about the instantiation of derived classes: `DerivedClassName()` creates a new instance of the class. Method references are resolved as follows: the corresponding class attribute is searched, descending the chain of base classes if necessary, and the method reference is valid if this yields a function object."
@@ -2047,7 +2047,7 @@
   {
    "cell_type": "code",
    "execution_count": 65,
-   "id": "1684c9dd",
+   "id": "647c72ab",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2061,7 +2061,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a13eb460",
+   "id": "f0207e7c",
    "metadata": {},
    "source": [
     "Python has two built-in functions that work with inheritance:\n",
@@ -2073,7 +2073,7 @@
   {
    "cell_type": "code",
    "execution_count": 66,
-   "id": "42f95f8c",
+   "id": "8b4b136d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2089,7 +2089,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b32025c9",
+   "id": "0a085391",
    "metadata": {},
    "source": [
     "### Multiple inheritance\n",
@@ -2104,7 +2104,7 @@
   {
    "cell_type": "code",
    "execution_count": 67,
-   "id": "d84abcfd",
+   "id": "9d658c5f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2137,7 +2137,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e58ea768",
+   "id": "7063e4a2",
    "metadata": {},
    "source": [
     "Python supports a form of multiple inheritances as well. A class definition with multiple base classes looks like this:"
@@ -2146,7 +2146,7 @@
   {
    "cell_type": "code",
    "execution_count": 68,
-   "id": "4d4d96ce",
+   "id": "1b859be1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2161,7 +2161,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "df800ea8",
+   "id": "15bbf0d3",
    "metadata": {},
    "source": [
     "For most purposes, in the simplest cases, you can think of the search for attributes inherited from a parent class as depth-first, left-to-right, not searching twice in the same class where there is an overlap in the hierarchy. Thus, if an attribute is not found in `CalendarClock`, it is searched for in `Clock`, then (recursively) in the base classes of `Clock`, and if it was not found there, it was searched for in `Calendar`, and so on.\n",
@@ -2204,7 +2204,7 @@
   {
    "cell_type": "code",
    "execution_count": 69,
-   "id": "fe3109ba",
+   "id": "83396c51",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2213,7 +2213,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b8a4cf7b",
+   "id": "dc4aeb61",
    "metadata": {},
    "source": [
     "There is a variant of the import statement that imports names from a module directly into the importing module’s symbol table. For example:"
@@ -2222,7 +2222,7 @@
   {
    "cell_type": "code",
    "execution_count": 70,
-   "id": "486d6dc4",
+   "id": "459700b3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2232,7 +2232,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a3f2d3eb",
+   "id": "2aafec1f",
    "metadata": {},
    "source": [
     "There is even a variant to import all names that a module defines. This imports all names except those beginning with an underscore `_`. In most cases Python programmers do not use this facility since it introduces an unknown set of names into the interpreter, possibly hiding some things you have already defined.\n",
@@ -2243,7 +2243,7 @@
   {
    "cell_type": "code",
    "execution_count": 71,
-   "id": "12c876e7",
+   "id": "4556bae1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2252,7 +2252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "768dda58",
+   "id": "dfbf85c8",
    "metadata": {},
    "source": [
     "It can also be used when utilizing from with similar effects:"
@@ -2261,7 +2261,7 @@
   {
    "cell_type": "code",
    "execution_count": 72,
-   "id": "72c36500",
+   "id": "3347d505",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2270,7 +2270,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e44fdc3d",
+   "id": "0490578b",
    "metadata": {},
    "source": [
     "When a module named spam is imported, the interpreter first searches for a built-in module with that name. If not found, it then searches for a file named spam.py in a list of directories given by the variable `sys.path`. `sys.path` is initialized from these locations:\n",
@@ -2283,7 +2283,7 @@
   {
    "cell_type": "code",
    "execution_count": 73,
-   "id": "0d0eb0ca",
+   "id": "b950bc24",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2299,7 +2299,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b5622a10",
+   "id": "412aa792",
    "metadata": {},
    "source": [
     "If you intend to use a function often you can assign it to a local name."
@@ -2308,7 +2308,7 @@
   {
    "cell_type": "code",
    "execution_count": 74,
-   "id": "a2d42455",
+   "id": "a46c6ad1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2318,7 +2318,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d939214f",
+   "id": "4de37c49",
    "metadata": {},
    "source": [
     "The built-in function `dir()` is used to find out which names a module defines. It returns a sorted list of strings."
@@ -2327,7 +2327,7 @@
   {
    "cell_type": "code",
    "execution_count": 75,
-   "id": "bf30bc71",
+   "id": "8bc003fc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2347,7 +2347,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de95d3c9",
+   "id": "dc8332f5",
    "metadata": {},
    "source": [
     "### Packages\n",
@@ -2362,7 +2362,7 @@
   {
    "cell_type": "code",
    "execution_count": 76,
-   "id": "bb4c06d9",
+   "id": "2b3ea1dc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2371,7 +2371,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "43ae2881",
+   "id": "f359e5a7",
    "metadata": {},
    "source": [
     "An alternative way of importing the submodule is:"
@@ -2380,7 +2380,7 @@
   {
    "cell_type": "code",
    "execution_count": 77,
-   "id": "3859d1a2",
+   "id": "236c6a94",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2390,7 +2390,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "92f3dcd8",
+   "id": "624de547",
    "metadata": {},
    "source": [
     "Yet another variation is to import the desired function or variable directly:"
@@ -2399,7 +2399,7 @@
   {
    "cell_type": "code",
    "execution_count": 78,
-   "id": "0ae03a64",
+   "id": "8d64ae7d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2408,7 +2408,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "776917fc",
+   "id": "2dab110a",
    "metadata": {},
    "source": [
     "Note that when using from package import item, the item can be either a submodule (or subpackage) of the package or some other name defined in the package, like a function, class, or variable.\n",
@@ -2421,7 +2421,7 @@
   {
    "cell_type": "code",
    "execution_count": 79,
-   "id": "0238a106",
+   "id": "36042f13",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2432,7 +2432,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09c846e6",
+   "id": "8ecc1c3b",
    "metadata": {},
    "source": [
     "## Errors and exceptions\n",
@@ -2462,7 +2462,7 @@
   {
    "cell_type": "code",
    "execution_count": 80,
-   "id": "3819f958",
+   "id": "3c7eb940",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2480,7 +2480,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "82232e45",
+   "id": "f7c407f4",
    "metadata": {},
    "source": [
     "Let's simulate an undefined variable access exception."
@@ -2489,7 +2489,7 @@
   {
    "cell_type": "code",
    "execution_count": 81,
-   "id": "ac31101e",
+   "id": "4b042f9a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2508,7 +2508,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e85218ae",
+   "id": "c62f8617",
    "metadata": {},
    "source": [
     "A `try` statement may have more than one `except` clause, to specify handlers for different exceptions. At most one handler will be executed. Handlers only handle exceptions that occur in the corresponding `try` clause, not in other handlers of the same `try` statement. An `except` clause may name multiple exceptions as a parenthesized tuple, for example:"
@@ -2517,7 +2517,7 @@
   {
    "cell_type": "code",
    "execution_count": 82,
-   "id": "0d93647b",
+   "id": "4485d983",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2535,7 +2535,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a71aaf75",
+   "id": "f6dffdd6",
    "metadata": {},
    "source": [
     "Exception handlers may be chained."
@@ -2544,7 +2544,7 @@
   {
    "cell_type": "code",
    "execution_count": 83,
-   "id": "3da425ec",
+   "id": "9e007c1b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2565,7 +2565,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e431612c",
+   "id": "2f1d1750",
    "metadata": {},
    "source": [
     "The `try … except` statement has an optional `else` clause, which, when present, must follow all `except` clauses. It is useful for code that must be executed if the `try` clause does not raise an exception. For example:"
@@ -2574,7 +2574,7 @@
   {
    "cell_type": "code",
    "execution_count": 84,
-   "id": "d66a8b59",
+   "id": "95c35150",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2597,7 +2597,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9a9935b1",
+   "id": "43c069b3",
    "metadata": {},
    "source": [
     "### Raising exceptions (raise statement)\n",
@@ -2614,7 +2614,7 @@
   {
    "cell_type": "code",
    "execution_count": 85,
-   "id": "058b206d",
+   "id": "0a748aee",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2634,7 +2634,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc3fe5f0",
+   "id": "94a640f0",
    "metadata": {},
    "source": [
     "Programs may name their own exceptions by creating a new exception class. Exceptions should typically be derived from the `Exception` class, either directly or indirectly. Most exceptions are defined with names that end in `Error,` similar to the naming of the standard exceptions. Many standard modules define their own exceptions to report errors that may occur in functions they define.\n",
@@ -2645,7 +2645,7 @@
   {
    "cell_type": "code",
    "execution_count": 86,
-   "id": "bac71cfa",
+   "id": "1b28324c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2667,7 +2667,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9aeb035",
+   "id": "25564b84",
    "metadata": {},
    "source": [
     "## Your turn! 🚀\n",
diff --git a/assets/code/prerequisites/__pycache__/fibonacci_module.cpython-39.pyc b/assets/code/prerequisites/__pycache__/fibonacci_module.cpython-39.pyc
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diff --git a/assets/code/prerequisites/sound_package/effects/__pycache__/echo.cpython-39.pyc b/assets/code/prerequisites/sound_package/effects/__pycache__/echo.cpython-39.pyc
index c1a07107c2e6fa47deb60d422e2a731917ae1789..4e23e8076951cae85c03664851737ccc7db2f408 100644
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diff --git a/assignments/README.html b/assignments/README.html
index 7c3513528a..1dbf3fa1c9 100644
--- a/assignments/README.html
+++ b/assignments/README.html
@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../data-science/data-visualization/data-visualization.html">
    6. Data visualization
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
-  <label for="toctree-checkbox-3">
+  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
+  <label for="toctree-checkbox-4">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -284,8 +306,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
-  <label for="toctree-checkbox-4">
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+  <label for="toctree-checkbox-5">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -311,8 +333,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
-  <label for="toctree-checkbox-5">
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    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -355,8 +377,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
-  <label for="toctree-checkbox-6">
+  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
+  <label for="toctree-checkbox-7">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -392,8 +414,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
-  <label for="toctree-checkbox-7">
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+  <label for="toctree-checkbox-8">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -431,8 +453,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../ml-advanced/clustering/clustering-models-for-machine-learning.html">
    14. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
-  <label for="toctree-checkbox-8">
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -453,8 +475,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
    15. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -480,8 +502,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
    16. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -633,8 +655,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="current reference internal" href="#">
    37. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
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   </label>
@@ -825,308 +847,303 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="ml-fundamentals/ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
+     37.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-fundamentals/linear-regression/loss-function.html">
-     37.48. Loss Function
+     37.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-fundamentals/ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
+     37.50. ML logistic regression - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-fundamentals/ml-logistic-regression-2.html">
-     37.52. ML logistic regression - assignment 2
+     37.51. ML logistic regression - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-fundamentals/ml-neural-network-1.html">
-     37.53. ML neural network - Assignment 1
+     37.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-fundamentals/regression-tools.html">
-     37.54. Regression tools
+     37.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-fundamentals/managing-data.html">
-     37.55. Managing data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-fundamentals/exploring-visualizations.html">
-     37.56. Exploring visualizations
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-fundamentals/try-a-different-model.html">
-     37.57. Try a different model
+     37.56. Try a different model
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-fundamentals/create-a-regression-model.html">
-     37.58. Create a regression model
+     37.57. Create a regression model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-fundamentals/linear-and-polynomial-regression.html">
-     37.59. Linear and polynomial regression
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-fundamentals/retrying-some-regression.html">
-     37.60. Retrying some regression
+     37.59. Retrying some regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
+     37.60. Pumpkin varieties and color
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.62. Delicious asian and indian cuisines
+     37.61. Delicious asian and indian cuisines
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-fundamentals/explore-classification-methods.html">
-     37.63. Explore classification methods
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="ml-fundamentals/linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
+     37.62. Explore classification methods
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.65. Kernel method assignment 1
+     37.63. Kernel method assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
+     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.67. Support Vector Machines (SVM) - Classification
+     37.65. Support Vector Machines (SVM) - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.68. Decision Trees - Intro and Regression
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.69. Decision Trees - Classification
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.70. Model selection assignment 1
+     37.68. Model selection assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.71. Learning Curve To Identify Overfit &amp; Underfit
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.72. Dropout and Batch Normalization
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.73. Lasso and Ridge Regression
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-advanced/model-selection/regularized-linear-models.html">
-     37.74. Regularized Linear Models
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-advanced/ensemble-learning/random-forests-for-classification.html">
-     37.76. Random forests for classification
+     37.74. Random forests for classification
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-advanced/ensemble-learning/decision-trees.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
-     37.79. Hyperparameter tuning gradient boosting
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
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     <a class="reference internal" href="ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
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     <a class="reference internal" href="machine-learning-productionization/data-engineering.html">
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     <a class="reference internal" href="machine-learning-productionization/debugging-in-classification.html">
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     <a class="reference internal" href="deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="deep-learning/cnn/object-recognition-in-images-using-cnn.html">
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-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
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     <a class="reference internal" href="deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
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      5.3. NumPy
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    </li>
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+    <label for="toctree-checkbox-3">
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/gradient-descent.html">
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+    <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
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      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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      5.3. NumPy
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    </li>
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+    <label for="toctree-checkbox-3">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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+    <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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      5.3. NumPy
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    </li>
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+    <label for="toctree-checkbox-3">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/gradient-descent.html">
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+    <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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+   <li class="toctree-l2 has-children">
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      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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+   <li class="toctree-l2 has-children">
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      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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+    <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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     <a class="reference internal" href="../deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
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     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
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     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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index f70b753361..1e2ad6c3ae 100644
--- a/assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html
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      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+    <label for="toctree-checkbox-3">
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/gradient-descent.html">
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+     37.48. Gradient descent
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+    <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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      5.3. NumPy
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    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+    <label for="toctree-checkbox-3">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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+    <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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      5.3. NumPy
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    </li>
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+    <label for="toctree-checkbox-3">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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+    <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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+   <li class="toctree-l2 has-children">
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      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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+    <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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+   <li class="toctree-l2 has-children">
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      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
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      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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+   <li class="toctree-l2 has-children">
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      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
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    <li class="toctree-l2">
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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     <a class="reference internal" href="../deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
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     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
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     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
-     37.117. Generative Adversarial Networks (GANs)
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     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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index d9e4f0d82e..5f07473c71 100644
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      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
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+    <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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+   <li class="toctree-l2 has-children">
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      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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+   <li class="toctree-l2 has-children">
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      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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      5.3. NumPy
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-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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+    <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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      5.3. NumPy
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    </li>
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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+    <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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   <section class="tex2jax_ignore mathjax_ignore" id="intro-to-autoencoders">
-<h1><span class="section-number">37.103. </span>Intro to Autoencoders<a class="headerlink" href="#intro-to-autoencoders" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.101. </span>Intro to Autoencoders<a class="headerlink" href="#intro-to-autoencoders" title="Permalink to this headline">#</a></h1>
 <p>This tutorial introduces autoencoders with three examples: the basics, image denoising, and anomaly detection.</p>
 <p>An autoencoder is a special type of neural network that is trained to copy its input to its output. For example, given an image of a handwritten digit, an autoencoder first encodes the image into a lower dimensional latent representation, then decodes the latent representation back to an image. An autoencoder learns to compress the data while minimizing the reconstruction error.</p>
 <p>To learn more about autoencoders, please consider reading chapter 14 from <a class="reference external" href="https://www.deeplearningbook.org/">Deep Learning</a> by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.</p>
 <section id="import-tensorflow-and-other-libraries">
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+<h2><span class="section-number">37.101.1. </span>Import TensorFlow and other libraries<a class="headerlink" href="#import-tensorflow-and-other-libraries" title="Permalink to this headline">#</a></h2>
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+<h2><span class="section-number">37.101.3. </span>Load the dataset<a class="headerlink" href="#load-the-dataset" title="Permalink to this headline">#</a></h2>
 <p>To start, you will train the basic autoencoder using the Fashion MNIST dataset. Each image in this dataset is 28x28 pixels.</p>
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 <p><img alt="Basic autoencoder results" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/autoencoder/autoencoder/intro_autoencoder_result.png" /></p>
 <p>Define an autoencoder with two Dense layers: an <code class="docutils literal notranslate"><span class="pre">encoder</span></code>, which compresses the images into a 64 dimensional latent vector, and a <code class="docutils literal notranslate"><span class="pre">decoder</span></code>, that reconstructs the original image from the latent space.</p>
 <p>To define your model, use the <a class="reference external" href="https://www.tensorflow.org/guide/keras/custom_layers_and_models">Keras Model Subclassing API</a>.</p>
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+<h2><span class="section-number">37.101.5. </span>Second example: Image denoising<a class="headerlink" href="#second-example-image-denoising" title="Permalink to this headline">#</a></h2>
 <p><img alt="Image denoising results" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/autoencoder/autoencoder/image_denoise_fmnist_results.png" /></p>
 <p>An autoencoder can also be trained to remove noise from images. In the following section, you will create a noisy version of the Fashion MNIST dataset by applying random noise to each image. You will then train an autoencoder using the noisy image as input, and the original image as the target.</p>
 <p>Let’s reimport the dataset to omit the modifications made earlier.</p>
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+<h3><span class="section-number">37.101.5.1. </span>Define a convolutional autoencoder<a class="headerlink" href="#define-a-convolutional-autoencoder" title="Permalink to this headline">#</a></h3>
 <p>In this example, you will train a convolutional autoencoder using  <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D">Conv2D</a> layers in the <code class="docutils literal notranslate"><span class="pre">encoder</span></code>, and <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2DTranspose">Conv2DTranspose</a> layers in the <code class="docutils literal notranslate"><span class="pre">decoder</span></code>.</p>
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 <p>In this example, you will train an autoencoder to detect anomalies on the <a class="reference external" href="http://www.timeseriesclassification.com/description.php?Dataset=ECG5000">ECG5000 dataset</a>. This dataset contains 5,000 <a class="reference external" href="https://en.wikipedia.org/wiki/Electrocardiography">Electrocardiograms</a>, each with 140 data points. You will use a simplified version of the dataset, where each example has been labeled either <code class="docutils literal notranslate"><span class="pre">0</span></code> (corresponding to an abnormal rhythm), or <code class="docutils literal notranslate"><span class="pre">1</span></code> (corresponding to a normal rhythm). You are interested in identifying the abnormal rhythms.</p>
 <p>Note: This is a labeled dataset, so you could phrase this as a supervised learning problem. The goal of this example is to illustrate anomaly detection concepts you can apply to larger datasets, where you do not have labels available (for example, if you had many thousands of normal rhythms, and only a small number of abnormal rhythms).</p>
 <p>How will you detect anomalies using an autoencoder? Recall that an autoencoder is trained to minimize reconstruction error. You will train an autoencoder on the normal rhythms only, then use it to reconstruct all the data. Our hypothesis is that the abnormal rhythms will have higher reconstruction error. You will then classify a rhythm as an anomaly if the reconstruction error surpasses a fixed threshold.</p>
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+<h3><span class="section-number">37.101.7.1. </span>Load ECG data<a class="headerlink" href="#load-ecg-data" title="Permalink to this headline">#</a></h3>
 <p>The dataset you will use is based on one from <a class="reference external" href="http://www.timeseriesclassification.com/description.php?Dataset=ECG5000">timeseriesclassification.com</a>.</p>
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+<h3><span class="section-number">37.101.7.3. </span>Detect anomalies<a class="headerlink" href="#detect-anomalies" title="Permalink to this headline">#</a></h3>
 <p>Detect anomalies by calculating whether the reconstruction loss is greater than a fixed threshold. In this tutorial, you will calculate the mean average error for normal examples from the training set, then classify future examples as anomalous if the reconstruction error is higher than one standard deviation from the training set.</p>
 <p>Plot the reconstruction error on normal ECGs from the training set</p>
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-<h2><span class="section-number">37.103.8. </span>Next steps<a class="headerlink" href="#next-steps" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.101.8. </span>Next steps<a class="headerlink" href="#next-steps" title="Permalink to this headline">#</a></h2>
 <p>To learn more about anomaly detection with autoencoders, check out this excellent <a class="reference external" href="https://anomagram.fastforwardlabs.com/#/">interactive example</a> built with TensorFlow.js by Victor Dibia. For a real-world use case, you can learn how <a class="reference external" href="https://blog.tensorflow.org/2020/04/how-airbus-detects-anomalies-iss-telemetry-data-tfx.html">Airbus Detects Anomalies in ISS Telemetry Data</a> using TensorFlow. To learn more about the basics, consider reading this <a class="reference external" href="https://blog.keras.io/building-autoencoders-in-keras.html">blog post</a> by François Chollet. For more details, check out chapter 14 from <a class="reference external" href="https://www.deeplearningbook.org/">Deep Learning</a> by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.103.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.101.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to TensorFlow Core for creating the open-source course <a class="reference external" href="https://www.tensorflow.org/tutorials/generative/autoencoder">autoencoder</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2686,13 +2703,13 @@ <h2><span class="section-number">37.103.9. </span>Acknowledgments<a class="heade
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+            <p class="prev-next-title"><span class="section-number">37.99. </span>Google Stock Price Prediction RNN</p>
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
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    <li class="toctree-l2">
     <a class="reference internal" href="../tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.97. Intro to TensorFlow for Deep Learning
+     37.95. Intro to TensorFlow for Deep Learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../rnn/google-stock-price-prediction-rnn.html">
-     37.101. Google Stock Price Prediction RNN
+     37.99. Google Stock Price Prediction RNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="autoencoder.html">
-     37.103. Intro to Autoencoders
+     37.101. Intro to Autoencoders
     </a>
    </li>
    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
+     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
+     37.103. Fun with Variational Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
+     37.104. Time Series Forecasting Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../nn-for-classification-assignment.html">
-     37.108. Neural Networks for Classification with TensorFlow
+     37.106. Neural Networks for Classification with TensorFlow
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../nn-classify-15-fruits-assignment.html">
-     37.109. NN Classify 15 Fruits Assignment
+     37.107. NN Classify 15 Fruits Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../dqn/dqn-on-foreign-exchange-market.html">
-     37.114. DQN On Foreign Exchange Market
+     37.112. DQN On Foreign Exchange Market
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../gan/art-by-gan.html">
-     37.115. Art by gan
+     37.113. Art by gan
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../gan/gan-introduction.html">
-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../overview/basic-classification-classify-images-of-clothing.html">
-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
     </a>
    </li>
   </ul>
@@ -1137,8 +1154,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../slides/introduction.html">
    38. Slides
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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@@ -1511,110 +1528,110 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   37.104.1. Introduction
+   37.102.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loading-and-scaling-datasets">
-   37.104.2. Loading and Scaling Datasets
+   37.102.2. Loading and Scaling Datasets
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#load-model">
-   37.104.3. Load model
+   37.102.3. Load model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-dataset-by-2d-plot">
-     37.104.3.1. Checking dataset by 2D plot
+     37.102.3.1. Checking dataset by 2D plot
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-dataset-by-3d-plot">
-     37.104.3.2. Checking dataset by 3D plot
+     37.102.3.2. Checking dataset by 3D plot
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#base-autoencoder">
-   37.104.4. Base Autoencoder
+   37.102.4. Base Autoencoder
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#modeling">
-     37.104.4.1. Modeling
+     37.102.4.1. Modeling
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#training">
-     37.104.4.2. Training
+     37.102.4.2. Training
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#plotting-the-latent-space-after-dimension-reduction">
-     37.104.4.3. Plotting the latent space after Dimension Reduction
+     37.102.4.3. Plotting the latent space after Dimension Reduction
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-results">
-     37.104.4.4. Checking results
+     37.102.4.4. Checking results
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#denoising-autoencoder">
-   37.104.5. Denoising Autoencoder
+   37.102.5. Denoising Autoencoder
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#adding-random-noise-to-the-image">
-     37.104.5.1. Adding random noise to the image.
+     37.102.5.1. Adding random noise to the image.
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#plotting-a-noisy-image">
-     37.104.5.2. Plotting a noisy image.
+     37.102.5.2. Plotting a noisy image.
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-noisy-dataset-using-demension-reduction">
-     37.104.5.3. Checking Noisy Dataset using Demension Reduction
+     37.102.5.3. Checking Noisy Dataset using Demension Reduction
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#noisy-dataset">
-       37.104.5.3.1. 1) Noisy Dataset
+       37.102.5.3.1. 1) Noisy Dataset
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#orignal-dataset">
-       37.104.5.3.2. 2) Orignal Dataset
+       37.102.5.3.2. 2) Orignal Dataset
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     37.104.5.4. Modeling
+     37.102.5.4. Modeling
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id2">
-     37.104.5.5. Training
+     37.102.5.5. Training
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id3">
-     37.104.5.6. Checking results
+     37.102.5.6. Checking results
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.104.6. Acknowledgments
+   37.102.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1638,110 +1655,110 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   37.104.1. Introduction
+   37.102.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loading-and-scaling-datasets">
-   37.104.2. Loading and Scaling Datasets
+   37.102.2. Loading and Scaling Datasets
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#load-model">
-   37.104.3. Load model
+   37.102.3. Load model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-dataset-by-2d-plot">
-     37.104.3.1. Checking dataset by 2D plot
+     37.102.3.1. Checking dataset by 2D plot
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-dataset-by-3d-plot">
-     37.104.3.2. Checking dataset by 3D plot
+     37.102.3.2. Checking dataset by 3D plot
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#base-autoencoder">
-   37.104.4. Base Autoencoder
+   37.102.4. Base Autoencoder
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#modeling">
-     37.104.4.1. Modeling
+     37.102.4.1. Modeling
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#training">
-     37.104.4.2. Training
+     37.102.4.2. Training
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#plotting-the-latent-space-after-dimension-reduction">
-     37.104.4.3. Plotting the latent space after Dimension Reduction
+     37.102.4.3. Plotting the latent space after Dimension Reduction
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-results">
-     37.104.4.4. Checking results
+     37.102.4.4. Checking results
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#denoising-autoencoder">
-   37.104.5. Denoising Autoencoder
+   37.102.5. Denoising Autoencoder
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#adding-random-noise-to-the-image">
-     37.104.5.1. Adding random noise to the image.
+     37.102.5.1. Adding random noise to the image.
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#plotting-a-noisy-image">
-     37.104.5.2. Plotting a noisy image.
+     37.102.5.2. Plotting a noisy image.
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-noisy-dataset-using-demension-reduction">
-     37.104.5.3. Checking Noisy Dataset using Demension Reduction
+     37.102.5.3. Checking Noisy Dataset using Demension Reduction
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#noisy-dataset">
-       37.104.5.3.1. 1) Noisy Dataset
+       37.102.5.3.1. 1) Noisy Dataset
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#orignal-dataset">
-       37.104.5.3.2. 2) Orignal Dataset
+       37.102.5.3.2. 2) Orignal Dataset
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     37.104.5.4. Modeling
+     37.102.5.4. Modeling
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id2">
-     37.104.5.5. Training
+     37.102.5.5. Training
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id3">
-     37.104.5.6. Checking results
+     37.102.5.6. Checking results
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.104.6. Acknowledgments
+   37.102.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1755,9 +1772,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="base-denoising-autoencoder-dimension-reduction">
-<h1><span class="section-number">37.104. </span>Base/Denoising Autoencoder &amp; Dimension Reduction<a class="headerlink" href="#base-denoising-autoencoder-dimension-reduction" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.102. </span>Base/Denoising Autoencoder &amp; Dimension Reduction<a class="headerlink" href="#base-denoising-autoencoder-dimension-reduction" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">37.104.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.102.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction/base_noise.gif" /></p>
 <p>Autoencoder is a neural network that simply copies input to output. In some ways, it looks like a simple neural network, but it makes a difficult neural network by constraining the network in various ways. For example, the number of neurons in the hidden layer is smaller than that of the input layer to compress the data (reduce the dimension), or add noise to the input data and then restore the original input. There are various autoencoders, such as learning These constraints prevent the autoencoder from simply copying the input directly to the output, and control it to learn how to represent the data efficiently.</p>
 <p>In this notebook, we will cover two autoencoders:</p>
@@ -1784,7 +1801,7 @@ <h2><span class="section-number">37.104.1. </span>Introduction<a class="headerli
 </div>
 </section>
 <section id="loading-and-scaling-datasets">
-<h2><span class="section-number">37.104.2. </span>Loading and Scaling Datasets<a class="headerlink" href="#loading-and-scaling-datasets" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.102.2. </span>Loading and Scaling Datasets<a class="headerlink" href="#loading-and-scaling-datasets" title="Permalink to this headline">#</a></h2>
 <p>Train a basic autoencoder using the Fashon MNIST dataset. Each image in this dataset is 28x28 pixels.
 Inputs are scaled for training.</p>
 <div class="cell docutils container">
@@ -1802,7 +1819,7 @@ <h2><span class="section-number">37.104.2. </span>Loading and Scaling Datasets<a
 </div>
 </section>
 <section id="load-model">
-<h2><span class="section-number">37.104.3. </span>Load model<a class="headerlink" href="#load-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.102.3. </span>Load model<a class="headerlink" href="#load-model" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
@@ -1858,7 +1875,7 @@ <h2><span class="section-number">37.104.3. </span>Load model<a class="headerlink
 </div>
 </div>
 <section id="checking-dataset-by-2d-plot">
-<h3><span class="section-number">37.104.3.1. </span>Checking dataset by 2D plot<a class="headerlink" href="#checking-dataset-by-2d-plot" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.102.3.1. </span>Checking dataset by 2D plot<a class="headerlink" href="#checking-dataset-by-2d-plot" title="Permalink to this headline">#</a></h3>
 <p>Autoencoding can be thought of as a kind of dimensionality reduction process. Therefore, after compressing the fashion MNIST dataset through UMAP in two dimensions, let’s check how it is mapped for each label.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1906,7 +1923,7 @@ <h3><span class="section-number">37.104.3.1. </span>Checking dataset by 2D plot<
 <p>Ref: <a class="reference external" href="https://umap-learn.readthedocs.io/en">https://umap-learn.readthedocs.io/en</a></p>
 </section>
 <section id="checking-dataset-by-3d-plot">
-<h3><span class="section-number">37.104.3.2. </span>Checking dataset by 3D plot<a class="headerlink" href="#checking-dataset-by-3d-plot" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.102.3.2. </span>Checking dataset by 3D plot<a class="headerlink" href="#checking-dataset-by-3d-plot" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">plotly</span>
@@ -1932,9 +1949,9 @@ <h3><span class="section-number">37.104.3.2. </span>Checking dataset by 3D plot<
 </section>
 </section>
 <section id="base-autoencoder">
-<h2><span class="section-number">37.104.4. </span>Base Autoencoder<a class="headerlink" href="#base-autoencoder" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.102.4. </span>Base Autoencoder<a class="headerlink" href="#base-autoencoder" title="Permalink to this headline">#</a></h2>
 <section id="modeling">
-<h3><span class="section-number">37.104.4.1. </span>Modeling<a class="headerlink" href="#modeling" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.102.4.1. </span>Modeling<a class="headerlink" href="#modeling" title="Permalink to this headline">#</a></h3>
 <p>An autoencoder always consists of two parts: an encoder and a decoder.</p>
 <ul class="simple">
 <li><p>Encoder (Recognition network): it transforms an input into an internal representation.</p></li>
@@ -1978,7 +1995,7 @@ <h3><span class="section-number">37.104.4.1. </span>Modeling<a class="headerlink
 </div>
 </section>
 <section id="training">
-<h3><span class="section-number">37.104.4.2. </span>Training<a class="headerlink" href="#training" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.102.4.2. </span>Training<a class="headerlink" href="#training" title="Permalink to this headline">#</a></h3>
 <p>Train the model using x_train as input and target. The encoder learns to compress the dataset into a latent space in 784 dimensions, and the decoder learns to reconstruct the original image.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2009,7 +2026,7 @@ <h3><span class="section-number">37.104.4.2. </span>Training<a class="headerlink
 </div>
 </section>
 <section id="plotting-the-latent-space-after-dimension-reduction">
-<h3><span class="section-number">37.104.4.3. </span>Plotting the latent space after Dimension Reduction<a class="headerlink" href="#plotting-the-latent-space-after-dimension-reduction" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.102.4.3. </span>Plotting the latent space after Dimension Reduction<a class="headerlink" href="#plotting-the-latent-space-after-dimension-reduction" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_test</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;class&#39;</span><span class="p">])</span>
@@ -2029,7 +2046,7 @@ <h3><span class="section-number">37.104.4.3. </span>Plotting the latent space af
 <p>The <span class="math notranslate nohighlight">\(28*28\)</span> dimension input is compressed into the <span class="math notranslate nohighlight">\(7*7\)</span> latent space by the encoder. The latent space is compressed into 2D using Dimension Reduction. Although it is an approximate expression, it can be seen that each class is well clustered in the compressed latent space.</p>
 </section>
 <section id="checking-results">
-<h3><span class="section-number">37.104.4.4. </span>Checking results<a class="headerlink" href="#checking-results" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.102.4.4. </span>Checking results<a class="headerlink" href="#checking-results" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">n</span> <span class="o">=</span> <span class="mi">4</span>
@@ -2059,7 +2076,7 @@ <h3><span class="section-number">37.104.4.4. </span>Checking results<a class="he
 </section>
 </section>
 <section id="denoising-autoencoder">
-<h2><span class="section-number">37.104.5. </span>Denoising Autoencoder<a class="headerlink" href="#denoising-autoencoder" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.102.5. </span>Denoising Autoencoder<a class="headerlink" href="#denoising-autoencoder" title="Permalink to this headline">#</a></h2>
 <p>Another way to constrain the autoencoder to learn meaningful features is to add noise to the input and train it to reconstruct the original noise-free input. Noise can be generated by adding Gaussian noise to the input as shown in the figure below, or by randomly turning off the input unit (node) like a dropout.</p>
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@@ -2082,7 +2099,7 @@ <h2><span class="section-number">37.104.5. </span>Denoising Autoencoder<a class=
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 </div>
 <section id="adding-random-noise-to-the-image">
-<h3><span class="section-number">37.104.5.1. </span>Adding random noise to the image.<a class="headerlink" href="#adding-random-noise-to-the-image" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.102.5.1. </span>Adding random noise to the image.<a class="headerlink" href="#adding-random-noise-to-the-image" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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@@ -2097,7 +2114,7 @@ <h3><span class="section-number">37.104.5.1. </span>Adding random noise to the i
 </div>
 </section>
 <section id="plotting-a-noisy-image">
-<h3><span class="section-number">37.104.5.2. </span>Plotting a noisy image.<a class="headerlink" href="#plotting-a-noisy-image" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.102.5.2. </span>Plotting a noisy image.<a class="headerlink" href="#plotting-a-noisy-image" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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@@ -2114,9 +2131,9 @@ <h3><span class="section-number">37.104.5.2. </span>Plotting a noisy image.<a cl
 </div>
 </section>
 <section id="checking-noisy-dataset-using-demension-reduction">
-<h3><span class="section-number">37.104.5.3. </span>Checking Noisy Dataset using Demension Reduction<a class="headerlink" href="#checking-noisy-dataset-using-demension-reduction" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.102.5.3. </span>Checking Noisy Dataset using Demension Reduction<a class="headerlink" href="#checking-noisy-dataset-using-demension-reduction" title="Permalink to this headline">#</a></h3>
 <section id="noisy-dataset">
-<h4><span class="section-number">37.104.5.3.1. </span>1) Noisy Dataset<a class="headerlink" href="#noisy-dataset" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.102.5.3.1. </span>1) Noisy Dataset<a class="headerlink" href="#noisy-dataset" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train_noisy_flat</span> <span class="o">=</span> <span class="n">x_train</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x_train_noisy</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
@@ -2142,7 +2159,7 @@ <h4><span class="section-number">37.104.5.3.1. </span>1) Noisy Dataset<a class="
 </div>
 </section>
 <section id="orignal-dataset">
-<h4><span class="section-number">37.104.5.3.2. </span>2) Orignal Dataset<a class="headerlink" href="#orignal-dataset" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.102.5.3.2. </span>2) Orignal Dataset<a class="headerlink" href="#orignal-dataset" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train_flat</span> <span class="o">=</span> <span class="n">x_train</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
@@ -2165,7 +2182,7 @@ <h4><span class="section-number">37.104.5.3.2. </span>2) Orignal Dataset<a class
 </section>
 </section>
 <section id="id1">
-<h3><span class="section-number">37.104.5.4. </span>Modeling<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.102.5.4. </span>Modeling<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Denoise</span><span class="p">(</span><span class="n">Model</span><span class="p">):</span>
@@ -2200,7 +2217,7 @@ <h3><span class="section-number">37.104.5.4. </span>Modeling<a class="headerlink
 </div>
 </section>
 <section id="id2">
-<h3><span class="section-number">37.104.5.5. </span>Training<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.102.5.5. </span>Training<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">autoencoder</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train_noisy</span><span class="p">,</span> <span class="n">x_train</span><span class="p">,</span>
@@ -2229,7 +2246,7 @@ <h3><span class="section-number">37.104.5.5. </span>Training<a class="headerlink
 </div>
 </section>
 <section id="id3">
-<h3><span class="section-number">37.104.5.6. </span>Checking results<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.102.5.6. </span>Checking results<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
 <p>Plots both the noisy and denoised images generated by the autoencoder.</p>
 <div class="cell docutils container">
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@@ -2268,7 +2285,7 @@ <h3><span class="section-number">37.104.5.6. </span>Checking results<a class="he
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.104.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.102.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to TOH SEOK KIM for creating the Kaggle open-source project <a class="reference external" href="https://www.kaggle.com/code/ohseokkim/base-denoising-autoencoder-dimension-reduction">Base/Denoising Autoencoder + Dimension Reduction</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2310,13 +2327,13 @@ <h2><span class="section-number">37.104.6. </span>Acknowledgments<a class="heade
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.103. </span>Intro to Autoencoders</p>
+            <p class="prev-next-title"><span class="section-number">37.101. </span>Intro to Autoencoders</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="variational-autoencoder-and-faces-generation.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">37.105. </span>Fun with Variational Autoencoders</p>
+        <p class="prev-next-title"><span class="section-number">37.103. </span>Fun with Variational Autoencoders</p>
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diff --git a/assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html b/assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html
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+++ b/assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html
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-    <title>37.105. Fun with Variational Autoencoders &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.103. Fun with Variational Autoencoders &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.106. Time Series Forecasting Assignment" href="../time-series-forecasting-assignment.html" />
-    <link rel="prev" title="37.104. Base/Denoising Autoencoder &amp; Dimension Reduction" href="base-denoising-autoencoder-dimension-reduction.html" />
+    <link rel="next" title="37.104. Time Series Forecasting Assignment" href="../time-series-forecasting-assignment.html" />
+    <link rel="prev" title="37.102. Base/Denoising Autoencoder &amp; Dimension Reduction" href="base-denoising-autoencoder-dimension-reduction.html" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
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   <a class="reference internal" href="../../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
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   <a class="reference internal" href="../../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
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   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
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   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
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-  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
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    16. Introduction to Gradient Boosting
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-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
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    37. Self-paced assignments
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-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-1.html">
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-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
+     37.46. Linear Regression Metrics
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/loss-function.html">
-     37.48. Loss Function
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
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     <a class="reference internal" href="../../ml-fundamentals/ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
+     37.50. ML logistic regression - assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/ml-logistic-regression-2.html">
-     37.52. ML logistic regression - assignment 2
+     37.51. ML logistic regression - assignment 2
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-     37.53. ML neural network - Assignment 1
+     37.52. ML neural network - Assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression-tools.html">
-     37.54. Regression tools
+     37.53. Regression tools
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    </li>
    <li class="toctree-l2">
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-     37.55. Managing data
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     <a class="reference internal" href="../../ml-fundamentals/exploring-visualizations.html">
-     37.56. Exploring visualizations
+     37.55. Exploring visualizations
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     <a class="reference internal" href="../../ml-fundamentals/try-a-different-model.html">
-     37.57. Try a different model
+     37.56. Try a different model
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+     37.57. Create a regression model
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     <a class="reference internal" href="../../ml-fundamentals/linear-and-polynomial-regression.html">
-     37.59. Linear and polynomial regression
+     37.58. Linear and polynomial regression
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     <a class="reference internal" href="../../ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
+     37.60. Pumpkin varieties and color
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     <a class="reference internal" href="../../ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.62. Delicious asian and indian cuisines
+     37.61. Delicious asian and indian cuisines
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     <a class="reference internal" href="../../ml-fundamentals/explore-classification-methods.html">
-     37.63. Explore classification methods
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-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
+     37.62. Explore classification methods
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     <a class="reference internal" href="../../ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.65. Kernel method assignment 1
+     37.63. Kernel method assignment 1
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     <a class="reference internal" href="../../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
+     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.105. Fun with Variational Autoencoders
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-     37.108. Neural Networks for Classification with TensorFlow
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-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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+   37.103.2. A bit of theory
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+   37.103.4. Explore the data
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-   37.105.7. Train autoencoder
+   37.103.7. Train autoencoder
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+   37.103.9. Adding smile and glasses
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 </ul>
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               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="fun-with-variational-autoencoders">
-<h1><span class="section-number">37.105. </span>Fun with Variational Autoencoders<a class="headerlink" href="#fun-with-variational-autoencoders" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.103. </span>Fun with Variational Autoencoders<a class="headerlink" href="#fun-with-variational-autoencoders" title="Permalink to this headline">#</a></h1>
 <p>This is a starter kernel to use <strong>Labelled Faces in the Wild (LFW) Dataset</strong> in order to maintain knowledge about main Autoencoder principles. PyTorch will be used for modelling.</p>
 <section id="fork-it-and-give-it-an-upvote">
-<h2><span class="section-number">37.105.1. </span>Fork it and give it an upvote.<a class="headerlink" href="#fork-it-and-give-it-an-upvote" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.103.1. </span>Fork it and give it an upvote.<a class="headerlink" href="#fork-it-and-give-it-an-upvote" title="Permalink to this headline">#</a></h2>
 <p><img alt="architecture" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/autoencoder/variational-autoencoder-and-faces-generation/architecture.png" /></p>
 <p>Useful links:</p>
 <ul class="simple">
@@ -1673,7 +1690,7 @@ <h2><span class="section-number">37.105.1. </span>Fork it and give it an upvote.
 </ul>
 </section>
 <section id="a-bit-of-theory">
-<h2><span class="section-number">37.105.2. </span>A bit of theory<a class="headerlink" href="#a-bit-of-theory" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.103.2. </span>A bit of theory<a class="headerlink" href="#a-bit-of-theory" title="Permalink to this headline">#</a></h2>
 <p>“Autoencoding” is a data compression algorithm where the compression and decompression functions are 1) data-specific, 2) lossy, and 3) learned automatically from examples rather than engineered by a human. Additionally, in almost all contexts where the term “autoencoder” is used, the compression and decompression functions are implemented with neural networks.</p>
 <ol class="simple">
 <li><p>Autoencoders are data-specific, which means that they will only be able to compress data similar to what they have been trained on. This is different from, say, the MPEG-2 Audio Layer III (MP3) compression algorithm, which only holds assumptions about “sound” in general, but not about specific types of sounds. An autoencoder trained on pictures of faces would do a rather poor job of compressing pictures of trees, because the features it would learn would be face-specific.</p></li>
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 <section id="load-datasets">
-<h2><span class="section-number">37.105.3. </span>Load datasets<a class="headerlink" href="#load-datasets" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.103.3. </span>Load datasets<a class="headerlink" href="#load-datasets" title="Permalink to this headline">#</a></h2>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">datasets_url</span> <span class="o">=</span> <span class="s2">&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/autoencoder/variational-autoencoder-and-faces-generation/lfw-deepfunneled.zip&quot;</span>
@@ -1791,7 +1808,7 @@ <h2><span class="section-number">37.105.3. </span>Load datasets<a class="headerl
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-<h2><span class="section-number">37.105.4. </span>Explore the data<a class="headerlink" href="#explore-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.103.4. </span>Explore the data<a class="headerlink" href="#explore-the-data" title="Permalink to this headline">#</a></h2>
 <p>Image data is collected from DATASET_PATH and a dataset is created in which the person information for each image is extracted and used for subsequent data analysis or processing. Finally, the filter() function is used to limit the size of the dataset by retaining only information about people who appear less than 25 times.</p>
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-<h2><span class="section-number">37.105.5. </span>Prepare the dataset<a class="headerlink" href="#prepare-the-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.103.5. </span>Prepare the dataset<a class="headerlink" href="#prepare-the-dataset" title="Permalink to this headline">#</a></h2>
 <blockquote>
 <div><ul class="simple">
 <li><p>Reads the attribute data from the txt file at the specified path and stores it in a DataFrame object called df_attrs. The txt file is tab delimited, skipping the first line.</p></li>
@@ -1919,7 +1936,7 @@ <h2><span class="section-number">37.105.5. </span>Prepare the dataset<a class="h
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-<h2><span class="section-number">37.105.6. </span>Building simple autoencoder<a class="headerlink" href="#building-simple-autoencoder" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.103.6. </span>Building simple autoencoder<a class="headerlink" href="#building-simple-autoencoder" title="Permalink to this headline">#</a></h2>
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-<h2><span class="section-number">37.105.7. </span>Train autoencoder<a class="headerlink" href="#train-autoencoder" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.103.7. </span>Train autoencoder<a class="headerlink" href="#train-autoencoder" title="Permalink to this headline">#</a></h2>
 <blockquote>
 <div><ul class="simple">
 <li><p>get_batch: It uses the Generator method to generate batches of a specified size by iterating over them. The amount of data generated is batch_size each time, until the entire data set is traversed.</p></li>
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 <section id="sampling">
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+<h2><span class="section-number">37.103.8. </span>Sampling<a class="headerlink" href="#sampling" title="Permalink to this headline">#</a></h2>
 <p>Let’s generate some samples from random vectors</p>
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 <section id="adding-smile-and-glasses">
-<h2><span class="section-number">37.105.9. </span>Adding smile and glasses<a class="headerlink" href="#adding-smile-and-glasses" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.103.9. </span>Adding smile and glasses<a class="headerlink" href="#adding-smile-and-glasses" title="Permalink to this headline">#</a></h2>
 <p>Let’s find some attributes like smiles or glasses on the photo and try to add it to the photos which don’t have it. We will use the second dataset for it. It contains a bunch of such attributes.</p>
 <div class="cell docutils container">
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@@ -2340,7 +2357,7 @@ <h2><span class="section-number">37.105.9. </span>Adding smile and glasses<a cla
 <p>While the concept is pretty straightforward the simple autoencoder have some disadvantages. Let’s explore them and try to do better.</p>
 </section>
 <section id="variational-autoencoder">
-<h2><span class="section-number">37.105.10. </span>Variational autoencoder<a class="headerlink" href="#variational-autoencoder" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.103.10. </span>Variational autoencoder<a class="headerlink" href="#variational-autoencoder" title="Permalink to this headline">#</a></h2>
 <p>So far we have trained our encoder to reconstruct the very same image that we’ve transfered to latent space. That means that when we’re trying to <strong>generate</strong> new image from the point decoder never met we’re getting <em>the best image it can produce</em>, but the quelity is not good enough.</p>
 <blockquote>
 <div><p><strong>In other words the encoded vectors may not be continuous in the latent space.</strong></p>
@@ -2516,12 +2533,12 @@ <h2><span class="section-number">37.105.10. </span>Variational autoencoder<a cla
 </div>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">37.105.11. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.103.11. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>Variational autoencoders are cool. Although models in this particular notebook are simple they let us design complex generative models of data, and fit them to large datasets. They can generate images of fictional celebrity faces and high-resolution digital artwork.
 These models also yield state-of-the-art machine learning results in image generation and reinforcement learning. Variational autoencoders (VAEs) were defined in 2013 by Kingma et al. and Rezende et al.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.105.12. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.103.12. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to SERGEI AVERKIEV for creating the Kaggle open-source project <a class="reference external" href="https://www.kaggle.com/code/averkij/variational-autoencoder-and-faces-generation">Variational Autoencoder and Faces Generation</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2563,13 +2580,13 @@ <h2><span class="section-number">37.105.12. </span>Acknowledgments<a class="head
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.104. </span>Base/Denoising Autoencoder &amp; Dimension Reduction</p>
+            <p class="prev-next-title"><span class="section-number">37.102. </span>Base/Denoising Autoencoder &amp; Dimension Reduction</p>
         </div>
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.106. </span>Time Series Forecasting Assignment</p>
+        <p class="prev-next-title"><span class="section-number">37.104. </span>Time Series Forecasting Assignment</p>
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     <i class="fas fa-angle-right"></i>
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diff --git a/assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html b/assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html
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--- a/assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html
+++ b/assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html
@@ -6,7 +6,7 @@
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-    <title>37.92. How to choose cnn architecture mnist &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.90. How to choose cnn architecture mnist &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="next" title="37.94. Sign Language Digits Classification with CNN" href="sign-language-digits-classification-with-cnn.html" />
-    <link rel="prev" title="37.91. Parameter play" href="../../ml-fundamentals/parameter-play.html" />
+    <link rel="next" title="37.92. Sign Language Digits Classification with CNN" href="sign-language-digits-classification-with-cnn.html" />
+    <link rel="prev" title="37.89. Parameter play" href="../../ml-fundamentals/parameter-play.html" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
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-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
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    13. Getting started with classification
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    15. Getting started with ensemble learning
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-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
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   <a class="reference internal" href="../../README.html">
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
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-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
+     37.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/loss-function.html">
-     37.48. Loss Function
+     37.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
+     37.50. ML logistic regression - assignment 1
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    </li>
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     <a class="reference internal" href="../../ml-fundamentals/ml-logistic-regression-2.html">
-     37.52. ML logistic regression - assignment 2
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     <a class="reference internal" href="../../ml-fundamentals/ml-neural-network-1.html">
-     37.53. ML neural network - Assignment 1
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    </li>
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     <a class="reference internal" href="../../ml-fundamentals/regression-tools.html">
-     37.54. Regression tools
+     37.53. Regression tools
     </a>
    </li>
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-     37.55. Managing data
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-     37.56. Exploring visualizations
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    </li>
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-     37.57. Try a different model
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-     37.59. Linear and polynomial regression
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     <a class="reference internal" href="../../ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
+     37.60. Pumpkin varieties and color
     </a>
    </li>
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     <a class="reference internal" href="../../ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.62. Delicious asian and indian cuisines
+     37.61. Delicious asian and indian cuisines
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     <a class="reference internal" href="../../ml-fundamentals/explore-classification-methods.html">
-     37.63. Explore classification methods
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-   <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
+     37.62. Explore classification methods
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     <a class="reference internal" href="../../ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.65. Kernel method assignment 1
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     <a class="reference internal" href="../../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
+     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.67. Support Vector Machines (SVM) - Classification
+     37.65. Support Vector Machines (SVM) - Classification
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.68. Decision Trees - Intro and Regression
+     37.66. Decision Trees - Intro and Regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.69. Decision Trees - Classification
+     37.67. Decision Trees - Classification
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.70. Model selection assignment 1
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     <a class="reference internal" href="../../ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.71. Learning Curve To Identify Overfit &amp; Underfit
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     <a class="reference internal" href="../../ml-advanced/model-selection/dropout-and-batch-normalization.html">
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     <a class="reference internal" href="../../ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.73. Lasso and Ridge Regression
+     37.71. Lasso and Ridge Regression
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     <a class="reference internal" href="../../ml-advanced/model-selection/regularized-linear-models.html">
-     37.74. Regularized Linear Models
+     37.72. Regularized Linear Models
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
-     37.75. Random forests intro and regression
+     37.73. Random forests intro and regression
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
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     <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
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     <a class="reference internal" href="../../machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
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     <a class="reference internal" href="sign-language-digits-classification-with-cnn.html">
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     <a class="reference internal" href="../lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
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     <a class="reference internal" href="../rnn/google-stock-price-prediction-rnn.html">
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     <a class="reference internal" href="../autoencoder/autoencoder.html">
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     <a class="reference internal" href="../autoencoder/base-denoising-autoencoder-dimension-reduction.html">
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     <a class="reference internal" href="../autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
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     <a class="reference internal" href="../time-series-forecasting-assignment.html">
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     <a class="reference internal" href="../nn-for-classification-assignment.html">
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     <a class="reference internal" href="../nn-classify-15-fruits-assignment.html">
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     <a class="reference internal" href="../gan/art-by-gan.html">
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     <a class="reference internal" href="../gan/gan-introduction.html">
-     37.117. Generative Adversarial Networks (GANs)
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    38. Slides
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     <a class="reference internal nav-link" href="#what-is-the-best-cnn-architecture-for-mnist">
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     <a class="reference internal nav-link" href="#basic-cnn-structure">
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-     37.92.3. Load libraries
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-     37.92.4. Load the data
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     <a class="reference internal nav-link" href="#prepare-data-for-neural-network">
-     37.92.5. Prepare data for neural network
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     </a>
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-     37.92.6. Global variables
+     37.90.6. Global variables
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     <a class="reference internal nav-link" href="#how-many-convolution-subsambling-pairs">
-     37.92.7. 1. How many convolution-subsambling pairs?
+     37.90.7. 1. How many convolution-subsambling pairs?
     </a>
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    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#experiment-1">
-     37.92.8. Experiment 1
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       </a>
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-       37.92.8.3. Plot accuracies
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       </a>
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-     37.92.9. 2. How many feature maps?
+     37.90.9. 2. How many feature maps?
     </a>
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     <a class="reference internal nav-link" href="#experiment-2">
-     37.92.10. Experiment 2
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-       37.92.10.1. Build convolutional neural networks
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       </a>
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-       37.92.10.2. Create validation set and train networks
+       37.90.10.2. Create validation set and train networks
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id3">
-       37.92.10.3. Plot accuracies
+       37.90.10.3. Plot accuracies
       </a>
      </li>
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-       37.92.10.4. Summary
+       37.90.10.4. Summary
       </a>
      </li>
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     <a class="reference internal nav-link" href="#how-large-a-dense-layer">
-     37.92.11. 3. How large a dense layer?
+     37.90.11. 3. How large a dense layer?
     </a>
    </li>
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-     37.92.12. Experiment 3
+     37.90.12. Experiment 3
     </a>
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-     37.92.13. Build convolutional neural networks
+     37.90.13. Build convolutional neural networks
     </a>
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-     37.92.14. Create validation set and train networks
+     37.90.14. Create validation set and train networks
     </a>
    </li>
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-     37.92.15. Plot accuracies
+     37.90.15. Plot accuracies
     </a>
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      <li class="toc-h3 nav-item toc-entry">
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-       37.92.15.1. Summary
+       37.90.15.1. Summary
       </a>
      </li>
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-     37.92.16. 4. How much dropout?
+     37.90.16. 4. How much dropout?
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#experiment-4">
-     37.92.17. Experiment 4
+     37.90.17. Experiment 4
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
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-     37.92.18. Build convolutional neural networks
+     37.90.18. Build convolutional neural networks
     </a>
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    <li class="toc-h2 nav-item toc-entry">
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-     37.92.19. Create validation set and train networks
+     37.90.19. Create validation set and train networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id11">
-     37.92.20. Plot accuracies
+     37.90.20. Plot accuracies
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id12">
-     37.92.21. Summary
+     37.90.21. Summary
     </a>
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    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#advanced-features">
-     37.92.22. 5. Advanced features
+     37.90.22. 5. Advanced features
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
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-     37.92.23. Build convolutional neural networks
+     37.90.23. Build convolutional neural networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id14">
-     37.92.24. Create validation set and train networks
+     37.90.24. Create validation set and train networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#create-more-training-images-via-data-augmentation-and-train-network">
-     37.92.25. Create more training images via data augmentation and train network
+     37.90.25. Create more training images via data augmentation and train network
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id15">
-     37.92.26. Plot accuracies
+     37.90.26. Plot accuracies
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id16">
-       37.92.26.1. Summary
+       37.90.26.1. Summary
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#conclusion">
-     37.92.27. Conclusion
+     37.90.27. Conclusion
     </a>
    </li>
   </ul>
  </li>
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-   37.93. Acknowledgments
+   37.91. Acknowledgments
   </a>
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 </ul>
@@ -1733,207 +1750,207 @@ <h2> Contents </h2>
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  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#">
-   37.92. How to choose cnn architecture mnist
+   37.90. How to choose cnn architecture mnist
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    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#what-is-the-best-cnn-architecture-for-mnist">
-     37.92.1. What is the best CNN architecture for MNIST?
+     37.90.1. What is the best CNN architecture for MNIST?
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#basic-cnn-structure">
-     37.92.2. Basic CNN structure
+     37.90.2. Basic CNN structure
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#load-libraries">
-     37.92.3. Load libraries
+     37.90.3. Load libraries
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#load-the-data">
-     37.92.4. Load the data
+     37.90.4. Load the data
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#prepare-data-for-neural-network">
-     37.92.5. Prepare data for neural network
+     37.90.5. Prepare data for neural network
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#global-variables">
-     37.92.6. Global variables
+     37.90.6. Global variables
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#how-many-convolution-subsambling-pairs">
-     37.92.7. 1. How many convolution-subsambling pairs?
+     37.90.7. 1. How many convolution-subsambling pairs?
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#experiment-1">
-     37.92.8. Experiment 1
+     37.90.8. Experiment 1
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#build-convolutional-neural-networks">
-       37.92.8.1. Build convolutional neural networks
+       37.90.8.1. Build convolutional neural networks
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#create-validation-set-and-train-networks">
-       37.92.8.2. Create validation set and train networks
+       37.90.8.2. Create validation set and train networks
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#plot-accuracies">
-       37.92.8.3. Plot accuracies
+       37.90.8.3. Plot accuracies
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#summary">
-       37.92.8.4. Summary
+       37.90.8.4. Summary
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#how-many-feature-maps">
-     37.92.9. 2. How many feature maps?
+     37.90.9. 2. How many feature maps?
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#experiment-2">
-     37.92.10. Experiment 2
+     37.90.10. Experiment 2
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
-       37.92.10.1. Build convolutional neural networks
+       37.90.10.1. Build convolutional neural networks
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id2">
-       37.92.10.2. Create validation set and train networks
+       37.90.10.2. Create validation set and train networks
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id3">
-       37.92.10.3. Plot accuracies
+       37.90.10.3. Plot accuracies
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id4">
-       37.92.10.4. Summary
+       37.90.10.4. Summary
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#how-large-a-dense-layer">
-     37.92.11. 3. How large a dense layer?
+     37.90.11. 3. How large a dense layer?
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#experiment-3">
-     37.92.12. Experiment 3
+     37.90.12. Experiment 3
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id5">
-     37.92.13. Build convolutional neural networks
+     37.90.13. Build convolutional neural networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id6">
-     37.92.14. Create validation set and train networks
+     37.90.14. Create validation set and train networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id7">
-     37.92.15. Plot accuracies
+     37.90.15. Plot accuracies
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id8">
-       37.92.15.1. Summary
+       37.90.15.1. Summary
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#how-much-dropout">
-     37.92.16. 4. How much dropout?
+     37.90.16. 4. How much dropout?
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#experiment-4">
-     37.92.17. Experiment 4
+     37.90.17. Experiment 4
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id9">
-     37.92.18. Build convolutional neural networks
+     37.90.18. Build convolutional neural networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id10">
-     37.92.19. Create validation set and train networks
+     37.90.19. Create validation set and train networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id11">
-     37.92.20. Plot accuracies
+     37.90.20. Plot accuracies
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id12">
-     37.92.21. Summary
+     37.90.21. Summary
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#advanced-features">
-     37.92.22. 5. Advanced features
+     37.90.22. 5. Advanced features
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id13">
-     37.92.23. Build convolutional neural networks
+     37.90.23. Build convolutional neural networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id14">
-     37.92.24. Create validation set and train networks
+     37.90.24. Create validation set and train networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#create-more-training-images-via-data-augmentation-and-train-network">
-     37.92.25. Create more training images via data augmentation and train network
+     37.90.25. Create more training images via data augmentation and train network
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id15">
-     37.92.26. Plot accuracies
+     37.90.26. Plot accuracies
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id16">
-       37.92.26.1. Summary
+       37.90.26.1. Summary
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#conclusion">
-     37.92.27. Conclusion
+     37.90.27. Conclusion
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.93. Acknowledgments
+   37.91. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1947,19 +1964,19 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="how-to-choose-cnn-architecture-mnist">
-<h1><span class="section-number">37.92. </span>How to choose cnn architecture mnist<a class="headerlink" href="#how-to-choose-cnn-architecture-mnist" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.90. </span>How to choose cnn architecture mnist<a class="headerlink" href="#how-to-choose-cnn-architecture-mnist" title="Permalink to this headline">#</a></h1>
 <section id="what-is-the-best-cnn-architecture-for-mnist">
-<h2><span class="section-number">37.92.1. </span>What is the best CNN architecture for MNIST?<a class="headerlink" href="#what-is-the-best-cnn-architecture-for-mnist" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.1. </span>What is the best CNN architecture for MNIST?<a class="headerlink" href="#what-is-the-best-cnn-architecture-for-mnist" title="Permalink to this headline">#</a></h2>
 <p>There are so many choices for CNN architecture. How do we choose the best one? First we must define what <strong>best</strong> means. The best may be the simplest, or it may be the most efficient at producing accuracy while minimizing computational complexity. In this kernel, we will run experiments to find the most accurate and efficient CNN architecture for classifying MNIST handwritten digits.</p>
 <p>The best known MNIST classifier found on the internet achieves 99.8% accuracy!! That’s amazing. The best Kaggle kernel MNIST classifier achieves 99.75% [posted here][<a class="reference external" href="https://www.kaggle.com/cdeotte/25-million-images-0-99757-mnist">https://www.kaggle.com/cdeotte/25-million-images-0-99757-mnist</a>]. This kernel demostrates the experiments used to determine that kernel’s CNN architecture.</p>
 </section>
 <section id="basic-cnn-structure">
-<h2><span class="section-number">37.92.2. </span>Basic CNN structure<a class="headerlink" href="#basic-cnn-structure" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.2. </span>Basic CNN structure<a class="headerlink" href="#basic-cnn-structure" title="Permalink to this headline">#</a></h2>
 <p>A typical CNN design begins with feature extraction and finishes with classification. Feature extraction is performed by alternating convolution layers with subsambling layers. Classification is performed with dense layers followed by a final softmax layer. For image classification, this architecture performs better than an entirely fully connected feed forward neural network.
 <img alt="extract" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/cnn/how-to-choose-cnn-architecture-mnist/extract.png" /></p>
 </section>
 <section id="load-libraries">
-<h2><span class="section-number">37.92.3. </span>Load libraries<a class="headerlink" href="#load-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.3. </span>Load libraries<a class="headerlink" href="#load-libraries" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
@@ -1986,7 +2003,7 @@ <h2><span class="section-number">37.92.3. </span>Load libraries<a class="headerl
 </div>
 </section>
 <section id="load-the-data">
-<h2><span class="section-number">37.92.4. </span>Load the data<a class="headerlink" href="#load-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.4. </span>Load the data<a class="headerlink" href="#load-the-data" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/cnn/mnist_train.csv&quot;</span><span class="p">)</span>
@@ -1997,7 +2014,7 @@ <h2><span class="section-number">37.92.4. </span>Load the data<a class="headerli
 </div>
 </section>
 <section id="prepare-data-for-neural-network">
-<h2><span class="section-number">37.92.5. </span>Prepare data for neural network<a class="headerlink" href="#prepare-data-for-neural-network" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.5. </span>Prepare data for neural network<a class="headerlink" href="#prepare-data-for-neural-network" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Y_train</span> <span class="o">=</span> <span class="n">train</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span>
@@ -2018,7 +2035,7 @@ <h2><span class="section-number">37.92.5. </span>Prepare data for neural network
 </div>
 </section>
 <section id="global-variables">
-<h2><span class="section-number">37.92.6. </span>Global variables<a class="headerlink" href="#global-variables" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.6. </span>Global variables<a class="headerlink" href="#global-variables" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">annealer</span> <span class="o">=</span> <span class="n">LearningRateScheduler</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="mf">1e-3</span> <span class="o">*</span> <span class="mf">0.95</span><span class="o">**</span><span class="n">x</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
@@ -2029,7 +2046,7 @@ <h2><span class="section-number">37.92.6. </span>Global variables<a class="heade
 </div>
 </section>
 <section id="how-many-convolution-subsambling-pairs">
-<h2><span class="section-number">37.92.7. </span>1. How many convolution-subsambling pairs?<a class="headerlink" href="#how-many-convolution-subsambling-pairs" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.7. </span>1. How many convolution-subsambling pairs?<a class="headerlink" href="#how-many-convolution-subsambling-pairs" title="Permalink to this headline">#</a></h2>
 <p>First question, how many pairs of convolution-subsampling should we use? For example, our network could have 1, 2, or 3:</p>
 <ul class="simple">
 <li><p>784 - <strong>[24C5-P2]</strong> - 256 - 10</p></li>
@@ -2039,10 +2056,10 @@ <h2><span class="section-number">37.92.7. </span>1. How many convolution-subsamb
 <p>It’s typical to increase the number of feature maps for each subsequent pair as shown here.</p>
 </section>
 <section id="experiment-1">
-<h2><span class="section-number">37.92.8. </span>Experiment 1<a class="headerlink" href="#experiment-1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.8. </span>Experiment 1<a class="headerlink" href="#experiment-1" title="Permalink to this headline">#</a></h2>
 <p>Let’s see whether one, two, or three pairs is best. We are not doing four pairs since the image will be reduced too small before then. The input image is 28x28. After one pair, it’s 14x14. After two, it’s 7x7. After three it’s 4x4 (or 3x3 if we don’t use padding=‘same’). It doesn’t make sense to do a fourth convolution.</p>
 <section id="build-convolutional-neural-networks">
-<h3><span class="section-number">37.92.8.1. </span>Build convolutional neural networks<a class="headerlink" href="#build-convolutional-neural-networks" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.90.8.1. </span>Build convolutional neural networks<a class="headerlink" href="#build-convolutional-neural-networks" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">nets</span> <span class="o">=</span> <span class="mi">3</span>
@@ -2078,7 +2095,7 @@ <h3><span class="section-number">37.92.8.1. </span>Build convolutional neural ne
 </div>
 </section>
 <section id="create-validation-set-and-train-networks">
-<h3><span class="section-number">37.92.8.2. </span>Create validation set and train networks<a class="headerlink" href="#create-validation-set-and-train-networks" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.90.8.2. </span>Create validation set and train networks<a class="headerlink" href="#create-validation-set-and-train-networks" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train2</span><span class="p">,</span> <span class="n">X_val2</span><span class="p">,</span> <span class="n">Y_train2</span><span class="p">,</span> <span class="n">Y_val2</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.333</span><span class="p">)</span>
@@ -2109,7 +2126,7 @@ <h3><span class="section-number">37.92.8.2. </span>Create validation set and tra
 </div>
 </section>
 <section id="plot-accuracies">
-<h3><span class="section-number">37.92.8.3. </span>Plot accuracies<a class="headerlink" href="#plot-accuracies" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.90.8.3. </span>Plot accuracies<a class="headerlink" href="#plot-accuracies" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
@@ -2128,12 +2145,12 @@ <h3><span class="section-number">37.92.8.3. </span>Plot accuracies<a class="head
 </div>
 </section>
 <section id="summary">
-<h3><span class="section-number">37.92.8.4. </span>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.90.8.4. </span>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">#</a></h3>
 <p>From the above experiment, it seems that 3 pairs of convolution-subsambling is slightly better than 2 pairs. However for efficiency, the improvement doesn’t warrant the additional computional cost, so let’s use 2.</p>
 </section>
 </section>
 <section id="how-many-feature-maps">
-<h2><span class="section-number">37.92.9. </span>2. How many feature maps?<a class="headerlink" href="#how-many-feature-maps" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.9. </span>2. How many feature maps?<a class="headerlink" href="#how-many-feature-maps" title="Permalink to this headline">#</a></h2>
 <p>In the previous experiement, we decided that two pairs is sufficient. How many feature maps should we include? For example, we could do</p>
 <ul class="simple">
 <li><p>784 - [<strong>8</strong>C5-P2] - [<strong>16</strong>C5-P2] - 256 - 10</p></li>
@@ -2145,9 +2162,9 @@ <h2><span class="section-number">37.92.9. </span>2. How many feature maps?<a cla
 </ul>
 </section>
 <section id="experiment-2">
-<h2><span class="section-number">37.92.10. </span>Experiment 2<a class="headerlink" href="#experiment-2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.10. </span>Experiment 2<a class="headerlink" href="#experiment-2" title="Permalink to this headline">#</a></h2>
 <section id="id1">
-<h3><span class="section-number">37.92.10.1. </span>Build convolutional neural networks<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.90.10.1. </span>Build convolutional neural networks<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">nets</span> <span class="o">=</span> <span class="mi">6</span>
@@ -2172,7 +2189,7 @@ <h3><span class="section-number">37.92.10.1. </span>Build convolutional neural n
 </div>
 </section>
 <section id="id2">
-<h3><span class="section-number">37.92.10.2. </span>Create validation set and train networks<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.90.10.2. </span>Create validation set and train networks<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train2</span><span class="p">,</span> <span class="n">X_val2</span><span class="p">,</span> <span class="n">Y_train2</span><span class="p">,</span> <span class="n">Y_val2</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.333</span><span class="p">)</span>
@@ -2203,7 +2220,7 @@ <h3><span class="section-number">37.92.10.2. </span>Create validation set and tr
 </div>
 </section>
 <section id="id3">
-<h3><span class="section-number">37.92.10.3. </span>Plot accuracies<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.90.10.3. </span>Plot accuracies<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
@@ -2222,12 +2239,12 @@ <h3><span class="section-number">37.92.10.3. </span>Plot accuracies<a class="hea
 </div>
 </section>
 <section id="id4">
-<h3><span class="section-number">37.92.10.4. </span>Summary<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.90.10.4. </span>Summary<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
 <p>From the above experiement, it appears that 32 maps in the first convolutional layer and 64 maps in the second convolutional layer is the best. Architectures with more maps only perform slightly better and are not worth the additonal computation cost.</p>
 </section>
 </section>
 <section id="how-large-a-dense-layer">
-<h2><span class="section-number">37.92.11. </span>3. How large a dense layer?<a class="headerlink" href="#how-large-a-dense-layer" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.11. </span>3. How large a dense layer?<a class="headerlink" href="#how-large-a-dense-layer" title="Permalink to this headline">#</a></h2>
 <p>In our previous experiment, we decided on 32 and 64 maps in our convolutional layers. How many dense units should we use? For example we could use</p>
 <ul class="simple">
 <li><p>784 - [32C5-P2] - [64C5-P2] - <strong>0</strong> - 10</p></li>
@@ -2241,10 +2258,10 @@ <h2><span class="section-number">37.92.11. </span>3. How large a dense layer?<a
 </ul>
 </section>
 <section id="experiment-3">
-<h2><span class="section-number">37.92.12. </span>Experiment 3<a class="headerlink" href="#experiment-3" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.12. </span>Experiment 3<a class="headerlink" href="#experiment-3" title="Permalink to this headline">#</a></h2>
 </section>
 <section id="id5">
-<h2><span class="section-number">37.92.13. </span>Build convolutional neural networks<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.13. </span>Build convolutional neural networks<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">nets</span> <span class="o">=</span> <span class="mi">8</span>
@@ -2269,7 +2286,7 @@ <h2><span class="section-number">37.92.13. </span>Build convolutional neural net
 </div>
 </section>
 <section id="id6">
-<h2><span class="section-number">37.92.14. </span>Create validation set and train networks<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.14. </span>Create validation set and train networks<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train2</span><span class="p">,</span> <span class="n">X_val2</span><span class="p">,</span> <span class="n">Y_train2</span><span class="p">,</span> <span class="n">Y_val2</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.333</span><span class="p">)</span>
@@ -2301,7 +2318,7 @@ <h2><span class="section-number">37.92.14. </span>Create validation set and trai
 </div>
 </section>
 <section id="id7">
-<h2><span class="section-number">37.92.15. </span>Plot accuracies<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.15. </span>Plot accuracies<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
@@ -2319,22 +2336,22 @@ <h2><span class="section-number">37.92.15. </span>Plot accuracies<a class="heade
 </div>
 </div>
 <section id="id8">
-<h3><span class="section-number">37.92.15.1. </span>Summary<a class="headerlink" href="#id8" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.90.15.1. </span>Summary<a class="headerlink" href="#id8" title="Permalink to this headline">#</a></h3>
 <p>From this experiment, it appears that 128 units is the best. Dense layers with more units only perform slightly better and are not worth the additional computational cost. (We also tested using two consecutive dense layers instead of one, but that showed no benefit over a single dense layer.)</p>
 </section>
 </section>
 <section id="how-much-dropout">
-<h2><span class="section-number">37.92.16. </span>4. How much dropout?<a class="headerlink" href="#how-much-dropout" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.16. </span>4. How much dropout?<a class="headerlink" href="#how-much-dropout" title="Permalink to this headline">#</a></h2>
 <p>Dropout will prevent our network from overfitting thus helping our network generalize better. How much dropout should we add after each layer?</p>
 <ul class="simple">
 <li><p>0%, 10%, 20%, 30%, 40%, 50%, 60%, or 70%</p></li>
 </ul>
 </section>
 <section id="experiment-4">
-<h2><span class="section-number">37.92.17. </span>Experiment 4<a class="headerlink" href="#experiment-4" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.17. </span>Experiment 4<a class="headerlink" href="#experiment-4" title="Permalink to this headline">#</a></h2>
 </section>
 <section id="id9">
-<h2><span class="section-number">37.92.18. </span>Build convolutional neural networks<a class="headerlink" href="#id9" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.18. </span>Build convolutional neural networks<a class="headerlink" href="#id9" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">nets</span> <span class="o">=</span> <span class="mi">8</span>
@@ -2361,7 +2378,7 @@ <h2><span class="section-number">37.92.18. </span>Build convolutional neural net
 </div>
 </section>
 <section id="id10">
-<h2><span class="section-number">37.92.19. </span>Create validation set and train networks<a class="headerlink" href="#id10" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.19. </span>Create validation set and train networks<a class="headerlink" href="#id10" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train2</span><span class="p">,</span> <span class="n">X_val2</span><span class="p">,</span> <span class="n">Y_train2</span><span class="p">,</span> <span class="n">Y_val2</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.333</span><span class="p">)</span>
@@ -2393,7 +2410,7 @@ <h2><span class="section-number">37.92.19. </span>Create validation set and trai
 </div>
 </section>
 <section id="id11">
-<h2><span class="section-number">37.92.20. </span>Plot accuracies<a class="headerlink" href="#id11" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.20. </span>Plot accuracies<a class="headerlink" href="#id11" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
@@ -2412,11 +2429,11 @@ <h2><span class="section-number">37.92.20. </span>Plot accuracies<a class="heade
 </div>
 </section>
 <section id="id12">
-<h2><span class="section-number">37.92.21. </span>Summary<a class="headerlink" href="#id12" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.21. </span>Summary<a class="headerlink" href="#id12" title="Permalink to this headline">#</a></h2>
 <p>From this experiment, it appears that 40% dropout is the best.</p>
 </section>
 <section id="advanced-features">
-<h2><span class="section-number">37.92.22. </span>5. Advanced features<a class="headerlink" href="#advanced-features" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.22. </span>5. Advanced features<a class="headerlink" href="#advanced-features" title="Permalink to this headline">#</a></h2>
 <p>Instead of using one convolution layer of size 5x5, you can mimic 5x5 by using two consecutive 3x3 layers and it will be more nonlinear. Instead of using a max pooling layer, you can subsample by using a convolution layer with strides=2 and it will be learnable. Lastly, does batch normalization help? And does data augmentation help? Let’s test all four of these</p>
 <ul class="simple">
 <li><p>replace ‘32C5’ with ‘32C3-32C3’</p></li>
@@ -2426,7 +2443,7 @@ <h2><span class="section-number">37.92.22. </span>5. Advanced features<a class="
 </ul>
 </section>
 <section id="id13">
-<h2><span class="section-number">37.92.23. </span>Build convolutional neural networks<a class="headerlink" href="#id13" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.23. </span>Build convolutional neural networks<a class="headerlink" href="#id13" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">nets</span> <span class="o">=</span> <span class="mi">5</span>
@@ -2545,7 +2562,7 @@ <h2><span class="section-number">37.92.23. </span>Build convolutional neural net
 </div>
 </section>
 <section id="id14">
-<h2><span class="section-number">37.92.24. </span>Create validation set and train networks<a class="headerlink" href="#id14" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.24. </span>Create validation set and train networks<a class="headerlink" href="#id14" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train2</span><span class="p">,</span> <span class="n">X_val2</span><span class="p">,</span> <span class="n">Y_train2</span><span class="p">,</span> <span class="n">Y_val2</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
@@ -2577,7 +2594,7 @@ <h2><span class="section-number">37.92.24. </span>Create validation set and trai
 </div>
 </section>
 <section id="create-more-training-images-via-data-augmentation-and-train-network">
-<h2><span class="section-number">37.92.25. </span>Create more training images via data augmentation and train network<a class="headerlink" href="#create-more-training-images-via-data-augmentation-and-train-network" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.25. </span>Create more training images via data augmentation and train network<a class="headerlink" href="#create-more-training-images-via-data-augmentation-and-train-network" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">datagen</span> <span class="o">=</span> <span class="n">ImageDataGenerator</span><span class="p">(</span>
@@ -2607,7 +2624,7 @@ <h2><span class="section-number">37.92.25. </span>Create more training images vi
 </div>
 </section>
 <section id="id15">
-<h2><span class="section-number">37.92.26. </span>Plot accuracies<a class="headerlink" href="#id15" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.26. </span>Plot accuracies<a class="headerlink" href="#id15" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
@@ -2625,12 +2642,12 @@ <h2><span class="section-number">37.92.26. </span>Plot accuracies<a class="heade
 </div>
 </div>
 <section id="id16">
-<h3><span class="section-number">37.92.26.1. </span>Summary<a class="headerlink" href="#id16" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.90.26.1. </span>Summary<a class="headerlink" href="#id16" title="Permalink to this headline">#</a></h3>
 <p>From this experiment, we see that each of the four advanced features improve accuracy. The first model uses no advanced features. The second uses only the double convolution layer trick. The third uses only the learnable subsambling layer trick. The third model uses both of those techniques plus batch normalization. The last model employs all three of those techniques plus data augmentation and achieves the best accuracy of 99.5%! (Or more if we train longer.) (Experiments determing the best data augmentation hyper-parameters are posted at the end of the kernel <a class="reference external" href="https://www.kaggle.com/cdeotte/25-million-images-0-99757-mnist">here</a>.)</p>
 </section>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">37.92.27. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.90.27. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>Training convolutional neural networks is a random process. This makes experiments difficult because each time you run the same experiment, you get different results. Therefore, you must run your experiments dozens of times and take an average. This kernel was run dozens of times and it seems that the best CNN architecture for classifying MNIST handwritten digits is 784 - [32C5-P2] - [64C5-P2] - 128 - 10 with 40% dropout. Afterward, more experiments show that replacing ‘32C5’ with ‘32C3-32C3’ improves accuracy. And replacing ‘P2’ with ‘32C5S2’ improves accuracy. And adding batch normalizaiton and data augmentation improve the CNN. The best CNN found from the experiments here becomes</p>
 <ul class="simple">
 <li><p><strong>784 - [32C3-32C3-32C5S2] - [64C3-64C3-64C5S2] - 128 - 10</strong></p></li>
@@ -2639,7 +2656,7 @@ <h2><span class="section-number">37.92.27. </span>Conclusion<a class="headerlink
 </section>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="acknowledgments">
-<h1><span class="section-number">37.93. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.91. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/cdeotte">Chris Deotte</a> for creating <a class="reference external" href="https://www.kaggle.com/code/cdeotte/how-to-choose-cnn-architecture-mnist">how-to-choose-cnn-architecture-mnist</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
@@ -2680,13 +2697,13 @@ <h1><span class="section-number">37.93. </span>Acknowledgments<a class="headerli
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.91. </span>Parameter play</p>
+            <p class="prev-next-title"><span class="section-number">37.89. </span>Parameter play</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="sign-language-digits-classification-with-cnn.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.94. </span>Sign Language Digits Classification with CNN</p>
+        <p class="prev-next-title"><span class="section-number">37.92. </span>Sign Language Digits Classification with CNN</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html b/assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html
index 9c14c1b60c..91fc07235f 100644
--- a/assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html
+++ b/assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html
@@ -6,7 +6,7 @@
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.96. Object Recognition in Images using CNN &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.94. Object Recognition in Images using CNN &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.97. Intro to TensorFlow for Deep Learning" href="../tensorflow/intro_to_tensorflow_for_deeplearning.html" />
-    <link rel="prev" title="37.94. Sign Language Digits Classification with CNN" href="sign-language-digits-classification-with-cnn.html" />
+    <link rel="next" title="37.95. Intro to TensorFlow for Deep Learning" href="../tensorflow/intro_to_tensorflow_for_deeplearning.html" />
+    <link rel="prev" title="37.92. Sign Language Digits Classification with CNN" href="sign-language-digits-classification-with-cnn.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
-  <label for="toctree-checkbox-3">
+  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
+  <label for="toctree-checkbox-4">
    <i class="fas fa-chevron-down">
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   </label>
@@ -284,8 +306,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
-  <label for="toctree-checkbox-4">
+  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
+  <label for="toctree-checkbox-5">
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   <a class="reference internal" href="../../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
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-  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
-  <label for="toctree-checkbox-5">
+  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
+  <label for="toctree-checkbox-6">
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   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
-  <label for="toctree-checkbox-6">
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   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
-  <label for="toctree-checkbox-7">
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   <a class="reference internal" href="../../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
    14. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
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   <a class="reference internal" href="../../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
    15. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
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+  <label for="toctree-checkbox-10">
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    16. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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@@ -633,8 +655,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../README.html">
    37. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
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@@ -825,308 +847,303 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
+     37.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/loss-function.html">
-     37.48. Loss Function
+     37.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
+     37.50. ML logistic regression - assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/ml-logistic-regression-2.html">
-     37.52. ML logistic regression - assignment 2
+     37.51. ML logistic regression - assignment 2
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/ml-neural-network-1.html">
-     37.53. ML neural network - Assignment 1
+     37.52. ML neural network - Assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression-tools.html">
-     37.54. Regression tools
+     37.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/managing-data.html">
-     37.55. Managing data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/exploring-visualizations.html">
-     37.56. Exploring visualizations
+     37.55. Exploring visualizations
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/try-a-different-model.html">
-     37.57. Try a different model
+     37.56. Try a different model
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    </li>
    <li class="toctree-l2">
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-     37.58. Create a regression model
+     37.57. Create a regression model
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-and-polynomial-regression.html">
-     37.59. Linear and polynomial regression
+     37.58. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/retrying-some-regression.html">
-     37.60. Retrying some regression
+     37.59. Retrying some regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
+     37.60. Pumpkin varieties and color
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.62. Delicious asian and indian cuisines
+     37.61. Delicious asian and indian cuisines
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/explore-classification-methods.html">
-     37.63. Explore classification methods
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
+     37.62. Explore classification methods
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.65. Kernel method assignment 1
+     37.63. Kernel method assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
+     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.67. Support Vector Machines (SVM) - Classification
+     37.65. Support Vector Machines (SVM) - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.68. Decision Trees - Intro and Regression
+     37.66. Decision Trees - Intro and Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.69. Decision Trees - Classification
+     37.67. Decision Trees - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.70. Model selection assignment 1
+     37.68. Model selection assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.71. Learning Curve To Identify Overfit &amp; Underfit
+     37.69. Learning Curve To Identify Overfit &amp; Underfit
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.72. Dropout and Batch Normalization
+     37.70. Dropout and Batch Normalization
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.73. Lasso and Ridge Regression
+     37.71. Lasso and Ridge Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/model-selection/regularized-linear-models.html">
-     37.74. Regularized Linear Models
+     37.72. Regularized Linear Models
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
-     37.75. Random forests intro and regression
+     37.73. Random forests intro and regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forests-for-classification.html">
-     37.76. Random forests for classification
+     37.74. Random forests for classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
-     37.77. Beyond random forests: more ensemble models
+     37.75. Beyond random forests: more ensemble models
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/decision-trees.html">
-     37.78. Decision trees
+     37.76. Decision trees
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
-     37.79. Hyperparameter tuning gradient boosting
+     37.77. Hyperparameter tuning gradient boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
-     37.80. Gradient boosting
+     37.78. Gradient boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/boosting-with-tuning.html">
-     37.81. Boosting with tuning
+     37.79. Boosting with tuning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
-     37.82. Random Forest Classifier with Feature Importance
+     37.80. Random Forest Classifier with Feature Importance
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
-     37.84. Data engineering
+     37.82. Data engineering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
-     37.85. Counterintuitive Challenges in ML Debugging
+     37.83. Counterintuitive Challenges in ML Debugging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../machine-learning-productionization/debugging-in-classification.html">
-     37.86. Case Study: Debugging in Classification
+     37.84. Case Study: Debugging in Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../machine-learning-productionization/debugging-in-regression.html">
-     37.87. Case Study: Debugging in Regression
+     37.85. Case Study: Debugging in Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/study-the-solvers.html">
-     37.88. Study the solvers
+     37.86. Study the solvers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/build-classification-models.html">
-     37.89. Build classification models
+     37.87. Build classification models
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/build-classification-model.html">
-     37.90. Build Classification Model
+     37.88. Build Classification Model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/parameter-play.html">
-     37.91. Parameter play
+     37.89. Parameter play
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="how-to-choose-cnn-architecture-mnist.html">
-     37.92. How to choose cnn architecture mnist
+     37.90. How to choose cnn architecture mnist
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="sign-language-digits-classification-with-cnn.html">
-     37.94. Sign Language Digits Classification with CNN
+     37.92. Sign Language Digits Classification with CNN
     </a>
    </li>
    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     37.96. Object Recognition in Images using CNN
+     37.94. Object Recognition in Images using CNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.97. Intro to TensorFlow for Deep Learning
+     37.95. Intro to TensorFlow for Deep Learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../rnn/google-stock-price-prediction-rnn.html">
-     37.101. Google Stock Price Prediction RNN
+     37.99. Google Stock Price Prediction RNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../autoencoder/autoencoder.html">
-     37.103. Intro to Autoencoders
+     37.101. Intro to Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
+     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
+     37.103. Fun with Variational Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
+     37.104. Time Series Forecasting Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../nn-for-classification-assignment.html">
-     37.108. Neural Networks for Classification with TensorFlow
+     37.106. Neural Networks for Classification with TensorFlow
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../nn-classify-15-fruits-assignment.html">
-     37.109. NN Classify 15 Fruits Assignment
+     37.107. NN Classify 15 Fruits Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../dqn/dqn-on-foreign-exchange-market.html">
-     37.114. DQN On Foreign Exchange Market
+     37.112. DQN On Foreign Exchange Market
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../gan/art-by-gan.html">
-     37.115. Art by gan
+     37.113. Art by gan
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../gan/gan-introduction.html">
-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../overview/basic-classification-classify-images-of-clothing.html">
-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
     </a>
    </li>
   </ul>
@@ -1135,8 +1152,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../slides/introduction.html">
    38. Slides
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -1509,63 +1526,63 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#about-dataset">
-   37.96.1. About Dataset
+   37.94.1. About Dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#table-of-contents">
-   37.96.2. Table of Contents
+   37.94.2. Table of Contents
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#import-libaries">
-     37.96.2.1. Import Libaries
+     37.94.2.1. Import Libaries
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploring-the-data">
-     37.96.2.2. Exploring the Data
+     37.94.2.2. Exploring the Data
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-and-validation-datasets">
-   37.96.3. Training and Validation Datasets
+   37.94.3. Training and Validation Datasets
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#training-data-single-batch-images">
-     37.96.3.1. training data single batch images
+     37.94.3.1. training data single batch images
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#convolutional-neural-network">
-   37.96.4. Convolutional Neural Network
+   37.94.4. Convolutional Neural Network
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#defining-the-model-convolutional-neural-network">
-     37.96.4.1. Defining the Model (Convolutional Neural Network)
+     37.94.4.1. Defining the Model (Convolutional Neural Network)
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-the-model">
-   37.96.5. Training the Model
+   37.94.5. Training the Model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#testing-with-individual-images">
-   37.96.6. Testing with individual images
+   37.94.6. Testing with individual images
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.96.7. Acknowledgments
+   37.94.7. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1589,63 +1606,63 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#about-dataset">
-   37.96.1. About Dataset
+   37.94.1. About Dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#table-of-contents">
-   37.96.2. Table of Contents
+   37.94.2. Table of Contents
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#import-libaries">
-     37.96.2.1. Import Libaries
+     37.94.2.1. Import Libaries
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploring-the-data">
-     37.96.2.2. Exploring the Data
+     37.94.2.2. Exploring the Data
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-and-validation-datasets">
-   37.96.3. Training and Validation Datasets
+   37.94.3. Training and Validation Datasets
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#training-data-single-batch-images">
-     37.96.3.1. training data single batch images
+     37.94.3.1. training data single batch images
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#convolutional-neural-network">
-   37.96.4. Convolutional Neural Network
+   37.94.4. Convolutional Neural Network
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#defining-the-model-convolutional-neural-network">
-     37.96.4.1. Defining the Model (Convolutional Neural Network)
+     37.94.4.1. Defining the Model (Convolutional Neural Network)
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-the-model">
-   37.96.5. Training the Model
+   37.94.5. Training the Model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#testing-with-individual-images">
-   37.96.6. Testing with individual images
+   37.94.6. Testing with individual images
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.96.7. Acknowledgments
+   37.94.7. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1659,18 +1676,18 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="object-recognition-in-images-using-cnn">
-<h1><span class="section-number">37.96. </span>Object Recognition in Images using CNN<a class="headerlink" href="#object-recognition-in-images-using-cnn" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.94. </span>Object Recognition in Images using CNN<a class="headerlink" href="#object-recognition-in-images-using-cnn" title="Permalink to this headline">#</a></h1>
 <section id="about-dataset">
-<h2><span class="section-number">37.96.1. </span>About Dataset<a class="headerlink" href="#about-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.94.1. </span>About Dataset<a class="headerlink" href="#about-dataset" title="Permalink to this headline">#</a></h2>
 <p>CIFAR-10 is an established computer-vision dataset used for object recognition. It is a subset of the 80
 million tiny images dataset and consists of 60,000 32x32 color images containing one of 10 object classes,
 with 6000 images per class. It was collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.</p>
 <p><img alt="cifar10" src="https://miro.medium.com/max/709/1*LyV7_xga4jUHdx4_jHk1PQ.png" /></p>
 </section>
 <section id="table-of-contents">
-<h2><span class="section-number">37.96.2. </span>Table of Contents<a class="headerlink" href="#table-of-contents" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.94.2. </span>Table of Contents<a class="headerlink" href="#table-of-contents" title="Permalink to this headline">#</a></h2>
 <section id="import-libaries">
-<h3><span class="section-number">37.96.2.1. </span>Import Libaries<a class="headerlink" href="#import-libaries" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.94.2.1. </span>Import Libaries<a class="headerlink" href="#import-libaries" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
@@ -1693,7 +1710,7 @@ <h3><span class="section-number">37.96.2.1. </span>Import Libaries<a class="head
 </div>
 </section>
 <section id="exploring-the-data">
-<h3><span class="section-number">37.96.2.2. </span>Exploring the Data<a class="headerlink" href="#exploring-the-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.94.2.2. </span>Exploring the Data<a class="headerlink" href="#exploring-the-data" title="Permalink to this headline">#</a></h3>
 <p>Extract from tar archive file</p>
 <p>The dataset is extracted to the directory <code class="docutils literal notranslate"><span class="pre">tmp/object-recognition-in-images-using-cnn/cifar10</span></code>. It contains 2 folders <code class="docutils literal notranslate"><span class="pre">train</span></code> and <code class="docutils literal notranslate"><span class="pre">test</span></code>, containing the training set (50000 images) and test set (10000 images) respectively. Each of them contains 10 folders, one for each class of images. Let’s verify this using <code class="docutils literal notranslate"><span class="pre">os.listdir</span></code>.</p>
 <div class="cell docutils container">
@@ -1864,7 +1881,7 @@ <h3><span class="section-number">37.96.2.2. </span>Exploring the Data<a class="h
 </section>
 </section>
 <section id="training-and-validation-datasets">
-<h2><span class="section-number">37.96.3. </span>Training and Validation Datasets<a class="headerlink" href="#training-and-validation-datasets" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.94.3. </span>Training and Validation Datasets<a class="headerlink" href="#training-and-validation-datasets" title="Permalink to this headline">#</a></h2>
 <p>While building real world machine learning models, it is quite common to split the dataset into 3 parts:</p>
 <ol class="simple">
 <li><p><strong>Training set</strong> - used to train the model i.e. compute the loss and adjust the weights of the model using gradient descent.</p></li>
@@ -1924,7 +1941,7 @@ <h2><span class="section-number">37.96.3. </span>Training and Validation Dataset
 </div>
 </div>
 <section id="training-data-single-batch-images">
-<h3><span class="section-number">37.96.3.1. </span>training data single batch images<a class="headerlink" href="#training-data-single-batch-images" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.94.3.1. </span>training data single batch images<a class="headerlink" href="#training-data-single-batch-images" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">show_images_batch</span><span class="p">(</span><span class="n">train_dl</span><span class="p">)</span>
@@ -1942,9 +1959,9 @@ <h3><span class="section-number">37.96.3.1. </span>training data single batch im
 </section>
 </section>
 <section id="convolutional-neural-network">
-<h2><span class="section-number">37.96.4. </span>Convolutional Neural Network<a class="headerlink" href="#convolutional-neural-network" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.94.4. </span>Convolutional Neural Network<a class="headerlink" href="#convolutional-neural-network" title="Permalink to this headline">#</a></h2>
 <section id="defining-the-model-convolutional-neural-network">
-<h3><span class="section-number">37.96.4.1. </span>Defining the Model (Convolutional Neural Network)<a class="headerlink" href="#defining-the-model-convolutional-neural-network" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.94.4.1. </span>Defining the Model (Convolutional Neural Network)<a class="headerlink" href="#defining-the-model-convolutional-neural-network" title="Permalink to this headline">#</a></h3>
 <p>The 2D convolution is a fairly simple operation at heart: you start with a kernel, which is simply a small matrix of weights. This kernel “slides” over the 2D input data, performing an elementwise multiplication with the part of the input it is currently on, and then summing up the results into a single output pixel.</p>
 <img src="https://miro.medium.com/max/1070/1*Zx-ZMLKab7VOCQTxdZ1OAw.gif" style="max-width:400px;">
 <p>Let us implement a convolution operation on a 1 channel image with a 3x3 kernel.</p>
@@ -2153,7 +2170,7 @@ <h3><span class="section-number">37.96.4.1. </span>Defining the Model (Convoluti
 </section>
 </section>
 <section id="training-the-model">
-<h2><span class="section-number">37.96.5. </span>Training the Model<a class="headerlink" href="#training-the-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.94.5. </span>Training the Model<a class="headerlink" href="#training-the-model" title="Permalink to this headline">#</a></h2>
 <p>In this mode, the result of every computation will have requires_grad=False, even when the inputs have requires_grad=True.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2269,7 +2286,7 @@ <h2><span class="section-number">37.96.5. </span>Training the Model<a class="hea
 <p>Initialy, both the training and validation losses seem to decrease over time. However, if we train the model for long enough,training loss continues to decrease, while the validation loss stops decreasing, and even starts to increase after a certain point(overfitting).</p>
 </section>
 <section id="testing-with-individual-images">
-<h2><span class="section-number">37.96.6. </span>Testing with individual images<a class="headerlink" href="#testing-with-individual-images" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.94.6. </span>Testing with individual images<a class="headerlink" href="#testing-with-individual-images" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">test_dataset</span> <span class="o">=</span> <span class="n">ImageFolder</span><span class="p">(</span><span class="n">data_dir</span><span class="o">+</span><span class="s1">&#39;/test&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ToTensor</span><span class="p">())</span>
@@ -2374,7 +2391,7 @@ <h2><span class="section-number">37.96.6. </span>Testing with individual images<
 </div>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.96.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.94.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to datajameson for creating the Kaggle notebook <a class="reference external" href="https://www.kaggle.com/code/datajameson/cifar-10-object-recognition-cnn-explained">Cifar-10 Object Recognition(CNN) Explained</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2416,13 +2433,13 @@ <h2><span class="section-number">37.96.7. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
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-            <p class="prev-next-title"><span class="section-number">37.94. </span>Sign Language Digits Classification with CNN</p>
+            <p class="prev-next-title"><span class="section-number">37.92. </span>Sign Language Digits Classification with CNN</p>
         </div>
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-        <p class="prev-next-title"><span class="section-number">37.97. </span>Intro to TensorFlow for Deep Learning</p>
+        <p class="prev-next-title"><span class="section-number">37.95. </span>Intro to TensorFlow for Deep Learning</p>
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diff --git a/assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html b/assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html
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+++ b/assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html
@@ -6,7 +6,7 @@
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-    <title>37.94. Sign Language Digits Classification with CNN &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.92. Sign Language Digits Classification with CNN &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="next" title="37.96. Object Recognition in Images using CNN" href="object-recognition-in-images-using-cnn.html" />
-    <link rel="prev" title="37.92. How to choose cnn architecture mnist" href="how-to-choose-cnn-architecture-mnist.html" />
+    <link rel="next" title="37.94. Object Recognition in Images using CNN" href="object-recognition-in-images-using-cnn.html" />
+    <link rel="prev" title="37.90. How to choose cnn architecture mnist" href="how-to-choose-cnn-architecture-mnist.html" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
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-  <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
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   <a class="reference internal" href="../../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
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   <a class="reference internal" href="../../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
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   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
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   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
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   <a class="reference internal" href="../../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
    14. Clustering models for Machine Learning
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-  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
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   <a class="reference internal" href="../../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
    15. Getting started with ensemble learning
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   <a class="reference internal" href="../../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
    16. Introduction to Gradient Boosting
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-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
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   <a class="reference internal" href="../../README.html">
    37. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
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@@ -825,308 +847,303 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
     </a>
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    <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/loss-function.html">
-     37.48. Loss Function
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
+     37.50. ML logistic regression - assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/ml-logistic-regression-2.html">
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+     37.51. ML logistic regression - assignment 2
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    </li>
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    </li>
    <li class="toctree-l2">
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-     37.54. Regression tools
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/managing-data.html">
-     37.55. Managing data
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    </li>
    <li class="toctree-l2">
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-     37.56. Exploring visualizations
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    </li>
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     <a class="reference internal" href="../../ml-fundamentals/try-a-different-model.html">
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    </li>
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     <a class="reference internal" href="../../ml-fundamentals/linear-and-polynomial-regression.html">
-     37.59. Linear and polynomial regression
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     <a class="reference internal" href="../../ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.62. Delicious asian and indian cuisines
+     37.61. Delicious asian and indian cuisines
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     <a class="reference internal" href="../../ml-fundamentals/explore-classification-methods.html">
-     37.63. Explore classification methods
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-    <a class="reference internal" href="../../ml-fundamentals/linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
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     <a class="reference internal" href="../../ml-advanced/kernel-method/kernel-method-assignment-1.html">
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     <a class="reference internal" href="../../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.67. Support Vector Machines (SVM) - Classification
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/kernel-method/decision_trees_for_regression.html">
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     <a class="reference internal" href="../../ml-advanced/kernel-method/decision_trees_for_classification.html">
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     <a class="reference internal" href="../../ml-advanced/model-selection/model-selection-assignment-1.html">
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     <a class="reference internal" href="../../ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.71. Learning Curve To Identify Overfit &amp; Underfit
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     <a class="reference internal" href="../../ml-advanced/model-selection/dropout-and-batch-normalization.html">
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
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     <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
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-     37.86. Case Study: Debugging in Classification
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-     37.87. Case Study: Debugging in Regression
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-     37.88. Study the solvers
+     37.86. Study the solvers
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-     37.89. Build classification models
+     37.87. Build classification models
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-     37.90. Build Classification Model
+     37.88. Build Classification Model
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-     37.91. Parameter play
+     37.89. Parameter play
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-     37.92. How to choose cnn architecture mnist
+     37.90. How to choose cnn architecture mnist
     </a>
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    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     37.94. Sign Language Digits Classification with CNN
+     37.92. Sign Language Digits Classification with CNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="object-recognition-in-images-using-cnn.html">
-     37.96. Object Recognition in Images using CNN
+     37.94. Object Recognition in Images using CNN
     </a>
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     <a class="reference internal" href="../tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.97. Intro to TensorFlow for Deep Learning
+     37.95. Intro to TensorFlow for Deep Learning
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     <a class="reference internal" href="../lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
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-     37.101. Google Stock Price Prediction RNN
+     37.99. Google Stock Price Prediction RNN
     </a>
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-     37.103. Intro to Autoencoders
+     37.101. Intro to Autoencoders
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     <a class="reference internal" href="../autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
+     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
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     <a class="reference internal" href="../autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
+     37.103. Fun with Variational Autoencoders
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    <li class="toctree-l2">
     <a class="reference internal" href="../time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
+     37.104. Time Series Forecasting Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../nn-for-classification-assignment.html">
-     37.108. Neural Networks for Classification with TensorFlow
+     37.106. Neural Networks for Classification with TensorFlow
     </a>
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     <a class="reference internal" href="../nn-classify-15-fruits-assignment.html">
-     37.109. NN Classify 15 Fruits Assignment
+     37.107. NN Classify 15 Fruits Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../dqn/dqn-on-foreign-exchange-market.html">
-     37.114. DQN On Foreign Exchange Market
+     37.112. DQN On Foreign Exchange Market
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-     37.115. Art by gan
+     37.113. Art by gan
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     <a class="reference internal" href="../gan/gan-introduction.html">
-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
     </a>
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     <a class="reference internal" href="../overview/basic-classification-classify-images-of-clothing.html">
-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
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    38. Slides
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-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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-   37.94. Sign Language Digits Classification with CNN
+   37.92. Sign Language Digits Classification with CNN
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     <a class="reference internal nav-link" href="#load-libraries">
-     37.94.1. Load libraries
+     37.92.1. Load libraries
     </a>
    </li>
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     <a class="reference internal nav-link" href="#load-data-from-numpy-file">
-     37.94.2. Load data from numpy file
+     37.92.2. Load data from numpy file
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    <li class="toc-h2 nav-item toc-entry">
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-     37.94.3. Preparing Data
+     37.92.3. Preparing Data
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    </li>
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-     37.94.4. Train Test Split
+     37.92.4. Train Test Split
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-     37.94.5. Implementation of CNN
+     37.92.5. Implementation of CNN
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    </li>
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     <a class="reference internal nav-link" href="#data-augmentation-with-keras-api">
-     37.94.6. Data Augmentation With Keras API
+     37.92.6. Data Augmentation With Keras API
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
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-     37.94.7. Changin zoom level
+     37.92.7. Changin zoom level
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-     37.94.9. Changing rotaion, zoom
+     37.92.9. Changing rotaion, zoom
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-     37.94.10. Model Implementation
+     37.92.10. Model Implementation
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    </li>
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+     37.92.11. Conclusion
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@@ -1595,69 +1612,69 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
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-   37.94. Sign Language Digits Classification with CNN
+   37.92. Sign Language Digits Classification with CNN
   </a>
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    <li class="toc-h2 nav-item toc-entry">
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-     37.94.1. Load libraries
+     37.92.1. Load libraries
     </a>
    </li>
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     <a class="reference internal nav-link" href="#load-data-from-numpy-file">
-     37.94.2. Load data from numpy file
+     37.92.2. Load data from numpy file
     </a>
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-     37.94.3. Preparing Data
+     37.92.3. Preparing Data
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-     37.94.4. Train Test Split
+     37.92.4. Train Test Split
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-     37.94.5. Implementation of CNN
+     37.92.5. Implementation of CNN
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-augmentation-with-keras-api">
-     37.94.6. Data Augmentation With Keras API
+     37.92.6. Data Augmentation With Keras API
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
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-     37.94.7. Changin zoom level
+     37.92.7. Changin zoom level
     </a>
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-     37.94.8. Changing rotaion
+     37.92.8. Changing rotaion
     </a>
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-     37.94.9. Changing rotaion, zoom
+     37.92.9. Changing rotaion, zoom
     </a>
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-     37.94.10. Model Implementation
+     37.92.10. Model Implementation
     </a>
    </li>
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-     37.94.11. Conclusion
+     37.92.11. Conclusion
     </a>
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-   37.95. Acknowledgments
+   37.93. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1671,9 +1688,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="sign-language-digits-classification-with-cnn">
-<h1><span class="section-number">37.94. </span>Sign Language Digits Classification with CNN<a class="headerlink" href="#sign-language-digits-classification-with-cnn" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.92. </span>Sign Language Digits Classification with CNN<a class="headerlink" href="#sign-language-digits-classification-with-cnn" title="Permalink to this headline">#</a></h1>
 <section id="load-libraries">
-<h2><span class="section-number">37.94.1. </span>Load libraries<a class="headerlink" href="#load-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.92.1. </span>Load libraries<a class="headerlink" href="#load-libraries" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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@@ -1692,7 +1709,7 @@ <h2><span class="section-number">37.94.1. </span>Load libraries<a class="headerl
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 </section>
 <section id="load-data-from-numpy-file">
-<h2><span class="section-number">37.94.2. </span>Load data from numpy file<a class="headerlink" href="#load-data-from-numpy-file" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.92.2. </span>Load data from numpy file<a class="headerlink" href="#load-data-from-numpy-file" title="Permalink to this headline">#</a></h2>
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@@ -1757,7 +1774,7 @@ <h2><span class="section-number">37.94.2. </span>Load data from numpy file<a cla
 </ul>
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-<h2><span class="section-number">37.94.3. </span>Preparing Data<a class="headerlink" href="#preparing-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.92.3. </span>Preparing Data<a class="headerlink" href="#preparing-data" title="Permalink to this headline">#</a></h2>
 <p>We will re-organize data to match labels and images correctly.</p>
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 <li><p>204-409   =&gt; 0</p></li>
@@ -1810,7 +1827,7 @@ <h2><span class="section-number">37.94.3. </span>Preparing Data<a class="headerl
 </ul>
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 <section id="train-test-split">
-<h2><span class="section-number">37.94.4. </span>Train Test Split<a class="headerlink" href="#train-test-split" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.92.4. </span>Train Test Split<a class="headerlink" href="#train-test-split" title="Permalink to this headline">#</a></h2>
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@@ -1830,10 +1847,10 @@ <h2><span class="section-number">37.94.4. </span>Train Test Split<a class="heade
 </ul>
 </section>
 <section id="implementation-of-cnn">
-<h2><span class="section-number">37.94.5. </span>Implementation of CNN<a class="headerlink" href="#implementation-of-cnn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.92.5. </span>Implementation of CNN<a class="headerlink" href="#implementation-of-cnn" title="Permalink to this headline">#</a></h2>
 </section>
 <section id="data-augmentation-with-keras-api">
-<h2><span class="section-number">37.94.6. </span>Data Augmentation With Keras API<a class="headerlink" href="#data-augmentation-with-keras-api" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.92.6. </span>Data Augmentation With Keras API<a class="headerlink" href="#data-augmentation-with-keras-api" title="Permalink to this headline">#</a></h2>
 <p>Data augmentation is a technique which generates new training samples without changing labels of images. To generate new samples, some features of images are changed like brightness, rotation or zoom level. To apply it, ImageDataGenerator class is used in KERAS API. This class refers parameters and changes images. After complete the changing process, it returns new samples. This is important! ImageDataGenerator returns only new images. It means that out training dataset consists of different from original dataset. It provides more generalizaton for model anf of course it is desirable.</p>
 <p>So, in implementation of CNN part, we will use data augmentation and we will change rotation and zoom level of images. we chose these parameters with a simple logic. Think of test data that we might encounter in real life. we don’t always hold our hand at 90 degrees. So it is quite possible that we have a rotational change when using sign language. Likewise, the zoom level of the photo to be taken may also change. So we thought we could train my model better by creating a more general data set with these two parameters.  Let’s take a closer look at these parameters.</p>
 <ul class="simple">
@@ -1870,7 +1887,7 @@ <h2><span class="section-number">37.94.6. </span>Data Augmentation With Keras AP
 </div>
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-<h2><span class="section-number">37.94.7. </span>Changin zoom level<a class="headerlink" href="#changin-zoom-level" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.92.7. </span>Changin zoom level<a class="headerlink" href="#changin-zoom-level" title="Permalink to this headline">#</a></h2>
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-<h2><span class="section-number">37.94.8. </span>Changing rotaion<a class="headerlink" href="#changing-rotaion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.92.8. </span>Changing rotaion<a class="headerlink" href="#changing-rotaion" title="Permalink to this headline">#</a></h2>
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@@ -1894,7 +1911,7 @@ <h2><span class="section-number">37.94.8. </span>Changing rotaion<a class="heade
 </div>
 </section>
 <section id="changing-rotaion-zoom">
-<h2><span class="section-number">37.94.9. </span>Changing rotaion, zoom<a class="headerlink" href="#changing-rotaion-zoom" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.92.9. </span>Changing rotaion, zoom<a class="headerlink" href="#changing-rotaion-zoom" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">datagen</span> <span class="o">=</span> <span class="n">ImageDataGenerator</span><span class="p">(</span><span class="n">zoom_range</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">rotation_range</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
@@ -1906,7 +1923,7 @@ <h2><span class="section-number">37.94.9. </span>Changing rotaion, zoom<a class=
 </div>
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 <section id="model-implementation">
-<h2><span class="section-number">37.94.10. </span>Model Implementation<a class="headerlink" href="#model-implementation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.92.10. </span>Model Implementation<a class="headerlink" href="#model-implementation" title="Permalink to this headline">#</a></h2>
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 </div>
 </section>
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-<h2><span class="section-number">37.94.11. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.92.11. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
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@@ -1989,7 +2006,7 @@ <h2><span class="section-number">37.94.11. </span>Conclusion<a class="headerlink
 </section>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="acknowledgments">
-<h1><span class="section-number">37.95. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.93. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/gorkemgunay">Görkem Günay</a> for creating <a class="reference external" href="https://www.kaggle.com/code/gorkemgunay/sign-language-digits-classification-with-cnn">sign-language-digits-classification-with-cnn</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
@@ -2030,13 +2047,13 @@ <h1><span class="section-number">37.95. </span>Acknowledgments<a class="headerli
         <i class="fas fa-angle-left"></i>
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             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.92. </span>How to choose cnn architecture mnist</p>
+            <p class="prev-next-title"><span class="section-number">37.90. </span>How to choose cnn architecture mnist</p>
         </div>
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-     37.114. DQN On Foreign Exchange Market
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     <a class="reference internal" href="../gan/art-by-gan.html">
-     37.115. Art by gan
+     37.113. Art by gan
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    </li>
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     <a class="reference internal" href="../gan/gan-introduction.html">
-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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+     37.116. Basic classification: Classify images of clothing
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   <a class="reference internal nav-link" href="#load-dataset">
-   37.114.1. Load dataset
+   37.112.1. Load dataset
   </a>
  </li>
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   <a class="reference internal nav-link" href="#define-envireonment">
-   37.114.2. Define envireonment
+   37.112.2. Define envireonment
   </a>
  </li>
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   <a class="reference internal nav-link" href="#agent-class">
-   37.114.3. Agent class
+   37.112.3. Agent class
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  </li>
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   <a class="reference internal nav-link" href="#train-the-dqn">
-   37.114.4. Train the DQN
+   37.112.4. Train the DQN
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgement">
-   37.114.5. Acknowledgement
+   37.112.5. Acknowledgement
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 </ul>
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                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#load-dataset">
-   37.114.1. Load dataset
+   37.112.1. Load dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#define-envireonment">
-   37.114.2. Define envireonment
+   37.112.2. Define envireonment
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#agent-class">
-   37.114.3. Agent class
+   37.112.3. Agent class
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#train-the-dqn">
-   37.114.4. Train the DQN
+   37.112.4. Train the DQN
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgement">
-   37.114.5. Acknowledgement
+   37.112.5. Acknowledgement
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  </li>
 </ul>
@@ -1587,9 +1604,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="dqn-on-foreign-exchange-market">
-<h1><span class="section-number">37.114. </span>DQN On Foreign Exchange Market<a class="headerlink" href="#dqn-on-foreign-exchange-market" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.112. </span>DQN On Foreign Exchange Market<a class="headerlink" href="#dqn-on-foreign-exchange-market" title="Permalink to this headline">#</a></h1>
 <section id="load-dataset">
-<h2><span class="section-number">37.114.1. </span>Load dataset<a class="headerlink" href="#load-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.112.1. </span>Load dataset<a class="headerlink" href="#load-dataset" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># This Python 3 environment comes with many helpful analytics libraries installed</span>
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-<h2><span class="section-number">37.114.2. </span>Define envireonment<a class="headerlink" href="#define-envireonment" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.112.2. </span>Define envireonment<a class="headerlink" href="#define-envireonment" title="Permalink to this headline">#</a></h2>
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-<h2><span class="section-number">37.114.3. </span>Agent class<a class="headerlink" href="#agent-class" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.112.3. </span>Agent class<a class="headerlink" href="#agent-class" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Deep Q-learning Agent</span>
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+<h2><span class="section-number">37.112.4. </span>Train the DQN<a class="headerlink" href="#train-the-dqn" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
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 <section id="acknowledgement">
-<h2><span class="section-number">37.114.5. </span>Acknowledgement<a class="headerlink" href="#acknowledgement" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.112.5. </span>Acknowledgement<a class="headerlink" href="#acknowledgement" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/emrebulbul23">emrebulbul23</a> for creating <a class="reference external" href="https://www.kaggle.com/code/emrebulbul23/dqn-on-foreign-exchange-market">DQN on foreign exchange market</a>. It inspired the majority of the content in this article.</p>
 </section>
 </section>
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         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.109. </span>NN Classify 15 Fruits Assignment</p>
+            <p class="prev-next-title"><span class="section-number">37.107. </span>NN Classify 15 Fruits Assignment</p>
         </div>
     </a>
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+        <p class="prev-next-title"><span class="section-number">37.113. </span>Art by gan</p>
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     <i class="fas fa-angle-right"></i>
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-    <link rel="prev" title="37.114. DQN On Foreign Exchange Market" href="../dqn/dqn-on-foreign-exchange-market.html" />
+    <link rel="next" title="37.115. Generative Adversarial Networks (GANs)" href="gan-introduction.html" />
+    <link rel="prev" title="37.112. DQN On Foreign Exchange Market" href="../dqn/dqn-on-foreign-exchange-market.html" />
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      5.3. NumPy
     </a>
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-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/working-with-data/data-preparation.html">
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-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-metrics.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
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     <a class="reference internal" href="../../ml-fundamentals/create-a-regression-model.html">
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-<h1><span class="section-number">37.115. </span>Art by gan<a class="headerlink" href="#art-by-gan" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.113. </span>Art by gan<a class="headerlink" href="#art-by-gan" title="Permalink to this headline">#</a></h1>
 <p>In this Notebook, we will build a Generative Adversarial Network  (GAN) to illustrate the workings of a Generative Adversarial Network and to generate images. Generative modelling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input data. As GANs work by identifying the patterns in the data, we will be using oil painted portraits. However, glancing over the dataset gives me an idea that it is going to be a long shot. The orientation and poses in the dataset vary vastly. Keeping that in mind we are still willing to give it a try. Only because portraits are our jam. We basically love oil painted portraits.</p>
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+<h2><span class="section-number">37.113.1. </span>Data loading &amp; Prepreprocessing<a class="headerlink" href="#data-loading-prepreprocessing" title="Permalink to this headline">#</a></h2>
 <p>For this project, We are using .jpg files of images of portraits. The dataset includes various artists. We are loading data as TensorFlow.Dataset, with a batch size of 64. We have reduced the image size to (64,64), presuming, it will be computationally less taxing on the GPU.</p>
 </section>
 <section id="loading-the-data">
-<h2><span class="section-number">37.115.2. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
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 <p>Most of the images are portraits. A portrait is a painting representation of a person, The face is predominantly depicted portraits along with expressions and postures. To represent the personality of the subject. Since our model is relative a smaller GAN we have reduced the size of the image.</p>
 </section>
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+<h2><span class="section-number">37.113.3. </span>Preprocessing the data<a class="headerlink" href="#preprocessing-the-data" title="Permalink to this headline">#</a></h2>
 <p><strong>Normalization:</strong> For the data normalization, we will convert the data in the range between 0 to 1. This helps in fast convergence and makes it easy for the computer to do calculations faster.
 Each of the three RGB channels in the image can take pixel values ranging from 0 to 256. Dividing it by 255 converts it to a range between 0 to 1.</p>
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+<h2><span class="section-number">37.113.4. </span>Building GAN<a class="headerlink" href="#building-gan" title="Permalink to this headline">#</a></h2>
 <p>GANs employs deep learning methods. It is a dexterous way of posing the problem as a supervised learning problem. It is composed of two models namely Generator and a Discriminator.</p>
 <p>Two models are trained simultaneously by an adversarial process. A generator (“the artist”) learns to create images that look like the dataset while a discriminator (“the art critic”) learns to tell real images apart from fakes.</p>
 <p>During training, the generator progressively becomes better at creating images that look real, while the discriminator becomes better at telling them apart. The process reaches equilibrium when the discriminator can no longer distinguish real images from fakes.</p>
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 <p>The Generator is a neural network that generates the images. It takes in a random noise as seed and outputs sample data. As the GAN’s training progresses the Generator output becomes more and more like the training set, as the Generator tries to improve the output so that the discrimination passes the output as a real image.</p>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">latent_dim</span> <span class="o">=</span> <span class="mi">100</span>
@@ -1879,12 +1896,12 @@ <h2><span class="section-number">37.115.6. </span>Building a generator<a class="
 <p>Clearly, the output is a random seed containing noise as the Generator is not trained yet.</p>
 </section>
 <section id="the-discriminator">
-<h2><span class="section-number">37.115.7. </span>The discriminator<a class="headerlink" href="#the-discriminator" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.113.7. </span>The discriminator<a class="headerlink" href="#the-discriminator" title="Permalink to this headline">#</a></h2>
 <p>In GANs the Generator works along with the Discriminator.</p>
 <p>The Discriminator network decided whether the data is fake aka created by the Generator or real i.e. from the original input data. To do so it applies a binary classification method using a sigmoid function to get an output in the range of 0 to 1.</p>
 </section>
 <section id="building-a-discriminator">
-<h2><span class="section-number">37.115.8. </span>Building a discriminator<a class="headerlink" href="#building-a-discriminator" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.113.8. </span>Building a discriminator<a class="headerlink" href="#building-a-discriminator" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">discriminator</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
@@ -1933,7 +1950,7 @@ <h2><span class="section-number">37.115.8. </span>Building a discriminator<a cla
 <p>Let us proceed and build the GAN architecture to train.</p>
 </section>
 <section id="gan-compilation">
-<h2><span class="section-number">37.115.9. </span>GAN compilation<a class="headerlink" href="#gan-compilation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.113.9. </span>GAN compilation<a class="headerlink" href="#gan-compilation" title="Permalink to this headline">#</a></h2>
 <p>GAN training has two sections:</p>
 <p><strong>Section 1</strong>: The Discriminator is trained while the Generator is idle.
 The discriminator is trained real images and random noise (from an untrained generator). This trains it to tell between fake and real. This accommodates the discriminator to predict as fakes.</p>
@@ -2010,7 +2027,7 @@ <h2><span class="section-number">37.115.9. </span>GAN compilation<a class="heade
 </div>
 </section>
 <section id="training-the-model">
-<h2><span class="section-number">37.115.10. </span>Training the model<a class="headerlink" href="#training-the-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.113.10. </span>Training the model<a class="headerlink" href="#training-the-model" title="Permalink to this headline">#</a></h2>
 <p>Calling the above created GAN function trains the generator and discriminator simultaneously.
 To implement the GAN we must define:</p>
 <ul class="simple">
@@ -2043,7 +2060,7 @@ <h2><span class="section-number">37.115.10. </span>Training the model<a class="h
 </div>
 </section>
 <section id="ploting-the-learning-curves">
-<h2><span class="section-number">37.115.11. </span>Ploting the Learning Curves<a class="headerlink" href="#ploting-the-learning-curves" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.113.11. </span>Ploting the Learning Curves<a class="headerlink" href="#ploting-the-learning-curves" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
@@ -2060,7 +2077,7 @@ <h2><span class="section-number">37.115.11. </span>Ploting the Learning Curves<a
 To get the art output we will create a function that saves the output portraits generated. We will be plotting the generated Portraits</p>
 </section>
 <section id="ai-makes-artwork">
-<h2><span class="section-number">37.115.12. </span>AI makes artwork<a class="headerlink" href="#ai-makes-artwork" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.113.12. </span>AI makes artwork<a class="headerlink" href="#ai-makes-artwork" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Number of images to be generate</span>
@@ -2100,12 +2117,12 @@ <h2><span class="section-number">37.115.12. </span>AI makes artwork<a class="hea
 </div>
 </section>
 <section id="conculsion">
-<h2><span class="section-number">37.115.13. </span>Conculsion<a class="headerlink" href="#conculsion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.113.13. </span>Conculsion<a class="headerlink" href="#conculsion" title="Permalink to this headline">#</a></h2>
 <p>In the evaluation of the model: We can see that the GAN picked up the patterns in the portraits. It worked quite well. For further improvement,  as GANs are notorious for being data-hungry, I would consider increasing the dataset. There are many inconsistencies in the data which is rather complicated for the GAN to learn. Cleaning the data with some consistencies in the portrait styles would certainly help. Training it longer i.e. for more epochs would also help. Lastly, one can always strive to make a  more robust architecture for the Neural Networks.</p>
 </section>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="acknowledgments">
-<h1><span class="section-number">37.116. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.114. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/karnikakapoor">Karnika Kapoor</a> for creating <a class="reference external" href="https://www.kaggle.com/code/karnikakapoor/art-by-gan">art-by-gan</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
@@ -2146,13 +2163,13 @@ <h1><span class="section-number">37.116. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
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-            <p class="prev-next-title"><span class="section-number">37.114. </span>DQN On Foreign Exchange Market</p>
+            <p class="prev-next-title"><span class="section-number">37.112. </span>DQN On Foreign Exchange Market</p>
         </div>
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+        <p class="prev-next-title"><span class="section-number">37.115. </span>Generative Adversarial Networks (GANs)</p>
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diff --git a/assignments/deep-learning/gan/gan-introduction.html b/assignments/deep-learning/gan/gan-introduction.html
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-    <title>37.117. Generative Adversarial Networks (GANs) &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.115. Generative Adversarial Networks (GANs) &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../../genindex.html" />
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-    <link rel="next" title="37.118. Basic classification: Classify images of clothing" href="../overview/basic-classification-classify-images-of-clothing.html" />
-    <link rel="prev" title="37.115. Art by gan" href="art-by-gan.html" />
+    <link rel="next" title="37.116. Basic classification: Classify images of clothing" href="../overview/basic-classification-classify-images-of-clothing.html" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-visualization/data-visualization.html">
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   <a class="reference internal" href="../../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
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   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
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-  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
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   <a class="reference internal" href="../../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
    14. Clustering models for Machine Learning
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    15. Getting started with ensemble learning
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    16. Introduction to Gradient Boosting
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   <a class="reference internal" href="../../README.html">
    37. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
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-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-1.html">
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-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
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    </li>
    <li class="toctree-l2">
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-     37.48. Loss Function
+     37.47. Loss Function
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
+     37.50. ML logistic regression - assignment 1
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     <a class="reference internal" href="../../ml-fundamentals/ml-logistic-regression-2.html">
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+     37.51. ML logistic regression - assignment 2
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-     37.53. ML neural network - Assignment 1
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    <li class="toctree-l2">
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-     37.54. Regression tools
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    </li>
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-     37.55. Managing data
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    </li>
    <li class="toctree-l2">
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-     37.56. Exploring visualizations
+     37.55. Exploring visualizations
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/try-a-different-model.html">
-     37.57. Try a different model
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    </li>
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-     37.58. Create a regression model
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     <a class="reference internal" href="../../ml-fundamentals/linear-and-polynomial-regression.html">
-     37.59. Linear and polynomial regression
+     37.58. Linear and polynomial regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/retrying-some-regression.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
+     37.60. Pumpkin varieties and color
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.62. Delicious asian and indian cuisines
+     37.61. Delicious asian and indian cuisines
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    <li class="toctree-l2">
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-     37.63. Explore classification methods
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-    <a class="reference internal" href="../../ml-fundamentals/linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
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     <a class="reference internal" href="../../ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.65. Kernel method assignment 1
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     <a class="reference internal" href="../../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/kernel-method/decision_trees_for_regression.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/kernel-method/decision_trees_for_classification.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/model-selection/model-selection-assignment-1.html">
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     <a class="reference internal" href="../../ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
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     <a class="reference internal" href="../../ml-advanced/model-selection/dropout-and-batch-normalization.html">
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     <a class="reference internal" href="../../ml-advanced/model-selection/regularized-linear-models.html">
-     37.74. Regularized Linear Models
+     37.72. Regularized Linear Models
     </a>
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
-     37.75. Random forests intro and regression
+     37.73. Random forests intro and regression
     </a>
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-     37.76. Random forests for classification
+     37.74. Random forests for classification
     </a>
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
-     37.77. Beyond random forests: more ensemble models
+     37.75. Beyond random forests: more ensemble models
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/decision-trees.html">
-     37.78. Decision trees
+     37.76. Decision trees
     </a>
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     <a class="reference internal" href="../../ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
-     37.79. Hyperparameter tuning gradient boosting
+     37.77. Hyperparameter tuning gradient boosting
     </a>
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-     37.80. Gradient boosting
+     37.78. Gradient boosting
     </a>
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-     37.81. Boosting with tuning
+     37.79. Boosting with tuning
     </a>
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
-     37.82. Random Forest Classifier with Feature Importance
+     37.80. Random Forest Classifier with Feature Importance
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
-     37.84. Data engineering
+     37.82. Data engineering
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
-     37.85. Counterintuitive Challenges in ML Debugging
+     37.83. Counterintuitive Challenges in ML Debugging
     </a>
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     <a class="reference internal" href="../../machine-learning-productionization/debugging-in-classification.html">
-     37.86. Case Study: Debugging in Classification
+     37.84. Case Study: Debugging in Classification
     </a>
    </li>
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     <a class="reference internal" href="../../machine-learning-productionization/debugging-in-regression.html">
-     37.87. Case Study: Debugging in Regression
+     37.85. Case Study: Debugging in Regression
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/study-the-solvers.html">
-     37.88. Study the solvers
+     37.86. Study the solvers
     </a>
    </li>
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     <a class="reference internal" href="../../ml-fundamentals/build-classification-models.html">
-     37.89. Build classification models
+     37.87. Build classification models
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     <a class="reference internal" href="../../ml-fundamentals/build-classification-model.html">
-     37.90. Build Classification Model
+     37.88. Build Classification Model
     </a>
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     <a class="reference internal" href="../../ml-fundamentals/parameter-play.html">
-     37.91. Parameter play
+     37.89. Parameter play
     </a>
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     <a class="reference internal" href="../cnn/how-to-choose-cnn-architecture-mnist.html">
-     37.92. How to choose cnn architecture mnist
+     37.90. How to choose cnn architecture mnist
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../cnn/sign-language-digits-classification-with-cnn.html">
-     37.94. Sign Language Digits Classification with CNN
+     37.92. Sign Language Digits Classification with CNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../cnn/object-recognition-in-images-using-cnn.html">
-     37.96. Object Recognition in Images using CNN
+     37.94. Object Recognition in Images using CNN
     </a>
    </li>
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     <a class="reference internal" href="../tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.97. Intro to TensorFlow for Deep Learning
+     37.95. Intro to TensorFlow for Deep Learning
     </a>
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     <a class="reference internal" href="../lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
     </a>
    </li>
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     <a class="reference internal" href="../rnn/google-stock-price-prediction-rnn.html">
-     37.101. Google Stock Price Prediction RNN
+     37.99. Google Stock Price Prediction RNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../autoencoder/autoencoder.html">
-     37.103. Intro to Autoencoders
+     37.101. Intro to Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
+     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
+     37.103. Fun with Variational Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
+     37.104. Time Series Forecasting Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../nn-for-classification-assignment.html">
-     37.108. Neural Networks for Classification with TensorFlow
+     37.106. Neural Networks for Classification with TensorFlow
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../nn-classify-15-fruits-assignment.html">
-     37.109. NN Classify 15 Fruits Assignment
+     37.107. NN Classify 15 Fruits Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../dqn/dqn-on-foreign-exchange-market.html">
-     37.114. DQN On Foreign Exchange Market
+     37.112. DQN On Foreign Exchange Market
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="art-by-gan.html">
-     37.115. Art by gan
+     37.113. Art by gan
     </a>
    </li>
    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../overview/basic-classification-classify-images-of-clothing.html">
-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
     </a>
    </li>
   </ul>
@@ -1135,8 +1152,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../slides/introduction.html">
    38. Slides
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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@@ -1509,91 +1526,91 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
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-   37.117.1. Loading data
+   37.115.1. Loading data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-libraries">
-   37.117.2. Importing the libraries
+   37.115.2. Importing the libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#some-dogs">
-   37.117.3. Some dogs
+   37.115.3. Some dogs
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#image-preprocessing">
-   37.117.4. Image Preprocessing
+   37.115.4. Image Preprocessing
   </a>
  </li>
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   <a class="reference internal nav-link" href="#weights">
-   37.117.5. Weights
+   37.115.5. Weights
   </a>
  </li>
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   <a class="reference internal nav-link" href="#generator">
-   37.117.6. Generator
+   37.115.6. Generator
   </a>
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   <a class="reference internal nav-link" href="#discriminator">
-   37.117.7. Discriminator
+   37.115.7. Discriminator
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#another-setup">
-   37.117.8. Another setup
+   37.115.8. Another setup
   </a>
  </li>
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   <a class="reference internal nav-link" href="#training">
-   37.117.9. Training
+   37.115.9. Training
   </a>
  </li>
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   <a class="reference internal nav-link" href="#best-public-training">
-   37.117.10. Best public training
+   37.115.10. Best public training
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#parameters">
-     37.117.10.1. Parameters
+     37.115.10.1. Parameters
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#initialize-models-and-optimizers">
-     37.117.10.2. Initialize models and optimizers
+     37.115.10.2. Initialize models and optimizers
     </a>
    </li>
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-   37.117.11. Show generated images
+   37.115.11. Show generated images
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-   37.117.12. Training Loop
+   37.115.12. Training Loop
   </a>
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   <a class="reference internal nav-link" href="#generation-example">
-   37.117.13. Generation example
+   37.115.13. Generation example
   </a>
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     <a class="reference internal nav-link" href="#save-models">
-     37.117.13.1. Save models
+     37.115.13.1. Save models
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgement">
-   37.117.14. Acknowledgement
+   37.115.14. Acknowledgement
   </a>
  </li>
 </ul>
@@ -1617,91 +1634,91 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loading-data">
-   37.117.1. Loading data
+   37.115.1. Loading data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-libraries">
-   37.117.2. Importing the libraries
+   37.115.2. Importing the libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#some-dogs">
-   37.117.3. Some dogs
+   37.115.3. Some dogs
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#image-preprocessing">
-   37.117.4. Image Preprocessing
+   37.115.4. Image Preprocessing
   </a>
  </li>
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   <a class="reference internal nav-link" href="#weights">
-   37.117.5. Weights
+   37.115.5. Weights
   </a>
  </li>
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-   37.117.6. Generator
+   37.115.6. Generator
   </a>
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-   37.117.7. Discriminator
+   37.115.7. Discriminator
   </a>
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   <a class="reference internal nav-link" href="#another-setup">
-   37.117.8. Another setup
+   37.115.8. Another setup
   </a>
  </li>
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   <a class="reference internal nav-link" href="#training">
-   37.117.9. Training
+   37.115.9. Training
   </a>
  </li>
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   <a class="reference internal nav-link" href="#best-public-training">
-   37.117.10. Best public training
+   37.115.10. Best public training
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#parameters">
-     37.117.10.1. Parameters
+     37.115.10.1. Parameters
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#initialize-models-and-optimizers">
-     37.117.10.2. Initialize models and optimizers
+     37.115.10.2. Initialize models and optimizers
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#show-generated-images">
-   37.117.11. Show generated images
+   37.115.11. Show generated images
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-loop">
-   37.117.12. Training Loop
+   37.115.12. Training Loop
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#generation-example">
-   37.117.13. Generation example
+   37.115.13. Generation example
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#save-models">
-     37.117.13.1. Save models
+     37.115.13.1. Save models
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgement">
-   37.117.14. Acknowledgement
+   37.115.14. Acknowledgement
   </a>
  </li>
 </ul>
@@ -1715,9 +1732,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="generative-adversarial-networks-gans">
-<h1><span class="section-number">37.117. </span>Generative Adversarial Networks (GANs)<a class="headerlink" href="#generative-adversarial-networks-gans" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.115. </span>Generative Adversarial Networks (GANs)<a class="headerlink" href="#generative-adversarial-networks-gans" title="Permalink to this headline">#</a></h1>
 <section id="loading-data">
-<h2><span class="section-number">37.117.1. </span>Loading data<a class="headerlink" href="#loading-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.115.1. </span>Loading data<a class="headerlink" href="#loading-data" title="Permalink to this headline">#</a></h2>
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 </div>
 </section>
 <section id="importing-the-libraries">
-<h2><span class="section-number">37.117.2. </span>Importing the libraries<a class="headerlink" href="#importing-the-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.115.2. </span>Importing the libraries<a class="headerlink" href="#importing-the-libraries" title="Permalink to this headline">#</a></h2>
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@@ -1781,7 +1798,7 @@ <h2><span class="section-number">37.117.2. </span>Importing the libraries<a clas
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 </section>
 <section id="some-dogs">
-<h2><span class="section-number">37.117.3. </span>Some dogs<a class="headerlink" href="#some-dogs" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.115.3. </span>Some dogs<a class="headerlink" href="#some-dogs" title="Permalink to this headline">#</a></h2>
 <p>The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world.</p>
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@@ -1804,7 +1821,7 @@ <h2><span class="section-number">37.117.3. </span>Some dogs<a class="headerlink"
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 <section id="image-preprocessing">
-<h2><span class="section-number">37.117.4. </span>Image Preprocessing<a class="headerlink" href="#image-preprocessing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.115.4. </span>Image Preprocessing<a class="headerlink" href="#image-preprocessing" title="Permalink to this headline">#</a></h2>
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@@ -1839,7 +1856,7 @@ <h2><span class="section-number">37.117.4. </span>Image Preprocessing<a class="h
 </div>
 </section>
 <section id="weights">
-<h2><span class="section-number">37.117.5. </span>Weights<a class="headerlink" href="#weights" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.115.5. </span>Weights<a class="headerlink" href="#weights" title="Permalink to this headline">#</a></h2>
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@@ -1855,7 +1872,7 @@ <h2><span class="section-number">37.117.5. </span>Weights<a class="headerlink" h
 </div>
 </section>
 <section id="generator">
-<h2><span class="section-number">37.117.6. </span>Generator<a class="headerlink" href="#generator" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.115.6. </span>Generator<a class="headerlink" href="#generator" title="Permalink to this headline">#</a></h2>
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@@ -1893,7 +1910,7 @@ <h2><span class="section-number">37.117.6. </span>Generator<a class="headerlink"
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 <section id="discriminator">
-<h2><span class="section-number">37.117.7. </span>Discriminator<a class="headerlink" href="#discriminator" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.115.7. </span>Discriminator<a class="headerlink" href="#discriminator" title="Permalink to this headline">#</a></h2>
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@@ -1926,7 +1943,7 @@ <h2><span class="section-number">37.117.7. </span>Discriminator<a class="headerl
 </div>
 </section>
 <section id="another-setup">
-<h2><span class="section-number">37.117.8. </span>Another setup<a class="headerlink" href="#another-setup" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.115.8. </span>Another setup<a class="headerlink" href="#another-setup" title="Permalink to this headline">#</a></h2>
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@@ -1992,7 +2009,7 @@ <h2><span class="section-number">37.117.8. </span>Another setup<a class="headerl
 </div>
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 <section id="training">
-<h2><span class="section-number">37.117.9. </span>Training<a class="headerlink" href="#training" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.115.9. </span>Training<a class="headerlink" href="#training" title="Permalink to this headline">#</a></h2>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">EPOCH</span> <span class="o">=</span> <span class="mi">0</span>
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 <section id="parameters">
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+<h3><span class="section-number">37.115.10.1. </span>Parameters<a class="headerlink" href="#parameters" title="Permalink to this headline">#</a></h3>
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 </div>
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+<h3><span class="section-number">37.115.10.2. </span>Initialize models and optimizers<a class="headerlink" href="#initialize-models-and-optimizers" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">netG</span> <span class="o">=</span> <span class="n">Generator</span><span class="p">(</span><span class="n">nz</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
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 <div class="cell docutils container">
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@@ -2136,7 +2153,7 @@ <h2><span class="section-number">37.117.11. </span>Show generated images<a class
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-<h2><span class="section-number">37.117.12. </span>Training Loop<a class="headerlink" href="#training-loop" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.115.12. </span>Training Loop<a class="headerlink" href="#training-loop" title="Permalink to this headline">#</a></h2>
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-<h2><span class="section-number">37.117.13. </span>Generation example<a class="headerlink" href="#generation-example" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.115.13. </span>Generation example<a class="headerlink" href="#generation-example" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">show_generated_img</span><span class="p">(</span><span class="mi">7</span><span class="p">)</span>
@@ -2247,7 +2264,7 @@ <h2><span class="section-number">37.117.13. </span>Generation example<a class="h
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 <section id="save-models">
-<h3><span class="section-number">37.117.13.1. </span>Save models<a class="headerlink" href="#save-models" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.115.13.1. </span>Save models<a class="headerlink" href="#save-models" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">netG</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="s1">&#39;generator.pth&#39;</span><span class="p">)</span>
@@ -2259,7 +2276,7 @@ <h3><span class="section-number">37.117.13.1. </span>Save models<a class="header
 </section>
 </section>
 <section id="acknowledgement">
-<h2><span class="section-number">37.117.14. </span>Acknowledgement<a class="headerlink" href="#acknowledgement" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.115.14. </span>Acknowledgement<a class="headerlink" href="#acknowledgement" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/jesucristo">jesucristo</a> for creating <a class="reference external" href="https://www.kaggle.com/code/jesucristo/gan-introduction/notebook">GAN Introduction</a>. It inspired the majority of the content in this article.</p>
 </section>
 </section>
@@ -2301,13 +2318,13 @@ <h2><span class="section-number">37.117.14. </span>Acknowledgement<a class="head
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.115. </span>Art by gan</p>
+            <p class="prev-next-title"><span class="section-number">37.113. </span>Art by gan</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../overview/basic-classification-classify-images-of-clothing.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.118. </span>Basic classification: Classify images of clothing</p>
+        <p class="prev-next-title"><span class="section-number">37.116. </span>Basic classification: Classify images of clothing</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html b/assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html
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-    <title>37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment &#8212; Ocademy Open Machine Learning Book</title>
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     <link rel="index" title="Index" href="../../../genindex.html" />
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-    <link rel="next" title="37.101. Google Stock Price Prediction RNN" href="../rnn/google-stock-price-prediction-rnn.html" />
-    <link rel="prev" title="37.97. Intro to TensorFlow for Deep Learning" href="../tensorflow/intro_to_tensorflow_for_deeplearning.html" />
+    <link rel="next" title="37.99. Google Stock Price Prediction RNN" href="../rnn/google-stock-price-prediction-rnn.html" />
+    <link rel="prev" title="37.95. Intro to TensorFlow for Deep Learning" href="../tensorflow/intro_to_tensorflow_for_deeplearning.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
-  <label for="toctree-checkbox-3">
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+  <label for="toctree-checkbox-4">
    <i class="fas fa-chevron-down">
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   <a class="reference internal" href="../../../data-science/data-science-lifecycle/data-science-lifecycle.html">
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-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
-  <label for="toctree-checkbox-4">
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-  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
-  <label for="toctree-checkbox-5">
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-  <label for="toctree-checkbox-6">
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    13. Getting started with classification
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   <a class="reference internal" href="../../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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-  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
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+  <label for="toctree-checkbox-9">
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
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-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
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-   <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-metrics.html">
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    <li class="toctree-l2">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/ml-logistic-regression-1.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/ml-logistic-regression-2.html">
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    <li class="toctree-l2">
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-     37.53. ML neural network - Assignment 1
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    <li class="toctree-l2">
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-     37.54. Regression tools
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    </li>
    <li class="toctree-l2">
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-     37.55. Managing data
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/exploring-visualizations.html">
-     37.56. Exploring visualizations
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/try-a-different-model.html">
-     37.57. Try a different model
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-     37.58. Create a regression model
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-and-polynomial-regression.html">
-     37.59. Linear and polynomial regression
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     <a class="reference internal" href="../../ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
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     <a class="reference internal" href="../../ml-fundamentals/delicious-asian-and-indian-cuisines.html">
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    <li class="toctree-l2">
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-     37.63. Explore classification methods
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-   <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
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     <a class="reference internal" href="../../ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.65. Kernel method assignment 1
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     <a class="reference internal" href="../../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/kernel-method/decision_trees_for_regression.html">
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     <a class="reference internal" href="../../ml-advanced/kernel-method/decision_trees_for_classification.html">
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+     37.76. Decision trees
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     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
-     37.82. Random Forest Classifier with Feature Importance
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     <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
-     37.84. Data engineering
+     37.82. Data engineering
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     <a class="reference internal" href="../../machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
-     37.85. Counterintuitive Challenges in ML Debugging
+     37.83. Counterintuitive Challenges in ML Debugging
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     <a class="reference internal" href="../../machine-learning-productionization/debugging-in-classification.html">
-     37.86. Case Study: Debugging in Classification
+     37.84. Case Study: Debugging in Classification
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     <a class="reference internal" href="../../machine-learning-productionization/debugging-in-regression.html">
-     37.87. Case Study: Debugging in Regression
+     37.85. Case Study: Debugging in Regression
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    </li>
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     <a class="reference internal" href="../../ml-fundamentals/study-the-solvers.html">
-     37.88. Study the solvers
+     37.86. Study the solvers
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+     37.88. Build Classification Model
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     <a class="reference internal" href="../../ml-fundamentals/parameter-play.html">
-     37.91. Parameter play
+     37.89. Parameter play
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     <a class="reference internal" href="../cnn/how-to-choose-cnn-architecture-mnist.html">
-     37.92. How to choose cnn architecture mnist
+     37.90. How to choose cnn architecture mnist
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    <li class="toctree-l2">
     <a class="reference internal" href="../cnn/sign-language-digits-classification-with-cnn.html">
-     37.94. Sign Language Digits Classification with CNN
+     37.92. Sign Language Digits Classification with CNN
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    </li>
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     <a class="reference internal" href="../cnn/object-recognition-in-images-using-cnn.html">
-     37.96. Object Recognition in Images using CNN
+     37.94. Object Recognition in Images using CNN
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     <a class="reference internal" href="../tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.97. Intro to TensorFlow for Deep Learning
+     37.95. Intro to TensorFlow for Deep Learning
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    </li>
    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
     </a>
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     <a class="reference internal" href="../rnn/google-stock-price-prediction-rnn.html">
-     37.101. Google Stock Price Prediction RNN
+     37.99. Google Stock Price Prediction RNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../autoencoder/autoencoder.html">
-     37.103. Intro to Autoencoders
+     37.101. Intro to Autoencoders
     </a>
    </li>
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     <a class="reference internal" href="../autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
+     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
+     37.103. Fun with Variational Autoencoders
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
+     37.104. Time Series Forecasting Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../nn-for-classification-assignment.html">
-     37.108. Neural Networks for Classification with TensorFlow
+     37.106. Neural Networks for Classification with TensorFlow
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../nn-classify-15-fruits-assignment.html">
-     37.109. NN Classify 15 Fruits Assignment
+     37.107. NN Classify 15 Fruits Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../dqn/dqn-on-foreign-exchange-market.html">
-     37.114. DQN On Foreign Exchange Market
+     37.112. DQN On Foreign Exchange Market
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../gan/art-by-gan.html">
-     37.115. Art by gan
+     37.113. Art by gan
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../gan/gan-introduction.html">
-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../overview/basic-classification-classify-images-of-clothing.html">
-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
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    38. Slides
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         <ul class="visible nav section-nav flex-column">
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#">
-   37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
+   37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
   </a>
   <ul class="visible nav section-nav flex-column">
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#load-libraries">
-     37.99.1. Load libraries
+     37.97.1. Load libraries
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-pre-processing">
-     37.99.2. Data Pre-processing
+     37.97.2. Data Pre-processing
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploratory-analysis">
-     37.99.3. Exploratory Analysis
+     37.97.3. Exploratory Analysis
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    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#lstm-model">
-     37.99.4. LSTM Model
+     37.97.4. LSTM Model
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgements">
-   37.100. Acknowledgements
+   37.98. Acknowledgements
   </a>
  </li>
 </ul>
@@ -1560,34 +1577,34 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#">
-   37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
+   37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
   </a>
   <ul class="visible nav section-nav flex-column">
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#load-libraries">
-     37.99.1. Load libraries
+     37.97.1. Load libraries
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-pre-processing">
-     37.99.2. Data Pre-processing
+     37.97.2. Data Pre-processing
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploratory-analysis">
-     37.99.3. Exploratory Analysis
+     37.97.3. Exploratory Analysis
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#lstm-model">
-     37.99.4. LSTM Model
+     37.97.4. LSTM Model
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgements">
-   37.100. Acknowledgements
+   37.98. Acknowledgements
   </a>
  </li>
 </ul>
@@ -1601,9 +1618,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="bitcoin-lstm-model-with-tweet-volume-and-sentiment">
-<h1><span class="section-number">37.99. </span>Bitcoin LSTM Model with Tweet Volume and Sentiment<a class="headerlink" href="#bitcoin-lstm-model-with-tweet-volume-and-sentiment" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.97. </span>Bitcoin LSTM Model with Tweet Volume and Sentiment<a class="headerlink" href="#bitcoin-lstm-model-with-tweet-volume-and-sentiment" title="Permalink to this headline">#</a></h1>
 <section id="load-libraries">
-<h2><span class="section-number">37.99.1. </span>Load libraries<a class="headerlink" href="#load-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.97.1. </span>Load libraries<a class="headerlink" href="#load-libraries" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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@@ -1634,7 +1651,7 @@ <h2><span class="section-number">37.99.1. </span>Load libraries<a class="headerl
 </div>
 </section>
 <section id="data-pre-processing">
-<h2><span class="section-number">37.99.2. </span>Data Pre-processing<a class="headerlink" href="#data-pre-processing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.97.2. </span>Data Pre-processing<a class="headerlink" href="#data-pre-processing" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">notclean</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span>
@@ -3333,7 +3350,7 @@ <h2><span class="section-number">37.99.2. </span>Data Pre-processing<a class="he
 </div>
 </section>
 <section id="exploratory-analysis">
-<h2><span class="section-number">37.99.3. </span>Exploratory Analysis<a class="headerlink" href="#exploratory-analysis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.97.3. </span>Exploratory Analysis<a class="headerlink" href="#exploratory-analysis" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 </div>
 </section>
 <section id="lstm-model">
-<h2><span class="section-number">37.99.4. </span>LSTM Model<a class="headerlink" href="#lstm-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.97.4. </span>LSTM Model<a class="headerlink" href="#lstm-model" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">math</span> <span class="kn">import</span> <span class="n">sqrt</span>
@@ -5494,7 +5511,7 @@ <h2><span class="section-number">37.99.4. </span>LSTM Model<a class="headerlink"
 </section>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="acknowledgements">
-<h1><span class="section-number">37.100. </span>Acknowledgements<a class="headerlink" href="#acknowledgements" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.98. </span>Acknowledgements<a class="headerlink" href="#acknowledgements" title="Permalink to this headline">#</a></h1>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/paul92s">Paul Simpson</a> for creating <a class="reference external" href="https://www.kaggle.com/code/paul92s/bitcoin-lstm-model-with-tweet-volume-and-sentiment">Bitcoin Lstm Model with Tweet Volume and Sentiment</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
@@ -5535,13 +5552,13 @@ <h1><span class="section-number">37.100. </span>Acknowledgements<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.97. </span>Intro to TensorFlow for Deep Learning</p>
+            <p class="prev-next-title"><span class="section-number">37.95. </span>Intro to TensorFlow for Deep Learning</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../rnn/google-stock-price-prediction-rnn.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.101. </span>Google Stock Price Prediction RNN</p>
+        <p class="prev-next-title"><span class="section-number">37.99. </span>Google Stock Price Prediction RNN</p>
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diff --git a/assignments/deep-learning/nn-classify-15-fruits-assignment.html b/assignments/deep-learning/nn-classify-15-fruits-assignment.html
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-    <title>37.109. NN Classify 15 Fruits Assignment &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.107. NN Classify 15 Fruits Assignment &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.114. DQN On Foreign Exchange Market" href="dqn/dqn-on-foreign-exchange-market.html" />
-    <link rel="prev" title="37.108. Neural Networks for Classification with TensorFlow" href="nn-for-classification-assignment.html" />
+    <link rel="next" title="37.112. DQN On Foreign Exchange Market" href="dqn/dqn-on-foreign-exchange-market.html" />
+    <link rel="prev" title="37.106. Neural Networks for Classification with TensorFlow" href="nn-for-classification-assignment.html" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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    6. Data visualization
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-  <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
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    7. Data Science lifecycle
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-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
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    8. Data Science in the cloud
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    11. Regression models for Machine Learning
   </a>
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-  <label for="toctree-checkbox-6">
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    13. Getting started with classification
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-    <a class="reference internal" href="../ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
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-     37.49. Gradient descent
+     37.48. Gradient descent
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      </a>
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+     37.110.3. Visualize the result with pictures of fruits
     </a>
    </li>
   </ul>
  </li>
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               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="nn-classify-15-fruits-assignment">
-<h1><span class="section-number">37.109. </span>NN Classify 15 Fruits Assignment<a class="headerlink" href="#nn-classify-15-fruits-assignment" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.107. </span>NN Classify 15 Fruits Assignment<a class="headerlink" href="#nn-classify-15-fruits-assignment" title="Permalink to this headline">#</a></h1>
 <p><img alt="Fruit Example" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/assignment/deep-learning/nn/examplefruit.png" /></p>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="data-collection">
-<h1><span class="section-number">37.110. </span>Data collection<a class="headerlink" href="#data-collection" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.108. </span>Data collection<a class="headerlink" href="#data-collection" title="Permalink to this headline">#</a></h1>
 <p>The database used in this study is comprising of 44406 fruit images, which we collected
 in a period of 6 months. The images where made with in our lab’s environment under different
 scenarios which we mention below. We captured all the images on a clear background with
@@ -1668,7 +1685,7 @@ <h1><span class="section-number">37.110. </span>Data collection<a class="headerl
 </ul>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="load-and-visualize-the-dataset">
-<h1><span class="section-number">37.111. </span>Load and visualize the dataset<a class="headerlink" href="#load-and-visualize-the-dataset" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.109. </span>Load and visualize the dataset<a class="headerlink" href="#load-and-visualize-the-dataset" title="Permalink to this headline">#</a></h1>
 <p>The database used in this study is comprising of 70549 fruit images, which were collected in a period of 6 months. The images where made with in a lab’s environment under different scenarios which we mention below. All the images were captured on a clear background with resolution of 320×258 pixels.</p>
 <p>Type of fruits in the dataset:</p>
 <ul class="simple">
@@ -1844,7 +1861,7 @@ <h1><span class="section-number">37.111. </span>Load and visualize the dataset<a
 </div>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="train-the-neural-network-from-scratch-with-keras-and-w-o-generator">
-<h1><span class="section-number">37.112. </span>Train the neural network from scratch with Keras and w/o generator<a class="headerlink" href="#train-the-neural-network-from-scratch-with-keras-and-w-o-generator" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.110. </span>Train the neural network from scratch with Keras and w/o generator<a class="headerlink" href="#train-the-neural-network-from-scratch-with-keras-and-w-o-generator" title="Permalink to this headline">#</a></h1>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># The pictures will be resized to have the same size for the neural network</span>
@@ -1861,7 +1878,7 @@ <h1><span class="section-number">37.112. </span>Train the neural network from sc
 </div>
 </div>
 <section id="create-and-train-the-nn-model">
-<h2><span class="section-number">37.112.1. </span>Create and train the NN Model<a class="headerlink" href="#create-and-train-the-nn-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.110.1. </span>Create and train the NN Model<a class="headerlink" href="#create-and-train-the-nn-model" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">cut_df</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">number_of_parts</span><span class="p">,</span> <span class="n">part</span><span class="p">):</span>
@@ -2040,7 +2057,7 @@ <h2><span class="section-number">37.112.1. </span>Create and train the NN Model<
 </div>
 </section>
 <section id="predictions-a-class-anchor-id-3-a">
-<h2><span class="section-number">37.112.2. </span>Predictions<a class="anchor" id="3"></a><a class="headerlink" href="#predictions-a-class-anchor-id-3-a" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.110.2. </span>Predictions<a class="anchor" id="3"></a><a class="headerlink" href="#predictions-a-class-anchor-id-3-a" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">warnings</span>
@@ -2091,7 +2108,7 @@ <h2><span class="section-number">37.112.2. </span>Predictions<a class="anchor" i
 </div>
 </section>
 <section id="visualize-the-result-with-pictures-of-fruits">
-<h2><span class="section-number">37.112.3. </span>Visualize the result with pictures of fruits<a class="headerlink" href="#visualize-the-result-with-pictures-of-fruits" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.110.3. </span>Visualize the result with pictures of fruits<a class="headerlink" href="#visualize-the-result-with-pictures-of-fruits" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span>
@@ -2110,7 +2127,7 @@ <h2><span class="section-number">37.112.3. </span>Visualize the result with pict
 <!-- <strong>The predictions are very good with around 97% accuracy using only 1/5 of the dataset to train the model.</strong> --></section>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="acknowledgments">
-<h1><span class="section-number">37.113. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.111. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/databeru"> DATALIRA </a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/databeru/classify-15-fruits-with-tensorflow-acc-99-6"> Classify 15 Fruits with TensorFlow </a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
@@ -2151,13 +2168,13 @@ <h1><span class="section-number">37.113. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.108. </span>Neural Networks for Classification with TensorFlow</p>
+            <p class="prev-next-title"><span class="section-number">37.106. </span>Neural Networks for Classification with TensorFlow</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="dqn/dqn-on-foreign-exchange-market.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.114. </span>DQN On Foreign Exchange Market</p>
+        <p class="prev-next-title"><span class="section-number">37.112. </span>DQN On Foreign Exchange Market</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/deep-learning/nn-for-classification-assignment.html b/assignments/deep-learning/nn-for-classification-assignment.html
index 5d6094267e..d33d6a2c33 100644
--- a/assignments/deep-learning/nn-for-classification-assignment.html
+++ b/assignments/deep-learning/nn-for-classification-assignment.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.108. Neural Networks for Classification with TensorFlow &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.106. Neural Networks for Classification with TensorFlow &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.109. NN Classify 15 Fruits Assignment" href="nn-classify-15-fruits-assignment.html" />
-    <link rel="prev" title="37.106. Time Series Forecasting Assignment" href="time-series-forecasting-assignment.html" />
+    <link rel="next" title="37.107. NN Classify 15 Fruits Assignment" href="nn-classify-15-fruits-assignment.html" />
+    <link rel="prev" title="37.104. Time Series Forecasting Assignment" href="time-series-forecasting-assignment.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
-  <label for="toctree-checkbox-3">
+  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
+  <label for="toctree-checkbox-4">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -284,8 +306,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
-  <label for="toctree-checkbox-4">
+  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
+  <label for="toctree-checkbox-5">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -311,8 +333,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
-  <label for="toctree-checkbox-5">
+  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
+  <label for="toctree-checkbox-6">
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   </label>
@@ -355,8 +377,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
-  <label for="toctree-checkbox-6">
+  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
+  <label for="toctree-checkbox-7">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -392,8 +414,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
-  <label for="toctree-checkbox-7">
+  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
+  <label for="toctree-checkbox-8">
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   </label>
@@ -431,8 +453,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
    14. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
-  <label for="toctree-checkbox-8">
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+  <label for="toctree-checkbox-9">
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@@ -453,8 +475,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
    15. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -480,8 +502,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
    16. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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   </label>
@@ -633,8 +655,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../README.html">
    37. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
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   </label>
@@ -825,308 +847,303 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../ml-fundamentals/ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
+     37.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/loss-function.html">
-     37.48. Loss Function
+     37.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
+     37.50. ML logistic regression - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-2.html">
-     37.52. ML logistic regression - assignment 2
+     37.51. ML logistic regression - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-neural-network-1.html">
-     37.53. ML neural network - Assignment 1
+     37.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression-tools.html">
-     37.54. Regression tools
+     37.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/managing-data.html">
-     37.55. Managing data
+     37.54. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/exploring-visualizations.html">
-     37.56. Exploring visualizations
+     37.55. Exploring visualizations
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/try-a-different-model.html">
-     37.57. Try a different model
+     37.56. Try a different model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/create-a-regression-model.html">
-     37.58. Create a regression model
+     37.57. Create a regression model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-and-polynomial-regression.html">
-     37.59. Linear and polynomial regression
+     37.58. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/retrying-some-regression.html">
-     37.60. Retrying some regression
+     37.59. Retrying some regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
+     37.60. Pumpkin varieties and color
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.62. Delicious asian and indian cuisines
+     37.61. Delicious asian and indian cuisines
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/explore-classification-methods.html">
-     37.63. Explore classification methods
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-fundamentals/linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
+     37.62. Explore classification methods
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.65. Kernel method assignment 1
+     37.63. Kernel method assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
+     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.67. Support Vector Machines (SVM) - Classification
+     37.65. Support Vector Machines (SVM) - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.68. Decision Trees - Intro and Regression
+     37.66. Decision Trees - Intro and Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.69. Decision Trees - Classification
+     37.67. Decision Trees - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.70. Model selection assignment 1
+     37.68. Model selection assignment 1
     </a>
    </li>
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-     37.79. Hyperparameter tuning gradient boosting
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-     37.82. Random Forest Classifier with Feature Importance
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     </a>
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-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
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    <li class="toctree-l2">
     <a class="reference internal" href="time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
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     <a class="current reference internal" href="#">
-     37.108. Neural Networks for Classification with TensorFlow
+     37.106. Neural Networks for Classification with TensorFlow
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-     37.109. NN Classify 15 Fruits Assignment
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-     37.114. DQN On Foreign Exchange Market
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     </a>
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-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
     </a>
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@@ -1135,8 +1152,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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    38. Slides
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         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#getting-started-binary-classifier">
-   37.108.1. Getting Started: Binary Classifier
+   37.106.1. Getting Started: Binary Classifier
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
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+     37.106.1.1. Getting the data
     </a>
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+   37.106.2. Taking a look in the data
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+   37.106.3. Preparing the Data
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-   37.108.4. Creating, Compiling and Training a Model
+   37.106.4. Creating, Compiling and Training a Model
   </a>
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+   37.106.5. Visualizing the Results
   </a>
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-   37.108.6. Evaluating the Model
+   37.106.6. Evaluating the Model
   </a>
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   <a class="reference internal nav-link" href="#going-beyond-binary-classifier-to-multiclass-classifier-10-fashions-classifier">
-   37.108.7. Going Beyond Binary Classifier to Multiclass Classifier: 10 Fashions Classifier
+   37.106.7. Going Beyond Binary Classifier to Multiclass Classifier: 10 Fashions Classifier
   </a>
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-   37.108.8. Getting the Fashion data
+   37.106.8. Getting the Fashion data
   </a>
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-   37.108.9. Looking in the Fashion Data
+   37.106.9. Looking in the Fashion Data
   </a>
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-   37.108.10. Preparing the Data
+   37.106.10. Preparing the Data
   </a>
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   <a class="reference internal nav-link" href="#id2">
-   37.108.11. Creating, Compiling, and Training a Model
+   37.106.11. Creating, Compiling, and Training a Model
   </a>
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-   37.108.12. Visualizing the Model Results
+   37.106.12. Visualizing the Model Results
   </a>
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-   37.108.13. Model Evaluation
+   37.106.13. Model Evaluation
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   <a class="reference internal nav-link" href="#controlling-training-with-callbacks">
-   37.108.14. Controlling Training with Callbacks
+   37.106.14. Controlling Training with Callbacks
   </a>
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    <li class="toc-h3 nav-item toc-entry">
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-     37.108.14.1. Implementing Callbacks
+     37.106.14.1. Implementing Callbacks
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#custom-callback">
-     37.108.14.2. Custom Callback
+     37.106.14.2. Custom Callback
     </a>
    </li>
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   <a class="reference internal nav-link" href="#using-tensorboard-for-model-visualization">
-   37.108.15. Using TensorBoard for Model Visualization
+   37.106.15. Using TensorBoard for Model Visualization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.108.16. Acknowledgments
+   37.106.16. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1627,101 +1644,101 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#getting-started-binary-classifier">
-   37.108.1. Getting Started: Binary Classifier
+   37.106.1. Getting Started: Binary Classifier
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#getting-the-data">
-     37.108.1.1. Getting the data
+     37.106.1.1. Getting the data
     </a>
    </li>
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-   37.108.2. Taking a look in the data
+   37.106.2. Taking a look in the data
   </a>
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-   37.108.3. Preparing the Data
+   37.106.3. Preparing the Data
   </a>
  </li>
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   <a class="reference internal nav-link" href="#creating-compiling-and-training-a-model">
-   37.108.4. Creating, Compiling and Training a Model
+   37.106.4. Creating, Compiling and Training a Model
   </a>
  </li>
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   <a class="reference internal nav-link" href="#visualizing-the-results">
-   37.108.5. Visualizing the Results
+   37.106.5. Visualizing the Results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#evaluating-the-model">
-   37.108.6. Evaluating the Model
+   37.106.6. Evaluating the Model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#going-beyond-binary-classifier-to-multiclass-classifier-10-fashions-classifier">
-   37.108.7. Going Beyond Binary Classifier to Multiclass Classifier: 10 Fashions Classifier
+   37.106.7. Going Beyond Binary Classifier to Multiclass Classifier: 10 Fashions Classifier
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#getting-the-fashion-data">
-   37.108.8. Getting the Fashion data
+   37.106.8. Getting the Fashion data
   </a>
  </li>
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   <a class="reference internal nav-link" href="#looking-in-the-fashion-data">
-   37.108.9. Looking in the Fashion Data
+   37.106.9. Looking in the Fashion Data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id1">
-   37.108.10. Preparing the Data
+   37.106.10. Preparing the Data
   </a>
  </li>
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   <a class="reference internal nav-link" href="#id2">
-   37.108.11. Creating, Compiling, and Training a Model
+   37.106.11. Creating, Compiling, and Training a Model
   </a>
  </li>
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   <a class="reference internal nav-link" href="#visualizing-the-model-results">
-   37.108.12. Visualizing the Model Results
+   37.106.12. Visualizing the Model Results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#model-evaluation">
-   37.108.13. Model Evaluation
+   37.106.13. Model Evaluation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#controlling-training-with-callbacks">
-   37.108.14. Controlling Training with Callbacks
+   37.106.14. Controlling Training with Callbacks
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#implementing-callbacks">
-     37.108.14.1. Implementing Callbacks
+     37.106.14.1. Implementing Callbacks
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#custom-callback">
-     37.108.14.2. Custom Callback
+     37.106.14.2. Custom Callback
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#using-tensorboard-for-model-visualization">
-   37.108.15. Using TensorBoard for Model Visualization
+   37.106.15. Using TensorBoard for Model Visualization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.108.16. Acknowledgments
+   37.106.16. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1735,14 +1752,14 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="neural-networks-for-classification-with-tensorflow">
-<h1><span class="section-number">37.108. </span>Neural Networks for Classification with TensorFlow<a class="headerlink" href="#neural-networks-for-classification-with-tensorflow" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.106. </span>Neural Networks for Classification with TensorFlow<a class="headerlink" href="#neural-networks-for-classification-with-tensorflow" title="Permalink to this headline">#</a></h1>
 <section id="getting-started-binary-classifier">
-<h2><span class="section-number">37.108.1. </span>Getting Started: Binary Classifier<a class="headerlink" href="#getting-started-binary-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.106.1. </span>Getting Started: Binary Classifier<a class="headerlink" href="#getting-started-binary-classifier" title="Permalink to this headline">#</a></h2>
 <p>We will first practice building neural networks for binary classifier. In binary classification, we have two classes.</p>
 <p>We will use a classical cancer dataset to predict if a given patient has a malignant or benign based on their medical information. We will get it from sklearn datasets. You can read more about the dataset.</p>
 <p>The dataset contains two labels: malignant, benign.</p>
 <section id="getting-the-data">
-<h3><span class="section-number">37.108.1.1. </span>Getting the data<a class="headerlink" href="#getting-the-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.106.1.1. </span>Getting the data<a class="headerlink" href="#getting-the-data" title="Permalink to this headline">#</a></h3>
 <p>We will get the data from sklearn datasets.</p>
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-<h2><span class="section-number">37.108.2. </span>Taking a look in the data<a class="headerlink" href="#taking-a-look-in-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.106.2. </span>Taking a look in the data<a class="headerlink" href="#taking-a-look-in-the-data" title="Permalink to this headline">#</a></h2>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Looking from the head </span>
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 </div>
 </section>
 <section id="preparing-the-data">
-<h2><span class="section-number">37.108.3. </span>Preparing the Data<a class="headerlink" href="#preparing-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.106.3. </span>Preparing the Data<a class="headerlink" href="#preparing-the-data" title="Permalink to this headline">#</a></h2>
 <p>The data from sklearn is reasonably cleaned. Let’s split the data into train and test sets, and we will follow with scaling the values to be between 0 and 1.</p>
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 <p>We are now ready to create, compile and train the model.</p>
 </section>
 <section id="creating-compiling-and-training-a-model">
-<h2><span class="section-number">37.108.4. </span>Creating, Compiling and Training a Model<a class="headerlink" href="#creating-compiling-and-training-a-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.106.4. </span>Creating, Compiling and Training a Model<a class="headerlink" href="#creating-compiling-and-training-a-model" title="Permalink to this headline">#</a></h2>
 <p>In TensorFlow, creating a model is only putting together an empty graphs. We are going to use Sequential API to stack the layers, from the input to output.</p>
 <p>In model compilaton, it’s where we specify the optimizer and loss function. Loss function is there for calculating the difference between the predictions and the actual output, and optimizer is there for reducing the loss.</p>
 <p>Also, if we are interested in tracking other metrics during training, we can specify them in <code class="docutils literal notranslate"><span class="pre">metric</span></code>.</p>
@@ -1947,7 +1964,7 @@ <h2><span class="section-number">37.108.4. </span>Creating, Compiling and Traini
 <p>‼️ If you retrain again, it will continue where it left. So, for example, if you train for 30 epochs, and you rerun the cell, it will train for same more epochs again.</p>
 </section>
 <section id="visualizing-the-results">
-<h2><span class="section-number">37.108.5. </span>Visualizing the Results<a class="headerlink" href="#visualizing-the-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.106.5. </span>Visualizing the Results<a class="headerlink" href="#visualizing-the-results" title="Permalink to this headline">#</a></h2>
 <p>Visualizing the model results after training is always a good way to learn what you can do to improve the performance.</p>
 <p>Let’s get a Pandas dataframe containing training loss and accuracy, and validation loss and accuracy.</p>
 <div class="cell docutils container">
@@ -1965,7 +1982,7 @@ <h2><span class="section-number">37.108.5. </span>Visualizing the Results<a clas
 <p>Let’s evaluate the model on the test set.</p>
 </section>
 <section id="evaluating-the-model">
-<h2><span class="section-number">37.108.6. </span>Evaluating the Model<a class="headerlink" href="#evaluating-the-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.106.6. </span>Evaluating the Model<a class="headerlink" href="#evaluating-the-model" title="Permalink to this headline">#</a></h2>
 <p>Quite often, you will want to test your model on the data that it never saw. This data is normally called <code class="docutils literal notranslate"><span class="pre">test</span> <span class="pre">set</span></code> and in more applied practice, you will only feed the test to the model after you have done your best to improve it.</p>
 <p>Let’s now evaluate the model on the test set. One thing to note here is that the test set must be preprocessed the same way we preprocessed the training set. The training set was rescaled and that was applied to the test set.</p>
 <p>If this is not obeyed, you would not know why you’re having poor results. Just look up on the next next cell how poor the accuracy will be if I evaluate the model on unscaled data when I trained it on scaled data.</p>
@@ -2083,13 +2100,13 @@ <h2><span class="section-number">37.108.6. </span>Evaluating the Model<a class="
 F1 score is the harmonic mean of precision and recall.</p>
 </section>
 <section id="going-beyond-binary-classifier-to-multiclass-classifier-10-fashions-classifier">
-<h2><span class="section-number">37.108.7. </span>Going Beyond Binary Classifier to Multiclass Classifier: 10 Fashions Classifier<a class="headerlink" href="#going-beyond-binary-classifier-to-multiclass-classifier-10-fashions-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.106.7. </span>Going Beyond Binary Classifier to Multiclass Classifier: 10 Fashions Classifier<a class="headerlink" href="#going-beyond-binary-classifier-to-multiclass-classifier-10-fashions-classifier" title="Permalink to this headline">#</a></h2>
 <p>So far, we have built a neural network for regression(in previous labs) and binary classification. And we have only been working with structured datasets(datasets in tabular format).</p>
 <p>Can the same neural networks we used be able to recognize images? In this next practice, we will turn the page to image classification. We will build a neural network for recognizing 10 different fashions and along the way, we will learn other things such as stopping the training upon a given condition is met, and using TensorBoard to visualize model.</p>
 <p>That is going to be cool! Let’s get started!</p>
 </section>
 <section id="getting-the-fashion-data">
-<h2><span class="section-number">37.108.8. </span>Getting the Fashion data<a class="headerlink" href="#getting-the-fashion-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.106.8. </span>Getting the Fashion data<a class="headerlink" href="#getting-the-fashion-data" title="Permalink to this headline">#</a></h2>
 <p>Let’s get the dataset from Keras.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2104,7 +2121,7 @@ <h2><span class="section-number">37.108.8. </span>Getting the Fashion data<a cla
 </div>
 </section>
 <section id="looking-in-the-fashion-data">
-<h2><span class="section-number">37.108.9. </span>Looking in the Fashion Data<a class="headerlink" href="#looking-in-the-fashion-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.106.9. </span>Looking in the Fashion Data<a class="headerlink" href="#looking-in-the-fashion-data" title="Permalink to this headline">#</a></h2>
 <p>As always, it is a best practice to peep into the images to see how they like.</p>
 <p>Let’s display the pixels values of a given image, image, and its corresponding label.</p>
 <div class="cell docutils container">
@@ -2175,7 +2192,7 @@ <h2><span class="section-number">37.108.9. </span>Looking in the Fashion Data<a
 <p>Now that we know the dataset that we are working with, let us do some few proprocessing before building a model.</p>
 </section>
 <section id="id1">
-<h2><span class="section-number">37.108.10. </span>Preparing the Data<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.106.10. </span>Preparing the Data<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
 <p>In many cases, real world images datasets are not that clean like fashion mnist.</p>
 <p>You may have to correct images that were incorrectly labeled, or you have labels in texts that need to be converted to numbers(most machine learning models accept numeric input), or scale the pixels values.</p>
 <p>The latter is what we are going to do. It is inarguable that scaling the images pixels to value between 0 and 1 increase the performance of the neural network, and hence the results. Let’s do it!!</p>
@@ -2194,7 +2211,7 @@ <h2><span class="section-number">37.108.10. </span>Preparing the Data<a class="h
 <p>We are now ready to build a neural network.</p>
 </section>
 <section id="id2">
-<h2><span class="section-number">37.108.11. </span>Creating, Compiling, and Training a Model<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.106.11. </span>Creating, Compiling, and Training a Model<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
 <p>There are few points to note before creating a model:</p>
 <ul class="simple">
 <li><p>When working with images, the shape of the input images has to be correctly provided. This is a common error done by many people, including me (before I learned it).</p></li>
@@ -2244,7 +2261,7 @@ <h2><span class="section-number">37.108.11. </span>Creating, Compiling, and Trai
 <p>But also, training mnist for 20 epochs is not slow that we would need to activate GPU. We will take an advantage of GPU in later labs.</p>
 </section>
 <section id="visualizing-the-model-results">
-<h2><span class="section-number">37.108.12. </span>Visualizing the Model Results<a class="headerlink" href="#visualizing-the-model-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.106.12. </span>Visualizing the Model Results<a class="headerlink" href="#visualizing-the-model-results" title="Permalink to this headline">#</a></h2>
 <p>Let’s visualize the model results to see how training went.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2263,7 +2280,7 @@ <h2><span class="section-number">37.108.12. </span>Visualizing the Model Results
 <p>Let’s see how the model performs on unseed data: test set.</p>
 </section>
 <section id="model-evaluation">
-<h2><span class="section-number">37.108.13. </span>Model Evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.106.13. </span>Model Evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># if you need a model trained, you can use this cell</span>
@@ -2294,11 +2311,11 @@ <h2><span class="section-number">37.108.13. </span>Model Evaluation<a class="hea
 <p>We could also find other Classification metrics based on True/False positives &amp; negatives such as precision, recall, but since we already saw how to compute them in the binary classifier, let’s see other interesting things: Controlling the training using callbacks and using TensorBoard.</p>
 </section>
 <section id="controlling-training-with-callbacks">
-<h2><span class="section-number">37.108.14. </span>Controlling Training with Callbacks<a class="headerlink" href="#controlling-training-with-callbacks" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.106.14. </span>Controlling Training with Callbacks<a class="headerlink" href="#controlling-training-with-callbacks" title="Permalink to this headline">#</a></h2>
 <p>We can use Callbacks functions to control the training.</p>
 <p>Take an example: we can stop training when the model is lo longer showing significant improvements on validation set. Or we can terminate training when a certain condition is met.</p>
 <section id="implementing-callbacks">
-<h3><span class="section-number">37.108.14.1. </span>Implementing Callbacks<a class="headerlink" href="#implementing-callbacks" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.106.14.1. </span>Implementing Callbacks<a class="headerlink" href="#implementing-callbacks" title="Permalink to this headline">#</a></h3>
 <p>There are various functionalities available in Keras Callbacks.</p>
 <p>Let’s start with how to use ModelCheckpoint to save the model when the performance on the validation set is best so far. By saving the best model on the validation set, we avoid things like overfitting which is a common issue in machine learning model training, neural network specifically. We also train for less time.</p>
 <p>I will rebuild a same model again.</p>
@@ -2474,7 +2491,7 @@ <h3><span class="section-number">37.108.14.1. </span>Implementing Callbacks<a cl
 </div>
 </section>
 <section id="custom-callback">
-<h3><span class="section-number">37.108.14.2. </span>Custom Callback<a class="headerlink" href="#custom-callback" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.106.14.2. </span>Custom Callback<a class="headerlink" href="#custom-callback" title="Permalink to this headline">#</a></h3>
 <p>Keras offers various functions for implementing custom callbacks that are very handy when you want to control the model training with a little bit of customization.</p>
 <p>You can do certain actions on almost every step of the training. Let’s stop the training when the accuracy is 95%.</p>
 <div class="cell docutils container">
@@ -2539,7 +2556,7 @@ <h3><span class="section-number">37.108.14.2. </span>Custom Callback<a class="he
 </section>
 </section>
 <section id="using-tensorboard-for-model-visualization">
-<h2><span class="section-number">37.108.15. </span>Using TensorBoard for Model Visualization<a class="headerlink" href="#using-tensorboard-for-model-visualization" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.106.15. </span>Using TensorBoard for Model Visualization<a class="headerlink" href="#using-tensorboard-for-model-visualization" title="Permalink to this headline">#</a></h2>
 <p>Tensorboard is incredible tool used by many people (and not just only TensorFlow developers) to experiment with machine learning.</p>
 <p>With TensorBoard, you can:</p>
 <ul class="simple">
@@ -2630,7 +2647,7 @@ <h2><span class="section-number">37.108.15. </span>Using TensorBoard for Model V
 <p>As you can see, TensorBoard is very useful. The fact that you can use it to visualize the performance metrics, model graphs, and datasets as well.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.108.16. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.106.16. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Jean de Dieu Nyandwi for creating the open-source course <a class="reference external" href="https://nyandwi.com/machine_learning_complete/"> machine learning complete </a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2672,13 +2689,13 @@ <h2><span class="section-number">37.108.16. </span>Acknowledgments<a class="head
         <i class="fas fa-angle-left"></i>
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             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.106. </span>Time Series Forecasting Assignment</p>
+            <p class="prev-next-title"><span class="section-number">37.104. </span>Time Series Forecasting Assignment</p>
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diff --git a/assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html b/assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html
index 44301b66a5..baeb8b07c8 100644
--- a/assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html
+++ b/assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.118. Basic classification: Classify images of clothing &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.116. Basic classification: Classify images of clothing &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -99,7 +99,7 @@
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
     <link rel="next" title="38. Slides" href="../../../slides/introduction.html" />
-    <link rel="prev" title="37.117. Generative Adversarial Networks (GANs)" href="../gan/gan-introduction.html" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
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   <a class="reference internal" href="../../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
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   <a class="reference internal" href="../../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
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@@ -355,8 +377,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
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@@ -392,8 +414,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
   </a>
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   <a class="reference internal" href="../../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
    14. Clustering models for Machine Learning
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    15. Getting started with ensemble learning
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   <a class="reference internal" href="../../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
    16. Introduction to Gradient Boosting
   </a>
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@@ -633,8 +655,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../README.html">
    37. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
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@@ -825,308 +847,303 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
+     37.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/loss-function.html">
-     37.48. Loss Function
+     37.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
+     37.50. ML logistic regression - assignment 1
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-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.67. Support Vector Machines (SVM) - Classification
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-     37.92. How to choose cnn architecture mnist
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-     37.96. Object Recognition in Images using CNN
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-     37.97. Intro to TensorFlow for Deep Learning
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-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
+     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
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-     37.105. Fun with Variational Autoencoders
+     37.103. Fun with Variational Autoencoders
     </a>
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-     37.108. Neural Networks for Classification with TensorFlow
+     37.106. Neural Networks for Classification with TensorFlow
     </a>
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-     37.109. NN Classify 15 Fruits Assignment
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     </a>
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-     37.114. DQN On Foreign Exchange Market
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     </a>
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+     37.113. Art by gan
     </a>
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     <a class="reference internal" href="../gan/gan-introduction.html">
-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
     </a>
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+     37.116. Basic classification: Classify images of clothing
     </a>
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         <ul class="visible nav section-nav flex-column">
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-   37.118.1. Load model
+   37.116.1. Load model
   </a>
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   <a class="reference internal nav-link" href="#overview-deep-learning-machine-learning-and-ai">
-   37.118.2. Overview: Deep learning, machine learning, and AI
+   37.116.2. Overview: Deep learning, machine learning, and AI
   </a>
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   <a class="reference internal nav-link" href="#overview-of-this-guide">
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+   37.116.3. Overview of this guide
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   <a class="reference internal nav-link" href="#import-the-fashion-mnist-dataset">
-   37.118.4. Import the Fashion MNIST dataset
+   37.116.4. Import the Fashion MNIST dataset
   </a>
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   <a class="reference internal nav-link" href="#explore-the-data">
-   37.118.5. Explore the data
+   37.116.5. Explore the data
   </a>
  </li>
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   <a class="reference internal nav-link" href="#preprocess-the-data">
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+   37.116.6. Preprocess the data
   </a>
  </li>
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   </a>
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+     37.116.7.1. Set up the layers
     </a>
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-     37.118.7.2. Compile the model
+     37.116.7.2. Compile the model
     </a>
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+     37.116.8.1. Feed the model
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-     37.118.8.2. Evaluate accuracy
+     37.116.8.2. Evaluate accuracy
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+     37.116.8.3. Make predictions
     </a>
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+     37.116.8.4. Verify predictions
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+   37.116.9. Use the trained model
   </a>
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   <a class="reference internal nav-link" href="#acknowledgments">
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+   37.116.10. Acknowledgments
   </a>
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 </ul>
@@ -1612,86 +1629,86 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#load-model">
-   37.118.1. Load model
+   37.116.1. Load model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#overview-deep-learning-machine-learning-and-ai">
-   37.118.2. Overview: Deep learning, machine learning, and AI
+   37.116.2. Overview: Deep learning, machine learning, and AI
   </a>
  </li>
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   <a class="reference internal nav-link" href="#overview-of-this-guide">
-   37.118.3. Overview of this guide
+   37.116.3. Overview of this guide
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   <a class="reference internal nav-link" href="#import-the-fashion-mnist-dataset">
-   37.118.4. Import the Fashion MNIST dataset
+   37.116.4. Import the Fashion MNIST dataset
   </a>
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   <a class="reference internal nav-link" href="#explore-the-data">
-   37.118.5. Explore the data
+   37.116.5. Explore the data
   </a>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#preprocess-the-data">
-   37.118.6. Preprocess the data
+   37.116.6. Preprocess the data
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   <a class="reference internal nav-link" href="#build-the-model">
-   37.118.7. Build the model
+   37.116.7. Build the model
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#set-up-the-layers">
-     37.118.7.1. Set up the layers
+     37.116.7.1. Set up the layers
     </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#compile-the-model">
-     37.118.7.2. Compile the model
+     37.116.7.2. Compile the model
     </a>
    </li>
   </ul>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#train-the-model">
-   37.118.8. Train the model
+   37.116.8. Train the model
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#feed-the-model">
-     37.118.8.1. Feed the model
+     37.116.8.1. Feed the model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#evaluate-accuracy">
-     37.118.8.2. Evaluate accuracy
+     37.116.8.2. Evaluate accuracy
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#make-predictions">
-     37.118.8.3. Make predictions
+     37.116.8.3. Make predictions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#verify-predictions">
-     37.118.8.4. Verify predictions
+     37.116.8.4. Verify predictions
     </a>
    </li>
   </ul>
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   <a class="reference internal nav-link" href="#use-the-trained-model">
-   37.118.9. Use the trained model
+   37.116.9. Use the trained model
   </a>
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.118.10. Acknowledgments
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@@ -1705,9 +1722,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="basic-classification-classify-images-of-clothing">
-<h1><span class="section-number">37.118. </span>Basic classification: Classify images of clothing<a class="headerlink" href="#basic-classification-classify-images-of-clothing" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.116. </span>Basic classification: Classify images of clothing<a class="headerlink" href="#basic-classification-classify-images-of-clothing" title="Permalink to this headline">#</a></h1>
 <section id="load-model">
-<h2><span class="section-number">37.118.1. </span>Load model<a class="headerlink" href="#load-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.116.1. </span>Load model<a class="headerlink" href="#load-model" title="Permalink to this headline">#</a></h2>
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 <section id="overview-deep-learning-machine-learning-and-ai">
-<h2><span class="section-number">37.118.2. </span>Overview: Deep learning, machine learning, and AI<a class="headerlink" href="#overview-deep-learning-machine-learning-and-ai" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.116.2. </span>Overview: Deep learning, machine learning, and AI<a class="headerlink" href="#overview-deep-learning-machine-learning-and-ai" title="Permalink to this headline">#</a></h2>
 <p><img alt="ai-vs-machine-learning-vs-deep-learning" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/overview/ai-vs-machine-learning-vs-deep-learning.png" /></p>
 <p>Consider the following definitions to understand deep learning vs. machine learning vs. AI:</p>
 <ul class="simple">
@@ -1770,7 +1787,7 @@ <h2><span class="section-number">37.118.2. </span>Overview: Deep learning, machi
 <p>By using machine learning and deep learning techniques, you can build computer systems and applications that do tasks that are commonly associated with human intelligence. These tasks include image recognition, speech recognition, and language translation.</p>
 </section>
 <section id="overview-of-this-guide">
-<h2><span class="section-number">37.118.3. </span>Overview of this guide<a class="headerlink" href="#overview-of-this-guide" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.116.3. </span>Overview of this guide<a class="headerlink" href="#overview-of-this-guide" title="Permalink to this headline">#</a></h2>
 <p>This guide provide a  simple sample of deep learning. It will help you understand the model framework for deep learning.</p>
 <p>This guide trains a neural network model to classify images of clothing, like sneakers and shirts. It’s okay if you don’t understand all the details; this is a fast-paced overview of a complete TensorFlow program with the details explained as you go.</p>
 <p>This guide uses <a class="reference external" href="https://www.tensorflow.org/guide/keras">tf.keras</a>, a high-level API to build and train models in TensorFlow.</p>
@@ -1790,7 +1807,7 @@ <h2><span class="section-number">37.118.3. </span>Overview of this guide<a class
 </div>
 </section>
 <section id="import-the-fashion-mnist-dataset">
-<h2><span class="section-number">37.118.4. </span>Import the Fashion MNIST dataset<a class="headerlink" href="#import-the-fashion-mnist-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.116.4. </span>Import the Fashion MNIST dataset<a class="headerlink" href="#import-the-fashion-mnist-dataset" title="Permalink to this headline">#</a></h2>
 <p>This guide uses the <a class="reference external" href="https://github.com/zalandoresearch/fashion-mnist">Fashion MNIST</a> dataset which contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 by 28 pixels), as seen here:</p>
 <table>
   <tr><td>
@@ -1876,7 +1893,7 @@ <h2><span class="section-number">37.118.4. </span>Import the Fashion MNIST datas
 </div>
 </section>
 <section id="explore-the-data">
-<h2><span class="section-number">37.118.5. </span>Explore the data<a class="headerlink" href="#explore-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.116.5. </span>Explore the data<a class="headerlink" href="#explore-the-data" title="Permalink to this headline">#</a></h2>
 <p>Let’s explore the format of the dataset before training the model. The following shows there are 60,000 images in the training set, with each image represented as 28 x 28 pixels:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1919,7 +1936,7 @@ <h2><span class="section-number">37.118.5. </span>Explore the data<a class="head
 </div>
 </section>
 <section id="preprocess-the-data">
-<h2><span class="section-number">37.118.6. </span>Preprocess the data<a class="headerlink" href="#preprocess-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.116.6. </span>Preprocess the data<a class="headerlink" href="#preprocess-the-data" title="Permalink to this headline">#</a></h2>
 <p>The data must be preprocessed before training the network. If you inspect the first image in the training set, you will see that the pixel values fall in the range of 0 to 255:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1960,10 +1977,10 @@ <h2><span class="section-number">37.118.6. </span>Preprocess the data<a class="h
 </div>
 </section>
 <section id="build-the-model">
-<h2><span class="section-number">37.118.7. </span>Build the model<a class="headerlink" href="#build-the-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.116.7. </span>Build the model<a class="headerlink" href="#build-the-model" title="Permalink to this headline">#</a></h2>
 <p>Building the neural network requires configuring the layers of the model, then compiling the model.</p>
 <section id="set-up-the-layers">
-<h3><span class="section-number">37.118.7.1. </span>Set up the layers<a class="headerlink" href="#set-up-the-layers" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.116.7.1. </span>Set up the layers<a class="headerlink" href="#set-up-the-layers" title="Permalink to this headline">#</a></h3>
 <p>The basic building block of a neural network is the <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras/layers"><em>layer</em></a>. Layers extract representations from the data fed into them. Hopefully, these representations are meaningful for the problem at hand.</p>
 <p>Most of deep learning consists of chaining together simple layers. Most layers, such as <code class="docutils literal notranslate"><span class="pre">tf.keras.layers.Dense</span></code>, have parameters that are learned during training.</p>
 <div class="cell docutils container">
@@ -1981,7 +1998,7 @@ <h3><span class="section-number">37.118.7.1. </span>Set up the layers<a class="h
 <p>After the pixels are flattened, the network consists of a sequence of two <code class="docutils literal notranslate"><span class="pre">tf.keras.layers.Dense</span></code> layers. These are densely connected, or fully connected, neural layers. The first <code class="docutils literal notranslate"><span class="pre">Dense</span></code> layer has 128 nodes (or neurons). The second (and last) layer returns a logits array with length of 10. Each node contains a score that indicates the current image belongs to one of the 10 classes.</p>
 </section>
 <section id="compile-the-model">
-<h3><span class="section-number">37.118.7.2. </span>Compile the model<a class="headerlink" href="#compile-the-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.116.7.2. </span>Compile the model<a class="headerlink" href="#compile-the-model" title="Permalink to this headline">#</a></h3>
 <p>Before the model is ready for training, it needs a few more settings. These are added during the model’s <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras/Model#compile"><em>compile</em></a> step:</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras/losses"><em>Loss function</em></a> —This measures how accurate the model is during training. You want to minimize this function to “steer” the model in the right direction.</p></li>
@@ -2000,7 +2017,7 @@ <h3><span class="section-number">37.118.7.2. </span>Compile the model<a class="h
 </section>
 </section>
 <section id="train-the-model">
-<h2><span class="section-number">37.118.8. </span>Train the model<a class="headerlink" href="#train-the-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.116.8. </span>Train the model<a class="headerlink" href="#train-the-model" title="Permalink to this headline">#</a></h2>
 <p>Training the neural network model requires the following steps:</p>
 <ol class="simple">
 <li><p>Feed the training data to the model. In this example, the training data is in the <code class="docutils literal notranslate"><span class="pre">train_images</span></code> and <code class="docutils literal notranslate"><span class="pre">train_labels</span></code> arrays.</p></li>
@@ -2009,7 +2026,7 @@ <h2><span class="section-number">37.118.8. </span>Train the model<a class="heade
 <li><p>Verify that the predictions match the labels from the <code class="docutils literal notranslate"><span class="pre">test_labels</span></code> array.</p></li>
 </ol>
 <section id="feed-the-model">
-<h3><span class="section-number">37.118.8.1. </span>Feed the model<a class="headerlink" href="#feed-the-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.116.8.1. </span>Feed the model<a class="headerlink" href="#feed-the-model" title="Permalink to this headline">#</a></h3>
 <p>To start training,  call the <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit"><code class="docutils literal notranslate"><span class="pre">model.fit</span></code></a> method—so called because it “fits” the model to the training data:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2029,7 +2046,7 @@ <h3><span class="section-number">37.118.8.1. </span>Feed the model<a class="head
 <p>As the model trains, the loss and accuracy metrics are displayed. This model reaches an accuracy of about 0.91 (or 91%) on the training data.</p>
 </section>
 <section id="evaluate-accuracy">
-<h3><span class="section-number">37.118.8.2. </span>Evaluate accuracy<a class="headerlink" href="#evaluate-accuracy" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.116.8.2. </span>Evaluate accuracy<a class="headerlink" href="#evaluate-accuracy" title="Permalink to this headline">#</a></h3>
 <p>Next, compare how the model performs on the test dataset:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2047,7 +2064,7 @@ <h3><span class="section-number">37.118.8.2. </span>Evaluate accuracy<a class="h
 </ul>
 </section>
 <section id="make-predictions">
-<h3><span class="section-number">37.118.8.3. </span>Make predictions<a class="headerlink" href="#make-predictions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.116.8.3. </span>Make predictions<a class="headerlink" href="#make-predictions" title="Permalink to this headline">#</a></h3>
 <p>With the model trained, you can use it to make predictions about some images.
 Attach a softmax layer to convert the model’s linear outputs—<a class="reference external" href="https://developers.google.com/machine-learning/glossary#logits">logits</a>—to probabilities, which should be easier to interpret.</p>
 <div class="cell docutils container">
@@ -2128,7 +2145,7 @@ <h3><span class="section-number">37.118.8.3. </span>Make predictions<a class="he
 </div>
 </section>
 <section id="verify-predictions">
-<h3><span class="section-number">37.118.8.4. </span>Verify predictions<a class="headerlink" href="#verify-predictions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.116.8.4. </span>Verify predictions<a class="headerlink" href="#verify-predictions" title="Permalink to this headline">#</a></h3>
 <p>With the model trained, you can use it to make predictions about some images.</p>
 <p>Let’s look at the 0th image, predictions, and prediction array. Correct prediction labels are blue and incorrect prediction labels are red. The number gives the percentage (out of 100) for the predicted label.</p>
 <div class="cell docutils container">
@@ -2180,7 +2197,7 @@ <h3><span class="section-number">37.118.8.4. </span>Verify predictions<a class="
 </section>
 </section>
 <section id="use-the-trained-model">
-<h2><span class="section-number">37.118.9. </span>Use the trained model<a class="headerlink" href="#use-the-trained-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.116.9. </span>Use the trained model<a class="headerlink" href="#use-the-trained-model" title="Permalink to this headline">#</a></h2>
 <p>Finally, use the trained model to make a prediction about a single image.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2233,7 +2250,7 @@ <h2><span class="section-number">37.118.9. </span>Use the trained model<a class=
 <p>And the model predicts a label as expected.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.118.10. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.116.10. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Tensorflow docs for creating the Tensorflow notebook <a class="reference external" href="https://www.tensorflow.org/tutorials/keras/classification">Basic classification: Classify images of clothing</a>. It inspires the majority of the content in this chapter.</p>
 <p>Thanks to Microsoft for providing <a class="reference external" href="https://learn.microsoft.com/en-us/azure/machine-learning/concept-deep-learning-vs-machine-learning?view=azureml-api-2">overview of deep learning, machine learning, and AI</a>.</p>
 </section>
@@ -2276,7 +2293,7 @@ <h2><span class="section-number">37.118.10. </span>Acknowledgments<a class="head
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.117. </span>Generative Adversarial Networks (GANs)</p>
+            <p class="prev-next-title"><span class="section-number">37.115. </span>Generative Adversarial Networks (GANs)</p>
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diff --git a/assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html b/assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html
index 2a15472526..17fe790f40 100644
--- a/assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html
+++ b/assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.101. Google Stock Price Prediction RNN &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.99. Google Stock Price Prediction RNN &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.103. Intro to Autoencoders" href="../autoencoder/autoencoder.html" />
-    <link rel="prev" title="37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment" href="../lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html" />
+    <link rel="next" title="37.101. Intro to Autoencoders" href="../autoencoder/autoencoder.html" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
   </a>
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@@ -284,8 +306,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
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@@ -311,8 +333,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
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@@ -355,8 +377,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
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@@ -392,8 +414,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
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@@ -431,8 +453,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
    14. Clustering models for Machine Learning
   </a>
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@@ -453,8 +475,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
    15. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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@@ -480,8 +502,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
    16. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
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@@ -633,8 +655,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../README.html">
    37. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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@@ -825,308 +847,303 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
+     37.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/loss-function.html">
-     37.48. Loss Function
+     37.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
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 <section id="load-libraries">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>  <span class="c1"># linear algebra</span>
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+<h1><span class="section-number">37.100. </span>Acknowledgements<a class="headerlink" href="#acknowledgements" title="Permalink to this headline">#</a></h1>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/ptheru">Priya</a> for creating <a class="reference external" href="https://www.kaggle.com/code/ptheru/google-stock-price-prediction-rnn">Google stock price prediction - RNN</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
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+            <p class="prev-next-title"><span class="section-number">37.97. </span>Bitcoin LSTM Model with Tweet Volume and Sentiment</p>
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      5.3. NumPy
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      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#creating-a-tensor-from-existing-functions">
-       37.97.4.4. 3.4 Creating a Tensor from Existing Functions
+       37.95.4.4. 3.4 Creating a Tensor from Existing Functions
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#selecting-data-in-tensor">
-       37.97.4.5. 3.5 Selecting Data in Tensor
+       37.95.4.5. 3.5 Selecting Data in Tensor
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#performing-operations-on-tensors">
-       37.97.4.6. 3.6 Performing Operations on Tensors
+       37.95.4.6. 3.6 Performing Operations on Tensors
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#manipulating-the-shape-of-tensor">
-       37.97.4.7. 3.7 Manipulating the Shape of Tensor
+       37.95.4.7. 3.7 Manipulating the Shape of Tensor
       </a>
      </li>
     </ul>
@@ -1585,7 +1602,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.98. Acknowledgments
+   37.96. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1609,74 +1626,74 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#">
-   37.97. Intro to TensorFlow for Deep Learning
+   37.95. Intro to TensorFlow for Deep Learning
   </a>
   <ul class="visible nav section-nav flex-column">
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#what-is-tensorflow">
-     37.97.1. 1. What is TensorFlow
+     37.95.1. 1. What is TensorFlow
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#tensorflow-model-apis">
-     37.97.2. 2. TensorFlow Model APIs
+     37.95.2. 2. TensorFlow Model APIs
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#sequential-api">
-       37.97.2.1. 2.1 Sequential API
+       37.95.2.1. 2.1 Sequential API
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#functional-api">
-       37.97.2.2. Functional API
+       37.95.2.2. Functional API
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#model-subclassing">
-     37.97.3. Model SubClassing
+     37.95.3. Model SubClassing
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-basics-of-tensors">
-     37.97.4. 3. The Basics of Tensors
+     37.95.4. 3. The Basics of Tensors
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#intro-to-tensors">
-       37.97.4.1. 3.1 Intro to Tensors
+       37.95.4.1. 3.1 Intro to Tensors
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#creating-a-tensor-with-tf-constant">
-       37.97.4.2. 3.2 Creating a Tensor with tf.constant()
+       37.95.4.2. 3.2 Creating a Tensor with tf.constant()
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#creating-a-tensor-with-tf-variable">
-       37.97.4.3. 3.3 Creating a Tensor with tf.Variable()
+       37.95.4.3. 3.3 Creating a Tensor with tf.Variable()
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#creating-a-tensor-from-existing-functions">
-       37.97.4.4. 3.4 Creating a Tensor from Existing Functions
+       37.95.4.4. 3.4 Creating a Tensor from Existing Functions
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#selecting-data-in-tensor">
-       37.97.4.5. 3.5 Selecting Data in Tensor
+       37.95.4.5. 3.5 Selecting Data in Tensor
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#performing-operations-on-tensors">
-       37.97.4.6. 3.6 Performing Operations on Tensors
+       37.95.4.6. 3.6 Performing Operations on Tensors
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#manipulating-the-shape-of-tensor">
-       37.97.4.7. 3.7 Manipulating the Shape of Tensor
+       37.95.4.7. 3.7 Manipulating the Shape of Tensor
       </a>
      </li>
     </ul>
@@ -1685,7 +1702,7 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.98. Acknowledgments
+   37.96. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1700,18 +1717,18 @@ <h2> Contents </h2>
                 
   <p><a name='0'></a></p>
 <section class="tex2jax_ignore mathjax_ignore" id="intro-to-tensorflow-for-deep-learning">
-<h1><span class="section-number">37.97. </span>Intro to TensorFlow for Deep Learning<a class="headerlink" href="#intro-to-tensorflow-for-deep-learning" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.95. </span>Intro to TensorFlow for Deep Learning<a class="headerlink" href="#intro-to-tensorflow-for-deep-learning" title="Permalink to this headline">#</a></h1>
 <section id="what-is-tensorflow">
-<h2><span class="section-number">37.97.1. </span>1. What is TensorFlow<a class="headerlink" href="#what-is-tensorflow" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.95.1. </span>1. What is TensorFlow<a class="headerlink" href="#what-is-tensorflow" title="Permalink to this headline">#</a></h2>
 <p>TensorFlow is an open source and an end to end platform used for building machine learning models. Being end to end, you can prepare data, build models, diagnose, improve, and deploy them.</p>
 <p>TensorFlow uses <a class="reference external" href="https://keras.io">Keras</a> at its backend. Keras is a well beautifully designed API for building deep learning models in popular fields such as Computer Vision and Natural Language Processing.</p>
 <p>TensorFlow has got a strong community, from users, learning resources and whole range of technical supports. Not only it powers majority of Google apps such as YouTube, Maps and Google Photos, it is also widely used across startups and other big techs. If you would like to know who is using TensorFlow, <a class="reference external" href="https://www.tensorflow.org/about/case-studies">here you go!</a>.</p>
 </section>
 <section id="tensorflow-model-apis">
-<h2><span class="section-number">37.97.2. </span>2. TensorFlow Model APIs<a class="headerlink" href="#tensorflow-model-apis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.95.2. </span>2. TensorFlow Model APIs<a class="headerlink" href="#tensorflow-model-apis" title="Permalink to this headline">#</a></h2>
 <p>TensorFlow being suited for variety of tasks, there are 3 ways to build deep learning models.</p>
 <section id="sequential-api">
-<h3><span class="section-number">37.97.2.1. </span>2.1 Sequential API<a class="headerlink" href="#sequential-api" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.95.2.1. </span>2.1 Sequential API<a class="headerlink" href="#sequential-api" title="Permalink to this headline">#</a></h3>
 <p>This is the simplest model building option. When building a model, you start from the input to the output, no other way around. This API is suited for tasks that don’t require multiple inputs or outputs, or skip connections.</p>
 <p>Sequential API can be a good API to use for things like image classification or regression tasks. With things like object detection and segmentation, we need another API, which is Functional API.</p>
 <p>Below is how a sequential model looks like.</p>
@@ -1732,7 +1749,7 @@ <h3><span class="section-number">37.97.2.1. </span>2.1 Sequential API<a class="h
 </div>
 </section>
 <section id="functional-api">
-<h3><span class="section-number">37.97.2.2. </span>Functional API<a class="headerlink" href="#functional-api" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.95.2.2. </span>Functional API<a class="headerlink" href="#functional-api" title="Permalink to this headline">#</a></h3>
 <p>This type of API makes it easy to build models that can take multiple inputs/outputs, or skip connections.</p>
 <p>It is well suited in advanced things like object detection and segmentation. In object detection, there are two main involved things.</p>
 <p>One is recognizing the object(classification) and other is localizing the object(regression: predicting the bounding boxes coordinates).</p>
@@ -1754,7 +1771,7 @@ <h3><span class="section-number">37.97.2.2. </span>Functional API<a class="heade
 </section>
 </section>
 <section id="model-subclassing">
-<h2><span class="section-number">37.97.3. </span>Model SubClassing<a class="headerlink" href="#model-subclassing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.95.3. </span>Model SubClassing<a class="headerlink" href="#model-subclassing" title="Permalink to this headline">#</a></h2>
 <p>SubClassing API is for building custom models and having full control of every step in model building and training.</p>
 <p>In most cases, Sequential and Functional API will be all you need to build almost anything.</p>
 <p>Below is how a subclassing model looks like.</p>
@@ -1784,9 +1801,9 @@ <h2><span class="section-number">37.97.3. </span>Model SubClassing<a class="head
 </div>
 </section>
 <section id="the-basics-of-tensors">
-<h2><span class="section-number">37.97.4. </span>3. The Basics of Tensors<a class="headerlink" href="#the-basics-of-tensors" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.95.4. </span>3. The Basics of Tensors<a class="headerlink" href="#the-basics-of-tensors" title="Permalink to this headline">#</a></h2>
 <section id="intro-to-tensors">
-<h3><span class="section-number">37.97.4.1. </span>3.1 Intro to Tensors<a class="headerlink" href="#intro-to-tensors" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.95.4.1. </span>3.1 Intro to Tensors<a class="headerlink" href="#intro-to-tensors" title="Permalink to this headline">#</a></h3>
 <p>A tensor is a multidimensional array of the same data type. A tensor can be a scalar (single number), a vector, or a matrix.</p>
 <p>If you have used NumPy, tensors are like NumPy arrays, except that tensors have GPU(Graphical Processing Unit) support.</p>
 <p>A typical tensor has the following information:</p>
@@ -1816,7 +1833,7 @@ <h3><span class="section-number">37.97.4.1. </span>3.1 Intro to Tensors<a class=
 </ul>
 </section>
 <section id="creating-a-tensor-with-tf-constant">
-<h3><span class="section-number">37.97.4.2. </span>3.2 Creating a Tensor with tf.constant()<a class="headerlink" href="#creating-a-tensor-with-tf-constant" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.95.4.2. </span>3.2 Creating a Tensor with tf.constant()<a class="headerlink" href="#creating-a-tensor-with-tf-constant" title="Permalink to this headline">#</a></h3>
 <p>A Tensor can be a scalar, a vector or a matrix.
 Let’s use <code class="docutils literal notranslate"><span class="pre">tf.constant()</span></code> to create these tensor types.</p>
 <p>A tensor created with <code class="docutils literal notranslate"><span class="pre">tf.constant()</span></code> is immutable.</p>
@@ -1961,7 +1978,7 @@ <h3><span class="section-number">37.97.4.2. </span>3.2 Creating a Tensor with tf
 </div>
 </section>
 <section id="creating-a-tensor-with-tf-variable">
-<h3><span class="section-number">37.97.4.3. </span>3.3 Creating a Tensor with tf.Variable()<a class="headerlink" href="#creating-a-tensor-with-tf-variable" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.95.4.3. </span>3.3 Creating a Tensor with tf.Variable()<a class="headerlink" href="#creating-a-tensor-with-tf-variable" title="Permalink to this headline">#</a></h3>
 <p>A tensor created with <code class="docutils literal notranslate"><span class="pre">tf.constant()</span></code> is immutable, it can not be changed. Such kind of tensor can not be used as weights in neural networks because they need to be changed/updated in backpropogation for example.</p>
 <p>With <code class="docutils literal notranslate"><span class="pre">tf.Variable()</span></code>, we can create tensors that can be mutable and thus can be used in things like updating the weights of neural networks like said above.</p>
 <p>Creating variable tensor is as simple as the former.</p>
@@ -1992,7 +2009,7 @@ <h3><span class="section-number">37.97.4.3. </span>3.3 Creating a Tensor with tf
 </div>
 </section>
 <section id="creating-a-tensor-from-existing-functions">
-<h3><span class="section-number">37.97.4.4. </span>3.4 Creating a Tensor from Existing Functions<a class="headerlink" href="#creating-a-tensor-from-existing-functions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.95.4.4. </span>3.4 Creating a Tensor from Existing Functions<a class="headerlink" href="#creating-a-tensor-from-existing-functions" title="Permalink to this headline">#</a></h3>
 <p>There some types of uniform tensors that you would not want to create from scratch, when in fact, they are already built.</p>
 <p>Take an example of 1’s tensor, 0’s, and random tensors. Let’s create them.</p>
 <div class="cell docutils container">
@@ -2106,7 +2123,7 @@ <h3><span class="section-number">37.97.4.4. </span>3.4 Creating a Tensor from Ex
 <p>You can learn more about Random number generation at <a class="reference external" href="https://www.tensorflow.org/guide/random_numbers#the_tfrandomgenerator_class">TensorFlow docs</a>.</p>
 </section>
 <section id="selecting-data-in-tensor">
-<h3><span class="section-number">37.97.4.5. </span>3.5 Selecting Data in Tensor<a class="headerlink" href="#selecting-data-in-tensor" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.95.4.5. </span>3.5 Selecting Data in Tensor<a class="headerlink" href="#selecting-data-in-tensor" title="Permalink to this headline">#</a></h3>
 <p>We can also select values in any tensor, both single dimensional tensor and multi dimensional tensor.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2155,7 +2172,7 @@ <h3><span class="section-number">37.97.4.5. </span>3.5 Selecting Data in Tensor<
 </div>
 </section>
 <section id="performing-operations-on-tensors">
-<h3><span class="section-number">37.97.4.6. </span>3.6 Performing Operations on Tensors<a class="headerlink" href="#performing-operations-on-tensors" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.95.4.6. </span>3.6 Performing Operations on Tensors<a class="headerlink" href="#performing-operations-on-tensors" title="Permalink to this headline">#</a></h3>
 <p>All numeric operations can be performed on tensor. Let’s see few of them.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2206,7 +2223,7 @@ <h3><span class="section-number">37.97.4.6. </span>3.6 Performing Operations on
 <p>You can learn more at official docs, <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/math"><code class="docutils literal notranslate"><span class="pre">tf.math()</span></code></a> specifically. Almost all maths operations can be done on tensors.</p>
 </section>
 <section id="manipulating-the-shape-of-tensor">
-<h3><span class="section-number">37.97.4.7. </span>3.7 Manipulating the Shape of Tensor<a class="headerlink" href="#manipulating-the-shape-of-tensor" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.95.4.7. </span>3.7 Manipulating the Shape of Tensor<a class="headerlink" href="#manipulating-the-shape-of-tensor" title="Permalink to this headline">#</a></h3>
 <p>There are times you would want to reshape a tensor. Here is how to go about it.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2273,7 +2290,7 @@ <h3><span class="section-number">37.97.4.7. </span>3.7 Manipulating the Shape of
 </section>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="acknowledgments">
-<h1><span class="section-number">37.98. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.96. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
 <p>Thanks to <a class="reference external" href="https://github.com/Nyandwi">Jean de Dieu Nyandwi</a> for creating <a class="reference external" href="https://github.com/Nyandwi/machine_learning_complete/blob/main/7_intro_to_artificial%20_neural_networks_and_tensorflow/2_intro_to_tensorflow_for_deeplearning.ipynb">2_intro_to_tensorflow_for_deeplearning</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
@@ -2314,13 +2331,13 @@ <h1><span class="section-number">37.98. </span>Acknowledgments<a class="headerli
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.96. </span>Object Recognition in Images using CNN</p>
+            <p class="prev-next-title"><span class="section-number">37.94. </span>Object Recognition in Images using CNN</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html" title="next page">
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.99. </span>Bitcoin LSTM Model with Tweet Volume and Sentiment</p>
+        <p class="prev-next-title"><span class="section-number">37.97. </span>Bitcoin LSTM Model with Tweet Volume and Sentiment</p>
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diff --git a/assignments/deep-learning/time-series-forecasting-assignment.html b/assignments/deep-learning/time-series-forecasting-assignment.html
index d89548c590..2a98be47e9 100644
--- a/assignments/deep-learning/time-series-forecasting-assignment.html
+++ b/assignments/deep-learning/time-series-forecasting-assignment.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.106. Time Series Forecasting Assignment &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.104. Time Series Forecasting Assignment &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.108. Neural Networks for Classification with TensorFlow" href="nn-for-classification-assignment.html" />
-    <link rel="prev" title="37.105. Fun with Variational Autoencoders" href="autoencoder/variational-autoencoder-and-faces-generation.html" />
+    <link rel="next" title="37.106. Neural Networks for Classification with TensorFlow" href="nn-for-classification-assignment.html" />
+    <link rel="prev" title="37.103. Fun with Variational Autoencoders" href="autoencoder/variational-autoencoder-and-faces-generation.html" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
-  <label for="toctree-checkbox-3">
+  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
+  <label for="toctree-checkbox-4">
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@@ -284,8 +306,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
-  <label for="toctree-checkbox-4">
+  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
+  <label for="toctree-checkbox-5">
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@@ -311,8 +333,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
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-    <a class="reference internal" href="../ml-fundamentals/ml-linear-regression-2.html">
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+    <a class="reference internal" href="../ml-fundamentals/linear-regression/california_housing.html">
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+     37.48. Gradient descent
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+    <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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        </li>
        <li class="toc-h4 nav-item toc-entry">
         <a class="reference internal nav-link" href="#simple-rnn">
-         37.106.4.2.3. Simple RNN
+         37.104.4.2.3. Simple RNN
         </a>
        </li>
        <li class="toc-h4 nav-item toc-entry">
         <a class="reference internal nav-link" href="#lstm">
-         37.106.4.2.4. LSTM
+         37.104.4.2.4. LSTM
         </a>
        </li>
       </ul>
@@ -1597,27 +1614,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2">
-     37.106.5. Task 2
+     37.104.5. Task 2
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id2">
-       37.106.5.1. ANN
+       37.104.5.1. ANN
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#rnn">
-       37.106.5.2. RNN
+       37.104.5.2. RNN
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id3">
-       37.106.5.3. CNN
+       37.104.5.3. CNN
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id4">
-       37.106.5.4. LSTM
+       37.104.5.4. LSTM
       </a>
      </li>
     </ul>
@@ -1626,7 +1643,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.107. Acknowledgments
+   37.105. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1650,86 +1667,86 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#">
-   37.106. Time Series Forecasting Assignment
+   37.104. Time Series Forecasting Assignment
   </a>
   <ul class="visible nav section-nav flex-column">
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#tasks">
-     37.106.1. Tasks
+     37.104.1. Tasks
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task1">
-       37.106.1.1. Task1
+       37.104.1.1. Task1
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task2">
-       37.106.1.2. Task2
+       37.104.1.2. Task2
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#imports">
-     37.106.2. Imports
+     37.104.2. Imports
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#loading-data">
-     37.106.3. Loading Data
+     37.104.3. Loading Data
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#csv-data-path">
-       37.106.3.1. CSV Data Path
+       37.104.3.1. CSV Data Path
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#reading-csv-file-with-pandas">
-       37.106.3.2. Reading CSV file with Pandas
+       37.104.3.2. Reading CSV file with Pandas
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#normalizing-data">
-       37.106.3.3. Normalizing Data
+       37.104.3.3. Normalizing Data
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     37.106.4. Task1
+     37.104.4. Task1
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#data-preparation">
-       37.106.4.1. Data Preparation
+       37.104.4.1. Data Preparation
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#modeling">
-       37.106.4.2. Modeling
+       37.104.4.2. Modeling
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h4 nav-item toc-entry">
         <a class="reference internal nav-link" href="#ann">
-         37.106.4.2.1. ANN
+         37.104.4.2.1. ANN
         </a>
        </li>
        <li class="toc-h4 nav-item toc-entry">
         <a class="reference internal nav-link" href="#cnn">
-         37.106.4.2.2. CNN
+         37.104.4.2.2. CNN
         </a>
        </li>
        <li class="toc-h4 nav-item toc-entry">
         <a class="reference internal nav-link" href="#simple-rnn">
-         37.106.4.2.3. Simple RNN
+         37.104.4.2.3. Simple RNN
         </a>
        </li>
        <li class="toc-h4 nav-item toc-entry">
         <a class="reference internal nav-link" href="#lstm">
-         37.106.4.2.4. LSTM
+         37.104.4.2.4. LSTM
         </a>
        </li>
       </ul>
@@ -1738,27 +1755,27 @@ <h2> Contents </h2>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2">
-     37.106.5. Task 2
+     37.104.5. Task 2
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id2">
-       37.106.5.1. ANN
+       37.104.5.1. ANN
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#rnn">
-       37.106.5.2. RNN
+       37.104.5.2. RNN
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id3">
-       37.106.5.3. CNN
+       37.104.5.3. CNN
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id4">
-       37.106.5.4. LSTM
+       37.104.5.4. LSTM
       </a>
      </li>
     </ul>
@@ -1767,7 +1784,7 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.107. Acknowledgments
+   37.105. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1781,21 +1798,21 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="time-series-forecasting-assignment">
-<h1><span class="section-number">37.106. </span>Time Series Forecasting Assignment<a class="headerlink" href="#time-series-forecasting-assignment" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.104. </span>Time Series Forecasting Assignment<a class="headerlink" href="#time-series-forecasting-assignment" title="Permalink to this headline">#</a></h1>
 <section id="tasks">
-<h2><span class="section-number">37.106.1. </span>Tasks<a class="headerlink" href="#tasks" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.104.1. </span>Tasks<a class="headerlink" href="#tasks" title="Permalink to this headline">#</a></h2>
 <p><img alt="types" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/assignment/deep-learning/time-series/time-series.jpg" /></p>
 <section id="task1">
-<h3><span class="section-number">37.106.1.1. </span>Task1<a class="headerlink" href="#task1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.104.1.1. </span>Task1<a class="headerlink" href="#task1" title="Permalink to this headline">#</a></h3>
 <p>Given 10 timesteps of features, predict the USDT of the next timestep (many to one)</p>
 </section>
 <section id="task2">
-<h3><span class="section-number">37.106.1.2. </span>Task2<a class="headerlink" href="#task2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.104.1.2. </span>Task2<a class="headerlink" href="#task2" title="Permalink to this headline">#</a></h3>
 <p>Given 10 timestaps of features, predict the USDT of those timesteps (many to many)</p>
 </section>
 </section>
 <section id="imports">
-<h2><span class="section-number">37.106.2. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.104.2. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -1821,9 +1838,9 @@ <h2><span class="section-number">37.106.2. </span>Imports<a class="headerlink" h
 </div>
 </section>
 <section id="loading-data">
-<h2><span class="section-number">37.106.3. </span>Loading Data<a class="headerlink" href="#loading-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.104.3. </span>Loading Data<a class="headerlink" href="#loading-data" title="Permalink to this headline">#</a></h2>
 <section id="csv-data-path">
-<h3><span class="section-number">37.106.3.1. </span>CSV Data Path<a class="headerlink" href="#csv-data-path" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.104.3.1. </span>CSV Data Path<a class="headerlink" href="#csv-data-path" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data_path</span> <span class="o">=</span> <span class="s1">&#39;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/time-series/timeseries.csv&#39;</span>
@@ -1833,7 +1850,7 @@ <h3><span class="section-number">37.106.3.1. </span>CSV Data Path<a class="heade
 </div>
 </section>
 <section id="reading-csv-file-with-pandas">
-<h3><span class="section-number">37.106.3.2. </span>Reading CSV file with Pandas<a class="headerlink" href="#reading-csv-file-with-pandas" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.104.3.2. </span>Reading CSV file with Pandas<a class="headerlink" href="#reading-csv-file-with-pandas" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path</span><span class="p">)</span>
@@ -1850,7 +1867,7 @@ <h3><span class="section-number">37.106.3.2. </span>Reading CSV file with Pandas
 </div>
 </section>
 <section id="normalizing-data">
-<h3><span class="section-number">37.106.3.3. </span>Normalizing Data<a class="headerlink" href="#normalizing-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.104.3.3. </span>Normalizing Data<a class="headerlink" href="#normalizing-data" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">preprocessing</span>
@@ -1877,9 +1894,9 @@ <h3><span class="section-number">37.106.3.3. </span>Normalizing Data<a class="he
 </section>
 </section>
 <section id="id1">
-<h2><span class="section-number">37.106.4. </span>Task1<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.104.4. </span>Task1<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
 <section id="data-preparation">
-<h3><span class="section-number">37.106.4.1. </span>Data Preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.104.4.1. </span>Data Preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline">#</a></h3>
 <p>Preparing the data for the first task</p>
 <p>We will try to predict the value of USDT on the 11th day using the data of other features from day 1 to 10</p>
 <div class="cell docutils container">
@@ -1982,9 +1999,9 @@ <h3><span class="section-number">37.106.4.1. </span>Data Preparation<a class="he
 </div>
 </section>
 <section id="modeling">
-<h3><span class="section-number">37.106.4.2. </span>Modeling<a class="headerlink" href="#modeling" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.104.4.2. </span>Modeling<a class="headerlink" href="#modeling" title="Permalink to this headline">#</a></h3>
 <section id="ann">
-<h4><span class="section-number">37.106.4.2.1. </span>ANN<a class="headerlink" href="#ann" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.104.4.2.1. </span>ANN<a class="headerlink" href="#ann" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">keras.callbacks</span> <span class="kn">import</span> <span class="n">EarlyStopping</span><span class="p">,</span> <span class="n">ModelCheckpoint</span>
@@ -2051,7 +2068,7 @@ <h4><span class="section-number">37.106.4.2.1. </span>ANN<a class="headerlink" h
 </div>
 </section>
 <section id="cnn">
-<h4><span class="section-number">37.106.4.2.2. </span>CNN<a class="headerlink" href="#cnn" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.104.4.2.2. </span>CNN<a class="headerlink" href="#cnn" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">cnn_builder</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">):</span>
@@ -2111,7 +2128,7 @@ <h4><span class="section-number">37.106.4.2.2. </span>CNN<a class="headerlink" h
 </div>
 </section>
 <section id="simple-rnn">
-<h4><span class="section-number">37.106.4.2.3. </span>Simple RNN<a class="headerlink" href="#simple-rnn" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.104.4.2.3. </span>Simple RNN<a class="headerlink" href="#simple-rnn" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">rnn_builder</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">):</span>
@@ -2171,7 +2188,7 @@ <h4><span class="section-number">37.106.4.2.3. </span>Simple RNN<a class="header
 </div>
 </section>
 <section id="lstm">
-<h4><span class="section-number">37.106.4.2.4. </span>LSTM<a class="headerlink" href="#lstm" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.104.4.2.4. </span>LSTM<a class="headerlink" href="#lstm" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">lstm_builder</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">):</span>
@@ -2226,7 +2243,7 @@ <h4><span class="section-number">37.106.4.2.4. </span>LSTM<a class="headerlink"
 </section>
 </section>
 <section id="task-2">
-<h2><span class="section-number">37.106.5. </span>Task 2<a class="headerlink" href="#task-2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.104.5. </span>Task 2<a class="headerlink" href="#task-2" title="Permalink to this headline">#</a></h2>
 <p>Just setting future to 10 and the window to 10</p>
 <div class="cell docutils container">
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@@ -2251,7 +2268,7 @@ <h2><span class="section-number">37.106.5. </span>Task 2<a class="headerlink" hr
 </div>
 <p>Same models</p>
 <section id="id2">
-<h3><span class="section-number">37.106.5.1. </span>ANN<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.104.5.1. </span>ANN<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">ann_builder</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">):</span>
@@ -2304,7 +2321,7 @@ <h3><span class="section-number">37.106.5.1. </span>ANN<a class="headerlink" hre
 </div>
 </section>
 <section id="rnn">
-<h3><span class="section-number">37.106.5.2. </span>RNN<a class="headerlink" href="#rnn" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.104.5.2. </span>RNN<a class="headerlink" href="#rnn" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">rnn_builder</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">):</span>
@@ -2364,7 +2381,7 @@ <h3><span class="section-number">37.106.5.2. </span>RNN<a class="headerlink" hre
 </div>
 </section>
 <section id="id3">
-<h3><span class="section-number">37.106.5.3. </span>CNN<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.104.5.3. </span>CNN<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">cnn_builder</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">):</span>
@@ -2424,7 +2441,7 @@ <h3><span class="section-number">37.106.5.3. </span>CNN<a class="headerlink" hre
 </div>
 </section>
 <section id="id4">
-<h3><span class="section-number">37.106.5.4. </span>LSTM<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.104.5.4. </span>LSTM<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">lstm_builder</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">):</span>
@@ -2486,7 +2503,7 @@ <h3><span class="section-number">37.106.5.4. </span>LSTM<a class="headerlink" hr
 </section>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="acknowledgments">
-<h1><span class="section-number">37.107. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.105. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
 <p>Thanks to Taha Bouhsine for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/skywolfmo/wts2-time-series-forecasting-with-tf"> WTS2: Time Series Forecasting with TF </a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
@@ -2527,13 +2544,13 @@ <h1><span class="section-number">37.107. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.105. </span>Fun with Variational Autoencoders</p>
+            <p class="prev-next-title"><span class="section-number">37.103. </span>Fun with Variational Autoencoders</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="nn-for-classification-assignment.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.108. </span>Neural Networks for Classification with TensorFlow</p>
+        <p class="prev-next-title"><span class="section-number">37.106. </span>Neural Networks for Classification with TensorFlow</p>
     </div>
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+    <link rel="next" title="37.84. Case Study: Debugging in Classification" href="debugging-in-classification.html" />
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan/art-by-gan.html">
-     37.115. Art by gan
+     37.113. Art by gan
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
     </a>
    </li>
   </ul>
@@ -1135,8 +1152,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../slides/introduction.html">
    38. Slides
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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@@ -1509,51 +1526,51 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#case-study-debugging-a-simple-model">
-   37.85.1. Case Study: Debugging a Simple Model
+   37.83.1. Case Study: Debugging a Simple Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#create-the-dataset">
-     37.85.1.1. Create the Dataset
+     37.83.1.1. Create the Dataset
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#fit-simple-data-with-simple-model">
-     37.85.1.2. Fit Simple Data with Simple Model
+     37.83.1.2. Fit Simple Data with Simple Model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#solution-getting-loss-to-decrease">
-     37.85.1.3. Solution: Getting Loss to Decrease
+     37.83.1.3. Solution: Getting Loss to Decrease
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#solution-reaching-convergence">
-     37.85.1.4. Solution: Reaching Convergence
+     37.83.1.4. Solution: Reaching Convergence
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#summary-of-case-study">
-     37.85.1.5. Summary of Case Study
+     37.83.1.5. Summary of Case Study
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exploding-gradients">
-   37.85.2. Exploding Gradients
+   37.83.2. Exploding Gradients
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#cause-high-learning-rate">
-     37.85.2.1. Cause: High Learning Rate
+     37.83.2.1. Cause: High Learning Rate
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion">
-   37.85.3. Conclusion
+   37.83.3. Conclusion
   </a>
  </li>
 </ul>
@@ -1577,51 +1594,51 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#case-study-debugging-a-simple-model">
-   37.85.1. Case Study: Debugging a Simple Model
+   37.83.1. Case Study: Debugging a Simple Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#create-the-dataset">
-     37.85.1.1. Create the Dataset
+     37.83.1.1. Create the Dataset
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#fit-simple-data-with-simple-model">
-     37.85.1.2. Fit Simple Data with Simple Model
+     37.83.1.2. Fit Simple Data with Simple Model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#solution-getting-loss-to-decrease">
-     37.85.1.3. Solution: Getting Loss to Decrease
+     37.83.1.3. Solution: Getting Loss to Decrease
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#solution-reaching-convergence">
-     37.85.1.4. Solution: Reaching Convergence
+     37.83.1.4. Solution: Reaching Convergence
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#summary-of-case-study">
-     37.85.1.5. Summary of Case Study
+     37.83.1.5. Summary of Case Study
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exploding-gradients">
-   37.85.2. Exploding Gradients
+   37.83.2. Exploding Gradients
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#cause-high-learning-rate">
-     37.85.2.1. Cause: High Learning Rate
+     37.83.2.1. Cause: High Learning Rate
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion">
-   37.85.3. Conclusion
+   37.83.3. Conclusion
   </a>
  </li>
 </ul>
@@ -1646,7 +1663,7 @@ <h2> Contents </h2>
 See the License for the specific language governing permissions and
 limitations under the License.</p>
 </details><section class="tex2jax_ignore mathjax_ignore" id="counterintuitive-challenges-in-ml-debugging">
-<h1><span class="section-number">37.85. </span>Counterintuitive Challenges in ML Debugging<a class="headerlink" href="#counterintuitive-challenges-in-ml-debugging" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.83. </span>Counterintuitive Challenges in ML Debugging<a class="headerlink" href="#counterintuitive-challenges-in-ml-debugging" title="Permalink to this headline">#</a></h1>
 <p>In this Colab, you will explore why ML debugging is harder than traditional debugging by debugging a simple regression problem, with one feature and one label. You will:</p>
 <ul class="simple">
 <li><p>Create the dataset.</p></li>
@@ -1656,9 +1673,9 @@ <h1><span class="section-number">37.85. </span>Counterintuitive Challenges in ML
 </ul>
 <p>Please <strong>make a copy</strong> of this Colab before running it. Click on <em>File</em>, and then click on <em>Save a copy in Drive</em>.</p>
 <section id="case-study-debugging-a-simple-model">
-<h2><span class="section-number">37.85.1. </span>Case Study: Debugging a Simple Model<a class="headerlink" href="#case-study-debugging-a-simple-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.83.1. </span>Case Study: Debugging a Simple Model<a class="headerlink" href="#case-study-debugging-a-simple-model" title="Permalink to this headline">#</a></h2>
 <section id="create-the-dataset">
-<h3><span class="section-number">37.85.1.1. </span>Create the Dataset<a class="headerlink" href="#create-the-dataset" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.83.1.1. </span>Create the Dataset<a class="headerlink" href="#create-the-dataset" title="Permalink to this headline">#</a></h3>
 <p>Run the cells below to load libraries.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1702,7 +1719,7 @@ <h3><span class="section-number">37.85.1.1. </span>Create the Dataset<a class="h
 </div>
 </section>
 <section id="fit-simple-data-with-simple-model">
-<h3><span class="section-number">37.85.1.2. </span>Fit Simple Data with Simple Model<a class="headerlink" href="#fit-simple-data-with-simple-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.83.1.2. </span>Fit Simple Data with Simple Model<a class="headerlink" href="#fit-simple-data-with-simple-model" title="Permalink to this headline">#</a></h3>
 <p>TensorFlow provides several different APIs. This Colab only demonstrates the Keras API since Keras lets you quickly train models in a few lines of code using high-level APIs. In Keras, the typical neural network is a <code class="docutils literal notranslate"><span class="pre">sequential</span></code> model with fully-connected, or <code class="docutils literal notranslate"><span class="pre">dense</span></code>, layers.</p>
 <p>Your dataset is simple. A neural network with just 1 neuron should learn your dataset. Define a neural network with 1 layer having 1 neuron using the model type <code class="docutils literal notranslate"><span class="pre">keras.Sequential</span></code> with the layer type <code class="docutils literal notranslate"><span class="pre">keras.layers.Dense</span></code>. To understand the Keras code, read the code comments. Then run the cell. The code prints the model summary to show a model with 1 layer and 2 parameters (weight and bias).</p>
 <div class="cell docutils container">
@@ -1753,7 +1770,7 @@ <h3><span class="section-number">37.85.1.2. </span>Fit Simple Data with Simple M
 <p>Consider these actions and experiment where necessary. Then read the following section for the solution.</p>
 </section>
 <section id="solution-getting-loss-to-decrease">
-<h3><span class="section-number">37.85.1.3. </span>Solution: Getting Loss to Decrease<a class="headerlink" href="#solution-getting-loss-to-decrease" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.83.1.3. </span>Solution: Getting Loss to Decrease<a class="headerlink" href="#solution-getting-loss-to-decrease" title="Permalink to this headline">#</a></h3>
 <p>Before trying to adjust specific model parameters, such as the hyperparameter values, you should first check for good development practices. Here, you should start with a linear model because of these two best practices:</p>
 <ul class="simple">
 <li><p>Regression: In a regression problem, the last layer must always be linear.</p></li>
@@ -1777,7 +1794,7 @@ <h3><span class="section-number">37.85.1.3. </span>Solution: Getting Loss to Dec
 <p>Your loss decreases, albeit slowly! You’re on the right track. How can you get your loss to converge? Experiment with the code above. For the solution, read the following section.</p>
 </section>
 <section id="solution-reaching-convergence">
-<h3><span class="section-number">37.85.1.4. </span>Solution: Reaching Convergence<a class="headerlink" href="#solution-reaching-convergence" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.83.1.4. </span>Solution: Reaching Convergence<a class="headerlink" href="#solution-reaching-convergence" title="Permalink to this headline">#</a></h3>
 <p>Your loss isn’t decreasing fast enough. From the guidance on <a class="reference external" href="https://developers.google.com/machine-learning/crash-course/reducing-loss/learning-rate">Learning Rate</a>, you know that you can increase the learning rate to train faster. Run the following code to increase the learning rate to 0.1. The the model reaches convergence quickly.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1807,7 +1824,7 @@ <h3><span class="section-number">37.85.1.4. </span>Solution: Reaching Convergenc
 <p>Yes, the predictions match the features very well.</p>
 </section>
 <section id="summary-of-case-study">
-<h3><span class="section-number">37.85.1.5. </span>Summary of Case Study<a class="headerlink" href="#summary-of-case-study" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.83.1.5. </span>Summary of Case Study<a class="headerlink" href="#summary-of-case-study" title="Permalink to this headline">#</a></h3>
 <p>When debugging ML models, you should first attempt to diagnose the problem and apply the appropriate fix. For example, if you had changed your optimizer using <code class="docutils literal notranslate"><span class="pre">optimizer='sgd'</span></code>, then your model also converges faster. However, the problem was not with the optimizer but with the learning rate. Changing the optimizer only helps because <code class="docutils literal notranslate"><span class="pre">optimizer='sgd'</span></code> has a higher default learning rate than <code class="docutils literal notranslate"><span class="pre">optimizer='adam'</span></code>.</p>
 <p>Alternatively, you could train the model for longer with the default learning rate. However, in real-world ML, models take long to train. You should keep your training cycles as short as possible. Therefore, increasing the learning rate is the correct fix.</p>
 <p>These options demonstrate how debugging in ML is n-dimensional, and therefore you must use your understanding of model mechanics to narrow down your options. Because running experiments in ML is time consuming, requires careful setup, and can be subject to reproducibility issues, it’s important to use your understanding of model mechanics to  narrow down options without having to experiment.</p>
@@ -1815,10 +1832,10 @@ <h3><span class="section-number">37.85.1.5. </span>Summary of Case Study<a class
 </section>
 </section>
 <section id="exploding-gradients">
-<h2><span class="section-number">37.85.2. </span>Exploding Gradients<a class="headerlink" href="#exploding-gradients" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.83.2. </span>Exploding Gradients<a class="headerlink" href="#exploding-gradients" title="Permalink to this headline">#</a></h2>
 <p>A common problem in model training is a loss that “explodes” or becomes <code class="docutils literal notranslate"><span class="pre">nan</span></code>. A common cause is anomalous feature data, such as outliers and <code class="docutils literal notranslate"><span class="pre">nan</span></code> values, or a high learning rate. The following sections demonstrate these causes.</p>
 <section id="cause-high-learning-rate">
-<h3><span class="section-number">37.85.2.1. </span>Cause: High Learning Rate<a class="headerlink" href="#cause-high-learning-rate" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.83.2.1. </span>Cause: High Learning Rate<a class="headerlink" href="#cause-high-learning-rate" title="Permalink to this headline">#</a></h3>
 <p>In this section, you will create data in the range [0,50] and show that the gradient explodes when you train the model using a learning rate of 0.01. Then you’ll reduce the learning rate to make the model converge.</p>
 <p>Create and visualize the data by running the following code.</p>
 <div class="cell docutils container">
@@ -1853,7 +1870,7 @@ <h3><span class="section-number">37.85.2.1. </span>Cause: High Learning Rate<a c
 </section>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">37.85.3. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.83.3. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>This Colab demonstrated the following principles.</p>
 <ul class="simple">
 <li><p>The n-dimensional nature of debugging in ML makes ML debugging hard.</p></li>
@@ -1901,13 +1918,13 @@ <h2><span class="section-number">37.85.3. </span>Conclusion<a class="headerlink"
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.84. </span>Data engineering</p>
+            <p class="prev-next-title"><span class="section-number">37.82. </span>Data engineering</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="debugging-in-classification.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.86. </span>Case Study: Debugging in Classification</p>
+        <p class="prev-next-title"><span class="section-number">37.84. </span>Case Study: Debugging in Classification</p>
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     <i class="fas fa-angle-right"></i>
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diff --git a/assignments/machine-learning-productionization/data-engineering.html b/assignments/machine-learning-productionization/data-engineering.html
index ff322afde5..99e317f5a2 100644
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.84. Data engineering &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.82. Data engineering &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.85. Counterintuitive Challenges in ML Debugging" href="counterintuitive-challenges-in-ml-debugging.html" />
-    <link rel="prev" title="37.82. Random Forest Classifier with Feature Importance" href="../ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html" />
+    <link rel="next" title="37.83. Counterintuitive Challenges in ML Debugging" href="counterintuitive-challenges-in-ml-debugging.html" />
+    <link rel="prev" title="37.80. Random Forest Classifier with Feature Importance" href="../ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html" />
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     <meta name="docsearch:language" content="None">
     
@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
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@@ -284,8 +306,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
-  <label for="toctree-checkbox-4">
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@@ -311,8 +333,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
-  <label for="toctree-checkbox-5">
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@@ -355,8 +377,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
-  <label for="toctree-checkbox-6">
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+  <label for="toctree-checkbox-7">
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@@ -392,8 +414,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
-  <label for="toctree-checkbox-7">
+  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
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@@ -431,8 +453,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
    14. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
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@@ -453,8 +475,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
    15. Getting started with ensemble learning
   </a>
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    16. Introduction to Gradient Boosting
   </a>
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    37. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
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     </a>
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-    </a>
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-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
     </a>
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-     37.47. Linear Regression Metrics
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     </a>
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+     37.47. Loss Function
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-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
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-     37.59. Linear and polynomial regression
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     </a>
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-     37.63. Explore classification methods
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-fundamentals/linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
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-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
+     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.67. Support Vector Machines (SVM) - Classification
+     37.65. Support Vector Machines (SVM) - Classification
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+     37.67. Decision Trees - Classification
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-     37.71. Learning Curve To Identify Overfit &amp; Underfit
+     37.69. Learning Curve To Identify Overfit &amp; Underfit
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-     37.72. Dropout and Batch Normalization
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-     37.77. Beyond random forests: more ensemble models
+     37.75. Beyond random forests: more ensemble models
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-     37.79. Hyperparameter tuning gradient boosting
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-     37.82. Random Forest Classifier with Feature Importance
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-     37.85. Counterintuitive Challenges in ML Debugging
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-     37.94. Sign Language Digits Classification with CNN
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-     37.96. Object Recognition in Images using CNN
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-     37.97. Intro to TensorFlow for Deep Learning
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-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
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-     37.101. Google Stock Price Prediction RNN
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-     37.103. Intro to Autoencoders
+     37.101. Intro to Autoencoders
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-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
+     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
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-     37.105. Fun with Variational Autoencoders
+     37.103. Fun with Variational Autoencoders
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-     37.106. Time Series Forecasting Assignment
+     37.104. Time Series Forecasting Assignment
     </a>
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-     37.108. Neural Networks for Classification with TensorFlow
+     37.106. Neural Networks for Classification with TensorFlow
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/nn-classify-15-fruits-assignment.html">
-     37.109. NN Classify 15 Fruits Assignment
+     37.107. NN Classify 15 Fruits Assignment
     </a>
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-     37.114. DQN On Foreign Exchange Market
+     37.112. DQN On Foreign Exchange Market
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-     37.115. Art by gan
+     37.113. Art by gan
     </a>
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     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
     </a>
    </li>
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     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
     </a>
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   <a class="reference internal" href="../../slides/introduction.html">
    38. Slides
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exploring-dataset">
-   37.84.1. Exploring dataset
+   37.82.1. Exploring dataset
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   <a class="reference internal nav-link" href="#dealing-with-missing-data">
-   37.84.2. Dealing with missing data
+   37.82.2. Dealing with missing data
   </a>
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   <a class="reference internal nav-link" href="#removing-duplicate-data">
-   37.84.3. Removing duplicate data
+   37.82.3. Removing duplicate data
   </a>
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-   37.84.4. Handle inconsistent data
+   37.82.4. Handle inconsistent data
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-   37.84.5. At last
+   37.82.5. At last
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.84.6. Acknowledgments
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  </li>
 </ul>
@@ -1558,32 +1575,32 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exploring-dataset">
-   37.84.1. Exploring dataset
+   37.82.1. Exploring dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dealing-with-missing-data">
-   37.84.2. Dealing with missing data
+   37.82.2. Dealing with missing data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#removing-duplicate-data">
-   37.84.3. Removing duplicate data
+   37.82.3. Removing duplicate data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#handle-inconsistent-data">
-   37.84.4. Handle inconsistent data
+   37.82.4. Handle inconsistent data
   </a>
  </li>
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-   37.84.5. At last
+   37.82.5. At last
   </a>
  </li>
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.84.6. Acknowledgments
+   37.82.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1597,7 +1614,7 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="data-engineering">
-<h1><span class="section-number">37.84. </span>Data engineering<a class="headerlink" href="#data-engineering" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.82. </span>Data engineering<a class="headerlink" href="#data-engineering" title="Permalink to this headline">#</a></h1>
 <p>This assignment focuses on techniques for cleaning and transforming the data to handle challenges of missing, inaccurate, or incomplete data. Please refer to <a class="reference external" href="#data-engineering">Machine Learning productionization - Data engineering</a> to learn more.</p>
 <p>Fill <code class="docutils literal notranslate"><span class="pre">____</span></code> pieces of the below implementation in order to pass the assertions.</p>
 <!-- 
@@ -1609,7 +1626,7 @@ <h1><span class="section-number">37.84. </span>Data engineering<a class="headerl
 
 Identifying the group of attributes that uniquely identify a single record is very important and needs validation from a domain expert. Removing duplicates on this group leaves you with distinct records necessary for model training. This group acts as a key to performing multiple aggregate and transformations operations on the dataset like calculating rolling mean, backfilling null values, missing value imputation (details on this in next point), etc.
 True duplicates, i.e., instances of the same data point, are usually removed. In this way, the increase of the sample weight on these points is eliminated, and the risk of any artificial inflation in the performance metrics is reduced --><section id="exploring-dataset">
-<h2><span class="section-number">37.84.1. </span>Exploring dataset<a class="headerlink" href="#exploring-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.82.1. </span>Exploring dataset<a class="headerlink" href="#exploring-dataset" title="Permalink to this headline">#</a></h2>
 <blockquote>
 <div><p><strong>Learning goal</strong>: By the end of this subsection, you should be comfortable finding general information about the data stored in pandas DataFrames.</p>
 </div></blockquote>
@@ -1669,7 +1686,7 @@ <h2><span class="section-number">37.84.1. </span>Exploring dataset<a class="head
 <p><strong>Takeaway</strong>: Even just by looking at the metadata about the information in a DataFrame or the first and last few values in one, you can get an immediate idea about the size, shape, and content of the data you are dealing with.</p>
 </section>
 <section id="dealing-with-missing-data">
-<h2><span class="section-number">37.84.2. </span>Dealing with missing data<a class="headerlink" href="#dealing-with-missing-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.82.2. </span>Dealing with missing data<a class="headerlink" href="#dealing-with-missing-data" title="Permalink to this headline">#</a></h2>
 <p>Missing data can cause inaccuracies as well as weak or biased results. Sometimes these can be resolved by a “reload” of the data, filling in the missing values with computation and code like Python, or simply just removing the value and corresponding data. There are numerous reasons for why data may be missing and the actions that are taken to resolve these missing values can be dependent on how and why they went missing in the first place.</p>
 <blockquote>
 <div><p><strong>Learning goal</strong>: By the end of this subsection, you should know how to replace or remove null values from DataFrames.</p>
@@ -1779,7 +1796,7 @@ <h2><span class="section-number">37.84.2. </span>Dealing with missing data<a cla
 </div>
 </section>
 <section id="removing-duplicate-data">
-<h2><span class="section-number">37.84.3. </span>Removing duplicate data<a class="headerlink" href="#removing-duplicate-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.82.3. </span>Removing duplicate data<a class="headerlink" href="#removing-duplicate-data" title="Permalink to this headline">#</a></h2>
 <p>Data that has more than one occurrence can produce inaccurate results and usually should be removed. This can be a common occurrence when joining two or more datasets together. However, there are instances where duplication in joined datasets contain pieces that can provide additional information and may need to be preserved.</p>
 <blockquote>
 <div><p><strong>Learning goal</strong>: By the end of this subsection, you should be comfortable identifying and removing duplicate values from DataFrames.</p>
@@ -1822,7 +1839,7 @@ <h2><span class="section-number">37.84.3. </span>Removing duplicate data<a class
 </div>
 </section>
 <section id="handle-inconsistent-data">
-<h2><span class="section-number">37.84.4. </span>Handle inconsistent data<a class="headerlink" href="#handle-inconsistent-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.82.4. </span>Handle inconsistent data<a class="headerlink" href="#handle-inconsistent-data" title="Permalink to this headline">#</a></h2>
 <p>Depending on the source, data can have inconsistencies in how it’s presented. This can cause problems in searching for and representing the value, where it’s seen within the dataset but is not properly represented in visualizations or query results. Common formatting problems involve resolving whitespace, dates, and data types. Resolving formatting issues is typically up to the people who are using the data. For example, standards on how dates and numbers are presented can differ by country.</p>
 <blockquote>
 <div><p><strong>Learning goal</strong>: By the end of this subsection, you should know how to handle the inconsistent data format in the DataFrame.</p>
@@ -1870,7 +1887,7 @@ <h2><span class="section-number">37.84.4. </span>Handle inconsistent data<a clas
 </div>
 </section>
 <section id="at-last">
-<h2><span class="section-number">37.84.5. </span>At last<a class="headerlink" href="#at-last" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.82.5. </span>At last<a class="headerlink" href="#at-last" title="Permalink to this headline">#</a></h2>
 <p>Let’s apply all the methods above to make the data to be clean.</p>
 <div class="cell docutils container">
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@@ -1903,7 +1920,7 @@ <h2><span class="section-number">37.84.5. </span>At last<a class="headerlink" hr
 </ul>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.84.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.82.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It contributes some of the content in this chapter.</p>
 </section>
 </section>
@@ -1945,13 +1962,13 @@ <h2><span class="section-number">37.84.6. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.82. </span>Random Forest Classifier with Feature Importance</p>
+            <p class="prev-next-title"><span class="section-number">37.80. </span>Random Forest Classifier with Feature Importance</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="counterintuitive-challenges-in-ml-debugging.html" title="next page">
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.85. </span>Counterintuitive Challenges in ML Debugging</p>
+        <p class="prev-next-title"><span class="section-number">37.83. </span>Counterintuitive Challenges in ML Debugging</p>
     </div>
     <i class="fas fa-angle-right"></i>
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diff --git a/assignments/machine-learning-productionization/debugging-in-classification.html b/assignments/machine-learning-productionization/debugging-in-classification.html
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+    <title>37.84. Case Study: Debugging in Classification &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.87. Case Study: Debugging in Regression" href="debugging-in-regression.html" />
-    <link rel="prev" title="37.85. Counterintuitive Challenges in ML Debugging" href="counterintuitive-challenges-in-ml-debugging.html" />
+    <link rel="next" title="37.85. Case Study: Debugging in Regression" href="debugging-in-regression.html" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
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-  <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
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@@ -284,8 +306,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
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   <a class="reference internal" href="../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
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   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
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   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
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   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
    14. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
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   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
    15. Getting started with ensemble learning
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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    16. Introduction to Gradient Boosting
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-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
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@@ -633,8 +655,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../README.html">
    37. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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@@ -825,308 +847,303 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../ml-fundamentals/ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
+     37.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/loss-function.html">
-     37.48. Loss Function
+     37.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
+     37.50. ML logistic regression - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-2.html">
-     37.52. ML logistic regression - assignment 2
+     37.51. ML logistic regression - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-neural-network-1.html">
-     37.53. ML neural network - Assignment 1
+     37.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression-tools.html">
-     37.54. Regression tools
+     37.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
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-     37.55. Managing data
+     37.54. Managing data
     </a>
    </li>
    <li class="toctree-l2">
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-     37.56. Exploring visualizations
+     37.55. Exploring visualizations
     </a>
    </li>
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-     37.57. Try a different model
+     37.56. Try a different model
     </a>
    </li>
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-     37.58. Create a regression model
+     37.57. Create a regression model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-and-polynomial-regression.html">
-     37.59. Linear and polynomial regression
+     37.58. Linear and polynomial regression
     </a>
    </li>
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-     37.60. Retrying some regression
+     37.59. Retrying some regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
+     37.60. Pumpkin varieties and color
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.62. Delicious asian and indian cuisines
+     37.61. Delicious asian and indian cuisines
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/explore-classification-methods.html">
-     37.63. Explore classification methods
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-fundamentals/linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
+     37.62. Explore classification methods
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.65. Kernel method assignment 1
+     37.63. Kernel method assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
+     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.67. Support Vector Machines (SVM) - Classification
+     37.65. Support Vector Machines (SVM) - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.68. Decision Trees - Intro and Regression
+     37.66. Decision Trees - Intro and Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.69. Decision Trees - Classification
+     37.67. Decision Trees - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.70. Model selection assignment 1
+     37.68. Model selection assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.71. Learning Curve To Identify Overfit &amp; Underfit
+     37.69. Learning Curve To Identify Overfit &amp; Underfit
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.72. Dropout and Batch Normalization
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.73. Lasso and Ridge Regression
+     37.71. Lasso and Ridge Regression
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/regularized-linear-models.html">
-     37.74. Regularized Linear Models
+     37.72. Regularized Linear Models
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
-     37.75. Random forests intro and regression
+     37.73. Random forests intro and regression
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-     37.76. Random forests for classification
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     <a class="reference internal" href="../ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
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     <a class="reference internal" href="../ml-fundamentals/build-classification-models.html">
-     37.89. Build classification models
+     37.87. Build classification models
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/build-classification-model.html">
-     37.90. Build Classification Model
+     37.88. Build Classification Model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/parameter-play.html">
-     37.91. Parameter play
+     37.89. Parameter play
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
-     37.92. How to choose cnn architecture mnist
+     37.90. How to choose cnn architecture mnist
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
-     37.94. Sign Language Digits Classification with CNN
+     37.92. Sign Language Digits Classification with CNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/cnn/object-recognition-in-images-using-cnn.html">
-     37.96. Object Recognition in Images using CNN
+     37.94. Object Recognition in Images using CNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.97. Intro to TensorFlow for Deep Learning
+     37.95. Intro to TensorFlow for Deep Learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.101. Google Stock Price Prediction RNN
+     37.99. Google Stock Price Prediction RNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/autoencoder/autoencoder.html">
-     37.103. Intro to Autoencoders
+     37.101. Intro to Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
+     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
+     37.103. Fun with Variational Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
+     37.104. Time Series Forecasting Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/nn-for-classification-assignment.html">
-     37.108. Neural Networks for Classification with TensorFlow
+     37.106. Neural Networks for Classification with TensorFlow
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/nn-classify-15-fruits-assignment.html">
-     37.109. NN Classify 15 Fruits Assignment
+     37.107. NN Classify 15 Fruits Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.114. DQN On Foreign Exchange Market
+     37.112. DQN On Foreign Exchange Market
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan/art-by-gan.html">
-     37.115. Art by gan
+     37.113. Art by gan
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
     </a>
    </li>
   </ul>
@@ -1135,8 +1152,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../slides/introduction.html">
    38. Slides
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
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@@ -1509,101 +1526,101 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#load-mnist-data">
-   37.86.1. Load MNIST Data
+   37.84.1. Load MNIST Data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#understanding-the-data-format">
-   37.86.2. Understanding the Data Format
+   37.84.2. Understanding the Data Format
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#do-you-have-imbalanced-classes">
-   37.86.3. Do you have Imbalanced Classes?
+   37.84.3. Do you have Imbalanced Classes?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#shuffle-and-split-dataset">
-   37.86.4. Shuffle and Split Dataset
+   37.84.4. Shuffle and Split Dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#process-data">
-   37.86.5. Process Data
+   37.84.5. Process Data
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#solution">
-     37.86.5.1. Solution
+     37.84.5.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#remove-all-zero-features">
-   37.86.6. Remove All-Zero Features?
+   37.84.6. Remove All-Zero Features?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#establish-baseline">
-   37.86.7. Establish Baseline
+   37.84.7. Establish Baseline
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#train-a-linear-model">
-   37.86.8. Train a Linear Model
+   37.84.8. Train a Linear Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     37.86.8.1. Solution
+     37.84.8.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#fixing-loss-calculation">
-   37.86.9. Fixing Loss Calculation
+   37.84.9. Fixing Loss Calculation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#train-a-nonlinear-model">
-   37.86.10. Train a Nonlinear Model
+   37.84.10. Train a Nonlinear Model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#adding-a-second-layer">
-   37.86.11. Adding a Second Layer
+   37.84.11. Adding a Second Layer
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#check-for-training-validation-data-skew">
-   37.86.12. Check for Training/Validation Data Skew
+   37.84.12. Check for Training/Validation Data Skew
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#apply-dropout-regularization">
-   37.86.13. Apply Dropout Regularization
+   37.84.13. Apply Dropout Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#check-accuracy-for-data-slices">
-   37.86.14. Check Accuracy for Data Slices
+   37.84.14. Check Accuracy for Data Slices
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#testing-for-anomalous-values">
-   37.86.15. Testing for Anomalous Values
+   37.84.15. Testing for Anomalous Values
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#optional-pass-tests-by-fixing-loss-calculation">
-   37.86.16. Optional: Pass Tests by Fixing Loss Calculation
+   37.84.16. Optional: Pass Tests by Fixing Loss Calculation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion">
-   37.86.17. Conclusion
+   37.84.17. Conclusion
   </a>
  </li>
 </ul>
@@ -1627,101 +1644,101 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#load-mnist-data">
-   37.86.1. Load MNIST Data
+   37.84.1. Load MNIST Data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#understanding-the-data-format">
-   37.86.2. Understanding the Data Format
+   37.84.2. Understanding the Data Format
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#do-you-have-imbalanced-classes">
-   37.86.3. Do you have Imbalanced Classes?
+   37.84.3. Do you have Imbalanced Classes?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#shuffle-and-split-dataset">
-   37.86.4. Shuffle and Split Dataset
+   37.84.4. Shuffle and Split Dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#process-data">
-   37.86.5. Process Data
+   37.84.5. Process Data
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#solution">
-     37.86.5.1. Solution
+     37.84.5.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#remove-all-zero-features">
-   37.86.6. Remove All-Zero Features?
+   37.84.6. Remove All-Zero Features?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#establish-baseline">
-   37.86.7. Establish Baseline
+   37.84.7. Establish Baseline
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#train-a-linear-model">
-   37.86.8. Train a Linear Model
+   37.84.8. Train a Linear Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     37.86.8.1. Solution
+     37.84.8.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#fixing-loss-calculation">
-   37.86.9. Fixing Loss Calculation
+   37.84.9. Fixing Loss Calculation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#train-a-nonlinear-model">
-   37.86.10. Train a Nonlinear Model
+   37.84.10. Train a Nonlinear Model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#adding-a-second-layer">
-   37.86.11. Adding a Second Layer
+   37.84.11. Adding a Second Layer
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#check-for-training-validation-data-skew">
-   37.86.12. Check for Training/Validation Data Skew
+   37.84.12. Check for Training/Validation Data Skew
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#apply-dropout-regularization">
-   37.86.13. Apply Dropout Regularization
+   37.84.13. Apply Dropout Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#check-accuracy-for-data-slices">
-   37.86.14. Check Accuracy for Data Slices
+   37.84.14. Check Accuracy for Data Slices
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#testing-for-anomalous-values">
-   37.86.15. Testing for Anomalous Values
+   37.84.15. Testing for Anomalous Values
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#optional-pass-tests-by-fixing-loss-calculation">
-   37.86.16. Optional: Pass Tests by Fixing Loss Calculation
+   37.84.16. Optional: Pass Tests by Fixing Loss Calculation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion">
-   37.86.17. Conclusion
+   37.84.17. Conclusion
   </a>
  </li>
 </ul>
@@ -1746,7 +1763,7 @@ <h2> Contents </h2>
 See the License for the specific language governing permissions and
 limitations under the License.</p>
 </details><section class="tex2jax_ignore mathjax_ignore" id="case-study-debugging-in-classification">
-<h1><span class="section-number">37.86. </span>Case Study: Debugging in Classification<a class="headerlink" href="#case-study-debugging-in-classification" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.84. </span>Case Study: Debugging in Classification<a class="headerlink" href="#case-study-debugging-in-classification" title="Permalink to this headline">#</a></h1>
 <p>This Colab quickly demonstrates a few concepts related to debugging classification models. You will explore potential problems in implementing these tasks:</p>
 <ul class="simple">
 <li><p>Calculating loss for classification problems.</p></li>
@@ -1756,7 +1773,7 @@ <h1><span class="section-number">37.86. </span>Case Study: Debugging in Classifi
 </ul>
 <p>Please <strong>make a copy</strong> of this Colab before running it. Click on <em>File</em>, and then click on <em>Save a copy in Drive</em>.</p>
 <section id="load-mnist-data">
-<h2><span class="section-number">37.86.1. </span>Load MNIST Data<a class="headerlink" href="#load-mnist-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.84.1. </span>Load MNIST Data<a class="headerlink" href="#load-mnist-data" title="Permalink to this headline">#</a></h2>
 <p>MNIST is a dataset of images of the numbers 0 to 9. The problem is to classify the images as numbers. Setup libraries and load the MNIST dataset. Display the first few rows to verify that the data loaded. You’ll explore the data format after the data loads.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1794,7 +1811,7 @@ <h2><span class="section-number">37.86.1. </span>Load MNIST Data<a class="header
 </div>
 </section>
 <section id="understanding-the-data-format">
-<h2><span class="section-number">37.86.2. </span>Understanding the Data Format<a class="headerlink" href="#understanding-the-data-format" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.84.2. </span>Understanding the Data Format<a class="headerlink" href="#understanding-the-data-format" title="Permalink to this headline">#</a></h2>
 <p>Each row represents one labeled example. Column 0 represents the label that a human rater has assigned for one handwritten digit. For example, if Column 0 contains ‘6’, then a human rater interpreted the handwritten character as the digit ‘6’.  The ten digits 0-9 are each represented, with a unique class label for each possible digit. Thus, this is a multi-class classification problem with 10 classes.</p>
 <p><img alt="img" src="https://www.tensorflow.org/versions/r0.11/images/MNIST-Matrix.png" /></p>
 <p>Columns 1 through 784 contain the feature values, one per pixel for the 28×28=784 pixel values. The pixel values are on a gray scale in which 0 represents white, 255 represents black, and values between 0 and 255 represent shades of gray. Most of the pixel values are 0; you may want to take a minute to confirm that they aren’t all 0.  Modify the form below and run the code to view data for a given example.</p>
@@ -1809,7 +1826,7 @@ <h2><span class="section-number">37.86.2. </span>Understanding the Data Format<a
 </div>
 </section>
 <section id="do-you-have-imbalanced-classes">
-<h2><span class="section-number">37.86.3. </span>Do you have Imbalanced Classes?<a class="headerlink" href="#do-you-have-imbalanced-classes" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.84.3. </span>Do you have Imbalanced Classes?<a class="headerlink" href="#do-you-have-imbalanced-classes" title="Permalink to this headline">#</a></h2>
 <p>As we read in the course, imbalanced classes make classification harder. Let’s look at the distribution of classes. Do you have imbalanced classes?</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1826,7 +1843,7 @@ <h2><span class="section-number">37.86.3. </span>Do you have Imbalanced Classes?
 <p>The preceding graph shows that the 10 classes are roughly equally represented.</p>
 </section>
 <section id="shuffle-and-split-dataset">
-<h2><span class="section-number">37.86.4. </span>Shuffle and Split Dataset<a class="headerlink" href="#shuffle-and-split-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.84.4. </span>Shuffle and Split Dataset<a class="headerlink" href="#shuffle-and-split-dataset" title="Permalink to this headline">#</a></h2>
 <p>As part of <a class="reference external" href="https://developers.google.com/machine-learning/testing-debugging/common/data-errors">Data Debugging</a> best practices, ensure your splits are statistically equivalent by shuffling your data to remove any pre-existing order.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1842,7 +1859,7 @@ <h2><span class="section-number">37.86.4. </span>Shuffle and Split Dataset<a cla
 </div>
 </section>
 <section id="process-data">
-<h2><span class="section-number">37.86.5. </span>Process Data<a class="headerlink" href="#process-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.84.5. </span>Process Data<a class="headerlink" href="#process-data" title="Permalink to this headline">#</a></h2>
 <p>Scale the data values to <code class="docutils literal notranslate"><span class="pre">[0,1]</span></code> since the values are bounded to <code class="docutils literal notranslate"><span class="pre">[0,255]</span></code> and do not contain outliers. Then check that the scaled data values are as expected by generating summary statistics using the <code class="docutils literal notranslate"><span class="pre">DataFrame.describe()</span></code> function.</p>
 <p>Run the following cell to scale data and generate statistics. This cell takes a few minutes to run.</p>
 <div class="cell docutils container">
@@ -1878,7 +1895,7 @@ <h2><span class="section-number">37.86.5. </span>Process Data<a class="headerlin
 </div>
 </div>
 <section id="solution">
-<h3><span class="section-number">37.86.5.1. </span>Solution<a class="headerlink" href="#solution" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.84.5.1. </span>Solution<a class="headerlink" href="#solution" title="Permalink to this headline">#</a></h3>
 <p>Start exploring your data by generating a high-level summary using <code class="docutils literal notranslate"><span class="pre">Dataframe.describe()</span></code>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1914,11 +1931,11 @@ <h3><span class="section-number">37.86.5.1. </span>Solution<a class="headerlink"
 </section>
 </section>
 <section id="remove-all-zero-features">
-<h2><span class="section-number">37.86.6. </span>Remove All-Zero Features?<a class="headerlink" href="#remove-all-zero-features" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.84.6. </span>Remove All-Zero Features?<a class="headerlink" href="#remove-all-zero-features" title="Permalink to this headline">#</a></h2>
 <p>You might think that getting NaNs and discovering that some features were all-zero is good luck because those features can be discarded. However, your training data and validation data might have different all-zero features. Since you should not use validation data to make modeling decisions, you cannot remove only those features that are all-zero in both. Furthermore, data in the future might have different characteristics. There are pros and cons in either case. This Colab keeps the features since reducing the feature set is not a concern.</p>
 </section>
 <section id="establish-baseline">
-<h2><span class="section-number">37.86.7. </span>Establish Baseline<a class="headerlink" href="#establish-baseline" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.84.7. </span>Establish Baseline<a class="headerlink" href="#establish-baseline" title="Permalink to this headline">#</a></h2>
 <p>Following development best practices, you should establish a baseline. The simplest baseline is predicting the most common class. You saw that the most common class is 1. Let’s check the accuracy when always predicting 1.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1930,7 +1947,7 @@ <h2><span class="section-number">37.86.7. </span>Establish Baseline<a class="hea
 <p>Your baseline accuracy is about 11%. Should be easy to beat, right?</p>
 </section>
 <section id="train-a-linear-model">
-<h2><span class="section-number">37.86.8. </span>Train a Linear Model<a class="headerlink" href="#train-a-linear-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.84.8. </span>Train a Linear Model<a class="headerlink" href="#train-a-linear-model" title="Permalink to this headline">#</a></h2>
 <p>Let’s start nice and easy with a linear model. All we need is an accuracy &gt; 11%.</p>
 <p>First, let’s define a function to plot our loss and accuracy curves. The function will also print the final loss and accuracy. Instead of using <code class="docutils literal notranslate"><span class="pre">verbose=1</span></code>, you can call the function.</p>
 <div class="cell docutils container">
@@ -1992,12 +2009,12 @@ <h2><span class="section-number">37.86.8. </span>Train a Linear Model<a class="h
 <p>Wow, that accuracy is terrible! What could the cause be?</p>
 <p>Hint: You followed the same procedure as for the previous regression problem. Do you need an adaptation for a classification problem? Experiment with the code above or skip to the solution below.</p>
 <section id="id1">
-<h3><span class="section-number">37.86.8.1. </span>Solution<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.84.8.1. </span>Solution<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <p>In regression, the last layer uses a linear activation function. In classification, the last layer cannot use a linear transform. Instead, one option is a softmax transform. Furthermore, in regression, the loss is calculated using MSE while in classification, loss is calculated using crossentropy. Before running your model, if you wrote a test to validate the output values, your test would detect the anomalous output. You’ll look at such a test later. Move onto the next section to fix the loss calculation.</p>
 </section>
 </section>
 <section id="fixing-loss-calculation">
-<h2><span class="section-number">37.86.9. </span>Fixing Loss Calculation<a class="headerlink" href="#fixing-loss-calculation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.84.9. </span>Fixing Loss Calculation<a class="headerlink" href="#fixing-loss-calculation" title="Permalink to this headline">#</a></h2>
 <p>Since your labels are integers instead of one-hot encodings, use <code class="docutils literal notranslate"><span class="pre">sparse_categorical_crossentropy</span></code> instead of <code class="docutils literal notranslate"><span class="pre">categorical_crossentropy</span></code> so that you avoid converting the integers to one-hot encoding.</p>
 <p>Retrain the model with the new loss calculation by running the following cell. Look through the code to note the changes. What do you think of the result?</p>
 <div class="cell docutils container">
@@ -2024,7 +2041,7 @@ <h2><span class="section-number">37.86.9. </span>Fixing Loss Calculation<a class
 <p>Your loss curves are much better. Your accuracy has improved too. You’re on the right track.</p>
 </section>
 <section id="train-a-nonlinear-model">
-<h2><span class="section-number">37.86.10. </span>Train a Nonlinear Model<a class="headerlink" href="#train-a-nonlinear-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.84.10. </span>Train a Nonlinear Model<a class="headerlink" href="#train-a-nonlinear-model" title="Permalink to this headline">#</a></h2>
 <p>Switch to a nonlinear model by modifying the code below to use relu activation functions instead of linear activation functions. Run the code. What do you observe?</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2049,7 +2066,7 @@ <h2><span class="section-number">37.86.10. </span>Train a Nonlinear Model<a clas
 <p>The quality of the nonlinear model is significantly better than of the linear model. Progress! Move onto the next section.</p>
 </section>
 <section id="adding-a-second-layer">
-<h2><span class="section-number">37.86.11. </span>Adding a Second Layer<a class="headerlink" href="#adding-a-second-layer" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.84.11. </span>Adding a Second Layer<a class="headerlink" href="#adding-a-second-layer" title="Permalink to this headline">#</a></h2>
 <p>Increasing the model’s capacity significantly improved your results. Perhaps you can continue this strategy by adding a second relu layer. Run the following code cell to train the model with another relu layer.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2076,7 +2093,7 @@ <h2><span class="section-number">37.86.11. </span>Adding a Second Layer<a class=
 <p>Does your model begin to overfit the training data if you train for long enough? (Your model starts overfitting training data at the point when your validation loss starts increasing.)</p>
 </section>
 <section id="check-for-training-validation-data-skew">
-<h2><span class="section-number">37.86.12. </span>Check for Training/Validation Data Skew<a class="headerlink" href="#check-for-training-validation-data-skew" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.84.12. </span>Check for Training/Validation Data Skew<a class="headerlink" href="#check-for-training-validation-data-skew" title="Permalink to this headline">#</a></h2>
 <p>Our validation accuracy is a little worse than our training accuracy. While this result is always expected, you should check for typical errors. The commonest cause is having different distributions of data and labels in training and validation. Confirm that the distribution of classes in training and validation data is similar.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2095,7 +2112,7 @@ <h2><span class="section-number">37.86.12. </span>Check for Training/Validation
 </div>
 </section>
 <section id="apply-dropout-regularization">
-<h2><span class="section-number">37.86.13. </span>Apply Dropout Regularization<a class="headerlink" href="#apply-dropout-regularization" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.84.13. </span>Apply Dropout Regularization<a class="headerlink" href="#apply-dropout-regularization" title="Permalink to this headline">#</a></h2>
 <p>Dropout regularization is a common regularization method that removes a random selection of a fixed number of units in a network layer for a single gradient step. Typically, dropout will improve generalization at a dropout rate of between 10% and 50% of neurons.</p>
 <p>Try to reduce the divergence between training and validation loss by using dropout regularization with values between 0.1 and 0.5. Dropout does not improve the results in this case. However, at a dropout of 0.5, the difference in loss decreases, though both training and validation loss decrease in absolute terms.</p>
 <div class="cell docutils container">
@@ -2138,7 +2155,7 @@ <h2><span class="section-number">37.86.13. </span>Apply Dropout Regularization<a
 0.5 | 0.97 | 0.94</p>
 </section>
 <section id="check-accuracy-for-data-slices">
-<h2><span class="section-number">37.86.14. </span>Check Accuracy for Data Slices<a class="headerlink" href="#check-accuracy-for-data-slices" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.84.14. </span>Check Accuracy for Data Slices<a class="headerlink" href="#check-accuracy-for-data-slices" title="Permalink to this headline">#</a></h2>
 <p>For classification problems, you should always check the metrics by class to ensure your model predicts well across all classes. Check accuracy on the 10 classes by running the next cell by using the function <code class="docutils literal notranslate"><span class="pre">sklearn.metrics.classification_report</span></code> from the scikit-learn library. In the output, the rows with indices 0 to 9 correspond to the classes for the labels 0 to 9. The columns “Precision”, “Recall”, and “<a class="reference external" href="https://en.wikipedia.org/wiki/F1_score">F1-Score</a>” correspond to the respective classification metrics for each class. “Support” is the number of examples for the class in question. For example, for the label “4”, when predicting on 464 examples labelled “4”, the model has a precision of 0.98, a recall of 0.97, and a F1 score of 0.98.</p>
 <p>The classification metrics are very uniform across all classes, which is perfect. In your classification problem, in case any metric is lower for a class, then you should investigate why the model has lower-quality predictions for that class.</p>
 <div class="cell docutils container">
@@ -2152,7 +2169,7 @@ <h2><span class="section-number">37.86.14. </span>Check Accuracy for Data Slices
 </div>
 </section>
 <section id="testing-for-anomalous-values">
-<h2><span class="section-number">37.86.15. </span>Testing for Anomalous Values<a class="headerlink" href="#testing-for-anomalous-values" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.84.15. </span>Testing for Anomalous Values<a class="headerlink" href="#testing-for-anomalous-values" title="Permalink to this headline">#</a></h2>
 <p>In the section <a class="reference external" href="https://colab.corp.google.com/google_src/cloud/karangill/mlcc/google3/engedu/ml/capitalg/colab/testing_debugging_classification.ipynb#scrollTo=B6AOgLcC5nwp">Train a Linear Model</a>, you debugged an incorrect calculation of loss. Before running your model, if you wrote a test to validate the output values, your test would detect the anomalous output. For example, you could test whether the distribution of predicted labels on the training dataset is similar to the actual distribution of training labels. A simple statistical implementation of this concept is to compare the standard deviation and mean of the predicted and actual labels.</p>
 <p>First, check the standard deviation and mean of the actual labels.</p>
 <div class="cell docutils container">
@@ -2231,7 +2248,7 @@ <h2><span class="section-number">37.86.15. </span>Testing for Anomalous Values<a
 <p>Given equally represented classes, the predicted labels are clearly skewed. From this plot, a possible explanation is that your loss calculation does not appear to be weighting all classes equally. This anomaly is a hint that your loss calculation is incorrect.</p>
 </section>
 <section id="optional-pass-tests-by-fixing-loss-calculation">
-<h2><span class="section-number">37.86.16. </span>Optional: Pass Tests by Fixing Loss Calculation<a class="headerlink" href="#optional-pass-tests-by-fixing-loss-calculation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.84.16. </span>Optional: Pass Tests by Fixing Loss Calculation<a class="headerlink" href="#optional-pass-tests-by-fixing-loss-calculation" title="Permalink to this headline">#</a></h2>
 <p>As an optional exercise, you can attempt to pass the tests by fixing the loss calculation. You will need to:</p>
 <ol class="simple">
 <li><p>Fix the loss calculation by setting the output layer’s activation to <code class="docutils literal notranslate"><span class="pre">softmax</span></code>.</p></li>
@@ -2241,7 +2258,7 @@ <h2><span class="section-number">37.86.16. </span>Optional: Pass Tests by Fixing
 </ol>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">37.86.17. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.84.17. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>The Colab demonstrated the following principles:</p>
 <ul class="simple">
 <li><p>Calculate loss correctly for your problem.</p></li>
@@ -2290,13 +2307,13 @@ <h2><span class="section-number">37.86.17. </span>Conclusion<a class="headerlink
         <i class="fas fa-angle-left"></i>
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-            <p class="prev-next-title"><span class="section-number">37.85. </span>Counterintuitive Challenges in ML Debugging</p>
+            <p class="prev-next-title"><span class="section-number">37.83. </span>Counterintuitive Challenges in ML Debugging</p>
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+++ b/assignments/machine-learning-productionization/debugging-in-regression.html
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.88. Study the solvers" href="../ml-fundamentals/study-the-solvers.html" />
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+    <link rel="next" title="37.86. Study the solvers" href="../ml-fundamentals/study-the-solvers.html" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
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   <a class="reference internal" href="../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
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   <a class="reference internal" href="../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
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@@ -355,8 +377,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
   </a>
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@@ -392,8 +414,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
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@@ -431,8 +453,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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@@ -480,8 +502,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
    16. Introduction to Gradient Boosting
   </a>
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@@ -633,8 +655,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../README.html">
    37. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
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@@ -825,308 +847,303 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../ml-fundamentals/ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
+     37.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/loss-function.html">
-     37.48. Loss Function
+     37.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
+     37.50. ML logistic regression - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-2.html">
-     37.52. ML logistic regression - assignment 2
+     37.51. ML logistic regression - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-neural-network-1.html">
-     37.53. ML neural network - Assignment 1
+     37.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression-tools.html">
-     37.54. Regression tools
+     37.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/managing-data.html">
-     37.55. Managing data
+     37.54. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/exploring-visualizations.html">
-     37.56. Exploring visualizations
+     37.55. Exploring visualizations
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/try-a-different-model.html">
-     37.57. Try a different model
+     37.56. Try a different model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/create-a-regression-model.html">
-     37.58. Create a regression model
+     37.57. Create a regression model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-and-polynomial-regression.html">
-     37.59. Linear and polynomial regression
+     37.58. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/retrying-some-regression.html">
-     37.60. Retrying some regression
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-     37.85. Counterintuitive Challenges in ML Debugging
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+     37.116. Basic classification: Classify images of clothing
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+   37.85.1. Setup
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+   37.85.3. Validate Input Data against Data Schema
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+   37.85.3. Validate Input Data against Data Schema
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+   37.85.7. Establish a Baseline
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+   37.85.9. Add Feature to Linear Model
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+   37.85.10. Use a Nonlinear Model
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+   37.85.12. Check for Implementation Bugs using Reduced Dataset
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+   37.85.13. Trying a Very Complex Model
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     <a class="reference internal nav-link" href="#results">
-     37.87.13.1. Results
+     37.85.13.1. Results
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion">
-   37.87.14. Conclusion
+   37.85.14. Conclusion
   </a>
  </li>
 </ul>
@@ -1806,7 +1823,7 @@ <h2> Contents </h2>
 See the License for the specific language governing permissions and
 limitations under the License.</p>
 </details><section class="tex2jax_ignore mathjax_ignore" id="case-study-debugging-in-regression">
-<h1><span class="section-number">37.87. </span>Case Study: Debugging in Regression<a class="headerlink" href="#case-study-debugging-in-regression" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.85. </span>Case Study: Debugging in Regression<a class="headerlink" href="#case-study-debugging-in-regression" title="Permalink to this headline">#</a></h1>
 <p>In this Colab, you will learn how to debug a regression problem through a case study. You will:</p>
 <ol class="simple">
 <li><p>Set up the problem.</p></li>
@@ -1817,7 +1834,7 @@ <h1><span class="section-number">37.87. </span>Case Study: Debugging in Regressi
 <li><p>Debug your chosen model.</p></li>
 </ol>
 <section id="setup">
-<h2><span class="section-number">37.87.1. </span>Setup<a class="headerlink" href="#setup" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.85.1. </span>Setup<a class="headerlink" href="#setup" title="Permalink to this headline">#</a></h2>
 <p>This Colab uses the wine quality dataset&lt;sup#[1]&lt;/sup#, which is hosted at <a class="reference external" href="https://archive.ics.uci.edu/ml/datasets/wine+quality">UCI</a>. This dataset contains data on the physicochemical properties of wine along with wine quality ratings. The problem is to predict wine quality (0-10) from physicochemical properties.</p>
 <p>Please <strong>make a copy</strong> of this Colab before running it. Click on <em>File</em>, and then click on <em>Save a copy in Drive</em>.</p>
 <p>&lt;small#[1] Modeling wine preferences by data mining from physicochemical properties. P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Decision Support Systems, Elsevier, 47(4):547-553, 2009.&lt;/small#</p>
@@ -1854,7 +1871,7 @@ <h2><span class="section-number">37.87.1. </span>Setup<a class="headerlink" href
 </div>
 </section>
 <section id="check-correlation-matrix">
-<h2><span class="section-number">37.87.2. </span>Check Correlation Matrix<a class="headerlink" href="#check-correlation-matrix" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.85.2. </span>Check Correlation Matrix<a class="headerlink" href="#check-correlation-matrix" title="Permalink to this headline">#</a></h2>
 <p>Before developing your ML model, you need to select features. To find informative features, check the correlation matrix by running the following cell. Which features are informative?</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1868,7 +1885,7 @@ <h2><span class="section-number">37.87.2. </span>Check Correlation Matrix<a clas
 <p><code class="docutils literal notranslate"><span class="pre">alcohol</span></code> is most highly correlated with <code class="docutils literal notranslate"><span class="pre">quality</span></code>. Looking for other informative features, notice that <code class="docutils literal notranslate"><span class="pre">volatile</span> <span class="pre">acidity</span></code> correlates with <code class="docutils literal notranslate"><span class="pre">quality</span></code> but not with <code class="docutils literal notranslate"><span class="pre">alcohol</span></code>, making it a good second feature. Remember that a correlation matrix is not helpful if predictive signals are encoded in combinations of features.</p>
 </section>
 <section id="validate-input-data-against-data-schema">
-<h2><span class="section-number">37.87.3. </span>Validate Input Data against Data Schema<a class="headerlink" href="#validate-input-data-against-data-schema" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.85.3. </span>Validate Input Data against Data Schema<a class="headerlink" href="#validate-input-data-against-data-schema" title="Permalink to this headline">#</a></h2>
 <p>Before processing your data, you should validate the data against a data schema as described in <a class="reference external" href="https://developers.google.com/machine-learning/testing-debugging/common/data-errors">Data and Feature Debugging</a>.</p>
 <p>First, define a function that validates data against a schema.</p>
 <div class="cell docutils container">
@@ -1959,7 +1976,7 @@ <h2><span class="section-number">37.87.3. </span>Validate Input Data against Dat
 </div>
 </div>
 <section id="solution">
-<h3><span class="section-number">37.87.3.1. </span>Solution<a class="headerlink" href="#solution" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.85.3.1. </span>Solution<a class="headerlink" href="#solution" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">wine_schema</span> <span class="o">=</span> <span class="p">{</span>
@@ -1995,7 +2012,7 @@ <h3><span class="section-number">37.87.3.1. </span>Solution<a class="headerlink"
 </section>
 </section>
 <section id="split-and-normalize-data">
-<h2><span class="section-number">37.87.4. </span>Split and Normalize Data<a class="headerlink" href="#split-and-normalize-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.85.4. </span>Split and Normalize Data<a class="headerlink" href="#split-and-normalize-data" title="Permalink to this headline">#</a></h2>
 <p>Split the dataset into data and labels.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2023,7 +2040,7 @@ <h2><span class="section-number">37.87.4. </span>Split and Normalize Data<a clas
 </div>
 </section>
 <section id="test-engineered-data">
-<h2><span class="section-number">37.87.5. </span>Test Engineered Data<a class="headerlink" href="#test-engineered-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.85.5. </span>Test Engineered Data<a class="headerlink" href="#test-engineered-data" title="Permalink to this headline">#</a></h2>
 <p>After normalizing your data, you should test your engineered data for errors as described in <a class="reference external" href="https://developers.google.com/machine-learning/testing-debugging/common/data-errors">Data and Feature Debugging</a>. In this section, you will test that engineering data:</p>
 <ul class="simple">
 <li><p>Has the expected number of rows and columns.</p></li>
@@ -2070,7 +2087,7 @@ <h2><span class="section-number">37.87.5. </span>Test Engineered Data<a class="h
 </div>
 </section>
 <section id="check-splits-for-statistical-equivalence">
-<h2><span class="section-number">37.87.6. </span>Check Splits for Statistical Equivalence<a class="headerlink" href="#check-splits-for-statistical-equivalence" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.85.6. </span>Check Splits for Statistical Equivalence<a class="headerlink" href="#check-splits-for-statistical-equivalence" title="Permalink to this headline">#</a></h2>
 <p>As described in the <a class="reference external" href="https://developers.google.com/machine-learning/testing-debugging/common/data-errors">Data Debugging</a> guidelines, before developing your model, you should check that your training and validation splits are equally representative. Assuming a training:validation split of 80:20, compare the mean and the standard deviation of the splits by running the next two code cells. Note that this comparison is not a rigorous test for statistical equivalence but simply a quick and dirty comparison of the splits.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2105,7 +2122,7 @@ <h2><span class="section-number">37.87.6. </span>Check Splits for Statistical Eq
 <p>Ensuring your splits are statistically equivalent is a good development practice. In general, following good development practices will simplify your model debugging. To learn about testing for statistical equivalence, see <a class="reference external" href="https://journals.sagepub.com/doi/10.1177/1948550617697177">Equivalence Tests Lakens, D.</a>.</p>
 </section>
 <section id="establish-a-baseline">
-<h2><span class="section-number">37.87.7. </span>Establish a Baseline<a class="headerlink" href="#establish-a-baseline" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.85.7. </span>Establish a Baseline<a class="headerlink" href="#establish-a-baseline" title="Permalink to this headline">#</a></h2>
 <p>For a regression problem, the simplest baseline to predict the average value. Run the following code to calculate the mean-squared error (MSE) loss on the training split using the average value as a baseline. Your loss is approximately 0.75. Any model should beat this loss to justify its use.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2118,7 +2135,7 @@ <h2><span class="section-number">37.87.7. </span>Establish a Baseline<a class="h
 </div>
 </section>
 <section id="linear-model">
-<h2><span class="section-number">37.87.8. </span>Linear Model<a class="headerlink" href="#linear-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.85.8. </span>Linear Model<a class="headerlink" href="#linear-model" title="Permalink to this headline">#</a></h2>
 <p>Following good ML dev practice, let’s start with a linear model that uses the most informative feature from the correlation matrix: <code class="docutils literal notranslate"><span class="pre">alcohol</span></code>. Even if this model performs badly, we can still use it as a baseline. This model should beat our previous baseline’s MSE of 0.75.</p>
 <p>First, let’s define a function to plot our loss and accuracy curves. The function will also print the final loss and accuracy. Instead of using <code class="docutils literal notranslate"><span class="pre">verbose=1</span></code>, you can call the function.</p>
 <div class="cell docutils container">
@@ -2172,7 +2189,7 @@ <h2><span class="section-number">37.87.8. </span>Linear Model<a class="headerlin
 </div>
 </div>
 <section id="hint">
-<h3><span class="section-number">37.87.8.1. </span>Hint<a class="headerlink" href="#hint" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.85.8.1. </span>Hint<a class="headerlink" href="#hint" title="Permalink to this headline">#</a></h3>
 <p>The loss decreases but very slowly. Possible fixes are:</p>
 <ul class="simple">
 <li><p>Increase number of epochs.</p></li>
@@ -2182,7 +2199,7 @@ <h3><span class="section-number">37.87.8.1. </span>Hint<a class="headerlink" hre
 <p>Play with these three parameters in the code above to decrease the loss.</p>
 </section>
 <section id="id1">
-<h3><span class="section-number">37.87.8.2. </span>Solution<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.85.8.2. </span>Solution<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <p>Run the following code cell to train the model using a reduced batch size of 100. Reducing the batch size leads to a greater decrease in loss per epoch. The minimum achievable loss is about 0.64. This is a significant increase over our baseline of 0.75.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2211,7 +2228,7 @@ <h3><span class="section-number">37.87.8.2. </span>Solution<a class="headerlink"
 </section>
 </section>
 <section id="add-feature-to-linear-model">
-<h2><span class="section-number">37.87.9. </span>Add Feature to Linear Model<a class="headerlink" href="#add-feature-to-linear-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.85.9. </span>Add Feature to Linear Model<a class="headerlink" href="#add-feature-to-linear-model" title="Permalink to this headline">#</a></h2>
 <p>Try adding a feature to the linear model. Since you need to combine the two features into one prediction for regression, you’ll also need to add a second layer. Modify the code below to implement the following changes:</p>
 <ol class="simple">
 <li><p>Add <code class="docutils literal notranslate"><span class="pre">'volatile</span> <span class="pre">acidity'</span></code> to the features in <code class="docutils literal notranslate"><span class="pre">wineFeaturesSimple</span></code>.</p></li>
@@ -2246,7 +2263,7 @@ <h2><span class="section-number">37.87.9. </span>Add Feature to Linear Model<a c
 </div>
 </div>
 <section id="id2">
-<h3><span class="section-number">37.87.9.1. </span>Solution<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.85.9.1. </span>Solution<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <p>Run the following code to add the second feature and the second layer. The training loss is about 0.59, a small decrease from the previous loss of 0.64.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2277,7 +2294,7 @@ <h3><span class="section-number">37.87.9.1. </span>Solution<a class="headerlink"
 </section>
 </section>
 <section id="use-a-nonlinear-model">
-<h2><span class="section-number">37.87.10. </span>Use a Nonlinear Model<a class="headerlink" href="#use-a-nonlinear-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.85.10. </span>Use a Nonlinear Model<a class="headerlink" href="#use-a-nonlinear-model" title="Permalink to this headline">#</a></h2>
 <p>Let’s try a nonlinear model. Modify the code below to make the following changes:</p>
 <ol class="simple">
 <li><p>Change the first layer to use <code class="docutils literal notranslate"><span class="pre">relu</span></code>. (Output layer stays linear since this is a regression problem.)</p></li>
@@ -2309,7 +2326,7 @@ <h2><span class="section-number">37.87.10. </span>Use a Nonlinear Model<a class=
 </div>
 </div>
 <section id="id3">
-<h3><span class="section-number">37.87.10.1. </span>Solution<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.85.10.1. </span>Solution<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
 <p>Run the following cell to use a <code class="docutils literal notranslate"><span class="pre">relu</span></code> activation in your first hidden layer. Your loss stays about the same, perhaps declining negligibly to 0.58.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2338,7 +2355,7 @@ <h3><span class="section-number">37.87.10.1. </span>Solution<a class="headerlink
 </section>
 </section>
 <section id="optimize-your-model">
-<h2><span class="section-number">37.87.11. </span>Optimize Your Model<a class="headerlink" href="#optimize-your-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.85.11. </span>Optimize Your Model<a class="headerlink" href="#optimize-your-model" title="Permalink to this headline">#</a></h2>
 <p>We have two features with one hidden layer but didn’t see an improvement. At this point, it’s tempting to use all your features with a high-capacity network. However, you must resist the temptation. Instead, follow the guidance in <a class="reference external" href="https://developers.google.com/machine-learning/testing-debugging/common/optimization">Model Optimization</a> to improve model performance. For a hint and for a discussion, see the following text sections.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2368,11 +2385,11 @@ <h2><span class="section-number">37.87.11. </span>Optimize Your Model<a class="h
 </div>
 </div>
 <section id="id4">
-<h3><span class="section-number">37.87.11.1. </span>Hint<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.85.11.1. </span>Hint<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
 <p>You can try to reduce loss by adding features, adding layers, or playing with the hyperparameters. Before adding more features, check the correlation matrix. Don’t expect your loss to decrease by much. Sadly, that is a common experience in machine learning!</p>
 </section>
 <section id="id5">
-<h3><span class="section-number">37.87.11.2. </span>Solution<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.85.11.2. </span>Solution<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
 <p>Run the following code to:</p>
 <ul class="simple">
 <li><p>Add the features chlorides and density.</p></li>
@@ -2410,7 +2427,7 @@ <h3><span class="section-number">37.87.11.2. </span>Solution<a class="headerlink
 </section>
 </section>
 <section id="check-for-implementation-bugs-using-reduced-dataset">
-<h2><span class="section-number">37.87.12. </span>Check for Implementation Bugs using Reduced Dataset<a class="headerlink" href="#check-for-implementation-bugs-using-reduced-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.85.12. </span>Check for Implementation Bugs using Reduced Dataset<a class="headerlink" href="#check-for-implementation-bugs-using-reduced-dataset" title="Permalink to this headline">#</a></h2>
 <p>Your loss isn’t decreasing by much. Perhaps your model has an implementation bug. From the <a class="reference external" href="https://developers.google.com/machine-learning/testing-debugging/common/model-errors">Model Debugging</a> guidelines, a quick test for implementation bugs is to obtain a low loss on a reduced dataset of, say, 10 examples. Remember, passing this test does not validate your modeling approach but only checks for basic implementation bugs. In your ML problem, if your model passes this test, then continue debugging your model to train on your full dataset.</p>
 <p>In the following code, experiment with the learning rate, batch size, and number of epochs. Can you reach a low loss? Choose hyperparameter values that let you iterate quickly.</p>
 <div class="cell docutils container">
@@ -2442,7 +2459,7 @@ <h2><span class="section-number">37.87.12. </span>Check for Implementation Bugs
 </div>
 </div>
 <section id="id6">
-<h3><span class="section-number">37.87.12.1. </span>Solution<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.85.12.1. </span>Solution<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h3>
 <p>Run the following code cell to train the model using these hyperparameter values:</p>
 <ul class="simple">
 <li><p>learning rate = 0.01</p></li>
@@ -2480,7 +2497,7 @@ <h3><span class="section-number">37.87.12.1. </span>Solution<a class="headerlink
 </section>
 </section>
 <section id="trying-a-very-complex-model">
-<h2><span class="section-number">37.87.13. </span>Trying a Very Complex Model<a class="headerlink" href="#trying-a-very-complex-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.85.13. </span>Trying a Very Complex Model<a class="headerlink" href="#trying-a-very-complex-model" title="Permalink to this headline">#</a></h2>
 <p>Let’s go all in and use a very complex model with all the features. For science! And to satisfy ourselves that a simple model is indeed better. Let’s use all 11 features with 3 fully-connected relu layers and a final linear layer. The next cell takes a while to run. Skip to the results in the cell after if you like.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2505,14 +2522,14 @@ <h2><span class="section-number">37.87.13. </span>Trying a Very Complex Model<a
 </div>
 </div>
 <section id="results">
-<h3><span class="section-number">37.87.13.1. </span>Results<a class="headerlink" href="#results" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.85.13.1. </span>Results<a class="headerlink" href="#results" title="Permalink to this headline">#</a></h3>
 <p>If you train for long enough, the minimum achievable MSE is around 0.52, which is a decrease of 0.04 from the previous best loss of 0.56. This decrease probably isn’t worth the performance and complexity cost of using all your features and a deeper network. However, that decision depends on the objectives you’re optimizing for.</p>
 <p>If you train the model long enough, the validation loss actually starts increasing while training loss continues to decrease. This divergence in loss curves means your model is overfitting. The overfitting results from the closer fit that your very complex model can learn. Stick with the simpler model. You’ll be happier and live longer!</p>
 <p>If you do want to optimize your loss, then play with the model to find the minimum achievable training loss before overfitting sets in. Try playing with the network parameters in the code cell above to achieve a loss of 0.51. But be warned—optimizing your loss could take a lot of trial and error…</p>
 </section>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">37.87.14. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.85.14. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>This Colab demonstrated the following principles:</p>
 <ul class="simple">
 <li><p>The most important step in machine learning is understanding your data.</p></li>
@@ -2561,13 +2578,13 @@ <h2><span class="section-number">37.87.14. </span>Conclusion<a class="headerlink
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.86. </span>Case Study: Debugging in Classification</p>
+            <p class="prev-next-title"><span class="section-number">37.84. </span>Case Study: Debugging in Classification</p>
         </div>
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.88. </span>Study the solvers</p>
+        <p class="prev-next-title"><span class="section-number">37.86. </span>Study the solvers</p>
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diff --git a/assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html b/assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html
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@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.77. Beyond random forests: more ensemble models &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.75. Beyond random forests: more ensemble models &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.78. Decision trees" href="decision-trees.html" />
-    <link rel="prev" title="37.76. Random forests for classification" href="random-forests-for-classification.html" />
+    <link rel="next" title="37.76. Decision trees" href="decision-trees.html" />
+    <link rel="prev" title="37.74. Random forests for classification" href="random-forests-for-classification.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
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-   37.77.4. Tasks and roles
+   37.75.4. Tasks and roles
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-exploratory-data-analysis">
-     37.77.4.1. Task 1: Exploratory data analysis
+     37.75.4.1. Task 1: Exploratory data analysis
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-more-data-exploration">
-     37.77.4.2. Task 2: More data exploration
+     37.75.4.2. Task 2: More data exploration
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-data-preprocessing">
-     37.77.4.3. Task 3: Data preprocessing
+     37.75.4.3. Task 3: Data preprocessing
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-training-ensemble-classifiers">
-     37.77.4.4. Task 4: Training ensemble classifiers
+     37.75.4.4. Task 4: Training ensemble classifiers
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#voting-classifier">
-       37.77.4.4.1. 4.1: Voting classifier
+       37.75.4.4.1. 4.1: Voting classifier
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#bagging-classifier">
-       37.77.4.4.2. 4.2: Bagging classifier
+       37.75.4.4.2. 4.2: Bagging classifier
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#gradient-boosting-classifier">
-       37.77.4.4.3. 4.3: Gradient boosting classifier
+       37.75.4.4.3. 4.3: Gradient boosting classifier
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#adaboost-classifier">
-       37.77.4.4.4. 4.4: AdaBoost classifier
+       37.75.4.4.4. 4.4: AdaBoost classifier
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#stacking-classifier">
-       37.77.4.4.5. 4.5: Stacking classifier
+       37.75.4.4.5. 4.5: Stacking classifier
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-5-evaluating-the-ensemble-model-on-the-test-set">
-     37.77.4.5. Task 5: Evaluating the ensemble model on the test set
+     37.75.4.5. Task 5: Evaluating the ensemble model on the test set
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#final-note">
-   37.77.5. Final note
+   37.75.5. Final note
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.77.6. Acknowledgments
+   37.75.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1612,86 +1629,86 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#ensemble-methods-for-classification">
-   37.77.1. Ensemble methods for classification
+   37.75.1. Ensemble methods for classification
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#imports">
-   37.77.2. Imports
+   37.75.2. Imports
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loading-the-data">
-   37.77.3. Loading the data
+   37.75.3. Loading the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#tasks-and-roles">
-   37.77.4. Tasks and roles
+   37.75.4. Tasks and roles
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-exploratory-data-analysis">
-     37.77.4.1. Task 1: Exploratory data analysis
+     37.75.4.1. Task 1: Exploratory data analysis
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-more-data-exploration">
-     37.77.4.2. Task 2: More data exploration
+     37.75.4.2. Task 2: More data exploration
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-data-preprocessing">
-     37.77.4.3. Task 3: Data preprocessing
+     37.75.4.3. Task 3: Data preprocessing
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-training-ensemble-classifiers">
-     37.77.4.4. Task 4: Training ensemble classifiers
+     37.75.4.4. Task 4: Training ensemble classifiers
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#voting-classifier">
-       37.77.4.4.1. 4.1: Voting classifier
+       37.75.4.4.1. 4.1: Voting classifier
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#bagging-classifier">
-       37.77.4.4.2. 4.2: Bagging classifier
+       37.75.4.4.2. 4.2: Bagging classifier
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#gradient-boosting-classifier">
-       37.77.4.4.3. 4.3: Gradient boosting classifier
+       37.75.4.4.3. 4.3: Gradient boosting classifier
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#adaboost-classifier">
-       37.77.4.4.4. 4.4: AdaBoost classifier
+       37.75.4.4.4. 4.4: AdaBoost classifier
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#stacking-classifier">
-       37.77.4.4.5. 4.5: Stacking classifier
+       37.75.4.4.5. 4.5: Stacking classifier
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-5-evaluating-the-ensemble-model-on-the-test-set">
-     37.77.4.5. Task 5: Evaluating the ensemble model on the test set
+     37.75.4.5. Task 5: Evaluating the ensemble model on the test set
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#final-note">
-   37.77.5. Final note
+   37.75.5. Final note
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.77.6. Acknowledgments
+   37.75.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1705,7 +1722,7 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="beyond-random-forests-more-ensemble-models">
-<h1><span class="section-number">37.77. </span>Beyond random forests: more ensemble models<a class="headerlink" href="#beyond-random-forests-more-ensemble-models" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.75. </span>Beyond random forests: more ensemble models<a class="headerlink" href="#beyond-random-forests-more-ensemble-models" title="Permalink to this headline">#</a></h1>
 <p>Ensembles are the combination of different machine learning models, where by averaging the results of all models trained together on the same dataset, the result can be away good than any individual model. These types of models can be used for all kinds of tasks, including classification, regression and detecting anomalies.</p>
 <p>In his book <em><a class="reference external" href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/">Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow</a></em>, <em>Aurelion Geron</em> gave a perfect analogy describing the ensemble learning: “Suppose you pose a complex question to thousands of random people, then aggregate their answers. In many cases you will find that this aggregated answer is better than an expert’s answer. This is called the <em>wisdom of the crowd</em>. Similarly, if you aggregate the predictions of a group of predictors (such as classifiers or regressors), you will often get better predictions than with the best individual predictor. A group of predictors is called an ensemble; thus, this technique is called Ensemble Learning, and an Ensemble Learning algorithm is called an Ensemble method.”</p>
 <p>Random Forests that was used in the previous assignment is an example of ensemble models. It combined different decision trees. Usually, these types of models are classified into three groups that will be covered in this assignment:</p>
@@ -1717,10 +1734,10 @@ <h1><span class="section-number">37.77. </span>Beyond random forests: more ensem
 <p>Most of the above algorithms are implemented in Sklearn, except XGBoost and Stacking which is implemented indirectly.</p>
 <p>Like said in the beginning, these types of models can be used for both regression and classification, but this assignment will consider classification domain and for giving focus on the ensemble models, we will use the same dataset we used previouly, in random forests.</p>
 <section id="ensemble-methods-for-classification">
-<h2><span class="section-number">37.77.1. </span>Ensemble methods for classification<a class="headerlink" href="#ensemble-methods-for-classification" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.75.1. </span>Ensemble methods for classification<a class="headerlink" href="#ensemble-methods-for-classification" title="Permalink to this headline">#</a></h2>
 </section>
 <section id="imports">
-<h2><span class="section-number">37.77.2. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.75.2. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -1735,7 +1752,7 @@ <h2><span class="section-number">37.77.2. </span>Imports<a class="headerlink" hr
 </div>
 </section>
 <section id="loading-the-data">
-<h2><span class="section-number">37.77.3. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.75.3. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
 <p>In this assignment, we will use Random forests to build a classifier that identify the increase or decrease of the electricity using “the data that was collected from the Australian New South Wales Electricity Market. In this market, prices are not fixed and are affected by demand and supply of the market. They are set every five minutes. Electricity transfers to/from the neighboring state of Victoria were done to alleviate fluctuations.”</p>
 <p>“The dataset contains 45,312 instances dated from 7 May 1996 to 5 December 1998. Each example of the dataset refers to a period of 30 minutes, i.e. there are 48 instances for each time period of one day. Each example on the dataset has 5 fields, the day of week, the time stamp, the New South Wales electricity demand, the Victoria electricity demand, the scheduled electricity transfer between states and the class label. The class label identifies the change of the price (UP or DOWN) in New South Wales relative to a moving average of the last 24 hours (and removes the impact of longer term price trends). Source: <a class="reference external" href="https://www.openml.org/d/151">Open ML electricity</a>.</p>
 <p>Here are the information about the features:</p>
@@ -1790,9 +1807,9 @@ <h2><span class="section-number">37.77.3. </span>Loading the data<a class="heade
 </div>
 </section>
 <section id="tasks-and-roles">
-<h2><span class="section-number">37.77.4. </span>Tasks and roles<a class="headerlink" href="#tasks-and-roles" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.75.4. </span>Tasks and roles<a class="headerlink" href="#tasks-and-roles" title="Permalink to this headline">#</a></h2>
 <section id="task-1-exploratory-data-analysis">
-<h3><span class="section-number">37.77.4.1. </span>Task 1: Exploratory data analysis<a class="headerlink" href="#task-1-exploratory-data-analysis" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.75.4.1. </span>Task 1: Exploratory data analysis<a class="headerlink" href="#task-1-exploratory-data-analysis" title="Permalink to this headline">#</a></h3>
 <p>Before doing exploratory analysis, as always, let’s split the data into training and test sets.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1895,7 +1912,7 @@ <h3><span class="section-number">37.77.4.1. </span>Task 1: Exploratory data anal
 <p>In the above correlation matrix, you can see that class feature is not there and this is because it still has categorical values.</p>
 </section>
 <section id="task-2-more-data-exploration">
-<h3><span class="section-number">37.77.4.2. </span>Task 2: More data exploration<a class="headerlink" href="#task-2-more-data-exploration" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.75.4.2. </span>Task 2: More data exploration<a class="headerlink" href="#task-2-more-data-exploration" title="Permalink to this headline">#</a></h3>
 <p>Before preprocessing the data, let’s take a look into specific features.</p>
 <p>Let’s see how many Ups/Downs are in the class feature.</p>
 <div class="cell docutils container">
@@ -1981,7 +1998,7 @@ <h3><span class="section-number">37.77.4.2. </span>Task 2: More data exploration
 </div>
 </section>
 <section id="task-3-data-preprocessing">
-<h3><span class="section-number">37.77.4.3. </span>Task 3: Data preprocessing<a class="headerlink" href="#task-3-data-preprocessing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.75.4.3. </span>Task 3: Data preprocessing<a class="headerlink" href="#task-3-data-preprocessing" title="Permalink to this headline">#</a></h3>
 <p>It is here that we prepare the data to be in the proper format for the machine learning model.</p>
 <p>Let’s encode the categorical feature <code class="docutils literal notranslate"><span class="pre">class</span></code>. But before that, let’s take training input data and labels.</p>
 <div class="cell docutils container">
@@ -2035,9 +2052,9 @@ <h3><span class="section-number">37.77.4.3. </span>Task 3: Data preprocessing<a
 <p>But again if you look at the dat, the <code class="docutils literal notranslate"><span class="pre">day</span></code> feature is not normalized as other features. We can normalize it or leave it but for now let’s go ahead and train the random forests classifier.</p>
 </section>
 <section id="task-4-training-ensemble-classifiers">
-<h3><span class="section-number">37.77.4.4. </span>Task 4: Training ensemble classifiers<a class="headerlink" href="#task-4-training-ensemble-classifiers" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.75.4.4. </span>Task 4: Training ensemble classifiers<a class="headerlink" href="#task-4-training-ensemble-classifiers" title="Permalink to this headline">#</a></h3>
 <section id="voting-classifier">
-<h4><span class="section-number">37.77.4.4.1. </span>4.1: Voting classifier<a class="headerlink" href="#voting-classifier" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.75.4.4.1. </span>4.1: Voting classifier<a class="headerlink" href="#voting-classifier" title="Permalink to this headline">#</a></h4>
 <p>Let’s assume that you have trained 3 different classifiers on the training data but none of these classifiers had an oustanding results.</p>
 <p>The idea of Voting ensemble technique is fairly simple. We can aggregate the results of all those 3 classifiers and the result will be good than any single classifier.</p>
 <p>Let’s train 3 classifiers on the training data and then we will go ahead and aggregate their results.</p>
@@ -2138,7 +2155,7 @@ <h4><span class="section-number">37.77.4.4.1. </span>4.1: Voting classifier<a cl
 <p>As we can see, it slightly outperformed all individual classifiers. It’s small increments in the accuracy but most of the time, it will be always better than any individual classifier.</p>
 </section>
 <section id="bagging-classifier">
-<h4><span class="section-number">37.77.4.4.2. </span>4.2: Bagging classifier<a class="headerlink" href="#bagging-classifier" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.75.4.4.2. </span>4.2: Bagging classifier<a class="headerlink" href="#bagging-classifier" title="Permalink to this headline">#</a></h4>
 <p>Instead of training different algorithms on the same data and average their results as Voting does, Bagging ensemble method train a single or multiple classifier/regressors on different subsets of the training data and aggregate the results on all subsets.</p>
 <p>When this is used with complex models (that overfit data easily) like decision trees, the overfitting can be reduced.</p>
 <p>Let’s use bagging to train 500 decision trees on different subsets of data and then average the predictions on those subsets.</p>
@@ -2171,7 +2188,7 @@ <h4><span class="section-number">37.77.4.4.2. </span>4.2: Bagging classifier<a c
 <p>Wow, this is much better. Bagging ensembles work well and they outperform Voting ensembles a lot. Other remarkable thing about them is that they are able to overcome the overfitting especially when used with decision trees (decision trees tend to overfit easily). Let’s now see other type of ensemble models: Gradient Boosting Classifier.</p>
 </section>
 <section id="gradient-boosting-classifier">
-<h4><span class="section-number">37.77.4.4.3. </span>4.3: Gradient boosting classifier<a class="headerlink" href="#gradient-boosting-classifier" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.75.4.4.3. </span>4.3: Gradient boosting classifier<a class="headerlink" href="#gradient-boosting-classifier" title="Permalink to this headline">#</a></h4>
 <p>Gradient boosting works like bagging methods. The only difference is that instead of training models on subsets of training data, models(decision trees) are trained in a sequence where each tree model takes the error of the previous tree to correct it and the sequence goes on…</p>
 <p>Simply, the initial model is trained on the full data, but then the next models minimizes the previous errors.</p>
 <p>Just like other models, gradient boosting classifier has hyperparameters but the most important ones are number of estimators or trees (<code class="docutils literal notranslate"><span class="pre">n_estimators</span></code>) and learning rate (<code class="docutils literal notranslate"><span class="pre">learning_rate</span></code>).</p>
@@ -2202,7 +2219,7 @@ <h4><span class="section-number">37.77.4.4.3. </span>4.3: Gradient boosting clas
 <p>For classification problems beyond two classes, Scikit-Learn recommend using <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradientBoostingClassifier">Histogram Gradient Boosting Classifier</a> which is a faster version of the Gradient Boosting Classifier. It is inspired by <a class="reference external" href="https://github.com/Microsoft/LightGBM">Microsoft Light Gradient Boosting Machine (LightGBM)</a>. LightGBM is fater, low memory usage, better performance, support distributed training and GPU, and can handle large datasets.</p>
 </section>
 <section id="adaboost-classifier">
-<h4><span class="section-number">37.77.4.4.4. </span>4.4: AdaBoost classifier<a class="headerlink" href="#adaboost-classifier" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.75.4.4.4. </span>4.4: AdaBoost classifier<a class="headerlink" href="#adaboost-classifier" title="Permalink to this headline">#</a></h4>
 <p>AdaBoost is another ensemble model in class of boosting methods. It’s very much like gradient boost but instead of minimizing the error of the consecutive models, it updates the weights.</p>
 <p>So, the first model (decision tree) is trained on the full training data, the next model weights are updated based off the previous weights and so forth.</p>
 <p>The main parameters to tune to make AdaBoost work well are number of estimators and the maximum depth of each estimator.</p>
@@ -2234,7 +2251,7 @@ <h4><span class="section-number">37.77.4.4.4. </span>4.4: AdaBoost classifier<a
 <p>Again, you can tune the number of estimators and the depth of the base estimator. The base estimator is Decision Trees by default.</p>
 </section>
 <section id="stacking-classifier">
-<h4><span class="section-number">37.77.4.4.5. </span>4.5: Stacking classifier<a class="headerlink" href="#stacking-classifier" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.75.4.4.5. </span>4.5: Stacking classifier<a class="headerlink" href="#stacking-classifier" title="Permalink to this headline">#</a></h4>
 <p>Also referred to as Stacked Generalization, it is an ensemble method employed to reduce the biases and the errors rate made by multiple predictors(individual models/estimators).</p>
 <p>Instead of averaging the predictions made by individual models or predictors, in stacking, multiple models are trained on the full training data and then the final model that is trained on different subsets of the training data takes the predictions of the former models and find the final predictions.</p>
 <p>From its <a class="reference external" href="http://machine-learning.martinsewell.com/ensembles/stacking/Wolpert1992.pdf">original paper</a>, it is noted that it can be seen as the sophisticated version of the cross validation (per the reason why the final estimator is trained on the subsets or folds of the training data).</p>
@@ -2274,7 +2291,7 @@ <h4><span class="section-number">37.77.4.4.5. </span>4.5: Stacking classifier<a
 </section>
 </section>
 <section id="task-5-evaluating-the-ensemble-model-on-the-test-set">
-<h3><span class="section-number">37.77.4.5. </span>Task 5: Evaluating the ensemble model on the test set<a class="headerlink" href="#task-5-evaluating-the-ensemble-model-on-the-test-set" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.75.4.5. </span>Task 5: Evaluating the ensemble model on the test set<a class="headerlink" href="#task-5-evaluating-the-ensemble-model-on-the-test-set" title="Permalink to this headline">#</a></h3>
 <p>Let’s say that all we have been doing was trying to find an ensemble model that can fit well the data and so we want to test it on the test set before shipping it into production.</p>
 <p>To narrow the choice down, we will use A Gradient Boosting method.</p>
 <p>As always, we will prepare the test set in the same way that we prepared the training set. Let’s go!</p>
@@ -2317,14 +2334,14 @@ <h3><span class="section-number">37.77.4.5. </span>Task 5: Evaluating the ensemb
 </section>
 </section>
 <section id="final-note">
-<h2><span class="section-number">37.77.5. </span>Final note<a class="headerlink" href="#final-note" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.75.5. </span>Final note<a class="headerlink" href="#final-note" title="Permalink to this headline">#</a></h2>
 <p>This notebook was all about ensemble learning methods. There is truly a <code class="docutils literal notranslate"><span class="pre">wisdom</span> <span class="pre">in</span> <span class="pre">the</span> <span class="pre">crowd</span></code>(CC: Aurelion Geron). By averaging the results of different models, we are able to improve the overall prediction.</p>
 <p>There is a notion that ensemble models are slow and expensive to run in production. It’s true, but with the advent of computation power and how incredible these types of ML models work well, that’s no longer issue for someone. They are complex algorithms but they can also reduce complexity. How?</p>
 <p>Think about it: Instead of building a single complex model (typically a neural network), you can build small models that will train and compute predictions faster and then average their results using a given ensemble method. By that way, you are leveraging ensemble methods to reduce complexity.</p>
 <p>To learn more about the ensemble models, you can refer to this chapter <code class="docutils literal notranslate"><span class="pre">Chapter</span> <span class="pre">7</span> <span class="pre">Ensemble</span> <span class="pre">Learning</span> <span class="pre">and</span> <span class="pre">Random</span> <span class="pre">Forests</span></code> of the book <a class="reference external" href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/">Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow</a>.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.77.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.75.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Nyandwi for creating the open-source course <a class="reference external" href="https://github.com/Nyandwi/machine_learning_complete">Machine Learning complete</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
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-       37.78.2.1.1. Entropy
+       37.76.2.1.1. Entropy
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-     37.78.2.4. Task 4: How a decision tree works with numerical features
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-     37.78.4.1. Task 1: Decision trees in a customer churn prediction task
+     37.76.4.1. Task 1: Decision trees in a customer churn prediction task
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-     37.78.4.2. Task 2: Complex case for decision trees
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-     37.78.4.3. Task 3: Decision trees in a task of MNIST handwritten digits recognition
+     37.76.4.3. Task 3: Decision trees in a task of MNIST handwritten digits recognition
     </a>
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-   37.78.5. Pros and cons of decision tree
+   37.76.5. Pros and cons of decision tree
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-   37.78.6. Acknowledgments
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+     37.76.2.1. Task 1: How to build a decision tree
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-     37.78.2.3. Task 3: Other quality criteria for splits in classification problems
+     37.76.2.3. Task 3: Other quality criteria for splits in classification problems
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+       37.76.2.3.1. Example
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-       37.78.2.3.2. How can we “read” such a tree?
+       37.76.2.3.2. How can we “read” such a tree?
       </a>
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     <a class="reference internal nav-link" href="#task-4-how-a-decision-tree-works-with-numerical-features">
-     37.78.2.4. Task 4: How a decision tree works with numerical features
+     37.76.2.4. Task 4: How a decision tree works with numerical features
     </a>
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     <a class="reference internal nav-link" href="#task-5-crucial-tree-parameters">
-     37.78.2.5. Task 5: Crucial tree parameters
+     37.76.2.5. Task 5: Crucial tree parameters
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-     37.78.2.6. Task 6: Class DecisionTreeClassifier in Scikit-learn
+     37.76.2.6. Task 6: Class DecisionTreeClassifier in Scikit-learn
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     <a class="reference internal nav-link" href="#task-7-decision-tree-in-a-regression-problem">
-     37.78.2.7. Task 7: Decision tree in a regression problem
+     37.76.2.7. Task 7: Decision tree in a regression problem
     </a>
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-       37.78.2.7.1. Example
+       37.76.2.7.1. Example
       </a>
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-   37.78.3. Choosing model parameters and cross-validation
+   37.76.3. Choosing model parameters and cross-validation
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+   37.76.4. Application examples and complex cases
   </a>
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     <a class="reference internal nav-link" href="#task-1-decision-trees-in-a-customer-churn-prediction-task">
-     37.78.4.1. Task 1: Decision trees in a customer churn prediction task
+     37.76.4.1. Task 1: Decision trees in a customer churn prediction task
     </a>
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-     37.78.4.2. Task 2: Complex case for decision trees
+     37.76.4.2. Task 2: Complex case for decision trees
     </a>
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-     37.78.4.3. Task 3: Decision trees in a task of MNIST handwritten digits recognition
+     37.76.4.3. Task 3: Decision trees in a task of MNIST handwritten digits recognition
     </a>
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-   37.78.5. Pros and cons of decision tree
+   37.76.5. Pros and cons of decision tree
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               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="decision-trees">
-<h1><span class="section-number">37.78. </span>Decision trees<a class="headerlink" href="#decision-trees" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.76. </span>Decision trees<a class="headerlink" href="#decision-trees" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">37.78.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.76.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>Before we dive into the material for this week’s article, let’s talk about the kind of problem that we are going to solve and its place in the exciting field of machine learning. T. Mitchell’s book “Machine Learning” (1997) gives a classic, general definition of machine learning as follows:</p>
 <blockquote>
 <div><p>A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.</p>
@@ -1801,14 +1818,14 @@ <h2><span class="section-number">37.78.1. </span>Introduction<a class="headerlin
 <p>A good overview is provided in the “Machine Learning basics” chapter of <a class="reference external" href="http://www.deeplearningbook.org">“Deep Learning”</a> (by Ian Goodfellow, Yoshua Bengio, Aaron Courville, 2016).</p>
 <p>Experience E  refers to data (we can’t go anywhere without it). Machine learning algorithms can be divided into those that are trained in supervised or unsupervised manner. In unsupervised learning tasks, one has a set consisting of instances described by a set of features. In supervised learning problems, there’s also a target variable, which is what we would like to be able to predict, known for each instance in a training set.</p>
 <section id="example">
-<h3><span class="section-number">37.78.1.1. </span>Example<a class="headerlink" href="#example" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.76.1.1. </span>Example<a class="headerlink" href="#example" title="Permalink to this headline">#</a></h3>
 <p>Classification and regression are supervised learning problems. For example, as a credit institution, we may want to predict loan defaults based on the data accumulated about our clients. Here, the experience E is the available training data: a set of instances (clients), a collection of features (such as age, salary, type of loan, past loan defaults, etc.) for each, and a target variable (whether they defaulted on the loan). This target variable is just a fact of loan default (1 or 0), so recall that this is a (binary) classification problem. If you were instead predicting by how much time the loan payment is overdue, this would become a regression problem.</p>
 <p>Finally, the third term used in the definition of machine learning is a metric of the algorithm’s performance evaluation P. Such metrics differ for various problems and algorithms, and we’ll discuss them as we study new algorithms. For now, we’ll refer to a simple metric for classification algorithms, the proportion of correct answers – accuracy – on the test set.</p>
 <p>Let’s take a look at two supervised learning problems: classification and regression.</p>
 </section>
 </section>
 <section id="decision-tree">
-<h2><span class="section-number">37.78.2. </span>Decision tree<a class="headerlink" href="#decision-tree" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.76.2. </span>Decision tree<a class="headerlink" href="#decision-tree" title="Permalink to this headline">#</a></h2>
 <p>We begin our overview of classification and regression methods with one of the most popular ones – a decision tree. Decision trees are used in everyday life decisions, not just in machine learning. Flow diagrams are actually visual representations of decision trees. For example, Higher School of Economics publishes information diagrams to make the lives of its employees easier. Here is a snippet of instructions for publishing a paper on the Institution portal.</p>
 <p><img align='center' src='https://habrastorage.org/webt/wh/ty/tf/whtytf2hxotbnhymqno6vmcivr4.png' width=60%><br></p>
 <p>In terms of machine learning, one can see it as a simple classifier that determines the appropriate form of publication (book, article, chapter of the book, preprint, publication in the “Higher School of Economics and the Media”) based on the content (book, pamphlet, paper), type of journal, original publication type (scientific journal, proceedings), etc.</p>
@@ -1818,18 +1835,18 @@ <h2><span class="section-number">37.78.2. </span>Decision tree<a class="headerli
 <p>The decision tree as a machine learning algorithm is essentially the same thing as the diagram shown above; we incorporate a stream of logical rules of the form “feature <span class="math notranslate nohighlight">\(a\)</span> value is less than <span class="math notranslate nohighlight">\(x\)</span> and feature <span class="math notranslate nohighlight">\(b\)</span> value is less than <span class="math notranslate nohighlight">\(y\)</span> … =&gt; Category 1” into a tree-like data structure. The advantage of this algorithm is that they are easily interpretable. For example, using the above scheme, the bank can explain to the client why they were denied for a loan: e.g the client does not own a house and her income is less than 5,000.</p>
 <p>As we’ll see later, many other models, although more accurate, do not have this property and can be regarded as more of a “black box” approach, where it is harder to interpret how the input data was transformed into the output. Due to this “understandability” and similarity to human decision-making (you can easily explain your model to your boss), decision trees have gained immense popularity. C4.5, a representative of this group of classification methods, is even the first in the list of 10 best data mining algorithms (“Top 10 Algorithms in Data Mining”, Knowledge and Information Systems, 2008. <a class="reference external" href="https://www.researchgate.net/publication/29467751_Top_10_algorithms_in_data_mining">ResearchGate</a>).</p>
 <section id="task-1-how-to-build-a-decision-tree">
-<h3><span class="section-number">37.78.2.1. </span>Task 1: How to build a decision tree<a class="headerlink" href="#task-1-how-to-build-a-decision-tree" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.76.2.1. </span>Task 1: How to build a decision tree<a class="headerlink" href="#task-1-how-to-build-a-decision-tree" title="Permalink to this headline">#</a></h3>
 <p>Earlier, we saw that the decision to grant a loan is made based on age, assets, income, and other variables. But what variable to look at first? Let’s discuss a simple example where all the variables are binary.</p>
 <p>Recall the game of “20 Questions”, which is often referenced when introducing decision trees. You’ve probably played this game – one person thinks of a celebrity while the other tries to guess by asking only “Yes” or “No” questions. What question will the guesser ask first? Of course, they will ask the one that narrows down the number of the remaining options the most. Asking “Is it Angelina Jolie?” would, in the case of a negative response, leave all but one celebrity in the realm of possibility. In contrast, asking “Is the celebrity a woman?” would reduce the possibilities to roughly half. That is to say, the “gender” feature separates the celebrity dataset much better than other features like “Angelina Jolie”, “Spanish”, or “loves football.” This reasoning corresponds to the concept of information gain based on entropy.</p>
 <section id="entropy">
-<h4><span class="section-number">37.78.2.1.1. </span>Entropy<a class="headerlink" href="#entropy" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.76.2.1.1. </span>Entropy<a class="headerlink" href="#entropy" title="Permalink to this headline">#</a></h4>
 <p>Shannon’s entropy is defined for a system with N possible states as follows:</p>
 <div class="math notranslate nohighlight">
 \[\Large S = -\sum_{i=1}^{N}p_i \log_2{p_i},\]</div>
 <p>where <span class="math notranslate nohighlight">\(p_i\)</span> is the probability of finding the system in the <span class="math notranslate nohighlight">\(i\)</span>-th state. This is a very important concept used in physics, information theory, and other areas. Entropy can be described as the degree of chaos in the system. The higher the entropy, the less ordered the system and vice versa. This will help us formalize “effective data splitting”, which we alluded to in the context of “20 Questions”.</p>
 </section>
 <section id="toy-example">
-<h4><span class="section-number">37.78.2.1.2. </span>Toy Example<a class="headerlink" href="#toy-example" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.76.2.1.2. </span>Toy Example<a class="headerlink" href="#toy-example" title="Permalink to this headline">#</a></h4>
 <p>To illustrate how entropy can help us identify good features for building a decision tree, let’s look at a toy example. We will predict the color of the ball based on its position.</p>
 <p><img align='center' src='https://habrastorage.org/webt/mu/vl/mt/muvlmtd2njeqf18trbldenpqvnm.png'><br></p>
 <p>There are 9 blue balls and 11 yellow balls. If we randomly pull out a ball, then it will be blue with probability <span class="math notranslate nohighlight">\(p_1=\frac{9}{20}\)</span> and yellow with probability <span class="math notranslate nohighlight">\(p_2=\frac{11}{20}\)</span>, which gives us an entropy <span class="math notranslate nohighlight">\(S_0 = -\frac{9}{20}\log_2{\frac{9}{20}}-\frac{11}{20}\log_2{\frac{11}{20}} \approx 1\)</span>. This value by itself may not tell us much, but let’s see how the value changes if we were to break the balls into two groups: with the position less than or equal to 12 and greater than 12.</p>
@@ -1847,7 +1864,7 @@ <h4><span class="section-number">37.78.2.1.2. </span>Toy Example<a class="header
 </section>
 </section>
 <section id="task-2-tree-building-algorithm">
-<h3><span class="section-number">37.78.2.2. </span>Task 2: Tree-building algorithm<a class="headerlink" href="#task-2-tree-building-algorithm" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.76.2.2. </span>Task 2: Tree-building algorithm<a class="headerlink" href="#task-2-tree-building-algorithm" title="Permalink to this headline">#</a></h3>
 <p>We can make sure that the tree built in the previous example is optimal: it took only 5 “questions” (conditioned on the variable <span class="math notranslate nohighlight">\(x\)</span>) to perfectly fit a decision tree to the training set. Under other split conditions, the resulting tree would be deeper, i.e. take more “questions” to reach an answer.</p>
 <p>At the heart of the popular algorithms for decision tree construction, such as ID3 or C4.5, lies the principle of greedy maximization of information gain: at each step, the algorithm chooses the variable that gives the greatest information gain upon splitting. Then the procedure is repeated recursively until the entropy is zero (or some small value to account for overfitting). Different algorithms use different heuristics for “early stopping” or “cut-off” to avoid constructing an overfitted tree.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">build</span><span class="p">(</span><span class="n">L</span><span class="p">):</span>
@@ -1863,7 +1880,7 @@ <h3><span class="section-number">37.78.2.2. </span>Task 2: Tree-building algorit
 </div>
 </section>
 <section id="task-3-other-quality-criteria-for-splits-in-classification-problems">
-<h3><span class="section-number">37.78.2.3. </span>Task 3: Other quality criteria for splits in classification problems<a class="headerlink" href="#task-3-other-quality-criteria-for-splits-in-classification-problems" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.76.2.3. </span>Task 3: Other quality criteria for splits in classification problems<a class="headerlink" href="#task-3-other-quality-criteria-for-splits-in-classification-problems" title="Permalink to this headline">#</a></h3>
 <p>We discussed how entropy allows us to formalize partitions in a tree. But this is only one heuristic; there exists others:</p>
 <ul class="simple">
 <li><p>Gini uncertainty (Gini impurity): <span class="math notranslate nohighlight">\(G = 1 - \sum\limits_k (p_k)^2\)</span>. Maximizing this criterion can be interpreted as the maximization of the number of pairs of objects of the same class that are in the same subtree (not to be confused with the Gini index).</p></li>
@@ -1915,7 +1932,7 @@ <h3><span class="section-number">37.78.2.3. </span>Task 3: Other quality criteri
 </div>
 </div>
 <section id="id1">
-<h4><span class="section-number">37.78.2.3.1. </span>Example<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.76.2.3.1. </span>Example<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
 <p>Let’s consider fitting a decision tree to some synthetic data. We will generate samples from two classes, both normal distributions but with different means.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1993,12 +2010,12 @@ <h4><span class="section-number">37.78.2.3.1. </span>Example<a class="headerlink
 <p>And how does the tree itself look? We see that the tree “cuts” the space into 8 rectangles, i.e. the tree has 8 leaves. Within each rectangle, the tree will make the prediction according to the majority label of the objects inside it.</p>
 <img src="https://habrastorage.org/webt/z0/yp/wm/z0ypwmvjko4r2mymcohy7ovevwe.png" /></section>
 <section id="how-can-we-read-such-a-tree">
-<h4><span class="section-number">37.78.2.3.2. </span>How can we “read” such a tree?<a class="headerlink" href="#how-can-we-read-such-a-tree" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.76.2.3.2. </span>How can we “read” such a tree?<a class="headerlink" href="#how-can-we-read-such-a-tree" title="Permalink to this headline">#</a></h4>
 <p>In the beginning, there were 200 samples (instances), 100 of each class. The entropy of the initial state was maximal, <span class="math notranslate nohighlight">\(S=1\)</span>. Then, the first partition of the samples into 2 groups was made by comparing the value of <span class="math notranslate nohighlight">\(x_2\)</span> with <span class="math notranslate nohighlight">\(1.211\)</span> (find this part of the border in the picture above). With that, the entropy of both left and right groups decreased. The process continues up to depth 3. In this visualization, the more samples of the first class, the darker the orange color of the vertex; the more samples of the second class, the darker the blue. At the beginning, the number of samples from two classes is equal, so the root node of the tree is white.</p>
 </section>
 </section>
 <section id="task-4-how-a-decision-tree-works-with-numerical-features">
-<h3><span class="section-number">37.78.2.4. </span>Task 4: How a decision tree works with numerical features<a class="headerlink" href="#task-4-how-a-decision-tree-works-with-numerical-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.76.2.4. </span>Task 4: How a decision tree works with numerical features<a class="headerlink" href="#task-4-how-a-decision-tree-works-with-numerical-features" title="Permalink to this headline">#</a></h3>
 <p>Suppose we have a numeric feature “Age” that has a lot of unique values. A decision tree will look for the best (according to some criterion of information gain) split by checking binary attributes such as “Age &lt;17”, “Age &lt; 22.87”, and so on. But what if the age range is large? Or what if another quantitative variable, “salary”, can also be “cut” in many ways? There will be too many binary attributes to select from at each step during tree construction. To resolve this problem, heuristics are usually used to limit the number of thresholds to which we compare the quantitative variable.</p>
 <p>Let’s consider an example. Suppose we have the following dataset:</p>
 <div class="cell docutils container">
@@ -2429,7 +2446,7 @@ <h3><span class="section-number">37.78.2.4. </span>Task 4: How a decision tree w
 <p>More examples of numeric feature discretization can be found in posts like <a class="reference external" href="http://kevinmeurer.com/a-simple-guide-to-entropy-based-discretization/">“A Simple Guide to Entropy-Based Discretization”</a> or <a class="reference external" href="http://clear-lines.com/blog/post/Discretizing-a-continuous-variable-using-Entropy.aspx">“Discretizing a continuous variable using Entropy”</a>. One of the most prominent scientific papers on this subject is “On the handling of continuous-valued attributes in decision tree generation” (UM Fayyad. KB Irani, “Machine Learning”, 1992).</p>
 </section>
 <section id="task-5-crucial-tree-parameters">
-<h3><span class="section-number">37.78.2.5. </span>Task 5: Crucial tree parameters<a class="headerlink" href="#task-5-crucial-tree-parameters" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.76.2.5. </span>Task 5: Crucial tree parameters<a class="headerlink" href="#task-5-crucial-tree-parameters" title="Permalink to this headline">#</a></h3>
 <p>Technically, you can build a decision tree until each leaf has exactly one instance, but this is not common in practice when building a single tree because it will be <em>overfitted</em>, or too tuned to the training set, and will not predict labels for new data well. At the bottom of the tree, at some great depth, there will be partitions on less important features (e.g. whether a client came from Leeds or New York). We can exaggerate this story further and find that all four clients who came to the bank for a loan in green trousers did not return the loan. Even if that were true in training, we do not want our classification model to generate such specific rules.</p>
 <p>There are two exceptions where the trees are built to the maximum depth:</p>
 <ul class="simple">
@@ -2445,7 +2462,7 @@ <h3><span class="section-number">37.78.2.5. </span>Task 5: Crucial tree paramete
 </ul>
 </section>
 <section id="task-6-class-decisiontreeclassifier-in-scikit-learn">
-<h3><span class="section-number">37.78.2.6. </span>Task 6: Class DecisionTreeClassifier in Scikit-learn<a class="headerlink" href="#task-6-class-decisiontreeclassifier-in-scikit-learn" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.76.2.6. </span>Task 6: Class DecisionTreeClassifier in Scikit-learn<a class="headerlink" href="#task-6-class-decisiontreeclassifier-in-scikit-learn" title="Permalink to this headline">#</a></h3>
 <p>The main parameters of the <a class="reference external" href="http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html"><code class="docutils literal notranslate"><span class="pre">sklearn.tree.DecisionTreeClassifier</span></code></a> class are:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">max_depth</span></code> – the maximum depth of the tree;</p></li>
@@ -2455,7 +2472,7 @@ <h3><span class="section-number">37.78.2.6. </span>Task 6: Class DecisionTreeCla
 <p>The parameters of the tree need to be set depending on input data, and it is usually done by means of <em>cross-validation</em>, more on this below.</p>
 </section>
 <section id="task-7-decision-tree-in-a-regression-problem">
-<h3><span class="section-number">37.78.2.7. </span>Task 7: Decision tree in a regression problem<a class="headerlink" href="#task-7-decision-tree-in-a-regression-problem" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.76.2.7. </span>Task 7: Decision tree in a regression problem<a class="headerlink" href="#task-7-decision-tree-in-a-regression-problem" title="Permalink to this headline">#</a></h3>
 <p>When predicting a numeric variable, the idea of a tree construction remains the same, but the quality criteria changes:</p>
 <ul class="simple">
 <li><p>Variance:</p></li>
@@ -2464,7 +2481,7 @@ <h3><span class="section-number">37.78.2.7. </span>Task 7: Decision tree in a re
 \[\Large D = \frac{1}{\ell} \sum\limits_{i =1}^{\ell} (y_i - \frac{1}{\ell} \sum\limits_{j=1}^{\ell} y_j)^2, \]</div>
 <p>where <span class="math notranslate nohighlight">\(\ell\)</span> is the number of samples in a leaf, <span class="math notranslate nohighlight">\(y_i\)</span> is the value of the target variable. Simply put, by minimizing the variance, we look for features that divide the training set in such a way that the values of the target feature in each leaf are roughly equal.</p>
 <section id="id2">
-<h4><span class="section-number">37.78.2.7.1. </span>Example<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.76.2.7.1. </span>Example<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
 <p>Let’s generate some data distributed by the function <span class="math notranslate nohighlight">\(f(x) = e^{-x ^ 2} + 1.5 * e^{-(x - 2) ^ 2}\)</span> with some noise. Then we will train a tree with this data and predictions that the tree makes.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2522,7 +2539,7 @@ <h4><span class="section-number">37.78.2.7.1. </span>Example<a class="headerlink
 </section>
 </section>
 <section id="choosing-model-parameters-and-cross-validation">
-<h2><span class="section-number">37.78.3. </span>Choosing model parameters and cross-validation<a class="headerlink" href="#choosing-model-parameters-and-cross-validation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.76.3. </span>Choosing model parameters and cross-validation<a class="headerlink" href="#choosing-model-parameters-and-cross-validation" title="Permalink to this headline">#</a></h2>
 <p>The main task of learning algorithms is to be able to <em>generalize</em> to unseen data. Since we cannot immediately check the model performance on new, incoming data (because we do not know the true values of the target variable yet), it is necessary to sacrifice a small portion of the data to check the quality of the model on it.</p>
 <p>This is often done in one of two ways:</p>
 <ul class="simple">
@@ -2535,9 +2552,9 @@ <h2><span class="section-number">37.78.3. </span>Choosing model parameters and c
 <p>Cross-validation is a very important technique in machine learning and can also be applied in statistics and econometrics. It helps with hyperparameter tuning, model comparison, feature evaluation, etc. More details can be found <a class="reference external" href="https://sebastianraschka.com/blog/2016/model-evaluation-selection-part1.html">here</a> (blog post by Sebastian Raschka) or in any classic textbook on machine (statistical) learning.</p>
 </section>
 <section id="application-examples-and-complex-cases">
-<h2><span class="section-number">37.78.4. </span>Application examples and complex cases<a class="headerlink" href="#application-examples-and-complex-cases" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.76.4. </span>Application examples and complex cases<a class="headerlink" href="#application-examples-and-complex-cases" title="Permalink to this headline">#</a></h2>
 <section id="task-1-decision-trees-in-a-customer-churn-prediction-task">
-<h3><span class="section-number">37.78.4.1. </span>Task 1: Decision trees in a customer churn prediction task<a class="headerlink" href="#task-1-decision-trees-in-a-customer-churn-prediction-task" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.76.4.1. </span>Task 1: Decision trees in a customer churn prediction task<a class="headerlink" href="#task-1-decision-trees-in-a-customer-churn-prediction-task" title="Permalink to this headline">#</a></h3>
 <p>Let’s read data into a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> and preprocess it. Store <em>State</em> in a separate <code class="docutils literal notranslate"><span class="pre">Series</span></code> object for now and remove it from the dataframe. We will
 train the first model without the <em>State</em> feature, and then we will see if it helps.</p>
 <div class="cell docutils container">
@@ -2862,7 +2879,7 @@ <h3><span class="section-number">37.78.4.1. </span>Task 1: Decision trees in a c
 </div>
 </section>
 <section id="task-2-complex-case-for-decision-trees">
-<h3><span class="section-number">37.78.4.2. </span>Task 2: Complex case for decision trees<a class="headerlink" href="#task-2-complex-case-for-decision-trees" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.76.4.2. </span>Task 2: Complex case for decision trees<a class="headerlink" href="#task-2-complex-case-for-decision-trees" title="Permalink to this headline">#</a></h3>
 <p>To continue the discussion of the pros and cons of the methods in question, let’s consider a simple classification task, where a tree would perform well but does it in an “overly complicated” manner. Let’s create a set of points on a plane (2 features), each point will be one of two classes (+1 for red, or -1 for yellow). If you look at it as a classification problem, it seems very simple: the classes are separated by a line.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2910,7 +2927,7 @@ <h3><span class="section-number">37.78.4.2. </span>Task 2: Complex case for deci
 <img src="https://habrastorage.org/webt/az/ld/yj/azldyj0ija_sxkk438m959qpf0w.png" /><p>Obviously, decision trees don’t work as well as linear classifiers in this case</p>
 </section>
 <section id="task-3-decision-trees-in-a-task-of-mnist-handwritten-digits-recognition">
-<h3><span class="section-number">37.78.4.3. </span>Task 3: Decision trees in a task of MNIST handwritten digits recognition<a class="headerlink" href="#task-3-decision-trees-in-a-task-of-mnist-handwritten-digits-recognition" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.76.4.3. </span>Task 3: Decision trees in a task of MNIST handwritten digits recognition<a class="headerlink" href="#task-3-decision-trees-in-a-task-of-mnist-handwritten-digits-recognition" title="Permalink to this headline">#</a></h3>
 <p>Now let’s have a look at how decision trees performs on a real-world task. We will use the <code class="docutils literal notranslate"><span class="pre">sklearn</span></code> built-in dataset on handwritten digits.</p>
 <p>Pictures here are 8x8 matrices (intensity of white color for each pixel). Then each such matrix is “unfolded” into a vector of length 64, and we obtain a feature description of an object.</p>
 <p>Let’s draw some handwritten digits. We see that they are distinguishable.</p>
@@ -3053,7 +3070,7 @@ <h3><span class="section-number">37.78.4.3. </span>Task 3: Decision trees in a t
 </section>
 </section>
 <section id="pros-and-cons-of-decision-tree">
-<h2><span class="section-number">37.78.5. </span>Pros and cons of decision tree<a class="headerlink" href="#pros-and-cons-of-decision-tree" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.76.5. </span>Pros and cons of decision tree<a class="headerlink" href="#pros-and-cons-of-decision-tree" title="Permalink to this headline">#</a></h2>
 <p>Pros:</p>
 <ul class="simple">
 <li><p>Generation of clear human-understandable classification rules, e.g. “if age &lt;25 and is interested in motorcycles, deny the loan”. This property is called interpretability of the model.</p></li>
@@ -3074,7 +3091,7 @@ <h2><span class="section-number">37.78.5. </span>Pros and cons of decision tree<
 </ul>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.78.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.76.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/kashnitsky">Yury Kashnitsky</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/kashnitsky/topic-3-decision-trees-and-knn/notebook">Decision Trees and kNN</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -3116,13 +3133,13 @@ <h2><span class="section-number">37.78.6. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
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-            <p class="prev-next-title"><span class="section-number">37.77. </span>Beyond random forests: more ensemble models</p>
+            <p class="prev-next-title"><span class="section-number">37.75. </span>Beyond random forests: more ensemble models</p>
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-        <p class="prev-next-title"><span class="section-number">37.79. </span>Hyperparameter tuning gradient boosting</p>
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diff --git a/assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html b/assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html
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--- a/assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html
+++ b/assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.82. Random Forest Classifier with Feature Importance &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.80. Random Forest Classifier with Feature Importance &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.84. Data engineering" href="../../machine-learning-productionization/data-engineering.html" />
-    <link rel="prev" title="37.81. Boosting with tuning" href="../gradient-boosting/boosting-with-tuning.html" />
+    <link rel="next" title="37.82. Data engineering" href="../../machine-learning-productionization/data-engineering.html" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
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       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#check-the-data-types-of-columns">
-       37.82.4.5. Check the data types of columns
+       37.80.4.5. Check the data types of columns
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#view-statistical-properties-of-dataset">
-       37.82.4.6. View statistical properties of dataset
+       37.80.4.6. View statistical properties of dataset
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#check-with-assert-statement">
-       37.82.4.7. Check with ASSERT statement
+       37.80.4.7. Check with ASSERT statement
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#functional-approach-to-eda">
-       37.82.4.8. Functional approach to EDA
+       37.80.4.8. Functional approach to EDA
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#explore-categorical-variables">
-     37.82.5. Explore Categorical Variables
+     37.80.5. Explore Categorical Variables
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#find-categorical-variables">
-       37.82.5.1. Find categorical variables
+       37.80.5.1. Find categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#preview-categorical-variables">
-       37.82.5.2. Preview categorical variables
+       37.80.5.2. Preview categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#summary-of-categorical-variables">
-       37.82.5.3. Summary of categorical variables
+       37.80.5.3. Summary of categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#frequency-distribution-of-categorical-variables">
-       37.82.5.4. Frequency distribution of categorical variables
+       37.80.5.4. Frequency distribution of categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#percentage-of-frequency-distribution-of-values">
-       37.82.5.5. Percentage of frequency distribution of values
+       37.80.5.5. Percentage of frequency distribution of values
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#explore-the-variables">
-       37.82.5.6. Explore the variables
+       37.80.5.6. Explore the variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#number-of-labels-cardinality">
-       37.82.5.7. Number of labels: Cardinality
+       37.80.5.7. Number of labels: Cardinality
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#declare-feature-vector-and-target-variable">
-     37.82.6. Declare feature vector and target variable
+     37.80.6. Declare feature vector and target variable
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#split-data-into-separate-training-and-test-set">
-     37.82.7. Split data into separate training and test set
+     37.80.7. Split data into separate training and test set
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#feature-engineering">
-     37.82.8. Feature Engineering
+     37.80.8. Feature Engineering
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#display-categorical-variables-in-training-set">
-       37.82.8.1. Display categorical variables in training set
+       37.80.8.1. Display categorical variables in training set
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#display-numerical-variables-in-training-set">
-       37.82.8.2. Display numerical variables in training set
+       37.80.8.2. Display numerical variables in training set
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#engineering-missing-values-in-categorical-variables">
-       37.82.8.3. Engineering missing values in categorical variables
+       37.80.8.3. Engineering missing values in categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#encode-categorical-variables">
-       37.82.8.4. Encode categorical variables
+       37.80.8.4. Encode categorical variables
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#feature-scaling">
-     37.82.9. Feature Scaling
+     37.80.9. Feature Scaling
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#random-forest-classifier-model-with-default-parameters">
-     37.82.10. Random Forest Classifier model with default parameters
+     37.80.10. Random Forest Classifier model with default parameters
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#random-forest-classifier-model-with-100-decision-trees">
-     37.82.11. Random Forest Classifier model with 100 Decision Trees
+     37.80.11. Random Forest Classifier model with 100 Decision Trees
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#build-the-random-forest-model-on-selected-features">
-     37.82.12. Build the Random Forest model on selected features
+     37.80.12. Build the Random Forest model on selected features
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#confusion-matrix">
-     37.82.13. Confusion matrix
+     37.80.13. Confusion matrix
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-report">
-     37.82.14. Classification Report
+     37.80.14. Classification Report
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.83. Acknowledgments
+   37.81. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1713,187 +1730,187 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#">
-   37.82. Random Forest Classifier with Feature Importance
+   37.80. Random Forest Classifier with Feature Importance
   </a>
   <ul class="visible nav section-nav flex-column">
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-problem-statement">
-     37.82.1. The problem statement
+     37.80.1. The problem statement
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#import-libraries">
-     37.82.2. Import libraries
+     37.80.2. Import libraries
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#import-dataset">
-     37.82.3. Import dataset
+     37.80.3. Import dataset
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploratory-data-analysis">
-     37.82.4. Exploratory data analysis
+     37.80.4. Exploratory data analysis
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#view-dimensions-of-dataset">
-       37.82.4.1. View dimensions of dataset
+       37.80.4.1. View dimensions of dataset
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#preview-the-dataset-a-class-anchor-id-4-2-a">
-       37.82.4.2. Preview the dataset
+       37.80.4.2. Preview the dataset
        <a class="anchor" id="4.2">
        </a>
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#rename-column-names">
-       37.82.4.3. Rename column names
+       37.80.4.3. Rename column names
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#view-summary-of-dataset">
-       37.82.4.4. View summary of dataset
+       37.80.4.4. View summary of dataset
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#check-the-data-types-of-columns">
-       37.82.4.5. Check the data types of columns
+       37.80.4.5. Check the data types of columns
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#view-statistical-properties-of-dataset">
-       37.82.4.6. View statistical properties of dataset
+       37.80.4.6. View statistical properties of dataset
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#check-with-assert-statement">
-       37.82.4.7. Check with ASSERT statement
+       37.80.4.7. Check with ASSERT statement
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#functional-approach-to-eda">
-       37.82.4.8. Functional approach to EDA
+       37.80.4.8. Functional approach to EDA
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#explore-categorical-variables">
-     37.82.5. Explore Categorical Variables
+     37.80.5. Explore Categorical Variables
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#find-categorical-variables">
-       37.82.5.1. Find categorical variables
+       37.80.5.1. Find categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#preview-categorical-variables">
-       37.82.5.2. Preview categorical variables
+       37.80.5.2. Preview categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#summary-of-categorical-variables">
-       37.82.5.3. Summary of categorical variables
+       37.80.5.3. Summary of categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#frequency-distribution-of-categorical-variables">
-       37.82.5.4. Frequency distribution of categorical variables
+       37.80.5.4. Frequency distribution of categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#percentage-of-frequency-distribution-of-values">
-       37.82.5.5. Percentage of frequency distribution of values
+       37.80.5.5. Percentage of frequency distribution of values
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#explore-the-variables">
-       37.82.5.6. Explore the variables
+       37.80.5.6. Explore the variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#number-of-labels-cardinality">
-       37.82.5.7. Number of labels: Cardinality
+       37.80.5.7. Number of labels: Cardinality
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#declare-feature-vector-and-target-variable">
-     37.82.6. Declare feature vector and target variable
+     37.80.6. Declare feature vector and target variable
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#split-data-into-separate-training-and-test-set">
-     37.82.7. Split data into separate training and test set
+     37.80.7. Split data into separate training and test set
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#feature-engineering">
-     37.82.8. Feature Engineering
+     37.80.8. Feature Engineering
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#display-categorical-variables-in-training-set">
-       37.82.8.1. Display categorical variables in training set
+       37.80.8.1. Display categorical variables in training set
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#display-numerical-variables-in-training-set">
-       37.82.8.2. Display numerical variables in training set
+       37.80.8.2. Display numerical variables in training set
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#engineering-missing-values-in-categorical-variables">
-       37.82.8.3. Engineering missing values in categorical variables
+       37.80.8.3. Engineering missing values in categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#encode-categorical-variables">
-       37.82.8.4. Encode categorical variables
+       37.80.8.4. Encode categorical variables
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#feature-scaling">
-     37.82.9. Feature Scaling
+     37.80.9. Feature Scaling
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#random-forest-classifier-model-with-default-parameters">
-     37.82.10. Random Forest Classifier model with default parameters
+     37.80.10. Random Forest Classifier model with default parameters
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#random-forest-classifier-model-with-100-decision-trees">
-     37.82.11. Random Forest Classifier model with 100 Decision Trees
+     37.80.11. Random Forest Classifier model with 100 Decision Trees
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#build-the-random-forest-model-on-selected-features">
-     37.82.12. Build the Random Forest model on selected features
+     37.80.12. Build the Random Forest model on selected features
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#confusion-matrix">
-     37.82.13. Confusion matrix
+     37.80.13. Confusion matrix
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-report">
-     37.82.14. Classification Report
+     37.80.14. Classification Report
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.83. Acknowledgments
+   37.81. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1907,13 +1924,13 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="random-forest-classifier-with-feature-importance">
-<h1><span class="section-number">37.82. </span>Random Forest Classifier with Feature Importance<a class="headerlink" href="#random-forest-classifier-with-feature-importance" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.80. </span>Random Forest Classifier with Feature Importance<a class="headerlink" href="#random-forest-classifier-with-feature-importance" title="Permalink to this headline">#</a></h1>
 <section id="the-problem-statement">
-<h2><span class="section-number">37.82.1. </span>The problem statement<a class="headerlink" href="#the-problem-statement" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.80.1. </span>The problem statement<a class="headerlink" href="#the-problem-statement" title="Permalink to this headline">#</a></h2>
 <p>We try to make predictions where the prediction task is to determine whether a person makes over 50K a year. We implement Random Forest Classification with Python and Scikit-Learn. So, to answer the question, we build a Random Forest classifier to predict whether a person makes over 50K a year.</p>
 </section>
 <section id="import-libraries">
-<h2><span class="section-number">37.82.2. </span>Import libraries<a class="headerlink" href="#import-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.80.2. </span>Import libraries<a class="headerlink" href="#import-libraries" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># This Python 3 environment comes with many helpful analytics libraries installed</span>
@@ -1958,7 +1975,7 @@ <h2><span class="section-number">37.82.2. </span>Import libraries<a class="heade
 </div>
 </section>
 <section id="import-dataset">
-<h2><span class="section-number">37.82.3. </span>Import dataset<a class="headerlink" href="#import-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.80.3. </span>Import dataset<a class="headerlink" href="#import-dataset" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="s1">&#39;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/random-forest-income_evaluation.csv&#39;</span>
@@ -1970,9 +1987,9 @@ <h2><span class="section-number">37.82.3. </span>Import dataset<a class="headerl
 </div>
 </section>
 <section id="exploratory-data-analysis">
-<h2><span class="section-number">37.82.4. </span>Exploratory data analysis<a class="headerlink" href="#exploratory-data-analysis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.80.4. </span>Exploratory data analysis<a class="headerlink" href="#exploratory-data-analysis" title="Permalink to this headline">#</a></h2>
 <section id="view-dimensions-of-dataset">
-<h3><span class="section-number">37.82.4.1. </span>View dimensions of dataset<a class="headerlink" href="#view-dimensions-of-dataset" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.80.4.1. </span>View dimensions of dataset<a class="headerlink" href="#view-dimensions-of-dataset" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># print the shape</span>
@@ -1984,7 +2001,7 @@ <h3><span class="section-number">37.82.4.1. </span>View dimensions of dataset<a
 <p>We can see that there are 32561 instances and 15 attributes in the data set.</p>
 </section>
 <section id="preview-the-dataset-a-class-anchor-id-4-2-a">
-<h3><span class="section-number">37.82.4.2. </span>Preview the dataset <a class="anchor" id="4.2"></a><a class="headerlink" href="#preview-the-dataset-a-class-anchor-id-4-2-a" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.80.4.2. </span>Preview the dataset <a class="anchor" id="4.2"></a><a class="headerlink" href="#preview-the-dataset-a-class-anchor-id-4-2-a" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
@@ -1994,7 +2011,7 @@ <h3><span class="section-number">37.82.4.2. </span>Preview the dataset <a class=
 </div>
 </section>
 <section id="rename-column-names">
-<h3><span class="section-number">37.82.4.3. </span>Rename column names<a class="headerlink" href="#rename-column-names" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.80.4.3. </span>Rename column names<a class="headerlink" href="#rename-column-names" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">col_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;age&#39;</span><span class="p">,</span> <span class="s1">&#39;workclass&#39;</span><span class="p">,</span> <span class="s1">&#39;fnlwgt&#39;</span><span class="p">,</span> <span class="s1">&#39;education&#39;</span><span class="p">,</span> <span class="s1">&#39;education_num&#39;</span><span class="p">,</span> <span class="s1">&#39;marital_status&#39;</span><span class="p">,</span> <span class="s1">&#39;occupation&#39;</span><span class="p">,</span> <span class="s1">&#39;relationship&#39;</span><span class="p">,</span>
@@ -2009,7 +2026,7 @@ <h3><span class="section-number">37.82.4.3. </span>Rename column names<a class="
 </div>
 </section>
 <section id="view-summary-of-dataset">
-<h3><span class="section-number">37.82.4.4. </span>View summary of dataset<a class="headerlink" href="#view-summary-of-dataset" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.80.4.4. </span>View summary of dataset<a class="headerlink" href="#view-summary-of-dataset" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
@@ -2019,7 +2036,7 @@ <h3><span class="section-number">37.82.4.4. </span>View summary of dataset<a cla
 </div>
 </section>
 <section id="check-the-data-types-of-columns">
-<h3><span class="section-number">37.82.4.5. </span>Check the data types of columns<a class="headerlink" href="#check-the-data-types-of-columns" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.80.4.5. </span>Check the data types of columns<a class="headerlink" href="#check-the-data-types-of-columns" title="Permalink to this headline">#</a></h3>
 <p>The above <a class="reference external" href="http://df.info">df.info</a>() command gives us the number of filled values along with the data types of columns.</p>
 <p>If we simply want to check the data type of a particular column, we can use the following command.</p>
 <div class="cell docutils container">
@@ -2031,7 +2048,7 @@ <h3><span class="section-number">37.82.4.5. </span>Check the data types of colum
 </div>
 </section>
 <section id="view-statistical-properties-of-dataset">
-<h3><span class="section-number">37.82.4.6. </span>View statistical properties of dataset<a class="headerlink" href="#view-statistical-properties-of-dataset" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.80.4.6. </span>View statistical properties of dataset<a class="headerlink" href="#view-statistical-properties-of-dataset" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
@@ -2065,7 +2082,7 @@ <h3><span class="section-number">37.82.4.6. </span>View statistical properties o
 </div>
 </section>
 <section id="check-with-assert-statement">
-<h3><span class="section-number">37.82.4.7. </span>Check with ASSERT statement<a class="headerlink" href="#check-with-assert-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.80.4.7. </span>Check with ASSERT statement<a class="headerlink" href="#check-with-assert-statement" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">assert</span> <span class="n">pd</span><span class="o">.</span><span class="n">notnull</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">()</span><span class="o">.</span><span class="n">all</span><span class="p">()</span>
@@ -2075,7 +2092,7 @@ <h3><span class="section-number">37.82.4.7. </span>Check with ASSERT statement<a
 </div>
 </section>
 <section id="functional-approach-to-eda">
-<h3><span class="section-number">37.82.4.8. </span>Functional approach to EDA<a class="headerlink" href="#functional-approach-to-eda" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.80.4.8. </span>Functional approach to EDA<a class="headerlink" href="#functional-approach-to-eda" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">initial_eda</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
@@ -2107,9 +2124,9 @@ <h3><span class="section-number">37.82.4.8. </span>Functional approach to EDA<a
 </section>
 </section>
 <section id="explore-categorical-variables">
-<h2><span class="section-number">37.82.5. </span>Explore Categorical Variables<a class="headerlink" href="#explore-categorical-variables" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.80.5. </span>Explore Categorical Variables<a class="headerlink" href="#explore-categorical-variables" title="Permalink to this headline">#</a></h2>
 <section id="find-categorical-variables">
-<h3><span class="section-number">37.82.5.1. </span>Find categorical variables<a class="headerlink" href="#find-categorical-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.80.5.1. </span>Find categorical variables<a class="headerlink" href="#find-categorical-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">categorical</span> <span class="o">=</span> <span class="p">[</span><span class="n">var</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="n">df</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span><span class="o">==</span><span class="s1">&#39;O&#39;</span><span class="p">]</span>
@@ -2123,7 +2140,7 @@ <h3><span class="section-number">37.82.5.1. </span>Find categorical variables<a
 </div>
 </section>
 <section id="preview-categorical-variables">
-<h3><span class="section-number">37.82.5.2. </span>Preview categorical variables<a class="headerlink" href="#preview-categorical-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.80.5.2. </span>Preview categorical variables<a class="headerlink" href="#preview-categorical-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="n">categorical</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
@@ -2133,10 +2150,10 @@ <h3><span class="section-number">37.82.5.2. </span>Preview categorical variables
 </div>
 </section>
 <section id="summary-of-categorical-variables">
-<h3><span class="section-number">37.82.5.3. </span>Summary of categorical variables<a class="headerlink" href="#summary-of-categorical-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.80.5.3. </span>Summary of categorical variables<a class="headerlink" href="#summary-of-categorical-variables" title="Permalink to this headline">#</a></h3>
 </section>
 <section id="frequency-distribution-of-categorical-variables">
-<h3><span class="section-number">37.82.5.4. </span>Frequency distribution of categorical variables<a class="headerlink" href="#frequency-distribution-of-categorical-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.80.5.4. </span>Frequency distribution of categorical variables<a class="headerlink" href="#frequency-distribution-of-categorical-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">categorical</span><span class="p">:</span> 
@@ -2148,7 +2165,7 @@ <h3><span class="section-number">37.82.5.4. </span>Frequency distribution of cat
 </div>
 </section>
 <section id="percentage-of-frequency-distribution-of-values">
-<h3><span class="section-number">37.82.5.5. </span>Percentage of frequency distribution of values<a class="headerlink" href="#percentage-of-frequency-distribution-of-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.80.5.5. </span>Percentage of frequency distribution of values<a class="headerlink" href="#percentage-of-frequency-distribution-of-values" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">categorical</span><span class="p">:</span>
@@ -2159,7 +2176,7 @@ <h3><span class="section-number">37.82.5.5. </span>Percentage of frequency distr
 </div>
 </section>
 <section id="explore-the-variables">
-<h3><span class="section-number">37.82.5.6. </span>Explore the variables<a class="headerlink" href="#explore-the-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.80.5.6. </span>Explore the variables<a class="headerlink" href="#explore-the-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># check for missing values</span>
@@ -2448,7 +2465,7 @@ <h3><span class="section-number">37.82.5.6. </span>Explore the variables<a class
 </div>
 </section>
 <section id="number-of-labels-cardinality">
-<h3><span class="section-number">37.82.5.7. </span>Number of labels: Cardinality<a class="headerlink" href="#number-of-labels-cardinality" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.80.5.7. </span>Number of labels: Cardinality<a class="headerlink" href="#number-of-labels-cardinality" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># check for cardinality in categorical variables</span>
@@ -2464,7 +2481,7 @@ <h3><span class="section-number">37.82.5.7. </span>Number of labels: Cardinality
 </section>
 </section>
 <section id="declare-feature-vector-and-target-variable">
-<h2><span class="section-number">37.82.6. </span>Declare feature vector and target variable<a class="headerlink" href="#declare-feature-vector-and-target-variable" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.80.6. </span>Declare feature vector and target variable<a class="headerlink" href="#declare-feature-vector-and-target-variable" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">&#39;income&#39;</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
@@ -2476,7 +2493,7 @@ <h2><span class="section-number">37.82.6. </span>Declare feature vector and targ
 </div>
 </section>
 <section id="split-data-into-separate-training-and-test-set">
-<h2><span class="section-number">37.82.7. </span>Split data into separate training and test set<a class="headerlink" href="#split-data-into-separate-training-and-test-set" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.80.7. </span>Split data into separate training and test set<a class="headerlink" href="#split-data-into-separate-training-and-test-set" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
@@ -2497,9 +2514,9 @@ <h2><span class="section-number">37.82.7. </span>Split data into separate traini
 </div>
 </section>
 <section id="feature-engineering">
-<h2><span class="section-number">37.82.8. </span>Feature Engineering<a class="headerlink" href="#feature-engineering" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.80.8. </span>Feature Engineering<a class="headerlink" href="#feature-engineering" title="Permalink to this headline">#</a></h2>
 <section id="display-categorical-variables-in-training-set">
-<h3><span class="section-number">37.82.8.1. </span>Display categorical variables in training set<a class="headerlink" href="#display-categorical-variables-in-training-set" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.80.8.1. </span>Display categorical variables in training set<a class="headerlink" href="#display-categorical-variables-in-training-set" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">categorical</span> <span class="o">=</span> <span class="p">[</span><span class="n">col</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">X_train</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="n">X_train</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">dtypes</span> <span class="o">==</span> <span class="s1">&#39;O&#39;</span><span class="p">]</span>
@@ -2511,7 +2528,7 @@ <h3><span class="section-number">37.82.8.1. </span>Display categorical variables
 </div>
 </section>
 <section id="display-numerical-variables-in-training-set">
-<h3><span class="section-number">37.82.8.2. </span>Display numerical variables in training set<a class="headerlink" href="#display-numerical-variables-in-training-set" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.80.8.2. </span>Display numerical variables in training set<a class="headerlink" href="#display-numerical-variables-in-training-set" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">numerical</span> <span class="o">=</span> <span class="p">[</span><span class="n">col</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">X_train</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="n">X_train</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">dtypes</span> <span class="o">!=</span> <span class="s1">&#39;O&#39;</span><span class="p">]</span>
@@ -2523,7 +2540,7 @@ <h3><span class="section-number">37.82.8.2. </span>Display numerical variables i
 </div>
 </section>
 <section id="engineering-missing-values-in-categorical-variables">
-<h3><span class="section-number">37.82.8.3. </span>Engineering missing values in categorical variables<a class="headerlink" href="#engineering-missing-values-in-categorical-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.80.8.3. </span>Engineering missing values in categorical variables<a class="headerlink" href="#engineering-missing-values-in-categorical-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># print percentage of missing values in the categorical variables in training set</span>
@@ -2596,7 +2613,7 @@ <h3><span class="section-number">37.82.8.3. </span>Engineering missing values in
 <p>We can see that there are no missing values in X_train and X_test.</p>
 </section>
 <section id="encode-categorical-variables">
-<h3><span class="section-number">37.82.8.4. </span>Encode categorical variables<a class="headerlink" href="#encode-categorical-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.80.8.4. </span>Encode categorical variables<a class="headerlink" href="#encode-categorical-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># preview categorical variables in X_train</span>
@@ -2663,7 +2680,7 @@ <h3><span class="section-number">37.82.8.4. </span>Encode categorical variables<
 </section>
 </section>
 <section id="feature-scaling">
-<h2><span class="section-number">37.82.9. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.80.9. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cols</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">columns</span>
@@ -2701,7 +2718,7 @@ <h2><span class="section-number">37.82.9. </span>Feature Scaling<a class="header
 <p>We now have X_train dataset ready to be fed into the Random Forest classifier. We will do it as follows.</p>
 </section>
 <section id="random-forest-classifier-model-with-default-parameters">
-<h2><span class="section-number">37.82.10. </span>Random Forest Classifier model with default parameters<a class="headerlink" href="#random-forest-classifier-model-with-default-parameters" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.80.10. </span>Random Forest Classifier model with default parameters<a class="headerlink" href="#random-forest-classifier-model-with-default-parameters" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># import Random Forest classifier</span>
@@ -2739,7 +2756,7 @@ <h2><span class="section-number">37.82.10. </span>Random Forest Classifier model
 </div>
 </section>
 <section id="random-forest-classifier-model-with-100-decision-trees">
-<h2><span class="section-number">37.82.11. </span>Random Forest Classifier model with 100 Decision Trees<a class="headerlink" href="#random-forest-classifier-model-with-100-decision-trees" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.80.11. </span>Random Forest Classifier model with 100 Decision Trees<a class="headerlink" href="#random-forest-classifier-model-with-100-decision-trees" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate the classifier with n_estimators = 100</span>
@@ -2795,7 +2812,7 @@ <h2><span class="section-number">37.82.11. </span>Random Forest Classifier model
 </div>
 </section>
 <section id="build-the-random-forest-model-on-selected-features">
-<h2><span class="section-number">37.82.12. </span>Build the Random Forest model on selected features<a class="headerlink" href="#build-the-random-forest-model-on-selected-features" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.80.12. </span>Build the Random Forest model on selected features<a class="headerlink" href="#build-the-random-forest-model-on-selected-features" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># drop the least important feature from X_train and X_test</span>
@@ -2835,7 +2852,7 @@ <h2><span class="section-number">37.82.12. </span>Build the Random Forest model
 </div>
 </section>
 <section id="confusion-matrix">
-<h2><span class="section-number">37.82.13. </span>Confusion matrix<a class="headerlink" href="#confusion-matrix" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.80.13. </span>Confusion matrix<a class="headerlink" href="#confusion-matrix" title="Permalink to this headline">#</a></h2>
 <p>A confusion matrix is a tool for summarizing the performance of a classification algorithm. A confusion matrix will give us a clear picture of classification model performance and the types of errors produced by the model. It gives us a summary of correct and incorrect predictions broken down by each category. The summary is represented in a tabular form.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2864,7 +2881,7 @@ <h2><span class="section-number">37.82.13. </span>Confusion matrix<a class="head
 </div>
 </section>
 <section id="classification-report">
-<h2><span class="section-number">37.82.14. </span>Classification Report<a class="headerlink" href="#classification-report" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.80.14. </span>Classification Report<a class="headerlink" href="#classification-report" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">classification_report</span>
@@ -2877,7 +2894,7 @@ <h2><span class="section-number">37.82.14. </span>Classification Report<a class=
 </section>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="acknowledgments">
-<h1><span class="section-number">37.83. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.81. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/prashant111">prashant111</a> for creating <a class="reference external" href="https://www.kaggle.com/code/prashant111/random-forest-classifier-feature-importance">random-forest-classifier-feature-importance</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
@@ -2918,13 +2935,13 @@ <h1><span class="section-number">37.83. </span>Acknowledgments<a class="headerli
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.81. </span>Boosting with tuning</p>
+            <p class="prev-next-title"><span class="section-number">37.79. </span>Boosting with tuning</p>
         </div>
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-        <p class="prev-next-title"><span class="section-number">37.84. </span>Data engineering</p>
+        <p class="prev-next-title"><span class="section-number">37.82. </span>Data engineering</p>
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.76. Random forests for classification &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.74. Random forests for classification &#8212; Ocademy Open Machine Learning Book</title>
     
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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+    </ul>
    </li>
    <li class="toctree-l2">
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   <a class="reference internal" href="../../../data-science/data-science-lifecycle/data-science-lifecycle.html">
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    38. Slides
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-     37.76.2.3. Task 3: Data preprocessing
+     37.74.2.3. Task 3: Data preprocessing
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     <a class="reference internal nav-link" href="#task-4-training-random-forests-classifier">
-     37.76.2.4. Task 4: Training random forests classifier
+     37.74.2.4. Task 4: Training random forests classifier
     </a>
    </li>
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-     37.76.2.5. Task5: Evaluating random forests classifier
+     37.74.2.5. Task5: Evaluating random forests classifier
     </a>
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-     37.76.2.6. Task 6: Improving random forests
+     37.74.2.6. Task 6: Improving random forests
     </a>
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-     37.76.2.7. Task 7: Evaluating the model on the test set
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                             <ul class="visible nav section-nav flex-column">
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+     37.74.2.1. Task 1: Exploratory data analysis
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+     37.74.2.4. Task 4: Training random forests classifier
     </a>
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+     37.74.2.5. Task5: Evaluating random forests classifier
     </a>
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-     37.76.2.6. Task 6: Improving random forests
+     37.74.2.6. Task 6: Improving random forests
     </a>
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-     37.76.2.7. Task 7: Evaluating the model on the test set
+     37.74.2.7. Task 7: Evaluating the model on the test set
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+   37.74.3. Acknowledgments
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@@ -1651,12 +1668,12 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="random-forests-for-classification">
-<h1><span class="section-number">37.76. </span>Random forests for classification<a class="headerlink" href="#random-forests-for-classification" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.74. </span>Random forests for classification<a class="headerlink" href="#random-forests-for-classification" title="Permalink to this headline">#</a></h1>
 <p>Random Forests are powerful machine learning algorithms used for supervised classification and regression. Random forests works by averaging the predictions of the multiple and randomized decision trees. Decision trees tends to overfit and so by combining multiple decision trees, the effect of overfitting can be minimized.</p>
 <p>Random Forests are type of ensemble models. More about ensembles models in the next assignment.</p>
 <p>Different to other learning algorithms, random forests provide a way to find the importance of each feature and this is implemented in Sklearn.</p>
 <section id="imports">
-<h2><span class="section-number">37.76.1. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.74.1. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -1671,7 +1688,7 @@ <h2><span class="section-number">37.76.1. </span>Imports<a class="headerlink" hr
 </div>
 </section>
 <section id="loading-the-data">
-<h2><span class="section-number">37.76.2. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.74.2. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
 <p>In this assignment, we will use Random forests to build a classifier that identify the increase or decrease of the electricity using “the data that was collected from the Australian New South Wales Electricity Market. In this market, prices are not fixed and are affected by demand and supply of the market. They are set every five minutes. Electricity transfers to/from the neighboring state of Victoria were done to alleviate fluctuations.”</p>
 <p>“The dataset contains 45,312 instances dated from 7 May 1996 to 5 December 1998. Each example of the dataset refers to a period of 30 minutes, i.e. there are 48 instances for each time period of one day. Each example on the dataset has 5 fields, the day of week, the time stamp, the New South Wales electricity demand, the Victoria electricity demand, the scheduled electricity transfer between states and the class label. The class label identifies the change of the price (UP or DOWN) in New South Wales relative to a moving average of the last 24 hours (and removes the impact of longer term price trends). Source: <a class="reference external" href="https://www.openml.org/d/151">Open ML electricity</a>.</p>
 <p>Here are the information about the features:</p>
@@ -1725,7 +1742,7 @@ <h2><span class="section-number">37.76.2. </span>Loading the data<a class="heade
 </div>
 </div>
 <section id="task-1-exploratory-data-analysis">
-<h3><span class="section-number">37.76.2.1. </span>Task 1: Exploratory data analysis<a class="headerlink" href="#task-1-exploratory-data-analysis" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.74.2.1. </span>Task 1: Exploratory data analysis<a class="headerlink" href="#task-1-exploratory-data-analysis" title="Permalink to this headline">#</a></h3>
 <p>Before doing exploratory analysis, as always, let’s split the data into training and test sets.</p>
 <div class="cell docutils container">
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@@ -1821,7 +1838,7 @@ <h3><span class="section-number">37.76.2.1. </span>Task 1: Exploratory data anal
 <p>In the above correlation matrix, you can see that class feature is not there and this is because it still has categorical values.</p>
 </section>
 <section id="task-2-more-data-exploration">
-<h3><span class="section-number">37.76.2.2. </span>Task 2: More data exploration<a class="headerlink" href="#task-2-more-data-exploration" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.74.2.2. </span>Task 2: More data exploration<a class="headerlink" href="#task-2-more-data-exploration" title="Permalink to this headline">#</a></h3>
 <p>Before preprocessing the data, let’s take a look into specific features.</p>
 <p>Let’s see how many Ups/Downs are in the class feature.</p>
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@@ -1907,7 +1924,7 @@ <h3><span class="section-number">37.76.2.2. </span>Task 2: More data exploration
 </div>
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-<h3><span class="section-number">37.76.2.3. </span>Task 3: Data preprocessing<a class="headerlink" href="#task-3-data-preprocessing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.74.2.3. </span>Task 3: Data preprocessing<a class="headerlink" href="#task-3-data-preprocessing" title="Permalink to this headline">#</a></h3>
 <p>It is here that we prepare the data to be in the proper format for the machine learning model.</p>
 <p>Let’s encode the categorical feature <code class="docutils literal notranslate"><span class="pre">class</span></code>. But before that, let’s take training input data and labels.</p>
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@@ -1946,7 +1963,7 @@ <h3><span class="section-number">37.76.2.3. </span>Task 3: Data preprocessing<a
 <p>But again if you look at the data, the <code class="docutils literal notranslate"><span class="pre">day</span></code> feature is not normalized as other features. We can normalize it or leave it but for now let’s go ahead and train the random forests classifier.</p>
 </section>
 <section id="task-4-training-random-forests-classifier">
-<h3><span class="section-number">37.76.2.4. </span>Task 4: Training random forests classifier<a class="headerlink" href="#task-4-training-random-forests-classifier" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.74.2.4. </span>Task 4: Training random forests classifier<a class="headerlink" href="#task-4-training-random-forests-classifier" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
@@ -1967,7 +1984,7 @@ <h3><span class="section-number">37.76.2.4. </span>Task 4: Training random fores
 </div>
 </section>
 <section id="task5-evaluating-random-forests-classifier">
-<h3><span class="section-number">37.76.2.5. </span>Task5: Evaluating random forests classifier<a class="headerlink" href="#task5-evaluating-random-forests-classifier" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.74.2.5. </span>Task5: Evaluating random forests classifier<a class="headerlink" href="#task5-evaluating-random-forests-classifier" title="Permalink to this headline">#</a></h3>
 <p>Let’s build 3 functions to display accuracy, confusion matrix, and classification report.</p>
 <ul class="simple">
 <li><p>Accuracy provide a percentage score of the model’s ability to make correct predictions.</p></li>
@@ -2058,7 +2075,7 @@ <h3><span class="section-number">37.76.2.5. </span>Task5: Evaluating random fore
 <p>The model clearly overfitted the data. Let’s see how we can regularize it.</p>
 </section>
 <section id="task-6-improving-random-forests">
-<h3><span class="section-number">37.76.2.6. </span>Task 6: Improving random forests<a class="headerlink" href="#task-6-improving-random-forests" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.74.2.6. </span>Task 6: Improving random forests<a class="headerlink" href="#task-6-improving-random-forests" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Random forest model parameters</span>
@@ -2160,7 +2177,7 @@ <h3><span class="section-number">37.76.2.6. </span>Task 6: Improving random fore
 <p>One way to improve the model can be to search more hyperparameters or adding more good data is always the best cure.</p>
 </section>
 <section id="task-7-evaluating-the-model-on-the-test-set">
-<h3><span class="section-number">37.76.2.7. </span>Task 7: Evaluating the model on the test set<a class="headerlink" href="#task-7-evaluating-the-model-on-the-test-set" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.74.2.7. </span>Task 7: Evaluating the model on the test set<a class="headerlink" href="#task-7-evaluating-the-model-on-the-test-set" title="Permalink to this headline">#</a></h3>
 <p>Let us evaluate the model on the test set. But we need first run the label_encoder on the class feature as we did in the training labels. Note that we only transform (not fit_transform).</p>
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@@ -2196,7 +2213,7 @@ <h3><span class="section-number">37.76.2.7. </span>Task 7: Evaluating the model
 <p>As you can see the model is no longer overfitting. On the training set, the accuracy was 79%, which is a figure very similar to the test set. And the model never saw the test data. To improve the model in the case like this, if is often best to add more data if possible.</p>
 </section>
 <section id="task-8-feature-importance">
-<h3><span class="section-number">37.76.2.8. </span>Task 8: Feature importance<a class="headerlink" href="#task-8-feature-importance" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.74.2.8. </span>Task 8: Feature importance<a class="headerlink" href="#task-8-feature-importance" title="Permalink to this headline">#</a></h3>
 <p>Different to other machine learning models, random forests can show how each feature contributed to the model generalization. Let’s find it.</p>
 <p>The results are values between 0 and 1. The closer to 1, the good the feature was to the model.</p>
 <div class="cell docutils container">
@@ -2216,7 +2233,7 @@ <h3><span class="section-number">37.76.2.8. </span>Task 8: Feature importance<a
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.76.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.74.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Nyandwi for creating the open-source course <a class="reference external" href="https://github.com/Nyandwi/machine_learning_complete">Machine Learning complete</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2258,13 +2275,13 @@ <h2><span class="section-number">37.76.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.75. </span>Random forests intro and regression</p>
+            <p class="prev-next-title"><span class="section-number">37.73. </span>Random forests intro and regression</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="beyond-random-forests-more-ensemble-models.html" title="next page">
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.77. </span>Beyond random forests: more ensemble models</p>
+        <p class="prev-next-title"><span class="section-number">37.75. </span>Beyond random forests: more ensemble models</p>
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.75. Random forests intro and regression &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.73. Random forests intro and regression &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.76. Random forests for classification" href="random-forests-for-classification.html" />
-    <link rel="prev" title="37.74. Regularized Linear Models" href="../model-selection/regularized-linear-models.html" />
+    <link rel="next" title="37.74. Random forests for classification" href="random-forests-for-classification.html" />
+    <link rel="prev" title="37.72. Regularized Linear Models" href="../model-selection/regularized-linear-models.html" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
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@@ -284,8 +306,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
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   <a class="reference internal" href="../../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
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  </li>
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.75.4. Acknowledgments
+   37.73.4. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1607,81 +1624,81 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#imports">
-   37.75.1. Imports
+   37.73.1. Imports
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loading-the-data">
-   37.75.2. Loading the data
+   37.73.2. Loading the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#tasks-and-roles">
-   37.75.3. Tasks and roles
+   37.73.3. Tasks and roles
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-exploratory-analysis">
-     37.75.3.1. Task 1: Exploratory analysis
+     37.73.3.1. Task 1: Exploratory analysis
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#part-1-the-histogram">
-       37.75.3.1.1. Part 1: The histogram
+       37.73.3.1.1. Part 1: The histogram
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#part-2-the-pairplot">
-       37.75.3.1.2. Part 2: The pairplot
+       37.73.3.1.2. Part 2: The pairplot
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#part-3-check-the-train-data">
-       37.75.3.1.3. Part 3: Check the train data
+       37.73.3.1.3. Part 3: Check the train data
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#part-4-look-the-correlation">
-       37.75.3.1.4. Part 4: Look the correlation
+       37.73.3.1.4. Part 4: Look the correlation
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-data-preprocessing">
-     37.75.3.2. Task 2: Data preprocessing
+     37.73.3.2. Task 2: Data preprocessing
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-training-random-forests-regressor">
-     37.75.3.3. Task 3: Training random forests regressor
+     37.73.3.3. Task 3: Training random forests regressor
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-evaluating-random-forests-regressor">
-     37.75.3.4. Task 4: Evaluating random forests regressor
+     37.73.3.4. Task 4: Evaluating random forests regressor
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-5-improving-random-forests">
-     37.75.3.5. Task 5: Improving random forests
+     37.73.3.5. Task 5: Improving random forests
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-6-feature-importance">
-     37.75.3.6. Task 6: Feature importance
+     37.73.3.6. Task 6: Feature importance
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-7-evaluating-the-model-on-the-test-set">
-     37.75.3.7. Task 7: Evaluating the Model on the Test Set
+     37.73.3.7. Task 7: Evaluating the Model on the Test Set
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.75.4. Acknowledgments
+   37.73.4. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1695,12 +1712,12 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="random-forests-intro-and-regression">
-<h1><span class="section-number">37.75. </span>Random forests intro and regression<a class="headerlink" href="#random-forests-intro-and-regression" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.73. </span>Random forests intro and regression<a class="headerlink" href="#random-forests-intro-and-regression" title="Permalink to this headline">#</a></h1>
 <p>Random Forests are powerful machine learning algorithms used for supervised classification and regression. Random forests works by averaging the predictions of the multiple and randomized decision trees. Decision trees tends to overfit and so by combining multiple decision trees, the effect of overfitting can be minimized.</p>
 <p>Random Forests are type of ensemble models. More about ensembles models in the next notebook.</p>
 <p>Different to other learning algorithms, random forests provide a way to find the importance of each feature and this is implemented in Sklearn.</p>
 <section id="imports">
-<h2><span class="section-number">37.75.1. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.73.1. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -1720,7 +1737,7 @@ <h2><span class="section-number">37.75.1. </span>Imports<a class="headerlink" hr
 </div>
 </section>
 <section id="loading-the-data">
-<h2><span class="section-number">37.75.2. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.73.2. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
 <p>In this regression task with random forests, we will use the Machine CPU (Central Processing Unit) dataset which is available at <a class="reference external" href="https://www.openml.org/t/5492">OpenML</a>.</p>
 <p>If you are reading this, it’s very likely that you know CPU or you have once(or many times) thought about it when you were buying your computer. In this notebook, we will predict the relative performance of the CPU given the following data:</p>
 <ul class="simple">
@@ -1773,9 +1790,9 @@ <h2><span class="section-number">37.75.2. </span>Loading the data<a class="heade
 </div>
 </section>
 <section id="tasks-and-roles">
-<h2><span class="section-number">37.75.3. </span>Tasks and roles<a class="headerlink" href="#tasks-and-roles" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.73.3. </span>Tasks and roles<a class="headerlink" href="#tasks-and-roles" title="Permalink to this headline">#</a></h2>
 <section id="task-1-exploratory-analysis">
-<h3><span class="section-number">37.75.3.1. </span>Task 1: Exploratory analysis<a class="headerlink" href="#task-1-exploratory-analysis" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.73.3.1. </span>Task 1: Exploratory analysis<a class="headerlink" href="#task-1-exploratory-analysis" title="Permalink to this headline">#</a></h3>
 <p>Before doing exploratory analysis, let’s get the training and test data.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1792,7 +1809,7 @@ <h3><span class="section-number">37.75.3.1. </span>Task 1: Exploratory analysis<
 </div>
 </div>
 <section id="part-1-the-histogram">
-<h4><span class="section-number">37.75.3.1.1. </span>Part 1: The histogram<a class="headerlink" href="#part-1-the-histogram" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.73.3.1.1. </span>Part 1: The histogram<a class="headerlink" href="#part-1-the-histogram" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">df_hist</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
@@ -1865,7 +1882,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="part-2-the-pairplot">
-<h4><span class="section-number">37.75.3.1.2. </span>Part 2: The pairplot<a class="headerlink" href="#part-2-the-pairplot" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.73.3.1.2. </span>Part 2: The pairplot<a class="headerlink" href="#part-2-the-pairplot" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">df_pairplot</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
@@ -1938,7 +1955,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="part-3-check-the-train-data">
-<h4><span class="section-number">37.75.3.1.3. </span>Part 3: Check the train data<a class="headerlink" href="#part-3-check-the-train-data" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.73.3.1.3. </span>Part 3: Check the train data<a class="headerlink" href="#part-3-check-the-train-data" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">df_desc</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
@@ -1978,7 +1995,7 @@ <h4><span class="section-number">37.75.3.1.3. </span>Part 3: Check the train dat
 <p>Great! We don’t have any missing values.</p>
 </section>
 <section id="part-4-look-the-correlation">
-<h4><span class="section-number">37.75.3.1.4. </span>Part 4: Look the correlation<a class="headerlink" href="#part-4-look-the-correlation" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.73.3.1.4. </span>Part 4: Look the correlation<a class="headerlink" href="#part-4-look-the-correlation" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">df_corr</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
@@ -2019,7 +2036,7 @@ <h4><span class="section-number">37.75.3.1.4. </span>Part 4: Look the correlatio
 </section>
 </section>
 <section id="task-2-data-preprocessing">
-<h3><span class="section-number">37.75.3.2. </span>Task 2: Data preprocessing<a class="headerlink" href="#task-2-data-preprocessing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.73.3.2. </span>Task 2: Data preprocessing<a class="headerlink" href="#task-2-data-preprocessing" title="Permalink to this headline">#</a></h3>
 <p>It is here that we prepare the data to be in the proper format for the machine learning model.
 Let’s set up a pipeline to scale features but before that, let’s take training input data and labels.</p>
 <div class="cell docutils container">
@@ -2043,7 +2060,7 @@ <h3><span class="section-number">37.75.3.2. </span>Task 2: Data preprocessing<a
 </div>
 </section>
 <section id="task-3-training-random-forests-regressor">
-<h3><span class="section-number">37.75.3.3. </span>Task 3: Training random forests regressor<a class="headerlink" href="#task-3-training-random-forests-regressor" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.73.3.3. </span>Task 3: Training random forests regressor<a class="headerlink" href="#task-3-training-random-forests-regressor" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestRegressor</span>
@@ -2059,7 +2076,7 @@ <h3><span class="section-number">37.75.3.3. </span>Task 3: Training random fores
 </div>
 </section>
 <section id="task-4-evaluating-random-forests-regressor">
-<h3><span class="section-number">37.75.3.4. </span>Task 4: Evaluating random forests regressor<a class="headerlink" href="#task-4-evaluating-random-forests-regressor" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.73.3.4. </span>Task 4: Evaluating random forests regressor<a class="headerlink" href="#task-4-evaluating-random-forests-regressor" title="Permalink to this headline">#</a></h3>
 <p>Let’s first check the root mean squarred errr on the training. It is not advised to evaluate the model on the test data since we haven’t improved it yet. we will make a function to make it easier and to avoid repetitions.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2090,7 +2107,7 @@ <h3><span class="section-number">37.75.3.4. </span>Task 4: Evaluating random for
 </div>
 </section>
 <section id="task-5-improving-random-forests">
-<h3><span class="section-number">37.75.3.5. </span>Task 5: Improving random forests<a class="headerlink" href="#task-5-improving-random-forests" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.73.3.5. </span>Task 5: Improving random forests<a class="headerlink" href="#task-5-improving-random-forests" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">forest_reg</span><span class="o">.</span><span class="n">get_params</span><span class="p">()</span>
@@ -2155,7 +2172,7 @@ <h3><span class="section-number">37.75.3.5. </span>Task 5: Improving random fore
 <p>Surprisingly, by searching model hyperparameters, the model did not improve. Can you guess why? We can observe many things while running Grid Search and reading about the <a class="reference external" href="https://scikit-learn.org/stable/modules/ensemble.html#forests-of-randomized-trees">random forests</a>. If you can’t get good results, set the <code class="docutils literal notranslate"><span class="pre">bootstrap</span></code> to False. It is true by default, and that means that you are training on samples of the training set instead of the whole training set. Try going back to the orginal model and change it to True and note how the prediction changes. Also learn more about the other hyperparameters.</p>
 </section>
 <section id="task-6-feature-importance">
-<h3><span class="section-number">37.75.3.6. </span>Task 6: Feature importance<a class="headerlink" href="#task-6-feature-importance" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.73.3.6. </span>Task 6: Feature importance<a class="headerlink" href="#task-6-feature-importance" title="Permalink to this headline">#</a></h3>
 <p>Different to other machine learning models, random forests can show how each feature contributed to the model generalization. Let’s find it.
 The results are values between 0 and 1. The closer to 1, the good the feature was to the model.</p>
 <div class="cell docutils container">
@@ -2173,7 +2190,7 @@ <h3><span class="section-number">37.75.3.6. </span>Task 6: Feature importance<a
 <p>It makes sense that the model was able to find that out. Main memory (RAM, Read Only Memory) and cache memory (which stores frequently used information thus facilitating faster processing and quick retrieval of information) are the two most factors of the CPU performance and if you are going to buy a new computer, you want to have high RAM and cache memory in order to have a powerful machine that can process/compute and retrieve things faster.</p>
 </section>
 <section id="task-7-evaluating-the-model-on-the-test-set">
-<h3><span class="section-number">37.75.3.7. </span>Task 7: Evaluating the Model on the Test Set<a class="headerlink" href="#task-7-evaluating-the-model-on-the-test-set" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.73.3.7. </span>Task 7: Evaluating the Model on the Test Set<a class="headerlink" href="#task-7-evaluating-the-model-on-the-test-set" title="Permalink to this headline">#</a></h3>
 <p>Let us evaluate the model on the test set. But we need first run the pipeline on the test data. Note that we only transform (not fit_transform).</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2204,7 +2221,7 @@ <h3><span class="section-number">37.75.3.7. </span>Task 7: Evaluating the Model
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.75.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.73.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Nyandwi for creating the open-source course <a class="reference external" href="https://github.com/Nyandwi/machine_learning_complete">Machine Learning complete</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2246,13 +2263,13 @@ <h2><span class="section-number">37.75.4. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.74. </span>Regularized Linear Models</p>
+            <p class="prev-next-title"><span class="section-number">37.72. </span>Regularized Linear Models</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="random-forests-for-classification.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.76. </span>Random forests for classification</p>
+        <p class="prev-next-title"><span class="section-number">37.74. </span>Random forests for classification</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html b/assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html
index 772dfd94c8..60d92a469e 100644
--- a/assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html
+++ b/assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.81. Boosting with tuning &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.79. Boosting with tuning &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.82. Random Forest Classifier with Feature Importance" href="../ensemble-learning/random-forest-classifier-feature-importance.html" />
-    <link rel="prev" title="37.80. Gradient boosting" href="gradient-boosting-assignment.html" />
+    <link rel="next" title="37.80. Random Forest Classifier with Feature Importance" href="../ensemble-learning/random-forest-classifier-feature-importance.html" />
+    <link rel="prev" title="37.78. Gradient boosting" href="gradient-boosting-assignment.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
-  <label for="toctree-checkbox-3">
+  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
+  <label for="toctree-checkbox-4">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -284,8 +306,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
-  <label for="toctree-checkbox-4">
+  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
+  <label for="toctree-checkbox-5">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -311,8 +333,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
   </a>
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       <a class="reference internal nav-link" href="#task-3-scatterplot">
-       37.81.2.5.3. Task 3: Scatterplot
+       37.79.2.5.3. Task 3: Scatterplot
       </a>
      </li>
     </ul>
@@ -1579,69 +1596,69 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-processing">
-   37.81.3. Data Processing
+   37.79.3. Data Processing
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#removing-redundant-features">
-     37.81.3.1. Removing redundant features
+     37.79.3.1. Removing redundant features
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-1-features-with-multicollinearity">
-       37.81.3.1.1. Task 1：Features with multicollinearity
+       37.79.3.1.1. Task 1：Features with multicollinearity
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-2-features-with-a-lot-of-missing-values">
-       37.81.3.1.2. Task 2: Features with a lot of missing values
+       37.79.3.1.2. Task 2: Features with a lot of missing values
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-3-useless-features-in-predicting-saleprice">
-       37.81.3.1.3. Task 3: Useless features in predicting SalePrice
+       37.79.3.1.3. Task 3: Useless features in predicting SalePrice
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-4-removing-features-that-have-mostly-just-1-value">
-       37.81.3.1.4. Task 4: Removing features that have mostly just 1 value
+       37.79.3.1.4. Task 4: Removing features that have mostly just 1 value
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dealing-with-outliers">
-     37.81.3.2. Dealing with outliers
+     37.79.3.2. Dealing with outliers
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#filling-missing-values">
-     37.81.3.3. Filling Missing Values
+     37.79.3.3. Filling Missing Values
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-1-ordinal-features">
-       37.81.3.3.1. Task 1: Ordinal features
+       37.79.3.3.1. Task 1: Ordinal features
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
-       37.81.3.3.2. Task 2: Categorical features
+       37.79.3.3.2. Task 2: Categorical features
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-3-numerical-features">
-       37.81.3.3.3. Task 3: Numerical features
+       37.79.3.3.3. Task 3: Numerical features
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-4-changing-data-type">
-       37.81.3.3.4. Task 4: Changing Data Type
+       37.79.3.3.4. Task 4: Changing Data Type
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-5-mapping-ordinal-features">
-       37.81.3.3.5. Task 5: Mapping ordinal features
+       37.79.3.3.5. Task 5: Mapping ordinal features
       </a>
      </li>
     </ul>
@@ -1650,74 +1667,74 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#feature-engineering">
-   37.81.4. Feature engineering
+   37.79.4. Feature engineering
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-binay-columns">
-     37.81.4.1. Task 1: Binay columns
+     37.79.4.1. Task 1: Binay columns
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-converting-categorical-to-numerical">
-     37.81.4.2. Task 2: Converting categorical to numerical
+     37.79.4.2. Task 2: Converting categorical to numerical
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-saleprice-distribution">
-     37.81.4.3. Task 3: SalePrice distribution
+     37.79.4.3. Task 3: SalePrice distribution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#modeling">
-   37.81.5. Modeling
+   37.79.5. Modeling
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#split-into-train-validation-set">
-     37.81.5.1. Split into train-validation set
+     37.79.5.1. Split into train-validation set
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#scaling-of-data">
-     37.81.5.2. Scaling of Data
+     37.79.5.2. Scaling of Data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#ensemble-algorithms">
-     37.81.5.3. Ensemble Algorithms
+     37.79.5.3. Ensemble Algorithms
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-1-ensembling-models">
-       37.81.5.3.1. Task 1: Ensembling models
+       37.79.5.3.1. Task 1: Ensembling models
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-2-xgboost">
-       37.81.5.3.2. Task 2: XGBoost
+       37.79.5.3.2. Task 2: XGBoost
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-3-lightgbm">
-       37.81.5.3.3. Task 3: LightGBM
+       37.79.5.3.3. Task 3: LightGBM
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-4-catboost">
-       37.81.5.3.4. Task 4: Catboost
+       37.79.5.3.4. Task 4: Catboost
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-5-training-and-evaluation">
-       37.81.5.3.5. Task 5: Training and evaluation
+       37.79.5.3.5. Task 5: Training and evaluation
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-6-blending">
-       37.81.5.3.6. Task 6 Blending
+       37.79.5.3.6. Task 6 Blending
       </a>
      </li>
     </ul>
@@ -1726,7 +1743,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.81.6. Acknowledgments
+   37.79.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1750,59 +1767,59 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   37.81.1. Introduction
+   37.79.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#understanding-data">
-   37.81.2. Understanding data
+   37.79.2. Understanding data
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-imports-and-load-data">
-     37.81.2.1. Task 1: Imports and load data
+     37.79.2.1. Task 1: Imports and load data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-concatenate-the-train-and-test-together">
-     37.81.2.2. Task 2: Concatenate the train and test together
+     37.79.2.2. Task 2: Concatenate the train and test together
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-isolate-numerical-and-categorical-columns">
-     37.81.2.3. Task 3: Isolate numerical and categorical columns
+     37.79.2.3. Task 3: Isolate numerical and categorical columns
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#univariate-analysis">
-     37.81.2.4. Univariate Analysis
+     37.79.2.4. Univariate Analysis
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-1-numeric-features">
-       37.81.2.4.1. Task 1: Numeric features
+       37.79.2.4.1. Task 1: Numeric features
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-2-categorical-features">
-       37.81.2.4.2. Task 2: Categorical features
+       37.79.2.4.2. Task 2: Categorical features
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#bi-variate-analysis">
-     37.81.2.5. Bi-Variate Analysis
+     37.79.2.5. Bi-Variate Analysis
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-1-correlation-matrix">
-       37.81.2.5.1. Task 1: Correlation matrix
+       37.79.2.5.1. Task 1: Correlation matrix
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-2-identifying-relationship-between-numerical-predictor-and-target-saleprice">
-       37.81.2.5.2. Task 2: Identifying relationship between Numerical Predictor and Target (
+       37.79.2.5.2. Task 2: Identifying relationship between Numerical Predictor and Target (
        <em>
         SalePrice
        </em>
@@ -1811,7 +1828,7 @@ <h2> Contents </h2>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-3-scatterplot">
-       37.81.2.5.3. Task 3: Scatterplot
+       37.79.2.5.3. Task 3: Scatterplot
       </a>
      </li>
     </ul>
@@ -1820,69 +1837,69 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-processing">
-   37.81.3. Data Processing
+   37.79.3. Data Processing
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#removing-redundant-features">
-     37.81.3.1. Removing redundant features
+     37.79.3.1. Removing redundant features
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-1-features-with-multicollinearity">
-       37.81.3.1.1. Task 1：Features with multicollinearity
+       37.79.3.1.1. Task 1：Features with multicollinearity
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-2-features-with-a-lot-of-missing-values">
-       37.81.3.1.2. Task 2: Features with a lot of missing values
+       37.79.3.1.2. Task 2: Features with a lot of missing values
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-3-useless-features-in-predicting-saleprice">
-       37.81.3.1.3. Task 3: Useless features in predicting SalePrice
+       37.79.3.1.3. Task 3: Useless features in predicting SalePrice
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-4-removing-features-that-have-mostly-just-1-value">
-       37.81.3.1.4. Task 4: Removing features that have mostly just 1 value
+       37.79.3.1.4. Task 4: Removing features that have mostly just 1 value
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dealing-with-outliers">
-     37.81.3.2. Dealing with outliers
+     37.79.3.2. Dealing with outliers
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#filling-missing-values">
-     37.81.3.3. Filling Missing Values
+     37.79.3.3. Filling Missing Values
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-1-ordinal-features">
-       37.81.3.3.1. Task 1: Ordinal features
+       37.79.3.3.1. Task 1: Ordinal features
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
-       37.81.3.3.2. Task 2: Categorical features
+       37.79.3.3.2. Task 2: Categorical features
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-3-numerical-features">
-       37.81.3.3.3. Task 3: Numerical features
+       37.79.3.3.3. Task 3: Numerical features
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-4-changing-data-type">
-       37.81.3.3.4. Task 4: Changing Data Type
+       37.79.3.3.4. Task 4: Changing Data Type
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-5-mapping-ordinal-features">
-       37.81.3.3.5. Task 5: Mapping ordinal features
+       37.79.3.3.5. Task 5: Mapping ordinal features
       </a>
      </li>
     </ul>
@@ -1891,74 +1908,74 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#feature-engineering">
-   37.81.4. Feature engineering
+   37.79.4. Feature engineering
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-binay-columns">
-     37.81.4.1. Task 1: Binay columns
+     37.79.4.1. Task 1: Binay columns
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-converting-categorical-to-numerical">
-     37.81.4.2. Task 2: Converting categorical to numerical
+     37.79.4.2. Task 2: Converting categorical to numerical
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-saleprice-distribution">
-     37.81.4.3. Task 3: SalePrice distribution
+     37.79.4.3. Task 3: SalePrice distribution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#modeling">
-   37.81.5. Modeling
+   37.79.5. Modeling
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#split-into-train-validation-set">
-     37.81.5.1. Split into train-validation set
+     37.79.5.1. Split into train-validation set
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#scaling-of-data">
-     37.81.5.2. Scaling of Data
+     37.79.5.2. Scaling of Data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#ensemble-algorithms">
-     37.81.5.3. Ensemble Algorithms
+     37.79.5.3. Ensemble Algorithms
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-1-ensembling-models">
-       37.81.5.3.1. Task 1: Ensembling models
+       37.79.5.3.1. Task 1: Ensembling models
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-2-xgboost">
-       37.81.5.3.2. Task 2: XGBoost
+       37.79.5.3.2. Task 2: XGBoost
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-3-lightgbm">
-       37.81.5.3.3. Task 3: LightGBM
+       37.79.5.3.3. Task 3: LightGBM
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-4-catboost">
-       37.81.5.3.4. Task 4: Catboost
+       37.79.5.3.4. Task 4: Catboost
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-5-training-and-evaluation">
-       37.81.5.3.5. Task 5: Training and evaluation
+       37.79.5.3.5. Task 5: Training and evaluation
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-6-blending">
-       37.81.5.3.6. Task 6 Blending
+       37.79.5.3.6. Task 6 Blending
       </a>
      </li>
     </ul>
@@ -1967,7 +1984,7 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.81.6. Acknowledgments
+   37.79.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1981,9 +1998,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="boosting-with-tuning">
-<h1><span class="section-number">37.81. </span>Boosting with tuning<a class="headerlink" href="#boosting-with-tuning" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.79. </span>Boosting with tuning<a class="headerlink" href="#boosting-with-tuning" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">37.81.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.79.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>In this assginment, we will take you through the step by step approach in solving a House Pricing regression problem. This notebook aims to:</p>
 <ol class="simple">
 <li><p>Provide insights on Housing Data</p></li>
@@ -1994,9 +2011,9 @@ <h2><span class="section-number">37.81.1. </span>Introduction<a class="headerlin
 <p>We hope that after reading this notebook, beginners will be more comfortable in tackling any learning problems and able to use the taught techniques to solve any problems from start to end. For non-beginners, hopefully you are able to get something out of it from this assignment and gain new insights and knowledge along the way.</p>
 </section>
 <section id="understanding-data">
-<h2><span class="section-number">37.81.2. </span>Understanding data<a class="headerlink" href="#understanding-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.79.2. </span>Understanding data<a class="headerlink" href="#understanding-data" title="Permalink to this headline">#</a></h2>
 <section id="task-1-imports-and-load-data">
-<h3><span class="section-number">37.81.2.1. </span>Task 1: Imports and load data<a class="headerlink" href="#task-1-imports-and-load-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.79.2.1. </span>Task 1: Imports and load data<a class="headerlink" href="#task-1-imports-and-load-data" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="c1"># linear algebra</span>
@@ -2029,7 +2046,7 @@ <h3><span class="section-number">37.81.2.1. </span>Task 1: Imports and load data
 <p>Just taking a quick glance at the top rows of the dataframe, we can see that there are some columns that are filled with <strong>NAN (Not a Number)</strong>. We will investigate this later on.</p>
 </section>
 <section id="task-2-concatenate-the-train-and-test-together">
-<h3><span class="section-number">37.81.2.2. </span>Task 2: Concatenate the train and test together<a class="headerlink" href="#task-2-concatenate-the-train-and-test-together" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.79.2.2. </span>Task 2: Concatenate the train and test together<a class="headerlink" href="#task-2-concatenate-the-train-and-test-together" title="Permalink to this headline">#</a></h3>
 <p>What we did here is first to concatenate the train and test together for extracting insights into the Housing Price data as a whole. It will also be more convenient for our preprocessing steps later on as we will only have 1 data reference</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2048,7 +2065,7 @@ <h3><span class="section-number">37.81.2.2. </span>Task 2: Concatenate the train
 </div>
 </section>
 <section id="task-3-isolate-numerical-and-categorical-columns">
-<h3><span class="section-number">37.81.2.3. </span>Task 3: Isolate numerical and categorical columns<a class="headerlink" href="#task-3-isolate-numerical-and-categorical-columns" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.79.2.3. </span>Task 3: Isolate numerical and categorical columns<a class="headerlink" href="#task-3-isolate-numerical-and-categorical-columns" title="Permalink to this headline">#</a></h3>
 <p>Lets isolate both the numerical and categorical columns since we will be applying different visualization techniques on them</p>
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@@ -2082,9 +2099,9 @@ <h3><span class="section-number">37.81.2.3. </span>Task 3: Isolate numerical and
 </div>
 </section>
 <section id="univariate-analysis">
-<h3><span class="section-number">37.81.2.4. </span>Univariate Analysis<a class="headerlink" href="#univariate-analysis" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.79.2.4. </span>Univariate Analysis<a class="headerlink" href="#univariate-analysis" title="Permalink to this headline">#</a></h3>
 <section id="task-1-numeric-features">
-<h4><span class="section-number">37.81.2.4.1. </span>Task 1: Numeric features<a class="headerlink" href="#task-1-numeric-features" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.79.2.4.1. </span>Task 1: Numeric features<a class="headerlink" href="#task-1-numeric-features" title="Permalink to this headline">#</a></h4>
 <p>For numerical features, we are always concerned about the <strong>distribution</strong> of these features, including the <strong>statistical characteristics</strong> of these columns e.g mean, median, mode. Hence  we will usually use <strong>Distribution plot</strong> to visualize their data distribution. <strong>Boxplots</strong> are also commonly used to unearth the statistical characteristics of each feature. More often than not, we use it to look for any outliers that we might need to filter out later on during the preprocessing step.</p>
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@@ -2132,7 +2149,7 @@ <h4><span class="section-number">37.81.2.4.1. </span>Task 1: Numeric features<a
 </div>
 </section>
 <section id="task-2-categorical-features">
-<h4><span class="section-number">37.81.2.4.2. </span>Task 2: Categorical features<a class="headerlink" href="#task-2-categorical-features" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.79.2.4.2. </span>Task 2: Categorical features<a class="headerlink" href="#task-2-categorical-features" title="Permalink to this headline">#</a></h4>
 <p>In the case of categorical features, we will often use countplots to visualize the count of each distinct value within each features. We can see that some categorical features like <strong>Utilities, Condition2</strong> consist of mainly just one value, which does not add any useful information. Thus, we will also remove them later on.</p>
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@@ -2150,11 +2167,11 @@ <h4><span class="section-number">37.81.2.4.2. </span>Task 2: Categorical feature
 </section>
 </section>
 <section id="bi-variate-analysis">
-<h3><span class="section-number">37.81.2.5. </span>Bi-Variate Analysis<a class="headerlink" href="#bi-variate-analysis" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.79.2.5. </span>Bi-Variate Analysis<a class="headerlink" href="#bi-variate-analysis" title="Permalink to this headline">#</a></h3>
 <p>Bi-variate analysis looks at 2 different features to identify any possible relationship or distinctive patterns between the 2 features. One of the commonly used technique is through the  <strong>Correlation Matrix</strong>. Correlation matrix is an effective tool to uncover linear relationship (Correlation) between any 2 continuous features. Correlation not only allow us to determine which features are important to Saleprice, but also as a mean to investigate any <strong>multicollinearity</strong> between our independent predictors.<br />
 Multicollinearity happens when 2 or more independent variables are highly correlated with one another. In such situation, it causes precision loss in our regression coefficients, affecting our ability to identify the most important features that are most useful to our model</p>
 <section id="task-1-correlation-matrix">
-<h4><span class="section-number">37.81.2.5.1. </span>Task 1: Correlation matrix<a class="headerlink" href="#task-1-correlation-matrix" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.79.2.5.1. </span>Task 1: Correlation matrix<a class="headerlink" href="#task-1-correlation-matrix" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span><span class="mi">12</span><span class="p">))</span>
@@ -2174,7 +2191,7 @@ <h4><span class="section-number">37.81.2.5.1. </span>Task 1: Correlation matrix<
 <p>From the correlation matrix we have identified the above variables which are highly correlated with each other. This finding will guide us in our preprocessing steps later on as we aim to remove highly correlated features to avoid performance loss in our model</p>
 </section>
 <section id="task-2-identifying-relationship-between-numerical-predictor-and-target-saleprice">
-<h4><span class="section-number">37.81.2.5.2. </span>Task 2: Identifying relationship between Numerical Predictor and Target (<em>SalePrice</em>)<a class="headerlink" href="#task-2-identifying-relationship-between-numerical-predictor-and-target-saleprice" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.79.2.5.2. </span>Task 2: Identifying relationship between Numerical Predictor and Target (<em>SalePrice</em>)<a class="headerlink" href="#task-2-identifying-relationship-between-numerical-predictor-and-target-saleprice" title="Permalink to this headline">#</a></h4>
 <p>Below, we sorted the strength of linear relationship between Saleprice and other features. OverallQual and GrLivArea has the strongest linear relationship with SalePrice. Hence, these 2 features will be important factor in predicting Housing Price</p>
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@@ -2187,7 +2204,7 @@ <h4><span class="section-number">37.81.2.5.2. </span>Task 2: Identifying relatio
 </div>
 </section>
 <section id="task-3-scatterplot">
-<h4><span class="section-number">37.81.2.5.3. </span>Task 3: Scatterplot<a class="headerlink" href="#task-3-scatterplot" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.79.2.5.3. </span>Task 3: Scatterplot<a class="headerlink" href="#task-3-scatterplot" title="Permalink to this headline">#</a></h4>
 <p>Using scatterplot can also help us to identify potential linear relationship between Numerical features. Although scatterplot does not provide quantitative evidence on the strength of linear relationship between our features, it is useful in helping us to visualize any sort of relationship that correlation matrix could not calculate. E.g Quadratic, Exponential relationships.</p>
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@@ -2204,7 +2221,7 @@ <h4><span class="section-number">37.81.2.5.3. </span>Task 3: Scatterplot<a class
 </section>
 </section>
 <section id="data-processing">
-<h2><span class="section-number">37.81.3. </span>Data Processing<a class="headerlink" href="#data-processing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.79.3. </span>Data Processing<a class="headerlink" href="#data-processing" title="Permalink to this headline">#</a></h2>
 <p>Now that we have more or less finished analysing our data and gaining insights through the various analysis and visualization, we will have to leverage on these insights to guide our preprocessing decision, so as to provide a clean and error-free data for our model to train on later on.</p>
 <p>We do not perform visualization and analysis just to create pretty graphs or for the sake of doing it, IT IS VITAL TO OUR PREPROCESSING!</p>
 <p>This section outlines the steps for Data Processing:</p>
@@ -2214,9 +2231,9 @@ <h2><span class="section-number">37.81.3. </span>Data Processing<a class="header
 <li><p>Filling in missing values</p></li>
 </ol>
 <section id="removing-redundant-features">
-<h3><span class="section-number">37.81.3.1. </span>Removing redundant features<a class="headerlink" href="#removing-redundant-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.79.3.1. </span>Removing redundant features<a class="headerlink" href="#removing-redundant-features" title="Permalink to this headline">#</a></h3>
 <section id="task-1-features-with-multicollinearity">
-<h4><span class="section-number">37.81.3.1.1. </span>Task 1：Features with multicollinearity<a class="headerlink" href="#task-1-features-with-multicollinearity" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.79.3.1.1. </span>Task 1：Features with multicollinearity<a class="headerlink" href="#task-1-features-with-multicollinearity" title="Permalink to this headline">#</a></h4>
 <p>From the above correlation matrix, we have pinpointed certain features that are highly correlated</p>
 <ul class="simple">
 <li><p>GarageYrBlt and YearBuilt</p></li>
@@ -2234,7 +2251,7 @@ <h4><span class="section-number">37.81.3.1.1. </span>Task 1：Features with mult
 </div>
 </section>
 <section id="task-2-features-with-a-lot-of-missing-values">
-<h4><span class="section-number">37.81.3.1.2. </span>Task 2: Features with a lot of missing values<a class="headerlink" href="#task-2-features-with-a-lot-of-missing-values" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.79.3.1.2. </span>Task 2: Features with a lot of missing values<a class="headerlink" href="#task-2-features-with-a-lot-of-missing-values" title="Permalink to this headline">#</a></h4>
 <p>Apart from these highly correlated features, we will also remove features that is not very useful in prediction due to many missing values. PoolQC, MiscFeature, Alley has too many missing values to provide any useful information</p>
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@@ -2256,7 +2273,7 @@ <h4><span class="section-number">37.81.3.1.2. </span>Task 2: Features with a lot
 </div>
 </section>
 <section id="task-3-useless-features-in-predicting-saleprice">
-<h4><span class="section-number">37.81.3.1.3. </span>Task 3: Useless features in predicting SalePrice<a class="headerlink" href="#task-3-useless-features-in-predicting-saleprice" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.79.3.1.3. </span>Task 3: Useless features in predicting SalePrice<a class="headerlink" href="#task-3-useless-features-in-predicting-saleprice" title="Permalink to this headline">#</a></h4>
 <p>We will also remove features that does not have any linear relationship with target <em>SalePrice</em>. We can see from the plot below that the MoSold and YrSold does not have any impact on the price of the house sold.</p>
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@@ -2279,7 +2296,7 @@ <h4><span class="section-number">37.81.3.1.3. </span>Task 3: Useless features in
 </div>
 </section>
 <section id="task-4-removing-features-that-have-mostly-just-1-value">
-<h4><span class="section-number">37.81.3.1.4. </span>Task 4: Removing features that have mostly just 1 value<a class="headerlink" href="#task-4-removing-features-that-have-mostly-just-1-value" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.79.3.1.4. </span>Task 4: Removing features that have mostly just 1 value<a class="headerlink" href="#task-4-removing-features-that-have-mostly-just-1-value" title="Permalink to this headline">#</a></h4>
 <p>Earlier during our Univariate analysis, we found that some features mostly consist of just a single value or 0s, which is not useful to us. Therefore, we set a user defined threshold at 96%. If a column has more than 96% of the same value, we will render the features to be useless and remove it, since there isnt much information we can extract from</p>
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@@ -2324,7 +2341,7 @@ <h4><span class="section-number">37.81.3.1.4. </span>Task 4: Removing features t
 </section>
 </section>
 <section id="dealing-with-outliers">
-<h3><span class="section-number">37.81.3.2. </span>Dealing with outliers<a class="headerlink" href="#dealing-with-outliers" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.79.3.2. </span>Dealing with outliers<a class="headerlink" href="#dealing-with-outliers" title="Permalink to this headline">#</a></h3>
 <p>Removing outliers will prevent our models performance from being affected by extreme values.<br />
 From our boxplot earlier, we have pinpointed the following features with extreme outliers:</p>
 <ul class="simple">
@@ -2367,7 +2384,7 @@ <h3><span class="section-number">37.81.3.2. </span>Dealing with outliers<a class
 </div>
 </section>
 <section id="filling-missing-values">
-<h3><span class="section-number">37.81.3.3. </span>Filling Missing Values<a class="headerlink" href="#filling-missing-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.79.3.3. </span>Filling Missing Values<a class="headerlink" href="#filling-missing-values" title="Permalink to this headline">#</a></h3>
 <p>Our machine learning model is unable to deal with missing values, thus we need to deal with them based on our understanding of the features. These missing values are denoted <strong>NAN</strong> as we have seen earlier during our data exploration.</p>
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@@ -2377,7 +2394,7 @@ <h3><span class="section-number">37.81.3.3. </span>Filling Missing Values<a clas
 </div>
 </div>
 <section id="task-1-ordinal-features">
-<h4><span class="section-number">37.81.3.3.1. </span>Task 1: Ordinal features<a class="headerlink" href="#task-1-ordinal-features" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.79.3.3.1. </span>Task 1: Ordinal features<a class="headerlink" href="#task-1-ordinal-features" title="Permalink to this headline">#</a></h4>
 <p>We will replace the ordinal missing values with NA, which will be mapped later on when we encode them into an ordered arrangement</p>
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@@ -2392,7 +2409,7 @@ <h4><span class="section-number">37.81.3.3.1. </span>Task 1: Ordinal features<a
 </div>
 </section>
 <section id="id1">
-<h4><span class="section-number">37.81.3.3.2. </span>Task 2: Categorical features<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.79.3.3.2. </span>Task 2: Categorical features<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
 <p>We will replace the missing value of our categorical features with the most frequent occurrence (mode) of the individual features.</p>
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@@ -2405,7 +2422,7 @@ <h4><span class="section-number">37.81.3.3.2. </span>Task 2: Categorical feature
 </div>
 </section>
 <section id="task-3-numerical-features">
-<h4><span class="section-number">37.81.3.3.3. </span>Task 3: Numerical features<a class="headerlink" href="#task-3-numerical-features" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.79.3.3.3. </span>Task 3: Numerical features<a class="headerlink" href="#task-3-numerical-features" title="Permalink to this headline">#</a></h4>
 <p>For <strong>Numerical Features</strong>, the common approach will be to replace the missing value with the mean of the feature distribution.<br />
 However, certain features like <em>LotFrontage</em> and <em>GarageArea</em> have wide variance in their distribution. Taking mean values across <em>Neighborhoods</em>, we will see that the mean varies alot from just taking the mean value of these individual column, since each neightborhood have different LotFrontage and GarageArea mean value. Hence, i decided to group these features by Neighborhoods to impute the respective mean values.</p>
 <p><strong>Note</strong>: My initial approach was based on the means of both train and test set. This exposed us to the issue of data leakage, where information from the test set is used to compute mean values. The right way to do it will be to impute solely based on the mean of the train data.</p>
@@ -2447,7 +2464,7 @@ <h4><span class="section-number">37.81.3.3.3. </span>Task 3: Numerical features<
 </div>
 </section>
 <section id="task-4-changing-data-type">
-<h4><span class="section-number">37.81.3.3.4. </span>Task 4: Changing Data Type<a class="headerlink" href="#task-4-changing-data-type" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.79.3.3.4. </span>Task 4: Changing Data Type<a class="headerlink" href="#task-4-changing-data-type" title="Permalink to this headline">#</a></h4>
 <p>Since <strong>MSSubClass</strong> is an integer column based on some mapped values in string notation, we change its data type to string value instead</p>
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@@ -2458,7 +2475,7 @@ <h4><span class="section-number">37.81.3.3.4. </span>Task 4: Changing Data Type<
 </div>
 </section>
 <section id="task-5-mapping-ordinal-features">
-<h4><span class="section-number">37.81.3.3.5. </span>Task 5: Mapping ordinal features<a class="headerlink" href="#task-5-mapping-ordinal-features" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.79.3.3.5. </span>Task 5: Mapping ordinal features<a class="headerlink" href="#task-5-mapping-ordinal-features" title="Permalink to this headline">#</a></h4>
 <p>There are some columns which are ordinal by nature, which represents the quality or condition of certain housing features. In this case, we will map the respective strings to a value. The better the quality, the higher the value</p>
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@@ -2491,7 +2508,7 @@ <h4><span class="section-number">37.81.3.3.5. </span>Task 5: Mapping ordinal fea
 </section>
 </section>
 <section id="feature-engineering">
-<h2><span class="section-number">37.81.4. </span>Feature engineering<a class="headerlink" href="#feature-engineering" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.79.4. </span>Feature engineering<a class="headerlink" href="#feature-engineering" title="Permalink to this headline">#</a></h2>
 <p>Feature Engineering is a technique by which we create new features that could potentially aid in predicting our target variable, which in this case, is <em>SalePrice</em>. In this notebook, we will create additional features based on our <strong>Domain Knowledge</strong> of the housing features</p>
 <p>Based on the current feature we have, the first additional featuire we can add would be <strong>TotalLot</strong>, which sums up both the <em>LotFrontage</em> and <em>LotArea</em> to identify the total area of land available as lot. We can also calculate the total number of surface area of the house, <strong>TotalSF</strong> by adding the area from basement and 2nd floor. <strong>TotalBath</strong> can also be used to tell us in total how many bathrooms are there in the house. We can also add all the different types of porches around the house and generalise into a total porch area, <strong>TotalPorch</strong>.</p>
 <ul class="simple">
@@ -2513,7 +2530,7 @@ <h2><span class="section-number">37.81.4. </span>Feature engineering<a class="he
 </div>
 </div>
 <section id="task-1-binay-columns">
-<h3><span class="section-number">37.81.4.1. </span>Task 1: Binay columns<a class="headerlink" href="#task-1-binay-columns" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.79.4.1. </span>Task 1: Binay columns<a class="headerlink" href="#task-1-binay-columns" title="Permalink to this headline">#</a></h3>
 <p>We also include simple feature engineering by creating binary columns for some features that can indicate the <strong>presence(1) / absence(0)</strong> of some features of the house</p>
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@@ -2528,7 +2545,7 @@ <h3><span class="section-number">37.81.4.1. </span>Task 1: Binay columns<a class
 </div>
 </section>
 <section id="task-2-converting-categorical-to-numerical">
-<h3><span class="section-number">37.81.4.2. </span>Task 2: Converting categorical to numerical<a class="headerlink" href="#task-2-converting-categorical-to-numerical" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.79.4.2. </span>Task 2: Converting categorical to numerical<a class="headerlink" href="#task-2-converting-categorical-to-numerical" title="Permalink to this headline">#</a></h3>
 <p>Lastly, because machine learning only learns from data that is numerical in nature, we will convert the remaining categorical columns into one-hot features using the <em>get_dummies()</em> method into numerical columns that is suitable for feeding into our machine learning algorithm.</p>
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@@ -2539,7 +2556,7 @@ <h3><span class="section-number">37.81.4.2. </span>Task 2: Converting categorica
 </div>
 </section>
 <section id="task-3-saleprice-distribution">
-<h3><span class="section-number">37.81.4.3. </span>Task 3: SalePrice distribution<a class="headerlink" href="#task-3-saleprice-distribution" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.79.4.3. </span>Task 3: SalePrice distribution<a class="headerlink" href="#task-3-saleprice-distribution" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
@@ -2571,10 +2588,10 @@ <h3><span class="section-number">37.81.4.3. </span>Task 3: SalePrice distributio
 </section>
 </section>
 <section id="modeling">
-<h2><span class="section-number">37.81.5. </span>Modeling<a class="headerlink" href="#modeling" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.79.5. </span>Modeling<a class="headerlink" href="#modeling" title="Permalink to this headline">#</a></h2>
 <p>This section will consist of scaling the data for better optimization in our training, and also introducing the varieties of ensembling methods that are used in this notebook for predicting the Housing price. We also try out hyperparameter tuning briefly, as i will be dedicating a new notebook that will explain more in details on the process of Hyperparameter Tuning as well as the mathematical aspect of the ensemble algorithms.</p>
 <section id="split-into-train-validation-set">
-<h3><span class="section-number">37.81.5.1. </span>Split into train-validation set<a class="headerlink" href="#split-into-train-validation-set" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.79.5.1. </span>Split into train-validation set<a class="headerlink" href="#split-into-train-validation-set" title="Permalink to this headline">#</a></h3>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">train</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
@@ -2586,7 +2603,7 @@ <h3><span class="section-number">37.81.5.1. </span>Split into train-validation s
 </div>
 </section>
 <section id="scaling-of-data">
-<h3><span class="section-number">37.81.5.2. </span>Scaling of Data<a class="headerlink" href="#scaling-of-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.79.5.2. </span>Scaling of Data<a class="headerlink" href="#scaling-of-data" title="Permalink to this headline">#</a></h3>
 <p><strong>RobustScaler</strong> is a transformation technique that removes the median and scales the data according to the quantile range (defaults to IQR: Interquartile Range). The IQR is the range between the 1st quartile (25th quantile) and the 3rd quartile (75th quantile). It is also robust to outliers, which makes it ideal for data where there are too many outliers that will drastically reduce the number of training data.</p>
 <p>Previously i fitted the RobustScaler on both Train and Test set, and that is a mistake on my side. By fitting the scaler on both train and testset, we exposed ourselves to the problem of <strong>Data Leakage</strong>. Data Leakage is a problem when information from outside the training dataset is used to create the model. If we fit the scaler on both training and test data, our training data characteristics will contain the distribution of our testset. As such, we are unknowningly passing in information about our test data into the final training data for training, which will not give us the opportunity to truly test our model on data it has never seen before.<br />
 <strong>Lesson Learnt</strong>: Fit the scaler just on training data, and then transforming it on both training and test data</p>
@@ -2604,7 +2621,7 @@ <h3><span class="section-number">37.81.5.2. </span>Scaling of Data<a class="head
 </div>
 </section>
 <section id="ensemble-algorithms">
-<h3><span class="section-number">37.81.5.3. </span>Ensemble Algorithms<a class="headerlink" href="#ensemble-algorithms" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.79.5.3. </span>Ensemble Algorithms<a class="headerlink" href="#ensemble-algorithms" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
@@ -2615,7 +2632,7 @@ <h3><span class="section-number">37.81.5.3. </span>Ensemble Algorithms<a class="
 </div>
 </div>
 <section id="task-1-ensembling-models">
-<h4><span class="section-number">37.81.5.3.1. </span>Task 1: Ensembling models<a class="headerlink" href="#task-1-ensembling-models" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.79.5.3.1. </span>Task 1: Ensembling models<a class="headerlink" href="#task-1-ensembling-models" title="Permalink to this headline">#</a></h4>
 <p>Ensembling methods are meta-algorithms which involves combining several machine learning models into one predictive model, aim at decreasing variance(reduce overfitting) and improving bias(improve accuracy).<br />
 The 3 main ensembling methods are <strong>Bagging, Boosting and Stacking</strong>.<br />
 In this notebook, we will focus mainly on <strong>Boosting</strong>, which is what we will be using for our prediction.</p>
@@ -2636,7 +2653,7 @@ <h4><span class="section-number">37.81.5.3.1. </span>Task 1: Ensembling models<a
 </div>
 </section>
 <section id="task-2-xgboost">
-<h4><span class="section-number">37.81.5.3.2. </span>Task 2: XGBoost<a class="headerlink" href="#task-2-xgboost" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.79.5.3.2. </span>Task 2: XGBoost<a class="headerlink" href="#task-2-xgboost" title="Permalink to this headline">#</a></h4>
 <p>Extreme Gradient Boost (XGB) is a boosting algorithm that uses the gradient boosting framework; where gradient descent algorithm is employed to minimize the errors in the sequential model. It improves on the gradient boosting framework with faster execution speed and improved performance.<br />
 <br/>
 <img src="https://miro.medium.com/max/1400/1*QJZ6W-Pck_W7RlIDwUIN9Q.jpeg" width=700/></p>
@@ -2676,7 +2693,7 @@ <h4><span class="section-number">37.81.5.3.2. </span>Task 2: XGBoost<a class="he
 </div>
 </section>
 <section id="task-3-lightgbm">
-<h4><span class="section-number">37.81.5.3.3. </span>Task 3: LightGBM<a class="headerlink" href="#task-3-lightgbm" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.79.5.3.3. </span>Task 3: LightGBM<a class="headerlink" href="#task-3-lightgbm" title="Permalink to this headline">#</a></h4>
 <p>LightBGM is another gradient boosting framework developed by Microsoft that is based on decision tree algorithm, designed to be efficient and distributed. Some of the advantages of implementing LightBGM compared to other boosting frameworks include:</p>
 <ol class="simple">
 <li><p>Faster training speed and higher efficiency (use histogram based algorithm i.e it buckets continuous feature values into discrete bins which fasten the training procedure)</p></li>
@@ -2728,7 +2745,7 @@ <h4><span class="section-number">37.81.5.3.3. </span>Task 3: LightGBM<a class="h
 </div>
 </section>
 <section id="task-4-catboost">
-<h4><span class="section-number">37.81.5.3.4. </span>Task 4: Catboost<a class="headerlink" href="#task-4-catboost" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.79.5.3.4. </span>Task 4: Catboost<a class="headerlink" href="#task-4-catboost" title="Permalink to this headline">#</a></h4>
 <p>Catboost is another alternative gradient boosting framework developed by Yandex. The word “Catboost” is derived from two words; “Category” and “Boosting”. This means that Catboost itself can deal with categorical features which usually has to be converted to numerical encodings in order to feed into traditional gradient boost frameworks and machine learning models.<br />
 The 2 critical features in Catboost algorithm is the use of <strong>ordered boosting</strong> and <strong>innovative algorithm for processing categorical features</strong>, which fight the prediction shift caused by a special kind of target leakage present in all existing implementations of gradient boosting algorithms.<br />
 Find out more <a class="reference external" href="https://towardsdatascience.com/https-medium-com-talperetz24-mastering-the-new-generation-of-gradient-boosting-db04062a7ea2">here</a></p>
@@ -2767,7 +2784,7 @@ <h4><span class="section-number">37.81.5.3.4. </span>Task 4: Catboost<a class="h
 </div>
 </section>
 <section id="task-5-training-and-evaluation">
-<h4><span class="section-number">37.81.5.3.5. </span>Task 5: Training and evaluation<a class="headerlink" href="#task-5-training-and-evaluation" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.79.5.3.5. </span>Task 5: Training and evaluation<a class="headerlink" href="#task-5-training-and-evaluation" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">mean_cross_val</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
@@ -2822,7 +2839,7 @@ <h4><span class="section-number">37.81.5.3.5. </span>Task 5: Training and evalua
 </div>
 </section>
 <section id="task-6-blending">
-<h4><span class="section-number">37.81.5.3.6. </span>Task 6 Blending<a class="headerlink" href="#task-6-blending" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.79.5.3.6. </span>Task 6 Blending<a class="headerlink" href="#task-6-blending" title="Permalink to this headline">#</a></h4>
 <p><strong>Blending</strong> is a technique that give different weightage to different algorithms that will affect their influence of the predictions. Such techniques can help to improve performance since it uses a variety of models as predictors. Special thanks to <a class="reference external" href="https://www.kaggle.com/itslek">&#64;itslek</a> for the implementation of blending. I have randomly chosen the weights for each models in this case, however, you can improve on this by futher tuning the weightage to be given to each model to achieve a better accuracy!</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2846,7 +2863,7 @@ <h4><span class="section-number">37.81.5.3.6. </span>Task 6 Blending<a class="he
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.81.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.79.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/angqx95">aqx</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/angqx95/data-science-workflow-top-2-with-tuning">Data Science Workflow TOP 2% (with Tuning)</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2888,13 +2905,13 @@ <h2><span class="section-number">37.81.6. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.80. </span>Gradient boosting</p>
+            <p class="prev-next-title"><span class="section-number">37.78. </span>Gradient boosting</p>
         </div>
     </a>
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-        <p class="prev-next-title"><span class="section-number">37.82. </span>Random Forest Classifier with Feature Importance</p>
+        <p class="prev-next-title"><span class="section-number">37.80. </span>Random Forest Classifier with Feature Importance</p>
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diff --git a/assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html b/assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html
index 0402517cdd..751c8b70d2 100644
--- a/assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html
+++ b/assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html
@@ -6,7 +6,7 @@
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
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+    <title>37.78. Gradient boosting &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.81. Boosting with tuning" href="boosting-with-tuning.html" />
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+    <link rel="next" title="37.79. Boosting with tuning" href="boosting-with-tuning.html" />
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@@ -238,10 +238,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/working-with-data/data-preparation.html">
@@ -254,8 +276,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
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@@ -286,8 +308,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
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   <a class="reference internal" href="../../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
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   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
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   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
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    15. Getting started with ensemble learning
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@@ -482,8 +504,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
    16. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
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@@ -635,8 +657,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../README.html">
    37. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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@@ -827,308 +849,303 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
+     37.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/loss-function.html">
-     37.48. Loss Function
+     37.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
+     37.50. ML logistic regression - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/ml-logistic-regression-2.html">
-     37.52. ML logistic regression - assignment 2
+     37.51. ML logistic regression - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/ml-neural-network-1.html">
-     37.53. ML neural network - Assignment 1
+     37.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression-tools.html">
-     37.54. Regression tools
+     37.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/managing-data.html">
-     37.55. Managing data
+     37.54. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/exploring-visualizations.html">
-     37.56. Exploring visualizations
+     37.55. Exploring visualizations
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/try-a-different-model.html">
-     37.57. Try a different model
+     37.56. Try a different model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/create-a-regression-model.html">
-     37.58. Create a regression model
+     37.57. Create a regression model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-and-polynomial-regression.html">
-     37.59. Linear and polynomial regression
+     37.58. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/retrying-some-regression.html">
-     37.60. Retrying some regression
+     37.59. Retrying some regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
+     37.60. Pumpkin varieties and color
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.62. Delicious asian and indian cuisines
+     37.61. Delicious asian and indian cuisines
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/explore-classification-methods.html">
-     37.63. Explore classification methods
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
+     37.62. Explore classification methods
     </a>
    </li>
    <li class="toctree-l2">
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-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
+     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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+     37.65. Support Vector Machines (SVM) - Classification
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     <a class="reference internal" href="../kernel-method/decision_trees_for_regression.html">
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+     37.66. Decision Trees - Intro and Regression
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+     37.67. Decision Trees - Classification
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     <a class="reference internal" href="../model-selection/learning-curve-to-identify-overfit-underfit.html">
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+     37.69. Learning Curve To Identify Overfit &amp; Underfit
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-     37.74. Regularized Linear Models
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-     37.77. Beyond random forests: more ensemble models
+     37.75. Beyond random forests: more ensemble models
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-     37.78. Decision trees
+     37.76. Decision trees
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-     37.79. Hyperparameter tuning gradient boosting
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-     37.80. Gradient boosting
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-     37.81. Boosting with tuning
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-     37.82. Random Forest Classifier with Feature Importance
+     37.80. Random Forest Classifier with Feature Importance
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    <li class="toctree-l2">
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-     37.84. Data engineering
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-     37.85. Counterintuitive Challenges in ML Debugging
+     37.83. Counterintuitive Challenges in ML Debugging
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-     37.86. Case Study: Debugging in Classification
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-     37.87. Case Study: Debugging in Regression
+     37.85. Case Study: Debugging in Regression
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-     37.88. Study the solvers
+     37.86. Study the solvers
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-     37.91. Parameter play
+     37.89. Parameter play
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-     37.92. How to choose cnn architecture mnist
+     37.90. How to choose cnn architecture mnist
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     <a class="reference internal" href="../../deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
-     37.94. Sign Language Digits Classification with CNN
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     <a class="reference internal" href="../../deep-learning/cnn/object-recognition-in-images-using-cnn.html">
-     37.96. Object Recognition in Images using CNN
+     37.94. Object Recognition in Images using CNN
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-     37.97. Intro to TensorFlow for Deep Learning
+     37.95. Intro to TensorFlow for Deep Learning
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-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
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     <a class="reference internal" href="../../deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.101. Google Stock Price Prediction RNN
+     37.99. Google Stock Price Prediction RNN
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     <a class="reference internal" href="../../deep-learning/autoencoder/autoencoder.html">
-     37.103. Intro to Autoencoders
+     37.101. Intro to Autoencoders
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     <a class="reference internal" href="../../deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
+     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
    </li>
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     <a class="reference internal" href="../../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
+     37.103. Fun with Variational Autoencoders
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     <a class="reference internal" href="../../deep-learning/time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
+     37.104. Time Series Forecasting Assignment
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     <a class="reference internal" href="../../deep-learning/nn-for-classification-assignment.html">
-     37.108. Neural Networks for Classification with TensorFlow
+     37.106. Neural Networks for Classification with TensorFlow
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     <a class="reference internal" href="../../deep-learning/nn-classify-15-fruits-assignment.html">
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+     37.107. NN Classify 15 Fruits Assignment
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-     37.114. DQN On Foreign Exchange Market
+     37.112. DQN On Foreign Exchange Market
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+     37.113. Art by gan
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     <a class="reference internal" href="../../deep-learning/gan/gan-introduction.html">
-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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     <a class="reference internal" href="../../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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+     37.116. Basic classification: Classify images of clothing
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    38. Slides
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@@ -1511,29 +1528,29 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-simulation">
-   37.80.1. Data simulation
+   37.78.1. Data simulation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#scatter-plot-of-data">
-   37.80.2. Scatter plot of data
+   37.78.2. Scatter plot of data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#gradient-boosting-decisiontrees-in-a-loop">
-   37.80.3. Gradient Boosting (DecisionTrees in a loop)
+   37.78.3. Gradient Boosting (DecisionTrees in a loop)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#maths-behind-this-logic">
-     37.80.3.1. Maths behind this logic
+     37.78.3.1. Maths behind this logic
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.80.4. Acknowledgments
+   37.78.4. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1557,29 +1574,29 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-simulation">
-   37.80.1. Data simulation
+   37.78.1. Data simulation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#scatter-plot-of-data">
-   37.80.2. Scatter plot of data
+   37.78.2. Scatter plot of data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#gradient-boosting-decisiontrees-in-a-loop">
-   37.80.3. Gradient Boosting (DecisionTrees in a loop)
+   37.78.3. Gradient Boosting (DecisionTrees in a loop)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#maths-behind-this-logic">
-     37.80.3.1. Maths behind this logic
+     37.78.3.1. Maths behind this logic
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.80.4. Acknowledgments
+   37.78.4. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1593,7 +1610,7 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="gradient-boosting">
-<h1><span class="section-number">37.80. </span>Gradient boosting<a class="headerlink" href="#gradient-boosting" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.78. </span>Gradient boosting<a class="headerlink" href="#gradient-boosting" title="Permalink to this headline">#</a></h1>
 <p>The logic of gradient boosting is very simple.
 One of the very basic assumption of linear regression is that it’s sum of residuals is 0. Although, tree based models are not based on any of such assumptions, but if we think logic behind these assumptions, we might argue that, if sum of residuals is not 0, then most probably there is some pattern in the residuals of our model which can be leveraged to make our model better.
 So, the intuition behind gradient boosting algorithm is to leverage the pattern in residuals and strenghten a weak prediction model, until our residuals become randomly distributed. Once we reach a stage that residuals do not have any pattern that could be modeled, we can stop modeling residuals. Algorithmically, we are minimizing our loss function, such that test loss reach it’s minima.</p>
@@ -1678,7 +1695,7 @@ <h1><span class="section-number">37.80. </span>Gradient boosting<a class="header
 </div>
 </div>
 <section id="data-simulation">
-<h2><span class="section-number">37.80.1. </span>Data simulation<a class="headerlink" href="#data-simulation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.78.1. </span>Data simulation<a class="headerlink" href="#data-simulation" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">50</span><span class="p">)</span>
@@ -1705,7 +1722,7 @@ <h2><span class="section-number">37.80.1. </span>Data simulation<a class="header
 </div>
 </section>
 <section id="scatter-plot-of-data">
-<h2><span class="section-number">37.80.2. </span>Scatter plot of data<a class="headerlink" href="#scatter-plot-of-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.78.2. </span>Scatter plot of data<a class="headerlink" href="#scatter-plot-of-data" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y</span><span class="o">.</span><span class="n">shape</span>
@@ -1727,7 +1744,7 @@ <h2><span class="section-number">37.80.2. </span>Scatter plot of data<a class="h
 </div>
 </section>
 <section id="gradient-boosting-decisiontrees-in-a-loop">
-<h2><span class="section-number">37.80.3. </span>Gradient Boosting (DecisionTrees in a loop)<a class="headerlink" href="#gradient-boosting-decisiontrees-in-a-loop" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.78.3. </span>Gradient Boosting (DecisionTrees in a loop)<a class="headerlink" href="#gradient-boosting-decisiontrees-in-a-loop" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">std_agg</span><span class="p">(</span><span class="n">cnt</span><span class="p">,</span> <span class="n">s1</span><span class="p">,</span> <span class="n">s2</span><span class="p">):</span> <span class="k">return</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">((</span><span class="n">s2</span><span class="o">/</span><span class="n">cnt</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="n">s1</span><span class="o">/</span><span class="n">cnt</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
@@ -1792,7 +1809,7 @@ <h2><span class="section-number">37.80.3. </span>Gradient Boosting (DecisionTree
 </div>
 <p>Errors are not changing much after <code class="docutils literal notranslate"><span class="pre">20th</span> <span class="pre">iteration</span></code> and pattern in residuals is also removed. Residuals look distributed around the mean</p>
 <section id="maths-behind-this-logic">
-<h3><span class="section-number">37.80.3.1. </span>Maths behind this logic<a class="headerlink" href="#maths-behind-this-logic" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.78.3.1. </span>Maths behind this logic<a class="headerlink" href="#maths-behind-this-logic" title="Permalink to this headline">#</a></h3>
 <p><span class="math notranslate nohighlight">\( Predictions = y_i^p \)</span><br />
 <span class="math notranslate nohighlight">\( Loss = L(y_i, y_i^p) \)</span><br />
 <span class="math notranslate nohighlight">\( Loss = MSE = \sum {(y_i - y_i^p)}^2 \)</span><br />
@@ -1803,7 +1820,7 @@ <h3><span class="section-number">37.80.3.1. </span>Maths behind this logic<a cla
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.80.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.78.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/grroverpr">Prince Grover</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/grroverpr/gradient-boosting-simplified/notebook">gradient-boosting-simplified</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1845,13 +1862,13 @@ <h2><span class="section-number">37.80.4. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.79. </span>Hyperparameter tuning gradient boosting</p>
+            <p class="prev-next-title"><span class="section-number">37.77. </span>Hyperparameter tuning gradient boosting</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="boosting-with-tuning.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.81. </span>Boosting with tuning</p>
+        <p class="prev-next-title"><span class="section-number">37.79. </span>Boosting with tuning</p>
     </div>
     <i class="fas fa-angle-right"></i>
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diff --git a/assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html b/assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html
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-    <title>37.79. Hyperparameter tuning gradient boosting &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.77. Hyperparameter tuning gradient boosting &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.80. Gradient boosting" href="gradient-boosting-assignment.html" />
-    <link rel="prev" title="37.78. Decision trees" href="../ensemble-learning/decision-trees.html" />
+    <link rel="next" title="37.78. Gradient boosting" href="gradient-boosting-assignment.html" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
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   <a class="reference internal nav-link" href="#imports">
-   37.79.4. Imports¶
+   37.77.4. Imports¶
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loading-the-data">
-   37.79.5. Loading the data
+   37.77.5. Loading the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exploratory-data-analysis">
-   37.79.6. Exploratory data analysis
+   37.77.6. Exploratory data analysis
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-city">
-     37.79.6.1. Task 1: City
+     37.77.6.1. Task 1: City
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task2-gender">
-     37.79.6.2. Task2: Gender
+     37.77.6.2. Task2: Gender
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-relevant-experience">
-     37.79.6.3. Task 3：Relevant experience
+     37.77.6.3. Task 3：Relevant experience
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#encoding">
-     37.79.6.4. Encoding:
+     37.77.6.4. Encoding:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-enrolled-university">
-     37.79.6.5. Task 4: Enrolled university
+     37.77.6.5. Task 4: Enrolled university
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-5-education-level">
-     37.79.6.6. Task 5: Education level
+     37.77.6.6. Task 5: Education level
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-6-major-discipline">
-     37.79.6.7. Task 6: Major discipline
+     37.77.6.7. Task 6: Major discipline
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-7-experience">
-     37.79.6.8. Task 7: Experience
+     37.77.6.8. Task 7: Experience
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-8-company-size">
-     37.79.6.9. Task 8:Company size
+     37.77.6.9. Task 8:Company size
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#we-can-see-that-most-candidates-work-in-small-companies-between-50-500">
-       37.79.6.9.1. We can see that most candidates work in small companies (between 50-500)
+       37.77.6.9.1. We can see that most candidates work in small companies (between 50-500)
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-9-company-type">
-     37.79.6.10. Task 9:Company type
+     37.77.6.10. Task 9:Company type
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-10-last-new-job">
-     37.79.6.11. Task 10: Last new job
+     37.77.6.11. Task 10: Last new job
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-11-pre-modeling-steps">
-     37.79.6.12. Task 11: Pre modeling steps
+     37.77.6.12. Task 11: Pre modeling steps
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#models-testing">
-   37.79.7. Models testing
+   37.77.7. Models testing
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#boosting">
-   37.79.8. Boosting:
+   37.77.8. Boosting:
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#gradient-boosting">
-   37.79.9. Gradient Boosting:
+   37.77.9. Gradient Boosting:
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#tree-specific-parameters">
-     37.79.9.1. Tree-specific parameters
+     37.77.9.1. Tree-specific parameters
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#boosting-parameters">
-     37.79.9.2. Boosting parameters:
+     37.77.9.2. Boosting parameters:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#miscellaneous-parameters">
-     37.79.9.3. Miscellaneous parameters
+     37.77.9.3. Miscellaneous parameters
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#hyperparameter-tuning-using-gridsearchcv">
-   37.79.10. Hyperparameter Tuning using GridSearchCV
+   37.77.10. Hyperparameter Tuning using GridSearchCV
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-initialize-model">
-     37.79.10.1. Task 1: Initialize model
+     37.77.10.1. Task 1: Initialize model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-remove-enrollee-id">
-     37.79.10.2. Task 2: Remove enrollee_id
+     37.77.10.2. Task 2: Remove enrollee_id
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-look-for-optimal-number">
-     37.79.10.3. Task 3: Look for optimal number
+     37.77.10.3. Task 3: Look for optimal number
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-find-learning-rate">
-     37.79.10.4. Task 4: Find learning rate
+     37.77.10.4. Task 4: Find learning rate
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-5-find-max-depth-and-min-samples-split">
-     37.79.10.5. Task 5: Find max_depth and min_samples_split
+     37.77.10.5. Task 5: Find max_depth and min_samples_split
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-6-find-min-samples-leaf">
-     37.79.10.6. Task 6: Find min_samples_leaf
+     37.77.10.6. Task 6: Find min_samples_leaf
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-7-auc">
-     37.79.10.7. Task 7: AUC
+     37.77.10.7. Task 7: AUC
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-8-find-max-features">
-     37.79.10.8. Task 8: Find max_features
+     37.77.10.8. Task 8: Find max_features
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-9-find-subsample-value">
-     37.79.10.9. Task 9: Find subsample value
+     37.77.10.9. Task 9: Find subsample value
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-10-improvement">
-     37.79.10.10. Task 10: Improvement
+     37.77.10.10. Task 10: Improvement
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-11-model-fitting">
-     37.79.10.11. Task 11: Model fitting
+     37.77.10.11. Task 11: Model fitting
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-12-prediction">
-     37.79.10.12. Task 12: Prediction
+     37.77.10.12. Task 12: Prediction
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-13-result">
-     37.79.10.13. Task 13: Result
+     37.77.10.13. Task 13: Result
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.79.11. Acknowledgments
+   37.77.11. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1753,227 +1770,227 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#context">
-   37.79.1. Context:
+   37.77.1. Context:
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#features">
-   37.79.2. Features
+   37.77.2. Features
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#plan">
-   37.79.3. Plan:
+   37.77.3. Plan:
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-preprocessing">
-     37.79.3.1. Data preprocessing:
+     37.77.3.1. Data preprocessing:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-visualization">
-     37.79.3.2. Data Visualization
+     37.77.3.2. Data Visualization
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#model-creation">
-     37.79.3.3. Model Creation:
+     37.77.3.3. Model Creation:
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#imports">
-   37.79.4. Imports¶
+   37.77.4. Imports¶
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loading-the-data">
-   37.79.5. Loading the data
+   37.77.5. Loading the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exploratory-data-analysis">
-   37.79.6. Exploratory data analysis
+   37.77.6. Exploratory data analysis
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-city">
-     37.79.6.1. Task 1: City
+     37.77.6.1. Task 1: City
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task2-gender">
-     37.79.6.2. Task2: Gender
+     37.77.6.2. Task2: Gender
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-relevant-experience">
-     37.79.6.3. Task 3：Relevant experience
+     37.77.6.3. Task 3：Relevant experience
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#encoding">
-     37.79.6.4. Encoding:
+     37.77.6.4. Encoding:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-enrolled-university">
-     37.79.6.5. Task 4: Enrolled university
+     37.77.6.5. Task 4: Enrolled university
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-5-education-level">
-     37.79.6.6. Task 5: Education level
+     37.77.6.6. Task 5: Education level
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-6-major-discipline">
-     37.79.6.7. Task 6: Major discipline
+     37.77.6.7. Task 6: Major discipline
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-7-experience">
-     37.79.6.8. Task 7: Experience
+     37.77.6.8. Task 7: Experience
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-8-company-size">
-     37.79.6.9. Task 8:Company size
+     37.77.6.9. Task 8:Company size
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#we-can-see-that-most-candidates-work-in-small-companies-between-50-500">
-       37.79.6.9.1. We can see that most candidates work in small companies (between 50-500)
+       37.77.6.9.1. We can see that most candidates work in small companies (between 50-500)
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-9-company-type">
-     37.79.6.10. Task 9:Company type
+     37.77.6.10. Task 9:Company type
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-10-last-new-job">
-     37.79.6.11. Task 10: Last new job
+     37.77.6.11. Task 10: Last new job
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-11-pre-modeling-steps">
-     37.79.6.12. Task 11: Pre modeling steps
+     37.77.6.12. Task 11: Pre modeling steps
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#models-testing">
-   37.79.7. Models testing
+   37.77.7. Models testing
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#boosting">
-   37.79.8. Boosting:
+   37.77.8. Boosting:
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#gradient-boosting">
-   37.79.9. Gradient Boosting:
+   37.77.9. Gradient Boosting:
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#tree-specific-parameters">
-     37.79.9.1. Tree-specific parameters
+     37.77.9.1. Tree-specific parameters
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#boosting-parameters">
-     37.79.9.2. Boosting parameters:
+     37.77.9.2. Boosting parameters:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#miscellaneous-parameters">
-     37.79.9.3. Miscellaneous parameters
+     37.77.9.3. Miscellaneous parameters
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#hyperparameter-tuning-using-gridsearchcv">
-   37.79.10. Hyperparameter Tuning using GridSearchCV
+   37.77.10. Hyperparameter Tuning using GridSearchCV
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-initialize-model">
-     37.79.10.1. Task 1: Initialize model
+     37.77.10.1. Task 1: Initialize model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-remove-enrollee-id">
-     37.79.10.2. Task 2: Remove enrollee_id
+     37.77.10.2. Task 2: Remove enrollee_id
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-look-for-optimal-number">
-     37.79.10.3. Task 3: Look for optimal number
+     37.77.10.3. Task 3: Look for optimal number
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-find-learning-rate">
-     37.79.10.4. Task 4: Find learning rate
+     37.77.10.4. Task 4: Find learning rate
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-5-find-max-depth-and-min-samples-split">
-     37.79.10.5. Task 5: Find max_depth and min_samples_split
+     37.77.10.5. Task 5: Find max_depth and min_samples_split
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-6-find-min-samples-leaf">
-     37.79.10.6. Task 6: Find min_samples_leaf
+     37.77.10.6. Task 6: Find min_samples_leaf
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-7-auc">
-     37.79.10.7. Task 7: AUC
+     37.77.10.7. Task 7: AUC
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-8-find-max-features">
-     37.79.10.8. Task 8: Find max_features
+     37.77.10.8. Task 8: Find max_features
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-9-find-subsample-value">
-     37.79.10.9. Task 9: Find subsample value
+     37.77.10.9. Task 9: Find subsample value
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-10-improvement">
-     37.79.10.10. Task 10: Improvement
+     37.77.10.10. Task 10: Improvement
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-11-model-fitting">
-     37.79.10.11. Task 11: Model fitting
+     37.77.10.11. Task 11: Model fitting
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-12-prediction">
-     37.79.10.12. Task 12: Prediction
+     37.77.10.12. Task 12: Prediction
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-13-result">
-     37.79.10.13. Task 13: Result
+     37.77.10.13. Task 13: Result
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.79.11. Acknowledgments
+   37.77.11. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1987,13 +2004,13 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="hyperparameter-tuning-gradient-boosting">
-<h1><span class="section-number">37.79. </span>Hyperparameter tuning gradient boosting<a class="headerlink" href="#hyperparameter-tuning-gradient-boosting" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.77. </span>Hyperparameter tuning gradient boosting<a class="headerlink" href="#hyperparameter-tuning-gradient-boosting" title="Permalink to this headline">#</a></h1>
 <section id="context">
-<h2><span class="section-number">37.79.1. </span>Context:<a class="headerlink" href="#context" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.77.1. </span>Context:<a class="headerlink" href="#context" title="Permalink to this headline">#</a></h2>
 <p>A company which is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which are conducted by the company. Many people sign up for their training. Company wants to know which of these candidates really want to work for the company after training, because it helps reducing the cost and time as well as increasing the quality of training and the planning of the courses. Information related to demographics, education, experience are in hands from candidates signup and enrollment.</p>
 </section>
 <section id="features">
-<h2><span class="section-number">37.79.2. </span>Features<a class="headerlink" href="#features" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.77.2. </span>Features<a class="headerlink" href="#features" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>enrollee_id : Unique ID for candidate</p></li>
 <li><p>city: City code</p></li>
@@ -2012,20 +2029,20 @@ <h2><span class="section-number">37.79.2. </span>Features<a class="headerlink" h
 </ul>
 </section>
 <section id="plan">
-<h2><span class="section-number">37.79.3. </span>Plan:<a class="headerlink" href="#plan" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.77.3. </span>Plan:<a class="headerlink" href="#plan" title="Permalink to this headline">#</a></h2>
 <p>We will treat each column in two phases:</p>
 <section id="data-preprocessing">
-<h3><span class="section-number">37.79.3.1. </span>Data preprocessing:<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.3.1. </span>Data preprocessing:<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Imputing missing values.</p></li>
 <li><p>Handling categorical data.</p></li>
 </ul>
 </section>
 <section id="data-visualization">
-<h3><span class="section-number">37.79.3.2. </span>Data Visualization<a class="headerlink" href="#data-visualization" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.3.2. </span>Data Visualization<a class="headerlink" href="#data-visualization" title="Permalink to this headline">#</a></h3>
 </section>
 <section id="model-creation">
-<h3><span class="section-number">37.79.3.3. </span>Model Creation:<a class="headerlink" href="#model-creation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.3.3. </span>Model Creation:<a class="headerlink" href="#model-creation" title="Permalink to this headline">#</a></h3>
 <p>Then we will test several model and choose the one with the best performance:</p>
 <ul class="simple">
 <li><p>Models: Logistic Regression-SVM-Gradient Boosting-KNN-RandomForest-XGBoost classifier.</p></li>
@@ -2035,7 +2052,7 @@ <h3><span class="section-number">37.79.3.3. </span>Model Creation:<a class="head
 </section>
 </section>
 <section id="imports">
-<h2><span class="section-number">37.79.4. </span>Imports¶<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.77.4. </span>Imports¶<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <p>Lets get started, first lets import our typical libraries:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2058,7 +2075,7 @@ <h2><span class="section-number">37.79.4. </span>Imports¶<a class="headerlink"
 </div>
 </section>
 <section id="loading-the-data">
-<h2><span class="section-number">37.79.5. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.77.5. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
 <p>Let’s import our train and test data.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2097,9 +2114,9 @@ <h2><span class="section-number">37.79.5. </span>Loading the data<a class="heade
 </div>
 </section>
 <section id="exploratory-data-analysis">
-<h2><span class="section-number">37.79.6. </span>Exploratory data analysis<a class="headerlink" href="#exploratory-data-analysis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.77.6. </span>Exploratory data analysis<a class="headerlink" href="#exploratory-data-analysis" title="Permalink to this headline">#</a></h2>
 <section id="task-1-city">
-<h3><span class="section-number">37.79.6.1. </span>Task 1: City<a class="headerlink" href="#task-1-city" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.6.1. </span>Task 1: City<a class="headerlink" href="#task-1-city" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">[</span><span class="s2">&quot;city&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
@@ -2192,7 +2209,7 @@ <h3><span class="section-number">37.79.6.1. </span>Task 1: City<a class="headerl
 </div>
 </section>
 <section id="task2-gender">
-<h3><span class="section-number">37.79.6.2. </span>Task2: Gender<a class="headerlink" href="#task2-gender" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.6.2. </span>Task2: Gender<a class="headerlink" href="#task2-gender" title="Permalink to this headline">#</a></h3>
 <p>It is obvious we have to encode the caregorical data and take care of missing data, there are a lot of ways to handle gender missing values such as replacing them with most common gender in the dataset, deleting those rows…etc.
 But I prefer to fill the gender missing values with “Other” since we may have candidates identify as non-binary.
 First lets take a look at our gender column:</p>
@@ -2260,7 +2277,7 @@ <h3><span class="section-number">37.79.6.2. </span>Task2: Gender<a class="header
 </div>
 </section>
 <section id="task-3-relevant-experience">
-<h3><span class="section-number">37.79.6.3. </span>Task 3：Relevant experience<a class="headerlink" href="#task-3-relevant-experience" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.6.3. </span>Task 3：Relevant experience<a class="headerlink" href="#task-3-relevant-experience" title="Permalink to this headline">#</a></h3>
 <p>Before making any decision we have to look at the values of this column:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2280,7 +2297,7 @@ <h3><span class="section-number">37.79.6.3. </span>Task 3：Relevant experience<
 </div>
 </section>
 <section id="encoding">
-<h3><span class="section-number">37.79.6.4. </span>Encoding:<a class="headerlink" href="#encoding" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.6.4. </span>Encoding:<a class="headerlink" href="#encoding" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">[</span><span class="s2">&quot;relevent_experience&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">train</span><span class="p">[</span><span class="s2">&quot;relevent_experience&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">({</span><span class="s2">&quot;Has relevent experience&quot;</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;No relevent experience&quot;</span><span class="p">:</span><span class="mi">0</span><span class="p">})</span>
@@ -2290,7 +2307,7 @@ <h3><span class="section-number">37.79.6.4. </span>Encoding:<a class="headerlink
 </div>
 </section>
 <section id="task-4-enrolled-university">
-<h3><span class="section-number">37.79.6.5. </span>Task 4: Enrolled university<a class="headerlink" href="#task-4-enrolled-university" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.6.5. </span>Task 4: Enrolled university<a class="headerlink" href="#task-4-enrolled-university" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">[</span><span class="s1">&#39;enrolled_university&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
@@ -2336,7 +2353,7 @@ <h3><span class="section-number">37.79.6.5. </span>Task 4: Enrolled university<a
 </div>
 </section>
 <section id="task-5-education-level">
-<h3><span class="section-number">37.79.6.6. </span>Task 5: Education level<a class="headerlink" href="#task-5-education-level" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.6.6. </span>Task 5: Education level<a class="headerlink" href="#task-5-education-level" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">[</span><span class="s1">&#39;education_level&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
@@ -2378,7 +2395,7 @@ <h3><span class="section-number">37.79.6.6. </span>Task 5: Education level<a cla
 </div>
 </section>
 <section id="task-6-major-discipline">
-<h3><span class="section-number">37.79.6.7. </span>Task 6: Major discipline<a class="headerlink" href="#task-6-major-discipline" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.6.7. </span>Task 6: Major discipline<a class="headerlink" href="#task-6-major-discipline" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">[</span><span class="s1">&#39;major_discipline&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
@@ -2418,7 +2435,7 @@ <h3><span class="section-number">37.79.6.7. </span>Task 6: Major discipline<a cl
 </div>
 </section>
 <section id="task-7-experience">
-<h3><span class="section-number">37.79.6.8. </span>Task 7: Experience<a class="headerlink" href="#task-7-experience" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.6.8. </span>Task 7: Experience<a class="headerlink" href="#task-7-experience" title="Permalink to this headline">#</a></h3>
 <p>The experience variable is an object indicating the minimum or maximum years of experience a candidate had, so deleting the operators won’t make a big difference.</p>
 <p>First we have to convert the column to string.</p>
 <div class="cell docutils container">
@@ -2462,7 +2479,7 @@ <h3><span class="section-number">37.79.6.8. </span>Task 7: Experience<a class="h
 </div>
 </section>
 <section id="task-8-company-size">
-<h3><span class="section-number">37.79.6.9. </span>Task 8:Company size<a class="headerlink" href="#task-8-company-size" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.6.9. </span>Task 8:Company size<a class="headerlink" href="#task-8-company-size" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">[</span><span class="s1">&#39;company_size&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
@@ -2480,7 +2497,7 @@ <h3><span class="section-number">37.79.6.9. </span>Task 8:Company size<a class="
 </div>
 </div>
 <section id="we-can-see-that-most-candidates-work-in-small-companies-between-50-500">
-<h4><span class="section-number">37.79.6.9.1. </span>We can see that most candidates work in small companies (between 50-500)<a class="headerlink" href="#we-can-see-that-most-candidates-work-in-small-companies-between-50-500" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.77.6.9.1. </span>We can see that most candidates work in small companies (between 50-500)<a class="headerlink" href="#we-can-see-that-most-candidates-work-in-small-companies-between-50-500" title="Permalink to this headline">#</a></h4>
 <p>Identify each interval with a number:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2507,7 +2524,7 @@ <h4><span class="section-number">37.79.6.9.1. </span>We can see that most candid
 </section>
 </section>
 <section id="task-9-company-type">
-<h3><span class="section-number">37.79.6.10. </span>Task 9:Company type<a class="headerlink" href="#task-9-company-type" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.6.10. </span>Task 9:Company type<a class="headerlink" href="#task-9-company-type" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">countplot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="s1">&#39;company_type&#39;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">)</span>
@@ -2544,7 +2561,7 @@ <h3><span class="section-number">37.79.6.10. </span>Task 9:Company type<a class=
 </div>
 </section>
 <section id="task-10-last-new-job">
-<h3><span class="section-number">37.79.6.11. </span>Task 10: Last new job<a class="headerlink" href="#task-10-last-new-job" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.6.11. </span>Task 10: Last new job<a class="headerlink" href="#task-10-last-new-job" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">[</span><span class="s1">&#39;last_new_job&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
@@ -2591,7 +2608,7 @@ <h3><span class="section-number">37.79.6.11. </span>Task 10: Last new job<a clas
 </div>
 </section>
 <section id="task-11-pre-modeling-steps">
-<h3><span class="section-number">37.79.6.12. </span>Task 11: Pre modeling steps<a class="headerlink" href="#task-11-pre-modeling-steps" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.6.12. </span>Task 11: Pre modeling steps<a class="headerlink" href="#task-11-pre-modeling-steps" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pred</span> <span class="o">=</span> <span class="n">train</span><span class="p">[</span><span class="s1">&#39;target&#39;</span><span class="p">]</span>
@@ -2616,7 +2633,7 @@ <h3><span class="section-number">37.79.6.12. </span>Task 11: Pre modeling steps<
 </section>
 </section>
 <section id="models-testing">
-<h2><span class="section-number">37.79.7. </span>Models testing<a class="headerlink" href="#models-testing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.77.7. </span>Models testing<a class="headerlink" href="#models-testing" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
@@ -2672,7 +2689,7 @@ <h2><span class="section-number">37.79.7. </span>Models testing<a class="headerl
 <p>Obviously,Gradient Boosting gives the best result.</p>
 </section>
 <section id="boosting">
-<h2><span class="section-number">37.79.8. </span>Boosting:<a class="headerlink" href="#boosting" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.77.8. </span>Boosting:<a class="headerlink" href="#boosting" title="Permalink to this headline">#</a></h2>
 <p><strong>Ensemble learning techniques:</strong> They combine the predictions of multiple machine learning models.</p>
 <p><strong>Boosting:</strong> Boosting algorithms play a crucial role in dealing with bias variance trade-off.  Unlike bagging algorithms, which only controls for high variance in a model, boosting controls both the aspects (bias &amp; variance), and is considered to be more effective.
 Boosting is an ensemble learning technique.</p>
@@ -2682,7 +2699,7 @@ <h2><span class="section-number">37.79.8. </span>Boosting:<a class="headerlink"
 <p><a href="https://ibb.co/PgstW8k"><img src="https://i.ibb.co/WfNsz1C/boosting.png" alt="boosting" border="0"></a></p>
 </section>
 <section id="gradient-boosting">
-<h2><span class="section-number">37.79.9. </span>Gradient Boosting:<a class="headerlink" href="#gradient-boosting" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.77.9. </span>Gradient Boosting:<a class="headerlink" href="#gradient-boosting" title="Permalink to this headline">#</a></h2>
 <p><img alt="image.png" src="../../../_images/boosting_visual_3.png" /></p>
 <p>The overall parameters of gradient boosting can be devided into three categories:</p>
 <ul class="simple">
@@ -2691,10 +2708,10 @@ <h2><span class="section-number">37.79.9. </span>Gradient Boosting:<a class="hea
 <li><p>Miscellaneous Parameters: Other parameters for overall functioning.</p></li>
 </ul>
 <section id="tree-specific-parameters">
-<h3><span class="section-number">37.79.9.1. </span>Tree-specific parameters<a class="headerlink" href="#tree-specific-parameters" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.9.1. </span>Tree-specific parameters<a class="headerlink" href="#tree-specific-parameters" title="Permalink to this headline">#</a></h3>
 </section>
 <section id="boosting-parameters">
-<h3><span class="section-number">37.79.9.2. </span>Boosting parameters:<a class="headerlink" href="#boosting-parameters" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.9.2. </span>Boosting parameters:<a class="headerlink" href="#boosting-parameters" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>learning_rate</p></li>
 <li><p>n_estimators</p></li>
@@ -2702,7 +2719,7 @@ <h3><span class="section-number">37.79.9.2. </span>Boosting parameters:<a class=
 </ul>
 </section>
 <section id="miscellaneous-parameters">
-<h3><span class="section-number">37.79.9.3. </span>Miscellaneous parameters<a class="headerlink" href="#miscellaneous-parameters" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.9.3. </span>Miscellaneous parameters<a class="headerlink" href="#miscellaneous-parameters" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>loss</p></li>
 <li><p>init</p></li>
@@ -2714,7 +2731,7 @@ <h3><span class="section-number">37.79.9.3. </span>Miscellaneous parameters<a cl
 </section>
 </section>
 <section id="hyperparameter-tuning-using-gridsearchcv">
-<h2><span class="section-number">37.79.10. </span>Hyperparameter Tuning using GridSearchCV<a class="headerlink" href="#hyperparameter-tuning-using-gridsearchcv" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.77.10. </span>Hyperparameter Tuning using GridSearchCV<a class="headerlink" href="#hyperparameter-tuning-using-gridsearchcv" title="Permalink to this headline">#</a></h2>
 <p><strong>GradientBoostingClassifier</strong>(*, loss=‘deviance’, learning_rate=0.1, n_estimators=100, subsample=1.0, criterion=‘friedman_mse’, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_decrease=0.0, min_impurity_split=None, init=None, random_state=None, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, validation_fraction=0.1, n_iter_no_change=None, tol=0.0001, ccp_alpha=0.0)</p>
 <p>Grid search is best described as exhuastive guess and check. We have a problem: find the hyperparameters that result in the best cross validation score, and a set of values to try in the hyperparameter grid - the domain. The grid search method for finding the answer is to try all combinations of values in the domain and hope that the best combination is in the grid (in reality, we will never know if we found the best settings unless we have an infinite hyperparameter grid which would then require an infinite amount of time to run).</p>
 <p>Grid search suffers from one limiting problem: it is extremely computationally expensive because we have to perform cross validation with every single combination of hyperparameters in the grid!</p>
@@ -2730,7 +2747,7 @@ <h2><span class="section-number">37.79.10. </span>Hyperparameter Tuning using Gr
 <li><p>It is ‘uninformed’. Results of one model don’t help creating the next model.</p></li>
 </ul>
 <section id="task-1-initialize-model">
-<h3><span class="section-number">37.79.10.1. </span>Task 1: Initialize model<a class="headerlink" href="#task-1-initialize-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.10.1. </span>Task 1: Initialize model<a class="headerlink" href="#task-1-initialize-model" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">pred</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
@@ -2755,7 +2772,7 @@ <h3><span class="section-number">37.79.10.1. </span>Task 1: Initialize model<a c
 </div>
 </section>
 <section id="task-2-remove-enrollee-id">
-<h3><span class="section-number">37.79.10.2. </span>Task 2: Remove enrollee_id<a class="headerlink" href="#task-2-remove-enrollee-id" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.10.2. </span>Task 2: Remove enrollee_id<a class="headerlink" href="#task-2-remove-enrollee-id" title="Permalink to this headline">#</a></h3>
 <p>We shouldn’t use the enrollee_id since it is unique for each candidate.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2773,7 +2790,7 @@ <h3><span class="section-number">37.79.10.2. </span>Task 2: Remove enrollee_id<a
 </div>
 </section>
 <section id="task-3-look-for-optimal-number">
-<h3><span class="section-number">37.79.10.3. </span>Task 3: Look for optimal number<a class="headerlink" href="#task-3-look-for-optimal-number" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.10.3. </span>Task 3: Look for optimal number<a class="headerlink" href="#task-3-look-for-optimal-number" title="Permalink to this headline">#</a></h3>
 <p>First we have to look for an optimal number of estimators:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2809,7 +2826,7 @@ <h3><span class="section-number">37.79.10.3. </span>Task 3: Look for optimal num
 </div>
 </section>
 <section id="task-4-find-learning-rate">
-<h3><span class="section-number">37.79.10.4. </span>Task 4: Find learning rate<a class="headerlink" href="#task-4-find-learning-rate" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.10.4. </span>Task 4: Find learning rate<a class="headerlink" href="#task-4-find-learning-rate" title="Permalink to this headline">#</a></h3>
 <p>As you can see that here we got 90 as the optimal estimators for 0.1 learning rate, it is close to 100 so we will increase the learning rate to 0.2. (I tried working with number of estimators as 90 but when I increased the learning rate I got slightly better results, I won’t include the whole process because it will take a lot of time to run)</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2830,7 +2847,7 @@ <h3><span class="section-number">37.79.10.4. </span>Task 4: Find learning rate<a
 </div>
 </section>
 <section id="task-5-find-max-depth-and-min-samples-split">
-<h3><span class="section-number">37.79.10.5. </span>Task 5: Find max_depth and min_samples_split<a class="headerlink" href="#task-5-find-max-depth-and-min-samples-split" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.10.5. </span>Task 5: Find max_depth and min_samples_split<a class="headerlink" href="#task-5-find-max-depth-and-min-samples-split" title="Permalink to this headline">#</a></h3>
 <p>The next step is to find the max_depth and min_samples_split.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2846,7 +2863,7 @@ <h3><span class="section-number">37.79.10.5. </span>Task 5: Find max_depth and m
 <p>So we got a maximum depth of 7 and minimum samples split is 700.</p>
 </section>
 <section id="task-6-find-min-samples-leaf">
-<h3><span class="section-number">37.79.10.6. </span>Task 6: Find min_samples_leaf<a class="headerlink" href="#task-6-find-min-samples-leaf" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.10.6. </span>Task 6: Find min_samples_leaf<a class="headerlink" href="#task-6-find-min-samples-leaf" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">param_test3</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;min_samples_split&#39;</span><span class="p">:</span><span class="nb">range</span><span class="p">(</span><span class="mi">400</span><span class="p">,</span><span class="mi">1200</span><span class="p">,</span><span class="mi">100</span><span class="p">),</span> <span class="s1">&#39;min_samples_leaf&#39;</span><span class="p">:</span><span class="nb">range</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span><span class="mi">71</span><span class="p">,</span><span class="mi">10</span><span class="p">)}</span>
@@ -2860,7 +2877,7 @@ <h3><span class="section-number">37.79.10.6. </span>Task 6: Find min_samples_lea
 </div>
 </section>
 <section id="task-7-auc">
-<h3><span class="section-number">37.79.10.7. </span>Task 7: AUC<a class="headerlink" href="#task-7-auc" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.10.7. </span>Task 7: AUC<a class="headerlink" href="#task-7-auc" title="Permalink to this headline">#</a></h3>
 <p>Lets write a function that returns the accuracy, auc score and the importance of each variable:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2909,7 +2926,7 @@ <h3><span class="section-number">37.79.10.7. </span>Task 7: AUC<a class="headerl
 </div>
 </section>
 <section id="task-8-find-max-features">
-<h3><span class="section-number">37.79.10.8. </span>Task 8: Find max_features<a class="headerlink" href="#task-8-find-max-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.10.8. </span>Task 8: Find max_features<a class="headerlink" href="#task-8-find-max-features" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">param_test4</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;max_features&#39;</span><span class="p">:</span><span class="nb">range</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">20</span><span class="p">,</span><span class="mi">2</span><span class="p">)}</span>
@@ -2930,7 +2947,7 @@ <h3><span class="section-number">37.79.10.8. </span>Task 8: Find max_features<a
 </div>
 </section>
 <section id="task-9-find-subsample-value">
-<h3><span class="section-number">37.79.10.9. </span>Task 9: Find subsample value<a class="headerlink" href="#task-9-find-subsample-value" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.10.9. </span>Task 9: Find subsample value<a class="headerlink" href="#task-9-find-subsample-value" title="Permalink to this headline">#</a></h3>
 <p>The next step would be try different subsample values.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2946,7 +2963,7 @@ <h3><span class="section-number">37.79.10.9. </span>Task 9: Find subsample value
 <p>We got 0.85 as the optimum subsample value.</p>
 </section>
 <section id="task-10-improvement">
-<h3><span class="section-number">37.79.10.10. </span>Task 10: Improvement<a class="headerlink" href="#task-10-improvement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.10.10. </span>Task 10: Improvement<a class="headerlink" href="#task-10-improvement" title="Permalink to this headline">#</a></h3>
 <p>Now, we need to lower the learning rate and increase the number of estimators to see if we get better results.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2976,7 +2993,7 @@ <h3><span class="section-number">37.79.10.10. </span>Task 10: Improvement<a clas
 <p>increasing the number of estimators got us a slightly better model.</p>
 </section>
 <section id="task-11-model-fitting">
-<h3><span class="section-number">37.79.10.11. </span>Task 11: Model fitting<a class="headerlink" href="#task-11-model-fitting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.10.11. </span>Task 11: Model fitting<a class="headerlink" href="#task-11-model-fitting" title="Permalink to this headline">#</a></h3>
 <p>Lets fit the model.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2994,7 +3011,7 @@ <h3><span class="section-number">37.79.10.11. </span>Task 11: Model fitting<a cl
 </div>
 </section>
 <section id="task-12-prediction">
-<h3><span class="section-number">37.79.10.12. </span>Task 12: Prediction<a class="headerlink" href="#task-12-prediction" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.10.12. </span>Task 12: Prediction<a class="headerlink" href="#task-12-prediction" title="Permalink to this headline">#</a></h3>
 <p>Make predictions on test and training set:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3027,7 +3044,7 @@ <h3><span class="section-number">37.79.10.12. </span>Task 12: Prediction<a class
 </div>
 </section>
 <section id="task-13-result">
-<h3><span class="section-number">37.79.10.13. </span>Task 13: Result<a class="headerlink" href="#task-13-result" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.77.10.13. </span>Task 13: Result<a class="headerlink" href="#task-13-result" title="Permalink to this headline">#</a></h3>
 <p>Save results to a dataframe</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3046,7 +3063,7 @@ <h3><span class="section-number">37.79.10.13. </span>Task 13: Result<a class="he
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.79.11. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.77.11. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/elyousfiomar">Omar El Yousfi</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/elyousfiomar/hyperparameter-tuning-gradient-boosting/notebook#Relevant-experience:">Hyperparameter tuning - Gradient boosting</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -3088,13 +3105,13 @@ <h2><span class="section-number">37.79.11. </span>Acknowledgments<a class="heade
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.78. </span>Decision trees</p>
+            <p class="prev-next-title"><span class="section-number">37.76. </span>Decision trees</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="gradient-boosting-assignment.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.80. </span>Gradient boosting</p>
+        <p class="prev-next-title"><span class="section-number">37.78. </span>Gradient boosting</p>
     </div>
     <i class="fas fa-angle-right"></i>
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diff --git a/assignments/ml-advanced/kernel-method/decision_trees_for_classification.html b/assignments/ml-advanced/kernel-method/decision_trees_for_classification.html
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     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.70. Model selection assignment 1" href="../model-selection/model-selection-assignment-1.html" />
-    <link rel="prev" title="37.68. Decision Trees - Intro and Regression" href="decision_trees_for_regression.html" />
+    <link rel="next" title="37.68. Model selection assignment 1" href="../model-selection/model-selection-assignment-1.html" />
+    <link rel="prev" title="37.66. Decision Trees - Intro and Regression" href="decision_trees_for_regression.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
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-  <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
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+  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
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   <a class="reference internal" href="../../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
-  <label for="toctree-checkbox-4">
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   <a class="reference internal" href="../../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
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-  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
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   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
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   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
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   <a class="reference internal" href="../../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
    14. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
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   <a class="reference internal" href="../../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
    15. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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   <a class="reference internal" href="../../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
    16. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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@@ -633,8 +655,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../README.html">
    37. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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@@ -825,308 +847,303 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
+     37.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/loss-function.html">
-     37.48. Loss Function
+     37.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
+     37.50. ML logistic regression - assignment 1
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/ml-logistic-regression-2.html">
-     37.52. ML logistic regression - assignment 2
+     37.51. ML logistic regression - assignment 2
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/ml-neural-network-1.html">
-     37.53. ML neural network - Assignment 1
+     37.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression-tools.html">
-     37.54. Regression tools
+     37.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/managing-data.html">
-     37.55. Managing data
+     37.54. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/exploring-visualizations.html">
-     37.56. Exploring visualizations
+     37.55. Exploring visualizations
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/try-a-different-model.html">
-     37.57. Try a different model
+     37.56. Try a different model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/create-a-regression-model.html">
-     37.58. Create a regression model
+     37.57. Create a regression model
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-and-polynomial-regression.html">
-     37.59. Linear and polynomial regression
+     37.58. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/retrying-some-regression.html">
-     37.60. Retrying some regression
+     37.59. Retrying some regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
+     37.60. Pumpkin varieties and color
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.62. Delicious asian and indian cuisines
+     37.61. Delicious asian and indian cuisines
     </a>
    </li>
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+   37.67.1. Decision Trees for Classification
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-     37.69.8.1. Evaluating the model on the test set
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+   37.67.4. 3 - Exploratory Analysis
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     <a class="reference internal nav-link" href="#splitting-data-into-training-and-test-sets">
-     37.69.4.1. Splitting Data into Training and Test sets
+     37.67.4.1. Splitting Data into Training and Test sets
     </a>
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-     37.69.4.2. Checking Summary Statistics
+     37.67.4.2. Checking Summary Statistics
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     <a class="reference internal nav-link" href="#checking-missing-values">
-     37.69.4.3. Checking Missing Values
+     37.67.4.3. Checking Missing Values
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     <a class="reference internal nav-link" href="#checking-categorical-features">
-     37.69.4.4. Checking Categorical Features
+     37.67.4.4. Checking Categorical Features
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   <a class="reference internal nav-link" href="#data-preprocessing">
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+   37.67.5. 4 - Data Preprocessing
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#handling-categorical-features">
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+     37.67.5.1. Handling Categorical Features
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+   37.67.6. 5 - Training Decision Tree Classifier
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   <a class="reference internal nav-link" href="#evaluating-decision-trees">
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+   37.67.7. 6 - Evaluating Decision Trees
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   <a class="reference internal nav-link" href="#improving-decision-trees">
-   37.69.8. 7 - Improving Decision Trees
+   37.67.8. 7 - Improving Decision Trees
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#evaluating-the-model-on-the-test-set">
-     37.69.8.1. Evaluating the model on the test set
+     37.67.8.1. Evaluating the model on the test set
     </a>
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+   37.67.9. Acknowledgments
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@@ -1713,7 +1730,7 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="decision-trees-classification">
-<h1><span class="section-number">37.69. </span>Decision Trees - Classification<a class="headerlink" href="#decision-trees-classification" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.67. </span>Decision Trees - Classification<a class="headerlink" href="#decision-trees-classification" title="Permalink to this headline">#</a></h1>
 <p>Decision Trees are supervised machine learning algorithms that are used for both regression and classification tasks. Trees are powerful algorithms that can handle complex datasets.</p>
 <p>In the previous notebook, we learnt to predict the continous values with decision trees (regression). This notebook is for classification.</p>
 <p>Different to other algorithms, here are two things that trees are very particular about:</p>
@@ -1732,9 +1749,9 @@ <h1><span class="section-number">37.69. </span>Decision Trees - Classification<a
 <p><img alt="Decision Trees.png" src="../../../_images/4XijLSXib.png" /></p>
 <p>A well-known downside of decision trees is that they tend to overfit the data easily(pretty much assumed they will always overfit at first). One way to overcome overfitting is to reduce the maximum depth of the decision tree (refered to as <code class="docutils literal notranslate"><span class="pre">max_depth</span></code>hyperparameter) in decision trees. We will see other techniques to avoid overfitting.</p>
 <section id="decision-trees-for-classification">
-<h2><span class="section-number">37.69.1. </span>Decision Trees for Classification<a class="headerlink" href="#decision-trees-for-classification" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.67.1. </span>Decision Trees for Classification<a class="headerlink" href="#decision-trees-for-classification" title="Permalink to this headline">#</a></h2>
 <section id="contents">
-<h3><span class="section-number">37.69.1.1. </span>Contents<a class="headerlink" href="#contents" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.67.1.1. </span>Contents<a class="headerlink" href="#contents" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>[1 - Imports]</p></li>
 <li><p>[2 - Loading the data]</p></li>
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+<h2><span class="section-number">37.67.2. </span>1 - Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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-<h2><span class="section-number">37.69.3. </span>2 - Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.67.3. </span>2 - Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
 <p>In this classification task with decision trees, we will use a car dataset that is avilable at <a class="reference external" href="https://www.openml.org/d/21">OpenML</a> to predict the car acceptability given the information about the car. We will load it with Sklearn <code class="docutils literal notranslate"><span class="pre">fetch_openml</span></code> function.</p>
 <p>The version of the data we are fetching is 2. In the version 1 of the dataset, the target class had 4 classes (unacc, acc, good, vgood) but in the second version, the majority class is Positive§ whereas the rest are Negative§. If you want to see version 1, you can change version parameter in a cell below.</p>
 <p>Here are the informations about the features:</p>
@@ -1982,10 +1999,10 @@ <h2><span class="section-number">37.69.3. </span>2 - Loading the data<a class="h
 </div>
 </section>
 <section id="exploratory-analysis">
-<h2><span class="section-number">37.69.4. </span>3 - Exploratory Analysis<a class="headerlink" href="#exploratory-analysis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.67.4. </span>3 - Exploratory Analysis<a class="headerlink" href="#exploratory-analysis" title="Permalink to this headline">#</a></h2>
 <p>Before doing exploratory analysis, let’s get the training and test data.</p>
 <section id="splitting-data-into-training-and-test-sets">
-<h3><span class="section-number">37.69.4.1. </span>Splitting Data into Training and Test sets<a class="headerlink" href="#splitting-data-into-training-and-test-sets" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.67.4.1. </span>Splitting Data into Training and Test sets<a class="headerlink" href="#splitting-data-into-training-and-test-sets" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
@@ -2005,7 +2022,7 @@ <h3><span class="section-number">37.69.4.1. </span>Splitting Data into Training
 </div>
 </section>
 <section id="checking-summary-statistics">
-<h3><span class="section-number">37.69.4.2. </span>Checking Summary Statistics<a class="headerlink" href="#checking-summary-statistics" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.67.4.2. </span>Checking Summary Statistics<a class="headerlink" href="#checking-summary-statistics" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Checking summary statistics</span>
@@ -2089,7 +2106,7 @@ <h3><span class="section-number">37.69.4.2. </span>Checking Summary Statistics<a
 </div>
 </section>
 <section id="checking-missing-values">
-<h3><span class="section-number">37.69.4.3. </span>Checking Missing Values<a class="headerlink" href="#checking-missing-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.67.4.3. </span>Checking Missing Values<a class="headerlink" href="#checking-missing-values" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Checking missing values</span>
@@ -2113,7 +2130,7 @@ <h3><span class="section-number">37.69.4.3. </span>Checking Missing Values<a cla
 <p>We don’t have any missing values.</p>
 </section>
 <section id="checking-categorical-features">
-<h3><span class="section-number">37.69.4.4. </span>Checking Categorical Features<a class="headerlink" href="#checking-categorical-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.67.4.4. </span>Checking Categorical Features<a class="headerlink" href="#checking-categorical-features" title="Permalink to this headline">#</a></h3>
 <p>Let’s inspect some categorical features that are in the dataset, almost all re. Let’s see that!</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2370,10 +2387,10 @@ <h3><span class="section-number">37.69.4.4. </span>Checking Categorical Features
 </section>
 </section>
 <section id="data-preprocessing">
-<h2><span class="section-number">37.69.5. </span>4 - Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.67.5. </span>4 - Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
 <p>It is here that we prepare the data to be in the proper format for the machine learning model.</p>
 <section id="handling-categorical-features">
-<h3><span class="section-number">37.69.5.1. </span>Handling Categorical Features<a class="headerlink" href="#handling-categorical-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.67.5.1. </span>Handling Categorical Features<a class="headerlink" href="#handling-categorical-features" title="Permalink to this headline">#</a></h3>
 <p>Decision trees don’t care if the features are scaled or not, and they can handle the categorical features. There is a <a class="reference external" href="https://scikit-learn.org/stable/modules/tree.html#tree">note on documentation</a> that the sklearn tree implementation doesn’t support categorical features, so I will go ahead and handle them. It’s fun anyways :)</p>
 <p>Before handling categorical features, let’s create a training input data and labels.</p>
 <div class="cell docutils container">
@@ -2423,7 +2440,7 @@ <h3><span class="section-number">37.69.5.1. </span>Handling Categorical Features
 </section>
 </section>
 <section id="training-decision-tree-classifier">
-<h2><span class="section-number">37.69.6. </span>5 - Training Decision Tree Classifier<a class="headerlink" href="#training-decision-tree-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.67.6. </span>5 - Training Decision Tree Classifier<a class="headerlink" href="#training-decision-tree-classifier" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="kn">import</span> <span class="n">DecisionTreeClassifier</span>
@@ -2454,7 +2471,7 @@ <h2><span class="section-number">37.69.6. </span>5 - Training Decision Tree Clas
 </div>
 </section>
 <section id="evaluating-decision-trees">
-<h2><span class="section-number">37.69.7. </span>6 - Evaluating Decision Trees<a class="headerlink" href="#evaluating-decision-trees" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.67.7. </span>6 - Evaluating Decision Trees<a class="headerlink" href="#evaluating-decision-trees" title="Permalink to this headline">#</a></h2>
 <p>Let’s build 3 functions to display accuracy, confusion matrix, and classification report. Classification report contains all useful metrics such as precision, recall, and f1 score.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2560,7 +2577,7 @@ <h2><span class="section-number">37.69.7. </span>6 - Evaluating Decision Trees<a
 <p>The model clearly overfitted the data. Let’s see how we can regularize it.</p>
 </section>
 <section id="improving-decision-trees">
-<h2><span class="section-number">37.69.8. </span>7 - Improving Decision Trees<a class="headerlink" href="#improving-decision-trees" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.67.8. </span>7 - Improving Decision Trees<a class="headerlink" href="#improving-decision-trees" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tree_clf</span><span class="o">.</span><span class="n">get_params</span><span class="p">()</span>
@@ -2763,7 +2780,7 @@ <h2><span class="section-number">37.69.8. </span>7 - Improving Decision Trees<a
 </ul>
 <p>The higher the precision and recall are, the higher the F1 score. But there is a tradeoff between them. Increasing precision will reduce recall, and vice versa. So it’s fair to say that it depends on the problem you’re trying to solve and the metrics you want to optimize for.</p>
 <section id="evaluating-the-model-on-the-test-set">
-<h3><span class="section-number">37.69.8.1. </span>Evaluating the model on the test set<a class="headerlink" href="#evaluating-the-model-on-the-test-set" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.67.8.1. </span>Evaluating the model on the test set<a class="headerlink" href="#evaluating-the-model-on-the-test-set" title="Permalink to this headline">#</a></h3>
 <p>It is only after we have improved the model that we can feed it to the test set. If we try to show the test set to the model while we are still training, we may cause a potential leak thus producing misleading predictions.</p>
 <p>Also, we will apply the same processing functions that we applied to the training set.</p>
 <div class="cell docutils container">
@@ -2843,7 +2860,7 @@ <h3><span class="section-number">37.69.8.1. </span>Evaluating the model on the t
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.69.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.67.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Jake VanderPlas for creating the open-source course <a class="reference external" href="https://github.com/jakevdp/PythonDataScienceHandbook"> Python Data Science Handbook </a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2885,13 +2902,13 @@ <h2><span class="section-number">37.69.9. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.68. </span>Decision Trees - Intro and Regression</p>
+            <p class="prev-next-title"><span class="section-number">37.66. </span>Decision Trees - Intro and Regression</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../model-selection/model-selection-assignment-1.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.70. </span>Model selection assignment 1</p>
+        <p class="prev-next-title"><span class="section-number">37.68. </span>Model selection assignment 1</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/ml-advanced/kernel-method/decision_trees_for_regression.html b/assignments/ml-advanced/kernel-method/decision_trees_for_regression.html
index 065187cd26..9e17bc1004 100644
--- a/assignments/ml-advanced/kernel-method/decision_trees_for_regression.html
+++ b/assignments/ml-advanced/kernel-method/decision_trees_for_regression.html
@@ -6,7 +6,7 @@
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.68. Decision Trees - Intro and Regression &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.66. Decision Trees - Intro and Regression &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.69. Decision Trees - Classification" href="decision_trees_for_classification.html" />
-    <link rel="prev" title="37.67. Support Vector Machines (SVM) - Classification" href="support_vector_machines_for_classification.html" />
+    <link rel="next" title="37.67. Decision Trees - Classification" href="decision_trees_for_classification.html" />
+    <link rel="prev" title="37.65. Support Vector Machines (SVM) - Classification" href="support_vector_machines_for_classification.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
-  <label for="toctree-checkbox-3">
+  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
+  <label for="toctree-checkbox-4">
    <i class="fas fa-chevron-down">
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   </label>
@@ -284,8 +306,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
-  <label for="toctree-checkbox-4">
+  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
+  <label for="toctree-checkbox-5">
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   </label>
@@ -311,8 +333,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
-  <label for="toctree-checkbox-5">
+  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
+  <label for="toctree-checkbox-6">
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@@ -355,8 +377,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
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+  <label for="toctree-checkbox-7">
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@@ -392,8 +414,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
-  <label for="toctree-checkbox-7">
+  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
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@@ -431,8 +453,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
    14. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
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@@ -453,8 +475,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
    15. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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@@ -480,8 +502,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
    16. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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@@ -633,8 +655,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../README.html">
    37. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
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@@ -825,308 +847,303 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
+     37.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/loss-function.html">
-     37.48. Loss Function
+     37.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
+     37.50. ML logistic regression - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/ml-logistic-regression-2.html">
-     37.52. ML logistic regression - assignment 2
+     37.51. ML logistic regression - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/ml-neural-network-1.html">
-     37.53. ML neural network - Assignment 1
+     37.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression-tools.html">
-     37.54. Regression tools
+     37.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/managing-data.html">
-     37.55. Managing data
+     37.54. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/exploring-visualizations.html">
-     37.56. Exploring visualizations
+     37.55. Exploring visualizations
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/try-a-different-model.html">
-     37.57. Try a different model
+     37.56. Try a different model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/create-a-regression-model.html">
-     37.58. Create a regression model
+     37.57. Create a regression model
     </a>
    </li>
    <li class="toctree-l2">
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+   37.66.7. 6 - Evaluating Decision Trees
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+   37.66.8. 7 - Improving Decision Trees
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+   37.66.9. Acknowledgments
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@@ -1641,7 +1658,7 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="decision-trees-intro-and-regression">
-<h1><span class="section-number">37.68. </span>Decision Trees - Intro and Regression<a class="headerlink" href="#decision-trees-intro-and-regression" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.66. </span>Decision Trees - Intro and Regression<a class="headerlink" href="#decision-trees-intro-and-regression" title="Permalink to this headline">#</a></h1>
 <p>Decision Trees are supervised machine learning algorithms that are used for both regression and classification tasks. Trees are powerful algorithms that can handle complex datasets.</p>
 <p>Here are 7 interesting facts about decision trees:</p>
 <ul class="simple">
@@ -1659,9 +1676,9 @@ <h1><span class="section-number">37.68. </span>Decision Trees - Intro and Regres
 <p>A well-known downside of decision trees is that they tend to overfit the data easily(pretty much assumed they will always overfit at first). One way to overcome overfitting is to reduce the maximum depth of the decision tree (refered to as <code class="docutils literal notranslate"><span class="pre">max_depth</span></code>hyperparameter) in decision trees. We will see other techniques to avoid overfitting.</p>
 <p>To motivate the superpower of decision trees, let’s use it for a regression task where instead of predicting class, we are predicting a continous value. In the next lab, we will use them for classification.</p>
 <section id="decision-trees-for-regression">
-<h2><span class="section-number">37.68.1. </span>Decision Trees for Regression<a class="headerlink" href="#decision-trees-for-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.66.1. </span>Decision Trees for Regression<a class="headerlink" href="#decision-trees-for-regression" title="Permalink to this headline">#</a></h2>
 <section id="contents">
-<h3><span class="section-number">37.68.1.1. </span>Contents<a class="headerlink" href="#contents" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.66.1.1. </span>Contents<a class="headerlink" href="#contents" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>[1 - Imports]</p></li>
 <li><p>[2 - Loading the data]</p></li>
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 </section>
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-<h2><span class="section-number">37.68.2. </span>1 - Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.66.2. </span>1 - Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -1689,7 +1706,7 @@ <h2><span class="section-number">37.68.2. </span>1 - Imports<a class="headerlink
 </div>
 </section>
 <section id="loading-the-data">
-<h2><span class="section-number">37.68.3. </span>2 - Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.66.3. </span>2 - Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
 <p>In this regression task with decision trees, we will use the Machine CPU (Central Processing Unit) data which is avilable at <a class="reference external" href="https://www.openml.org/t/5492">OpenML</a>. We will load it with Sklearn <code class="docutils literal notranslate"><span class="pre">fetch_openml</span></code> function.</p>
 <p>If you are reading this, it’s very likely that you know CPU or you have once(or many times) thought about it when you were buying your computer. In this notebook, we will predict the relative performance of the CPU given the following data:</p>
 <ul class="simple">
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 </div>
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 <section id="exploratory-analysis">
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+<h2><span class="section-number">37.66.4. </span>3 - Exploratory Analysis<a class="headerlink" href="#exploratory-analysis" title="Permalink to this headline">#</a></h2>
 <p>Before doing exploratory analysis, let’s get the training and test data.</p>
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 <p>We don’t have any missing values.</p>
 </section>
 <section id="data-preprocessing">
-<h2><span class="section-number">37.68.5. </span>4 - Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.66.5. </span>4 - Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
 <p>It is here that we prepare the data to be in the proper format for the machine learning model.</p>
 <p>Decision trees don’t care if the features are scaled or not. Let’s set up a pipeline to scale features, and we will use it to verify that notion.</p>
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-<h2><span class="section-number">37.68.6. </span>5 - Training Decision Tree Regressor<a class="headerlink" href="#training-decision-tree-regressor" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.66.6. </span>5 - Training Decision Tree Regressor<a class="headerlink" href="#training-decision-tree-regressor" title="Permalink to this headline">#</a></h2>
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@@ -2205,7 +2222,7 @@ <h2><span class="section-number">37.68.6. </span>5 - Training Decision Tree Regr
 </div>
 </section>
 <section id="evaluating-decision-trees">
-<h2><span class="section-number">37.68.7. </span>6 - Evaluating Decision Trees<a class="headerlink" href="#evaluating-decision-trees" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.66.7. </span>6 - Evaluating Decision Trees<a class="headerlink" href="#evaluating-decision-trees" title="Permalink to this headline">#</a></h2>
 <p>Let’s first check the root mean squarred errr on the training. It is not advised to evaluate the model on the test data since we haven’t improved it yet. I will make a function to make it easier and to avoid repetitions.</p>
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 </section>
 <section id="improving-decision-trees">
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@@ -2382,7 +2399,7 @@ <h2><span class="section-number">37.68.8. </span>7 - Improving Decision Trees<a
 <p>This is the end of the notebook. We have learned the fundamental idea behind the decision trees, and used it to predict the CPU performance. In the next lab, we will use it for classification task and we will use a real world dataset so that we can practically improve the decision trees model.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.68.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.66.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Jake VanderPlas for creating the open-source course <a class="reference external" href="https://github.com/jakevdp/PythonDataScienceHandbook"> Python Data Science Handbook </a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2424,13 +2441,13 @@ <h2><span class="section-number">37.68.9. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.67. </span>Support Vector Machines (SVM) - Classification</p>
+            <p class="prev-next-title"><span class="section-number">37.65. </span>Support Vector Machines (SVM) - Classification</p>
         </div>
     </a>
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-        <p class="prev-next-title"><span class="section-number">37.69. </span>Decision Trees - Classification</p>
+        <p class="prev-next-title"><span class="section-number">37.67. </span>Decision Trees - Classification</p>
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
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+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
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     <a class="reference internal nav-link" href="#run-svm-with-sigmoid-kernel-and-c-100-0">
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     </a>
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     <a class="reference internal nav-link" href="#comments">
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    </li>
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-   37.65.17. Confusion matrix
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   <a class="reference internal nav-link" href="#classification-metrices">
-   37.65.18. Classification metrices
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     <a class="reference internal nav-link" href="#classification-report">
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     <a class="reference internal nav-link" href="#classification-accuracy">
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-error">
-     37.65.18.3. Classification error
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     <a class="reference internal nav-link" href="#precision">
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     <a class="reference internal nav-link" href="#recall">
-     37.65.18.5. Recall
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#true-positive-rate">
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#false-positive-rate">
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     <a class="reference internal nav-link" href="#specificity">
-     37.65.18.8. Specificity
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#f1-score">
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+     37.63.18.9. f1-score
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    </li>
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     <a class="reference internal nav-link" href="#support">
-     37.65.18.10. Support
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     </a>
    </li>
   </ul>
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   <a class="reference internal nav-link" href="#roc-auc">
-   37.65.19. ROC - AUC
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     <a class="reference internal nav-link" href="#roc-curve">
-     37.65.19.1. ROC Curve
+     37.63.19.1. ROC Curve
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id4">
-     37.65.19.2. ROC  AUC
+     37.63.19.2. ROC  AUC
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id5">
-     37.65.19.3. Comments
+     37.63.19.3. Comments
     </a>
    </li>
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   <a class="reference internal nav-link" href="#stratified-k-fold-cross-validation-with-shuffle-split">
-   37.65.20. Stratified k-fold Cross Validation with shuffle split
+   37.63.20. Stratified k-fold Cross Validation with shuffle split
   </a>
   <ul class="nav section-nav flex-column">
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-     37.65.20.1. Stratified k-Fold Cross Validation with shuffle split with  linear kernel
+     37.63.20.1. Stratified k-Fold Cross Validation with shuffle split with  linear kernel
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#stratified-k-fold-cross-validation-with-shuffle-split-with-rbf-kernel">
-     37.65.20.2. Stratified k-Fold Cross Validation with shuffle split with rbf kernel
+     37.63.20.2. Stratified k-Fold Cross Validation with shuffle split with rbf kernel
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id6">
-     37.65.20.3. Comments
+     37.63.20.3. Comments
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#hyperparameter-optimization-using-gridsearch-cv">
-   37.65.21. Hyperparameter Optimization using GridSearch CV
+   37.63.21. Hyperparameter Optimization using GridSearch CV
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id7">
-     37.65.21.1. Comments
+     37.63.21.1. Comments
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#results-and-conclusion">
-   37.65.22. Results and conclusion
+   37.63.22. Results and conclusion
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.65.23. Acknowledgments
+   37.63.23. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1928,74 +1945,74 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction-to-support-vector-machines">
-   37.65.1. Introduction to Support Vector Machines
+   37.63.1. Introduction to Support Vector Machines
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#support-vector-machines-intuition">
-   37.65.2. Support Vector Machines intuition
+   37.63.2. Support Vector Machines intuition
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#hyperplane">
-     37.65.2.1. Hyperplane
+     37.63.2.1. Hyperplane
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#support-vectors">
-     37.65.2.2. Support Vectors
+     37.63.2.2. Support Vectors
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#margin">
-     37.65.2.3. Margin
+     37.63.2.3. Margin
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#margin-in-svm">
-     37.65.2.4. Margin in SVM
+     37.63.2.4. Margin in SVM
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#svm-under-the-hood">
-     37.65.2.5. SVM Under the hood
+     37.63.2.5. SVM Under the hood
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#maximum-margin-hyperplane">
-     37.65.2.6. Maximum margin hyperplane
+     37.63.2.6. Maximum margin hyperplane
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#problem-with-dispersed-datasets">
-     37.65.2.7. Problem with dispersed datasets
+     37.63.2.7. Problem with dispersed datasets
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#kernel-trick-transformation-of-input-space-to-higher-dimensional-space">
-     37.65.2.8. Kernel trick - transformation of input space to higher dimensional space
+     37.63.2.8. Kernel trick - transformation of input space to higher dimensional space
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#kernel-trick">
-   37.65.3. Kernel trick
+   37.63.3. Kernel trick
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#kernel-function">
-     37.65.3.1. Kernel function
+     37.63.3.1. Kernel function
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#linear-kernel">
-     37.65.3.2. Linear kernel
+     37.63.3.2. Linear kernel
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
-       37.65.3.2.1. Linear Kernel
+       37.63.3.2.1. Linear Kernel
       </a>
      </li>
     </ul>
@@ -2004,27 +2021,27 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#polynomial-kernel">
-   37.65.4. Polynomial Kernel
+   37.63.4. Polynomial Kernel
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id2">
-     37.65.4.1. Polynomial Kernel
+     37.63.4.1. Polynomial Kernel
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#radial-basis-function-kernel">
-     37.65.4.2. Radial Basis Function Kernel
+     37.63.4.2. Radial Basis Function Kernel
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id3">
-       37.65.4.2.1. Radial Basis Function kernel
+       37.63.4.2.1. Radial Basis Function kernel
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#svm-classification-with-rbf-kernel">
-       37.65.4.2.2. SVM Classification with rbf kernel
+       37.63.4.2.2. SVM Classification with rbf kernel
       </a>
      </li>
     </ul>
@@ -2033,297 +2050,297 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#svm-scikit-learn-libraries">
-   37.65.5. SVM Scikit-Learn libraries
+   37.63.5. SVM Scikit-Learn libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dataset-description">
-   37.65.6. Dataset description
+   37.63.6. Dataset description
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#attribute-information">
-     37.65.6.1. Attribute Information:
+     37.63.6.1. Attribute Information:
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#import-libraries">
-   37.65.7. Import libraries
+   37.63.7. Import libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#import-dataset">
-   37.65.8. Import dataset
+   37.63.8. Import dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exploratory-data-analysis">
-   37.65.9. Exploratory data analysis
+   37.63.9. Exploratory data analysis
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#explore-missing-values-in-variables">
-     37.65.9.1. Explore missing values in variables
+     37.63.9.1. Explore missing values in variables
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#summary-of-numerical-variables">
-     37.65.9.2. Summary of numerical variables
+     37.63.9.2. Summary of numerical variables
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#outliers-in-numerical-variables">
-     37.65.9.3. Outliers in numerical variables
+     37.63.9.3. Outliers in numerical variables
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#handle-outliers-with-svms">
-     37.65.9.4. Handle outliers with SVMs
+     37.63.9.4. Handle outliers with SVMs
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#check-the-distribution-of-variables">
-     37.65.9.5. Check the distribution of variables
+     37.63.9.5. Check the distribution of variables
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#declare-feature-vector-and-target-variable">
-   37.65.10. Declare feature vector and target variable
+   37.63.10. Declare feature vector and target variable
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#split-data-into-separate-training-and-test-set">
-   37.65.11. Split data into separate training and test set
+   37.63.11. Split data into separate training and test set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#feature-scaling">
-   37.65.12. Feature Scaling
+   37.63.12. Feature Scaling
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#run-svm-with-default-hyperparameters">
-   37.65.13. Run SVM with default hyperparameters
+   37.63.13. Run SVM with default hyperparameters
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-rbf-kernel-and-c-100-0">
-     37.65.13.1. Run SVM with rbf kernel and C=100.0
+     37.63.13.1. Run SVM with rbf kernel and C=100.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-rbf-kernel-and-c-1000-0">
-     37.65.13.2. Run SVM with rbf kernel and C=1000.0
+     37.63.13.2. Run SVM with rbf kernel and C=1000.0
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#run-svm-with-linear-kernel">
-   37.65.14. Run SVM with linear kernel
+   37.63.14. Run SVM with linear kernel
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-linear-kernel-and-c-1-0">
-     37.65.14.1. Run SVM with linear kernel and C=1.0
+     37.63.14.1. Run SVM with linear kernel and C=1.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-linear-kernel-and-c-100-0">
-     37.65.14.2. Run SVM with linear kernel and C=100.0
+     37.63.14.2. Run SVM with linear kernel and C=100.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-linear-kernel-and-c-1000-0">
-     37.65.14.3. Run SVM with linear kernel and C=1000.0
+     37.63.14.3. Run SVM with linear kernel and C=1000.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#compare-the-train-set-and-test-set-accuracy">
-     37.65.14.4. Compare the train-set and test-set accuracy
+     37.63.14.4. Compare the train-set and test-set accuracy
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#check-for-overfitting-and-underfitting">
-     37.65.14.5. Check for overfitting and underfitting
+     37.63.14.5. Check for overfitting and underfitting
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#compare-model-accuracy-with-null-accuracy">
-     37.65.14.6. Compare model accuracy with null accuracy
+     37.63.14.6. Compare model accuracy with null accuracy
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#run-svm-with-polynomial-kernel">
-   37.65.15. Run SVM with polynomial kernel
+   37.63.15. Run SVM with polynomial kernel
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-polynomial-kernel-and-c-1-0">
-     37.65.15.1. Run SVM with polynomial kernel and C=1.0
+     37.63.15.1. Run SVM with polynomial kernel and C=1.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-polynomial-kernel-and-c-100-0">
-     37.65.15.2. Run SVM with polynomial kernel and C=100.0
+     37.63.15.2. Run SVM with polynomial kernel and C=100.0
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#run-svm-with-sigmoid-kernel">
-   37.65.16. Run SVM with sigmoid kernel
+   37.63.16. Run SVM with sigmoid kernel
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-sigmoid-kernel-and-c-1-0">
-     37.65.16.1. Run SVM with sigmoid kernel and C=1.0
+     37.63.16.1. Run SVM with sigmoid kernel and C=1.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-sigmoid-kernel-and-c-100-0">
-     37.65.16.2. Run SVM with sigmoid kernel and C=100.0
+     37.63.16.2. Run SVM with sigmoid kernel and C=100.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#comments">
-     37.65.16.3. Comments
+     37.63.16.3. Comments
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#confusion-matrix">
-   37.65.17. Confusion matrix
+   37.63.17. Confusion matrix
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#classification-metrices">
-   37.65.18. Classification metrices
+   37.63.18. Classification metrices
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-report">
-     37.65.18.1. Classification Report
+     37.63.18.1. Classification Report
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-accuracy">
-     37.65.18.2. Classification accuracy
+     37.63.18.2. Classification accuracy
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-error">
-     37.65.18.3. Classification error
+     37.63.18.3. Classification error
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#precision">
-     37.65.18.4. Precision
+     37.63.18.4. Precision
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#recall">
-     37.65.18.5. Recall
+     37.63.18.5. Recall
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#true-positive-rate">
-     37.65.18.6. True Positive Rate
+     37.63.18.6. True Positive Rate
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#false-positive-rate">
-     37.65.18.7. False Positive Rate
+     37.63.18.7. False Positive Rate
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#specificity">
-     37.65.18.8. Specificity
+     37.63.18.8. Specificity
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#f1-score">
-     37.65.18.9. f1-score
+     37.63.18.9. f1-score
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#support">
-     37.65.18.10. Support
+     37.63.18.10. Support
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#roc-auc">
-   37.65.19. ROC - AUC
+   37.63.19. ROC - AUC
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#roc-curve">
-     37.65.19.1. ROC Curve
+     37.63.19.1. ROC Curve
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id4">
-     37.65.19.2. ROC  AUC
+     37.63.19.2. ROC  AUC
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id5">
-     37.65.19.3. Comments
+     37.63.19.3. Comments
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#stratified-k-fold-cross-validation-with-shuffle-split">
-   37.65.20. Stratified k-fold Cross Validation with shuffle split
+   37.63.20. Stratified k-fold Cross Validation with shuffle split
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#stratified-k-fold-cross-validation-with-shuffle-split-with-linear-kernel">
-     37.65.20.1. Stratified k-Fold Cross Validation with shuffle split with  linear kernel
+     37.63.20.1. Stratified k-Fold Cross Validation with shuffle split with  linear kernel
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#stratified-k-fold-cross-validation-with-shuffle-split-with-rbf-kernel">
-     37.65.20.2. Stratified k-Fold Cross Validation with shuffle split with rbf kernel
+     37.63.20.2. Stratified k-Fold Cross Validation with shuffle split with rbf kernel
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id6">
-     37.65.20.3. Comments
+     37.63.20.3. Comments
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#hyperparameter-optimization-using-gridsearch-cv">
-   37.65.21. Hyperparameter Optimization using GridSearch CV
+   37.63.21. Hyperparameter Optimization using GridSearch CV
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id7">
-     37.65.21.1. Comments
+     37.63.21.1. Comments
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#results-and-conclusion">
-   37.65.22. Results and conclusion
+   37.63.22. Results and conclusion
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.65.23. Acknowledgments
+   37.63.23. Acknowledgments
   </a>
  </li>
 </ul>
@@ -2337,36 +2354,36 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="kernel-method-assignment-1">
-<h1><span class="section-number">37.65. </span>Kernel method assignment 1<a class="headerlink" href="#kernel-method-assignment-1" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.63. </span>Kernel method assignment 1<a class="headerlink" href="#kernel-method-assignment-1" title="Permalink to this headline">#</a></h1>
 <p>Support Vector Machines (SVMs in short) are supervised machine learning algorithms that are used for classification and regression purposes. In this kernel, We build a Support Vector Machines classifier to classify a Pulsar star. We have used the <strong>Predicting a Pulsar Star</strong> dataset for this project.</p>
 <section id="introduction-to-support-vector-machines">
-<h2><span class="section-number">37.65.1. </span>Introduction to Support Vector Machines<a class="headerlink" href="#introduction-to-support-vector-machines" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.63.1. </span>Introduction to Support Vector Machines<a class="headerlink" href="#introduction-to-support-vector-machines" title="Permalink to this headline">#</a></h2>
 <p><strong>Support Vector Machines</strong> (SVMs in short) are machine learning algorithms that are used for classification and regression purposes. SVMs are one of the powerful machine learning algorithms for classification, regression and outlier detection purposes. An SVM classifier builds a model that assigns new data points to one of the given categories. Thus, it can be viewed as a non-probabilistic binary linear classifier.</p>
 <p>The original SVM algorithm was developed by Vladimir N Vapnik and Alexey Ya. Chervonenkis in 1963. At that time, the algorithm was in early stages. The only possibility is to draw hyperplanes for linear classifier. In 1992, Bernhard E. Boser, Isabelle M Guyon and Vladimir N Vapnik suggested a way to create non-linear classifiers by applying the kernel trick to maximum-margin hyperplanes. The current standard was proposed by Corinna Cortes and Vapnik in 1993 and published in 1995.</p>
 <p>SVMs can be used for linear classification purposes. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using the <strong>kernel trick</strong>. It enable us to implicitly map the inputs into high dimensional feature spaces.</p>
 </section>
 <section id="support-vector-machines-intuition">
-<h2><span class="section-number">37.65.2. </span>Support Vector Machines intuition<a class="headerlink" href="#support-vector-machines-intuition" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.63.2. </span>Support Vector Machines intuition<a class="headerlink" href="#support-vector-machines-intuition" title="Permalink to this headline">#</a></h2>
 <p>Now, we should be familiar with some SVM terminology.</p>
 <section id="hyperplane">
-<h3><span class="section-number">37.65.2.1. </span>Hyperplane<a class="headerlink" href="#hyperplane" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.2.1. </span>Hyperplane<a class="headerlink" href="#hyperplane" title="Permalink to this headline">#</a></h3>
 <p>A hyperplane is a decision boundary which separates between given set of data points having different class labels. The SVM classifier separates data points using a hyperplane with the maximum amount of margin. This hyperplane is known as the <code class="docutils literal notranslate"><span class="pre">maximum</span> <span class="pre">margin</span> <span class="pre">hyperplane</span></code> and the linear classifier it defines is known as the <code class="docutils literal notranslate"><span class="pre">maximum</span> <span class="pre">margin</span> <span class="pre">classifier</span></code>.</p>
 </section>
 <section id="support-vectors">
-<h3><span class="section-number">37.65.2.2. </span>Support Vectors<a class="headerlink" href="#support-vectors" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.2.2. </span>Support Vectors<a class="headerlink" href="#support-vectors" title="Permalink to this headline">#</a></h3>
 <p>Support vectors are the sample data points, which are closest to the hyperplane.  These data points will define the separating line or hyperplane better by calculating margins.</p>
 </section>
 <section id="margin">
-<h3><span class="section-number">37.65.2.3. </span>Margin<a class="headerlink" href="#margin" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.2.3. </span>Margin<a class="headerlink" href="#margin" title="Permalink to this headline">#</a></h3>
 <p>A margin is a separation gap between the two lines on the closest data points. It is calculated as the perpendicular distance from the line to support vectors or closest data points. In SVMs, we try to maximize this separation gap so that we get maximum margin.</p>
 <p>The following diagram illustrates these concepts visually.</p>
 </section>
 <section id="margin-in-svm">
-<h3><span class="section-number">37.65.2.4. </span>Margin in SVM<a class="headerlink" href="#margin-in-svm" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.2.4. </span>Margin in SVM<a class="headerlink" href="#margin-in-svm" title="Permalink to this headline">#</a></h3>
 <p><img alt="Margin in SVM" src="https://static.wixstatic.com/media/8f929f_7ecacdcf69d2450087cb4a898ef90837~mv2.png" /></p>
 </section>
 <section id="svm-under-the-hood">
-<h3><span class="section-number">37.65.2.5. </span>SVM Under the hood<a class="headerlink" href="#svm-under-the-hood" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.2.5. </span>SVM Under the hood<a class="headerlink" href="#svm-under-the-hood" title="Permalink to this headline">#</a></h3>
 <p>In SVMs, our main objective is to select a hyperplane with the maximum possible margin between support vectors in the given dataset. SVM searches for the maximum margin hyperplane in the following 2 step process –</p>
 <ol class="simple">
 <li><p>Generate hyperplanes which segregates the classes in the best possible way. There are many hyperplanes that might classify the data. We should look for the best hyperplane that represents the largest separation, or margin, between the two classes.</p></li>
@@ -2375,78 +2392,78 @@ <h3><span class="section-number">37.65.2.5. </span>SVM Under the hood<a class="h
 <p>The following diagram illustrates the concept of <strong>maximum margin</strong> and <strong>maximum margin hyperplane</strong> in a clear manner.</p>
 </section>
 <section id="maximum-margin-hyperplane">
-<h3><span class="section-number">37.65.2.6. </span>Maximum margin hyperplane<a class="headerlink" href="#maximum-margin-hyperplane" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.2.6. </span>Maximum margin hyperplane<a class="headerlink" href="#maximum-margin-hyperplane" title="Permalink to this headline">#</a></h3>
 <p><img alt="Maximum margin hyperplane" src="https://static.packt-cdn.com/products/9781783555130/graphics/3547_03_07.jpg" /></p>
 </section>
 <section id="problem-with-dispersed-datasets">
-<h3><span class="section-number">37.65.2.7. </span>Problem with dispersed datasets<a class="headerlink" href="#problem-with-dispersed-datasets" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.2.7. </span>Problem with dispersed datasets<a class="headerlink" href="#problem-with-dispersed-datasets" title="Permalink to this headline">#</a></h3>
 <p>Sometimes, the sample data points are so dispersed that it is not possible to separate them using a linear hyperplane.
 In such a situation, SVMs uses a <code class="docutils literal notranslate"><span class="pre">kernel</span> <span class="pre">trick</span></code> to transform the input space to a higher dimensional space as shown in the diagram below. It uses a mapping function to transform the 2-D input space into the 3-D input space. Now, we can easily segregate the data points using linear separation.</p>
 </section>
 <section id="kernel-trick-transformation-of-input-space-to-higher-dimensional-space">
-<h3><span class="section-number">37.65.2.8. </span>Kernel trick - transformation of input space to higher dimensional space<a class="headerlink" href="#kernel-trick-transformation-of-input-space-to-higher-dimensional-space" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.2.8. </span>Kernel trick - transformation of input space to higher dimensional space<a class="headerlink" href="#kernel-trick-transformation-of-input-space-to-higher-dimensional-space" title="Permalink to this headline">#</a></h3>
 <p><img alt="Kernel trick" src="http://www.aionlinecourse.com/uploads/tutorials/2019/07/11_21_kernel_svm_3.png" /></p>
 </section>
 </section>
 <section id="kernel-trick">
-<h2><span class="section-number">37.65.3. </span>Kernel trick<a class="headerlink" href="#kernel-trick" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.63.3. </span>Kernel trick<a class="headerlink" href="#kernel-trick" title="Permalink to this headline">#</a></h2>
 <p>In practice, SVM algorithm is implemented using a <code class="docutils literal notranslate"><span class="pre">kernel</span></code>. It uses a technique called the <code class="docutils literal notranslate"><span class="pre">kernel</span> <span class="pre">trick</span></code>. In simple words, a <code class="docutils literal notranslate"><span class="pre">kernel</span></code> is just a function that maps the data to a higher dimension where data is separable. A kernel transforms a low-dimensional input data space into a higher dimensional space. So, it converts non-linear separable problems to linear separable problems by adding more dimensions to it. Thus, the kernel trick helps us to build a more accurate classifier. Hence, it is useful in non-linear separation problems.</p>
 <p>We can define a kernel function as follows-</p>
 <section id="kernel-function">
-<h3><span class="section-number">37.65.3.1. </span>Kernel function<a class="headerlink" href="#kernel-function" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.3.1. </span>Kernel function<a class="headerlink" href="#kernel-function" title="Permalink to this headline">#</a></h3>
 <p><img alt="Kernel function" src="https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTodZptqcRor0LGo8Qn7_kJB9n9BACMt6jgIPZ4C3g_rgh_uSRZLQ&amp;s" /></p>
 <p>In the context of SVMs, there are 4 popular kernels – <code class="docutils literal notranslate"><span class="pre">Linear</span> <span class="pre">kernel</span></code>,<code class="docutils literal notranslate"><span class="pre">Polynomial</span> <span class="pre">kernel</span></code>,<code class="docutils literal notranslate"><span class="pre">Radial</span> <span class="pre">Basis</span> <span class="pre">Function</span> <span class="pre">(RBF)</span> <span class="pre">kernel</span></code> (also called Gaussian kernel) and <code class="docutils literal notranslate"><span class="pre">Sigmoid</span> <span class="pre">kernel</span></code>. These are described below -</p>
 </section>
 <section id="linear-kernel">
-<h3><span class="section-number">37.65.3.2. </span>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.3.2. </span>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to this headline">#</a></h3>
 <p>In linear kernel, the kernel function takes the form of a linear function as follows-</p>
 <p><strong>linear kernel : K(xi , xj ) = xiT xj</strong></p>
 <p>Linear kernel is used when the data is linearly separable. It means that data can be separated using a single line. It is one of the most common kernels to be used. It is mostly used when there are large number of features in a dataset. Linear kernel is often used for text classification purposes.</p>
 <p>Training with a linear kernel is usually faster, because we only need to optimize the C regularization parameter. When training with other kernels, we also need to optimize the γ parameter. So, performing a grid search will usually take more time.</p>
 <p>Linear kernel can be visualized with the following figure.</p>
 <section id="id1">
-<h4><span class="section-number">37.65.3.2.1. </span>Linear Kernel<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.63.3.2.1. </span>Linear Kernel<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
 <p><img alt="Linear Kernel" src="https://scikit-learn.org/stable/_images/sphx_glr_plot_svm_kernels_thumb.png" /></p>
 </section>
 </section>
 </section>
 <section id="polynomial-kernel">
-<h2><span class="section-number">37.65.4. </span>Polynomial Kernel<a class="headerlink" href="#polynomial-kernel" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.63.4. </span>Polynomial Kernel<a class="headerlink" href="#polynomial-kernel" title="Permalink to this headline">#</a></h2>
 <p>Polynomial kernel represents the similarity of vectors (training samples) in a feature space over polynomials of the original variables. The polynomial kernel looks not only at the given features of input samples to determine their similarity, but also combinations of the input samples.</p>
 <p>For degree-d polynomials, the polynomial kernel is defined as follows –</p>
 <p><strong>Polynomial kernel : K(xi , xj ) = (γxiT xj + r)d , γ &gt; 0</strong></p>
 <p>Polynomial kernel is very popular in Natural Language Processing. The most common degree is d = 2 (quadratic), since larger degrees tend to overfit on NLP problems. It can be visualized with the following diagram.</p>
 <section id="id2">
-<h3><span class="section-number">37.65.4.1. </span>Polynomial Kernel<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.4.1. </span>Polynomial Kernel<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <p><img alt="Polynomial Kernel" src="https://www.researchgate.net/profile/Cheng_Soon_Ong/publication/23442384/figure/fig12/AS:341444054274063&#64;1458418014823/The-effect-of-the-degree-of-a-polynomial-kernel-The-polynomial-kernel-of-degree-1-leads.png" /></p>
 </section>
 <section id="radial-basis-function-kernel">
-<h3><span class="section-number">37.65.4.2. </span>Radial Basis Function Kernel<a class="headerlink" href="#radial-basis-function-kernel" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.4.2. </span>Radial Basis Function Kernel<a class="headerlink" href="#radial-basis-function-kernel" title="Permalink to this headline">#</a></h3>
 <p>Radial basis function kernel is a general purpose kernel. It is used when we have no prior knowledge about the data. The RBF kernel on two samples x and y is defined by the following equation –</p>
 <section id="id3">
-<h4><span class="section-number">37.65.4.2.1. </span>Radial Basis Function kernel<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.63.4.2.1. </span>Radial Basis Function kernel<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h4>
 <p>![RBK Kernel](data:image/png;base64,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)</p>
 <p>The following diagram demonstrates the SVM classification with rbf kernel.</p>
 </section>
 <section id="svm-classification-with-rbf-kernel">
-<h4><span class="section-number">37.65.4.2.2. </span>SVM Classification with rbf kernel<a class="headerlink" href="#svm-classification-with-rbf-kernel" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">37.63.4.2.2. </span>SVM Classification with rbf kernel<a class="headerlink" href="#svm-classification-with-rbf-kernel" title="Permalink to this headline">#</a></h4>
 <p><img alt="SVM Classification with rbf kernel" src="https://www.researchgate.net/profile/Periklis_Gogas/publication/286180566/figure/fig5/AS:304327777374210&#64;1449568804246/An-example-of-an-SVM-classification-using-the-RBF-kernel-The-two-classes-are-separated.png" /></p>
 </section>
 </section>
 </section>
 <section id="svm-scikit-learn-libraries">
-<h2><span class="section-number">37.65.5. </span>SVM Scikit-Learn libraries<a class="headerlink" href="#svm-scikit-learn-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.63.5. </span>SVM Scikit-Learn libraries<a class="headerlink" href="#svm-scikit-learn-libraries" title="Permalink to this headline">#</a></h2>
 <p>Scikit-Learn provides useful libraries to implement Support Vector Machine algorithm on a dataset. There are many libraries that can help us to implement SVM smoothly. We just need to call the library with parameters that suit to our needs. In this project, I am dealing with a classification task. So, I will mention the Scikit-Learn libraries for SVM classification purposes.</p>
 <p>First, there is a <strong>LinearSVC()</strong> classifier. As the name suggests, this classifier uses only linear kernel. In LinearSVC() classifier, we don’t pass the value of kernel since it is used only for linear classification purposes.</p>
 <p>Scikit-Learn provides two other classifiers - <strong>SVC()</strong> and <strong>NuSVC()</strong> which are used for classification purposes. These classifiers are mostly similar with some difference in parameters. <strong>NuSVC()</strong> is similar to <strong>SVC()</strong> but uses a parameter to control the number of support vectors. We pass the values of kernel, gamma and C along with other parameters. By default kernel parameter uses rbf as its value but we can pass values like poly, linear, sigmoid or callable function.</p>
 </section>
 <section id="dataset-description">
-<h2><span class="section-number">37.65.6. </span>Dataset description<a class="headerlink" href="#dataset-description" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.63.6. </span>Dataset description<a class="headerlink" href="#dataset-description" title="Permalink to this headline">#</a></h2>
 <p>I have used the <strong>Predicting a Pulsar Star</strong> dataset for this project.</p>
 <p>Pulsars are a rare type of Neutron star that produce radio emission detectable here on Earth. They are of considerable scientific interest as probes of space-time, the inter-stellar medium, and states of matter. Classification algorithms in particular are being adopted, which treat the data sets as binary classification problems. Here the legitimate pulsar examples form  minority positive class and spurious examples form the majority negative class.</p>
 <p>The data set shared here contains 16,259 spurious examples caused by RFI/noise, and 1,639 real pulsar examples. Each row lists the variables first, and the class label is the final entry. The class labels used are 0 (negative) and 1 (positive).</p>
 <section id="attribute-information">
-<h3><span class="section-number">37.65.6.1. </span>Attribute Information:<a class="headerlink" href="#attribute-information" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.6.1. </span>Attribute Information:<a class="headerlink" href="#attribute-information" title="Permalink to this headline">#</a></h3>
 <p>Each candidate is described by 8 continuous variables, and a single class variable. The first four are simple statistics obtained from the integrated pulse profile. The remaining four variables are similarly obtained from the DM-SNR curve . These are summarised below:</p>
 <ol class="simple">
 <li><p>Mean of the integrated profile.</p></li>
@@ -2462,7 +2479,7 @@ <h3><span class="section-number">37.65.6.1. </span>Attribute Information:<a clas
 </section>
 </section>
 <section id="import-libraries">
-<h2><span class="section-number">37.65.7. </span>Import libraries<a class="headerlink" href="#import-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.63.7. </span>Import libraries<a class="headerlink" href="#import-libraries" title="Permalink to this headline">#</a></h2>
 <p>I will start off by importing the required Python libraries.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2480,7 +2497,7 @@ <h2><span class="section-number">37.65.7. </span>Import libraries<a class="heade
 </div>
 </section>
 <section id="import-dataset">
-<h2><span class="section-number">37.65.8. </span>Import dataset<a class="headerlink" href="#import-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.63.8. </span>Import dataset<a class="headerlink" href="#import-dataset" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="s1">&#39;../../../assets/data/pulsar_stars.csv&#39;</span>
@@ -2492,7 +2509,7 @@ <h2><span class="section-number">37.65.8. </span>Import dataset<a class="headerl
 </div>
 </section>
 <section id="exploratory-data-analysis">
-<h2><span class="section-number">37.65.9. </span>Exploratory data analysis<a class="headerlink" href="#exploratory-data-analysis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.63.9. </span>Exploratory data analysis<a class="headerlink" href="#exploratory-data-analysis" title="Permalink to this headline">#</a></h2>
 <p>Now, we will explore the data to gain insights about the data.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2762,7 +2779,7 @@ <h2><span class="section-number">37.65.9. </span>Exploratory data analysis<a cla
 </div>
 <p>We can see that there are no missing values in the dataset and all the variables are numerical variables.</p>
 <section id="explore-missing-values-in-variables">
-<h3><span class="section-number">37.65.9.1. </span>Explore missing values in variables<a class="headerlink" href="#explore-missing-values-in-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.9.1. </span>Explore missing values in variables<a class="headerlink" href="#explore-missing-values-in-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># check for missing values in variables</span>
@@ -2789,7 +2806,7 @@ <h3><span class="section-number">37.65.9.1. </span>Explore missing values in var
 <p>We can see that there are no missing values in the dataset.</p>
 </section>
 <section id="summary-of-numerical-variables">
-<h3><span class="section-number">37.65.9.2. </span>Summary of numerical variables<a class="headerlink" href="#summary-of-numerical-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.9.2. </span>Summary of numerical variables<a class="headerlink" href="#summary-of-numerical-variables" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>There are 9 numerical variables in the dataset.</p></li>
 <li><p>8 are continuous variables and 1 is discrete variable.</p></li>
@@ -2798,7 +2815,7 @@ <h3><span class="section-number">37.65.9.2. </span>Summary of numerical variable
 </ul>
 </section>
 <section id="outliers-in-numerical-variables">
-<h3><span class="section-number">37.65.9.3. </span>Outliers in numerical variables<a class="headerlink" href="#outliers-in-numerical-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.9.3. </span>Outliers in numerical variables<a class="headerlink" href="#outliers-in-numerical-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># view summary statistics in numerical variables</span>
@@ -3006,7 +3023,7 @@ <h3><span class="section-number">37.65.9.3. </span>Outliers in numerical variabl
 <p>The above boxplots confirm that there are lot of outliers in these variables.</p>
 </section>
 <section id="handle-outliers-with-svms">
-<h3><span class="section-number">37.65.9.4. </span>Handle outliers with SVMs<a class="headerlink" href="#handle-outliers-with-svms" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.9.4. </span>Handle outliers with SVMs<a class="headerlink" href="#handle-outliers-with-svms" title="Permalink to this headline">#</a></h3>
 <p>There are 2 variants of SVMs. They are <code class="docutils literal notranslate"><span class="pre">hard-margin</span> <span class="pre">variant</span> <span class="pre">of</span> <span class="pre">SVM</span></code> and <code class="docutils literal notranslate"><span class="pre">soft-margin</span> <span class="pre">variant</span> <span class="pre">of</span> <span class="pre">SVM</span></code>.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">hard-margin</span> <span class="pre">variant</span> <span class="pre">of</span> <span class="pre">SVM</span></code> does not deal with outliers. In this case, we want to find the hyperplane with maximum margin such that every training point is correctly classified with margin at least 1. This technique does not handle outliers well.</p>
 <p>Another version of SVM is called <code class="docutils literal notranslate"><span class="pre">soft-margin</span> <span class="pre">variant</span> <span class="pre">of</span> <span class="pre">SVM</span></code>. In this case, we can have a few points incorrectly classified or
@@ -3014,7 +3031,7 @@ <h3><span class="section-number">37.65.9.4. </span>Handle outliers with SVMs<a c
 <p>The message is that since the dataset contains outliers, so the value of C should be high while training the model.</p>
 </section>
 <section id="check-the-distribution-of-variables">
-<h3><span class="section-number">37.65.9.5. </span>Check the distribution of variables<a class="headerlink" href="#check-the-distribution-of-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.9.5. </span>Check the distribution of variables<a class="headerlink" href="#check-the-distribution-of-variables" title="Permalink to this headline">#</a></h3>
 <p>Now, I will plot the histograms to check distributions to find out if they are normal or skewed.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3088,7 +3105,7 @@ <h3><span class="section-number">37.65.9.5. </span>Check the distribution of var
 </section>
 </section>
 <section id="declare-feature-vector-and-target-variable">
-<h2><span class="section-number">37.65.10. </span>Declare feature vector and target variable<a class="headerlink" href="#declare-feature-vector-and-target-variable" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.63.10. </span>Declare feature vector and target variable<a class="headerlink" href="#declare-feature-vector-and-target-variable" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">&#39;target_class&#39;</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
@@ -3100,7 +3117,7 @@ <h2><span class="section-number">37.65.10. </span>Declare feature vector and tar
 </div>
 </section>
 <section id="split-data-into-separate-training-and-test-set">
-<h2><span class="section-number">37.65.11. </span>Split data into separate training and test set<a class="headerlink" href="#split-data-into-separate-training-and-test-set" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.63.11. </span>Split data into separate training and test set<a class="headerlink" href="#split-data-into-separate-training-and-test-set" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># split X and y into training and testing sets</span>
@@ -3128,7 +3145,7 @@ <h2><span class="section-number">37.65.11. </span>Split data into separate train
 </div>
 </section>
 <section id="feature-scaling">
-<h2><span class="section-number">37.65.12. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.63.12. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cols</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">columns</span>
@@ -3294,7 +3311,7 @@ <h2><span class="section-number">37.65.12. </span>Feature Scaling<a class="heade
 <p>We now have <code class="docutils literal notranslate"><span class="pre">X_train</span></code> dataset ready to be fed into the Logistic Regression classifier. I will do it as follows.</p>
 </section>
 <section id="run-svm-with-default-hyperparameters">
-<h2><span class="section-number">37.65.13. </span>Run SVM with default hyperparameters<a class="headerlink" href="#run-svm-with-default-hyperparameters" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.63.13. </span>Run SVM with default hyperparameters<a class="headerlink" href="#run-svm-with-default-hyperparameters" title="Permalink to this headline">#</a></h2>
 <p>Default hyperparameter means C=1.0,  kernel=<code class="docutils literal notranslate"><span class="pre">rbf</span></code> and gamma=<code class="docutils literal notranslate"><span class="pre">auto</span></code> among other parameters.</p>
 <div class="cell docutils container">
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@@ -3330,7 +3347,7 @@ <h2><span class="section-number">37.65.13. </span>Run SVM with default hyperpara
 </div>
 </div>
 <section id="run-svm-with-rbf-kernel-and-c-100-0">
-<h3><span class="section-number">37.65.13.1. </span>Run SVM with rbf kernel and C=100.0<a class="headerlink" href="#run-svm-with-rbf-kernel-and-c-100-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.13.1. </span>Run SVM with rbf kernel and C=100.0<a class="headerlink" href="#run-svm-with-rbf-kernel-and-c-100-0" title="Permalink to this headline">#</a></h3>
 <p>We have seen that there are outliers in our dataset. So, we should increase the value of C as higher C means fewer outliers.
 So, I will run SVM with kernel=<code class="docutils literal notranslate"><span class="pre">rbf</span></code> and C=100.0.</p>
 <div class="cell docutils container">
@@ -3362,7 +3379,7 @@ <h3><span class="section-number">37.65.13.1. </span>Run SVM with rbf kernel and
 <p>Now, I will further increase the value of C=1000.0 and check accuracy.</p>
 </section>
 <section id="run-svm-with-rbf-kernel-and-c-1000-0">
-<h3><span class="section-number">37.65.13.2. </span>Run SVM with rbf kernel and C=1000.0<a class="headerlink" href="#run-svm-with-rbf-kernel-and-c-1000-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.13.2. </span>Run SVM with rbf kernel and C=1000.0<a class="headerlink" href="#run-svm-with-rbf-kernel-and-c-1000-0" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate classifier with rbf kernel and C=1000</span>
@@ -3392,9 +3409,9 @@ <h3><span class="section-number">37.65.13.2. </span>Run SVM with rbf kernel and
 </section>
 </section>
 <section id="run-svm-with-linear-kernel">
-<h2><span class="section-number">37.65.14. </span>Run SVM with linear kernel<a class="headerlink" href="#run-svm-with-linear-kernel" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.63.14. </span>Run SVM with linear kernel<a class="headerlink" href="#run-svm-with-linear-kernel" title="Permalink to this headline">#</a></h2>
 <section id="run-svm-with-linear-kernel-and-c-1-0">
-<h3><span class="section-number">37.65.14.1. </span>Run SVM with linear kernel and C=1.0<a class="headerlink" href="#run-svm-with-linear-kernel-and-c-1-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.14.1. </span>Run SVM with linear kernel and C=1.0<a class="headerlink" href="#run-svm-with-linear-kernel-and-c-1-0" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate classifier with linear kernel and C=1.0</span>
@@ -3422,7 +3439,7 @@ <h3><span class="section-number">37.65.14.1. </span>Run SVM with linear kernel a
 </div>
 </section>
 <section id="run-svm-with-linear-kernel-and-c-100-0">
-<h3><span class="section-number">37.65.14.2. </span>Run SVM with linear kernel and C=100.0<a class="headerlink" href="#run-svm-with-linear-kernel-and-c-100-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.14.2. </span>Run SVM with linear kernel and C=100.0<a class="headerlink" href="#run-svm-with-linear-kernel-and-c-100-0" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate classifier with linear kernel and C=100.0</span>
@@ -3450,7 +3467,7 @@ <h3><span class="section-number">37.65.14.2. </span>Run SVM with linear kernel a
 </div>
 </section>
 <section id="run-svm-with-linear-kernel-and-c-1000-0">
-<h3><span class="section-number">37.65.14.3. </span>Run SVM with linear kernel and C=1000.0<a class="headerlink" href="#run-svm-with-linear-kernel-and-c-1000-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.14.3. </span>Run SVM with linear kernel and C=1000.0<a class="headerlink" href="#run-svm-with-linear-kernel-and-c-1000-0" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate classifier with linear kernel and C=1000.0</span>
@@ -3480,7 +3497,7 @@ <h3><span class="section-number">37.65.14.3. </span>Run SVM with linear kernel a
 <p>Here, <strong>y_test</strong> are the true class labels and <strong>y_pred</strong> are the predicted class labels in the test-set.</p>
 </section>
 <section id="compare-the-train-set-and-test-set-accuracy">
-<h3><span class="section-number">37.65.14.4. </span>Compare the train-set and test-set accuracy<a class="headerlink" href="#compare-the-train-set-and-test-set-accuracy" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.14.4. </span>Compare the train-set and test-set accuracy<a class="headerlink" href="#compare-the-train-set-and-test-set-accuracy" title="Permalink to this headline">#</a></h3>
 <p>Now, I will compare the train-set and test-set accuracy to check for overfitting.</p>
 <div class="cell docutils container">
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@@ -3511,7 +3528,7 @@ <h3><span class="section-number">37.65.14.4. </span>Compare the train-set and te
 <p>We can see that the training set and test-set accuracy are very much comparable.</p>
 </section>
 <section id="check-for-overfitting-and-underfitting">
-<h3><span class="section-number">37.65.14.5. </span>Check for overfitting and underfitting<a class="headerlink" href="#check-for-overfitting-and-underfitting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.14.5. </span>Check for overfitting and underfitting<a class="headerlink" href="#check-for-overfitting-and-underfitting" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># print the scores on training and test set</span>
@@ -3532,7 +3549,7 @@ <h3><span class="section-number">37.65.14.5. </span>Check for overfitting and un
 <p>The training-set accuracy score is 0.9783 while the test-set accuracy to be 0.9830. These two values are quite comparable. So, there is no question of overfitting.</p>
 </section>
 <section id="compare-model-accuracy-with-null-accuracy">
-<h3><span class="section-number">37.65.14.6. </span>Compare model accuracy with null accuracy<a class="headerlink" href="#compare-model-accuracy-with-null-accuracy" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.14.6. </span>Compare model accuracy with null accuracy<a class="headerlink" href="#compare-model-accuracy-with-null-accuracy" title="Permalink to this headline">#</a></h3>
 <p>So, the model accuracy is 0.9832. But, we cannot say that our model is very good based on the above accuracy. We must compare it with the <strong>null accuracy</strong>. Null accuracy is the accuracy that could be achieved by always predicting the most frequent class.</p>
 <p>So, we should first check the class distribution in the test set.</p>
 <div class="cell docutils container">
@@ -3572,9 +3589,9 @@ <h3><span class="section-number">37.65.14.6. </span>Compare model accuracy with
 </section>
 </section>
 <section id="run-svm-with-polynomial-kernel">
-<h2><span class="section-number">37.65.15. </span>Run SVM with polynomial kernel<a class="headerlink" href="#run-svm-with-polynomial-kernel" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.63.15. </span>Run SVM with polynomial kernel<a class="headerlink" href="#run-svm-with-polynomial-kernel" title="Permalink to this headline">#</a></h2>
 <section id="run-svm-with-polynomial-kernel-and-c-1-0">
-<h3><span class="section-number">37.65.15.1. </span>Run SVM with polynomial kernel and C=1.0<a class="headerlink" href="#run-svm-with-polynomial-kernel-and-c-1-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.15.1. </span>Run SVM with polynomial kernel and C=1.0<a class="headerlink" href="#run-svm-with-polynomial-kernel-and-c-1-0" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate classifier with polynomial kernel and C=1.0</span>
@@ -3602,7 +3619,7 @@ <h3><span class="section-number">37.65.15.1. </span>Run SVM with polynomial kern
 </div>
 </section>
 <section id="run-svm-with-polynomial-kernel-and-c-100-0">
-<h3><span class="section-number">37.65.15.2. </span>Run SVM with polynomial kernel and C=100.0<a class="headerlink" href="#run-svm-with-polynomial-kernel-and-c-100-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.15.2. </span>Run SVM with polynomial kernel and C=100.0<a class="headerlink" href="#run-svm-with-polynomial-kernel-and-c-100-0" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate classifier with polynomial kernel and C=100.0</span>
@@ -3632,9 +3649,9 @@ <h3><span class="section-number">37.65.15.2. </span>Run SVM with polynomial kern
 </section>
 </section>
 <section id="run-svm-with-sigmoid-kernel">
-<h2><span class="section-number">37.65.16. </span>Run SVM with sigmoid kernel<a class="headerlink" href="#run-svm-with-sigmoid-kernel" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.63.16. </span>Run SVM with sigmoid kernel<a class="headerlink" href="#run-svm-with-sigmoid-kernel" title="Permalink to this headline">#</a></h2>
 <section id="run-svm-with-sigmoid-kernel-and-c-1-0">
-<h3><span class="section-number">37.65.16.1. </span>Run SVM with sigmoid kernel and C=1.0<a class="headerlink" href="#run-svm-with-sigmoid-kernel-and-c-1-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.16.1. </span>Run SVM with sigmoid kernel and C=1.0<a class="headerlink" href="#run-svm-with-sigmoid-kernel-and-c-1-0" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate classifier with sigmoid kernel and C=1.0</span>
@@ -3662,7 +3679,7 @@ <h3><span class="section-number">37.65.16.1. </span>Run SVM with sigmoid kernel
 </div>
 </section>
 <section id="run-svm-with-sigmoid-kernel-and-c-100-0">
-<h3><span class="section-number">37.65.16.2. </span>Run SVM with sigmoid kernel and C=100.0<a class="headerlink" href="#run-svm-with-sigmoid-kernel-and-c-100-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.16.2. </span>Run SVM with sigmoid kernel and C=100.0<a class="headerlink" href="#run-svm-with-sigmoid-kernel-and-c-100-0" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate classifier with sigmoid kernel and C=100.0</span>
@@ -3691,7 +3708,7 @@ <h3><span class="section-number">37.65.16.2. </span>Run SVM with sigmoid kernel
 <p>We can see that sigmoid kernel is also performing poorly just like with polynomial kernel.</p>
 </section>
 <section id="comments">
-<h3><span class="section-number">37.65.16.3. </span>Comments<a class="headerlink" href="#comments" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.16.3. </span>Comments<a class="headerlink" href="#comments" title="Permalink to this headline">#</a></h3>
 <p>We get maximum accuracy with <code class="docutils literal notranslate"><span class="pre">rbf</span></code> and <code class="docutils literal notranslate"><span class="pre">linear</span></code> kernel with C=100.0. and the accuracy is 0.9832. Based on the above analysis we can conclude that our classification model accuracy is very good. Our model is doing a very good job in terms of predicting the class labels.</p>
 <p>But, this is not true. Here, we have an imbalanced dataset. The problem is that accuracy is an inadequate measure for quantifying predictive performance in the imbalanced dataset problem.</p>
 <p>So, we must explore alternative metrices that provide better guidance in selecting models. In particular, we would like to know the underlying distribution of values and the type of errors our classifer is making.</p>
@@ -3699,7 +3716,7 @@ <h3><span class="section-number">37.65.16.3. </span>Comments<a class="headerlink
 </section>
 </section>
 <section id="confusion-matrix">
-<h2><span class="section-number">37.65.17. </span>Confusion matrix<a class="headerlink" href="#confusion-matrix" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.63.17. </span>Confusion matrix<a class="headerlink" href="#confusion-matrix" title="Permalink to this headline">#</a></h2>
 <p>A confusion matrix is a tool for summarizing the performance of a classification algorithm. A confusion matrix will give us a clear picture of classification model performance and the types of errors produced by the model. It gives us a summary of correct and incorrect predictions broken down by each category. The summary is represented in a tabular form.</p>
 <p>Four types of outcomes are possible while evaluating a classification model performance. These four outcomes are described below:-</p>
 <p><strong>True Positives (TP)</strong> – True Positives occur when we predict an observation belongs to a certain class and the observation actually belongs to that class.</p>
@@ -3772,9 +3789,9 @@ <h2><span class="section-number">37.65.17. </span>Confusion matrix<a class="head
 </div>
 </section>
 <section id="classification-metrices">
-<h2><span class="section-number">37.65.18. </span>Classification metrices<a class="headerlink" href="#classification-metrices" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.63.18. </span>Classification metrices<a class="headerlink" href="#classification-metrices" title="Permalink to this headline">#</a></h2>
 <section id="classification-report">
-<h3><span class="section-number">37.65.18.1. </span>Classification Report<a class="headerlink" href="#classification-report" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.18.1. </span>Classification Report<a class="headerlink" href="#classification-report" title="Permalink to this headline">#</a></h3>
 <p><strong>Classification report</strong> is another way to evaluate the classification model performance. It displays the  <strong>precision</strong>, <strong>recall</strong>, <strong>f1</strong> and <strong>support</strong> scores for the model. I have described these terms in later.</p>
 <p>We can print a classification report as follows:-</p>
 <div class="cell docutils container">
@@ -3800,7 +3817,7 @@ <h3><span class="section-number">37.65.18.1. </span>Classification Report<a clas
 </div>
 </section>
 <section id="classification-accuracy">
-<h3><span class="section-number">37.65.18.2. </span>Classification accuracy<a class="headerlink" href="#classification-accuracy" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.18.2. </span>Classification accuracy<a class="headerlink" href="#classification-accuracy" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">TP</span> <span class="o">=</span> <span class="n">cm</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span>
@@ -3829,7 +3846,7 @@ <h3><span class="section-number">37.65.18.2. </span>Classification accuracy<a cl
 </div>
 </section>
 <section id="classification-error">
-<h3><span class="section-number">37.65.18.3. </span>Classification error<a class="headerlink" href="#classification-error" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.18.3. </span>Classification error<a class="headerlink" href="#classification-error" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># print classification error</span>
@@ -3848,7 +3865,7 @@ <h3><span class="section-number">37.65.18.3. </span>Classification error<a class
 </div>
 </section>
 <section id="precision">
-<h3><span class="section-number">37.65.18.4. </span>Precision<a class="headerlink" href="#precision" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.18.4. </span>Precision<a class="headerlink" href="#precision" title="Permalink to this headline">#</a></h3>
 <p><strong>Precision</strong> can be defined as the percentage of correctly predicted positive outcomes out of all the predicted positive outcomes. It can be given as the ratio of true positives (TP) to the sum of true and false positives (TP + FP).</p>
 <p>So, <strong>Precision</strong> identifies the proportion of correctly predicted positive outcome. It is more concerned with the positive class than the negative class.</p>
 <p>Mathematically, precision can be defined as the ratio of <code class="docutils literal notranslate"><span class="pre">TP</span> <span class="pre">to</span> <span class="pre">(TP</span> <span class="pre">+</span> <span class="pre">FP)</span></code>.</p>
@@ -3871,7 +3888,7 @@ <h3><span class="section-number">37.65.18.4. </span>Precision<a class="headerlin
 </div>
 </section>
 <section id="recall">
-<h3><span class="section-number">37.65.18.5. </span>Recall<a class="headerlink" href="#recall" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.18.5. </span>Recall<a class="headerlink" href="#recall" title="Permalink to this headline">#</a></h3>
 <p>Recall can be defined as the percentage of correctly predicted positive outcomes out of all the actual positive outcomes.
 It can be given as the ratio of true positives (TP) to the sum of true positives and false negatives (TP + FN). <strong>Recall</strong> is also called <strong>Sensitivity</strong>.</p>
 <p><strong>Recall</strong> identifies the proportion of correctly predicted actual positives.</p>
@@ -3892,7 +3909,7 @@ <h3><span class="section-number">37.65.18.5. </span>Recall<a class="headerlink"
 </div>
 </section>
 <section id="true-positive-rate">
-<h3><span class="section-number">37.65.18.6. </span>True Positive Rate<a class="headerlink" href="#true-positive-rate" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.18.6. </span>True Positive Rate<a class="headerlink" href="#true-positive-rate" title="Permalink to this headline">#</a></h3>
 <p><strong>True Positive Rate</strong> is synonymous with <strong>Recall</strong>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3911,7 +3928,7 @@ <h3><span class="section-number">37.65.18.6. </span>True Positive Rate<a class="
 </div>
 </section>
 <section id="false-positive-rate">
-<h3><span class="section-number">37.65.18.7. </span>False Positive Rate<a class="headerlink" href="#false-positive-rate" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.18.7. </span>False Positive Rate<a class="headerlink" href="#false-positive-rate" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">false_positive_rate</span> <span class="o">=</span> <span class="n">FP</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">FP</span> <span class="o">+</span> <span class="n">TN</span><span class="p">)</span>
@@ -3929,7 +3946,7 @@ <h3><span class="section-number">37.65.18.7. </span>False Positive Rate<a class=
 </div>
 </section>
 <section id="specificity">
-<h3><span class="section-number">37.65.18.8. </span>Specificity<a class="headerlink" href="#specificity" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.18.8. </span>Specificity<a class="headerlink" href="#specificity" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">specificity</span> <span class="o">=</span> <span class="n">TN</span> <span class="o">/</span> <span class="p">(</span><span class="n">TN</span> <span class="o">+</span> <span class="n">FP</span><span class="p">)</span>
@@ -3946,20 +3963,20 @@ <h3><span class="section-number">37.65.18.8. </span>Specificity<a class="headerl
 </div>
 </section>
 <section id="f1-score">
-<h3><span class="section-number">37.65.18.9. </span>f1-score<a class="headerlink" href="#f1-score" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.18.9. </span>f1-score<a class="headerlink" href="#f1-score" title="Permalink to this headline">#</a></h3>
 <p><strong>f1-score</strong> is the weighted harmonic mean of precision and recall. The best possible <strong>f1-score</strong> would be 1.0 and the worst
 would be 0.0.  <strong>f1-score</strong> is the harmonic mean of precision and recall. So, <strong>f1-score</strong> is always lower than accuracy measures as they embed precision and recall into their computation. The weighted average of <code class="docutils literal notranslate"><span class="pre">f1-score</span></code> should be used to
 compare classifier models, not global accuracy.</p>
 </section>
 <section id="support">
-<h3><span class="section-number">37.65.18.10. </span>Support<a class="headerlink" href="#support" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.18.10. </span>Support<a class="headerlink" href="#support" title="Permalink to this headline">#</a></h3>
 <p><strong>Support</strong> is the actual number of occurrences of the class in our dataset.</p>
 </section>
 </section>
 <section id="roc-auc">
-<h2><span class="section-number">37.65.19. </span>ROC - AUC<a class="headerlink" href="#roc-auc" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.63.19. </span>ROC - AUC<a class="headerlink" href="#roc-auc" title="Permalink to this headline">#</a></h2>
 <section id="roc-curve">
-<h3><span class="section-number">37.65.19.1. </span>ROC Curve<a class="headerlink" href="#roc-curve" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.19.1. </span>ROC Curve<a class="headerlink" href="#roc-curve" title="Permalink to this headline">#</a></h3>
 <p>Another tool to measure the classification model performance visually is <strong>ROC Curve</strong>. ROC Curve stands for <strong>Receiver Operating Characteristic Curve</strong>. An <strong>ROC Curve</strong> is a plot which shows the performance of a classification model at various
 classification threshold levels.</p>
 <p>The <strong>ROC Curve</strong> plots the <strong>True Positive Rate (TPR)</strong> against the <strong>False Positive Rate (FPR)</strong> at various threshold levels.</p>
@@ -3999,7 +4016,7 @@ <h3><span class="section-number">37.65.19.1. </span>ROC Curve<a class="headerlin
 <p>ROC curve help us to choose a threshold level that balances sensitivity and specificity for a particular context.</p>
 </section>
 <section id="id4">
-<h3><span class="section-number">37.65.19.2. </span>ROC  AUC<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.19.2. </span>ROC  AUC<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
 <p><strong>ROC AUC</strong> stands for <strong>Receiver Operating Characteristic - Area Under Curve</strong>. It is a technique to compare classifier performance. In this technique, we measure the <code class="docutils literal notranslate"><span class="pre">area</span> <span class="pre">under</span> <span class="pre">the</span> <span class="pre">curve</span> <span class="pre">(AUC)</span></code>. A perfect classifier will have a ROC AUC equal to 1, whereas a purely random classifier will have a ROC AUC equal to 0.5.</p>
 <p>So, <strong>ROC AUC</strong> is the percentage of the ROC plot that is underneath the curve.</p>
 <div class="cell docutils container">
@@ -4022,7 +4039,7 @@ <h3><span class="section-number">37.65.19.2. </span>ROC  AUC<a class="headerlink
 </div>
 </section>
 <section id="id5">
-<h3><span class="section-number">37.65.19.3. </span>Comments<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.19.3. </span>Comments<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>ROC AUC is a single number summary of classifier performance. The higher the value, the better the classifier.</p></li>
 <li><p>ROC AUC of our model approaches towards 1. So, we can conclude that our classifier does a good job in classifying the pulsar star.</p></li>
@@ -4048,12 +4065,12 @@ <h3><span class="section-number">37.65.19.3. </span>Comments<a class="headerlink
 </section>
 </section>
 <section id="stratified-k-fold-cross-validation-with-shuffle-split">
-<h2><span class="section-number">37.65.20. </span>Stratified k-fold Cross Validation with shuffle split<a class="headerlink" href="#stratified-k-fold-cross-validation-with-shuffle-split" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.63.20. </span>Stratified k-fold Cross Validation with shuffle split<a class="headerlink" href="#stratified-k-fold-cross-validation-with-shuffle-split" title="Permalink to this headline">#</a></h2>
 <p>k-fold cross-validation is a very useful technique to evaluate model performance. But, it fails here because we have a imbalnced dataset. So, in the case of imbalanced dataset, I will use another technique to evaluate model performance. It is called <code class="docutils literal notranslate"><span class="pre">stratified</span> <span class="pre">k-fold</span> <span class="pre">cross-validation</span></code>.</p>
 <p>In <code class="docutils literal notranslate"><span class="pre">stratified</span> <span class="pre">k-fold</span> <span class="pre">cross-validation</span></code>, we split the data such that the proportions between classes are the same in each fold as they are in the whole dataset.</p>
 <p>Moreover, I will shuffle the data before splitting because shuffling yields much better result.</p>
 <section id="stratified-k-fold-cross-validation-with-shuffle-split-with-linear-kernel">
-<h3><span class="section-number">37.65.20.1. </span>Stratified k-Fold Cross Validation with shuffle split with  linear kernel<a class="headerlink" href="#stratified-k-fold-cross-validation-with-shuffle-split-with-linear-kernel" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.20.1. </span>Stratified k-Fold Cross Validation with shuffle split with  linear kernel<a class="headerlink" href="#stratified-k-fold-cross-validation-with-shuffle-split-with-linear-kernel" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">KFold</span>
@@ -4102,7 +4119,7 @@ <h3><span class="section-number">37.65.20.1. </span>Stratified k-Fold Cross Vali
 </div>
 </section>
 <section id="stratified-k-fold-cross-validation-with-shuffle-split-with-rbf-kernel">
-<h3><span class="section-number">37.65.20.2. </span>Stratified k-Fold Cross Validation with shuffle split with rbf kernel<a class="headerlink" href="#stratified-k-fold-cross-validation-with-shuffle-split-with-rbf-kernel" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.20.2. </span>Stratified k-Fold Cross Validation with shuffle split with rbf kernel<a class="headerlink" href="#stratified-k-fold-cross-validation-with-shuffle-split-with-rbf-kernel" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">rbf_svc</span><span class="o">=</span><span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;rbf&#39;</span><span class="p">)</span>
@@ -4145,13 +4162,13 @@ <h3><span class="section-number">37.65.20.2. </span>Stratified k-Fold Cross Vali
 </div>
 </section>
 <section id="id6">
-<h3><span class="section-number">37.65.20.3. </span>Comments<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.20.3. </span>Comments<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h3>
 <p>I obtain higher average stratified k-fold cross-validation score of 0.9789 with linear kernel but the model accuracy is 0.9832.
 So, stratified cross-validation technique does not help to improve the model performance.</p>
 </section>
 </section>
 <section id="hyperparameter-optimization-using-gridsearch-cv">
-<h2><span class="section-number">37.65.21. </span>Hyperparameter Optimization using GridSearch CV<a class="headerlink" href="#hyperparameter-optimization-using-gridsearch-cv" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.63.21. </span>Hyperparameter Optimization using GridSearch CV<a class="headerlink" href="#hyperparameter-optimization-using-gridsearch-cv" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># import GridSearchCV</span>
@@ -4217,7 +4234,7 @@ <h2><span class="section-number">37.65.21. </span>Hyperparameter Optimization us
 </div>
 </div>
 <section id="id7">
-<h3><span class="section-number">37.65.21.1. </span>Comments<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.63.21.1. </span>Comments<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Our original model test accuracy is 0.9832 while GridSearch CV score on test-set is 0.9835.</p></li>
 <li><p>So, GridSearch CV helps to identify the parameters that will improve the performance for this particular model.</p></li>
@@ -4228,7 +4245,7 @@ <h3><span class="section-number">37.65.21.1. </span>Comments<a class="headerlink
 </section>
 </section>
 <section id="results-and-conclusion">
-<h2><span class="section-number">37.65.22. </span>Results and conclusion<a class="headerlink" href="#results-and-conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.63.22. </span>Results and conclusion<a class="headerlink" href="#results-and-conclusion" title="Permalink to this headline">#</a></h2>
 <ol class="simple">
 <li><p>There are outliers in our dataset. So, as we increase the value of C to limit fewer outliers, the accuracy increased. This is true with different kinds of kernels.</p></li>
 <li><p>We get maximum accuracy with <code class="docutils literal notranslate"><span class="pre">rbf</span></code> and <code class="docutils literal notranslate"><span class="pre">linear</span></code> kernel with C=100.0 and the accuracy is 0.9832. So, we can conclude that our model is doing a very good job in terms of predicting the class labels. But, this is not true. Here, we have an imbalanced dataset. Accuracy is an inadequate measure for quantifying predictive performance in the imbalanced dataset problem. So, we must explore <code class="docutils literal notranslate"><span class="pre">confusion</span> <span class="pre">matrix</span></code> that provide better guidance in selecting models.</p></li>
@@ -4238,7 +4255,7 @@ <h2><span class="section-number">37.65.22. </span>Results and conclusion<a class
 </ol>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.65.23. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.63.23. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to PRASHANT BANERJEE for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/prashant111/svm-classifier-tutorial"> SVM Classifier Tutorial </a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -4276,17 +4293,17 @@ <h2><span class="section-number">37.65.23. </span>Acknowledgments<a class="heade
                 
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-     37.49. Gradient descent
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-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.82. Random Forest Classifier with Feature Importance
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-     37.97. Intro to TensorFlow for Deep Learning
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     <a class="reference internal" href="../../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
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     <a class="reference internal" href="../../deep-learning/time-series-forecasting-assignment.html">
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     <a class="reference internal" href="../../deep-learning/nn-for-classification-assignment.html">
-     37.108. Neural Networks for Classification with TensorFlow
+     37.106. Neural Networks for Classification with TensorFlow
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     <a class="reference internal" href="../../deep-learning/nn-classify-15-fruits-assignment.html">
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-     37.114. DQN On Foreign Exchange Market
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+     37.113. Art by gan
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-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
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    38. Slides
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+   37.65.1. Contents
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   <a class="reference internal nav-link" href="#imports">
-   37.67.2. 1 - Imports
+   37.65.2. 1 - Imports
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   <a class="reference internal nav-link" href="#exploratory-analysis">
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+   37.65.4. 3 - Exploratory Analysis
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+   37.65.5. 4 - Data Preprocessing
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-   37.67.6. 5 - Training Support Vector Classifier
+   37.65.6. 5 - Training Support Vector Classifier
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+   37.65.5. 4 - Data Preprocessing
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+   37.65.6. 5 - Training Support Vector Classifier
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+   37.65.7. 6 - Evaluating Support Vector Classifier
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+   37.65.8. 7 - Improving Support Vector Classifier
   </a>
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.67.9. Acknowledgments
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   </a>
  </li>
 </ul>
@@ -1628,7 +1645,7 @@ <h2> Contents </h2>
                 
   <p><a name='0'></a></p>
 <section class="tex2jax_ignore mathjax_ignore" id="support-vector-machines-svm-classification">
-<h1><span class="section-number">37.67. </span>Support Vector Machines (SVM) - Classification<a class="headerlink" href="#support-vector-machines-svm-classification" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.65. </span>Support Vector Machines (SVM) - Classification<a class="headerlink" href="#support-vector-machines-svm-classification" title="Permalink to this headline">#</a></h1>
 <p>Support Vector Machines are the type of supervised learning algorithms used for regression, classification and detecting outliers. SVMs are remarkably one of the powerful models in classical machine learning suited for handling complex and high dimensional datasets.</p>
 <p>With SVM supporting different kernels (linear, polynomial, Radial Basis Function(rbf), and sigmoid), SVM can tackle different kinds of datasets, both linear and non linear.</p>
 <p>While the maths behind the SVMs are beyond the scope of this notebook, here is the idea behind SVMs:</p>
@@ -1690,7 +1707,7 @@ <h1><span class="section-number">37.67. </span>Support Vector Machines (SVM) - C
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-<h2><span class="section-number">37.67.1. </span>Contents<a class="headerlink" href="#contents" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.65.1. </span>Contents<a class="headerlink" href="#contents" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="#1">1 - Imports</a></p></li>
 <li><p><a class="reference external" href="#2">2 - Loading the data</a></p></li>
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-<h2><span class="section-number">37.67.2. </span>1 - Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.65.2. </span>1 - Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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-<h2><span class="section-number">37.67.3. </span>2 - Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.65.3. </span>2 - Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
 <p>We will get Iris data from Sklearn datasets. Setting <code class="docutils literal notranslate"><span class="pre">as_frame</span></code> parameter to <code class="docutils literal notranslate"><span class="pre">True</span></code> will return data as a Pandas Dataframe.</p>
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 </section>
 <section id="exploratory-analysis">
-<h2><span class="section-number">37.67.4. </span>3 - Exploratory Analysis<a class="headerlink" href="#exploratory-analysis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.65.4. </span>3 - Exploratory Analysis<a class="headerlink" href="#exploratory-analysis" title="Permalink to this headline">#</a></h2>
 <p>Before exploring some insight in data, let’s split it into test and train set.</p>
 <div class="cell docutils container">
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@@ -1936,7 +1953,7 @@ <h2><span class="section-number">37.67.4. </span>3 - Exploratory Analysis<a clas
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 </section>
 <section id="data-preprocessing">
-<h2><span class="section-number">37.67.5. </span>4 - Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.65.5. </span>4 - Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
 <p>The features already have small values but let’s scale them to be between 0 and 1. SVM work well with scaled values. I will set up a pipeline to handle that.</p>
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@@ -1972,7 +1989,7 @@ <h2><span class="section-number">37.67.5. </span>4 - Data Preprocessing<a class=
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 </section>
 <section id="training-support-vector-classifier">
-<h2><span class="section-number">37.67.6. </span>5 - Training Support Vector Classifier<a class="headerlink" href="#training-support-vector-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.65.6. </span>5 - Training Support Vector Classifier<a class="headerlink" href="#training-support-vector-classifier" title="Permalink to this headline">#</a></h2>
 <p>We are going to train two classifiers: Linear SVC and SVC that we can use different kernels. SVM supports <code class="docutils literal notranslate"><span class="pre">linear</span></code>, <code class="docutils literal notranslate"><span class="pre">polynomial</span></code>, <code class="docutils literal notranslate"><span class="pre">sigmoid</span></code> and <code class="docutils literal notranslate"><span class="pre">rbf</span></code> kernels.</p>
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@@ -2000,7 +2017,7 @@ <h2><span class="section-number">37.67.6. </span>5 - Training Support Vector Cla
 <p><a name='6'></a></p>
 </section>
 <section id="evaluating-support-vector-classifier">
-<h2><span class="section-number">37.67.7. </span>6 - Evaluating Support Vector Classifier<a class="headerlink" href="#evaluating-support-vector-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.65.7. </span>6 - Evaluating Support Vector Classifier<a class="headerlink" href="#evaluating-support-vector-classifier" title="Permalink to this headline">#</a></h2>
 <p>Let’s first check the accuracy on the training. For this step since we are trying to find model to improve further, we won’t touch test set yet.</p>
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@@ -2076,7 +2093,7 @@ <h2><span class="section-number">37.67.7. </span>6 - Evaluating Support Vector C
 <p><a name='7'></a></p>
 </section>
 <section id="improving-support-vector-classifier">
-<h2><span class="section-number">37.67.8. </span>7 - Improving Support Vector Classifier<a class="headerlink" href="#improving-support-vector-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.65.8. </span>7 - Improving Support Vector Classifier<a class="headerlink" href="#improving-support-vector-classifier" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchCV</span>
@@ -3555,7 +3572,7 @@ <h2><span class="section-number">37.67.8. </span>7 - Improving Support Vector Cl
 <p>This is the end of the lab which was all about using Support Vector Machines for classification task. As you can see, SVM is a robust algorithm given how it supports different kernels. These kernels are what make it suitable for both linear and non linear problems. In real world, many datasets are not linear. So when you can’t get good results with linear models, try things like SVM with polynomial kernel.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.67.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.65.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Jake VanderPlas for creating the open-source course <a class="reference external" href="https://github.com/jakevdp/PythonDataScienceHandbook"> Python Data Science Handbook </a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -3597,13 +3614,13 @@ <h2><span class="section-number">37.67.9. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
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-            <p class="prev-next-title"><span class="section-number">37.66. </span>Support Vector Machines (SVM) - Intro and SVM for Regression</p>
+            <p class="prev-next-title"><span class="section-number">37.64. </span>Support Vector Machines (SVM) - Intro and SVM for Regression</p>
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-        <p class="prev-next-title"><span class="section-number">37.68. </span>Decision Trees - Intro and Regression</p>
+        <p class="prev-next-title"><span class="section-number">37.66. </span>Decision Trees - Intro and Regression</p>
     </div>
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diff --git a/assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html b/assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html
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-    <link rel="prev" title="37.65. Kernel method assignment 1" href="kernel-method-assignment-1.html" />
+    <link rel="next" title="37.65. Support Vector Machines (SVM) - Classification" href="support_vector_machines_for_classification.html" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/working-with-data/data-preparation.html">
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    6. Data visualization
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    7. Data Science lifecycle
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
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-   <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-metrics.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/ml-logistic-regression-1.html">
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-     37.63. Explore classification methods
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-    <a class="reference internal" href="../../ml-fundamentals/linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
     <a class="reference internal" href="kernel-method-assignment-1.html">
-     37.65. Kernel method assignment 1
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    <li class="toctree-l2 current active">
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-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="support_vector_machines_for_classification.html">
-     37.67. Support Vector Machines (SVM) - Classification
+     37.65. Support Vector Machines (SVM) - Classification
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    <li class="toctree-l2">
     <a class="reference internal" href="decision_trees_for_regression.html">
-     37.68. Decision Trees - Intro and Regression
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    <li class="toctree-l2">
     <a class="reference internal" href="decision_trees_for_classification.html">
-     37.69. Decision Trees - Classification
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    </li>
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     <a class="reference internal" href="../model-selection/model-selection-assignment-1.html">
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-     37.76. Random forests for classification
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-     37.101. Google Stock Price Prediction RNN
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-     37.105. Fun with Variational Autoencoders
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-     37.108. Neural Networks for Classification with TensorFlow
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-     37.114. DQN On Foreign Exchange Market
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     <a class="reference internal" href="../../deep-learning/gan/gan-introduction.html">
-     37.117. Generative Adversarial Networks (GANs)
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    38. Slides
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-   37.66.1. Contents
+   37.64.1. Contents
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   <a class="reference internal nav-link" href="#imports">
-   37.66.2. 1 - Imports
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+   37.64.3. 2 - Loading the data
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-   37.66.6. 5 - Training Support Vector Regressor
+   37.64.6. 5 - Training Support Vector Regressor
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   <a class="reference internal nav-link" href="#acknowledgments">
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   </a>
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-   37.66.6. 5 - Training Support Vector Regressor
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-   37.66.7. 6 - Evaluating Support Vector Regressor
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-   37.66.8. 7 - Improving Support Vector Regressor
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.66.9. Acknowledgments
+   37.64.9. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1628,7 +1645,7 @@ <h2> Contents </h2>
                 
   <p><a name='0'></a></p>
 <section class="tex2jax_ignore mathjax_ignore" id="support-vector-machines-svm-intro-and-svm-for-regression">
-<h1><span class="section-number">37.66. </span>Support Vector Machines (SVM) - Intro and SVM for Regression<a class="headerlink" href="#support-vector-machines-svm-intro-and-svm-for-regression" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.64. </span>Support Vector Machines (SVM) - Intro and SVM for Regression<a class="headerlink" href="#support-vector-machines-svm-intro-and-svm-for-regression" title="Permalink to this headline">#</a></h1>
 <p>Support Vector Machines are the type of supervised learning algorithms used for regression, classification and detecting outliers. SVMs are remarkably one of the powerful models in classical machine learning suited for handling complex and high dimensional datasets.</p>
 <p>With SVM supporting different kernels (linear, polynomial, Radial Basis Function(rbf), and sigmoid), SVM can tackle different kinds of datasets, both linear and non linear.</p>
 <p>While the maths behind the SVMs are beyond the scope of this notebook, here is the idea behind SVMs:</p>
@@ -1636,7 +1653,7 @@ <h1><span class="section-number">37.66. </span>Support Vector Machines (SVM) - I
 <p><img alt="SVM" src="../../../_images/1200px-Kernel_Machine.svg.png" /></p>
 <p>SMVs are widely used for classification. But to motivate that, let’s start with regression. For the purpose of examining how powerful this algorithm is, we will use the same dataset that we used in linear regression notebook, and hopefully the difference in performance will be notable.</p>
 <section id="contents">
-<h2><span class="section-number">37.66.1. </span>Contents<a class="headerlink" href="#contents" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.64.1. </span>Contents<a class="headerlink" href="#contents" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="#1">1 - Imports</a></p></li>
 <li><p><a class="reference external" href="#2">2 - Loading the data</a></p></li>
@@ -1649,7 +1666,7 @@ <h2><span class="section-number">37.66.1. </span>Contents<a class="headerlink" h
 <p><a name='1'></a></p>
 </section>
 <section id="imports">
-<h2><span class="section-number">37.66.2. </span>1 - Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.64.2. </span>1 - Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -1666,7 +1683,7 @@ <h2><span class="section-number">37.66.2. </span>1 - Imports<a class="headerlink
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 </section>
 <section id="loading-the-data">
-<h2><span class="section-number">37.66.3. </span>2 - Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.64.3. </span>2 - Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cal_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;../../assets/data/housing.csv&quot;</span><span class="p">)</span>
@@ -1789,7 +1806,7 @@ <h2><span class="section-number">37.66.3. </span>2 - Loading the data<a class="h
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 </section>
 <section id="exploratory-analysis">
-<h2><span class="section-number">37.66.4. </span>3 - Exploratory Analysis<a class="headerlink" href="#exploratory-analysis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.64.4. </span>3 - Exploratory Analysis<a class="headerlink" href="#exploratory-analysis" title="Permalink to this headline">#</a></h2>
 <p>This is going to be a quick glance through the dataset. The full exploratory data analysis was done in the linear regression notebook[ADD LINK]. Before anything, let’s split the data into training and test set.</p>
 <div class="cell docutils container">
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 </section>
 <section id="data-preprocessing">
-<h2><span class="section-number">37.66.5. </span>4 - Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.64.5. </span>4 - Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
 <p>To do:</p>
 <ul class="simple">
 <li><p>Handle missing values</p></li>
@@ -2132,7 +2149,7 @@ <h2><span class="section-number">37.66.5. </span>4 - Data Preprocessing<a class=
 <p><a name='5'></a></p>
 </section>
 <section id="training-support-vector-regressor">
-<h2><span class="section-number">37.66.6. </span>5 - Training Support Vector Regressor<a class="headerlink" href="#training-support-vector-regressor" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.64.6. </span>5 - Training Support Vector Regressor<a class="headerlink" href="#training-support-vector-regressor" title="Permalink to this headline">#</a></h2>
 <p>In regression, instead of separating the classes with decision boundary  like in classification, SVR fits the training data points on the boundary margin but keep them off.</p>
 <p>We can implement it quite easily with Scikit-Learn.</p>
 <div class="cell docutils container">
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 <p><a name='6'></a></p>
 </section>
 <section id="evaluating-support-vector-regressor">
-<h2><span class="section-number">37.66.7. </span>6 - Evaluating Support Vector Regressor<a class="headerlink" href="#evaluating-support-vector-regressor" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.64.7. </span>6 - Evaluating Support Vector Regressor<a class="headerlink" href="#evaluating-support-vector-regressor" title="Permalink to this headline">#</a></h2>
 <p>Let us evaluate the two models we created on the training set before evaluating on the test set. This is a good practice. Since finding a good model can take many iterations of improvements, it’s not advised to touch the test set until the model is good enough. Otherwise it would fail to make predictions on the new data.</p>
 <p>As always, we evaluate regression models with mean squarred error, but the commonly used one is root mean squarred error.</p>
 <div class="cell docutils container">
@@ -2206,7 +2223,7 @@ <h2><span class="section-number">37.66.7. </span>6 - Evaluating Support Vector R
 <p><a name='7'></a></p>
 </section>
 <section id="improving-support-vector-regressor">
-<h2><span class="section-number">37.66.8. </span>7 - Improving Support Vector Regressor<a class="headerlink" href="#improving-support-vector-regressor" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.64.8. </span>7 - Improving Support Vector Regressor<a class="headerlink" href="#improving-support-vector-regressor" title="Permalink to this headline">#</a></h2>
 <p>Let use Randomized Search to improve the SVR model. Few notes about the parameters:</p>
 <ul class="simple">
 <li><p><em>Gamma(y)</em>: This is a regularization hyperparameter. When gamma is small, the model can underfit. It is too high, model can overfit.</p></li>
@@ -2339,7 +2356,7 @@ <h2><span class="section-number">37.66.8. </span>7 - Improving Support Vector Re
 <p>This is the end of the notebook. It was a practical introduction to using Support Vector Machines for regression. In the next lab, we will take a further step, where we will do classification with SVM.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.66.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.64.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Jake VanderPlas for creating the open-source course <a class="reference external" href="https://github.com/jakevdp/PythonDataScienceHandbook"> Python Data Science Handbook </a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2381,13 +2398,13 @@ <h2><span class="section-number">37.66.9. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.65. </span>Kernel method assignment 1</p>
+            <p class="prev-next-title"><span class="section-number">37.63. </span>Kernel method assignment 1</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="support_vector_machines_for_classification.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">37.67. </span>Support Vector Machines (SVM) - Classification</p>
+        <p class="prev-next-title"><span class="section-number">37.65. </span>Support Vector Machines (SVM) - Classification</p>
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-    <title>37.72. Dropout and Batch Normalization &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.70. Dropout and Batch Normalization &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.73. Lasso and Ridge Regression" href="lasso-and-ridge-regression.html" />
-    <link rel="prev" title="37.71. Learning Curve To Identify Overfit &amp; Underfit" href="learning-curve-to-identify-overfit-underfit.html" />
+    <link rel="next" title="37.71. Lasso and Ridge Regression" href="lasso-and-ridge-regression.html" />
+    <link rel="prev" title="37.69. Learning Curve To Identify Overfit &amp; Underfit" href="learning-curve-to-identify-overfit-underfit.html" />
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      5.3. NumPy
     </a>
    </li>
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
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-     37.72.3.1. Adding Batch Normalization
+     37.70.3.1. Adding Batch Normalization
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#example-using-dropout-and-batch-normalization">
-   37.72.4. Example - Using Dropout and Batch Normalization
+   37.70.4. Example - Using Dropout and Batch Normalization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.72.5. Acknowledgments
+   37.70.5. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1567,41 +1584,41 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   37.72.1. Introduction
+   37.70.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dropout">
-   37.72.2. Dropout
+   37.70.2. Dropout
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#adding-dropout">
-     37.72.2.1. Adding Dropout
+     37.70.2.1. Adding Dropout
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#batch-normalization">
-   37.72.3. Batch Normalization
+   37.70.3. Batch Normalization
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#adding-batch-normalization">
-     37.72.3.1. Adding Batch Normalization
+     37.70.3.1. Adding Batch Normalization
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#example-using-dropout-and-batch-normalization">
-   37.72.4. Example - Using Dropout and Batch Normalization
+   37.70.4. Example - Using Dropout and Batch Normalization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.72.5. Acknowledgments
+   37.70.5. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1615,14 +1632,14 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="dropout-and-batch-normalization">
-<h1><span class="section-number">37.72. </span>Dropout and Batch Normalization<a class="headerlink" href="#dropout-and-batch-normalization" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.70. </span>Dropout and Batch Normalization<a class="headerlink" href="#dropout-and-batch-normalization" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">37.72.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.70.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>There’s more to the world of deep learning than just dense layers. There are dozens of kinds of layers you might add to a model. (Try browsing through the Keras docs for a sample!) Some are like dense layers and define connections between neurons, and others can do preprocessing or transformations of other sorts.</p>
 <p>In this lesson, we’ll learn about a two kinds of special layers, not containing any neurons themselves, but that add some functionality that can sometimes benefit a model in various ways. Both are commonly used in modern architectures.</p>
 </section>
 <section id="dropout">
-<h2><span class="section-number">37.72.2. </span>Dropout<a class="headerlink" href="#dropout" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.70.2. </span>Dropout<a class="headerlink" href="#dropout" title="Permalink to this headline">#</a></h2>
 <p>The first of these is the “dropout layer”, which can help correct overfitting.</p>
 <p>In the last lesson we talked about how overfitting is caused by the network learning spurious patterns in the training data. To recognize these spurious patterns a network will often rely on very a specific combinations of weight, a kind of “conspiracy” of weights. Being so specific, they tend to be fragile: remove one and the conspiracy falls apart.</p>
 <p>This is the idea behind <strong>dropout</strong>. To break up these conspiracies, we randomly drop out some fraction of a layer’s input units every step of training, making it much harder for the network to learn those spurious patterns in the training data. Instead, it has to search for broad, general patterns, whose weight patterns tend to be more robust.</p>
@@ -1630,7 +1647,7 @@ <h2><span class="section-number">37.72.2. </span>Dropout<a class="headerlink" hr
 <em>Here, 50% dropout has been added between the two hidden layers.</em></p>
 <p>You could also think about dropout as creating a kind of ensemble of networks. The predictions will no longer be made by one big network, but instead by a committee of smaller networks. Individuals in the committee tend to make different kinds of mistakes, but be right at the same time, making the committee as a whole better than any individual. (If you’re familiar with random forests as an ensemble of decision trees, it’s the same idea.)</p>
 <section id="adding-dropout">
-<h3><span class="section-number">37.72.2.1. </span>Adding Dropout<a class="headerlink" href="#adding-dropout" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.70.2.1. </span>Adding Dropout<a class="headerlink" href="#adding-dropout" title="Permalink to this headline">#</a></h3>
 <p>In Keras, the dropout rate argument <em>rate</em> defines what percentage of the input units to shut off. Put the <em>Dropout</em> layer just before the layer you want the dropout applied to:</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">keras</span><span class="o">.</span><span class="n">Sequential</span><span class="p">([</span>
     <span class="c1"># ...</span>
@@ -1643,13 +1660,13 @@ <h3><span class="section-number">37.72.2.1. </span>Adding Dropout<a class="heade
 </section>
 </section>
 <section id="batch-normalization">
-<h2><span class="section-number">37.72.3. </span>Batch Normalization<a class="headerlink" href="#batch-normalization" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.70.3. </span>Batch Normalization<a class="headerlink" href="#batch-normalization" title="Permalink to this headline">#</a></h2>
 <p>The next special layer we’ll look at performs “batch normalization” (or “batchnorm”), which can help correct training that is slow or unstable.</p>
 <p>With neural networks, it’s generally a good idea to put all of your data on a common scale, perhaps with something like scikit-learn’s <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html">StandardScaler</a> or <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html">MinMaxScaler</a>. The reason is that SGD will shift the network weights in proportion to how large an activation the data produces. Features that tend to produce activations of very different sizes can make for unstable training behavior.</p>
 <p>Now, if it’s good to normalize the data before it goes into the network, maybe also normalizing inside the network would be better! In fact, we have a special kind of layer that can do this, the <strong>batch normalization layer</strong>. A batch normalization layer looks at each batch as it comes in, first normalizing the batch with its own mean and standard deviation, and then also putting the data on a new scale with two trainable rescaling parameters. Batchnorm, in effect, performs a kind of coordinated rescaling of its inputs.</p>
 <p>Most often, batchnorm is added as an aid to the optimization process (though it can sometimes also help prediction performance). Models with batchnorm tend to need fewer epochs to complete training. Moreover, batchnorm can also fix various problems that can cause the training to get “stuck”. Consider adding batch normalization to your models, especially if you’re having trouble during training.</p>
 <section id="adding-batch-normalization">
-<h3><span class="section-number">37.72.3.1. </span>Adding Batch Normalization<a class="headerlink" href="#adding-batch-normalization" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.70.3.1. </span>Adding Batch Normalization<a class="headerlink" href="#adding-batch-normalization" title="Permalink to this headline">#</a></h3>
 <p>It seems that batch normalization can be used at almost any point in a network. You can put it after a layer…</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">),</span>
 <span class="n">layers</span><span class="o">.</span><span class="n">BatchNormalization</span><span class="p">(),</span>
@@ -1665,7 +1682,7 @@ <h3><span class="section-number">37.72.3.1. </span>Adding Batch Normalization<a
 </section>
 </section>
 <section id="example-using-dropout-and-batch-normalization">
-<h2><span class="section-number">37.72.4. </span>Example - Using Dropout and Batch Normalization<a class="headerlink" href="#example-using-dropout-and-batch-normalization" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.70.4. </span>Example - Using Dropout and Batch Normalization<a class="headerlink" href="#example-using-dropout-and-batch-normalization" title="Permalink to this headline">#</a></h2>
 <p>Let’s continue developing the Red Wine model. Now we’ll increase the capacity even more, but add dropout to control overfitting and batch normalization to speed up optimization. This time, we’ll also leave off standardizing the data, to demonstrate how batch normalization can stabalize the training.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1752,7 +1769,7 @@ <h2><span class="section-number">37.72.4. </span>Example - Using Dropout and Bat
 <p>You’ll typically get better performance if you standardize your data before using it for training. That we were able to use the raw data at all, however, shows how effective batch normalization can be on more difficult datasets.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.72.5. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.70.5. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/ryanholbrook">Ryan Holbrook</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/ryanholbrook/dropout-and-batch-normalization">Learning Curve To Identify Overfit &amp; Underfit</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1794,13 +1811,13 @@ <h2><span class="section-number">37.72.5. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.71. </span>Learning Curve To Identify Overfit &amp; Underfit</p>
+            <p class="prev-next-title"><span class="section-number">37.69. </span>Learning Curve To Identify Overfit &amp; Underfit</p>
         </div>
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-        <p class="prev-next-title"><span class="section-number">37.73. </span>Lasso and Ridge Regression</p>
+        <p class="prev-next-title"><span class="section-number">37.71. </span>Lasso and Ridge Regression</p>
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+++ b/assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html
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-    <title>37.73. Lasso and Ridge Regression &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.71. Lasso and Ridge Regression &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.74. Regularized Linear Models" href="regularized-linear-models.html" />
-    <link rel="prev" title="37.72. Dropout and Batch Normalization" href="dropout-and-batch-normalization.html" />
+    <link rel="next" title="37.72. Regularized Linear Models" href="regularized-linear-models.html" />
+    <link rel="prev" title="37.70. Dropout and Batch Normalization" href="dropout-and-batch-normalization.html" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
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@@ -284,8 +306,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
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   <a class="reference internal" href="../../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
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-  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
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   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
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   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
   </a>
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   <a class="reference internal" href="../../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
    14. Clustering models for Machine Learning
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   <a class="reference internal" href="../../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
    15. Getting started with ensemble learning
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   <a class="reference internal" href="../../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
    16. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
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@@ -633,8 +655,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../README.html">
    37. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
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@@ -825,308 +847,303 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
     </a>
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    <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
+     37.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/loss-function.html">
-     37.48. Loss Function
+     37.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
+     37.50. ML logistic regression - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/ml-logistic-regression-2.html">
-     37.52. ML logistic regression - assignment 2
+     37.51. ML logistic regression - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/ml-neural-network-1.html">
-     37.53. ML neural network - Assignment 1
+     37.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression-tools.html">
-     37.54. Regression tools
+     37.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/managing-data.html">
-     37.55. Managing data
+     37.54. Managing data
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/exploring-visualizations.html">
-     37.56. Exploring visualizations
+     37.55. Exploring visualizations
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/try-a-different-model.html">
-     37.57. Try a different model
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-     37.64. Linear Regression Implementation from Scratch
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-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
+     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.82. Random Forest Classifier with Feature Importance
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-     37.105. Fun with Variational Autoencoders
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-     37.106. Time Series Forecasting Assignment
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-     37.108. Neural Networks for Classification with TensorFlow
+     37.106. Neural Networks for Classification with TensorFlow
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-     37.114. DQN On Foreign Exchange Market
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-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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    38. Slides
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-   37.73.2. Regularization classes
+   37.71.2. Regularization classes
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   </a>
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   <a class="reference internal nav-link" href="#acknowledgments">
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   </a>
  </li>
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.73.9. Acknowledgments
+   37.71.9. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1627,7 +1644,7 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="lasso-and-ridge-regression">
-<h1><span class="section-number">37.73. </span>Lasso and Ridge Regression<a class="headerlink" href="#lasso-and-ridge-regression" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.71. </span>Lasso and Ridge Regression<a class="headerlink" href="#lasso-and-ridge-regression" title="Permalink to this headline">#</a></h1>
 <div class="cell docutils container">
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@@ -1641,7 +1658,7 @@ <h1><span class="section-number">37.73. </span>Lasso and Ridge Regression<a clas
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 <section id="common-regression-class">
-<h2><span class="section-number">37.73.1. </span>Common Regression class<a class="headerlink" href="#common-regression-class" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.71.1. </span>Common Regression class<a class="headerlink" href="#common-regression-class" title="Permalink to this headline">#</a></h2>
 <p>The most of things common in Lasson and Ridge Regression. <br/>
 The only different between two regression is, what regularization it is using. <br/></p>
 <ul class="simple">
@@ -1746,7 +1763,7 @@ <h2><span class="section-number">37.73.1. </span>Common Regression class<a class
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 <section id="regularization-classes">
-<h2><span class="section-number">37.73.2. </span>Regularization classes<a class="headerlink" href="#regularization-classes" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.71.2. </span>Regularization classes<a class="headerlink" href="#regularization-classes" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 </div>
 </section>
 <section id="data-creation">
-<h2><span class="section-number">37.73.3. </span>Data creation<a class="headerlink" href="#data-creation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.71.3. </span>Data creation<a class="headerlink" href="#data-creation" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 </div>
 </section>
 <section id="lasson-regression-from-scratch">
-<h2><span class="section-number">37.73.4. </span>Lasson Regression from scratch<a class="headerlink" href="#lasson-regression-from-scratch" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.71.4. </span>Lasson Regression from scratch<a class="headerlink" href="#lasson-regression-from-scratch" title="Permalink to this headline">#</a></h2>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">LassoRegression</span><span class="p">(</span><span class="n">Regression</span><span class="p">):</span>
@@ -2042,7 +2059,7 @@ <h2><span class="section-number">37.73.4. </span>Lasson Regression from scratch<
 </div>
 </section>
 <section id="lasso-regression-using-skicit-learn">
-<h2><span class="section-number">37.73.5. </span>Lasso Regression using skicit-learn<a class="headerlink" href="#lasso-regression-using-skicit-learn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.71.5. </span>Lasso Regression using skicit-learn<a class="headerlink" href="#lasso-regression-using-skicit-learn" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">Lasso</span>
@@ -2085,11 +2102,11 @@ <h2><span class="section-number">37.73.5. </span>Lasso Regression using skicit-l
 </div>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">37.73.6. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.71.6. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>Our model (from scratch) also works great as compared to skiti-learn model. Both the models are giving 0.99…% r2_socre which is good.</p>
 </section>
 <section id="ridge-regression-from-scratch">
-<h2><span class="section-number">37.73.7. </span>Ridge Regression from scratch<a class="headerlink" href="#ridge-regression-from-scratch" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.71.7. </span>Ridge Regression from scratch<a class="headerlink" href="#ridge-regression-from-scratch" title="Permalink to this headline">#</a></h2>
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@@ -2171,7 +2188,7 @@ <h2><span class="section-number">37.73.7. </span>Ridge Regression from scratch<a
 </div>
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 <section id="ridge-regression-using-scikit-learn">
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 <section id="acknowledgments">
-<h2><span class="section-number">37.73.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.71.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Pavithra Devi M for creating the Notebook <a class="reference external" href="https://www.kaggle.com/code/ninjaac/lasso-and-ridge-regression-from-scratch">Lasso and Ridge Regression from scratch</a>, lisensed under the <a class="reference external" href="https://www.apache.org/licenses/LICENSE-2.0">Apache 2.0</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2256,13 +2273,13 @@ <h2><span class="section-number">37.73.9. </span>Acknowledgments<a class="header
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-     37.108. Neural Networks for Classification with TensorFlow
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-     37.114. DQN On Foreign Exchange Market
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-     37.115. Art by gan
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-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
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         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
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-   37.71.1. Imports
+   37.69.1. Imports
   </a>
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   <a class="reference internal nav-link" href="#overfitting-and-underfitting-refresher">
-   37.71.2. Overfitting and Underfitting Refresher
+   37.69.2. Overfitting and Underfitting Refresher
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#overfitting-aka-variance">
-     37.71.2.1. Overfitting (aka Variance):
+     37.69.2.1. Overfitting (aka Variance):
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#reasons-for-overfitting">
-     37.71.2.2. Reasons for overfitting
+     37.69.2.2. Reasons for overfitting
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#underfitting-aka-bias">
-     37.71.2.3. Underfitting (aka Bias):
+     37.69.2.3. Underfitting (aka Bias):
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#reasons-for-underfitting">
-     37.71.2.4. Reasons for underfitting
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     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#modeling-iris-data-using-logistic-regression">
-   37.71.3. Modeling (Iris data) using Logistic Regression
+   37.69.3. Modeling (Iris data) using Logistic Regression
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#identifying-and-handling-multicollinearity-using-vif">
-   37.71.4. Identifying and handling Multicollinearity using VIF
+   37.69.4. Identifying and handling Multicollinearity using VIF
   </a>
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   <a class="reference internal nav-link" href="#introduction-to-learning-curve">
-   37.71.5. Introduction to Learning Curve
+   37.69.5. Introduction to Learning Curve
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-   37.71.6. Learning Curve of a Good Fit Model
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-     37.71.6.1. Interpreting the training loss
+     37.69.6.1. Interpreting the training loss
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-     37.71.6.2. Interpreting the validation loss
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-     37.71.7.2. Interpreting the validation loss
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-   37.71.8. Learning Curve of an Underfit Model
+   37.69.8. Learning Curve of an Underfit Model
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-     37.71.8.1. Interpreting the training loss
+     37.69.8.1. Interpreting the training loss
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-     37.71.8.2. Interpreting the validation loss
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-   37.71.9. Acknowledgments
+   37.69.9. Acknowledgments
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                             <ul class="visible nav section-nav flex-column">
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-   37.71.1. Imports
+   37.69.1. Imports
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+   37.69.2. Overfitting and Underfitting Refresher
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#overfitting-aka-variance">
-     37.71.2.1. Overfitting (aka Variance):
+     37.69.2.1. Overfitting (aka Variance):
     </a>
    </li>
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-     37.71.2.2. Reasons for overfitting
+     37.69.2.2. Reasons for overfitting
     </a>
    </li>
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     <a class="reference internal nav-link" href="#underfitting-aka-bias">
-     37.71.2.3. Underfitting (aka Bias):
+     37.69.2.3. Underfitting (aka Bias):
     </a>
    </li>
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-     37.71.2.4. Reasons for underfitting
+     37.69.2.4. Reasons for underfitting
     </a>
    </li>
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-   37.71.3. Modeling (Iris data) using Logistic Regression
+   37.69.3. Modeling (Iris data) using Logistic Regression
   </a>
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-   37.71.4. Identifying and handling Multicollinearity using VIF
+   37.69.4. Identifying and handling Multicollinearity using VIF
   </a>
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-   37.71.5. Introduction to Learning Curve
+   37.69.5. Introduction to Learning Curve
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-   37.71.6. Learning Curve of a Good Fit Model
+   37.69.6. Learning Curve of a Good Fit Model
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#interpreting-the-training-loss">
-     37.71.6.1. Interpreting the training loss
+     37.69.6.1. Interpreting the training loss
     </a>
    </li>
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     <a class="reference internal nav-link" href="#interpreting-the-validation-loss">
-     37.71.6.2. Interpreting the validation loss
+     37.69.6.2. Interpreting the validation loss
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    </li>
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-   37.71.7. Learning Curve of an Overfit Model
+   37.69.7. Learning Curve of an Overfit Model
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     37.71.7.1. Interpreting the training loss
+     37.69.7.1. Interpreting the training loss
     </a>
    </li>
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-     37.71.7.2. Interpreting the validation loss
+     37.69.7.2. Interpreting the validation loss
     </a>
    </li>
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-   37.71.8. Learning Curve of an Underfit Model
+   37.69.8. Learning Curve of an Underfit Model
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    <li class="toc-h3 nav-item toc-entry">
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-     37.71.8.1. Interpreting the training loss
+     37.69.8.1. Interpreting the training loss
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     <a class="reference internal nav-link" href="#id4">
-     37.71.8.2. Interpreting the validation loss
+     37.69.8.2. Interpreting the validation loss
     </a>
    </li>
   </ul>
  </li>
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-   37.71.9. Acknowledgments
+   37.69.9. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1743,9 +1760,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="learning-curve-to-identify-overfit-underfit">
-<h1><span class="section-number">37.71. </span>Learning Curve To Identify Overfit &amp; Underfit<a class="headerlink" href="#learning-curve-to-identify-overfit-underfit" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.69. </span>Learning Curve To Identify Overfit &amp; Underfit<a class="headerlink" href="#learning-curve-to-identify-overfit-underfit" title="Permalink to this headline">#</a></h1>
 <section id="imports">
-<h2><span class="section-number">37.71.1. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.69.1. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -1765,24 +1782,24 @@ <h2><span class="section-number">37.71.1. </span>Imports<a class="headerlink" hr
 </div>
 </section>
 <section id="overfitting-and-underfitting-refresher">
-<h2><span class="section-number">37.71.2. </span>Overfitting and Underfitting Refresher<a class="headerlink" href="#overfitting-and-underfitting-refresher" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.69.2. </span>Overfitting and Underfitting Refresher<a class="headerlink" href="#overfitting-and-underfitting-refresher" title="Permalink to this headline">#</a></h2>
 <section id="overfitting-aka-variance">
-<h3><span class="section-number">37.71.2.1. </span>Overfitting (aka Variance):<a class="headerlink" href="#overfitting-aka-variance" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.69.2.1. </span>Overfitting (aka Variance):<a class="headerlink" href="#overfitting-aka-variance" title="Permalink to this headline">#</a></h3>
 <p>A model is said to be overfit if the model is overtrained on the data such that it even learns the noise from the data. In the figure below, the third image shows overfitting where the model has learnt each and every example so perfectly that it misclassifies an unseen/new example. For a model that’s overfit we have a perfect/close to perfect training set score while a poor test/validation score.</p>
 </section>
 <section id="reasons-for-overfitting">
-<h3><span class="section-number">37.71.2.2. </span>Reasons for overfitting<a class="headerlink" href="#reasons-for-overfitting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.69.2.2. </span>Reasons for overfitting<a class="headerlink" href="#reasons-for-overfitting" title="Permalink to this headline">#</a></h3>
 <ol class="simple">
 <li><p>Using a complex model for a simple problem which picks up the noise from the data. Example: Using a neural network on Iris dataset</p></li>
 <li><p>Small datasets, as the training set may not be a right representation of the universe</p></li>
 </ol>
 </section>
 <section id="underfitting-aka-bias">
-<h3><span class="section-number">37.71.2.3. </span>Underfitting (aka Bias):<a class="headerlink" href="#underfitting-aka-bias" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.69.2.3. </span>Underfitting (aka Bias):<a class="headerlink" href="#underfitting-aka-bias" title="Permalink to this headline">#</a></h3>
 <p>A model is said to be underfit if the model is unable to learn the patterns in the data properly. In the figure below, the first image shows overfitting where the model hasn’t fully learnt each and every example. In such cases we see a low score on both the training set and test/validation set</p>
 </section>
 <section id="reasons-for-underfitting">
-<h3><span class="section-number">37.71.2.4. </span>Reasons for underfitting<a class="headerlink" href="#reasons-for-underfitting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.69.2.4. </span>Reasons for underfitting<a class="headerlink" href="#reasons-for-underfitting" title="Permalink to this headline">#</a></h3>
 <ol class="simple">
 <li><p>Using a simple model for a complex problem which doesn’t pick up all the patterns from the data. Example: Using a logistic regression for image classification</p></li>
 <li><p>The underlying data has no inherent pattern. Example, trying to predict a student’s marks with his father’s weight.</p></li>
@@ -1790,7 +1807,7 @@ <h3><span class="section-number">37.71.2.4. </span>Reasons for underfitting<a cl
 </section>
 </section>
 <section id="modeling-iris-data-using-logistic-regression">
-<h2><span class="section-number">37.71.3. </span>Modeling (Iris data) using Logistic Regression<a class="headerlink" href="#modeling-iris-data-using-logistic-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.69.3. </span>Modeling (Iris data) using Logistic Regression<a class="headerlink" href="#modeling-iris-data-using-logistic-regression" title="Permalink to this headline">#</a></h2>
 <p>In this section we’ll fit a LogisticRegressor to the Iris dataset and identify underfitting, overfitting and good fit.</p>
 <div class="cell docutils container">
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@@ -2403,7 +2420,7 @@ <h2><span class="section-number">37.71.3. </span>Modeling (Iris data) using Logi
 </div>
 </section>
 <section id="identifying-and-handling-multicollinearity-using-vif">
-<h2><span class="section-number">37.71.4. </span>Identifying and handling Multicollinearity using VIF<a class="headerlink" href="#identifying-and-handling-multicollinearity-using-vif" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.69.4. </span>Identifying and handling Multicollinearity using VIF<a class="headerlink" href="#identifying-and-handling-multicollinearity-using-vif" title="Permalink to this headline">#</a></h2>
 <p>The correlation matrix below shows high multicollinearity among input features. VIF &gt; 5 or 10 implies multicollinearity.</p>
 <div class="cell docutils container">
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@@ -2519,11 +2536,11 @@ <h2><span class="section-number">37.71.4. </span>Identifying and handling Multic
 </div>
 </section>
 <section id="introduction-to-learning-curve">
-<h2><span class="section-number">37.71.5. </span>Introduction to Learning Curve<a class="headerlink" href="#introduction-to-learning-curve" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.69.5. </span>Introduction to Learning Curve<a class="headerlink" href="#introduction-to-learning-curve" title="Permalink to this headline">#</a></h2>
 <p>Learning curve is a plot that plots the training and validation loss for a sample of training examples by incrementally increasing them. Learning curves helps us in identifying if adding additional training examples could improve the validation score or not. If a model is overfit then adding additional training examples might improve the model performance on unseen data. Similarly, if a model is underfit then adding training examples doesn’t help</p>
 </section>
 <section id="learning-curve-of-a-good-fit-model">
-<h2><span class="section-number">37.71.6. </span>Learning Curve of a Good Fit Model<a class="headerlink" href="#learning-curve-of-a-good-fit-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.69.6. </span>Learning Curve of a Good Fit Model<a class="headerlink" href="#learning-curve-of-a-good-fit-model" title="Permalink to this headline">#</a></h2>
 <p>Below we’ll use the function created above to get a good fit model. We’ll set the inverse regularization variable/parameter ‘c’ to 1 (i.e. we are not performing any regularization)</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2561,11 +2578,11 @@ <h2><span class="section-number">37.71.6. </span>Learning Curve of a Good Fit Mo
 </div>
 <p>The cross validation accuracy and training accuracy calculated above are close to each other</p>
 <section id="interpreting-the-training-loss">
-<h3><span class="section-number">37.71.6.1. </span>Interpreting the training loss<a class="headerlink" href="#interpreting-the-training-loss" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.69.6.1. </span>Interpreting the training loss<a class="headerlink" href="#interpreting-the-training-loss" title="Permalink to this headline">#</a></h3>
 <p>Learning curve of a good fit model has a moderately high training loss at the beginning which gradually lowers upon adding training examples and flattens gradually indicating addition of more training examples doesn’t improve the model performance on training data</p>
 </section>
 <section id="interpreting-the-validation-loss">
-<h3><span class="section-number">37.71.6.2. </span>Interpreting the validation loss<a class="headerlink" href="#interpreting-the-validation-loss" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.69.6.2. </span>Interpreting the validation loss<a class="headerlink" href="#interpreting-the-validation-loss" title="Permalink to this headline">#</a></h3>
 <p>Learning curve of a good fit model has a high validation loss at the beginning which gradually lowers upon adding training examples and flattens gradually indicating addition of more training examples doesn’t improve the model performance on unseen data</p>
 <p>We can also see that upon adding a reasonable number of training examples, both the training and validation loss are close to each other</p>
 <p><strong>Typical Features</strong></p>
@@ -2576,7 +2593,7 @@ <h3><span class="section-number">37.71.6.2. </span>Interpreting the validation l
 </section>
 </section>
 <section id="learning-curve-of-an-overfit-model">
-<h2><span class="section-number">37.71.7. </span>Learning Curve of an Overfit Model<a class="headerlink" href="#learning-curve-of-an-overfit-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.69.7. </span>Learning Curve of an Overfit Model<a class="headerlink" href="#learning-curve-of-an-overfit-model" title="Permalink to this headline">#</a></h2>
 <p>Below we’ll use the function created above to get an overfit model. We’ll set the inverse regularization variable/parameter ‘c’ to 10000 (high value of ‘c’ causes overfitting)</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2614,11 +2631,11 @@ <h2><span class="section-number">37.71.7. </span>Learning Curve of an Overfit Mo
 </div>
 <p>The standard deviation in cross validation accuracy is high compared to underfit and good fit model. Training accuracy is higher than cross validation accuracy, typical to an overfit model, but not too high to detect overfitting. But Overfitting can be detected from the learning curve.</p>
 <section id="id1">
-<h3><span class="section-number">37.71.7.1. </span>Interpreting the training loss<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.69.7.1. </span>Interpreting the training loss<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <p>Learning curve of an overfit model has a very low training loss at the beginning which gradually increases very slightly upon adding training examples and doesn’t flatten.</p>
 </section>
 <section id="id2">
-<h3><span class="section-number">37.71.7.2. </span>Interpreting the validation loss<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.69.7.2. </span>Interpreting the validation loss<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <p>Learning curve of an overfit model has a high validation loss at the beginning which gradually lowers upon adding training examples and doesn’t flatten, indicating addition of more training examples can improve the model performance on unseen data</p>
 <p>We can also see that the training and validation losses are far away from each other, which may come close to each other upon adding additional training data</p>
 <p><strong>Typical Features</strong></p>
@@ -2630,7 +2647,7 @@ <h3><span class="section-number">37.71.7.2. </span>Interpreting the validation l
 </section>
 </section>
 <section id="learning-curve-of-an-underfit-model">
-<h2><span class="section-number">37.71.8. </span>Learning Curve of an Underfit Model<a class="headerlink" href="#learning-curve-of-an-underfit-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.69.8. </span>Learning Curve of an Underfit Model<a class="headerlink" href="#learning-curve-of-an-underfit-model" title="Permalink to this headline">#</a></h2>
 <p>Below we’ll use the function created above to get an underfit model. We’ll set the inverse regularization variable/parameter ‘c’ to 1/10000 (low value of ‘c’ causes underfitting)</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2668,11 +2685,11 @@ <h2><span class="section-number">37.71.8. </span>Learning Curve of an Underfit M
 </div>
 <p>The standard deviation in cross validation accuracy is low compared to overfit and good fit model. Underfitting can be detected from the learning curve.</p>
 <section id="id3">
-<h3><span class="section-number">37.71.8.1. </span>Interpreting the training loss<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.69.8.1. </span>Interpreting the training loss<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
 <p>Learning curve of an underfit model has a low training loss at the beginning which gradually increases upon adding training examples and suddenly falls to the minimum at the end.</p>
 </section>
 <section id="id4">
-<h3><span class="section-number">37.71.8.2. </span>Interpreting the validation loss<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.69.8.2. </span>Interpreting the validation loss<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
 <p>Learning curve of an underfit model has a high validation loss at the beginning which gradually lowers upon adding training examples and suddenly falls to the minimum at the end, indicating addition of more training examples can’t improve the model performance on unseen data</p>
 <p>We can also see that the training and validation losses are very close to each other at the end. Adding additional training examples doesn’t improve the performance on unseen data.</p>
 <p><strong>Typical Features</strong></p>
@@ -2705,7 +2722,7 @@ <h3><span class="section-number">37.71.8.2. </span>Interpreting the validation l
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.71.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.69.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/ksvmuralidhar">KSV MURALIDHAR</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/ksvmuralidhar/learning-curve-to-identify-overfit-underfit/notebook">Learning Curve To Identify Overfit &amp; Underfit</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2747,13 +2764,13 @@ <h2><span class="section-number">37.71.9. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
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-            <p class="prev-next-title"><span class="section-number">37.70. </span>Model selection assignment 1</p>
+            <p class="prev-next-title"><span class="section-number">37.68. </span>Model selection assignment 1</p>
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-        <p class="prev-next-title"><span class="section-number">37.72. </span>Dropout and Batch Normalization</p>
+        <p class="prev-next-title"><span class="section-number">37.70. </span>Dropout and Batch Normalization</p>
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diff --git a/assignments/ml-advanced/model-selection/model-selection-assignment-1.html b/assignments/ml-advanced/model-selection/model-selection-assignment-1.html
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      5.3. NumPy
     </a>
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-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
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@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-visualization/data-visualization.html">
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   <a class="reference internal" href="../../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
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   <a class="reference internal" href="../../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
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   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
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   <a class="reference internal" href="../../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
    16. Introduction to Gradient Boosting
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@@ -633,8 +655,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../README.html">
    37. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
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     </a>
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-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
     </a>
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     <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
+     37.46. Linear Regression Metrics
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/loss-function.html">
-     37.48. Loss Function
+     37.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
     </a>
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     <a class="reference internal" href="../../ml-fundamentals/ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
+     37.50. ML logistic regression - assignment 1
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     <a class="reference internal" href="../../ml-fundamentals/ml-logistic-regression-2.html">
-     37.52. ML logistic regression - assignment 2
+     37.51. ML logistic regression - assignment 2
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     <a class="reference internal" href="../../ml-fundamentals/ml-neural-network-1.html">
-     37.53. ML neural network - Assignment 1
+     37.52. ML neural network - Assignment 1
     </a>
    </li>
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     <a class="reference internal" href="../../ml-fundamentals/regression-tools.html">
-     37.54. Regression tools
+     37.53. Regression tools
     </a>
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     <a class="reference internal" href="../../ml-fundamentals/managing-data.html">
-     37.55. Managing data
+     37.54. Managing data
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     <a class="reference internal" href="../../ml-fundamentals/exploring-visualizations.html">
-     37.56. Exploring visualizations
+     37.55. Exploring visualizations
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     <a class="reference internal" href="../../ml-fundamentals/try-a-different-model.html">
-     37.57. Try a different model
+     37.56. Try a different model
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     <a class="reference internal" href="../../ml-fundamentals/create-a-regression-model.html">
-     37.58. Create a regression model
+     37.57. Create a regression model
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     <a class="reference internal" href="../../ml-fundamentals/linear-and-polynomial-regression.html">
-     37.59. Linear and polynomial regression
+     37.58. Linear and polynomial regression
     </a>
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     <a class="reference internal" href="../../ml-fundamentals/retrying-some-regression.html">
-     37.60. Retrying some regression
+     37.59. Retrying some regression
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     <a class="reference internal" href="../../ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
+     37.60. Pumpkin varieties and color
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     <a class="reference internal" href="../../ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.62. Delicious asian and indian cuisines
+     37.61. Delicious asian and indian cuisines
     </a>
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     <a class="reference internal" href="../../ml-fundamentals/explore-classification-methods.html">
-     37.63. Explore classification methods
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-   <li class="toctree-l2">
-    <a class="reference internal" href="../../ml-fundamentals/linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
+     37.62. Explore classification methods
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../kernel-method/kernel-method-assignment-1.html">
-     37.65. Kernel method assignment 1
+     37.63. Kernel method assignment 1
     </a>
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     <a class="reference internal" href="../kernel-method/support_vector_machines_for_regression.html">
-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
+     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../kernel-method/support_vector_machines_for_classification.html">
-     37.67. Support Vector Machines (SVM) - Classification
+     37.65. Support Vector Machines (SVM) - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../kernel-method/decision_trees_for_regression.html">
-     37.68. Decision Trees - Intro and Regression
+     37.66. Decision Trees - Intro and Regression
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../kernel-method/decision_trees_for_classification.html">
-     37.69. Decision Trees - Classification
+     37.67. Decision Trees - Classification
     </a>
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    <li class="toctree-l2 current active">
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-     37.70. Model selection assignment 1
+     37.68. Model selection assignment 1
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-     37.71. Learning Curve To Identify Overfit &amp; Underfit
+     37.69. Learning Curve To Identify Overfit &amp; Underfit
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    <li class="toctree-l2">
     <a class="reference internal" href="dropout-and-batch-normalization.html">
-     37.72. Dropout and Batch Normalization
+     37.70. Dropout and Batch Normalization
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     <a class="reference internal" href="lasso-and-ridge-regression.html">
-     37.73. Lasso and Ridge Regression
+     37.71. Lasso and Ridge Regression
     </a>
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     <a class="reference internal" href="regularized-linear-models.html">
-     37.74. Regularized Linear Models
+     37.72. Regularized Linear Models
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-     37.75. Random forests intro and regression
+     37.73. Random forests intro and regression
     </a>
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     <a class="reference internal" href="../ensemble-learning/random-forests-for-classification.html">
-     37.76. Random forests for classification
+     37.74. Random forests for classification
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     <a class="reference internal" href="../ensemble-learning/beyond-random-forests-more-ensemble-models.html">
-     37.77. Beyond random forests: more ensemble models
+     37.75. Beyond random forests: more ensemble models
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-     37.78. Decision trees
+     37.76. Decision trees
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     <a class="reference internal" href="../gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
-     37.79. Hyperparameter tuning gradient boosting
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-     37.96. Object Recognition in Images using CNN
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-     37.97. Intro to TensorFlow for Deep Learning
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     <a class="reference internal" href="../../deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.101. Google Stock Price Prediction RNN
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     <a class="reference internal" href="../../deep-learning/autoencoder/autoencoder.html">
-     37.103. Intro to Autoencoders
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-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
+     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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     <a class="reference internal" href="../../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
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    <li class="toctree-l2">
     <a class="reference internal" href="../../deep-learning/time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
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    </li>
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     <a class="reference internal" href="../../deep-learning/nn-for-classification-assignment.html">
-     37.108. Neural Networks for Classification with TensorFlow
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     <a class="reference internal" href="../../deep-learning/nn-classify-15-fruits-assignment.html">
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     <a class="reference internal" href="../../deep-learning/gan/gan-introduction.html">
-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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     <a class="reference internal" href="../../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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+   37.68.1. Common Regression class
   </a>
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-   37.70.2. Regularization classes
+   37.68.2. Regularization classes
   </a>
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   <a class="reference internal nav-link" href="#data-creation">
-   37.70.3. Data creation
+   37.68.3. Data creation
   </a>
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   </a>
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   </a>
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+   37.68.8. Ridge Regression using scikit-learn
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
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+   37.68.9. Acknowledgments
   </a>
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-   37.70.2. Regularization classes
+   37.68.2. Regularization classes
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  </li>
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   <a class="reference internal nav-link" href="#data-creation">
-   37.70.3. Data creation
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   </a>
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+   37.68.4. Lasson Regression from scratch
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   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion">
-   37.70.6. Conclusion
+   37.68.6. Conclusion
   </a>
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-   37.70.7. Ridge Regression from scratch
+   37.68.7. Ridge Regression from scratch
   </a>
  </li>
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   <a class="reference internal nav-link" href="#ridge-regression-using-scikit-learn">
-   37.70.8. Ridge Regression using scikit-learn
+   37.68.8. Ridge Regression using scikit-learn
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.70.9. Acknowledgments
+   37.68.9. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1627,7 +1644,7 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="model-selection-assignment-1">
-<h1><span class="section-number">37.70. </span>Model selection assignment 1<a class="headerlink" href="#model-selection-assignment-1" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.68. </span>Model selection assignment 1<a class="headerlink" href="#model-selection-assignment-1" title="Permalink to this headline">#</a></h1>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -1641,7 +1658,7 @@ <h1><span class="section-number">37.70. </span>Model selection assignment 1<a cl
 </div>
 </div>
 <section id="common-regression-class">
-<h2><span class="section-number">37.70.1. </span>Common Regression class<a class="headerlink" href="#common-regression-class" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.68.1. </span>Common Regression class<a class="headerlink" href="#common-regression-class" title="Permalink to this headline">#</a></h2>
 <p>The most of things common in Lasson and Ridge Regression. <br/>
 The only different between two regression is, what regularization it is using. <br/></p>
 <ul class="simple">
@@ -1746,7 +1763,7 @@ <h2><span class="section-number">37.70.1. </span>Common Regression class<a class
 </div>
 </section>
 <section id="regularization-classes">
-<h2><span class="section-number">37.70.2. </span>Regularization classes<a class="headerlink" href="#regularization-classes" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.68.2. </span>Regularization classes<a class="headerlink" href="#regularization-classes" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create the regularization class we want.</span>
@@ -1782,7 +1799,7 @@ <h2><span class="section-number">37.70.2. </span>Regularization classes<a class=
 </div>
 </section>
 <section id="data-creation">
-<h2><span class="section-number">37.70.3. </span>Data creation<a class="headerlink" href="#data-creation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.68.3. </span>Data creation<a class="headerlink" href="#data-creation" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define the traning data.</span>
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 </div>
 </section>
 <section id="lasson-regression-from-scratch">
-<h2><span class="section-number">37.70.4. </span>Lasson Regression from scratch<a class="headerlink" href="#lasson-regression-from-scratch" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.68.4. </span>Lasson Regression from scratch<a class="headerlink" href="#lasson-regression-from-scratch" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">LassoRegression</span><span class="p">(</span><span class="n">Regression</span><span class="p">):</span>
@@ -2042,7 +2059,7 @@ <h2><span class="section-number">37.70.4. </span>Lasson Regression from scratch<
 </div>
 </section>
 <section id="lasso-regression-using-skicit-learn">
-<h2><span class="section-number">37.70.5. </span>Lasso Regression using skicit-learn<a class="headerlink" href="#lasso-regression-using-skicit-learn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.68.5. </span>Lasso Regression using skicit-learn<a class="headerlink" href="#lasso-regression-using-skicit-learn" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">Lasso</span>
@@ -2085,11 +2102,11 @@ <h2><span class="section-number">37.70.5. </span>Lasso Regression using skicit-l
 </div>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">37.70.6. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.68.6. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>Our model (from scratch) also works great as compared to skiti-learn model. Both the models are giving 0.99…% r2_socre which is good.</p>
 </section>
 <section id="ridge-regression-from-scratch">
-<h2><span class="section-number">37.70.7. </span>Ridge Regression from scratch<a class="headerlink" href="#ridge-regression-from-scratch" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.68.7. </span>Ridge Regression from scratch<a class="headerlink" href="#ridge-regression-from-scratch" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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@@ -2171,7 +2188,7 @@ <h2><span class="section-number">37.70.7. </span>Ridge Regression from scratch<a
 </div>
 </section>
 <section id="ridge-regression-using-scikit-learn">
-<h2><span class="section-number">37.70.8. </span>Ridge Regression using scikit-learn<a class="headerlink" href="#ridge-regression-using-scikit-learn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.68.8. </span>Ridge Regression using scikit-learn<a class="headerlink" href="#ridge-regression-using-scikit-learn" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">Ridge</span>
@@ -2214,7 +2231,7 @@ <h2><span class="section-number">37.70.8. </span>Ridge Regression using scikit-l
 </div>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.70.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.68.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Pavithra Devi M for creating the Notebook <a class="reference external" href="https://www.kaggle.com/code/ninjaac/lasso-and-ridge-regression-from-scratch">Lasso and Ridge Regression from scratch</a>, lisensed under the <a class="reference external" href="https://www.apache.org/licenses/LICENSE-2.0">Apache 2.0</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2256,13 +2273,13 @@ <h2><span class="section-number">37.70.9. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.69. </span>Decision Trees - Classification</p>
+            <p class="prev-next-title"><span class="section-number">37.67. </span>Decision Trees - Classification</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="learning-curve-to-identify-overfit-underfit.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.71. </span>Learning Curve To Identify Overfit &amp; Underfit</p>
+        <p class="prev-next-title"><span class="section-number">37.69. </span>Learning Curve To Identify Overfit &amp; Underfit</p>
     </div>
     <i class="fas fa-angle-right"></i>
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diff --git a/assignments/ml-advanced/model-selection/regularized-linear-models.html b/assignments/ml-advanced/model-selection/regularized-linear-models.html
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-    <title>37.74. Regularized Linear Models &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.72. Regularized Linear Models &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.75. Random forests intro and regression" href="../ensemble-learning/random-forests-intro-and-regression.html" />
-    <link rel="prev" title="37.73. Lasso and Ridge Regression" href="lasso-and-ridge-regression.html" />
+    <link rel="next" title="37.73. Random forests intro and regression" href="../ensemble-learning/random-forests-intro-and-regression.html" />
+    <link rel="prev" title="37.71. Lasso and Ridge Regression" href="lasso-and-ridge-regression.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
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-   37.74.2. Data preprocessing:
+   37.72.2. Data preprocessing:
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#models">
-   37.74.3. Models
+   37.72.3. Models
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#adding-an-xgboost-model">
-   37.74.4. Adding an xgboost model:
+   37.72.4. Adding an xgboost model:
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.74.5. Acknowledgments
+   37.72.5. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1587,9 +1604,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="regularized-linear-models">
-<h1><span class="section-number">37.74. </span>Regularized Linear Models<a class="headerlink" href="#regularized-linear-models" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.72. </span>Regularized Linear Models<a class="headerlink" href="#regularized-linear-models" title="Permalink to this headline">#</a></h1>
 <section id="imports">
-<h2><span class="section-number">37.74.1. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.72.1. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
@@ -1843,7 +1860,7 @@ <h2><span class="section-number">37.74.1. </span>Imports<a class="headerlink" hr
 </div>
 </section>
 <section id="data-preprocessing">
-<h2><span class="section-number">37.74.2. </span>Data preprocessing:<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.72.2. </span>Data preprocessing:<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
 <p>We’re not going to do anything fancy here:</p>
 <ul class="simple">
 <li><p>First I’ll transform the skewed numeric features by taking log(feature + 1) - this will make the features more normal</p></li>
@@ -1916,7 +1933,7 @@ <h2><span class="section-number">37.74.2. </span>Data preprocessing:<a class="he
 </div>
 </section>
 <section id="models">
-<h2><span class="section-number">37.74.3. </span>Models<a class="headerlink" href="#models" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.72.3. </span>Models<a class="headerlink" href="#models" title="Permalink to this headline">#</a></h2>
 <p>Now we are going to use regularized linear regression models from the scikit learn module. I’m going to try both l_1(Lasso) and l_2(Ridge) regularization. I’ll also define a function that returns the cross-validation rmse error so we can evaluate our models and pick the best tuning par</p>
 <div class="cell docutils container">
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@@ -2065,7 +2082,7 @@ <h2><span class="section-number">37.74.3. </span>Models<a class="headerlink" hre
 <p>The residual plot looks pretty <a class="reference external" href="http://good.To">good.To</a> wrap it up let’s predict on the test set and submit on the leaderboard:</p>
 </section>
 <section id="adding-an-xgboost-model">
-<h2><span class="section-number">37.74.4. </span>Adding an xgboost model:<a class="headerlink" href="#adding-an-xgboost-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.72.4. </span>Adding an xgboost model:<a class="headerlink" href="#adding-an-xgboost-model" title="Permalink to this headline">#</a></h2>
 <p>Let’s add an xgboost model to our linear model to see if we can improve our score:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2169,7 +2186,7 @@ <h2><span class="section-number">37.74.4. </span>Adding an xgboost model:<a clas
 </div>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.74.5. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.72.5. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Alexandru Papiu for creating the open-source project <a class="reference external" href="https://www.kaggle.com/code/apapiu/regularized-linear-models">Regularized Linear Models</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2211,13 +2228,13 @@ <h2><span class="section-number">37.74.5. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.73. </span>Lasso and Ridge Regression</p>
+            <p class="prev-next-title"><span class="section-number">37.71. </span>Lasso and Ridge Regression</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../ensemble-learning/random-forests-intro-and-regression.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.75. </span>Random forests intro and regression</p>
+        <p class="prev-next-title"><span class="section-number">37.73. </span>Random forests intro and regression</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/ml-fundamentals/build-classification-model.html b/assignments/ml-fundamentals/build-classification-model.html
index 4a207d43f2..138be47f6b 100644
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.90. Build Classification Model &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.88. Build Classification Model &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.91. Parameter play" href="parameter-play.html" />
-    <link rel="prev" title="37.89. Build classification models" href="build-classification-models.html" />
+    <link rel="next" title="37.89. Parameter play" href="parameter-play.html" />
+    <link rel="prev" title="37.87. Build classification models" href="build-classification-models.html" />
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     <meta name="docsearch:language" content="None">
     
@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
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   <a class="reference internal" href="../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
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   <a class="reference internal" href="../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
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    11. Regression models for Machine Learning
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-  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
-  <label for="toctree-checkbox-6">
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   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
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-  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
-  <label for="toctree-checkbox-7">
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   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
    14. Clustering models for Machine Learning
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-  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
-  <label for="toctree-checkbox-8">
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   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
    15. Getting started with ensemble learning
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
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   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
    16. Introduction to Gradient Boosting
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-  <label for="toctree-checkbox-10">
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+  <label for="toctree-checkbox-11">
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@@ -633,8 +655,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../README.html">
    37. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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@@ -825,308 +847,303 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
     </a>
    </li>
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-    <a class="reference internal" href="ml-linear-regression-1.html">
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-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
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     </a>
    </li>
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     <a class="reference internal" href="linear-regression/loss-function.html">
-     37.48. Loss Function
+     37.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
+     37.50. ML logistic regression - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-2.html">
-     37.52. ML logistic regression - assignment 2
+     37.51. ML logistic regression - assignment 2
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-neural-network-1.html">
-     37.53. ML neural network - Assignment 1
+     37.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
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-     37.54. Regression tools
+     37.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
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-     37.55. Managing data
+     37.54. Managing data
     </a>
    </li>
    <li class="toctree-l2">
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-     37.56. Exploring visualizations
+     37.55. Exploring visualizations
     </a>
    </li>
    <li class="toctree-l2">
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-     37.57. Try a different model
+     37.56. Try a different model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="create-a-regression-model.html">
-     37.58. Create a regression model
+     37.57. Create a regression model
     </a>
    </li>
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-     37.59. Linear and polynomial regression
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     </a>
    </li>
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-     37.60. Retrying some regression
+     37.59. Retrying some regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
+     37.60. Pumpkin varieties and color
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="delicious-asian-and-indian-cuisines.html">
-     37.62. Delicious asian and indian cuisines
+     37.61. Delicious asian and indian cuisines
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="explore-classification-methods.html">
-     37.63. Explore classification methods
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
+     37.62. Explore classification methods
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.65. Kernel method assignment 1
+     37.63. Kernel method assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
+     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.67. Support Vector Machines (SVM) - Classification
+     37.65. Support Vector Machines (SVM) - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.68. Decision Trees - Intro and Regression
+     37.66. Decision Trees - Intro and Regression
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.69. Decision Trees - Classification
+     37.67. Decision Trees - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.70. Model selection assignment 1
+     37.68. Model selection assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.71. Learning Curve To Identify Overfit &amp; Underfit
+     37.69. Learning Curve To Identify Overfit &amp; Underfit
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.72. Dropout and Batch Normalization
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.73. Lasso and Ridge Regression
+     37.71. Lasso and Ridge Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/regularized-linear-models.html">
-     37.74. Regularized Linear Models
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
-     37.75. Random forests intro and regression
+     37.73. Random forests intro and regression
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forests-for-classification.html">
-     37.76. Random forests for classification
+     37.74. Random forests for classification
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
-     37.77. Beyond random forests: more ensemble models
+     37.75. Beyond random forests: more ensemble models
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/ensemble-learning/decision-trees.html">
-     37.78. Decision trees
+     37.76. Decision trees
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
-     37.79. Hyperparameter tuning gradient boosting
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    </li>
    <li class="toctree-l2">
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-     37.80. Gradient boosting
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     <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
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    </li>
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     </a>
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     <a class="reference internal" href="../deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.97. Intro to TensorFlow for Deep Learning
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     <a class="reference internal" href="../deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
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     <a class="reference internal" href="../deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.101. Google Stock Price Prediction RNN
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     <a class="reference internal" href="../deep-learning/autoencoder/autoencoder.html">
-     37.103. Intro to Autoencoders
+     37.101. Intro to Autoencoders
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    </li>
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     <a class="reference internal" href="../deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
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     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
+     37.103. Fun with Variational Autoencoders
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    </li>
    <li class="toctree-l2">
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-     37.106. Time Series Forecasting Assignment
+     37.104. Time Series Forecasting Assignment
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    </li>
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-     37.108. Neural Networks for Classification with TensorFlow
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    <li class="toctree-l2">
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-     37.109. NN Classify 15 Fruits Assignment
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-     37.114. DQN On Foreign Exchange Market
+     37.112. DQN On Foreign Exchange Market
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    </li>
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     <a class="reference internal" href="../deep-learning/gan/art-by-gan.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
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+     37.115. Generative Adversarial Networks (GANs)
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.90.1. Acknowledgments
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 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
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 <section id="acknowledgments">
-<h2><span class="section-number">37.90.1. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.88.1. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
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         <i class="fas fa-angle-left"></i>
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-            <p class="prev-next-title"><span class="section-number">37.89. </span>Build classification models</p>
+            <p class="prev-next-title"><span class="section-number">37.87. </span>Build classification models</p>
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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    </li>
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.68. Decision Trees - Intro and Regression
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     </a>
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-     37.72. Dropout and Batch Normalization
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-     37.77. Beyond random forests: more ensemble models
+     37.75. Beyond random forests: more ensemble models
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-     37.79. Hyperparameter tuning gradient boosting
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-     37.80. Gradient boosting
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-     37.81. Boosting with tuning
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-     37.82. Random Forest Classifier with Feature Importance
+     37.80. Random Forest Classifier with Feature Importance
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-     37.87. Case Study: Debugging in Regression
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-     37.88. Study the solvers
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-     37.96. Object Recognition in Images using CNN
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-     37.97. Intro to TensorFlow for Deep Learning
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     <a class="reference internal" href="../deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.101. Google Stock Price Prediction RNN
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-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
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     </a>
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     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
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-     37.106. Time Series Forecasting Assignment
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-     37.108. Neural Networks for Classification with TensorFlow
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     </a>
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     <a class="reference internal" href="../deep-learning/nn-classify-15-fruits-assignment.html">
-     37.109. NN Classify 15 Fruits Assignment
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-     37.114. DQN On Foreign Exchange Market
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-     37.115. Art by gan
+     37.113. Art by gan
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     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
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@@ -1135,8 +1152,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#problem-formulation">
-   37.89.1. 1. Problem Formulation
+   37.87.1. 1. Problem Formulation
   </a>
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   <a class="reference internal nav-link" href="#finding-the-data">
-   37.89.2. 2. Finding the Data
+   37.87.2. 2. Finding the Data
   </a>
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   <a class="reference internal nav-link" href="#exploring-insights-in-data-or-eda">
-   37.89.3. 3. Exploring Insights in Data or EDA
+   37.87.3. 3. Exploring Insights in Data or EDA
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-summary-statistics">
-     37.89.3.1. Checking summary statistics
+     37.87.3.1. Checking summary statistics
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-missing-values">
-     37.89.3.2. Checking missing values
+     37.87.3.2. Checking missing values
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-correlation-between-features">
-     37.89.3.3. Checking Correlation Between Features
+     37.87.3.3. Checking Correlation Between Features
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#more-data-exploration">
-     37.89.3.4. More Data Exploration
+     37.87.3.4. More Data Exploration
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploring-categorical-features">
-     37.89.3.5. Exploring Categorical Features
+     37.87.3.5. Exploring Categorical Features
     </a>
    </li>
   </ul>
  </li>
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   <a class="reference internal nav-link" href="#data-preprocessing">
-   37.89.4. 4. Data Preprocessing
+   37.87.4. 4. Data Preprocessing
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-     37.89.4.1. Feature Scaling
+     37.87.4.1. Feature Scaling
     </a>
    </li>
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-   37.89.5. 5. Creating and Training a Logistic Regression Model
+   37.87.5. 5. Creating and Training a Logistic Regression Model
   </a>
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+     37.87.5.1. Trying other models
     </a>
    </li>
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   <a class="reference internal nav-link" href="#model-evaluation">
-   37.89.6. 6.  Model Evaluation
+   37.87.6. 6.  Model Evaluation
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-performance-metrics">
-     37.89.6.1. Classification Performance Metrics
+     37.87.6.1. Classification Performance Metrics
     </a>
    </li>
   </ul>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.89.7. Acknowledgments
+   37.87.7. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1611,85 +1628,85 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#problem-formulation">
-   37.89.1. 1. Problem Formulation
+   37.87.1. 1. Problem Formulation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#finding-the-data">
-   37.89.2. 2. Finding the Data
+   37.87.2. 2. Finding the Data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exploring-insights-in-data-or-eda">
-   37.89.3. 3. Exploring Insights in Data or EDA
+   37.87.3. 3. Exploring Insights in Data or EDA
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-summary-statistics">
-     37.89.3.1. Checking summary statistics
+     37.87.3.1. Checking summary statistics
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-missing-values">
-     37.89.3.2. Checking missing values
+     37.87.3.2. Checking missing values
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-correlation-between-features">
-     37.89.3.3. Checking Correlation Between Features
+     37.87.3.3. Checking Correlation Between Features
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#more-data-exploration">
-     37.89.3.4. More Data Exploration
+     37.87.3.4. More Data Exploration
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploring-categorical-features">
-     37.89.3.5. Exploring Categorical Features
+     37.87.3.5. Exploring Categorical Features
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-preprocessing">
-   37.89.4. 4. Data Preprocessing
+   37.87.4. 4. Data Preprocessing
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#feature-scaling">
-     37.89.4.1. Feature Scaling
+     37.87.4.1. Feature Scaling
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#creating-and-training-a-logistic-regression-model">
-   37.89.5. 5. Creating and Training a Logistic Regression Model
+   37.87.5. 5. Creating and Training a Logistic Regression Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#trying-other-models">
-     37.89.5.1. Trying other models
+     37.87.5.1. Trying other models
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#model-evaluation">
-   37.89.6. 6.  Model Evaluation
+   37.87.6. 6.  Model Evaluation
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-performance-metrics">
-     37.89.6.1. Classification Performance Metrics
+     37.87.6.1. Classification Performance Metrics
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.89.7. Acknowledgments
+   37.87.7. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1703,19 +1720,19 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="build-classification-models">
-<h1><span class="section-number">37.89. </span>Build classification models<a class="headerlink" href="#build-classification-models" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.87. </span>Build classification models<a class="headerlink" href="#build-classification-models" title="Permalink to this headline">#</a></h1>
 <p><a name='0'></a></p>
 <p>In this lab, we will learn about classification where the task is to predict the class or category. Both regression and classification are the main two types of supervised learning.</p>
 <p><a name='1'></a></p>
 <section id="problem-formulation">
-<h2><span class="section-number">37.89.1. </span>1. Problem Formulation<a class="headerlink" href="#problem-formulation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.87.1. </span>1. Problem Formulation<a class="headerlink" href="#problem-formulation" title="Permalink to this headline">#</a></h2>
 <p>Let’s say you have an idea of a revolutionary mobile phone and you want to establish a start up, but you know little about the price of the mobile phones. You are interested in learning that!</p>
 <p>Fortunately, there is this mobile dataset <a class="reference external" href="https://www.kaggle.com/iabhishekofficial/mobile-price-classification?select=train.csv">on Kaggle</a> that you can use to learn about the price ranges of mobiles based on their features such as wifi &amp; bluetooth supports etc…</p>
 <p>So, to make it simple, you have a dataset containing the features of mobiles and the problem is to predict the price range, not the exact price.</p>
 <p><a name='2'></a></p>
 </section>
 <section id="finding-the-data">
-<h2><span class="section-number">37.89.2. </span>2. Finding the Data<a class="headerlink" href="#finding-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.87.2. </span>2. Finding the Data<a class="headerlink" href="#finding-the-data" title="Permalink to this headline">#</a></h2>
 <p>The data that we are going to use is found on Kaggle.</p>
 <p>Here are the details of the features. It is 21 features. The target feature is <code class="docutils literal notranslate"><span class="pre">price</span> <span class="pre">range</span></code> and it has four price ranges: <code class="docutils literal notranslate"><span class="pre">0(low</span> <span class="pre">cost),</span> <span class="pre">1(medium</span> <span class="pre">cost),</span> <span class="pre">2(high</span> <span class="pre">cost)</span> <span class="pre">and</span> <span class="pre">3(very</span> <span class="pre">high</span> <span class="pre">cost).</span></code></p>
 <ul class="simple">
@@ -1832,7 +1849,7 @@ <h2><span class="section-number">37.89.2. </span>2. Finding the Data<a class="he
 <p><a name='3'></a></p>
 </section>
 <section id="exploring-insights-in-data-or-eda">
-<h2><span class="section-number">37.89.3. </span>3. Exploring Insights in Data or EDA<a class="headerlink" href="#exploring-insights-in-data-or-eda" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.87.3. </span>3. Exploring Insights in Data or EDA<a class="headerlink" href="#exploring-insights-in-data-or-eda" title="Permalink to this headline">#</a></h2>
 <p>In this part, we are going to learn more about the dataset. Let’s start with the summary statistics, but before that, I will copy the training data in order to get it easily when we mess up down the road.</p>
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 <section id="checking-summary-statistics">
-<h3><span class="section-number">37.89.3.1. </span>Checking summary statistics<a class="headerlink" href="#checking-summary-statistics" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.87.3.1. </span>Checking summary statistics<a class="headerlink" href="#checking-summary-statistics" title="Permalink to this headline">#</a></h3>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mobile_train</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
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 </div>
 </section>
 <section id="checking-missing-values">
-<h3><span class="section-number">37.89.3.2. </span>Checking missing values<a class="headerlink" href="#checking-missing-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.87.3.2. </span>Checking missing values<a class="headerlink" href="#checking-missing-values" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mobile_train</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
@@ -1863,7 +1880,7 @@ <h3><span class="section-number">37.89.3.2. </span>Checking missing values<a cla
 <p>We are lucky to not have missing values. If we had some, we would have to fill them with some strategies such as mean, remove them compleletly or leave them as they are. None of those 3 options is always the right choice for imputing missing values in all kinds of problems. it depends on the problem and the size of your dataset. Take an example, by removing a feature, you’re loosing data. And by filling the values, you’re adding noise or so. There is a trade off when it comes to imputing the missing values.</p>
 </section>
 <section id="checking-correlation-between-features">
-<h3><span class="section-number">37.89.3.3. </span>Checking Correlation Between Features<a class="headerlink" href="#checking-correlation-between-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.87.3.3. </span>Checking Correlation Between Features<a class="headerlink" href="#checking-correlation-between-features" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">correlation</span> <span class="o">=</span> <span class="n">mobile_train</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span>
@@ -1892,7 +1909,7 @@ <h3><span class="section-number">37.89.3.3. </span>Checking Correlation Between
 <p>Let’s take that insights from words to visualization.</p>
 </section>
 <section id="more-data-exploration">
-<h3><span class="section-number">37.89.3.4. </span>More Data Exploration<a class="headerlink" href="#more-data-exploration" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.87.3.4. </span>More Data Exploration<a class="headerlink" href="#more-data-exploration" title="Permalink to this headline">#</a></h3>
 <p>Again, let’s see what we have in the price ranges.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2010,7 +2027,7 @@ <h3><span class="section-number">37.89.3.4. </span>More Data Exploration<a class
 </div>
 </section>
 <section id="exploring-categorical-features">
-<h3><span class="section-number">37.89.3.5. </span>Exploring Categorical Features<a class="headerlink" href="#exploring-categorical-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.87.3.5. </span>Exploring Categorical Features<a class="headerlink" href="#exploring-categorical-features" title="Permalink to this headline">#</a></h3>
 <p>Even if all features have numerical values, but there some of them which are categorical. Let’s inspect them!! It might be that they are already encoded!</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2107,7 +2124,7 @@ <h3><span class="section-number">37.89.3.5. </span>Exploring Categorical Feature
 </section>
 </section>
 <section id="data-preprocessing">
-<h2><span class="section-number">37.89.4. </span>4. Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.87.4. </span>4. Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
 <p>In this part, we will prepare the data to be in proper format for ML models. We will do things like feature scaling. We would also had to encode categorical features but they are already encoded. Before we proceed with feature scaling, let’s take training labels from training data.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2118,7 +2135,7 @@ <h2><span class="section-number">37.89.4. </span>4. Data Preprocessing<a class="
 </div>
 </div>
 <section id="feature-scaling">
-<h3><span class="section-number">37.89.4.1. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.87.4.1. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h3>
 <p>This is the only data processing type that we have to take care of here. Let’s make a function that can take handle that.</p>
 <p>We will normalize the data with sklearn <code class="docutils literal notranslate"><span class="pre">MinMaxScaler</span></code> where the numerical values will be scaled to values between 0 and 1.</p>
 <div class="cell docutils container">
@@ -2153,7 +2170,7 @@ <h3><span class="section-number">37.89.4.1. </span>Feature Scaling<a class="head
 </section>
 </section>
 <section id="creating-and-training-a-logistic-regression-model">
-<h2><span class="section-number">37.89.5. </span>5. Creating and Training a Logistic Regression Model<a class="headerlink" href="#creating-and-training-a-logistic-regression-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.87.5. </span>5. Creating and Training a Logistic Regression Model<a class="headerlink" href="#creating-and-training-a-logistic-regression-model" title="Permalink to this headline">#</a></h2>
 <p>We will start by importing <code class="docutils literal notranslate"><span class="pre">LogisticRegression</span></code> models available in Sklearn linear models and then proceed with model training.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2176,7 +2193,7 @@ <h2><span class="section-number">37.89.5. </span>5. Creating and Training a Logi
 <p>Using <code class="docutils literal notranslate"><span class="pre">log_model.score()</span></code>, we can get an accuracy of the model confidence on the training data and labels. 94.7% is not that bad.</p>
 <p>Let’s train another linear classifier called SGD (Stockastic Gradient Descent) classifier on the same dataset.</p>
 <section id="trying-other-models">
-<h3><span class="section-number">37.89.5.1. </span>Trying other models<a class="headerlink" href="#trying-other-models" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.87.5.1. </span>Trying other models<a class="headerlink" href="#trying-other-models" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">SGDClassifier</span>
@@ -2217,7 +2234,7 @@ <h3><span class="section-number">37.89.5.1. </span>Trying other models<a class="
 </section>
 </section>
 <section id="model-evaluation">
-<h2><span class="section-number">37.89.6. </span>6.  Model Evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.87.6. </span>6.  Model Evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
 <p>Evaluating classification models is not as simple as for regression models. When you have skewed dataset or imbalances, you can get a high accuracy and you can think the model did well when in fact it didn’t.</p>
 <p>In this part, we are going to learn how to evaluate classification models. But first, let’s evaluate the model with cross validation.</p>
 <p>Here is an idea behind cross validation: We want to divide the training set into different training and validation subsets so that we can iteratively train and validate the models on those subsets.</p>
@@ -2324,7 +2341,7 @@ <h2><span class="section-number">37.89.6. </span>6.  Model Evaluation<a class="h
 </div>
 </div>
 <section id="classification-performance-metrics">
-<h3><span class="section-number">37.89.6.1. </span>Classification Performance Metrics<a class="headerlink" href="#classification-performance-metrics" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.87.6.1. </span>Classification Performance Metrics<a class="headerlink" href="#classification-performance-metrics" title="Permalink to this headline">#</a></h3>
 <p>Here are commom classification metrics:</p>
 <ul class="simple">
 <li><p>Accuracy</p></li>
@@ -2397,7 +2414,7 @@ <h3><span class="section-number">37.89.6.1. </span>Classification Performance Me
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.89.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.87.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2439,13 +2456,13 @@ <h2><span class="section-number">37.89.7. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.88. </span>Study the solvers</p>
+            <p class="prev-next-title"><span class="section-number">37.86. </span>Study the solvers</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="build-classification-model.html" title="next page">
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.90. </span>Build Classification Model</p>
+        <p class="prev-next-title"><span class="section-number">37.88. </span>Build Classification Model</p>
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diff --git a/assignments/ml-fundamentals/create-a-regression-model.html b/assignments/ml-fundamentals/create-a-regression-model.html
index 1e94820cb9..c6ac2aa259 100644
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@@ -6,7 +6,7 @@
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.58. Create a regression model &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.57. Create a regression model &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.59. Linear and polynomial regression" href="linear-and-polynomial-regression.html" />
-    <link rel="prev" title="37.57. Try a different model" href="try-a-different-model.html" />
+    <link rel="next" title="37.58. Linear and polynomial regression" href="linear-and-polynomial-regression.html" />
+    <link rel="prev" title="37.56. Try a different model" href="try-a-different-model.html" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
   </a>
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@@ -284,8 +306,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
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@@ -311,8 +333,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
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-  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
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@@ -355,8 +377,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
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@@ -392,8 +414,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
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@@ -431,8 +453,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
    14. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
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@@ -453,8 +475,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
    15. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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@@ -480,8 +502,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
    16. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
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@@ -633,8 +655,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../README.html">
    37. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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@@ -825,308 +847,303 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
+     37.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/loss-function.html">
-     37.48. Loss Function
+     37.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
+     37.50. ML logistic regression - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-2.html">
-     37.52. ML logistic regression - assignment 2
+     37.51. ML logistic regression - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-neural-network-1.html">
-     37.53. ML neural network - Assignment 1
+     37.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="regression-tools.html">
-     37.54. Regression tools
+     37.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="managing-data.html">
-     37.55. Managing data
+     37.54. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="exploring-visualizations.html">
-     37.56. Exploring visualizations
+     37.55. Exploring visualizations
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="try-a-different-model.html">
-     37.57. Try a different model
+     37.56. Try a different model
     </a>
    </li>
    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     37.58. Create a regression model
+     37.57. Create a regression model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-and-polynomial-regression.html">
-     37.59. Linear and polynomial regression
+     37.58. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="retrying-some-regression.html">
-     37.60. Retrying some regression
+     37.59. Retrying some regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
+     37.60. Pumpkin varieties and color
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="delicious-asian-and-indian-cuisines.html">
-     37.62. Delicious asian and indian cuisines
+     37.61. Delicious asian and indian cuisines
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="explore-classification-methods.html">
-     37.63. Explore classification methods
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
+     37.62. Explore classification methods
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.65. Kernel method assignment 1
+     37.63. Kernel method assignment 1
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    </li>
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-     37.108. Neural Networks for Classification with TensorFlow
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-     37.114. DQN On Foreign Exchange Market
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    </li>
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     <a class="reference internal" href="../deep-learning/gan/art-by-gan.html">
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-     37.117. Generative Adversarial Networks (GANs)
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-   37.58.1. Instructions
+   37.57.1. Instructions
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+   37.57.2. Rubric
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-   37.58.3. Acknowledgments
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 </ul>
@@ -1560,17 +1577,17 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.58.3. Acknowledgments
+   37.57.3. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1584,13 +1601,13 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="create-a-regression-model">
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 <section id="instructions">
-<h2><span class="section-number">37.58.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.57.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>In this section you were shown how to build a model using both Linear and Polynomial Regression. Using this knowledge, find a dataset or use one of Scikit-learn’s built-in sets to build a fresh model. Explain in your notebook why you chose the technique you did, and demonstrate your model’s accuracy. If it is not accurate, explain why.</p>
 </section>
 <section id="rubric">
-<h2><span class="section-number">37.58.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
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 <table class="colwidths-auto table">
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 </table>
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 <section id="acknowledgments">
-<h2><span class="section-number">37.58.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.57.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1651,13 +1668,13 @@ <h2><span class="section-number">37.58.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.57. </span>Try a different model</p>
+            <p class="prev-next-title"><span class="section-number">37.56. </span>Try a different model</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="linear-and-polynomial-regression.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.59. </span>Linear and polynomial regression</p>
+        <p class="prev-next-title"><span class="section-number">37.58. </span>Linear and polynomial regression</p>
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diff --git a/assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html b/assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html
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-    <title>37.62. Delicious asian and indian cuisines &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.61. Delicious asian and indian cuisines &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="next" title="37.63. Explore classification methods" href="explore-classification-methods.html" />
-    <link rel="prev" title="37.61. Pumpkin varieties and color" href="pumpkin-varieties-and-color.html" />
+    <link rel="next" title="37.62. Explore classification methods" href="explore-classification-methods.html" />
+    <link rel="prev" title="37.60. Pumpkin varieties and color" href="pumpkin-varieties-and-color.html" />
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      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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    7. Data Science lifecycle
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    8. Data Science in the cloud
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    11. Regression models for Machine Learning
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-    <a class="reference internal" href="ml-linear-regression-2.html">
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     <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
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+    <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
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-     37.91. Parameter play
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-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
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     <a class="reference internal" href="../deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
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     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
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-     37.106. Time Series Forecasting Assignment
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    </li>
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     <a class="reference internal" href="../deep-learning/nn-for-classification-assignment.html">
-     37.108. Neural Networks for Classification with TensorFlow
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     <a class="reference internal" href="../deep-learning/nn-classify-15-fruits-assignment.html">
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+<h1><span class="section-number">37.61. </span>Delicious asian and indian cuisines<a class="headerlink" href="#delicious-asian-and-indian-cuisines" title="Permalink to this headline">#</a></h1>
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
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      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
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-    <a class="reference internal" href="ml-linear-regression-2.html">
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-     37.49. Gradient descent
+     37.48. Gradient descent
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+   </li>
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+    <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
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               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="explore-classification-methods">
-<h1><span class="section-number">37.63. </span>Explore classification methods<a class="headerlink" href="#explore-classification-methods" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.62. </span>Explore classification methods<a class="headerlink" href="#explore-classification-methods" title="Permalink to this headline">#</a></h1>
 <section id="instructions">
-<h2><span class="section-number">37.63.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.62.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>In <a class="reference external" href="https://scikit-learn.org/stable/supervised_learning.html">Scikit-learn documentation</a> you’ll find a large list of ways to classify data. Do a little scavenger hunt in these docs: your goals is to look for classification methods and match a dataset in this curriculum, a question you can ask of it, and a technique of classification. Create a spreadsheet or table in a .doc file and explain how the dataset would work with the classification algorithm.</p>
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+<h2><span class="section-number">37.62.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
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+            <p class="prev-next-title"><span class="section-number">37.61. </span>Delicious asian and indian cuisines</p>
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diff --git a/assignments/ml-fundamentals/exploring-visualizations.html b/assignments/ml-fundamentals/exploring-visualizations.html
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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-    <a class="reference internal" href="ml-linear-regression-2.html">
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+   </li>
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+    <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
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    </li>
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+     37.113. Art by gan
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    </li>
    <li class="toctree-l2">
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-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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+     37.116. Basic classification: Classify images of clothing
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   </a>
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+   37.55.1. Rubric
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-   37.56.1. Rubric
+   37.55.1. Rubric
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.56.2. Acknowledgments
+   37.55.2. Acknowledgments
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 </ul>
@@ -1574,10 +1591,10 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="exploring-visualizations">
-<h1><span class="section-number">37.56. </span>Exploring visualizations<a class="headerlink" href="#exploring-visualizations" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.55. </span>Exploring visualizations<a class="headerlink" href="#exploring-visualizations" title="Permalink to this headline">#</a></h1>
 <p>There are several different libraries that are available for data visualization. Create some visualizations using the Pumpkin data in this lesson with matplotlib and seaborn in a sample notebook. Which libraries are easier to work with?</p>
 <section id="rubric">
-<h2><span class="section-number">37.56.1. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.55.1. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Criteria</p></th>
@@ -1596,7 +1613,7 @@ <h2><span class="section-number">37.56.1. </span>Rubric<a class="headerlink" hre
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 <section id="acknowledgments">
-<h2><span class="section-number">37.56.2. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.55.2. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1638,13 +1655,13 @@ <h2><span class="section-number">37.56.2. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.55. </span>Managing data</p>
+            <p class="prev-next-title"><span class="section-number">37.54. </span>Managing data</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="try-a-different-model.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.57. </span>Try a different model</p>
+        <p class="prev-next-title"><span class="section-number">37.56. </span>Try a different model</p>
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-    <link rel="prev" title="37.58. Create a regression model" href="create-a-regression-model.html" />
+    <link rel="next" title="37.59. Retrying some regression" href="retrying-some-regression.html" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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    6. Data visualization
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    7. Data Science lifecycle
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-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
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    6. Data visualization
   </a>
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      6.1. Visualizing distributions
     </a>
    </li>
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-    <a class="reference internal" href="../../data-science/data-visualization/visualization-proportions.html">
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      6.2. Visualizing proportions
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    </li>
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-    <a class="reference internal" href="../../data-science/data-visualization/visualization-relationships.html">
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      6.3. Visualizing relationships: all about honey 🍯
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    </li>
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-    <a class="reference internal" href="../../data-science/data-visualization/meaningful-visualizations.html">
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      6.4. Making meaningful visualizations
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    7. Data Science lifecycle
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      7.1. Introduction to the Data Science lifecycle
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      7.2. Analyzing
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      7.3. Communication
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-  <a class="reference internal" href="../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
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    8. Data Science in the cloud
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-    <a class="reference internal" href="../../data-science/data-science-in-the-cloud/introduction.html">
+    <a class="reference internal" href="../../../data-science/data-science-in-the-cloud/introduction.html">
      8.1. Introduction
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../../data-science/data-science-in-the-cloud/the-low-code-no-code-way.html">
+    <a class="reference internal" href="../../../data-science/data-science-in-the-cloud/the-low-code-no-code-way.html">
      8.2. The “low code/no code” way
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    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../../data-science/data-science-in-the-cloud/the-azure-ml-sdk-way.html">
+    <a class="reference internal" href="../../../data-science/data-science-in-the-cloud/the-azure-ml-sdk-way.html">
      8.3. Data Science in the cloud: The “Azure ML SDK” way
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    9. Data Science in the real world
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    10. Machine Learning overview
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-  <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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    11. Regression models for Machine Learning
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+    <a class="reference internal" href="../../../ml-fundamentals/regression/tools-of-the-trade.html">
      11.1. Tools of the trade
     </a>
    </li>
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-    <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
+    <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
      11.2. Managing data
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    </li>
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-    <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
+    <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
      11.3. Linear and polynomial regression
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    </li>
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-    <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
+    <a class="reference internal" href="../../../ml-fundamentals/regression/logistic-regression.html">
      11.4. Logistic regression
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-  <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
+  <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
    12. Build a web app to use a Machine Learning model
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-  <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
+  <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
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-  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
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      13.1. Introduction to classification
     </a>
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-    <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
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      13.2. More classifiers
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-    <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
+    <a class="reference internal" href="../../../ml-fundamentals/classification/yet-other-classifiers.html">
      13.3. Yet other classifiers
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-    <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
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      13.4. Applied Machine Learning : build a web app
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-  <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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    14. Clustering models for Machine Learning
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-  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
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-    <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
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      14.1. Introduction to clustering
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    </li>
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-    <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
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      14.2. K-Means clustering
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    15. Getting started with ensemble learning
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      15.1. Bagging
     </a>
    </li>
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-    <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
+    <a class="reference internal" href="../../../ml-advanced/ensemble-learning/random-forest.html">
      15.2. Random forest
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-    <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
+    <a class="reference internal" href="../../../ml-advanced/ensemble-learning/feature-importance.html">
      15.3. Feature importance
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    16. Introduction to Gradient Boosting
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      16.1. Gradient Boosting
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-    <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
+    <a class="reference internal" href="../../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
      16.2. Gradient boosting example
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-    <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
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      16.3. XGBoost
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+    <a class="reference internal" href="../../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
      16.4. XGBoost + k-fold CV + Feature Importance
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    17. Unsupervised learning
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    18. Kernel method
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    19. Model selection
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    20. Intro to Deep Learning
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    21. Convolutional Neural Networks
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    23. Recurrent Neural Networks
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    24. Autoencoder
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    28. Image classification
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    29. Image segmentation
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    30. Diffusion Model
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    31. Object detection
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    32. Overview
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    33. Problem framing
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    34. Data engineering
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    35. Model training &amp; evaluation
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    36. Model deployment
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-  <a class="reference internal" href="../README.html">
+  <a class="reference internal" href="../../README.html">
    37. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
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      37.3. First assignment
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    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../set-up-env/second-assignment.html">
+    <a class="reference internal" href="../../set-up-env/second-assignment.html">
      37.4. Second assignment
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    <li class="toctree-l2">
-    <a class="reference internal" href="../project-plan-template.html">
+    <a class="reference internal" href="../../project-plan-template.html">
      37.5. Project Plan​ Template
     </a>
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    <li class="toctree-l2">
-    <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
+    <a class="reference internal" href="../../prerequisites/python-programming-introduction.html">
      37.6. Python programming introduction
     </a>
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    <li class="toctree-l2">
-    <a class="reference internal" href="../prerequisites/python-programming-basics.html">
+    <a class="reference internal" href="../../prerequisites/python-programming-basics.html">
      37.7. Python programming basics
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    <li class="toctree-l2">
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+    <a class="reference internal" href="../../prerequisites/python-programming-advanced.html">
      37.8. Python programming advanced
     </a>
    </li>
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-    <a class="reference internal" href="../data-science/analyzing-text-about-data-science.html">
+    <a class="reference internal" href="../../data-science/analyzing-text-about-data-science.html">
      37.9. Analyzing text about Data Science
     </a>
    </li>
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-    <a class="reference internal" href="../data-science/data-science-scenarios.html">
+    <a class="reference internal" href="../../data-science/data-science-scenarios.html">
      37.10. Data Science scenarios
     </a>
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-    <a class="reference internal" href="../data-science/write-a-data-ethics-case-study.html">
+    <a class="reference internal" href="../../data-science/write-a-data-ethics-case-study.html">
      37.11. Write a data ethics case study
     </a>
    </li>
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-    <a class="reference internal" href="../data-science/lines-scatters-and-bars.html">
+    <a class="reference internal" href="../../data-science/lines-scatters-and-bars.html">
      37.12. Lines, scatters and bars
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      37.13. Apply your skills
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      37.14. Try it in Excel
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    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/dive-into-the-beehive.html">
+    <a class="reference internal" href="../../data-science/dive-into-the-beehive.html">
      37.15. Dive into the beehive
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/build-your-own-custom-vis.html">
+    <a class="reference internal" href="../../data-science/build-your-own-custom-vis.html">
      37.16. Build your own custom vis
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/classifying-datasets.html">
+    <a class="reference internal" href="../../data-science/classifying-datasets.html">
      37.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/small-diabetes-study.html">
+    <a class="reference internal" href="../../data-science/small-diabetes-study.html">
      37.18. Small diabetes study
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/introduction-to-statistics-and-probability.html">
+    <a class="reference internal" href="../../data-science/introduction-to-statistics-and-probability.html">
      37.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/displaying-airport-data.html">
+    <a class="reference internal" href="../../data-science/displaying-airport-data.html">
      37.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/soda-profits.html">
+    <a class="reference internal" href="../../data-science/soda-profits.html">
      37.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/analyzing-COVID-19-papers.html">
+    <a class="reference internal" href="../../data-science/analyzing-COVID-19-papers.html">
      37.22. Analyzing COVID-19 papers
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/estimation-of-COVID-19-pandemic.html">
+    <a class="reference internal" href="../../data-science/estimation-of-COVID-19-pandemic.html">
      37.23. Estimation of COVID-19 pandemic
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/data-processing-in-python.html">
+    <a class="reference internal" href="../../data-science/data-processing-in-python.html">
      37.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/evaluating-data-from-a-form.html">
+    <a class="reference internal" href="../../data-science/evaluating-data-from-a-form.html">
      37.25. Evaluating data from a form
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/data-preparation.html">
+    <a class="reference internal" href="../../data-science/data-preparation.html">
      37.26. Data preparation
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/analyzing-data.html">
+    <a class="reference internal" href="../../data-science/analyzing-data.html">
      37.27. Analyzing data
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/nyc-taxi-data-in-winter-and-summer.html">
+    <a class="reference internal" href="../../data-science/nyc-taxi-data-in-winter-and-summer.html">
      37.28. NYC taxi data in winter and summer
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/matplotlib-applied.html">
+    <a class="reference internal" href="../../data-science/matplotlib-applied.html">
      37.29. Matplotlib applied
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/tell-a-story.html">
+    <a class="reference internal" href="../../data-science/tell-a-story.html">
      37.35. Tell a story
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/explore-a-planetary-computer-dataset.html">
+    <a class="reference internal" href="../../data-science/explore-a-planetary-computer-dataset.html">
      37.36. Explore a planetary computer dataset
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/exploring-for-anwser.html">
+    <a class="reference internal" href="../../data-science/exploring-for-anwser.html">
      37.37. Exploring for answers
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/market-research.html">
+    <a class="reference internal" href="../../data-science/market-research.html">
      37.38. Market research
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/low-code-no-code-data-science-project-on-azure-ml.html">
+    <a class="reference internal" href="../../data-science/low-code-no-code-data-science-project-on-azure-ml.html">
      37.39. Low code/no code Data Science project on Azure ML
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/data-science-project-using-azure-ml-sdk.html">
+    <a class="reference internal" href="../../data-science/data-science-project-using-azure-ml-sdk.html">
      37.40. Data Science project using Azure ML SDK
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
+    <a class="reference internal" href="../../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
      37.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="ml-overview-iris.html">
+    <a class="reference internal" href="../ml-overview-iris.html">
      37.42. Machine Learning overview - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="ml-overview-mnist-digits.html">
+    <a class="reference internal" href="../ml-overview-mnist-digits.html">
      37.43. Machine Learning overview - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="regression-with-scikit-learn.html">
+    <a class="reference internal" href="../regression-with-scikit-learn.html">
      37.44. Regression with Scikit-learn
     </a>
    </li>
    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     37.45. ML linear regression - assignment 1
+     37.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="linear-regression-metrics.html">
+     37.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
+    <a class="reference internal" href="loss-function.html">
+     37.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="linear-regression/loss-function.html">
-     37.48. Loss Function
+    <a class="reference internal" href="gradient-descent.html">
+     37.48. Gradient descent
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+    <a class="reference internal" href="linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
+    <a class="reference internal" href="../ml-logistic-regression-1.html">
+     37.50. ML logistic regression - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="ml-logistic-regression-2.html">
-     37.52. ML logistic regression - assignment 2
+    <a class="reference internal" href="../ml-logistic-regression-2.html">
+     37.51. ML logistic regression - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="ml-neural-network-1.html">
-     37.53. ML neural network - Assignment 1
+    <a class="reference internal" href="../ml-neural-network-1.html">
+     37.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="regression-tools.html">
-     37.54. Regression tools
+    <a class="reference internal" href="../regression-tools.html">
+     37.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="managing-data.html">
-     37.55. Managing data
+    <a class="reference internal" href="../managing-data.html">
+     37.54. Managing data
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="exploring-visualizations.html">
-     37.56. Exploring visualizations
+    <a class="reference internal" href="../exploring-visualizations.html">
+     37.55. Exploring visualizations
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="try-a-different-model.html">
-     37.57. Try a different model
+    <a class="reference internal" href="../try-a-different-model.html">
+     37.56. Try a different model
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="create-a-regression-model.html">
-     37.58. Create a regression model
+    <a class="reference internal" href="../create-a-regression-model.html">
+     37.57. Create a regression model
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="linear-and-polynomial-regression.html">
-     37.59. Linear and polynomial regression
+    <a class="reference internal" href="../linear-and-polynomial-regression.html">
+     37.58. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="retrying-some-regression.html">
-     37.60. Retrying some regression
+    <a class="reference internal" href="../retrying-some-regression.html">
+     37.59. Retrying some regression
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
+    <a class="reference internal" href="../pumpkin-varieties-and-color.html">
+     37.60. Pumpkin varieties and color
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="delicious-asian-and-indian-cuisines.html">
-     37.62. Delicious asian and indian cuisines
+    <a class="reference internal" href="../delicious-asian-and-indian-cuisines.html">
+     37.61. Delicious asian and indian cuisines
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="explore-classification-methods.html">
-     37.63. Explore classification methods
+    <a class="reference internal" href="../explore-classification-methods.html">
+     37.62. Explore classification methods
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
+    <a class="reference internal" href="../../ml-advanced/kernel-method/kernel-method-assignment-1.html">
+     37.63. Kernel method assignment 1
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.65. Kernel method assignment 1
+    <a class="reference internal" href="../../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
+     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
+    <a class="reference internal" href="../../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
+     37.65. Support Vector Machines (SVM) - Classification
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.67. Support Vector Machines (SVM) - Classification
+    <a class="reference internal" href="../../ml-advanced/kernel-method/decision_trees_for_regression.html">
+     37.66. Decision Trees - Intro and Regression
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.68. Decision Trees - Intro and Regression
+    <a class="reference internal" href="../../ml-advanced/kernel-method/decision_trees_for_classification.html">
+     37.67. Decision Trees - Classification
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.69. Decision Trees - Classification
+    <a class="reference internal" href="../../ml-advanced/model-selection/model-selection-assignment-1.html">
+     37.68. Model selection assignment 1
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.70. Model selection assignment 1
+    <a class="reference internal" href="../../ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
+     37.69. Learning Curve To Identify Overfit &amp; Underfit
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.71. Learning Curve To Identify Overfit &amp; Underfit
+    <a class="reference internal" href="../../ml-advanced/model-selection/dropout-and-batch-normalization.html">
+     37.70. Dropout and Batch Normalization
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.72. Dropout and Batch Normalization
+    <a class="reference internal" href="../../ml-advanced/model-selection/lasso-and-ridge-regression.html">
+     37.71. Lasso and Ridge Regression
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.73. Lasso and Ridge Regression
+    <a class="reference internal" href="../../ml-advanced/model-selection/regularized-linear-models.html">
+     37.72. Regularized Linear Models
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/model-selection/regularized-linear-models.html">
-     37.74. Regularized Linear Models
+    <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
+     37.73. Random forests intro and regression
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
-     37.75. Random forests intro and regression
+    <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forests-for-classification.html">
+     37.74. Random forests for classification
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forests-for-classification.html">
-     37.76. Random forests for classification
+    <a class="reference internal" href="../../ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
+     37.75. Beyond random forests: more ensemble models
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
-     37.77. Beyond random forests: more ensemble models
+    <a class="reference internal" href="../../ml-advanced/ensemble-learning/decision-trees.html">
+     37.76. Decision trees
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/ensemble-learning/decision-trees.html">
-     37.78. Decision trees
+    <a class="reference internal" href="../../ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
+     37.77. Hyperparameter tuning gradient boosting
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
-     37.79. Hyperparameter tuning gradient boosting
+    <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
+     37.78. Gradient boosting
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
-     37.80. Gradient boosting
+    <a class="reference internal" href="../../ml-advanced/gradient-boosting/boosting-with-tuning.html">
+     37.79. Boosting with tuning
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/gradient-boosting/boosting-with-tuning.html">
-     37.81. Boosting with tuning
+    <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
+     37.80. Random Forest Classifier with Feature Importance
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
-     37.82. Random Forest Classifier with Feature Importance
+    <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
+     37.82. Data engineering
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
-     37.84. Data engineering
+    <a class="reference internal" href="../../machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
+     37.83. Counterintuitive Challenges in ML Debugging
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
-     37.85. Counterintuitive Challenges in ML Debugging
+    <a class="reference internal" href="../../machine-learning-productionization/debugging-in-classification.html">
+     37.84. Case Study: Debugging in Classification
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../machine-learning-productionization/debugging-in-classification.html">
-     37.86. Case Study: Debugging in Classification
+    <a class="reference internal" href="../../machine-learning-productionization/debugging-in-regression.html">
+     37.85. Case Study: Debugging in Regression
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../machine-learning-productionization/debugging-in-regression.html">
-     37.87. Case Study: Debugging in Regression
+    <a class="reference internal" href="../study-the-solvers.html">
+     37.86. Study the solvers
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="study-the-solvers.html">
-     37.88. Study the solvers
+    <a class="reference internal" href="../build-classification-models.html">
+     37.87. Build classification models
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="build-classification-models.html">
-     37.89. Build classification models
+    <a class="reference internal" href="../build-classification-model.html">
+     37.88. Build Classification Model
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="build-classification-model.html">
-     37.90. Build Classification Model
+    <a class="reference internal" href="../parameter-play.html">
+     37.89. Parameter play
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="parameter-play.html">
-     37.91. Parameter play
+    <a class="reference internal" href="../../deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
+     37.90. How to choose cnn architecture mnist
     </a>
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-    <a class="reference internal" href="../deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
-     37.92. How to choose cnn architecture mnist
+    <a class="reference internal" href="../../deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
+     37.92. Sign Language Digits Classification with CNN
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
-     37.94. Sign Language Digits Classification with CNN
+    <a class="reference internal" href="../../deep-learning/cnn/object-recognition-in-images-using-cnn.html">
+     37.94. Object Recognition in Images using CNN
     </a>
    </li>
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-    <a class="reference internal" href="../deep-learning/cnn/object-recognition-in-images-using-cnn.html">
-     37.96. Object Recognition in Images using CNN
+    <a class="reference internal" href="../../deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
+     37.95. Intro to TensorFlow for Deep Learning
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.97. Intro to TensorFlow for Deep Learning
+    <a class="reference internal" href="../../deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
+     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
+    <a class="reference internal" href="../../deep-learning/rnn/google-stock-price-prediction-rnn.html">
+     37.99. Google Stock Price Prediction RNN
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.101. Google Stock Price Prediction RNN
+    <a class="reference internal" href="../../deep-learning/autoencoder/autoencoder.html">
+     37.101. Intro to Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../deep-learning/autoencoder/autoencoder.html">
-     37.103. Intro to Autoencoders
+    <a class="reference internal" href="../../deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
+     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
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-    <a class="reference internal" href="../deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
+    <a class="reference internal" href="../../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
+     37.103. Fun with Variational Autoencoders
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-    <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
+    <a class="reference internal" href="../../deep-learning/time-series-forecasting-assignment.html">
+     37.104. Time Series Forecasting Assignment
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-    <a class="reference internal" href="../deep-learning/time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
+    <a class="reference internal" href="../../deep-learning/nn-for-classification-assignment.html">
+     37.106. Neural Networks for Classification with TensorFlow
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-    <a class="reference internal" href="../deep-learning/nn-for-classification-assignment.html">
-     37.108. Neural Networks for Classification with TensorFlow
+    <a class="reference internal" href="../../deep-learning/nn-classify-15-fruits-assignment.html">
+     37.107. NN Classify 15 Fruits Assignment
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-    <a class="reference internal" href="../deep-learning/nn-classify-15-fruits-assignment.html">
-     37.109. NN Classify 15 Fruits Assignment
+    <a class="reference internal" href="../../deep-learning/dqn/dqn-on-foreign-exchange-market.html">
+     37.112. DQN On Foreign Exchange Market
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-    <a class="reference internal" href="../deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.114. DQN On Foreign Exchange Market
+    <a class="reference internal" href="../../deep-learning/gan/art-by-gan.html">
+     37.113. Art by gan
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-    <a class="reference internal" href="../deep-learning/gan/art-by-gan.html">
-     37.115. Art by gan
+    <a class="reference internal" href="../../deep-learning/gan/gan-introduction.html">
+     37.115. Generative Adversarial Networks (GANs)
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-     37.117. Generative Adversarial Networks (GANs)
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-    <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.118. Basic classification: Classify images of clothing
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+     37.116. Basic classification: Classify images of clothing
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    38. Slides
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      38.1. Python programming introduction
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      38.2. Python programming basics
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      38.3. Python programming advanced
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      38.4. Data Science introduction
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-    <a class="reference internal" href="../../slides/data-science/relational-vs-non-relational-database.html">
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      38.5. Relational vs. non-relational database
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      38.6. NumPy and Pandas
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      38.8. Data Science lifecycle
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      38.9. Data Science in the cloud
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      38.10. Data Science in real world
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-    <a class="reference internal" href="../../slides/ml-fundamentals/ml-overview.html">
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      38.11. Machine Learning overview
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-    <a class="reference internal" href="../../slides/ml-fundamentals/linear-regression.html">
+    <a class="reference internal" href="../../../slides/ml-fundamentals/linear-regression.html">
      38.12. Linear Regression
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-    <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression.html">
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      38.13. Logistic Regression
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      38.14. Logistic Regression
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-    <a class="reference internal" href="../../slides/ml-fundamentals/neural-network.html">
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      38.15. Neural Network
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-    <a class="reference internal" href="../../slides/ml-fundamentals/build-an-ml-web-app.html">
+    <a class="reference internal" href="../../../slides/ml-fundamentals/build-an-ml-web-app.html">
      38.16. Build an machine learning web application
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-    <a class="reference internal" href="../../slides/ml-advanced/unsupervised-learning.html">
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      38.17. Unsupervised learning
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-    <a class="reference internal" href="../../slides/ml-advanced/kernel-method.html">
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      38.18. Kernel method
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      38.19. Model Selection
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      38.20. Convolutional Neural Network
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-  <section class="tex2jax_ignore mathjax_ignore" id="ml-linear-regression-assignment-1">
-<h1><span class="section-number">37.45. </span>ML linear regression - assignment 1<a class="headerlink" href="#ml-linear-regression-assignment-1" title="Permalink to this headline">#</a></h1>
+  <section class="tex2jax_ignore mathjax_ignore" id="linear-regression-california-housing">
+<h1><span class="section-number">37.45. </span>Linear regression - California Housing<a class="headerlink" href="#linear-regression-california-housing" title="Permalink to this headline">#</a></h1>
 <p>In this assignment, we will predict the price of a house by its region, size, number of bedrooms, etc.</p>
 <section id="introduction">
 <h2><span class="section-number">37.45.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
@@ -2424,7 +2441,7 @@ <h4><span class="section-number">37.45.2.1.4. </span>Checking values in the cate
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.axes._subplots.AxesSubplot at 0x1f49b239f08&gt;
 </pre></div>
 </div>
-<img alt="../../_images/ml-linear-regression-1_18_1.png" src="../../_images/ml-linear-regression-1_18_1.png" />
+<img alt="../../../_images/california_housing_18_1.png" src="../../../_images/california_housing_18_1.png" />
 </div>
 </div>
 </section>
@@ -2464,7 +2481,7 @@ <h4><span class="section-number">37.45.2.1.5. </span>Checking Correlation Betwee
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.axes._subplots.AxesSubplot at 0x1f4a26c7b08&gt;
 </pre></div>
 </div>
-<img alt="../../_images/ml-linear-regression-1_21_1.png" src="../../_images/ml-linear-regression-1_21_1.png" />
+<img alt="../../../_images/california_housing_21_1.png" src="../../../_images/california_housing_21_1.png" />
 </div>
 </div>
 <p>Some features like total_bedrooms and households are highly correlated. Same things for <code class="docutils literal notranslate"><span class="pre">total_bedrooms</span></code> and <code class="docutils literal notranslate"><span class="pre">total_rooms</span></code> and that makes sense because for many houses, the number of people who stay in that particular house (<code class="docutils literal notranslate"><span class="pre">households</span></code>) goes with the number of available rooms(<code class="docutils literal notranslate"><span class="pre">total_rooms</span></code>) and <code class="docutils literal notranslate"><span class="pre">bed_rooms</span></code>.</p>
@@ -2485,7 +2502,7 @@ <h4><span class="section-number">37.45.2.1.6. </span>Plotting geographical featu
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.axes._subplots.AxesSubplot at 0x1f4a26efc48&gt;
 </pre></div>
 </div>
-<img alt="../../_images/ml-linear-regression-1_23_1.png" src="../../_images/ml-linear-regression-1_23_1.png" />
+<img alt="../../../_images/california_housing_23_1.png" src="../../../_images/california_housing_23_1.png" />
 </div>
 </div>
 <div class="cell docutils container">
@@ -2500,7 +2517,7 @@ <h4><span class="section-number">37.45.2.1.6. </span>Plotting geographical featu
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.axes._subplots.AxesSubplot at 0x1f4a2788808&gt;
 </pre></div>
 </div>
-<img alt="../../_images/ml-linear-regression-1_24_1.png" src="../../_images/ml-linear-regression-1_24_1.png" />
+<img alt="../../../_images/california_housing_24_1.png" src="../../../_images/california_housing_24_1.png" />
 </div>
 </div>
 <p>It makes sense that the most expensive houses are those close to sea. We can verify that with the <code class="docutils literal notranslate"><span class="pre">ocean_proximity</span></code>.</p>
@@ -2517,7 +2534,7 @@ <h4><span class="section-number">37.45.2.1.6. </span>Plotting geographical featu
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.axes._subplots.AxesSubplot at 0x1f4ad02ae08&gt;
 </pre></div>
 </div>
-<img alt="../../_images/ml-linear-regression-1_26_1.png" src="../../_images/ml-linear-regression-1_26_1.png" />
+<img alt="../../../_images/california_housing_26_1.png" src="../../../_images/california_housing_26_1.png" />
 </div>
 </div>
 <p>Yup, all houses near the ocean are very expensive compared to other areas.</p>
@@ -2536,7 +2553,7 @@ <h4><span class="section-number">37.45.2.1.7. </span>Exploring Relationship Betw
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.axes._subplots.AxesSubplot at 0x1f4a27bb588&gt;
 </pre></div>
 </div>
-<img alt="../../_images/ml-linear-regression-1_29_1.png" src="../../_images/ml-linear-regression-1_29_1.png" />
+<img alt="../../../_images/california_housing_29_1.png" src="../../../_images/california_housing_29_1.png" />
 </div>
 </div>
 <p>There are times you want to quickly see different plots to draw insights from the data. In that case, you can use grid plots. Seaborn, a visualization library provides a handy function for that.</p>
@@ -2550,7 +2567,7 @@ <h4><span class="section-number">37.45.2.1.7. </span>Exploring Relationship Betw
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;seaborn.axisgrid.PairGrid at 0x1f4ad061988&gt;
 </pre></div>
 </div>
-<img alt="../../_images/ml-linear-regression-1_31_1.png" src="../../_images/ml-linear-regression-1_31_1.png" />
+<img alt="../../../_images/california_housing_31_1.png" src="../../../_images/california_housing_31_1.png" />
 </div>
 </div>
 <p>As you can see, it plots the relationship between all numerical features and histograms of each feature as well. But it’s slow…</p>
@@ -3328,7 +3345,7 @@ <h2><span class="section-number">37.45.3. </span>Acknowledgments<a class="header
         },
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     <!-- Previous / next buttons -->
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-    <title>37.49. Gradient descent &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.48. Gradient descent &#8212; Ocademy Open Machine Learning Book</title>
     
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      5.3. NumPy
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-     37.49.1. Session Objective
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#math-feel-free-to-skip-if-you-find-it-difficult">
+     37.48.2.1. Math (feel free to skip if you find it difficult)
     </a>
    </li>
-   <li class="toc-h2 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#lets-explore-and-gain-intuition">
-     37.49.2. Let’s Explore and Gain Intuition
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#partial-derivatives">
+     37.48.2.2. Partial Derivatives
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#partial-derivative-with-respect-to-m">
+     37.48.2.3. Partial Derivative with Respect to
+     <span class="math notranslate nohighlight">
+      \(m\)
+     </span>
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#partial-derivative-with-respect-to-b">
+     37.48.2.4. Partial Derivative with Respect to
+     <span class="math notranslate nohighlight">
+      \(b\)
+     </span>
     </a>
-    <ul class="nav section-nav flex-column">
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#math-feel-free-to-skip-if-you-find-it-difficult">
-       37.49.2.1. Math (feel free to skip if you find it difficult)
-      </a>
-     </li>
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#partial-derivatives">
-       37.49.2.2. Partial Derivatives
-      </a>
-     </li>
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#partial-derivative-with-respect-to-m">
-       37.49.2.3. Partial Derivative with Respect to
-       <span class="math notranslate nohighlight">
-        \(m\)
-       </span>
-      </a>
-     </li>
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#partial-derivative-with-respect-to-b">
-       37.49.2.4. Partial Derivative with Respect to
-       <span class="math notranslate nohighlight">
-        \(b\)
-       </span>
-      </a>
-     </li>
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#the-final-function">
-       37.49.2.5. The Final Function
-      </a>
-     </li>
-    </ul>
    </li>
-   <li class="toc-h2 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#time-to-code">
-     37.49.3. Time to code!
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#the-final-function">
+     37.48.2.5. The Final Function
     </a>
    </li>
   </ul>
  </li>
- <li class="toc-h1 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#linear-regression-with-gradient-descent">
-   37.50. Linear Regression With Gradient Descent
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#time-to-code">
+   37.48.3. Time to code!
   </a>
  </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#one-dimensional-gradient-descent">
+   37.48.4. 1. One-dimensional gradient descent
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#lets-try">
+     37.48.4.1. Let’s try!
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#two-dimensional-gradient-descent">
+   37.48.5. 2. Two-dimensional gradient descent
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#id1">
+     37.48.5.1. Let’s try!
+    </a>
+   </li>
+  </ul>
+ </li>
 </ul>
 
     </nav>
@@ -1588,65 +1617,77 @@ <h2> Contents </h2>
                         </div>
                         <nav aria-label="Page">
                             <ul class="visible nav section-nav flex-column">
- <li class="toc-h1 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#">
-   37.49. Gradient descent
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#session-objective">
+   37.48.1. Session Objective
   </a>
-  <ul class="visible nav section-nav flex-column">
-   <li class="toc-h2 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#session-objective">
-     37.49.1. Session Objective
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#lets-explore-and-gain-intuition">
+   37.48.2. Let’s Explore and Gain Intuition
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#math-feel-free-to-skip-if-you-find-it-difficult">
+     37.48.2.1. Math (feel free to skip if you find it difficult)
     </a>
    </li>
-   <li class="toc-h2 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#lets-explore-and-gain-intuition">
-     37.49.2. Let’s Explore and Gain Intuition
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#partial-derivatives">
+     37.48.2.2. Partial Derivatives
     </a>
-    <ul class="nav section-nav flex-column">
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#math-feel-free-to-skip-if-you-find-it-difficult">
-       37.49.2.1. Math (feel free to skip if you find it difficult)
-      </a>
-     </li>
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#partial-derivatives">
-       37.49.2.2. Partial Derivatives
-      </a>
-     </li>
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#partial-derivative-with-respect-to-m">
-       37.49.2.3. Partial Derivative with Respect to
-       <span class="math notranslate nohighlight">
-        \(m\)
-       </span>
-      </a>
-     </li>
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#partial-derivative-with-respect-to-b">
-       37.49.2.4. Partial Derivative with Respect to
-       <span class="math notranslate nohighlight">
-        \(b\)
-       </span>
-      </a>
-     </li>
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#the-final-function">
-       37.49.2.5. The Final Function
-      </a>
-     </li>
-    </ul>
    </li>
-   <li class="toc-h2 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#time-to-code">
-     37.49.3. Time to code!
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#partial-derivative-with-respect-to-m">
+     37.48.2.3. Partial Derivative with Respect to
+     <span class="math notranslate nohighlight">
+      \(m\)
+     </span>
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#partial-derivative-with-respect-to-b">
+     37.48.2.4. Partial Derivative with Respect to
+     <span class="math notranslate nohighlight">
+      \(b\)
+     </span>
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#the-final-function">
+     37.48.2.5. The Final Function
     </a>
    </li>
   </ul>
  </li>
- <li class="toc-h1 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#linear-regression-with-gradient-descent">
-   37.50. Linear Regression With Gradient Descent
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#time-to-code">
+   37.48.3. Time to code!
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#one-dimensional-gradient-descent">
+   37.48.4. 1. One-dimensional gradient descent
   </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#lets-try">
+     37.48.4.1. Let’s try!
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#two-dimensional-gradient-descent">
+   37.48.5. 2. Two-dimensional gradient descent
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#id1">
+     37.48.5.1. Let’s try!
+    </a>
+   </li>
+  </ul>
  </li>
 </ul>
 
@@ -1659,9 +1700,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="gradient-descent">
-<h1><span class="section-number">37.49. </span>Gradient descent<a class="headerlink" href="#gradient-descent" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.48. </span>Gradient descent<a class="headerlink" href="#gradient-descent" title="Permalink to this headline">#</a></h1>
 <section id="session-objective">
-<h2><span class="section-number">37.49.1. </span>Session Objective<a class="headerlink" href="#session-objective" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.48.1. </span>Session Objective<a class="headerlink" href="#session-objective" title="Permalink to this headline">#</a></h2>
 <p>In previous sessions, we’ve delved into the application of Linear Regression and Logistic Regression models. You may find the code relatively straightforward and intuitive at this point. However, you might be pondering questions like:</p>
 <ul class="simple">
 <li><p>What exactly occurs when we invoke the <code class="docutils literal notranslate"><span class="pre">.fit()</span></code> function?</p></li>
@@ -1670,7 +1711,7 @@ <h2><span class="section-number">37.49.1. </span>Session Objective<a class="head
 <p>This session is designed to provide insight into the functionality of the <code class="docutils literal notranslate"><span class="pre">.fit()</span></code> method, which is responsible for training machine learning models and fine-tuning model parameters. The underlying technique at play here is known as “Gradient Descent.”</p>
 </section>
 <section id="lets-explore-and-gain-intuition">
-<h2><span class="section-number">37.49.2. </span>Let’s Explore and Gain Intuition<a class="headerlink" href="#lets-explore-and-gain-intuition" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.48.2. </span>Let’s Explore and Gain Intuition<a class="headerlink" href="#lets-explore-and-gain-intuition" title="Permalink to this headline">#</a></h2>
 <p>To further enhance your understanding and gain a playful insight into Gradient Descent, you can explore the following resources:</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://github.com/lilipads/gradient_descent_viz">Gradient Descent Visualization</a>: This GitHub repository offers a visualization of the Gradient Descent algorithm, which can be a valuable resource for understanding the optimization process.</p></li>
@@ -1678,7 +1719,7 @@ <h2><span class="section-number">37.49.2. </span>Let’s Explore and Gain Intuit
 </ul>
 <p>These resources will help you build an intuitive grasp of Gradient Descent and its role in training machine learning models. Enjoy your exploration!</p>
 <section id="math-feel-free-to-skip-if-you-find-it-difficult">
-<h3><span class="section-number">37.49.2.1. </span>Math (feel free to skip if you find it difficult)<a class="headerlink" href="#math-feel-free-to-skip-if-you-find-it-difficult" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.48.2.1. </span>Math (feel free to skip if you find it difficult)<a class="headerlink" href="#math-feel-free-to-skip-if-you-find-it-difficult" title="Permalink to this headline">#</a></h3>
 <p>The fundamental concept behind gradient descent is rather straightforward: it involves the gradual adjustment of parameters, such as the slope (<span class="math notranslate nohighlight">\(m\)</span>) and the intercept (<span class="math notranslate nohighlight">\(b\)</span>) in our regression equation $y = mx + b, with the aim of minimizing a cost function. This cost function is typically a metric that quantifies the disparity between our model’s predicted results and the actual values. In regression scenarios, the widely employed cost function is the <code class="docutils literal notranslate"><span class="pre">mean</span> <span class="pre">squared</span> <span class="pre">error</span></code> (MSE). When dealing with classification problems, a different set of parameters must be fine-tuned.</p>
 <p>The MSE (Mean Squared Error) is mathematically expressed as:</p>
 <div class="math notranslate nohighlight">
@@ -1689,7 +1730,7 @@ <h3><span class="section-number">37.49.2.1. </span>Math (feel free to skip if yo
 <p>Our primary challenge is to determine the optimal adjustments to parameters <span class="math notranslate nohighlight">\(m\)</span> and $b” to minimize the MSE effectively.</p>
 </section>
 <section id="partial-derivatives">
-<h3><span class="section-number">37.49.2.2. </span>Partial Derivatives<a class="headerlink" href="#partial-derivatives" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.48.2.2. </span>Partial Derivatives<a class="headerlink" href="#partial-derivatives" title="Permalink to this headline">#</a></h3>
 <p>In our pursuit of minimizing the Mean Squared Error (MSE), we turn to partial derivatives to understand how each individual parameter influences the MSE. The term “partial” signifies that we are taking derivatives with respect to individual parameters, in this case, <span class="math notranslate nohighlight">\(m\)</span> and $b, separately.</p>
 <p>Consider the following formula, which closely resembles the MSE, but now we’ve introduced the function <span class="math notranslate nohighlight">\(f(m, b)\)</span> into it. The addition of this function doesn’t significantly alter the essence of the calculation, but it allows us to input specific values for <span class="math notranslate nohighlight">\(m\)</span> and <span class="math notranslate nohighlight">\(b\)</span> to compute the result.</p>
 <div class="math notranslate nohighlight">
@@ -1697,7 +1738,7 @@ <h3><span class="section-number">37.49.2.2. </span>Partial Derivatives<a class="
 <p>For the purposes of calculating partial derivatives, we can temporarily disregard the summation and the terms preceding it, focusing solely on the expression <span class="math notranslate nohighlight">\(y - (mx + b)^2\)</span>. This expression serves as a better starting point for the subsequent partial derivative calculations.</p>
 </section>
 <section id="partial-derivative-with-respect-to-m">
-<h3><span class="section-number">37.49.2.3. </span>Partial Derivative with Respect to <span class="math notranslate nohighlight">\(m\)</span><a class="headerlink" href="#partial-derivative-with-respect-to-m" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.48.2.3. </span>Partial Derivative with Respect to <span class="math notranslate nohighlight">\(m\)</span><a class="headerlink" href="#partial-derivative-with-respect-to-m" title="Permalink to this headline">#</a></h3>
 <p>When we calculate the partial derivative with respect to the parameter <span class="math notranslate nohighlight">\(m,&quot; we isolate the parameter \)</span>m” and treat <span class="math notranslate nohighlight">\(b\)</span> as a constant (effectively setting it to 0 for differentiation purposes). To compute this derivative, we utilize the chain rule, which is a fundamental concept in calculus.</p>
 <p>The chain rule is expressed as follows:</p>
 <div class="math notranslate nohighlight">
@@ -1722,7 +1763,7 @@ <h3><span class="section-number">37.49.2.3. </span>Partial Derivative with Respe
 <p>Here, <span class="math notranslate nohighlight">\(\frac{df}{dm}\)</span> signifies the partial derivative of function <span class="math notranslate nohighlight">\(f\)</span> (as previously defined) with respect to the parameter <span class="math notranslate nohighlight">\(m\)</span>. We can now insert this derivative into our summation to complete the calculation.</p>
 </section>
 <section id="partial-derivative-with-respect-to-b">
-<h3><span class="section-number">37.49.2.4. </span>Partial Derivative with Respect to <span class="math notranslate nohighlight">\(b\)</span><a class="headerlink" href="#partial-derivative-with-respect-to-b" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.48.2.4. </span>Partial Derivative with Respect to <span class="math notranslate nohighlight">\(b\)</span><a class="headerlink" href="#partial-derivative-with-respect-to-b" title="Permalink to this headline">#</a></h3>
 <p>The process for computing the partial derivative with respect to the parameter <span class="math notranslate nohighlight">\(b&quot; is analogous to our previous derivation with respect to \)</span>m. We still apply the same rules and utilize the chain rule:</p>
 <ol class="simple">
 <li><p>The derivative of <span class="math notranslate nohighlight">\(()^2\)</span> is <span class="math notranslate nohighlight">\(2()\)</span>, which corresponds to how <span class="math notranslate nohighlight">\(x^2\)</span> becomes <span class="math notranslate nohighlight">\(2x\)</span>.</p></li>
@@ -1741,7 +1782,7 @@ <h3><span class="section-number">37.49.2.4. </span>Partial Derivative with Respe
 <p>Similarly to the previous case, <span class="math notranslate nohighlight">\(\frac{df}{db}\)</span> represents the partial derivative of function <span class="math notranslate nohighlight">\(f\)</span> with respect to the parameter $b”. Inserting this derivative into our summation concludes the computation.</p>
 </section>
 <section id="the-final-function">
-<h3><span class="section-number">37.49.2.5. </span>The Final Function<a class="headerlink" href="#the-final-function" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.48.2.5. </span>The Final Function<a class="headerlink" href="#the-final-function" title="Permalink to this headline">#</a></h3>
 <p>Before delving into the code, there are a few essential details to address:</p>
 <ol class="simple">
 <li><p>Gradient descent is an iterative process, and with each iteration (referred to as an “epoch”), we incrementally reduce the Mean Squared Error (MSE). At each iteration, we apply our derived functions to update the values of parameters <span class="math notranslate nohighlight">\(m\)</span> and <span class="math notranslate nohighlight">\(b\)</span>.</p></li>
@@ -1752,7 +1793,7 @@ <h3><span class="section-number">37.49.2.5. </span>The Final Function<a class="h
 </section>
 </section>
 <section id="time-to-code">
-<h2><span class="section-number">37.49.3. </span>Time to code!<a class="headerlink" href="#time-to-code" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.48.3. </span>Time to code!<a class="headerlink" href="#time-to-code" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -1762,69 +1803,287 @@ <h2><span class="section-number">37.49.3. </span>Time to code!<a class="headerli
 <span class="kn">import</span> <span class="nn">sklearn</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 <span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span> <span class="nn">mpl_toolkits.mplot3d</span> <span class="kn">import</span> <span class="n">Axes3D</span>
 </pre></div>
 </div>
 </div>
 </div>
 </section>
-</section>
-<section class="tex2jax_ignore mathjax_ignore" id="linear-regression-with-gradient-descent">
-<h1><span class="section-number">37.50. </span>Linear Regression With Gradient Descent<a class="headerlink" href="#linear-regression-with-gradient-descent" title="Permalink to this headline">#</a></h1>
+<section id="one-dimensional-gradient-descent">
+<h2><span class="section-number">37.48.4. </span>1. One-dimensional gradient descent<a class="headerlink" href="#one-dimensional-gradient-descent" title="Permalink to this headline">#</a></h2>
+<p>One-dimensional gradient descent is a simple optimization algorithm used to find the minimum (or maximum) of a univariate function.</p>
+<p>Here are the main steps:</p>
+<blockquote>
+<div><p>step 1: Choose an initial point on the function curve. For example, we choose an initial x value.</p>
+</div></blockquote>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">LinearRegression</span><span class="p">:</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.0003</span><span class="p">,</span> <span class="n">n_iters</span><span class="o">=</span><span class="mi">3000</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">=</span> <span class="n">learning_rate</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_iters</span> <span class="o">=</span> <span class="n">n_iters</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-        <span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="c1"># Initialize parameters</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">n_features</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="mi">0</span>
-
-        <span class="c1"># Gradient Descent</span>
-        <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_iters</span><span class="p">):</span>
-            <span class="c1"># Approximate y with a linear combination of weights and x, plus bias</span>
-            <span class="n">y_predicted</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span>
-
-            <span class="c1"># Compute gradients</span>
-            <span class="n">dw</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">n_samples</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="p">(</span><span class="n">y_predicted</span> <span class="o">-</span> <span class="n">y</span><span class="p">))</span>
-            <span class="n">db</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">n_samples</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">y_predicted</span> <span class="o">-</span> <span class="n">y</span><span class="p">)</span>
-            
-            <span class="c1"># Update parameters</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">-=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">*</span> <span class="n">dw</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">-=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">*</span> <span class="n">db</span>
-
-    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-        <span class="n">y_predicted</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span>
-        <span class="k">return</span> <span class="n">y_predicted</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="mi">10</span>
+</pre></div>
+</div>
+</div>
+</div>
+<blockquote>
+<div><p>step 2: Compute the derivative of the function at the current point. In this case, the derivative of the function is computed as follows:</p>
+</div></blockquote>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">df</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="k">return</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">5</span>
+</pre></div>
+</div>
+</div>
+</div>
+<blockquote>
+<div><p>step 3: Update the position along the function curve using the gradient and the learning rate. The learning rate determines the size of the step.</p>
+</div></blockquote>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.3</span>
+<span class="n">x</span> <span class="o">-=</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">df</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<blockquote>
+<div><p>step 4: Repeat the process for a specified number of iterations.</p>
+</div></blockquote>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">max_iter</span> <span class="o">=</span> <span class="mi">100</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">max_iter</span><span class="p">):</span>
+    <span class="n">gradient</span> <span class="o">=</span> <span class="n">df</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+    <span class="n">x</span> <span class="o">-=</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">gradient</span>
+</pre></div>
+</div>
+</div>
+</div>
+<blockquote>
+<div><p>step 5: Output the result, which is the minimum value of x that corresponds to the minimum of the function.</p>
+</div></blockquote>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;x_min:&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="lets-try">
+<h3><span class="section-number">37.48.4.1. </span>Let’s try!<a class="headerlink" href="#lets-try" title="Permalink to this headline">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define the target function</span>
+<span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">x</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">5</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">10</span>
 
-<span class="c1"># Load data and perform linear regression</span>
-<span class="n">prostate</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="s2">&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/prostate.data&quot;</span><span class="p">)</span>
-<span class="n">prostate</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">prostate</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="c1"># Define the guidance of the target function</span>
+<span class="k">def</span> <span class="nf">df</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="k">return</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">5</span>
 
-<span class="n">X</span> <span class="o">=</span> <span class="n">prostate</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s2">&quot;lpsa&quot;</span><span class="p">,</span> <span class="s2">&quot;train&quot;</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">prostate</span><span class="p">[</span><span class="s2">&quot;lpsa&quot;</span><span class="p">]</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;f(x)&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define gradient descent function</span>
+<span class="k">def</span> <span class="nf">gradient_descent</span><span class="p">(</span><span class="n">learning_rate</span><span class="p">,</span> <span class="n">max_iter</span><span class="p">,</span> <span class="n">x_init</span><span class="p">):</span>
+    <span class="c1"># step 1:start with an initial value for the input variable, denoted as x_int.</span>
+    <span class="n">x</span> <span class="o">=</span> <span class="n">x_init</span>
+    <span class="n">x_history</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="p">]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">max_iter</span><span class="p">):</span>
+        <span class="c1"># step 2:compute the derivative of the function at the current point.</span>
+        <span class="n">gradient</span> <span class="o">=</span> <span class="n">df</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">x</span> <span class="o">-=</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">gradient</span>
+        <span class="c1"># step 3:update the position along the function curve.</span>
+        <span class="n">x_history</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+    <span class="c1"># step 4:repeat the process.</span>
+    <span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">x_history</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set the learning rate and initial value</span>
+        <span class="c1"># learning rate: the size of the step for gradient descent.</span>
+        <span class="c1"># max_iter: number of gradient descent iterations.</span>
+<span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.3</span>
+<span class="n">max_iter</span> <span class="o">=</span> <span class="mi">100</span>
+<span class="n">x_init</span> <span class="o">=</span> <span class="mi">10</span>
+
+<span class="c1"># Run gradient descent function</span>
+<span class="n">x_min</span><span class="p">,</span> <span class="n">x_history</span> <span class="o">=</span> <span class="n">gradient_descent</span><span class="p">(</span><span class="n">learning_rate</span><span class="p">,</span> <span class="n">max_iter</span><span class="p">,</span> <span class="n">x_init</span><span class="p">)</span>
+
+<span class="c1"># Draw the target function and the process of gradient descent</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="n">y_history</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">x_history</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;f(x)&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x_history</span><span class="p">,</span> <span class="n">y_history</span><span class="p">,</span> <span class="s1">&#39;ro-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Gradient Descent&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;x&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;f(x)&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;One-Dimensional Gradient Descent&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 
-<span class="n">regressor</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;x_min:&#39;</span><span class="p">,</span> <span class="n">x_min</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>you can change <code class="docutils literal notranslate"><span class="pre">learning</span> <span class="pre">rate</span></code> and  <code class="docutils literal notranslate"><span class="pre">max_iter</span></code> to see different images.</p>
+</section>
+</section>
+<section id="two-dimensional-gradient-descent">
+<h2><span class="section-number">37.48.5. </span>2. Two-dimensional gradient descent<a class="headerlink" href="#two-dimensional-gradient-descent" title="Permalink to this headline">#</a></h2>
+<p>Two-dimensional gradient descent is an optimization algorithm used to find the minimum (or maximum) of a function with two input variables.</p>
+<p>Here are the main steps:</p>
+<blockquote>
+<div><p>step 1: Choose an initial point on the function surface by specifying initial values for both x and y.</p>
+</div></blockquote>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_init</span> <span class="o">=</span> <span class="o">-</span><span class="mi">5</span>
+<span class="n">y_init</span> <span class="o">=</span> <span class="mi">5</span>
+</pre></div>
+</div>
+</div>
+</div>
+<blockquote>
+<div><p>step 2: Compute the partial derivatives of the function at the current point. These partial derivatives represent the rate of change of the function with respect to x and y.</p>
+</div></blockquote>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">dfx</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <span class="k">return</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">5</span>
 
-<span class="n">regressor</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="n">y_pred</span> <span class="o">=</span> <span class="n">regressor</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">dfy</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <span class="k">return</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">y</span> <span class="o">+</span> <span class="mi">2</span>
+</pre></div>
+</div>
+</div>
+</div>
+<blockquote>
+<div><p>step 3: Update the positions along the function surface for both x and y using their respective partial derivatives and the learning rate.</p>
+</div></blockquote>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.1</span>
+<span class="n">x</span> <span class="o">-=</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">dfx</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">-=</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">dfy</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<blockquote>
+<div><p>step 4: Repeat the process for a specified number of iterations.</p>
+</div></blockquote>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">max_iter</span> <span class="o">=</span> <span class="mi">100</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">max_iter</span><span class="p">):</span>
+    <span class="n">gradient_x</span> <span class="o">=</span> <span class="n">dfx</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+    <span class="n">gradient_y</span> <span class="o">=</span> <span class="n">dfy</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+    <span class="n">x</span> <span class="o">-=</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">gradient_x</span>
+    <span class="n">y</span> <span class="o">-=</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">gradient_y</span>
+</pre></div>
+</div>
+</div>
+</div>
+<blockquote>
+<div><p>step 5: Output the results, which are the minimum values of x and y that correspond to the minimum of the function.</p>
+</div></blockquote>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;x_min:&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;y_min:&#39;</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="id1">
+<h3><span class="section-number">37.48.5.1. </span>Let’s try!<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define the target function</span>
+<span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">x</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="n">y</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">5</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">y</span> <span class="o">+</span> <span class="mi">10</span>
 
-<span class="nb">print</span><span class="p">(</span><span class="n">regressor</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">y</span> <span class="o">-</span> <span class="n">y_pred</span><span class="p">)</span>
+<span class="c1"># Take the partial derivative of the objective function with respect to x</span>
+<span class="k">def</span> <span class="nf">dfx</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <span class="k">return</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">5</span>
 
-<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
+<span class="c1"># Take the partial derivative of the objective function with respect to y</span>
+<span class="k">def</span> <span class="nf">dfy</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <span class="k">return</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">y</span> <span class="o">+</span> <span class="mi">2</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define the two-dimensional gradient descent function</span>
+<span class="k">def</span> <span class="nf">gradient_descent</span><span class="p">(</span><span class="n">learning_rate</span><span class="p">,</span> <span class="n">max_iter</span><span class="p">,</span> <span class="n">x_init</span><span class="p">,</span> <span class="n">y_init</span><span class="p">):</span>
+    <span class="c1"># step 1:start with an initial value for the two input variables, denoted as (x_init, y_init).</span>
+    <span class="n">x</span> <span class="o">=</span> <span class="n">x_init</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="n">y_init</span>
+    <span class="n">x_history</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="p">]</span>
+    <span class="n">y_history</span> <span class="o">=</span> <span class="p">[</span><span class="n">y</span><span class="p">]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">max_iter</span><span class="p">):</span>
+        <span class="c1"># step 2:compute the partial derivatives of the function at the current point.</span>
+        <span class="n">gradient_x</span> <span class="o">=</span> <span class="n">dfx</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+        <span class="n">gradient_y</span> <span class="o">=</span> <span class="n">dfy</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+        <span class="n">x</span> <span class="o">-=</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">gradient_x</span>
+        <span class="n">y</span> <span class="o">-=</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">gradient_y</span>
+        <span class="c1"># step 3:update the positions along the function surface.</span>
+        <span class="n">x_history</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">y_history</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+    <span class="c1"># step 4:repeat the process.</span>
+    <span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">x_history</span><span class="p">,</span> <span class="n">y_history</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set the learning rate and initial value</span>
+<span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.1</span>
+<span class="n">max_iter</span> <span class="o">=</span> <span class="mi">100</span>
+<span class="n">x_init</span> <span class="o">=</span> <span class="o">-</span><span class="mi">5</span>
+<span class="n">y_init</span> <span class="o">=</span> <span class="mi">5</span>
+
+<span class="c1"># Run two-dimensional gradient descent function</span>
+<span class="n">x_min</span><span class="p">,</span> <span class="n">y_min</span><span class="p">,</span> <span class="n">x_history</span><span class="p">,</span> <span class="n">y_history</span> <span class="o">=</span> <span class="n">gradient_descent</span><span class="p">(</span><span class="n">learning_rate</span><span class="p">,</span> <span class="n">max_iter</span><span class="p">,</span> <span class="n">x_init</span><span class="p">,</span> <span class="n">y_init</span><span class="p">)</span>
+
+<span class="c1"># Draw the target function and the process of gradient descent</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
+<span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="n">Z</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
+
+<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="s1">&#39;3d&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot_surface</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;viridis&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x_history</span><span class="p">,</span> <span class="n">y_history</span><span class="p">,</span> <span class="n">f</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">x_history</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y_history</span><span class="p">)),</span> <span class="s1">&#39;ro-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Gradient Descent&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;x&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;y&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_zlabel</span><span class="p">(</span><span class="s1">&#39;f(x, y)&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Two-Dimensional Gradient Descent&#39;</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;x_min:&#39;</span><span class="p">,</span> <span class="n">x_min</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;y_min:&#39;</span><span class="p">,</span> <span class="n">y_min</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 </div>
+</section>
+</section>
 </section>
 
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@@ -1864,13 +2123,13 @@ <h1><span class="section-number">37.50. </span>Linear Regression With Gradient D
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+            <p class="prev-next-title"><span class="section-number">37.47. </span>Loss Function</p>
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-        <p class="prev-next-title"><span class="section-number">37.51. </span>ML logistic regression - assignment 1</p>
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similarity index 68%
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+    <title>37.49. Linear Regression Implementation from Scratch &#8212; Ocademy Open Machine Learning Book</title>
     
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    <li class="toctree-l2">
-    <a class="reference internal" href="../../data-science/introduction/defining-data.html">
+    <a class="reference internal" href="../../../data-science/introduction/defining-data.html">
      4.3. Defining data
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../../data-science/introduction/introduction-to-statistics-and-probability.html">
+    <a class="reference internal" href="../../../data-science/introduction/introduction-to-statistics-and-probability.html">
      4.4. Introduction to statistics and probability
     </a>
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-  <a class="reference internal" href="../../data-science/working-with-data/working-with-data.html">
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    5. Working with data
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-    <a class="reference internal" href="../../data-science/working-with-data/relational-databases.html">
+    <a class="reference internal" href="../../../data-science/working-with-data/relational-databases.html">
      5.1. Relational databases
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../../data-science/working-with-data/non-relational-data.html">
+    <a class="reference internal" href="../../../data-science/working-with-data/non-relational-data.html">
      5.2. Non-relational data
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../../data-science/working-with-data/numpy.html">
+    <a class="reference internal" href="../../../data-science/working-with-data/numpy.html">
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
+   <li class="toctree-l2 has-children">
+    <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
+    <a class="reference internal" href="../../../data-science/working-with-data/data-preparation.html">
      5.5. Data preparation
     </a>
    </li>
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  <li class="toctree-l1 has-children">
-  <a class="reference internal" href="../../data-science/data-visualization/data-visualization.html">
+  <a class="reference internal" href="../../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
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-  <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
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-    <a class="reference internal" href="../../data-science/data-visualization/visualization-distributions.html">
+    <a class="reference internal" href="../../../data-science/data-visualization/visualization-distributions.html">
      6.1. Visualizing distributions
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../../data-science/data-visualization/visualization-proportions.html">
+    <a class="reference internal" href="../../../data-science/data-visualization/visualization-proportions.html">
      6.2. Visualizing proportions
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../../data-science/data-visualization/visualization-relationships.html">
+    <a class="reference internal" href="../../../data-science/data-visualization/visualization-relationships.html">
      6.3. Visualizing relationships: all about honey 🍯
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../../data-science/data-visualization/meaningful-visualizations.html">
+    <a class="reference internal" href="../../../data-science/data-visualization/meaningful-visualizations.html">
      6.4. Making meaningful visualizations
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-  <a class="reference internal" href="../../data-science/data-science-lifecycle/data-science-lifecycle.html">
+  <a class="reference internal" href="../../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
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-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
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      7.1. Introduction to the Data Science lifecycle
     </a>
    </li>
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-    <a class="reference internal" href="../../data-science/data-science-lifecycle/analyzing.html">
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      7.2. Analyzing
     </a>
    </li>
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-    <a class="reference internal" href="../../data-science/data-science-lifecycle/communication.html">
+    <a class="reference internal" href="../../../data-science/data-science-lifecycle/communication.html">
      7.3. Communication
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-  <a class="reference internal" href="../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
+  <a class="reference internal" href="../../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
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-  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
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-    <a class="reference internal" href="../../data-science/data-science-in-the-cloud/introduction.html">
+    <a class="reference internal" href="../../../data-science/data-science-in-the-cloud/introduction.html">
      8.1. Introduction
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../../data-science/data-science-in-the-cloud/the-low-code-no-code-way.html">
+    <a class="reference internal" href="../../../data-science/data-science-in-the-cloud/the-low-code-no-code-way.html">
      8.2. The “low code/no code” way
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    </li>
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-    <a class="reference internal" href="../../data-science/data-science-in-the-cloud/the-azure-ml-sdk-way.html">
+    <a class="reference internal" href="../../../data-science/data-science-in-the-cloud/the-azure-ml-sdk-way.html">
      8.3. Data Science in the cloud: The “Azure ML SDK” way
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-  <a class="reference internal" href="../../data-science/data-science-in-the-wild.html">
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    9. Data Science in the real world
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-  <a class="reference internal" href="../../ml-fundamentals/ml-overview.html">
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    10. Machine Learning overview
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-  <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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    11. Regression models for Machine Learning
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-  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
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-    <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
+    <a class="reference internal" href="../../../ml-fundamentals/regression/tools-of-the-trade.html">
      11.1. Tools of the trade
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    </li>
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-    <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
+    <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
      11.2. Managing data
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    </li>
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-    <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
+    <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
      11.3. Linear and polynomial regression
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    </li>
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-    <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
+    <a class="reference internal" href="../../../ml-fundamentals/regression/logistic-regression.html">
      11.4. Logistic regression
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-  <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
+  <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
    12. Build a web app to use a Machine Learning model
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-  <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
+  <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
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-  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
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      13.1. Introduction to classification
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-    <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
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      13.2. More classifiers
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-    <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
+    <a class="reference internal" href="../../../ml-fundamentals/classification/yet-other-classifiers.html">
      13.3. Yet other classifiers
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-    <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
+    <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
      13.4. Applied Machine Learning : build a web app
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-  <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
+  <a class="reference internal" href="../../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
    14. Clustering models for Machine Learning
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-  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
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-    <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
+    <a class="reference internal" href="../../../ml-advanced/clustering/introduction-to-clustering.html">
      14.1. Introduction to clustering
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    </li>
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-    <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
+    <a class="reference internal" href="../../../ml-advanced/clustering/k-means-clustering.html">
      14.2. K-Means clustering
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    </li>
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-  <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
+  <a class="reference internal" href="../../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
    15. Getting started with ensemble learning
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-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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-    <a class="reference internal" href="../../ml-advanced/ensemble-learning/bagging.html">
+    <a class="reference internal" href="../../../ml-advanced/ensemble-learning/bagging.html">
      15.1. Bagging
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    </li>
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-    <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
+    <a class="reference internal" href="../../../ml-advanced/ensemble-learning/random-forest.html">
      15.2. Random forest
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-    <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
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      15.3. Feature importance
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    16. Introduction to Gradient Boosting
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      16.1. Gradient Boosting
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-    <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
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      16.2. Gradient boosting example
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      16.3. XGBoost
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      16.4. XGBoost + k-fold CV + Feature Importance
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    17. Unsupervised learning
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    18. Kernel method
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    19. Model selection
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    20. Intro to Deep Learning
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    21. Convolutional Neural Networks
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    29. Image segmentation
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    32. Overview
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    37. Self-paced assignments
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+    <a class="reference internal" href="../../set-up-env/first-assignment.html">
      37.3. First assignment
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+    <a class="reference internal" href="../../set-up-env/second-assignment.html">
      37.4. Second assignment
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      37.5. Project Plan​ Template
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+    <a class="reference internal" href="../../prerequisites/python-programming-introduction.html">
      37.6. Python programming introduction
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+    <a class="reference internal" href="../../prerequisites/python-programming-basics.html">
      37.7. Python programming basics
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-    <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
+    <a class="reference internal" href="../../prerequisites/python-programming-advanced.html">
      37.8. Python programming advanced
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+    <a class="reference internal" href="../../data-science/analyzing-text-about-data-science.html">
      37.9. Analyzing text about Data Science
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      37.10. Data Science scenarios
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-    <a class="reference internal" href="../data-science/write-a-data-ethics-case-study.html">
+    <a class="reference internal" href="../../data-science/write-a-data-ethics-case-study.html">
      37.11. Write a data ethics case study
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+    <a class="reference internal" href="../../data-science/lines-scatters-and-bars.html">
      37.12. Lines, scatters and bars
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      37.13. Apply your skills
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+    <a class="reference internal" href="../../data-science/try-it-in-excel.html">
      37.14. Try it in Excel
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+    <a class="reference internal" href="../../data-science/dive-into-the-beehive.html">
      37.15. Dive into the beehive
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-    <a class="reference internal" href="../data-science/build-your-own-custom-vis.html">
+    <a class="reference internal" href="../../data-science/build-your-own-custom-vis.html">
      37.16. Build your own custom vis
     </a>
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-    <a class="reference internal" href="../data-science/classifying-datasets.html">
+    <a class="reference internal" href="../../data-science/classifying-datasets.html">
      37.17. Classifying datasets
     </a>
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-    <a class="reference internal" href="../data-science/small-diabetes-study.html">
+    <a class="reference internal" href="../../data-science/small-diabetes-study.html">
      37.18. Small diabetes study
     </a>
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-    <a class="reference internal" href="../data-science/introduction-to-statistics-and-probability.html">
+    <a class="reference internal" href="../../data-science/introduction-to-statistics-and-probability.html">
      37.19. Introduction to probability and statistics
     </a>
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-    <a class="reference internal" href="../data-science/displaying-airport-data.html">
+    <a class="reference internal" href="../../data-science/displaying-airport-data.html">
      37.20. Displaying airport data
     </a>
    </li>
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-    <a class="reference internal" href="../data-science/soda-profits.html">
+    <a class="reference internal" href="../../data-science/soda-profits.html">
      37.21. Soda profits
     </a>
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-    <a class="reference internal" href="../data-science/analyzing-COVID-19-papers.html">
+    <a class="reference internal" href="../../data-science/analyzing-COVID-19-papers.html">
      37.22. Analyzing COVID-19 papers
     </a>
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-    <a class="reference internal" href="../data-science/estimation-of-COVID-19-pandemic.html">
+    <a class="reference internal" href="../../data-science/estimation-of-COVID-19-pandemic.html">
      37.23. Estimation of COVID-19 pandemic
     </a>
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-    <a class="reference internal" href="../data-science/data-processing-in-python.html">
+    <a class="reference internal" href="../../data-science/data-processing-in-python.html">
      37.24. Data processing in Python
     </a>
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-    <a class="reference internal" href="../data-science/evaluating-data-from-a-form.html">
+    <a class="reference internal" href="../../data-science/evaluating-data-from-a-form.html">
      37.25. Evaluating data from a form
     </a>
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-    <a class="reference internal" href="../data-science/data-preparation.html">
+    <a class="reference internal" href="../../data-science/data-preparation.html">
      37.26. Data preparation
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-    <a class="reference internal" href="../data-science/analyzing-data.html">
+    <a class="reference internal" href="../../data-science/analyzing-data.html">
      37.27. Analyzing data
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-    <a class="reference internal" href="../data-science/nyc-taxi-data-in-winter-and-summer.html">
+    <a class="reference internal" href="../../data-science/nyc-taxi-data-in-winter-and-summer.html">
      37.28. NYC taxi data in winter and summer
     </a>
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-    <a class="reference internal" href="../data-science/matplotlib-applied.html">
+    <a class="reference internal" href="../../data-science/matplotlib-applied.html">
      37.29. Matplotlib applied
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-    <a class="reference internal" href="../data-science/tell-a-story.html">
+    <a class="reference internal" href="../../data-science/tell-a-story.html">
      37.35. Tell a story
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-    <a class="reference internal" href="../data-science/explore-a-planetary-computer-dataset.html">
+    <a class="reference internal" href="../../data-science/explore-a-planetary-computer-dataset.html">
      37.36. Explore a planetary computer dataset
     </a>
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-    <a class="reference internal" href="../data-science/exploring-for-anwser.html">
+    <a class="reference internal" href="../../data-science/exploring-for-anwser.html">
      37.37. Exploring for answers
     </a>
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-    <a class="reference internal" href="../data-science/market-research.html">
+    <a class="reference internal" href="../../data-science/market-research.html">
      37.38. Market research
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-    <a class="reference internal" href="../data-science/low-code-no-code-data-science-project-on-azure-ml.html">
+    <a class="reference internal" href="../../data-science/low-code-no-code-data-science-project-on-azure-ml.html">
      37.39. Low code/no code Data Science project on Azure ML
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-    <a class="reference internal" href="../data-science/data-science-project-using-azure-ml-sdk.html">
+    <a class="reference internal" href="../../data-science/data-science-project-using-azure-ml-sdk.html">
      37.40. Data Science project using Azure ML SDK
     </a>
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-    <a class="reference internal" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
+    <a class="reference internal" href="../../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
      37.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
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-    <a class="reference internal" href="ml-overview-iris.html">
+    <a class="reference internal" href="../ml-overview-iris.html">
      37.42. Machine Learning overview - assignment 1
     </a>
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-    <a class="reference internal" href="ml-overview-mnist-digits.html">
+    <a class="reference internal" href="../ml-overview-mnist-digits.html">
      37.43. Machine Learning overview - assignment 2
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+    <a class="reference internal" href="../regression-with-scikit-learn.html">
      37.44. Regression with Scikit-learn
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-    <a class="reference internal" href="ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
+    <a class="reference internal" href="california_housing.html">
+     37.45. Linear regression - California Housing
     </a>
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-    <a class="reference internal" href="ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="linear-regression-metrics.html">
+     37.46. Linear Regression Metrics
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-    <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
+    <a class="reference internal" href="loss-function.html">
+     37.47. Loss Function
     </a>
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-    <a class="reference internal" href="linear-regression/loss-function.html">
-     37.48. Loss Function
+    <a class="reference internal" href="gradient-descent.html">
+     37.48. Gradient descent
     </a>
    </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+   <li class="toctree-l2 current active">
+    <a class="current reference internal" href="#">
+     37.49. Linear Regression Implementation from Scratch
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-     37.51. ML logistic regression - assignment 1
+    <a class="reference internal" href="../ml-logistic-regression-1.html">
+     37.50. ML logistic regression - assignment 1
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-    <a class="reference internal" href="ml-logistic-regression-2.html">
-     37.52. ML logistic regression - assignment 2
+    <a class="reference internal" href="../ml-logistic-regression-2.html">
+     37.51. ML logistic regression - assignment 2
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-    <a class="reference internal" href="ml-neural-network-1.html">
-     37.53. ML neural network - Assignment 1
+    <a class="reference internal" href="../ml-neural-network-1.html">
+     37.52. ML neural network - Assignment 1
     </a>
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-    <a class="reference internal" href="regression-tools.html">
-     37.54. Regression tools
+    <a class="reference internal" href="../regression-tools.html">
+     37.53. Regression tools
     </a>
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-    <a class="reference internal" href="managing-data.html">
-     37.55. Managing data
+    <a class="reference internal" href="../managing-data.html">
+     37.54. Managing data
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-    <a class="reference internal" href="exploring-visualizations.html">
-     37.56. Exploring visualizations
+    <a class="reference internal" href="../exploring-visualizations.html">
+     37.55. Exploring visualizations
     </a>
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-    <a class="reference internal" href="try-a-different-model.html">
-     37.57. Try a different model
+    <a class="reference internal" href="../try-a-different-model.html">
+     37.56. Try a different model
     </a>
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-    <a class="reference internal" href="create-a-regression-model.html">
-     37.58. Create a regression model
+    <a class="reference internal" href="../create-a-regression-model.html">
+     37.57. Create a regression model
     </a>
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-    <a class="reference internal" href="linear-and-polynomial-regression.html">
-     37.59. Linear and polynomial regression
+    <a class="reference internal" href="../linear-and-polynomial-regression.html">
+     37.58. Linear and polynomial regression
     </a>
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-    <a class="reference internal" href="retrying-some-regression.html">
-     37.60. Retrying some regression
+    <a class="reference internal" href="../retrying-some-regression.html">
+     37.59. Retrying some regression
     </a>
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-    <a class="reference internal" href="pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
+    <a class="reference internal" href="../pumpkin-varieties-and-color.html">
+     37.60. Pumpkin varieties and color
     </a>
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-    <a class="reference internal" href="delicious-asian-and-indian-cuisines.html">
-     37.62. Delicious asian and indian cuisines
+    <a class="reference internal" href="../delicious-asian-and-indian-cuisines.html">
+     37.61. Delicious asian and indian cuisines
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-    <a class="reference internal" href="explore-classification-methods.html">
-     37.63. Explore classification methods
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-    <a class="current reference internal" href="#">
-     37.64. Linear Regression Implementation from Scratch
+    <a class="reference internal" href="../explore-classification-methods.html">
+     37.62. Explore classification methods
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-    <a class="reference internal" href="../ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.65. Kernel method assignment 1
+    <a class="reference internal" href="../../ml-advanced/kernel-method/kernel-method-assignment-1.html">
+     37.63. Kernel method assignment 1
     </a>
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-    <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
+    <a class="reference internal" href="../../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
+     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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-    <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.67. Support Vector Machines (SVM) - Classification
+    <a class="reference internal" href="../../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
+     37.65. Support Vector Machines (SVM) - Classification
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-     37.68. Decision Trees - Intro and Regression
+    <a class="reference internal" href="../../ml-advanced/kernel-method/decision_trees_for_regression.html">
+     37.66. Decision Trees - Intro and Regression
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-    <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.69. Decision Trees - Classification
+    <a class="reference internal" href="../../ml-advanced/kernel-method/decision_trees_for_classification.html">
+     37.67. Decision Trees - Classification
     </a>
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-     37.70. Model selection assignment 1
+    <a class="reference internal" href="../../ml-advanced/model-selection/model-selection-assignment-1.html">
+     37.68. Model selection assignment 1
     </a>
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-    <a class="reference internal" href="../ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.71. Learning Curve To Identify Overfit &amp; Underfit
+    <a class="reference internal" href="../../ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
+     37.69. Learning Curve To Identify Overfit &amp; Underfit
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-    <a class="reference internal" href="../ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.72. Dropout and Batch Normalization
+    <a class="reference internal" href="../../ml-advanced/model-selection/dropout-and-batch-normalization.html">
+     37.70. Dropout and Batch Normalization
     </a>
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-    <a class="reference internal" href="../ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.73. Lasso and Ridge Regression
+    <a class="reference internal" href="../../ml-advanced/model-selection/lasso-and-ridge-regression.html">
+     37.71. Lasso and Ridge Regression
     </a>
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-    <a class="reference internal" href="../ml-advanced/model-selection/regularized-linear-models.html">
-     37.74. Regularized Linear Models
+    <a class="reference internal" href="../../ml-advanced/model-selection/regularized-linear-models.html">
+     37.72. Regularized Linear Models
     </a>
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-    <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
-     37.75. Random forests intro and regression
+    <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
+     37.73. Random forests intro and regression
     </a>
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-    <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forests-for-classification.html">
-     37.76. Random forests for classification
+    <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forests-for-classification.html">
+     37.74. Random forests for classification
     </a>
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-    <a class="reference internal" href="../ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
-     37.77. Beyond random forests: more ensemble models
+    <a class="reference internal" href="../../ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
+     37.75. Beyond random forests: more ensemble models
     </a>
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-    <a class="reference internal" href="../ml-advanced/ensemble-learning/decision-trees.html">
-     37.78. Decision trees
+    <a class="reference internal" href="../../ml-advanced/ensemble-learning/decision-trees.html">
+     37.76. Decision trees
     </a>
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-    <a class="reference internal" href="../ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
-     37.79. Hyperparameter tuning gradient boosting
+    <a class="reference internal" href="../../ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
+     37.77. Hyperparameter tuning gradient boosting
     </a>
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-    <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
-     37.80. Gradient boosting
+    <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
+     37.78. Gradient boosting
     </a>
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-    <a class="reference internal" href="../ml-advanced/gradient-boosting/boosting-with-tuning.html">
-     37.81. Boosting with tuning
+    <a class="reference internal" href="../../ml-advanced/gradient-boosting/boosting-with-tuning.html">
+     37.79. Boosting with tuning
     </a>
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-    <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
-     37.82. Random Forest Classifier with Feature Importance
+    <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
+     37.80. Random Forest Classifier with Feature Importance
     </a>
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-    <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
-     37.84. Data engineering
+    <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
+     37.82. Data engineering
     </a>
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-    <a class="reference internal" href="../machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
-     37.85. Counterintuitive Challenges in ML Debugging
+    <a class="reference internal" href="../../machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
+     37.83. Counterintuitive Challenges in ML Debugging
     </a>
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-    <a class="reference internal" href="../machine-learning-productionization/debugging-in-classification.html">
-     37.86. Case Study: Debugging in Classification
+    <a class="reference internal" href="../../machine-learning-productionization/debugging-in-classification.html">
+     37.84. Case Study: Debugging in Classification
     </a>
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-    <a class="reference internal" href="../machine-learning-productionization/debugging-in-regression.html">
-     37.87. Case Study: Debugging in Regression
+    <a class="reference internal" href="../../machine-learning-productionization/debugging-in-regression.html">
+     37.85. Case Study: Debugging in Regression
     </a>
    </li>
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-    <a class="reference internal" href="study-the-solvers.html">
-     37.88. Study the solvers
+    <a class="reference internal" href="../study-the-solvers.html">
+     37.86. Study the solvers
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="build-classification-models.html">
-     37.89. Build classification models
+    <a class="reference internal" href="../build-classification-models.html">
+     37.87. Build classification models
     </a>
    </li>
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-    <a class="reference internal" href="build-classification-model.html">
-     37.90. Build Classification Model
+    <a class="reference internal" href="../build-classification-model.html">
+     37.88. Build Classification Model
     </a>
    </li>
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-    <a class="reference internal" href="parameter-play.html">
-     37.91. Parameter play
+    <a class="reference internal" href="../parameter-play.html">
+     37.89. Parameter play
     </a>
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-    <a class="reference internal" href="../deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
-     37.92. How to choose cnn architecture mnist
+    <a class="reference internal" href="../../deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
+     37.90. How to choose cnn architecture mnist
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-    <a class="reference internal" href="../deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
-     37.94. Sign Language Digits Classification with CNN
+    <a class="reference internal" href="../../deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
+     37.92. Sign Language Digits Classification with CNN
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-    <a class="reference internal" href="../deep-learning/cnn/object-recognition-in-images-using-cnn.html">
-     37.96. Object Recognition in Images using CNN
+    <a class="reference internal" href="../../deep-learning/cnn/object-recognition-in-images-using-cnn.html">
+     37.94. Object Recognition in Images using CNN
     </a>
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-    <a class="reference internal" href="../deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.97. Intro to TensorFlow for Deep Learning
+    <a class="reference internal" href="../../deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
+     37.95. Intro to TensorFlow for Deep Learning
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-    <a class="reference internal" href="../deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
+    <a class="reference internal" href="../../deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
+     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
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-    <a class="reference internal" href="../deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.101. Google Stock Price Prediction RNN
+    <a class="reference internal" href="../../deep-learning/rnn/google-stock-price-prediction-rnn.html">
+     37.99. Google Stock Price Prediction RNN
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-    <a class="reference internal" href="../deep-learning/autoencoder/autoencoder.html">
-     37.103. Intro to Autoencoders
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+     37.101. Intro to Autoencoders
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-    <a class="reference internal" href="../deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
+    <a class="reference internal" href="../../deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
+     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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-    <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
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+     37.103. Fun with Variational Autoencoders
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-     37.106. Time Series Forecasting Assignment
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+     37.104. Time Series Forecasting Assignment
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-     37.108. Neural Networks for Classification with TensorFlow
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+     37.106. Neural Networks for Classification with TensorFlow
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-     37.109. NN Classify 15 Fruits Assignment
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+     37.107. NN Classify 15 Fruits Assignment
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-     37.114. DQN On Foreign Exchange Market
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+     37.112. DQN On Foreign Exchange Market
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-     37.115. Art by gan
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+     37.113. Art by gan
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-    <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
-     37.117. Generative Adversarial Networks (GANs)
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+     37.115. Generative Adversarial Networks (GANs)
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-     37.118. Basic classification: Classify images of clothing
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+     37.116. Basic classification: Classify images of clothing
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    38. Slides
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-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
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      38.1. Python programming introduction
     </a>
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      38.2. Python programming basics
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      38.3. Python programming advanced
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      38.4. Data Science introduction
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      38.5. Relational vs. non-relational database
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      38.6. NumPy and Pandas
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      38.7. Data visualization
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      38.8. Data Science lifecycle
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+    <a class="reference internal" href="../../../slides/data-science/data-science-in-the-cloud.html">
      38.9. Data Science in the cloud
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      38.10. Data Science in real world
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      38.11. Machine Learning overview
     </a>
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      38.12. Linear Regression
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      38.13. Logistic Regression
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      38.14. Logistic Regression
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      38.15. Neural Network
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-    <a class="reference internal" href="../../slides/ml-fundamentals/build-an-ml-web-app.html">
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      38.16. Build an machine learning web application
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      38.17. Unsupervised learning
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      38.18. Kernel method
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      38.19. Model Selection
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      38.20. Convolutional Neural Network
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      38.21. Generative Adversarial Network
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+        <a href="https://github.com/ocademy-ai/machine-learning//issues/new?title=Issue%20on%20page%20%2Fassignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html&body=Your%20issue%20content%20here."
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-   37.64.1. Introduction
+   37.49.1. Introduction
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-   37.64.2. Data Preparation
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-   37.64.3. Model Development
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               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="linear-regression-implementation-from-scratch">
-<h1><span class="section-number">37.64. </span>Linear Regression Implementation from Scratch<a class="headerlink" href="#linear-regression-implementation-from-scratch" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.49. </span>Linear Regression Implementation from Scratch<a class="headerlink" href="#linear-regression-implementation-from-scratch" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">37.64.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.49.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>Linear regression is a widely used method in data analysis to describe the relationship between independent variables and a dependent variable using a linear equation. It aims to minimize the error between predicted and actual values by finding the best-fit line or surface. Linear regression can be used for predicting trends, exploring relationships, and identifying patterns in the data.</p>
 <p>In Python, we can use libraries like NumPy and Pandas for data handling and analysis. We will also use the Matplotlib library for visualizing data and model performance. Here’s a simple Python code snippet to demonstrate how a linear regression model works:</p>
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-<img alt="../../_images/linear-regression-implementation-from-scratch_4_0.png" src="../../_images/linear-regression-implementation-from-scratch_4_0.png" />
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-<h2><span class="section-number">37.64.2. </span>Data Preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.49.2. </span>Data Preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline">#</a></h2>
 <p>Before we can start developing the linear regression model, it is important to prepare the data appropriately. This involves importing the necessary libraries, loading the dataset, performing exploratory data analysis, and cleaning and preprocessing the data.</p>
 <p>Let’s take a look at the code snippet below to understand how we can perform these steps:</p>
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 y的缺失值数量： 0
 </pre></div>
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-<img alt="../../_images/linear-regression-implementation-from-scratch_6_1.png" src="../../_images/linear-regression-implementation-from-scratch_6_1.png" />
+<img alt="../../../_images/linear-regression-from-scratch_6_1.png" src="../../../_images/linear-regression-from-scratch_6_1.png" />
 </div>
 </div>
 <p>In this code snippet, we first import the necessary libraries, such as NumPy and Pandas. Then, we load the dataset using the read_csv() function from Pandas, replacing ‘data.csv’ with the actual path to your dataset.</p>
@@ -1682,7 +1699,7 @@ <h2><span class="section-number">37.64.2. </span>Data Preparation<a class="heade
 <p>Then, we perform any necessary preprocessing steps, such as feature scaling or encoding categorical variables. Finally, we split the dataset into input features (X) and the target variable (y), convert them to NumPy arrays using np.array(), and verify their dimensions using shape.</p>
 </section>
 <section id="model-development">
-<h2><span class="section-number">37.64.3. </span>Model Development<a class="headerlink" href="#model-development" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.49.3. </span>Model Development<a class="headerlink" href="#model-development" title="Permalink to this headline">#</a></h2>
 <p>Once we have prepared the data, we can proceed with developing the linear regression model. This involves deriving the mathematical formula for linear regression, implementing the formula in code, defining a cost function, and implementing the gradient descent algorithm to train the model.</p>
 <p>Let’s take a look at the code snippet below to understand how we can develop the linear regression model:</p>
 <div class="cell docutils container">
@@ -1737,7 +1754,7 @@ <h2><span class="section-number">37.64.3. </span>Model Development<a class="head
 <p>Finally, we train the model using the <code class="docutils literal notranslate"><span class="pre">fit()</span></code> method, make predictions using the <code class="docutils literal notranslate"><span class="pre">predict()</span></code> method, and evaluate the model’s performance using the mean squared error (MSE).</p>
 </section>
 <section id="model-evaluation">
-<h2><span class="section-number">37.64.4. </span>Model Evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.49.4. </span>Model Evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
 <p>After training the linear regression model, it is important to evaluate its performance to assess how well it is able to make predictions. In this section, we will discuss some commonly used evaluation metrics for regression models.</p>
 <p>When evaluating a machine learning model, we often use certain metrics to measure its performance. Here are some commonly used metrics and plotting methods:</p>
 <ol class="simple">
@@ -1791,13 +1808,13 @@ <h2><span class="section-number">37.64.4. </span>Model Evaluation<a class="heade
 Shape of y: (100, 1)
 </pre></div>
 </div>
-<img alt="../../_images/linear-regression-implementation-from-scratch_12_1.png" src="../../_images/linear-regression-implementation-from-scratch_12_1.png" />
+<img alt="../../../_images/linear-regression-from-scratch_12_1.png" src="../../../_images/linear-regression-from-scratch_12_1.png" />
 </div>
 </div>
 <p>The scatter plot shows the relationship between the features (X) and the actual values (blue dots), along with the predicted values (red line). This visualization helps us understand how well the model captures the underlying patterns in the data.</p>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">37.64.5. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.49.5. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>In this assignment, we implemented a simple linear regression model and trained and predicted using the from-scratch method. Through this implementation process, we gained a deeper understanding of the linear regression model and learned how to use gradient descent algorithm to minimize the loss function.</p>
 <p>We evaluated the performance of the model and used several common evaluation metrics to measure the accuracy of the model’s predictions. Based on our results, the linear regression model performed well. The values of mean absolute error and root mean squared error were relatively small, indicating that the errors between the model’s predictions and actual results were small. And the value of R-squared was close to 1, indicating that the model was able to explain the variability in the data well.</p>
 <p>Although our model performed well on this task, we also need to be aware of its limitations. Linear regression models assume a linear relationship between input features and target variables. If the data has a nonlinear relationship, the model may not fit the data well. In addition, it may also be affected by problems such as outliers and multicollinearity.</p>
@@ -1811,7 +1828,7 @@ <h2><span class="section-number">37.64.5. </span>Conclusion<a class="headerlink"
 <p>In conclusion, implementing a linear regression model from scratch is a great way to gain a deeper understanding of how machine learning algorithms work. We hope that this assignment has helped you to better understand the concepts and techniques involved in building and training machine learning models.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.64.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.49.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to the ChatGPT platform for providing the inspiration and guidance throughout this assignment.</p>
 </section>
 </section>
@@ -1829,7 +1846,7 @@ <h2><span class="section-number">37.64.6. </span>Acknowledgments<a class="header
         },
         kernelOptions: {
             kernelName: "python3",
-            path: "./assignments/ml-fundamentals"
+            path: "./assignments/ml-fundamentals/linear-regression"
         },
         predefinedOutput: true
     }
@@ -1849,17 +1866,17 @@ <h2><span class="section-number">37.64.6. </span>Acknowledgments<a class="header
                 
     <!-- Previous / next buttons -->
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+    <a class='left-prev' id="prev-link" href="gradient-descent.html" title="previous page">
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-            <p class="prev-next-title"><span class="section-number">37.63. </span>Explore classification methods</p>
+            <p class="prev-next-title"><span class="section-number">37.48. </span>Gradient descent</p>
         </div>
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-    <a class='right-next' id="next-link" href="../ml-advanced/kernel-method/kernel-method-assignment-1.html" title="next page">
+    <a class='right-next' id="next-link" href="../ml-logistic-regression-1.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">37.65. </span>Kernel method assignment 1</p>
+        <p class="prev-next-title"><span class="section-number">37.50. </span>ML logistic regression - assignment 1</p>
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@@ -1888,7 +1905,7 @@ <h2><span class="section-number">37.64.6. </span>Acknowledgments<a class="header
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--- a/assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html
+++ b/assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.47. Linear Regression Metrics &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.46. Linear Regression Metrics &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -100,8 +100,8 @@
     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.48. Loss Function" href="loss-function.html" />
-    <link rel="prev" title="37.46. ML linear regression - assignment 2" href="../ml-linear-regression-2.html" />
+    <link rel="next" title="37.47. Loss Function" href="loss-function.html" />
+    <link rel="prev" title="37.45. Linear regression - California Housing" href="california_housing.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -238,10 +238,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/working-with-data/data-preparation.html">
@@ -254,8 +276,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
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@@ -286,8 +308,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
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@@ -313,8 +335,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
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@@ -357,8 +379,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
-  <label for="toctree-checkbox-6">
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@@ -394,8 +416,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
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@@ -433,8 +455,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
    14. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
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@@ -455,8 +477,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
    15. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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@@ -482,8 +504,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
    16. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
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   </label>
@@ -635,8 +657,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../README.html">
    37. Self-paced assignments
   </a>
-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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   </label>
@@ -827,308 +849,303 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="california_housing.html">
+     37.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     37.47. Linear Regression Metrics
+     37.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="loss-function.html">
-     37.48. Loss Function
+     37.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
+     37.50. ML logistic regression - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-logistic-regression-2.html">
-     37.52. ML logistic regression - assignment 2
+     37.51. ML logistic regression - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-neural-network-1.html">
-     37.53. ML neural network - Assignment 1
+     37.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../regression-tools.html">
-     37.54. Regression tools
+     37.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../managing-data.html">
-     37.55. Managing data
+     37.54. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../exploring-visualizations.html">
-     37.56. Exploring visualizations
+     37.55. Exploring visualizations
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../try-a-different-model.html">
-     37.57. Try a different model
+     37.56. Try a different model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../create-a-regression-model.html">
-     37.58. Create a regression model
+     37.57. Create a regression model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../linear-and-polynomial-regression.html">
-     37.59. Linear and polynomial regression
+     37.58. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../retrying-some-regression.html">
-     37.60. Retrying some regression
+     37.59. Retrying some regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
+     37.60. Pumpkin varieties and color
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../delicious-asian-and-indian-cuisines.html">
-     37.62. Delicious asian and indian cuisines
+     37.61. Delicious asian and indian cuisines
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../explore-classification-methods.html">
-     37.63. Explore classification methods
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
+     37.62. Explore classification methods
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.65. Kernel method assignment 1
+     37.63. Kernel method assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
+     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.67. Support Vector Machines (SVM) - Classification
+     37.65. Support Vector Machines (SVM) - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.68. Decision Trees - Intro and Regression
+     37.66. Decision Trees - Intro and Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.69. Decision Trees - Classification
+     37.67. Decision Trees - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.70. Model selection assignment 1
+     37.68. Model selection assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.71. Learning Curve To Identify Overfit &amp; Underfit
+     37.69. Learning Curve To Identify Overfit &amp; Underfit
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.72. Dropout and Batch Normalization
+     37.70. Dropout and Batch Normalization
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.73. Lasso and Ridge Regression
+     37.71. Lasso and Ridge Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/model-selection/regularized-linear-models.html">
-     37.74. Regularized Linear Models
+     37.72. Regularized Linear Models
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
-     37.75. Random forests intro and regression
+     37.73. Random forests intro and regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forests-for-classification.html">
-     37.76. Random forests for classification
+     37.74. Random forests for classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
-     37.77. Beyond random forests: more ensemble models
+     37.75. Beyond random forests: more ensemble models
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/decision-trees.html">
-     37.78. Decision trees
+     37.76. Decision trees
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
-     37.79. Hyperparameter tuning gradient boosting
+     37.77. Hyperparameter tuning gradient boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
-     37.80. Gradient boosting
+     37.78. Gradient boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/boosting-with-tuning.html">
-     37.81. Boosting with tuning
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-     37.82. Random Forest Classifier with Feature Importance
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     <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
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     <a class="reference internal" href="../../machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
-     37.85. Counterintuitive Challenges in ML Debugging
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     <a class="reference internal" href="../../machine-learning-productionization/debugging-in-classification.html">
-     37.86. Case Study: Debugging in Classification
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     </a>
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     <a class="reference internal" href="../../machine-learning-productionization/debugging-in-regression.html">
-     37.87. Case Study: Debugging in Regression
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../study-the-solvers.html">
-     37.88. Study the solvers
+     37.86. Study the solvers
     </a>
    </li>
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     <a class="reference internal" href="../build-classification-models.html">
-     37.89. Build classification models
+     37.87. Build classification models
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-     37.90. Build Classification Model
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-     37.91. Parameter play
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-     37.92. How to choose cnn architecture mnist
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     <a class="reference internal" href="../../deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
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     <a class="reference internal" href="../../deep-learning/cnn/object-recognition-in-images-using-cnn.html">
-     37.96. Object Recognition in Images using CNN
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     <a class="reference internal" href="../../deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.97. Intro to TensorFlow for Deep Learning
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     <a class="reference internal" href="../../deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
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     <a class="reference internal" href="../../deep-learning/rnn/google-stock-price-prediction-rnn.html">
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-     37.103. Intro to Autoencoders
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     <a class="reference internal" href="../../deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
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     <a class="reference internal" href="../../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
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     <a class="reference internal" href="../../deep-learning/time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
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     <a class="reference internal" href="../../deep-learning/nn-for-classification-assignment.html">
-     37.108. Neural Networks for Classification with TensorFlow
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     <a class="reference internal" href="../../deep-learning/nn-classify-15-fruits-assignment.html">
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-     37.114. DQN On Foreign Exchange Market
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-     37.115. Art by gan
+     37.113. Art by gan
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     <a class="reference internal" href="../../deep-learning/gan/gan-introduction.html">
-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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     <a class="reference internal" href="../../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
     </a>
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@@ -1137,8 +1154,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../slides/introduction.html">
    38. Slides
   </a>
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         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#mean-squared-error-mse">
-   37.47.1. Mean Squared Error (MSE)
+   37.46.1. Mean Squared Error (MSE)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-formula">
-     37.47.1.1. The Formula
+     37.46.1.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#interpretation">
-     37.47.1.2. Interpretation
+     37.46.1.2. Interpretation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#python-implementation">
-     37.47.1.3. Python Implementation
+     37.46.1.3. Python Implementation
     </a>
    </li>
   </ul>
  </li>
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   <a class="reference internal nav-link" href="#root-mean-squared-error-rmse">
-   37.47.2. Root Mean Squared Error (RMSE)
+   37.46.2. Root Mean Squared Error (RMSE)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     37.47.2.1. The Formula
+     37.46.2.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
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-     37.47.2.2. Python Implementation
+     37.46.2.2. Python Implementation
     </a>
    </li>
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   <a class="reference internal nav-link" href="#r-squared-r2-score">
-   37.47.3. R-squared (R2) Score
+   37.46.3. R-squared (R2) Score
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id3">
-     37.47.3.1. The Formula
+     37.46.3.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id4">
-     37.47.3.2. Python Implementation
+     37.46.3.2. Python Implementation
     </a>
    </li>
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   <a class="reference internal nav-link" href="#residual-analysis">
-   37.47.4. Residual Analysis
+   37.46.4. Residual Analysis
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id5">
-     37.47.4.1. Python Implementation
+     37.46.4.1. Python Implementation
     </a>
    </li>
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     <a class="reference internal nav-link" href="#id6">
-     37.47.4.2. Interpretation
+     37.46.4.2. Interpretation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#adjusted-r-squared">
-   37.47.5. Adjusted R-squared
+  <a class="reference internal nav-link" href="#mean-absolute-error-mae">
+   37.46.5. Mean Absolute Error (MAE)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id7">
-     37.47.5.1. The Formula
+     37.46.5.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id8">
-     37.47.5.2. Python Implementation
+     37.46.5.2. Python Implementation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id9">
-     37.47.5.3. Interpretation
+     37.46.5.3. Interpretation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#mean-absolute-error-mae">
-   37.47.6. Mean Absolute Error (MAE)
+  <a class="reference internal nav-link" href="#mean-absolute-percentage-error-mape">
+   37.46.6. Mean Absolute Percentage Error (MAPE)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
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-     37.47.6.1. The Formula
+     37.46.6.1. The Formula
     </a>
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-     37.47.6.2. Python Implementation
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#id12">
-     37.47.6.3. Interpretation
-    </a>
-   </li>
-  </ul>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#mean-absolute-percentage-error-mape">
-   37.47.7. Mean Absolute Percentage Error (MAPE)
-  </a>
-  <ul class="nav section-nav flex-column">
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#id13">
-     37.47.7.1. The Formula
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#id14">
-     37.47.7.2. Interpretation
+     37.46.6.2. Interpretation
     </a>
    </li>
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   <a class="reference internal nav-link" href="#conclusion-and-recap">
-   37.47.8. Conclusion and Recap
+   37.46.7. Conclusion and Recap
   </a>
  </li>
 </ul>
@@ -1669,141 +1664,119 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#mean-squared-error-mse">
-   37.47.1. Mean Squared Error (MSE)
+   37.46.1. Mean Squared Error (MSE)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-formula">
-     37.47.1.1. The Formula
+     37.46.1.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#interpretation">
-     37.47.1.2. Interpretation
+     37.46.1.2. Interpretation
     </a>
    </li>
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     <a class="reference internal nav-link" href="#python-implementation">
-     37.47.1.3. Python Implementation
+     37.46.1.3. Python Implementation
     </a>
    </li>
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-   37.47.2. Root Mean Squared Error (RMSE)
+   37.46.2. Root Mean Squared Error (RMSE)
   </a>
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    <li class="toc-h3 nav-item toc-entry">
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-     37.47.2.1. The Formula
+     37.46.2.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id2">
-     37.47.2.2. Python Implementation
+     37.46.2.2. Python Implementation
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-   37.47.3. R-squared (R2) Score
+   37.46.3. R-squared (R2) Score
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id3">
-     37.47.3.1. The Formula
+     37.46.3.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id4">
-     37.47.3.2. Python Implementation
+     37.46.3.2. Python Implementation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#residual-analysis">
-   37.47.4. Residual Analysis
+   37.46.4. Residual Analysis
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id5">
-     37.47.4.1. Python Implementation
+     37.46.4.1. Python Implementation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id6">
-     37.47.4.2. Interpretation
+     37.46.4.2. Interpretation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#adjusted-r-squared">
-   37.47.5. Adjusted R-squared
+  <a class="reference internal nav-link" href="#mean-absolute-error-mae">
+   37.46.5. Mean Absolute Error (MAE)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id7">
-     37.47.5.1. The Formula
+     37.46.5.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id8">
-     37.47.5.2. Python Implementation
+     37.46.5.2. Python Implementation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id9">
-     37.47.5.3. Interpretation
+     37.46.5.3. Interpretation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#mean-absolute-error-mae">
-   37.47.6. Mean Absolute Error (MAE)
+  <a class="reference internal nav-link" href="#mean-absolute-percentage-error-mape">
+   37.46.6. Mean Absolute Percentage Error (MAPE)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id10">
-     37.47.6.1. The Formula
+     37.46.6.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id11">
-     37.47.6.2. Python Implementation
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#id12">
-     37.47.6.3. Interpretation
-    </a>
-   </li>
-  </ul>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#mean-absolute-percentage-error-mape">
-   37.47.7. Mean Absolute Percentage Error (MAPE)
-  </a>
-  <ul class="nav section-nav flex-column">
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#id13">
-     37.47.7.1. The Formula
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#id14">
-     37.47.7.2. Interpretation
+     37.46.6.2. Interpretation
     </a>
    </li>
   </ul>
  </li>
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   <a class="reference internal nav-link" href="#conclusion-and-recap">
-   37.47.8. Conclusion and Recap
+   37.46.7. Conclusion and Recap
   </a>
  </li>
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@@ -1816,40 +1789,17 @@ <h2> Contents </h2>
                 
               <div>
                 
-  <div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Install the necessary dependencies</span>
-
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">sys</span>
-<span class="o">!{</span>sys.executable<span class="o">}</span><span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>--quiet<span class="w"> </span>pandas<span class="w"> </span>scikit-learn<span class="w"> </span>numpy<span class="w"> </span>matplotlib<span class="w"> </span>jupyterlab_myst<span class="w"> </span>ipython
-</pre></div>
-</div>
-</div>
-</div>
-<hr class="docutils" />
-<p>license:
-code: MIT
-content: CC-BY-4.0
-github: <a class="reference external" href="https://github.com/ocademy-ai/machine-learning">https://github.com/ocademy-ai/machine-learning</a>
-venue: By Ocademy
-open_access: true
-bibliography:</p>
-<ul class="simple">
-<li><p><a class="reference external" href="https://raw.githubusercontent.com/ocademy-ai/machine-learning/main/open-machine-learning-jupyter-book/references.bib">https://raw.githubusercontent.com/ocademy-ai/machine-learning/main/open-machine-learning-jupyter-book/references.bib</a></p></li>
-</ul>
-<hr class="docutils" />
-<section class="tex2jax_ignore mathjax_ignore" id="linear-regression-metrics">
-<h1><span class="section-number">37.47. </span>Linear Regression Metrics<a class="headerlink" href="#linear-regression-metrics" title="Permalink to this headline">#</a></h1>
+  <section class="tex2jax_ignore mathjax_ignore" id="linear-regression-metrics">
+<h1><span class="section-number">37.46. </span>Linear Regression Metrics<a class="headerlink" href="#linear-regression-metrics" title="Permalink to this headline">#</a></h1>
 <p>Linear regression is a fundamental and widely used technique in machine learning and statistics for predicting continuous values based on input variables. It finds its application in various domains, from finance and economics to healthcare and engineering. When using linear regression, it’s essential to assess the model’s performance accurately. This is where linear regression metrics come into play.</p>
 <p>In this tutorial, we will delve into the world of linear regression metrics, exploring the key evaluation measures that allow us to gauge how well a linear regression model fits the data and makes predictions. These metrics provide valuable insights into the model’s accuracy, precision, and ability to capture the underlying relationships between variables.</p>
 <p>We will cover essential concepts such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R2) score, and Mean Absolute Error (MAE). Understanding these metrics is crucial for data scientists, machine learning practitioners, and anyone looking to harness the power of linear regression for predictive modeling.</p>
 <p>Whether you are building models for price predictions, sales forecasts, or any other regression task, mastering these metrics will empower you to make informed decisions and fine-tune your models for optimal performance. Let’s embark on this journey to explore the intricacies of linear regression metrics and enhance our ability to assess and improve regression models.</p>
 <section id="mean-squared-error-mse">
-<h2><span class="section-number">37.47.1. </span>Mean Squared Error (MSE)<a class="headerlink" href="#mean-squared-error-mse" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.46.1. </span>Mean Squared Error (MSE)<a class="headerlink" href="#mean-squared-error-mse" title="Permalink to this headline">#</a></h2>
 <p>In the realm of linear regression metrics, one fundamental measure of model performance is the <strong>Mean Squared Error (MSE)</strong>. MSE serves as a valuable indicator of how well your linear regression model aligns its predictions with the actual data points. This metric quantifies the average of the squared differences between predicted values and observed values.</p>
 <section id="the-formula">
-<h3><span class="section-number">37.47.1.1. </span>The Formula<a class="headerlink" href="#the-formula" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.46.1.1. </span>The Formula<a class="headerlink" href="#the-formula" title="Permalink to this headline">#</a></h3>
 <p>Mathematically, the MSE is computed using the following formula:</p>
 <div class="math notranslate nohighlight">
 \[ MSE = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2 \]</div>
@@ -1861,11 +1811,11 @@ <h3><span class="section-number">37.47.1.1. </span>The Formula<a class="headerli
 </ul>
 </section>
 <section id="interpretation">
-<h3><span class="section-number">37.47.1.2. </span>Interpretation<a class="headerlink" href="#interpretation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.46.1.2. </span>Interpretation<a class="headerlink" href="#interpretation" title="Permalink to this headline">#</a></h3>
 <p>A lower MSE value indicates that the model’s predictions are closer to the actual values, signifying better model performance. Conversely, a higher MSE suggests that the model’s predictions deviate more from the true values, indicating poorer performance.</p>
 </section>
 <section id="python-implementation">
-<h3><span class="section-number">37.47.1.3. </span>Python Implementation<a class="headerlink" href="#python-implementation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.46.1.3. </span>Python Implementation<a class="headerlink" href="#python-implementation" title="Permalink to this headline">#</a></h3>
 <p>Let’s take a look at how to calculate MSE in Python. We’ll use a simple example with sample data:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1896,10 +1846,10 @@ <h3><span class="section-number">37.47.1.3. </span>Python Implementation<a class
 </section>
 </section>
 <section id="root-mean-squared-error-rmse">
-<h2><span class="section-number">37.47.2. </span>Root Mean Squared Error (RMSE)<a class="headerlink" href="#root-mean-squared-error-rmse" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.46.2. </span>Root Mean Squared Error (RMSE)<a class="headerlink" href="#root-mean-squared-error-rmse" title="Permalink to this headline">#</a></h2>
 <p>The Root Mean Squared Error (RMSE) is another important metric for evaluating the performance of a linear regression model. It is the square root of the Mean Squared Error (MSE) and provides a measure of the average magnitude of error between actual and predicted values.</p>
 <section id="id1">
-<h3><span class="section-number">37.47.2.1. </span>The Formula<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.46.2.1. </span>The Formula<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <p>The formula for RMSE is:</p>
 <div class="math notranslate nohighlight">
 \[ RMSE = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2} \]</div>
@@ -1911,7 +1861,7 @@ <h3><span class="section-number">37.47.2.1. </span>The Formula<a class="headerli
 </ul>
 </section>
 <section id="id2">
-<h3><span class="section-number">37.47.2.2. </span>Python Implementation<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.46.2.2. </span>Python Implementation<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Calculate RMSE</span>
@@ -1931,10 +1881,10 @@ <h3><span class="section-number">37.47.2.2. </span>Python Implementation<a class
 </section>
 </section>
 <section id="r-squared-r2-score">
-<h2><span class="section-number">37.47.3. </span>R-squared (R2) Score<a class="headerlink" href="#r-squared-r2-score" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.46.3. </span>R-squared (R2) Score<a class="headerlink" href="#r-squared-r2-score" title="Permalink to this headline">#</a></h2>
 <p>The R-squared (R2) score, also known as the coefficient of determination, is a measure that indicates the proportion of the variance in the dependent variable that is predictable from the independent variables. It provides insight into how well the model is performing compared to a simple mean.</p>
 <section id="id3">
-<h3><span class="section-number">37.47.3.1. </span>The Formula<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.46.3.1. </span>The Formula<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
 <p>The formula for R2 score is:</p>
 <div class="math notranslate nohighlight">
 \[ R^2 = 1 - \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{\sum_{i=1}^{n} (y_i - \bar{y})^2} \]</div>
@@ -1947,7 +1897,7 @@ <h3><span class="section-number">37.47.3.1. </span>The Formula<a class="headerli
 </ul>
 </section>
 <section id="id4">
-<h3><span class="section-number">37.47.3.2. </span>Python Implementation<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.46.3.2. </span>Python Implementation<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Calculate the mean of actual values</span>
@@ -1976,10 +1926,10 @@ <h3><span class="section-number">37.47.3.2. </span>Python Implementation<a class
 </section>
 </section>
 <section id="residual-analysis">
-<h2><span class="section-number">37.47.4. </span>Residual Analysis<a class="headerlink" href="#residual-analysis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.46.4. </span>Residual Analysis<a class="headerlink" href="#residual-analysis" title="Permalink to this headline">#</a></h2>
 <p>Residual analysis is a critical step in evaluating the performance of a linear regression model. It involves examining the differences between the observed and predicted values (i.e., the residuals). This helps us understand if there are any patterns or trends that the model might have missed.</p>
 <section id="id5">
-<h3><span class="section-number">37.47.4.1. </span>Python Implementation<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.46.4.1. </span>Python Implementation<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
@@ -2001,66 +1951,22 @@ <h3><span class="section-number">37.47.4.1. </span>Python Implementation<a class
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../../../_images/linear-regression-metrics_30_0.png" src="../../../_images/linear-regression-metrics_30_0.png" />
+<img alt="../../../_images/linear-regression-metrics_28_0.png" src="../../../_images/linear-regression-metrics_28_0.png" />
 </div>
 </div>
 </section>
 <section id="id6">
-<h3><span class="section-number">37.47.4.2. </span>Interpretation<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.46.4.2. </span>Interpretation<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h3>
 <p>If the residuals are randomly scattered around the red line, it suggests that the model is capturing the underlying patterns in the data well.
 If there’s a clear pattern in the residuals (e.g., a curve or a trend), it indicates that the model might be missing some important features.
 Residual analysis is a crucial step in understanding the limitations of the model and can provide insights into potential areas of improvement.</p>
 </section>
 </section>
-<section id="adjusted-r-squared">
-<h2><span class="section-number">37.47.5. </span>Adjusted R-squared<a class="headerlink" href="#adjusted-r-squared" title="Permalink to this headline">#</a></h2>
-<p>The adjusted R-squared is a modified version of the R-squared score that takes into account the number of independent variables in the model. It penalizes the inclusion of unnecessary variables that do not significantly contribute to explaining the variance in the dependent variable.</p>
-<section id="id7">
-<h3><span class="section-number">37.47.5.1. </span>The Formula<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h3>
-<p>The formula for Adjusted R-squared is:
-$<span class="math notranslate nohighlight">\( \text{Adjusted } R^2 = 1 - \frac{{(1 - R^2)(n - 1)}}{{n - k - 1}} \)</span>$
-Where:</p>
-<ul class="simple">
-<li><p><span class="math notranslate nohighlight">\(n\)</span> is the number of data points</p></li>
-<li><p><span class="math notranslate nohighlight">\(k\)</span> is the number of independent variables</p></li>
-</ul>
-</section>
-<section id="id8">
-<h3><span class="section-number">37.47.5.2. </span>Python Implementation<a class="headerlink" href="#id8" title="Permalink to this headline">#</a></h3>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define the number of independent variables (k)</span>
-<span class="sd">&#39;&#39;&#39;</span>
-<span class="sd">In this code, you&#39;ll need to replace num_independent_variables with the actual number of independent variables in your model.</span>
-<span class="sd">&#39;&#39;&#39;</span>
-<span class="n">num_independent_variables</span> <span class="o">=</span> <span class="mi">2</span>  <span class="c1"># Replace with the actual number of independent variables</span>
-
-<span class="c1"># Calculate adjusted R2 score</span>
-<span class="n">adjusted_r2_score</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="p">((</span><span class="mi">1</span> <span class="o">-</span> <span class="n">r2_score</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">actual_values</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span> <span class="o">/</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">actual_values</span><span class="p">)</span> <span class="o">-</span> <span class="n">num_independent_variables</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
-
-<span class="c1"># Print the adjusted R2 score</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Adjusted R-squared Score:&quot;</span><span class="p">,</span> <span class="n">adjusted_r2_score</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Adjusted R-squared Score: 0.7552043176561298
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="id9">
-<h3><span class="section-number">37.47.5.3. </span>Interpretation<a class="headerlink" href="#id9" title="Permalink to this headline">#</a></h3>
-<p>The adjusted R-squared score provides a more accurate representation of the model’s explanatory power, especially when dealing with multiple independent variables.
-A higher adjusted R-squared indicates that a larger proportion of the variance in the dependent variable is explained by the independent variables.</p>
-</section>
-</section>
 <section id="mean-absolute-error-mae">
-<h2><span class="section-number">37.47.6. </span>Mean Absolute Error (MAE)<a class="headerlink" href="#mean-absolute-error-mae" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.46.5. </span>Mean Absolute Error (MAE)<a class="headerlink" href="#mean-absolute-error-mae" title="Permalink to this headline">#</a></h2>
 <p>The Mean Absolute Error (MAE) is a metric that measures the average absolute differences between actual and predicted values. Unlike MSE, MAE does not square the differences, which makes it less sensitive to outliers.</p>
-<section id="id10">
-<h3><span class="section-number">37.47.6.1. </span>The Formula<a class="headerlink" href="#id10" title="Permalink to this headline">#</a></h3>
+<section id="id7">
+<h3><span class="section-number">37.46.5.1. </span>The Formula<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h3>
 <p>The formula for Mean Absolute Errord is:
 $<span class="math notranslate nohighlight">\( MAE = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i| \)</span>$
 Where:</p>
@@ -2070,8 +1976,8 @@ <h3><span class="section-number">37.47.6.1. </span>The Formula<a class="headerli
 <li><p><span class="math notranslate nohighlight">\( \hat{y}_i \)</span> represents the predicted value of the dependent variable</p></li>
 </ul>
 </section>
-<section id="id11">
-<h3><span class="section-number">37.47.6.2. </span>Python Implementation<a class="headerlink" href="#id11" title="Permalink to this headline">#</a></h3>
+<section id="id8">
+<h3><span class="section-number">37.46.5.2. </span>Python Implementation<a class="headerlink" href="#id8" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Calculate absolute differences between actual and predicted values</span>
@@ -2092,16 +1998,16 @@ <h3><span class="section-number">37.47.6.2. </span>Python Implementation<a class
 </div>
 </div>
 </section>
-<section id="id12">
-<h3><span class="section-number">37.47.6.3. </span>Interpretation<a class="headerlink" href="#id12" title="Permalink to this headline">#</a></h3>
+<section id="id9">
+<h3><span class="section-number">37.46.5.3. </span>Interpretation<a class="headerlink" href="#id9" title="Permalink to this headline">#</a></h3>
 <p>The MAE gives us an average of how far off our predictions are from the actual values. It provides a more intuitive understanding of error compared to MSE.</p>
 </section>
 </section>
 <section id="mean-absolute-percentage-error-mape">
-<h2><span class="section-number">37.47.7. </span>Mean Absolute Percentage Error (MAPE)<a class="headerlink" href="#mean-absolute-percentage-error-mape" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.46.6. </span>Mean Absolute Percentage Error (MAPE)<a class="headerlink" href="#mean-absolute-percentage-error-mape" title="Permalink to this headline">#</a></h2>
 <p>The Mean Absolute Percentage Error (MAPE) is a metric used to evaluate the accuracy of a model’s predictions in percentage terms. It measures the average absolute percentage difference between actual and predicted values.</p>
-<section id="id13">
-<h3><span class="section-number">37.47.7.1. </span>The Formula<a class="headerlink" href="#id13" title="Permalink to this headline">#</a></h3>
+<section id="id10">
+<h3><span class="section-number">37.46.6.1. </span>The Formula<a class="headerlink" href="#id10" title="Permalink to this headline">#</a></h3>
 <p>The formula for MAPE is:</p>
 <div class="math notranslate nohighlight">
 \[ MAPE = \frac{1}{n} \sum_{i=1}^{n} \left| \frac{y_i - \hat{y}_i}{y_i} \right| \times 100 \]</div>
@@ -2112,8 +2018,8 @@ <h3><span class="section-number">37.47.7.1. </span>The Formula<a class="headerli
 <li><p><span class="math notranslate nohighlight">\( \hat{y}_i \)</span> represents the predicted value of the dependent variable</p></li>
 </ul>
 </section>
-<section id="id14">
-<h3><span class="section-number">37.47.7.2. </span>Interpretation<a class="headerlink" href="#id14" title="Permalink to this headline">#</a></h3>
+<section id="id11">
+<h3><span class="section-number">37.46.6.2. </span>Interpretation<a class="headerlink" href="#id11" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Calculate absolute percentage differences between actual and predicted values</span>
@@ -2136,13 +2042,12 @@ <h3><span class="section-number">37.47.7.2. </span>Interpretation<a class="heade
 </section>
 </section>
 <section id="conclusion-and-recap">
-<h2><span class="section-number">37.47.8. </span>Conclusion and Recap<a class="headerlink" href="#conclusion-and-recap" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.46.7. </span>Conclusion and Recap<a class="headerlink" href="#conclusion-and-recap" title="Permalink to this headline">#</a></h2>
 <p>In this tutorial, we covered several important metrics used to evaluate the performance of linear regression models. Here’s a quick recap:</p>
 <ul class="simple">
 <li><p>Mean Squared Error (MSE): Measures the average squared difference between actual and predicted values.</p></li>
 <li><p>Root Mean Squared Error (RMSE): The square root of MSE, providing a measure of the average magnitude of error.</p></li>
 <li><p>R-squared (R2) Score: Indicates the proportion of the variance in the dependent variable that is predictable from the independent variables.</p></li>
-<li><p>Adjusted R-squared: A modified R2 score that considers the number of independent variables in the model.</p></li>
 <li><p>Mean Absolute Error (MAE): Measures the average absolute differences between actual and predicted values.</p></li>
 <li><p>Mean Absolute Percentage Error (MAPE): Measures the average absolute percentage difference between actual and predicted values.</p></li>
 </ul>
@@ -2184,17 +2089,17 @@ <h2><span class="section-number">37.47.8. </span>Conclusion and Recap<a class="h
                 
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-        <p class="prev-next-title"><span class="section-number">37.48. </span>Loss Function</p>
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diff --git a/assignments/ml-fundamentals/linear-regression/loss-function.html b/assignments/ml-fundamentals/linear-regression/loss-function.html
index fd4f27fc91..155cd8a23f 100644
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+++ b/assignments/ml-fundamentals/linear-regression/loss-function.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.48. Loss Function &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.47. Loss Function &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
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     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="37.49. Gradient descent" href="gradient-descent.html" />
-    <link rel="prev" title="37.47. Linear Regression Metrics" href="linear-regression-metrics.html" />
+    <link rel="next" title="37.48. Gradient descent" href="gradient-descent.html" />
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@@ -238,10 +238,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/working-with-data/data-preparation.html">
@@ -254,8 +276,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
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@@ -286,8 +308,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
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@@ -313,8 +335,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-   37.48.6. Common Loss Functions
+   37.47.6. Common Loss Functions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#regression-loss-functions">
-     37.48.6.1. Regression Loss Functions
+     37.47.6.1. Regression Loss Functions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-loss-functions">
-     37.48.6.2. Classification Loss Functions
+     37.47.6.2. Classification Loss Functions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#structured-loss-functions">
-     37.48.6.3. Structured Loss Functions
+     37.47.6.3. Structured Loss Functions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#regularization">
-     37.48.6.4. Regularization
+     37.47.6.4. Regularization
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion">
-   37.48.7. Conclusion
+   37.47.7. Conclusion
   </a>
  </li>
 </ul>
@@ -1631,103 +1648,103 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#objective-of-this-section">
-   37.48.1. Objective of this section
+   37.47.1. Objective of this section
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#concept-of-loss-function">
-   37.48.2. Concept of loss function
+   37.47.2. Concept of loss function
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#difference-between-a-loss-function-and-a-cost-function">
-   37.48.3. Difference between a Loss Function and a Cost Function
+   37.47.3. Difference between a Loss Function and a Cost Function
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#classification-of-loss-functions">
-   37.48.4. Classification of loss functions
+   37.47.4. Classification of loss functions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#regression-losses">
-     37.48.4.1. Regression Losses
+     37.47.4.1. Regression Losses
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-losses">
-     37.48.4.2. Classification Losses
+     37.47.4.2. Classification Losses
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#structured-losses">
-     37.48.4.3. Structured Losses
+     37.47.4.3. Structured Losses
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#regularization-losses">
-     37.48.4.4. Regularization Losses
+     37.47.4.4. Regularization Losses
     </a>
    </li>
   </ul>
  </li>
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   <a class="reference internal nav-link" href="#empirical-risk-and-structural-risk">
-   37.48.5. Empirical Risk and Structural Risk
+   37.47.5. Empirical Risk and Structural Risk
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#definition">
-     37.48.5.1. Definition
+     37.47.5.1. Definition
     </a>
    </li>
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     <a class="reference internal nav-link" href="#a-detaphor">
-     37.48.5.2. A Detaphor
+     37.47.5.2. A Detaphor
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#mathematical-explanation">
-     37.48.5.3. Mathematical Explanation
+     37.47.5.3. Mathematical Explanation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#cost-function-and-objective-function">
-     37.48.5.4. Cost Function and Objective Function
+     37.47.5.4. Cost Function and Objective Function
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#common-loss-functions">
-   37.48.6. Common Loss Functions
+   37.47.6. Common Loss Functions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#regression-loss-functions">
-     37.48.6.1. Regression Loss Functions
+     37.47.6.1. Regression Loss Functions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-loss-functions">
-     37.48.6.2. Classification Loss Functions
+     37.47.6.2. Classification Loss Functions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#structured-loss-functions">
-     37.48.6.3. Structured Loss Functions
+     37.47.6.3. Structured Loss Functions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#regularization">
-     37.48.6.4. Regularization
+     37.47.6.4. Regularization
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion">
-   37.48.7. Conclusion
+   37.47.7. Conclusion
   </a>
  </li>
 </ul>
@@ -1741,15 +1758,15 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="loss-function">
-<h1><span class="section-number">37.48. </span>Loss Function<a class="headerlink" href="#loss-function" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.47. </span>Loss Function<a class="headerlink" href="#loss-function" title="Permalink to this headline">#</a></h1>
 <section id="objective-of-this-section">
-<h2><span class="section-number">37.48.1. </span>Objective of this section<a class="headerlink" href="#objective-of-this-section" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.47.1. </span>Objective of this section<a class="headerlink" href="#objective-of-this-section" title="Permalink to this headline">#</a></h2>
 <p>We have already learned math and code for “Gradient Descent”, as well as other optimization techniques.</p>
 <p>In this section, we will learn more about loss functions.</p>
 <p>As a learner, you can focus on learning the L1, L2 loss, and Classification Losses in the regression loss function in this section. And learn about other loss functions.</p>
 </section>
 <section id="concept-of-loss-function">
-<h2><span class="section-number">37.48.2. </span>Concept of loss function<a class="headerlink" href="#concept-of-loss-function" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.47.2. </span>Concept of loss function<a class="headerlink" href="#concept-of-loss-function" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>A loss function gauges the disparity between the model’s predictions and the actual values. Simply put, it indicates how “off” our model is. By optimizing this function, our objective is to identify parameters that bring the model’s predictions as close as possible to the true values.</p></li>
 <li><p>The function we want to minimize or maximize is called the objective function or criterion. When we are minimizing it, we may also call it the cost function, loss function, or error function.
@@ -1757,7 +1774,7 @@ <h2><span class="section-number">37.48.2. </span>Concept of loss function<a clas
 </ul>
 </section>
 <section id="difference-between-a-loss-function-and-a-cost-function">
-<h2><span class="section-number">37.48.3. </span>Difference between a Loss Function and a Cost Function<a class="headerlink" href="#difference-between-a-loss-function-and-a-cost-function" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.47.3. </span>Difference between a Loss Function and a Cost Function<a class="headerlink" href="#difference-between-a-loss-function-and-a-cost-function" title="Permalink to this headline">#</a></h2>
 <p>A loss function evaluates the error for a single training example, and it is occasionally referred to as an error function. In contrast, a cost function represents the <strong>average loss</strong> across the entire training dataset. Optimization strategies are designed to minimize this cost function.</p>
 <p>For a simple sample:</p>
 <p>The corresponding cost function of L1 Loss is the Mean of these Squared Errors (MSE).
@@ -1765,9 +1782,9 @@ <h2><span class="section-number">37.48.3. </span>Difference between a Loss Funct
 <p>However, these terms are frequently used interchangeably in practical settings, they aren’t precisely equivalent. From a definitional standpoint, the cost function represents an aggregation or average of the loss functions.</p>
 </section>
 <section id="classification-of-loss-functions">
-<h2><span class="section-number">37.48.4. </span>Classification of loss functions<a class="headerlink" href="#classification-of-loss-functions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.47.4. </span>Classification of loss functions<a class="headerlink" href="#classification-of-loss-functions" title="Permalink to this headline">#</a></h2>
 <section id="regression-losses">
-<h3><span class="section-number">37.48.4.1. </span>Regression Losses<a class="headerlink" href="#regression-losses" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.47.4.1. </span>Regression Losses<a class="headerlink" href="#regression-losses" title="Permalink to this headline">#</a></h3>
 <p>These are employed when the objective is to predict a continuous outcome.</p>
 <ul class="simple">
 <li><p>Mean Squared Error (MSE): Measures the average squared discrepancies between predictions and actual values, emphasizing larger errors.</p></li>
@@ -1779,7 +1796,7 @@ <h3><span class="section-number">37.48.4.1. </span>Regression Losses<a class="he
 </ul>
 </section>
 <section id="classification-losses">
-<h3><span class="section-number">37.48.4.2. </span>Classification Losses<a class="headerlink" href="#classification-losses" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.47.4.2. </span>Classification Losses<a class="headerlink" href="#classification-losses" title="Permalink to this headline">#</a></h3>
 <p>Utilized for tasks requiring the prediction of discrete categories.</p>
 <ul class="simple">
 <li><p>Cross Entropy Loss: Quantifies the dissimilarity between the predicted probability distribution and the actual class distribution.</p></li>
@@ -1789,7 +1806,7 @@ <h3><span class="section-number">37.48.4.2. </span>Classification Losses<a class
 </ul>
 </section>
 <section id="structured-losses">
-<h3><span class="section-number">37.48.4.3. </span>Structured Losses<a class="headerlink" href="#structured-losses" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.47.4.3. </span>Structured Losses<a class="headerlink" href="#structured-losses" title="Permalink to this headline">#</a></h3>
 <p>Tailored for intricate tasks involving structured data patterns.</p>
 <ul class="simple">
 <li><p>Sequence Generation Loss: Emblematic examples include the CTC (Connectionist Temporal Classification) designed for undertakings such as speech and text identification.</p></li>
@@ -1797,7 +1814,7 @@ <h3><span class="section-number">37.48.4.3. </span>Structured Losses<a class="he
 </ul>
 </section>
 <section id="regularization-losses">
-<h3><span class="section-number">37.48.4.4. </span>Regularization Losses<a class="headerlink" href="#regularization-losses" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.47.4.4. </span>Regularization Losses<a class="headerlink" href="#regularization-losses" title="Permalink to this headline">#</a></h3>
 <p>Rather than directly influencing the model’s predictions, these losses are integrated into the objective function to counteract excessive model complexity.</p>
 <ul class="simple">
 <li><p>L1 Regularization (Lasso): Enforces sparsity by compelling certain model coefficients to be exactly zero.</p></li>
@@ -1806,9 +1823,9 @@ <h3><span class="section-number">37.48.4.4. </span>Regularization Losses<a class
 </section>
 </section>
 <section id="empirical-risk-and-structural-risk">
-<h2><span class="section-number">37.48.5. </span>Empirical Risk and Structural Risk<a class="headerlink" href="#empirical-risk-and-structural-risk" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.47.5. </span>Empirical Risk and Structural Risk<a class="headerlink" href="#empirical-risk-and-structural-risk" title="Permalink to this headline">#</a></h2>
 <section id="definition">
-<h3><span class="section-number">37.48.5.1. </span>Definition<a class="headerlink" href="#definition" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.47.5.1. </span>Definition<a class="headerlink" href="#definition" title="Permalink to this headline">#</a></h3>
 <p>Perhaps you’ve heard of these two concepts before. In the realms of machine learning and statistics, the concepts of empirical risk and structural risk are intricately tied to loss functions. <strong>However, these terms aren’t directly categories of loss functions perse.</strong> Let’s first clarify these concepts:</p>
 <ol class="simple">
 <li><p><strong>Empirical Risk:</strong> Refers to the average loss of a model over a given dataset. Minimizing empirical risk focuses on reducing errors explicitly on the training data.</p></li>
@@ -1821,7 +1838,7 @@ <h3><span class="section-number">37.48.5.1. </span>Definition<a class="headerlin
 </ul>
 </section>
 <section id="a-detaphor">
-<h3><span class="section-number">37.48.5.2. </span>A Detaphor<a class="headerlink" href="#a-detaphor" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.47.5.2. </span>A Detaphor<a class="headerlink" href="#a-detaphor" title="Permalink to this headline">#</a></h3>
 <p>Maybe it’s still abtract. So, now imagine you’re a tailor trying to make a dress for a client.</p>
 <ul class="simple">
 <li><p><strong>Empirical Risk:</strong> This is like ensuring the dress fits the client perfectly based on a single fitting session. You measure every contour and make the dress to match those exact measurements. The dress is a perfect fit for the client on that particular day.</p></li>
@@ -1835,7 +1852,7 @@ <h3><span class="section-number">37.48.5.2. </span>A Detaphor<a class="headerlin
 <p>Factoring in <strong>Structural Risk</strong> ensures the model isn’t overly tailored to the training data and can generalize well to new, unseen data. It’s about ensuring a balance between a perfect fit and adaptability.</p>
 </section>
 <section id="mathematical-explanation">
-<h3><span class="section-number">37.48.5.3. </span>Mathematical Explanation<a class="headerlink" href="#mathematical-explanation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.47.5.3. </span>Mathematical Explanation<a class="headerlink" href="#mathematical-explanation" title="Permalink to this headline">#</a></h3>
 <p>Now you have a general understanding of the meaning of empirical risk and structural risk. Let’s delve into a more mathematical perspective:</p>
 <p>Given a dataset <span class="math notranslate nohighlight">\(\mathcal{D}\)</span> comprising input-output pairs <span class="math notranslate nohighlight">\((x_1, y_1)\)</span>, <span class="math notranslate nohighlight">\((x_2, y_2)\)</span>, … <span class="math notranslate nohighlight">\((x_n, y_n)\)</span> and a model <span class="math notranslate nohighlight">\(f\)</span> parameterized by <span class="math notranslate nohighlight">\(\theta\)</span>, the empirical risk and structural risk can be formally defined as follows:</p>
 <p><strong>Empirical Risk(Cost Function):</strong>
@@ -1865,15 +1882,15 @@ <h3><span class="section-number">37.48.5.3. </span>Mathematical Explanation<a cl
 <p>In essence, while empirical risk aims for performance on the current dataset, structural risk aims for good performance on new data by penalizing overly complex models.</p>
 </section>
 <section id="cost-function-and-objective-function">
-<h3><span class="section-number">37.48.5.4. </span>Cost Function and Objective Function<a class="headerlink" href="#cost-function-and-objective-function" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.47.5.4. </span>Cost Function and Objective Function<a class="headerlink" href="#cost-function-and-objective-function" title="Permalink to this headline">#</a></h3>
 <p>The empirical risk and cost functions are in many cases the same and represent the average loss on the training data.</p>
 <p>Structural risk is often viewed as an objective function, especially when regularization is considered. But the term “objective function” is broader and is not limited to structural risk but can also include other optimization objectives.</p>
 </section>
 </section>
 <section id="common-loss-functions">
-<h2><span class="section-number">37.48.6. </span>Common Loss Functions<a class="headerlink" href="#common-loss-functions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.47.6. </span>Common Loss Functions<a class="headerlink" href="#common-loss-functions" title="Permalink to this headline">#</a></h2>
 <section id="regression-loss-functions">
-<h3><span class="section-number">37.48.6.1. </span>Regression Loss Functions<a class="headerlink" href="#regression-loss-functions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.47.6.1. </span>Regression Loss Functions<a class="headerlink" href="#regression-loss-functions" title="Permalink to this headline">#</a></h3>
 <ol class="simple">
 <li><p><strong>Mean Squared Error, MSE</strong></p></li>
 </ol>
@@ -1949,7 +1966,7 @@ <h3><span class="section-number">37.48.6.1. </span>Regression Loss Functions<a c
 <p>Corresponds to MSE.</p>
 </section>
 <section id="classification-loss-functions">
-<h3><span class="section-number">37.48.6.2. </span>Classification Loss Functions<a class="headerlink" href="#classification-loss-functions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.47.6.2. </span>Classification Loss Functions<a class="headerlink" href="#classification-loss-functions" title="Permalink to this headline">#</a></h3>
 <ol class="simple">
 <li><p><strong>Cross Entropy Loss</strong></p></li>
 </ol>
@@ -2050,7 +2067,7 @@ <h3><span class="section-number">37.48.6.2. </span>Classification Loss Functions
 </div>
 </section>
 <section id="structured-loss-functions">
-<h3><span class="section-number">37.48.6.3. </span>Structured Loss Functions<a class="headerlink" href="#structured-loss-functions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.47.6.3. </span>Structured Loss Functions<a class="headerlink" href="#structured-loss-functions" title="Permalink to this headline">#</a></h3>
 <ol class="simple">
 <li><p><strong>CTC Loss (Connectionist Temporal Classification)</strong></p></li>
 </ol>
@@ -2109,7 +2126,7 @@ <h3><span class="section-number">37.48.6.3. </span>Structured Loss Functions<a c
 </div>
 </section>
 <section id="regularization">
-<h3><span class="section-number">37.48.6.4. </span>Regularization<a class="headerlink" href="#regularization" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.47.6.4. </span>Regularization<a class="headerlink" href="#regularization" title="Permalink to this headline">#</a></h3>
 <ol class="simple">
 <li><p><strong>L1 Regularization (Lasso)</strong></p></li>
 </ol>
@@ -2143,7 +2160,7 @@ <h3><span class="section-number">37.48.6.4. </span>Regularization<a class="heade
 </section>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">37.48.7. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.47.7. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>Loss functions hold a pivotal role in machine learning. By minimizing the loss, we enhance the accuracy of our model’s predictions. A deep understanding of various loss functions aids in selecting the most appropriate optimization technique for specific challenges.</p>
 </section>
 </section>
@@ -2185,13 +2202,13 @@ <h2><span class="section-number">37.48.7. </span>Conclusion<a class="headerlink"
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.47. </span>Linear Regression Metrics</p>
+            <p class="prev-next-title"><span class="section-number">37.46. </span>Linear Regression Metrics</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="gradient-descent.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.49. </span>Gradient descent</p>
+        <p class="prev-next-title"><span class="section-number">37.48. </span>Gradient descent</p>
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diff --git a/assignments/ml-fundamentals/managing-data.html b/assignments/ml-fundamentals/managing-data.html
index ee9916687e..01246324c6 100644
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+++ b/assignments/ml-fundamentals/managing-data.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>37.55. Managing data &#8212; Ocademy Open Machine Learning Book</title>
+    <title>37.54. Managing data &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.56. Exploring visualizations" href="exploring-visualizations.html" />
-    <link rel="prev" title="37.54. Regression tools" href="regression-tools.html" />
+    <link rel="next" title="37.55. Exploring visualizations" href="exploring-visualizations.html" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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-    <a class="reference internal" href="ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
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     <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
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     <a class="reference internal" href="linear-regression/loss-function.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
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     <a class="reference internal" href="ml-logistic-regression-1.html">
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-  DEEP LEARNING
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-</p>
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- <li class="toctree-l1">
-  <a class="reference internal" href="../../deep-learning/dl-overview.html">
-   20. Intro to Deep Learning
-  </a>
- </li>
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-  <a class="reference internal" href="../../deep-learning/cnn.html">
-   21. Convolutional Neural Networks
-  </a>
- </li>
- <li class="toctree-l1">
-  <a class="reference internal" href="../../deep-learning/gan.html">
-   22. Generative adversarial networks
-  </a>
- </li>
- <li class="toctree-l1">
-  <a class="reference internal" href="../../deep-learning/rnn.html">
-   23. Recurrent Neural Networks
-  </a>
- </li>
- <li class="toctree-l1">
-  <a class="reference internal" href="../../deep-learning/autoencoder.html">
-   24. Autoencoder
-  </a>
- </li>
- <li class="toctree-l1">
-  <a class="reference internal" href="../../deep-learning/lstm.html">
-   25. Long-short term memory
-  </a>
- </li>
- <li class="toctree-l1">
-  <a class="reference internal" href="../../deep-learning/time-series.html">
-   26. Time series
-  </a>
- </li>
- <li class="toctree-l1">
-  <a class="reference internal" href="../../deep-learning/dqn.html">
-   27. Deep Q-learning
-  </a>
- </li>
- <li class="toctree-l1">
-  <a class="reference internal" href="../../deep-learning/image-classification.html">
-   28. Image classification
-  </a>
- </li>
- <li class="toctree-l1">
-  <a class="reference internal" href="../../deep-learning/image-segmentation.html">
-   29. Image segmentation
-  </a>
- </li>
- <li class="toctree-l1">
-  <a class="reference internal" href="../../deep-learning/difussion-model.html">
-   30. Diffusion Model
-  </a>
- </li>
- <li class="toctree-l1">
-  <a class="reference internal" href="../../deep-learning/object-detection.html">
-   31. Object detection
-  </a>
- </li>
-</ul>
-<p aria-level="2" class="caption" role="heading">
- <span class="caption-text">
-  MACHINE LEARNING OPERATIONS
- </span>
-</p>
-<ul class="nav bd-sidenav">
- <li class="toctree-l1">
-  <a class="reference internal" href="../../machine-learning-productionization/overview.html">
-   32. Overview
-  </a>
- </li>
- <li class="toctree-l1">
-  <a class="reference internal" href="../../machine-learning-productionization/problem-framing.html">
-   33. Problem framing
-  </a>
- </li>
- <li class="toctree-l1">
-  <a class="reference internal" href="../../machine-learning-productionization/data-engineering.html">
-   34. Data engineering
-  </a>
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- <li class="toctree-l1">
-  <a class="reference internal" href="../../machine-learning-productionization/model-training-and-evaluation.html">
-   35. Model training &amp; evaluation
-  </a>
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-  <a class="reference internal" href="../../machine-learning-productionization/model-deployment.html">
-   36. Model deployment
-  </a>
- </li>
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-<p aria-level="2" class="caption" role="heading">
- <span class="caption-text">
-  OTHERS
- </span>
-</p>
-<ul class="current nav bd-sidenav">
- <li class="toctree-l1 current active has-children">
-  <a class="reference internal" href="../README.html">
-   37. Self-paced assignments
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-    <a class="reference internal" href="../set-up-env/first-assignment.html">
-     37.3. First assignment
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../set-up-env/second-assignment.html">
-     37.4. Second assignment
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../project-plan-template.html">
-     37.5. Project Plan​ Template
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-     37.6. Python programming introduction
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-     37.7. Python programming basics
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-     37.8. Python programming advanced
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/analyzing-text-about-data-science.html">
-     37.9. Analyzing text about Data Science
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/data-science-scenarios.html">
-     37.10. Data Science scenarios
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/write-a-data-ethics-case-study.html">
-     37.11. Write a data ethics case study
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/lines-scatters-and-bars.html">
-     37.12. Lines, scatters and bars
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/apply-your-skills.html">
-     37.13. Apply your skills
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/try-it-in-excel.html">
-     37.14. Try it in Excel
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/dive-into-the-beehive.html">
-     37.15. Dive into the beehive
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/build-your-own-custom-vis.html">
-     37.16. Build your own custom vis
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/classifying-datasets.html">
-     37.17. Classifying datasets
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/small-diabetes-study.html">
-     37.18. Small diabetes study
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/introduction-to-statistics-and-probability.html">
-     37.19. Introduction to probability and statistics
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/displaying-airport-data.html">
-     37.20. Displaying airport data
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/soda-profits.html">
-     37.21. Soda profits
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/analyzing-COVID-19-papers.html">
-     37.22. Analyzing COVID-19 papers
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/estimation-of-COVID-19-pandemic.html">
-     37.23. Estimation of COVID-19 pandemic
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/data-processing-in-python.html">
-     37.24. Data processing in Python
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/evaluating-data-from-a-form.html">
-     37.25. Evaluating data from a form
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/data-preparation.html">
-     37.26. Data preparation
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/analyzing-data.html">
-     37.27. Analyzing data
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/nyc-taxi-data-in-winter-and-summer.html">
-     37.28. NYC taxi data in winter and summer
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/matplotlib-applied.html">
-     37.29. Matplotlib applied
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/tell-a-story.html">
-     37.35. Tell a story
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/explore-a-planetary-computer-dataset.html">
-     37.36. Explore a planetary computer dataset
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/exploring-for-anwser.html">
-     37.37. Exploring for answers
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/market-research.html">
-     37.38. Market research
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     37.39. Low code/no code Data Science project on Azure ML
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/data-science-project-using-azure-ml-sdk.html">
-     37.40. Data Science project using Azure ML SDK
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     37.41. Data Science in the cloud: The “Azure ML SDK” way
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="ml-overview-iris.html">
-     37.42. Machine Learning overview - assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="ml-overview-mnist-digits.html">
-     37.43. Machine Learning overview - assignment 2
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="regression-with-scikit-learn.html">
-     37.44. Regression with Scikit-learn
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2 current active">
-    <a class="current reference internal" href="#">
-     37.46. ML linear regression - assignment 2
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="linear-regression/loss-function.html">
-     37.48. Loss Function
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.49. Gradient descent
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="ml-logistic-regression-2.html">
-     37.52. ML logistic regression - assignment 2
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="ml-neural-network-1.html">
-     37.53. ML neural network - Assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="regression-tools.html">
-     37.54. Regression tools
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="managing-data.html">
-     37.55. Managing data
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="exploring-visualizations.html">
-     37.56. Exploring visualizations
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="try-a-different-model.html">
-     37.57. Try a different model
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="create-a-regression-model.html">
-     37.58. Create a regression model
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="linear-and-polynomial-regression.html">
-     37.59. Linear and polynomial regression
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="retrying-some-regression.html">
-     37.60. Retrying some regression
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="delicious-asian-and-indian-cuisines.html">
-     37.62. Delicious asian and indian cuisines
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="explore-classification-methods.html">
-     37.63. Explore classification methods
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.65. Kernel method assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.67. Support Vector Machines (SVM) - Classification
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.68. Decision Trees - Intro and Regression
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.69. Decision Trees - Classification
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.70. Model selection assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.71. Learning Curve To Identify Overfit &amp; Underfit
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.72. Dropout and Batch Normalization
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.73. Lasso and Ridge Regression
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/model-selection/regularized-linear-models.html">
-     37.74. Regularized Linear Models
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
-     37.75. Random forests intro and regression
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forests-for-classification.html">
-     37.76. Random forests for classification
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
-     37.77. Beyond random forests: more ensemble models
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/ensemble-learning/decision-trees.html">
-     37.78. Decision trees
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
-     37.79. Hyperparameter tuning gradient boosting
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
-     37.80. Gradient boosting
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/gradient-boosting/boosting-with-tuning.html">
-     37.81. Boosting with tuning
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
-     37.82. Random Forest Classifier with Feature Importance
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
-     37.84. Data engineering
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
-     37.85. Counterintuitive Challenges in ML Debugging
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../machine-learning-productionization/debugging-in-classification.html">
-     37.86. Case Study: Debugging in Classification
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../machine-learning-productionization/debugging-in-regression.html">
-     37.87. Case Study: Debugging in Regression
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="study-the-solvers.html">
-     37.88. Study the solvers
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="build-classification-models.html">
-     37.89. Build classification models
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="build-classification-model.html">
-     37.90. Build Classification Model
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="parameter-play.html">
-     37.91. Parameter play
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
-     37.92. How to choose cnn architecture mnist
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
-     37.94. Sign Language Digits Classification with CNN
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../deep-learning/cnn/object-recognition-in-images-using-cnn.html">
-     37.96. Object Recognition in Images using CNN
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.97. Intro to TensorFlow for Deep Learning
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.101. Google Stock Price Prediction RNN
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../deep-learning/autoencoder/autoencoder.html">
-     37.103. Intro to Autoencoders
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../deep-learning/time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../deep-learning/nn-for-classification-assignment.html">
-     37.108. Neural Networks for Classification with TensorFlow
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../deep-learning/nn-classify-15-fruits-assignment.html">
-     37.109. NN Classify 15 Fruits Assignment
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.114. DQN On Foreign Exchange Market
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../deep-learning/gan/art-by-gan.html">
-     37.115. Art by gan
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-  <section class="tex2jax_ignore mathjax_ignore" id="ml-linear-regression-assignment-2">
-<h1><span class="section-number">37.46. </span>ML linear regression - assignment 2<a class="headerlink" href="#ml-linear-regression-assignment-2" title="Permalink to this headline">#</a></h1>
-<p>In this assignment, we will explain the principle of one-dimensional gradient descent and multidimensional gradient descent.</p>
-<section id="one-dimensional-gradient-descent">
-<h2><span class="section-number">37.46.1. </span>One-dimensional gradient descent<a class="headerlink" href="#one-dimensional-gradient-descent" title="Permalink to this headline">#</a></h2>
-<p>One-dimensional gradient descent is a simple optimization algorithm used to find the minimum (or maximum) of a univariate function.</p>
-<p>Here are the main steps:</p>
-<blockquote>
-<div><p>step 1: choose an initial point on the function curve.</p>
-</div></blockquote>
-<blockquote>
-<div><p>step 2: compute the derivative of the function at the current point.</p>
-</div></blockquote>
-<blockquote>
-<div><p>step 3: update the position along the function curve.</p>
-</div></blockquote>
-<blockquote>
-<div><p>step 4: repeat the process.</p>
-</div></blockquote>
-<blockquote>
-<div><p>step 5: output the result.</p>
-</div></blockquote>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define the target function</span>
-<span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">x</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">5</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">10</span>
-
-<span class="c1"># Define the guidance of the target function</span>
-<span class="k">def</span> <span class="nf">df</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-    <span class="k">return</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">5</span>
-
-<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;f(x)&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x2587be67a00&gt;]
-</pre></div>
-</div>
-<img alt="../../_images/ml-linear-regression-2_4_1.png" src="../../_images/ml-linear-regression-2_4_1.png" />
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define gradient descent function</span>
-<span class="k">def</span> <span class="nf">gradient_descent</span><span class="p">(</span><span class="n">learning_rate</span><span class="p">,</span> <span class="n">max_iter</span><span class="p">,</span> <span class="n">x_init</span><span class="p">):</span>
-    <span class="c1"># step 1:start with an initial value for the input variable, denoted as x_int.</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="n">x_init</span>
-    <span class="n">x_history</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="p">]</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">max_iter</span><span class="p">):</span>
-        <span class="c1"># step 2:compute the derivative of the function at the current point.</span>
-        <span class="n">gradient</span> <span class="o">=</span> <span class="n">df</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="n">x</span> <span class="o">-=</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">gradient</span>
-        <span class="c1"># step 3:update the position along the function curve.</span>
-        <span class="n">x_history</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-    <span class="c1"># step 4:repeat the process.</span>
-    <span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">x_history</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set the learning rate and initial value</span>
-        <span class="c1"># learning rate: the size of the step for gradient descent.</span>
-        <span class="c1"># max_iter: number of gradient descent iterations.</span>
-<span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.3</span>
-<span class="n">max_iter</span> <span class="o">=</span> <span class="mi">100</span>
-<span class="n">x_init</span> <span class="o">=</span> <span class="mi">10</span>
-
-<span class="c1"># Run gradient descent function</span>
-<span class="n">x_min</span><span class="p">,</span> <span class="n">x_history</span> <span class="o">=</span> <span class="n">gradient_descent</span><span class="p">(</span><span class="n">learning_rate</span><span class="p">,</span> <span class="n">max_iter</span><span class="p">,</span> <span class="n">x_init</span><span class="p">)</span>
-
-<span class="c1"># Draw the target function and the process of gradient descent</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-<span class="n">y_history</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">x_history</span><span class="p">))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;f(x)&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x_history</span><span class="p">,</span> <span class="n">y_history</span><span class="p">,</span> <span class="s1">&#39;ro-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Gradient Descent&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;x&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;f(x)&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;One-Dimensional Gradient Descent&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;x_min:&#39;</span><span class="p">,</span> <span class="n">x_min</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<img alt="../../_images/ml-linear-regression-2_6_0.png" src="../../_images/ml-linear-regression-2_6_0.png" />
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>x_min: -2.5
-</pre></div>
-</div>
-</div>
-</div>
-<p>you can change <code class="docutils literal notranslate"><span class="pre">learning</span> <span class="pre">rate</span></code> and  <code class="docutils literal notranslate"><span class="pre">max_iter</span></code> to see different images.</p>
-</section>
-<section id="multidimensional-gradient-descent">
-<h2><span class="section-number">37.46.2. </span>Multidimensional gradient descent<a class="headerlink" href="#multidimensional-gradient-descent" title="Permalink to this headline">#</a></h2>
-<p>Two-dimensional gradient descent is an optimization algorithm used to find the minimum (or maximum) of a function with two input variables.</p>
-<p>Here are the main steps:</p>
-<blockquote>
-<div><p>step 1: choose an initial point on the function curve.</p>
-</div></blockquote>
-<blockquote>
-<div><p>step 2: compute the partial derivatives of the function at the current point.</p>
-</div></blockquote>
-<blockquote>
-<div><p>step 3: update the position along the function curve.</p>
-</div></blockquote>
-<blockquote>
-<div><p>step 4: repeat the process.</p>
-</div></blockquote>
-<blockquote>
-<div><p>step 5: output the result.</p>
-</div></blockquote>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">mpl_toolkits.mplot3d</span> <span class="kn">import</span> <span class="n">Axes3D</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define the target function</span>
-<span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">x</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="n">y</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">5</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">y</span> <span class="o">+</span> <span class="mi">10</span>
-
-<span class="c1"># Take the partial derivative of the objective function with respect to x</span>
-<span class="k">def</span> <span class="nf">dfx</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-    <span class="k">return</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">5</span>
-
-<span class="c1"># Take the partial derivative of the objective function with respect to y</span>
-<span class="k">def</span> <span class="nf">dfy</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-    <span class="k">return</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">y</span> <span class="o">+</span> <span class="mi">2</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define the two-dimensional gradient descent function</span>
-<span class="k">def</span> <span class="nf">gradient_descent</span><span class="p">(</span><span class="n">learning_rate</span><span class="p">,</span> <span class="n">max_iter</span><span class="p">,</span> <span class="n">x_init</span><span class="p">,</span> <span class="n">y_init</span><span class="p">):</span>
-    <span class="c1"># step 1:start with an initial value for the two input variables, denoted as (x_init, y_init).</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="n">x_init</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="n">y_init</span>
-    <span class="n">x_history</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="p">]</span>
-    <span class="n">y_history</span> <span class="o">=</span> <span class="p">[</span><span class="n">y</span><span class="p">]</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">max_iter</span><span class="p">):</span>
-        <span class="c1"># step 2:compute the partial derivatives of the function at the current point.</span>
-        <span class="n">gradient_x</span> <span class="o">=</span> <span class="n">dfx</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-        <span class="n">gradient_y</span> <span class="o">=</span> <span class="n">dfy</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-        <span class="n">x</span> <span class="o">-=</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">gradient_x</span>
-        <span class="n">y</span> <span class="o">-=</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">gradient_y</span>
-        <span class="c1"># step 3:update the positions along the function surface.</span>
-        <span class="n">x_history</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="n">y_history</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-    <span class="c1"># step 4:repeat the process.</span>
-    <span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">x_history</span><span class="p">,</span> <span class="n">y_history</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set the learning rate and initial value</span>
-<span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.1</span>
-<span class="n">max_iter</span> <span class="o">=</span> <span class="mi">100</span>
-<span class="n">x_init</span> <span class="o">=</span> <span class="o">-</span><span class="mi">5</span>
-<span class="n">y_init</span> <span class="o">=</span> <span class="mi">5</span>
-
-<span class="c1"># Run two-dimensional gradient descent function</span>
-<span class="n">x_min</span><span class="p">,</span> <span class="n">y_min</span><span class="p">,</span> <span class="n">x_history</span><span class="p">,</span> <span class="n">y_history</span> <span class="o">=</span> <span class="n">gradient_descent</span><span class="p">(</span><span class="n">learning_rate</span><span class="p">,</span> <span class="n">max_iter</span><span class="p">,</span> <span class="n">x_init</span><span class="p">,</span> <span class="n">y_init</span><span class="p">)</span>
-
-<span class="c1"># Draw the target function and the process of gradient descent</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-<span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="n">Z</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
-
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="s1">&#39;3d&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot_surface</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;viridis&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x_history</span><span class="p">,</span> <span class="n">y_history</span><span class="p">,</span> <span class="n">f</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">x_history</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y_history</span><span class="p">)),</span> <span class="s1">&#39;ro-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Gradient Descent&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;x&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;y&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_zlabel</span><span class="p">(</span><span class="s1">&#39;f(x, y)&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Two-Dimensional Gradient Descent&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;x_min:&#39;</span><span class="p">,</span> <span class="n">x_min</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;y_min:&#39;</span><span class="p">,</span> <span class="n">y_min</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<img alt="../../_images/ml-linear-regression-2_13_0.png" src="../../_images/ml-linear-regression-2_13_0.png" />
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>x_min: -2.5000000005092593
-y_min: -0.9999999987777783
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-</section>
-
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index 1f503607e1..1c99bdd38d 100644
--- a/assignments/ml-fundamentals/ml-logistic-regression-1.html
+++ b/assignments/ml-fundamentals/ml-logistic-regression-1.html
@@ -6,7 +6,7 @@
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
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+    <title>37.50. ML logistic regression - assignment 1 &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../genindex.html" />
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-    <link rel="next" title="37.52. ML logistic regression - assignment 2" href="ml-logistic-regression-2.html" />
-    <link rel="prev" title="37.49. Gradient descent" href="linear-regression/gradient-descent.html" />
+    <link rel="next" title="37.51. ML logistic regression - assignment 2" href="ml-logistic-regression-2.html" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
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   <a class="reference internal" href="../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
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@@ -311,8 +333,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
   </a>
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@@ -355,8 +377,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
   </a>
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@@ -392,8 +414,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
-  <label for="toctree-checkbox-7">
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-     37.45. ML linear regression - assignment 1
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-   <li class="toctree-l2">
-    <a class="reference internal" href="ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
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-     37.47. Linear Regression Metrics
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
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+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
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     <a class="reference internal" href="regression-tools.html">
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-     37.103. Intro to Autoencoders
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     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
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     <a class="reference internal" href="../deep-learning/nn-classify-15-fruits-assignment.html">
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     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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+   37.50.1. Build classification models：Predict the price range
   </a>
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+   37.50.2. 1. Problem Formulation
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+   37.50.3. 2. Finding the Data
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    <li class="toc-h3 nav-item toc-entry">
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-     37.51.4.1. Checking summary statistics
+     37.50.4.1. Checking summary statistics
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-missing-values">
-     37.51.4.2. Checking missing values
+     37.50.4.2. Checking missing values
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-correlation-between-features">
-     37.51.4.3. Checking Correlation Between Features
+     37.50.4.3. Checking Correlation Between Features
     </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#more-data-exploration">
-     37.51.4.4. More Data Exploration
+     37.50.4.4. More Data Exploration
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    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploring-categorical-features">
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+     37.50.4.5. Exploring Categorical Features
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+   37.50.5. 4. Data Preprocessing
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+   37.50.6. 5. Creating and Training a Logistic Regression Model
   </a>
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+     37.50.6.1. Trying other models
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+   37.50.7. 6.  Model Evaluation
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+     37.50.7.1. Classification Performance Metrics
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+   37.50.8. Acknowledgments
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 </ul>
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                             <ul class="visible nav section-nav flex-column">
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-   37.51.1. Build classification models：Predict the price range
+   37.50.1. Build classification models：Predict the price range
   </a>
  </li>
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   <a class="reference internal nav-link" href="#problem-formulation">
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+   37.50.2. 1. Problem Formulation
   </a>
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   <a class="reference internal nav-link" href="#finding-the-data">
-   37.51.3. 2. Finding the Data
+   37.50.3. 2. Finding the Data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exploring-insights-in-data-or-eda">
-   37.51.4. 3. Exploring Insights in Data or EDA
+   37.50.4. 3. Exploring Insights in Data or EDA
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-summary-statistics">
-     37.51.4.1. Checking summary statistics
+     37.50.4.1. Checking summary statistics
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-missing-values">
-     37.51.4.2. Checking missing values
+     37.50.4.2. Checking missing values
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-correlation-between-features">
-     37.51.4.3. Checking Correlation Between Features
+     37.50.4.3. Checking Correlation Between Features
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#more-data-exploration">
-     37.51.4.4. More Data Exploration
+     37.50.4.4. More Data Exploration
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploring-categorical-features">
-     37.51.4.5. Exploring Categorical Features
+     37.50.4.5. Exploring Categorical Features
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-preprocessing">
-   37.51.5. 4. Data Preprocessing
+   37.50.5. 4. Data Preprocessing
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#feature-scaling">
-     37.51.5.1. Feature Scaling
+     37.50.5.1. Feature Scaling
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#creating-and-training-a-logistic-regression-model">
-   37.51.6. 5. Creating and Training a Logistic Regression Model
+   37.50.6. 5. Creating and Training a Logistic Regression Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#trying-other-models">
-     37.51.6.1. Trying other models
+     37.50.6.1. Trying other models
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#model-evaluation">
-   37.51.7. 6.  Model Evaluation
+   37.50.7. 6.  Model Evaluation
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-performance-metrics">
-     37.51.7.1. Classification Performance Metrics
+     37.50.7.1. Classification Performance Metrics
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.51.8. Acknowledgments
+   37.50.8. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1713,21 +1730,21 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="ml-logistic-regression-assignment-1">
-<h1><span class="section-number">37.51. </span>ML logistic regression - assignment 1<a class="headerlink" href="#ml-logistic-regression-assignment-1" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.50. </span>ML logistic regression - assignment 1<a class="headerlink" href="#ml-logistic-regression-assignment-1" title="Permalink to this headline">#</a></h1>
 <section id="build-classification-models-predict-the-price-range">
-<h2><span class="section-number">37.51.1. </span>Build classification models：Predict the price range<a class="headerlink" href="#build-classification-models-predict-the-price-range" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.50.1. </span>Build classification models：Predict the price range<a class="headerlink" href="#build-classification-models-predict-the-price-range" title="Permalink to this headline">#</a></h2>
 <p>In this lab, we will learn about classification where the task is to predict the class or category. Both regression and classification are the main two types of supervised learning.</p>
 <p><a name='1'></a></p>
 </section>
 <section id="problem-formulation">
-<h2><span class="section-number">37.51.2. </span>1. Problem Formulation<a class="headerlink" href="#problem-formulation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.50.2. </span>1. Problem Formulation<a class="headerlink" href="#problem-formulation" title="Permalink to this headline">#</a></h2>
 <p>Let’s say you have an idea of a revolutionary mobile phone and you want to establish a start up, but you know little about the price of the mobile phones. You are interested in learning that!</p>
 <p>Fortunately, there is this mobile dataset <a class="reference external" href="https://www.kaggle.com/iabhishekofficial/mobile-price-classification?select=train.csv">on Kaggle</a> that you can use to learn about the price ranges of mobiles based on their features such as wifi &amp; bluetooth supports etc…</p>
 <p>So, to make it simple, you have a dataset containing the features of mobiles and the problem is to predict the price range, not the exact price.</p>
 <p><a name='2'></a></p>
 </section>
 <section id="finding-the-data">
-<h2><span class="section-number">37.51.3. </span>2. Finding the Data<a class="headerlink" href="#finding-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.50.3. </span>2. Finding the Data<a class="headerlink" href="#finding-the-data" title="Permalink to this headline">#</a></h2>
 <p>The data that we are going to use is found on Kaggle.</p>
 <p>Here are the details of the features. It is 21 features. The target feature is <code class="docutils literal notranslate"><span class="pre">price</span> <span class="pre">range</span></code> and it has four price ranges: <code class="docutils literal notranslate"><span class="pre">0(low</span> <span class="pre">cost),</span> <span class="pre">1(medium</span> <span class="pre">cost),</span> <span class="pre">2(high</span> <span class="pre">cost)</span> <span class="pre">and</span> <span class="pre">3(very</span> <span class="pre">high</span> <span class="pre">cost).</span></code></p>
 <ul class="simple">
@@ -1849,7 +1866,7 @@ <h2><span class="section-number">37.51.3. </span>2. Finding the Data<a class="he
 <p><a name='3'></a></p>
 </section>
 <section id="exploring-insights-in-data-or-eda">
-<h2><span class="section-number">37.51.4. </span>3. Exploring Insights in Data or EDA<a class="headerlink" href="#exploring-insights-in-data-or-eda" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.50.4. </span>3. Exploring Insights in Data or EDA<a class="headerlink" href="#exploring-insights-in-data-or-eda" title="Permalink to this headline">#</a></h2>
 <p>In this part, we are going to learn more about the dataset. Let’s start with the summary statistics, but before that, I will copy the training data in order to get it easily when we mess up down the road.</p>
 <div class="cell docutils container">
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@@ -1859,7 +1876,7 @@ <h2><span class="section-number">37.51.4. </span>3. Exploring Insights in Data o
 </div>
 </div>
 <section id="checking-summary-statistics">
-<h3><span class="section-number">37.51.4.1. </span>Checking summary statistics<a class="headerlink" href="#checking-summary-statistics" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.50.4.1. </span>Checking summary statistics<a class="headerlink" href="#checking-summary-statistics" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mobile_train</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
@@ -1869,7 +1886,7 @@ <h3><span class="section-number">37.51.4.1. </span>Checking summary statistics<a
 </div>
 </section>
 <section id="checking-missing-values">
-<h3><span class="section-number">37.51.4.2. </span>Checking missing values<a class="headerlink" href="#checking-missing-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.50.4.2. </span>Checking missing values<a class="headerlink" href="#checking-missing-values" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mobile_train</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
@@ -1880,7 +1897,7 @@ <h3><span class="section-number">37.51.4.2. </span>Checking missing values<a cla
 <p>We are lucky to not have missing values. If we had some, we would have to fill them with some strategies such as mean, remove them compleletly or leave them as they are. None of those 3 options is always the right choice for imputing missing values in all kinds of problems. it depends on the problem and the size of your dataset. Take an example, by removing a feature, you’re loosing data. And by filling the values, you’re adding noise or so. There is a trade off when it comes to imputing the missing values.</p>
 </section>
 <section id="checking-correlation-between-features">
-<h3><span class="section-number">37.51.4.3. </span>Checking Correlation Between Features<a class="headerlink" href="#checking-correlation-between-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.50.4.3. </span>Checking Correlation Between Features<a class="headerlink" href="#checking-correlation-between-features" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">correlation</span> <span class="o">=</span> <span class="n">mobile_train</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span>
@@ -1909,7 +1926,7 @@ <h3><span class="section-number">37.51.4.3. </span>Checking Correlation Between
 <p>Let’s take that insights from words to visualization.</p>
 </section>
 <section id="more-data-exploration">
-<h3><span class="section-number">37.51.4.4. </span>More Data Exploration<a class="headerlink" href="#more-data-exploration" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.50.4.4. </span>More Data Exploration<a class="headerlink" href="#more-data-exploration" title="Permalink to this headline">#</a></h3>
 <p>Again, let’s see what we have in the price ranges.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2027,7 +2044,7 @@ <h3><span class="section-number">37.51.4.4. </span>More Data Exploration<a class
 </div>
 </section>
 <section id="exploring-categorical-features">
-<h3><span class="section-number">37.51.4.5. </span>Exploring Categorical Features<a class="headerlink" href="#exploring-categorical-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.50.4.5. </span>Exploring Categorical Features<a class="headerlink" href="#exploring-categorical-features" title="Permalink to this headline">#</a></h3>
 <p>Even if all features have numerical values, but there some of them which are categorical. Let’s inspect them!! It might be that they are already encoded!</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2117,7 +2134,7 @@ <h3><span class="section-number">37.51.4.5. </span>Exploring Categorical Feature
 </section>
 </section>
 <section id="data-preprocessing">
-<h2><span class="section-number">37.51.5. </span>4. Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.50.5. </span>4. Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
 <p>In this part, we will prepare the data to be in proper format for ML models. We will do things like feature scaling. We would also had to encode categorical features but they are already encoded. Before we proceed with feature scaling, let’s take training labels from training data.</p>
 <div class="cell docutils container">
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@@ -2128,7 +2145,7 @@ <h2><span class="section-number">37.51.5. </span>4. Data Preprocessing<a class="
 </div>
 </div>
 <section id="feature-scaling">
-<h3><span class="section-number">37.51.5.1. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.50.5.1. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h3>
 <p>This is the only data processing type that we have to take care of here. Let’s make a function that can take handle that.</p>
 <p>We will normalize the data with sklearn <code class="docutils literal notranslate"><span class="pre">MinMaxScaler</span></code> where the numerical values will be scaled to values between 0 and 1.</p>
 <div class="cell docutils container">
@@ -2163,7 +2180,7 @@ <h3><span class="section-number">37.51.5.1. </span>Feature Scaling<a class="head
 </section>
 </section>
 <section id="creating-and-training-a-logistic-regression-model">
-<h2><span class="section-number">37.51.6. </span>5. Creating and Training a Logistic Regression Model<a class="headerlink" href="#creating-and-training-a-logistic-regression-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.50.6. </span>5. Creating and Training a Logistic Regression Model<a class="headerlink" href="#creating-and-training-a-logistic-regression-model" title="Permalink to this headline">#</a></h2>
 <p>We will start by importing <code class="docutils literal notranslate"><span class="pre">LogisticRegression</span></code> models available in Sklearn linear models and then proceed with model training.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2186,7 +2203,7 @@ <h2><span class="section-number">37.51.6. </span>5. Creating and Training a Logi
 <p>Using <code class="docutils literal notranslate"><span class="pre">log_model.score()</span></code>, we can get an accuracy of the model confidence on the training data and labels. 94.7% is not that bad.</p>
 <p>Let’s train another linear classifier called SGD (Stockastic Gradient Descent) classifier on the same dataset.</p>
 <section id="trying-other-models">
-<h3><span class="section-number">37.51.6.1. </span>Trying other models<a class="headerlink" href="#trying-other-models" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.50.6.1. </span>Trying other models<a class="headerlink" href="#trying-other-models" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">SGDClassifier</span>
@@ -2227,7 +2244,7 @@ <h3><span class="section-number">37.51.6.1. </span>Trying other models<a class="
 </section>
 </section>
 <section id="model-evaluation">
-<h2><span class="section-number">37.51.7. </span>6.  Model Evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.50.7. </span>6.  Model Evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
 <p>Evaluating classification models is not as simple as for regression models. When you have skewed dataset or imbalances, you can get a high accuracy and you can think the model did well when in fact it didn’t.</p>
 <p>In this part, we are going to learn how to evaluate classification models. But first, let’s evaluate the model with cross validation.</p>
 <p>Here is an idea behind cross validation: We want to divide the training set into different training and validation subsets so that we can iteratively train and validate the models on those subsets.</p>
@@ -2334,7 +2351,7 @@ <h2><span class="section-number">37.51.7. </span>6.  Model Evaluation<a class="h
 </div>
 </div>
 <section id="classification-performance-metrics">
-<h3><span class="section-number">37.51.7.1. </span>Classification Performance Metrics<a class="headerlink" href="#classification-performance-metrics" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">37.50.7.1. </span>Classification Performance Metrics<a class="headerlink" href="#classification-performance-metrics" title="Permalink to this headline">#</a></h3>
 <p>Here are commom classification metrics:</p>
 <ul class="simple">
 <li><p>Accuracy</p></li>
@@ -2407,7 +2424,7 @@ <h3><span class="section-number">37.51.7.1. </span>Classification Performance Me
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.51.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.50.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Jean de Dieu Nyandwi for creating the open source course <a class="reference external" href="https://github.com/Nyandwi/machine_learning_complete">machine_learning_complete</a> . It inspires the majority of the content in this chapter.Original Notebook source from <a class="reference external" href="https://github.com/Nyandwi/machine_learning_complete/blob/main/6_classical_machine_learning_with_scikit-learn/2_linear_models_for_classification.ipynb">Linear Models for Classification</a></p>
 </section>
 </section>
@@ -2445,17 +2462,17 @@ <h2><span class="section-number">37.51.8. </span>Acknowledgments<a class="header
                 
     <!-- Previous / next buttons -->
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diff --git a/assignments/ml-fundamentals/ml-logistic-regression-2.html b/assignments/ml-fundamentals/ml-logistic-regression-2.html
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+    <title>37.51. ML logistic regression - assignment 2 &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="37.53. ML neural network - Assignment 1" href="ml-neural-network-1.html" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
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   <a class="reference internal" href="../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
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-    <a class="reference internal" href="ml-linear-regression-2.html">
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-     37.49. Gradient descent
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+    <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
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+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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   <a class="reference internal" href="../../data-science/data-visualization/data-visualization.html">
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   <a class="reference internal" href="../README.html">
    37. Self-paced assignments
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-    <a class="reference internal" href="ml-linear-regression-1.html">
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-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
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+     37.45. Linear regression - California Housing
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     <a class="reference internal" href="linear-regression/loss-function.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
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    </li>
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     <a class="reference internal" href="ml-logistic-regression-2.html">
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-     37.63. Explore classification methods
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-     37.64. Linear Regression Implementation from Scratch
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     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
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     <a class="reference internal" href="../ml-advanced/model-selection/lasso-and-ridge-regression.html">
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     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
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   <a class="reference internal" href="../../slides/introduction.html">
    38. Slides
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-  <label for="toctree-checkbox-12">
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   <a class="reference internal nav-link" href="#the-road-ahead">
-   37.53.1. The road ahead
+   37.52.1. The road ahead
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#nonlinearity-layer">
-   37.53.2. Nonlinearity layer
+   37.52.2. Nonlinearity layer
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dense-layer">
-   37.53.3. Dense layer
+   37.52.3. Dense layer
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#testing-the-dense-layer">
-   37.53.4. Testing the dense layer
+   37.52.4. Testing the dense layer
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#the-loss-function">
-   37.53.5. The loss function
+   37.52.5. The loss function
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#full-network">
-   37.53.6. Full network
+   37.52.6. Full network
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#backprop">
-   37.53.7. Backprop
+   37.52.7. Backprop
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-loop">
-   37.53.8. Training loop
+   37.52.8. Training loop
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.53.9. Acknowledgments
+   37.52.9. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1575,47 +1592,47 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#the-road-ahead">
-   37.53.1. The road ahead
+   37.52.1. The road ahead
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#nonlinearity-layer">
-   37.53.2. Nonlinearity layer
+   37.52.2. Nonlinearity layer
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dense-layer">
-   37.53.3. Dense layer
+   37.52.3. Dense layer
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#testing-the-dense-layer">
-   37.53.4. Testing the dense layer
+   37.52.4. Testing the dense layer
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#the-loss-function">
-   37.53.5. The loss function
+   37.52.5. The loss function
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#full-network">
-   37.53.6. Full network
+   37.52.6. Full network
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#backprop">
-   37.53.7. Backprop
+   37.52.7. Backprop
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-loop">
-   37.53.8. Training loop
+   37.52.8. Training loop
   </a>
  </li>
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.53.9. Acknowledgments
+   37.52.9. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1629,7 +1646,7 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="ml-neural-network-assignment-1">
-<h1><span class="section-number">37.53. </span>ML neural network - Assignment 1<a class="headerlink" href="#ml-neural-network-assignment-1" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.52. </span>ML neural network - Assignment 1<a class="headerlink" href="#ml-neural-network-assignment-1" title="Permalink to this headline">#</a></h1>
 <p>In this notebook, we’re going to build a neural network using naught but pure numpy and your willpower. It’s going to be fun, I promise!</p>
 <div class="cell docutils container">
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@@ -1687,7 +1704,7 @@ <h1><span class="section-number">37.53. </span>ML neural network - Assignment 1<
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 </div>
 <section id="the-road-ahead">
-<h2><span class="section-number">37.53.1. </span>The road ahead<a class="headerlink" href="#the-road-ahead" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.52.1. </span>The road ahead<a class="headerlink" href="#the-road-ahead" title="Permalink to this headline">#</a></h2>
 <p>We’re going to build a neural network that classifies MNIST digits. To do so, we’ll need a few building blocks:</p>
 <ul class="simple">
 <li><p>Dense layer - a fully-connected layer, <span class="math notranslate nohighlight">\(f(X)=X \cdot W + \vec{b}\)</span></p></li>
@@ -1698,7 +1715,7 @@ <h2><span class="section-number">37.53.1. </span>The road ahead<a class="headerl
 <p>Let’s approach them one at a time.</p>
 </section>
 <section id="nonlinearity-layer">
-<h2><span class="section-number">37.53.2. </span>Nonlinearity layer<a class="headerlink" href="#nonlinearity-layer" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.52.2. </span>Nonlinearity layer<a class="headerlink" href="#nonlinearity-layer" title="Permalink to this headline">#</a></h2>
 <p>This is the simplest layer you can get: it simply applies a nonlinearity to each element of your network.</p>
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@@ -1722,7 +1739,7 @@ <h2><span class="section-number">37.53.2. </span>Nonlinearity layer<a class="hea
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 </section>
 <section id="dense-layer">
-<h2><span class="section-number">37.53.3. </span>Dense layer<a class="headerlink" href="#dense-layer" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.52.3. </span>Dense layer<a class="headerlink" href="#dense-layer" title="Permalink to this headline">#</a></h2>
 <p>Now let’s build something more complicated. Unlike nonlinearity, a dense layer actually has something to learn.</p>
 <p>A dense layer applies affine transformation. In a vectorized form, it can be described as:
 $<span class="math notranslate nohighlight">\(f(X)= X \cdot W + \vec b \)</span>$</p>
@@ -1781,7 +1798,7 @@ <h2><span class="section-number">37.53.3. </span>Dense layer<a class="headerlink
 </div>
 </section>
 <section id="testing-the-dense-layer">
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+<h2><span class="section-number">37.52.4. </span>Testing the dense layer<a class="headerlink" href="#testing-the-dense-layer" title="Permalink to this headline">#</a></h2>
 <p>Here we have a few tests to make sure your dense layer works properly. You can just run them, get 3 “well done”s and forget they ever existed.</p>
 <p>… or not get 3 “well done”s and go fix stuff. If that is the case, here are some tips for you:</p>
 <ul class="simple">
@@ -1819,7 +1836,7 @@ <h2><span class="section-number">37.53.4. </span>Testing the dense layer<a class
 </div>
 </section>
 <section id="the-loss-function">
-<h2><span class="section-number">37.53.5. </span>The loss function<a class="headerlink" href="#the-loss-function" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.52.5. </span>The loss function<a class="headerlink" href="#the-loss-function" title="Permalink to this headline">#</a></h2>
 <p>Since we want to predict probabilities, it would be logical for us to define softmax nonlinearity on top of our network and compute loss given predicted probabilities. However, there is a better way to do so.</p>
 <p>If you write down the expression for crossentropy as a function of softmax logits (a), you’ll see:</p>
 <div class="math notranslate nohighlight">
@@ -1859,7 +1876,7 @@ <h2><span class="section-number">37.53.5. </span>The loss function<a class="head
 </div>
 </section>
 <section id="full-network">
-<h2><span class="section-number">37.53.6. </span>Full network<a class="headerlink" href="#full-network" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.52.6. </span>Full network<a class="headerlink" href="#full-network" title="Permalink to this headline">#</a></h2>
 <p>Now let’s combine what we’ve just built into a working neural network. As we announced, we’re gonna use this monster to classify handwritten digits, so let’s get them loaded.</p>
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 </div>
 </section>
 <section id="backprop">
-<h2><span class="section-number">37.53.7. </span>Backprop<a class="headerlink" href="#backprop" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.52.7. </span>Backprop<a class="headerlink" href="#backprop" title="Permalink to this headline">#</a></h2>
 <p>You can now define the backpropagation step for the neural network. Please read the docstring.</p>
 <div class="cell docutils container">
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@@ -2009,7 +2026,7 @@ <h2><span class="section-number">37.53.7. </span>Backprop<a class="headerlink" h
 <p>If your implementation of forward and backward are correct, your accuracy should grow from 90~93% to &gt;97% with the default network.</p>
 </section>
 <section id="training-loop">
-<h2><span class="section-number">37.53.8. </span>Training loop<a class="headerlink" href="#training-loop" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.52.8. </span>Training loop<a class="headerlink" href="#training-loop" title="Permalink to this headline">#</a></h2>
 <p>As usual, we split data into minibatches, feed each such minibatch into the network and update weights.</p>
 <div class="cell docutils container">
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@@ -2077,7 +2094,7 @@ <h2><span class="section-number">37.53.8. </span>Training loop<a class="headerli
 <p>Good hunting!</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.53.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.52.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/yandexdataschool">yandexdataschool</a> for creating the open-source course <a class="reference external" href="https://github.com/yandexdataschool/Practical_DL"> Practical_DL</a>. It inspires the majority of the content in this assignment.</p>
 </section>
 </section>
@@ -2119,13 +2136,13 @@ <h2><span class="section-number">37.53.9. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.52. </span>ML logistic regression - assignment 2</p>
+            <p class="prev-next-title"><span class="section-number">37.51. </span>ML logistic regression - assignment 2</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="regression-tools.html" title="next page">
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.54. </span>Regression tools</p>
+        <p class="prev-next-title"><span class="section-number">37.53. </span>Regression tools</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/ml-fundamentals/ml-overview-iris.html b/assignments/ml-fundamentals/ml-overview-iris.html
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      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
   </a>
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   <a class="reference internal" href="../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
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   <a class="reference internal" href="../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
-  <label for="toctree-checkbox-5">
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    11. Regression models for Machine Learning
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    13. Getting started with classification
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-  <label for="toctree-checkbox-8">
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    16. Introduction to Gradient Boosting
   </a>
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-  <label for="toctree-checkbox-10">
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+  <label for="toctree-checkbox-11">
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@@ -633,8 +655,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../README.html">
    37. Self-paced assignments
   </a>
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-  <label for="toctree-checkbox-11">
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     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
+     37.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/loss-function.html">
-     37.48. Loss Function
+     37.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
+     37.50. ML logistic regression - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-2.html">
-     37.52. ML logistic regression - assignment 2
+     37.51. ML logistic regression - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-neural-network-1.html">
-     37.53. ML neural network - Assignment 1
+     37.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="regression-tools.html">
-     37.54. Regression tools
+     37.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="managing-data.html">
-     37.55. Managing data
+     37.54. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="exploring-visualizations.html">
-     37.56. Exploring visualizations
+     37.55. Exploring visualizations
     </a>
    </li>
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     <a class="reference internal" href="create-a-regression-model.html">
-     37.58. Create a regression model
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-and-polynomial-regression.html">
-     37.59. Linear and polynomial regression
+     37.58. Linear and polynomial regression
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    <li class="toctree-l2">
     <a class="reference internal" href="retrying-some-regression.html">
-     37.60. Retrying some regression
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    <li class="toctree-l2">
     <a class="reference internal" href="pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
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     <a class="reference internal" href="delicious-asian-and-indian-cuisines.html">
-     37.62. Delicious asian and indian cuisines
+     37.61. Delicious asian and indian cuisines
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     <a class="reference internal" href="explore-classification-methods.html">
-     37.63. Explore classification methods
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-    <a class="reference internal" href="linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
+     37.62. Explore classification methods
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
+     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.67. Support Vector Machines (SVM) - Classification
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     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.68. Decision Trees - Intro and Regression
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     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_classification.html">
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     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
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      5.3. NumPy
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    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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-    <a class="reference internal" href="ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
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     <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
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     <a class="reference internal" href="linear-regression/loss-function.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
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+   </li>
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+    <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-1.html">
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     <a class="reference internal" href="ml-logistic-regression-2.html">
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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-     37.108. Neural Networks for Classification with TensorFlow
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-     37.109. NN Classify 15 Fruits Assignment
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    </li>
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+     37.113. Art by gan
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-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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-   37.91.3. Acknowledgments
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               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="parameter-play">
-<h1><span class="section-number">37.91. </span>Parameter play<a class="headerlink" href="#parameter-play" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.89. </span>Parameter play<a class="headerlink" href="#parameter-play" title="Permalink to this headline">#</a></h1>
 <section id="instructions">
-<h2><span class="section-number">37.91.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.89.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>There are a lot of parameters that are set by default when working with these classifiers. Intellisense in VS Code can help you dig into them. Adopt one of the ML Classification Techniques in this section and retrain models tweaking various parameter values. Build a notebook explaining why some changes help the model quality while others degrade it. Be detailed in your answer.</p>
 </section>
 <section id="rubric">
-<h2><span class="section-number">37.91.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
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 <section id="acknowledgments">
-<h2><span class="section-number">37.91.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.89.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1651,13 +1668,13 @@ <h2><span class="section-number">37.91.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
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+            <p class="prev-next-title"><span class="section-number">37.88. </span>Build Classification Model</p>
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-    <link rel="next" title="37.62. Delicious asian and indian cuisines" href="delicious-asian-and-indian-cuisines.html" />
-    <link rel="prev" title="37.60. Retrying some regression" href="retrying-some-regression.html" />
+    <link rel="next" title="37.61. Delicious asian and indian cuisines" href="delicious-asian-and-indian-cuisines.html" />
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      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
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    11. Regression models for Machine Learning
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+<h1><span class="section-number">37.60. </span>Pumpkin varieties and color<a class="headerlink" href="#pumpkin-varieties-and-color" title="Permalink to this headline">#</a></h1>
 <p>Load up required libraries and dataset. Convert the data to a dataframe containing a subset of the data:</p>
 <p>Let’s look at the relationship between color and variety</p>
 <div class="cell docutils container">
@@ -1563,7 +1580,7 @@ <h1><span class="section-number">37.61. </span>Pumpkin varieties and color<a cla
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-<h2><span class="section-number">37.61.1. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.60.1. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
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         <div class="prev-next-info">
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-            <p class="prev-next-title"><span class="section-number">37.60. </span>Retrying some regression</p>
+            <p class="prev-next-title"><span class="section-number">37.59. </span>Retrying some regression</p>
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-    <link rel="prev" title="37.53. ML neural network - Assignment 1" href="ml-neural-network-1.html" />
+    <link rel="next" title="37.54. Managing data" href="managing-data.html" />
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      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../data-science/data-visualization/data-visualization.html">
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   <a class="reference internal" href="../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
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   <a class="reference internal" href="../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
   </a>
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@@ -355,8 +377,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
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-  <label for="toctree-checkbox-7">
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@@ -431,8 +453,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
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-  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
-  <label for="toctree-checkbox-8">
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   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
    15. Getting started with ensemble learning
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   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
    16. Introduction to Gradient Boosting
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@@ -633,8 +655,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../README.html">
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@@ -825,308 +847,303 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-    <a class="reference internal" href="ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
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-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
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    </li>
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     <a class="reference internal" href="linear-regression/loss-function.html">
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-1.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-logistic-regression-2.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-neural-network-1.html">
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     <a class="reference internal" href="try-a-different-model.html">
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     <a class="reference internal" href="create-a-regression-model.html">
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     <a class="reference internal" href="retrying-some-regression.html">
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     <a class="reference internal" href="pumpkin-varieties-and-color.html">
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     <a class="reference internal" href="delicious-asian-and-indian-cuisines.html">
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-     37.64. Linear Regression Implementation from Scratch
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     <a class="reference internal" href="../ml-advanced/kernel-method/kernel-method-assignment-1.html">
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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     <a class="reference internal" href="../ml-advanced/model-selection/model-selection-assignment-1.html">
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     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
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+     37.116. Basic classification: Classify images of clothing
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@@ -1135,8 +1152,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../slides/introduction.html">
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@@ -1510,7 +1527,7 @@ <h1>Regression tools</h1>
               <div>
                 
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
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      5.4. Pandas
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+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
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-    <link rel="prev" title="37.59. Linear and polynomial regression" href="linear-and-polynomial-regression.html" />
+    <link rel="next" title="37.60. Pumpkin varieties and color" href="pumpkin-varieties-and-color.html" />
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
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      5.4. Pandas
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
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+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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-     37.108. Neural Networks for Classification with TensorFlow
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    </li>
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     <a class="reference internal" href="../deep-learning/gan/art-by-gan.html">
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-   37.60.1. Instructions
+   37.59.1. Instructions
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-   37.60.3. Acknowledgments
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 <section id="instructions">
-<h2><span class="section-number">37.60.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.59.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>In the section, you used a subset of the pumpkin data. Now, go back to the original data and try to use all of it, cleaned and standardized, to build a Logistic Regression model.</p>
 </section>
 <section id="rubric">
-<h2><span class="section-number">37.60.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
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 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Criteria</p></th>
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 <section id="acknowledgments">
-<h2><span class="section-number">37.60.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.59.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1651,13 +1668,13 @@ <h2><span class="section-number">37.60.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.59. </span>Linear and polynomial regression</p>
+            <p class="prev-next-title"><span class="section-number">37.58. </span>Linear and polynomial regression</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="pumpkin-varieties-and-color.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.61. </span>Pumpkin varieties and color</p>
+        <p class="prev-next-title"><span class="section-number">37.60. </span>Pumpkin varieties and color</p>
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     <i class="fas fa-angle-right"></i>
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-    <link rel="prev" title="37.87. Case Study: Debugging in Regression" href="../machine-learning-productionization/debugging-in-regression.html" />
+    <link rel="next" title="37.87. Build classification models" href="build-classification-models.html" />
+    <link rel="prev" title="37.85. Case Study: Debugging in Regression" href="../machine-learning-productionization/debugging-in-regression.html" />
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      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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-    <a class="reference internal" href="ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
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     <a class="reference internal" href="linear-regression/loss-function.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
     </a>
    </li>
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     <a class="reference internal" href="ml-logistic-regression-1.html">
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    </li>
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    </li>
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-     37.61. Pumpkin varieties and color
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     </a>
    </li>
    <li class="toctree-l2">
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-     37.62. Delicious asian and indian cuisines
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     </a>
    </li>
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-     37.63. Explore classification methods
-    </a>
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-    <a class="reference internal" href="linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.67. Support Vector Machines (SVM) - Classification
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    <li class="toctree-l2">
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-     37.69. Decision Trees - Classification
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-     37.71. Learning Curve To Identify Overfit &amp; Underfit
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-     37.72. Dropout and Batch Normalization
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    </li>
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+     37.106. Neural Networks for Classification with TensorFlow
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    <li class="toctree-l2">
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-     37.109. NN Classify 15 Fruits Assignment
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    </li>
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     <a class="reference internal" href="../deep-learning/gan/art-by-gan.html">
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+     37.113. Art by gan
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    </li>
    <li class="toctree-l2">
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-   37.88.1. Instructions
+   37.86.1. Instructions
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-   37.88.2. Rubric
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-   37.88.1. Instructions
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+   37.86.2. Rubric
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.88.3. Acknowledgments
+   37.86.3. Acknowledgments
   </a>
  </li>
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               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="study-the-solvers">
-<h1><span class="section-number">37.88. </span>Study the solvers<a class="headerlink" href="#study-the-solvers" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.86. </span>Study the solvers<a class="headerlink" href="#study-the-solvers" title="Permalink to this headline">#</a></h1>
 <section id="instructions">
-<h2><span class="section-number">37.88.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.86.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>In this section you learned about the various solvers that pair algorithms with a machine learning process to create an accurate model. Walk through the solvers listed in the section and pick two. In your own words, compare and contrast these two solvers. What kind of problem do they address? How do they work with various data structures? Why would you pick one over another?</p>
 </section>
 <section id="rubric">
-<h2><span class="section-number">37.88.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
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 <table class="colwidths-auto table">
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 <section id="acknowledgments">
-<h2><span class="section-number">37.88.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.86.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1569,13 +1586,13 @@ <h2><span class="section-number">37.88.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.87. </span>Case Study: Debugging in Regression</p>
+            <p class="prev-next-title"><span class="section-number">37.85. </span>Case Study: Debugging in Regression</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="build-classification-models.html" title="next page">
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">37.89. </span>Build classification models</p>
+        <p class="prev-next-title"><span class="section-number">37.87. </span>Build classification models</p>
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-    <link rel="prev" title="37.56. Exploring visualizations" href="exploring-visualizations.html" />
+    <link rel="next" title="37.57. Create a regression model" href="create-a-regression-model.html" />
+    <link rel="prev" title="37.55. Exploring visualizations" href="exploring-visualizations.html" />
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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    8. Data Science in the cloud
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-    <a class="reference internal" href="ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
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+     37.45. Linear regression - California Housing
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     <a class="reference internal" href="linear-regression/loss-function.html">
-     37.48. Loss Function
+     37.47. Loss Function
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="linear-regression/linear-regression-from-scratch.html">
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    </li>
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    </li>
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     <a class="reference internal" href="ml-neural-network-1.html">
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="regression-tools.html">
-     37.54. Regression tools
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    </li>
    <li class="toctree-l2">
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+     37.54. Managing data
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    </li>
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-     37.56. Exploring visualizations
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     </a>
    </li>
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     <a class="current reference internal" href="#">
-     37.57. Try a different model
+     37.56. Try a different model
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    </li>
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     <a class="reference internal" href="create-a-regression-model.html">
-     37.58. Create a regression model
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-and-polynomial-regression.html">
-     37.59. Linear and polynomial regression
+     37.58. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
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     <a class="reference internal" href="pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
+     37.60. Pumpkin varieties and color
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-     37.62. Delicious asian and indian cuisines
+     37.61. Delicious asian and indian cuisines
     </a>
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-     37.63. Explore classification methods
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-   <li class="toctree-l2">
-    <a class="reference internal" href="linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
+     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.67. Support Vector Machines (SVM) - Classification
+     37.65. Support Vector Machines (SVM) - Classification
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.68. Decision Trees - Intro and Regression
+     37.66. Decision Trees - Intro and Regression
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    </li>
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     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.69. Decision Trees - Classification
+     37.67. Decision Trees - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.70. Model selection assignment 1
+     37.68. Model selection assignment 1
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     <a class="reference internal" href="../ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.71. Learning Curve To Identify Overfit &amp; Underfit
+     37.69. Learning Curve To Identify Overfit &amp; Underfit
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.72. Dropout and Batch Normalization
+     37.70. Dropout and Batch Normalization
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.73. Lasso and Ridge Regression
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection/regularized-linear-models.html">
-     37.74. Regularized Linear Models
+     37.72. Regularized Linear Models
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     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
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+     37.74. Random forests for classification
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    </li>
    <li class="toctree-l2">
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-     37.78. Decision trees
+     37.76. Decision trees
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-     37.79. Hyperparameter tuning gradient boosting
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-     37.80. Gradient boosting
+     37.78. Gradient boosting
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     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
-     37.82. Random Forest Classifier with Feature Importance
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../machine-learning-productionization/data-engineering.html">
-     37.84. Data engineering
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-     37.85. Counterintuitive Challenges in ML Debugging
+     37.83. Counterintuitive Challenges in ML Debugging
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../machine-learning-productionization/debugging-in-classification.html">
-     37.86. Case Study: Debugging in Classification
+     37.84. Case Study: Debugging in Classification
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    <li class="toctree-l2">
     <a class="reference internal" href="../machine-learning-productionization/debugging-in-regression.html">
-     37.87. Case Study: Debugging in Regression
+     37.85. Case Study: Debugging in Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="study-the-solvers.html">
-     37.88. Study the solvers
+     37.86. Study the solvers
     </a>
    </li>
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-     37.89. Build classification models
+     37.87. Build classification models
     </a>
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    <li class="toctree-l2">
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-     37.90. Build Classification Model
+     37.88. Build Classification Model
     </a>
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    <li class="toctree-l2">
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-     37.91. Parameter play
+     37.89. Parameter play
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+     37.90. How to choose cnn architecture mnist
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     <a class="reference internal" href="../deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
-     37.94. Sign Language Digits Classification with CNN
+     37.92. Sign Language Digits Classification with CNN
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    </li>
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     <a class="reference internal" href="../deep-learning/cnn/object-recognition-in-images-using-cnn.html">
-     37.96. Object Recognition in Images using CNN
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     <a class="reference internal" href="../deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.97. Intro to TensorFlow for Deep Learning
+     37.95. Intro to TensorFlow for Deep Learning
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     <a class="reference internal" href="../deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.101. Google Stock Price Prediction RNN
+     37.99. Google Stock Price Prediction RNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/autoencoder/autoencoder.html">
-     37.103. Intro to Autoencoders
+     37.101. Intro to Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
+     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
+     37.103. Fun with Variational Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
+     37.104. Time Series Forecasting Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/nn-for-classification-assignment.html">
-     37.108. Neural Networks for Classification with TensorFlow
+     37.106. Neural Networks for Classification with TensorFlow
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/nn-classify-15-fruits-assignment.html">
-     37.109. NN Classify 15 Fruits Assignment
+     37.107. NN Classify 15 Fruits Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.114. DQN On Foreign Exchange Market
+     37.112. DQN On Foreign Exchange Market
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan/art-by-gan.html">
-     37.115. Art by gan
+     37.113. Art by gan
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
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   <a class="reference internal" href="../../slides/introduction.html">
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   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#instructions">
-   37.57.1. Instructions
+   37.56.1. Instructions
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#rubric">
-   37.57.2. Rubric
+   37.56.2. Rubric
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  </li>
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   <a class="reference internal nav-link" href="#acknowledgments">
-   37.57.3. Acknowledgments
+   37.56.3. Acknowledgments
   </a>
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 </ul>
@@ -1478,17 +1495,17 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
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-   37.57.1. Instructions
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   <a class="reference internal nav-link" href="#rubric">
-   37.57.2. Rubric
+   37.56.2. Rubric
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   37.57.3. Acknowledgments
+   37.56.3. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1502,13 +1519,13 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="try-a-different-model">
-<h1><span class="section-number">37.57. </span>Try a different model<a class="headerlink" href="#try-a-different-model" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37.56. </span>Try a different model<a class="headerlink" href="#try-a-different-model" title="Permalink to this headline">#</a></h1>
 <section id="instructions">
-<h2><span class="section-number">37.57.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.56.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>Now that you have built one web app using a trained Regression model, use one of the models from an earlier Regression section to redo this web app. You can keep the style or design it differently to reflect the pumpkin data. Be careful to change the inputs to reflect your model’s training method.</p>
 </section>
 <section id="rubric">
-<h2><span class="section-number">37.57.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.56.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
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 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">37.57.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.56.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1569,13 +1586,13 @@ <h2><span class="section-number">37.57.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">37.56. </span>Exploring visualizations</p>
+            <p class="prev-next-title"><span class="section-number">37.55. </span>Exploring visualizations</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="create-a-regression-model.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">37.58. </span>Create a regression model</p>
+        <p class="prev-next-title"><span class="section-number">37.57. </span>Create a regression model</p>
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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   <a class="reference internal" href="../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
   </a>
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    7. Data Science lifecycle
   </a>
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    8. Data Science in the cloud
   </a>
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    11. Regression models for Machine Learning
   </a>
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
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+    <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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     <a class="reference internal" href="../ml-fundamentals/pumpkin-varieties-and-color.html">
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     <a class="reference internal" href="../ml-fundamentals/delicious-asian-and-indian-cuisines.html">
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     <a class="reference internal" href="../ml-fundamentals/explore-classification-methods.html">
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-     37.64. Linear Regression Implementation from Scratch
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
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+    <label for="toctree-checkbox-3">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
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+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
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+    <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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     <a class="reference internal" href="../ml-fundamentals/delicious-asian-and-indian-cuisines.html">
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
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    <li class="toctree-l2">
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
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      5.4. Pandas
     </a>
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+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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    </li>
    <li class="toctree-l2">
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   <a class="reference internal" href="../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
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     <a class="reference internal" href="ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
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     <a class="reference internal" href="ml-fundamentals/pumpkin-varieties-and-color.html">
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     <a class="reference internal" href="ml-fundamentals/delicious-asian-and-indian-cuisines.html">
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     <a class="reference internal" href="ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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     <a class="reference internal" href="deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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      5.3. NumPy
     </a>
    </li>
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
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+    <label for="toctree-checkbox-3">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
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+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
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+    <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
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+    <label for="toctree-checkbox-3">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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   </a>
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   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
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   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
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-    <a class="reference internal" href="../ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
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     <a class="reference internal" href="../ml-fundamentals/linear-regression/loss-function.html">
-     37.48. Loss Function
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
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     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/ml-logistic-regression-2.html">
-     37.52. ML logistic regression - assignment 2
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     <a class="reference internal" href="../ml-fundamentals/ml-neural-network-1.html">
-     37.53. ML neural network - Assignment 1
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     <a class="reference internal" href="../ml-fundamentals/regression-tools.html">
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     <a class="reference internal" href="../ml-fundamentals/managing-data.html">
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     <a class="reference internal" href="../ml-fundamentals/try-a-different-model.html">
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     <a class="reference internal" href="../ml-fundamentals/create-a-regression-model.html">
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     <a class="reference internal" href="../ml-fundamentals/linear-and-polynomial-regression.html">
-     37.59. Linear and polynomial regression
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     <a class="reference internal" href="../ml-fundamentals/retrying-some-regression.html">
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     <a class="reference internal" href="../ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
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     <a class="reference internal" href="../ml-fundamentals/delicious-asian-and-indian-cuisines.html">
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     <a class="reference internal" href="../ml-fundamentals/explore-classification-methods.html">
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-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.67. Support Vector Machines (SVM) - Classification
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     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.68. Decision Trees - Intro and Regression
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     <a class="reference internal" href="../ml-advanced/kernel-method/decision_trees_for_classification.html">
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     <a class="reference internal" href="../ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.70. Model selection assignment 1
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     <a class="reference internal" href="../ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.71. Learning Curve To Identify Overfit &amp; Underfit
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     <a class="reference internal" href="../ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.72. Dropout and Batch Normalization
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     <a class="reference internal" href="../ml-advanced/model-selection/lasso-and-ridge-regression.html">
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     <a class="reference internal" href="../ml-advanced/model-selection/regularized-linear-models.html">
-     37.74. Regularized Linear Models
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     <a class="reference internal" href="../ml-advanced/ensemble-learning/random-forests-for-classification.html">
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     <a class="reference internal" href="../ml-fundamentals/parameter-play.html">
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-     37.96. Object Recognition in Images using CNN
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     <a class="reference internal" href="../deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.97. Intro to TensorFlow for Deep Learning
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     <a class="reference internal" href="../deep-learning/rnn/google-stock-price-prediction-rnn.html">
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     <a class="reference internal" href="../deep-learning/autoencoder/autoencoder.html">
-     37.103. Intro to Autoencoders
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     <a class="reference internal" href="../deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
+     37.103. Fun with Variational Autoencoders
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
+     37.104. Time Series Forecasting Assignment
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/nn-for-classification-assignment.html">
-     37.108. Neural Networks for Classification with TensorFlow
+     37.106. Neural Networks for Classification with TensorFlow
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/nn-classify-15-fruits-assignment.html">
-     37.109. NN Classify 15 Fruits Assignment
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     <a class="reference internal" href="../deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.114. DQN On Foreign Exchange Market
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     <a class="reference internal" href="../deep-learning/gan/art-by-gan.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
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   <a class="reference internal" href="../../slides/introduction.html">
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   </a>
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+++ b/data-science/data-science-in-the-cloud/data-science-in-the-cloud.html
@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../data-visualization/data-visualization.html">
    6. Data visualization
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+      <a class="reference internal" href="../working-with-data/pandas/introduction-and-data-structures.html">
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+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
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+     37.48. Gradient descent
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+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
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      5.4. Pandas
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+      <a class="reference internal" href="../working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+      <a class="reference internal" href="../working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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+     </li>
+    </ul>
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="working-with-data/pandas.html">
      5.4. Pandas
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+       5.4.1. Introduction and Data Structures
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+      <a class="reference internal" href="working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
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+      <a class="reference internal" href="working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="working-with-data/data-preparation.html">
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      5.4. Pandas
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+       5.4.1. Introduction and Data Structures
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+      <a class="reference internal" href="../working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
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+      <a class="reference internal" href="../working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
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    <li class="toctree-l2">
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+      <a class="reference internal" href="../working-with-data/pandas/advanced-pandas-techniques.html">
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+      <a class="reference internal" href="../working-with-data/pandas/advanced-pandas-techniques.html">
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+      <a class="reference internal" href="../working-with-data/pandas/advanced-pandas-techniques.html">
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+      <a class="reference internal" href="../working-with-data/pandas/advanced-pandas-techniques.html">
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+      <a class="reference internal" href="../working-with-data/pandas/advanced-pandas-techniques.html">
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
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     <a class="reference internal" href="../../assignments/deep-learning/time-series-forecasting-assignment.html">
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+     37.104. Time Series Forecasting Assignment
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     <a class="reference internal" href="../../assignments/deep-learning/nn-for-classification-assignment.html">
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+     37.115. Generative Adversarial Networks (GANs)
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/tmp/ipykernel_5984/1933666654.py:1: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
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-/tmp/ipykernel_6364/1933666654.py:1: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
+/tmp/ipykernel_5984/1933666654.py:1: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.
   sns.kdeplot(
 </pre></div>
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@@ -1964,7 +1981,7 @@ <h2><span class="section-number">6.1.4. </span>Density plots<a class="headerlink
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 <div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/tmp/ipykernel_6364/49960699.py:1: UserWarning: KDE cannot be estimated (0 variance or perfect covariance). Pass `warn_singular=False` to disable this warning.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/tmp/ipykernel_5984/49960699.py:1: UserWarning: KDE cannot be estimated (0 variance or perfect covariance). Pass `warn_singular=False` to disable this warning.
   sns.kdeplot(data=filteredBirds, x=&quot;MinLength&quot;, y=&quot;MaxLength&quot;, hue=&quot;ConservationStatus&quot;)
 </pre></div>
 </div>
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../working-with-data/pandas.html">
      5.4. Pandas
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+      <a class="reference internal" href="../working-with-data/pandas/introduction-and-data-structures.html">
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+      <a class="reference internal" href="../working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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    </li>
    <li class="toctree-l2">
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+     37.48. Gradient descent
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+      <a class="reference internal" href="../working-with-data/pandas/advanced-pandas-techniques.html">
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               <div>
                 
-  <section class="tex2jax_ignore mathjax_ignore" id="data-science-ethics">
+  <hr class="docutils" />
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+<section class="tex2jax_ignore mathjax_ignore" id="data-science-ethics">
 <h1><span class="section-number">4.2. </span>Data Science ethics<a class="headerlink" href="#data-science-ethics" title="Permalink to this headline">#</a></h1>
 <p>We are all data citizens living in a datafied world.</p>
 <p>Market trends tell us that by 2022, 1-in-3 large organizations will buy and sell their data through online <a class="reference external" href="https://www.gartner.com/smarterwithgartner/gartner-top-10-trends-in-data-and-analytics-for-2020/">Marketplaces and Exchanges</a>. As <strong>app developers</strong>, we’ll find it easier and cheaper to integrate data-driven insights and algorithm-driven automation into daily user experiences. But as AI becomes pervasive, we’ll also need to understand the potential harms caused by the <a class="reference external" href="https://www.youtube.com/watch?v=TQHs8SA1qpk">weaponization</a> of such algorithms at scale.</p>
 <p>Trends also indicate that we will create and consume over <a class="reference external" href="https://www.statista.com/statistics/871513/worldwide-data-created/">180 zettabytes</a> of data by 2025. As <strong>Data Scientists</strong>, this gives us unprecedented levels of access to personal data. This means we can build behavioral profiles of users and influence decision-making in ways that create an <a class="reference external" href="https://www.verywellmind.com/what-is-the-illusion-of-choice-5224973">illusion of free choice</a> while potentially nudging users towards outcomes we prefer. It also raises broader questions on data privacy and user protections.</p>
 <p>Data ethics are now <em>necessary guardrails</em> for data science and engineering, helping us minimize potential harms and unintended consequences from our data-driven actions. The <a class="reference external" href="https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020/">Gartner Hype Cycle for AI</a> identifies relevant trends in digital ethics, responsible AI, and AI governance as key drivers for larger megatrends around the <em>democratization</em> and <em>industrialization</em> of AI.</p>
 <figure class="align-default" id="gartner-hype-cycle-for-ai-2020">
-<img alt="../../_images/hype-cycle-for-ai.png" src="../../_images/hype-cycle-for-ai.png" />
+<img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/hype-cycle-for-ai.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/hype-cycle-for-ai.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 4.3 </span><span class="caption-text">Gartner Hype Cycle for AI - 2020<span id="id1">[<a class="reference internal" href="#id52" title="Kamala Kanta MISHRA (Kamal). Gartner hype cycle for ai 2021. October 2021. URL: https://medium.com/&#64;mishra.kamal/gartner-hype-cycle-for-ai-2021-3c11c1d80358 (visited on 2022-10-01).">MKamal21</a>]</span></span><a class="headerlink" href="#gartner-hype-cycle-for-ai-2020" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 4.3 </span><span class="caption-text">Gartner Hype Cycle for AI - 2020</span><a class="headerlink" href="#gartner-hype-cycle-for-ai-2020" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>In this section, we’ll explore the fascinating area of data ethics - from core concepts and challenges, to case studies and applied AI concepts like governance - that help establish an ethics culture in teams and organizations that work with data and AI.</p>
@@ -1723,7 +1736,7 @@ <h3><span class="section-number">4.2.2.1. </span>Ethics principles<a class="head
 <figure class="align-default" id="responsible-ai-at-microsoft">
 <img alt="https://docs.microsoft.com/en-gb/azure/cognitive-services/personalizer/media/ethics-and-responsible-use/ai-values-future-computed.png" src="https://docs.microsoft.com/en-gb/azure/cognitive-services/personalizer/media/ethics-and-responsible-use/ai-values-future-computed.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 4.4 </span><span class="caption-text">Responsible AI at Microsoft<span id="id2">[<a class="reference internal" href="#id53" title="jcodella. Ethics and responsible use - personalizer - azure cognitive services. URL: https://learn.microsoft.com/en-us/azure/cognitive-services/personalizer/ethics-responsible-use (visited on 2022-10-01).">jcodella</a>]</span></span><a class="headerlink" href="#responsible-ai-at-microsoft" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 4.4 </span><span class="caption-text">Responsible AI at Microsoft</span><a class="headerlink" href="#responsible-ai-at-microsoft" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Let’s briefly explore these principles. <em>Transparency</em> and <em>accountability</em> are foundational values that other principles built upon - so let’s begin there:</p>
@@ -1940,7 +1953,7 @@ <h3><span class="section-number">4.2.3.4. </span>Ethics culture<a class="headerl
 </section>
 <section id="your-turn">
 <h2><span class="section-number">4.2.4. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
-<p><a class="reference internal" href="../../assignments/data-science/write-a-data-ethics-case-study.html"><span class="doc std std-doc">Write a data ethics case study</span></a></p>
+<p><a class="reference external" href="https://static-1300131294.cos.qp-shanghai.myqcloud.com/assignments/data-science/write-a-data-ethics-case-study.md">Write a data ethics case study</a></p>
 </section>
 <section id="self-study">
 <h2><span class="section-number">4.2.5. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
@@ -1957,17 +1970,7 @@ <h2><span class="section-number">4.2.5. </span>Self study<a class="headerlink" h
 <h2><span class="section-number">4.2.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 <hr class="docutils" />
-<div class="docutils container" id="id3">
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-<dt class="label" id="id52"><span class="brackets"><a class="fn-backref" href="#id1">MKamal21</a></span></dt>
-<dd><p>Kamala Kanta MISHRA (Kamal). Gartner hype cycle for ai 2021. October 2021. URL: <a class="reference external" href="https://medium.com/&#64;mishra.kamal/gartner-hype-cycle-for-ai-2021-3c11c1d80358">https://medium.com/&#64;mishra.kamal/gartner-hype-cycle-for-ai-2021-3c11c1d80358</a> (visited on 2022-10-01).</p>
-</dd>
-<dt class="label" id="id53"><span class="brackets"><a class="fn-backref" href="#id2">jcodella</a></span></dt>
-<dd><p>jcodella. Ethics and responsible use - personalizer - azure cognitive services. URL: <a class="reference external" href="https://learn.microsoft.com/en-us/azure/cognitive-services/personalizer/ethics-responsible-use">https://learn.microsoft.com/en-us/azure/cognitive-services/personalizer/ethics-responsible-use</a> (visited on 2022-10-01).</p>
-</dd>
-</dl>
-</div>
-</section>
+<span class="target" id="id1"></span></section>
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+      <a class="reference internal" href="../working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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@@ -1653,7 +1653,20 @@ <h2> Contents </h2>
                 
               <div>
                 
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 <h1><span class="section-number">4.1. </span>Defining data science<a class="headerlink" href="#defining-data-science" title="Permalink to this headline">#</a></h1>
 <section id="what-is-data">
 <h2><span class="section-number">4.1.1. </span>What is data?<a class="headerlink" href="#what-is-data" title="Permalink to this headline">#</a></h2>
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 <h2><span class="section-number">4.1.3. </span>Other related fields<a class="headerlink" href="#other-related-fields" title="Permalink to this headline">#</a></h2>
 <p>Since data is pervasive, Data Science itself is also a broad field, touching many other disciplines.</p>
 <p><strong>Databases</strong></p>
-<p>A critical consideration is <strong>how to store</strong> the data, i.e. how to structure it in a way that allows faster processing. There are different types of databases that store structured and unstructured data, which <a class="reference internal" href="../working-with-data/working-with-data.html"><span class="doc std std-doc">we will consider in our book</span></a>.</p>
+<p>A critical consideration is <strong>how to store</strong> the data, i.e. how to structure it in a way that allows faster processing. There are different types of databases that store structured and unstructured data, which <a class="reference external" href="https://static-1300131294.cos.qp-shanghai.myqcloud.com/working-with-data/working-with-data.md">we will consider in our book</a>.</p>
 <p><strong>Big Data</strong></p>
 <p>Often we need to store and process very large quantities of data with a relatively simple structure. There are special approaches and tools to store that data in a distributed manner on a computer cluster and process it efficiently.</p>
 <p><strong>Machine Learning</strong></p>
-<p>One way to understand data is to <strong>build a model</strong> that will be able to predict the desired outcome. Developing models from data is called <strong>machine learning</strong>. You may want to have a look at our <a class="reference internal" href="../../ml-fundamentals/ml-overview.html"><span class="doc std std-doc">Machine Learning</span></a>-related chapters to learn more about it.</p>
+<p>One way to understand data is to <strong>build a model</strong> that will be able to predict the desired outcome. Developing models from data is called <strong>machine learning</strong>. You may want to have a look at our <a class="reference external" href="https://static-1300131294.cos.qp-shanghai.myqcloud.com/ml-fundamentals/ml-overview.md">Machine Learning</a>-related chapters to learn more about it.</p>
 <p><strong>Artificial Intelligence</strong></p>
 <p>An area of Machine Learning known as artificial intelligence (AI) also relies on data, and it involves building high-complexity models that mimic human thought processes. AI methods often allow us to turn unstructured data (e.g. natural language) into structured insights.</p>
 <p><strong>Visualization</strong></p>
-<p>Vast amounts of data are incomprehensible to a human being, but once we create useful visualizations using that data, we can make more sense of the data, and draw some conclusions. Thus, it is important to know many ways to visualize information - something that we will cover in the <a class="reference internal" href="../data-visualization/data-visualization.html"><span class="doc std std-doc">Data visualization</span></a> section. Related fields also include <strong>Infographics</strong> and <strong>Human-Computer Interaction</strong> in general.</p>
+<p>Vast amounts of data are incomprehensible to a human being, but once we create useful visualizations using that data, we can make more sense of the data, and draw some conclusions. Thus, it is important to know many ways to visualize information - something that we will cover in the <a class="reference external" href="https://static-1300131294.cos.qp-shanghai.myqcloud.com/data-visualization/data-visualization.md">Data visualization</a> section. Related fields also include <strong>Infographics</strong> and <strong>Human-Computer Interaction</strong> in general.</p>
 </section>
 <section id="types-of-data">
 <h2><span class="section-number">4.1.4. </span>Types of data<a class="headerlink" href="#types-of-data" title="Permalink to this headline">#</a></h2>
@@ -1767,7 +1780,7 @@ <h2><span class="section-number">4.1.6. </span>What you can do with data<a class
 </ul>
 <p><strong>Data processing.</strong> This is the most exciting part of the data journey, which involves converting the data from its original form into a form that can be used for visualization/model training. When dealing with unstructured data such as text or images, we may need to use some AI techniques to extract <strong>features</strong> from the data, thus converting it to a structured form.</p>
 <p><strong>Visualization / human insights.</strong> Oftentimes, in order to understand the data, we need to visualize it. Having many different visualization techniques in our toolbox, we can find the right view to make an insight. Often, a data scientist needs to “play with data”, visualizing it many times and looking for some relationships. Also, we may use statistical techniques to test hypotheses or prove a correlation between different pieces of data.</p>
-<p><strong>Training a predictive model.</strong> Because the ultimate goal of Data Science is to be able to make decisions based on data, we may want to use the techniques of <a class="reference internal" href="../../ml-fundamentals/ml-overview.html"><span class="doc std std-doc">Machine Learning</span></a> to build a predictive model. We can then use this to make predictions using new datasets with similar structures.</p>
+<p><strong>Training a predictive model.</strong> Because the ultimate goal of Data Science is to be able to make decisions based on data, we may want to use the techniques of <a class="reference external" href="https://static-1300131294.cos.qp-shanghai.myqcloud.com/ml-fundamentals/ml-overview.md">Machine Learning</a> to build a predictive model. We can then use this to make predictions using new datasets with similar structures.</p>
 <p>Of course, depending on the actual data, some steps might be missing (e.g., when we already have the data in the database, or when we do not need model training), or some steps might be repeated several times (such as data processing).</p>
 </section>
 <section id="digitalization-and-digital-transformation">
@@ -1786,15 +1799,15 @@ <h2><span class="section-number">4.1.7. </span>Digitalization and digital transf
 <h2><span class="section-number">4.1.8. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>In this challenge, we will try to find concepts relevant to the field of Data Science by looking at texts. We will take a Wikipedia article on Data Science, download and process the text, and then build a word cloud like this one:</p>
 <figure class="align-default" id="world-of-data-science">
-<img alt="../../_images/data-science-word-cloud.png" src="../../_images/data-science-word-cloud.png" />
+<img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/data-science-word-cloud.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/data-science-word-cloud.png" />
 <figcaption>
 <p><span class="caption-number">Fig. 4.2 </span><span class="caption-text">World of Data Science<span id="id1">[<a class="reference internal" href="#id50" title="Raffaele Nolli. How to identify the most requested skills on the data science job market, with data science. July 2020. URL: https://towardsdatascience.com/how-to-identify-the-most-requested-skills-on-the-data-science-job-market-with-data-science-726845ca9638 (visited on 2022-10-01).">Nol20</a>]</span></span><a class="headerlink" href="#world-of-data-science" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
-<p>Visit <a class="reference internal" href="../../assignments/data-science/analyzing-text-about-data-science.html"><span class="doc std std-doc">Analyzing text about data science</span></a> to read through the code. You can also run the code, and see how it performs all data transformations in real-time.</p>
+<p>Visit <a class="reference external" href="https://static-1300131294.cos.qp-shanghai.myqcloud.com/assignments/data-science/analyzing-text-about-data-science.ipynb">Analyzing text about data science</a> to read through the code. You can also run the code, and see how it performs all data transformations in real-time.</p>
 <ul class="simple">
 <li><p><strong>Task 1</strong>: Modify the code above to find out related concepts for the fields of <strong>Big Data</strong> and <strong>Machine Learning</strong>.</p></li>
-<li><p><strong>Task 2</strong>: Think about <a class="reference internal" href="../../assignments/data-science/data-science-scenarios.html"><span class="doc std std-doc">Data Science scenarios</span></a>.</p></li>
+<li><p><strong>Task 2</strong>: Think about <a class="reference external" href="https://static-1300131294.cos.qp-shanghai.myqcloud.com/assignments/data-science/data-science-scenarios.md">Data Science scenarios</a>.</p></li>
 </ul>
 </section>
 <section id="self-study">
diff --git a/data-science/introduction/defining-data.html b/data-science/introduction/defining-data.html
index 392b50bd8f..b46abf64a0 100644
--- a/data-science/introduction/defining-data.html
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../data-visualization/data-visualization.html">
    6. Data visualization
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
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+  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
+  <label for="toctree-checkbox-4">
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   </label>
@@ -284,8 +306,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
-  <label for="toctree-checkbox-4">
+  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
+  <label for="toctree-checkbox-5">
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   </label>
@@ -311,8 +333,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
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+  <label for="toctree-checkbox-6">
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@@ -355,8 +377,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
-  <label for="toctree-checkbox-6">
+  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
+  <label for="toctree-checkbox-7">
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@@ -392,8 +414,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
-  <label for="toctree-checkbox-7">
+  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
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@@ -431,8 +453,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
    14. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
-  <label for="toctree-checkbox-8">
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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@@ -453,8 +475,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
    15. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
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@@ -480,8 +502,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
    16. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
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@@ -633,8 +655,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../assignments/README.html">
    37. Self-paced assignments
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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@@ -825,308 +847,303 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
+     37.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.48. Loss Function
+     37.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
+     37.50. ML logistic regression - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.52. ML logistic regression - assignment 2
+     37.51. ML logistic regression - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
-     37.53. ML neural network - Assignment 1
+     37.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html">
-     37.54. Regression tools
+     37.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/managing-data.html">
-     37.55. Managing data
+     37.54. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/exploring-visualizations.html">
-     37.56. Exploring visualizations
+     37.55. Exploring visualizations
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/try-a-different-model.html">
-     37.57. Try a different model
+     37.56. Try a different model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/create-a-regression-model.html">
-     37.58. Create a regression model
+     37.57. Create a regression model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
-     37.59. Linear and polynomial regression
+     37.58. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/retrying-some-regression.html">
-     37.60. Retrying some regression
+     37.59. Retrying some regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
+     37.60. Pumpkin varieties and color
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.62. Delicious asian and indian cuisines
+     37.61. Delicious asian and indian cuisines
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/explore-classification-methods.html">
-     37.63. Explore classification methods
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
+     37.62. Explore classification methods
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.65. Kernel method assignment 1
+     37.63. Kernel method assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
+     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.67. Support Vector Machines (SVM) - Classification
+     37.65. Support Vector Machines (SVM) - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.68. Decision Trees - Intro and Regression
+     37.66. Decision Trees - Intro and Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.69. Decision Trees - Classification
+     37.67. Decision Trees - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.70. Model selection assignment 1
+     37.68. Model selection assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.71. Learning Curve To Identify Overfit &amp; Underfit
+     37.69. Learning Curve To Identify Overfit &amp; Underfit
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.72. Dropout and Batch Normalization
+     37.70. Dropout and Batch Normalization
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.73. Lasso and Ridge Regression
+     37.71. Lasso and Ridge Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/regularized-linear-models.html">
-     37.74. Regularized Linear Models
+     37.72. Regularized Linear Models
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
-     37.75. Random forests intro and regression
+     37.73. Random forests intro and regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-for-classification.html">
-     37.76. Random forests for classification
+     37.74. Random forests for classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
-     37.77. Beyond random forests: more ensemble models
+     37.75. Beyond random forests: more ensemble models
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/decision-trees.html">
-     37.78. Decision trees
+     37.76. Decision trees
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
-     37.79. Hyperparameter tuning gradient boosting
+     37.77. Hyperparameter tuning gradient boosting
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
-     37.80. Gradient boosting
+     37.78. Gradient boosting
     </a>
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+<section class="tex2jax_ignore mathjax_ignore" id="defining-data">
 <h1><span class="section-number">4.3. </span>Defining data<a class="headerlink" href="#defining-data" title="Permalink to this headline">#</a></h1>
 <p>Data is facts, information, observations, and measurements that are used to make discoveries and support informed decisions. A data point is a single unit of data within a dataset, which is a collection of data points. Datasets may come in different formats and structures, and will usually be based on their source, or where the data came from. For example, a company’s monthly earnings might be in a spreadsheet but hourly heart rate data from a smartwatch may be in <a class="reference external" href="https://stackoverflow.com/a/383699">JSON</a> format. It’s common for data scientists to work with different types of data within a dataset.</p>
 <p>This section focuses on identifying and classifying data by its characteristics and its sources.</p>
@@ -1652,7 +1665,7 @@ <h2><span class="section-number">4.3.1. </span>How data is described<a class="he
 <section id="sources-of-data">
 <h2><span class="section-number">4.3.2. </span>Sources of data<a class="headerlink" href="#sources-of-data" title="Permalink to this headline">#</a></h2>
 <p>A data source is an initial location of where the data was generated, or where it “lives” and will vary based on how and when it was collected. Data generated by its user(s) are known as primary data while secondary data comes from a source that has collected data for general use. For example, a group of scientists collecting observations in a rainforest would be considered primary and if they decide to share it with other scientists it would be considered secondary to those that use it.</p>
-<p>Databases are a common source and rely on a database management system to host and maintain the data where users use commands called queries to explore the data. Files as data sources can be audio, image, and video files as well as spreadsheets like Excel. Internet sources are a common location for hosting data, where databases, as well as files, can be found. Application programming interfaces, also known as APIs allow programmers to create ways to share data with external users through the internet, while the process of web scraping extracts data from a web page. The sections in <a class="reference internal" href="../working-with-data/working-with-data.html"><span class="doc std std-doc">Working with data</span></a> focus on how to use various data sources.</p>
+<p>Databases are a common source and rely on a database management system to host and maintain the data where users use commands called queries to explore the data. Files as data sources can be audio, image, and video files as well as spreadsheets like Excel. Internet sources are a common location for hosting data, where databases, as well as files, can be found. Application programming interfaces, also known as APIs allow programmers to create ways to share data with external users through the internet, while the process of web scraping extracts data from a web page. The sections in <a class="reference external" href="https://static-1300131294.cos.qp-shanghai.myqcloud.com/working-with-data/working-with-data.md">Working with data</a> focus on how to use various data sources.</p>
 </section>
 <section id="your-turn">
 <h2><span class="section-number">4.3.3. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
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 <h3><span class="section-number">4.3.3.2. </span>Task 2<a class="headerlink" href="#task-2" title="Permalink to this headline">#</a></h3>
-<p><a class="reference internal" href="../../assignments/data-science/classifying-datasets.html"><span class="doc std std-doc">Classifying datasets</span></a></p>
+<p><a class="reference external" href="https://static-1300131294.cos.qp-shanghai.myqcloud.com/assignments/data-science/classifying-datasets.md">Classifying datasets</a></p>
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      5.3. NumPy
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
-     37.53. ML neural network - Assignment 1
+     37.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html">
-     37.54. Regression tools
+     37.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/managing-data.html">
-     37.55. Managing data
+     37.54. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/exploring-visualizations.html">
-     37.56. Exploring visualizations
+     37.55. Exploring visualizations
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/try-a-different-model.html">
-     37.57. Try a different model
+     37.56. Try a different model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/create-a-regression-model.html">
-     37.58. Create a regression model
+     37.57. Create a regression model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
-     37.59. Linear and polynomial regression
+     37.58. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/retrying-some-regression.html">
-     37.60. Retrying some regression
+     37.59. Retrying some regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
+     37.60. Pumpkin varieties and color
     </a>
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     <a class="reference internal" href="../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.62. Delicious asian and indian cuisines
+     37.61. Delicious asian and indian cuisines
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/explore-classification-methods.html">
-     37.63. Explore classification methods
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
+     37.62. Explore classification methods
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     37.65. Kernel method assignment 1
+     37.63. Kernel method assignment 1
     </a>
    </li>
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
+     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.67. Support Vector Machines (SVM) - Classification
+     37.65. Support Vector Machines (SVM) - Classification
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.68. Decision Trees - Intro and Regression
+     37.66. Decision Trees - Intro and Regression
     </a>
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.69. Decision Trees - Classification
+     37.67. Decision Trees - Classification
     </a>
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-     37.70. Model selection assignment 1
+     37.68. Model selection assignment 1
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.71. Learning Curve To Identify Overfit &amp; Underfit
+     37.69. Learning Curve To Identify Overfit &amp; Underfit
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     37.72. Dropout and Batch Normalization
+     37.70. Dropout and Batch Normalization
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     37.73. Lasso and Ridge Regression
+     37.71. Lasso and Ridge Regression
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-     37.74. Regularized Linear Models
+     37.72. Regularized Linear Models
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
-     37.75. Random forests intro and regression
+     37.73. Random forests intro and regression
     </a>
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-     37.76. Random forests for classification
+     37.74. Random forests for classification
     </a>
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
-     37.77. Beyond random forests: more ensemble models
+     37.75. Beyond random forests: more ensemble models
     </a>
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-     37.78. Decision trees
+     37.76. Decision trees
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     <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
-     37.79. Hyperparameter tuning gradient boosting
+     37.77. Hyperparameter tuning gradient boosting
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-     37.80. Gradient boosting
+     37.78. Gradient boosting
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html">
-     37.81. Boosting with tuning
+     37.79. Boosting with tuning
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
-     37.82. Random Forest Classifier with Feature Importance
+     37.80. Random Forest Classifier with Feature Importance
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/machine-learning-productionization/data-engineering.html">
-     37.84. Data engineering
+     37.82. Data engineering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
-     37.85. Counterintuitive Challenges in ML Debugging
+     37.83. Counterintuitive Challenges in ML Debugging
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/machine-learning-productionization/debugging-in-classification.html">
-     37.86. Case Study: Debugging in Classification
+     37.84. Case Study: Debugging in Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/machine-learning-productionization/debugging-in-regression.html">
-     37.87. Case Study: Debugging in Regression
+     37.85. Case Study: Debugging in Regression
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/study-the-solvers.html">
-     37.88. Study the solvers
+     37.86. Study the solvers
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/build-classification-models.html">
-     37.89. Build classification models
+     37.87. Build classification models
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/build-classification-model.html">
-     37.90. Build Classification Model
+     37.88. Build Classification Model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/parameter-play.html">
-     37.91. Parameter play
+     37.89. Parameter play
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     <a class="reference internal" href="../../assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
-     37.92. How to choose cnn architecture mnist
+     37.90. How to choose cnn architecture mnist
     </a>
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     <a class="reference internal" href="../../assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
-     37.94. Sign Language Digits Classification with CNN
+     37.92. Sign Language Digits Classification with CNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html">
-     37.96. Object Recognition in Images using CNN
+     37.94. Object Recognition in Images using CNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     37.97. Intro to TensorFlow for Deep Learning
+     37.95. Intro to TensorFlow for Deep Learning
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.101. Google Stock Price Prediction RNN
+     37.99. Google Stock Price Prediction RNN
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/autoencoder.html">
-     37.103. Intro to Autoencoders
+     37.101. Intro to Autoencoders
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
+     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
+     37.103. Fun with Variational Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
+     37.104. Time Series Forecasting Assignment
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/nn-for-classification-assignment.html">
-     37.108. Neural Networks for Classification with TensorFlow
+     37.106. Neural Networks for Classification with TensorFlow
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
-     37.109. NN Classify 15 Fruits Assignment
+     37.107. NN Classify 15 Fruits Assignment
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.114. DQN On Foreign Exchange Market
+     37.112. DQN On Foreign Exchange Market
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/gan/art-by-gan.html">
-     37.115. Art by gan
+     37.113. Art by gan
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     <a class="reference internal" href="../../assignments/deep-learning/gan/gan-introduction.html">
-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
     </a>
    </li>
   </ul>
@@ -1137,8 +1154,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../slides/introduction.html">
    38. Slides
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -1309,7 +1326,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <div class="menu-dropdown__content">
     <ul>
       <li>
-        <a href="https://mybinder.org/v2/gh/ocademy-ai/machine-learning/release?urlpath=lab/tree/open-machine-learning-jupyter-book/data-science/introduction/introduction-to-statistics-and-probability.md"
+        <a href="https://mybinder.org/v2/gh/ocademy-ai/machine-learning/release?urlpath=lab/tree/open-machine-learning-jupyter-book/data-science/introduction/introduction-to-statistics-and-probability.ipynb"
    class="headerbtn"
    data-toggle="tooltip"
 data-placement="left"
@@ -1327,7 +1344,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
       </li>
       
       <li>
-        <a href="https://colab.research.google.com/github/ocademy-ai/machine-learning/blob/release/open-machine-learning-jupyter-book/data-science/introduction/introduction-to-statistics-and-probability.md"
+        <a href="https://colab.research.google.com/github/ocademy-ai/machine-learning/blob/release/open-machine-learning-jupyter-book/data-science/introduction/introduction-to-statistics-and-probability.ipynb"
    class="headerbtn"
    data-toggle="tooltip"
 data-placement="left"
@@ -1422,7 +1439,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
       </li>
       
       <li>
-        <a href="https://github.com/ocademy-ai/machine-learning/edit/release/open-machine-learning-jupyter-book/data-science/introduction/introduction-to-statistics-and-probability.md"
+        <a href="https://github.com/ocademy-ai/machine-learning/edit/release/open-machine-learning-jupyter-book/data-science/introduction/introduction-to-statistics-and-probability.ipynb"
    class="headerbtn"
    data-toggle="tooltip"
 data-placement="left"
@@ -1454,23 +1471,6 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    class="headerbtn"
    data-toggle="tooltip"
 data-placement="left"
-title="Download notebook file"
->
-  
-
-<span class="headerbtn__icon-container">
-  <i class="fas fa-code"></i>
-  </span>
-<span class="headerbtn__text-container">.ipynb</span>
-</a>
-
-      </li>
-      
-      <li>
-        <a href="../../_sources/data-science/introduction/introduction-to-statistics-and-probability.md"
-   class="headerbtn"
-   data-toggle="tooltip"
-data-placement="left"
 title="Download source file"
 >
   
@@ -1478,7 +1478,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <span class="headerbtn__icon-container">
   <i class="fas fa-file"></i>
   </span>
-<span class="headerbtn__text-container">.md</span>
+<span class="headerbtn__text-container">.ipynb</span>
 </a>
 
       </li>
@@ -1695,15 +1695,68 @@ <h2> Contents </h2>
                 
               <div>
                 
-  <section class="tex2jax_ignore mathjax_ignore" id="introduction-to-statistics-and-probability">
+  <hr class="docutils" />
+<p>jupytext:
+cell_metadata_filter: -all
+formats: md:myst
+text_representation:
+extension: .md
+format_name: myst
+format_version: 0.13
+jupytext_version: 1.11.5
+kernelspec:
+display_name: Python 3
+language: python
+name: python3</p>
+<section class="tex2jax_ignore mathjax_ignore" id="introduction-to-statistics-and-probability">
 <h1><span class="section-number">4.4. </span>Introduction to statistics and probability<a class="headerlink" href="#introduction-to-statistics-and-probability" title="Permalink to this headline">#</a></h1>
 <p>Statistics and Probability Theory are two highly related areas of Mathematics that are highly relevant to Data Science. It is possible to operate with data without deep knowledge of mathematics, but it is still better to know at least some basic concepts. Here we will present a short introduction that will help you get started.</p>
-<div class="admonition seealso">
-<p class="admonition-title">See also</p>
+<div class="cell tag_hide-input tag_output-scoll docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># install the necessary dependencies</span>
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="o">!{</span>sys.executable<span class="o">}</span><span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>--quiet<span class="w"> </span>pandas<span class="w"> </span>numpy<span class="w"> </span>matplotlib<span class="w"> </span>jupyterlab_myst<span class="w"> </span>pygments
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_hide-input tag_output-scoll docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+<span class="n">display</span><span class="p">(</span><span class="n">HTML</span><span class="p">(</span><span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">&lt;div class=&quot;yt-container&quot;&gt;</span>
+<span class="s2">   &lt;iframe width=&quot;560&quot; height=&quot;315&quot; src=&quot;https://www.youtube.com/embed/Z5Zy85g4Yjw&quot; title=&quot;YouTube video player&quot; frameborder=&quot;0&quot; allow=&quot;accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture&quot; allowfullscreen&gt;&lt;/iframe&gt;</span>
+<span class="s2">&lt;/div&gt;</span>
+<span class="s2">&quot;&quot;&quot;</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
 <div class="yt-container">
    <iframe width="560" height="315" src="https://www.youtube.com/embed/Z5Zy85g4Yjw" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
 </div>
+</div></div>
 </div>
+<p>execute:
+exclude_patterns:
+- ‘assignments/<em>’
+- ‘assignments/**/</em>’
+- ‘deep-learning/<em>’
+- ‘slides/</em>’
+- ‘slides/**/*’
+- ‘ml-fundamentals/classification/introduction-to-classification.md’
+- ‘ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.md’
+- ‘ml-fundamentals/regression/managing-data.md’
+- ‘ml-fundamentals/parameter-optimization/loss-function.md’
+- ‘ml-advanced/ensemble-learning/random-forest.md’
+- ‘ml-advanced/clustering/introduction-to-clustering.md’
+- ‘ml-advanced/clustering/k-means-clustering.md’
+- ‘ml-advanced/unsupervised-learning.md’
+- ‘data-science/data-visualization/visualization-distributions.md’</p>
 <section id="probability-and-random-variables">
 <h2><span class="section-number">4.4.1. </span>Probability and random variables<a class="headerlink" href="#probability-and-random-variables" title="Permalink to this headline">#</a></h2>
 <p><strong>Probability</strong> is a number between 0 and 1 that expresses how probable an <strong>event</strong> is. It is defined as a number of positive outcomes (that lead to the event), divided by a total number of outcomes, given that all outcomes are equally probable. For example, when we roll a dice, the probability that we get an even number is <span class="math notranslate nohighlight">\(3/6 = 0.5\)</span>.</p>
@@ -1743,31 +1796,59 @@ <h2><span class="section-number">4.4.4. </span>Mode, median and quartiles<a clas
 <li><p>the third quartile, or <span class="math notranslate nohighlight">\(Q3\)</span>, is a value that <span class="math notranslate nohighlight">\(75%\)</span> of the data falls below it.</p></li>
 </ul>
 <p>Graphically we can represent the relationship between the median and quartiles in a diagram called the <strong>box plot</strong>:</p>
-<p><img alt="" src="../../_images/boxplot_explanation.png" /></p>
+<p><img alt="" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/boxplot_explanation.png" /></p>
 <p>Here we also compute the <strong>inter-quartile range</strong> <span class="math notranslate nohighlight">\(IQR=Q3-Q1\)</span>, and so-called <strong>outliers</strong> - values, that lie outside the boundaries <span class="math notranslate nohighlight">\([Q1-1.5*IQR, Q3+1.5*IQR]\)</span>.</p>
 <p>For the finite distribution that contains a small number of possible values, a good “typical” value is the one that appears the most frequently, which is called <strong>mode</strong>. It is often applied to categorical data, such as colors. Consider a situation where we have two groups of people - some that strongly prefer red, and others who prefer blue. If we code colors by numbers, the mean value for a favorite color would be somewhere in the orange-green spectrum, which does not indicate the actual preference for either group. However, the mode would be either one of the colors, or both colors, if the number of people voting for them is equal (in this case we call the sample <strong>multimodal</strong>).</p>
 </section>
 <section id="real-world-data">
 <h2><span class="section-number">4.4.5. </span>Real-world data<a class="headerlink" href="#real-world-data" title="Permalink to this headline">#</a></h2>
 <p>When we analyze data from real life, they often are not random variables as such, in the sense that we do not perform experiments with unknown results. For example, consider a team of baseball players, and their body data, such as height, weight and age. Those numbers are not exactly random, but we can still apply the same mathematical concepts. For example, a sequence of people’s weights can be considered to be a sequence of values drawn from some random variable. Below is the sequence of weights of actual baseball players from <a class="reference external" href="http://mlb.mlb.com/index.jsp">Major League Baseball</a>, taken from <a class="reference external" href="http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights">this dataset</a> (for your convenience, only the first 20 values are shown):</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="mf">180.0</span><span class="p">,</span> <span class="mf">215.0</span><span class="p">,</span> <span class="mf">210.0</span><span class="p">,</span> <span class="mf">210.0</span><span class="p">,</span> <span class="mf">188.0</span><span class="p">,</span> <span class="mf">176.0</span><span class="p">,</span> <span class="mf">209.0</span><span class="p">,</span> <span class="mf">200.0</span><span class="p">,</span> <span class="mf">231.0</span><span class="p">,</span> <span class="mf">180.0</span><span class="p">,</span> <span class="mf">188.0</span><span class="p">,</span> <span class="mf">180.0</span><span class="p">,</span> <span class="mf">185.0</span><span class="p">,</span> <span class="mf">160.0</span><span class="p">,</span> <span class="mf">180.0</span><span class="p">,</span> <span class="mf">185.0</span><span class="p">,</span> <span class="mf">197.0</span><span class="p">,</span> <span class="mf">189.0</span><span class="p">,</span> <span class="mf">185.0</span><span class="p">,</span> <span class="mf">219.0</span><span class="p">]</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="mf">180.0</span><span class="p">,</span> <span class="mf">215.0</span><span class="p">,</span> <span class="mf">210.0</span><span class="p">,</span> <span class="mf">210.0</span><span class="p">,</span> <span class="mf">188.0</span><span class="p">,</span> <span class="mf">176.0</span><span class="p">,</span> <span class="mf">209.0</span><span class="p">,</span> <span class="mf">200.0</span><span class="p">,</span> <span class="mf">231.0</span><span class="p">,</span> <span class="mf">180.0</span><span class="p">,</span> <span class="mf">188.0</span><span class="p">,</span> <span class="mf">180.0</span><span class="p">,</span> <span class="mf">185.0</span><span class="p">,</span> <span class="mf">160.0</span><span class="p">,</span> <span class="mf">180.0</span><span class="p">,</span> <span class="mf">185.0</span><span class="p">,</span> <span class="mf">197.0</span><span class="p">,</span> <span class="mf">189.0</span><span class="p">,</span> <span class="mf">185.0</span><span class="p">,</span> <span class="mf">219.0</span><span class="p">]</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[180.0,
+ 215.0,
+ 210.0,
+ 210.0,
+ 188.0,
+ 176.0,
+ 209.0,
+ 200.0,
+ 231.0,
+ 180.0,
+ 188.0,
+ 180.0,
+ 185.0,
+ 160.0,
+ 180.0,
+ 185.0,
+ 197.0,
+ 189.0,
+ 185.0,
+ 219.0]
+</pre></div>
+</div>
+</div>
+</div>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>To see the example of working with this dataset, have a look at the <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html"><span class="doc std std-doc">accompanying notebook</span></a>. There are also a number of challenges throughout this section, and you may complete them by adding some code to that notebook. If you are not sure how to operate on data, do not worry - we will come back to working with data using Python at a later time. If you do not know how to run code in Jupyter Notebook, have a look at <a class="reference external" href="https://soshnikov.com/education/how-to-execute-notebooks-from-github/">this article</a>.</p>
+<p>To see the example of working with this dataset, have a look at the <a class="reference external" href="https://static-1300131294.cos.qp-shanghai.myqcloud.com/assignments/data-science/introduction-to-statistics-and-probability.ipynb">accompanying notebook</a>. There are also a number of challenges throughout this section, and you may complete them by adding some code to that notebook. If you are not sure how to operate on data, do not worry - we will come back to working with data using Python at a later time. If you do not know how to run code in Jupyter Notebook, have a look at <a class="reference external" href="https://soshnikov.com/education/how-to-execute-notebooks-from-github/">this article</a>.</p>
 </div>
 <p>Here is the box plot showing the mean, median, and quartiles for our data:</p>
-<p><img alt="Weight Box Plot" src="../../_images/weight-boxplot.png" /></p>
+<p><img alt="Weight Box Plot" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/weight-boxplot.png" /></p>
 <p>Since our data contains information about different player <strong>roles</strong>, we can also do the box plot by role - it will allow us to get an idea on how parameter values differ across roles. This time we will consider height:</p>
-<p><img alt="Box plot by role" src="../../_images/boxplot_byrole.png" /></p>
+<p><img alt="Box plot by role" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/boxplot_byrole.png" /></p>
 <p>This diagram suggests that, on average, the height of first basemen is higher than the height of second basemen. Later in this section, we will learn how we can test this hypothesis more formally, and how to demonstrate that our data is statistically significant to show that.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>When working with real-world data, we assume that all data points are samples drawn from some probability distribution. This assumption allows us to apply machine learning techniques and build working predictive models.</p>
 </div>
 <p>To see what the distribution of our data is, we can plot a graph called a <strong>histogram</strong>. The X-axis would contain a number of different weight intervals (so-called <strong>bins</strong>), and the vertical axis would show the number of times our random variable sample was inside a given interval.</p>
-<p><img alt="Histogram of real-world data" src="../../_images/weight-histogram.png" /></p>
+<p><img alt="Histogram of real-world data" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/weight-histogram.png" /></p>
 <p>From this histogram, you can see that all values are centered around a certain mean weight, and the further we go from that weight - the fewer weights of that value are encountered. I.e., it is very improbable that the weight of a baseball player would be very different from the mean weight. The variance of weights shows the extent to which weights are likely to differ from the mean.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1778,12 +1859,28 @@ <h2><span class="section-number">4.4.5. </span>Real-world data<a class="headerli
 <h2><span class="section-number">4.4.6. </span>Normal distribution<a class="headerlink" href="#normal-distribution" title="Permalink to this headline">#</a></h2>
 <p>The distribution of weights that we have seen above is very typical, and many measurements from the real world follow the same type of distribution, but with different mean and variance. This distribution is called <strong>normal distribution</strong>, and it plays a very important role in statistics.</p>
 <p>Using normal distribution is the correct way to generate random weights of potential baseball players. Once we know the mean weight <code class="docutils literal notranslate"><span class="pre">mean</span></code> and standard deviation <code class="docutils literal notranslate"><span class="pre">std</span></code>, we can generate 1000 weight samples in the following way:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">samples</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">,</span> <span class="mi">1000</span><span class="p">)</span>
+<div class="cell tag_output-scoll tag_hide-input docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">statistics</span> <span class="kn">import</span> <span class="n">mean</span>
+<span class="n">mean</span> <span class="o">=</span> <span class="mi">10</span>
+<span class="n">std</span> <span class="o">=</span> <span class="mi">2</span>
+<span class="n">samples</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">,</span> <span class="mi">1000</span><span class="p">)</span>
+<span class="n">samples</span><span class="p">[:</span><span class="mi">20</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([ 9.07364156, 15.55486073, 10.3530279 , 11.18327947, 10.22882358,
+        8.6559162 ,  9.88222676, 12.00043378,  7.40916247,  9.90606733,
+        8.46852095,  7.72093691, 11.37244833, 11.10944738, 10.34108564,
+       10.18614641,  4.12508105,  9.44605898, 11.27196741,  9.39155156])
 </pre></div>
 </div>
+</div>
+</div>
 <p>If we plot the histogram of the generated samples we will see a picture very similar to the one shown above. And if we increase the number of samples and the number of bins, we can generate a picture of a normal distribution that is more close to ideal:</p>
 <figure class="align-default" id="normal-distribution-with-mean-0-and-std-dev-1">
-<img alt="../../_images/normal-histogram.png" src="../../_images/normal-histogram.png" />
+<img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/normal-histogram.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/normal-histogram.png" />
 <figcaption>
 <p><span class="caption-number">Fig. 4.5 </span><span class="caption-text">Normal distribution with <span class="math notranslate nohighlight">\(mean=0\)</span> and <span class="math notranslate nohighlight">\(std.dev=1\)</span></span><a class="headerlink" href="#normal-distribution-with-mean-0-and-std-dev-1" title="Permalink to this image">#</a></p>
 </figcaption>
@@ -1808,7 +1905,7 @@ <h2><span class="section-number">4.4.7. </span>Confidence intervals<a class="hea
 <p class="admonition-title">Note</p>
 <p>We also omit the discussion of an important concept of <a class="reference external" href="https://en.wikipedia.org/wiki/Degrees_of_freedom_(statistics)">degrees of freedom</a>, which is important in relation to student distribution. You can refer to more complete books on statistics to understand this concept deeper.</p>
 </div>
-<p>An example of calculating the confidence interval for weights and heights is given in the <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html"><span class="doc std std-doc">accompanying notebooks</span></a>.</p>
+<p>An example of calculating the confidence interval for weights and heights is given in the <a class="reference external" href="https://static-1300131294.cos.qp-shanghai.myqcloud.com/assignments/data-science/introduction-to-statistics-and-probability.ipynb">accompanying notebooks</a>.</p>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>p</p></th>
@@ -1831,7 +1928,7 @@ <h2><span class="section-number">4.4.7. </span>Confidence intervals<a class="hea
 </section>
 <section id="hypothesis-testing">
 <h2><span class="section-number">4.4.8. </span>Hypothesis testing<a class="headerlink" href="#hypothesis-testing" title="Permalink to this headline">#</a></h2>
-<p>In our baseball players dataset, there are different player roles, that can be summarized below (look at the <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html"><span class="doc std std-doc">accompanying notebooks</span></a>) to see how this table can be calculated):</p>
+<p>In our baseball players dataset, there are different player roles, that can be summarized below (look at the <a class="reference external" href="https://static-1300131294.cos.qp-shanghai.myqcloud.com/assignments/data-science/introduction-to-statistics-and-probability.ipynb">accompanying notebooks</a>) to see how this table can be calculated):</p>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Role</p></th>
@@ -1922,7 +2019,13 @@ <h2><span class="section-number">4.4.8. </span>Hypothesis testing<a class="heade
 <p>In the student t-test, we compute the so-called <strong>t-value</strong>, which indicates the difference between means, taking into account the variance. It is demonstrated that the t-value follows <strong>student distribution</strong>, which allows us to get the threshold value for a given confidence level <span class="math notranslate nohighlight">\(p\)</span> (this can be computed, or looked up in the numerical tables). We then compare the t-value to this threshold to approve or reject the hypothesis.</p>
 <p>In Python, we can use the <strong>SciPy</strong> package, which includes <code class="docutils literal notranslate"><span class="pre">ttest_ind</span></code> function (in addition to many other useful statistical functions!). It computes the t-value for us, and also does the reverse lookup of the confidence p-value, so that we can just look at the confidence to draw the conclusion.</p>
 <p>For example, our comparison between the heights of first and second basemen gives us the following results:</p>
-<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">ttest_ind</span>
+<div class="cell tag_output-scoll tag_hide-input docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/SOCR_MLB.tsv&quot;</span><span class="p">,</span>
+                 <span class="n">sep</span><span class="o">=</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Name&#39;</span><span class="p">,</span> <span class="s1">&#39;Team&#39;</span><span class="p">,</span> <span class="s1">&#39;Role&#39;</span><span class="p">,</span> <span class="s1">&#39;Height&#39;</span><span class="p">,</span> <span class="s1">&#39;Weight&#39;</span><span class="p">,</span> <span class="s1">&#39;Age&#39;</span><span class="p">])</span>
+<span class="n">df</span>
+
+<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">ttest_ind</span>
 
 <span class="n">tval</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="n">ttest_ind</span><span class="p">(</span>
   <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;Role&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;First_Baseman&#39;</span><span class="p">,</span> 
@@ -1934,10 +2037,32 @@ <h2><span class="section-number">4.4.8. </span>Hypothesis testing<a class="heade
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;T-value = </span><span class="si">{</span><span class="n">tval</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="se">\n</span><span class="s2">P-value: </span><span class="si">{</span><span class="n">pval</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">T</span><span class="o">-</span><span class="n">value</span> <span class="o">=</span> <span class="mf">7.65</span>
-<span class="n">P</span><span class="o">-</span><span class="n">value</span><span class="p">:</span> <span class="mf">9.137321189738925e-12</span>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>T-value = -0.44
+P-value: 0.6594504178995297
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_hide-input tag_output-scoll docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#T-value = 7.65</span>
+<span class="n">T_value</span> <span class="o">=</span> <span class="mf">7.65</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;T-value: </span><span class="si">{</span><span class="n">T_value</span><span class="si">:}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="c1">#P-value: 9.137321189738925e-12</span>
+<span class="n">P_value</span> <span class="o">=</span> <span class="mf">9.137321189738925e-12</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;P-value: </span><span class="si">{</span><span class="n">P_value</span><span class="si">:</span><span class="s2">.15e</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>T-value: 7.65
+P-value: 9.137321189738925e-12
 </pre></div>
 </div>
+</div>
+</div>
 <p>In our case, the p-value is very low, meaning that there is strong evidence supporting that the first basemen are taller.</p>
 <p>There are also different other types of hypotheses that we might want to test, for example:</p>
 <ul class="simple">
@@ -1965,23 +2090,46 @@ <h2><span class="section-number">4.4.10. </span>Covariance and correlation<a cla
 <p>Mathematically, the main concept that shows the relation between two random variables is <strong>covariance</strong>, which is computed like this: <span class="math notranslate nohighlight">\(Cov(X, Y) = E[(X-E(X))(Y-E(Y))]\)</span>. We compute the deviation of both variables from their mean values, and then the product of those deviations. If both variables deviate together, the product would always be a positive value, which would add up to positive covariance. If both variables deviate out of sync (i.e. one falls below average when another one rises above average), we will always get negative numbers, which will add up to negative covariance. If the deviations are not dependent, they will add up to roughly zero.</p>
 <p>The absolute value of covariance does not tell us much about how large the correlation is, because it depends on the magnitude of actual values. To normalize it, we can divide covariance by the standard deviation of both variables, to get a <strong>correlation</strong>. The good thing is that correlation is always in the range of <span class="math notranslate nohighlight">\([-1,1]\)</span>, where <span class="math notranslate nohighlight">\(1\)</span> indicates a strong positive correlation between values, <span class="math notranslate nohighlight">\(-1\)</span> - strong negative correlation, and <span class="math notranslate nohighlight">\(0\)</span> - no correlation at all (variables are independent).</p>
 <p><strong>Example</strong>: we can compute the correlation between weights and heights of baseball players from the dataset mentioned above:</p>
-<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">corrcoef</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">heights</span><span class="p">))</span>
+<div class="cell tag_output-scoll tag_hide-input docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">heights</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;Height&#39;</span><span class="p">]</span>
+<span class="n">weights</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;Weight&#39;</span><span class="p">]</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">corrcoef</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">heights</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[nan nan]
+ [nan  1.]]
 </pre></div>
 </div>
+</div>
+</div>
 <p>As a result, we get the <strong>correlation matrix</strong> like this one:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">array</span><span class="p">([[</span><span class="mf">1.</span>        <span class="p">,</span> <span class="mf">0.52959196</span><span class="p">],</span>
+<div class="cell tag_output-scoll tag_hide-input docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.</span>        <span class="p">,</span> <span class="mf">0.52959196</span><span class="p">],</span>
        <span class="p">[</span><span class="mf">0.52959196</span><span class="p">,</span> <span class="mf">1.</span>        <span class="p">]])</span>
+<span class="n">a</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([[1.        , 0.52959196],
+       [0.52959196, 1.        ]])
 </pre></div>
 </div>
+</div>
+</div>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>Correlation matrix <span class="math notranslate nohighlight">\(C\)</span> can be computed for any number of input sequences <span class="math notranslate nohighlight">\(S_1, ..., S_n\)</span>. The value of <span class="math notranslate nohighlight">\(C_{ij}\)</span> is the correlation between <span class="math notranslate nohighlight">\(S_i\)</span> and <span class="math notranslate nohighlight">\(S_j\)</span>, and diagonal elements are always <span class="math notranslate nohighlight">\(1\)</span> (which is also self-correlation of <span class="math notranslate nohighlight">\(S_i\)</span>).</p>
 </div>
 <p>In our case, the value <span class="math notranslate nohighlight">\(0.53\)</span> indicates that there is some correlation between the weight and height of a person. We can also make the scatter plot of one value against the other to see the relationship visually:</p>
-<p><img alt="Relationship between weight and height" src="../../_images/weight-height-relationship.png" /></p>
+<p><img alt="Relationship between weight and height" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/weight-height-relationship.png" /></p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>More examples of correlation and covariance can be found in <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html"><span class="doc std std-doc">accompanying notebook</span></a>.</p>
+<p>More examples of correlation and covariance can be found in <a class="reference external" href="https://static-1300131294.cos.qp-shanghai.myqcloud.com/assignments/data-science/introduction-to-statistics-and-probability.ipynb">accompanying notebook</a>.</p>
 </div>
 </section>
 <section id="conclusion">
@@ -2005,8 +2153,8 @@ <h2><span class="section-number">4.4.12. </span>Your turn! 🚀<a class="headerl
 <li><p>Shortstops are taller than second basemen</p></li>
 </ol>
 <ul class="simple">
-<li><p><a class="reference internal" href="../../assignments/data-science/small-diabetes-study.html"><span class="doc std std-doc">Small diabetes study</span></a>.</p></li>
-<li><p><a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html"><span class="doc std std-doc">Introduction to probability and statistics</span></a>.</p></li>
+<li><p><a class="reference external" href="https://static-1300131294.cos.qp-shanghai.myqcloud.com/assignments/data-science/small-diabetes-study.ipynb">Small diabetes study</a>.</p></li>
+<li><p><a class="reference external" href="https://static-1300131294.cos.qp-shanghai.myqcloud.com/assignments/data-science/introduction-to-statistics-and-probability.ipynb">Introduction to probability and statistics</a>.</p></li>
 </ul>
 </section>
 <section id="self-study">
diff --git a/data-science/introduction/introduction.html b/data-science/introduction/introduction.html
index ad0c624591..341f527f73 100644
--- a/data-science/introduction/introduction.html
+++ b/data-science/introduction/introduction.html
@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../data-visualization/data-visualization.html">
    6. Data visualization
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
-  <label for="toctree-checkbox-3">
+  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
+  <label for="toctree-checkbox-4">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -284,8 +306,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
-  <label for="toctree-checkbox-4">
+  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
+  <label for="toctree-checkbox-5">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -311,8 +333,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
-  <label for="toctree-checkbox-5">
+  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
+  <label for="toctree-checkbox-6">
    <i class="fas fa-chevron-down">
    </i>
   </label>
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   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
-  <label for="toctree-checkbox-6">
+  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
+  <label for="toctree-checkbox-7">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -392,8 +414,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
-  <label for="toctree-checkbox-7">
+  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
+  <label for="toctree-checkbox-8">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -431,8 +453,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
    14. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
-  <label for="toctree-checkbox-8">
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -453,8 +475,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
    15. Getting started with ensemble learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
-  <label for="toctree-checkbox-9">
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -480,8 +502,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
    16. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -633,8 +655,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../assignments/README.html">
    37. Self-paced assignments
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
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@@ -825,308 +847,303 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
     </a>
    </li>
    <li class="toctree-l2">
-    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
+     37.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.48. Loss Function
+     37.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
+     37.50. ML logistic regression - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.52. ML logistic regression - assignment 2
+     37.51. ML logistic regression - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
-     37.53. ML neural network - Assignment 1
+     37.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html">
-     37.54. Regression tools
+     37.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/managing-data.html">
-     37.55. Managing data
+     37.54. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/exploring-visualizations.html">
-     37.56. Exploring visualizations
+     37.55. Exploring visualizations
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/try-a-different-model.html">
-     37.57. Try a different model
+     37.56. Try a different model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/create-a-regression-model.html">
-     37.58. Create a regression model
+     37.57. Create a regression model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
-     37.59. Linear and polynomial regression
+     37.58. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/retrying-some-regression.html">
-     37.60. Retrying some regression
+     37.59. Retrying some regression
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
+     37.60. Pumpkin varieties and color
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     37.62. Delicious asian and indian cuisines
+     37.61. Delicious asian and indian cuisines
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/explore-classification-methods.html">
-     37.63. Explore classification methods
-    </a>
-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
+     37.62. Explore classification methods
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.67. Support Vector Machines (SVM) - Classification
+     37.65. Support Vector Machines (SVM) - Classification
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.68. Decision Trees - Intro and Regression
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.69. Decision Trees - Classification
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.71. Learning Curve To Identify Overfit &amp; Underfit
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/regularized-linear-models.html">
-     37.74. Regularized Linear Models
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
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     <a class="reference internal" href="../../assignments/machine-learning-productionization/data-engineering.html">
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 <h1><span class="section-number">4. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h1>
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-<img alt="../../_images/the-data-science-venn-diagram.png" src="../../_images/the-data-science-venn-diagram.png" />
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+<p><span class="caption-number">Fig. 4.1 </span><span class="caption-text">The data science venn diagram{cite}:TheDataScienceVennDiagram:</span><a class="headerlink" href="#the-data-science-venn-diagram" title="Permalink to this image">#</a></p>
 </figcaption>
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-    <link rel="prev" title="5.4. Pandas" href="pandas.html" />
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      5.3. NumPy
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+      <a class="reference internal" href="pandas/data-selection.html">
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+      <a class="reference internal" href="pandas/advanced-pandas-techniques.html">
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     <a class="reference internal" href="../../assignments/deep-learning/time-series-forecasting-assignment.html">
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+     37.104. Time Series Forecasting Assignment
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 <p><strong>Learning goal:</strong> By the end of this subsection, you should be comfortable finding general information about the data stored in pandas DataFrames.</p>
 </div>
-<p>Once you have loaded your data into Pandas, it will more likely than not be in a DataFrame(refer to the previous <a class="reference internal" href="pandas.html"><span class="doc std std-doc">section</span></a> for a detailed overview). However, if the data set in your DataFrame has 60,000 rows and 400 columns, how do you even begin to get a sense of what you’re working with? Fortunately, <a class="reference external" href="https://pandas.pydata.org/">Pandas</a> provides some convenient tools to quickly look at overall information about a DataFrame in addition to the first few and last few rows.</p>
+<p>Once you have loaded your data into Pandas, it will more likely than not be in a DataFrame. However, if the data set in your DataFrame has 60,000 rows and 400 columns, how do you even begin to get a sense of what you’re working with? Fortunately, <a class="reference external" href="https://pandas.pydata.org/">Pandas</a> provides some convenient tools to quickly look at overall information about a DataFrame in addition to the first few and last few rows.</p>
 <p>In order to explore this functionality, we will import the Python scikit-learn library and use an iconic dataset: the <strong>Iris data set</strong>.</p>
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-    <a class='left-prev' id="prev-link" href="pandas.html" title="previous page">
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         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">5.4. </span>Pandas</p>
+            <p class="prev-next-title"><span class="section-number">5.4.3. </span>Advanced Pandas Techniques</p>
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="pandas.html">
      5.4. Pandas
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+      <a class="reference internal" href="pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     <li class="toctree-l3">
+      <a class="reference internal" href="pandas/data-selection.html">
+       5.4.2. Data Selection
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+     <li class="toctree-l3">
+      <a class="reference internal" href="pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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+    </ul>
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+     37.48. Gradient descent
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 <!-- Table of contents -->
 <div class="col-md-3 bd-toc show noprint">
-    <div class="tocsection onthispage pt-5 pb-3">
-        <i class="fas fa-list"></i> Contents
-    </div>
-    <nav id="bd-toc-nav" aria-label="Page">
-        <ul class="visible nav section-nav flex-column">
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#introducing-pandas-objects">
-   5.4.1. Introducing Pandas objects
-  </a>
-  <ul class="nav section-nav flex-column">
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#series">
-     5.4.1.1. Series
-    </a>
-    <ul class="nav section-nav flex-column">
-     <li class="toc-h4 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#create-a-series">
-       5.4.1.1.1. Create a Series
-      </a>
-      <ul class="nav section-nav flex-column">
-       <li class="toc-h5 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#from-ndarray">
-         5.4.1.1.1.1. From ndarray
-        </a>
-       </li>
-       <li class="toc-h5 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#from-dict">
-         5.4.1.1.1.2. From dict
-        </a>
-       </li>
-       <li class="toc-h5 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#from-scalar-value">
-         5.4.1.1.1.3. From scalar value
-        </a>
-       </li>
-      </ul>
-     </li>
-     <li class="toc-h4 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#series-is-ndarray-like">
-       5.4.1.1.2. Series is ndarray-like
-      </a>
-     </li>
-     <li class="toc-h4 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#series-is-dict-like">
-       5.4.1.1.3. Series is dict-like
-      </a>
-     </li>
-     <li class="toc-h4 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#vectorized-operations-and-label-alignment-with-series">
-       5.4.1.1.4. Vectorized operations and label alignment with Series
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-     </li>
-     <li class="toc-h4 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#name-attribute">
-       5.4.1.1.5. Name attribute
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-     </li>
-    </ul>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#dataframe">
-     5.4.1.2. DataFrame
-    </a>
-    <ul class="nav section-nav flex-column">
-     <li class="toc-h4 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#create-a-dataframe">
-       5.4.1.2.1. Create a Dataframe
-      </a>
-      <ul class="nav section-nav flex-column">
-       <li class="toc-h5 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#from-dict-of-series-or-dicts">
-         5.4.1.2.1.1. From dict of
-         <code class="docutils literal notranslate">
-          <span class="pre">
-           Series
-          </span>
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-         or dicts
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-       <li class="toc-h5 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#from-dict-of-ndarrays-lists">
-         5.4.1.2.1.2. From dict of ndarrays / lists
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-       <li class="toc-h5 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#from-structured-or-record-array">
-         5.4.1.2.1.3. From structured or record array
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-        <a class="reference internal nav-link" href="#from-a-list-of-dicts">
-         5.4.1.2.1.4. From a list of dicts
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-       </li>
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-        <a class="reference internal nav-link" href="#from-a-dict-of-tuples">
-         5.4.1.2.1.5. From a dict of tuples
-        </a>
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-       <li class="toc-h5 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#from-a-series">
-         5.4.1.2.1.6. From a Series
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-       </li>
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-        <a class="reference internal nav-link" href="#from-a-list-of-namedtuples">
-         5.4.1.2.1.7. From a list of namedtuples
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-       <li class="toc-h5 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#from-a-list-of-dataclasses">
-         5.4.1.2.1.8. From a list of dataclasses
-        </a>
-       </li>
-      </ul>
-     </li>
-     <li class="toc-h4 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#column-selection-addition-deletion">
-       5.4.1.2.2. Column selection, addition, deletion
-      </a>
-     </li>
-     <li class="toc-h4 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#assigning-new-columns-in-method-chains">
-       5.4.1.2.3. Assigning new columns in method chains
-      </a>
-     </li>
-     <li class="toc-h4 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#indexing-selection">
-       5.4.1.2.4. Indexing / selection
-      </a>
-     </li>
-     <li class="toc-h4 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#data-alignment-and-arithmetic">
-       5.4.1.2.5. Data alignment and arithmetic
-      </a>
-     </li>
-     <li class="toc-h4 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#transposing">
-       5.4.1.2.6. Transposing
-      </a>
-     </li>
-    </ul>
-   </li>
-  </ul>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#data-indexing-and-selection">
-   5.4.2. Data indexing and selection
-  </a>
-  <ul class="nav section-nav flex-column">
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#different-choices-for-indexing">
-     5.4.2.1. Different choices for indexing
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#basics">
-     5.4.2.2. Basics
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#attribute-access">
-     5.4.2.3. Attribute access
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#slicing-ranges">
-     5.4.2.4. Slicing ranges
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#selection-by-label">
-     5.4.2.5. Selection by label
-    </a>
-    <ul class="nav section-nav flex-column">
-     <li class="toc-h4 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#slicing-with-labels">
-       5.4.2.5.1. Slicing with labels
-      </a>
-     </li>
-    </ul>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#selection-by-position">
-     5.4.2.6. Selection by position
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#selection-by-callable">
-     5.4.2.7. Selection by callable
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#combining-positional-and-label-based-indexing">
-     5.4.2.8. Combining positional and label-based indexing
-    </a>
-   </li>
-  </ul>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#combining-datasets-concat-merge-and-join">
-   5.4.3. Combining datasets: concat, merge and join
-  </a>
-  <ul class="nav section-nav flex-column">
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#concat">
-     5.4.3.1. concat
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#merge">
-     5.4.3.2. merge
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#join">
-     5.4.3.3. join
-    </a>
-   </li>
-  </ul>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#aggregation-and-grouping">
-   5.4.4. Aggregation and grouping
-  </a>
-  <ul class="nav section-nav flex-column">
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#hierarchical-indexes">
-     5.4.4.1. Hierarchical Indexes
-    </a>
-   </li>
-  </ul>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#pivot-table">
-   5.4.5. Pivot table
-  </a>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#high-performance-pandas-eval-and-query">
-   5.4.6. High-performance Pandas: eval() and query()
-  </a>
-  <ul class="nav section-nav flex-column">
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#eval">
-     5.4.6.1. eval()
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#query">
-     5.4.6.2. query()
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#processing-image-data">
-     5.4.6.3. Processing image data
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#assignment">
-     5.4.6.4. Assignment
-    </a>
-   </li>
-  </ul>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#self-study">
-   5.4.7. Self study
-  </a>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#acknowledgments">
-   5.4.8. Acknowledgments
-  </a>
- </li>
-</ul>
-
-    </nav>
 </div>
     </div>
     <div class="article row">
@@ -1809,285 +1454,6 @@ <h1>Pandas</h1>
                 <div id="print-main-content">
                     <div id="jb-print-toc">
                         
-                        <div>
-                            <h2> Contents </h2>
-                        </div>
-                        <nav aria-label="Page">
-                            <ul class="visible nav section-nav flex-column">
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#introducing-pandas-objects">
-   5.4.1. Introducing Pandas objects
-  </a>
-  <ul class="nav section-nav flex-column">
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#series">
-     5.4.1.1. Series
-    </a>
-    <ul class="nav section-nav flex-column">
-     <li class="toc-h4 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#create-a-series">
-       5.4.1.1.1. Create a Series
-      </a>
-      <ul class="nav section-nav flex-column">
-       <li class="toc-h5 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#from-ndarray">
-         5.4.1.1.1.1. From ndarray
-        </a>
-       </li>
-       <li class="toc-h5 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#from-dict">
-         5.4.1.1.1.2. From dict
-        </a>
-       </li>
-       <li class="toc-h5 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#from-scalar-value">
-         5.4.1.1.1.3. From scalar value
-        </a>
-       </li>
-      </ul>
-     </li>
-     <li class="toc-h4 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#series-is-ndarray-like">
-       5.4.1.1.2. Series is ndarray-like
-      </a>
-     </li>
-     <li class="toc-h4 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#series-is-dict-like">
-       5.4.1.1.3. Series is dict-like
-      </a>
-     </li>
-     <li class="toc-h4 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#vectorized-operations-and-label-alignment-with-series">
-       5.4.1.1.4. Vectorized operations and label alignment with Series
-      </a>
-     </li>
-     <li class="toc-h4 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#name-attribute">
-       5.4.1.1.5. Name attribute
-      </a>
-     </li>
-    </ul>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#dataframe">
-     5.4.1.2. DataFrame
-    </a>
-    <ul class="nav section-nav flex-column">
-     <li class="toc-h4 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#create-a-dataframe">
-       5.4.1.2.1. Create a Dataframe
-      </a>
-      <ul class="nav section-nav flex-column">
-       <li class="toc-h5 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#from-dict-of-series-or-dicts">
-         5.4.1.2.1.1. From dict of
-         <code class="docutils literal notranslate">
-          <span class="pre">
-           Series
-          </span>
-         </code>
-         or dicts
-        </a>
-       </li>
-       <li class="toc-h5 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#from-dict-of-ndarrays-lists">
-         5.4.1.2.1.2. From dict of ndarrays / lists
-        </a>
-       </li>
-       <li class="toc-h5 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#from-structured-or-record-array">
-         5.4.1.2.1.3. From structured or record array
-        </a>
-       </li>
-       <li class="toc-h5 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#from-a-list-of-dicts">
-         5.4.1.2.1.4. From a list of dicts
-        </a>
-       </li>
-       <li class="toc-h5 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#from-a-dict-of-tuples">
-         5.4.1.2.1.5. From a dict of tuples
-        </a>
-       </li>
-       <li class="toc-h5 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#from-a-series">
-         5.4.1.2.1.6. From a Series
-        </a>
-       </li>
-       <li class="toc-h5 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#from-a-list-of-namedtuples">
-         5.4.1.2.1.7. From a list of namedtuples
-        </a>
-       </li>
-       <li class="toc-h5 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#from-a-list-of-dataclasses">
-         5.4.1.2.1.8. From a list of dataclasses
-        </a>
-       </li>
-      </ul>
-     </li>
-     <li class="toc-h4 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#column-selection-addition-deletion">
-       5.4.1.2.2. Column selection, addition, deletion
-      </a>
-     </li>
-     <li class="toc-h4 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#assigning-new-columns-in-method-chains">
-       5.4.1.2.3. Assigning new columns in method chains
-      </a>
-     </li>
-     <li class="toc-h4 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#indexing-selection">
-       5.4.1.2.4. Indexing / selection
-      </a>
-     </li>
-     <li class="toc-h4 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#data-alignment-and-arithmetic">
-       5.4.1.2.5. Data alignment and arithmetic
-      </a>
-     </li>
-     <li class="toc-h4 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#transposing">
-       5.4.1.2.6. Transposing
-      </a>
-     </li>
-    </ul>
-   </li>
-  </ul>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#data-indexing-and-selection">
-   5.4.2. Data indexing and selection
-  </a>
-  <ul class="nav section-nav flex-column">
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#different-choices-for-indexing">
-     5.4.2.1. Different choices for indexing
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#basics">
-     5.4.2.2. Basics
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#attribute-access">
-     5.4.2.3. Attribute access
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#slicing-ranges">
-     5.4.2.4. Slicing ranges
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#selection-by-label">
-     5.4.2.5. Selection by label
-    </a>
-    <ul class="nav section-nav flex-column">
-     <li class="toc-h4 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#slicing-with-labels">
-       5.4.2.5.1. Slicing with labels
-      </a>
-     </li>
-    </ul>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#selection-by-position">
-     5.4.2.6. Selection by position
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#selection-by-callable">
-     5.4.2.7. Selection by callable
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#combining-positional-and-label-based-indexing">
-     5.4.2.8. Combining positional and label-based indexing
-    </a>
-   </li>
-  </ul>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#combining-datasets-concat-merge-and-join">
-   5.4.3. Combining datasets: concat, merge and join
-  </a>
-  <ul class="nav section-nav flex-column">
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#concat">
-     5.4.3.1. concat
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#merge">
-     5.4.3.2. merge
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#join">
-     5.4.3.3. join
-    </a>
-   </li>
-  </ul>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#aggregation-and-grouping">
-   5.4.4. Aggregation and grouping
-  </a>
-  <ul class="nav section-nav flex-column">
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#hierarchical-indexes">
-     5.4.4.1. Hierarchical Indexes
-    </a>
-   </li>
-  </ul>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#pivot-table">
-   5.4.5. Pivot table
-  </a>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#high-performance-pandas-eval-and-query">
-   5.4.6. High-performance Pandas: eval() and query()
-  </a>
-  <ul class="nav section-nav flex-column">
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#eval">
-     5.4.6.1. eval()
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#query">
-     5.4.6.2. query()
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#processing-image-data">
-     5.4.6.3. Processing image data
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#assignment">
-     5.4.6.4. Assignment
-    </a>
-   </li>
-  </ul>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#self-study">
-   5.4.7. Self study
-  </a>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#acknowledgments">
-   5.4.8. Acknowledgments
-  </a>
- </li>
-</ul>
-
-                        </nav>
                     </div>
                 </div>
             </div>
@@ -2097,7654 +1463,14 @@ <h2> Contents </h2>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="pandas">
 <h1><span class="section-number">5.4. </span>Pandas<a class="headerlink" href="#pandas" title="Permalink to this headline">#</a></h1>
-<p>Pandas is a fast, powerful, flexible and easy-to-use open-source data analysis and manipulation tool, built on top of the Python programming language.</p>
-<section id="introducing-pandas-objects">
-<h2><span class="section-number">5.4.1. </span>Introducing Pandas objects<a class="headerlink" href="#introducing-pandas-objects" title="Permalink to this headline">#</a></h2>
-<p>We’ll start with a quick, non-comprehensive overview of the fundamental data structures in Pandas to get you started. The fundamental behavior about data types, indexing, axis labeling, and alignment apply across all of the objects. To get started, import NumPy and load Pandas into your namespace:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-</pre></div>
-</div>
-</div>
-</div>
-<section id="series">
-<h3><span class="section-number">5.4.1.1. </span>Series<a class="headerlink" href="#series" title="Permalink to this headline">#</a></h3>
-<p><code class="docutils literal notranslate"><span class="pre">Series</span></code> is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). The axis labels are collectively referred to as the <strong>index</strong>. The basic method to create a <code class="docutils literal notranslate"><span class="pre">Series</span></code> is to call:</p>
-<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">index</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Here, <code class="docutils literal notranslate"><span class="pre">data</span></code> can be many different things:</p>
-<ul class="simple">
-<li><p>a Python dict</p></li>
-<li><p>an ndarray</p></li>
-<li><p>a scalar value (like 5)</p></li>
-</ul>
-<p>The passed <strong>index</strong> is a list of axis labels. Thus, this separates into a few cases depending on what the <strong>data is</strong>:</p>
-<section id="create-a-series">
-<h4><span class="section-number">5.4.1.1.1. </span>Create a Series<a class="headerlink" href="#create-a-series" title="Permalink to this headline">#</a></h4>
-<section id="from-ndarray">
-<h5><span class="section-number">5.4.1.1.1.1. </span>From ndarray<a class="headerlink" href="#from-ndarray" title="Permalink to this headline">#</a></h5>
-<p>If <code class="docutils literal notranslate"><span class="pre">data</span></code> is an ndarray, <strong>index</strong> must be the same length as the <strong>data</strong>. If no index is passed, one will be created having values <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">...,</span> <span class="pre">len(data)</span> <span class="pre">-</span> <span class="pre">1]</span></code>.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">),</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">,</span> <span class="s2">&quot;d&quot;</span><span class="p">,</span> <span class="s2">&quot;e&quot;</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a   -0.742503
-b   -0.342277
-c    2.575226
-d   -1.022013
-e   -0.549587
-dtype: float64
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="o">.</span><span class="n">index</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Index([&#39;a&#39;, &#39;b&#39;, &#39;c&#39;, &#39;d&#39;, &#39;e&#39;], dtype=&#39;object&#39;)
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
-</pre></div>
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-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    0.456269
-1   -0.744398
-2    1.151727
-3    1.031141
-4    0.833176
-dtype: float64
-</pre></div>
-</div>
-</div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Pandas supports non-unique index values. If an operation that does not support duplicate index values is attempted, an exception will be raised at that time.</p>
-</div>
-</section>
-<section id="from-dict">
-<h5><span class="section-number">5.4.1.1.1.2. </span>From dict<a class="headerlink" href="#from-dict" title="Permalink to this headline">#</a></h5>
-<p><code class="docutils literal notranslate"><span class="pre">Series</span></code> can be instantiated from dicts:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">d</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;b&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;a&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">}</span>
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-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
-</pre></div>
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-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>b    1
-a    0
-c    2
-dtype: int64
-</pre></div>
-</div>
-</div>
-</div>
-<p>If an index is passed, the values in data corresponding to the labels in the index will be pulled out.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">d</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;a&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">:</span> <span class="mf">2.0</span><span class="p">}</span>
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-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    0.0
-b    1.0
-c    2.0
-dtype: float64
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">,</span> <span class="s2">&quot;d&quot;</span><span class="p">,</span> <span class="s2">&quot;a&quot;</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>b    1.0
-c    2.0
-d    NaN
-a    0.0
-dtype: float64
-</pre></div>
-</div>
-</div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>NaN (not a number) is the standard missing data marker used in Pandas.</p>
-</div>
-</section>
-<section id="from-scalar-value">
-<h5><span class="section-number">5.4.1.1.1.3. </span>From scalar value<a class="headerlink" href="#from-scalar-value" title="Permalink to this headline">#</a></h5>
-<p>If <code class="docutils literal notranslate"><span class="pre">data</span></code> is a scalar value, an index must be provided. The value will be repeated to match the length of <strong>index</strong>.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="mf">5.0</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">,</span> <span class="s2">&quot;d&quot;</span><span class="p">,</span> <span class="s2">&quot;e&quot;</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    5.0
-b    5.0
-c    5.0
-d    5.0
-e    5.0
-dtype: float64
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-</section>
-<section id="series-is-ndarray-like">
-<h4><span class="section-number">5.4.1.1.2. </span>Series is ndarray-like<a class="headerlink" href="#series-is-ndarray-like" title="Permalink to this headline">#</a></h4>
-<p><code class="docutils literal notranslate"><span class="pre">Series</span></code> acts very similarly to a <code class="docutils literal notranslate"><span class="pre">ndarray</span></code> and is a valid argument to most NumPy functions. However, operations such as slicing will also slice the index.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.7425028341431366
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a   -0.742503
-b   -0.342277
-c    2.575226
-dtype: float64
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="p">[</span><span class="n">s</span> <span class="o">&gt;</span> <span class="n">s</span><span class="o">.</span><span class="n">median</span><span class="p">()]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>b   -0.342277
-c    2.575226
-dtype: float64
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="p">[[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>e   -0.549587
-d   -1.022013
-b   -0.342277
-dtype: float64
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a     0.475921
-b     0.710151
-c    13.134280
-d     0.359870
-e     0.577188
-dtype: float64
-</pre></div>
-</div>
-</div>
-</div>
-<p>Like a NumPy array, a Pandas Series has a single <code class="docutils literal notranslate"><span class="pre">dtype</span></code>.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="o">.</span><span class="n">dtype</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>dtype(&#39;float64&#39;)
-</pre></div>
-</div>
-</div>
-</div>
-<p>If you need the actual array backing a <code class="docutils literal notranslate"><span class="pre">Series</span></code>, use <code class="docutils literal notranslate"><span class="pre">Series.array</span></code>.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="o">.</span><span class="n">array</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;PandasArray&gt;
-[ -0.7425028341431366, -0.34227718263016094,   2.5752256412522594,
-  -1.0220127417608955,  -0.5495868399385304]
-Length: 5, dtype: float64
-</pre></div>
-</div>
-</div>
-</div>
-<p>While <code class="docutils literal notranslate"><span class="pre">Series</span></code> is ndarray-like, if you need an actual ndarray, then use <code class="docutils literal notranslate"><span class="pre">Series.to_numpy()</span></code>.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([-0.74250283, -0.34227718,  2.57522564, -1.02201274, -0.54958684])
-</pre></div>
-</div>
-</div>
-</div>
-<p>Even if the <code class="docutils literal notranslate"><span class="pre">Series</span></code> is backed by an <code class="docutils literal notranslate"><span class="pre">ExtensionArray</span></code>, <code class="docutils literal notranslate"><span class="pre">Series.to_numpy()</span></code> will return a NumPy ndarray.</p>
-</section>
-<section id="series-is-dict-like">
-<h4><span class="section-number">5.4.1.1.3. </span>Series is dict-like<a class="headerlink" href="#series-is-dict-like" title="Permalink to this headline">#</a></h4>
-<p>A <code class="docutils literal notranslate"><span class="pre">Series</span></code> is also like a fixed-size dict in that you can get and set values by index label:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.7425028341431366
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="p">[</span><span class="s2">&quot;e&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">12.0</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    -0.742503
-b    -0.342277
-c     2.575226
-d    -1.022013
-e    12.000000
-dtype: float64
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="s2">&quot;e&quot;</span> <span class="ow">in</span> <span class="n">s</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>True
-</pre></div>
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-</div>
-</div>
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-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="s2">&quot;f&quot;</span> <span class="ow">in</span> <span class="n">s</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>False
-</pre></div>
-</div>
-</div>
-</div>
-<p>If a label is not contained in the index, an exception is raised:</p>
-<div class="cell tag_raises-exception docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="p">[</span><span class="s2">&quot;f&quot;</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
-<span class="ne">KeyError</span><span class="g g-Whitespace">                                  </span>Traceback (most recent call last)
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexes/base.py:3803,</span> in <span class="ni">Index.get_loc</span><span class="nt">(self, key, method, tolerance)</span>
-<span class="g g-Whitespace">   </span><span class="mi">3802</span> <span class="k">try</span><span class="p">:</span>
-<span class="ne">-&gt; </span><span class="mi">3803</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_engine</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="n">casted_key</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">3804</span> <span class="k">except</span> <span class="ne">KeyError</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/_libs/index.pyx:138,</span> in <span class="ni">pandas._libs.index.IndexEngine.get_loc</span><span class="nt">()</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/_libs/index.pyx:165,</span> in <span class="ni">pandas._libs.index.IndexEngine.get_loc</span><span class="nt">()</span>
-
-<span class="nn">File pandas/_libs/hashtable_class_helper.pxi:5745,</span> in <span class="ni">pandas._libs.hashtable.PyObjectHashTable.get_item</span><span class="nt">()</span>
-
-<span class="nn">File pandas/_libs/hashtable_class_helper.pxi:5753,</span> in <span class="ni">pandas._libs.hashtable.PyObjectHashTable.get_item</span><span class="nt">()</span>
-
-<span class="ne">KeyError</span>: &#39;f&#39;
-
-<span class="n">The</span> <span class="n">above</span> <span class="n">exception</span> <span class="n">was</span> <span class="n">the</span> <span class="n">direct</span> <span class="n">cause</span> <span class="n">of</span> <span class="n">the</span> <span class="n">following</span> <span class="n">exception</span><span class="p">:</span>
-
-<span class="ne">KeyError</span><span class="g g-Whitespace">                                  </span>Traceback (most recent call last)
-<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">25</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
-<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">s</span><span class="p">[</span><span class="s2">&quot;f&quot;</span><span class="p">]</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/series.py:981,</span> in <span class="ni">Series.__getitem__</span><span class="nt">(self, key)</span>
-<span class="g g-Whitespace">    </span><span class="mi">978</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_values</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
-<span class="g g-Whitespace">    </span><span class="mi">980</span> <span class="k">elif</span> <span class="n">key_is_scalar</span><span class="p">:</span>
-<span class="ne">--&gt; </span><span class="mi">981</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_value</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-<span class="g g-Whitespace">    </span><span class="mi">983</span> <span class="k">if</span> <span class="n">is_hashable</span><span class="p">(</span><span class="n">key</span><span class="p">):</span>
-<span class="g g-Whitespace">    </span><span class="mi">984</span>     <span class="c1"># Otherwise index.get_value will raise InvalidIndexError</span>
-<span class="g g-Whitespace">    </span><span class="mi">985</span>     <span class="k">try</span><span class="p">:</span>
-<span class="g g-Whitespace">    </span><span class="mi">986</span>         <span class="c1"># For labels that don&#39;t resolve as scalars like tuples and frozensets</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/series.py:1089,</span> in <span class="ni">Series._get_value</span><span class="nt">(self, label, takeable)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1086</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_values</span><span class="p">[</span><span class="n">label</span><span class="p">]</span>
-<span class="g g-Whitespace">   </span><span class="mi">1088</span> <span class="c1"># Similar to Index.get_value, but we do not fall back to positional</span>
-<span class="ne">-&gt; </span><span class="mi">1089</span> <span class="n">loc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="n">label</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1090</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">_get_values_for_loc</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loc</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexes/base.py:3805,</span> in <span class="ni">Index.get_loc</span><span class="nt">(self, key, method, tolerance)</span>
-<span class="g g-Whitespace">   </span><span class="mi">3803</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_engine</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="n">casted_key</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">3804</span> <span class="k">except</span> <span class="ne">KeyError</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
-<span class="ne">-&gt; </span><span class="mi">3805</span>     <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="n">key</span><span class="p">)</span> <span class="kn">from</span> <span class="nn">err</span>
-<span class="g g-Whitespace">   </span><span class="mi">3806</span> <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
-<span class="g g-Whitespace">   </span><span class="mi">3807</span>     <span class="c1"># If we have a listlike key, _check_indexing_error will raise</span>
-<span class="g g-Whitespace">   </span><span class="mi">3808</span>     <span class="c1">#  InvalidIndexError. Otherwise we fall through and re-raise</span>
-<span class="g g-Whitespace">   </span><span class="mi">3809</span>     <span class="c1">#  the TypeError.</span>
-<span class="g g-Whitespace">   </span><span class="mi">3810</span>     <span class="bp">self</span><span class="o">.</span><span class="n">_check_indexing_error</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-
-<span class="ne">KeyError</span>: &#39;f&#39;
-</pre></div>
-</div>
-</div>
-</div>
-<p>Using the <code class="docutils literal notranslate"><span class="pre">Series.get()</span></code> method, a missing label will return None or specified default:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;f&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;f&quot;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>nan
-</pre></div>
-</div>
-</div>
-</div>
-<p>These labels can also be accessed by <code class="docutils literal notranslate"><span class="pre">attribute</span></code>.</p>
-</section>
-<section id="vectorized-operations-and-label-alignment-with-series">
-<h4><span class="section-number">5.4.1.1.4. </span>Vectorized operations and label alignment with Series<a class="headerlink" href="#vectorized-operations-and-label-alignment-with-series" title="Permalink to this headline">#</a></h4>
-<p>When working with raw NumPy arrays, looping through value-by-value is usually not necessary. The same is true when working with <code class="docutils literal notranslate"><span class="pre">Series</span></code> in Pandas. <code class="docutils literal notranslate"><span class="pre">Series</span></code> can also be passed into most NumPy methods expecting an ndarray.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">+</span> <span class="n">s</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    -1.485006
-b    -0.684554
-c     5.150451
-d    -2.044025
-e    24.000000
-dtype: float64
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">*</span> <span class="mi">2</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    -1.485006
-b    -0.684554
-c     5.150451
-d    -2.044025
-e    24.000000
-dtype: float64
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a         0.475921
-b         0.710151
-c        13.134280
-d         0.359870
-e    162754.791419
-dtype: float64
-</pre></div>
-</div>
-</div>
-</div>
-<p>A key difference between <code class="docutils literal notranslate"><span class="pre">Series</span></code> and ndarray is that operations between <code class="docutils literal notranslate"><span class="pre">Series</span></code> automatically align the data based on the label. Thus, you can write computations without giving consideration to whether the <code class="docutils literal notranslate"><span class="pre">Series</span></code> involved have the same labels.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">+</span> <span class="n">s</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a         NaN
-b   -0.684554
-c    5.150451
-d   -2.044025
-e         NaN
-dtype: float64
-</pre></div>
-</div>
-</div>
-</div>
-<p>The result of an operation between unaligned <code class="docutils literal notranslate"><span class="pre">Series</span></code> will have the <strong>union</strong> of the indexes involved. If a label is not found in one <code class="docutils literal notranslate"><span class="pre">Series</span></code> or the other, the result will be marked as missing <code class="docutils literal notranslate"><span class="pre">NaN</span></code>. Being able to write code without doing any explicit data alignment grants immense freedom and flexibility in interactive data analysis and research. The integrated data alignment features of the Pandas data structures set Pandas apart from the majority of related tools for working with labeled data.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>In general, we chose to make the default result of operations between differently indexed objects yield the <strong>union</strong> of the indexes in order to avoid loss of information. Having an index label, though the data is missing, is typically important information as part of a computation. You of course have the option of dropping labels with missing data via the <code class="docutils literal notranslate"><span class="pre">dropna</span></code> function.</p>
-</div>
-</section>
-<section id="name-attribute">
-<h4><span class="section-number">5.4.1.1.5. </span>Name attribute<a class="headerlink" href="#name-attribute" title="Permalink to this headline">#</a></h4>
-<p><code class="docutils literal notranslate"><span class="pre">Series</span></code> also has a <code class="docutils literal notranslate"><span class="pre">name</span></code> attribute:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;something&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    0.182830
-1   -0.931166
-2    0.144336
-3    0.318988
-4    0.426142
-Name: something, dtype: float64
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="o">.</span><span class="n">name</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;something&#39;
-</pre></div>
-</div>
-</div>
-</div>
-<p>The <code class="docutils literal notranslate"><span class="pre">Series</span></code> <code class="docutils literal notranslate"><span class="pre">name</span></code> can be assigned automatically in many cases, in particular, when selecting a single column from a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>, the <code class="docutils literal notranslate"><span class="pre">name</span></code> will be assigned the column label.</p>
-<p>You can rename a <code class="docutils literal notranslate"><span class="pre">Series</span></code> with the <code class="docutils literal notranslate"><span class="pre">pandas.Series.rename()</span></code> method.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s2</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="s2">&quot;different&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s2</span><span class="o">.</span><span class="n">name</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;different&#39;
-</pre></div>
-</div>
-</div>
-</div>
-<p>Note that <code class="docutils literal notranslate"><span class="pre">s</span></code> and <code class="docutils literal notranslate"><span class="pre">s2</span></code> refer to different objects.</p>
-</section>
-</section>
-<section id="dataframe">
-<h3><span class="section-number">5.4.1.2. </span>DataFrame<a class="headerlink" href="#dataframe" title="Permalink to this headline">#</a></h3>
-<p><code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table, or a <code class="docutils literal notranslate"><span class="pre">dict</span></code> of <code class="docutils literal notranslate"><span class="pre">Series</span></code> objects. It is generally the most commonly used Pandas object. Like <code class="docutils literal notranslate"><span class="pre">Series</span></code>, <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> accepts many different kinds of input:</p>
-<ul class="simple">
-<li><p>Dict of 1D ndarrays, lists, dicts, or <code class="docutils literal notranslate"><span class="pre">Series</span></code></p></li>
-<li><p>2-D <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></p></li>
-<li><p>Structured or record ndarray</p></li>
-<li><p>A <code class="docutils literal notranslate"><span class="pre">Series</span></code></p></li>
-<li><p>Another <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code></p></li>
+<p>–</p>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="pandas/introduction-and-data-structures.html">5.4.1. Introduction and Data Structures</a></li>
+<li class="toctree-l1"><a class="reference internal" href="pandas/data-selection.html">5.4.2. Data Selection</a></li>
+<li class="toctree-l1"><a class="reference internal" href="pandas/advanced-pandas-techniques.html">5.4.3. Advanced Pandas Techniques</a></li>
 </ul>
-<p>Along with the data, you can optionally pass <strong>index</strong> (row labels) and <strong>columns</strong> (column labels) arguments. If you pass an index and / or columns, you are guaranteeing the index and / or columns of the resulting <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>. Thus, a <code class="docutils literal notranslate"><span class="pre">dict</span></code> of Series plus a specific index will discard all data not matching up to the passed index.</p>
-<p>If axis labels are not passed, they will be constructed from the input data based on common sense rules.</p>
-<section id="create-a-dataframe">
-<h4><span class="section-number">5.4.1.2.1. </span>Create a Dataframe<a class="headerlink" href="#create-a-dataframe" title="Permalink to this headline">#</a></h4>
-<section id="from-dict-of-series-or-dicts">
-<h5><span class="section-number">5.4.1.2.1.1. </span>From dict of <code class="docutils literal notranslate"><span class="pre">Series</span></code> or dicts<a class="headerlink" href="#from-dict-of-series-or-dicts" title="Permalink to this headline">#</a></h5>
-<p>The resulting <strong>index</strong> will be the <strong>union</strong> of the indexes of the various Series. If there are any nested dicts, these will first be converted to Series. If no columns are passed, the columns will be the ordered list of <code class="docutils literal notranslate"><span class="pre">dict</span></code> keys.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">d</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="s2">&quot;one&quot;</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">]),</span>
-    <span class="s2">&quot;two&quot;</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">,</span> <span class="s2">&quot;d&quot;</span><span class="p">]),</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>one</th>
-      <th>two</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>a</th>
-      <td>1.0</td>
-      <td>1.0</td>
-    </tr>
-    <tr>
-      <th>b</th>
-      <td>2.0</td>
-      <td>2.0</td>
-    </tr>
-    <tr>
-      <th>c</th>
-      <td>3.0</td>
-      <td>3.0</td>
-    </tr>
-    <tr>
-      <th>d</th>
-      <td>NaN</td>
-      <td>4.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;d&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;a&quot;</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>one</th>
-      <th>two</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>d</th>
-      <td>NaN</td>
-      <td>4.0</td>
-    </tr>
-    <tr>
-      <th>b</th>
-      <td>2.0</td>
-      <td>2.0</td>
-    </tr>
-    <tr>
-      <th>a</th>
-      <td>1.0</td>
-      <td>1.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;d&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;a&quot;</span><span class="p">],</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;two&quot;</span><span class="p">,</span> <span class="s2">&quot;three&quot;</span><span class="p">])</span>
-</pre></div>
 </div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>two</th>
-      <th>three</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>d</th>
-      <td>4.0</td>
-      <td>NaN</td>
-    </tr>
-    <tr>
-      <th>b</th>
-      <td>2.0</td>
-      <td>NaN</td>
-    </tr>
-    <tr>
-      <th>a</th>
-      <td>1.0</td>
-      <td>NaN</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>The row and column labels can be accessed respectively by accessing the <strong>index</strong> and <strong>columns</strong> attributes:</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>When a particular set of columns is passed along with a dict of data, the passed columns override the keys in the dict.</p>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">index</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Index([&#39;a&#39;, &#39;b&#39;, &#39;c&#39;, &#39;d&#39;], dtype=&#39;object&#39;)
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">columns</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Index([&#39;one&#39;, &#39;two&#39;], dtype=&#39;object&#39;)
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="from-dict-of-ndarrays-lists">
-<h5><span class="section-number">5.4.1.2.1.2. </span>From dict of ndarrays / lists<a class="headerlink" href="#from-dict-of-ndarrays-lists" title="Permalink to this headline">#</a></h5>
-<p>The ndarrays must all be the same length. If an index is passed, it must also be the same length as the arrays. If no index is passed, the result will be <code class="docutils literal notranslate"><span class="pre">range(n)</span></code>, where <code class="docutils literal notranslate"><span class="pre">n</span></code> is the array length.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">d</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;one&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">],</span> <span class="s2">&quot;two&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]}</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
-</pre></div>
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-<div class="cell_output docutils container">
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-      <th></th>
-      <th>one</th>
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-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1.0</td>
-      <td>4.0</td>
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-      <td>3.0</td>
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-      <th>2</th>
-      <td>3.0</td>
-      <td>2.0</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>4.0</td>
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-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">,</span> <span class="s2">&quot;d&quot;</span><span class="p">])</span>
-</pre></div>
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-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>one</th>
-      <th>two</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>a</th>
-      <td>1.0</td>
-      <td>4.0</td>
-    </tr>
-    <tr>
-      <th>b</th>
-      <td>2.0</td>
-      <td>3.0</td>
-    </tr>
-    <tr>
-      <th>c</th>
-      <td>3.0</td>
-      <td>2.0</td>
-    </tr>
-    <tr>
-      <th>d</th>
-      <td>4.0</td>
-      <td>1.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-</section>
-<section id="from-structured-or-record-array">
-<h5><span class="section-number">5.4.1.2.1.3. </span>From structured or record array<a class="headerlink" href="#from-structured-or-record-array" title="Permalink to this headline">#</a></h5>
-<p>This case is handled identically to a dict of arrays.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;i4&quot;</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;B&quot;</span><span class="p">,</span> <span class="s2">&quot;f4&quot;</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;C&quot;</span><span class="p">,</span> <span class="s2">&quot;a10&quot;</span><span class="p">)])</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data</span><span class="p">[:]</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="s2">&quot;Hello&quot;</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="s2">&quot;World&quot;</span><span class="p">)]</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
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-    .dataframe tbody tr th {
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-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-      <th>C</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>2.0</td>
-      <td>b'Hello'</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>2</td>
-      <td>3.0</td>
-      <td>b'World'</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;first&quot;</span><span class="p">,</span> <span class="s2">&quot;second&quot;</span><span class="p">])</span>
-</pre></div>
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-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-      <th>C</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>first</th>
-      <td>1</td>
-      <td>2.0</td>
-      <td>b'Hello'</td>
-    </tr>
-    <tr>
-      <th>second</th>
-      <td>2</td>
-      <td>3.0</td>
-      <td>b'World'</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">,</span> <span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>C</th>
-      <th>A</th>
-      <th>B</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>b'Hello'</td>
-      <td>1</td>
-      <td>2.0</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>b'World'</td>
-      <td>2</td>
-      <td>3.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>DataFrame is not intended to work exactly like a 2-dimensional NumPy ndarray.</p>
-</div>
-</section>
-<section id="from-a-list-of-dicts">
-<h5><span class="section-number">5.4.1.2.1.4. </span>From a list of dicts<a class="headerlink" href="#from-a-list-of-dicts" title="Permalink to this headline">#</a></h5>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data2</span> <span class="o">=</span> <span class="p">[{</span><span class="s2">&quot;a&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">},</span> <span class="p">{</span><span class="s2">&quot;a&quot;</span><span class="p">:</span> <span class="mi">5</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">:</span> <span class="mi">20</span><span class="p">}]</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data2</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
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-
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-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>a</th>
-      <th>b</th>
-      <th>c</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>2</td>
-      <td>NaN</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>5</td>
-      <td>10</td>
-      <td>20.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data2</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;first&quot;</span><span class="p">,</span> <span class="s2">&quot;second&quot;</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>a</th>
-      <th>b</th>
-      <th>c</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>first</th>
-      <td>1</td>
-      <td>2</td>
-      <td>NaN</td>
-    </tr>
-    <tr>
-      <th>second</th>
-      <td>5</td>
-      <td>10</td>
-      <td>20.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data2</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>a</th>
-      <th>b</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>2</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>5</td>
-      <td>10</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-</section>
-<section id="from-a-dict-of-tuples">
-<h5><span class="section-number">5.4.1.2.1.5. </span>From a dict of tuples<a class="headerlink" href="#from-a-dict-of-tuples" title="Permalink to this headline">#</a></h5>
-<p>You can automatically create a MultiIndexed frame by passing a tuples dictionary.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
-    <span class="p">{</span>
-        <span class="p">(</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">):</span> <span class="p">{(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">):</span> <span class="mi">1</span><span class="p">,</span> <span class="p">(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">):</span> <span class="mi">2</span><span class="p">},</span>
-        <span class="p">(</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;a&quot;</span><span class="p">):</span> <span class="p">{(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">):</span> <span class="mi">3</span><span class="p">,</span> <span class="p">(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">):</span> <span class="mi">4</span><span class="p">},</span>
-        <span class="p">(</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">):</span> <span class="p">{(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">):</span> <span class="mi">5</span><span class="p">,</span> <span class="p">(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">):</span> <span class="mi">6</span><span class="p">},</span>
-        <span class="p">(</span><span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;a&quot;</span><span class="p">):</span> <span class="p">{(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">):</span> <span class="mi">7</span><span class="p">,</span> <span class="p">(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">):</span> <span class="mi">8</span><span class="p">},</span>
-        <span class="p">(</span><span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">):</span> <span class="p">{(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;D&quot;</span><span class="p">):</span> <span class="mi">9</span><span class="p">,</span> <span class="p">(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">):</span> <span class="mi">10</span><span class="p">},</span>
-    <span class="p">}</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead tr th {
-        text-align: left;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr>
-      <th></th>
-      <th></th>
-      <th colspan="3" halign="left">a</th>
-      <th colspan="2" halign="left">b</th>
-    </tr>
-    <tr>
-      <th></th>
-      <th></th>
-      <th>b</th>
-      <th>a</th>
-      <th>c</th>
-      <th>a</th>
-      <th>b</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th rowspan="3" valign="top">A</th>
-      <th>B</th>
-      <td>1.0</td>
-      <td>4.0</td>
-      <td>5.0</td>
-      <td>8.0</td>
-      <td>10.0</td>
-    </tr>
-    <tr>
-      <th>C</th>
-      <td>2.0</td>
-      <td>3.0</td>
-      <td>6.0</td>
-      <td>7.0</td>
-      <td>NaN</td>
-    </tr>
-    <tr>
-      <th>D</th>
-      <td>NaN</td>
-      <td>NaN</td>
-      <td>NaN</td>
-      <td>NaN</td>
-      <td>9.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-</section>
-<section id="from-a-series">
-<h5><span class="section-number">5.4.1.2.1.6. </span>From a Series<a class="headerlink" href="#from-a-series" title="Permalink to this headline">#</a></h5>
-<p>The result will be a DataFrame with the same index as the input Series, and with one column whose name is the original name of the Series (only if no other column name provided).</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ser</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">),</span> <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s2">&quot;abc&quot;</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;ser&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">ser</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>ser</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>a</th>
-      <td>0</td>
-    </tr>
-    <tr>
-      <th>b</th>
-      <td>1</td>
-    </tr>
-    <tr>
-      <th>c</th>
-      <td>2</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-</section>
-<section id="from-a-list-of-namedtuples">
-<h5><span class="section-number">5.4.1.2.1.7. </span>From a list of namedtuples<a class="headerlink" href="#from-a-list-of-namedtuples" title="Permalink to this headline">#</a></h5>
-<p>The field names of the first <code class="docutils literal notranslate"><span class="pre">namedtuple</span></code> in the list determine the columns of the <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>. The remaining namedtuples (or tuples) are simply unpacked and their values are fed into the rows of the <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>. If any of those tuples is shorter than the first <code class="docutils literal notranslate"><span class="pre">namedtuple</span></code> then the later columns in the corresponding row are marked as missing values. If any are longer than the first <code class="docutils literal notranslate"><span class="pre">namedtuple</span></code> , a <code class="docutils literal notranslate"><span class="pre">ValueError</span></code> is raised.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">namedtuple</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Point</span> <span class="o">=</span> <span class="n">namedtuple</span><span class="p">(</span><span class="s2">&quot;Point&quot;</span><span class="p">,</span> <span class="s2">&quot;x y&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="n">Point</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">Point</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)])</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>x</th>
-      <th>y</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>0</td>
-      <td>0</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>0</td>
-      <td>3</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>2</td>
-      <td>3</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Point3D</span> <span class="o">=</span> <span class="n">namedtuple</span><span class="p">(</span><span class="s2">&quot;Point3D&quot;</span><span class="p">,</span> <span class="s2">&quot;x y z&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="n">Point3D</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">Point3D</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">Point</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)])</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>x</th>
-      <th>y</th>
-      <th>z</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>0</td>
-      <td>0</td>
-      <td>0.0</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>0</td>
-      <td>3</td>
-      <td>5.0</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>2</td>
-      <td>3</td>
-      <td>NaN</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-</section>
-<section id="from-a-list-of-dataclasses">
-<h5><span class="section-number">5.4.1.2.1.8. </span>From a list of dataclasses<a class="headerlink" href="#from-a-list-of-dataclasses" title="Permalink to this headline">#</a></h5>
-<p>Data Classes as introduced in PEP557, can be passed into the DataFrame constructor. Passing a list of dataclasses is equivalent to passing a list of dictionaries.</p>
-<p>Please be aware, that all values in the list should be dataclasses, mixing types in the list would result in a <code class="docutils literal notranslate"><span class="pre">TypeError</span></code>.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">dataclasses</span> <span class="kn">import</span> <span class="n">make_dataclass</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Point</span> <span class="o">=</span> <span class="n">make_dataclass</span><span class="p">(</span><span class="s2">&quot;Point&quot;</span><span class="p">,</span> <span class="p">[(</span><span class="s2">&quot;x&quot;</span><span class="p">,</span> <span class="nb">int</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;y&quot;</span><span class="p">,</span> <span class="nb">int</span><span class="p">)])</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="n">Point</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">Point</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">Point</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)])</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>x</th>
-      <th>y</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>0</td>
-      <td>0</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>0</td>
-      <td>3</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>2</td>
-      <td>3</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-</section>
-</section>
-<section id="column-selection-addition-deletion">
-<h4><span class="section-number">5.4.1.2.2. </span>Column selection, addition, deletion<a class="headerlink" href="#column-selection-addition-deletion" title="Permalink to this headline">#</a></h4>
-<p>You can treat a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> semantically like a dict of like-indexed <code class="docutils literal notranslate"><span class="pre">Series</span></code> objects. Getting, setting, and deleting columns works with the same syntax as the analogous dict operations:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
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-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>one</th>
-      <th>two</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>a</th>
-      <td>1.0</td>
-      <td>1.0</td>
-    </tr>
-    <tr>
-      <th>b</th>
-      <td>2.0</td>
-      <td>2.0</td>
-    </tr>
-    <tr>
-      <th>c</th>
-      <td>3.0</td>
-      <td>3.0</td>
-    </tr>
-    <tr>
-      <th>d</th>
-      <td>NaN</td>
-      <td>4.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;one&quot;</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    1.0
-b    2.0
-c    3.0
-d    NaN
-Name: one, dtype: float64
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;three&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;one&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;two&quot;</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;flag&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;one&quot;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">2</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>one</th>
-      <th>two</th>
-      <th>three</th>
-      <th>flag</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>a</th>
-      <td>1.0</td>
-      <td>1.0</td>
-      <td>1.0</td>
-      <td>False</td>
-    </tr>
-    <tr>
-      <th>b</th>
-      <td>2.0</td>
-      <td>2.0</td>
-      <td>4.0</td>
-      <td>False</td>
-    </tr>
-    <tr>
-      <th>c</th>
-      <td>3.0</td>
-      <td>3.0</td>
-      <td>9.0</td>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>d</th>
-      <td>NaN</td>
-      <td>4.0</td>
-      <td>NaN</td>
-      <td>False</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>Columns can be deleted or popped like with a dict:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">del</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;two&quot;</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">three</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;three&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
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-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>one</th>
-      <th>flag</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>a</th>
-      <td>1.0</td>
-      <td>False</td>
-    </tr>
-    <tr>
-      <th>b</th>
-      <td>2.0</td>
-      <td>False</td>
-    </tr>
-    <tr>
-      <th>c</th>
-      <td>3.0</td>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>d</th>
-      <td>NaN</td>
-      <td>False</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>When inserting a scalar value, it will naturally be propagated to fill the column:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;foo&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;bar&quot;</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>one</th>
-      <th>flag</th>
-      <th>foo</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>a</th>
-      <td>1.0</td>
-      <td>False</td>
-      <td>bar</td>
-    </tr>
-    <tr>
-      <th>b</th>
-      <td>2.0</td>
-      <td>False</td>
-      <td>bar</td>
-    </tr>
-    <tr>
-      <th>c</th>
-      <td>3.0</td>
-      <td>True</td>
-      <td>bar</td>
-    </tr>
-    <tr>
-      <th>d</th>
-      <td>NaN</td>
-      <td>False</td>
-      <td>bar</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>When inserting a <code class="docutils literal notranslate"><span class="pre">Series</span></code> that does not have the same index as the <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>, it will be conformed to the DataFrame’s index:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;one_trunc&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;one&quot;</span><span class="p">][:</span><span class="mi">2</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:1000px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Ad%20%3D%20%7B%0A%20%20%20%20%22one%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29,%0A%20%20%20%20%22two%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0,%204.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22,%20%22d%22%5D%29,%0A%7D%0Adf%20%3D%20pd.DataFrame%28d%29%0Adf%5B%22flag%22%5D%20%3D%20df%5B%22one%22%5D%20%3E%202%0Adel%20df%5B%22two%22%5D%0Adf%5B%22foo%22%5D%20%3D%20%22bar%22%0Adf%5B%22one%22%5D%5B%3A2%5D&d=2023-07-13&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div><div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>one</th>
-      <th>flag</th>
-      <th>foo</th>
-      <th>one_trunc</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>a</th>
-      <td>1.0</td>
-      <td>False</td>
-      <td>bar</td>
-      <td>1.0</td>
-    </tr>
-    <tr>
-      <th>b</th>
-      <td>2.0</td>
-      <td>False</td>
-      <td>bar</td>
-      <td>2.0</td>
-    </tr>
-    <tr>
-      <th>c</th>
-      <td>3.0</td>
-      <td>True</td>
-      <td>bar</td>
-      <td>NaN</td>
-    </tr>
-    <tr>
-      <th>d</th>
-      <td>NaN</td>
-      <td>False</td>
-      <td>bar</td>
-      <td>NaN</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>You can insert raw ndarrays but their length must match the length of the DataFrame’s index.</p>
-<p>By default, columns get inserted at the end. <code class="docutils literal notranslate"><span class="pre">DataFrame.insert()</span></code> inserts at a particular location in the columns:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;bar&quot;</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;one&quot;</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>one</th>
-      <th>bar</th>
-      <th>flag</th>
-      <th>foo</th>
-      <th>one_trunc</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>a</th>
-      <td>1.0</td>
-      <td>1.0</td>
-      <td>False</td>
-      <td>bar</td>
-      <td>1.0</td>
-    </tr>
-    <tr>
-      <th>b</th>
-      <td>2.0</td>
-      <td>2.0</td>
-      <td>False</td>
-      <td>bar</td>
-      <td>2.0</td>
-    </tr>
-    <tr>
-      <th>c</th>
-      <td>3.0</td>
-      <td>3.0</td>
-      <td>True</td>
-      <td>bar</td>
-      <td>NaN</td>
-    </tr>
-    <tr>
-      <th>d</th>
-      <td>NaN</td>
-      <td>NaN</td>
-      <td>False</td>
-      <td>bar</td>
-      <td>NaN</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-</section>
-<section id="assigning-new-columns-in-method-chains">
-<h4><span class="section-number">5.4.1.2.3. </span>Assigning new columns in method chains<a class="headerlink" href="#assigning-new-columns-in-method-chains" title="Permalink to this headline">#</a></h4>
-<p>DataFrame has an <code class="docutils literal notranslate"><span class="pre">assign()</span></code> method that allows you to easily create new columns that are potentially derived from existing columns.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">iris</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;../../assets/data/iris.csv&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">iris</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>SepalLength</th>
-      <th>SepalWidth</th>
-      <th>PetalLength</th>
-      <th>PetalWidth</th>
-      <th>Name</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>5.1</td>
-      <td>3.5</td>
-      <td>1.4</td>
-      <td>0.2</td>
-      <td>Iris-setosa</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>4.9</td>
-      <td>3.0</td>
-      <td>1.4</td>
-      <td>0.2</td>
-      <td>Iris-setosa</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>4.7</td>
-      <td>3.2</td>
-      <td>1.3</td>
-      <td>0.2</td>
-      <td>Iris-setosa</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>4.6</td>
-      <td>3.1</td>
-      <td>1.5</td>
-      <td>0.2</td>
-      <td>Iris-setosa</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>5.0</td>
-      <td>3.6</td>
-      <td>1.4</td>
-      <td>0.2</td>
-      <td>Iris-setosa</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">iris</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">sepal_ratio</span><span class="o">=</span><span class="n">iris</span><span class="p">[</span><span class="s2">&quot;SepalWidth&quot;</span><span class="p">]</span> <span class="o">/</span> <span class="n">iris</span><span class="p">[</span><span class="s2">&quot;SepalLength&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>SepalLength</th>
-      <th>SepalWidth</th>
-      <th>PetalLength</th>
-      <th>PetalWidth</th>
-      <th>Name</th>
-      <th>sepal_ratio</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>5.1</td>
-      <td>3.5</td>
-      <td>1.4</td>
-      <td>0.2</td>
-      <td>Iris-setosa</td>
-      <td>0.686275</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>4.9</td>
-      <td>3.0</td>
-      <td>1.4</td>
-      <td>0.2</td>
-      <td>Iris-setosa</td>
-      <td>0.612245</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>4.7</td>
-      <td>3.2</td>
-      <td>1.3</td>
-      <td>0.2</td>
-      <td>Iris-setosa</td>
-      <td>0.680851</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>4.6</td>
-      <td>3.1</td>
-      <td>1.5</td>
-      <td>0.2</td>
-      <td>Iris-setosa</td>
-      <td>0.673913</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>5.0</td>
-      <td>3.6</td>
-      <td>1.4</td>
-      <td>0.2</td>
-      <td>Iris-setosa</td>
-      <td>0.720000</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>In the example above, we inserted a precomputed value. We can also pass in a function of one argument to be evaluated on the DataFrame being assigned to.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">iris</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">sepal_ratio</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;SepalWidth&quot;</span><span class="p">]</span> <span class="o">/</span> <span class="n">x</span><span class="p">[</span><span class="s2">&quot;SepalLength&quot;</span><span class="p">]))</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>SepalLength</th>
-      <th>SepalWidth</th>
-      <th>PetalLength</th>
-      <th>PetalWidth</th>
-      <th>Name</th>
-      <th>sepal_ratio</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>5.1</td>
-      <td>3.5</td>
-      <td>1.4</td>
-      <td>0.2</td>
-      <td>Iris-setosa</td>
-      <td>0.686275</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>4.9</td>
-      <td>3.0</td>
-      <td>1.4</td>
-      <td>0.2</td>
-      <td>Iris-setosa</td>
-      <td>0.612245</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>4.7</td>
-      <td>3.2</td>
-      <td>1.3</td>
-      <td>0.2</td>
-      <td>Iris-setosa</td>
-      <td>0.680851</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>4.6</td>
-      <td>3.1</td>
-      <td>1.5</td>
-      <td>0.2</td>
-      <td>Iris-setosa</td>
-      <td>0.673913</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>5.0</td>
-      <td>3.6</td>
-      <td>1.4</td>
-      <td>0.2</td>
-      <td>Iris-setosa</td>
-      <td>0.720000</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p><code class="docutils literal notranslate"><span class="pre">assign()</span></code> <strong>always</strong> returns a copy of the data, leaving the original DataFrame untouched.</p>
-<p>Passing a callable, as opposed to an actual value to be inserted, is useful when you don’t have a reference to the DataFrame at hand. This is common when using <code class="docutils literal notranslate"><span class="pre">assign()</span></code> in a chain of operations. For example, we can limit the DataFrame to just those observations with a Sepal Length greater than 5, calculate the ratio, and plot:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="p">(</span>
-    <span class="n">iris</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="s2">&quot;SepalLength &gt; 5&quot;</span><span class="p">)</span>
-    <span class="o">.</span><span class="n">assign</span><span class="p">(</span>
-        <span class="n">SepalRatio</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">SepalWidth</span> <span class="o">/</span> <span class="n">x</span><span class="o">.</span><span class="n">SepalLength</span><span class="p">,</span>
-        <span class="n">PetalRatio</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">PetalWidth</span> <span class="o">/</span> <span class="n">x</span><span class="o">.</span><span class="n">PetalLength</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s2">&quot;scatter&quot;</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">&quot;SepalRatio&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;PetalRatio&quot;</span><span class="p">)</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;AxesSubplot: xlabel=&#39;SepalRatio&#39;, ylabel=&#39;PetalRatio&#39;&gt;
-</pre></div>
-</div>
-<img alt="../../_images/pandas_118_1.png" src="../../_images/pandas_118_1.png" />
-</div>
-</div>
-<p>Since a function is passed in, the function is computed on the DataFrame being assigned to. Importantly, this is the DataFrame that’s been filtered to those rows with sepal length greater than 5. The filtering happens first, and then the ratio calculations. This is an example where we didn’t have a reference to the filtered DataFrame available.</p>
-<p>The function signature for <code class="docutils literal notranslate"><span class="pre">assign()</span></code> is simply <code class="docutils literal notranslate"><span class="pre">**kwargs</span></code>. The keys are the column names for the new fields, and the values are either a value to be inserted (for example, a <code class="docutils literal notranslate"><span class="pre">Series</span></code> or NumPy array), or a function of one argument to be called on the <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>. A copy of the original <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> is returned, with the new values inserted.</p>
-<p>The order of <code class="docutils literal notranslate"><span class="pre">**kwargs</span></code> is preserved. This allows for dependent assignment, where an expression later in <code class="docutils literal notranslate"><span class="pre">**kwargs</span></code> can refer to a column created earlier in the same <code class="docutils literal notranslate"><span class="pre">assign()</span></code>.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfa</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">&quot;A&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="s2">&quot;B&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]})</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfa</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">C</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="s2">&quot;A&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">x</span><span class="p">[</span><span class="s2">&quot;B&quot;</span><span class="p">],</span> <span class="n">D</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="s2">&quot;A&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">x</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-      <th>C</th>
-      <th>D</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>4</td>
-      <td>5</td>
-      <td>6</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>2</td>
-      <td>5</td>
-      <td>7</td>
-      <td>9</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>3</td>
-      <td>6</td>
-      <td>9</td>
-      <td>12</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>In the second expression, <code class="docutils literal notranslate"><span class="pre">x['C']</span></code> will refer to the newly created column, that’s equal to <code class="docutils literal notranslate"><span class="pre">dfa['A']</span> <span class="pre">+</span> <span class="pre">dfa['B']</span></code>.</p>
-</section>
-<section id="indexing-selection">
-<h4><span class="section-number">5.4.1.2.4. </span>Indexing / selection<a class="headerlink" href="#indexing-selection" title="Permalink to this headline">#</a></h4>
-<p>The basics of indexing are as follows:</p>
-<table class="colwidths-auto table">
-<thead>
-<tr class="row-odd"><th class="text-align:left head"><p>Operation</p></th>
-<th class="text-align:left head"><p>Syntax</p></th>
-<th class="text-align:left head"><p>Result</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td class="text-align:left"><p>Select column</p></td>
-<td class="text-align:left"><p><code class="docutils literal notranslate"><span class="pre">df[col]</span></code></p></td>
-<td class="text-align:left"><p>Series</p></td>
-</tr>
-<tr class="row-odd"><td class="text-align:left"><p>Select row by label</p></td>
-<td class="text-align:left"><p><code class="docutils literal notranslate"><span class="pre">df.loc[label]</span></code></p></td>
-<td class="text-align:left"><p>Series</p></td>
-</tr>
-<tr class="row-even"><td class="text-align:left"><p>Select row by integer location</p></td>
-<td class="text-align:left"><p><code class="docutils literal notranslate"><span class="pre">df.iloc[loc]</span></code></p></td>
-<td class="text-align:left"><p>Series</p></td>
-</tr>
-<tr class="row-odd"><td class="text-align:left"><p>Slice rows</p></td>
-<td class="text-align:left"><p><code class="docutils literal notranslate"><span class="pre">df[5:10]</span> </code></p></td>
-<td class="text-align:left"><p>DataFrame</p></td>
-</tr>
-<tr class="row-even"><td class="text-align:left"><p>Select rows by boolean vector</p></td>
-<td class="text-align:left"><p><code class="docutils literal notranslate"><span class="pre">df[bool_vec]</span></code></p></td>
-<td class="text-align:left"><p>DataFrame</p></td>
-</tr>
-</tbody>
-</table>
-<p>Row selection, for example, returns a <code class="docutils literal notranslate"><span class="pre">Series</span></code> whose index is the columns of the <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">&quot;b&quot;</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:730px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Ad%20%3D%20%7B%0A%20%20%20%20%22one%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29,%0A%20%20%20%20%22two%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0,%204.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22,%20%22d%22%5D%29,%0A%7D%0Adf%20%3D%20pd.DataFrame%28d%29%0Adf%5B%22flag%22%5D%20%3D%20df%5B%22one%22%5D%20%3E%202%0Adel%20df%5B%22two%22%5D%0Adf%5B%22foo%22%5D%20%3D%20%22bar%22%0Adf%5B%22one_trunc%22%5D%20%3Ddf.insert%281,%20%22bar%22,%20df%5B%22one%22%5D%29%0Adf.loc%5B%22b%22%5D&d=2023-07-13&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div><div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>one           3.0
-bar           3.0
-flag         True
-foo           bar
-one_trunc     NaN
-Name: c, dtype: object
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="data-alignment-and-arithmetic">
-<h4><span class="section-number">5.4.1.2.5. </span>Data alignment and arithmetic<a class="headerlink" href="#data-alignment-and-arithmetic" title="Permalink to this headline">#</a></h4>
-<p>Data alignment between <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> objects automatically aligns on <strong>both</strong> the columns and the index (row labels)**. Again, the resulting object will have the union of the column and row labels.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">,</span> <span class="s2">&quot;D&quot;</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">+</span> <span class="n">df2</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-      <th>C</th>
-      <th>D</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>-1.034449</td>
-      <td>0.718916</td>
-      <td>0.140419</td>
-      <td>NaN</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>1.765848</td>
-      <td>0.010122</td>
-      <td>0.636368</td>
-      <td>NaN</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>-0.855305</td>
-      <td>0.230352</td>
-      <td>0.573435</td>
-      <td>NaN</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>-0.509938</td>
-      <td>1.352540</td>
-      <td>-0.631656</td>
-      <td>NaN</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>-1.162460</td>
-      <td>1.902037</td>
-      <td>-0.058588</td>
-      <td>NaN</td>
-    </tr>
-    <tr>
-      <th>5</th>
-      <td>-0.052771</td>
-      <td>-1.345608</td>
-      <td>3.168266</td>
-      <td>NaN</td>
-    </tr>
-    <tr>
-      <th>6</th>
-      <td>-1.440110</td>
-      <td>-0.183173</td>
-      <td>0.280195</td>
-      <td>NaN</td>
-    </tr>
-    <tr>
-      <th>7</th>
-      <td>NaN</td>
-      <td>NaN</td>
-      <td>NaN</td>
-      <td>NaN</td>
-    </tr>
-    <tr>
-      <th>8</th>
-      <td>NaN</td>
-      <td>NaN</td>
-      <td>NaN</td>
-      <td>NaN</td>
-    </tr>
-    <tr>
-      <th>9</th>
-      <td>NaN</td>
-      <td>NaN</td>
-      <td>NaN</td>
-      <td>NaN</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>When doing an operation between <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> and <code class="docutils literal notranslate"><span class="pre">Series</span></code>, the default behavior is to align the <code class="docutils literal notranslate"><span class="pre">Series</span></code> <strong>index</strong> on the <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> <strong>columns</strong>, thus broadcasting row-wise. For example:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">-</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-      <th>C</th>
-      <th>D</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>0.000000</td>
-      <td>0.000000</td>
-      <td>0.000000</td>
-      <td>0.000000</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>0.537551</td>
-      <td>-1.578889</td>
-      <td>-1.614495</td>
-      <td>1.598048</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>1.573832</td>
-      <td>-0.441195</td>
-      <td>-1.954656</td>
-      <td>1.888938</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>0.325417</td>
-      <td>0.719227</td>
-      <td>-3.141645</td>
-      <td>-0.030097</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>0.351423</td>
-      <td>0.296638</td>
-      <td>-1.667557</td>
-      <td>2.071918</td>
-    </tr>
-    <tr>
-      <th>5</th>
-      <td>0.706899</td>
-      <td>-1.667650</td>
-      <td>-1.156199</td>
-      <td>0.343590</td>
-    </tr>
-    <tr>
-      <th>6</th>
-      <td>0.394004</td>
-      <td>-1.852758</td>
-      <td>-2.264064</td>
-      <td>-0.252468</td>
-    </tr>
-    <tr>
-      <th>7</th>
-      <td>1.875723</td>
-      <td>-0.890963</td>
-      <td>-3.212639</td>
-      <td>0.738720</td>
-    </tr>
-    <tr>
-      <th>8</th>
-      <td>1.322616</td>
-      <td>-0.894232</td>
-      <td>-0.643626</td>
-      <td>1.804562</td>
-    </tr>
-    <tr>
-      <th>9</th>
-      <td>0.666647</td>
-      <td>0.375275</td>
-      <td>-1.202656</td>
-      <td>0.855678</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>Arithmetic operations with scalars operate element-wise:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">*</span> <span class="mi">5</span> <span class="o">+</span> <span class="mi">2</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-      <th>C</th>
-      <th>D</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>-2.447541</td>
-      <td>7.522482</td>
-      <td>10.973291</td>
-      <td>-2.429113</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>0.240213</td>
-      <td>-0.371962</td>
-      <td>2.900815</td>
-      <td>5.561129</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>5.421622</td>
-      <td>5.316509</td>
-      <td>1.200010</td>
-      <td>7.015574</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>-0.820455</td>
-      <td>11.118616</td>
-      <td>-4.734936</td>
-      <td>-2.579596</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>-0.690424</td>
-      <td>9.005674</td>
-      <td>2.635504</td>
-      <td>7.930478</td>
-    </tr>
-    <tr>
-      <th>5</th>
-      <td>1.086953</td>
-      <td>-0.815768</td>
-      <td>5.192298</td>
-      <td>-0.711166</td>
-    </tr>
-    <tr>
-      <th>6</th>
-      <td>-0.477520</td>
-      <td>-1.741306</td>
-      <td>-0.347029</td>
-      <td>-3.691456</td>
-    </tr>
-    <tr>
-      <th>7</th>
-      <td>6.931074</td>
-      <td>3.067669</td>
-      <td>-5.089906</td>
-      <td>1.264485</td>
-    </tr>
-    <tr>
-      <th>8</th>
-      <td>4.165539</td>
-      <td>3.051320</td>
-      <td>7.755160</td>
-      <td>6.593695</td>
-    </tr>
-    <tr>
-      <th>9</th>
-      <td>0.885692</td>
-      <td>9.398856</td>
-      <td>4.960012</td>
-      <td>1.849276</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="mi">1</span> <span class="o">/</span> <span class="n">df</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-      <th>C</th>
-      <th>D</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>-1.124217</td>
-      <td>0.905390</td>
-      <td>0.557209</td>
-      <td>-1.128894</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>-2.841254</td>
-      <td>-2.107959</td>
-      <td>5.550530</td>
-      <td>1.404049</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>1.461295</td>
-      <td>1.507609</td>
-      <td>-6.250077</td>
-      <td>0.996895</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>-1.772764</td>
-      <td>0.548329</td>
-      <td>-0.742398</td>
-      <td>-1.091799</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>-1.858443</td>
-      <td>0.713707</td>
-      <td>7.867773</td>
-      <td>0.843102</td>
-    </tr>
-    <tr>
-      <th>5</th>
-      <td>-5.476172</td>
-      <td>-1.775714</td>
-      <td>1.566270</td>
-      <td>-1.844225</td>
-    </tr>
-    <tr>
-      <th>6</th>
-      <td>-2.018147</td>
-      <td>-1.336432</td>
-      <td>-2.130353</td>
-      <td>-0.878510</td>
-    </tr>
-    <tr>
-      <th>7</th>
-      <td>1.013978</td>
-      <td>4.683100</td>
-      <td>-0.705228</td>
-      <td>-6.797955</td>
-    </tr>
-    <tr>
-      <th>8</th>
-      <td>2.308894</td>
-      <td>4.755925</td>
-      <td>0.868786</td>
-      <td>1.088448</td>
-    </tr>
-    <tr>
-      <th>9</th>
-      <td>-4.487090</td>
-      <td>0.675780</td>
-      <td>1.689182</td>
-      <td>-33.173253</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">**</span> <span class="mi">4</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
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-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-      <th>C</th>
-      <th>D</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>0.626037</td>
-      <td>1.488186</td>
-      <td>10.373539</td>
-      <td>6.157256e-01</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>0.015345</td>
-      <td>0.050647</td>
-      <td>0.001054</td>
-      <td>2.573183e-01</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>0.219305</td>
-      <td>0.193573</td>
-      <td>0.000655</td>
-      <td>1.012518e+00</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>0.101250</td>
-      <td>11.062052</td>
-      <td>3.291954</td>
-      <td>7.037668e-01</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>0.083831</td>
-      <td>3.854071</td>
-      <td>0.000261</td>
-      <td>1.979150e+00</td>
-    </tr>
-    <tr>
-      <th>5</th>
-      <td>0.001112</td>
-      <td>0.100579</td>
-      <td>0.166163</td>
-      <td>8.644588e-02</td>
-    </tr>
-    <tr>
-      <th>6</th>
-      <td>0.060282</td>
-      <td>0.313482</td>
-      <td>0.048550</td>
-      <td>1.678856e+00</td>
-    </tr>
-    <tr>
-      <th>7</th>
-      <td>0.945989</td>
-      <td>0.002079</td>
-      <td>4.042796</td>
-      <td>4.682598e-04</td>
-    </tr>
-    <tr>
-      <th>8</th>
-      <td>0.035187</td>
-      <td>0.001955</td>
-      <td>1.755293</td>
-      <td>7.124734e-01</td>
-    </tr>
-    <tr>
-      <th>9</th>
-      <td>0.002467</td>
-      <td>4.794885</td>
-      <td>0.122827</td>
-      <td>8.257484e-07</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>Boolean operators operate element-wise as well:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">&quot;a&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;b&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]},</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">bool</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">&quot;a&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;b&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]},</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">bool</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span> <span class="o">&amp;</span> <span class="n">df2</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
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-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>a</th>
-      <th>b</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>False</td>
-      <td>False</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>False</td>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>True</td>
-      <td>False</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span> <span class="o">|</span> <span class="n">df2</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>a</th>
-      <th>b</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>True</td>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>True</td>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>True</td>
-      <td>True</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span> <span class="o">^</span> <span class="n">df2</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
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-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>a</th>
-      <th>b</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>True</td>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>True</td>
-      <td>False</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>False</td>
-      <td>True</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">-</span><span class="n">df1</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>a</th>
-      <th>b</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>False</td>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>True</td>
-      <td>False</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>False</td>
-      <td>False</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-</section>
-<section id="transposing">
-<h4><span class="section-number">5.4.1.2.6. </span>Transposing<a class="headerlink" href="#transposing" title="Permalink to this headline">#</a></h4>
-<p>To transpose, access the <code class="docutils literal notranslate"><span class="pre">T</span></code> attribute or <code class="docutils literal notranslate"><span class="pre">DataFrame.transpose()</span></code>, similar to an ndarray:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>0</th>
-      <th>1</th>
-      <th>2</th>
-      <th>3</th>
-      <th>4</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>A</th>
-      <td>-0.889508</td>
-      <td>-0.351957</td>
-      <td>0.684324</td>
-      <td>-0.564091</td>
-      <td>-0.538085</td>
-    </tr>
-    <tr>
-      <th>B</th>
-      <td>1.104496</td>
-      <td>-0.474392</td>
-      <td>0.663302</td>
-      <td>1.823723</td>
-      <td>1.401135</td>
-    </tr>
-    <tr>
-      <th>C</th>
-      <td>1.794658</td>
-      <td>0.180163</td>
-      <td>-0.159998</td>
-      <td>-1.346987</td>
-      <td>0.127101</td>
-    </tr>
-    <tr>
-      <th>D</th>
-      <td>-0.885823</td>
-      <td>0.712226</td>
-      <td>1.003115</td>
-      <td>-0.915919</td>
-      <td>1.186096</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-</section>
-</section>
-</section>
-<section id="data-indexing-and-selection">
-<h2><span class="section-number">5.4.2. </span>Data indexing and selection<a class="headerlink" href="#data-indexing-and-selection" title="Permalink to this headline">#</a></h2>
-<p>The axis labeling information in Pandas objects serves many purposes:</p>
-<ul class="simple">
-<li><p>Identifies data (i.e. provides metadata) using known indicators, important for analysis, visualization, and interactive console display.</p></li>
-<li><p>Enables automatic and explicit data alignment.</p></li>
-<li><p>Allows intuitive getting and setting of subsets of the data set.</p></li>
-</ul>
-<p>In this section, we will focus on the final point: namely, how to slice, dice, and generally get and set subsets of Pandas objects. The primary focus will be on Series and DataFrame as they have received more development attention in this area.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>The Python and NumPy indexing operators <code class="docutils literal notranslate"><span class="pre">[]</span></code> and attribute operator <code class="docutils literal notranslate"><span class="pre">.</span></code> provide quick and easy access to Pandas data structures across a wide range of use cases. This makes interactive work intuitive, as there’s little new to learn if you already know how to deal with Python dictionaries and NumPy arrays. However, since the type of the data to be accessed isn’t known in advance, directly using standard operators has some optimization limits. For production code, we recommended that you take advantage of the optimized Pandas data access methods exposed in this chapter.</p>
-</div>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>Whether a copy or a reference is returned for a setting operation, may depend on the context. This is sometimes called <code class="docutils literal notranslate"><span class="pre">chained</span> <span class="pre">assignment</span></code> and should be avoided.</p>
-</div>
-<section id="different-choices-for-indexing">
-<h3><span class="section-number">5.4.2.1. </span>Different choices for indexing<a class="headerlink" href="#different-choices-for-indexing" title="Permalink to this headline">#</a></h3>
-<p>Object selection has had a number of user-requested additions in order to support more explicit location-based indexing. Pandas now supports three types of multi-axis indexing.</p>
-<ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">.loc</span></code> is primarily label based, but may also be used with a boolean array. <code class="docutils literal notranslate"><span class="pre">.loc</span></code> will raise <code class="docutils literal notranslate"><span class="pre">KeyError</span></code> when the items are not found. Allowed inputs are:</p>
-<ul>
-<li><p>A single label, e.g. <code class="docutils literal notranslate"><span class="pre">5</span></code> or <code class="docutils literal notranslate"><span class="pre">'a'</span></code> (Note that <code class="docutils literal notranslate"><span class="pre">5</span></code> is interpreted as a label of the index. This use is not an integer position along the index.).</p></li>
-<li><p>A list or array of labels <code class="docutils literal notranslate"><span class="pre">['a',</span> <span class="pre">'b',</span> <span class="pre">'c']</span></code>.</p></li>
-<li><p>A slice object with labels <code class="docutils literal notranslate"><span class="pre">'a':'f'</span></code> (Note that contrary to usual Python slices, both the start and the stop are included, when present in the index!)</p></li>
-<li><p>A boolean array (any <code class="docutils literal notranslate"><span class="pre">NA</span></code> values will be treated as <code class="docutils literal notranslate"><span class="pre">False</span></code>).</p></li>
-<li><p>A <code class="docutils literal notranslate"><span class="pre">callable</span></code> function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above).</p></li>
-</ul>
-</li>
-<li><p><code class="docutils literal notranslate"><span class="pre">.iloc</span></code> is primarily integer position based (from <code class="docutils literal notranslate"><span class="pre">0</span></code> to <code class="docutils literal notranslate"><span class="pre">length-1</span></code> of the axis), but may also be used with a boolean array. <code class="docutils literal notranslate"><span class="pre">.iloc</span></code> will raise <code class="docutils literal notranslate"><span class="pre">IndexError</span></code> if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing. (this conforms with Python/NumPy slice semantics). Allowed inputs are:</p>
-<ul>
-<li><p>An integer e.g. <code class="docutils literal notranslate"><span class="pre">5</span></code>.</p></li>
-<li><p>A list or array of integers <code class="docutils literal notranslate"><span class="pre">[4,</span> <span class="pre">3,</span> <span class="pre">0]</span></code>.</p></li>
-<li><p>A slice object with ints <code class="docutils literal notranslate"><span class="pre">1:7</span></code>.</p></li>
-<li><p>A boolean array (any <code class="docutils literal notranslate"><span class="pre">NA</span></code> values will be treated as <code class="docutils literal notranslate"><span class="pre">False</span></code>).</p></li>
-<li><p>A <code class="docutils literal notranslate"><span class="pre">callable</span></code> function with one argument (the calling Series or DataFrame) that returns valid output for indexing (one of the above).</p></li>
-</ul>
-</li>
-<li><p><code class="docutils literal notranslate"><span class="pre">.loc</span></code>, <code class="docutils literal notranslate"><span class="pre">.iloc</span></code>, and also <code class="docutils literal notranslate"><span class="pre">[]</span></code> indexing can accept a <code class="docutils literal notranslate"><span class="pre">callable</span></code> as indexer.</p></li>
-</ul>
-<p>Getting values from an object with multi-axes selection uses the following notation (using <code class="docutils literal notranslate"><span class="pre">.loc</span></code> as an example, but the following applies to <code class="docutils literal notranslate"><span class="pre">.iloc</span></code> as well). Any of the axes accessors may be the null slice <code class="docutils literal notranslate"><span class="pre">:</span></code>. Axes left out of the specification are assumed to be <code class="docutils literal notranslate"><span class="pre">:</span></code>, e.g. <code class="docutils literal notranslate"><span class="pre">p.loc['a']</span></code> is equivalent to <code class="docutils literal notranslate"><span class="pre">p.loc['a',</span> <span class="pre">:]</span></code>.</p>
-<table class="colwidths-auto table">
-<thead>
-<tr class="row-odd"><th class="text-align:left head"><p><strong>Object Type</strong></p></th>
-<th class="text-align:left head"><p><strong>Indexers</strong></p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td class="text-align:left"><p>Series</p></td>
-<td class="text-align:left"><p><code class="docutils literal notranslate"><span class="pre">s.loc[indexer]</span></code></p></td>
-</tr>
-<tr class="row-odd"><td class="text-align:left"><p>DataFrame</p></td>
-<td class="text-align:left"><p><code class="docutils literal notranslate"><span class="pre">df.loc[row_indexer,</span> <span class="pre">column_indexer]</span></code></p></td>
-</tr>
-</tbody>
-</table>
-</section>
-<section id="basics">
-<h3><span class="section-number">5.4.2.2. </span>Basics<a class="headerlink" href="#basics" title="Permalink to this headline">#</a></h3>
-<p>As mentioned when introducing the data structures in the last section, the primary function of indexing with <code class="docutils literal notranslate"><span class="pre">[]</span></code> (a.k.a.<code class="docutils literal notranslate"> <span class="pre">__getitem__</span></code> for those familiar with implementing class behavior in Python) is selecting out lower-dimensional slices. The following table shows return type values when indexing Pandas objects with <code class="docutils literal notranslate"><span class="pre">[]</span></code>:</p>
-<table class="colwidths-auto table">
-<thead>
-<tr class="row-odd"><th class="text-align:left head"><p><strong>Object Type</strong></p></th>
-<th class="text-align:left head"><p><strong>Selection</strong></p></th>
-<th class="text-align:left head"><p>Return Value Type</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td class="text-align:left"><p>Series</p></td>
-<td class="text-align:left"><p><code class="docutils literal notranslate"><span class="pre">series[label]</span></code></p></td>
-<td class="text-align:left"><p>scalar value</p></td>
-</tr>
-<tr class="row-odd"><td class="text-align:left"><p>DataFrame</p></td>
-<td class="text-align:left"><p><code class="docutils literal notranslate"><span class="pre">frame[colname]</span></code></p></td>
-<td class="text-align:left"><p><code class="docutils literal notranslate"><span class="pre">Series</span></code> corresponding to colname</p></td>
-</tr>
-</tbody>
-</table>
-<p>Here we construct a simple time series data set to use for illustrating the indexing functionality:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dates</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span><span class="s1">&#39;1/1/2000&#39;</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
-                  <span class="n">index</span><span class="o">=</span><span class="n">dates</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">,</span> <span class="s1">&#39;D&#39;</span><span class="p">])</span>
-<span class="n">df</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-      <th>C</th>
-      <th>D</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>2000-01-01</th>
-      <td>0.560977</td>
-      <td>0.210545</td>
-      <td>0.158079</td>
-      <td>0.492279</td>
-    </tr>
-    <tr>
-      <th>2000-01-02</th>
-      <td>1.280369</td>
-      <td>-0.817745</td>
-      <td>-1.522940</td>
-      <td>-1.275264</td>
-    </tr>
-    <tr>
-      <th>2000-01-03</th>
-      <td>0.156302</td>
-      <td>-0.313090</td>
-      <td>-1.386411</td>
-      <td>-0.757229</td>
-    </tr>
-    <tr>
-      <th>2000-01-04</th>
-      <td>0.528869</td>
-      <td>-0.542066</td>
-      <td>1.345872</td>
-      <td>-0.706867</td>
-    </tr>
-    <tr>
-      <th>2000-01-05</th>
-      <td>-2.466330</td>
-      <td>0.731123</td>
-      <td>-2.066561</td>
-      <td>-1.477377</td>
-    </tr>
-    <tr>
-      <th>2000-01-06</th>
-      <td>-0.300497</td>
-      <td>0.207552</td>
-      <td>-0.639468</td>
-      <td>-0.950777</td>
-    </tr>
-    <tr>
-      <th>2000-01-07</th>
-      <td>-2.626322</td>
-      <td>-1.072332</td>
-      <td>-1.523701</td>
-      <td>-1.489816</td>
-    </tr>
-    <tr>
-      <th>2000-01-08</th>
-      <td>-0.960255</td>
-      <td>-0.402632</td>
-      <td>-0.397366</td>
-      <td>0.085742</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>None of the indexing functionality is time series specific unless specifically stated.</p>
-</div>
-<p>Thus, as per above, we have the most basic indexing using <code class="docutils literal notranslate"><span class="pre">[]</span></code>:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">]</span>
-
-<span class="n">s</span><span class="p">[</span><span class="n">dates</span><span class="p">[</span><span class="mi">5</span><span class="p">]]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.3004967298995864
-</pre></div>
-</div>
-</div>
-</div>
-<p>You can pass a list of columns to <code class="docutils literal notranslate"><span class="pre">[]</span></code> to select columns in that order. If a column is not contained in the DataFrame, an exception will be raised. Multiple columns can also be set in this manner:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
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-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-      <th>C</th>
-      <th>D</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>2000-01-01</th>
-      <td>0.560977</td>
-      <td>0.210545</td>
-      <td>0.158079</td>
-      <td>0.492279</td>
-    </tr>
-    <tr>
-      <th>2000-01-02</th>
-      <td>1.280369</td>
-      <td>-0.817745</td>
-      <td>-1.522940</td>
-      <td>-1.275264</td>
-    </tr>
-    <tr>
-      <th>2000-01-03</th>
-      <td>0.156302</td>
-      <td>-0.313090</td>
-      <td>-1.386411</td>
-      <td>-0.757229</td>
-    </tr>
-    <tr>
-      <th>2000-01-04</th>
-      <td>0.528869</td>
-      <td>-0.542066</td>
-      <td>1.345872</td>
-      <td>-0.706867</td>
-    </tr>
-    <tr>
-      <th>2000-01-05</th>
-      <td>-2.466330</td>
-      <td>0.731123</td>
-      <td>-2.066561</td>
-      <td>-1.477377</td>
-    </tr>
-    <tr>
-      <th>2000-01-06</th>
-      <td>-0.300497</td>
-      <td>0.207552</td>
-      <td>-0.639468</td>
-      <td>-0.950777</td>
-    </tr>
-    <tr>
-      <th>2000-01-07</th>
-      <td>-2.626322</td>
-      <td>-1.072332</td>
-      <td>-1.523701</td>
-      <td>-1.489816</td>
-    </tr>
-    <tr>
-      <th>2000-01-08</th>
-      <td>-0.960255</td>
-      <td>-0.402632</td>
-      <td>-0.397366</td>
-      <td>0.085742</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[[</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;A&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">]]</span>
-<span class="n">df</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
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-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-      <th>C</th>
-      <th>D</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>2000-01-01</th>
-      <td>0.210545</td>
-      <td>0.560977</td>
-      <td>0.158079</td>
-      <td>0.492279</td>
-    </tr>
-    <tr>
-      <th>2000-01-02</th>
-      <td>-0.817745</td>
-      <td>1.280369</td>
-      <td>-1.522940</td>
-      <td>-1.275264</td>
-    </tr>
-    <tr>
-      <th>2000-01-03</th>
-      <td>-0.313090</td>
-      <td>0.156302</td>
-      <td>-1.386411</td>
-      <td>-0.757229</td>
-    </tr>
-    <tr>
-      <th>2000-01-04</th>
-      <td>-0.542066</td>
-      <td>0.528869</td>
-      <td>1.345872</td>
-      <td>-0.706867</td>
-    </tr>
-    <tr>
-      <th>2000-01-05</th>
-      <td>0.731123</td>
-      <td>-2.466330</td>
-      <td>-2.066561</td>
-      <td>-1.477377</td>
-    </tr>
-    <tr>
-      <th>2000-01-06</th>
-      <td>0.207552</td>
-      <td>-0.300497</td>
-      <td>-0.639468</td>
-      <td>-0.950777</td>
-    </tr>
-    <tr>
-      <th>2000-01-07</th>
-      <td>-1.072332</td>
-      <td>-2.626322</td>
-      <td>-1.523701</td>
-      <td>-1.489816</td>
-    </tr>
-    <tr>
-      <th>2000-01-08</th>
-      <td>-0.402632</td>
-      <td>-0.960255</td>
-      <td>-0.397366</td>
-      <td>0.085742</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>You may find this useful for applying a transform (in-place) to a subset of the columns.</p>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>Pandas aligns all AXES when setting <code class="docutils literal notranslate"><span class="pre">Series</span></code> and <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> from <code class="docutils literal notranslate"><span class="pre">.loc</span></code>, and <code class="docutils literal notranslate"><span class="pre">.iloc</span></code>.</p>
-<p>This will not modify <code class="docutils literal notranslate"><span class="pre">df</span></code> because the column alignment is before value assignment.</p>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">]]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
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-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>2000-01-01</th>
-      <td>0.210545</td>
-      <td>0.560977</td>
-    </tr>
-    <tr>
-      <th>2000-01-02</th>
-      <td>-0.817745</td>
-      <td>1.280369</td>
-    </tr>
-    <tr>
-      <th>2000-01-03</th>
-      <td>-0.313090</td>
-      <td>0.156302</td>
-    </tr>
-    <tr>
-      <th>2000-01-04</th>
-      <td>-0.542066</td>
-      <td>0.528869</td>
-    </tr>
-    <tr>
-      <th>2000-01-05</th>
-      <td>0.731123</td>
-      <td>-2.466330</td>
-    </tr>
-    <tr>
-      <th>2000-01-06</th>
-      <td>0.207552</td>
-      <td>-0.300497</td>
-    </tr>
-    <tr>
-      <th>2000-01-07</th>
-      <td>-1.072332</td>
-      <td>-2.626322</td>
-    </tr>
-    <tr>
-      <th>2000-01-08</th>
-      <td>-0.402632</td>
-      <td>-0.960255</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;A&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">]]</span>
-<span class="n">df</span><span class="p">[[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">]]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>2000-01-01</th>
-      <td>0.210545</td>
-      <td>0.560977</td>
-    </tr>
-    <tr>
-      <th>2000-01-02</th>
-      <td>-0.817745</td>
-      <td>1.280369</td>
-    </tr>
-    <tr>
-      <th>2000-01-03</th>
-      <td>-0.313090</td>
-      <td>0.156302</td>
-    </tr>
-    <tr>
-      <th>2000-01-04</th>
-      <td>-0.542066</td>
-      <td>0.528869</td>
-    </tr>
-    <tr>
-      <th>2000-01-05</th>
-      <td>0.731123</td>
-      <td>-2.466330</td>
-    </tr>
-    <tr>
-      <th>2000-01-06</th>
-      <td>0.207552</td>
-      <td>-0.300497</td>
-    </tr>
-    <tr>
-      <th>2000-01-07</th>
-      <td>-1.072332</td>
-      <td>-2.626322</td>
-    </tr>
-    <tr>
-      <th>2000-01-08</th>
-      <td>-0.402632</td>
-      <td>-0.960255</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>The correct way to swap column values is by using raw values:</p>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;A&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
-<span class="n">df</span><span class="p">[[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">]]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>2000-01-01</th>
-      <td>0.560977</td>
-      <td>0.210545</td>
-    </tr>
-    <tr>
-      <th>2000-01-02</th>
-      <td>1.280369</td>
-      <td>-0.817745</td>
-    </tr>
-    <tr>
-      <th>2000-01-03</th>
-      <td>0.156302</td>
-      <td>-0.313090</td>
-    </tr>
-    <tr>
-      <th>2000-01-04</th>
-      <td>0.528869</td>
-      <td>-0.542066</td>
-    </tr>
-    <tr>
-      <th>2000-01-05</th>
-      <td>-2.466330</td>
-      <td>0.731123</td>
-    </tr>
-    <tr>
-      <th>2000-01-06</th>
-      <td>-0.300497</td>
-      <td>0.207552</td>
-    </tr>
-    <tr>
-      <th>2000-01-07</th>
-      <td>-2.626322</td>
-      <td>-1.072332</td>
-    </tr>
-    <tr>
-      <th>2000-01-08</th>
-      <td>-0.960255</td>
-      <td>-0.402632</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-</section>
-<section id="attribute-access">
-<h3><span class="section-number">5.4.2.3. </span>Attribute access<a class="headerlink" href="#attribute-access" title="Permalink to this headline">#</a></h3>
-<p>You may access an index on a <code class="docutils literal notranslate"><span class="pre">Series</span></code> or column on a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> directly as an attribute:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sa</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">&#39;abc&#39;</span><span class="p">))</span>
-<span class="n">dfa</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sa</span><span class="o">.</span><span class="n">b</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfa</span><span class="o">.</span><span class="n">A</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2000-01-01    0.560977
-2000-01-02    1.280369
-2000-01-03    0.156302
-2000-01-04    0.528869
-2000-01-05   -2.466330
-2000-01-06   -0.300497
-2000-01-07   -2.626322
-2000-01-08   -0.960255
-Freq: D, Name: A, dtype: float64
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sa</span><span class="o">.</span><span class="n">a</span> <span class="o">=</span> <span class="mi">5</span>
-<span class="n">sa</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    5
-b    2
-c    3
-dtype: int64
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfa</span><span class="o">.</span><span class="n">A</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dfa</span><span class="o">.</span><span class="n">index</span><span class="p">)))</span>  <span class="c1"># ok if A already exists</span>
-<span class="n">dfa</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
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-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-      <th>C</th>
-      <th>D</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>2000-01-01</th>
-      <td>0</td>
-      <td>0.210545</td>
-      <td>0.158079</td>
-      <td>0.492279</td>
-    </tr>
-    <tr>
-      <th>2000-01-02</th>
-      <td>1</td>
-      <td>-0.817745</td>
-      <td>-1.522940</td>
-      <td>-1.275264</td>
-    </tr>
-    <tr>
-      <th>2000-01-03</th>
-      <td>2</td>
-      <td>-0.313090</td>
-      <td>-1.386411</td>
-      <td>-0.757229</td>
-    </tr>
-    <tr>
-      <th>2000-01-04</th>
-      <td>3</td>
-      <td>-0.542066</td>
-      <td>1.345872</td>
-      <td>-0.706867</td>
-    </tr>
-    <tr>
-      <th>2000-01-05</th>
-      <td>4</td>
-      <td>0.731123</td>
-      <td>-2.066561</td>
-      <td>-1.477377</td>
-    </tr>
-    <tr>
-      <th>2000-01-06</th>
-      <td>5</td>
-      <td>0.207552</td>
-      <td>-0.639468</td>
-      <td>-0.950777</td>
-    </tr>
-    <tr>
-      <th>2000-01-07</th>
-      <td>6</td>
-      <td>-1.072332</td>
-      <td>-1.523701</td>
-      <td>-1.489816</td>
-    </tr>
-    <tr>
-      <th>2000-01-08</th>
-      <td>7</td>
-      <td>-0.402632</td>
-      <td>-0.397366</td>
-      <td>0.085742</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfa</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dfa</span><span class="o">.</span><span class="n">index</span><span class="p">)))</span>  <span class="c1"># use this form to create a new column</span>
-<span class="n">dfa</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
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-
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-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-      <th>C</th>
-      <th>D</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>2000-01-01</th>
-      <td>0</td>
-      <td>0.210545</td>
-      <td>0.158079</td>
-      <td>0.492279</td>
-    </tr>
-    <tr>
-      <th>2000-01-02</th>
-      <td>1</td>
-      <td>-0.817745</td>
-      <td>-1.522940</td>
-      <td>-1.275264</td>
-    </tr>
-    <tr>
-      <th>2000-01-03</th>
-      <td>2</td>
-      <td>-0.313090</td>
-      <td>-1.386411</td>
-      <td>-0.757229</td>
-    </tr>
-    <tr>
-      <th>2000-01-04</th>
-      <td>3</td>
-      <td>-0.542066</td>
-      <td>1.345872</td>
-      <td>-0.706867</td>
-    </tr>
-    <tr>
-      <th>2000-01-05</th>
-      <td>4</td>
-      <td>0.731123</td>
-      <td>-2.066561</td>
-      <td>-1.477377</td>
-    </tr>
-    <tr>
-      <th>2000-01-06</th>
-      <td>5</td>
-      <td>0.207552</td>
-      <td>-0.639468</td>
-      <td>-0.950777</td>
-    </tr>
-    <tr>
-      <th>2000-01-07</th>
-      <td>6</td>
-      <td>-1.072332</td>
-      <td>-1.523701</td>
-      <td>-1.489816</td>
-    </tr>
-    <tr>
-      <th>2000-01-08</th>
-      <td>7</td>
-      <td>-0.402632</td>
-      <td>-0.397366</td>
-      <td>0.085742</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<ul class="simple">
-<li><p>You can use this access only if the index element is a valid Python identifier, e.g. s.1 is not allowed. See here for an explanation of valid identifiers.</p></li>
-<li><p>The attribute will not be available if it conflicts with an existing method name, e.g. s.min is not allowed, but s[‘min’] is possible.</p></li>
-<li><p>Similarly, the attribute will not be available if it conflicts with any of the following list: index, major_axis, minor_axis, items.</p></li>
-<li><p>In any of these cases, standard indexing will still work, e.g. s[‘1’], s[‘min’], and s[‘index’] will access the corresponding element or column.</p></li>
-</ul>
-</div>
-<p>If you are using the IPython environment, you may also use tab-completion to see these accessible attributes.</p>
-<p>You can also assign a <code class="docutils literal notranslate"><span class="pre">dict</span></code> to a row of a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;x&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="s1">&#39;y&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]})</span>
-<span class="n">x</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;x&#39;</span><span class="p">:</span> <span class="mi">9</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">:</span> <span class="mi">99</span><span class="p">}</span>
-<span class="n">x</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>x</th>
-      <th>y</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>3</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>9</td>
-      <td>99</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>3</td>
-      <td>5</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>You can use attribute access to modify an existing element of a Series or column of a DataFrame, but be careful; if you try to use attribute access to create a new column, it creates a new attribute rather than a new column. In 0.21.0 and later, this will raise a <code class="docutils literal notranslate"><span class="pre">UserWarning</span></code>:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;one&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">]})</span>
-<span class="n">df</span><span class="o">.</span><span class="n">two</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/tmp/ipykernel_6653/269534380.py:2: UserWarning: Pandas doesn&#39;t allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  df.two = [4, 5, 6]
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
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-    .dataframe tbody tr th {
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-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>one</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1.0</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>2.0</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>3.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-</section>
-<section id="slicing-ranges">
-<h3><span class="section-number">5.4.2.4. </span>Slicing ranges<a class="headerlink" href="#slicing-ranges" title="Permalink to this headline">#</a></h3>
-<p>For now, we explain the semantics of slicing using the [] operator.</p>
-<p>With Series, the syntax works exactly as with an ndarray, returning a slice of the values and the corresponding labels:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2000-01-01    0.560977
-2000-01-02    1.280369
-2000-01-03    0.156302
-2000-01-04    0.528869
-2000-01-05   -2.466330
-2000-01-06   -0.300497
-2000-01-07   -2.626322
-2000-01-08   -0.960255
-Freq: D, Name: A, dtype: float64
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2000-01-01    0.560977
-2000-01-02    1.280369
-2000-01-03    0.156302
-2000-01-04    0.528869
-2000-01-05   -2.466330
-Freq: D, Name: A, dtype: float64
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="p">[::</span><span class="mi">2</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2000-01-01    0.560977
-2000-01-03    0.156302
-2000-01-05   -2.466330
-2000-01-07   -2.626322
-Freq: 2D, Name: A, dtype: float64
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2000-01-08   -0.960255
-2000-01-07   -2.626322
-2000-01-06   -0.300497
-2000-01-05   -2.466330
-2000-01-04    0.528869
-2000-01-03    0.156302
-2000-01-02    1.280369
-2000-01-01    0.560977
-Freq: -1D, Name: A, dtype: float64
-</pre></div>
-</div>
-</div>
-</div>
-<p>Note that setting works as well:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s2</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-<span class="n">s2</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
-<span class="n">s2</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2000-01-01    0.000000
-2000-01-02    0.000000
-2000-01-03    0.000000
-2000-01-04    0.000000
-2000-01-05    0.000000
-2000-01-06   -0.300497
-2000-01-07   -2.626322
-2000-01-08   -0.960255
-Freq: D, Name: A, dtype: float64
-</pre></div>
-</div>
-</div>
-</div>
-<p>With DataFrame, slicing inside of <code class="docutils literal notranslate"><span class="pre">[]</span></code> slices the rows. This is provided largely as a convenience since it is such a common operation.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
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-
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-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>one</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1.0</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>2.0</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>3.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-</pre></div>
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-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>one</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>2</th>
-      <td>3.0</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>2.0</td>
-    </tr>
-    <tr>
-      <th>0</th>
-      <td>1.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-</section>
-<section id="selection-by-label">
-<h3><span class="section-number">5.4.2.5. </span>Selection by label<a class="headerlink" href="#selection-by-label" title="Permalink to this headline">#</a></h3>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>Whether a copy or a reference is returned for a setting operation, may depend on the context. This is sometimes called <code class="docutils literal notranslate"><span class="pre">chained</span> <span class="pre">assignment</span></code> and should be avoided.</p>
-</div>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p><code class="docutils literal notranslate"><span class="pre">.loc</span></code> is strict when you present slicers that are not compatible (or convertible) with the index type. For example using integers in a <code class="docutils literal notranslate"><span class="pre">DatetimeIndex</span></code>. These will raise a <code class="docutils literal notranslate"><span class="pre">TypeError</span></code>.</p>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfl</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
-                   <span class="n">columns</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">&#39;ABCD&#39;</span><span class="p">),</span>
-                   <span class="n">index</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span><span class="s1">&#39;20130101&#39;</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">5</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell tag_raises-exception docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfl</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">2</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
-<span class="ne">TypeError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
-<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">127</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
-<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">dfl</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">2</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexing.py:1073,</span> in <span class="ni">_LocationIndexer.__getitem__</span><span class="nt">(self, key)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1070</span> <span class="n">axis</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">axis</span> <span class="ow">or</span> <span class="mi">0</span>
-<span class="g g-Whitespace">   </span><span class="mi">1072</span> <span class="n">maybe_callable</span> <span class="o">=</span> <span class="n">com</span><span class="o">.</span><span class="n">apply_if_callable</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">obj</span><span class="p">)</span>
-<span class="ne">-&gt; </span><span class="mi">1073</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_getitem_axis</span><span class="p">(</span><span class="n">maybe_callable</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexing.py:1290,</span> in <span class="ni">_LocIndexer._getitem_axis</span><span class="nt">(self, key, axis)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1288</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">slice</span><span class="p">):</span>
-<span class="g g-Whitespace">   </span><span class="mi">1289</span>     <span class="bp">self</span><span class="o">.</span><span class="n">_validate_key</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">axis</span><span class="p">)</span>
-<span class="ne">-&gt; </span><span class="mi">1290</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_slice_axis</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1291</span> <span class="k">elif</span> <span class="n">com</span><span class="o">.</span><span class="n">is_bool_indexer</span><span class="p">(</span><span class="n">key</span><span class="p">):</span>
-<span class="g g-Whitespace">   </span><span class="mi">1292</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_getbool_axis</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexing.py:1324,</span> in <span class="ni">_LocIndexer._get_slice_axis</span><span class="nt">(self, slice_obj, axis)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1321</span>     <span class="k">return</span> <span class="n">obj</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">deep</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1323</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">obj</span><span class="o">.</span><span class="n">_get_axis</span><span class="p">(</span><span class="n">axis</span><span class="p">)</span>
-<span class="ne">-&gt; </span><span class="mi">1324</span> <span class="n">indexer</span> <span class="o">=</span> <span class="n">labels</span><span class="o">.</span><span class="n">slice_indexer</span><span class="p">(</span><span class="n">slice_obj</span><span class="o">.</span><span class="n">start</span><span class="p">,</span> <span class="n">slice_obj</span><span class="o">.</span><span class="n">stop</span><span class="p">,</span> <span class="n">slice_obj</span><span class="o">.</span><span class="n">step</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1326</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">indexer</span><span class="p">,</span> <span class="nb">slice</span><span class="p">):</span>
-<span class="g g-Whitespace">   </span><span class="mi">1327</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">obj</span><span class="o">.</span><span class="n">_slice</span><span class="p">(</span><span class="n">indexer</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexes/datetimes.py:809,</span> in <span class="ni">DatetimeIndex.slice_indexer</span><span class="nt">(self, start, end, step, kind)</span>
-<span class="g g-Whitespace">    </span><span class="mi">801</span> <span class="c1"># GH#33146 if start and end are combinations of str and None and Index is not</span>
-<span class="g g-Whitespace">    </span><span class="mi">802</span> <span class="c1"># monotonic, we can not use Index.slice_indexer because it does not honor the</span>
-<span class="g g-Whitespace">    </span><span class="mi">803</span> <span class="c1"># actual elements, is only searching for start and end</span>
-<span class="g g-Whitespace">    </span><span class="mi">804</span> <span class="k">if</span> <span class="p">(</span>
-<span class="g g-Whitespace">    </span><span class="mi">805</span>     <span class="n">check_str_or_none</span><span class="p">(</span><span class="n">start</span><span class="p">)</span>
-<span class="g g-Whitespace">    </span><span class="mi">806</span>     <span class="ow">or</span> <span class="n">check_str_or_none</span><span class="p">(</span><span class="n">end</span><span class="p">)</span>
-<span class="g g-Whitespace">    </span><span class="mi">807</span>     <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_monotonic_increasing</span>
-<span class="g g-Whitespace">    </span><span class="mi">808</span> <span class="p">):</span>
-<span class="ne">--&gt; </span><span class="mi">809</span>     <span class="k">return</span> <span class="n">Index</span><span class="o">.</span><span class="n">slice_indexer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">,</span> <span class="n">step</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="n">kind</span><span class="p">)</span>
-<span class="g g-Whitespace">    </span><span class="mi">811</span> <span class="n">mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
-<span class="g g-Whitespace">    </span><span class="mi">812</span> <span class="n">deprecation_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexes/base.py:6602,</span> in <span class="ni">Index.slice_indexer</span><span class="nt">(self, start, end, step, kind)</span>
-<span class="g g-Whitespace">   </span><span class="mi">6559</span><span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="g g-Whitespace">   </span><span class="mi">6560</span><span class="sd"> Compute the slice indexer for input labels and step.</span>
-<span class="g g-Whitespace">   </span><span class="mi">6561</span><span class="sd"> </span>
-<span class="sd">   (...)</span>
-<span class="g g-Whitespace">   </span><span class="mi">6598</span><span class="sd"> slice(1, 3, None)</span>
-<span class="g g-Whitespace">   </span><span class="mi">6599</span><span class="sd"> &quot;&quot;&quot;</span>
-<span class="g g-Whitespace">   </span><span class="mi">6600</span> <span class="bp">self</span><span class="o">.</span><span class="n">_deprecated_arg</span><span class="p">(</span><span class="n">kind</span><span class="p">,</span> <span class="s2">&quot;kind&quot;</span><span class="p">,</span> <span class="s2">&quot;slice_indexer&quot;</span><span class="p">)</span>
-<span class="ne">-&gt; </span><span class="mi">6602</span> <span class="n">start_slice</span><span class="p">,</span> <span class="n">end_slice</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">slice_locs</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="n">step</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">6604</span> <span class="c1"># return a slice</span>
-<span class="g g-Whitespace">   </span><span class="mi">6605</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">is_scalar</span><span class="p">(</span><span class="n">start_slice</span><span class="p">):</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexes/base.py:6810,</span> in <span class="ni">Index.slice_locs</span><span class="nt">(self, start, end, step, kind)</span>
-<span class="g g-Whitespace">   </span><span class="mi">6808</span> <span class="n">start_slice</span> <span class="o">=</span> <span class="kc">None</span>
-<span class="g g-Whitespace">   </span><span class="mi">6809</span> <span class="k">if</span> <span class="n">start</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-<span class="ne">-&gt; </span><span class="mi">6810</span>     <span class="n">start_slice</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_slice_bound</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="s2">&quot;left&quot;</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">6811</span> <span class="k">if</span> <span class="n">start_slice</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-<span class="g g-Whitespace">   </span><span class="mi">6812</span>     <span class="n">start_slice</span> <span class="o">=</span> <span class="mi">0</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexes/base.py:6719,</span> in <span class="ni">Index.get_slice_bound</span><span class="nt">(self, label, side, kind)</span>
-<span class="g g-Whitespace">   </span><span class="mi">6715</span> <span class="n">original_label</span> <span class="o">=</span> <span class="n">label</span>
-<span class="g g-Whitespace">   </span><span class="mi">6717</span> <span class="c1"># For datetime indices label may be a string that has to be converted</span>
-<span class="g g-Whitespace">   </span><span class="mi">6718</span> <span class="c1"># to datetime boundary according to its resolution.</span>
-<span class="ne">-&gt; </span><span class="mi">6719</span> <span class="n">label</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_maybe_cast_slice_bound</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">side</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">6721</span> <span class="c1"># we need to look up the label</span>
-<span class="g g-Whitespace">   </span><span class="mi">6722</span> <span class="k">try</span><span class="p">:</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexes/datetimes.py:767,</span> in <span class="ni">DatetimeIndex._maybe_cast_slice_bound</span><span class="nt">(self, label, side, kind)</span>
-<span class="g g-Whitespace">    </span><span class="mi">762</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">date</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">datetime</span><span class="p">):</span>
-<span class="g g-Whitespace">    </span><span class="mi">763</span>     <span class="c1"># Pandas supports slicing with dates, treated as datetimes at midnight.</span>
-<span class="g g-Whitespace">    </span><span class="mi">764</span>     <span class="c1"># https://github.com/pandas-dev/pandas/issues/31501</span>
-<span class="g g-Whitespace">    </span><span class="mi">765</span>     <span class="n">label</span> <span class="o">=</span> <span class="n">Timestamp</span><span class="p">(</span><span class="n">label</span><span class="p">)</span><span class="o">.</span><span class="n">to_pydatetime</span><span class="p">()</span>
-<span class="ne">--&gt; </span><span class="mi">767</span> <span class="n">label</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">_maybe_cast_slice_bound</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">side</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="n">kind</span><span class="p">)</span>
-<span class="g g-Whitespace">    </span><span class="mi">768</span> <span class="bp">self</span><span class="o">.</span><span class="n">_deprecate_mismatched_indexing</span><span class="p">(</span><span class="n">label</span><span class="p">)</span>
-<span class="g g-Whitespace">    </span><span class="mi">769</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_maybe_cast_for_get_loc</span><span class="p">(</span><span class="n">label</span><span class="p">)</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexes/datetimelike.py:320,</span> in <span class="ni">DatetimeIndexOpsMixin._maybe_cast_slice_bound</span><span class="nt">(self, label, side, kind)</span>
-<span class="g g-Whitespace">    </span><span class="mi">318</span>     <span class="k">return</span> <span class="n">lower</span> <span class="k">if</span> <span class="n">side</span> <span class="o">==</span> <span class="s2">&quot;left&quot;</span> <span class="k">else</span> <span class="n">upper</span>
-<span class="g g-Whitespace">    </span><span class="mi">319</span> <span class="k">elif</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="o">.</span><span class="n">_recognized_scalars</span><span class="p">):</span>
-<span class="ne">--&gt; </span><span class="mi">320</span>     <span class="k">raise</span> <span class="bp">self</span><span class="o">.</span><span class="n">_invalid_indexer</span><span class="p">(</span><span class="s2">&quot;slice&quot;</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span>
-<span class="g g-Whitespace">    </span><span class="mi">322</span> <span class="k">return</span> <span class="n">label</span>
-
-<span class="ne">TypeError</span>: cannot do slice indexing on DatetimeIndex with these indexers [2] of type int
-</pre></div>
-</div>
-</div>
-</div>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>String likes in slicing can be convertible to the type of the index and lead to natural slicing.</p>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dfl</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;20130102&#39;</span><span class="p">:</span><span class="s1">&#39;20130104&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:730px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%20%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dpd.date_range%28'20130101',%20periods%3D5%29%29%0Adfl%0Adfl.loc%5B'20130102'%3A'20130104'%5D&d=2023-07-13&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>Pandas will raise a <code class="docutils literal notranslate"><span class="pre">KeyError</span></code> if indexing with a list with missing labels.</p>
-</div>
-<p>Pandas provides a suite of methods in order to have <strong>purely label-based indexing</strong>. This is a strict inclusion-based protocol. Every label asked for must be in the index, or a <code class="docutils literal notranslate"><span class="pre">KeyError</span></code> will be raised. When slicing, both the start bound <strong>AND</strong> the stop bound are included, if present in the index. Integers are valid labels, but they refer to the label <strong>and not the position</strong>.</p>
-<ul class="simple">
-<li><p>The <code class="docutils literal notranslate"><span class="pre">.loc</span></code> attribute is the primary access method. The following are valid inputs:</p></li>
-<li><p>A single label, e.g. <code class="docutils literal notranslate"><span class="pre">5</span></code> or <code class="docutils literal notranslate"><span class="pre">'a'</span></code> (Note that <code class="docutils literal notranslate"><span class="pre">5</span></code> is interpreted as a label of the index. This use is not an integer position along the index.).</p></li>
-<li><p>A list or array of labels <code class="docutils literal notranslate"><span class="pre">['a',</span> <span class="pre">'b',</span> <span class="pre">'c']</span></code>.</p></li>
-<li><p>A slice object with labels <code class="docutils literal notranslate"><span class="pre">'a':'f'</span></code> (Note that contrary to usual Python slices, both the start and the stop are included, when present in the index!</p></li>
-<li><p>A boolean array.</p></li>
-<li><p>A <code class="docutils literal notranslate"><span class="pre">callable</span></code>.</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">6</span><span class="p">),</span> <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">&#39;abcdef&#39;</span><span class="p">))</span>
-<span class="n">s1</span>
-<span class="n">s1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;c&#39;</span><span class="p">:]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:630px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%286%29,%20index%3Dlist%28'abcdef'%29%29%0As1.loc%5B'c'%3A%5D%0A&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:630px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%286%29,%20index%3Dlist%28'abcdef'%29%29%0As1.loc%5B'b'%5D%0A&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<p>Note that the setting works as well:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;c&#39;</span><span class="p">:]</span> <span class="o">=</span> <span class="mi">0</span>
-<span class="n">s1</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:630px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%286%29,%20index%3Dlist%28'abcdef'%29%29%0As1.loc%5B'c'%3A%5D%20%3D%200%0As1&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<p>With a DataFrame:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
-                   <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">&#39;abcdef&#39;</span><span class="p">),</span>
-                   <span class="n">columns</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">&#39;ABCD&#39;</span><span class="p">))</span>
-<span class="n">df1</span>
-<span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[[</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">],</span> <span class="p">:]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:680px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B%5B'a',%20'b',%20'd'%5D,%20%3A%5D%0A&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<p>Accessing via label slices:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;d&#39;</span><span class="p">:,</span> <span class="s1">&#39;A&#39;</span><span class="p">:</span><span class="s1">&#39;C&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:710px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B'd'%3A,%20'A'%3A'C'%5D%0A&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<p>For getting a cross-section using a label (equivalent to <code class="docutils literal notranslate"><span class="pre">df.xs('a')</span></code>):</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:690px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B'a'%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<p>For getting values with a boolean array:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:650px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B'a'%5D%20%3E%200&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:700px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B%3A,%20df1.loc%5B'a'%5D%20%3E%200%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<p>NA values in a boolean array propagate as <code class="docutils literal notranslate"><span class="pre">False</span></code>:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mask</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">NA</span><span class="p">,</span> <span class="kc">False</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">&quot;boolean&quot;</span><span class="p">)</span>
-<span class="n">mask</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;BooleanArray&gt;
-[True, False, True, False, &lt;NA&gt;, False]
-Length: 6, dtype: boolean
-</pre></div>
-</div>
-</div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:680px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Amask%20%3D%20pd.array%28%5BTrue,%20False,%20True,%20False,%20pd.NA,%20False%5D,%20dtype%3D%22boolean%22%29%0Adf1%5Bmask%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<p>For getting a value explicitly:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;A&#39;</span><span class="p">]</span> <span class="c1"># this is also equivalent to ``df1.at[&#39;a&#39;,&#39;A&#39;]``</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:680px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B'a',%20'A'%5D%20&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<section id="slicing-with-labels">
-<h4><span class="section-number">5.4.2.5.1. </span>Slicing with labels<a class="headerlink" href="#slicing-with-labels" title="Permalink to this headline">#</a></h4>
-<p>When using <code class="docutils literal notranslate"><span class="pre">.loc</span></code> with slices, if both the start and the stop labels are present in the index, then elements located between the two (including them) are returned:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="s1">&#39;abcde&#39;</span><span class="p">),</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
-<span class="n">s</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">3</span><span class="p">:</span><span class="mi">5</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:580px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28'abcde'%29,%20index%3D%5B0,%203,%202,%205,%204%5D%29%0As.loc%5B3%3A5%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<p>If at least one of the two is absent, but the index is sorted, and can be compared against start and stop labels, then slicing will still work as expected, by selecting labels which rank between the two:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:600px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28'abcde'%29,%20index%3D%5B0,%203,%202,%205,%204%5D%29%0As.sort_index%28%29&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">6</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:820px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28'abcde'%29,%20index%3D%5B0,%203,%202,%205,%204%5D%29%0As.sort_index%28%29.loc%5B1%3A6%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<p>However, if at least one of the two is absent and the index is not sorted, an error will be raised (since doing otherwise would be computationally expensive, as well as potentially ambiguous for mixed-type indexes). For instance, in the above example, <code class="docutils literal notranslate"><span class="pre">s.loc[1:6]</span></code> would raise <code class="docutils literal notranslate"><span class="pre">KeyError</span></code>.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="s1">&#39;abcdef&#39;</span><span class="p">),</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
-<span class="n">s</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">3</span><span class="p">:</span><span class="mi">5</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:650px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28'abcdef'%29,%20index%3D%5B0,%203,%202,%205,%204,%202%5D%29%0As.loc%5B3%3A5%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<p>Also, if the index has duplicate labels and either the start or the stop label is duplicated, an error will be raised. For instance, in the above example, <code class="docutils literal notranslate"><span class="pre">s.loc[2:5]</span></code> would raise a <code class="docutils literal notranslate"><span class="pre">KeyError</span></code>.</p>
-</section>
-</section>
-<section id="selection-by-position">
-<h3><span class="section-number">5.4.2.6. </span>Selection by position<a class="headerlink" href="#selection-by-position" title="Permalink to this headline">#</a></h3>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>Whether a copy or a reference is returned for a setting operation, may depend on the context. This is sometimes called <code class="docutils literal notranslate"><span class="pre">chained</span> <span class="pre">assignment</span></code> and should be avoided.</p>
-</div>
-<p>Pandas provides a suite of methods in order to get purely integer-based indexing. The semantics follow closely Python and NumPy slicing. These are 0-based indexing. When slicing, the start bound is included, while the upper bound is excluded. Trying to use a non-integer, even a valid label will raise an <code class="docutils literal notranslate"><span class="pre">IndexError</span></code>.</p>
-<p>The <code class="docutils literal notranslate"><span class="pre">.iloc</span></code> attribute is the primary access method. The following are valid inputs:</p>
-<ul class="simple">
-<li><p>An integer e.g. <code class="docutils literal notranslate"><span class="pre">5</span></code>.</p></li>
-<li><p>A list or array of integers <code class="docutils literal notranslate"><span class="pre">[4,</span> <span class="pre">3,</span> <span class="pre">0]</span></code>.</p></li>
-<li><p>A slice object with ints <code class="docutils literal notranslate"><span class="pre">1:7</span></code>.</p></li>
-<li><p>A boolean array.</p></li>
-<li><p>A <code class="docutils literal notranslate"><span class="pre">callable</span></code>.</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">),</span> <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
-<span class="n">s1</span>
-<span class="n">s1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:600px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%285%29,%20index%3Dlist%28range%280,%2010,%202%29%29%29%0As1.iloc%5B%3A3%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:600px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%285%29,%20index%3Dlist%28range%280,%2010,%202%29%29%29%0As1.iloc%5B3%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<p>Note that setting works as well:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
-<span class="n">s1</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:620px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%285%29,%20index%3Dlist%28range%280,%2010,%202%29%29%29%0As1.iloc%5B%3A3%5D%20%3D%200%0As1&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<p>With a DataFrame,Select via integer slicing:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
-                   <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span>
-                   <span class="n">columns</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
-<span class="n">df1</span>
-<span class="n">df1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:680px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B%3A3%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:700px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1%3A5,%202%3A4%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<p>Select via integer list:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:700px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B%5B1,%203,%205%5D,%20%5B1,%203%5D%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span> <span class="p">:]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:680px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1%3A3,%20%3A%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:700px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B%3A,%201%3A3%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="c1"># this is also equivalent to ``df1.iat[1,1]``</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:680px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1,%201%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<p>For getting a cross-section using an integer position (equiv to <code class="docutils literal notranslate"><span class="pre">df.xs(1)</span></code>):</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:700px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<p>Out-of-range slice indexes are handled gracefully just as in Python/NumPy.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="s1">&#39;abcdef&#39;</span><span class="p">)</span> <span class="c1"># these are allowed in Python/NumPy.</span>
-<span class="n">x</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;a&#39;, &#39;b&#39;, &#39;c&#39;, &#39;d&#39;, &#39;e&#39;, &#39;f&#39;]
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">[</span><span class="mi">4</span><span class="p">:</span><span class="mi">10</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;e&#39;, &#39;f&#39;]
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">[</span><span class="mi">8</span><span class="p">:</span><span class="mi">10</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[]
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-<span class="n">s</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    a
-1    b
-2    c
-3    d
-4    e
-5    f
-dtype: object
-</pre></div>
-</div>
-</div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">4</span><span class="p">:</span><span class="mi">10</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:600px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Ax%20%3D%20list%28'abcdef'%29%20%0As%20%3D%20pd.Series%28x%29%0As.iloc%5B4%3A10%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div><div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">8</span><span class="p">:</span><span class="mi">10</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Series([], dtype: object)
-</pre></div>
-</div>
-</div>
-</div>
-<p>Note that using slices that go out of bounds can result in an empty axis (e.g. an empty DataFrame being returned).</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfl</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">&#39;AB&#39;</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dfl</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:630px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%202%29,%20columns%3Dlist%28'AB'%29%29%0Adfl%0Adfl.iloc%5B%3A,%202%3A3%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dfl</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:630px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%202%29,%20columns%3Dlist%28'AB'%29%29%0Adfl.iloc%5B%3A,%201%3A3%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dfl</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">4</span><span class="p">:</span><span class="mi">6</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:600px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%202%29,%20columns%3Dlist%28'AB'%29%29%0Adfl.iloc%5B4%3A6%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<p>A single indexer that is out of bounds will raise an <code class="docutils literal notranslate"><span class="pre">IndexError</span></code>. A list of indexers where any element is out of bounds will raise an <code class="docutils literal notranslate"><span class="pre">IndexError</span></code>.</p>
-<div class="cell tag_raises-exception docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfl</span><span class="o">.</span><span class="n">iloc</span><span class="p">[[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
-<span class="ne">IndexError</span><span class="g g-Whitespace">                                </span>Traceback (most recent call last)
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexing.py:1587,</span> in <span class="ni">_iLocIndexer._get_list_axis</span><span class="nt">(self, key, axis)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1586</span> <span class="k">try</span><span class="p">:</span>
-<span class="ne">-&gt; </span><span class="mi">1587</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">obj</span><span class="o">.</span><span class="n">_take_with_is_copy</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1588</span> <span class="k">except</span> <span class="ne">IndexError</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
-<span class="g g-Whitespace">   </span><span class="mi">1589</span>     <span class="c1"># re-raise with different error message</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/generic.py:3902,</span> in <span class="ni">NDFrame._take_with_is_copy</span><span class="nt">(self, indices, axis)</span>
-<span class="g g-Whitespace">   </span><span class="mi">3895</span><span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="g g-Whitespace">   </span><span class="mi">3896</span><span class="sd"> Internal version of the `take` method that sets the `_is_copy`</span>
-<span class="g g-Whitespace">   </span><span class="mi">3897</span><span class="sd"> attribute to keep track of the parent dataframe (using in indexing</span>
-<span class="sd">   (...)</span>
-<span class="g g-Whitespace">   </span><span class="mi">3900</span><span class="sd"> See the docstring of `take` for full explanation of the parameters.</span>
-<span class="g g-Whitespace">   </span><span class="mi">3901</span><span class="sd"> &quot;&quot;&quot;</span>
-<span class="ne">-&gt; </span><span class="mi">3902</span> <span class="n">result</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_take</span><span class="p">(</span><span class="n">indices</span><span class="o">=</span><span class="n">indices</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">3903</span> <span class="c1"># Maybe set copy if we didn&#39;t actually change the index.</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/generic.py:3886,</span> in <span class="ni">NDFrame._take</span><span class="nt">(self, indices, axis, convert_indices)</span>
-<span class="g g-Whitespace">   </span><span class="mi">3884</span> <span class="bp">self</span><span class="o">.</span><span class="n">_consolidate_inplace</span><span class="p">()</span>
-<span class="ne">-&gt; </span><span class="mi">3886</span> <span class="n">new_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mgr</span><span class="o">.</span><span class="n">take</span><span class="p">(</span>
-<span class="g g-Whitespace">   </span><span class="mi">3887</span>     <span class="n">indices</span><span class="p">,</span>
-<span class="g g-Whitespace">   </span><span class="mi">3888</span>     <span class="n">axis</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_block_manager_axis</span><span class="p">(</span><span class="n">axis</span><span class="p">),</span>
-<span class="g g-Whitespace">   </span><span class="mi">3889</span>     <span class="n">verify</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-<span class="g g-Whitespace">   </span><span class="mi">3890</span>     <span class="n">convert_indices</span><span class="o">=</span><span class="n">convert_indices</span><span class="p">,</span>
-<span class="g g-Whitespace">   </span><span class="mi">3891</span> <span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">3892</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_constructor</span><span class="p">(</span><span class="n">new_data</span><span class="p">)</span><span class="o">.</span><span class="n">__finalize__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;take&quot;</span><span class="p">)</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/internals/managers.py:977,</span> in <span class="ni">BaseBlockManager.take</span><span class="nt">(self, indexer, axis, verify, convert_indices)</span>
-<span class="g g-Whitespace">    </span><span class="mi">976</span> <span class="k">if</span> <span class="n">convert_indices</span><span class="p">:</span>
-<span class="ne">--&gt; </span><span class="mi">977</span>     <span class="n">indexer</span> <span class="o">=</span> <span class="n">maybe_convert_indices</span><span class="p">(</span><span class="n">indexer</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">verify</span><span class="o">=</span><span class="n">verify</span><span class="p">)</span>
-<span class="g g-Whitespace">    </span><span class="mi">979</span> <span class="n">new_labels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">axes</span><span class="p">[</span><span class="n">axis</span><span class="p">]</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="n">indexer</span><span class="p">)</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexers/utils.py:286,</span> in <span class="ni">maybe_convert_indices</span><span class="nt">(indices, n, verify)</span>
-<span class="g g-Whitespace">    </span><span class="mi">285</span>     <span class="k">if</span> <span class="n">mask</span><span class="o">.</span><span class="n">any</span><span class="p">():</span>
-<span class="ne">--&gt; </span><span class="mi">286</span>         <span class="k">raise</span> <span class="ne">IndexError</span><span class="p">(</span><span class="s2">&quot;indices are out-of-bounds&quot;</span><span class="p">)</span>
-<span class="g g-Whitespace">    </span><span class="mi">287</span> <span class="k">return</span> <span class="n">indices</span>
-
-<span class="ne">IndexError</span>: indices are out-of-bounds
-
-<span class="n">The</span> <span class="n">above</span> <span class="n">exception</span> <span class="n">was</span> <span class="n">the</span> <span class="n">direct</span> <span class="n">cause</span> <span class="n">of</span> <span class="n">the</span> <span class="n">following</span> <span class="n">exception</span><span class="p">:</span>
-
-<span class="ne">IndexError</span><span class="g g-Whitespace">                                </span>Traceback (most recent call last)
-<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">135</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
-<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">dfl</span><span class="o">.</span><span class="n">iloc</span><span class="p">[[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]]</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexing.py:1073,</span> in <span class="ni">_LocationIndexer.__getitem__</span><span class="nt">(self, key)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1070</span> <span class="n">axis</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">axis</span> <span class="ow">or</span> <span class="mi">0</span>
-<span class="g g-Whitespace">   </span><span class="mi">1072</span> <span class="n">maybe_callable</span> <span class="o">=</span> <span class="n">com</span><span class="o">.</span><span class="n">apply_if_callable</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">obj</span><span class="p">)</span>
-<span class="ne">-&gt; </span><span class="mi">1073</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_getitem_axis</span><span class="p">(</span><span class="n">maybe_callable</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexing.py:1616,</span> in <span class="ni">_iLocIndexer._getitem_axis</span><span class="nt">(self, key, axis)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1614</span> <span class="c1"># a list of integers</span>
-<span class="g g-Whitespace">   </span><span class="mi">1615</span> <span class="k">elif</span> <span class="n">is_list_like_indexer</span><span class="p">(</span><span class="n">key</span><span class="p">):</span>
-<span class="ne">-&gt; </span><span class="mi">1616</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_list_axis</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1618</span> <span class="c1"># a single integer</span>
-<span class="g g-Whitespace">   </span><span class="mi">1619</span> <span class="k">else</span><span class="p">:</span>
-<span class="g g-Whitespace">   </span><span class="mi">1620</span>     <span class="n">key</span> <span class="o">=</span> <span class="n">item_from_zerodim</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexing.py:1590,</span> in <span class="ni">_iLocIndexer._get_list_axis</span><span class="nt">(self, key, axis)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1587</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">obj</span><span class="o">.</span><span class="n">_take_with_is_copy</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1588</span> <span class="k">except</span> <span class="ne">IndexError</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
-<span class="g g-Whitespace">   </span><span class="mi">1589</span>     <span class="c1"># re-raise with different error message</span>
-<span class="ne">-&gt; </span><span class="mi">1590</span>     <span class="k">raise</span> <span class="ne">IndexError</span><span class="p">(</span><span class="s2">&quot;positional indexers are out-of-bounds&quot;</span><span class="p">)</span> <span class="kn">from</span> <span class="nn">err</span>
-
-<span class="ne">IndexError</span>: positional indexers are out-of-bounds
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell tag_raises-exception docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfl</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
-<span class="ne">IndexError</span><span class="g g-Whitespace">                                </span>Traceback (most recent call last)
-<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">136</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
-<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">dfl</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexing.py:1067,</span> in <span class="ni">_LocationIndexer.__getitem__</span><span class="nt">(self, key)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1065</span>     <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_scalar_access</span><span class="p">(</span><span class="n">key</span><span class="p">):</span>
-<span class="g g-Whitespace">   </span><span class="mi">1066</span>         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">obj</span><span class="o">.</span><span class="n">_get_value</span><span class="p">(</span><span class="o">*</span><span class="n">key</span><span class="p">,</span> <span class="n">takeable</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_takeable</span><span class="p">)</span>
-<span class="ne">-&gt; </span><span class="mi">1067</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_getitem_tuple</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1068</span> <span class="k">else</span><span class="p">:</span>
-<span class="g g-Whitespace">   </span><span class="mi">1069</span>     <span class="c1"># we by definition only have the 0th axis</span>
-<span class="g g-Whitespace">   </span><span class="mi">1070</span>     <span class="n">axis</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">axis</span> <span class="ow">or</span> <span class="mi">0</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexing.py:1563,</span> in <span class="ni">_iLocIndexer._getitem_tuple</span><span class="nt">(self, tup)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1561</span> <span class="k">def</span> <span class="nf">_getitem_tuple</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tup</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">):</span>
-<span class="ne">-&gt; </span><span class="mi">1563</span>     <span class="n">tup</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_validate_tuple_indexer</span><span class="p">(</span><span class="n">tup</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1564</span>     <span class="k">with</span> <span class="n">suppress</span><span class="p">(</span><span class="n">IndexingError</span><span class="p">):</span>
-<span class="g g-Whitespace">   </span><span class="mi">1565</span>         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_getitem_lowerdim</span><span class="p">(</span><span class="n">tup</span><span class="p">)</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexing.py:873,</span> in <span class="ni">_LocationIndexer._validate_tuple_indexer</span><span class="nt">(self, key)</span>
-<span class="g g-Whitespace">    </span><span class="mi">871</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">key</span><span class="p">):</span>
-<span class="g g-Whitespace">    </span><span class="mi">872</span>     <span class="k">try</span><span class="p">:</span>
-<span class="ne">--&gt; </span><span class="mi">873</span>         <span class="bp">self</span><span class="o">.</span><span class="n">_validate_key</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
-<span class="g g-Whitespace">    </span><span class="mi">874</span>     <span class="k">except</span> <span class="ne">ValueError</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
-<span class="g g-Whitespace">    </span><span class="mi">875</span>         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-<span class="g g-Whitespace">    </span><span class="mi">876</span>             <span class="s2">&quot;Location based indexing can only have &quot;</span>
-<span class="g g-Whitespace">    </span><span class="mi">877</span>             <span class="sa">f</span><span class="s2">&quot;[</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">_valid_types</span><span class="si">}</span><span class="s2">] types&quot;</span>
-<span class="g g-Whitespace">    </span><span class="mi">878</span>         <span class="p">)</span> <span class="kn">from</span> <span class="nn">err</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexing.py:1466,</span> in <span class="ni">_iLocIndexer._validate_key</span><span class="nt">(self, key, axis)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1464</span>     <span class="k">return</span>
-<span class="g g-Whitespace">   </span><span class="mi">1465</span> <span class="k">elif</span> <span class="n">is_integer</span><span class="p">(</span><span class="n">key</span><span class="p">):</span>
-<span class="ne">-&gt; </span><span class="mi">1466</span>     <span class="bp">self</span><span class="o">.</span><span class="n">_validate_integer</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">axis</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1467</span> <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
-<span class="g g-Whitespace">   </span><span class="mi">1468</span>     <span class="c1"># a tuple should already have been caught by this point</span>
-<span class="g g-Whitespace">   </span><span class="mi">1469</span>     <span class="c1"># so don&#39;t treat a tuple as a valid indexer</span>
-<span class="g g-Whitespace">   </span><span class="mi">1470</span>     <span class="k">raise</span> <span class="n">IndexingError</span><span class="p">(</span><span class="s2">&quot;Too many indexers&quot;</span><span class="p">)</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/indexing.py:1557,</span> in <span class="ni">_iLocIndexer._validate_integer</span><span class="nt">(self, key, axis)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1555</span> <span class="n">len_axis</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">obj</span><span class="o">.</span><span class="n">_get_axis</span><span class="p">(</span><span class="n">axis</span><span class="p">))</span>
-<span class="g g-Whitespace">   </span><span class="mi">1556</span> <span class="k">if</span> <span class="n">key</span> <span class="o">&gt;=</span> <span class="n">len_axis</span> <span class="ow">or</span> <span class="n">key</span> <span class="o">&lt;</span> <span class="o">-</span><span class="n">len_axis</span><span class="p">:</span>
-<span class="ne">-&gt; </span><span class="mi">1557</span>     <span class="k">raise</span> <span class="ne">IndexError</span><span class="p">(</span><span class="s2">&quot;single positional indexer is out-of-bounds&quot;</span><span class="p">)</span>
-
-<span class="ne">IndexError</span>: single positional indexer is out-of-bounds
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="selection-by-callable">
-<h3><span class="section-number">5.4.2.7. </span>Selection by callable<a class="headerlink" href="#selection-by-callable" title="Permalink to this headline">#</a></h3>
-<p><code class="docutils literal notranslate"><span class="pre">.loc</span></code>, <code class="docutils literal notranslate"><span class="pre">.iloc</span></code>, and also <code class="docutils literal notranslate"><span class="pre">[]</span></code> indexing can accept a <code class="docutils literal notranslate"><span class="pre">callable</span></code> as indexer. The <code class="docutils literal notranslate"><span class="pre">callable</span></code> must be a function with one argument (the calling Series or DataFrame) that returns valid output for indexing.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
-                   <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">&#39;abcdef&#39;</span><span class="p">),</span>
-                   <span class="n">columns</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">&#39;ABCD&#39;</span><span class="p">))</span>
-<span class="n">df1</span>
-<span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="k">lambda</span> <span class="n">df</span><span class="p">:</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:700px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5Blambda%20df%3A%20df%5B'A'%5D%20%3E%200,%20%3A%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="k">lambda</span> <span class="n">df</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">]]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:700px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1.loc%5B%3A,%20lambda%20df%3A%20%5B'A',%20'B'%5D%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="k">lambda</span> <span class="n">df</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:700px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1.iloc%5B%3A,%20lambda%20df%3A%20%5B0,%201%5D%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="p">[</span><span class="k">lambda</span> <span class="n">df</span><span class="p">:</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:650px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%5Blambda%20df%3A%20df.columns%5B0%5D%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<p>You can use callable indexing in <code class="docutils literal notranslate"><span class="pre">Series</span></code>.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="k">lambda</span> <span class="n">s</span><span class="p">:</span> <span class="n">s</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:920px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%5B'A'%5D.loc%5Blambda%20s%3A%20s%20%3E%200%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-</section>
-<section id="combining-positional-and-label-based-indexing">
-<h3><span class="section-number">5.4.2.8. </span>Combining positional and label-based indexing<a class="headerlink" href="#combining-positional-and-label-based-indexing" title="Permalink to this headline">#</a></h3>
-<p>If you wish to get the 0th and the 2nd elements from the index in the <code class="docutils literal notranslate"><span class="pre">'A'</span></code> column, you can do:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dfd</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;A&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
-                    <span class="s1">&#39;B&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]},</span>
-                   <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">&#39;abc&#39;</span><span class="p">))</span>
-<span class="n">dfd</span>
-<span class="n">dfd</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">dfd</span><span class="o">.</span><span class="n">index</span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]],</span> <span class="s1">&#39;A&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:550px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adfd%20%3D%20pd.DataFrame%28%7B'A'%3A%20%5B1,%202,%203%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'B'%3A%20%5B4,%205,%206%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abc'%29%29%0Adfd.loc%5Bdfd.index%5B%5B0,%202%5D%5D,%20'A'%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<p>This can also be expressed using <code class="docutils literal notranslate"><span class="pre">.iloc</span></code>, by explicitly getting locations on the indexers, and using positional indexing to select things.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dfd</span><span class="o">.</span><span class="n">iloc</span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">dfd</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">)]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:550px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adfd%20%3D%20pd.DataFrame%28%7B'A'%3A%20%5B1,%202,%203%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'B'%3A%20%5B4,%205,%206%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abc'%29%29%0Adfd.iloc%5B%5B0,%202%5D,%20dfd.columns.get_loc%28'A'%29%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<p>For getting multiple indexers, using <code class="docutils literal notranslate"><span class="pre">.get_indexer</span></code>:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dfd</span><span class="o">.</span><span class="n">iloc</span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">dfd</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">get_indexer</span><span class="p">([</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">])]</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:550px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adfd%20%3D%20pd.DataFrame%28%7B'A'%3A%20%5B1,%202,%203%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'B'%3A%20%5B4,%205,%206%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abc'%29%29%0Adfd.iloc%5B%5B0,%202%5D,%20dfd.columns.get_indexer%28%5B'A',%20'B'%5D%29%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-</section>
-</section>
-<section id="combining-datasets-concat-merge-and-join">
-<h2><span class="section-number">5.4.3. </span>Combining datasets: concat, merge and join<a class="headerlink" href="#combining-datasets-concat-merge-and-join" title="Permalink to this headline">#</a></h2>
-<section id="concat">
-<h3><span class="section-number">5.4.3.1. </span>concat<a class="headerlink" href="#concat" title="Permalink to this headline">#</a></h3>
-<ul class="simple">
-<li><p>Concatenate Pandas objects along a particular axis.</p></li>
-<li><p>Allows optional set logic along the other axes.</p></li>
-<li><p>Can also add a layer of hierarchical indexing on the concatenation axis, which may be useful if the labels are the same (or overlapping) on the passed axis number.</p></li>
-</ul>
-<p>For example:</p>
-<p>Combine two <code class="docutils literal notranslate"><span class="pre">Series</span></code>.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">])</span>
-<span class="n">s2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">])</span>
-<span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">s1</span><span class="p">,</span> <span class="n">s2</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    a
-1    b
-0    c
-1    d
-dtype: object
-</pre></div>
-</div>
-</div>
-</div>
-<p>Clear the existing index and reset it in the result by setting the <code class="docutils literal notranslate"><span class="pre">ignore_index</span></code> option to <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">s1</span><span class="p">,</span> <span class="n">s2</span><span class="p">],</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    a
-1    b
-2    c
-3    d
-dtype: object
-</pre></div>
-</div>
-</div>
-</div>
-<p>Add a hierarchical index at the outermost level of the data with the <code class="docutils literal notranslate"><span class="pre">keys</span></code> option.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">s1</span><span class="p">,</span> <span class="n">s2</span><span class="p">],</span> <span class="n">keys</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;s1&#39;</span><span class="p">,</span> <span class="s1">&#39;s2&#39;</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>s1  0    a
-    1    b
-s2  0    c
-    1    d
-dtype: object
-</pre></div>
-</div>
-</div>
-</div>
-<p>Label the index keys you create with the <code class="docutils literal notranslate"><span class="pre">names</span></code> option.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">s1</span><span class="p">,</span> <span class="n">s2</span><span class="p">],</span> <span class="n">keys</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;s1&#39;</span><span class="p">,</span> <span class="s1">&#39;s2&#39;</span><span class="p">],</span>
-          <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Series name&#39;</span><span class="p">,</span> <span class="s1">&#39;Row ID&#39;</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Series name  Row ID
-s1           0         a
-             1         b
-s2           0         c
-             1         d
-dtype: object
-</pre></div>
-</div>
-</div>
-</div>
-<p>Combine two <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> objects with identical columns.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([[</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">]],</span>
-                   <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;letter&#39;</span><span class="p">,</span> <span class="s1">&#39;number&#39;</span><span class="p">])</span>
-<span class="n">df1</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
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-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>letter</th>
-      <th>number</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>a</td>
-      <td>1</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>b</td>
-      <td>2</td>
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-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([[</span><span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="mi">4</span><span class="p">]],</span>
-                   <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;letter&#39;</span><span class="p">,</span> <span class="s1">&#39;number&#39;</span><span class="p">])</span>
-<span class="n">df2</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>letter</th>
-      <th>number</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>c</td>
-      <td>3</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>d</td>
-      <td>4</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>letter</th>
-      <th>number</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>a</td>
-      <td>1</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>b</td>
-      <td>2</td>
-    </tr>
-    <tr>
-      <th>0</th>
-      <td>c</td>
-      <td>3</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>d</td>
-      <td>4</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>Combine <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> objects with overlapping columns and return everything. Columns outside the intersection will be filled with <code class="docutils literal notranslate"><span class="pre">NaN</span></code> values.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df3</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([[</span><span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s1">&#39;cat&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="s1">&#39;dog&#39;</span><span class="p">]],</span>
-                   <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;letter&#39;</span><span class="p">,</span> <span class="s1">&#39;number&#39;</span><span class="p">,</span> <span class="s1">&#39;animal&#39;</span><span class="p">])</span>
-<span class="n">df3</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>letter</th>
-      <th>number</th>
-      <th>animal</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>c</td>
-      <td>3</td>
-      <td>cat</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>d</td>
-      <td>4</td>
-      <td>dog</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df1</span><span class="p">,</span> <span class="n">df3</span><span class="p">],</span> <span class="n">sort</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>letter</th>
-      <th>number</th>
-      <th>animal</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>a</td>
-      <td>1</td>
-      <td>NaN</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>b</td>
-      <td>2</td>
-      <td>NaN</td>
-    </tr>
-    <tr>
-      <th>0</th>
-      <td>c</td>
-      <td>3</td>
-      <td>cat</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>d</td>
-      <td>4</td>
-      <td>dog</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>Combine DataFrame objects with overlapping columns and return only those that are shared by passing inner to the join keyword argument.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df1</span><span class="p">,</span> <span class="n">df3</span><span class="p">],</span> <span class="n">join</span><span class="o">=</span><span class="s2">&quot;inner&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>letter</th>
-      <th>number</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>a</td>
-      <td>1</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>b</td>
-      <td>2</td>
-    </tr>
-    <tr>
-      <th>0</th>
-      <td>c</td>
-      <td>3</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>d</td>
-      <td>4</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>Combine <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> objects horizontally along the x-axis by passing in <code class="docutils literal notranslate"><span class="pre">axis=1</span></code>.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df4</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([[</span><span class="s1">&#39;bird&#39;</span><span class="p">,</span> <span class="s1">&#39;polly&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;monkey&#39;</span><span class="p">,</span> <span class="s1">&#39;george&#39;</span><span class="p">]],</span>
-                   <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;animal&#39;</span><span class="p">,</span> <span class="s1">&#39;name&#39;</span><span class="p">])</span>
-<span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df1</span><span class="p">,</span> <span class="n">df4</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>letter</th>
-      <th>number</th>
-      <th>animal</th>
-      <th>name</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>a</td>
-      <td>1</td>
-      <td>bird</td>
-      <td>polly</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>b</td>
-      <td>2</td>
-      <td>monkey</td>
-      <td>george</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>Prevent the result from including duplicate index values with the <code class="docutils literal notranslate"><span class="pre">verify_integrity</span></code> option.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df5</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="mi">1</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">])</span>
-<span class="n">df5</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>0</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>a</th>
-      <td>1</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df6</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="mi">2</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">])</span>
-<span class="n">df6</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>0</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>a</th>
-      <td>2</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell tag_raises-exception docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df5</span><span class="p">,</span> <span class="n">df6</span><span class="p">],</span> <span class="n">verify_integrity</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
-<span class="ne">ValueError</span><span class="g g-Whitespace">                                </span>Traceback (most recent call last)
-<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">150</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
-<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df5</span><span class="p">,</span> <span class="n">df6</span><span class="p">],</span> <span class="n">verify_integrity</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/util/_decorators.py:331,</span> in <span class="ni">deprecate_nonkeyword_arguments.&lt;locals&gt;.decorate.&lt;locals&gt;.wrapper</span><span class="nt">(*args, **kwargs)</span>
-<span class="g g-Whitespace">    </span><span class="mi">325</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">num_allow_args</span><span class="p">:</span>
-<span class="g g-Whitespace">    </span><span class="mi">326</span>     <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
-<span class="g g-Whitespace">    </span><span class="mi">327</span>         <span class="n">msg</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">arguments</span><span class="o">=</span><span class="n">_format_argument_list</span><span class="p">(</span><span class="n">allow_args</span><span class="p">)),</span>
-<span class="g g-Whitespace">    </span><span class="mi">328</span>         <span class="ne">FutureWarning</span><span class="p">,</span>
-<span class="g g-Whitespace">    </span><span class="mi">329</span>         <span class="n">stacklevel</span><span class="o">=</span><span class="n">find_stack_level</span><span class="p">(),</span>
-<span class="g g-Whitespace">    </span><span class="mi">330</span>     <span class="p">)</span>
-<span class="ne">--&gt; </span><span class="mi">331</span> <span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/reshape/concat.py:368,</span> in <span class="ni">concat</span><span class="nt">(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)</span>
-<span class="g g-Whitespace">    </span><span class="mi">146</span> <span class="nd">@deprecate_nonkeyword_arguments</span><span class="p">(</span><span class="n">version</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">allowed_args</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;objs&quot;</span><span class="p">])</span>
-<span class="g g-Whitespace">    </span><span class="mi">147</span> <span class="k">def</span> <span class="nf">concat</span><span class="p">(</span>
-<span class="g g-Whitespace">    </span><span class="mi">148</span>     <span class="n">objs</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">NDFrame</span><span class="p">]</span> <span class="o">|</span> <span class="n">Mapping</span><span class="p">[</span><span class="n">HashableT</span><span class="p">,</span> <span class="n">NDFrame</span><span class="p">],</span>
-   <span class="p">(</span><span class="o">...</span><span class="p">)</span>
-<span class="g g-Whitespace">    </span><span class="mi">157</span>     <span class="n">copy</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-<span class="g g-Whitespace">    </span><span class="mi">158</span> <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span> <span class="o">|</span> <span class="n">Series</span><span class="p">:</span>
-<span class="g g-Whitespace">    </span><span class="mi">159</span><span class="w">     </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="g g-Whitespace">    </span><span class="mi">160</span><span class="sd">     Concatenate pandas objects along a particular axis.</span>
-<span class="g g-Whitespace">    </span><span class="mi">161</span><span class="sd"> </span>
-<span class="sd">   (...)</span>
-<span class="g g-Whitespace">    </span><span class="mi">366</span><span class="sd">     1   3   4</span>
-<span class="g g-Whitespace">    </span><span class="mi">367</span><span class="sd">     &quot;&quot;&quot;</span>
-<span class="ne">--&gt; </span><span class="mi">368</span>     <span class="n">op</span> <span class="o">=</span> <span class="n">_Concatenator</span><span class="p">(</span>
-<span class="g g-Whitespace">    </span><span class="mi">369</span>         <span class="n">objs</span><span class="p">,</span>
-<span class="g g-Whitespace">    </span><span class="mi">370</span>         <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span>
-<span class="g g-Whitespace">    </span><span class="mi">371</span>         <span class="n">ignore_index</span><span class="o">=</span><span class="n">ignore_index</span><span class="p">,</span>
-<span class="g g-Whitespace">    </span><span class="mi">372</span>         <span class="n">join</span><span class="o">=</span><span class="n">join</span><span class="p">,</span>
-<span class="g g-Whitespace">    </span><span class="mi">373</span>         <span class="n">keys</span><span class="o">=</span><span class="n">keys</span><span class="p">,</span>
-<span class="g g-Whitespace">    </span><span class="mi">374</span>         <span class="n">levels</span><span class="o">=</span><span class="n">levels</span><span class="p">,</span>
-<span class="g g-Whitespace">    </span><span class="mi">375</span>         <span class="n">names</span><span class="o">=</span><span class="n">names</span><span class="p">,</span>
-<span class="g g-Whitespace">    </span><span class="mi">376</span>         <span class="n">verify_integrity</span><span class="o">=</span><span class="n">verify_integrity</span><span class="p">,</span>
-<span class="g g-Whitespace">    </span><span class="mi">377</span>         <span class="n">copy</span><span class="o">=</span><span class="n">copy</span><span class="p">,</span>
-<span class="g g-Whitespace">    </span><span class="mi">378</span>         <span class="n">sort</span><span class="o">=</span><span class="n">sort</span><span class="p">,</span>
-<span class="g g-Whitespace">    </span><span class="mi">379</span>     <span class="p">)</span>
-<span class="g g-Whitespace">    </span><span class="mi">381</span>     <span class="k">return</span> <span class="n">op</span><span class="o">.</span><span class="n">get_result</span><span class="p">()</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/reshape/concat.py:563,</span> in <span class="ni">_Concatenator.__init__</span><span class="nt">(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)</span>
-<span class="g g-Whitespace">    </span><span class="mi">560</span> <span class="bp">self</span><span class="o">.</span><span class="n">verify_integrity</span> <span class="o">=</span> <span class="n">verify_integrity</span>
-<span class="g g-Whitespace">    </span><span class="mi">561</span> <span class="bp">self</span><span class="o">.</span><span class="n">copy</span> <span class="o">=</span> <span class="n">copy</span>
-<span class="ne">--&gt; </span><span class="mi">563</span> <span class="bp">self</span><span class="o">.</span><span class="n">new_axes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_new_axes</span><span class="p">()</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/reshape/concat.py:633,</span> in <span class="ni">_Concatenator._get_new_axes</span><span class="nt">(self)</span>
-<span class="g g-Whitespace">    </span><span class="mi">631</span> <span class="k">def</span> <span class="nf">_get_new_axes</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="n">Index</span><span class="p">]:</span>
-<span class="g g-Whitespace">    </span><span class="mi">632</span>     <span class="n">ndim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_result_dim</span><span class="p">()</span>
-<span class="ne">--&gt; </span><span class="mi">633</span>     <span class="k">return</span> <span class="p">[</span>
-<span class="g g-Whitespace">    </span><span class="mi">634</span>         <span class="bp">self</span><span class="o">.</span><span class="n">_get_concat_axis</span> <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">bm_axis</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_comb_axis</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-<span class="nn">    635         for i</span> in <span class="ni">range</span><span class="nt">(ndim)</span>
-<span class="g g-Whitespace">    </span><span class="mi">636</span>     <span class="p">]</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/reshape/concat.py:634,</span> in <span class="ni">&lt;listcomp&gt;</span><span class="nt">(.0)</span>
-<span class="g g-Whitespace">    </span><span class="mi">631</span> <span class="k">def</span> <span class="nf">_get_new_axes</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="n">Index</span><span class="p">]:</span>
-<span class="g g-Whitespace">    </span><span class="mi">632</span>     <span class="n">ndim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_result_dim</span><span class="p">()</span>
-<span class="g g-Whitespace">    </span><span class="mi">633</span>     <span class="k">return</span> <span class="p">[</span>
-<span class="ne">--&gt; </span><span class="mi">634</span>         <span class="bp">self</span><span class="o">.</span><span class="n">_get_concat_axis</span> <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">bm_axis</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_comb_axis</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-<span class="nn">    635         for i</span> in <span class="ni">range</span><span class="nt">(ndim)</span>
-<span class="g g-Whitespace">    </span><span class="mi">636</span>     <span class="p">]</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/_libs/properties.pyx:36,</span> in <span class="ni">pandas._libs.properties.CachedProperty.__get__</span><span class="nt">()</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/reshape/concat.py:697,</span> in <span class="ni">_Concatenator._get_concat_axis</span><span class="nt">(self)</span>
-<span class="g g-Whitespace">    </span><span class="mi">692</span> <span class="k">else</span><span class="p">:</span>
-<span class="g g-Whitespace">    </span><span class="mi">693</span>     <span class="n">concat_axis</span> <span class="o">=</span> <span class="n">_make_concat_multiindex</span><span class="p">(</span>
-<span class="g g-Whitespace">    </span><span class="mi">694</span>         <span class="n">indexes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">keys</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">levels</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">names</span>
-<span class="g g-Whitespace">    </span><span class="mi">695</span>     <span class="p">)</span>
-<span class="ne">--&gt; </span><span class="mi">697</span> <span class="bp">self</span><span class="o">.</span><span class="n">_maybe_check_integrity</span><span class="p">(</span><span class="n">concat_axis</span><span class="p">)</span>
-<span class="g g-Whitespace">    </span><span class="mi">699</span> <span class="k">return</span> <span class="n">concat_axis</span>
-
-<span class="nn">File /usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/reshape/concat.py:705,</span> in <span class="ni">_Concatenator._maybe_check_integrity</span><span class="nt">(self, concat_index)</span>
-<span class="g g-Whitespace">    </span><span class="mi">703</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">concat_index</span><span class="o">.</span><span class="n">is_unique</span><span class="p">:</span>
-<span class="g g-Whitespace">    </span><span class="mi">704</span>     <span class="n">overlap</span> <span class="o">=</span> <span class="n">concat_index</span><span class="p">[</span><span class="n">concat_index</span><span class="o">.</span><span class="n">duplicated</span><span class="p">()]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
-<span class="ne">--&gt; </span><span class="mi">705</span>     <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Indexes have overlapping values: </span><span class="si">{</span><span class="n">overlap</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-
-<span class="ne">ValueError</span>: Indexes have overlapping values: Index([&#39;a&#39;], dtype=&#39;object&#39;)
-</pre></div>
-</div>
-</div>
-</div>
-<p>Append a single row to the end of a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> object.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df7</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;a&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">},</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="n">df7</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>a</th>
-      <th>b</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>2</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">new_row</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">({</span><span class="s1">&#39;a&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">:</span> <span class="mi">4</span><span class="p">})</span>
-<span class="n">new_row</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    3
-b    4
-dtype: int64
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df7</span><span class="p">,</span> <span class="n">new_row</span><span class="o">.</span><span class="n">to_frame</span><span class="p">()</span><span class="o">.</span><span class="n">T</span><span class="p">],</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>a</th>
-      <th>b</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>2</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>3</td>
-      <td>4</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p><code class="docutils literal notranslate"><span class="pre">append()</span></code> has been deprecated since version 1.4.0: Use <code class="docutils literal notranslate"><span class="pre">concat()</span></code> instead.</p>
-</div>
-</section>
-<section id="merge">
-<h3><span class="section-number">5.4.3.2. </span>merge<a class="headerlink" href="#merge" title="Permalink to this headline">#</a></h3>
-<ul class="simple">
-<li><p>Merge DataFrame or named Series objects with a database-style join.</p></li>
-<li><p>A named Series object is treated as a DataFrame with a single named column.</p></li>
-<li><p>The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on. When performing a cross-merge, no column specifications to merge on are allowed.</p></li>
-</ul>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>If both key columns contain rows where the key is a null value, those rows will be matched against each other. This is different from usual SQL join behaviour and can lead to unexpected results.</p>
-</div>
-<p>For example:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;lkey&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;foo&#39;</span><span class="p">,</span> <span class="s1">&#39;bar&#39;</span><span class="p">,</span> <span class="s1">&#39;baz&#39;</span><span class="p">,</span> <span class="s1">&#39;foo&#39;</span><span class="p">],</span>
-                    <span class="s1">&#39;value&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">]})</span>
-<span class="n">df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;rkey&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;foo&#39;</span><span class="p">,</span> <span class="s1">&#39;bar&#39;</span><span class="p">,</span> <span class="s1">&#39;baz&#39;</span><span class="p">,</span> <span class="s1">&#39;foo&#39;</span><span class="p">],</span>
-                    <span class="s1">&#39;value&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">]})</span>
-</pre></div>
-</div>
-<p>Merge DataFrames <code class="docutils literal notranslate"><span class="pre">df1</span></code> and <code class="docutils literal notranslate"><span class="pre">df2</span></code> with specified left and right suffixes appended to any overlapping columns.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">df2</span><span class="p">,</span> <span class="n">left_on</span><span class="o">=</span><span class="s1">&#39;lkey&#39;</span><span class="p">,</span> <span class="n">right_on</span><span class="o">=</span><span class="s1">&#39;rkey&#39;</span><span class="p">,</span> <span class="n">suffixes</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;_left&#39;</span><span class="p">,</span> <span class="s1">&#39;_right&#39;</span><span class="p">))</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:800px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf1%20%3D%20pd.DataFrame%28%7B'lkey'%3A%20%5B'foo',%20'bar',%20'baz',%20'foo'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'value'%3A%20%5B1,%202,%203,%205%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B'rkey'%3A%20%5B'foo',%20'bar',%20'baz',%20'foo'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'value'%3A%20%5B5,%206,%207,%208%5D%7D%29%0Adf1.merge%28df2,%20left_on%3D'lkey',%20right_on%3D'rkey'%29&d=2023-05-27&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<p>Merge DataFrames <code class="docutils literal notranslate"><span class="pre">df1</span></code> and <code class="docutils literal notranslate"><span class="pre">df2</span></code>, but raise an exception if the DataFrames have any overlapping columns.</p>
-<div class="cell tag_raises-exception docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">df2</span><span class="p">,</span> <span class="n">left_on</span><span class="o">=</span><span class="s1">&#39;lkey&#39;</span><span class="p">,</span> <span class="n">right_on</span><span class="o">=</span><span class="s1">&#39;rkey&#39;</span><span class="p">,</span> <span class="n">suffixes</span><span class="o">=</span><span class="p">(</span><span class="kc">False</span><span class="p">,</span> <span class="kc">False</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
-<span class="ne">KeyError</span><span class="g g-Whitespace">                                  </span>Traceback (most recent call last)
-<span class="nn">/tmp/ipykernel_6653/726409595.py</span> in <span class="ni">?</span><span class="nt">()</span>
-<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">df1</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">df2</span><span class="p">,</span> <span class="n">left_on</span><span class="o">=</span><span class="s1">&#39;lkey&#39;</span><span class="p">,</span> <span class="n">right_on</span><span class="o">=</span><span class="s1">&#39;rkey&#39;</span><span class="p">,</span> <span class="n">suffixes</span><span class="o">=</span><span class="p">(</span><span class="kc">False</span><span class="p">,</span> <span class="kc">False</span><span class="p">))</span>
-
-<span class="nn">/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/frame.py</span> in <span class="ni">?</span><span class="nt">(self, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)</span>
-<span class="g g-Whitespace">  </span><span class="mi">10086</span>         <span class="n">validate</span><span class="p">:</span> <span class="nb">str</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-<span class="g g-Whitespace">  </span><span class="mi">10087</span>     <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
-<span class="g g-Whitespace">  </span><span class="mi">10088</span>         <span class="kn">from</span> <span class="nn">pandas.core.reshape.merge</span> <span class="kn">import</span> <span class="n">merge</span>
-<span class="g g-Whitespace">  </span><span class="mi">10089</span> 
-<span class="ne">&gt; </span><span class="mi">10090</span>         <span class="k">return</span> <span class="n">merge</span><span class="p">(</span>
-<span class="g g-Whitespace">  </span><span class="mi">10091</span>             <span class="bp">self</span><span class="p">,</span>
-<span class="g g-Whitespace">  </span><span class="mi">10092</span>             <span class="n">right</span><span class="p">,</span>
-<span class="g g-Whitespace">  </span><span class="mi">10093</span>             <span class="n">how</span><span class="o">=</span><span class="n">how</span><span class="p">,</span>
-
-<span class="nn">/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/reshape/merge.py</span> in <span class="ni">?</span><span class="nt">(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)</span>
-<span class="g g-Whitespace">    </span><span class="mi">106</span>     <span class="n">copy</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-<span class="g g-Whitespace">    </span><span class="mi">107</span>     <span class="n">indicator</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-<span class="g g-Whitespace">    </span><span class="mi">108</span>     <span class="n">validate</span><span class="p">:</span> <span class="nb">str</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-<span class="g g-Whitespace">    </span><span class="mi">109</span> <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
-<span class="ne">--&gt; </span><span class="mi">110</span>     <span class="n">op</span> <span class="o">=</span> <span class="n">_MergeOperation</span><span class="p">(</span>
-<span class="g g-Whitespace">    </span><span class="mi">111</span>         <span class="n">left</span><span class="p">,</span>
-<span class="g g-Whitespace">    </span><span class="mi">112</span>         <span class="n">right</span><span class="p">,</span>
-<span class="g g-Whitespace">    </span><span class="mi">113</span>         <span class="n">how</span><span class="o">=</span><span class="n">how</span><span class="p">,</span>
-
-<span class="nn">/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/reshape/merge.py</span> in <span class="ni">?</span><span class="nt">(self, left, right, how, on, left_on, right_on, axis, left_index, right_index, sort, suffixes, indicator, validate)</span>
-<span class="g g-Whitespace">    </span><span class="mi">699</span>         <span class="p">(</span>
-<span class="g g-Whitespace">    </span><span class="mi">700</span>             <span class="bp">self</span><span class="o">.</span><span class="n">left_join_keys</span><span class="p">,</span>
-<span class="g g-Whitespace">    </span><span class="mi">701</span>             <span class="bp">self</span><span class="o">.</span><span class="n">right_join_keys</span><span class="p">,</span>
-<span class="g g-Whitespace">    </span><span class="mi">702</span>             <span class="bp">self</span><span class="o">.</span><span class="n">join_names</span><span class="p">,</span>
-<span class="ne">--&gt; </span><span class="mi">703</span>         <span class="p">)</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_merge_keys</span><span class="p">()</span>
-<span class="g g-Whitespace">    </span><span class="mi">704</span> 
-<span class="g g-Whitespace">    </span><span class="mi">705</span>         <span class="c1"># validate the merge keys dtypes. We may need to coerce</span>
-<span class="g g-Whitespace">    </span><span class="mi">706</span>         <span class="c1"># to avoid incompatible dtypes</span>
-
-<span class="nn">/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/reshape/merge.py</span> in <span class="ni">?</span><span class="nt">(self)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1158</span>                         <span class="c1"># Then we&#39;re either Hashable or a wrong-length arraylike,</span>
-<span class="g g-Whitespace">   </span><span class="mi">1159</span>                         <span class="c1">#  the latter of which will raise</span>
-<span class="g g-Whitespace">   </span><span class="mi">1160</span>                         <span class="n">rk</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">Hashable</span><span class="p">,</span> <span class="n">rk</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1161</span>                         <span class="k">if</span> <span class="n">rk</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-<span class="ne">-&gt; </span><span class="mi">1162</span>                             <span class="n">right_keys</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">right</span><span class="o">.</span><span class="n">_get_label_or_level_values</span><span class="p">(</span><span class="n">rk</span><span class="p">))</span>
-<span class="g g-Whitespace">   </span><span class="mi">1163</span>                         <span class="k">else</span><span class="p">:</span>
-<span class="g g-Whitespace">   </span><span class="mi">1164</span>                             <span class="c1"># work-around for merge_asof(right_index=True)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1165</span>                             <span class="n">right_keys</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">right</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
-
-<span class="nn">/usr/share/miniconda/envs/open-machine-learning-jupyter-book/lib/python3.9/site-packages/pandas/core/generic.py</span> in <span class="ni">?</span><span class="nt">(self, key, axis)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1846</span>                 <span class="o">.</span><span class="n">get_level_values</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>  <span class="c1"># type: ignore[assignment]</span>
-<span class="g g-Whitespace">   </span><span class="mi">1847</span>                 <span class="o">.</span><span class="n">_values</span>
-<span class="g g-Whitespace">   </span><span class="mi">1848</span>             <span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1849</span>         <span class="k">else</span><span class="p">:</span>
-<span class="ne">-&gt; </span><span class="mi">1850</span>             <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1851</span> 
-<span class="g g-Whitespace">   </span><span class="mi">1852</span>         <span class="c1"># Check for duplicates</span>
-<span class="g g-Whitespace">   </span><span class="mi">1853</span>         <span class="k">if</span> <span class="n">values</span><span class="o">.</span><span class="n">ndim</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-
-<span class="ne">KeyError</span>: &#39;rkey&#39;
-</pre></div>
-</div>
-</div>
-</div>
-<p>Using <code class="docutils literal notranslate"><span class="pre">how</span></code> parameter decide the type of merge to be performed.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;a&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;foo&#39;</span><span class="p">,</span> <span class="s1">&#39;bar&#39;</span><span class="p">],</span> <span class="s1">&#39;b&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]})</span>
-<span class="n">df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;a&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;foo&#39;</span><span class="p">,</span> <span class="s1">&#39;baz&#39;</span><span class="p">],</span> <span class="s1">&#39;c&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]})</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">df2</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s1">&#39;inner&#39;</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s1">&#39;a&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:600px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf1%20%3D%20pd.DataFrame%28%7B'a'%3A%20%5B'foo',%20'bar'%5D,%20'b'%3A%20%5B1,%202%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B'a'%3A%20%5B'foo',%20'baz'%5D,%20'c'%3A%20%5B3,%204%5D%7D%29%0Adf1.merge%28df2,%20how%3D'inner',%20on%3D'a'%29&d=2023-07-15&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">df2</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s1">&#39;a&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:630px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf1%20%3D%20pd.DataFrame%28%7B'a'%3A%20%5B'foo',%20'bar'%5D,%20'b'%3A%20%5B1,%202%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B'a'%3A%20%5B'foo',%20'baz'%5D,%20'c'%3A%20%5B3,%204%5D%7D%29%0Adf1.merge%28df2,%20how%3D'left',%20on%3D'a'%29&d=2023-07-15&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;left&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;foo&#39;</span><span class="p">,</span> <span class="s1">&#39;bar&#39;</span><span class="p">]})</span>
-<span class="n">df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;right&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">]})</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">df2</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s1">&#39;cross&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:670px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf1%20%3D%20pd.DataFrame%28%7B'left'%3A%20%5B'foo',%20'bar'%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B'right'%3A%20%5B7,%208%5D%7D%29%0Adf1.merge%28df2,%20how%3D'cross'%29&d=2023-07-15&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-</section>
-<section id="join">
-<h3><span class="section-number">5.4.3.3. </span>join<a class="headerlink" href="#join" title="Permalink to this headline">#</a></h3>
-<ul class="simple">
-<li><p>Join columns of another DataFrame.</p></li>
-<li><p>Join columns with other DataFrame either on index or on a key column. Efficiently join multiple DataFrame objects by index at once by passing a list.</p></li>
-</ul>
-<p>For example:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;key&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;K0&#39;</span><span class="p">,</span> <span class="s1">&#39;K1&#39;</span><span class="p">,</span> <span class="s1">&#39;K2&#39;</span><span class="p">,</span> <span class="s1">&#39;K3&#39;</span><span class="p">,</span> <span class="s1">&#39;K4&#39;</span><span class="p">,</span> <span class="s1">&#39;K5&#39;</span><span class="p">],</span>
-                   <span class="s1">&#39;A&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;A0&#39;</span><span class="p">,</span> <span class="s1">&#39;A1&#39;</span><span class="p">,</span> <span class="s1">&#39;A2&#39;</span><span class="p">,</span> <span class="s1">&#39;A3&#39;</span><span class="p">,</span> <span class="s1">&#39;A4&#39;</span><span class="p">,</span> <span class="s1">&#39;A5&#39;</span><span class="p">]})</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">other</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;key&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;K0&#39;</span><span class="p">,</span> <span class="s1">&#39;K1&#39;</span><span class="p">,</span> <span class="s1">&#39;K2&#39;</span><span class="p">],</span>
-                      <span class="s1">&#39;B&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;B0&#39;</span><span class="p">,</span> <span class="s1">&#39;B1&#39;</span><span class="p">,</span> <span class="s1">&#39;B2&#39;</span><span class="p">]})</span>                     
-</pre></div>
-</div>
-</div>
-</div>
-<p>Join DataFrames using their indexes.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">lsuffix</span><span class="o">=</span><span class="s1">&#39;_caller&#39;</span><span class="p">,</span> <span class="n">rsuffix</span><span class="o">=</span><span class="s1">&#39;_other&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:830px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf%20%3D%20pd.DataFrame%28%7B'key'%3A%20%5B'K0',%20'K1',%20'K2',%20'K3',%20'K4',%20'K5'%5D,'A'%3A%20%5B'A0',%20'A1',%20'A2',%20'A3',%20'A4',%20'A5'%5D%7D%29%0Aother%20%3D%20pd.DataFrame%28%7B'key'%3A%20%5B'K0',%20'K1',%20'K2'%5D,'B'%3A%20%5B'B0',%20'B1',%20'B2'%5D%7D%29%0Adf.join%28other,%20lsuffix%3D'_caller',%20rsuffix%3D'_other'%29&d=2023-07-15&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<p>If we want to join using the <code class="docutils literal notranslate"><span class="pre">key</span></code> columns, we need to set <code class="docutils literal notranslate"><span class="pre">key</span></code> to be the index in both <code class="docutils literal notranslate"><span class="pre">df</span></code> and <code class="docutils literal notranslate"><span class="pre">other</span></code>. The joined DataFrame will have <code class="docutils literal notranslate"><span class="pre">key</span></code> as its index.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s1">&#39;key&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s1">&#39;key&#39;</span><span class="p">))</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:1170px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf%20%3D%20pd.DataFrame%28%7B'key'%3A%20%5B'K0',%20'K1',%20'K2',%20'K3',%20'K4',%20'K5'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'A'%3A%20%5B'A0',%20'A1',%20'A2',%20'A3',%20'A4',%20'A5'%5D%7D%29%0Aother%20%3D%20pd.DataFrame%28%7B'key'%3A%20%5B'K0',%20'K1',%20'K2'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'B'%3A%20%5B'B0',%20'B1',%20'B2'%5D%7D%29%0Adf.set_index%28'key'%29.join%28other.set_index%28'key'%29%29&d=2023-05-27&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<p>Another option to join using the key columns is to use the <code class="docutils literal notranslate"><span class="pre">on</span></code> parameter. <code class="docutils literal notranslate"><span class="pre">DataFrame.join</span></code> always uses <code class="docutils literal notranslate"><span class="pre">other</span></code>’s index but we can use any column in <code class="docutils literal notranslate"><span class="pre">df</span></code>. This method preserves the original DataFrame’s index in the result.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s1">&#39;key&#39;</span><span class="p">),</span> <span class="n">on</span><span class="o">=</span><span class="s1">&#39;key&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>key</th>
-      <th>A</th>
-      <th>B</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>K0</td>
-      <td>A0</td>
-      <td>B0</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>K1</td>
-      <td>A1</td>
-      <td>B1</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>K2</td>
-      <td>A2</td>
-      <td>B2</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>K3</td>
-      <td>A3</td>
-      <td>NaN</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>K4</td>
-      <td>A4</td>
-      <td>NaN</td>
-    </tr>
-    <tr>
-      <th>5</th>
-      <td>K5</td>
-      <td>A5</td>
-      <td>NaN</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>Using non-unique key values shows how they are matched.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;key&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;K0&#39;</span><span class="p">,</span> <span class="s1">&#39;K1&#39;</span><span class="p">,</span> <span class="s1">&#39;K1&#39;</span><span class="p">,</span> <span class="s1">&#39;K3&#39;</span><span class="p">,</span> <span class="s1">&#39;K0&#39;</span><span class="p">,</span> <span class="s1">&#39;K1&#39;</span><span class="p">],</span>
-                   <span class="s1">&#39;A&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;A0&#39;</span><span class="p">,</span> <span class="s1">&#39;A1&#39;</span><span class="p">,</span> <span class="s1">&#39;A2&#39;</span><span class="p">,</span> <span class="s1">&#39;A3&#39;</span><span class="p">,</span> <span class="s1">&#39;A4&#39;</span><span class="p">,</span> <span class="s1">&#39;A5&#39;</span><span class="p">]})</span>
-<span class="n">df</span>                   
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>key</th>
-      <th>A</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>K0</td>
-      <td>A0</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>K1</td>
-      <td>A1</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>K1</td>
-      <td>A2</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>K3</td>
-      <td>A3</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>K0</td>
-      <td>A4</td>
-    </tr>
-    <tr>
-      <th>5</th>
-      <td>K1</td>
-      <td>A5</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s1">&#39;key&#39;</span><span class="p">),</span> <span class="n">on</span><span class="o">=</span><span class="s1">&#39;key&#39;</span><span class="p">,</span> <span class="n">validate</span><span class="o">=</span><span class="s1">&#39;m:1&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>key</th>
-      <th>A</th>
-      <th>B</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>K0</td>
-      <td>A0</td>
-      <td>B0</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>K1</td>
-      <td>A1</td>
-      <td>B1</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>K1</td>
-      <td>A2</td>
-      <td>B1</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>K3</td>
-      <td>A3</td>
-      <td>NaN</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>K0</td>
-      <td>A4</td>
-      <td>B0</td>
-    </tr>
-    <tr>
-      <th>5</th>
-      <td>K1</td>
-      <td>A5</td>
-      <td>B1</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-</section>
-</section>
-<section id="aggregation-and-grouping">
-<h2><span class="section-number">5.4.4. </span>Aggregation and grouping<a class="headerlink" href="#aggregation-and-grouping" title="Permalink to this headline">#</a></h2>
-<p>Group <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> using a mapper or by a <code class="docutils literal notranslate"><span class="pre">Series</span></code> of columns.</p>
-<p>A <code class="docutils literal notranslate"><span class="pre">groupby</span></code> operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.</p>
-<p>For example:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;Animal&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;Falcon&#39;</span><span class="p">,</span> <span class="s1">&#39;Falcon&#39;</span><span class="p">,</span>
-                              <span class="s1">&#39;Parrot&#39;</span><span class="p">,</span> <span class="s1">&#39;Parrot&#39;</span><span class="p">],</span>
-                   <span class="s1">&#39;Max Speed&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">380.</span><span class="p">,</span> <span class="mf">370.</span><span class="p">,</span> <span class="mf">24.</span><span class="p">,</span> <span class="mf">26.</span><span class="p">]})</span>
-<span class="n">df</span>
-<span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;Animal&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:785px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf%20%3D%20pd.DataFrame%28%7B'Animal'%3A%20%5B'Falcon',%20'Falcon',%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'Parrot',%20'Parrot'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'Max%20Speed'%3A%20%5B380.,%20370.,%2024.,%2026.%5D%7D%29%0Adf%0Adf.groupby%28%5B'Animal'%5D%29.mean%28%29&d=2023-05-27&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<section id="hierarchical-indexes">
-<h3><span class="section-number">5.4.4.1. </span>Hierarchical Indexes<a class="headerlink" href="#hierarchical-indexes" title="Permalink to this headline">#</a></h3>
-<p>#################</p>
-<p>We can <code class="docutils literal notranslate"><span class="pre">groupby</span></code> different levels of a hierarchical index using the <code class="docutils literal notranslate"><span class="pre">level</span></code> parameter:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">arrays</span> <span class="o">=</span> <span class="p">[[</span><span class="s1">&#39;Falcon&#39;</span><span class="p">,</span> <span class="s1">&#39;Falcon&#39;</span><span class="p">,</span> <span class="s1">&#39;Parrot&#39;</span><span class="p">,</span> <span class="s1">&#39;Parrot&#39;</span><span class="p">],</span>
-          <span class="p">[</span><span class="s1">&#39;Captive&#39;</span><span class="p">,</span> <span class="s1">&#39;Wild&#39;</span><span class="p">,</span> <span class="s1">&#39;Captive&#39;</span><span class="p">,</span> <span class="s1">&#39;Wild&#39;</span><span class="p">]]</span>
-<span class="n">index</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">(</span><span class="n">arrays</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;Animal&#39;</span><span class="p">,</span> <span class="s1">&#39;Type&#39;</span><span class="p">))</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;Max Speed&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">390.</span><span class="p">,</span> <span class="mf">350.</span><span class="p">,</span> <span class="mf">30.</span><span class="p">,</span> <span class="mf">20.</span><span class="p">]},</span>
-                  <span class="n">index</span><span class="o">=</span><span class="n">index</span><span class="p">)</span>
-<span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:810px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aarrays%20%3D%20%5B%5B'Falcon',%20'Falcon',%20'Parrot',%20'Parrot'%5D,%0A%20%20%20%20%20%20%20%20%20%20%5B'Captive',%20'Wild',%20'Captive',%20'Wild'%5D%5D%0Aindex%20%3D%20pd.MultiIndex.from_arrays%28arrays,%20names%3D%28'Animal',%20'Type'%29%29%0Adf%20%3D%20pd.DataFrame%28%7B'Max%20Speed'%3A%20%5B390.,%20350.,%2030.,%2020.%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dindex%29%0Adf.groupby%28level%3D0%29.mean%28%29&d=2023-07-15&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="s2">&quot;Type&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:810px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aarrays%20%3D%20%5B%5B'Falcon',%20'Falcon',%20'Parrot',%20'Parrot'%5D,%0A%20%20%20%20%20%20%20%20%20%20%5B'Captive',%20'Wild',%20'Captive',%20'Wild'%5D%5D%0Aindex%20%3D%20pd.MultiIndex.from_arrays%28arrays,%20names%3D%28'Animal',%20'Type'%29%29%0Adf%20%3D%20pd.DataFrame%28%7B'Max%20Speed'%3A%20%5B390.,%20350.,%2030.,%2020.%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dindex%29%0Adf.groupby%28level%3D%22Type%22%29.mean%28%29&d=2023-07-15&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<p>We can also choose to include NA in group keys or not by setting <code class="docutils literal notranslate"><span class="pre">dropna</span></code> parameter, the default setting is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">l</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]]</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">])</span>
-<span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;b&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:770px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Al%20%3D%20%5B%5B1,%202,%203%5D,%20%5B1,%20None,%204%5D,%20%5B2,%201,%203%5D,%20%5B1,%202,%202%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%5B%22b%22%5D%29.sum%28%29%0A&d=2023-07-15&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;b&quot;</span><span class="p">],</span> <span class="n">dropna</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:750px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Al%20%3D%20%5B%5B1,%202,%203%5D,%20%5B1,%20None,%204%5D,%20%5B2,%201,%203%5D,%20%5B1,%202,%202%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%5B%22b%22%5D,%20dropna%3DFalse%29.sum%28%29&d=2023-05-27&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">l</span> <span class="o">=</span> <span class="p">[[</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">],</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="mf">12.3</span><span class="p">,</span> <span class="mf">33.</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="mf">12.3</span><span class="p">,</span> <span class="mi">123</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">])</span>
-<span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s2">&quot;a&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:750px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Al%20%3D%20%5B%5B%22a%22,%2012,%2012%5D,%20%5BNone,%2012.3,%2033.%5D,%20%5B%22b%22,%2012.3,%20123%5D,%20%5B%22a%22,%201,%201%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%22a%22%29.sum%28%29&d=2023-07-15&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="n">dropna</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-</pre></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:750px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Al%20%3D%20%5B%5B%22a%22,%2012,%2012%5D,%20%5BNone,%2012.3,%2033.%5D,%20%5B%22b%22,%2012.3,%20123%5D,%20%5B%22a%22,%201,%201%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%22a%22,%20dropna%3DFalse%29.sum%28%29%0A&d=2023-07-15&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-<p>When using <code class="docutils literal notranslate"><span class="pre">.apply()</span></code>, use <code class="docutils literal notranslate"><span class="pre">group_keys</span></code> to include or exclude the group keys. The <code class="docutils literal notranslate"><span class="pre">group_keys</span></code> argument defaults to <code class="docutils literal notranslate"><span class="pre">True</span></code> (include).</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;Animal&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;Falcon&#39;</span><span class="p">,</span> <span class="s1">&#39;Falcon&#39;</span><span class="p">,</span>
-                              <span class="s1">&#39;Parrot&#39;</span><span class="p">,</span> <span class="s1">&#39;Parrot&#39;</span><span class="p">],</span>
-                   <span class="s1">&#39;Max Speed&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">380.</span><span class="p">,</span> <span class="mf">370.</span><span class="p">,</span> <span class="mf">24.</span><span class="p">,</span> <span class="mf">26.</span><span class="p">]})</span>
-<span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">&quot;Animal&quot;</span><span class="p">,</span> <span class="n">group_keys</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th></th>
-      <th>Animal</th>
-      <th>Max Speed</th>
-    </tr>
-    <tr>
-      <th>Animal</th>
-      <th></th>
-      <th></th>
-      <th></th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th rowspan="2" valign="top">Falcon</th>
-      <th>0</th>
-      <td>Falcon</td>
-      <td>380.0</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>Falcon</td>
-      <td>370.0</td>
-    </tr>
-    <tr>
-      <th rowspan="2" valign="top">Parrot</th>
-      <th>2</th>
-      <td>Parrot</td>
-      <td>24.0</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>Parrot</td>
-      <td>26.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">&quot;Animal&quot;</span><span class="p">,</span> <span class="n">group_keys</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>Animal</th>
-      <th>Max Speed</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>Falcon</td>
-      <td>380.0</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>Falcon</td>
-      <td>370.0</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>Parrot</td>
-      <td>24.0</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>Parrot</td>
-      <td>26.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-</section>
-</section>
-<section id="pivot-table">
-<h2><span class="section-number">5.4.5. </span>Pivot table<a class="headerlink" href="#pivot-table" title="Permalink to this headline">#</a></h2>
-<p>Create a spreadsheet-style pivot table as a DataFrame.</p>
-<p>The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">&quot;A&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;foo&quot;</span><span class="p">,</span> <span class="s2">&quot;foo&quot;</span><span class="p">,</span> <span class="s2">&quot;foo&quot;</span><span class="p">,</span> <span class="s2">&quot;foo&quot;</span><span class="p">,</span> <span class="s2">&quot;foo&quot;</span><span class="p">,</span>
-                         <span class="s2">&quot;bar&quot;</span><span class="p">,</span> <span class="s2">&quot;bar&quot;</span><span class="p">,</span> <span class="s2">&quot;bar&quot;</span><span class="p">,</span> <span class="s2">&quot;bar&quot;</span><span class="p">],</span>
-                   <span class="s2">&quot;B&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;one&quot;</span><span class="p">,</span> <span class="s2">&quot;one&quot;</span><span class="p">,</span> <span class="s2">&quot;one&quot;</span><span class="p">,</span> <span class="s2">&quot;two&quot;</span><span class="p">,</span> <span class="s2">&quot;two&quot;</span><span class="p">,</span>
-                         <span class="s2">&quot;one&quot;</span><span class="p">,</span> <span class="s2">&quot;one&quot;</span><span class="p">,</span> <span class="s2">&quot;two&quot;</span><span class="p">,</span> <span class="s2">&quot;two&quot;</span><span class="p">],</span>
-                   <span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;small&quot;</span><span class="p">,</span> <span class="s2">&quot;large&quot;</span><span class="p">,</span> <span class="s2">&quot;large&quot;</span><span class="p">,</span> <span class="s2">&quot;small&quot;</span><span class="p">,</span>
-                         <span class="s2">&quot;small&quot;</span><span class="p">,</span> <span class="s2">&quot;large&quot;</span><span class="p">,</span> <span class="s2">&quot;small&quot;</span><span class="p">,</span> <span class="s2">&quot;small&quot;</span><span class="p">,</span>
-                         <span class="s2">&quot;large&quot;</span><span class="p">],</span>
-                   <span class="s2">&quot;D&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span>
-                   <span class="s2">&quot;E&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">9</span><span class="p">]})</span>
-<span class="n">df</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-      <th>C</th>
-      <th>D</th>
-      <th>E</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>foo</td>
-      <td>one</td>
-      <td>small</td>
-      <td>1</td>
-      <td>2</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>foo</td>
-      <td>one</td>
-      <td>large</td>
-      <td>2</td>
-      <td>4</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>foo</td>
-      <td>one</td>
-      <td>large</td>
-      <td>2</td>
-      <td>5</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>foo</td>
-      <td>two</td>
-      <td>small</td>
-      <td>3</td>
-      <td>5</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>foo</td>
-      <td>two</td>
-      <td>small</td>
-      <td>3</td>
-      <td>6</td>
-    </tr>
-    <tr>
-      <th>5</th>
-      <td>bar</td>
-      <td>one</td>
-      <td>large</td>
-      <td>4</td>
-      <td>6</td>
-    </tr>
-    <tr>
-      <th>6</th>
-      <td>bar</td>
-      <td>one</td>
-      <td>small</td>
-      <td>5</td>
-      <td>8</td>
-    </tr>
-    <tr>
-      <th>7</th>
-      <td>bar</td>
-      <td>two</td>
-      <td>small</td>
-      <td>6</td>
-      <td>9</td>
-    </tr>
-    <tr>
-      <th>8</th>
-      <td>bar</td>
-      <td>two</td>
-      <td>large</td>
-      <td>7</td>
-      <td>9</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>This first example aggregates values by taking the sum.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">table</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="s1">&#39;D&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">],</span>
-                    <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;C&#39;</span><span class="p">],</span> <span class="n">aggfunc</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">)</span>
-<span class="n">table</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>C</th>
-      <th>large</th>
-      <th>small</th>
-    </tr>
-    <tr>
-      <th>A</th>
-      <th>B</th>
-      <th></th>
-      <th></th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th rowspan="2" valign="top">bar</th>
-      <th>one</th>
-      <td>4.0</td>
-      <td>5.0</td>
-    </tr>
-    <tr>
-      <th>two</th>
-      <td>7.0</td>
-      <td>6.0</td>
-    </tr>
-    <tr>
-      <th rowspan="2" valign="top">foo</th>
-      <th>one</th>
-      <td>4.0</td>
-      <td>1.0</td>
-    </tr>
-    <tr>
-      <th>two</th>
-      <td>NaN</td>
-      <td>6.0</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>We can also fill in missing values using the <code class="docutils literal notranslate"><span class="pre">fill_value</span></code> parameter.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">table</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="s1">&#39;D&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">],</span>
-                    <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;C&#39;</span><span class="p">],</span> <span class="n">aggfunc</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">table</span>
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-</div>
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-<div class="cell_output docutils container">
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-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>C</th>
-      <th>large</th>
-      <th>small</th>
-    </tr>
-    <tr>
-      <th>A</th>
-      <th>B</th>
-      <th></th>
-      <th></th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th rowspan="2" valign="top">bar</th>
-      <th>one</th>
-      <td>4</td>
-      <td>5</td>
-    </tr>
-    <tr>
-      <th>two</th>
-      <td>7</td>
-      <td>6</td>
-    </tr>
-    <tr>
-      <th rowspan="2" valign="top">foo</th>
-      <th>one</th>
-      <td>4</td>
-      <td>1</td>
-    </tr>
-    <tr>
-      <th>two</th>
-      <td>0</td>
-      <td>6</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>The next example aggregates by taking the mean across multiple columns.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">table</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;D&#39;</span><span class="p">,</span> <span class="s1">&#39;E&#39;</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">],</span>
-                    <span class="n">aggfunc</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;D&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">,</span>
-                             <span class="s1">&#39;E&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">})</span>
-<span class="n">table</span>
-</pre></div>
-</div>
-</div>
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-<div class="output text_html"><div>
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-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th></th>
-      <th>D</th>
-      <th>E</th>
-    </tr>
-    <tr>
-      <th>A</th>
-      <th>C</th>
-      <th></th>
-      <th></th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th rowspan="2" valign="top">bar</th>
-      <th>large</th>
-      <td>5.500000</td>
-      <td>7.500000</td>
-    </tr>
-    <tr>
-      <th>small</th>
-      <td>5.500000</td>
-      <td>8.500000</td>
-    </tr>
-    <tr>
-      <th rowspan="2" valign="top">foo</th>
-      <th>large</th>
-      <td>2.000000</td>
-      <td>4.500000</td>
-    </tr>
-    <tr>
-      <th>small</th>
-      <td>2.333333</td>
-      <td>4.333333</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>We can also calculate multiple types of aggregations for any given value column.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">table</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;D&#39;</span><span class="p">,</span> <span class="s1">&#39;E&#39;</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">],</span>
-                    <span class="n">aggfunc</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;D&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">,</span>
-                             <span class="s1">&#39;E&#39;</span><span class="p">:</span> <span class="p">[</span><span class="nb">min</span><span class="p">,</span> <span class="nb">max</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">]})</span>
-<span class="n">table</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
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-    .dataframe thead tr:last-of-type th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr>
-      <th></th>
-      <th></th>
-      <th>D</th>
-      <th colspan="3" halign="left">E</th>
-    </tr>
-    <tr>
-      <th></th>
-      <th></th>
-      <th>mean</th>
-      <th>max</th>
-      <th>mean</th>
-      <th>min</th>
-    </tr>
-    <tr>
-      <th>A</th>
-      <th>C</th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th rowspan="2" valign="top">bar</th>
-      <th>large</th>
-      <td>5.500000</td>
-      <td>9</td>
-      <td>7.500000</td>
-      <td>6</td>
-    </tr>
-    <tr>
-      <th>small</th>
-      <td>5.500000</td>
-      <td>9</td>
-      <td>8.500000</td>
-      <td>8</td>
-    </tr>
-    <tr>
-      <th rowspan="2" valign="top">foo</th>
-      <th>large</th>
-      <td>2.000000</td>
-      <td>5</td>
-      <td>4.500000</td>
-      <td>4</td>
-    </tr>
-    <tr>
-      <th>small</th>
-      <td>2.333333</td>
-      <td>6</td>
-      <td>4.333333</td>
-      <td>2</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-</section>
-<section id="high-performance-pandas-eval-and-query">
-<h2><span class="section-number">5.4.6. </span>High-performance Pandas: eval() and query()<a class="headerlink" href="#high-performance-pandas-eval-and-query" title="Permalink to this headline">#</a></h2>
-<section id="eval">
-<h3><span class="section-number">5.4.6.1. </span>eval()<a class="headerlink" href="#eval" title="Permalink to this headline">#</a></h3>
-<p>Evaluate a string describing operations on DataFrame columns.</p>
-<p>Operates on columns only, not specific rows or elements. This allows <code class="docutils literal notranslate"><span class="pre">eval</span></code> to run arbitrary code, which can make you vulnerable to code injection if you pass user input to this function.</p>
-<p>For example:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;A&#39;</span><span class="p">:</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span> <span class="s1">&#39;B&#39;</span><span class="p">:</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)})</span>
-<span class="n">df</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
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-
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-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>10</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>2</td>
-      <td>8</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>3</td>
-      <td>6</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>4</td>
-      <td>4</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>5</td>
-      <td>2</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">&#39;A + B&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    11
-1    10
-2     9
-3     8
-4     7
-dtype: int64
-</pre></div>
-</div>
-</div>
-</div>
-<p>The assignment is allowed though by default the original <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> is not modified.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">&#39;C = A + B&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
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-
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-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-      <th>C</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>10</td>
-      <td>11</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>2</td>
-      <td>8</td>
-      <td>10</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>3</td>
-      <td>6</td>
-      <td>9</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>4</td>
-      <td>4</td>
-      <td>8</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>5</td>
-      <td>2</td>
-      <td>7</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>10</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>2</td>
-      <td>8</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>3</td>
-      <td>6</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>4</td>
-      <td>4</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>5</td>
-      <td>2</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>Use <code class="docutils literal notranslate"><span class="pre">inplace=True</span></code> to modify the original DataFrame.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">&#39;C = A + B&#39;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">df</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-      <th>C</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>10</td>
-      <td>11</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>2</td>
-      <td>8</td>
-      <td>10</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>3</td>
-      <td>6</td>
-      <td>9</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>4</td>
-      <td>4</td>
-      <td>8</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>5</td>
-      <td>2</td>
-      <td>7</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>Multiple columns can be assigned using multi-line expressions:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span>
-<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
-<span class="sd">    C = A + B</span>
-<span class="sd">    D = A - B</span>
-<span class="sd">    &#39;&#39;&#39;</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-      <th>C</th>
-      <th>D</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>10</td>
-      <td>11</td>
-      <td>-9</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>2</td>
-      <td>8</td>
-      <td>10</td>
-      <td>-6</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>3</td>
-      <td>6</td>
-      <td>9</td>
-      <td>-3</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>4</td>
-      <td>4</td>
-      <td>8</td>
-      <td>0</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>5</td>
-      <td>2</td>
-      <td>7</td>
-      <td>3</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-</section>
-<section id="query">
-<h3><span class="section-number">5.4.6.2. </span>query()<a class="headerlink" href="#query" title="Permalink to this headline">#</a></h3>
-<p>Query the columns of a DataFrame with a boolean expression.</p>
-<p>For example:</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
-    <span class="s1">&#39;A&#39;</span><span class="p">:</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span>
-    <span class="s1">&#39;B&#39;</span><span class="p">:</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">),</span>
-    <span class="s1">&#39;C C&#39;</span><span class="p">:</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-<span class="p">})</span>
-<span class="n">df</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
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-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-      <th>C C</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>10</td>
-      <td>10</td>
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-      <td>8</td>
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-      <td>6</td>
-      <td>8</td>
-    </tr>
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-      <td>4</td>
-      <td>4</td>
-      <td>7</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>5</td>
-      <td>2</td>
-      <td>6</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="s1">&#39;A &gt; B&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
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-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-      <th>C C</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>4</th>
-      <td>5</td>
-      <td>2</td>
-      <td>6</td>
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-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>The previous expression is equivalent to</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">A</span> <span class="o">&gt;</span> <span class="n">df</span><span class="o">.</span><span class="n">B</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
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-    .dataframe tbody tr th {
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-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-      <th>C C</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>4</th>
-      <td>5</td>
-      <td>2</td>
-      <td>6</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>For columns with spaces in their name, you can use backtick quoting.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="s1">&#39;B == `C C`&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
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-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-      <th>C C</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>10</td>
-      <td>10</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<p>The previous expression is equivalent to</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">B</span> <span class="o">==</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;C C&#39;</span><span class="p">]]</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-      <th>C C</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>10</td>
-      <td>10</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-<div class='full-width docutils' >
-  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
-    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
-    <div class="pandastutor inner" style="height:660px;">
-      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf%20%3D%20pd.DataFrame%28%7B%0A%20%20%20%20'A'%3A%20range%281,%206%29,%0A%20%20%20%20'B'%3A%20range%2810,%200,%20-2%29,%0A%20%20%20%20'C%20C'%3A%20range%2810,%205,%20-1%29%0A%7D%29%0A%0Adf%5Bdf.B%20%3D%3D%20df%5B'C%20C'%5D%5D&d=2023-07-13&lang=py&v=v1"> </iframe>
-    </div>
-  </div>
-</div>
-## Your turn! 🚀
-</section>
-<section id="processing-image-data">
-<h3><span class="section-number">5.4.6.3. </span>Processing image data<a class="headerlink" href="#processing-image-data" title="Permalink to this headline">#</a></h3>
-<p>Recently, very powerful AI models have been developed that allow us to understand images. There are many tasks that can be solved using pre-trained neural networks, or cloud services. Some examples include:</p>
-<ul class="simple">
-<li><p><strong>Image Classification</strong>, can help you categorize the image into one of the pre-defined classes. You can easily train your own image classifiers using services such as <a class="reference external" href="https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum">Custom Vision</a></p></li>
-<li><p><strong>Object Detection</strong> to detect different objects in the image. Services such as <a class="reference external" href="https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum">computer vision</a> can detect a number of common objects, and you can train <a class="reference external" href="https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum">Custom Vision</a> model to detect some specific objects of interest.</p></li>
-<li><p><strong>Face Detection</strong>, including Age, Gender and Emotion detection. This can be done via <a class="reference external" href="https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-77958-bethanycheum">Face API</a>.</p></li>
-</ul>
-<p>All those cloud services can be called using <a class="reference external" href="https://docs.microsoft.com/samples/azure-samples/cognitive-services-python-sdk-samples/cognitive-services-python-sdk-samples/?WT.mc_id=academic-77958-bethanycheum">Python SDKs</a>, and thus can be easily incorporated into your data exploration workflow.</p>
-<p>Here are some examples of exploring data from Image data sources:</p>
-<ul class="simple">
-<li><p>In the blog post <a class="reference external" href="https://soshnikov.com/azure/how-to-learn-data-science-without-coding/">How to Learn Data Science without Coding</a> we explore Instagram photos, trying to understand what makes people give more likes to a photo. We first extract as much information from pictures as possible using <a class="reference external" href="https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum">computer vision</a>, and then use <a class="reference external" href="https://docs.microsoft.com/azure/machine-learning/concept-automated-ml/?WT.mc_id=academic-77958-bethanycheum">Azure Machine Learning AutoML</a> to build the interpretable model.</p></li>
-<li><p>In <a class="reference external" href="https://github.com/CloudAdvocacy/FaceStudies">Facial Studies Workshop</a> we use <a class="reference external" href="https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-77958-bethanycheum">Face API</a> to extract emotions from people on photographs from events, in order to try to understand what makes people happy.</p></li>
-</ul>
-</section>
-<section id="assignment">
-<h3><span class="section-number">5.4.6.4. </span>Assignment<a class="headerlink" href="#assignment" title="Permalink to this headline">#</a></h3>
-<p><a class="reference internal" href="../../assignments/data-science/data-processing-in-python.html"><span class="doc std std-doc">Perform more detailed data study for the challenges above</span></a></p>
-</section>
-</section>
-<section id="self-study">
-<h2><span class="section-number">5.4.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
-<p>In this chapter, we’ve covered many of the basics of using Pandas effectively for data analysis. Still, much has been omitted from our discussion. To learn more about Pandas, we recommend the following resources:</p>
-<ul class="simple">
-<li><p><a class="reference external" href="http://pandas.pydata.org/">Pandas online documentation</a>: This is the go-to source for complete documentation of the package. While the examples in the documentation tend to be small generated datasets, the description of the options is complete and generally very useful for understanding the use of various functions.</p></li>
-<li><p><a class="reference external" href="http://shop.oreilly.com/product/0636920023784.do"><em>Python for Data Analysis</em></a> Written by Wes McKinney (the original creator of Pandas), this book contains much more detail on the Pandas package than we had room for in this chapter. In particular, he takes a deep dive into tools for time series, which were his bread and butter as a financial consultant. The book also has many entertaining examples of applying Pandas to gain insight from real-world datasets. Keep in mind, though, that the book is now several years old, and the Pandas package has quite a few new features that this book does not cover (but be on the lookout for a new edition in 2017).</p></li>
-<li><p><a class="reference external" href="http://stackoverflow.com/questions/tagged/pandas">Stack Overflow</a>: Pandas has so many users that any question you have has likely been asked and answered on Stack Overflow. Using Pandas is a case where some Google-Fu is your best friend. Simply go to your favorite search engine and type in the question, problem, or error you’re coming across–more than likely you’ll find your answer on a Stack Overflow page.</p></li>
-<li><p><a class="reference external" href="http://pyvideo.org/search?q=pandas">Pandas on PyVideo</a>: From PyCon to SciPy to PyData, many conferences have featured tutorials from Pandas developers and power users. The PyCon tutorials in particular tend to be given by very well-vetted presenters.</p></li>
-</ul>
-<p>Using these resources, combined with the walk-through given in this chapter, my hope is that you’ll be poised to use Pandas to tackle any data analysis problem you come across!</p>
-</section>
-<section id="acknowledgments">
-<h2><span class="section-number">5.4.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
-<p>Thanks for <a class="reference external" href="https://pandas.pydata.org/docs/user_guide/index.html">Pandas user guide</a>. It contributes the majority of the content in this chapter.</p>
-</section>
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+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../prerequisites/python-programming-advanced.html">
+   3. Python programming advanced
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  DATA SCIENCE
+ </span>
+</p>
+<ul class="current nav bd-sidenav">
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../introduction/introduction.html">
+   4. Introduction
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/>
+  <label for="toctree-checkbox-1">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../introduction/defining-data-science.html">
+     4.1. Defining data science
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../introduction/data-science-ethics.html">
+     4.2. Data Science ethics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../introduction/defining-data.html">
+     4.3. Defining data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../introduction/introduction-to-statistics-and-probability.html">
+     4.4. Introduction to statistics and probability
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 current active has-children">
+  <a class="reference internal" href="../working-with-data.html">
+   5. Working with data
+  </a>
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/>
+  <label for="toctree-checkbox-2">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul class="current">
+   <li class="toctree-l2">
+    <a class="reference internal" href="../relational-databases.html">
+     5.1. Relational databases
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../non-relational-data.html">
+     5.2. Non-relational data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../numpy.html">
+     5.3. NumPy
+    </a>
+   </li>
+   <li class="toctree-l2 current active has-children">
+    <a class="reference internal" href="../pandas.html">
+     5.4. Pandas
+    </a>
+    <input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul class="current">
+     <li class="toctree-l3">
+      <a class="reference internal" href="introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3 current active">
+      <a class="current reference internal" href="#">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-preparation.html">
+     5.5. Data preparation
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../data-visualization/data-visualization.html">
+   6. Data visualization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
+  <label for="toctree-checkbox-4">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-visualization/visualization-distributions.html">
+     6.1. Visualizing distributions
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-visualization/visualization-proportions.html">
+     6.2. Visualizing proportions
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-visualization/visualization-relationships.html">
+     6.3. Visualizing relationships: all about honey 🍯
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-visualization/meaningful-visualizations.html">
+     6.4. Making meaningful visualizations
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../data-science-lifecycle/data-science-lifecycle.html">
+   7. Data Science lifecycle
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
+  <label for="toctree-checkbox-5">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science-lifecycle/introduction.html">
+     7.1. Introduction to the Data Science lifecycle
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science-lifecycle/analyzing.html">
+     7.2. Analyzing
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science-lifecycle/communication.html">
+     7.3. Communication
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../data-science-in-the-cloud/data-science-in-the-cloud.html">
+   8. Data Science in the cloud
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
+  <label for="toctree-checkbox-6">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science-in-the-cloud/introduction.html">
+     8.1. Introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science-in-the-cloud/the-low-code-no-code-way.html">
+     8.2. The “low code/no code” way
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science-in-the-cloud/the-azure-ml-sdk-way.html">
+     8.3. Data Science in the cloud: The “Azure ML SDK” way
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../data-science-in-the-wild.html">
+   9. Data Science in the real world
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  MACHINE LEARNING BASICS
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-overview.html">
+   10. Machine Learning overview
+  </a>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
+   11. Regression models for Machine Learning
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
+  <label for="toctree-checkbox-7">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/regression/tools-of-the-trade.html">
+     11.1. Tools of the trade
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
+     11.2. Managing data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
+     11.3. Linear and polynomial regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/regression/logistic-regression.html">
+     11.4. Logistic regression
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
+   12. Build a web app to use a Machine Learning model
+  </a>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
+   13. Getting started with classification
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
+  <label for="toctree-checkbox-8">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
+     13.1. Introduction to classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/classification/more-classifiers.html">
+     13.2. More classifiers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/classification/yet-other-classifiers.html">
+     13.3. Yet other classifiers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
+     13.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  ADVANCED MACHINE LEARNING
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
+   14. Clustering models for Machine Learning
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/clustering/introduction-to-clustering.html">
+     14.1. Introduction to clustering
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/clustering/k-means-clustering.html">
+     14.2. K-Means clustering
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
+   15. Getting started with ensemble learning
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/ensemble-learning/bagging.html">
+     15.1. Bagging
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/ensemble-learning/random-forest.html">
+     15.2. Random forest
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/ensemble-learning/feature-importance.html">
+     15.3. Feature importance
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
+   16. Introduction to Gradient Boosting
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/gradient-boosting/gradient-boosting.html">
+     16.1. Gradient Boosting
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
+     16.2. Gradient boosting example
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/gradient-boosting/xgboost.html">
+     16.3. XGBoost
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
+     16.4. XGBoost + k-fold CV + Feature Importance
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/unsupervised-learning.html">
+   17. Unsupervised learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/kernel-method.html">
+   18. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/model-selection.html">
+   19. Model selection
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  DEEP LEARNING
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/dl-overview.html">
+   20. Intro to Deep Learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/cnn.html">
+   21. Convolutional Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/gan.html">
+   22. Generative adversarial networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/rnn.html">
+   23. Recurrent Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/autoencoder.html">
+   24. Autoencoder
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/lstm.html">
+   25. Long-short term memory
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/time-series.html">
+   26. Time series
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/dqn.html">
+   27. Deep Q-learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/image-classification.html">
+   28. Image classification
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/image-segmentation.html">
+   29. Image segmentation
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/difussion-model.html">
+   30. Diffusion Model
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/object-detection.html">
+   31. Object detection
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  MACHINE LEARNING OPERATIONS
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../machine-learning-productionization/overview.html">
+   32. Overview
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../machine-learning-productionization/problem-framing.html">
+   33. Problem framing
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../machine-learning-productionization/data-engineering.html">
+   34. Data engineering
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../machine-learning-productionization/model-training-and-evaluation.html">
+   35. Model training &amp; evaluation
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../machine-learning-productionization/model-deployment.html">
+   36. Model deployment
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  OTHERS
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../assignments/README.html">
+   37. Self-paced assignments
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/set-up-env/first-assignment.html">
+     37.3. First assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/set-up-env/second-assignment.html">
+     37.4. Second assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/project-plan-template.html">
+     37.5. Project Plan​ Template
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/prerequisites/python-programming-introduction.html">
+     37.6. Python programming introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/prerequisites/python-programming-basics.html">
+     37.7. Python programming basics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/prerequisites/python-programming-advanced.html">
+     37.8. Python programming advanced
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/analyzing-text-about-data-science.html">
+     37.9. Analyzing text about Data Science
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/data-science-scenarios.html">
+     37.10. Data Science scenarios
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/write-a-data-ethics-case-study.html">
+     37.11. Write a data ethics case study
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/lines-scatters-and-bars.html">
+     37.12. Lines, scatters and bars
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/apply-your-skills.html">
+     37.13. Apply your skills
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/try-it-in-excel.html">
+     37.14. Try it in Excel
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/dive-into-the-beehive.html">
+     37.15. Dive into the beehive
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/build-your-own-custom-vis.html">
+     37.16. Build your own custom vis
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/classifying-datasets.html">
+     37.17. Classifying datasets
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/small-diabetes-study.html">
+     37.18. Small diabetes study
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/introduction-to-statistics-and-probability.html">
+     37.19. Introduction to probability and statistics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/displaying-airport-data.html">
+     37.20. Displaying airport data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/soda-profits.html">
+     37.21. Soda profits
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/analyzing-COVID-19-papers.html">
+     37.22. Analyzing COVID-19 papers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
+     37.23. Estimation of COVID-19 pandemic
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/data-processing-in-python.html">
+     37.24. Data processing in Python
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/evaluating-data-from-a-form.html">
+     37.25. Evaluating data from a form
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/data-preparation.html">
+     37.26. Data preparation
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/analyzing-data.html">
+     37.27. Analyzing data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
+     37.28. NYC taxi data in winter and summer
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/matplotlib-applied.html">
+     37.29. Matplotlib applied
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/tell-a-story.html">
+     37.35. Tell a story
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/explore-a-planetary-computer-dataset.html">
+     37.36. Explore a planetary computer dataset
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/exploring-for-anwser.html">
+     37.37. Exploring for answers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/market-research.html">
+     37.38. Market research
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
+     37.39. Low code/no code Data Science project on Azure ML
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
+     37.40. Data Science project using Azure ML SDK
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
+     37.41. Data Science in the cloud: The “Azure ML SDK” way
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/ml-overview-iris.html">
+     37.42. Machine Learning overview - assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
+     37.43. Machine Learning overview - assignment 2
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
+     37.44. Regression with Scikit-learn
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
+     37.46. Linear Regression Metrics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/linear-regression/loss-function.html">
+     37.47. Loss Function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
+     37.50. ML logistic regression - assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
+     37.51. ML logistic regression - assignment 2
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/ml-neural-network-1.html">
+     37.52. ML neural network - Assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/regression-tools.html">
+     37.53. Regression tools
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/managing-data.html">
+     37.54. Managing data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/exploring-visualizations.html">
+     37.55. Exploring visualizations
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/try-a-different-model.html">
+     37.56. Try a different model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/create-a-regression-model.html">
+     37.57. Create a regression model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
+     37.58. Linear and polynomial regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/retrying-some-regression.html">
+     37.59. Retrying some regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
+     37.60. Pumpkin varieties and color
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
+     37.61. Delicious asian and indian cuisines
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/explore-classification-methods.html">
+     37.62. Explore classification methods
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
+     37.63. Kernel method assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
+     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
+     37.65. Support Vector Machines (SVM) - Classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
+     37.66. Decision Trees - Intro and Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
+     37.67. Decision Trees - Classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
+     37.68. Model selection assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
+     37.69. Learning Curve To Identify Overfit &amp; Underfit
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
+     37.70. Dropout and Batch Normalization
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
+     37.71. Lasso and Ridge Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/model-selection/regularized-linear-models.html">
+     37.72. Regularized Linear Models
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
+     37.73. Random forests intro and regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/ensemble-learning/random-forests-for-classification.html">
+     37.74. Random forests for classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
+     37.75. Beyond random forests: more ensemble models
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/ensemble-learning/decision-trees.html">
+     37.76. Decision trees
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
+     37.77. Hyperparameter tuning gradient boosting
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
+     37.78. Gradient boosting
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html">
+     37.79. Boosting with tuning
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
+     37.80. Random Forest Classifier with Feature Importance
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/machine-learning-productionization/data-engineering.html">
+     37.82. Data engineering
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
+     37.83. Counterintuitive Challenges in ML Debugging
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/machine-learning-productionization/debugging-in-classification.html">
+     37.84. Case Study: Debugging in Classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/machine-learning-productionization/debugging-in-regression.html">
+     37.85. Case Study: Debugging in Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/study-the-solvers.html">
+     37.86. Study the solvers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/build-classification-models.html">
+     37.87. Build classification models
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/build-classification-model.html">
+     37.88. Build Classification Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/parameter-play.html">
+     37.89. Parameter play
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
+     37.90. How to choose cnn architecture mnist
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
+     37.92. Sign Language Digits Classification with CNN
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html">
+     37.94. Object Recognition in Images using CNN
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
+     37.95. Intro to TensorFlow for Deep Learning
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
+     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
+     37.99. Google Stock Price Prediction RNN
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/autoencoder/autoencoder.html">
+     37.101. Intro to Autoencoders
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
+     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
+     37.103. Fun with Variational Autoencoders
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/time-series-forecasting-assignment.html">
+     37.104. Time Series Forecasting Assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/nn-for-classification-assignment.html">
+     37.106. Neural Networks for Classification with TensorFlow
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
+     37.107. NN Classify 15 Fruits Assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
+     37.112. DQN On Foreign Exchange Market
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/gan/art-by-gan.html">
+     37.113. Art by gan
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/gan/gan-introduction.html">
+     37.115. Generative Adversarial Networks (GANs)
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
+     37.116. Basic classification: Classify images of clothing
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../slides/introduction.html">
+   38. Slides
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/python-programming/python-programming-introduction.html">
+     38.1. Python programming introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/python-programming/python-programming-basics.html">
+     38.2. Python programming basics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/python-programming/python-programming-advanced.html">
+     38.3. Python programming advanced
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/data-science/data-science-introduction.html">
+     38.4. Data Science introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/data-science/relational-vs-non-relational-database.html">
+     38.5. Relational vs. non-relational database
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/data-science/numpy-and-pandas.html">
+     38.6. NumPy and Pandas
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/data-science/data-visualization.html">
+     38.7. Data visualization
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/data-science/data-science-lifecycle.html">
+     38.8. Data Science lifecycle
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/data-science/data-science-in-the-cloud.html">
+     38.9. Data Science in the cloud
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/data-science/data-science-in-real-world.html">
+     38.10. Data Science in real world
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-fundamentals/ml-overview.html">
+     38.11. Machine Learning overview
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-fundamentals/linear-regression.html">
+     38.12. Linear Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-fundamentals/logistic-regression.html">
+     38.13. Logistic Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-fundamentals/logistic-regression-condensed.html">
+     38.14. Logistic Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-fundamentals/neural-network.html">
+     38.15. Neural Network
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-fundamentals/build-an-ml-web-app.html">
+     38.16. Build an machine learning web application
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-advanced/unsupervised-learning.html">
+     38.17. Unsupervised learning
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-advanced/kernel-method.html">
+     38.18. Kernel method
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-advanced/model-selection.html">
+     38.19. Model Selection
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/deep-learning/cnn.html">
+     38.20. Convolutional Neural Network
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/deep-learning/gan.html">
+     38.21. Generative Adversarial Network
+    </a>
+   </li>
+  </ul>
+ </li>
+</ul>
+
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+</nav></div>
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+<div class="col-md-3 bd-toc show noprint">
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+        <i class="fas fa-list"></i> Contents
+    </div>
+    <nav id="bd-toc-nav" aria-label="Page">
+        <ul class="visible nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#overview">
+   5.4.3.1. Overview
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#combining-datasets-concat-merge-and-join">
+   5.4.3.2. Combining datasets: concat, merge and join
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#concat">
+     5.4.3.2.1. concat
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#merge">
+     5.4.3.2.2. merge
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#join">
+     5.4.3.2.3. join
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#aggregation-and-grouping">
+   5.4.3.3. Aggregation and grouping
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#hierarchical-indexes">
+     5.4.3.3.1. Hierarchical Indexes
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#pivot-table">
+   5.4.3.4. Pivot table
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#high-performance-pandas-eval-and-query">
+   5.4.3.5. High-performance Pandas: eval() and query()
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#eval">
+     5.4.3.5.1. eval()
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#query">
+     5.4.3.5.2. query()
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#your-turn">
+   5.4.3.6. Your turn! 🚀
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#processing-image-data">
+     5.4.3.6.1. Processing image data
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#assignment">
+     5.4.3.6.2. Assignment
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#self-study">
+   5.4.3.7. Self study
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#acknowledgments">
+   5.4.3.8. Acknowledgments
+  </a>
+ </li>
+</ul>
+
+    </nav>
+</div>
+    </div>
+    <div class="article row">
+        <div class="col pl-md-3 pl-lg-5 content-container">
+            <!-- Table of contents that is only displayed when printing the page -->
+            <div id="jb-print-docs-body" class="onlyprint">
+                <h1>Advanced Pandas Techniques</h1>
+                <!-- Table of contents -->
+                <div id="print-main-content">
+                    <div id="jb-print-toc">
+                        
+                        <div>
+                            <h2> Contents </h2>
+                        </div>
+                        <nav aria-label="Page">
+                            <ul class="visible nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#overview">
+   5.4.3.1. Overview
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#combining-datasets-concat-merge-and-join">
+   5.4.3.2. Combining datasets: concat, merge and join
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#concat">
+     5.4.3.2.1. concat
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#merge">
+     5.4.3.2.2. merge
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#join">
+     5.4.3.2.3. join
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#aggregation-and-grouping">
+   5.4.3.3. Aggregation and grouping
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#hierarchical-indexes">
+     5.4.3.3.1. Hierarchical Indexes
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#pivot-table">
+   5.4.3.4. Pivot table
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#high-performance-pandas-eval-and-query">
+   5.4.3.5. High-performance Pandas: eval() and query()
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#eval">
+     5.4.3.5.1. eval()
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#query">
+     5.4.3.5.2. query()
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#your-turn">
+   5.4.3.6. Your turn! 🚀
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#processing-image-data">
+     5.4.3.6.1. Processing image data
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#assignment">
+     5.4.3.6.2. Assignment
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#self-study">
+   5.4.3.7. Self study
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#acknowledgments">
+   5.4.3.8. Acknowledgments
+  </a>
+ </li>
+</ul>
+
+                        </nav>
+                    </div>
+                </div>
+            </div>
+            <main id="main-content" role="main">
+                
+              <div>
+                
+  <section class="tex2jax_ignore mathjax_ignore" id="advanced-pandas-techniques">
+<h1><span class="section-number">5.4.3. </span>Advanced Pandas Techniques<a class="headerlink" href="#advanced-pandas-techniques" title="Permalink to this headline">#</a></h1>
+<section id="overview">
+<h2><span class="section-number">5.4.3.1. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h2>
+<p>In this section, we’ll continue to introduce combining datasets: concat, merge and join along with data aggregation and grouping.</p>
+<p>Import NumPy and load Pandas into your namespace:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Install the necessary dependencies</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="o">!{</span>sys.executable<span class="o">}</span><span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>--quiet<span class="w"> </span>jupyterlab_myst<span class="w"> </span>ipython
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="combining-datasets-concat-merge-and-join">
+<h2><span class="section-number">5.4.3.2. </span>Combining datasets: concat, merge and join<a class="headerlink" href="#combining-datasets-concat-merge-and-join" title="Permalink to this headline">#</a></h2>
+<section id="concat">
+<h3><span class="section-number">5.4.3.2.1. </span>concat<a class="headerlink" href="#concat" title="Permalink to this headline">#</a></h3>
+<ul class="simple">
+<li><p>Concatenate Pandas objects along a particular axis.</p></li>
+<li><p>Allows optional set logic along the other axes.</p></li>
+<li><p>Can also add a layer of hierarchical indexing on the concatenation axis, which may be useful if the labels are the same (or overlapping) on the passed axis number.</p></li>
+</ul>
+<p>For example:</p>
+<p>Combine two <code class="docutils literal notranslate"><span class="pre">Series</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">])</span>
+<span class="n">s2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">])</span>
+<span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">s1</span><span class="p">,</span> <span class="n">s2</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    a
+1    b
+0    c
+1    d
+dtype: object
+</pre></div>
+</div>
+</div>
+</div>
+<p>Clear the existing index and reset it in the result by setting the <code class="docutils literal notranslate"><span class="pre">ignore_index</span></code> option to <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">s1</span><span class="p">,</span> <span class="n">s2</span><span class="p">],</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    a
+1    b
+2    c
+3    d
+dtype: object
+</pre></div>
+</div>
+</div>
+</div>
+<p>Add a hierarchical index at the outermost level of the data with the <code class="docutils literal notranslate"><span class="pre">keys</span></code> option.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">s1</span><span class="p">,</span> <span class="n">s2</span><span class="p">],</span> <span class="n">keys</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;s1&#39;</span><span class="p">,</span> <span class="s1">&#39;s2&#39;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>s1  0    a
+    1    b
+s2  0    c
+    1    d
+dtype: object
+</pre></div>
+</div>
+</div>
+</div>
+<p>Label the index keys you create with the <code class="docutils literal notranslate"><span class="pre">names</span></code> option.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">s1</span><span class="p">,</span> <span class="n">s2</span><span class="p">],</span> <span class="n">keys</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;s1&#39;</span><span class="p">,</span> <span class="s1">&#39;s2&#39;</span><span class="p">],</span>
+          <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Series name&#39;</span><span class="p">,</span> <span class="s1">&#39;Row ID&#39;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Series name  Row ID
+s1           0         a
+             1         b
+s2           0         c
+             1         d
+dtype: object
+</pre></div>
+</div>
+</div>
+</div>
+<p>Combine two <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> objects with identical columns.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([[</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">]],</span>
+                   <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;letter&#39;</span><span class="p">,</span> <span class="s1">&#39;number&#39;</span><span class="p">])</span>
+<span class="n">df1</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>letter</th>
+      <th>number</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>a</td>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>b</td>
+      <td>2</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([[</span><span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="mi">4</span><span class="p">]],</span>
+                   <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;letter&#39;</span><span class="p">,</span> <span class="s1">&#39;number&#39;</span><span class="p">])</span>
+<span class="n">df2</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>letter</th>
+      <th>number</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>c</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>d</td>
+      <td>4</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>letter</th>
+      <th>number</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>a</td>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>b</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <th>0</th>
+      <td>c</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>d</td>
+      <td>4</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>Combine <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> objects with overlapping columns and return everything. Columns outside the intersection will be filled with <code class="docutils literal notranslate"><span class="pre">NaN</span></code> values.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df3</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([[</span><span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s1">&#39;cat&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="s1">&#39;dog&#39;</span><span class="p">]],</span>
+                   <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;letter&#39;</span><span class="p">,</span> <span class="s1">&#39;number&#39;</span><span class="p">,</span> <span class="s1">&#39;animal&#39;</span><span class="p">])</span>
+<span class="n">df3</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>letter</th>
+      <th>number</th>
+      <th>animal</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>c</td>
+      <td>3</td>
+      <td>cat</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>d</td>
+      <td>4</td>
+      <td>dog</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df1</span><span class="p">,</span> <span class="n">df3</span><span class="p">],</span> <span class="n">sort</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>letter</th>
+      <th>number</th>
+      <th>animal</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>a</td>
+      <td>1</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>b</td>
+      <td>2</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>0</th>
+      <td>c</td>
+      <td>3</td>
+      <td>cat</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>d</td>
+      <td>4</td>
+      <td>dog</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>Combine DataFrame objects with overlapping columns and return only those that are shared by passing inner to the join keyword argument.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df1</span><span class="p">,</span> <span class="n">df3</span><span class="p">],</span> <span class="n">join</span><span class="o">=</span><span class="s2">&quot;inner&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>letter</th>
+      <th>number</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>a</td>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>b</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <th>0</th>
+      <td>c</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>d</td>
+      <td>4</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>Combine <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> objects horizontally along the x-axis by passing in <code class="docutils literal notranslate"><span class="pre">axis=1</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df4</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([[</span><span class="s1">&#39;bird&#39;</span><span class="p">,</span> <span class="s1">&#39;polly&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;monkey&#39;</span><span class="p">,</span> <span class="s1">&#39;george&#39;</span><span class="p">]],</span>
+                   <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;animal&#39;</span><span class="p">,</span> <span class="s1">&#39;name&#39;</span><span class="p">])</span>
+<span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df1</span><span class="p">,</span> <span class="n">df4</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>letter</th>
+      <th>number</th>
+      <th>animal</th>
+      <th>name</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>a</td>
+      <td>1</td>
+      <td>bird</td>
+      <td>polly</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>b</td>
+      <td>2</td>
+      <td>monkey</td>
+      <td>george</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>Prevent the result from including duplicate index values with the <code class="docutils literal notranslate"><span class="pre">verify_integrity</span></code> option.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df5</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="mi">1</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">])</span>
+<span class="n">df5</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>0</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>a</th>
+      <td>1</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df6</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="mi">2</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">])</span>
+<span class="n">df6</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>0</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>a</th>
+      <td>2</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_raises-exception docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df5</span><span class="p">,</span> <span class="n">df6</span><span class="p">],</span> <span class="n">verify_integrity</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">ValueError</span><span class="g g-Whitespace">                                </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">15</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
+<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df5</span><span class="p">,</span> <span class="n">df6</span><span class="p">],</span> <span class="n">verify_integrity</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="nn">File F:\anaconda\envs\py39\lib\site-packages\pandas\core\reshape\concat.py:393,</span> in <span class="ni">concat</span><span class="nt">(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)</span>
+<span class="g g-Whitespace">    </span><span class="mi">378</span>     <span class="n">copy</span> <span class="o">=</span> <span class="kc">False</span>
+<span class="g g-Whitespace">    </span><span class="mi">380</span> <span class="n">op</span> <span class="o">=</span> <span class="n">_Concatenator</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">381</span>     <span class="n">objs</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">382</span>     <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span>
+   <span class="p">(</span><span class="o">...</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">390</span>     <span class="n">sort</span><span class="o">=</span><span class="n">sort</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">391</span> <span class="p">)</span>
+<span class="ne">--&gt; </span><span class="mi">393</span> <span class="k">return</span> <span class="n">op</span><span class="o">.</span><span class="n">get_result</span><span class="p">()</span>
+
+<span class="nn">File F:\anaconda\envs\py39\lib\site-packages\pandas\core\reshape\concat.py:667,</span> in <span class="ni">_Concatenator.get_result</span><span class="nt">(self)</span>
+<span class="g g-Whitespace">    </span><span class="mi">665</span> <span class="k">for</span> <span class="n">obj</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">objs</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">666</span>     <span class="n">indexers</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="ne">--&gt; </span><span class="mi">667</span>     <span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">new_labels</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">new_axes</span><span class="p">):</span>
+<span class="g g-Whitespace">    </span><span class="mi">668</span>         <span class="c1"># ::-1 to convert BlockManager ax to DataFrame ax</span>
+<span class="g g-Whitespace">    </span><span class="mi">669</span>         <span class="k">if</span> <span class="n">ax</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">bm_axis</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">670</span>             <span class="c1"># Suppress reindexing on concat axis</span>
+<span class="g g-Whitespace">    </span><span class="mi">671</span>             <span class="k">continue</span>
+
+<span class="nn">File properties.pyx:36,</span> in <span class="ni">pandas._libs.properties.CachedProperty.__get__</span><span class="nt">()</span>
+
+<span class="nn">File F:\anaconda\envs\py39\lib\site-packages\pandas\core\reshape\concat.py:698,</span> in <span class="ni">_Concatenator.new_axes</span><span class="nt">(self)</span>
+<span class="g g-Whitespace">    </span><span class="mi">695</span> <span class="nd">@cache_readonly</span>
+<span class="g g-Whitespace">    </span><span class="mi">696</span> <span class="k">def</span> <span class="nf">new_axes</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="n">Index</span><span class="p">]:</span>
+<span class="g g-Whitespace">    </span><span class="mi">697</span>     <span class="n">ndim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_result_dim</span><span class="p">()</span>
+<span class="ne">--&gt; </span><span class="mi">698</span>     <span class="k">return</span> <span class="p">[</span>
+<span class="g g-Whitespace">    </span><span class="mi">699</span>         <span class="bp">self</span><span class="o">.</span><span class="n">_get_concat_axis</span> <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">bm_axis</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_comb_axis</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+<span class="nn">    700         for i</span> in <span class="ni">range</span><span class="nt">(ndim)</span>
+<span class="g g-Whitespace">    </span><span class="mi">701</span>     <span class="p">]</span>
+
+<span class="nn">File F:\anaconda\envs\py39\lib\site-packages\pandas\core\reshape\concat.py:699,</span> in <span class="ni">&lt;listcomp&gt;</span><span class="nt">(.0)</span>
+<span class="g g-Whitespace">    </span><span class="mi">695</span> <span class="nd">@cache_readonly</span>
+<span class="g g-Whitespace">    </span><span class="mi">696</span> <span class="k">def</span> <span class="nf">new_axes</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="n">Index</span><span class="p">]:</span>
+<span class="g g-Whitespace">    </span><span class="mi">697</span>     <span class="n">ndim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_result_dim</span><span class="p">()</span>
+<span class="g g-Whitespace">    </span><span class="mi">698</span>     <span class="k">return</span> <span class="p">[</span>
+<span class="ne">--&gt; </span><span class="mi">699</span>         <span class="bp">self</span><span class="o">.</span><span class="n">_get_concat_axis</span> <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">bm_axis</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_comb_axis</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+<span class="nn">    700         for i</span> in <span class="ni">range</span><span class="nt">(ndim)</span>
+<span class="g g-Whitespace">    </span><span class="mi">701</span>     <span class="p">]</span>
+
+<span class="nn">File properties.pyx:36,</span> in <span class="ni">pandas._libs.properties.CachedProperty.__get__</span><span class="nt">()</span>
+
+<span class="nn">File F:\anaconda\envs\py39\lib\site-packages\pandas\core\reshape\concat.py:762,</span> in <span class="ni">_Concatenator._get_concat_axis</span><span class="nt">(self)</span>
+<span class="g g-Whitespace">    </span><span class="mi">757</span> <span class="k">else</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">758</span>     <span class="n">concat_axis</span> <span class="o">=</span> <span class="n">_make_concat_multiindex</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">759</span>         <span class="n">indexes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">keys</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">levels</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">names</span>
+<span class="g g-Whitespace">    </span><span class="mi">760</span>     <span class="p">)</span>
+<span class="ne">--&gt; </span><span class="mi">762</span> <span class="bp">self</span><span class="o">.</span><span class="n">_maybe_check_integrity</span><span class="p">(</span><span class="n">concat_axis</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">764</span> <span class="k">return</span> <span class="n">concat_axis</span>
+
+<span class="nn">File F:\anaconda\envs\py39\lib\site-packages\pandas\core\reshape\concat.py:770,</span> in <span class="ni">_Concatenator._maybe_check_integrity</span><span class="nt">(self, concat_index)</span>
+<span class="g g-Whitespace">    </span><span class="mi">768</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">concat_index</span><span class="o">.</span><span class="n">is_unique</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">769</span>     <span class="n">overlap</span> <span class="o">=</span> <span class="n">concat_index</span><span class="p">[</span><span class="n">concat_index</span><span class="o">.</span><span class="n">duplicated</span><span class="p">()]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+<span class="ne">--&gt; </span><span class="mi">770</span>     <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Indexes have overlapping values: </span><span class="si">{</span><span class="n">overlap</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+<span class="ne">ValueError</span>: Indexes have overlapping values: Index([&#39;a&#39;], dtype=&#39;object&#39;)
+</pre></div>
+</div>
+</div>
+</div>
+<p>Append a single row to the end of a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> object.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df7</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;a&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">},</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">df7</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>a</th>
+      <th>b</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>2</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">new_row</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">({</span><span class="s1">&#39;a&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">:</span> <span class="mi">4</span><span class="p">})</span>
+<span class="n">new_row</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    3
+b    4
+dtype: int64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df7</span><span class="p">,</span> <span class="n">new_row</span><span class="o">.</span><span class="n">to_frame</span><span class="p">()</span><span class="o">.</span><span class="n">T</span><span class="p">],</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>a</th>
+      <th>b</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>3</td>
+      <td>4</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">append()</span></code> has been deprecated since version 1.4.0: Use <code class="docutils literal notranslate"><span class="pre">concat()</span></code> instead.</p>
+</div>
+</section>
+<section id="merge">
+<h3><span class="section-number">5.4.3.2.2. </span>merge<a class="headerlink" href="#merge" title="Permalink to this headline">#</a></h3>
+<ul class="simple">
+<li><p>Merge DataFrame or named Series objects with a database-style join.</p></li>
+<li><p>A named Series object is treated as a DataFrame with a single named column.</p></li>
+<li><p>The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on. When performing a cross-merge, no column specifications to merge on are allowed.</p></li>
+</ul>
+<p>If both key columns contain rows where the key is a null value, those rows will be matched against each other. This is different from usual SQL join behaviour and can lead to unexpected results.</p>
+<p>For example:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;lkey&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;foo&#39;</span><span class="p">,</span> <span class="s1">&#39;bar&#39;</span><span class="p">,</span> <span class="s1">&#39;baz&#39;</span><span class="p">,</span> <span class="s1">&#39;foo&#39;</span><span class="p">],</span>
+                    <span class="s1">&#39;value&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">]})</span>
+<span class="n">df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;rkey&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;foo&#39;</span><span class="p">,</span> <span class="s1">&#39;bar&#39;</span><span class="p">,</span> <span class="s1">&#39;baz&#39;</span><span class="p">,</span> <span class="s1">&#39;foo&#39;</span><span class="p">],</span>
+                    <span class="s1">&#39;value&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">]})</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Merge DataFrames <code class="docutils literal notranslate"><span class="pre">df1</span></code> and <code class="docutils literal notranslate"><span class="pre">df2</span></code> with specified left and right suffixes appended to any overlapping columns.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">df2</span><span class="p">,</span> <span class="n">left_on</span><span class="o">=</span><span class="s1">&#39;lkey&#39;</span><span class="p">,</span> <span class="n">right_on</span><span class="o">=</span><span class="s1">&#39;rkey&#39;</span><span class="p">,</span> <span class="n">suffixes</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;_left&#39;</span><span class="p">,</span> <span class="s1">&#39;_right&#39;</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>lkey</th>
+      <th>value_left</th>
+      <th>rkey</th>
+      <th>value_right</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>foo</td>
+      <td>1</td>
+      <td>foo</td>
+      <td>5</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>foo</td>
+      <td>1</td>
+      <td>foo</td>
+      <td>8</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>foo</td>
+      <td>5</td>
+      <td>foo</td>
+      <td>5</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>foo</td>
+      <td>5</td>
+      <td>foo</td>
+      <td>8</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>bar</td>
+      <td>2</td>
+      <td>bar</td>
+      <td>6</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>baz</td>
+      <td>3</td>
+      <td>baz</td>
+      <td>7</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:800px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf1%20%3D%20pd.DataFrame%28%7B&#39;lkey&#39;%3A%20%5B&#39;foo&#39;,%20&#39;bar&#39;,%20&#39;baz&#39;,%20&#39;foo&#39;%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20&#39;value&#39;%3A%20%5B1,%202,%203,%205%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B&#39;rkey&#39;%3A%20%5B&#39;foo&#39;,%20&#39;bar&#39;,%20&#39;baz&#39;,%20&#39;foo&#39;%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20&#39;value&#39;%3A%20%5B5,%206,%207,%208%5D%7D%29%0Adf1.merge%28df2,%20left_on%3D&#39;lkey&#39;,%20right_on%3D&#39;rkey&#39;%29&amp;d=2023-05-27&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:800px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf1%20%3D%20pd.DataFrame%28%7B'lkey'%3A%20%5B'foo',%20'bar',%20'baz',%20'foo'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'value'%3A%20%5B1,%202,%203,%205%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B'rkey'%3A%20%5B'foo',%20'bar',%20'baz',%20'foo'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'value'%3A%20%5B5,%206,%207,%208%5D%7D%29%0Adf1.merge%28df2,%20left_on%3D'lkey',%20right_on%3D'rkey'%29&d=2023-05-27&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+</div></div>
+</div>
+<p>Merge DataFrames <code class="docutils literal notranslate"><span class="pre">df1</span></code> and <code class="docutils literal notranslate"><span class="pre">df2</span></code>, but raise an exception if the DataFrames have any overlapping columns.</p>
+<div class="cell tag_raises-exception docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">df2</span><span class="p">,</span> <span class="n">left_on</span><span class="o">=</span><span class="s1">&#39;lkey&#39;</span><span class="p">,</span> <span class="n">right_on</span><span class="o">=</span><span class="s1">&#39;rkey&#39;</span><span class="p">,</span> <span class="n">suffixes</span><span class="o">=</span><span class="p">(</span><span class="kc">False</span><span class="p">,</span> <span class="kc">False</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">ValueError</span><span class="g g-Whitespace">                                </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">22</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
+<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">df1</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">df2</span><span class="p">,</span> <span class="n">left_on</span><span class="o">=</span><span class="s1">&#39;lkey&#39;</span><span class="p">,</span> <span class="n">right_on</span><span class="o">=</span><span class="s1">&#39;rkey&#39;</span><span class="p">,</span> <span class="n">suffixes</span><span class="o">=</span><span class="p">(</span><span class="kc">False</span><span class="p">,</span> <span class="kc">False</span><span class="p">))</span>
+
+<span class="nn">File F:\anaconda\envs\py39\lib\site-packages\pandas\core\frame.py:10490,</span> in <span class="ni">DataFrame.merge</span><span class="nt">(self, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)</span>
+<span class="g g-Whitespace">  </span><span class="mi">10471</span> <span class="nd">@Substitution</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
+<span class="g g-Whitespace">  </span><span class="mi">10472</span> <span class="nd">@Appender</span><span class="p">(</span><span class="n">_merge_doc</span><span class="p">,</span> <span class="n">indents</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="g g-Whitespace">  </span><span class="mi">10473</span> <span class="k">def</span> <span class="nf">merge</span><span class="p">(</span>
+   <span class="p">(</span><span class="o">...</span><span class="p">)</span>
+<span class="g g-Whitespace">  </span><span class="mi">10486</span>     <span class="n">validate</span><span class="p">:</span> <span class="n">MergeValidate</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+<span class="g g-Whitespace">  </span><span class="mi">10487</span> <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
+<span class="g g-Whitespace">  </span><span class="mi">10488</span>     <span class="kn">from</span> <span class="nn">pandas.core.reshape.merge</span> <span class="kn">import</span> <span class="n">merge</span>
+<span class="ne">&gt; </span><span class="mi">10490</span>     <span class="k">return</span> <span class="n">merge</span><span class="p">(</span>
+<span class="g g-Whitespace">  </span><span class="mi">10491</span>         <span class="bp">self</span><span class="p">,</span>
+<span class="g g-Whitespace">  </span><span class="mi">10492</span>         <span class="n">right</span><span class="p">,</span>
+<span class="g g-Whitespace">  </span><span class="mi">10493</span>         <span class="n">how</span><span class="o">=</span><span class="n">how</span><span class="p">,</span>
+<span class="g g-Whitespace">  </span><span class="mi">10494</span>         <span class="n">on</span><span class="o">=</span><span class="n">on</span><span class="p">,</span>
+<span class="g g-Whitespace">  </span><span class="mi">10495</span>         <span class="n">left_on</span><span class="o">=</span><span class="n">left_on</span><span class="p">,</span>
+<span class="g g-Whitespace">  </span><span class="mi">10496</span>         <span class="n">right_on</span><span class="o">=</span><span class="n">right_on</span><span class="p">,</span>
+<span class="g g-Whitespace">  </span><span class="mi">10497</span>         <span class="n">left_index</span><span class="o">=</span><span class="n">left_index</span><span class="p">,</span>
+<span class="g g-Whitespace">  </span><span class="mi">10498</span>         <span class="n">right_index</span><span class="o">=</span><span class="n">right_index</span><span class="p">,</span>
+<span class="g g-Whitespace">  </span><span class="mi">10499</span>         <span class="n">sort</span><span class="o">=</span><span class="n">sort</span><span class="p">,</span>
+<span class="g g-Whitespace">  </span><span class="mi">10500</span>         <span class="n">suffixes</span><span class="o">=</span><span class="n">suffixes</span><span class="p">,</span>
+<span class="g g-Whitespace">  </span><span class="mi">10501</span>         <span class="n">copy</span><span class="o">=</span><span class="n">copy</span><span class="p">,</span>
+<span class="g g-Whitespace">  </span><span class="mi">10502</span>         <span class="n">indicator</span><span class="o">=</span><span class="n">indicator</span><span class="p">,</span>
+<span class="g g-Whitespace">  </span><span class="mi">10503</span>         <span class="n">validate</span><span class="o">=</span><span class="n">validate</span><span class="p">,</span>
+<span class="g g-Whitespace">  </span><span class="mi">10504</span>     <span class="p">)</span>
+
+<span class="nn">File F:\anaconda\envs\py39\lib\site-packages\pandas\core\reshape\merge.py:183,</span> in <span class="ni">merge</span><span class="nt">(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)</span>
+<span class="g g-Whitespace">    </span><span class="mi">168</span> <span class="k">else</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">169</span>     <span class="n">op</span> <span class="o">=</span> <span class="n">_MergeOperation</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">170</span>         <span class="n">left_df</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">171</span>         <span class="n">right_df</span><span class="p">,</span>
+   <span class="p">(</span><span class="o">...</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">181</span>         <span class="n">validate</span><span class="o">=</span><span class="n">validate</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">182</span>     <span class="p">)</span>
+<span class="ne">--&gt; </span><span class="mi">183</span>     <span class="k">return</span> <span class="n">op</span><span class="o">.</span><span class="n">get_result</span><span class="p">(</span><span class="n">copy</span><span class="o">=</span><span class="n">copy</span><span class="p">)</span>
+
+<span class="nn">File F:\anaconda\envs\py39\lib\site-packages\pandas\core\reshape\merge.py:885,</span> in <span class="ni">_MergeOperation.get_result</span><span class="nt">(self, copy)</span>
+<span class="g g-Whitespace">    </span><span class="mi">881</span>     <span class="bp">self</span><span class="o">.</span><span class="n">left</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_indicator_pre_merge</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">left</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">right</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">883</span> <span class="n">join_index</span><span class="p">,</span> <span class="n">left_indexer</span><span class="p">,</span> <span class="n">right_indexer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_join_info</span><span class="p">()</span>
+<span class="ne">--&gt; </span><span class="mi">885</span> <span class="n">result</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reindex_and_concat</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">886</span>     <span class="n">join_index</span><span class="p">,</span> <span class="n">left_indexer</span><span class="p">,</span> <span class="n">right_indexer</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="n">copy</span>
+<span class="g g-Whitespace">    </span><span class="mi">887</span> <span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">888</span> <span class="n">result</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">__finalize__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_merge_type</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">890</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">indicator</span><span class="p">:</span>
+
+<span class="nn">File F:\anaconda\envs\py39\lib\site-packages\pandas\core\reshape\merge.py:837,</span> in <span class="ni">_MergeOperation._reindex_and_concat</span><span class="nt">(self, join_index, left_indexer, right_indexer, copy)</span>
+<span class="g g-Whitespace">    </span><span class="mi">834</span> <span class="n">left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">left</span><span class="p">[:]</span>
+<span class="g g-Whitespace">    </span><span class="mi">835</span> <span class="n">right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">right</span><span class="p">[:]</span>
+<span class="ne">--&gt; </span><span class="mi">837</span> <span class="n">llabels</span><span class="p">,</span> <span class="n">rlabels</span> <span class="o">=</span> <span class="n">_items_overlap_with_suffix</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">838</span>     <span class="bp">self</span><span class="o">.</span><span class="n">left</span><span class="o">.</span><span class="n">_info_axis</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">right</span><span class="o">.</span><span class="n">_info_axis</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">suffixes</span>
+<span class="g g-Whitespace">    </span><span class="mi">839</span> <span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">841</span> <span class="k">if</span> <span class="n">left_indexer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">is_range_indexer</span><span class="p">(</span><span class="n">left_indexer</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">left</span><span class="p">)):</span>
+<span class="g g-Whitespace">    </span><span class="mi">842</span>     <span class="c1"># Pinning the index here (and in the right code just below) is not</span>
+<span class="g g-Whitespace">    </span><span class="mi">843</span>     <span class="c1">#  necessary, but makes the `.take` more performant if we have e.g.</span>
+<span class="g g-Whitespace">    </span><span class="mi">844</span>     <span class="c1">#  a MultiIndex for left.index.</span>
+<span class="g g-Whitespace">    </span><span class="mi">845</span>     <span class="n">lmgr</span> <span class="o">=</span> <span class="n">left</span><span class="o">.</span><span class="n">_mgr</span><span class="o">.</span><span class="n">reindex_indexer</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">846</span>         <span class="n">join_index</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">847</span>         <span class="n">left_indexer</span><span class="p">,</span>
+   <span class="p">(</span><span class="o">...</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">852</span>         <span class="n">use_na_proxy</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">853</span>     <span class="p">)</span>
+
+<span class="nn">File F:\anaconda\envs\py39\lib\site-packages\pandas\core\reshape\merge.py:2655,</span> in <span class="ni">_items_overlap_with_suffix</span><span class="nt">(left, right, suffixes)</span>
+<span class="g g-Whitespace">   </span><span class="mi">2652</span> <span class="n">lsuffix</span><span class="p">,</span> <span class="n">rsuffix</span> <span class="o">=</span> <span class="n">suffixes</span>
+<span class="g g-Whitespace">   </span><span class="mi">2654</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">lsuffix</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">rsuffix</span><span class="p">:</span>
+<span class="ne">-&gt; </span><span class="mi">2655</span>     <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;columns overlap but no suffix specified: </span><span class="si">{</span><span class="n">to_rename</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">2657</span> <span class="k">def</span> <span class="nf">renamer</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">suffix</span><span class="p">:</span> <span class="nb">str</span> <span class="o">|</span> <span class="kc">None</span><span class="p">):</span>
+<span class="g g-Whitespace">   </span><span class="mi">2658</span><span class="w">     </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="g g-Whitespace">   </span><span class="mi">2659</span><span class="sd">     Rename the left and right indices.</span>
+<span class="g g-Whitespace">   </span><span class="mi">2660</span><span class="sd"> </span>
+<span class="sd">   (...)</span>
+<span class="g g-Whitespace">   </span><span class="mi">2671</span><span class="sd">     x : renamed column name</span>
+<span class="g g-Whitespace">   </span><span class="mi">2672</span><span class="sd">     &quot;&quot;&quot;</span>
+
+<span class="ne">ValueError</span>: columns overlap but no suffix specified: Index([&#39;value&#39;], dtype=&#39;object&#39;)
+</pre></div>
+</div>
+</div>
+</div>
+<p>Using <code class="docutils literal notranslate"><span class="pre">how</span></code> parameter decide the type of merge to be performed.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;a&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;foo&#39;</span><span class="p">,</span> <span class="s1">&#39;bar&#39;</span><span class="p">],</span> <span class="s1">&#39;b&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]})</span>
+<span class="n">df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;a&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;foo&#39;</span><span class="p">,</span> <span class="s1">&#39;baz&#39;</span><span class="p">],</span> <span class="s1">&#39;c&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]})</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">df2</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s1">&#39;inner&#39;</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s1">&#39;a&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>a</th>
+      <th>b</th>
+      <th>c</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>foo</td>
+      <td>1</td>
+      <td>3</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:600px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf1%20%3D%20pd.DataFrame%28%7B&#39;a&#39;%3A%20%5B&#39;foo&#39;,%20&#39;bar&#39;%5D,%20&#39;b&#39;%3A%20%5B1,%202%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B&#39;a&#39;%3A%20%5B&#39;foo&#39;,%20&#39;baz&#39;%5D,%20&#39;c&#39;%3A%20%5B3,%204%5D%7D%29%0Adf1.merge%28df2,%20how%3D&#39;inner&#39;,%20on%3D&#39;a&#39;%29&amp;d=2023-07-15&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:600px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf1%20%3D%20pd.DataFrame%28%7B'a'%3A%20%5B'foo',%20'bar'%5D,%20'b'%3A%20%5B1,%202%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B'a'%3A%20%5B'foo',%20'baz'%5D,%20'c'%3A%20%5B3,%204%5D%7D%29%0Adf1.merge%28df2,%20how%3D'inner',%20on%3D'a'%29&d=2023-07-15&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+</div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">df2</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s1">&#39;a&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>a</th>
+      <th>b</th>
+      <th>c</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>foo</td>
+      <td>1</td>
+      <td>3.0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>bar</td>
+      <td>2</td>
+      <td>NaN</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:630px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf1%20%3D%20pd.DataFrame%28%7B&#39;a&#39;%3A%20%5B&#39;foo&#39;,%20&#39;bar&#39;%5D,%20&#39;b&#39;%3A%20%5B1,%202%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B&#39;a&#39;%3A%20%5B&#39;foo&#39;,%20&#39;baz&#39;%5D,%20&#39;c&#39;%3A%20%5B3,%204%5D%7D%29%0Adf1.merge%28df2,%20how%3D&#39;left&#39;,%20on%3D&#39;a&#39;%29&amp;d=2023-07-15&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:630px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf1%20%3D%20pd.DataFrame%28%7B'a'%3A%20%5B'foo',%20'bar'%5D,%20'b'%3A%20%5B1,%202%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B'a'%3A%20%5B'foo',%20'baz'%5D,%20'c'%3A%20%5B3,%204%5D%7D%29%0Adf1.merge%28df2,%20how%3D'left',%20on%3D'a'%29&d=2023-07-15&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+</div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;left&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;foo&#39;</span><span class="p">,</span> <span class="s1">&#39;bar&#39;</span><span class="p">]})</span>
+<span class="n">df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;right&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">]})</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">df2</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s1">&#39;cross&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>left</th>
+      <th>right</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>foo</td>
+      <td>7</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>foo</td>
+      <td>8</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>bar</td>
+      <td>7</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>bar</td>
+      <td>8</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:670px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf1%20%3D%20pd.DataFrame%28%7B&#39;left&#39;%3A%20%5B&#39;foo&#39;,%20&#39;bar&#39;%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B&#39;right&#39;%3A%20%5B7,%208%5D%7D%29%0Adf1.merge%28df2,%20how%3D&#39;cross&#39;%29&amp;d=2023-07-15&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:670px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf1%20%3D%20pd.DataFrame%28%7B'left'%3A%20%5B'foo',%20'bar'%5D%7D%29%0Adf2%20%3D%20pd.DataFrame%28%7B'right'%3A%20%5B7,%208%5D%7D%29%0Adf1.merge%28df2,%20how%3D'cross'%29&d=2023-07-15&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+
+
+</div></div>
+</div>
+</section>
+<section id="join">
+<h3><span class="section-number">5.4.3.2.3. </span>join<a class="headerlink" href="#join" title="Permalink to this headline">#</a></h3>
+<ul class="simple">
+<li><p>Join columns of another DataFrame.</p></li>
+<li><p>Join columns with other DataFrame either on index or on a key column. Efficiently join multiple DataFrame objects by index at once by passing a list.</p></li>
+</ul>
+<p>For example:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;key&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;K0&#39;</span><span class="p">,</span> <span class="s1">&#39;K1&#39;</span><span class="p">,</span> <span class="s1">&#39;K2&#39;</span><span class="p">,</span> <span class="s1">&#39;K3&#39;</span><span class="p">,</span> <span class="s1">&#39;K4&#39;</span><span class="p">,</span> <span class="s1">&#39;K5&#39;</span><span class="p">],</span>
+                   <span class="s1">&#39;A&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;A0&#39;</span><span class="p">,</span> <span class="s1">&#39;A1&#39;</span><span class="p">,</span> <span class="s1">&#39;A2&#39;</span><span class="p">,</span> <span class="s1">&#39;A3&#39;</span><span class="p">,</span> <span class="s1">&#39;A4&#39;</span><span class="p">,</span> <span class="s1">&#39;A5&#39;</span><span class="p">]})</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">other</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;key&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;K0&#39;</span><span class="p">,</span> <span class="s1">&#39;K1&#39;</span><span class="p">,</span> <span class="s1">&#39;K2&#39;</span><span class="p">],</span>
+                      <span class="s1">&#39;B&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;B0&#39;</span><span class="p">,</span> <span class="s1">&#39;B1&#39;</span><span class="p">,</span> <span class="s1">&#39;B2&#39;</span><span class="p">]})</span>                     
+</pre></div>
+</div>
+</div>
+</div>
+<p>Join DataFrames using their indexes.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">lsuffix</span><span class="o">=</span><span class="s1">&#39;_caller&#39;</span><span class="p">,</span> <span class="n">rsuffix</span><span class="o">=</span><span class="s1">&#39;_other&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>key_caller</th>
+      <th>A</th>
+      <th>key_other</th>
+      <th>B</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>K0</td>
+      <td>A0</td>
+      <td>K0</td>
+      <td>B0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>K1</td>
+      <td>A1</td>
+      <td>K1</td>
+      <td>B1</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>K2</td>
+      <td>A2</td>
+      <td>K2</td>
+      <td>B2</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>K3</td>
+      <td>A3</td>
+      <td>NaN</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>K4</td>
+      <td>A4</td>
+      <td>NaN</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>K5</td>
+      <td>A5</td>
+      <td>NaN</td>
+      <td>NaN</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:830px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf%20%3D%20pd.DataFrame%28%7B&#39;key&#39;%3A%20%5B&#39;K0&#39;,%20&#39;K1&#39;,%20&#39;K2&#39;,%20&#39;K3&#39;,%20&#39;K4&#39;,%20&#39;K5&#39;%5D,&#39;A&#39;%3A%20%5B&#39;A0&#39;,%20&#39;A1&#39;,%20&#39;A2&#39;,%20&#39;A3&#39;,%20&#39;A4&#39;,%20&#39;A5&#39;%5D%7D%29%0Aother%20%3D%20pd.DataFrame%28%7B&#39;key&#39;%3A%20%5B&#39;K0&#39;,%20&#39;K1&#39;,%20&#39;K2&#39;%5D,&#39;B&#39;%3A%20%5B&#39;B0&#39;,%20&#39;B1&#39;,%20&#39;B2&#39;%5D%7D%29%0Adf.join%28other,%20lsuffix%3D&#39;_caller&#39;,%20rsuffix%3D&#39;_other&#39;%29&amp;d=2023-07-15&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:830px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adf%20%3D%20pd.DataFrame%28%7B'key'%3A%20%5B'K0',%20'K1',%20'K2',%20'K3',%20'K4',%20'K5'%5D,'A'%3A%20%5B'A0',%20'A1',%20'A2',%20'A3',%20'A4',%20'A5'%5D%7D%29%0Aother%20%3D%20pd.DataFrame%28%7B'key'%3A%20%5B'K0',%20'K1',%20'K2'%5D,'B'%3A%20%5B'B0',%20'B1',%20'B2'%5D%7D%29%0Adf.join%28other,%20lsuffix%3D'_caller',%20rsuffix%3D'_other'%29&d=2023-07-15&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+
+
+</div></div>
+</div>
+<p>If we want to join using the <code class="docutils literal notranslate"><span class="pre">key</span></code> columns, we need to set <code class="docutils literal notranslate"><span class="pre">key</span></code> to be the index in both <code class="docutils literal notranslate"><span class="pre">df</span></code> and <code class="docutils literal notranslate"><span class="pre">other</span></code>. The joined DataFrame will have <code class="docutils literal notranslate"><span class="pre">key</span></code> as its index.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s1">&#39;key&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s1">&#39;key&#39;</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+    </tr>
+    <tr>
+      <th>key</th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>K0</th>
+      <td>A0</td>
+      <td>B0</td>
+    </tr>
+    <tr>
+      <th>K1</th>
+      <td>A1</td>
+      <td>B1</td>
+    </tr>
+    <tr>
+      <th>K2</th>
+      <td>A2</td>
+      <td>B2</td>
+    </tr>
+    <tr>
+      <th>K3</th>
+      <td>A3</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>K4</th>
+      <td>A4</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>K5</th>
+      <td>A5</td>
+      <td>NaN</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:1170px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf%20%3D%20pd.DataFrame%28%7B&#39;key&#39;%3A%20%5B&#39;K0&#39;,%20&#39;K1&#39;,%20&#39;K2&#39;,%20&#39;K3&#39;,%20&#39;K4&#39;,%20&#39;K5&#39;%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20&#39;A&#39;%3A%20%5B&#39;A0&#39;,%20&#39;A1&#39;,%20&#39;A2&#39;,%20&#39;A3&#39;,%20&#39;A4&#39;,%20&#39;A5&#39;%5D%7D%29%0Aother%20%3D%20pd.DataFrame%28%7B&#39;key&#39;%3A%20%5B&#39;K0&#39;,%20&#39;K1&#39;,%20&#39;K2&#39;%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20&#39;B&#39;%3A%20%5B&#39;B0&#39;,%20&#39;B1&#39;,%20&#39;B2&#39;%5D%7D%29%0Adf.set_index%28&#39;key&#39;%29.join%28other.set_index%28&#39;key&#39;%29%29&amp;d=2023-05-27&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:1170px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf%20%3D%20pd.DataFrame%28%7B'key'%3A%20%5B'K0',%20'K1',%20'K2',%20'K3',%20'K4',%20'K5'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'A'%3A%20%5B'A0',%20'A1',%20'A2',%20'A3',%20'A4',%20'A5'%5D%7D%29%0Aother%20%3D%20pd.DataFrame%28%7B'key'%3A%20%5B'K0',%20'K1',%20'K2'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'B'%3A%20%5B'B0',%20'B1',%20'B2'%5D%7D%29%0Adf.set_index%28'key'%29.join%28other.set_index%28'key'%29%29&d=2023-05-27&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+
+
+</div></div>
+</div>
+<p>Another option to join using the key columns is to use the <code class="docutils literal notranslate"><span class="pre">on</span></code> parameter. <code class="docutils literal notranslate"><span class="pre">DataFrame.join</span></code> always uses <code class="docutils literal notranslate"><span class="pre">other</span></code>’s index but we can use any column in <code class="docutils literal notranslate"><span class="pre">df</span></code>. This method preserves the original DataFrame’s index in the result.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s1">&#39;key&#39;</span><span class="p">),</span> <span class="n">on</span><span class="o">=</span><span class="s1">&#39;key&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>key</th>
+      <th>A</th>
+      <th>B</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>K0</td>
+      <td>A0</td>
+      <td>B0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>K1</td>
+      <td>A1</td>
+      <td>B1</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>K2</td>
+      <td>A2</td>
+      <td>B2</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>K3</td>
+      <td>A3</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>K4</td>
+      <td>A4</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>K5</td>
+      <td>A5</td>
+      <td>NaN</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>Using non-unique key values shows how they are matched.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;key&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;K0&#39;</span><span class="p">,</span> <span class="s1">&#39;K1&#39;</span><span class="p">,</span> <span class="s1">&#39;K1&#39;</span><span class="p">,</span> <span class="s1">&#39;K3&#39;</span><span class="p">,</span> <span class="s1">&#39;K0&#39;</span><span class="p">,</span> <span class="s1">&#39;K1&#39;</span><span class="p">],</span>
+                   <span class="s1">&#39;A&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;A0&#39;</span><span class="p">,</span> <span class="s1">&#39;A1&#39;</span><span class="p">,</span> <span class="s1">&#39;A2&#39;</span><span class="p">,</span> <span class="s1">&#39;A3&#39;</span><span class="p">,</span> <span class="s1">&#39;A4&#39;</span><span class="p">,</span> <span class="s1">&#39;A5&#39;</span><span class="p">]})</span>
+<span class="n">df</span>                   
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>key</th>
+      <th>A</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>K0</td>
+      <td>A0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>K1</td>
+      <td>A1</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>K1</td>
+      <td>A2</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>K3</td>
+      <td>A3</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>K0</td>
+      <td>A4</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>K1</td>
+      <td>A5</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s1">&#39;key&#39;</span><span class="p">),</span> <span class="n">on</span><span class="o">=</span><span class="s1">&#39;key&#39;</span><span class="p">,</span> <span class="n">validate</span><span class="o">=</span><span class="s1">&#39;m:1&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>key</th>
+      <th>A</th>
+      <th>B</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>K0</td>
+      <td>A0</td>
+      <td>B0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>K1</td>
+      <td>A1</td>
+      <td>B1</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>K1</td>
+      <td>A2</td>
+      <td>B1</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>K3</td>
+      <td>A3</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>K0</td>
+      <td>A4</td>
+      <td>B0</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>K1</td>
+      <td>A5</td>
+      <td>B1</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+</section>
+</section>
+<section id="aggregation-and-grouping">
+<h2><span class="section-number">5.4.3.3. </span>Aggregation and grouping<a class="headerlink" href="#aggregation-and-grouping" title="Permalink to this headline">#</a></h2>
+<p>Group <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> using a mapper or by a <code class="docutils literal notranslate"><span class="pre">Series</span></code> of columns.</p>
+<p>A <code class="docutils literal notranslate"><span class="pre">groupby</span></code> operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.</p>
+<p>For example:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;Animal&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;Falcon&#39;</span><span class="p">,</span> <span class="s1">&#39;Falcon&#39;</span><span class="p">,</span>
+                              <span class="s1">&#39;Parrot&#39;</span><span class="p">,</span> <span class="s1">&#39;Parrot&#39;</span><span class="p">],</span>
+                   <span class="s1">&#39;Max Speed&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">380.</span><span class="p">,</span> <span class="mf">370.</span><span class="p">,</span> <span class="mf">24.</span><span class="p">,</span> <span class="mf">26.</span><span class="p">]})</span>
+<span class="n">df</span>
+<span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;Animal&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Max Speed</th>
+    </tr>
+    <tr>
+      <th>Animal</th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>Falcon</th>
+      <td>375.0</td>
+    </tr>
+    <tr>
+      <th>Parrot</th>
+      <td>25.0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:785px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf%20%3D%20pd.DataFrame%28%7B&#39;Animal&#39;%3A%20%5B&#39;Falcon&#39;,%20&#39;Falcon&#39;,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20&#39;Parrot&#39;,%20&#39;Parrot&#39;%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20&#39;Max%20Speed&#39;%3A%20%5B380.,%20370.,%2024.,%2026.%5D%7D%29%0Adf%0Adf.groupby%28%5B&#39;Animal&#39;%5D%29.mean%28%29&amp;d=2023-05-27&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:785px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf%20%3D%20pd.DataFrame%28%7B'Animal'%3A%20%5B'Falcon',%20'Falcon',%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'Parrot',%20'Parrot'%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'Max%20Speed'%3A%20%5B380.,%20370.,%2024.,%2026.%5D%7D%29%0Adf%0Adf.groupby%28%5B'Animal'%5D%29.mean%28%29&d=2023-05-27&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+
+
+</div></div>
+</div>
+<section id="hierarchical-indexes">
+<h3><span class="section-number">5.4.3.3.1. </span>Hierarchical Indexes<a class="headerlink" href="#hierarchical-indexes" title="Permalink to this headline">#</a></h3>
+<p>We can <code class="docutils literal notranslate"><span class="pre">groupby</span></code> different levels of a hierarchical index using the <code class="docutils literal notranslate"><span class="pre">level</span></code> parameter:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">arrays</span> <span class="o">=</span> <span class="p">[[</span><span class="s1">&#39;Falcon&#39;</span><span class="p">,</span> <span class="s1">&#39;Falcon&#39;</span><span class="p">,</span> <span class="s1">&#39;Parrot&#39;</span><span class="p">,</span> <span class="s1">&#39;Parrot&#39;</span><span class="p">],</span>
+          <span class="p">[</span><span class="s1">&#39;Captive&#39;</span><span class="p">,</span> <span class="s1">&#39;Wild&#39;</span><span class="p">,</span> <span class="s1">&#39;Captive&#39;</span><span class="p">,</span> <span class="s1">&#39;Wild&#39;</span><span class="p">]]</span>
+<span class="n">index</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">(</span><span class="n">arrays</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;Animal&#39;</span><span class="p">,</span> <span class="s1">&#39;Type&#39;</span><span class="p">))</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;Max Speed&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">390.</span><span class="p">,</span> <span class="mf">350.</span><span class="p">,</span> <span class="mf">30.</span><span class="p">,</span> <span class="mf">20.</span><span class="p">]},</span>
+                  <span class="n">index</span><span class="o">=</span><span class="n">index</span><span class="p">)</span>
+<span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Max Speed</th>
+    </tr>
+    <tr>
+      <th>Animal</th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>Falcon</th>
+      <td>370.0</td>
+    </tr>
+    <tr>
+      <th>Parrot</th>
+      <td>25.0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:810px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aarrays%20%3D%20%5B%5B&#39;Falcon&#39;,%20&#39;Falcon&#39;,%20&#39;Parrot&#39;,%20&#39;Parrot&#39;%5D,%0A%20%20%20%20%20%20%20%20%20%20%5B&#39;Captive&#39;,%20&#39;Wild&#39;,%20&#39;Captive&#39;,%20&#39;Wild&#39;%5D%5D%0Aindex%20%3D%20pd.MultiIndex.from_arrays%28arrays,%20names%3D%28&#39;Animal&#39;,%20&#39;Type&#39;%29%29%0Adf%20%3D%20pd.DataFrame%28%7B&#39;Max%20Speed&#39;%3A%20%5B390.,%20350.,%2030.,%2020.%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dindex%29%0Adf.groupby%28level%3D0%29.mean%28%29&amp;d=2023-07-15&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:810px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aarrays%20%3D%20%5B%5B'Falcon',%20'Falcon',%20'Parrot',%20'Parrot'%5D,%0A%20%20%20%20%20%20%20%20%20%20%5B'Captive',%20'Wild',%20'Captive',%20'Wild'%5D%5D%0Aindex%20%3D%20pd.MultiIndex.from_arrays%28arrays,%20names%3D%28'Animal',%20'Type'%29%29%0Adf%20%3D%20pd.DataFrame%28%7B'Max%20Speed'%3A%20%5B390.,%20350.,%2030.,%2020.%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dindex%29%0Adf.groupby%28level%3D0%29.mean%28%29&d=2023-07-15&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+
+</div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="s2">&quot;Type&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Max Speed</th>
+    </tr>
+    <tr>
+      <th>Type</th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>Captive</th>
+      <td>210.0</td>
+    </tr>
+    <tr>
+      <th>Wild</th>
+      <td>185.0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:810px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aarrays%20%3D%20%5B%5B&#39;Falcon&#39;,%20&#39;Falcon&#39;,%20&#39;Parrot&#39;,%20&#39;Parrot&#39;%5D,%0A%20%20%20%20%20%20%20%20%20%20%5B&#39;Captive&#39;,%20&#39;Wild&#39;,%20&#39;Captive&#39;,%20&#39;Wild&#39;%5D%5D%0Aindex%20%3D%20pd.MultiIndex.from_arrays%28arrays,%20names%3D%28&#39;Animal&#39;,%20&#39;Type&#39;%29%29%0Adf%20%3D%20pd.DataFrame%28%7B&#39;Max%20Speed&#39;%3A%20%5B390.,%20350.,%2030.,%2020.%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dindex%29%0Adf.groupby%28level%3D%22Type%22%29.mean%28%29&amp;d=2023-07-15&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:810px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aarrays%20%3D%20%5B%5B'Falcon',%20'Falcon',%20'Parrot',%20'Parrot'%5D,%0A%20%20%20%20%20%20%20%20%20%20%5B'Captive',%20'Wild',%20'Captive',%20'Wild'%5D%5D%0Aindex%20%3D%20pd.MultiIndex.from_arrays%28arrays,%20names%3D%28'Animal',%20'Type'%29%29%0Adf%20%3D%20pd.DataFrame%28%7B'Max%20Speed'%3A%20%5B390.,%20350.,%2030.,%2020.%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dindex%29%0Adf.groupby%28level%3D%22Type%22%29.mean%28%29&d=2023-07-15&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+
+</div></div>
+</div>
+<p>We can also choose to include NA in group keys or not by setting <code class="docutils literal notranslate"><span class="pre">dropna</span></code> parameter, the default setting is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">l</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]]</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">])</span>
+<span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;b&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>a</th>
+      <th>c</th>
+    </tr>
+    <tr>
+      <th>b</th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>1.0</th>
+      <td>2</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <th>2.0</th>
+      <td>2</td>
+      <td>5</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:770px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Al%20%3D%20%5B%5B1,%202,%203%5D,%20%5B1,%20None,%204%5D,%20%5B2,%201,%203%5D,%20%5B1,%202,%202%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%5B%22b%22%5D%29.sum%28%29%0A&amp;d=2023-07-15&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:770px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Al%20%3D%20%5B%5B1,%202,%203%5D,%20%5B1,%20None,%204%5D,%20%5B2,%201,%203%5D,%20%5B1,%202,%202%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%5B%22b%22%5D%29.sum%28%29%0A&d=2023-07-15&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+</div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;b&quot;</span><span class="p">],</span> <span class="n">dropna</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>a</th>
+      <th>c</th>
+    </tr>
+    <tr>
+      <th>b</th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>1.0</th>
+      <td>2</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <th>2.0</th>
+      <td>2</td>
+      <td>5</td>
+    </tr>
+    <tr>
+      <th>NaN</th>
+      <td>1</td>
+      <td>4</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:750px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Al%20%3D%20%5B%5B1,%202,%203%5D,%20%5B1,%20None,%204%5D,%20%5B2,%201,%203%5D,%20%5B1,%202,%202%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%5B%22b%22%5D,%20dropna%3DFalse%29.sum%28%29&amp;d=2023-05-27&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:750px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Al%20%3D%20%5B%5B1,%202,%203%5D,%20%5B1,%20None,%204%5D,%20%5B2,%201,%203%5D,%20%5B1,%202,%202%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%5B%22b%22%5D,%20dropna%3DFalse%29.sum%28%29&d=2023-05-27&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+</div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">l</span> <span class="o">=</span> <span class="p">[[</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">],</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="mf">12.3</span><span class="p">,</span> <span class="mf">33.</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="mf">12.3</span><span class="p">,</span> <span class="mi">123</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">])</span>
+<span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s2">&quot;a&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>b</th>
+      <th>c</th>
+    </tr>
+    <tr>
+      <th>a</th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>a</th>
+      <td>13.0</td>
+      <td>13.0</td>
+    </tr>
+    <tr>
+      <th>b</th>
+      <td>12.3</td>
+      <td>123.0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:750px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Al%20%3D%20%5B%5B%22a%22,%2012,%2012%5D,%20%5BNone,%2012.3,%2033.%5D,%20%5B%22b%22,%2012.3,%20123%5D,%20%5B%22a%22,%201,%201%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%22a%22%29.sum%28%29&amp;d=2023-07-15&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:750px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Al%20%3D%20%5B%5B%22a%22,%2012,%2012%5D,%20%5BNone,%2012.3,%2033.%5D,%20%5B%22b%22,%2012.3,%20123%5D,%20%5B%22a%22,%201,%201%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%22a%22%29.sum%28%29&d=2023-07-15&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+
+</div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="n">dropna</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>b</th>
+      <th>c</th>
+    </tr>
+    <tr>
+      <th>a</th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>a</th>
+      <td>13.0</td>
+      <td>13.0</td>
+    </tr>
+    <tr>
+      <th>b</th>
+      <td>12.3</td>
+      <td>123.0</td>
+    </tr>
+    <tr>
+      <th>NaN</th>
+      <td>12.3</td>
+      <td>33.0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:750px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Al%20%3D%20%5B%5B%22a%22,%2012,%2012%5D,%20%5BNone,%2012.3,%2033.%5D,%20%5B%22b%22,%2012.3,%20123%5D,%20%5B%22a%22,%201,%201%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%22a%22,%20dropna%3DFalse%29.sum%28%29%0A&amp;d=2023-07-15&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:750px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Al%20%3D%20%5B%5B%22a%22,%2012,%2012%5D,%20%5BNone,%2012.3,%2033.%5D,%20%5B%22b%22,%2012.3,%20123%5D,%20%5B%22a%22,%201,%201%5D%5D%0Adf%20%3D%20pd.DataFrame%28l,%20columns%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29%0Adf.groupby%28by%3D%22a%22,%20dropna%3DFalse%29.sum%28%29%0A&d=2023-07-15&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+
+</div></div>
+</div>
+<p>When using <code class="docutils literal notranslate"><span class="pre">.apply()</span></code>, use <code class="docutils literal notranslate"><span class="pre">group_keys</span></code> to include or exclude the group keys. The <code class="docutils literal notranslate"><span class="pre">group_keys</span></code> argument defaults to <code class="docutils literal notranslate"><span class="pre">True</span></code> (include).</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;Animal&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;Falcon&#39;</span><span class="p">,</span> <span class="s1">&#39;Falcon&#39;</span><span class="p">,</span>
+                              <span class="s1">&#39;Parrot&#39;</span><span class="p">,</span> <span class="s1">&#39;Parrot&#39;</span><span class="p">],</span>
+                   <span class="s1">&#39;Max Speed&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">380.</span><span class="p">,</span> <span class="mf">370.</span><span class="p">,</span> <span class="mf">24.</span><span class="p">,</span> <span class="mf">26.</span><span class="p">]})</span>
+<span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">&quot;Animal&quot;</span><span class="p">,</span> <span class="n">group_keys</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th></th>
+      <th>Animal</th>
+      <th>Max Speed</th>
+    </tr>
+    <tr>
+      <th>Animal</th>
+      <th></th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th rowspan="2" valign="top">Falcon</th>
+      <th>0</th>
+      <td>Falcon</td>
+      <td>380.0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>Falcon</td>
+      <td>370.0</td>
+    </tr>
+    <tr>
+      <th rowspan="2" valign="top">Parrot</th>
+      <th>2</th>
+      <td>Parrot</td>
+      <td>24.0</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>Parrot</td>
+      <td>26.0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">&quot;Animal&quot;</span><span class="p">,</span> <span class="n">group_keys</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Animal</th>
+      <th>Max Speed</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>Falcon</td>
+      <td>380.0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>Falcon</td>
+      <td>370.0</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>Parrot</td>
+      <td>24.0</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>Parrot</td>
+      <td>26.0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+</section>
+</section>
+<section id="pivot-table">
+<h2><span class="section-number">5.4.3.4. </span>Pivot table<a class="headerlink" href="#pivot-table" title="Permalink to this headline">#</a></h2>
+<p>Create a spreadsheet-style pivot table as a DataFrame.</p>
+<p>The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">&quot;A&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;foo&quot;</span><span class="p">,</span> <span class="s2">&quot;foo&quot;</span><span class="p">,</span> <span class="s2">&quot;foo&quot;</span><span class="p">,</span> <span class="s2">&quot;foo&quot;</span><span class="p">,</span> <span class="s2">&quot;foo&quot;</span><span class="p">,</span>
+                         <span class="s2">&quot;bar&quot;</span><span class="p">,</span> <span class="s2">&quot;bar&quot;</span><span class="p">,</span> <span class="s2">&quot;bar&quot;</span><span class="p">,</span> <span class="s2">&quot;bar&quot;</span><span class="p">],</span>
+                   <span class="s2">&quot;B&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;one&quot;</span><span class="p">,</span> <span class="s2">&quot;one&quot;</span><span class="p">,</span> <span class="s2">&quot;one&quot;</span><span class="p">,</span> <span class="s2">&quot;two&quot;</span><span class="p">,</span> <span class="s2">&quot;two&quot;</span><span class="p">,</span>
+                         <span class="s2">&quot;one&quot;</span><span class="p">,</span> <span class="s2">&quot;one&quot;</span><span class="p">,</span> <span class="s2">&quot;two&quot;</span><span class="p">,</span> <span class="s2">&quot;two&quot;</span><span class="p">],</span>
+                   <span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;small&quot;</span><span class="p">,</span> <span class="s2">&quot;large&quot;</span><span class="p">,</span> <span class="s2">&quot;large&quot;</span><span class="p">,</span> <span class="s2">&quot;small&quot;</span><span class="p">,</span>
+                         <span class="s2">&quot;small&quot;</span><span class="p">,</span> <span class="s2">&quot;large&quot;</span><span class="p">,</span> <span class="s2">&quot;small&quot;</span><span class="p">,</span> <span class="s2">&quot;small&quot;</span><span class="p">,</span>
+                         <span class="s2">&quot;large&quot;</span><span class="p">],</span>
+                   <span class="s2">&quot;D&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span>
+                   <span class="s2">&quot;E&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">9</span><span class="p">]})</span>
+<span class="n">df</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C</th>
+      <th>D</th>
+      <th>E</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>foo</td>
+      <td>one</td>
+      <td>small</td>
+      <td>1</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>foo</td>
+      <td>one</td>
+      <td>large</td>
+      <td>2</td>
+      <td>4</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>foo</td>
+      <td>one</td>
+      <td>large</td>
+      <td>2</td>
+      <td>5</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>foo</td>
+      <td>two</td>
+      <td>small</td>
+      <td>3</td>
+      <td>5</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>foo</td>
+      <td>two</td>
+      <td>small</td>
+      <td>3</td>
+      <td>6</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>bar</td>
+      <td>one</td>
+      <td>large</td>
+      <td>4</td>
+      <td>6</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>bar</td>
+      <td>one</td>
+      <td>small</td>
+      <td>5</td>
+      <td>8</td>
+    </tr>
+    <tr>
+      <th>7</th>
+      <td>bar</td>
+      <td>two</td>
+      <td>small</td>
+      <td>6</td>
+      <td>9</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>bar</td>
+      <td>two</td>
+      <td>large</td>
+      <td>7</td>
+      <td>9</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>This first example aggregates values by taking the sum.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">table</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="s1">&#39;D&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">],</span>
+                    <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;C&#39;</span><span class="p">],</span> <span class="n">aggfunc</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">)</span>
+<span class="n">table</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>C:\Users\87554\AppData\Local\Temp\ipykernel_26368\2135498425.py:1: FutureWarning: The provided callable &lt;function sum at 0x0000017F450A19D0&gt; is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string &quot;sum&quot; instead.
+  table = pd.pivot_table(df, values=&#39;D&#39;, index=[&#39;A&#39;, &#39;B&#39;],
+</pre></div>
+</div>
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>C</th>
+      <th>large</th>
+      <th>small</th>
+    </tr>
+    <tr>
+      <th>A</th>
+      <th>B</th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th rowspan="2" valign="top">bar</th>
+      <th>one</th>
+      <td>4.0</td>
+      <td>5.0</td>
+    </tr>
+    <tr>
+      <th>two</th>
+      <td>7.0</td>
+      <td>6.0</td>
+    </tr>
+    <tr>
+      <th rowspan="2" valign="top">foo</th>
+      <th>one</th>
+      <td>4.0</td>
+      <td>1.0</td>
+    </tr>
+    <tr>
+      <th>two</th>
+      <td>NaN</td>
+      <td>6.0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>We can also fill in missing values using the <code class="docutils literal notranslate"><span class="pre">fill_value</span></code> parameter.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">table</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="s1">&#39;D&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">],</span>
+                    <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;C&#39;</span><span class="p">],</span> <span class="n">aggfunc</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">table</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>C:\Users\87554\AppData\Local\Temp\ipykernel_26368\3213005518.py:1: FutureWarning: The provided callable &lt;function sum at 0x0000017F450A19D0&gt; is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string &quot;sum&quot; instead.
+  table = pd.pivot_table(df, values=&#39;D&#39;, index=[&#39;A&#39;, &#39;B&#39;],
+</pre></div>
+</div>
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>C</th>
+      <th>large</th>
+      <th>small</th>
+    </tr>
+    <tr>
+      <th>A</th>
+      <th>B</th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th rowspan="2" valign="top">bar</th>
+      <th>one</th>
+      <td>4</td>
+      <td>5</td>
+    </tr>
+    <tr>
+      <th>two</th>
+      <td>7</td>
+      <td>6</td>
+    </tr>
+    <tr>
+      <th rowspan="2" valign="top">foo</th>
+      <th>one</th>
+      <td>4</td>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>two</th>
+      <td>0</td>
+      <td>6</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>The next example aggregates by taking the mean across multiple columns.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">table</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;D&#39;</span><span class="p">,</span> <span class="s1">&#39;E&#39;</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">],</span>
+                    <span class="n">aggfunc</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;D&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">,</span>
+                             <span class="s1">&#39;E&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">})</span>
+<span class="n">table</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>We can also calculate multiple types of aggregations for any given value column.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">table</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;D&#39;</span><span class="p">,</span> <span class="s1">&#39;E&#39;</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">],</span>
+                    <span class="n">aggfunc</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;D&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">,</span>
+                             <span class="s1">&#39;E&#39;</span><span class="p">:</span> <span class="p">[</span><span class="nb">min</span><span class="p">,</span> <span class="nb">max</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">]})</span>
+<span class="n">table</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="high-performance-pandas-eval-and-query">
+<h2><span class="section-number">5.4.3.5. </span>High-performance Pandas: eval() and query()<a class="headerlink" href="#high-performance-pandas-eval-and-query" title="Permalink to this headline">#</a></h2>
+<section id="eval">
+<h3><span class="section-number">5.4.3.5.1. </span>eval()<a class="headerlink" href="#eval" title="Permalink to this headline">#</a></h3>
+<p>Evaluate a string describing operations on DataFrame columns.</p>
+<p>Operates on columns only, not specific rows or elements. This allows <code class="docutils literal notranslate"><span class="pre">eval</span></code> to run arbitrary code, which can make you vulnerable to code injection if you pass user input to this function.</p>
+<p>For example:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;A&#39;</span><span class="p">:</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span> <span class="s1">&#39;B&#39;</span><span class="p">:</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)})</span>
+<span class="n">df</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>10</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>2</td>
+      <td>8</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>3</td>
+      <td>6</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>4</td>
+      <td>4</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>5</td>
+      <td>2</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">&#39;A + B&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    11
+1    10
+2     9
+3     8
+4     7
+dtype: int64
+</pre></div>
+</div>
+</div>
+</div>
+<p>The assignment is allowed though by default the original <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> is not modified.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">&#39;C = A + B&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>10</td>
+      <td>11</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>2</td>
+      <td>8</td>
+      <td>10</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>3</td>
+      <td>6</td>
+      <td>9</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>4</td>
+      <td>4</td>
+      <td>8</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>5</td>
+      <td>2</td>
+      <td>7</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>10</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>2</td>
+      <td>8</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>3</td>
+      <td>6</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>4</td>
+      <td>4</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>5</td>
+      <td>2</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>Use <code class="docutils literal notranslate"><span class="pre">inplace=True</span></code> to modify the original DataFrame.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">&#39;C = A + B&#39;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">df</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>10</td>
+      <td>11</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>2</td>
+      <td>8</td>
+      <td>10</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>3</td>
+      <td>6</td>
+      <td>9</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>4</td>
+      <td>4</td>
+      <td>8</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>5</td>
+      <td>2</td>
+      <td>7</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>Multiple columns can be assigned using multi-line expressions:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    C = A + B</span>
+<span class="sd">    D = A - B</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C</th>
+      <th>D</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>10</td>
+      <td>11</td>
+      <td>-9</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>2</td>
+      <td>8</td>
+      <td>10</td>
+      <td>-6</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>3</td>
+      <td>6</td>
+      <td>9</td>
+      <td>-3</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>4</td>
+      <td>4</td>
+      <td>8</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>5</td>
+      <td>2</td>
+      <td>7</td>
+      <td>3</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+</section>
+<section id="query">
+<h3><span class="section-number">5.4.3.5.2. </span>query()<a class="headerlink" href="#query" title="Permalink to this headline">#</a></h3>
+<p>Query the columns of a DataFrame with a boolean expression.</p>
+<p>For example:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
+    <span class="s1">&#39;A&#39;</span><span class="p">:</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span>
+    <span class="s1">&#39;B&#39;</span><span class="p">:</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">),</span>
+    <span class="s1">&#39;C C&#39;</span><span class="p">:</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+<span class="p">})</span>
+<span class="n">df</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C C</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>10</td>
+      <td>10</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>2</td>
+      <td>8</td>
+      <td>9</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>3</td>
+      <td>6</td>
+      <td>8</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>4</td>
+      <td>4</td>
+      <td>7</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>5</td>
+      <td>2</td>
+      <td>6</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="s1">&#39;A &gt; B&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C C</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>4</th>
+      <td>5</td>
+      <td>2</td>
+      <td>6</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>The previous expression is equivalent to</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">A</span> <span class="o">&gt;</span> <span class="n">df</span><span class="o">.</span><span class="n">B</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C C</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>4</th>
+      <td>5</td>
+      <td>2</td>
+      <td>6</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>For columns with spaces in their name, you can use backtick quoting.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="s1">&#39;B == `C C`&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C C</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>10</td>
+      <td>10</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>The previous expression is equivalent to</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
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+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:660px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf%20%3D%20pd.DataFrame%28%7B%0A%20%20%20%20&#39;A&#39;%3A%20range%281,%206%29,%0A%20%20%20%20&#39;B&#39;%3A%20range%2810,%200,%20-2%29,%0A%20%20%20%20&#39;C%20C&#39;%3A%20range%2810,%205,%20-1%29%0A%7D%29%0A%0Adf%5Bdf.B%20%3D%3D%20df%5B&#39;C%20C&#39;%5D%5D&amp;d=2023-07-13&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
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+</pre></div>
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+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:660px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Adf%20%3D%20pd.DataFrame%28%7B%0A%20%20%20%20'A'%3A%20range%281,%206%29,%0A%20%20%20%20'B'%3A%20range%2810,%200,%20-2%29,%0A%20%20%20%20'C%20C'%3A%20range%2810,%205,%20-1%29%0A%7D%29%0A%0Adf%5Bdf.B%20%3D%3D%20df%5B'C%20C'%5D%5D&d=2023-07-13&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+</div></div>
+</div>
+</section>
+</section>
+<section id="your-turn">
+<h2><span class="section-number">5.4.3.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<section id="processing-image-data">
+<h3><span class="section-number">5.4.3.6.1. </span>Processing image data<a class="headerlink" href="#processing-image-data" title="Permalink to this headline">#</a></h3>
+<p>Recently, very powerful AI models have been developed that allow us to understand images. There are many tasks that can be solved using pre-trained neural networks, or cloud services. Some examples include:</p>
+<ul class="simple">
+<li><p><strong>Image Classification</strong>, can help you categorize the image into one of the pre-defined classes. You can easily train your own image classifiers using services such as <a class="reference external" href="https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum">Custom Vision</a></p></li>
+<li><p><strong>Object Detection</strong> to detect different objects in the image. Services such as <a class="reference external" href="https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum">computer vision</a> can detect a number of common objects, and you can train <a class="reference external" href="https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum">Custom Vision</a> model to detect some specific objects of interest.</p></li>
+<li><p><strong>Face Detection</strong>, including Age, Gender and Emotion detection. This can be done via <a class="reference external" href="https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-77958-bethanycheum">Face API</a>.</p></li>
+</ul>
+<p>All those cloud services can be called using <a class="reference external" href="https://docs.microsoft.com/samples/azure-samples/cognitive-services-python-sdk-samples/cognitive-services-python-sdk-samples/?WT.mc_id=academic-77958-bethanycheum">Python SDKs</a>, and thus can be easily incorporated into your data exploration workflow.</p>
+<p>Here are some examples of exploring data from Image data sources:</p>
+<ul class="simple">
+<li><p>In the blog post <a class="reference external" href="https://soshnikov.com/azure/how-to-learn-data-science-without-coding/">How to Learn Data Science without Coding</a> we explore Instagram photos, trying to understand what makes people give more likes to a photo. We first extract as much information from pictures as possible using <a class="reference external" href="https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum">computer vision</a>, and then use <a class="reference external" href="https://docs.microsoft.com/azure/machine-learning/concept-automated-ml/?WT.mc_id=academic-77958-bethanycheum">Azure Machine Learning AutoML</a> to build the interpretable model.</p></li>
+<li><p>In <a class="reference external" href="https://github.com/CloudAdvocacy/FaceStudies">Facial Studies Workshop</a> we use <a class="reference external" href="https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-77958-bethanycheum">Face API</a> to extract emotions from people on photographs from events, in order to try to understand what makes people happy.</p></li>
+</ul>
+</section>
+<section id="assignment">
+<h3><span class="section-number">5.4.3.6.2. </span>Assignment<a class="headerlink" href="#assignment" title="Permalink to this headline">#</a></h3>
+<p><a class="reference internal" href="../../../assignments/data-science/data-processing-in-python.html"><span class="doc std std-doc">Perform more detailed data study for the challenges above</span></a></p>
+</section>
+</section>
+<section id="self-study">
+<h2><span class="section-number">5.4.3.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<p>In this chapter, we’ve covered many of the basics of using Pandas effectively for data analysis. Still, much has been omitted from our discussion. To learn more about Pandas, we recommend the following resources:</p>
+<ul class="simple">
+<li><p><a class="reference external" href="http://pandas.pydata.org/">Pandas online documentation</a>: This is the go-to source for complete documentation of the package. While the examples in the documentation tend to be small generated datasets, the description of the options is complete and generally very useful for understanding the use of various functions.</p></li>
+<li><p><a class="reference external" href="http://shop.oreilly.com/product/0636920023784.do"><em>Python for Data Analysis</em></a> Written by Wes McKinney (the original creator of Pandas), this book contains much more detail on the Pandas package than we had room for in this chapter. In particular, he takes a deep dive into tools for time series, which were his bread and butter as a financial consultant. The book also has many entertaining examples of applying Pandas to gain insight from real-world datasets. Keep in mind, though, that the book is now several years old, and the Pandas package has quite a few new features that this book does not cover (but be on the lookout for a new edition in 2017).</p></li>
+<li><p><a class="reference external" href="http://stackoverflow.com/questions/tagged/pandas">Stack Overflow</a>: Pandas has so many users that any question you have has likely been asked and answered on Stack Overflow. Using Pandas is a case where some Google-Fu is your best friend. Simply go to your favorite search engine and type in the question, problem, or error you’re coming across-more than likely you’ll find your answer on a Stack Overflow page.</p></li>
+<li><p><a class="reference external" href="http://pyvideo.org/search?q=pandas">Pandas on PyVideo</a>: From PyCon to SciPy to PyData, many conferences have featured tutorials from Pandas developers and power users. The PyCon tutorials in particular tend to be given by very well-vetted presenters.</p></li>
+</ul>
+<p>Using these resources, combined with the walk-through given in this chapter, my hope is that you’ll be poised to use Pandas to tackle any data analysis problem you come across!</p>
+</section>
+<section id="acknowledgments">
+<h2><span class="section-number">5.4.3.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<p>Thanks for <a class="reference external" href="https://pandas.pydata.org/docs/user_guide/index.html">Pandas user guide</a>. It contributes the majority of the content in this chapter.</p>
+</section>
+</section>
+
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+ <span class="caption-text">
+  PREREQUISITES
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../prerequisites/python-programming-introduction.html">
+   1. Python programming introduction
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../prerequisites/python-programming-basics.html">
+   2. Python programming basics
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../prerequisites/python-programming-advanced.html">
+   3. Python programming advanced
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  DATA SCIENCE
+ </span>
+</p>
+<ul class="current nav bd-sidenav">
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../introduction/introduction.html">
+   4. Introduction
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/>
+  <label for="toctree-checkbox-1">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../introduction/defining-data-science.html">
+     4.1. Defining data science
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../introduction/data-science-ethics.html">
+     4.2. Data Science ethics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../introduction/defining-data.html">
+     4.3. Defining data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../introduction/introduction-to-statistics-and-probability.html">
+     4.4. Introduction to statistics and probability
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 current active has-children">
+  <a class="reference internal" href="../working-with-data.html">
+   5. Working with data
+  </a>
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/>
+  <label for="toctree-checkbox-2">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul class="current">
+   <li class="toctree-l2">
+    <a class="reference internal" href="../relational-databases.html">
+     5.1. Relational databases
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../non-relational-data.html">
+     5.2. Non-relational data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../numpy.html">
+     5.3. NumPy
+    </a>
+   </li>
+   <li class="toctree-l2 current active has-children">
+    <a class="reference internal" href="../pandas.html">
+     5.4. Pandas
+    </a>
+    <input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul class="current">
+     <li class="toctree-l3">
+      <a class="reference internal" href="introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3 current active">
+      <a class="current reference internal" href="#">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-preparation.html">
+     5.5. Data preparation
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../data-visualization/data-visualization.html">
+   6. Data visualization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
+  <label for="toctree-checkbox-4">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-visualization/visualization-distributions.html">
+     6.1. Visualizing distributions
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-visualization/visualization-proportions.html">
+     6.2. Visualizing proportions
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-visualization/visualization-relationships.html">
+     6.3. Visualizing relationships: all about honey 🍯
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-visualization/meaningful-visualizations.html">
+     6.4. Making meaningful visualizations
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../data-science-lifecycle/data-science-lifecycle.html">
+   7. Data Science lifecycle
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
+  <label for="toctree-checkbox-5">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science-lifecycle/introduction.html">
+     7.1. Introduction to the Data Science lifecycle
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science-lifecycle/analyzing.html">
+     7.2. Analyzing
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science-lifecycle/communication.html">
+     7.3. Communication
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../data-science-in-the-cloud/data-science-in-the-cloud.html">
+   8. Data Science in the cloud
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
+  <label for="toctree-checkbox-6">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science-in-the-cloud/introduction.html">
+     8.1. Introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science-in-the-cloud/the-low-code-no-code-way.html">
+     8.2. The “low code/no code” way
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science-in-the-cloud/the-azure-ml-sdk-way.html">
+     8.3. Data Science in the cloud: The “Azure ML SDK” way
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../data-science-in-the-wild.html">
+   9. Data Science in the real world
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  MACHINE LEARNING BASICS
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-overview.html">
+   10. Machine Learning overview
+  </a>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
+   11. Regression models for Machine Learning
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
+  <label for="toctree-checkbox-7">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/regression/tools-of-the-trade.html">
+     11.1. Tools of the trade
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
+     11.2. Managing data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
+     11.3. Linear and polynomial regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/regression/logistic-regression.html">
+     11.4. Logistic regression
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
+   12. Build a web app to use a Machine Learning model
+  </a>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
+   13. Getting started with classification
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
+  <label for="toctree-checkbox-8">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
+     13.1. Introduction to classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/classification/more-classifiers.html">
+     13.2. More classifiers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/classification/yet-other-classifiers.html">
+     13.3. Yet other classifiers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
+     13.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  ADVANCED MACHINE LEARNING
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
+   14. Clustering models for Machine Learning
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/clustering/introduction-to-clustering.html">
+     14.1. Introduction to clustering
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/clustering/k-means-clustering.html">
+     14.2. K-Means clustering
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
+   15. Getting started with ensemble learning
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/ensemble-learning/bagging.html">
+     15.1. Bagging
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/ensemble-learning/random-forest.html">
+     15.2. Random forest
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/ensemble-learning/feature-importance.html">
+     15.3. Feature importance
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
+   16. Introduction to Gradient Boosting
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/gradient-boosting/gradient-boosting.html">
+     16.1. Gradient Boosting
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
+     16.2. Gradient boosting example
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/gradient-boosting/xgboost.html">
+     16.3. XGBoost
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
+     16.4. XGBoost + k-fold CV + Feature Importance
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/unsupervised-learning.html">
+   17. Unsupervised learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/kernel-method.html">
+   18. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/model-selection.html">
+   19. Model selection
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  DEEP LEARNING
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/dl-overview.html">
+   20. Intro to Deep Learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/cnn.html">
+   21. Convolutional Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/gan.html">
+   22. Generative adversarial networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/rnn.html">
+   23. Recurrent Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/autoencoder.html">
+   24. Autoencoder
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/lstm.html">
+   25. Long-short term memory
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/time-series.html">
+   26. Time series
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/dqn.html">
+   27. Deep Q-learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/image-classification.html">
+   28. Image classification
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/image-segmentation.html">
+   29. Image segmentation
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/difussion-model.html">
+   30. Diffusion Model
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/object-detection.html">
+   31. Object detection
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  MACHINE LEARNING OPERATIONS
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../machine-learning-productionization/overview.html">
+   32. Overview
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../machine-learning-productionization/problem-framing.html">
+   33. Problem framing
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../machine-learning-productionization/data-engineering.html">
+   34. Data engineering
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../machine-learning-productionization/model-training-and-evaluation.html">
+   35. Model training &amp; evaluation
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../machine-learning-productionization/model-deployment.html">
+   36. Model deployment
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  OTHERS
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../assignments/README.html">
+   37. Self-paced assignments
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/set-up-env/first-assignment.html">
+     37.3. First assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/set-up-env/second-assignment.html">
+     37.4. Second assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/project-plan-template.html">
+     37.5. Project Plan​ Template
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/prerequisites/python-programming-introduction.html">
+     37.6. Python programming introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/prerequisites/python-programming-basics.html">
+     37.7. Python programming basics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/prerequisites/python-programming-advanced.html">
+     37.8. Python programming advanced
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/analyzing-text-about-data-science.html">
+     37.9. Analyzing text about Data Science
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/data-science-scenarios.html">
+     37.10. Data Science scenarios
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/write-a-data-ethics-case-study.html">
+     37.11. Write a data ethics case study
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/lines-scatters-and-bars.html">
+     37.12. Lines, scatters and bars
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/apply-your-skills.html">
+     37.13. Apply your skills
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/try-it-in-excel.html">
+     37.14. Try it in Excel
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/dive-into-the-beehive.html">
+     37.15. Dive into the beehive
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/build-your-own-custom-vis.html">
+     37.16. Build your own custom vis
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/classifying-datasets.html">
+     37.17. Classifying datasets
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/small-diabetes-study.html">
+     37.18. Small diabetes study
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/introduction-to-statistics-and-probability.html">
+     37.19. Introduction to probability and statistics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/displaying-airport-data.html">
+     37.20. Displaying airport data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/soda-profits.html">
+     37.21. Soda profits
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/analyzing-COVID-19-papers.html">
+     37.22. Analyzing COVID-19 papers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
+     37.23. Estimation of COVID-19 pandemic
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/data-processing-in-python.html">
+     37.24. Data processing in Python
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/evaluating-data-from-a-form.html">
+     37.25. Evaluating data from a form
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/data-preparation.html">
+     37.26. Data preparation
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/analyzing-data.html">
+     37.27. Analyzing data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
+     37.28. NYC taxi data in winter and summer
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/matplotlib-applied.html">
+     37.29. Matplotlib applied
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/tell-a-story.html">
+     37.35. Tell a story
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/explore-a-planetary-computer-dataset.html">
+     37.36. Explore a planetary computer dataset
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/exploring-for-anwser.html">
+     37.37. Exploring for answers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/market-research.html">
+     37.38. Market research
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
+     37.39. Low code/no code Data Science project on Azure ML
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
+     37.40. Data Science project using Azure ML SDK
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
+     37.41. Data Science in the cloud: The “Azure ML SDK” way
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/ml-overview-iris.html">
+     37.42. Machine Learning overview - assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
+     37.43. Machine Learning overview - assignment 2
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
+     37.44. Regression with Scikit-learn
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
+     37.46. Linear Regression Metrics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/linear-regression/loss-function.html">
+     37.47. Loss Function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
+     37.50. ML logistic regression - assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
+     37.51. ML logistic regression - assignment 2
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/ml-neural-network-1.html">
+     37.52. ML neural network - Assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/regression-tools.html">
+     37.53. Regression tools
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/managing-data.html">
+     37.54. Managing data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/exploring-visualizations.html">
+     37.55. Exploring visualizations
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/try-a-different-model.html">
+     37.56. Try a different model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/create-a-regression-model.html">
+     37.57. Create a regression model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
+     37.58. Linear and polynomial regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/retrying-some-regression.html">
+     37.59. Retrying some regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
+     37.60. Pumpkin varieties and color
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
+     37.61. Delicious asian and indian cuisines
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/explore-classification-methods.html">
+     37.62. Explore classification methods
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
+     37.63. Kernel method assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
+     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
+     37.65. Support Vector Machines (SVM) - Classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
+     37.66. Decision Trees - Intro and Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
+     37.67. Decision Trees - Classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
+     37.68. Model selection assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
+     37.69. Learning Curve To Identify Overfit &amp; Underfit
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
+     37.70. Dropout and Batch Normalization
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
+     37.71. Lasso and Ridge Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/model-selection/regularized-linear-models.html">
+     37.72. Regularized Linear Models
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
+     37.73. Random forests intro and regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/ensemble-learning/random-forests-for-classification.html">
+     37.74. Random forests for classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
+     37.75. Beyond random forests: more ensemble models
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/ensemble-learning/decision-trees.html">
+     37.76. Decision trees
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
+     37.77. Hyperparameter tuning gradient boosting
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
+     37.78. Gradient boosting
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html">
+     37.79. Boosting with tuning
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
+     37.80. Random Forest Classifier with Feature Importance
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/machine-learning-productionization/data-engineering.html">
+     37.82. Data engineering
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
+     37.83. Counterintuitive Challenges in ML Debugging
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/machine-learning-productionization/debugging-in-classification.html">
+     37.84. Case Study: Debugging in Classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/machine-learning-productionization/debugging-in-regression.html">
+     37.85. Case Study: Debugging in Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/study-the-solvers.html">
+     37.86. Study the solvers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/build-classification-models.html">
+     37.87. Build classification models
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/build-classification-model.html">
+     37.88. Build Classification Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/parameter-play.html">
+     37.89. Parameter play
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
+     37.90. How to choose cnn architecture mnist
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
+     37.92. Sign Language Digits Classification with CNN
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html">
+     37.94. Object Recognition in Images using CNN
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
+     37.95. Intro to TensorFlow for Deep Learning
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
+     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
+     37.99. Google Stock Price Prediction RNN
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/autoencoder/autoencoder.html">
+     37.101. Intro to Autoencoders
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
+     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
+     37.103. Fun with Variational Autoencoders
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/time-series-forecasting-assignment.html">
+     37.104. Time Series Forecasting Assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/nn-for-classification-assignment.html">
+     37.106. Neural Networks for Classification with TensorFlow
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
+     37.107. NN Classify 15 Fruits Assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
+     37.112. DQN On Foreign Exchange Market
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/gan/art-by-gan.html">
+     37.113. Art by gan
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/gan/gan-introduction.html">
+     37.115. Generative Adversarial Networks (GANs)
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
+     37.116. Basic classification: Classify images of clothing
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../slides/introduction.html">
+   38. Slides
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/python-programming/python-programming-introduction.html">
+     38.1. Python programming introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/python-programming/python-programming-basics.html">
+     38.2. Python programming basics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/python-programming/python-programming-advanced.html">
+     38.3. Python programming advanced
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/data-science/data-science-introduction.html">
+     38.4. Data Science introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/data-science/relational-vs-non-relational-database.html">
+     38.5. Relational vs. non-relational database
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/data-science/numpy-and-pandas.html">
+     38.6. NumPy and Pandas
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/data-science/data-visualization.html">
+     38.7. Data visualization
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/data-science/data-science-lifecycle.html">
+     38.8. Data Science lifecycle
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/data-science/data-science-in-the-cloud.html">
+     38.9. Data Science in the cloud
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/data-science/data-science-in-real-world.html">
+     38.10. Data Science in real world
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-fundamentals/ml-overview.html">
+     38.11. Machine Learning overview
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-fundamentals/linear-regression.html">
+     38.12. Linear Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-fundamentals/logistic-regression.html">
+     38.13. Logistic Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-fundamentals/logistic-regression-condensed.html">
+     38.14. Logistic Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-fundamentals/neural-network.html">
+     38.15. Neural Network
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-fundamentals/build-an-ml-web-app.html">
+     38.16. Build an machine learning web application
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-advanced/unsupervised-learning.html">
+     38.17. Unsupervised learning
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-advanced/kernel-method.html">
+     38.18. Kernel method
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-advanced/model-selection.html">
+     38.19. Model Selection
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/deep-learning/cnn.html">
+     38.20. Convolutional Neural Network
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/deep-learning/gan.html">
+     38.21. Generative Adversarial Network
+    </a>
+   </li>
+  </ul>
+ </li>
+</ul>
+
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+>
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+  <i class="fas fa-file"></i>
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+<label for="__page-toc"
+  class="headerbtn headerbtn-page-toc"
+  
+>
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+    </div>
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+
+<!-- Table of contents -->
+<div class="col-md-3 bd-toc show noprint">
+    <div class="tocsection onthispage pt-5 pb-3">
+        <i class="fas fa-list"></i> Contents
+    </div>
+    <nav id="bd-toc-nav" aria-label="Page">
+        <ul class="visible nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#overview">
+   5.4.2.1. Overview
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#selection-by-label">
+   5.4.2.2. Selection by label
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#slicing-with-labels">
+   5.4.2.3. Slicing with labels
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#selection-by-position">
+   5.4.2.4. Selection by position
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#selection-by-callable">
+   5.4.2.5. Selection by callable
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#combining-positional-and-label-based-indexing">
+     5.4.2.5.1. Combining positional and label-based indexing
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#acknowledgments">
+   5.4.2.6. Acknowledgments
+  </a>
+ </li>
+</ul>
+
+    </nav>
+</div>
+    </div>
+    <div class="article row">
+        <div class="col pl-md-3 pl-lg-5 content-container">
+            <!-- Table of contents that is only displayed when printing the page -->
+            <div id="jb-print-docs-body" class="onlyprint">
+                <h1>Data Selection</h1>
+                <!-- Table of contents -->
+                <div id="print-main-content">
+                    <div id="jb-print-toc">
+                        
+                        <div>
+                            <h2> Contents </h2>
+                        </div>
+                        <nav aria-label="Page">
+                            <ul class="visible nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#overview">
+   5.4.2.1. Overview
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#selection-by-label">
+   5.4.2.2. Selection by label
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#slicing-with-labels">
+   5.4.2.3. Slicing with labels
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#selection-by-position">
+   5.4.2.4. Selection by position
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#selection-by-callable">
+   5.4.2.5. Selection by callable
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#combining-positional-and-label-based-indexing">
+     5.4.2.5.1. Combining positional and label-based indexing
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#acknowledgments">
+   5.4.2.6. Acknowledgments
+  </a>
+ </li>
+</ul>
+
+                        </nav>
+                    </div>
+                </div>
+            </div>
+            <main id="main-content" role="main">
+                
+              <div>
+                
+  <section class="tex2jax_ignore mathjax_ignore" id="data-selection">
+<h1><span class="section-number">5.4.2. </span>Data Selection<a class="headerlink" href="#data-selection" title="Permalink to this headline">#</a></h1>
+<section id="overview">
+<h2><span class="section-number">5.4.2.1. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h2>
+<p>In this section, we’ll focus on how to slice, dice, and generally get and set subsets of Pandas objects.</p>
+<p>Import NumPy and load Pandas into your namespace:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Install the necessary dependencies</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="o">!{</span>sys.executable<span class="o">}</span><span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>--quiet<span class="w"> </span>jupyterlab_myst<span class="w"> </span>ipython
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="selection-by-label">
+<h2><span class="section-number">5.4.2.2. </span>Selection by label<a class="headerlink" href="#selection-by-label" title="Permalink to this headline">#</a></h2>
+<p>Whether a copy or a reference is returned for a setting operation, may depend on the context. This is sometimes called <code class="docutils literal notranslate"><span class="pre">chained</span> <span class="pre">assignment</span></code> and should be avoided.</p>
+<p><code class="docutils literal notranslate"><span class="pre">.loc</span></code> is strict when you present slicers that are not compatible (or convertible) with the index type. For example using integers in a <code class="docutils literal notranslate"><span class="pre">DatetimeIndex</span></code>. These will raise a <code class="docutils literal notranslate"><span class="pre">TypeError</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfl</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
+                   <span class="n">columns</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">&#39;ABCD&#39;</span><span class="p">),</span>
+                   <span class="n">index</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span><span class="s1">&#39;20130101&#39;</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">5</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_raises-exception docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfl</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">2</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">TypeError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">line</span> <span class="mi">2</span>
+<span class="g g-Whitespace">      </span><span class="mi">1</span> <span class="c1">#:tags: [&quot;raises-exception&quot;]</span>
+<span class="ne">----&gt; </span><span class="mi">2</span> <span class="n">dfl</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">2</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\indexing.py:1073,</span> in <span class="ni">_LocationIndexer.__getitem__</span><span class="nt">(self, key)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1070</span> <span class="n">axis</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">axis</span> <span class="ow">or</span> <span class="mi">0</span>
+<span class="g g-Whitespace">   </span><span class="mi">1072</span> <span class="n">maybe_callable</span> <span class="o">=</span> <span class="n">com</span><span class="o">.</span><span class="n">apply_if_callable</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">obj</span><span class="p">)</span>
+<span class="ne">-&gt; </span><span class="mi">1073</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_getitem_axis</span><span class="p">(</span><span class="n">maybe_callable</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\indexing.py:1290,</span> in <span class="ni">_LocIndexer._getitem_axis</span><span class="nt">(self, key, axis)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1288</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">slice</span><span class="p">):</span>
+<span class="g g-Whitespace">   </span><span class="mi">1289</span>     <span class="bp">self</span><span class="o">.</span><span class="n">_validate_key</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">axis</span><span class="p">)</span>
+<span class="ne">-&gt; </span><span class="mi">1290</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_slice_axis</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1291</span> <span class="k">elif</span> <span class="n">com</span><span class="o">.</span><span class="n">is_bool_indexer</span><span class="p">(</span><span class="n">key</span><span class="p">):</span>
+<span class="g g-Whitespace">   </span><span class="mi">1292</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_getbool_axis</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\indexing.py:1324,</span> in <span class="ni">_LocIndexer._get_slice_axis</span><span class="nt">(self, slice_obj, axis)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1321</span>     <span class="k">return</span> <span class="n">obj</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">deep</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1323</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">obj</span><span class="o">.</span><span class="n">_get_axis</span><span class="p">(</span><span class="n">axis</span><span class="p">)</span>
+<span class="ne">-&gt; </span><span class="mi">1324</span> <span class="n">indexer</span> <span class="o">=</span> <span class="n">labels</span><span class="o">.</span><span class="n">slice_indexer</span><span class="p">(</span><span class="n">slice_obj</span><span class="o">.</span><span class="n">start</span><span class="p">,</span> <span class="n">slice_obj</span><span class="o">.</span><span class="n">stop</span><span class="p">,</span> <span class="n">slice_obj</span><span class="o">.</span><span class="n">step</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1326</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">indexer</span><span class="p">,</span> <span class="nb">slice</span><span class="p">):</span>
+<span class="g g-Whitespace">   </span><span class="mi">1327</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">obj</span><span class="o">.</span><span class="n">_slice</span><span class="p">(</span><span class="n">indexer</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\indexes\datetimes.py:809,</span> in <span class="ni">DatetimeIndex.slice_indexer</span><span class="nt">(self, start, end, step, kind)</span>
+<span class="g g-Whitespace">    </span><span class="mi">801</span> <span class="c1"># GH#33146 if start and end are combinations of str and None and Index is not</span>
+<span class="g g-Whitespace">    </span><span class="mi">802</span> <span class="c1"># monotonic, we can not use Index.slice_indexer because it does not honor the</span>
+<span class="g g-Whitespace">    </span><span class="mi">803</span> <span class="c1"># actual elements, is only searching for start and end</span>
+<span class="g g-Whitespace">    </span><span class="mi">804</span> <span class="k">if</span> <span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">805</span>     <span class="n">check_str_or_none</span><span class="p">(</span><span class="n">start</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">806</span>     <span class="ow">or</span> <span class="n">check_str_or_none</span><span class="p">(</span><span class="n">end</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">807</span>     <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_monotonic_increasing</span>
+<span class="g g-Whitespace">    </span><span class="mi">808</span> <span class="p">):</span>
+<span class="ne">--&gt; </span><span class="mi">809</span>     <span class="k">return</span> <span class="n">Index</span><span class="o">.</span><span class="n">slice_indexer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">,</span> <span class="n">step</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="n">kind</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">811</span> <span class="n">mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">812</span> <span class="n">deprecation_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\indexes\base.py:6559,</span> in <span class="ni">Index.slice_indexer</span><span class="nt">(self, start, end, step, kind)</span>
+<span class="g g-Whitespace">   </span><span class="mi">6516</span><span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="g g-Whitespace">   </span><span class="mi">6517</span><span class="sd"> Compute the slice indexer for input labels and step.</span>
+<span class="g g-Whitespace">   </span><span class="mi">6518</span><span class="sd"> </span>
+<span class="sd">   (...)</span>
+<span class="g g-Whitespace">   </span><span class="mi">6555</span><span class="sd"> slice(1, 3, None)</span>
+<span class="g g-Whitespace">   </span><span class="mi">6556</span><span class="sd"> &quot;&quot;&quot;</span>
+<span class="g g-Whitespace">   </span><span class="mi">6557</span> <span class="bp">self</span><span class="o">.</span><span class="n">_deprecated_arg</span><span class="p">(</span><span class="n">kind</span><span class="p">,</span> <span class="s2">&quot;kind&quot;</span><span class="p">,</span> <span class="s2">&quot;slice_indexer&quot;</span><span class="p">)</span>
+<span class="ne">-&gt; </span><span class="mi">6559</span> <span class="n">start_slice</span><span class="p">,</span> <span class="n">end_slice</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">slice_locs</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="n">step</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">6561</span> <span class="c1"># return a slice</span>
+<span class="g g-Whitespace">   </span><span class="mi">6562</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">is_scalar</span><span class="p">(</span><span class="n">start_slice</span><span class="p">):</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\indexes\base.py:6767,</span> in <span class="ni">Index.slice_locs</span><span class="nt">(self, start, end, step, kind)</span>
+<span class="g g-Whitespace">   </span><span class="mi">6765</span> <span class="n">start_slice</span> <span class="o">=</span> <span class="kc">None</span>
+<span class="g g-Whitespace">   </span><span class="mi">6766</span> <span class="k">if</span> <span class="n">start</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+<span class="ne">-&gt; </span><span class="mi">6767</span>     <span class="n">start_slice</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_slice_bound</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="s2">&quot;left&quot;</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">6768</span> <span class="k">if</span> <span class="n">start_slice</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+<span class="g g-Whitespace">   </span><span class="mi">6769</span>     <span class="n">start_slice</span> <span class="o">=</span> <span class="mi">0</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\indexes\base.py:6676,</span> in <span class="ni">Index.get_slice_bound</span><span class="nt">(self, label, side, kind)</span>
+<span class="g g-Whitespace">   </span><span class="mi">6672</span> <span class="n">original_label</span> <span class="o">=</span> <span class="n">label</span>
+<span class="g g-Whitespace">   </span><span class="mi">6674</span> <span class="c1"># For datetime indices label may be a string that has to be converted</span>
+<span class="g g-Whitespace">   </span><span class="mi">6675</span> <span class="c1"># to datetime boundary according to its resolution.</span>
+<span class="ne">-&gt; </span><span class="mi">6676</span> <span class="n">label</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_maybe_cast_slice_bound</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">side</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">6678</span> <span class="c1"># we need to look up the label</span>
+<span class="g g-Whitespace">   </span><span class="mi">6679</span> <span class="k">try</span><span class="p">:</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\indexes\datetimes.py:767,</span> in <span class="ni">DatetimeIndex._maybe_cast_slice_bound</span><span class="nt">(self, label, side, kind)</span>
+<span class="g g-Whitespace">    </span><span class="mi">762</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">date</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">datetime</span><span class="p">):</span>
+<span class="g g-Whitespace">    </span><span class="mi">763</span>     <span class="c1"># Pandas supports slicing with dates, treated as datetimes at midnight.</span>
+<span class="g g-Whitespace">    </span><span class="mi">764</span>     <span class="c1"># https://github.com/pandas-dev/pandas/issues/31501</span>
+<span class="g g-Whitespace">    </span><span class="mi">765</span>     <span class="n">label</span> <span class="o">=</span> <span class="n">Timestamp</span><span class="p">(</span><span class="n">label</span><span class="p">)</span><span class="o">.</span><span class="n">to_pydatetime</span><span class="p">()</span>
+<span class="ne">--&gt; </span><span class="mi">767</span> <span class="n">label</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">_maybe_cast_slice_bound</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">side</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="n">kind</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">768</span> <span class="bp">self</span><span class="o">.</span><span class="n">_deprecate_mismatched_indexing</span><span class="p">(</span><span class="n">label</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">769</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_maybe_cast_for_get_loc</span><span class="p">(</span><span class="n">label</span><span class="p">)</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\indexes\datetimelike.py:320,</span> in <span class="ni">DatetimeIndexOpsMixin._maybe_cast_slice_bound</span><span class="nt">(self, label, side, kind)</span>
+<span class="g g-Whitespace">    </span><span class="mi">318</span>     <span class="k">return</span> <span class="n">lower</span> <span class="k">if</span> <span class="n">side</span> <span class="o">==</span> <span class="s2">&quot;left&quot;</span> <span class="k">else</span> <span class="n">upper</span>
+<span class="g g-Whitespace">    </span><span class="mi">319</span> <span class="k">elif</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="o">.</span><span class="n">_recognized_scalars</span><span class="p">):</span>
+<span class="ne">--&gt; </span><span class="mi">320</span>     <span class="k">raise</span> <span class="bp">self</span><span class="o">.</span><span class="n">_invalid_indexer</span><span class="p">(</span><span class="s2">&quot;slice&quot;</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">322</span> <span class="k">return</span> <span class="n">label</span>
+
+<span class="ne">TypeError</span>: cannot do slice indexing on DatetimeIndex with these indexers [2] of type int
+</pre></div>
+</div>
+</div>
+</div>
+<p>String likes in slicing can be convertible to the type of the index and lead to natural slicing.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfl</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;20130102&#39;</span><span class="p">:</span><span class="s1">&#39;20130104&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C</th>
+      <th>D</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2013-01-02</th>
+      <td>-0.529216</td>
+      <td>1.223634</td>
+      <td>-0.783708</td>
+      <td>-1.209286</td>
+    </tr>
+    <tr>
+      <th>2013-01-03</th>
+      <td>-1.570743</td>
+      <td>-0.316004</td>
+      <td>-1.132640</td>
+      <td>-0.464328</td>
+    </tr>
+    <tr>
+      <th>2013-01-04</th>
+      <td>1.390855</td>
+      <td>-0.319271</td>
+      <td>-1.093100</td>
+      <td>-1.090622</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;link rel=&quot;stylesheet&quot; href=&quot;https://ocademy-ai.github.io/machine-learning/_static/style.css&quot;&gt;</span>
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:730px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%20%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28&#39;ABCD&#39;%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dpd.date_range%28&#39;20130101&#39;,%20periods%3D5%29%29%0Adfl%0Adfl.loc%5B&#39;20130102&#39;%3A&#39;20130104&#39;%5D&amp;d=2023-07-13&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<link rel="stylesheet" href="https://ocademy-ai.github.io/machine-learning/_static/style.css">
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:730px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%20%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dpd.date_range%28'20130101',%20periods%3D5%29%29%0Adfl%0Adfl.loc%5B'20130102'%3A'20130104'%5D&d=2023-07-13&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+</div></div>
+</div>
+<p>Pandas will raise a <code class="docutils literal notranslate"><span class="pre">KeyError</span></code> if indexing with a list with missing labels.</p>
+<p>Pandas provides a suite of methods in order to have <strong>purely label-based indexing</strong>. This is a strict inclusion-based protocol. Every label asked for must be in the index, or a <code class="docutils literal notranslate"><span class="pre">KeyError</span></code> will be raised. When slicing, both the start bound <strong>AND</strong> the stop bound are included, if present in the index. Integers are valid labels, but they refer to the label <strong>and not the position</strong>.</p>
+<ul class="simple">
+<li><p>The <code class="docutils literal notranslate"><span class="pre">.loc</span></code> attribute is the primary access method. The following are valid inputs:</p></li>
+<li><p>A single label, e.g. <code class="docutils literal notranslate"><span class="pre">5</span></code> or <code class="docutils literal notranslate"><span class="pre">'a'</span></code> (Note that <code class="docutils literal notranslate"><span class="pre">5</span></code> is interpreted as a label of the index. This use is not an integer position along the index.).</p></li>
+<li><p>A list or array of labels <code class="docutils literal notranslate"><span class="pre">['a',</span> <span class="pre">'b',</span> <span class="pre">'c']</span></code>.</p></li>
+<li><p>A slice object with labels <code class="docutils literal notranslate"><span class="pre">'a':'f'</span></code> (Note that contrary to usual Python slices, both the start and the stop are included, when present in the index!</p></li>
+<li><p>A boolean array.</p></li>
+<li><p>A <code class="docutils literal notranslate"><span class="pre">callable</span></code>.</p></li>
+</ul>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">6</span><span class="p">),</span> <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">&#39;abcdef&#39;</span><span class="p">))</span>
+<span class="n">s1</span>
+<span class="n">s1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;c&#39;</span><span class="p">:]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>c    0.697303
+d   -1.412259
+e    0.104600
+f    1.718896
+dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;link rel=&quot;stylesheet&quot; href=&quot;https://ocademy-ai.github.io/machine-learning/_static/style.css&quot;&gt;</span>
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:730px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%286%29,%20index%3Dlist%28&#39;abcdef&#39;%29%29%0As1.loc%5B&#39;c&#39;%3A%5D%0A&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<link rel="stylesheet" href="https://ocademy-ai.github.io/machine-learning/_static/style.css">
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:730px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%286%29,%20index%3Dlist%28'abcdef'%29%29%0As1.loc%5B'c'%3A%5D%0A&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+</div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.3374040853531507
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;link rel=&quot;stylesheet&quot; href=&quot;https://ocademy-ai.github.io/machine-learning/_static/style.css&quot;&gt;</span>
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:630px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%286%29,%20index%3Dlist%28&#39;abcdef&#39;%29%29%0As1.loc%5B&#39;b&#39;%5D%0A&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<link rel="stylesheet" href="https://ocademy-ai.github.io/machine-learning/_static/style.css">
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:630px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%286%29,%20index%3Dlist%28'abcdef'%29%29%0As1.loc%5B'b'%5D%0A&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+</div></div>
+</div>
+<p>Note that the setting works as well:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;c&#39;</span><span class="p">:]</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="n">s1</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a   -0.985634
+b   -0.337404
+c    0.000000
+d    0.000000
+e    0.000000
+f    0.000000
+dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;link rel=&quot;stylesheet&quot; href=&quot;https://ocademy-ai.github.io/machine-learning/_static/style.css&quot;&gt;</span>
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:630px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%286%29,%20index%3Dlist%28&#39;abcdef&#39;%29%29%0As1.loc%5B&#39;c&#39;%3A%5D%20%3D%200%0As1&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<link rel="stylesheet" href="https://ocademy-ai.github.io/machine-learning/_static/style.css">
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:630px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%286%29,%20index%3Dlist%28'abcdef'%29%29%0As1.loc%5B'c'%3A%5D%20%3D%200%0As1&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+</div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
+                   <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">&#39;abcdef&#39;</span><span class="p">),</span>
+                   <span class="n">columns</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">&#39;ABCD&#39;</span><span class="p">))</span>
+<span class="n">df1</span>
+<span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[[</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">],</span> <span class="p">:]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C</th>
+      <th>D</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>a</th>
+      <td>1.401532</td>
+      <td>-1.744216</td>
+      <td>-0.212177</td>
+      <td>-1.295240</td>
+    </tr>
+    <tr>
+      <th>b</th>
+      <td>0.965335</td>
+      <td>-1.586035</td>
+      <td>-2.275384</td>
+      <td>0.615352</td>
+    </tr>
+    <tr>
+      <th>d</th>
+      <td>-0.131692</td>
+      <td>-0.910665</td>
+      <td>-1.286641</td>
+      <td>0.340830</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+
+<span class="sd">&lt;link rel=&quot;stylesheet&quot; href=&quot;https://ocademy-ai.github.io/machine-learning/_static/style.css&quot;&gt;</span>
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:680px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28&#39;abcdef&#39;%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28&#39;ABCD&#39;%29%29%0Adf1%0Adf1.loc%5B%5B&#39;a&#39;,%20&#39;b&#39;,%20&#39;d&#39;%5D,%20%3A%5D%0A&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+
+<link rel="stylesheet" href="https://ocademy-ai.github.io/machine-learning/_static/style.css">
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:680px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B%5B'a',%20'b',%20'd'%5D,%20%3A%5D%0A&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+</div></div>
+</div>
+<p>Accessing via label slices:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;d&#39;</span><span class="p">:,</span> <span class="s1">&#39;A&#39;</span><span class="p">:</span><span class="s1">&#39;C&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>d</th>
+      <td>-0.131692</td>
+      <td>-0.910665</td>
+      <td>-1.286641</td>
+    </tr>
+    <tr>
+      <th>e</th>
+      <td>0.238683</td>
+      <td>-0.169771</td>
+      <td>1.322003</td>
+    </tr>
+    <tr>
+      <th>f</th>
+      <td>1.511896</td>
+      <td>-0.247248</td>
+      <td>3.169958</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>For getting a cross-section using a label (equivalent to <code class="docutils literal notranslate"><span class="pre">df.xs('a')</span></code>):</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>A    1.401532
+B   -1.744216
+C   -0.212177
+D   -1.295240
+Name: a, dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:690px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28&#39;abcdef&#39;%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28&#39;ABCD&#39;%29%29%0Adf1%0Adf1.loc%5B&#39;a&#39;%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:690px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B'a'%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+</div></div>
+</div>
+<p>For getting values with a boolean array:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>A     True
+B    False
+C    False
+D    False
+Name: a, dtype: bool
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:650px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28&#39;abcdef&#39;%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28&#39;ABCD&#39;%29%29%0Adf1%0Adf1.loc%5B&#39;a&#39;%5D%20%3E%200&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:650px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B'a'%5D%20%3E%200&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+</div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>a</th>
+      <td>1.401532</td>
+    </tr>
+    <tr>
+      <th>b</th>
+      <td>0.965335</td>
+    </tr>
+    <tr>
+      <th>c</th>
+      <td>-0.097299</td>
+    </tr>
+    <tr>
+      <th>d</th>
+      <td>-0.131692</td>
+    </tr>
+    <tr>
+      <th>e</th>
+      <td>0.238683</td>
+    </tr>
+    <tr>
+      <th>f</th>
+      <td>1.511896</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:700px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28&#39;abcdef&#39;%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28&#39;ABCD&#39;%29%29%0Adf1%0Adf1.loc%5B%3A,%20df1.loc%5B&#39;a&#39;%5D%20%3E%200%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:700px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B%3A,%20df1.loc%5B'a'%5D%20%3E%200%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+</div></div>
+</div>
+<p>NA values in a boolean array propagate as <code class="docutils literal notranslate"><span class="pre">False</span></code>:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mask</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">NA</span><span class="p">,</span> <span class="kc">False</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">&quot;boolean&quot;</span><span class="p">)</span>
+<span class="n">mask</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;BooleanArray&gt;
+[True, False, True, False, &lt;NA&gt;, False]
+Length: 6, dtype: boolean
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C</th>
+      <th>D</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>a</th>
+      <td>1.401532</td>
+      <td>-1.744216</td>
+      <td>-0.212177</td>
+      <td>-1.295240</td>
+    </tr>
+    <tr>
+      <th>c</th>
+      <td>-0.097299</td>
+      <td>-0.834496</td>
+      <td>0.188575</td>
+      <td>-0.271869</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:680px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28&#39;abcdef&#39;%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28&#39;ABCD&#39;%29%29%0Amask%20%3D%20pd.array%28%5BTrue,%20False,%20True,%20False,%20pd.NA,%20False%5D,%20dtype%3D%22boolean%22%29%0Adf1%5Bmask%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:680px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Amask%20%3D%20pd.array%28%5BTrue,%20False,%20True,%20False,%20pd.NA,%20False%5D,%20dtype%3D%22boolean%22%29%0Adf1%5Bmask%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+</div></div>
+</div>
+<p>For getting a value explicitly:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;A&#39;</span><span class="p">]</span> <span class="c1"># this is also equivalent to ``df1.at[&#39;a&#39;,&#39;A&#39;]``</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>1.4015323563287203
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:680px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28&#39;abcdef&#39;%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28&#39;ABCD&#39;%29%29%0Adf1%0Adf1.loc%5B&#39;a&#39;,%20&#39;A&#39;%5D%20&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:680px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5B'a',%20'A'%5D%20&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+
+</div></div>
+</div>
+</section>
+<section id="slicing-with-labels">
+<h2><span class="section-number">5.4.2.3. </span>Slicing with labels<a class="headerlink" href="#slicing-with-labels" title="Permalink to this headline">#</a></h2>
+<p>When using <code class="docutils literal notranslate"><span class="pre">.loc</span></code> with slices, if both the start and the stop labels are present in the index, then elements located between the two (including them) are returned:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="s1">&#39;abcde&#39;</span><span class="p">),</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
+<span class="n">s</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">3</span><span class="p">:</span><span class="mi">5</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>3    b
+2    c
+5    d
+dtype: object
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:580px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28&#39;abcde&#39;%29,%20index%3D%5B0,%203,%202,%205,%204%5D%29%0As.loc%5B3%3A5%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:580px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28'abcde'%29,%20index%3D%5B0,%203,%202,%205,%204%5D%29%0As.loc%5B3%3A5%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+
+
+</div></div>
+</div>
+<p>If at least one of the two is absent, but the index is sorted, and can be compared against start and stop labels, then slicing will still work as expected, by selecting labels which rank between the two:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    a
+2    c
+3    b
+4    e
+5    d
+dtype: object
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:600px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28&#39;abcde&#39;%29,%20index%3D%5B0,%203,%202,%205,%204%5D%29%0As.sort_index%28%29&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:600px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28'abcde'%29,%20index%3D%5B0,%203,%202,%205,%204%5D%29%0As.sort_index%28%29&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+
+</div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">6</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2    c
+3    b
+4    e
+5    d
+dtype: object
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:820px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28&#39;abcde&#39;%29,%20index%3D%5B0,%203,%202,%205,%204%5D%29%0As.sort_index%28%29.loc%5B1%3A6%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:820px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28'abcde'%29,%20index%3D%5B0,%203,%202,%205,%204%5D%29%0As.sort_index%28%29.loc%5B1%3A6%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+
+</div></div>
+</div>
+<p>However, if at least one of the two is absent and the index is not sorted, an error will be raised (since doing otherwise would be computationally expensive, as well as potentially ambiguous for mixed-type indexes). For instance, in the above example, <code class="docutils literal notranslate"><span class="pre">s.loc[1:6]</span></code> would raise <code class="docutils literal notranslate"><span class="pre">KeyError</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="s1">&#39;abcdef&#39;</span><span class="p">),</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
+<span class="n">s</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">3</span><span class="p">:</span><span class="mi">5</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>3    b
+2    c
+5    d
+dtype: object
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:650px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28&#39;abcdef&#39;%29,%20index%3D%5B0,%203,%202,%205,%204,%202%5D%29%0As.loc%5B3%3A5%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:650px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0As%20%3D%20pd.Series%28list%28'abcdef'%29,%20index%3D%5B0,%203,%202,%205,%204,%202%5D%29%0As.loc%5B3%3A5%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+</div></div>
+</div>
+<p>Also, if the index has duplicate labels and either the start or the stop label is duplicated, an error will be raised. For instance, in the above example, <code class="docutils literal notranslate"><span class="pre">s.loc[2:5]</span></code> would raise a <code class="docutils literal notranslate"><span class="pre">KeyError</span></code>.</p>
+</section>
+<section id="selection-by-position">
+<h2><span class="section-number">5.4.2.4. </span>Selection by position<a class="headerlink" href="#selection-by-position" title="Permalink to this headline">#</a></h2>
+<p>Whether a copy or a reference is returned for a setting operation, may depend on the context. This is sometimes called <code class="docutils literal notranslate"><span class="pre">chained</span> <span class="pre">assignment</span></code> and should be avoided.</p>
+<p>Pandas provides a suite of methods in order to get purely integer-based indexing. The semantics follow closely Python and NumPy slicing. These are 0-based indexing. When slicing, the start bound is included, while the upper bound is excluded. Trying to use a non-integer, even a valid label will raise an <code class="docutils literal notranslate"><span class="pre">IndexError</span></code>.</p>
+<p>The <code class="docutils literal notranslate"><span class="pre">.iloc</span></code> attribute is the primary access method. The following are valid inputs:</p>
+<ul class="simple">
+<li><p>An integer e.g. <code class="docutils literal notranslate"><span class="pre">5</span></code>.</p></li>
+<li><p>A list or array of integers <code class="docutils literal notranslate"><span class="pre">[4,</span> <span class="pre">3,</span> <span class="pre">0]</span></code>.</p></li>
+<li><p>A slice object with ints <code class="docutils literal notranslate"><span class="pre">1:7</span></code>.</p></li>
+<li><p>A boolean array.</p></li>
+<li><p>A <code class="docutils literal notranslate"><span class="pre">callable</span></code>.</p></li>
+</ul>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">),</span> <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
+<span class="n">s1</span>
+<span class="n">s1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    0.531403
+2    1.164702
+4   -0.384782
+dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:600px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%285%29,%20index%3Dlist%28range%280,%2010,%202%29%29%29%0As1.iloc%5B%3A3%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:600px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%285%29,%20index%3Dlist%28range%280,%2010,%202%29%29%29%0As1.iloc%5B%3A3%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+</div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.21764232439885461
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:600px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%285%29,%20index%3Dlist%28range%280,%2010,%202%29%29%29%0As1.iloc%5B%3A3%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:600px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%285%29,%20index%3Dlist%28range%280,%2010,%202%29%29%29%0As1.iloc%5B%3A3%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+</div></div>
+</div>
+<p>Note that setting works as well:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="n">s1</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    0.000000
+2    0.000000
+4    0.000000
+6    0.217642
+8    1.458410
+dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:620px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%285%29,%20index%3Dlist%28range%280,%2010,%202%29%29%29%0As1.iloc%5B%3A3%5D%20%3D%200%0As1&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:620px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0As1%20%3D%20pd.Series%28np.random.randn%285%29,%20index%3Dlist%28range%280,%2010,%202%29%29%29%0As1.iloc%5B%3A3%5D%20%3D%200%0As1&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+</div></div>
+</div>
+<p>With a DataFrame,Select via integer slicing:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
+                   <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span>
+                   <span class="n">columns</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
+<span class="n">df1</span>
+<span class="n">df1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>0</th>
+      <th>2</th>
+      <th>4</th>
+      <th>6</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>-1.194412</td>
+      <td>-0.126540</td>
+      <td>0.496297</td>
+      <td>-0.194096</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>-1.116951</td>
+      <td>1.041856</td>
+      <td>-0.662633</td>
+      <td>0.493678</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>1.497028</td>
+      <td>-0.260497</td>
+      <td>0.697729</td>
+      <td>-1.092215</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:680px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B%3A3%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:680px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B%3A3%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+</div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>4</th>
+      <th>6</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2</th>
+      <td>-0.662633</td>
+      <td>0.493678</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>0.697729</td>
+      <td>-1.092215</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>0.715370</td>
+      <td>1.528302</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>-0.548232</td>
+      <td>0.081242</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>Select via integer list:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>2</th>
+      <th>6</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2</th>
+      <td>1.041856</td>
+      <td>0.493678</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>-0.177517</td>
+      <td>1.528302</td>
+    </tr>
+    <tr>
+      <th>10</th>
+      <td>0.666088</td>
+      <td>-0.595855</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:700px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1%3A5,%202%3A4%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:700px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1%3A5,%202%3A4%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+</div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span> <span class="p">:]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>0</th>
+      <th>2</th>
+      <th>4</th>
+      <th>6</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2</th>
+      <td>-1.116951</td>
+      <td>1.041856</td>
+      <td>-0.662633</td>
+      <td>0.493678</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>1.497028</td>
+      <td>-0.260497</td>
+      <td>0.697729</td>
+      <td>-1.092215</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:680px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1%3A3,%20%3A%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:680px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1%3A3,%20%3A%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+
+</div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>2</th>
+      <th>4</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>-0.126540</td>
+      <td>0.496297</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>1.041856</td>
+      <td>-0.662633</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>-0.260497</td>
+      <td>0.697729</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>-0.177517</td>
+      <td>0.715370</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>-0.980550</td>
+      <td>-0.548232</td>
+    </tr>
+    <tr>
+      <th>10</th>
+      <td>0.666088</td>
+      <td>0.114509</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:700px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B%3A,%201%3A3%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:700px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B%3A,%201%3A3%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+</div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="c1"># this is also equivalent to ``df1.iat[1,1]``</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>1.0418559735628448
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:680px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1,%201%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:680px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1,%201%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+</div></div>
+</div>
+<p>For getting a cross-section using an integer position (equiv to <code class="docutils literal notranslate"><span class="pre">df.xs(1)</span></code>):</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0   -1.116951
+2    1.041856
+4   -0.662633
+6    0.493678
+Name: 2, dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:700px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:700px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28range%280,%2012,%202%29%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28range%280,%208,%202%29%29%29%0Adf1%0Adf1.iloc%5B1%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+</div></div>
+</div>
+<p>Out-of-range slice indexes are handled gracefully just as in Python/NumPy.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="s1">&#39;abcdef&#39;</span><span class="p">)</span> <span class="c1"># these are allowed in Python/NumPy.</span>
+<span class="n">x</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;a&#39;, &#39;b&#39;, &#39;c&#39;, &#39;d&#39;, &#39;e&#39;, &#39;f&#39;]
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">[</span><span class="mi">4</span><span class="p">:</span><span class="mi">10</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;e&#39;, &#39;f&#39;]
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">[</span><span class="mi">8</span><span class="p">:</span><span class="mi">10</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[]
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="n">s</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    a
+1    b
+2    c
+3    d
+4    e
+5    f
+dtype: object
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">4</span><span class="p">:</span><span class="mi">10</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>4    e
+5    f
+dtype: object
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:600px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Ax%20%3D%20list%28&#39;abcdef&#39;%29%20%0As%20%3D%20pd.Series%28x%29%0As.iloc%5B4%3A10%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:600px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Ax%20%3D%20list%28'abcdef'%29%20%0As%20%3D%20pd.Series%28x%29%0As.iloc%5B4%3A10%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+</div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">8</span><span class="p">:</span><span class="mi">10</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Series([], dtype: object)
+</pre></div>
+</div>
+</div>
+</div>
+<p>Note that using slices that go out of bounds can result in an empty axis (e.g. an empty DataFrame being returned).</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfl</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">&#39;AB&#39;</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfl</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+    </tr>
+    <tr>
+      <th>1</th>
+    </tr>
+    <tr>
+      <th>2</th>
+    </tr>
+    <tr>
+      <th>3</th>
+    </tr>
+    <tr>
+      <th>4</th>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:630px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%202%29,%20columns%3Dlist%28&#39;AB&#39;%29%29%0Adfl%0Adfl.iloc%5B%3A,%202%3A3%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:630px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%202%29,%20columns%3Dlist%28'AB'%29%29%0Adfl%0Adfl.iloc%5B%3A,%202%3A3%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+</div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfl</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>B</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>0.700611</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>-0.358047</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>0.620409</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>0.953488</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>-2.263445</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:630px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%202%29,%20columns%3Dlist%28&#39;AB&#39;%29%29%0Adfl.iloc%5B%3A,%201%3A3%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:630px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%202%29,%20columns%3Dlist%28'AB'%29%29%0Adfl.iloc%5B%3A,%201%3A3%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+</div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfl</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">4</span><span class="p">:</span><span class="mi">6</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>4</th>
+      <td>0.374726</td>
+      <td>-2.263445</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:600px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%202%29,%20columns%3Dlist%28&#39;AB&#39;%29%29%0Adfl.iloc%5B4%3A6%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:600px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adfl%20%3D%20pd.DataFrame%28np.random.randn%285,%202%29,%20columns%3Dlist%28'AB'%29%29%0Adfl.iloc%5B4%3A6%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+</div></div>
+</div>
+<p>A single indexer that is out of bounds will raise an <code class="docutils literal notranslate"><span class="pre">IndexError</span></code>. A list of indexers where any element is out of bounds will raise an <code class="docutils literal notranslate"><span class="pre">IndexError</span></code>.</p>
+<div class="cell tag_raises-exception docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfl</span><span class="o">.</span><span class="n">iloc</span><span class="p">[[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">IndexError</span><span class="g g-Whitespace">                                </span>Traceback (most recent call last)
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\indexing.py:1587,</span> in <span class="ni">_iLocIndexer._get_list_axis</span><span class="nt">(self, key, axis)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1586</span> <span class="k">try</span><span class="p">:</span>
+<span class="ne">-&gt; </span><span class="mi">1587</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">obj</span><span class="o">.</span><span class="n">_take_with_is_copy</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1588</span> <span class="k">except</span> <span class="ne">IndexError</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+<span class="g g-Whitespace">   </span><span class="mi">1589</span>     <span class="c1"># re-raise with different error message</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\generic.py:3902,</span> in <span class="ni">NDFrame._take_with_is_copy</span><span class="nt">(self, indices, axis)</span>
+<span class="g g-Whitespace">   </span><span class="mi">3895</span><span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="g g-Whitespace">   </span><span class="mi">3896</span><span class="sd"> Internal version of the `take` method that sets the `_is_copy`</span>
+<span class="g g-Whitespace">   </span><span class="mi">3897</span><span class="sd"> attribute to keep track of the parent dataframe (using in indexing</span>
+<span class="sd">   (...)</span>
+<span class="g g-Whitespace">   </span><span class="mi">3900</span><span class="sd"> See the docstring of `take` for full explanation of the parameters.</span>
+<span class="g g-Whitespace">   </span><span class="mi">3901</span><span class="sd"> &quot;&quot;&quot;</span>
+<span class="ne">-&gt; </span><span class="mi">3902</span> <span class="n">result</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_take</span><span class="p">(</span><span class="n">indices</span><span class="o">=</span><span class="n">indices</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">3903</span> <span class="c1"># Maybe set copy if we didn&#39;t actually change the index.</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\generic.py:3886,</span> in <span class="ni">NDFrame._take</span><span class="nt">(self, indices, axis, convert_indices)</span>
+<span class="g g-Whitespace">   </span><span class="mi">3884</span> <span class="bp">self</span><span class="o">.</span><span class="n">_consolidate_inplace</span><span class="p">()</span>
+<span class="ne">-&gt; </span><span class="mi">3886</span> <span class="n">new_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mgr</span><span class="o">.</span><span class="n">take</span><span class="p">(</span>
+<span class="g g-Whitespace">   </span><span class="mi">3887</span>     <span class="n">indices</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">3888</span>     <span class="n">axis</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_block_manager_axis</span><span class="p">(</span><span class="n">axis</span><span class="p">),</span>
+<span class="g g-Whitespace">   </span><span class="mi">3889</span>     <span class="n">verify</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">3890</span>     <span class="n">convert_indices</span><span class="o">=</span><span class="n">convert_indices</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">3891</span> <span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">3892</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_constructor</span><span class="p">(</span><span class="n">new_data</span><span class="p">)</span><span class="o">.</span><span class="n">__finalize__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;take&quot;</span><span class="p">)</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\internals\managers.py:975,</span> in <span class="ni">BaseBlockManager.take</span><span class="nt">(self, indexer, axis, verify, convert_indices)</span>
+<span class="g g-Whitespace">    </span><span class="mi">974</span> <span class="k">if</span> <span class="n">convert_indices</span><span class="p">:</span>
+<span class="ne">--&gt; </span><span class="mi">975</span>     <span class="n">indexer</span> <span class="o">=</span> <span class="n">maybe_convert_indices</span><span class="p">(</span><span class="n">indexer</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">verify</span><span class="o">=</span><span class="n">verify</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">977</span> <span class="n">new_labels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">axes</span><span class="p">[</span><span class="n">axis</span><span class="p">]</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="n">indexer</span><span class="p">)</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\indexers\utils.py:286,</span> in <span class="ni">maybe_convert_indices</span><span class="nt">(indices, n, verify)</span>
+<span class="g g-Whitespace">    </span><span class="mi">285</span>     <span class="k">if</span> <span class="n">mask</span><span class="o">.</span><span class="n">any</span><span class="p">():</span>
+<span class="ne">--&gt; </span><span class="mi">286</span>         <span class="k">raise</span> <span class="ne">IndexError</span><span class="p">(</span><span class="s2">&quot;indices are out-of-bounds&quot;</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">287</span> <span class="k">return</span> <span class="n">indices</span>
+
+<span class="ne">IndexError</span>: indices are out-of-bounds
+
+<span class="n">The</span> <span class="n">above</span> <span class="n">exception</span> <span class="n">was</span> <span class="n">the</span> <span class="n">direct</span> <span class="n">cause</span> <span class="n">of</span> <span class="n">the</span> <span class="n">following</span> <span class="n">exception</span><span class="p">:</span>
+
+<span class="ne">IndexError</span><span class="g g-Whitespace">                                </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">67</span><span class="p">],</span> <span class="n">line</span> <span class="mi">2</span>
+<span class="g g-Whitespace">      </span><span class="mi">1</span> <span class="c1">#:tags: [&quot;raises-exception&quot;]</span>
+<span class="ne">----&gt; </span><span class="mi">2</span> <span class="n">dfl</span><span class="o">.</span><span class="n">iloc</span><span class="p">[[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]]</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\indexing.py:1073,</span> in <span class="ni">_LocationIndexer.__getitem__</span><span class="nt">(self, key)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1070</span> <span class="n">axis</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">axis</span> <span class="ow">or</span> <span class="mi">0</span>
+<span class="g g-Whitespace">   </span><span class="mi">1072</span> <span class="n">maybe_callable</span> <span class="o">=</span> <span class="n">com</span><span class="o">.</span><span class="n">apply_if_callable</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">obj</span><span class="p">)</span>
+<span class="ne">-&gt; </span><span class="mi">1073</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_getitem_axis</span><span class="p">(</span><span class="n">maybe_callable</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\indexing.py:1616,</span> in <span class="ni">_iLocIndexer._getitem_axis</span><span class="nt">(self, key, axis)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1614</span> <span class="c1"># a list of integers</span>
+<span class="g g-Whitespace">   </span><span class="mi">1615</span> <span class="k">elif</span> <span class="n">is_list_like_indexer</span><span class="p">(</span><span class="n">key</span><span class="p">):</span>
+<span class="ne">-&gt; </span><span class="mi">1616</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_list_axis</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1618</span> <span class="c1"># a single integer</span>
+<span class="g g-Whitespace">   </span><span class="mi">1619</span> <span class="k">else</span><span class="p">:</span>
+<span class="g g-Whitespace">   </span><span class="mi">1620</span>     <span class="n">key</span> <span class="o">=</span> <span class="n">item_from_zerodim</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\indexing.py:1590,</span> in <span class="ni">_iLocIndexer._get_list_axis</span><span class="nt">(self, key, axis)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1587</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">obj</span><span class="o">.</span><span class="n">_take_with_is_copy</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1588</span> <span class="k">except</span> <span class="ne">IndexError</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+<span class="g g-Whitespace">   </span><span class="mi">1589</span>     <span class="c1"># re-raise with different error message</span>
+<span class="ne">-&gt; </span><span class="mi">1590</span>     <span class="k">raise</span> <span class="ne">IndexError</span><span class="p">(</span><span class="s2">&quot;positional indexers are out-of-bounds&quot;</span><span class="p">)</span> <span class="kn">from</span> <span class="nn">err</span>
+
+<span class="ne">IndexError</span>: positional indexers are out-of-bounds
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_raises-exception docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfl</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">IndexError</span><span class="g g-Whitespace">                                </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">68</span><span class="p">],</span> <span class="n">line</span> <span class="mi">2</span>
+<span class="g g-Whitespace">      </span><span class="mi">1</span> <span class="c1">#:tags: [&quot;raises-exception&quot;]</span>
+<span class="ne">----&gt; </span><span class="mi">2</span> <span class="n">dfl</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\indexing.py:1067,</span> in <span class="ni">_LocationIndexer.__getitem__</span><span class="nt">(self, key)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1065</span>     <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_scalar_access</span><span class="p">(</span><span class="n">key</span><span class="p">):</span>
+<span class="g g-Whitespace">   </span><span class="mi">1066</span>         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">obj</span><span class="o">.</span><span class="n">_get_value</span><span class="p">(</span><span class="o">*</span><span class="n">key</span><span class="p">,</span> <span class="n">takeable</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_takeable</span><span class="p">)</span>
+<span class="ne">-&gt; </span><span class="mi">1067</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_getitem_tuple</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1068</span> <span class="k">else</span><span class="p">:</span>
+<span class="g g-Whitespace">   </span><span class="mi">1069</span>     <span class="c1"># we by definition only have the 0th axis</span>
+<span class="g g-Whitespace">   </span><span class="mi">1070</span>     <span class="n">axis</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">axis</span> <span class="ow">or</span> <span class="mi">0</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\indexing.py:1563,</span> in <span class="ni">_iLocIndexer._getitem_tuple</span><span class="nt">(self, tup)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1561</span> <span class="k">def</span> <span class="nf">_getitem_tuple</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tup</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">):</span>
+<span class="ne">-&gt; </span><span class="mi">1563</span>     <span class="n">tup</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_validate_tuple_indexer</span><span class="p">(</span><span class="n">tup</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1564</span>     <span class="k">with</span> <span class="n">suppress</span><span class="p">(</span><span class="n">IndexingError</span><span class="p">):</span>
+<span class="g g-Whitespace">   </span><span class="mi">1565</span>         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_getitem_lowerdim</span><span class="p">(</span><span class="n">tup</span><span class="p">)</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\indexing.py:873,</span> in <span class="ni">_LocationIndexer._validate_tuple_indexer</span><span class="nt">(self, key)</span>
+<span class="g g-Whitespace">    </span><span class="mi">871</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">key</span><span class="p">):</span>
+<span class="g g-Whitespace">    </span><span class="mi">872</span>     <span class="k">try</span><span class="p">:</span>
+<span class="ne">--&gt; </span><span class="mi">873</span>         <span class="bp">self</span><span class="o">.</span><span class="n">_validate_key</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">874</span>     <span class="k">except</span> <span class="ne">ValueError</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">875</span>         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">876</span>             <span class="s2">&quot;Location based indexing can only have &quot;</span>
+<span class="g g-Whitespace">    </span><span class="mi">877</span>             <span class="sa">f</span><span class="s2">&quot;[</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">_valid_types</span><span class="si">}</span><span class="s2">] types&quot;</span>
+<span class="g g-Whitespace">    </span><span class="mi">878</span>         <span class="p">)</span> <span class="kn">from</span> <span class="nn">err</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\indexing.py:1466,</span> in <span class="ni">_iLocIndexer._validate_key</span><span class="nt">(self, key, axis)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1464</span>     <span class="k">return</span>
+<span class="g g-Whitespace">   </span><span class="mi">1465</span> <span class="k">elif</span> <span class="n">is_integer</span><span class="p">(</span><span class="n">key</span><span class="p">):</span>
+<span class="ne">-&gt; </span><span class="mi">1466</span>     <span class="bp">self</span><span class="o">.</span><span class="n">_validate_integer</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">axis</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1467</span> <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
+<span class="g g-Whitespace">   </span><span class="mi">1468</span>     <span class="c1"># a tuple should already have been caught by this point</span>
+<span class="g g-Whitespace">   </span><span class="mi">1469</span>     <span class="c1"># so don&#39;t treat a tuple as a valid indexer</span>
+<span class="g g-Whitespace">   </span><span class="mi">1470</span>     <span class="k">raise</span> <span class="n">IndexingError</span><span class="p">(</span><span class="s2">&quot;Too many indexers&quot;</span><span class="p">)</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\indexing.py:1557,</span> in <span class="ni">_iLocIndexer._validate_integer</span><span class="nt">(self, key, axis)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1555</span> <span class="n">len_axis</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">obj</span><span class="o">.</span><span class="n">_get_axis</span><span class="p">(</span><span class="n">axis</span><span class="p">))</span>
+<span class="g g-Whitespace">   </span><span class="mi">1556</span> <span class="k">if</span> <span class="n">key</span> <span class="o">&gt;=</span> <span class="n">len_axis</span> <span class="ow">or</span> <span class="n">key</span> <span class="o">&lt;</span> <span class="o">-</span><span class="n">len_axis</span><span class="p">:</span>
+<span class="ne">-&gt; </span><span class="mi">1557</span>     <span class="k">raise</span> <span class="ne">IndexError</span><span class="p">(</span><span class="s2">&quot;single positional indexer is out-of-bounds&quot;</span><span class="p">)</span>
+
+<span class="ne">IndexError</span>: single positional indexer is out-of-bounds
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="selection-by-callable">
+<h2><span class="section-number">5.4.2.5. </span>Selection by callable<a class="headerlink" href="#selection-by-callable" title="Permalink to this headline">#</a></h2>
+<p><code class="docutils literal notranslate"><span class="pre">.loc</span></code>, <code class="docutils literal notranslate"><span class="pre">.iloc</span></code>, and also <code class="docutils literal notranslate"><span class="pre">[]</span></code> indexing can accept a <code class="docutils literal notranslate"><span class="pre">callable</span></code> as indexer. The <code class="docutils literal notranslate"><span class="pre">callable</span></code> must be a function with one argument (the calling Series or DataFrame) that returns valid output for indexing.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
+                   <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">&#39;abcdef&#39;</span><span class="p">),</span>
+                   <span class="n">columns</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">&#39;ABCD&#39;</span><span class="p">))</span>
+<span class="n">df1</span>
+<span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="k">lambda</span> <span class="n">df</span><span class="p">:</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C</th>
+      <th>D</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>d</th>
+      <td>0.852422</td>
+      <td>-0.452728</td>
+      <td>-0.384272</td>
+      <td>0.370443</td>
+    </tr>
+    <tr>
+      <th>e</th>
+      <td>0.263762</td>
+      <td>-0.053398</td>
+      <td>0.135893</td>
+      <td>0.618338</td>
+    </tr>
+    <tr>
+      <th>f</th>
+      <td>0.857898</td>
+      <td>0.456715</td>
+      <td>0.556336</td>
+      <td>-1.022002</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:700px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28&#39;abcdef&#39;%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28&#39;ABCD&#39;%29%29%0Adf1%0Adf1.loc%5Blambda%20df%3A%20df%5B&#39;A&#39;%5D%20%3E%200,%20%3A%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:700px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%20%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%0Adf1.loc%5Blambda%20df%3A%20df%5B'A'%5D%20%3E%200,%20%3A%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+</div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="k">lambda</span> <span class="n">df</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">]]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>a</th>
+      <td>-1.863049</td>
+      <td>1.979038</td>
+    </tr>
+    <tr>
+      <th>b</th>
+      <td>-0.336175</td>
+      <td>-1.192626</td>
+    </tr>
+    <tr>
+      <th>c</th>
+      <td>-0.317314</td>
+      <td>2.267986</td>
+    </tr>
+    <tr>
+      <th>d</th>
+      <td>0.852422</td>
+      <td>-0.452728</td>
+    </tr>
+    <tr>
+      <th>e</th>
+      <td>0.263762</td>
+      <td>-0.053398</td>
+    </tr>
+    <tr>
+      <th>f</th>
+      <td>0.857898</td>
+      <td>0.456715</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:700px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28&#39;abcdef&#39;%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28&#39;ABCD&#39;%29%29%0Adf1.loc%5B%3A,%20lambda%20df%3A%20%5B&#39;A&#39;,%20&#39;B&#39;%5D%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:700px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1.loc%5B%3A,%20lambda%20df%3A%20%5B'A',%20'B'%5D%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+
+</div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="k">lambda</span> <span class="n">df</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>a</th>
+      <td>-1.863049</td>
+      <td>1.979038</td>
+    </tr>
+    <tr>
+      <th>b</th>
+      <td>-0.336175</td>
+      <td>-1.192626</td>
+    </tr>
+    <tr>
+      <th>c</th>
+      <td>-0.317314</td>
+      <td>2.267986</td>
+    </tr>
+    <tr>
+      <th>d</th>
+      <td>0.852422</td>
+      <td>-0.452728</td>
+    </tr>
+    <tr>
+      <th>e</th>
+      <td>0.263762</td>
+      <td>-0.053398</td>
+    </tr>
+    <tr>
+      <th>f</th>
+      <td>0.857898</td>
+      <td>0.456715</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:700px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28&#39;abcdef&#39;%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28&#39;ABCD&#39;%29%29%0Adf1.iloc%5B%3A,%20lambda%20df%3A%20%5B0,%201%5D%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:700px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1.iloc%5B%3A,%20lambda%20df%3A%20%5B0,%201%5D%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+</div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="p">[</span><span class="k">lambda</span> <span class="n">df</span><span class="p">:</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a   -1.863049
+b   -0.336175
+c   -0.317314
+d    0.852422
+e    0.263762
+f    0.857898
+Name: A, dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:650px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28&#39;abcdef&#39;%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28&#39;ABCD&#39;%29%29%0Adf1%5Blambda%20df%3A%20df.columns%5B0%5D%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:650px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%5Blambda%20df%3A%20df.columns%5B0%5D%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+</div></div>
+</div>
+<p>You can use callable indexing in <code class="docutils literal notranslate"><span class="pre">Series</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="k">lambda</span> <span class="n">s</span><span class="p">:</span> <span class="n">s</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>d    0.852422
+e    0.263762
+f    0.857898
+Name: A, dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:920px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28&#39;abcdef&#39;%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28&#39;ABCD&#39;%29%29%0Adf1%5B&#39;A&#39;%5D.loc%5Blambda%20s%3A%20s%20%3E%200%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:920px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Aimport%20random%0Anp.random.seed%2810%29%0Adf1%20%3D%20pd.DataFrame%28np.random.randn%286,%204%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abcdef'%29,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20columns%3Dlist%28'ABCD'%29%29%0Adf1%5B'A'%5D.loc%5Blambda%20s%3A%20s%20%3E%200%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+</div></div>
+</div>
+<section id="combining-positional-and-label-based-indexing">
+<h3><span class="section-number">5.4.2.5.1. </span>Combining positional and label-based indexing<a class="headerlink" href="#combining-positional-and-label-based-indexing" title="Permalink to this headline">#</a></h3>
+<p>If you wish to get the 0th and the 2nd elements from the index in the <code class="docutils literal notranslate"><span class="pre">'A'</span></code> column, you can do:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfd</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;A&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
+                    <span class="s1">&#39;B&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]},</span>
+                   <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">&#39;abc&#39;</span><span class="p">))</span>
+<span class="n">dfd</span>
+<span class="n">dfd</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">dfd</span><span class="o">.</span><span class="n">index</span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]],</span> <span class="s1">&#39;A&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    1
+c    3
+Name: A, dtype: int64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:550px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adfd%20%3D%20pd.DataFrame%28%7B&#39;A&#39;%3A%20%5B1,%202,%203%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20&#39;B&#39;%3A%20%5B4,%205,%206%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28&#39;abc&#39;%29%29%0Adfd.loc%5Bdfd.index%5B%5B0,%202%5D%5D,%20&#39;A&#39;%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:550px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adfd%20%3D%20pd.DataFrame%28%7B'A'%3A%20%5B1,%202,%203%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'B'%3A%20%5B4,%205,%206%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abc'%29%29%0Adfd.loc%5Bdfd.index%5B%5B0,%202%5D%5D,%20'A'%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+</div></div>
+</div>
+<p>This can also be expressed using <code class="docutils literal notranslate"><span class="pre">.iloc</span></code>, by explicitly getting locations on the indexers, and using positional indexing to select things.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfd</span><span class="o">.</span><span class="n">iloc</span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">dfd</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">)]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    1
+c    3
+Name: A, dtype: int64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:550px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adfd%20%3D%20pd.DataFrame%28%7B&#39;A&#39;%3A%20%5B1,%202,%203%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20&#39;B&#39;%3A%20%5B4,%205,%206%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28&#39;abc&#39;%29%29%0Adfd.iloc%5B%5B0,%202%5D,%20dfd.columns.get_loc%28&#39;A&#39;%29%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:550px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adfd%20%3D%20pd.DataFrame%28%7B'A'%3A%20%5B1,%202,%203%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'B'%3A%20%5B4,%205,%206%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abc'%29%29%0Adfd.iloc%5B%5B0,%202%5D,%20dfd.columns.get_loc%28'A'%29%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+
+</div></div>
+</div>
+<p>For getting multiple indexers, using <code class="docutils literal notranslate"><span class="pre">.get_indexer</span></code>:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfd</span><span class="o">.</span><span class="n">iloc</span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">dfd</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">get_indexer</span><span class="p">([</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">])]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>a</th>
+      <td>1</td>
+      <td>4</td>
+    </tr>
+    <tr>
+      <th>c</th>
+      <td>3</td>
+      <td>6</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:550px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adfd%20%3D%20pd.DataFrame%28%7B&#39;A&#39;%3A%20%5B1,%202,%203%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20&#39;B&#39;%3A%20%5B4,%205,%206%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28&#39;abc&#39;%29%29%0Adfd.iloc%5B%5B0,%202%5D,%20dfd.columns.get_indexer%28%5B&#39;A&#39;,%20&#39;B&#39;%5D%29%5D&amp;d=2023-07-14&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:550px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Adfd%20%3D%20pd.DataFrame%28%7B'A'%3A%20%5B1,%202,%203%5D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20'B'%3A%20%5B4,%205,%206%5D%7D,%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20index%3Dlist%28'abc'%29%29%0Adfd.iloc%5B%5B0,%202%5D,%20dfd.columns.get_indexer%28%5B'A',%20'B'%5D%29%5D&d=2023-07-14&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+
+</div></div>
+</div>
+</section>
+</section>
+<section id="acknowledgments">
+<h2><span class="section-number">5.4.2.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<p>Thanks for <a class="reference external" href="https://pandas.pydata.org/docs/user_guide/index.html">Pandas user guide</a>. It contributes the majority of the content in this chapter.</p>
+</section>
+</section>
+
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+    <div class="bd-toc-item active">
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+ <span class="caption-text">
+  PREREQUISITES
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../prerequisites/python-programming-introduction.html">
+   1. Python programming introduction
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../prerequisites/python-programming-basics.html">
+   2. Python programming basics
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../prerequisites/python-programming-advanced.html">
+   3. Python programming advanced
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  DATA SCIENCE
+ </span>
+</p>
+<ul class="current nav bd-sidenav">
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../introduction/introduction.html">
+   4. Introduction
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/>
+  <label for="toctree-checkbox-1">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../introduction/defining-data-science.html">
+     4.1. Defining data science
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../introduction/data-science-ethics.html">
+     4.2. Data Science ethics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../introduction/defining-data.html">
+     4.3. Defining data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../introduction/introduction-to-statistics-and-probability.html">
+     4.4. Introduction to statistics and probability
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 current active has-children">
+  <a class="reference internal" href="../working-with-data.html">
+   5. Working with data
+  </a>
+  <input checked="" class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/>
+  <label for="toctree-checkbox-2">
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+   </i>
+  </label>
+  <ul class="current">
+   <li class="toctree-l2">
+    <a class="reference internal" href="../relational-databases.html">
+     5.1. Relational databases
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../non-relational-data.html">
+     5.2. Non-relational data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../numpy.html">
+     5.3. NumPy
+    </a>
+   </li>
+   <li class="toctree-l2 current active has-children">
+    <a class="reference internal" href="../pandas.html">
+     5.4. Pandas
+    </a>
+    <input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
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+     </i>
+    </label>
+    <ul class="current">
+     <li class="toctree-l3 current active">
+      <a class="current reference internal" href="#">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../data-preparation.html">
+     5.5. Data preparation
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../data-visualization/data-visualization.html">
+   6. Data visualization
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
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+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-visualization/visualization-distributions.html">
+     6.1. Visualizing distributions
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-visualization/visualization-proportions.html">
+     6.2. Visualizing proportions
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-visualization/visualization-relationships.html">
+     6.3. Visualizing relationships: all about honey 🍯
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-visualization/meaningful-visualizations.html">
+     6.4. Making meaningful visualizations
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../data-science-lifecycle/data-science-lifecycle.html">
+   7. Data Science lifecycle
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
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+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science-lifecycle/introduction.html">
+     7.1. Introduction to the Data Science lifecycle
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science-lifecycle/analyzing.html">
+     7.2. Analyzing
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science-lifecycle/communication.html">
+     7.3. Communication
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../data-science-in-the-cloud/data-science-in-the-cloud.html">
+   8. Data Science in the cloud
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
+  <label for="toctree-checkbox-6">
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+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science-in-the-cloud/introduction.html">
+     8.1. Introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science-in-the-cloud/the-low-code-no-code-way.html">
+     8.2. The “low code/no code” way
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../data-science-in-the-cloud/the-azure-ml-sdk-way.html">
+     8.3. Data Science in the cloud: The “Azure ML SDK” way
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../data-science-in-the-wild.html">
+   9. Data Science in the real world
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  MACHINE LEARNING BASICS
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/ml-overview.html">
+   10. Machine Learning overview
+  </a>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
+   11. Regression models for Machine Learning
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
+  <label for="toctree-checkbox-7">
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+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/regression/tools-of-the-trade.html">
+     11.1. Tools of the trade
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/regression/managing-data.html">
+     11.2. Managing data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
+     11.3. Linear and polynomial regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/regression/logistic-regression.html">
+     11.4. Logistic regression
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
+   12. Build a web app to use a Machine Learning model
+  </a>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-fundamentals/classification/getting-started-with-classification.html">
+   13. Getting started with classification
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
+  <label for="toctree-checkbox-8">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/classification/introduction-to-classification.html">
+     13.1. Introduction to classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/classification/more-classifiers.html">
+     13.2. More classifiers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/classification/yet-other-classifiers.html">
+     13.3. Yet other classifiers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
+     13.4. Applied Machine Learning : build a web app
+    </a>
+   </li>
+  </ul>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  ADVANCED MACHINE LEARNING
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
+   14. Clustering models for Machine Learning
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/clustering/introduction-to-clustering.html">
+     14.1. Introduction to clustering
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/clustering/k-means-clustering.html">
+     14.2. K-Means clustering
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
+   15. Getting started with ensemble learning
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
+  <label for="toctree-checkbox-10">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/ensemble-learning/bagging.html">
+     15.1. Bagging
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/ensemble-learning/random-forest.html">
+     15.2. Random forest
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/ensemble-learning/feature-importance.html">
+     15.3. Feature importance
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
+   16. Introduction to Gradient Boosting
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/gradient-boosting/gradient-boosting.html">
+     16.1. Gradient Boosting
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
+     16.2. Gradient boosting example
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/gradient-boosting/xgboost.html">
+     16.3. XGBoost
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
+     16.4. XGBoost + k-fold CV + Feature Importance
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/unsupervised-learning.html">
+   17. Unsupervised learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/kernel-method.html">
+   18. Kernel method
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../ml-advanced/model-selection.html">
+   19. Model selection
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  DEEP LEARNING
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/dl-overview.html">
+   20. Intro to Deep Learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/cnn.html">
+   21. Convolutional Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/gan.html">
+   22. Generative adversarial networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/rnn.html">
+   23. Recurrent Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/autoencoder.html">
+   24. Autoencoder
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/lstm.html">
+   25. Long-short term memory
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/time-series.html">
+   26. Time series
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/dqn.html">
+   27. Deep Q-learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/image-classification.html">
+   28. Image classification
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/image-segmentation.html">
+   29. Image segmentation
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/difussion-model.html">
+   30. Diffusion Model
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../deep-learning/object-detection.html">
+   31. Object detection
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  MACHINE LEARNING OPERATIONS
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../machine-learning-productionization/overview.html">
+   32. Overview
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../machine-learning-productionization/problem-framing.html">
+   33. Problem framing
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../machine-learning-productionization/data-engineering.html">
+   34. Data engineering
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../machine-learning-productionization/model-training-and-evaluation.html">
+   35. Model training &amp; evaluation
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="../../../machine-learning-productionization/model-deployment.html">
+   36. Model deployment
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  OTHERS
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../assignments/README.html">
+   37. Self-paced assignments
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
+  <label for="toctree-checkbox-12">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/set-up-env/first-assignment.html">
+     37.3. First assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/set-up-env/second-assignment.html">
+     37.4. Second assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/project-plan-template.html">
+     37.5. Project Plan​ Template
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/prerequisites/python-programming-introduction.html">
+     37.6. Python programming introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/prerequisites/python-programming-basics.html">
+     37.7. Python programming basics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/prerequisites/python-programming-advanced.html">
+     37.8. Python programming advanced
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/analyzing-text-about-data-science.html">
+     37.9. Analyzing text about Data Science
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/data-science-scenarios.html">
+     37.10. Data Science scenarios
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/write-a-data-ethics-case-study.html">
+     37.11. Write a data ethics case study
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/lines-scatters-and-bars.html">
+     37.12. Lines, scatters and bars
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/apply-your-skills.html">
+     37.13. Apply your skills
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/try-it-in-excel.html">
+     37.14. Try it in Excel
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/dive-into-the-beehive.html">
+     37.15. Dive into the beehive
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/build-your-own-custom-vis.html">
+     37.16. Build your own custom vis
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/classifying-datasets.html">
+     37.17. Classifying datasets
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/small-diabetes-study.html">
+     37.18. Small diabetes study
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/introduction-to-statistics-and-probability.html">
+     37.19. Introduction to probability and statistics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/displaying-airport-data.html">
+     37.20. Displaying airport data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/soda-profits.html">
+     37.21. Soda profits
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/analyzing-COVID-19-papers.html">
+     37.22. Analyzing COVID-19 papers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
+     37.23. Estimation of COVID-19 pandemic
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/data-processing-in-python.html">
+     37.24. Data processing in Python
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/evaluating-data-from-a-form.html">
+     37.25. Evaluating data from a form
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/data-preparation.html">
+     37.26. Data preparation
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/analyzing-data.html">
+     37.27. Analyzing data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
+     37.28. NYC taxi data in winter and summer
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/matplotlib-applied.html">
+     37.29. Matplotlib applied
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/tell-a-story.html">
+     37.35. Tell a story
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/explore-a-planetary-computer-dataset.html">
+     37.36. Explore a planetary computer dataset
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/exploring-for-anwser.html">
+     37.37. Exploring for answers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/market-research.html">
+     37.38. Market research
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
+     37.39. Low code/no code Data Science project on Azure ML
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
+     37.40. Data Science project using Azure ML SDK
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
+     37.41. Data Science in the cloud: The “Azure ML SDK” way
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/ml-overview-iris.html">
+     37.42. Machine Learning overview - assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
+     37.43. Machine Learning overview - assignment 2
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
+     37.44. Regression with Scikit-learn
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
+     37.46. Linear Regression Metrics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/linear-regression/loss-function.html">
+     37.47. Loss Function
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
+     37.50. ML logistic regression - assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
+     37.51. ML logistic regression - assignment 2
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/ml-neural-network-1.html">
+     37.52. ML neural network - Assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/regression-tools.html">
+     37.53. Regression tools
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/managing-data.html">
+     37.54. Managing data
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/exploring-visualizations.html">
+     37.55. Exploring visualizations
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/try-a-different-model.html">
+     37.56. Try a different model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/create-a-regression-model.html">
+     37.57. Create a regression model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
+     37.58. Linear and polynomial regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/retrying-some-regression.html">
+     37.59. Retrying some regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
+     37.60. Pumpkin varieties and color
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
+     37.61. Delicious asian and indian cuisines
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/explore-classification-methods.html">
+     37.62. Explore classification methods
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
+     37.63. Kernel method assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
+     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
+     37.65. Support Vector Machines (SVM) - Classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
+     37.66. Decision Trees - Intro and Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
+     37.67. Decision Trees - Classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
+     37.68. Model selection assignment 1
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
+     37.69. Learning Curve To Identify Overfit &amp; Underfit
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
+     37.70. Dropout and Batch Normalization
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
+     37.71. Lasso and Ridge Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/model-selection/regularized-linear-models.html">
+     37.72. Regularized Linear Models
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
+     37.73. Random forests intro and regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/ensemble-learning/random-forests-for-classification.html">
+     37.74. Random forests for classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
+     37.75. Beyond random forests: more ensemble models
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/ensemble-learning/decision-trees.html">
+     37.76. Decision trees
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
+     37.77. Hyperparameter tuning gradient boosting
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
+     37.78. Gradient boosting
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html">
+     37.79. Boosting with tuning
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
+     37.80. Random Forest Classifier with Feature Importance
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/machine-learning-productionization/data-engineering.html">
+     37.82. Data engineering
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html">
+     37.83. Counterintuitive Challenges in ML Debugging
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/machine-learning-productionization/debugging-in-classification.html">
+     37.84. Case Study: Debugging in Classification
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/machine-learning-productionization/debugging-in-regression.html">
+     37.85. Case Study: Debugging in Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/study-the-solvers.html">
+     37.86. Study the solvers
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/build-classification-models.html">
+     37.87. Build classification models
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/build-classification-model.html">
+     37.88. Build Classification Model
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/ml-fundamentals/parameter-play.html">
+     37.89. Parameter play
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
+     37.90. How to choose cnn architecture mnist
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
+     37.92. Sign Language Digits Classification with CNN
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html">
+     37.94. Object Recognition in Images using CNN
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
+     37.95. Intro to TensorFlow for Deep Learning
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
+     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
+     37.99. Google Stock Price Prediction RNN
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/autoencoder/autoencoder.html">
+     37.101. Intro to Autoencoders
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
+     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
+     37.103. Fun with Variational Autoencoders
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/time-series-forecasting-assignment.html">
+     37.104. Time Series Forecasting Assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/nn-for-classification-assignment.html">
+     37.106. Neural Networks for Classification with TensorFlow
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
+     37.107. NN Classify 15 Fruits Assignment
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
+     37.112. DQN On Foreign Exchange Market
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/gan/art-by-gan.html">
+     37.113. Art by gan
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/gan/gan-introduction.html">
+     37.115. Generative Adversarial Networks (GANs)
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
+     37.116. Basic classification: Classify images of clothing
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toctree-l1 has-children">
+  <a class="reference internal" href="../../../slides/introduction.html">
+   38. Slides
+  </a>
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
+   <i class="fas fa-chevron-down">
+   </i>
+  </label>
+  <ul>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/python-programming/python-programming-introduction.html">
+     38.1. Python programming introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/python-programming/python-programming-basics.html">
+     38.2. Python programming basics
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/python-programming/python-programming-advanced.html">
+     38.3. Python programming advanced
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/data-science/data-science-introduction.html">
+     38.4. Data Science introduction
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/data-science/relational-vs-non-relational-database.html">
+     38.5. Relational vs. non-relational database
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/data-science/numpy-and-pandas.html">
+     38.6. NumPy and Pandas
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/data-science/data-visualization.html">
+     38.7. Data visualization
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/data-science/data-science-lifecycle.html">
+     38.8. Data Science lifecycle
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/data-science/data-science-in-the-cloud.html">
+     38.9. Data Science in the cloud
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/data-science/data-science-in-real-world.html">
+     38.10. Data Science in real world
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-fundamentals/ml-overview.html">
+     38.11. Machine Learning overview
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-fundamentals/linear-regression.html">
+     38.12. Linear Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-fundamentals/logistic-regression.html">
+     38.13. Logistic Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-fundamentals/logistic-regression-condensed.html">
+     38.14. Logistic Regression
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-fundamentals/neural-network.html">
+     38.15. Neural Network
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-fundamentals/build-an-ml-web-app.html">
+     38.16. Build an machine learning web application
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-advanced/unsupervised-learning.html">
+     38.17. Unsupervised learning
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-advanced/kernel-method.html">
+     38.18. Kernel method
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/ml-advanced/model-selection.html">
+     38.19. Model Selection
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/deep-learning/cnn.html">
+     38.20. Convolutional Neural Network
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../../slides/deep-learning/gan.html">
+     38.21. Generative Adversarial Network
+    </a>
+   </li>
+  </ul>
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+        <i class="fas fa-list"></i> Contents
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+        <ul class="visible nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#introducing-pandas-objects">
+   5.4.1.1. Introducing Pandas objects
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#overview">
+   5.4.1.2. Overview
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#series">
+     5.4.1.2.1. Series
+    </a>
+    <ul class="nav section-nav flex-column">
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#create-a-series">
+       5.4.1.2.1.1. Create a Series
+      </a>
+      <ul class="nav section-nav flex-column">
+       <li class="toc-h5 nav-item toc-entry">
+        <a class="reference internal nav-link" href="#from-ndarray">
+         5.4.1.2.1.1.1. From ndarray
+        </a>
+       </li>
+       <li class="toc-h5 nav-item toc-entry">
+        <a class="reference internal nav-link" href="#from-dict">
+         5.4.1.2.1.1.2. From dict
+        </a>
+       </li>
+       <li class="toc-h5 nav-item toc-entry">
+        <a class="reference internal nav-link" href="#from-scalar-value">
+         5.4.1.2.1.1.3. From scalar value
+        </a>
+       </li>
+      </ul>
+     </li>
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#series-is-ndarray-like">
+       5.4.1.2.1.2. Series is ndarray-like
+      </a>
+     </li>
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#series-is-dict-like">
+       5.4.1.2.1.3. Series is dict-like
+      </a>
+     </li>
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#vectorized-operations-and-label-alignment-with-series">
+       5.4.1.2.1.4. Vectorized operations and label alignment with Series
+      </a>
+     </li>
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#name-attribute">
+       5.4.1.2.1.5. Name attribute
+      </a>
+     </li>
+    </ul>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#dataframe">
+     5.4.1.2.2. DataFrame
+    </a>
+    <ul class="nav section-nav flex-column">
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#create-a-dataframe">
+       5.4.1.2.2.1. Create a Dataframe
+      </a>
+      <ul class="nav section-nav flex-column">
+       <li class="toc-h5 nav-item toc-entry">
+        <a class="reference internal nav-link" href="#from-dict-of-series-or-dicts">
+         5.4.1.2.2.1.1. From dict of
+         <code class="docutils literal notranslate">
+          <span class="pre">
+           Series
+          </span>
+         </code>
+         or dicts
+        </a>
+       </li>
+       <li class="toc-h5 nav-item toc-entry">
+        <a class="reference internal nav-link" href="#from-dict-of-ndarrays-lists">
+         5.4.1.2.2.1.2. From dict of ndarrays / lists
+        </a>
+       </li>
+       <li class="toc-h5 nav-item toc-entry">
+        <a class="reference internal nav-link" href="#from-structured-or-record-array">
+         5.4.1.2.2.1.3. From structured or record array
+        </a>
+       </li>
+       <li class="toc-h5 nav-item toc-entry">
+        <a class="reference internal nav-link" href="#from-a-list-of-dicts">
+         5.4.1.2.2.1.4. From a list of dicts
+        </a>
+       </li>
+       <li class="toc-h5 nav-item toc-entry">
+        <a class="reference internal nav-link" href="#from-a-dict-of-tuples">
+         5.4.1.2.2.1.5. From a dict of tuples
+        </a>
+       </li>
+       <li class="toc-h5 nav-item toc-entry">
+        <a class="reference internal nav-link" href="#from-a-series">
+         5.4.1.2.2.1.6. From a Series
+        </a>
+       </li>
+       <li class="toc-h5 nav-item toc-entry">
+        <a class="reference internal nav-link" href="#from-a-list-of-namedtuples">
+         5.4.1.2.2.1.7. From a list of namedtuples
+        </a>
+       </li>
+       <li class="toc-h5 nav-item toc-entry">
+        <a class="reference internal nav-link" href="#from-a-list-of-dataclasses">
+         5.4.1.2.2.1.8. From a list of dataclasses
+        </a>
+       </li>
+      </ul>
+     </li>
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#column-selection-addition-deletion">
+       5.4.1.2.2.2. Column selection, addition, deletion
+      </a>
+     </li>
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#assigning-new-columns-in-method-chains">
+       5.4.1.2.2.3. Assigning new columns in method chains
+      </a>
+     </li>
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#indexing-selection">
+       5.4.1.2.2.4. Indexing / selection
+      </a>
+     </li>
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#data-alignment-and-arithmetic">
+       5.4.1.2.2.5. Data alignment and arithmetic
+      </a>
+     </li>
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#transposing">
+       5.4.1.2.2.6. Transposing
+      </a>
+     </li>
+    </ul>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#data-indexing-and-selection">
+   5.4.1.3. Data indexing and selection
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#different-choices-for-indexing">
+     5.4.1.3.1. Different choices for indexing
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#basics">
+     5.4.1.3.2. Basics
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#attribute-access">
+     5.4.1.3.3. Attribute access
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#slicing-ranges">
+     5.4.1.3.4. Slicing ranges
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#acknowledgments">
+   5.4.1.4. Acknowledgments
+  </a>
+ </li>
+</ul>
+
+    </nav>
+</div>
+    </div>
+    <div class="article row">
+        <div class="col pl-md-3 pl-lg-5 content-container">
+            <!-- Table of contents that is only displayed when printing the page -->
+            <div id="jb-print-docs-body" class="onlyprint">
+                <h1>Introduction and Data Structures</h1>
+                <!-- Table of contents -->
+                <div id="print-main-content">
+                    <div id="jb-print-toc">
+                        
+                        <div>
+                            <h2> Contents </h2>
+                        </div>
+                        <nav aria-label="Page">
+                            <ul class="visible nav section-nav flex-column">
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#introducing-pandas-objects">
+   5.4.1.1. Introducing Pandas objects
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#overview">
+   5.4.1.2. Overview
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#series">
+     5.4.1.2.1. Series
+    </a>
+    <ul class="nav section-nav flex-column">
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#create-a-series">
+       5.4.1.2.1.1. Create a Series
+      </a>
+      <ul class="nav section-nav flex-column">
+       <li class="toc-h5 nav-item toc-entry">
+        <a class="reference internal nav-link" href="#from-ndarray">
+         5.4.1.2.1.1.1. From ndarray
+        </a>
+       </li>
+       <li class="toc-h5 nav-item toc-entry">
+        <a class="reference internal nav-link" href="#from-dict">
+         5.4.1.2.1.1.2. From dict
+        </a>
+       </li>
+       <li class="toc-h5 nav-item toc-entry">
+        <a class="reference internal nav-link" href="#from-scalar-value">
+         5.4.1.2.1.1.3. From scalar value
+        </a>
+       </li>
+      </ul>
+     </li>
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#series-is-ndarray-like">
+       5.4.1.2.1.2. Series is ndarray-like
+      </a>
+     </li>
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#series-is-dict-like">
+       5.4.1.2.1.3. Series is dict-like
+      </a>
+     </li>
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#vectorized-operations-and-label-alignment-with-series">
+       5.4.1.2.1.4. Vectorized operations and label alignment with Series
+      </a>
+     </li>
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#name-attribute">
+       5.4.1.2.1.5. Name attribute
+      </a>
+     </li>
+    </ul>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#dataframe">
+     5.4.1.2.2. DataFrame
+    </a>
+    <ul class="nav section-nav flex-column">
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#create-a-dataframe">
+       5.4.1.2.2.1. Create a Dataframe
+      </a>
+      <ul class="nav section-nav flex-column">
+       <li class="toc-h5 nav-item toc-entry">
+        <a class="reference internal nav-link" href="#from-dict-of-series-or-dicts">
+         5.4.1.2.2.1.1. From dict of
+         <code class="docutils literal notranslate">
+          <span class="pre">
+           Series
+          </span>
+         </code>
+         or dicts
+        </a>
+       </li>
+       <li class="toc-h5 nav-item toc-entry">
+        <a class="reference internal nav-link" href="#from-dict-of-ndarrays-lists">
+         5.4.1.2.2.1.2. From dict of ndarrays / lists
+        </a>
+       </li>
+       <li class="toc-h5 nav-item toc-entry">
+        <a class="reference internal nav-link" href="#from-structured-or-record-array">
+         5.4.1.2.2.1.3. From structured or record array
+        </a>
+       </li>
+       <li class="toc-h5 nav-item toc-entry">
+        <a class="reference internal nav-link" href="#from-a-list-of-dicts">
+         5.4.1.2.2.1.4. From a list of dicts
+        </a>
+       </li>
+       <li class="toc-h5 nav-item toc-entry">
+        <a class="reference internal nav-link" href="#from-a-dict-of-tuples">
+         5.4.1.2.2.1.5. From a dict of tuples
+        </a>
+       </li>
+       <li class="toc-h5 nav-item toc-entry">
+        <a class="reference internal nav-link" href="#from-a-series">
+         5.4.1.2.2.1.6. From a Series
+        </a>
+       </li>
+       <li class="toc-h5 nav-item toc-entry">
+        <a class="reference internal nav-link" href="#from-a-list-of-namedtuples">
+         5.4.1.2.2.1.7. From a list of namedtuples
+        </a>
+       </li>
+       <li class="toc-h5 nav-item toc-entry">
+        <a class="reference internal nav-link" href="#from-a-list-of-dataclasses">
+         5.4.1.2.2.1.8. From a list of dataclasses
+        </a>
+       </li>
+      </ul>
+     </li>
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#column-selection-addition-deletion">
+       5.4.1.2.2.2. Column selection, addition, deletion
+      </a>
+     </li>
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#assigning-new-columns-in-method-chains">
+       5.4.1.2.2.3. Assigning new columns in method chains
+      </a>
+     </li>
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#indexing-selection">
+       5.4.1.2.2.4. Indexing / selection
+      </a>
+     </li>
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#data-alignment-and-arithmetic">
+       5.4.1.2.2.5. Data alignment and arithmetic
+      </a>
+     </li>
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#transposing">
+       5.4.1.2.2.6. Transposing
+      </a>
+     </li>
+    </ul>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#data-indexing-and-selection">
+   5.4.1.3. Data indexing and selection
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#different-choices-for-indexing">
+     5.4.1.3.1. Different choices for indexing
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#basics">
+     5.4.1.3.2. Basics
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#attribute-access">
+     5.4.1.3.3. Attribute access
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#slicing-ranges">
+     5.4.1.3.4. Slicing ranges
+    </a>
+   </li>
+  </ul>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#acknowledgments">
+   5.4.1.4. Acknowledgments
+  </a>
+ </li>
+</ul>
+
+                        </nav>
+                    </div>
+                </div>
+            </div>
+            <main id="main-content" role="main">
+                
+              <div>
+                
+  <section class="tex2jax_ignore mathjax_ignore" id="introduction-and-data-structures">
+<h1><span class="section-number">5.4.1. </span>Introduction and Data Structures<a class="headerlink" href="#introduction-and-data-structures" title="Permalink to this headline">#</a></h1>
+<p>Pandas is a fast, powerful, flexible and easy-to-use open-source data analysis and manipulation tool, built on top of the Python programming language.</p>
+<section id="introducing-pandas-objects">
+<h2><span class="section-number">5.4.1.1. </span>Introducing Pandas objects<a class="headerlink" href="#introducing-pandas-objects" title="Permalink to this headline">#</a></h2>
+<p>In 3 sections, we’ll start with a quick, non-comprehensive overview of the fundamental data structures in Pandas to get you started. The fundamental behavior about data types, indexing, axis labeling, and alignment apply across all of the objects.</p>
+</section>
+<section id="overview">
+<h2><span class="section-number">5.4.1.2. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h2>
+<p>In this section, we’ll introduce two data structure of pandas and the basic concept of data indexing and selection.</p>
+<p>To get started, import NumPy and load Pandas into your namespace:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Install the necessary dependencies</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="o">!{</span>sys.executable<span class="o">}</span><span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>--quiet<span class="w"> </span>jupyterlab_myst<span class="w"> </span>ipython
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+</pre></div>
+</div>
+</div>
+</div>
+<section id="series">
+<h3><span class="section-number">5.4.1.2.1. </span>Series<a class="headerlink" href="#series" title="Permalink to this headline">#</a></h3>
+<p><code class="docutils literal notranslate"><span class="pre">Series</span></code> is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). The axis labels are collectively referred to as the <strong>index</strong>. The basic method to create a <code class="docutils literal notranslate"><span class="pre">Series</span></code> is to call:</p>
+<p><code class="docutils literal notranslate"><span class="pre">s</span> <span class="pre">=</span> <span class="pre">pd.Series(data,</span> <span class="pre">index=index)</span></code></p>
+<p>Here, <code class="docutils literal notranslate"><span class="pre">data</span></code> can be many different things:</p>
+<ul class="simple">
+<li><p>a Python dict</p></li>
+<li><p>an ndarray</p></li>
+<li><p>a scalar value (like 5)</p></li>
+</ul>
+<p>The passed <strong>index</strong> is a list of axis labels. Thus, this separates into a few cases depending on what the <strong>data is</strong>:</p>
+<section id="create-a-series">
+<h4><span class="section-number">5.4.1.2.1.1. </span>Create a Series<a class="headerlink" href="#create-a-series" title="Permalink to this headline">#</a></h4>
+<section id="from-ndarray">
+<h5><span class="section-number">5.4.1.2.1.1.1. </span>From ndarray<a class="headerlink" href="#from-ndarray" title="Permalink to this headline">#</a></h5>
+<p>If <code class="docutils literal notranslate"><span class="pre">data</span></code> is an ndarray, <strong>index</strong> must be the same length as the <strong>data</strong>. If no index is passed, one will be created having values <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">...,</span> <span class="pre">len(data)</span> <span class="pre">-</span> <span class="pre">1]</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">),</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">,</span> <span class="s2">&quot;d&quot;</span><span class="p">,</span> <span class="s2">&quot;e&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    0.142361
+b    0.407910
+c   -0.894226
+d    1.311313
+e    0.710528
+dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="o">.</span><span class="n">index</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Index([&#39;a&#39;, &#39;b&#39;, &#39;c&#39;, &#39;d&#39;, &#39;e&#39;], dtype=&#39;object&#39;)
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    0.131905
+1   -0.703499
+2    1.530060
+3   -0.073598
+4   -0.892724
+dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Pandas supports non-unique index values. If an operation that does not support duplicate index values is attempted, an exception will be raised at that time.</p>
+</div>
+</section>
+<section id="from-dict">
+<h5><span class="section-number">5.4.1.2.1.1.2. </span>From dict<a class="headerlink" href="#from-dict" title="Permalink to this headline">#</a></h5>
+<p><code class="docutils literal notranslate"><span class="pre">Series</span></code> can be instantiated from dicts:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">d</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;b&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;a&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">}</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>b    1
+a    0
+c    2
+dtype: int64
+</pre></div>
+</div>
+</div>
+</div>
+<p>If an index is passed, the values in data corresponding to the labels in the index will be pulled out.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">d</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;a&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">:</span> <span class="mf">2.0</span><span class="p">}</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    0.0
+b    1.0
+c    2.0
+dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">,</span> <span class="s2">&quot;d&quot;</span><span class="p">,</span> <span class="s2">&quot;a&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>b    1.0
+c    2.0
+d    NaN
+a    0.0
+dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>NaN (not a number) is the standard missing data marker used in Pandas.</p>
+</div>
+</section>
+<section id="from-scalar-value">
+<h5><span class="section-number">5.4.1.2.1.1.3. </span>From scalar value<a class="headerlink" href="#from-scalar-value" title="Permalink to this headline">#</a></h5>
+<p>If <code class="docutils literal notranslate"><span class="pre">data</span></code> is a scalar value, an index must be provided. The value will be repeated to match the length of <strong>index</strong>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="mf">5.0</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">,</span> <span class="s2">&quot;d&quot;</span><span class="p">,</span> <span class="s2">&quot;e&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    5.0
+b    5.0
+c    5.0
+d    5.0
+e    5.0
+dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+<section id="series-is-ndarray-like">
+<h4><span class="section-number">5.4.1.2.1.2. </span>Series is ndarray-like<a class="headerlink" href="#series-is-ndarray-like" title="Permalink to this headline">#</a></h4>
+<p><code class="docutils literal notranslate"><span class="pre">Series</span></code> acts very similarly to a <code class="docutils literal notranslate"><span class="pre">ndarray</span></code> and is a valid argument to most NumPy functions. However, operations such as slicing will also slice the index.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.14236085563166023
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    0.142361
+b    0.407910
+c   -0.894226
+dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="p">[</span><span class="n">s</span> <span class="o">&gt;</span> <span class="n">s</span><span class="o">.</span><span class="n">median</span><span class="p">()]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>d    1.311313
+e    0.710528
+dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="p">[[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>e    0.710528
+d    1.311313
+b    0.407910
+dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    1.152993
+b    1.503672
+c    0.408924
+d    3.711042
+e    2.035066
+dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<p>Like a NumPy array, a Pandas Series has a single <code class="docutils literal notranslate"><span class="pre">dtype</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="o">.</span><span class="n">dtype</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>dtype(&#39;float64&#39;)
+</pre></div>
+</div>
+</div>
+</div>
+<p>If you need the actual array backing a <code class="docutils literal notranslate"><span class="pre">Series</span></code>, use <code class="docutils literal notranslate"><span class="pre">Series.array</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="o">.</span><span class="n">array</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;PandasArray&gt;
+[0.14236085563166023,  0.4079103331875502, -0.8942262958393035,
+  1.3113126514940179,  0.7105280060827549]
+Length: 5, dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<p>While <code class="docutils literal notranslate"><span class="pre">Series</span></code> is ndarray-like, if you need an actual ndarray, then use <code class="docutils literal notranslate"><span class="pre">Series.to_numpy()</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([ 0.14236086,  0.40791033, -0.8942263 ,  1.31131265,  0.71052801])
+</pre></div>
+</div>
+</div>
+</div>
+<p>Even if the <code class="docutils literal notranslate"><span class="pre">Series</span></code> is backed by an <code class="docutils literal notranslate"><span class="pre">ExtensionArray</span></code>, <code class="docutils literal notranslate"><span class="pre">Series.to_numpy()</span></code> will return a NumPy ndarray.</p>
+</section>
+<section id="series-is-dict-like">
+<h4><span class="section-number">5.4.1.2.1.3. </span>Series is dict-like<a class="headerlink" href="#series-is-dict-like" title="Permalink to this headline">#</a></h4>
+<p>A <code class="docutils literal notranslate"><span class="pre">Series</span></code> is also like a fixed-size dict in that you can get and set values by index label:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.14236085563166023
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="p">[</span><span class="s2">&quot;e&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">12.0</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a     0.142361
+b     0.407910
+c    -0.894226
+d     1.311313
+e    12.000000
+dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="s2">&quot;e&quot;</span> <span class="ow">in</span> <span class="n">s</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>True
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="s2">&quot;f&quot;</span> <span class="ow">in</span> <span class="n">s</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>False
+</pre></div>
+</div>
+</div>
+</div>
+<p>If a label is not contained in the index, an exception is raised:</p>
+<div class="cell tag_raises-exception docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="p">[</span><span class="s2">&quot;f&quot;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">KeyError</span><span class="g g-Whitespace">                                  </span>Traceback (most recent call last)
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\indexes\base.py:3802,</span> in <span class="ni">Index.get_loc</span><span class="nt">(self, key, method, tolerance)</span>
+<span class="g g-Whitespace">   </span><span class="mi">3801</span> <span class="k">try</span><span class="p">:</span>
+<span class="ne">-&gt; </span><span class="mi">3802</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_engine</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="n">casted_key</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">3803</span> <span class="k">except</span> <span class="ne">KeyError</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\_libs\index.pyx:138,</span> in <span class="ni">pandas._libs.index.IndexEngine.get_loc</span><span class="nt">()</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\_libs\index.pyx:165,</span> in <span class="ni">pandas._libs.index.IndexEngine.get_loc</span><span class="nt">()</span>
+
+<span class="nn">File pandas\_libs\hashtable_class_helper.pxi:5745,</span> in <span class="ni">pandas._libs.hashtable.PyObjectHashTable.get_item</span><span class="nt">()</span>
+
+<span class="nn">File pandas\_libs\hashtable_class_helper.pxi:5753,</span> in <span class="ni">pandas._libs.hashtable.PyObjectHashTable.get_item</span><span class="nt">()</span>
+
+<span class="ne">KeyError</span>: &#39;f&#39;
+
+<span class="n">The</span> <span class="n">above</span> <span class="n">exception</span> <span class="n">was</span> <span class="n">the</span> <span class="n">direct</span> <span class="n">cause</span> <span class="n">of</span> <span class="n">the</span> <span class="n">following</span> <span class="n">exception</span><span class="p">:</span>
+
+<span class="ne">KeyError</span><span class="g g-Whitespace">                                  </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">35</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
+<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">s</span><span class="p">[</span><span class="s2">&quot;f&quot;</span><span class="p">]</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\series.py:981,</span> in <span class="ni">Series.__getitem__</span><span class="nt">(self, key)</span>
+<span class="g g-Whitespace">    </span><span class="mi">978</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_values</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+<span class="g g-Whitespace">    </span><span class="mi">980</span> <span class="k">elif</span> <span class="n">key_is_scalar</span><span class="p">:</span>
+<span class="ne">--&gt; </span><span class="mi">981</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_value</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">983</span> <span class="k">if</span> <span class="n">is_hashable</span><span class="p">(</span><span class="n">key</span><span class="p">):</span>
+<span class="g g-Whitespace">    </span><span class="mi">984</span>     <span class="c1"># Otherwise index.get_value will raise InvalidIndexError</span>
+<span class="g g-Whitespace">    </span><span class="mi">985</span>     <span class="k">try</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">986</span>         <span class="c1"># For labels that don&#39;t resolve as scalars like tuples and frozensets</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\series.py:1089,</span> in <span class="ni">Series._get_value</span><span class="nt">(self, label, takeable)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1086</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_values</span><span class="p">[</span><span class="n">label</span><span class="p">]</span>
+<span class="g g-Whitespace">   </span><span class="mi">1088</span> <span class="c1"># Similar to Index.get_value, but we do not fall back to positional</span>
+<span class="ne">-&gt; </span><span class="mi">1089</span> <span class="n">loc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="n">label</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1090</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">_get_values_for_loc</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loc</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span>
+
+<span class="nn">File F:\anaconda\Lib\site-packages\pandas\core\indexes\base.py:3804,</span> in <span class="ni">Index.get_loc</span><span class="nt">(self, key, method, tolerance)</span>
+<span class="g g-Whitespace">   </span><span class="mi">3802</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_engine</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="n">casted_key</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">3803</span> <span class="k">except</span> <span class="ne">KeyError</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+<span class="ne">-&gt; </span><span class="mi">3804</span>     <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="n">key</span><span class="p">)</span> <span class="kn">from</span> <span class="nn">err</span>
+<span class="g g-Whitespace">   </span><span class="mi">3805</span> <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
+<span class="g g-Whitespace">   </span><span class="mi">3806</span>     <span class="c1"># If we have a listlike key, _check_indexing_error will raise</span>
+<span class="g g-Whitespace">   </span><span class="mi">3807</span>     <span class="c1">#  InvalidIndexError. Otherwise we fall through and re-raise</span>
+<span class="g g-Whitespace">   </span><span class="mi">3808</span>     <span class="c1">#  the TypeError.</span>
+<span class="g g-Whitespace">   </span><span class="mi">3809</span>     <span class="bp">self</span><span class="o">.</span><span class="n">_check_indexing_error</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+
+<span class="ne">KeyError</span>: &#39;f&#39;
+</pre></div>
+</div>
+</div>
+</div>
+<p>Using the <code class="docutils literal notranslate"><span class="pre">Series.get()</span></code> method, a missing label will return None or specified default:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;f&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;f&quot;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>nan
+</pre></div>
+</div>
+</div>
+</div>
+<p>These labels can also be accessed by <code class="docutils literal notranslate"><span class="pre">attribute</span></code>.</p>
+</section>
+<section id="vectorized-operations-and-label-alignment-with-series">
+<h4><span class="section-number">5.4.1.2.1.4. </span>Vectorized operations and label alignment with Series<a class="headerlink" href="#vectorized-operations-and-label-alignment-with-series" title="Permalink to this headline">#</a></h4>
+<p>When working with raw NumPy arrays, looping through value-by-value is usually not necessary. The same is true when working with <code class="docutils literal notranslate"><span class="pre">Series</span></code> in Pandas. <code class="docutils literal notranslate"><span class="pre">Series</span></code> can also be passed into most NumPy methods expecting an ndarray.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">+</span> <span class="n">s</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a     0.284722
+b     0.815821
+c    -1.788453
+d     2.622625
+e    24.000000
+dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">*</span> <span class="mi">2</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a     0.284722
+b     0.815821
+c    -1.788453
+d     2.622625
+e    24.000000
+dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a         1.152993
+b         1.503672
+c         0.408924
+d         3.711042
+e    162754.791419
+dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<p>A key difference between <code class="docutils literal notranslate"><span class="pre">Series</span></code> and ndarray is that operations between <code class="docutils literal notranslate"><span class="pre">Series</span></code> automatically align the data based on the label. Thus, you can write computations without giving consideration to whether the <code class="docutils literal notranslate"><span class="pre">Series</span></code> involved have the same labels.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">+</span> <span class="n">s</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a         NaN
+b    0.815821
+c   -1.788453
+d    2.622625
+e         NaN
+dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<p>The result of an operation between unaligned <code class="docutils literal notranslate"><span class="pre">Series</span></code> will have the <strong>union</strong> of the indexes involved. If a label is not found in one <code class="docutils literal notranslate"><span class="pre">Series</span></code> or the other, the result will be marked as missing <code class="docutils literal notranslate"><span class="pre">NaN</span></code>. Being able to write code without doing any explicit data alignment grants immense freedom and flexibility in interactive data analysis and research. The integrated data alignment features of the Pandas data structures set Pandas apart from the majority of related tools for working with labeled data.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>In general, we chose to make the default result of operations between differently indexed objects yield the <strong>union</strong> of the indexes in order to avoid loss of information. Having an index label, though the data is missing, is typically important information as part of a computation. You of course have the option of dropping labels with missing data via the <code class="docutils literal notranslate"><span class="pre">dropna</span></code> function.</p>
+</div>
+</section>
+<section id="name-attribute">
+<h4><span class="section-number">5.4.1.2.1.5. </span>Name attribute<a class="headerlink" href="#name-attribute" title="Permalink to this headline">#</a></h4>
+<p><code class="docutils literal notranslate"><span class="pre">Series</span></code> also has a <code class="docutils literal notranslate"><span class="pre">name</span></code> attribute:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;something&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    0.836867
+1   -0.187063
+2    0.180988
+3    0.434802
+4    1.175946
+Name: something, dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="o">.</span><span class="n">name</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;something&#39;
+</pre></div>
+</div>
+</div>
+</div>
+<p>The <code class="docutils literal notranslate"><span class="pre">Series</span></code> <code class="docutils literal notranslate"><span class="pre">name</span></code> can be assigned automatically in many cases, in particular, when selecting a single column from a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>, the <code class="docutils literal notranslate"><span class="pre">name</span></code> will be assigned the column label.</p>
+<p>You can rename a <code class="docutils literal notranslate"><span class="pre">Series</span></code> with the <code class="docutils literal notranslate"><span class="pre">pandas.Series.rename()</span></code> method.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s2</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="s2">&quot;different&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s2</span><span class="o">.</span><span class="n">name</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;different&#39;
+</pre></div>
+</div>
+</div>
+</div>
+<p>Note that <code class="docutils literal notranslate"><span class="pre">s</span></code> and <code class="docutils literal notranslate"><span class="pre">s2</span></code> refer to different objects.</p>
+</section>
+</section>
+<section id="dataframe">
+<h3><span class="section-number">5.4.1.2.2. </span>DataFrame<a class="headerlink" href="#dataframe" title="Permalink to this headline">#</a></h3>
+<p><code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table, or a <code class="docutils literal notranslate"><span class="pre">dict</span></code> of <code class="docutils literal notranslate"><span class="pre">Series</span></code> objects. It is generally the most commonly used Pandas object. Like <code class="docutils literal notranslate"><span class="pre">Series</span></code>, <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> accepts many different kinds of input:</p>
+<ul class="simple">
+<li><p>Dict of 1D ndarrays, lists, dicts, or <code class="docutils literal notranslate"><span class="pre">Series</span></code></p></li>
+<li><p>2-D <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></p></li>
+<li><p>Structured or record ndarray</p></li>
+<li><p>A <code class="docutils literal notranslate"><span class="pre">Series</span></code></p></li>
+<li><p>Another <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code></p></li>
+</ul>
+<p>Along with the data, you can optionally pass <strong>index</strong> (row labels) and <strong>columns</strong> (column labels) arguments. If you pass an index and / or columns, you are guaranteeing the index and / or columns of the resulting <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>. Thus, a <code class="docutils literal notranslate"><span class="pre">dict</span></code> of Series plus a specific index will discard all data not matching up to the passed index.</p>
+<p>If axis labels are not passed, they will be constructed from the input data based on common sense rules.</p>
+<section id="create-a-dataframe">
+<h4><span class="section-number">5.4.1.2.2.1. </span>Create a Dataframe<a class="headerlink" href="#create-a-dataframe" title="Permalink to this headline">#</a></h4>
+<section id="from-dict-of-series-or-dicts">
+<h5><span class="section-number">5.4.1.2.2.1.1. </span>From dict of <code class="docutils literal notranslate"><span class="pre">Series</span></code> or dicts<a class="headerlink" href="#from-dict-of-series-or-dicts" title="Permalink to this headline">#</a></h5>
+<p>The resulting <strong>index</strong> will be the <strong>union</strong> of the indexes of the various Series. If there are any nested dicts, these will first be converted to Series. If no columns are passed, the columns will be the ordered list of <code class="docutils literal notranslate"><span class="pre">dict</span></code> keys.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">d</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;one&quot;</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">]),</span>
+    <span class="s2">&quot;two&quot;</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">,</span> <span class="s2">&quot;d&quot;</span><span class="p">]),</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>one</th>
+      <th>two</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>a</th>
+      <td>1.0</td>
+      <td>1.0</td>
+    </tr>
+    <tr>
+      <th>b</th>
+      <td>2.0</td>
+      <td>2.0</td>
+    </tr>
+    <tr>
+      <th>c</th>
+      <td>3.0</td>
+      <td>3.0</td>
+    </tr>
+    <tr>
+      <th>d</th>
+      <td>NaN</td>
+      <td>4.0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;d&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;a&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>one</th>
+      <th>two</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>d</th>
+      <td>NaN</td>
+      <td>4.0</td>
+    </tr>
+    <tr>
+      <th>b</th>
+      <td>2.0</td>
+      <td>2.0</td>
+    </tr>
+    <tr>
+      <th>a</th>
+      <td>1.0</td>
+      <td>1.0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;d&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;a&quot;</span><span class="p">],</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;two&quot;</span><span class="p">,</span> <span class="s2">&quot;three&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>two</th>
+      <th>three</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>d</th>
+      <td>4.0</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>b</th>
+      <td>2.0</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>a</th>
+      <td>1.0</td>
+      <td>NaN</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>The row and column labels can be accessed respectively by accessing the <strong>index</strong> and <strong>columns</strong> attributes:</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>When a particular set of columns is passed along with a dict of data, the passed columns override the keys in the dict.</p>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">index</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Index([&#39;a&#39;, &#39;b&#39;, &#39;c&#39;, &#39;d&#39;], dtype=&#39;object&#39;)
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">columns</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Index([&#39;one&#39;, &#39;two&#39;], dtype=&#39;object&#39;)
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="from-dict-of-ndarrays-lists">
+<h5><span class="section-number">5.4.1.2.2.1.2. </span>From dict of ndarrays / lists<a class="headerlink" href="#from-dict-of-ndarrays-lists" title="Permalink to this headline">#</a></h5>
+<p>The ndarrays must all be the same length. If an index is passed, it must also be the same length as the arrays. If no index is passed, the result will be <code class="docutils literal notranslate"><span class="pre">range(n)</span></code>, where <code class="docutils literal notranslate"><span class="pre">n</span></code> is the array length.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">d</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;one&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">],</span> <span class="s2">&quot;two&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]}</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>one</th>
+      <th>two</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1.0</td>
+      <td>4.0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>2.0</td>
+      <td>3.0</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>3.0</td>
+      <td>2.0</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>4.0</td>
+      <td>1.0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">,</span> <span class="s2">&quot;d&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>one</th>
+      <th>two</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>a</th>
+      <td>1.0</td>
+      <td>4.0</td>
+    </tr>
+    <tr>
+      <th>b</th>
+      <td>2.0</td>
+      <td>3.0</td>
+    </tr>
+    <tr>
+      <th>c</th>
+      <td>3.0</td>
+      <td>2.0</td>
+    </tr>
+    <tr>
+      <th>d</th>
+      <td>4.0</td>
+      <td>1.0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+</section>
+<section id="from-structured-or-record-array">
+<h5><span class="section-number">5.4.1.2.2.1.3. </span>From structured or record array<a class="headerlink" href="#from-structured-or-record-array" title="Permalink to this headline">#</a></h5>
+<p>This case is handled identically to a dict of arrays.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;i4&quot;</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;B&quot;</span><span class="p">,</span> <span class="s2">&quot;f4&quot;</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;C&quot;</span><span class="p">,</span> <span class="s2">&quot;a10&quot;</span><span class="p">)])</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data</span><span class="p">[:]</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="s2">&quot;Hello&quot;</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="s2">&quot;World&quot;</span><span class="p">)]</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>2.0</td>
+      <td>b'Hello'</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>2</td>
+      <td>3.0</td>
+      <td>b'World'</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;first&quot;</span><span class="p">,</span> <span class="s2">&quot;second&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>first</th>
+      <td>1</td>
+      <td>2.0</td>
+      <td>b'Hello'</td>
+    </tr>
+    <tr>
+      <th>second</th>
+      <td>2</td>
+      <td>3.0</td>
+      <td>b'World'</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">,</span> <span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>C</th>
+      <th>A</th>
+      <th>B</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>b'Hello'</td>
+      <td>1</td>
+      <td>2.0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>b'World'</td>
+      <td>2</td>
+      <td>3.0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>DataFrame is not intended to work exactly like a 2-dimensional NumPy ndarray.</p>
+</div>
+</section>
+<section id="from-a-list-of-dicts">
+<h5><span class="section-number">5.4.1.2.2.1.4. </span>From a list of dicts<a class="headerlink" href="#from-a-list-of-dicts" title="Permalink to this headline">#</a></h5>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data2</span> <span class="o">=</span> <span class="p">[{</span><span class="s2">&quot;a&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">},</span> <span class="p">{</span><span class="s2">&quot;a&quot;</span><span class="p">:</span> <span class="mi">5</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">:</span> <span class="mi">20</span><span class="p">}]</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data2</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>a</th>
+      <th>b</th>
+      <th>c</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>2</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>5</td>
+      <td>10</td>
+      <td>20.0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data2</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;first&quot;</span><span class="p">,</span> <span class="s2">&quot;second&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>a</th>
+      <th>b</th>
+      <th>c</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>first</th>
+      <td>1</td>
+      <td>2</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>second</th>
+      <td>5</td>
+      <td>10</td>
+      <td>20.0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data2</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>a</th>
+      <th>b</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>5</td>
+      <td>10</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+</section>
+<section id="from-a-dict-of-tuples">
+<h5><span class="section-number">5.4.1.2.2.1.5. </span>From a dict of tuples<a class="headerlink" href="#from-a-dict-of-tuples" title="Permalink to this headline">#</a></h5>
+<p>You can automatically create a MultiIndexed frame by passing a tuples dictionary.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
+    <span class="p">{</span>
+        <span class="p">(</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">):</span> <span class="p">{(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">):</span> <span class="mi">1</span><span class="p">,</span> <span class="p">(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">):</span> <span class="mi">2</span><span class="p">},</span>
+        <span class="p">(</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;a&quot;</span><span class="p">):</span> <span class="p">{(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">):</span> <span class="mi">3</span><span class="p">,</span> <span class="p">(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">):</span> <span class="mi">4</span><span class="p">},</span>
+        <span class="p">(</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">):</span> <span class="p">{(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">):</span> <span class="mi">5</span><span class="p">,</span> <span class="p">(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">):</span> <span class="mi">6</span><span class="p">},</span>
+        <span class="p">(</span><span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;a&quot;</span><span class="p">):</span> <span class="p">{(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">):</span> <span class="mi">7</span><span class="p">,</span> <span class="p">(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">):</span> <span class="mi">8</span><span class="p">},</span>
+        <span class="p">(</span><span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">):</span> <span class="p">{(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;D&quot;</span><span class="p">):</span> <span class="mi">9</span><span class="p">,</span> <span class="p">(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">):</span> <span class="mi">10</span><span class="p">},</span>
+    <span class="p">}</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead tr th {
+        text-align: left;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr>
+      <th></th>
+      <th></th>
+      <th colspan="3" halign="left">a</th>
+      <th colspan="2" halign="left">b</th>
+    </tr>
+    <tr>
+      <th></th>
+      <th></th>
+      <th>b</th>
+      <th>a</th>
+      <th>c</th>
+      <th>a</th>
+      <th>b</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th rowspan="3" valign="top">A</th>
+      <th>B</th>
+      <td>1.0</td>
+      <td>4.0</td>
+      <td>5.0</td>
+      <td>8.0</td>
+      <td>10.0</td>
+    </tr>
+    <tr>
+      <th>C</th>
+      <td>2.0</td>
+      <td>3.0</td>
+      <td>6.0</td>
+      <td>7.0</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>D</th>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>9.0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+</section>
+<section id="from-a-series">
+<h5><span class="section-number">5.4.1.2.2.1.6. </span>From a Series<a class="headerlink" href="#from-a-series" title="Permalink to this headline">#</a></h5>
+<p>The result will be a DataFrame with the same index as the input Series, and with one column whose name is the original name of the Series (only if no other column name provided).</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ser</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">),</span> <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s2">&quot;abc&quot;</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;ser&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">ser</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>ser</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>a</th>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>b</th>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>c</th>
+      <td>2</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+</section>
+<section id="from-a-list-of-namedtuples">
+<h5><span class="section-number">5.4.1.2.2.1.7. </span>From a list of namedtuples<a class="headerlink" href="#from-a-list-of-namedtuples" title="Permalink to this headline">#</a></h5>
+<p>The field names of the first <code class="docutils literal notranslate"><span class="pre">namedtuple</span></code> in the list determine the columns of the <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>. The remaining namedtuples (or tuples) are simply unpacked and their values are fed into the rows of the <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>. If any of those tuples is shorter than the first <code class="docutils literal notranslate"><span class="pre">namedtuple</span></code> then the later columns in the corresponding row are marked as missing values. If any are longer than the first <code class="docutils literal notranslate"><span class="pre">namedtuple</span></code> , a <code class="docutils literal notranslate"><span class="pre">ValueError</span></code> is raised.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">namedtuple</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Point</span> <span class="o">=</span> <span class="n">namedtuple</span><span class="p">(</span><span class="s2">&quot;Point&quot;</span><span class="p">,</span> <span class="s2">&quot;x y&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="n">Point</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">Point</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>x</th>
+      <th>y</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>0</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>0</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>2</td>
+      <td>3</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Point3D</span> <span class="o">=</span> <span class="n">namedtuple</span><span class="p">(</span><span class="s2">&quot;Point3D&quot;</span><span class="p">,</span> <span class="s2">&quot;x y z&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="n">Point3D</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">Point3D</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">Point</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>x</th>
+      <th>y</th>
+      <th>z</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>0</td>
+      <td>0</td>
+      <td>0.0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>0</td>
+      <td>3</td>
+      <td>5.0</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>2</td>
+      <td>3</td>
+      <td>NaN</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+</section>
+<section id="from-a-list-of-dataclasses">
+<h5><span class="section-number">5.4.1.2.2.1.8. </span>From a list of dataclasses<a class="headerlink" href="#from-a-list-of-dataclasses" title="Permalink to this headline">#</a></h5>
+<p>Data Classes as introduced in PEP557, can be passed into the DataFrame constructor. Passing a list of dataclasses is equivalent to passing a list of dictionaries.</p>
+<p>Please be aware, that all values in the list should be dataclasses, mixing types in the list would result in a <code class="docutils literal notranslate"><span class="pre">TypeError</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">dataclasses</span> <span class="kn">import</span> <span class="n">make_dataclass</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Point</span> <span class="o">=</span> <span class="n">make_dataclass</span><span class="p">(</span><span class="s2">&quot;Point&quot;</span><span class="p">,</span> <span class="p">[(</span><span class="s2">&quot;x&quot;</span><span class="p">,</span> <span class="nb">int</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;y&quot;</span><span class="p">,</span> <span class="nb">int</span><span class="p">)])</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="n">Point</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">Point</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">Point</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>x</th>
+      <th>y</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>0</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>0</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>2</td>
+      <td>3</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+</section>
+</section>
+<section id="column-selection-addition-deletion">
+<h4><span class="section-number">5.4.1.2.2.2. </span>Column selection, addition, deletion<a class="headerlink" href="#column-selection-addition-deletion" title="Permalink to this headline">#</a></h4>
+<p>You can treat a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> semantically like a dict of like-indexed <code class="docutils literal notranslate"><span class="pre">Series</span></code> objects. Getting, setting, and deleting columns works with the same syntax as the analogous dict operations:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>one</th>
+      <th>two</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>a</th>
+      <td>1.0</td>
+      <td>1.0</td>
+    </tr>
+    <tr>
+      <th>b</th>
+      <td>2.0</td>
+      <td>2.0</td>
+    </tr>
+    <tr>
+      <th>c</th>
+      <td>3.0</td>
+      <td>3.0</td>
+    </tr>
+    <tr>
+      <th>d</th>
+      <td>NaN</td>
+      <td>4.0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;one&quot;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    1.0
+b    2.0
+c    3.0
+d    NaN
+Name: one, dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;three&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;one&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;two&quot;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;flag&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;one&quot;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">2</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>one</th>
+      <th>two</th>
+      <th>three</th>
+      <th>flag</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>a</th>
+      <td>1.0</td>
+      <td>1.0</td>
+      <td>1.0</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <th>b</th>
+      <td>2.0</td>
+      <td>2.0</td>
+      <td>4.0</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <th>c</th>
+      <td>3.0</td>
+      <td>3.0</td>
+      <td>9.0</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>d</th>
+      <td>NaN</td>
+      <td>4.0</td>
+      <td>NaN</td>
+      <td>False</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>Columns can be deleted or popped like with a dict:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">del</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;two&quot;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">three</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;three&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>one</th>
+      <th>flag</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>a</th>
+      <td>1.0</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <th>b</th>
+      <td>2.0</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <th>c</th>
+      <td>3.0</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>d</th>
+      <td>NaN</td>
+      <td>False</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>When inserting a scalar value, it will naturally be propagated to fill the column:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;foo&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;bar&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>one</th>
+      <th>flag</th>
+      <th>foo</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>a</th>
+      <td>1.0</td>
+      <td>False</td>
+      <td>bar</td>
+    </tr>
+    <tr>
+      <th>b</th>
+      <td>2.0</td>
+      <td>False</td>
+      <td>bar</td>
+    </tr>
+    <tr>
+      <th>c</th>
+      <td>3.0</td>
+      <td>True</td>
+      <td>bar</td>
+    </tr>
+    <tr>
+      <th>d</th>
+      <td>NaN</td>
+      <td>False</td>
+      <td>bar</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>When inserting a <code class="docutils literal notranslate"><span class="pre">Series</span></code> that does not have the same index as the <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>, it will be conformed to the DataFrame’s index:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;one_trunc&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;one&quot;</span><span class="p">][:</span><span class="mi">2</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;link rel=&quot;stylesheet&quot; href=&quot;https://ocademy-ai.github.io/machine-learning/_static/style.css&quot;&gt;</span>
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:730px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Ad%20%3D%20%7B%0A%20%20%20%20%22one%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29,%0A%20%20%20%20%22two%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0,%204.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22,%20%22d%22%5D%29,%0A%7D%0Adf%20%3D%20pd.DataFrame%28d%29%0Adf%5B%22flag%22%5D%20%3D%20df%5B%22one%22%5D%20%3E%202%0Adel%20df%5B%22two%22%5D%0Adf%5B%22foo%22%5D%20%3D%20%22bar%22%0Adf%5B%22one_trunc%22%5D%20%3Ddf.insert%281,%20%22bar%22,%20df%5B%22one%22%5D%29%0Adf.loc%5B%22b%22%5D&amp;d=2023-09-19&amp;lang=py&amp;v=v1</span>
+<span class="sd">&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<link rel="stylesheet" href="https://ocademy-ai.github.io/machine-learning/_static/style.css">
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:730px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Ad%20%3D%20%7B%0A%20%20%20%20%22one%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29,%0A%20%20%20%20%22two%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0,%204.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22,%20%22d%22%5D%29,%0A%7D%0Adf%20%3D%20pd.DataFrame%28d%29%0Adf%5B%22flag%22%5D%20%3D%20df%5B%22one%22%5D%20%3E%202%0Adel%20df%5B%22two%22%5D%0Adf%5B%22foo%22%5D%20%3D%20%22bar%22%0Adf%5B%22one_trunc%22%5D%20%3Ddf.insert%281,%20%22bar%22,%20df%5B%22one%22%5D%29%0Adf.loc%5B%22b%22%5D&d=2023-09-19&lang=py&v=v1
+"> </iframe>
+    </div>
+  </div>
+</div>
+
+</div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>one</th>
+      <th>flag</th>
+      <th>foo</th>
+      <th>one_trunc</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>a</th>
+      <td>1.0</td>
+      <td>False</td>
+      <td>bar</td>
+      <td>1.0</td>
+    </tr>
+    <tr>
+      <th>b</th>
+      <td>2.0</td>
+      <td>False</td>
+      <td>bar</td>
+      <td>2.0</td>
+    </tr>
+    <tr>
+      <th>c</th>
+      <td>3.0</td>
+      <td>True</td>
+      <td>bar</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>d</th>
+      <td>NaN</td>
+      <td>False</td>
+      <td>bar</td>
+      <td>NaN</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>You can insert raw ndarrays but their length must match the length of the DataFrame’s index.</p>
+<p>By default, columns get inserted at the end. <code class="docutils literal notranslate"><span class="pre">DataFrame.insert()</span></code> inserts at a particular location in the columns:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;bar&quot;</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;one&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>one</th>
+      <th>bar</th>
+      <th>flag</th>
+      <th>foo</th>
+      <th>one_trunc</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>a</th>
+      <td>1.0</td>
+      <td>1.0</td>
+      <td>False</td>
+      <td>bar</td>
+      <td>1.0</td>
+    </tr>
+    <tr>
+      <th>b</th>
+      <td>2.0</td>
+      <td>2.0</td>
+      <td>False</td>
+      <td>bar</td>
+      <td>2.0</td>
+    </tr>
+    <tr>
+      <th>c</th>
+      <td>3.0</td>
+      <td>3.0</td>
+      <td>True</td>
+      <td>bar</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>d</th>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>False</td>
+      <td>bar</td>
+      <td>NaN</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+</section>
+<section id="assigning-new-columns-in-method-chains">
+<h4><span class="section-number">5.4.1.2.2.3. </span>Assigning new columns in method chains<a class="headerlink" href="#assigning-new-columns-in-method-chains" title="Permalink to this headline">#</a></h4>
+<p>DataFrame has an <code class="docutils literal notranslate"><span class="pre">assign()</span></code> method that allows you to easily create new columns that are potentially derived from existing columns.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">iris</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/data-science/working-with-data/pandas/iris.csv&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">iris</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>SepalLength</th>
+      <th>SepalWidth</th>
+      <th>PetalLength</th>
+      <th>PetalWidth</th>
+      <th>Name</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>5.1</td>
+      <td>3.5</td>
+      <td>1.4</td>
+      <td>0.2</td>
+      <td>Iris-setosa</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>4.9</td>
+      <td>3.0</td>
+      <td>1.4</td>
+      <td>0.2</td>
+      <td>Iris-setosa</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>4.7</td>
+      <td>3.2</td>
+      <td>1.3</td>
+      <td>0.2</td>
+      <td>Iris-setosa</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>4.6</td>
+      <td>3.1</td>
+      <td>1.5</td>
+      <td>0.2</td>
+      <td>Iris-setosa</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>5.0</td>
+      <td>3.6</td>
+      <td>1.4</td>
+      <td>0.2</td>
+      <td>Iris-setosa</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">iris</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">sepal_ratio</span><span class="o">=</span><span class="n">iris</span><span class="p">[</span><span class="s2">&quot;SepalWidth&quot;</span><span class="p">]</span> <span class="o">/</span> <span class="n">iris</span><span class="p">[</span><span class="s2">&quot;SepalLength&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>SepalLength</th>
+      <th>SepalWidth</th>
+      <th>PetalLength</th>
+      <th>PetalWidth</th>
+      <th>Name</th>
+      <th>sepal_ratio</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>5.1</td>
+      <td>3.5</td>
+      <td>1.4</td>
+      <td>0.2</td>
+      <td>Iris-setosa</td>
+      <td>0.686275</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>4.9</td>
+      <td>3.0</td>
+      <td>1.4</td>
+      <td>0.2</td>
+      <td>Iris-setosa</td>
+      <td>0.612245</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>4.7</td>
+      <td>3.2</td>
+      <td>1.3</td>
+      <td>0.2</td>
+      <td>Iris-setosa</td>
+      <td>0.680851</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>4.6</td>
+      <td>3.1</td>
+      <td>1.5</td>
+      <td>0.2</td>
+      <td>Iris-setosa</td>
+      <td>0.673913</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>5.0</td>
+      <td>3.6</td>
+      <td>1.4</td>
+      <td>0.2</td>
+      <td>Iris-setosa</td>
+      <td>0.720000</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>In the example above, we inserted a precomputed value. We can also pass in a function of one argument to be evaluated on the DataFrame being assigned to.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">iris</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">sepal_ratio</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;SepalWidth&quot;</span><span class="p">]</span> <span class="o">/</span> <span class="n">x</span><span class="p">[</span><span class="s2">&quot;SepalLength&quot;</span><span class="p">]))</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>SepalLength</th>
+      <th>SepalWidth</th>
+      <th>PetalLength</th>
+      <th>PetalWidth</th>
+      <th>Name</th>
+      <th>sepal_ratio</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>5.1</td>
+      <td>3.5</td>
+      <td>1.4</td>
+      <td>0.2</td>
+      <td>Iris-setosa</td>
+      <td>0.686275</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>4.9</td>
+      <td>3.0</td>
+      <td>1.4</td>
+      <td>0.2</td>
+      <td>Iris-setosa</td>
+      <td>0.612245</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>4.7</td>
+      <td>3.2</td>
+      <td>1.3</td>
+      <td>0.2</td>
+      <td>Iris-setosa</td>
+      <td>0.680851</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>4.6</td>
+      <td>3.1</td>
+      <td>1.5</td>
+      <td>0.2</td>
+      <td>Iris-setosa</td>
+      <td>0.673913</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>5.0</td>
+      <td>3.6</td>
+      <td>1.4</td>
+      <td>0.2</td>
+      <td>Iris-setosa</td>
+      <td>0.720000</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">assign()</span></code> <strong>always</strong> returns a copy of the data, leaving the original DataFrame untouched.</p>
+<p>Passing a callable, as opposed to an actual value to be inserted, is useful when you don’t have a reference to the DataFrame at hand. This is common when using <code class="docutils literal notranslate"><span class="pre">assign()</span></code> in a chain of operations. For example, we can limit the DataFrame to just those observations with a Sepal Length greater than 5, calculate the ratio, and plot:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="p">(</span>
+    <span class="n">iris</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="s2">&quot;SepalLength &gt; 5&quot;</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">assign</span><span class="p">(</span>
+        <span class="n">SepalRatio</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">SepalWidth</span> <span class="o">/</span> <span class="n">x</span><span class="o">.</span><span class="n">SepalLength</span><span class="p">,</span>
+        <span class="n">PetalRatio</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">PetalWidth</span> <span class="o">/</span> <span class="n">x</span><span class="o">.</span><span class="n">PetalLength</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s2">&quot;scatter&quot;</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">&quot;SepalRatio&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;PetalRatio&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;Axes: xlabel=&#39;SepalRatio&#39;, ylabel=&#39;PetalRatio&#39;&gt;
+</pre></div>
+</div>
+<img alt="../../../_images/introduction-and-data-structures_122_1.png" src="../../../_images/introduction-and-data-structures_122_1.png" />
+</div>
+</div>
+<p>Since a function is passed in, the function is computed on the DataFrame being assigned to. Importantly, this is the DataFrame that’s been filtered to those rows with sepal length greater than 5. The filtering happens first, and then the ratio calculations. This is an example where we didn’t have a reference to the filtered DataFrame available.</p>
+<p>The function signature for <code class="docutils literal notranslate"><span class="pre">assign()</span></code> is simply <code class="docutils literal notranslate"><span class="pre">**kwargs</span></code>. The keys are the column names for the new fields, and the values are either a value to be inserted (for example, a <code class="docutils literal notranslate"><span class="pre">Series</span></code> or NumPy array), or a function of one argument to be called on the <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>. A copy of the original <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> is returned, with the new values inserted.</p>
+<p>The order of <code class="docutils literal notranslate"><span class="pre">**kwargs</span></code> is preserved. This allows for dependent assignment, where an expression later in <code class="docutils literal notranslate"><span class="pre">**kwargs</span></code> can refer to a column created earlier in the same <code class="docutils literal notranslate"><span class="pre">assign()</span></code>.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfa</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">&quot;A&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="s2">&quot;B&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]})</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfa</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">C</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="s2">&quot;A&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">x</span><span class="p">[</span><span class="s2">&quot;B&quot;</span><span class="p">],</span> <span class="n">D</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="s2">&quot;A&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">x</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C</th>
+      <th>D</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>4</td>
+      <td>5</td>
+      <td>6</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>2</td>
+      <td>5</td>
+      <td>7</td>
+      <td>9</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>3</td>
+      <td>6</td>
+      <td>9</td>
+      <td>12</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>In the second expression, <code class="docutils literal notranslate"><span class="pre">x['C']</span></code> will refer to the newly created column, that’s equal to <code class="docutils literal notranslate"><span class="pre">dfa['A']</span> <span class="pre">+</span> <span class="pre">dfa['B']</span></code>.</p>
+</section>
+<section id="indexing-selection">
+<h4><span class="section-number">5.4.1.2.2.4. </span>Indexing / selection<a class="headerlink" href="#indexing-selection" title="Permalink to this headline">#</a></h4>
+<p>The basics of indexing are as follows:</p>
+<table class="colwidths-auto table">
+<thead>
+<tr class="row-odd"><th class="text-align:left head"><p>Operation</p></th>
+<th class="text-align:left head"><p>Syntax</p></th>
+<th class="text-align:left head"><p>Result</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td class="text-align:left"><p>Select column</p></td>
+<td class="text-align:left"><p><code class="docutils literal notranslate"><span class="pre">df[col]</span></code></p></td>
+<td class="text-align:left"><p>Series</p></td>
+</tr>
+<tr class="row-odd"><td class="text-align:left"><p>Select row by label</p></td>
+<td class="text-align:left"><p><code class="docutils literal notranslate"><span class="pre">df.loc[label]</span></code></p></td>
+<td class="text-align:left"><p>Series</p></td>
+</tr>
+<tr class="row-even"><td class="text-align:left"><p>Select row by integer location</p></td>
+<td class="text-align:left"><p><code class="docutils literal notranslate"><span class="pre">df.iloc[loc]</span></code></p></td>
+<td class="text-align:left"><p>Series</p></td>
+</tr>
+<tr class="row-odd"><td class="text-align:left"><p>Slice rows</p></td>
+<td class="text-align:left"><p><code class="docutils literal notranslate"><span class="pre">df[5:10]</span> </code></p></td>
+<td class="text-align:left"><p>DataFrame</p></td>
+</tr>
+<tr class="row-even"><td class="text-align:left"><p>Select rows by boolean vector</p></td>
+<td class="text-align:left"><p><code class="docutils literal notranslate"><span class="pre">df[bool_vec]</span></code></p></td>
+<td class="text-align:left"><p>DataFrame</p></td>
+</tr>
+</tbody>
+</table>
+<p>Row selection, for example, returns a <code class="docutils literal notranslate"><span class="pre">Series</span></code> whose index is the columns of the <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">&quot;b&quot;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>one            2.0
+bar            2.0
+flag         False
+foo            bar
+one_trunc      2.0
+Name: b, dtype: object
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell tag_hide-cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">HTML</span><span class="p">(</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="sd">&lt;link rel=&quot;stylesheet&quot; href=&quot;https://ocademy-ai.github.io/machine-learning/_static/style.css&quot;&gt;</span>
+
+<span class="sd">&lt;div class=&#39;full-width docutils&#39; &gt;</span>
+<span class="sd">  &lt;div class=&quot;admonition note pandastutor&quot; name=&quot;html-admonition&quot; style=&quot;margin-right:20%&quot;&gt;</span>
+<span class="sd">    &lt;p class=&quot;admonition-title pandastutor&quot;&gt;Let&#39;s visualize it! 🎥&lt;/p&gt;</span>
+<span class="sd">    &lt;div class=&quot;pandastutor inner&quot; style=&quot;height:730px;&quot;&gt;</span>
+<span class="sd">      &lt;iframe frameborder=&quot;0&quot; scrolling=&quot;no&quot; src=&quot;https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Ad%20%3D%20%7B%0A%20%20%20%20%22one%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29,%0A%20%20%20%20%22two%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0,%204.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22,%20%22d%22%5D%29,%0A%7D%0Adf%20%3D%20pd.DataFrame%28d%29%0Adf%5B%22flag%22%5D%20%3D%20df%5B%22one%22%5D%20%3E%202%0Adel%20df%5B%22two%22%5D%0Adf%5B%22foo%22%5D%20%3D%20%22bar%22%0Adf%5B%22one_trunc%22%5D%20%3Ddf.insert%281,%20%22bar%22,%20df%5B%22one%22%5D%29%0Adf.loc%5B%22b%22%5D&amp;d=2023-09-19&amp;lang=py&amp;v=v1&quot;&gt; &lt;/iframe&gt;</span>
+<span class="sd">    &lt;/div&gt;</span>
+<span class="sd">  &lt;/div&gt;</span>
+<span class="sd">&lt;/div&gt;</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html">
+
+
+<link rel="stylesheet" href="https://ocademy-ai.github.io/machine-learning/_static/style.css">
+
+<div class='full-width docutils' >
+  <div class="admonition note pandastutor" name="html-admonition" style="margin-right:20%">
+    <p class="admonition-title pandastutor">Let's visualize it! 🎥</p>
+    <div class="pandastutor inner" style="height:730px;">
+      <iframe frameborder="0" scrolling="no" src="https://pandastutor.com/vis.html#code=import%20pandas%20as%20pd%0Aimport%20io%0Aimport%20numpy%20as%20np%0Ad%20%3D%20%7B%0A%20%20%20%20%22one%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22%5D%29,%0A%20%20%20%20%22two%22%3A%20pd.Series%28%5B1.0,%202.0,%203.0,%204.0%5D,%20index%3D%5B%22a%22,%20%22b%22,%20%22c%22,%20%22d%22%5D%29,%0A%7D%0Adf%20%3D%20pd.DataFrame%28d%29%0Adf%5B%22flag%22%5D%20%3D%20df%5B%22one%22%5D%20%3E%202%0Adel%20df%5B%22two%22%5D%0Adf%5B%22foo%22%5D%20%3D%20%22bar%22%0Adf%5B%22one_trunc%22%5D%20%3Ddf.insert%281,%20%22bar%22,%20df%5B%22one%22%5D%29%0Adf.loc%5B%22b%22%5D&d=2023-09-19&lang=py&v=v1"> </iframe>
+    </div>
+  </div>
+</div>
+
+</div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>one           3.0
+bar           3.0
+flag         True
+foo           bar
+one_trunc     NaN
+Name: c, dtype: object
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="data-alignment-and-arithmetic">
+<h4><span class="section-number">5.4.1.2.2.5. </span>Data alignment and arithmetic<a class="headerlink" href="#data-alignment-and-arithmetic" title="Permalink to this headline">#</a></h4>
+<p>Data alignment between <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> objects automatically aligns on <strong>both</strong> the columns and the index (row labels)**. Again, the resulting object will have the union of the column and row labels.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">,</span> <span class="s2">&quot;D&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">+</span> <span class="n">df2</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C</th>
+      <th>D</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>-2.675459</td>
+      <td>-0.133581</td>
+      <td>1.148420</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>-1.979453</td>
+      <td>-0.736220</td>
+      <td>-4.194590</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>-1.373036</td>
+      <td>0.939902</td>
+      <td>-1.952070</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>0.813456</td>
+      <td>-0.228460</td>
+      <td>-0.634051</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>-0.287748</td>
+      <td>1.054761</td>
+      <td>-2.133658</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>0.697426</td>
+      <td>1.493623</td>
+      <td>-1.633845</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>-0.249357</td>
+      <td>1.432554</td>
+      <td>1.585387</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>7</th>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>NaN</td>
+    </tr>
+    <tr>
+      <th>9</th>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>NaN</td>
+      <td>NaN</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>When doing an operation between <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> and <code class="docutils literal notranslate"><span class="pre">Series</span></code>, the default behavior is to align the <code class="docutils literal notranslate"><span class="pre">Series</span></code> <strong>index</strong> on the <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> <strong>columns</strong>, thus broadcasting row-wise. For example:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">-</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C</th>
+      <th>D</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>0.000000</td>
+      <td>0.000000</td>
+      <td>0.000000</td>
+      <td>0.000000</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1.176653</td>
+      <td>0.958183</td>
+      <td>-3.117564</td>
+      <td>-0.681690</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>0.935730</td>
+      <td>1.163312</td>
+      <td>-1.466732</td>
+      <td>-1.218909</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>1.854724</td>
+      <td>0.293515</td>
+      <td>-0.472388</td>
+      <td>0.755568</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>1.604946</td>
+      <td>3.032983</td>
+      <td>-0.818088</td>
+      <td>0.248440</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>2.207410</td>
+      <td>1.803085</td>
+      <td>-1.369634</td>
+      <td>-1.490638</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>4.231287</td>
+      <td>1.862016</td>
+      <td>1.764553</td>
+      <td>-1.377419</td>
+    </tr>
+    <tr>
+      <th>7</th>
+      <td>1.182000</td>
+      <td>1.136687</td>
+      <td>-0.938919</td>
+      <td>-0.008524</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>1.579137</td>
+      <td>1.203216</td>
+      <td>-0.808539</td>
+      <td>-1.454299</td>
+    </tr>
+    <tr>
+      <th>9</th>
+      <td>1.807712</td>
+      <td>0.360691</td>
+      <td>-1.850980</td>
+      <td>-0.663877</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>Arithmetic operations with scalars operate element-wise:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">*</span> <span class="mi">5</span> <span class="o">+</span> <span class="mi">2</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C</th>
+      <th>D</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>-7.038225</td>
+      <td>-3.616662</td>
+      <td>2.249668</td>
+      <td>0.911815</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>-1.154961</td>
+      <td>1.174252</td>
+      <td>-13.338150</td>
+      <td>-2.496637</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>-2.359573</td>
+      <td>2.199900</td>
+      <td>-5.083991</td>
+      <td>-5.182731</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>2.235394</td>
+      <td>-2.149088</td>
+      <td>-0.112270</td>
+      <td>4.689655</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>0.986504</td>
+      <td>11.548255</td>
+      <td>-1.840774</td>
+      <td>2.154016</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>3.998827</td>
+      <td>5.398765</td>
+      <td>-4.598502</td>
+      <td>-6.541375</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>14.118211</td>
+      <td>5.693418</td>
+      <td>11.072431</td>
+      <td>-5.975282</td>
+    </tr>
+    <tr>
+      <th>7</th>
+      <td>-1.128225</td>
+      <td>2.066773</td>
+      <td>-2.444925</td>
+      <td>0.869196</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>0.857461</td>
+      <td>2.399419</td>
+      <td>-1.793024</td>
+      <td>-6.359682</td>
+    </tr>
+    <tr>
+      <th>9</th>
+      <td>2.000335</td>
+      <td>-1.813207</td>
+      <td>-7.005231</td>
+      <td>-2.407570</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="mi">1</span> <span class="o">/</span> <span class="n">df</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C</th>
+      <th>D</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>-0.553206</td>
+      <td>-0.890208</td>
+      <td>20.026571</td>
+      <td>-4.594807</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>-1.584805</td>
+      <td>-6.055120</td>
+      <td>-0.325985</td>
+      <td>-1.111942</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>-1.146901</td>
+      <td>25.012499</td>
+      <td>-0.705817</td>
+      <td>-0.696114</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>21.240986</td>
+      <td>-1.205084</td>
+      <td>-2.367122</td>
+      <td>1.858975</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>-4.933419</td>
+      <td>0.523656</td>
+      <td>-1.301821</td>
+      <td>32.464111</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>2.501467</td>
+      <td>1.471123</td>
+      <td>-0.757748</td>
+      <td>-0.585386</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>0.412602</td>
+      <td>1.353760</td>
+      <td>0.551120</td>
+      <td>-0.626937</td>
+    </tr>
+    <tr>
+      <th>7</th>
+      <td>-1.598350</td>
+      <td>74.880783</td>
+      <td>-1.124878</td>
+      <td>-4.421634</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>-4.376220</td>
+      <td>12.518198</td>
+      <td>-1.318209</td>
+      <td>-0.598109</td>
+    </tr>
+    <tr>
+      <th>9</th>
+      <td>14927.997228</td>
+      <td>-1.311232</td>
+      <td>-0.555233</td>
+      <td>-1.134412</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">**</span> <span class="mi">4</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C</th>
+      <th>D</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1.067708e+01</td>
+      <td>1.592330e+00</td>
+      <td>0.000006</td>
+      <td>2.243525e-03</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1.585244e-01</td>
+      <td>7.438906e-04</td>
+      <td>88.554761</td>
+      <td>6.541410e-01</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>5.779576e-01</td>
+      <td>2.554887e-06</td>
+      <td>4.029322</td>
+      <td>4.258713e+00</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>4.912486e-06</td>
+      <td>4.741661e-01</td>
+      <td>0.031851</td>
+      <td>8.373485e-02</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>1.688138e-03</td>
+      <td>1.329891e+01</td>
+      <td>0.348173</td>
+      <td>9.002973e-07</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>2.553999e-02</td>
+      <td>2.135033e-01</td>
+      <td>3.033203</td>
+      <td>8.515912e+00</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>3.450436e+01</td>
+      <td>2.977377e-01</td>
+      <td>10.839637</td>
+      <td>6.472977e+00</td>
+    </tr>
+    <tr>
+      <th>7</th>
+      <td>1.532188e-01</td>
+      <td>3.180669e-08</td>
+      <td>0.624565</td>
+      <td>2.616188e-03</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>2.726487e-03</td>
+      <td>4.072234e-05</td>
+      <td>0.331179</td>
+      <td>7.814101e+00</td>
+    </tr>
+    <tr>
+      <th>9</th>
+      <td>2.013696e-17</td>
+      <td>3.382842e-01</td>
+      <td>10.522029</td>
+      <td>6.038328e-01</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>Boolean operators operate element-wise as well:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">&quot;a&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;b&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]},</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">bool</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">&quot;a&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;b&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]},</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">bool</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span> <span class="o">&amp;</span> <span class="n">df2</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>a</th>
+      <th>b</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>False</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>False</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>True</td>
+      <td>False</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span> <span class="o">|</span> <span class="n">df2</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>a</th>
+      <th>b</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>True</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>True</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>True</td>
+      <td>True</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df1</span> <span class="o">^</span> <span class="n">df2</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>a</th>
+      <th>b</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>True</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>True</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>False</td>
+      <td>True</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">-</span><span class="n">df1</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>a</th>
+      <th>b</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>False</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>True</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>False</td>
+      <td>False</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+</section>
+<section id="transposing">
+<h4><span class="section-number">5.4.1.2.2.6. </span>Transposing<a class="headerlink" href="#transposing" title="Permalink to this headline">#</a></h4>
+<p>To transpose, access the <code class="docutils literal notranslate"><span class="pre">T</span></code> attribute or <code class="docutils literal notranslate"><span class="pre">DataFrame.transpose()</span></code>, similar to an ndarray:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>0</th>
+      <th>1</th>
+      <th>2</th>
+      <th>3</th>
+      <th>4</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>A</th>
+      <td>-1.807645</td>
+      <td>-0.630992</td>
+      <td>-0.871915</td>
+      <td>0.047079</td>
+      <td>-0.202699</td>
+    </tr>
+    <tr>
+      <th>B</th>
+      <td>-1.123332</td>
+      <td>-0.165150</td>
+      <td>0.039980</td>
+      <td>-0.829818</td>
+      <td>1.909651</td>
+    </tr>
+    <tr>
+      <th>C</th>
+      <td>0.049934</td>
+      <td>-3.067630</td>
+      <td>-1.416798</td>
+      <td>-0.422454</td>
+      <td>-0.768155</td>
+    </tr>
+    <tr>
+      <th>D</th>
+      <td>-0.217637</td>
+      <td>-0.899327</td>
+      <td>-1.436546</td>
+      <td>0.537931</td>
+      <td>0.030803</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+</section>
+</section>
+</section>
+<section id="data-indexing-and-selection">
+<h2><span class="section-number">5.4.1.3. </span>Data indexing and selection<a class="headerlink" href="#data-indexing-and-selection" title="Permalink to this headline">#</a></h2>
+<p>The axis labeling information in Pandas objects serves many purposes:</p>
+<ul class="simple">
+<li><p>Identifies data (i.e. provides metadata) using known indicators, important for analysis, visualization, and interactive console display.</p></li>
+<li><p>Enables automatic and explicit data alignment.</p></li>
+<li><p>Allows intuitive getting and setting of subsets of the data set.</p></li>
+</ul>
+<p>In this section, we will focus on the final point: namely, how to slice, dice, and generally get and set subsets of Pandas objects. The primary focus will be on Series and DataFrame as they have received more development attention in this area.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The Python and NumPy indexing operators <code class="docutils literal notranslate"><span class="pre">[]</span></code> and attribute operator <code class="docutils literal notranslate"><span class="pre">.</span></code> provide quick and easy access to Pandas data structures across a wide range of use cases. This makes interactive work intuitive, as there’s little new to learn if you already know how to deal with Python dictionaries and NumPy arrays. However, since the type of the data to be accessed isn’t known in advance, directly using standard operators has some optimization limits. For production code, we recommended that you take advantage of the optimized Pandas data access methods exposed in this chapter.</p>
+</div>
+<p>Whether a copy or a reference is returned for a setting operation, may depend on the context. This is sometimes called <code class="docutils literal notranslate"><span class="pre">chained</span> <span class="pre">assignment</span></code> and should be avoided.</p>
+<section id="different-choices-for-indexing">
+<h3><span class="section-number">5.4.1.3.1. </span>Different choices for indexing<a class="headerlink" href="#different-choices-for-indexing" title="Permalink to this headline">#</a></h3>
+<p>Object selection has had a number of user-requested additions in order to support more explicit location-based indexing. Pandas now supports three types of multi-axis indexing.</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">.loc</span></code> is primarily label based, but may also be used with a boolean array. <code class="docutils literal notranslate"><span class="pre">.loc</span></code> will raise <code class="docutils literal notranslate"><span class="pre">KeyError</span></code> when the items are not found. Allowed inputs are:</p>
+<ul>
+<li><p>A single label, e.g. <code class="docutils literal notranslate"><span class="pre">5</span></code> or <code class="docutils literal notranslate"><span class="pre">'a'</span></code> (Note that <code class="docutils literal notranslate"><span class="pre">5</span></code> is interpreted as a label of the index. This use is not an integer position along the index.).</p></li>
+<li><p>A list or array of labels <code class="docutils literal notranslate"><span class="pre">['a',</span> <span class="pre">'b',</span> <span class="pre">'c']</span></code>.</p></li>
+<li><p>A slice object with labels <code class="docutils literal notranslate"><span class="pre">'a':'f'</span></code> (Note that contrary to usual Python slices, both the start and the stop are included, when present in the index!)</p></li>
+<li><p>A boolean array (any <code class="docutils literal notranslate"><span class="pre">NA</span></code> values will be treated as <code class="docutils literal notranslate"><span class="pre">False</span></code>).</p></li>
+<li><p>A <code class="docutils literal notranslate"><span class="pre">callable</span></code> function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above).</p></li>
+</ul>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">.iloc</span></code> is primarily integer position based (from <code class="docutils literal notranslate"><span class="pre">0</span></code> to <code class="docutils literal notranslate"><span class="pre">length-1</span></code> of the axis), but may also be used with a boolean array. <code class="docutils literal notranslate"><span class="pre">.iloc</span></code> will raise <code class="docutils literal notranslate"><span class="pre">IndexError</span></code> if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing. (this conforms with Python/NumPy slice semantics). Allowed inputs are:</p>
+<ul>
+<li><p>An integer e.g. <code class="docutils literal notranslate"><span class="pre">5</span></code>.</p></li>
+<li><p>A list or array of integers <code class="docutils literal notranslate"><span class="pre">[4,</span> <span class="pre">3,</span> <span class="pre">0]</span></code>.</p></li>
+<li><p>A slice object with ints <code class="docutils literal notranslate"><span class="pre">1:7</span></code>.</p></li>
+<li><p>A boolean array (any <code class="docutils literal notranslate"><span class="pre">NA</span></code> values will be treated as <code class="docutils literal notranslate"><span class="pre">False</span></code>).</p></li>
+<li><p>A <code class="docutils literal notranslate"><span class="pre">callable</span></code> function with one argument (the calling Series or DataFrame) that returns valid output for indexing (one of the above).</p></li>
+</ul>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">.loc</span></code>, <code class="docutils literal notranslate"><span class="pre">.iloc</span></code>, and also <code class="docutils literal notranslate"><span class="pre">[]</span></code> indexing can accept a <code class="docutils literal notranslate"><span class="pre">callable</span></code> as indexer.</p></li>
+</ul>
+<p>Getting values from an object with multi-axes selection uses the following notation (using <code class="docutils literal notranslate"><span class="pre">.loc</span></code> as an example, but the following applies to <code class="docutils literal notranslate"><span class="pre">.iloc</span></code> as well). Any of the axes accessors may be the null slice <code class="docutils literal notranslate"><span class="pre">:</span></code>. Axes left out of the specification are assumed to be <code class="docutils literal notranslate"><span class="pre">:</span></code>, e.g. <code class="docutils literal notranslate"><span class="pre">p.loc['a']</span></code> is equivalent to <code class="docutils literal notranslate"><span class="pre">p.loc['a',</span> <span class="pre">:]</span></code>.</p>
+<table class="colwidths-auto table">
+<thead>
+<tr class="row-odd"><th class="text-align:left head"><p><strong>Object Type</strong></p></th>
+<th class="text-align:left head"><p><strong>Indexers</strong></p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td class="text-align:left"><p>Series</p></td>
+<td class="text-align:left"><p><code class="docutils literal notranslate"><span class="pre">s.loc[indexer]</span></code></p></td>
+</tr>
+<tr class="row-odd"><td class="text-align:left"><p>DataFrame</p></td>
+<td class="text-align:left"><p><code class="docutils literal notranslate"><span class="pre">df.loc[row_indexer,</span> <span class="pre">column_indexer]</span></code></p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="basics">
+<h3><span class="section-number">5.4.1.3.2. </span>Basics<a class="headerlink" href="#basics" title="Permalink to this headline">#</a></h3>
+<p>As mentioned when introducing the data structures in the last section, the primary function of indexing with <code class="docutils literal notranslate"><span class="pre">[]</span></code> (a.k.a.<code class="docutils literal notranslate"> <span class="pre">__getitem__</span></code> for those familiar with implementing class behavior in Python) is selecting out lower-dimensional slices. The following table shows return type values when indexing Pandas objects with <code class="docutils literal notranslate"><span class="pre">[]</span></code>:</p>
+<table class="colwidths-auto table">
+<thead>
+<tr class="row-odd"><th class="text-align:left head"><p><strong>Object Type</strong></p></th>
+<th class="text-align:left head"><p><strong>Selection</strong></p></th>
+<th class="text-align:left head"><p>Return Value Type</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td class="text-align:left"><p>Series</p></td>
+<td class="text-align:left"><p><code class="docutils literal notranslate"><span class="pre">series[label]</span></code></p></td>
+<td class="text-align:left"><p>scalar value</p></td>
+</tr>
+<tr class="row-odd"><td class="text-align:left"><p>DataFrame</p></td>
+<td class="text-align:left"><p><code class="docutils literal notranslate"><span class="pre">frame[colname]</span></code></p></td>
+<td class="text-align:left"><p><code class="docutils literal notranslate"><span class="pre">Series</span></code> corresponding to colname</p></td>
+</tr>
+</tbody>
+</table>
+<p>Here we construct a simple time series data set to use for illustrating the indexing functionality:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dates</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span><span class="s1">&#39;1/1/2000&#39;</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
+                  <span class="n">index</span><span class="o">=</span><span class="n">dates</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">,</span> <span class="s1">&#39;D&#39;</span><span class="p">])</span>
+<span class="n">df</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C</th>
+      <th>D</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2000-01-01</th>
+      <td>0.311438</td>
+      <td>2.122763</td>
+      <td>2.206814</td>
+      <td>-1.488590</td>
+    </tr>
+    <tr>
+      <th>2000-01-02</th>
+      <td>-0.459154</td>
+      <td>1.697299</td>
+      <td>0.852562</td>
+      <td>2.389648</td>
+    </tr>
+    <tr>
+      <th>2000-01-03</th>
+      <td>1.408196</td>
+      <td>-0.326012</td>
+      <td>-0.137593</td>
+      <td>-0.287003</td>
+    </tr>
+    <tr>
+      <th>2000-01-04</th>
+      <td>0.926176</td>
+      <td>1.213350</td>
+      <td>0.039471</td>
+      <td>1.068784</td>
+    </tr>
+    <tr>
+      <th>2000-01-05</th>
+      <td>-0.490948</td>
+      <td>0.108342</td>
+      <td>0.553074</td>
+      <td>1.213043</td>
+    </tr>
+    <tr>
+      <th>2000-01-06</th>
+      <td>-1.378264</td>
+      <td>0.374637</td>
+      <td>0.776962</td>
+      <td>-0.125644</td>
+    </tr>
+    <tr>
+      <th>2000-01-07</th>
+      <td>0.628157</td>
+      <td>-1.239929</td>
+      <td>0.761446</td>
+      <td>-0.847097</td>
+    </tr>
+    <tr>
+      <th>2000-01-08</th>
+      <td>0.291123</td>
+      <td>1.568350</td>
+      <td>1.051489</td>
+      <td>0.526787</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>None of the indexing functionality is time series specific unless specifically stated.</p>
+</div>
+<p>Thus, as per above, we have the most basic indexing using <code class="docutils literal notranslate"><span class="pre">[]</span></code>:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">]</span>
+
+<span class="n">s</span><span class="p">[</span><span class="n">dates</span><span class="p">[</span><span class="mi">5</span><span class="p">]]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-1.3782643792341864
+</pre></div>
+</div>
+</div>
+</div>
+<p>You can pass a list of columns to <code class="docutils literal notranslate"><span class="pre">[]</span></code> to select columns in that order. If a column is not contained in the DataFrame, an exception will be raised. Multiple columns can also be set in this manner:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C</th>
+      <th>D</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2000-01-01</th>
+      <td>0.311438</td>
+      <td>2.122763</td>
+      <td>2.206814</td>
+      <td>-1.488590</td>
+    </tr>
+    <tr>
+      <th>2000-01-02</th>
+      <td>-0.459154</td>
+      <td>1.697299</td>
+      <td>0.852562</td>
+      <td>2.389648</td>
+    </tr>
+    <tr>
+      <th>2000-01-03</th>
+      <td>1.408196</td>
+      <td>-0.326012</td>
+      <td>-0.137593</td>
+      <td>-0.287003</td>
+    </tr>
+    <tr>
+      <th>2000-01-04</th>
+      <td>0.926176</td>
+      <td>1.213350</td>
+      <td>0.039471</td>
+      <td>1.068784</td>
+    </tr>
+    <tr>
+      <th>2000-01-05</th>
+      <td>-0.490948</td>
+      <td>0.108342</td>
+      <td>0.553074</td>
+      <td>1.213043</td>
+    </tr>
+    <tr>
+      <th>2000-01-06</th>
+      <td>-1.378264</td>
+      <td>0.374637</td>
+      <td>0.776962</td>
+      <td>-0.125644</td>
+    </tr>
+    <tr>
+      <th>2000-01-07</th>
+      <td>0.628157</td>
+      <td>-1.239929</td>
+      <td>0.761446</td>
+      <td>-0.847097</td>
+    </tr>
+    <tr>
+      <th>2000-01-08</th>
+      <td>0.291123</td>
+      <td>1.568350</td>
+      <td>1.051489</td>
+      <td>0.526787</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[[</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;A&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">]]</span>
+<span class="n">df</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C</th>
+      <th>D</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2000-01-01</th>
+      <td>2.122763</td>
+      <td>0.311438</td>
+      <td>2.206814</td>
+      <td>-1.488590</td>
+    </tr>
+    <tr>
+      <th>2000-01-02</th>
+      <td>1.697299</td>
+      <td>-0.459154</td>
+      <td>0.852562</td>
+      <td>2.389648</td>
+    </tr>
+    <tr>
+      <th>2000-01-03</th>
+      <td>-0.326012</td>
+      <td>1.408196</td>
+      <td>-0.137593</td>
+      <td>-0.287003</td>
+    </tr>
+    <tr>
+      <th>2000-01-04</th>
+      <td>1.213350</td>
+      <td>0.926176</td>
+      <td>0.039471</td>
+      <td>1.068784</td>
+    </tr>
+    <tr>
+      <th>2000-01-05</th>
+      <td>0.108342</td>
+      <td>-0.490948</td>
+      <td>0.553074</td>
+      <td>1.213043</td>
+    </tr>
+    <tr>
+      <th>2000-01-06</th>
+      <td>0.374637</td>
+      <td>-1.378264</td>
+      <td>0.776962</td>
+      <td>-0.125644</td>
+    </tr>
+    <tr>
+      <th>2000-01-07</th>
+      <td>-1.239929</td>
+      <td>0.628157</td>
+      <td>0.761446</td>
+      <td>-0.847097</td>
+    </tr>
+    <tr>
+      <th>2000-01-08</th>
+      <td>1.568350</td>
+      <td>0.291123</td>
+      <td>1.051489</td>
+      <td>0.526787</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>You may find this useful for applying a transform (in-place) to a subset of the columns.</p>
+<p>Pandas aligns all AXES when setting <code class="docutils literal notranslate"><span class="pre">Series</span></code> and <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> from <code class="docutils literal notranslate"><span class="pre">.loc</span></code>, and <code class="docutils literal notranslate"><span class="pre">.iloc</span></code>.</p>
+<p>This will not modify <code class="docutils literal notranslate"><span class="pre">df</span></code> because the column alignment is before value assignment.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">]]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2000-01-01</th>
+      <td>2.122763</td>
+      <td>0.311438</td>
+    </tr>
+    <tr>
+      <th>2000-01-02</th>
+      <td>1.697299</td>
+      <td>-0.459154</td>
+    </tr>
+    <tr>
+      <th>2000-01-03</th>
+      <td>-0.326012</td>
+      <td>1.408196</td>
+    </tr>
+    <tr>
+      <th>2000-01-04</th>
+      <td>1.213350</td>
+      <td>0.926176</td>
+    </tr>
+    <tr>
+      <th>2000-01-05</th>
+      <td>0.108342</td>
+      <td>-0.490948</td>
+    </tr>
+    <tr>
+      <th>2000-01-06</th>
+      <td>0.374637</td>
+      <td>-1.378264</td>
+    </tr>
+    <tr>
+      <th>2000-01-07</th>
+      <td>-1.239929</td>
+      <td>0.628157</td>
+    </tr>
+    <tr>
+      <th>2000-01-08</th>
+      <td>1.568350</td>
+      <td>0.291123</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;A&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">]]</span>
+<span class="n">df</span><span class="p">[[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">]]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2000-01-01</th>
+      <td>2.122763</td>
+      <td>0.311438</td>
+    </tr>
+    <tr>
+      <th>2000-01-02</th>
+      <td>1.697299</td>
+      <td>-0.459154</td>
+    </tr>
+    <tr>
+      <th>2000-01-03</th>
+      <td>-0.326012</td>
+      <td>1.408196</td>
+    </tr>
+    <tr>
+      <th>2000-01-04</th>
+      <td>1.213350</td>
+      <td>0.926176</td>
+    </tr>
+    <tr>
+      <th>2000-01-05</th>
+      <td>0.108342</td>
+      <td>-0.490948</td>
+    </tr>
+    <tr>
+      <th>2000-01-06</th>
+      <td>0.374637</td>
+      <td>-1.378264</td>
+    </tr>
+    <tr>
+      <th>2000-01-07</th>
+      <td>-1.239929</td>
+      <td>0.628157</td>
+    </tr>
+    <tr>
+      <th>2000-01-08</th>
+      <td>1.568350</td>
+      <td>0.291123</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>The correct way to swap column values is by using raw values:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;A&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+<span class="n">df</span><span class="p">[[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">]]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2000-01-01</th>
+      <td>0.311438</td>
+      <td>2.122763</td>
+    </tr>
+    <tr>
+      <th>2000-01-02</th>
+      <td>-0.459154</td>
+      <td>1.697299</td>
+    </tr>
+    <tr>
+      <th>2000-01-03</th>
+      <td>1.408196</td>
+      <td>-0.326012</td>
+    </tr>
+    <tr>
+      <th>2000-01-04</th>
+      <td>0.926176</td>
+      <td>1.213350</td>
+    </tr>
+    <tr>
+      <th>2000-01-05</th>
+      <td>-0.490948</td>
+      <td>0.108342</td>
+    </tr>
+    <tr>
+      <th>2000-01-06</th>
+      <td>-1.378264</td>
+      <td>0.374637</td>
+    </tr>
+    <tr>
+      <th>2000-01-07</th>
+      <td>0.628157</td>
+      <td>-1.239929</td>
+    </tr>
+    <tr>
+      <th>2000-01-08</th>
+      <td>0.291123</td>
+      <td>1.568350</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+</section>
+<section id="attribute-access">
+<h3><span class="section-number">5.4.1.3.3. </span>Attribute access<a class="headerlink" href="#attribute-access" title="Permalink to this headline">#</a></h3>
+<p>You may access an index on a <code class="docutils literal notranslate"><span class="pre">Series</span></code> or column on a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> directly as an attribute:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sa</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s1">&#39;abc&#39;</span><span class="p">))</span>
+<span class="n">dfa</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sa</span><span class="o">.</span><span class="n">b</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfa</span><span class="o">.</span><span class="n">A</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2000-01-01    0.311438
+2000-01-02   -0.459154
+2000-01-03    1.408196
+2000-01-04    0.926176
+2000-01-05   -0.490948
+2000-01-06   -1.378264
+2000-01-07    0.628157
+2000-01-08    0.291123
+Freq: D, Name: A, dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sa</span><span class="o">.</span><span class="n">a</span> <span class="o">=</span> <span class="mi">5</span>
+<span class="n">sa</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    5
+b    2
+c    3
+dtype: int64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfa</span><span class="o">.</span><span class="n">A</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dfa</span><span class="o">.</span><span class="n">index</span><span class="p">)))</span>  <span class="c1"># ok if A already exists</span>
+<span class="n">dfa</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C</th>
+      <th>D</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2000-01-01</th>
+      <td>0</td>
+      <td>2.122763</td>
+      <td>2.206814</td>
+      <td>-1.488590</td>
+    </tr>
+    <tr>
+      <th>2000-01-02</th>
+      <td>1</td>
+      <td>1.697299</td>
+      <td>0.852562</td>
+      <td>2.389648</td>
+    </tr>
+    <tr>
+      <th>2000-01-03</th>
+      <td>2</td>
+      <td>-0.326012</td>
+      <td>-0.137593</td>
+      <td>-0.287003</td>
+    </tr>
+    <tr>
+      <th>2000-01-04</th>
+      <td>3</td>
+      <td>1.213350</td>
+      <td>0.039471</td>
+      <td>1.068784</td>
+    </tr>
+    <tr>
+      <th>2000-01-05</th>
+      <td>4</td>
+      <td>0.108342</td>
+      <td>0.553074</td>
+      <td>1.213043</td>
+    </tr>
+    <tr>
+      <th>2000-01-06</th>
+      <td>5</td>
+      <td>0.374637</td>
+      <td>0.776962</td>
+      <td>-0.125644</td>
+    </tr>
+    <tr>
+      <th>2000-01-07</th>
+      <td>6</td>
+      <td>-1.239929</td>
+      <td>0.761446</td>
+      <td>-0.847097</td>
+    </tr>
+    <tr>
+      <th>2000-01-08</th>
+      <td>7</td>
+      <td>1.568350</td>
+      <td>1.051489</td>
+      <td>0.526787</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dfa</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dfa</span><span class="o">.</span><span class="n">index</span><span class="p">)))</span>  <span class="c1"># use this form to create a new column</span>
+<span class="n">dfa</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C</th>
+      <th>D</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2000-01-01</th>
+      <td>0</td>
+      <td>2.122763</td>
+      <td>2.206814</td>
+      <td>-1.488590</td>
+    </tr>
+    <tr>
+      <th>2000-01-02</th>
+      <td>1</td>
+      <td>1.697299</td>
+      <td>0.852562</td>
+      <td>2.389648</td>
+    </tr>
+    <tr>
+      <th>2000-01-03</th>
+      <td>2</td>
+      <td>-0.326012</td>
+      <td>-0.137593</td>
+      <td>-0.287003</td>
+    </tr>
+    <tr>
+      <th>2000-01-04</th>
+      <td>3</td>
+      <td>1.213350</td>
+      <td>0.039471</td>
+      <td>1.068784</td>
+    </tr>
+    <tr>
+      <th>2000-01-05</th>
+      <td>4</td>
+      <td>0.108342</td>
+      <td>0.553074</td>
+      <td>1.213043</td>
+    </tr>
+    <tr>
+      <th>2000-01-06</th>
+      <td>5</td>
+      <td>0.374637</td>
+      <td>0.776962</td>
+      <td>-0.125644</td>
+    </tr>
+    <tr>
+      <th>2000-01-07</th>
+      <td>6</td>
+      <td>-1.239929</td>
+      <td>0.761446</td>
+      <td>-0.847097</td>
+    </tr>
+    <tr>
+      <th>2000-01-08</th>
+      <td>7</td>
+      <td>1.568350</td>
+      <td>1.051489</td>
+      <td>0.526787</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<ul class="simple">
+<li><p>You can use this access only if the index element is a valid Python identifier, e.g. s.1 is not allowed. See here for an explanation of valid identifiers.</p></li>
+<li><p>The attribute will not be available if it conflicts with an existing method name, e.g. s.min is not allowed, but s[‘min’] is possible.</p></li>
+<li><p>Similarly, the attribute will not be available if it conflicts with any of the following list: index, major_axis, minor_axis, items.</p></li>
+<li><p>In any of these cases, standard indexing will still work, e.g. s[‘1’], s[‘min’], and s[‘index’] will access the corresponding element or column.</p></li>
+</ul>
+<p>If you are using the IPython environment, you may also use tab-completion to see these accessible attributes.</p>
+<p>You can also assign a <code class="docutils literal notranslate"><span class="pre">dict</span></code> to a row of a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;x&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="s1">&#39;y&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]})</span>
+<span class="n">x</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;x&#39;</span><span class="p">:</span> <span class="mi">9</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">:</span> <span class="mi">99</span><span class="p">}</span>
+<span class="n">x</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>x</th>
+      <th>y</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>9</td>
+      <td>99</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>3</td>
+      <td>5</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<p>You can use attribute access to modify an existing element of a Series or column of a DataFrame, but be careful; if you try to use attribute access to create a new column, it creates a new attribute rather than a new column. In 0.21.0 and later, this will raise a <code class="docutils literal notranslate"><span class="pre">UserWarning</span></code>:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;one&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">]})</span>
+<span class="n">df</span><span class="o">.</span><span class="n">two</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>C:\Users\87554\AppData\Local\Temp\ipykernel_46616\269534380.py:2: UserWarning: Pandas doesn&#39;t allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  df.two = [4, 5, 6]
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>one</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1.0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>2.0</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>3.0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+</section>
+<section id="slicing-ranges">
+<h3><span class="section-number">5.4.1.3.4. </span>Slicing ranges<a class="headerlink" href="#slicing-ranges" title="Permalink to this headline">#</a></h3>
+<p>For now, we explain the semantics of slicing using the [] operator.</p>
+<p>With Series, the syntax works exactly as with an ndarray, returning a slice of the values and the corresponding labels:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2000-01-01    0.311438
+2000-01-02   -0.459154
+2000-01-03    1.408196
+2000-01-04    0.926176
+2000-01-05   -0.490948
+2000-01-06   -1.378264
+2000-01-07    0.628157
+2000-01-08    0.291123
+Freq: D, Name: A, dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2000-01-01    0.311438
+2000-01-02   -0.459154
+2000-01-03    1.408196
+2000-01-04    0.926176
+2000-01-05   -0.490948
+Freq: D, Name: A, dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="p">[::</span><span class="mi">2</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2000-01-01    0.311438
+2000-01-03    1.408196
+2000-01-05   -0.490948
+2000-01-07    0.628157
+Freq: 2D, Name: A, dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2000-01-08    0.291123
+2000-01-07    0.628157
+2000-01-06   -1.378264
+2000-01-05   -0.490948
+2000-01-04    0.926176
+2000-01-03    1.408196
+2000-01-02   -0.459154
+2000-01-01    0.311438
+Freq: -1D, Name: A, dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<p>Note that setting works as well:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">s2</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+<span class="n">s2</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="n">s2</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2000-01-01    0.000000
+2000-01-02    0.000000
+2000-01-03    0.000000
+2000-01-04    0.000000
+2000-01-05    0.000000
+2000-01-06   -1.378264
+2000-01-07    0.628157
+2000-01-08    0.291123
+Freq: D, Name: A, dtype: float64
+</pre></div>
+</div>
+</div>
+</div>
+<p>With DataFrame, slicing inside of <code class="docutils literal notranslate"><span class="pre">[]</span></code> slices the rows. This is provided largely as a convenience since it is such a common operation.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>one</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1.0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>2.0</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>3.0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>one</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2</th>
+      <td>3.0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>2.0</td>
+    </tr>
+    <tr>
+      <th>0</th>
+      <td>1.0</td>
+    </tr>
+  </tbody>
+</table>
+</div></div></div>
+</div>
+</section>
+</section>
+<section id="acknowledgments">
+<h2><span class="section-number">5.4.1.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<p>Thanks for <a class="reference external" href="https://pandas.pydata.org/docs/user_guide/index.html">Pandas user guide</a>. It contributes the majority of the content in this chapter.</p>
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    </li>
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="pandas.html">
      5.4. Pandas
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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+    </ul>
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-    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
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+     37.48. Gradient descent
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+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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-     37.114. DQN On Foreign Exchange Market
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      5.3. NumPy
     </a>
    </li>
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
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+    <label for="toctree-checkbox-3">
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+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/working-with-data/data-preparation.html">
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    6. Data visualization
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   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
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-     37.46. ML linear regression - assignment 2
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     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
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-     37.114. DQN On Foreign Exchange Market
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      5.3. NumPy
     </a>
    </li>
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
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+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
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    6. Data visualization
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    11. Regression models for Machine Learning
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   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
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     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
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+     37.48. Gradient descent
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-     37.82. Random Forest Classifier with Feature Importance
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-     37.114. DQN On Foreign Exchange Market
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+     37.116. Basic classification: Classify images of clothing
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   </a>
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      5.3. NumPy
     </a>
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
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+    <label for="toctree-checkbox-3">
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+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
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    6. Data visualization
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   </a>
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    8. Data Science in the cloud
   </a>
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    11. Regression models for Machine Learning
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    13. Getting started with classification
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-    <a class="reference internal" href="../assignments/ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
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     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
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-     37.81. Boosting with tuning
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-     37.82. Random Forest Classifier with Feature Importance
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-     37.85. Counterintuitive Challenges in ML Debugging
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-     37.86. Case Study: Debugging in Classification
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-     37.92. How to choose cnn architecture mnist
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-     37.94. Sign Language Digits Classification with CNN
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-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
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-     37.101. Google Stock Price Prediction RNN
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-     37.103. Intro to Autoencoders
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-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
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-     37.105. Fun with Variational Autoencoders
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     <a class="reference internal" href="../assignments/deep-learning/time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
+     37.104. Time Series Forecasting Assignment
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-     37.108. Neural Networks for Classification with TensorFlow
+     37.106. Neural Networks for Classification with TensorFlow
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-     37.109. NN Classify 15 Fruits Assignment
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-     37.114. DQN On Foreign Exchange Market
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-     37.115. Art by gan
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-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
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   </a>
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      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/working-with-data/data-preparation.html">
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    6. Data visualization
   </a>
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    7. Data Science lifecycle
   </a>
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    8. Data Science in the cloud
   </a>
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    11. Regression models for Machine Learning
   </a>
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   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
   </a>
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    14. Clustering models for Machine Learning
   </a>
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    15. Getting started with ensemble learning
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    16. Introduction to Gradient Boosting
   </a>
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   </a>
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-     37.45. ML linear regression - assignment 1
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-    <a class="reference internal" href="../assignments/ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
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    </li>
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+     37.47. Loss Function
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     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
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+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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-     37.78. Decision trees
+     37.76. Decision trees
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-     37.79. Hyperparameter tuning gradient boosting
+     37.77. Hyperparameter tuning gradient boosting
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-     37.80. Gradient boosting
+     37.78. Gradient boosting
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-     37.81. Boosting with tuning
+     37.79. Boosting with tuning
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-     37.82. Random Forest Classifier with Feature Importance
+     37.80. Random Forest Classifier with Feature Importance
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    </li>
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     <a class="reference internal" href="../assignments/machine-learning-productionization/data-engineering.html">
-     37.84. Data engineering
+     37.82. Data engineering
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-     37.85. Counterintuitive Challenges in ML Debugging
+     37.83. Counterintuitive Challenges in ML Debugging
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     <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-classification.html">
-     37.86. Case Study: Debugging in Classification
+     37.84. Case Study: Debugging in Classification
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-     37.87. Case Study: Debugging in Regression
+     37.85. Case Study: Debugging in Regression
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-     37.88. Study the solvers
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-     37.89. Build classification models
+     37.87. Build classification models
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-     37.90. Build Classification Model
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-     37.91. Parameter play
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-     37.92. How to choose cnn architecture mnist
+     37.90. How to choose cnn architecture mnist
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     <a class="reference internal" href="../assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
-     37.94. Sign Language Digits Classification with CNN
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     <a class="reference internal" href="../assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html">
-     37.96. Object Recognition in Images using CNN
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-     37.97. Intro to TensorFlow for Deep Learning
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-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
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-     37.101. Google Stock Price Prediction RNN
+     37.99. Google Stock Price Prediction RNN
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-     37.103. Intro to Autoencoders
+     37.101. Intro to Autoencoders
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     <a class="reference internal" href="../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
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     </a>
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     <a class="reference internal" href="../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
+     37.103. Fun with Variational Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
+     37.104. Time Series Forecasting Assignment
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    </li>
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     <a class="reference internal" href="../assignments/deep-learning/nn-for-classification-assignment.html">
-     37.108. Neural Networks for Classification with TensorFlow
+     37.106. Neural Networks for Classification with TensorFlow
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
-     37.109. NN Classify 15 Fruits Assignment
+     37.107. NN Classify 15 Fruits Assignment
     </a>
    </li>
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     <a class="reference internal" href="../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.114. DQN On Foreign Exchange Market
+     37.112. DQN On Foreign Exchange Market
     </a>
    </li>
    <li class="toctree-l2">
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-     37.115. Art by gan
+     37.113. Art by gan
     </a>
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-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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    </li>
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     <a class="reference internal" href="../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
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   </ul>
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   <a class="reference internal" href="../slides/introduction.html">
    38. Slides
   </a>
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      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/working-with-data/data-preparation.html">
@@ -254,8 +276,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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    6. Data visualization
   </a>
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   <a class="reference internal" href="../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
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   <a class="reference internal" href="../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
   </a>
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   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
   </a>
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   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
   </a>
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   <a class="reference internal" href="../ml-advanced/clustering/clustering-models-for-machine-learning.html">
    14. Clustering models for Machine Learning
   </a>
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   <a class="reference internal" href="../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
    15. Getting started with ensemble learning
   </a>
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   <a class="reference internal" href="../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
    16. Introduction to Gradient Boosting
   </a>
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   <a class="reference internal" href="../assignments/README.html">
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   </a>
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-    <a class="reference internal" href="../assignments/ml-fundamentals/ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
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-   <li class="toctree-l2">
-    <a class="reference internal" href="../assignments/ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
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    </li>
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-     37.47. Linear Regression Metrics
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-     37.48. Loss Function
+     37.47. Loss Function
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     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
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+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
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     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-2.html">
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     <a class="reference internal" href="../assignments/ml-fundamentals/ml-neural-network-1.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
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-     37.76. Random forests for classification
+     37.74. Random forests for classification
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-     37.77. Beyond random forests: more ensemble models
+     37.75. Beyond random forests: more ensemble models
     </a>
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     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/decision-trees.html">
-     37.78. Decision trees
+     37.76. Decision trees
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     <a class="reference internal" href="../assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
-     37.79. Hyperparameter tuning gradient boosting
+     37.77. Hyperparameter tuning gradient boosting
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     <a class="reference internal" href="../assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
-     37.80. Gradient boosting
+     37.78. Gradient boosting
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     <a class="reference internal" href="../assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html">
-     37.81. Boosting with tuning
+     37.79. Boosting with tuning
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-     37.82. Random Forest Classifier with Feature Importance
+     37.80. Random Forest Classifier with Feature Importance
     </a>
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-     37.84. Data engineering
+     37.82. Data engineering
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-     37.85. Counterintuitive Challenges in ML Debugging
+     37.83. Counterintuitive Challenges in ML Debugging
     </a>
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     <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-classification.html">
-     37.86. Case Study: Debugging in Classification
+     37.84. Case Study: Debugging in Classification
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     <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-regression.html">
-     37.87. Case Study: Debugging in Regression
+     37.85. Case Study: Debugging in Regression
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-     37.88. Study the solvers
+     37.86. Study the solvers
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-     37.89. Build classification models
+     37.87. Build classification models
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-     37.90. Build Classification Model
+     37.88. Build Classification Model
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    </li>
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-     37.91. Parameter play
+     37.89. Parameter play
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-     37.92. How to choose cnn architecture mnist
+     37.90. How to choose cnn architecture mnist
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     <a class="reference internal" href="../assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
-     37.94. Sign Language Digits Classification with CNN
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     <a class="reference internal" href="../assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html">
-     37.96. Object Recognition in Images using CNN
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-     37.97. Intro to TensorFlow for Deep Learning
+     37.95. Intro to TensorFlow for Deep Learning
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-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
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-     37.101. Google Stock Price Prediction RNN
+     37.99. Google Stock Price Prediction RNN
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-     37.103. Intro to Autoencoders
+     37.101. Intro to Autoencoders
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     <a class="reference internal" href="../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
+     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
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     <a class="reference internal" href="../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
+     37.103. Fun with Variational Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
+     37.104. Time Series Forecasting Assignment
     </a>
    </li>
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     <a class="reference internal" href="../assignments/deep-learning/nn-for-classification-assignment.html">
-     37.108. Neural Networks for Classification with TensorFlow
+     37.106. Neural Networks for Classification with TensorFlow
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
-     37.109. NN Classify 15 Fruits Assignment
+     37.107. NN Classify 15 Fruits Assignment
     </a>
    </li>
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-     37.114. DQN On Foreign Exchange Market
+     37.112. DQN On Foreign Exchange Market
     </a>
    </li>
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-     37.115. Art by gan
+     37.113. Art by gan
     </a>
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-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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    </li>
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     <a class="reference internal" href="../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
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   </ul>
@@ -1137,8 +1154,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../slides/introduction.html">
    38. Slides
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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    6. Data visualization
   </a>
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   <a class="reference internal" href="../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
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   <a class="reference internal" href="../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
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   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
   </a>
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   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
   </a>
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    14. Clustering models for Machine Learning
   </a>
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    15. Getting started with ensemble learning
   </a>
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   <a class="reference internal" href="../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
    16. Introduction to Gradient Boosting
   </a>
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   <a class="reference internal" href="../assignments/README.html">
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   </a>
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-    <a class="reference internal" href="../assignments/ml-fundamentals/ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
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-   <li class="toctree-l2">
-    <a class="reference internal" href="../assignments/ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
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    </li>
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     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
+     37.46. Linear Regression Metrics
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     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.48. Loss Function
+     37.47. Loss Function
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     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
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+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
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     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-2.html">
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     <a class="reference internal" href="../assignments/ml-fundamentals/ml-neural-network-1.html">
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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-     37.74. Regularized Linear Models
+     37.72. Regularized Linear Models
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-     37.75. Random forests intro and regression
+     37.73. Random forests intro and regression
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-     37.76. Random forests for classification
+     37.74. Random forests for classification
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     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
-     37.77. Beyond random forests: more ensemble models
+     37.75. Beyond random forests: more ensemble models
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/decision-trees.html">
-     37.78. Decision trees
+     37.76. Decision trees
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     <a class="reference internal" href="../assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
-     37.79. Hyperparameter tuning gradient boosting
+     37.77. Hyperparameter tuning gradient boosting
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     <a class="reference internal" href="../assignments/ml-advanced/gradient-boosting/gradient-boosting-assignment.html">
-     37.80. Gradient boosting
+     37.78. Gradient boosting
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     <a class="reference internal" href="../assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html">
-     37.81. Boosting with tuning
+     37.79. Boosting with tuning
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-     37.82. Random Forest Classifier with Feature Importance
+     37.80. Random Forest Classifier with Feature Importance
     </a>
    </li>
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     <a class="reference internal" href="../assignments/machine-learning-productionization/data-engineering.html">
-     37.84. Data engineering
+     37.82. Data engineering
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-     37.85. Counterintuitive Challenges in ML Debugging
+     37.83. Counterintuitive Challenges in ML Debugging
     </a>
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     <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-classification.html">
-     37.86. Case Study: Debugging in Classification
+     37.84. Case Study: Debugging in Classification
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-regression.html">
-     37.87. Case Study: Debugging in Regression
+     37.85. Case Study: Debugging in Regression
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-     37.88. Study the solvers
+     37.86. Study the solvers
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-     37.89. Build classification models
+     37.87. Build classification models
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-     37.90. Build Classification Model
+     37.88. Build Classification Model
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    </li>
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-     37.91. Parameter play
+     37.89. Parameter play
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-     37.92. How to choose cnn architecture mnist
+     37.90. How to choose cnn architecture mnist
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     <a class="reference internal" href="../assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
-     37.94. Sign Language Digits Classification with CNN
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     <a class="reference internal" href="../assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html">
-     37.96. Object Recognition in Images using CNN
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-     37.97. Intro to TensorFlow for Deep Learning
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-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     37.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
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     <a class="reference internal" href="../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.101. Google Stock Price Prediction RNN
+     37.99. Google Stock Price Prediction RNN
     </a>
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     <a class="reference internal" href="../assignments/deep-learning/autoencoder/autoencoder.html">
-     37.103. Intro to Autoencoders
+     37.101. Intro to Autoencoders
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     <a class="reference internal" href="../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
+     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
+     37.103. Fun with Variational Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
+     37.104. Time Series Forecasting Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/nn-for-classification-assignment.html">
-     37.108. Neural Networks for Classification with TensorFlow
+     37.106. Neural Networks for Classification with TensorFlow
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
-     37.109. NN Classify 15 Fruits Assignment
+     37.107. NN Classify 15 Fruits Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.114. DQN On Foreign Exchange Market
+     37.112. DQN On Foreign Exchange Market
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/gan/art-by-gan.html">
-     37.115. Art by gan
+     37.113. Art by gan
     </a>
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     <a class="reference internal" href="../assignments/deep-learning/gan/gan-introduction.html">
-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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    </li>
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     <a class="reference internal" href="../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
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   </ul>
@@ -1135,8 +1152,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../slides/introduction.html">
    38. Slides
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../data-science/data-visualization/data-visualization.html">
    6. Data visualization
   </a>
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   <a class="reference internal" href="../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
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   <a class="reference internal" href="../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
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   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
   </a>
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   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
   </a>
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   <a class="reference internal" href="../ml-advanced/clustering/clustering-models-for-machine-learning.html">
    14. Clustering models for Machine Learning
   </a>
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   <a class="reference internal" href="../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
    15. Getting started with ensemble learning
   </a>
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   <a class="reference internal" href="../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
    16. Introduction to Gradient Boosting
   </a>
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@@ -633,8 +655,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../assignments/README.html">
    37. Self-paced assignments
   </a>
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     </a>
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-    <a class="reference internal" href="../assignments/ml-fundamentals/ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
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-   <li class="toctree-l2">
-    <a class="reference internal" href="../assignments/ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
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    </li>
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     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
+     37.46. Linear Regression Metrics
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     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.48. Loss Function
+     37.47. Loss Function
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
+     37.50. ML logistic regression - assignment 1
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     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-2.html">
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     <a class="reference internal" href="../assignments/ml-fundamentals/ml-neural-network-1.html">
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+     37.53. Regression tools
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
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     <a class="reference internal" href="../assignments/deep-learning/time-series-forecasting-assignment.html">
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-     37.114. DQN On Foreign Exchange Market
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      5.3. NumPy
     </a>
    </li>
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
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+    <label for="toctree-checkbox-3">
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+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
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    6. Data visualization
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-     37.49. Gradient descent
+     37.48. Gradient descent
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+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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-     37.70. Model selection assignment 1
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-     37.71. Learning Curve To Identify Overfit &amp; Underfit
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-     37.74. Regularized Linear Models
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-     37.78. Decision trees
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-     37.79. Hyperparameter tuning gradient boosting
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-     37.80. Gradient boosting
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-     37.81. Boosting with tuning
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-     37.82. Random Forest Classifier with Feature Importance
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-     37.86. Case Study: Debugging in Classification
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-     37.94. Sign Language Digits Classification with CNN
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-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
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-     37.101. Google Stock Price Prediction RNN
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     <a class="reference internal" href="../assignments/deep-learning/time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
+     37.104. Time Series Forecasting Assignment
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-     37.114. DQN On Foreign Exchange Market
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-     37.117. Generative Adversarial Networks (GANs)
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-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
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   </a>
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      5.3. NumPy
     </a>
    </li>
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
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+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
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    6. Data visualization
   </a>
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   </a>
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   <a class="reference internal" href="../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
   </a>
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    11. Regression models for Machine Learning
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   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
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    16. Introduction to Gradient Boosting
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-     37.46. ML linear regression - assignment 2
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     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
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+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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-     37.114. DQN On Foreign Exchange Market
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   </a>
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      5.3. NumPy
     </a>
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
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+    <label for="toctree-checkbox-3">
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+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
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    6. Data visualization
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    11. Regression models for Machine Learning
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-     37.46. ML linear regression - assignment 2
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-     37.49. Gradient descent
+     37.48. Gradient descent
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+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.68. Decision Trees - Intro and Regression
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-     37.70. Model selection assignment 1
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-     37.71. Learning Curve To Identify Overfit &amp; Underfit
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-     37.72. Dropout and Batch Normalization
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-     37.79. Hyperparameter tuning gradient boosting
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-     37.80. Gradient boosting
+     37.78. Gradient boosting
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-     37.82. Random Forest Classifier with Feature Importance
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-     37.86. Case Study: Debugging in Classification
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-     37.92. How to choose cnn architecture mnist
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-     37.94. Sign Language Digits Classification with CNN
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-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
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-     37.101. Google Stock Price Prediction RNN
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-     37.103. Intro to Autoencoders
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-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
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-     37.105. Fun with Variational Autoencoders
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     <a class="reference internal" href="../assignments/deep-learning/time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
+     37.104. Time Series Forecasting Assignment
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-     37.108. Neural Networks for Classification with TensorFlow
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-     37.114. DQN On Foreign Exchange Market
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-     37.115. Art by gan
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-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
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   </a>
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      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
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+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/working-with-data/data-preparation.html">
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    6. Data visualization
   </a>
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    7. Data Science lifecycle
   </a>
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   <a class="reference internal" href="../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
   </a>
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    11. Regression models for Machine Learning
   </a>
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   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
   </a>
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    14. Clustering models for Machine Learning
   </a>
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    15. Getting started with ensemble learning
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    16. Introduction to Gradient Boosting
   </a>
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-     37.45. ML linear regression - assignment 1
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-    <a class="reference internal" href="../assignments/ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
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    </li>
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+     37.47. Loss Function
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     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
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+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
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-     37.114. DQN On Foreign Exchange Market
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
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+      <a class="reference internal" href="data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
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+     <li class="toctree-l3">
+      <a class="reference internal" href="data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
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-    <a class="reference internal" href="assignments/ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
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+     37.48. Gradient descent
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      5.3. NumPy
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-     37.64. Linear Regression Implementation from Scratch
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-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.67. Support Vector Machines (SVM) - Classification
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-     37.68. Decision Trees - Intro and Regression
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-     37.71. Learning Curve To Identify Overfit &amp; Underfit
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-     37.82. Random Forest Classifier with Feature Importance
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-     37.87. Case Study: Debugging in Regression
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-     37.92. How to choose cnn architecture mnist
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-     37.94. Sign Language Digits Classification with CNN
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-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
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-     37.101. Google Stock Price Prediction RNN
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-     37.103. Intro to Autoencoders
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     <a class="reference internal" href="assignments/deep-learning/time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
+     37.104. Time Series Forecasting Assignment
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-     37.109. NN Classify 15 Fruits Assignment
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-     37.114. DQN On Foreign Exchange Market
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-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
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   </a>
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      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
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+    <label for="toctree-checkbox-3">
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
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    6. Data visualization
   </a>
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    7. Data Science lifecycle
   </a>
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    8. Data Science in the cloud
   </a>
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    11. Regression models for Machine Learning
   </a>
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   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
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   </a>
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    14. Clustering models for Machine Learning
   </a>
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    16. Introduction to Gradient Boosting
   </a>
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   </a>
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-    <a class="reference internal" href="../assignments/ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
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    </li>
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+     37.47. Loss Function
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-     37.49. Gradient descent
+     37.48. Gradient descent
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+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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      5.3. NumPy
     </a>
    </li>
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
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+    <label for="toctree-checkbox-3">
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+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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    </li>
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+     37.48. Gradient descent
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-     37.60. Retrying some regression
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-     37.61. Pumpkin varieties and color
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-     37.63. Explore classification methods
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-     37.64. Linear Regression Implementation from Scratch
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-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
+     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.67. Support Vector Machines (SVM) - Classification
+     37.65. Support Vector Machines (SVM) - Classification
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-     37.68. Decision Trees - Intro and Regression
+     37.66. Decision Trees - Intro and Regression
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-     37.69. Decision Trees - Classification
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-     37.70. Model selection assignment 1
+     37.68. Model selection assignment 1
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-     37.71. Learning Curve To Identify Overfit &amp; Underfit
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-     37.79. Hyperparameter tuning gradient boosting
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-     37.82. Random Forest Classifier with Feature Importance
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-     37.94. Sign Language Digits Classification with CNN
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-     37.97. Intro to TensorFlow for Deep Learning
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-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
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-     37.101. Google Stock Price Prediction RNN
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-     37.103. Intro to Autoencoders
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     <a class="reference internal" href="../assignments/deep-learning/time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
+     37.104. Time Series Forecasting Assignment
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-     37.109. NN Classify 15 Fruits Assignment
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-     37.114. DQN On Foreign Exchange Market
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-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
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   </a>
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      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
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    6. Data visualization
   </a>
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    8. Data Science in the cloud
   </a>
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    11. Regression models for Machine Learning
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   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
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    16. Introduction to Gradient Boosting
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-    <a class="reference internal" href="../assignments/ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
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    </li>
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-     37.49. Gradient descent
+     37.48. Gradient descent
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+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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-     37.61. Pumpkin varieties and color
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+     37.61. Delicious asian and indian cuisines
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-     37.63. Explore classification methods
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-     37.64. Linear Regression Implementation from Scratch
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-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
+     37.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.67. Support Vector Machines (SVM) - Classification
+     37.65. Support Vector Machines (SVM) - Classification
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-     37.68. Decision Trees - Intro and Regression
+     37.66. Decision Trees - Intro and Regression
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-     37.69. Decision Trees - Classification
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-     37.70. Model selection assignment 1
+     37.68. Model selection assignment 1
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-     37.71. Learning Curve To Identify Overfit &amp; Underfit
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-     37.72. Dropout and Batch Normalization
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-     37.74. Regularized Linear Models
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-     37.78. Decision trees
+     37.76. Decision trees
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-     37.79. Hyperparameter tuning gradient boosting
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-     37.80. Gradient boosting
+     37.78. Gradient boosting
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-     37.81. Boosting with tuning
+     37.79. Boosting with tuning
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-     37.82. Random Forest Classifier with Feature Importance
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+     37.82. Data engineering
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-     37.85. Counterintuitive Challenges in ML Debugging
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-     37.86. Case Study: Debugging in Classification
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-     37.87. Case Study: Debugging in Regression
+     37.85. Case Study: Debugging in Regression
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-     37.94. Sign Language Digits Classification with CNN
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-     37.97. Intro to TensorFlow for Deep Learning
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-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
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-     37.101. Google Stock Price Prediction RNN
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-     37.103. Intro to Autoencoders
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-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
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-     37.105. Fun with Variational Autoencoders
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     <a class="reference internal" href="../assignments/deep-learning/time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
+     37.104. Time Series Forecasting Assignment
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     <a class="reference internal" href="../assignments/deep-learning/nn-for-classification-assignment.html">
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-     37.114. DQN On Foreign Exchange Market
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-     37.115. Art by gan
+     37.113. Art by gan
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-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
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   <a class="reference internal" href="../slides/introduction.html">
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   </a>
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      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/working-with-data/data-preparation.html">
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    6. Data visualization
   </a>
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   </a>
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   <a class="reference internal" href="../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
   </a>
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    11. Regression models for Machine Learning
   </a>
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   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
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    14. Clustering models for Machine Learning
   </a>
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    16. Introduction to Gradient Boosting
   </a>
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-     37.45. ML linear regression - assignment 1
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-    <a class="reference internal" href="../assignments/ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
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-     37.49. Gradient descent
+     37.48. Gradient descent
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+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
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   </a>
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      5.3. NumPy
     </a>
    </li>
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
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+    <label for="toctree-checkbox-3">
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+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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    </li>
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     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
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+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
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-     37.52. ML logistic regression - assignment 2
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     <a class="reference internal" href="../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
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     <a class="reference internal" href="../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
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-    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression-implementation-from-scratch.html">
-     37.64. Linear Regression Implementation from Scratch
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-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.71. Learning Curve To Identify Overfit &amp; Underfit
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     <a class="reference internal" href="../assignments/deep-learning/time-series-forecasting-assignment.html">
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     <a class="reference internal" href="../assignments/deep-learning/nn-for-classification-assignment.html">
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-     37.114. DQN On Foreign Exchange Market
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      5.3. NumPy
     </a>
    </li>
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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-    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-linear-regression-2.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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-    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-linear-regression-2.html">
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+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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+   <li class="toctree-l2 has-children">
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     <a class="reference internal" href="../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/working-with-data/data-preparation.html">
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    6. Data visualization
   </a>
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    7. Data Science lifecycle
   </a>
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   <a class="reference internal" href="../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
   </a>
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   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
   </a>
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@@ -392,8 +414,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
   </a>
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   <a class="reference internal" href="clustering/clustering-models-for-machine-learning.html">
    14. Clustering models for Machine Learning
   </a>
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   <a class="reference internal" href="ensemble-learning/getting-started-with-ensemble-learning.html">
    15. Getting started with ensemble learning
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   <a class="reference internal" href="gradient-boosting/introduction-to-gradient-boosting.html">
    16. Introduction to Gradient Boosting
   </a>
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@@ -633,8 +655,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../assignments/README.html">
    37. Self-paced assignments
   </a>
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-    <a class="reference internal" href="../assignments/ml-fundamentals/ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
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-   <li class="toctree-l2">
-    <a class="reference internal" href="../assignments/ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.48. Loss Function
+     37.47. Loss Function
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-2.html">
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     <a class="reference internal" href="../assignments/ml-fundamentals/ml-neural-network-1.html">
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     <a class="reference internal" href="../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
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     <a class="reference internal" href="../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
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-     37.64. Linear Regression Implementation from Scratch
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
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     <a class="reference internal" href="../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
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"}}]}}, 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"}}]}}}, 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yWcBqBfJ5VANYfPNbMfe//xl6bZ3ZVRJRCchmFxGLxaAoEQwPD0HXdQCFu33cbjc0TTN2pq2urjGxUioHDCy0evk8vO89BMdPD0PQl3aMXL7mDMBx+Eeo6VEx86OfQa/ymlcnEZWEn/70JxgY6DO+bmlphSSF4fcH4XQ6zSuMyhIDC62a84ffh/Mnj570cSGfh1VR4P78P2PhDvaQItpKpqePQZYj2L17DzyewvqTYDCE2dk4RDEMUZTg9VabXCWVMwYWWjXXl/4PdIsVgpY/6fcIWh7ur34ZCx/9GGC3b2B1RLTRFhaSUFUFqipjcnISAOB2u7F373kAgFBIhChKXJdC64KBhVZH12F/7jenDCvLLNPHYO3vQ36ppwcRbR65XA59fTHIsoyhoQFoWuHysNVqRWenD42NTcb3Lq9PIVoPDCy0OroOaGfQ6XRpbQsRlb/lxbJAIbA88cRjxp4pzc3bIIphBAJBuN1us0qkLYCBhVbHYkE+EIS1JwrhuDevE9FdLuSXdqUkovI1MxOHosiIx+N405uuBgC4XC7s2LELdrsdoigZfX6Iio2BhVYt9d4PoPJv/+aU36NbrUjfdAvATZ+IylIqlUJPTxSKEsH4+JgxHo/HUVtbCCcXX3ypWeXRFsbAQquWvukWuL/ypcJZlvxr17LoViv0qios/F8f2fjiiGjNdF3H+PgYXnzxdxgY6Ed+aX5bLBa0t3dCkrjzLJmPgYVWr7ISM9/9IaoP3QT7C7+DbrMBuRyEpc3jtJZWzD70bWidPrMrJaLT0HUd+XweNlvhz0AymURvbwwA0NDQCFGUEAqJqKjwmFkmkYGBhc6I3tyMmcNPwP7sM3B+51uwTk7A0VCH+auuQerAnwA2/koRlbJEIrG0RX4EwaCICy7YB0EQ4PN14bzzzkcwGEJ9fYPZZRK9Bv+60JkTBGT3X4zs/oths1ngqPUgG08CuTO4i4iINkwmk0EsFoUsRzA6OmKM9/bGcMEF+wAUtsvft2+/WSUSnRYDCxHRJvbkk49DUWTjNmRBENDW1g5RDMPv95tcHdHqMbAQEW0Suq4jHp9GbW2dsbuspunI5XKora2DJIURCnEBLZUnBhYiojI3Pz8PVVWgKBFMTx/DddfdiKamZgDA3r3nYefOXWhsbOIW+VTWGFiIiMpQNruIWCwGVZUxNDRo7EZrs9kwPX3MCCzLe6cQlTsGFiKiMjM9fQyPPPJtZLNZY6ylpRWiKCEQCMHpdJpYHVFxMLAQEZW46eljmJubg8/XBQCoqamFw+FARUUFRDEMUZTg9VabWyRRkTGwEBGVoFRqwViXMjk5CY/Hg3e+8y9gsVhgsVjwtrfdgMrKSq5LoS2DgYWIqERks1n09/dBUSIYHByAttQh3WKxoKmpGZlMGm53BQCgqqrKzFKJNhwDCxFRifjtb5/FkSMvGl83NTVDFMMIBkNwu93mFUZUAhhYiIhMMDMTh6rK6OzsQnPzNgBAICCip6fH2C+Fd/gQvYqBhYhog6TTaUSjKhQlgvHxMQDA/HzSCCxNTU145zvfxXUpRCfAwEJEVES6rqOvrxeKEkF/fx/y+TyAwhb5HR0+486f5TEiOjEGFiKiInvmmf9EIjELAGhoaIQoSgiFRFRUeEyujKh8MLAQnUwyCedPfwzL5AQ0bzUWD74JekOD2VVRCUskElBVGYOD/Xj3u28FUDhrsnv3HszNzUGSwqiv5+8Q0VowsBD9sXweFf/4P+H+P1+AJZmEbrFA0DToNhvS19+E5P/8HPRK3lJKBZlMBrFYFIoiY2RkGABgsQjo7e1FQ0MrAOCcc/aYWSLRpsDAQnQ8XUfVbR+E85FvQ1jqzSIs7YUh5HJwffubsL3yMma+/2PAw9P5W9n09DSef/436OvrRS6XA1A4m9LW1o7t28Po7OzEwkLO5CqJNg8GFqLjOA4/Ctd3Hz7p40I+D9sffo+K+/4FCx/92AZWRmbTdR25XA52u90Yi0ZVAEBtbR0kKYxgUERVVRVsNgucTicDC9E6YmAhOo77S/dBt1ohLN3JcSKCpsH9wBex8NcfBWycQptdMjkPRSlskV9XV4crr7wKAFBXV4eLLno92tra0djYxDt8iIqM77ZEy3Qd9l89fcqwsswyOQFrXy/ywdAGFEYbLZtdRG9v4VbkoaFB6EuXB5PJeeRyOdiWguq5577OzDKJykI2m0VfXwyKIuPmm6+D1epa0/NseGBJJpO4+uqrMT4+ju985zs455xzNroEopNbRVgxLC4Wrw4yzW9/+2scOfICstmsMdbS0gpRlOD3B42wQkQnp+s6RkaGoSgyYrEoFpfeL7PZLFyuMgksX/jCF4yNk4hKiiAg39UNa1+vseD2ZHS7A1pHxwYVRsU0PT2NqqpK2O0OAIDNZkc2m0V1dTVEMQxRlOD1VptcJVF5iMfjUJQIFEXG/PycMe71VkMURbhca++JtaGBpaenB//2b/+Gj33sY/jUpz61kS9NtCrpd78Pnk/991N+j261In3djdCrvBtUFa23VGoB0agKWY5gcnICBw5cCUkKAwDC4TC2bduGbdtauC6FaBVSqZTRcmJiYtwYdzqdCASCCIUktLS0QhAEWCyWNb/OhgaWz3zmM7j55pvR3d29kS9LtGrpWw7B/eX7YRkaPOFaFt1ihe5yIfXXt5tQHZ2NXC5nbJE/ODgAbel2dYvFYuxCCwBudwXc7gqzyiQqC7lcDv39fVCUCAYG+lfMp46OTohiGF1d3et6CXXDAsvhw4ehKAruuecevPzyy+vynFbr2pMarY/lY7BpjkVtDeb+4zCqbnwbrJGjxh1DxuZxdbWY/8Z3IEhSya1Y33THYh1lMhk89NDXkE6njbGWlm3Grchu99pPU58Ij0Xp4LFYP7quY2xsDLIcQU+PumI+bdvWDEnajmAwhIqKkwf+szlpuSHvualUCnfffTduv/12VFZWrtvzer3r+yZDa7epjkWtBPzhJeAnP4Hw4IPA8DCE2lrg+uthueEGeNe4YGyjbKpjsUbxeBwjIyPYuXPn0ogHHR2tmJ2dxfbt27Fz507U1dUVvQ4ei9LBY7F2MzMzeOWVV3D06FHMzMwAKASPxsY6Yz7V19cXvY4NCSz33nsv6uvrcd11163r8yYSKeTz2ro+J50Zq9UCr9e9OY/F/jcU/ne8VB5IJc2p5zQ29bFYhXQ6baxLGRsbhcViQU1No9Fg8JJLDsDtdhvrUuLx4h3HrX4sSgmPxdpkMhljPo2OjhjjdrsdgUAQoiihra3dWJOy2vlUXe1e8zqWogeW4eFhPPDAA/j85z+PubnCiuGFhQXj/5PJJDxr3OI8n9eQy/EXsBTwWJSOrXQs8vk8Bgb6oSgR9Pf3GXcgCoKA1tZ2JJNpOByFT9YOhwv5vA7g1HeArW99W+dYlDoei9PL5/MYHOyHoijo71/ZcqK9vQOhkAS/32/cUadpMNaurNZpbsA8paIHlqGhIWSzWXzgAx94zWOHDh3Cnj178PDDJ98KnYjoZGT5KJ588gnj6/r6BoiihFBIhMezfpefiTYrXdcxOTkBRZERjapIpRaMx+rq6iGKYYRC4rou51irogeW7du34+tf//qKsaNHj+Kzn/0s7rrrLm4cR0SrkkgkoKoyqqtrEFzaYdjvD+J3v3sefn8AoiihoaHR5CqJysPc3BxUVYaiyIjHp43xiooKhEIiRDGM+vqGkrq1v+iBxev1Yt++fSd8bOfOncctiiMiWmlxcRGxWBSyHMHIyDAAoLl5mxFYXC4XbrnlUEm9qRKVqsJ86oGiFObTcssJm82Grq5uiGIYHR2dZ7VXSjGV2p2ZREQYHByALB9Fb29sxXX01tY2iGIYuq4bIYVhhejkNE3D0NAgVFVGLBZDLvdqy4nW1jZIUhjd3QE4nU4Tq1wdUwLLvn37IMuyGS9NRGXgpZeOoL+/DwBQW1u7dB1dQlVVlbmFEZWJY8emIMsRRKMKkslX7+Cpqakx5pPXW167dfMMCxGZJpmch6oqUFUFb3rTm4030J07dy31HpHQ2NjEsyhEq7CwkISqKlAUGVNTk8a4y+VCIBCCJG1HU1P5zicGFiLaUNnsInp7C1vkDw0NGtfRVVXG6153AQDA5+uGz8cWHkSnk81m0dcXg6LIGBwcMOaT1WqFz9cFUQyjs9MHq9VqcqVnj4GFiDZEMjmPX//6WcRiUWSzr15Hb2lphShK8PuDJlZHVD50XcfIyDAURUYsFsXi4qLx2LZtLRBFCYFACK4S35X7TDGwEFHRLC4uwuEobDLlcDiMsLJ8uUcUJVRX15hbJFGZiMfjUJQIFEXG/PycMV5V5YUkSQiFJNTU1JpYYXExsBDRukqlFowtvfP5PG688e0QBAF2uwOXXPIGVFdXY9u2lrK9jk60kVKpFKJRFYoSwcTEuDHudDoRCAQRCkloaWndEvOJgYWIztqpWs3PzSXg9VYDAMLh7WaWSVQWlueTqsro7+9bMZ86OjohimH4fF2w2+0mV7qxGFiI6KwcPfoyfvWrp5HJZIyxxsYmSFIYwWAIbvfJW80TUYGu6xgfH4OiRNDTE0U6nTYea2xsNLbI38rziYGFiM7I7OwMbDab0aunosKDTCaDyspKY3+Huro6k6skKg+JxCwURYaiRDA7O2uMV1ZWIhQqrPOqq6s3scLSwcBCRKeVTqfR06NCUWSMjY3i3HNfh4suej0AoKOjE3/6p29Da2vblriOTnS2MpmMMZ9GR0eMcbvdvtQXK4zW1raS3SLfLAwsRHRC+XweAwP9UBQZ/f29yOfzAApb4S8svNrR1WKxoK2t3awyicpCPp/H0NAAZLkwn45vOdHe3gFRDKO7uxt2u8PkSksXAwsRvYau6/j2t7+BeDxujNXXN0AUJYRConE5iIhOTtd1TE5OGLs5p1KvBv26unpjXUplJefTajCwEBHm5ubQ1xfDrl27IQjCUqPBdmQyGeM6ekNDo9llEpWF+fl5qKoMWY4gHp82xt3uCoRCojGfeAn1zDCwEG1RJ2o139DQiJaWVgDAvn37cckll/E6OtEqZLOLiMViUJQIhoeHjC3ybTYburq6IYphdHR0cj6dBQYWoi1kudW8okTQ29u7otX8H69DKYd280Rm0jQNw8NDUJQIYrHYivnU0tIKSQrD7w9yLq0TBhaiLWRsbBQ/+tEPjK9ra2sRCkll2WqeyCzT08cgyxGoqoxkMmmM19TULF1CDXM+FQEDC9EmlUzOQ1UVAMDevecBKHzqa2xsQnPzNohiuKxbzRNtpIWFJFRVgaLImJqaNMZdLhcCgRAkKYympmbOpyJiYCHaRLLZLBRFxtGjR41W8y6XG+ecswdWqxWCIOC6627kmyrRKmSzWfT390KWZQwNDRhb5FutVvh8XQiFJHR2+mCz8U/pRuB/ZaJNYGxsFLL8CkZGBpBIJKFphQV/y63mlxcAAmBYIToFXdcxMjIMVVXQ06NicXHReGz5zGQgEITb7Taxyq2JgYWoTOm6boSP3t4YIpGjcLsd8HqrEQwWbp2srq4xt0iiMhGPx6GqMhRFxtxcwhivqvJCFAu39tfU1JpYITGwEJWRVGoB0agKWY7gwgsvQmenDwAgSWHk81lceOF5cLurkc/rp3kmIkqlUohGVaiqjPHxMWPc4XAgEAghFBLZcqKEMLAQlbjlVvOKEsHAQL9xHV1RIkZgqaurxxvfeAC1tR7E40kADCxEJ5LL5ZZaThTm03LLCYvFgvb2TkiSBJ+vG3a73eRK6Y8xsBCVqFwuh6ef/iV6eqLIZDLGeGNjEyQpjGAwZGJ1ROVD13WMjo7i6NGj6OlRkU6njccaGxshioX5VFHhMbFKOh0GFqISkk6n4XK5ABTuRBgbG0Mmkzmu1XwYdXV1JldJVB4SiVn09KgYHIxhdHTCWIzu8XgQCkmQpDDq6upNrpJWi4GFyGTpdBqxWBSKIuPYsSkcOvQe2O12CIKAiy56PaxWK1vNE61SJpNBLBaFLEcwOjoCi0WA2+2AzWZHd7cfohjmfCpTDCxEJsjn8xgc7DdazS9fRxcEAePjY2hv7wAA+HxdJlZJVB7y+TyGhgaM+ZTL5QAU5lN7ewcuuOBcNDa2QhD4J6+c8egRbbCBgX489tjPkE6njLG6unpIUqHVvMfDVvNEp6PrOqamJqEoMlRVQSq1YDxWW1u3NJ8k1NR4jcXouZxmYsV0thhYiIpsbm4O2WzWWHtSU1ODdDqFiorlVvNh1Nc38NZJolWYn59f2iI/gunpY8a42708nyQ0NDRyPm1CDCxERbC4uIhYrAeKEsHIyDB8vi5cffU1AACvtxpve9v1aGpq5nV0olXIZhcRi8WgKBEMDw8ZOzfbbDZ0dXVDFCW0t3fCarWaXCkVEwML0TrRNA1DQ4NQlAh6e3tXtJrP5/PQNM0IKNu2tZhVJlFZ0DQNw8NDUFUZPT09K+ZTS0srJCkMvz8Ip9NpYpW0kRhYiNbJ4cM/Qn9/n/F1bW0tQiEJoZDEVvNEqzQ9fQyyHIGqykgmk8Z4TU3N0q39ErzeahMrJLMwsBCtQTJZuI4eDu8w9k3p6PBhfHwcwWAIohhGU1MTr6MTrcLCQhKqqkBVZUxOThrjLpcLgUAIkhRGU1Mz59MWx8BCtErZbBZ9fTEoiozBwQHoug6Hw4EdO3YBAMLh7dixYyevoxOtQjabRX9/L2RZxtDQgNFywmq1orPTB1EMo7PTB5uNf6aogL8JtHXk8xASs9CdLqCiYlU/stxqXlFkxGLRFa3mt21rQcVxz8PeI0SnVtgifwSKIqOnR10xn5qbt0EUwwgEgnC73SZWSaWKgYU2PcvIMNz33wvXg1+FZaltfPa885F6/4eQedv1wCnu1EmlFvAf//Hvxl0JXm+10Wq+urpmI8onKnszM3EoigxFkTG3NAcBoKrKa8ynmppaEyukcsDAQpua7aUjqP7zt0KYn4OwtJssANhe/B28f/k+pH96GHNf+CJgtRqt5mdnZ3DJJZcBACoqPAgGQ7Db7RDFMLZta+F1dKJVSKVS6OmJQlEiGB8fM8YdDgcCgRBEUUJLSyvnE60aAwttXskkqm9622vCCgAIy9fL//27GGluxpH9F2NgoB+apkEQBOzZcy6qqqoAAAcPvmnDSycqR/l8HgMD/VCUCPr7+4yWExaLBe3tnZAkCT5fNy+f0powsNCm5Xrk2xCmpnCiz2+TFgtetNuh2O1IffdhLDQ1A1ar0Wre4XBseL1E5UjXdUxMjEOWI+jpUZFOp43HludTMBhCRYXHxCppM2BgoU3L+Z1vAYIALK0/0QEjvIxbLPj9UiipWljATqsV/pvewVbzRKuUSMwu9fGRMTMzY4x7PB5jv5T6+gbzCqRNh4GFNi3L1CTSug7VbscrNhuCuRzOzxZ2ywzlchjOZhHOZtGRz2O+rh4ZhhWiU8pkMojFopDlCEZHR4xxm80Ov98PUQyjra2dLSeoKIoeWJ588kl88YtfRDQaxfz8PJqbm3Hw4EHcdtttxhoBovWUz+cxONiPp1xuDFRVYXn1SlYQjMDiBPCm405d60uNCYloJU3TMDg4AEWJoK+vF7lcDgAgCALa2tohimH4/X7Y7byMSsVV9MAyMzOD3bt349Zbb0VNTQ1UVcU999wDVVXxwAMPFPvlaYv51a+eRiRyFOl0CrZgCI6+GBryeezM5RDOZk/4M5q3GouXvnFjCyUqYbquY2pqcumSj4JUasF4rLa2DpIURjAo8kMnbaiiB5Zrr712xdf79u2Dw+HAJz7xCYyPj6O5ubnYJdAmtrCQXLGYL5FIIJ1OoaKiAqEbb8a+XzyOpuQCLFr+hD+vCwJS7/8QsLS9PtFWNj9faDmhKBFMTx8zxt3uCoRCIkIhEY2NbDlB5jBlDUtNTQ2AwtbMRGdqcXERsVgPVFXG8PAQbrrpFtTWFjadOvfc8xAOb0dHRycsFgts3/gOcMO10BcWVtzarFssgKZh8eprsPDRj5n1r0Jkumx2EbFYDKoqY2ho0Ngk0WazwefrhiRJaG/vZMsJMt2GBZZ8Po9cLodoNIrPf/7zOHDgANrb2zfq5anMLbeaV5QIYrHYilbzo6PDRmBpalp5xi533vmIP/403P/n83D/24MQFgqntvPhHUh94C+RvukdAN+IaYvRNG2p5UQEsVjPig+PLS2tEEUJgUAITqfTxCqJVhL05ThdZJdddhnGx8cBAJdeein++Z//eUUflrVIJFLI57X1KI/WyGq1wOt1F/VYTE1N4Yc//P5rWs1L0valVvPe1T3R4iKEY8cAlxN6TW3hludNZCOOBa1OqR6LY8eOQVEiUBQZ8/Pzxnh1dQ1EUYIkhVFdXW1iheuvVI/FVlVd7V7zXWQbFlgikcjS1udR3HvvvWhvb8dXvvIVnmak10gmk5idnUVraysAIJfL4b777oMgCJAkCTt27EBLC7fIJ1qNhYUFHD16FK+88gomJiaMcafTacyn1lZukU+lb8MCy/EikQiuvfZa/NM//ROuuuqqNT8PE7P51uvTSzabRV9fL2Q5gsHBAVRVeXHLLbcab6JTU5Oora1jwD0FfpIsHWYfi+X5pCiy0XICKGyR7/N1QZLC8Pm6YLNt/q24zD4WtNLZnGEx5bdVkiTY7XYMDAyc1fPk8xpyOf4CloK1HAtd15euo8uIxaIrWs07HE7Mzy8YbeZrauqh6+DxXgXOi9KxkcdC13WMjo5AUWT09Kgr5lNTU7OxRf7ynAK21nzivCgNZ3OKxJTAcuTIEWSzWS663eKeeeY/8fvfv2h8XVXlhSRJCIXYap5otWZm4lBVGbIsY24uYYxXVlZBksIIhSRjUTpROSt6YLntttuwa9cuSJIEl8uFSCSCL3/5y5AkCQcPHiz2y1OJKKxfUtHW1mb06+nq6kYk8gpbzROdoXQ6jWhUhaJEMD4+Zow7HA74/UGIooTW1jbOJ9pUih5Ydu/ejUcffRT3338/dF1HW1sbbrjhBrz3ve9lR9xNLpfLrWg1r2kadu/ei4svvhQA0NrahkOH3sNW80SrkM/nV8yn/NK+QoIgoKOjE6IooavLz/lEm1bRA8sHPvABfOADHyj2y1CJ0HUd4+NjUJQIenqir2k1X1//aoNBQRD45kp0CrquY2JiHIoiIxpVkU6njMcaGhohihKCwRA8nkoTqyTaGJt/iThtKF3X8ZOfPIqFpQ3allvNS1LYuBRERKeWSCSgqjIUJYKZmRlj3OPxIBgUIUlh1Nc3mFcgkQkYWGjNMpkMZLkHx46N4ZJLDgAo3Da5Y8dOzM3NQRTDaG1tY6t5olXIZDKIxaJQFBkjI8PGuM1mh9/vhyiG0dbWzvlEWxYDC52RfD6PoaEByLKM/v5eaFoebrcD3d0iWloKd31dcMFFJldJVB40TcPg4AAUJYK+vl7kcjkAhculra1tEMUw/P4A1/sRgYGFVimRmMVLL/3+Na3mGxsbcP75565Ym0JEJ6frOqamJqGqMlRVMS6fAkBtbR0kKYxgUERVVZWJVRKVHgYWOild143bIlOptLFnynKreVGUsG1bM+rqKhGPJ7kpE9EpzM/PQ1UVKEoE09PHjHGXy23Mp8bGJt6KTHQSDCy0wnKreUWJwOv14g1vKKxNaWpqwu7de9He3oH29g5ji3y+uRKdXDa7CFXtgaJEMDQ0iOVOKDabDT5fNyRJQnt7J1tOEK0CAwtB0zQMDw8ttZqPIZcrtJp3Op245JI3wGq1QhAEY/8UIjq5wrqUITz7bB9+//uXkcm8ukV+S0srRFFCIBCC0+k0sUqi8sPAssUdOfICjhx5Aclk0hirqamBKIYRCon85Ee0StPTx6AoMlRVxsJCEm63A9lsFtXV1RDFMERRgtdbbXaZRGWLgWWLWVhIwul0GUFkcTGDZDIJl8uFQCAESQqjqamZl3qIViGVWjDWpUxOThrjbrcLu3efg7a2bjQ0cF0K0XpgYNkCstks+vt7IcsyhoYGcOWVV8HvDwAAwuGdqK9vRGenb0u0mic6W7lcDn19vVCUCAYHB6BphcXmFosFPl8XQiEJgYAfjY3VXIxOtI74F2qT0nUdIyPDUFXlNa3mJybGjcBSVVXF2yeJTkPXdYyOjkBVZfT0RJHJZIzHmpqaIYphBIMhuN1uAIDNxs3diNYbA8smlE6n8Z3vfGtFq/mqKi8kSUIoJKGmhq3miVZjdnYGihKBoihIJGaN8crKKoiiBFEMo7aW84loIzCwbAKpVAqTkxPo7PQBAFwuF1wuFzKZNAKBEERRQktLK6+jE61COp1GT48KRZExNjZqjNvtdmM+tba2cT4RrUImk0FPj4q+vl686U1vPqvnYmApU7lczmg1PzDQD0EQcOjQe4xbJQ8e/BN4PJXshky0Cvl83phP/f19yOfzAAr7DHV0dEIUJXR1+TmfiFYhn89jcLAfiqKgv//VlhODg/1obDxnzc/LwFJGdF3H+PgYFEVGT4+KdDptPNbY2Ihkct4ILLzsQ3Rquq5jYmICihJBNKoinU4Zj9XXN0AUJYRCIjyeShOrJCofiUQCv//9i4hG1RUtXOrq6iGKhTtQzwYDSxl5+eU/4KmnfmF87fF4EApJkKQw6urYy4doNRKJBKLRwq3I8XjcGK+oqEAoJEEUJTQ0NJpYIVH50DTN6CCey2Xx0ktHABRauIiiCFEMo76+YV0uoTKwlKjlVvOVlVXo6OgEAHR3d+PXv34G3d2FVvOtrW1sNU+0CouLi4jFopDlCEZGho1xm82O7u5uiGIY7e0dnE9Eq1CYT4WWEx6PB1dc8ScACmdS9u49D62tbejo6Fz3+cTAUkLy+TyGhgYgy7Jx3a+jo9MILB5PJd71rvdyvxSiVdA0DUNDg1CUCHp7Y8Z1dEEQ0NraBlGU4PcH4XA4TK6UqPSdrIWLzWbHZZdljfVd+/dfXLQa+JevBExOTixt6a2suO5XW1uH9vaOFV2TGVaITm1qahKKEoGqKlhYOH4+1S61nJC49xDRGfj971/Eiy/+7iQtXKQNW4zOv34l4Jln/tM4Te12Vxit5hsaGnnrJNEqJJPzS1vkyzh2bMoYd7ncCIVEhEISmpq4RT7RaiwsJGG3O4wgks/njRYuwWDh75MZLVwYWDZQNruIWCwGVZVxxRVXwu2uAABs374TFRUVEEW2midarWx2Eb29hS3yh4YGoes6AMBqtcLn64YkSejo8HE+Ea1CNptFX18MiiJjcHAAV1xxJUIhCQAgioUNRzs7zZ1PDCxFpmkaRkaGl6779SCbLVz3U1UFu3fvBYClHTMlE6skKg/LLSdkOYJYLGrMJwBoaWlFKCQiEAjB5XKZWCVReVieT4oiIxaLrmjhMjk5YQQWj6cS3d3m397PwFIkqdQCXnzxBaiqvOK633Kr+a4uv4nVEZWX6enppXUpMubn541xr7faCPzV1TXmFUhUZrLZRXzrW99Y0cKlMJ/Ekm3hwsCyjo6/Hx0Q8NJLR5DP5+FyuRAIhCBJYVOu+xGVo1RqAdGoClmOYHJywhh3Op0IBIIQxTC2bWvhfCJahVQqhYmJMfh83QAAu92ByspKLC5mymY+MbCcpWw2i/7+XsiyjGx2EX/2Z9cBANxuNy64YB+qq2vQ2enj3T1Eq5DL5dDXV1iXMjg4AE3TAAAWiwWdnT6IYhg+XxfnE9Eq5HI59Pf3GS1cAODQofcYXcUPHDgIt7uibFpOcNavwXKr+eUt8o+/7jc3N2fcMnnuua8zq0SisqHrOsbGRqEoEfT0RJHJZIzHmpqaIYoSgsGQsUidiE7u1RYuhfl0ohYuy4HF6602q8w1YWA5Q6oq49e/fnbFdb+qKu9S3xHu70C0WrOzM1CUCBRFQSIxa4xXVlYt3dofRl1dnYkVEpUfWT6KJ554zPi6srLSaDlR7i1cGFhOI5VKQRAE464DQRAwN5eAw+GA3x9kq3miM5BOp9HTo0JRZIyNjRrjdrsdfn8QkhTmfCJapUwmg54eFRUVFcaNHD5fN5xOJ7q6ujddCxcGlhPI5XJGq/mBgX6cf/6FOO+88wEUfhmuvPJN8Pm6y+a6H5GZ8vn80nwqtJzI5/MACuG/o6MToiihq8vP+US0Cidq4dLS0moEFrfbjXe9672bcv8hBpYlhVbz45DlCHp61BXX/SYnJ41/ttvtCAZFM0okKhuF+TQBRYkgGlWRTqeMx+rrG5YuoYrweMzf24GoHExOTkCWC/Pp+BYudXX18Pm6V7Rw2YxhBWBgAVB4c/3ud7+1Iph4PB7jul99fYOJ1RGVj7m5OaiqDEWJIB6PG+MVFRXGfGpoaDSxQqLy9JvfPGvc6eN2Vxj7pWylFi5bMrBkMhkMDg4gGAwBKJyarqurRzw+A7/fD1EMo62tfdNc9yMqpkKr+ShkOWL0xAIKXVy7uwvX0dvbOzifiFZhuYWLokTwxjdeYdzIEQ7vgMPhgCiG0dHRuSXn05YJLJqmYXBwAIoSQV9f4bpfTU2N8Wlv3779uPTSN8BuZ6t5otPRNA1DQ4NQlAh6e3uNVvMA0NbWDlGU4PcH4XBwPhGdjqZpGB4eWmrhEjPmUzSqGNtjBAJBBAJBM8s03aYOLLquL7Wal6GqyorrfrW1dSvWqfBaOtHpFeZTBKqqYGHh+PlUa7Sa5639RKuTSqXw4ou/e00Ll5qaGohiGIFAyMTqSs+mDiwjI8P4wQ++Z3ztdlcgGAxBFCU0NrLVPNFqJJPzUFUFiiLj2LEpY9zlciMUKlxHb2rifCJajXw+byyKtVqt+MMfXkIul2ULl1XYNIFl+bqfrusIh7cDALZta0FlZRWam7dBkiS0t3du2tXTROspm11Eb28vVLXQal7XdQCFN1ifrxuSJKGjw9xW80Tl4vgWLqnUAq6//iYAgMPhwL59F6GqyovOTs6n0ynrwHL8dfRYrAfZbBaVlZWQpDAEQYDVasU73nErfwmIVmG51bwsRxCLRZHNvrouZdu2FoiihEAgZGyiSEQntzyfFEVGLBZd0cJlZiZudEPevXuvSRWWn7INLKlUCg8++FUkEnPGWHV1NUQxjFwuZ2xCxbBCdGrT09NLtyLLmJ9/dT4VWs0XbkWurq4xr0CiMhONKnj22V+9poWLJBVauCyHFTozZRtYMpkM5ufn4XQ6l9alhNHcvI3X/YhWIZVaQDSqQpYjmJycMMadTmfZtJonKhWpVGFjxOWmgjabzWjhEggU1k22tLRyPp2lsg0sdrsdV131ZrS1dbLVPNEq/HGreU3TAAAWiwWdnT6EQhK6uro5n4hW4Y9buOzdex4uvPAiAEBHhw9XXnkVfL4utpxYR0V/Z/rxj3+MH/zgB3j55ZeRSCTg8/lw66234rrrrjurtOnxeBAIBJHLaetYLdHmcnyr+WhURSaTMR5rbGyCJIURDIbgdleYWCVReTh+PvX0RFdsjTE9fcz4Z6vVamxMSuun6IHlq1/9Ktra2nDHHXegtrYWzzzzDD7xiU9gbGwMt912W7FfnmhLmp2dWdp/SMbs7KwxXllZaeyXUldXZ2KFROVF13U88si3MTExboxVVlYaLSfq6upNrG5rKHpguffee1e8Me7fvx8zMzP4yle+gv/yX/7LltxemKgY0uk0fv/7Hvz2ty9gePjVLfLtdjv8/iAkqdBqntfRiU4vk8mgv78PoZAIQRAgCAIaGhoQj0/D7w9AFAvziX/DNk7RA8uJPsVt374dDz/8MBYWFlBZyR1midYqn89jcLAfsixjcLAPDocVqdQiBEFAR0cnRLGwLoUtJ4hOL5/PY2hoALIso7+/0MLF6/Vi27YWAMAFF+zD619/CeeTSUxZXff888+jubn5rMOK1cpka7blY8BjsXF0XTdazRdaThTuULBYBDQ0NKOrq9BzhO0mzMN5UTpOdyyWW7jIcgSKsrKFS0NDPTQtD5ut8LNeL9tOnK2zOcG74YHlueeew6OPPoqPfexjZ/1cXq97HSqi9cBjUXyJRAJHjx7FK6+8gunpaWO8oaEW4XAYO3bsQFNTk4kV0h/jvCgdJzsWw8PD+I//eMT4ur6+ZsV84iXU0rGhgWVsbAy333479u3bh0OHDp318yUSKeTzvEvITFarBV6vm8eiSBYXFxGLRSHLEQwPDxtb5NtstqXr6JLRan75EySPhfk4L0rH8ccinU6jp6cHuVwOu3adAwBwu6vhdFagoaERkhRGR8erLVxmZhZO9dS0BtXV7jWv+9mwwJJIJPD+978fNTU1uOeee9ZloVI+r/G25hLBY7F+jm850dvba7SaB4DW1jZIUhh+fxAOh2Pp+2HsqQLwWJQSHgvzaZqGvr4+/OY3v0M02oNcLgu3uwKhUNgIJjfc8A7jb5Kug8esiJY+c63JhgSWdDqND37wg5ibm8O3vvUttp8nOoFjx6YgyxFEo8oJW82HQhK8Xq+JFRKVj+npacjyUfT0KNC0LFKpRWiajpqaGoRCEjTt1a7JvNOnPBQ9sORyOXzkIx9BLBbDQw89hObm5mK/JFHZWFhIQlUVKIqMqalJY9zlchstJ3gdnejMRSIv48iRF2GxCKit9SIQkBAMSmhqauZ8KlNFDyx33XUXnnjiCdxxxx2Yn5/Hiy++aDy2Y8cO47Q20VaRzWbR1xeDosgYHBww1qVYrVb4fF0QxTBbzROtUjabRX9/L2RZxp49e9He3gEAEMUwEokEtm/fjr17d2JuLsNLPWWu6IHl6aefBgDcfffdr3nsscceQ3t7e7FLIDLdqVrNNzdvgySFEQiE4HK5TKySqDzouo7R0REoioyeHtWYT263ywgsDQ2NuOqqt8Bmsyz1x8qc4hmpHBQ9sDz++OPFfgmikhWPx6EoESiKjPn5OWPc662GKIpsNU90BnK5HH73u+egKDLm5hLGeFWVF6JY2CKfNi+2ZSVaZ6lUCtGoCkWJrOg74nQ6EQgEIYphbNvWwuvoRKuQy+WMDuJWqxWxWBRzcwk4HA4EAiGIooSWllbOpy2AgYVoHeRyOfT39xmt5pdvM7ZYLEtb5IfR1dVtvPES0cnlcjkMDPRDUSIYGxvDLbccgt1uhyAIOP/8fQB0+HzdsNvtZpdKG4jvnkRrdHyr+WhURSbz6jXyxsYmSFIYwWAIbneFiVUSlQdd1zExMQ5ZjqCnR0U6nTYeGxkZhs/XBQAIBkMmVUhmY2AhOkOzszNQFBmqKmN2dtYYf7XVfPiETT+J6MSGh4fwy18+gZmZGWPM4/EszScJ9fUN5hVHJYOBhWgV0uk0YrEoFEXG6OiIMW632+H3ByGKElvNE61SJpNBJpMxNkL0eDyYmZmBzWaH3++HKIbR1tbO+UQrMLAQnUQ+n8fgYD9kWcbAQB9yuRwAQBAEtLd3QBTD6O7uZqt5olXI5/MYGhqAosjo6+tFZ6cPb3rTmwEANTW1uPrqt6CtrZ3ziU6KgYXoOLquY3JyYumSj4J0OmU8VldXv7RFvojKykoTqyQqD7quY2pq0phPqdSrzQQTiQQ0TTPOonR1+c0qk8oEAwsRgLm5OaiqDEWJIB6PG+MVFRUIhUSIYhj19Q28dZJoFXRdhyAI+PnPf4JoVDXGC00HRYRCIhob2XKCzgwDC21Zi4uLiMV6oCgRjIwMG1vk22w2dHV1QxQLreZ5HZ3o9LLZRcRiMXR1dRstV7Zta0FfXy98vm5IkoT29k62nKA1Y2ChLUXTNAwNDUJVZcRiMeRyWeOx1tY2SFIY3d0BOJ1OE6skKg+api21nIigp6cHuVwWl19+BcLhHQAASdoOUQxzPtG6YGChLeHYsSnIcgTRqIJkMmmM19TULK1LkYw7Fojo1Kanj0GWI1BVecV8qq6uhsXy6hkUNrel9cTAQpvWwkISqqpAUWRMTU0a4y6XC4FACJK0HU1NvI5OdCYWFpJ4+OFvGJdQC/Op0HKiuXkb5xMVDQMLbSrZbBZ9fTEoiozBwQHjTdVqtcLn64IohtHZ6eN1dKJVyGaz6O/vRTw+jQsuuAgAUFHhQUeHD1arxZhPbDlBG4G/ZVT2dF1fuo4uIxaLGq3mgcKiP1GUEAiE4HK5TKySqDzouo7R0REoioyeHhWLi4sQBAHbt+8ybud/85uv4ZkU2nAMLFS24vE4VFWGLEcwPz9njFdVeSFJEkIhCTU1tSZWSFQ+EolZyPJRyLKMubmEMV5ZWQVJCsNieTWgMKyQGRhYqKykUilEoyoUJYKJiXFjnK3mic7O8PAQnnvutwAK8+n4lhOcT1QKGFio5B3far6/vw+apgEALBYLOjo6IYph+HxdbDVPtAr5fN6YT21tHdi16xwAgN8fRF9fL4LBELq6/JxPVHIYWKgk6bqO8fGxpf0doitazTc2Nhpb5LvdFSZWSVQedF3HxMQ4ZDmCnh7VmE9zc3NGYHE6nbj66mvMLJPolBhYqKQkErNQlMIW+bOzs8Z4ZWWl0Wq+rq7exAqJyssLL/wOkcjLmJmZMcY8Ho8xn4jKBQMLmS6TyaCnR4WiyBgdHTHG7XY7/P4ARDGM1tY2bpFPtArZ7OKKjscTE2OYmZmBzWaH3++HKIbR1tbO+URlh4GFTLHcal6WZfT39yKXywEo3H3Q1tYOUQzD7/ez1TzRKmiahsHBAShKBH19fbjxxptRXV0DANi9ey98vm74/QHuPEtljYGFNoyu65icnICqKq9pNV9XV2+sS1ne64GITk7XdUxNTUJRZESjChYWXp1PfX292LPnXABAS0srWlpazSqTaN0wsFDRzc3NIRpVIMsRxOPTxvhyq3lRlNDQ0MhbJ4lWKR6P46c//TGmp48ZYy6X25hPjY1NJlZHVBwMLFQUy63mFSWC4eEhY4t8m82Grq5uiGIY7e0d3CKfaBWy2UXMzc0ZC84rKysxPz8Hm80Gn68boigubZfP+USbFwMLrRtN0zA8PARFiSAWiyGXyxqPtbS0QpLC8PuDbDVPtAqapi21nIggFutBZWUlbrrpFgiCALvdjquvvgb19Q2cT7RlMLDQWZucnMRvfvMCIpGjK1rN19TULK1LkeD1ek2skKh8TE8fg6LIUFUZ8/PzxrimaUilFlBR4QEAtLa2mVUikSkYWGhNFhaSUFUF0aiCZHIWqdQiNE1fajUfgiSF0dTUzHUpRGfgN795Fs8//1vja6fTiWAwBFEMo7l5G+cTbWkMLLRqy63mZVnG0NAANE2DxSLA43HB7w8gEBDZap5olXK5HPr6etHQ0GA06WxpaYXFYoHP14VQSILP18X5RLSEM4FOSdf1pevoMmKxKBYXF43Hmpu3Yfv27bjggr1IpzXkcpqJlRKVPl3XMTo6AlWV0dMTRSaTwZ49e/H6118KAGhra8ehQ+9mywmiE2BgoROKx+NQVRmKsrLVfFWVF6JY2NK7pqYWNpsFbrcb6XTyFM9GtLXNzs5AUSJQFAWJxPEtJ6qMNSlAoaEnwwrRiTGwkCGVSiEaVaGqMsbHx4xxh8OBQCAEUZTQ0tLK6+hEZ0DTNHzve981Nkq02+3GfGptbeN8IlolBpYtLpfLGa3mBwb6kc/nARQ+6bW3d0KSJPh83Ww1T7QK+XweAwP9GBrqx9ve9lYAhbkkihLi8WmIooSuLj/nE9EaMLBsQSdrNQ8AjY2NEMUwgsHQilPVRHRihfk0AUWJIBpVkU6nYLEIOP/8vaiqKmz0tn//xTyTQnSWGFi2kERi1tjf4USt5iUpbOykSbSVWCw9cLu/Dqv1KAA7stn9SKdvga7XnvRnFhaSOHr0FShKZMV8qqioQDi8HV6vF0sbPJdxWMnAZnsBgpCCpnUgnw+aXRBtYQwsm1wmk0EsFoUsRzA6OmKM22x2BAIBiGIYra1tbDVPW1QOlZV/A7f7S9B1K4A8AAEOxw/h8dyFubn/F5nMO0/4k4lEAr/5zbMACvPJ7/cjFJLQ3t4Bh8OGmhoP4vFyXYyeQkXFP8Lt/hIslhljNJu9EMnkx5HNXmFeabRlMbBsQvl8HkNDA1AUGX19vcjlcgAKn/La2tohimH4/X7Y7Ww1T1tbZeXfwOX6MgBAEPJLo4XTIrqegdf7XzAz40Q0egFUVYbbXYGLLy7cgtzcvA2SFEZrazv8/gAcjs0yn1KoqflT2Gy/hSCs3KrAZnsO1dV/jrm5e5HJvMOk+mirYmDZJI5vNa+qinFHAgDU1tZBkgpb5FdWVppYJVHpsFqjcLu/dMLHdB2YnLTg5ZftOHr0TkxNfRiAAKfTiX379sNms0EQBBw4cOXGFr0BPJ7PnjCsAIAgaNB1oKrqw8hmL4am+UyokLYqBpYyNz8/D1WVIcsRxOPTxrjbXYFQSEQoJKKxsamMr6ETFYfL9TXouvW4MysFf/iDDc8/78DU1HLn43lUVg6hu/sahELSJu+InILL9eUThpVlglAIdG73V5BM/t3GlUZbHgNLGcpmFxGLxaAoEQwPD0FfWtlns9nQ1dUNUZTQ3t65yd9Yic6O1foKgDwWFwGbDVhexhWPWzA1ZYXFoiMQyGHHDg2NjS1YXLzM1Ho3gt3+a1gsc6f9PkHIw+H4AQMLbSgGljKhaRqGh4eWWs3HkMtljcdaWlohSWH4/UG2midaBV3XMTioQ1VdUFUb3vzmFILBwpmWXbuyqK7WEQpl4XYDum5BMrk19k0RhIXTf5PxveW6oJjKVdEDS39/P7785S/jyJEjUFUVfr8fP/zhD4v9spvG9PQxyHIEqiojmXz1DaKmpgahUGGLfK+32sQKicrH9PQ0FCUCVVWQyVTAbrdDEIC+PpsRWGprddTWvvqBQBA0ZLP7zSp5Q2la26q+T9ct0LSOIldDtFLRA4uqqnjyySexZ88eaJpmXL6gk1tYSEJVFaiqjMnJSWPc5XIhEAhBksJoamrmuhSiVcpkMviP//h3TE5OGGNO5/nYs+cx7NiRQmvridds6LoVudwu5HKv26hSTZXL7UYuF4bVKkMQTvVerSGV+ouNKosIwAYElgMHDuDgwYMAgDvuuAN/+MMfiv2SZSmbzaK/vxeyLGNoaACaVngDtVqt6Oz0QRTD6Oz0sdU80SrkcjkcOzaF5uZtAACn0wld12GxWIz55PN1obKyBlVVf4UTfY4q7MviwPz8PRtbvKkEJJN/i+rqQyf9Dl23QtPakMn8+QbWRbQBgYUbkp2crusYGRmGqiro6VGxuLhoPNbcvA2iGEYgEITb7TaxSqLyoOs6xsZGoSgR9PREkc9reNe73mPsj/LGNx5AZWXlim7I6fRfQNfd8HjuhNU6vhRSdAiChnx+O+bmvoBcbq85/0ImWVz8M8zP3w2P5+MALMZdVLouANChaa2YmfkPAOwqTRurrD+uW63lGYZmZuKQ5QgURUYikTDGa2qqIYqFLfJrak6+JXgpWT4G5XosNpOteixmZ2cgyxHIsoxEYtYYr6qqQjKZQEVFEwCgpWXbCX8+n387EokbYLf/FBZLYWv+XO71yOdfB0DAWk5qlvuxyGZvQyJxAE7nF+Fw/AhACprWiUzmvVhcvAkWiwfl8lm03I/FZnM2KxnKOrB4veVz5iGVSkGWZbzyyisYHR01xqurKyFJErZv34729vayXZdSTsdis9tKx+IPf/gDfvKTnxhfe70eiKKIHTt2oKOj4wzn0w3rXl95H4sLlv5XYLEUbv/2lGlP1PI+FgSUeWBJJFLI50++wZHZcrkc+vv7IMsRDAz0I58vnFq1WCzo6OiEJIXR1dVttJqfmVn9LYWlwmq1wOt1l/yx2Ao2+7HI5/Po7++Dy+VCa2vhbhavtwGZTA7t7R2QpDC6u/0lMZ82+7EoJzwWpaW62r3mpSJlHVjyeQ25XGn9AhZazY9DliPo6VGRTqeNxxobGyGKYQSDIVRUvPoxpdT+HdaiFI/FVrWZjkVhPk1AUSKIRlWk0yl0dvrwlre0AADcbg9uvfU9cLlcxs+U0r/7ZjoW5Y7HojSczY3CZR1YSkkikYCqyq9pNe/xeIz9UurrG8wrkKiMJBIJRKMKFCWCeDxujFdUVKCurh66rhuXe44PK0S0eTGwnIVMJoNYLApZjmB0dMQYX241L4phtLW1804pojP0+OM/M+aUzWZHd3c3RDGM9vYOzieiLarogSWVSuHJJ58EAAwPD2N+fh6HDx8GAFx44YWoq6srdgnrStM0DA4OQFEi6OvrRS6XAwAIgoC2tnaIYhh+vx92+2ZpNU9UPJqmYWhoEKoq4/Wvv9S4hV+SwrBYLEvzKWDcmkxEW5egF3nr2aGhIVxxxRUnfOzrX/869u3bt+bnjseTG3JNUtd1TE1NQlFkqKqCVOrVxXy1tXWQpDBCIQmVlZVFr6XU2GwW1NZ6NuxY0MmV07EozKfCFvkLC4X5dMklb8A55+wGgBWXfMpROR2LzY7HorTU1XnWfIt50c+wtLe3Q5blYr9MUczPz0NVC9fRp6ePGeNudwVCIRGiKKGhobGs31iJNkomk8HRoy9DUWQcOzZljLtcbgSDIbS2thpjnFNE9Me4huWPZLOLiMViUFUZQ0ODRu8jm80Gn68bkiShvb0TVqvV5EqJSt/xZ0o0LY9f//pX0DQNVqvVmE8dHT7OJyI6LQYWFK6jj4wMQ1EiiMV6kM2+2qm1paUVoighEAjB6XSaWCVReVhuOSHLEaRSKbzlLW8FUDgzuXfveaisrEQgENpUd/cIwjSczu/Aah2ArjuRz/8JgBNfCieitdnSgWV6+tjSuhQZ8/Pzxnh1dTVEMQxRlOD1VptYIVH5mJ6eXrq1X8b8/JwxnkjMGvNo3779ZpVXJHl4PJ+B230PgCwKb6k6BOEfAeyCxfIAgLCpFRJtFlsusKRSC8a6lMnJSWPc5XIhEAhCFMNobt7Ga+hEq9TXF8Nzz/0Wk5MTxpjT6TTmU1WV18Tqiquy8r/B5XoAgrB870L2uEePoqrqIGZmnkA+HzKjPKJNZUsElmw2i/7+PihKBIODA9C0wkpxi8UCn68LohhGZ6cPtrV0OSPaYnK5HHRdM27dz2azmJycgMViQWenD6GQhK6ubpPmUx52+y9gtQ5C191YXLwcut5UlFey2Z6H2/3lU9YiCEl4PP8dicTDRamBaCvZtH+hdV3H6OgIFEVGT4+KxcVF47GmpmZji/zlfR+I6OR0XcfY2CgUJYKenijOPfd1OPfc1wEAurr8uOSSy5bmU4VpNbpcX0NFxf+A1TpmjOm6FZnM2zA//4/Q9fp1fr0vQ9dtEITcSb9HEPJwOH4Ci2UImta+rq9PtNVsusAyMxOHqsqQZRlzcwljvLKyytgvpba21sQKicrH7OwMFKWwLiWRmDXGh4YGjcBit9txzjl7zCoRAFBR8Tl4PJ95TZ8SQcjD6fwebLYXMDPz83UNLXb7r04ZVl6tQYfN9gIWF7dqYCmENofj5wDS0LQupNPvYICjM7YpAks6nUY0qkJVZYyNjRrjDocDfn8QoiihtbWN61KIVknXdfzoRz/A4OCAMWa32+H3ByFJYaNbcimwWl+Gx/MZAMCJprgg5GG19sHj+TTm5/+/dXzlM9lzs6j7c5Ysm+3X8HrfBat1BLq+/OdGR0XF/0A6/W7Mz/8jALuZJVIZKdvAous6YrEeHD16FP39fcjn8wAKG051dPggiiK6ul5tNU9EJ5fP5zE2Noq2tsKnXkEQ4HK5luZTJ0SxsC6lFFtOuN1fWtWlGZfr35BM3gVdX587/3K5c2G1DqzqLEsut2tdXrOc2GxHUFPzViwvRP7j/04u11cgCHOYm/sSAH6YpNMr28CSSCTw4x//CJpW+OTS0NAIUZQQDIbg8Wy9LfKJzpSu65icnDBaTqTTKdx00ztQV1e4bHLhhRdh//6LS34+ORw/X+WlmTRstueRzR5Yl9dNp98Hl+u7p/weXbcim70UmuZfl9csJx7PnQCyEIT8CR8XBB0u17eRSn0AudzaW7TQ1lG2gUXXdXg8Hvj9IUhSGPX1DWaXRFQW5ubmlvZLiSAejxvjFRUVmJtLGIGlfPYgyqz6OwVh9d97Otns65FOvw1O5/chCCfqUWMBYEcy+el1e81yYbH0wOF48rTfp+s2uN1fwtwcAwudXtkGFo/Hg0OH3g2NvayIVm10dATf//4jK1pOdHf7EQpJ6OjohMWytqZkZsrnRVgskyf9JL/ye4Pr+MoC5ua+CF2vgsv1IAoBpTBeOOPTjLm5B5HLmbsg2Qw220ur+j5ByMFme77I1dBmUbaBxW63w2JZNPZUIaKVNE3D0NAgstksAoHCH+qmpma4XG7U1tZCksLw+4NwOEpvXcqZSKffA4fjl6f8Hl23IJe7sAgbuDkwP/8vWFj4v+FyPQSrtR+67kY+fyUqK69HPp8BsBXfo85kTQrXr9DqlG1gIaITm5qawiuvvAJVlbGwsICqKi/8/gAEQYDVasU73nFr2YeU42Uyb0U2uwc22x9OeJZF1wUAApLJTxStBk3zYWHhb42vbTYLCm+v63cJqpzkcnuh68JxOwCfWGGNDy8H0eowsBBtAsnkPGKxKAYHYxgcHDEWo7tcbvh8XcjlssYdPpsprBTYMTv776iuvh52+/PQdSsEIb+0J4sAwIFE4gFks5eaXOfWoWk+LC4ehMPx+Ckv1QlCHun0+zawMipnDCxEm8Bzz/0GkcgrcLsdsFqt6OrqgiRJ6OjwwWq1ml1e0el6PWZmHoPd/jhcrgdhtfZC1yuwuPhmpNO3rPsut3R6yeT/hN1+OYDUSc98pdN/gVzuvI0vjsoSAwtRGdF1HSMjw1AUGTt27EJzczMAQBTDmJmJ44ILzkVTUztsts12FmU1LMhmDyKbPWh2IQQgn5cwM/MTeL1/AZtNXdo4TgCQB2BFKnUbkslPmlwllRMGFqIyEI/HoSgRKIqM+fk5AIDVajUCS0tLK6677gbU1noQjyeRy23FhZ5UavL5cxCPPwe7/Wk4HI8BSEHTfEinb+RZLzpjDCxEJSqXy+Ho0VegKBFMTIwb406nE4FAYYt8otInIJu9BNnsJWYXQmWOgYWohOi6bvS8slqtePHF5zE/Pw+LxbK0RX4YXV3dsNk4dYloa+G7HpHJdF3H+PgYFCWCkZFh3HjjO2CxWCAIAs4993xoWh6hkAi3u8LsUomITMPAQmSSRGIWshyBqsqYnZ01xoeGBtHZ6QMA7Np1jlnlERGVFAYWog02NjaKZ599BqOjI8aY3W6H3x+AKIbR2tpmYnVERKWJgYWoyPL5PBYXF+F2uwEU+veMjo5AEAS0t3dAFMPo7u42NnYjIqLXYmAhKgJd1zE5OQFFkaGqCnw+Hw4cuBIAUF/fgDe84XJ0dnahsrLS5EqJiMoDAwvROpqbm4OqylAUGfH4tDE+NjYKTdOMxbQ7duwysUoiovLDwEK0Tn7xi8cRibwCvdDEBjabDV1d3RDFMDo6OmGxWEyukIiofDGwEK2BpmkYHh7Ctm0tsNvtAICqqirouo7W1jZIUhjd3QE4nU6TKyUi2hwYWIjOwLFjU5DlCKJRBclkEldeeRWCwRAAYMeOXQiFJHi9XpOrJCLafBhYiE5jYSEJVVWgKDKmpiaNcZfLjcXFjPG12+027gQiIqL1xcBCdArJ5Dz+9V+/Bk0rNBO0Wq3w+bogimF0dvpgtVpNrpCIaGtgYCFaous6RkaGMT09jXPO2Q0A8Hgq0djYBEEQIIoSAoEQXC6XyZUSEW09DCy05cXjcShKBIoiY35+DlarFaGQaASTt771z4yFtUREZA4GFtqSUqkUolEVihLBxMS4Me5wOBAIhJDL5YwxhhUiIvMxsNCWpKoynn76KQCAxWJBR0cnRDEMn6+LAYWIqAQxsNCmpus6xsfHoCgRtLa2IRgUAQDBYAiqqiAUEhEMhlBR4TG5UiIiOhUGFtqUEolZKIoMRYlgdnYWQGGtynJgqajw4LrrbjSzRCIiOgMMLLSpRCKvIBI5itHREWPMbrfD7w9AFMMmVkZERGeDgYXKmq7rEATB+FpVFYyOjkAQBLS3d0AUw+ju7obd7jCxSiIiOlsMLFR2dF3H5OQEVFVBNKrg+utvgsdTCQDYvXsP2ts7EQqJqKysNLlSIiJaLxsSWHp6evCZz3wGL7zwAjweD6699lp85CMfgcPBT720enNzc4hGFchyBPH4tDHe0xPF7t17AQA+Xzd8vm6TKiQiomIpemCZnZ3Fu971LnR1deGee+7B+Pg47r77bqTTaXzyk58s9svTJjAzE8dTTz2J4eEh6LoOALDZbOjq6oYohtHe3mFyhUREVGxFDyzf/OY3kUwm8S//8i+oqakBAOTzedx111344Ac/iObm5mKXQGVG0zQsLCwYl3Tc7gqMjY1C13W0tLRCksLw+4NwOp0mV0pERBul6IHll7/8Jfbv32+EFQC4+uqr8alPfQpPP/00/vzP/7zYJVCZmJ4+BlmOQFVlVFR4cP31NwEAnE4nDhy4Eo2NjfB6q02ukoiIzFD0wBKLxXDdddetGPN6vWhsbEQsFjur57ZaLWf183T2lo/BWo/FwkISilJYlzI1NWmM67qGxcU0KioqAACSJJ59sZvc2R4LWj88FqWDx6K0HHdT5xkremBJJBLwer2vGa+urjY29Forr9d9Vj9P62ctx+LZZ5/Fr371K2iaBgDweFzw+/3YsWMHuru7YbPxJra14LwoHTwWpYPHovyV9V+ERCKFfF4zu4wtzWq1wOt1n/ZY6LqOkZFhVFfXGGtTbDY3ksk0mpu3QZLCCAZDcLsLbypzcxkAmY34V9g0VnssqPh4LEoHj0Vpqa52w2JZ29muogcWr9eLubm514zPzs6iuvrs1iPk8xpyOf4CloKTHYt4PA5VlaEoMubmEjj//AtxwQX7AABtbZ246aZbUFNTa3w/j+fZ47woHTwWpYPHojQs3ei5JkUPLH6//zVrVebm5jA5OQm/31/slycTpFIp9PREoSgRjI+PGeOFfXde/W212WwrwgoREdHJFD2wXHbZZbjvvvtWrGU5fPgwLBYLLr744mK/PG0wTdPwzW8+hHQ6BQCwWCzo6OiEKIbh83XBbrebXCEREZWjogeWm2++GQ8++CA+/OEP44Mf/CDGx8fxuc99DjfffDP3YClzuq5jdHQUr7wyju3b9wAoBJTubj+mpiYgioV1KRUVHpMrJSKiclf0wFJdXY2vfe1r+PSnP40Pf/jD8Hg8uP7663H77bcX+6WpSBKJWSiKDEWJYG4uAbfbgYaGFtTVNQIALrnkMt7hQ0RE62pD/qoEAgF89atf3YiXoiLJZDKIxaKQ5QhGR0eMcYfDgR07dsBme/VSD8MKERGtN/5loVWZmBjHL37xOABAEAS0tbVDFMMQxSCammoRjye5Ap+IiIqGgYVW0HUdU1OTUBQZbrcb5513PgCgra0d7e0daG/vRCgkHreXCnePJCKi4mNgIQDA/Pw8VFWGLEcQj08DACoqKrB373mwWCywWCx461v/zNwiiYhoy2Jg2eJisR68/PJLGB4egr60o4/NZoPP1w1JkkyujoiIqICBZYvRNA2CIEBY6kA1MjKMoaFBAEBLSyskKQy/Pwin02lmmURERCswsGwR09PHIMsRqKqMK674E7S1tQMAtm/fAZfLBVGU4PWeXasEIiKiYmFg2cQWFpKIRlUoSgSTk5PGeE+PagSW+voG1Nc3mFUiERHRqjCwbELpdBqPPfYzDA0NQNMKtxpbrVZ0dvogimF0dvpMrpCIiOjMMLBsArquI5GYRXV1DQDA6XRidjYOTdPQ3LwNoZCEYDAEt9ttbqFERERrxMBSxmZm4ktb5MtYXMzg0KH3wGazQRAEvOENB1BR4UFtLbshExFR+WNgKTOpVAo9PVEoSgTj42PGuMPhwPT0MTQ1FRpKLq9RISIi2gwYWMqIosj4xS8eQz6fB1DojNze3glJkuDzdcNut5/mGYiIiMoTA0uJ0nUdExPjsFqtaGgodEFubGxCPp9HY2MjRDGMYDCEigqPyZUSEREVHwNLiUkkElBVGYoSwczMDAKBEP7kT64CANTW1uLtb38namq4LoWIiLYWBpYSkMlkEItFIcsRjI6OGOM2mx0OhwO6rhs70zKsEBHRVsTAUgJ+9KMfGAtoBUFAW1s7RDEMv98Pu91hcnVERETmY2DZQLquY2pqEqoq44IL9hlhxO8PYHFxEZIURigkobKy0uRKiYiISgsDywaYn5+HqipQlAimp48BAOrrGyFJYQDA7t17sWfPucZlHyIiIlqJgaVIstksYrEeKEoEw8ND0HUdAGCz2eDzdaO6+tVGgxaLxawyiYiIygIDS5Gk02k8/vjPjK9bWlohihICgRCcTqeJlREREZUfBpZ1MD19DLIcQSaTwRvfeAAAUFVVhXB4B6qqqiCKErze6tM8CxEREZ0MA8sapVILxrqUyclJAIVLOxdeuM/YzO3yy68ws0QiIqJNg4HlDA0PD+HIkRcwODgATdMAAFarFZ2dPohiGA4HL/cQERGtNwaW09B1HZqmwWq1Aih0SO7v7wMANDU1G1vku91uE6skIiLa3BhYTmJmJg5VlSHLMvbuPQ+7dp0DAAgEQkgmkwiFJNTWctdZIiKijcDAcpx0Oo1oVIWiRIydZwEgFosagcXlcuHCCy8yq0QiIqItiYEFhcs+P//5T9DbG0M+nwdQWEDb3t4JSZLg83WbXCEREdHWtiUDi67riMenUVdXD6DQvyebzSKfz6OhoRGiKCEUEo27fYiIiMhcWyqwJBIJqKoMRYlgZmYGt9xyyNgf5cILL8K+fftRX99gcpVERET0xzZ9YMlkMojFopDlCEZHR4xxm82OqakpI7A0NDSaVSIRERGdxqYOLKOjI/jhD7+PXC4HoHDpp7W1DaIYht8fgMPhMLlCIiIiWo1NE1h0XcfU1CQWFxfR1tYOAGhoaIAgCKitrYMkhREMiqiqqjK5UiIiIjpTZR9Yksl5KEphi/zp6WOora3DTTe9A4IgwG534KabbkFlZSUEQTC7VCIiIlqjsg0si4uL+MEP/h0DAwPQdR0AYLPZUFdXj1wuC7u9cLmHZ1SIiIjKX9kGloWFBQwOFsJKS0srRFFCIBCC08lePkRERJtN2QaWQmfkixAIhIw7fYiIiGhzKtvAUlVVhQsuuBC5nGZ2KURERFRkFrMLWCsuoiUiIto6yjawEBER0dbBwEJEREQlr+iB5emnn8ZHP/pRHDx4EJIk4e///u+L/ZJERES0yRQ9sDz11FOIRCK44IIL4PV6i/1yREREtAkV/S6hv/mbv8Edd9wBAPj1r39d7JcjIiKiTajoZ1gsFi6TISIiorNTtvuwAEB1tRtLu/KTSZbvLuexMB+PRengsSgdPBalxWJZ+5YkZR1YePamdPBYlA4ei9LBY1E6eCzK3xkHlrm5OUxMTJz2+zo6OuBwONZUFBEREdHxzjiwHD58GHfeeedpv+/RRx9FIBBYU1FERERExzvjwHLDDTfghhtuKEYtRERERCfEi3pERERU8oq+6HZ4eBgvvfQSACCVSmFgYACHDx8GAFx11VXFfnkiIiLaBARdL+6NXo888gg+/vGPn/AxWZaL+dJERES0SRQ9sBARERGdLa5hISIiopLHwEJEREQlj4GFiIiISh4DCxEREZU8BhYiIiIqeQwsREREVPLKPrA8/fTT+OhHP4qDBw9CkiT8/d//vdklbQk9PT1497vfjb179+Liiy/G5z73OSwuLppd1pbU39+PT37yk7j22muxY8cOXHPNNWaXtCX9+Mc/xl/+5V/isssuw969e3HttdfiO9/5DrhzhDmefPJJvPOd78RFF12EXbt24YorrsBnP/tZzM3NmV3alpdMJnHZZZdBkiRjY9nVKPpOt8X21FNPIRKJ4IILLsDs7KzZ5WwJs7OzeNe73oWuri7cc889GB8fx9133410Oo1PfvKTZpe35aiqiieffBJ79uyBpmn8A2mSr371q2hra8Mdd9yB2tpaPPPMM/jEJz6BsbEx3HbbbWaXt+XMzMxg9+7duPXWW1FTUwNVVXHPPfdAVVU88MADZpe3pX3hC19APp8/8x/Uy1w+nzf++fLLL9fvuusuE6vZGu677z597969ejweN8a++c1v6tu3b9fHxsbMK2yLOn4OfOxjH9Pf8pa3mFjN1nXs2LHXjN155536eeedt+IYkXm+9a1v6aIo8n3KRNFoVN+7d6/+jW98QxdFUf/973+/6p8t+0tCFkvZ/yuUnV/+8pfYv38/ampqjLGrr74amqbh6aefNq+wLYpzoDTU1dW9Zmz79u2Yn5/HwsKCCRXRH1t+z8pms+YWsoV95jOfwc0334zu7u4z/lm+09EZi8Vi8Pv9K8a8Xi8aGxsRi8VMqoqo9Dz//PNobm5GZWWl2aVsWfl8HplMBi+//DI+//nP48CBA2hvbze7rC3p8OHDUBQFH/7wh9f082W/hoU2XiKRgNfrfc14dXU11xERLXnuuefw6KOP4mMf+5jZpWxpl19+OcbHxwEAl156Kf7X//pfJle0NaVSKdx99924/fbb1xzgSy6wzM3NYWJi4rTf19HRAYfDsQEVERGdmbGxMdx+++3Yt28fDh06ZHY5W9r999+PVCqFaDSKe++9Fx/60Ifwla98BVar1ezStpR7770X9fX1uO6669b8HCUXWA4fPow777zztN/36KOPIhAIbEBF9Me8Xu8Jbw2cnZ1FdXW1CRURlY5EIoH3v//9qKmpwT333MM1RiYLh8MAgHPPPRfnnHMOrr32WvzsZz/DVVddZXJlW8fw8DAeeOABfP7znzf+diyv61pYWEAymYTH4znt85RcYLnhhhtwww03mF0GnYLf73/NWpW5uTlMTk6+Zm0L0VaSTqfxwQ9+EHNzc/jWt76Fqqoqs0ui40iSBLvdjoGBAbNL2VKGhoaQzWbxgQ984DWPHTp0CHv27MHDDz982ucpucBCpe+yyy7Dfffdt2Ity+HDh2GxWHDxxRebXB2ROXK5HD7ykY8gFovhoYceQnNzs9kl0R85cuQIstksF91usO3bt+PrX//6irGjR4/is5/9LO666y6cc845q3qesg8sw8PDxk55qVQKAwMDOHz4MADwlF+R3HzzzXjwwQfx4Q9/GB/84AcxPj6Oz33uc7j55pv5Jm2CVCqFJ598EkBhPszPzxtz4MILLzzh7ba0/u666y488cQTuOOOOzA/P48XX3zReGzHjh1cc7fBbrvtNuzatQuSJMHlciESieDLX/4yJEnCwYMHzS5vS/F6vdi3b98JH9u5cyd27ty5qucRdL28t8V85JFH8PGPf/yEj8myvMHVbB09PT349Kc/jRdeeAEejwfXXnstbr/9dr4pm2BoaAhXXHHFCR/7+te/ftI3ClpfBw4cwPDw8Akfe+yxx/ipfoPdf//9ePTRRzEwMABd19HW1oYrr7wS733ve3mbeQn49a9/jUOHDuE73/nOqs+wlH1gISIios2Py9eJiIio5DGwEBERUcljYCEiIqKSx8BCREREJY+BhYiIiEoeAwsRERGVPAYWIiIiKnkMLERERFTyGFiIiIio5DGwEBERUcljYCEiIqKS9/8DQLeYnUD1XxEAAAAASUVORK5CYII="}}]}}, 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"}}]}}}, 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diff --git a/ml-advanced/model-selection.html b/ml-advanced/model-selection.html
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      5.3. NumPy
     </a>
    </li>
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+   <li class="toctree-l2 has-children">
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+    <label for="toctree-checkbox-3">
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+     </i>
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+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
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+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/working-with-data/data-preparation.html">
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-  <label for="toctree-checkbox-3">
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-  <label for="toctree-checkbox-5">
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-  <label for="toctree-checkbox-6">
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    13. Getting started with classification
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-    <a class="reference internal" href="../assignments/ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
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     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
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+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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     <a class="reference internal" href="../assignments/ml-fundamentals/ml-neural-network-1.html">
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     <a class="reference internal" href="../assignments/deep-learning/nn-for-classification-assignment.html">
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     <a class="reference internal" href="../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
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   <a class="reference internal" href="../slides/introduction.html">
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      5.3. NumPy
     </a>
    </li>
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
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+    <label for="toctree-checkbox-3">
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+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/working-with-data/data-preparation.html">
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    6. Data visualization
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   <a class="reference internal" href="../data-science/data-science-lifecycle/data-science-lifecycle.html">
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    11. Regression models for Machine Learning
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    13. Getting started with classification
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   <a class="reference internal" href="../assignments/README.html">
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-    <a class="reference internal" href="../assignments/ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
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+     37.47. Loss Function
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     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
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+   <li class="toctree-l2">
+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-1.html">
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     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-2.html">
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     <a class="reference internal" href="../assignments/ml-fundamentals/ml-neural-network-1.html">
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    </li>
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     <a class="reference internal" href="../assignments/ml-fundamentals/regression-tools.html">
-     37.54. Regression tools
+     37.53. Regression tools
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/managing-data.html">
-     37.55. Managing data
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     <a class="reference internal" href="../assignments/ml-fundamentals/exploring-visualizations.html">
-     37.56. Exploring visualizations
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     <a class="reference internal" href="../assignments/ml-fundamentals/try-a-different-model.html">
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     <a class="reference internal" href="../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
-     37.59. Linear and polynomial regression
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     <a class="reference internal" href="../assignments/ml-fundamentals/retrying-some-regression.html">
-     37.60. Retrying some regression
+     37.59. Retrying some regression
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     <a class="reference internal" href="../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
-     37.61. Pumpkin varieties and color
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     <a class="reference internal" href="../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
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     <a class="reference internal" href="../assignments/ml-fundamentals/explore-classification-methods.html">
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-     37.64. Linear Regression Implementation from Scratch
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     37.67. Support Vector Machines (SVM) - Classification
+     37.65. Support Vector Machines (SVM) - Classification
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.68. Decision Trees - Intro and Regression
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
-     37.69. Decision Trees - Classification
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     <a class="reference internal" href="../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
-     37.70. Model selection assignment 1
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     <a class="reference internal" href="../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     37.71. Learning Curve To Identify Overfit &amp; Underfit
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     <a class="reference internal" href="../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
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     <a class="reference internal" href="../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
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     <a class="reference internal" href="../assignments/ml-advanced/model-selection/regularized-linear-models.html">
-     37.74. Regularized Linear Models
+     37.72. Regularized Linear Models
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     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
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     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/beyond-random-forests-more-ensemble-models.html">
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     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/decision-trees.html">
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-     37.82. Random Forest Classifier with Feature Importance
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-     37.85. Counterintuitive Challenges in ML Debugging
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     <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-classification.html">
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     <a class="reference internal" href="../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
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     <a class="reference internal" href="../assignments/deep-learning/time-series-forecasting-assignment.html">
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+     37.104. Time Series Forecasting Assignment
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     <a class="reference internal" href="../assignments/deep-learning/nn-for-classification-assignment.html">
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-     37.114. DQN On Foreign Exchange Market
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     <a class="reference internal" href="../assignments/deep-learning/gan/gan-introduction.html">
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+     37.115. Generative Adversarial Networks (GANs)
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     <a class="reference internal" href="../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
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   <a class="reference internal" href="../slides/introduction.html">
    38. Slides
   </a>
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+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
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+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/working-with-data/data-preparation.html">
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   <a class="reference internal" href="../data-science/data-visualization/data-visualization.html">
    6. Data visualization
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   <a class="reference internal" href="../data-science/data-science-lifecycle/data-science-lifecycle.html">
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   <a class="reference internal" href="../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
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+     37.48. Gradient descent
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      5.3. NumPy
     </a>
    </li>
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
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+    <label for="toctree-checkbox-3">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
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+     </li>
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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-    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-linear-regression-2.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
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+     37.48. Gradient descent
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+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
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+       5.4.3. Advanced Pandas Techniques
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+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>ingredients: Index([&#39;bell_pepper&#39;, &#39;black_bean&#39;, &#39;chicken&#39;, &#39;honey&#39;, &#39;onion&#39;, &#39;pepper&#39;,
-       &#39;sake&#39;, &#39;sesame_oil&#39;, &#39;soy_sauce&#39;, &#39;vegetable_oil&#39;],
-      dtype=&#39;object&#39;)
-cuisine: japanese
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>ingredients: Index([&#39;black_pepper&#39;, &#39;egg&#39;, &#39;scallion&#39;, &#39;soy_sauce&#39;], dtype=&#39;object&#39;)
+cuisine: korean
 </pre></div>
 </div>
 </div>
@@ -2244,23 +2259,23 @@ <h2><span class="section-number">13.2.4. </span>Exercise - apply logistic regres
   <tbody>
     <tr>
       <th>chinese</th>
-      <td>0.869888</td>
+      <td>0.529949</td>
     </tr>
     <tr>
-      <th>japanese</th>
-      <td>0.077357</td>
+      <th>korean</th>
+      <td>0.294966</td>
     </tr>
     <tr>
-      <th>korean</th>
-      <td>0.042823</td>
+      <th>japanese</th>
+      <td>0.136638</td>
     </tr>
     <tr>
       <th>thai</th>
-      <td>0.006624</td>
+      <td>0.037765</td>
     </tr>
     <tr>
       <th>indian</th>
-      <td>0.003308</td>
+      <td>0.000681</td>
     </tr>
   </tbody>
 </table>
@@ -2281,15 +2296,15 @@ <h2><span class="section-number">13.2.4. </span>Exercise - apply logistic regres
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>              precision    recall  f1-score   support
 
-     chinese       0.76      0.68      0.72       236
-      indian       0.95      0.88      0.91       257
-    japanese       0.75      0.80      0.77       247
-      korean       0.84      0.84      0.84       225
-        thai       0.78      0.87      0.82       234
+     chinese       0.75      0.70      0.73       253
+      indian       0.91      0.90      0.90       230
+    japanese       0.73      0.82      0.77       223
+      korean       0.85      0.78      0.82       238
+        thai       0.79      0.82      0.80       255
 
-    accuracy                           0.81      1199
-   macro avg       0.81      0.81      0.81      1199
-weighted avg       0.82      0.81      0.81      1199
+    accuracy                           0.80      1199
+   macro avg       0.81      0.81      0.80      1199
+weighted avg       0.81      0.80      0.80      1199
 </pre></div>
 </div>
 </div>
diff --git a/ml-fundamentals/classification/yet-other-classifiers.html b/ml-fundamentals/classification/yet-other-classifiers.html
index 1f03187718..65ffba8589 100644
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+++ b/ml-fundamentals/classification/yet-other-classifiers.html
@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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   <a class="reference internal" href="../../data-science/data-visualization/data-visualization.html">
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-  <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
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   <a class="reference internal" href="../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
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-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
-  <label for="toctree-checkbox-4">
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+  <label for="toctree-checkbox-5">
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   <a class="reference internal" href="../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
-  <label for="toctree-checkbox-5">
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+  <label for="toctree-checkbox-6">
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   <a class="reference internal" href="../regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
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-  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
-  <label for="toctree-checkbox-6">
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   <a class="reference internal" href="getting-started-with-classification.html">
    13. Getting started with classification
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-  <input checked="" class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
-  <label for="toctree-checkbox-7">
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   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
    14. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
-  <label for="toctree-checkbox-8">
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   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
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    16. Introduction to Gradient Boosting
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" type="checkbox"/>
-  <label for="toctree-checkbox-10">
+  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
+  <label for="toctree-checkbox-11">
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@@ -633,8 +655,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../assignments/README.html">
    37. Self-paced assignments
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-  <input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" type="checkbox"/>
-  <label for="toctree-checkbox-11">
+  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
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-    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-linear-regression-1.html">
-     37.45. ML linear regression - assignment 1
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-   </li>
-   <li class="toctree-l2">
-    <a class="reference internal" href="../../assignments/ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
+     37.45. Linear regression - California Housing
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     37.47. Linear Regression Metrics
+     37.46. Linear Regression Metrics
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     37.48. Loss Function
+     37.47. Loss Function
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
+    </a>
+   </li>
+   <li class="toctree-l2">
+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
+     37.49. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     37.51. ML logistic regression - assignment 1
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     37.52. ML logistic regression - assignment 2
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
-     37.53. ML neural network - Assignment 1
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    </li>
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     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html">
-     37.54. Regression tools
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     <a class="reference internal" href="../../assignments/ml-fundamentals/managing-data.html">
-     37.55. Managing data
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     <a class="reference internal" href="../../assignments/ml-fundamentals/exploring-visualizations.html">
-     37.56. Exploring visualizations
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     <a class="reference internal" href="../../assignments/ml-fundamentals/try-a-different-model.html">
-     37.57. Try a different model
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     <a class="reference internal" href="../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.101. Google Stock Price Prediction RNN
+     37.99. Google Stock Price Prediction RNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/autoencoder.html">
-     37.103. Intro to Autoencoders
+     37.101. Intro to Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
+     37.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
+     37.103. Fun with Variational Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
+     37.104. Time Series Forecasting Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/nn-for-classification-assignment.html">
-     37.108. Neural Networks for Classification with TensorFlow
+     37.106. Neural Networks for Classification with TensorFlow
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
-     37.109. NN Classify 15 Fruits Assignment
+     37.107. NN Classify 15 Fruits Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     37.114. DQN On Foreign Exchange Market
+     37.112. DQN On Foreign Exchange Market
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/gan/art-by-gan.html">
-     37.115. Art by gan
+     37.113. Art by gan
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/gan/gan-introduction.html">
-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
     </a>
    </li>
   </ul>
@@ -1135,8 +1152,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../slides/introduction.html">
    38. Slides
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/>
-  <label for="toctree-checkbox-12">
+  <input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" type="checkbox"/>
+  <label for="toctree-checkbox-13">
    <i class="fas fa-chevron-down">
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@@ -1822,18 +1839,18 @@ <h3><span class="section-number">13.3.4.1. </span>Exercise - apply a linear SVC<
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 78.6% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 78.2% 
               precision    recall  f1-score   support
 
-     chinese       0.70      0.75      0.72       254
-      indian       0.89      0.90      0.89       240
-    japanese       0.80      0.75      0.77       246
-      korean       0.80      0.70      0.75       227
-        thai       0.76      0.83      0.80       232
+     chinese       0.69      0.72      0.71       240
+      indian       0.88      0.93      0.91       253
+    japanese       0.75      0.75      0.75       240
+      korean       0.80      0.73      0.77       250
+        thai       0.78      0.77      0.78       216
 
-    accuracy                           0.79      1199
-   macro avg       0.79      0.79      0.79      1199
-weighted avg       0.79      0.79      0.79      1199
+    accuracy                           0.78      1199
+   macro avg       0.78      0.78      0.78      1199
+weighted avg       0.78      0.78      0.78      1199
 </pre></div>
 </div>
 </div>
@@ -1857,31 +1874,33 @@ <h3><span class="section-number">13.3.5.1. </span>Exercise - apply the K-Neighbo
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 78.6% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 78.2% 
               precision    recall  f1-score   support
 
-     chinese       0.70      0.75      0.72       254
-      indian       0.89      0.90      0.89       240
-    japanese       0.80      0.75      0.77       246
-      korean       0.80      0.70      0.75       227
-        thai       0.76      0.83      0.80       232
+     chinese       0.69      0.72      0.71       240
+      indian       0.88      0.93      0.91       253
+    japanese       0.75      0.75      0.75       240
+      korean       0.80      0.73      0.77       250
+        thai       0.78      0.77      0.78       216
 
-    accuracy                           0.79      1199
-   macro avg       0.79      0.79      0.79      1199
-weighted avg       0.79      0.79      0.79      1199
+    accuracy                           0.78      1199
+   macro avg       0.78      0.78      0.78      1199
+weighted avg       0.78      0.78      0.78      1199
 
-Accuracy (train) for KNN classifier: 74.0% 
-              precision    recall  f1-score   support
+Accuracy (train) for KNN classifier: 72.7% 
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>              precision    recall  f1-score   support
 
-     chinese       0.70      0.69      0.70       254
-      indian       0.88      0.84      0.86       240
-    japanese       0.64      0.82      0.72       246
-      korean       0.84      0.54      0.66       227
-        thai       0.71      0.80      0.75       232
+     chinese       0.68      0.71      0.70       240
+      indian       0.86      0.84      0.85       253
+    japanese       0.63      0.85      0.72       240
+      korean       0.88      0.52      0.65       250
+        thai       0.67      0.72      0.70       216
 
-    accuracy                           0.74      1199
-   macro avg       0.76      0.74      0.74      1199
-weighted avg       0.75      0.74      0.74      1199
+    accuracy                           0.73      1199
+   macro avg       0.75      0.73      0.72      1199
+weighted avg       0.75      0.73      0.73      1199
 </pre></div>
 </div>
 </div>
@@ -1909,45 +1928,47 @@ <h3><span class="section-number">13.3.6.1. </span>Exercise - apply a Support Vec
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 78.6% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 78.2% 
               precision    recall  f1-score   support
 
-     chinese       0.70      0.75      0.72       254
-      indian       0.89      0.90      0.89       240
-    japanese       0.80      0.75      0.77       246
-      korean       0.80      0.70      0.75       227
-        thai       0.76      0.83      0.80       232
+     chinese       0.69      0.72      0.71       240
+      indian       0.88      0.93      0.91       253
+    japanese       0.75      0.75      0.75       240
+      korean       0.80      0.73      0.77       250
+        thai       0.78      0.77      0.78       216
 
-    accuracy                           0.79      1199
-   macro avg       0.79      0.79      0.79      1199
-weighted avg       0.79      0.79      0.79      1199
+    accuracy                           0.78      1199
+   macro avg       0.78      0.78      0.78      1199
+weighted avg       0.78      0.78      0.78      1199
 
-Accuracy (train) for KNN classifier: 74.0% 
-              precision    recall  f1-score   support
+Accuracy (train) for KNN classifier: 72.7% 
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>              precision    recall  f1-score   support
 
-     chinese       0.70      0.69      0.70       254
-      indian       0.88      0.84      0.86       240
-    japanese       0.64      0.82      0.72       246
-      korean       0.84      0.54      0.66       227
-        thai       0.71      0.80      0.75       232
+     chinese       0.68      0.71      0.70       240
+      indian       0.86      0.84      0.85       253
+    japanese       0.63      0.85      0.72       240
+      korean       0.88      0.52      0.65       250
+        thai       0.67      0.72      0.70       216
 
-    accuracy                           0.74      1199
-   macro avg       0.76      0.74      0.74      1199
-weighted avg       0.75      0.74      0.74      1199
+    accuracy                           0.73      1199
+   macro avg       0.75      0.73      0.72      1199
+weighted avg       0.75      0.73      0.73      1199
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for SVC: 81.6% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for SVC: 81.2% 
               precision    recall  f1-score   support
 
-     chinese       0.77      0.74      0.75       254
-      indian       0.92      0.93      0.92       240
-    japanese       0.79      0.78      0.78       246
-      korean       0.84      0.76      0.80       227
-        thai       0.77      0.89      0.83       232
+     chinese       0.75      0.76      0.75       240
+      indian       0.91      0.91      0.91       253
+    japanese       0.81      0.80      0.81       240
+      korean       0.86      0.77      0.81       250
+        thai       0.74      0.82      0.78       216
 
-    accuracy                           0.82      1199
-   macro avg       0.82      0.82      0.82      1199
-weighted avg       0.82      0.82      0.81      1199
+    accuracy                           0.81      1199
+   macro avg       0.81      0.81      0.81      1199
+weighted avg       0.81      0.81      0.81      1199
 </pre></div>
 </div>
 </div>
@@ -1972,73 +1993,75 @@ <h2><span class="section-number">13.3.7. </span>Ensemble Classifiers<a class="he
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 78.6% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 78.2% 
               precision    recall  f1-score   support
 
-     chinese       0.70      0.75      0.72       254
-      indian       0.89      0.90      0.89       240
-    japanese       0.80      0.75      0.77       246
-      korean       0.80      0.70      0.75       227
-        thai       0.76      0.83      0.80       232
+     chinese       0.69      0.72      0.71       240
+      indian       0.88      0.93      0.91       253
+    japanese       0.75      0.75      0.75       240
+      korean       0.80      0.73      0.77       250
+        thai       0.78      0.77      0.78       216
 
-    accuracy                           0.79      1199
-   macro avg       0.79      0.79      0.79      1199
-weighted avg       0.79      0.79      0.79      1199
+    accuracy                           0.78      1199
+   macro avg       0.78      0.78      0.78      1199
+weighted avg       0.78      0.78      0.78      1199
 
-Accuracy (train) for KNN classifier: 74.0% 
-              precision    recall  f1-score   support
+Accuracy (train) for KNN classifier: 72.7% 
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>              precision    recall  f1-score   support
 
-     chinese       0.70      0.69      0.70       254
-      indian       0.88      0.84      0.86       240
-    japanese       0.64      0.82      0.72       246
-      korean       0.84      0.54      0.66       227
-        thai       0.71      0.80      0.75       232
+     chinese       0.68      0.71      0.70       240
+      indian       0.86      0.84      0.85       253
+    japanese       0.63      0.85      0.72       240
+      korean       0.88      0.52      0.65       250
+        thai       0.67      0.72      0.70       216
 
-    accuracy                           0.74      1199
-   macro avg       0.76      0.74      0.74      1199
-weighted avg       0.75      0.74      0.74      1199
+    accuracy                           0.73      1199
+   macro avg       0.75      0.73      0.72      1199
+weighted avg       0.75      0.73      0.73      1199
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for SVC: 81.6% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for SVC: 81.2% 
               precision    recall  f1-score   support
 
-     chinese       0.77      0.74      0.75       254
-      indian       0.92      0.93      0.92       240
-    japanese       0.79      0.78      0.78       246
-      korean       0.84      0.76      0.80       227
-        thai       0.77      0.89      0.83       232
+     chinese       0.75      0.76      0.75       240
+      indian       0.91      0.91      0.91       253
+    japanese       0.81      0.80      0.81       240
+      korean       0.86      0.77      0.81       250
+        thai       0.74      0.82      0.78       216
 
-    accuracy                           0.82      1199
-   macro avg       0.82      0.82      0.82      1199
-weighted avg       0.82      0.82      0.81      1199
+    accuracy                           0.81      1199
+   macro avg       0.81      0.81      0.81      1199
+weighted avg       0.81      0.81      0.81      1199
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for RFST: 84.2% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for RFST: 83.8% 
               precision    recall  f1-score   support
 
-     chinese       0.84      0.79      0.81       254
-      indian       0.90      0.93      0.91       240
-    japanese       0.85      0.81      0.83       246
-      korean       0.82      0.80      0.81       227
-        thai       0.80      0.88      0.84       232
+     chinese       0.81      0.78      0.79       240
+      indian       0.89      0.94      0.92       253
+    japanese       0.83      0.82      0.82       240
+      korean       0.84      0.79      0.82       250
+        thai       0.81      0.86      0.83       216
 
     accuracy                           0.84      1199
    macro avg       0.84      0.84      0.84      1199
 weighted avg       0.84      0.84      0.84      1199
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for ADA: 69.2% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for ADA: 70.2% 
               precision    recall  f1-score   support
 
-     chinese       0.66      0.44      0.53       254
-      indian       0.90      0.87      0.89       240
-    japanese       0.63      0.63      0.63       246
-      korean       0.60      0.77      0.67       227
-        thai       0.68      0.78      0.73       232
+     chinese       0.64      0.45      0.53       240
+      indian       0.87      0.86      0.86       253
+    japanese       0.69      0.62      0.65       240
+      korean       0.62      0.79      0.70       250
+        thai       0.69      0.79      0.74       216
 
-    accuracy                           0.69      1199
-   macro avg       0.70      0.70      0.69      1199
-weighted avg       0.70      0.69      0.69      1199
+    accuracy                           0.70      1199
+   macro avg       0.70      0.70      0.70      1199
+weighted avg       0.70      0.70      0.70      1199
 </pre></div>
 </div>
 </div>
diff --git a/ml-fundamentals/ml-overview.html b/ml-fundamentals/ml-overview.html
index 7a303b9cbb..c42f8b4235 100644
--- a/ml-fundamentals/ml-overview.html
+++ b/ml-fundamentals/ml-overview.html
@@ -238,10 +238,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/working-with-data/data-preparation.html">
@@ -254,8 +276,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../data-science/data-visualization/data-visualization.html">
    6. Data visualization
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
-  <label for="toctree-checkbox-3">
+  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
+  <label for="toctree-checkbox-4">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -286,8 +308,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
-  <label for="toctree-checkbox-4">
+  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
+  <label for="toctree-checkbox-5">
    <i class="fas fa-chevron-down">
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   </label>
@@ -313,8 +335,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
    8. Data Science in the cloud
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
-  <label for="toctree-checkbox-5">
+  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
+  <label for="toctree-checkbox-6">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -357,8 +379,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="regression/regression-models-for-machine-learning.html">
    11. Regression models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
-  <label for="toctree-checkbox-6">
+  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
+  <label for="toctree-checkbox-7">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -394,8 +416,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="classification/getting-started-with-classification.html">
    13. Getting started with classification
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
-  <label for="toctree-checkbox-7">
+  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
+  <label for="toctree-checkbox-8">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -433,8 +455,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../ml-advanced/clustering/clustering-models-for-machine-learning.html">
    14. Clustering models for Machine Learning
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
-  <label for="toctree-checkbox-8">
+  <input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/>
+  <label for="toctree-checkbox-9">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -455,8 +477,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
    15. Getting started with ensemble learning
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-    <a class="reference internal" href="../assignments/ml-fundamentals/ml-linear-regression-2.html">
-     37.46. ML linear regression - assignment 2
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-     37.49. Gradient descent
+     37.48. Gradient descent
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+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
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+    <label for="toctree-checkbox-3">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
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+     </li>
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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    </li>
    <li class="toctree-l2">
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-     37.108. Neural Networks for Classification with TensorFlow
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     <a class="reference internal" href="../../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
-     37.109. NN Classify 15 Fruits Assignment
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-     37.117. Generative Adversarial Networks (GANs)
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
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+    <label for="toctree-checkbox-3">
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+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
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+      </a>
+     </li>
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+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/working-with-data/data-preparation.html">
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-  <label for="toctree-checkbox-5">
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    <i class="fas fa-chevron-down">
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-  <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
-  <label for="toctree-checkbox-8">
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-    <a class="reference internal" href="../assignments/ml-fundamentals/ml-linear-regression-2.html">
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     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
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-     37.64. Linear Regression Implementation from Scratch
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-     37.66. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     37.67. Support Vector Machines (SVM) - Classification
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     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     37.68. Decision Trees - Intro and Regression
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-     37.94. Sign Language Digits Classification with CNN
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-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
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     <a class="reference internal" href="../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     37.101. Google Stock Price Prediction RNN
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-     37.105. Fun with Variational Autoencoders
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     <a class="reference internal" href="../assignments/deep-learning/time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
+     37.104. Time Series Forecasting Assignment
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     <a class="reference internal" href="../assignments/deep-learning/nn-for-classification-assignment.html">
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-     37.114. DQN On Foreign Exchange Market
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     <a class="reference internal" href="../assignments/deep-learning/gan/gan-introduction.html">
-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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     <a class="reference internal" href="../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
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   <a class="reference internal" href="../slides/introduction.html">
    38. Slides
   </a>
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@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
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+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
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    6. Data visualization
   </a>
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   <a class="reference internal" href="../data-science/data-science-lifecycle/data-science-lifecycle.html">
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   <a class="reference internal" href="../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
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    11. Regression models for Machine Learning
   </a>
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   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
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    14. Clustering models for Machine Learning
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    15. Getting started with ensemble learning
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    16. Introduction to Gradient Boosting
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-    <a class="reference internal" href="../assignments/ml-fundamentals/ml-linear-regression-2.html">
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     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     37.49. Gradient descent
+     37.48. Gradient descent
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+    <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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-     37.64. Linear Regression Implementation from Scratch
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-     37.67. Support Vector Machines (SVM) - Classification
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-     37.68. Decision Trees - Intro and Regression
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-     37.99. Bitcoin LSTM Model with Tweet Volume and Sentiment
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-     37.104. Base/Denoising Autoencoder &amp; Dimension Reduction
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     <a class="reference internal" href="../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     37.105. Fun with Variational Autoencoders
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/time-series-forecasting-assignment.html">
-     37.106. Time Series Forecasting Assignment
+     37.104. Time Series Forecasting Assignment
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     <a class="reference internal" href="../assignments/deep-learning/nn-for-classification-assignment.html">
-     37.108. Neural Networks for Classification with TensorFlow
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     <a class="reference internal" href="../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
-     37.109. NN Classify 15 Fruits Assignment
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-     37.114. DQN On Foreign Exchange Market
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    </li>
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-     37.117. Generative Adversarial Networks (GANs)
+     37.115. Generative Adversarial Networks (GANs)
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     <a class="reference internal" href="../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     37.118. Basic classification: Classify images of clothing
+     37.116. Basic classification: Classify images of clothing
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   <a class="reference internal" href="../slides/introduction.html">
    38. Slides
   </a>
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      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
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    6. Data visualization
   </a>
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   <a class="reference internal" href="../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
   </a>
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    8. Data Science in the cloud
   </a>
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    11. Regression models for Machine Learning
   </a>
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   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
    13. Getting started with classification
   </a>
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    14. Clustering models for Machine Learning
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    15. Getting started with ensemble learning
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
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+      <a class="reference internal" href="data-science/working-with-data/pandas/data-selection.html">
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+      <a class="reference internal" href="data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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    <li class="toctree-l2">
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    6. Data visualization
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    11. Regression models for Machine Learning
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9,170],And:[31,32,40,41,43,48,49,50,52,56,58,62,68,74,77,79,81,93,101,103,105,113,117,120,124,127,128,131,134,137,138,139,140,141,145,154,156,166,170,174,178,182,185,192],As:[1,3,7,8,33,34,36,40,41,43,47,48,49,50,51,52,53,54,56,57,58,59,60,61,68,77,79,81,83,84,100,101,107,110,113,115,117,118,120,121,122,130,131,132,137,138,139,145,148,149,153,154,155,156,160,163,164,165,166,169,170,171,175,177,180,189,191,192],At:[28,40,48,50,56,59,68,75,81,107,117,120,122,128,138,139,141,145,149,150,155,163,166,168,169,170,177,189,190],BE:[93,94,169,170],BUT:[93,94,169,170],BY:[76,102,139,144],Be:[86,92,105,109,120,157],Being:[43,62,102,105,121],But:[33,38,40,43,48,49,50,52,53,56,57,58,59,61,64,68,77,79,81,101,105,110,113,122,125,127,129,135,137,138,139,141,144,148,149,150,152,154,155,156,163,165,168,169,170,171,182,186],By:[7,18,29,40,41,46,49,52,53,54,57,59,68,76,77,79,81,100,110,115,118,120,121,122,126,135,137,138,140,143,144,145,146,153,156,160,163,164,165,169,180],FOR:[93,94,169,170],For:[7,19,29,30,31,32,35,36,38,39,40,41,43,45,46,47,48,49,50,51,54,59,60,61,64,66,67,68,69,71,72,75,77,79,85,86,87,89,90,91,92,101,102,103,112,113,114,115,117,118,120,121,124,127,128,129,131,133,135,137,138,139,140,141,143,144,145,146,148,149,150,153,154,155,156,157,160,161,162,163,164,165,166,168,169,170,171,172,182,186,189,190,192],IN:[25,83,93,94,169,170],IS:[22,45,47,48,54,93,94,98,143,169,170],IT:[54,100,138],If:[1,7,14,16,18,29,33,34,35,39,40,41,43,45,48,49,50,51,52,53,54,57,58,59,60,62,64,66,68,69,74,76,77,79,81,83,93,94,96,101,102,104,105,107,109,110,111,112,113,114,115,117,118,119,120,121,122,124,126,127,128,129,130,132,134,137,138,139,141,143,145,148,149,150,152,154,155,156,157,160,162,163,164,165,166,168,169,170,171,178,184,186,189,191,192,193],In:[1,3,7,8,9,11,12,13,14,16,18,19,21,24,28,29,30,31,32,33,36,39,40,41,43,45,46,47,48,49,50,52,53,54,57,58,59,60,61,62,64,66,68,69,71,74,75,76,77,79,80,81,83,84,89,90,91,93,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,133,135,137,138,139,140,141,143,144,145,146,148,149,150,151,152,153,154,155,156,157,159,160,161,162,163,164,165,166,167,168,169,170,171,172,175,177,178,179,184,186,187,188,189,190,192,193],Is:[50,94,98,100,104,107,112,113,114,126,138,139,143,157,163,165,166,193],It:[0,1,2,3,4,5,7,8,9,10,11,13,14,15,16,17,18,19,20,23,24,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,71,72,74,76,77,79,81,83,84,85,86,87,89,90,91,92,100,101,102,103,104,105,107,109,110,111,112,113,114,115,117,118,119,120,121,125,126,127,128,129,130,131,132,133,134,135,137,138,139,140,141,142,143,144,145,146,148,149,150,151,152,153,154,155,156,157,160,161,162,163,164,166,168,169,170,171,173,174,180,184,185,186,187,189,190,192],Its:[127,148,153,165],NEAR:[61,79],NO:[93,94,169,170],NOT:[83,93,94,120,169,170],Near:[110,176],No:[7,20,29,33,50,54,56,64,83,89,94,99,101,102,111,128,141,145,154,156,161,169,170,173,176],Not:[7,40,43,49,52,54,56,68,81,102,112,118,119,132,149,161,164,165,170,185,192],OF:[22,45,47,48,93,94,169,170],ON:[122,178],ONE:7,OR:[22,45,47,48,93,94,120,169,170],Of:[50,102,103,105,115,156,171,172],On:[49,50,52,57,58,59,60,61,66,68,79,81,102,105,107,139,145,149,152,153,156,157,163,164,168,169,175,185],One:[1,7,11,28,40,43,49,50,52,53,54,55,57,58,59,66,84,99,104,105,107,109,111,115,117,120,125,127,133,139,144,146,148,154,163,164,166,169,170,171,175,176,177,182,184,189,192],Or:[32,40,50,58,79,103,105,120,127,128,139,143,146,163,169,170,185,189,192],Such:[1,7,30,40,43,49,50,54,117,139,140,164,169,191],THAT:83,THE:[93,94,169,170],TO:[54,93,94,134,169,170],That:[31,32,40,43,48,49,50,52,57,61,62,68,79,81,105,110,117,120,122,128,144,146,149,150,156,157,163,165,170,171,189],The:[0,3,5,6,7,8,12,13,14,15,16,18,19,24,25,26,28,29,30,31,32,33,34,35,37,39,40,41,45,46,47,48,49,50,52,54,55,56,57,58,59,60,61,62,63,64,65,66,68,71,74,77,79,81,84,85,91,92,93,94,100,103,104,105,107,109,110,111,113,114,115,116,117,119,120,121,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,143,144,145,146,148,149,150,151,152,153,154,157,160,161,163,164,165,171,172,174,175,176,180,181,183,184,185,186,187,188,189,190,193],Then:[7,31,45,47,50,56,66,74,76,83,102,105,117,120,121,125,126,129,132,134,139,140,141,144,145,146,148,149,153,157,164,168,169,170,171,185],There:[0,1,3,7,18,28,29,30,32,34,36,37,39,40,41,43,46,47,49,50,51,54,57,59,60,62,64,68,72,79,81,86,101,102,105,107,109,110,111,114,115,117,118,119,120,121,122,124,125,127,130,132,134,135,137,138,139,140,141,143,144,146,148,149,152,156,157,159,160,162,163,164,165,167,168,169,170,171,189,190,191,192],These:[7,30,31,39,41,45,49,54,56,59,60,75,76,77,79,100,102,105,107,110,113,114,119,120,121,130,138,144,148,149,153,155,156,163,169,171,173,175,177,178,180,191],To:[0,1,7,14,18,22,29,33,34,36,40,41,45,46,48,49,50,52,54,58,61,62,66,68,74,75,79,83,93,100,101,102,103,105,110,111,112,113,115,117,118,120,121,122,125,126,127,129,130,131,132,135,136,137,138,139,140,141,145,146,149,150,152,153,154,156,157,160,163,164,165,166,168,169,170,171,175,176,178,181,184,185,189,191,192,193],WITH:[93,94,169,170],Will:[141,165,171],With:[7,40,41,43,47,50,56,60,61,62,85,100,104,105,109,110,112,113,114,115,119,120,121,122,127,138,139,146,148,149,154,156,163,169,178,185,190],_0:149,_1:146,_2:146,_:[18,29,31,33,37,41,51,56,75,82,124,125,126,128,129,130,131,132,134,135,146,149,156,169,170,180,186,187,192],____:[3,12,22,24,25,46,93,94,98],_____:[24,93],______:[12,14,25],_______:14,________:14,_________:14,_________________________________________________________________:29,____i:94,__abs__:93,__add__:93,__all__:169,__annotations__:[169,191],__builtins__:169,__cached__:169,__call__:[63,65,126,131],__class__:[37,169],__dict__:[75,187,188],__doc__:[169,191],__eq__:93,__file__:[125,169],__finalize__:121,__future__:[37,125],__get__:121,__getitem__:121,__init__:[29,30,31,33,35,36,37,43,55,63,65,74,75,82,83,95,121,126,128,130,131,132,133,150,169,186,187,191],__iter__:33,__len__:33,__loader__:169,__main__:[35,125,128,157],__mul__:93,__name__:[35,37,125,128,157,169],__operators__:132,__package__:169,__repr__:55,__spec__:169,__str__:93,__sub__:93,__truediv__:93,__version__:[41,156,191],_aspp:131,_attach:[119,178],_bin:54,_branch:131,_build_model:35,_bunch:[57,58],_caller:121,_check_indexing_error:121,_check_params_vs_input:144,_concaten:121,_consolidate_inplac:121,_constructor:121,_conv_block:131,_conv_bn_relu:131,_conv_relu:131,_data:121,_decor:121,_deeplabv3:131,_deprecate_mismatched_index:121,_deprecated_arg:121,_engin:121,_etag:[119,178],_fcn_16:131,_fcn_32:131,_fcn_8:131,_format_argument_list:121,_fuse_bn_tensor:130,_get_axi:121,_get_block_manager_axi:121,_get_comb_axi:121,_get_concat_axi:121,_get_label_or_level_valu:121,_get_list_axi:121,_get_merge_kei:121,_get_new_ax:121,_get_result_dim:121,_get_slice_axi:121,_get_valu:121,_get_values_for_loc:121,_getbool_axi:121,_getitem_axi:121,_getitem_lowerdim:121,_getitem_tupl:121,_i:[76,77,146,156],_identity_block:131,_ilocindex:121,_index:56,_invalid_index:121,_is_copi:121,_is_scalar_access:121,_j:[146,156],_k:128,_kmean:144,_label:57,_left:121,_lib:121,_locationindex:121,_locindex:121,_m:128,_make_concat_multiindex:121,_make_stag:130,_maybe_cast_for_get_loc:121,_maybe_cast_slice_bound:121,_maybe_check_integr:121,_mergeoper:121,_method:177,_mgr:121,_novalu:177,_other:121,_pad_1x1_to_3x3_tensor:130,_pickl:125,_recognized_scalar:121,_rid:[119,178],_right:121,_sec_1:94,_segnet:131,_self:[119,178],_sigmoid:[82,187],_skip:3,_slice:121,_subplot:79,_sum:177,_t:[119,178],_t_sne:184,_take:121,_take_with_is_copi:121,_takeabl:121,_valid_typ:121,_validate_integ:121,_validate_kei:121,_validate_tuple_index:121,_valu:121,a0958ad901d7:119,a0:[121,177],a10:121,a1:[120,121,177],a1gkdhua8we2lilmxcctgfiycqfttwx6tljchvsbz6sfau8wquo8541xaz2myyziork:59,a21453:170,a23:[169,191],a2:[120,121,177],a3:121,a3z5kdkfn3tbq:59,a4:121,a5:121,a7yia1n5fo6efhugqfis3dhueyjsa:59,a_:83,a_dict:170,a_i:[83,145],a_list:170,a_n:148,aaaaaa:[154,182],aafter:155,aaron:[29,50,77,129],ab:[50,63,65,76,93,94,121,125,126,133,156,169,170,192],abadi:129,abbeel:126,abbrevi:[122,126],abc:[94,121,170,177,193],abcd:[7,118,121,177],abcdef:121,abcmous:[113,174],abhinav:[137,141],abil:[43,52,54,68,74,76,81,109,127,137,138,148,154,157,163,169,171,182,189],abl:[3,7,10,11,14,16,20,31,40,49,50,52,53,54,57,61,62,74,79,102,105,111,113,115,119,120,121,127,135,138,140,143,149,152,155,157,160,163,164,165,166,168,174,184,187,188,191],abnorm:29,abnorml:66,abo:38,aboslut:155,about:[1,4,7,11,12,13,15,16,17,18,19,22,23,26,28,29,31,40,41,43,46,47,48,49,50,52,53,54,57,58,59,60,61,62,66,68,76,77,81,84,85,91,100,101,102,103,104,105,107,109,110,111,113,115,116,117,118,119,120,121,122,123,127,128,131,133,135,136,137,138,139,140,141,142,143,144,145,148,149,150,152,153,155,156,157,159,161,162,163,164,166,167,168,169,170,171,172,174,175,178,186,189,191,193],abov:[0,1,7,11,14,19,26,29,32,36,40,43,45,46,47,48,49,50,51,52,53,54,57,58,59,60,64,66,68,79,81,93,94,105,109,111,115,117,120,121,122,125,126,127,128,130,131,133,135,137,138,139,140,143,144,145,146,148,152,153,155,156,160,163,164,165,166,167,168,169,170,171,176,185,186],above_cutoff:156,abracadabra:170,abraham:193,abs_vector:[170,192],absenc:[54,184],absent:121,absolut:[47,74,77,84,93,117,120,139,148,152,155,169,170,171,192],absolute_error:76,absolute_import:125,absolute_percentage_error:76,abspath:125,abtract:77,abund:[111,176],ac:156,academ:[113,116,136,174],academi:191,acc:[33,39,47,49,52,57,125,134,190],acc_and_loss:125,acc_output:125,acceler:[101,111,112,139,141,164,176],accept:[16,40,57,68,81,84,101,104,107,113,120,121,130,139,141,149,153,163,169,175,189,190],access:[6,14,16,38,41,68,77,79,81,100,102,103,105,107,109,113,119,137,140,141,157,164,169,170,174,175,177,185,191,192],accessor:121,accident:149,acclaim:153,accommod:[7,36,47,118,170],accompani:[117,139,164],accomplish:[89,139,149,163,184,189],accord:[18,45,50,54,63,65,100,109,110,111,112,117,120,121,130,137,140,143,145,148,153,157,161,163,164,165,166,168,184],accordingli:[41,55,74,125,143,169,191],account:[0,6,8,14,16,50,76,77,93,102,103,113,117,119,120,145,149,164,165,174,177],accumul:[1,50,93,107,135,146,163,170,189],accur:[15,32,33,41,50,54,59,68,69,76,81,91,102,107,113,114,117,125,127,131,133,137,140,141,145,152,155,160,163,164,165,175],accuraci:[29,33,34,39,40,48,49,50,51,52,54,56,57,60,64,68,69,74,76,77,81,83,84,85,103,113,118,125,131,134,139,140,144,145,146,148,149,151,152,154,156,157,161,162,164,165,172,174,180,184],accuracy_of_batch:125,accuracy_scor:[29,30,39,49,50,51,52,56,57,59,60,68,81,84,148,153,157,161,162,165,184,187,188],achiev:[32,33,40,48,50,54,56,59,104,107,120,128,129,130,131,137,138,139,140,148,149,150,151,153,154,155,156,169],aci_servic:[9,101],aci_service_nam:[9,101],aciconfig:[9,101],acid:48,aciwebservic:[9,101],acm:[113,137,174],acoust:[142,143,144],acquir:[6,104,107,139,175],acquisit:[3,103,107,115,135,172,174],acronym:110,across:[33,43,47,54,68,77,81,103,113,115,117,120,121,122,125,135,137,138,139,141,149,153,156,169,170,172,174],act:[3,14,22,24,35,53,62,94,105,113,120,121,125,128,133,163,164,177,189],act_greedi:35,act_valu:35,action:[0,7,35,40,45,46,93,94,100,104,105,113,115,118,119,120,129,138,140,157,161,163,169,170,174,189],action_s:35,actions_count:35,activ:[0,29,30,32,33,34,35,36,39,40,41,43,44,45,47,48,56,57,62,77,83,113,124,125,126,130,131,132,133,134,140,149,155,156,160,174,179,180,185,190],activateion:132,activespac:178,actor:169,actual:[7,38,40,43,46,47,48,50,51,52,56,57,59,60,66,68,74,75,76,77,79,81,83,84,85,93,102,112,114,115,117,118,119,120,121,125,126,127,135,137,143,149,151,153,155,156,160,163,164,165,169,171,173,175,179,186,189],actual_result:[3,14,22,24,53,94],actual_valu:[38,76],acut:148,ad:[1,7,18,22,29,32,36,38,41,43,45,48,50,52,54,59,64,68,74,81,93,94,112,114,117,119,125,126,130,136,138,139,148,149,150,152,154,163,164,170,186,192],ada:162,adaboost:[149,162],adaboost_clf:49,adaboostclassifi:[49,56,161,162],adagradoptim:139,adam:[29,30,31,32,33,34,35,36,37,38,39,40,41,42,44,45,47,48,62,124,126,130,131,132,180],adamax:36,adamharlei:179,adamoptim:[125,129,139],adapt:[47,58,62,77,100,113,130,135,139,149,174,190],adaptiveaveragepooling2d:130,add:[1,7,9,14,17,18,30,31,32,33,34,35,36,38,39,41,42,43,44,45,46,47,50,52,54,61,62,63,65,66,74,77,93,94,109,111,114,117,119,121,122,124,125,126,130,131,132,133,138,148,149,150,152,153,154,155,157,162,163,164,165,166,168,169,170,171,178,180,182,185,186,189,190,192],add_1:132,add_:31,add_artist:[111,176],add_legend:143,add_selectbox:185,add_slid:185,add_subplot:[35,37,47,80,128],add_trick:169,add_weight:130,addison:138,addit:[1,7,18,23,32,41,46,54,59,64,66,74,75,77,79,93,104,105,107,109,113,114,117,118,119,120,122,128,130,131,132,133,135,139,141,145,149,150,152,153,154,156,164,170,171,177,178,192,193],addition:[31,74,118,120,122,138,143,145,149,154,178,182],additon:32,addon:126,address:[74,75,91,104,105,107,113,135,137,138,141,145,152,163,169,174,175],adel:148,adequ:[4,5,6,8,10,11,13,16,17,19,20,21,23,24,26,27,28,69,71,72,86,89,90,91,92,117,139],adher:[48,107],adjac:169,adject:[170,192],adjunct:163,adjust:[29,33,36,45,55,57,58,75,77,130,139,148,149,154,162],adjusted_r2_scor:76,admin2:140,admin:178,administr:[141,171],adobe_premier:38,adolesc:169,adopt:[59,86,103,113,138,139,141,145,149,172,174,193],adult:[169,191],advanc:[35,43,66,103,113,121,136,138,139,141,145,163,170,178,181],advantag:[40,50,54,68,81,121,130,138,141,148,151,152,154,170],advent:[49,127,138],advers:28,adversari:[36,141,181],advertis:105,advic:139,advis:[7,46,53,58,61,118,148],advoc:113,ae:[31,126],aebf:[119,178],aeroplan:7,aerospik:178,aesthet:22,affect:[7,17,33,39,49,52,54,56,58,68,74,81,83,103,105,107,113,117,118,128,130,139,146,152,154,155,169,172,174,182],affer:134,affin:[83,143],affinity_matrix_:156,afford:[7,79,118,163],african:[113,141,174],afro:[143,144],afropop:[143,144],after:[0,7,14,29,32,33,34,35,36,39,40,41,47,48,49,50,51,54,55,56,57,60,62,64,74,79,83,105,109,115,117,118,120,122,124,125,130,131,132,134,135,138,139,140,143,144,146,148,149,152,153,157,162,163,164,166,169,170,171,185,187,188,190,191,192],afterward:[32,120],afzal:137,ag:[9,18,22,50,51,79,89,93,94,101,102,115,117,119,120,121,138,143,145,146,150,153,160,163,167,168,169,170,171,177,178,187,188,189,191,192,193],again:[7,14,17,40,41,47,49,50,51,52,53,57,58,59,68,81,83,118,121,126,144,149,153,155,165,166,169,170,171,184,185],against:[0,18,41,47,50,59,76,77,105,113,115,117,121,135,139,149,155,157,168,183],agaricu:111,age_distribut:24,age_median_imput:22,age_sal_tre:50,age_tre:50,agefil:22,agenc:[105,163],agenda:[103,172],agent:[113,163,189],ageron:156,agg:[18,38,156],aggfunc:121,agglom:143,agglomerativeclust:156,aggreg:[7,14,49,77,107,112,125,145,148,153],aghdkaaa:130,aghdkaab:130,agil:[137,138],agnost:138,ago:[125,127,149],agre:[22,45,47,48,113],agricultur:[103,112,163,166,172,189],ahead:[49,52,57,105,135],ahnjovq9nfghs6fj4piqib3brpgnscyflm6riahdtaeyfclw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160,163,169,171,179,180,182,183,190,193],complex_numb:169,complex_number_1:[170,192],complex_number_2:[170,192],complex_number__1:192,complexnumb:169,complexnumberwithconstructor:169,compli:113,complianc:[22,45,47,48,113,174],compliant:113,complic:[36,50,83,115,120,137,138,139,149,152,155,156,166,180],compon:[75,102,103,109,119,124,128,135,137,138,139,140,145,149,152,154,163,171,172],components_:[156,184],compos:[36,37,61,79,83,124,125,129,137,138,149,186],compose_greet_func:169,compose_greet_func_with_closur:169,composit:[120,148],compound:[170,177,192],compound_stmt:169,comprehend:49,comprehens:[94,109,121,139,164],compress:[29,30,31,107,124,127,130],compris:[39,77,102,138],compromis:[7,118],comput:[3,7,18,22,29,32,33,36,40,41,43,46,47,49,50,53,54,58,59,66,74,75,76,80,82,83,85,100,103,104,107,115,117,118,119,121,124,125,126,127,129,130,133,134,136,137,138,139,141,145,146,148,149,153,154,155,160,163,165,168,170,172,173,174,185,186,187,189,192,193],computation:[33,36,50,56,120,121,127,130,135,151],computationn:33,compute_reciproc:177,compute_target:[9,101],con:[7,47,56,102,113,157],concat:[22,30,36,38,42,54,56,66,126,130,131,132,133,135,160,169,170],concat_axi:121,concat_index:121,concaten:[34,38,55,74,120,121,126,131,133,166,170,180,186,187,188,192],concatenated_str:169,concav:126,conceiv:[140,169],concentr:143,concept:[3,18,29,31,47,50,59,74,75,76,102,103,115,117,119,120,125,129,130,136,137,138,139,140,146,149,153,154,161,165,168,170,179,192],conceptu:149,concern:[7,47,54,58,59,79,107,110,113,118,137,138,141,149,163,164,176,189],concis:[120,145,163,169,170,192],conclud:[56,59,75,103,109,117,139,146],conclus:[24,50,104,113,115,164],concret:[141,163,184],concurr:[85,101,102],conda:0,condens:128,condit:[3,22,31,39,40,45,47,48,50,54,94,103,113,120,126,130,139,146,148,149,164,165,169,170,171,191,192],condition2:54,condorcet:145,conduct:[56,101,113,174],conf:18,conf_conv:133,conf_matrix:[52,57],confer:[105,109,121,137,141],confid:[40,41,48,68,81,112,130,139,140,143,145,149],config:[9,38,50,66,132,135,145,148,184,185],configur:[10,41,45,47,100,102,134,137,138,139,163,166,167,169],confirm:[14,30,45,47,48,59,104,107,113,156,164,174,175],conflict:[94,105,113,121],conform:[114,121,137,139],confus:[7,34,40,50,52,57,60,68,81,84,105,118,120,146,148,153,155,160,169],confusingli:156,confusion_matrix:[34,39,40,51,52,57,59,60,68,81,84,85,161,162,165,187,188],confusion_mtx:34,congratul:[101,102,164,165,168,171],conjug:93,conjunct:115,connect:[6,30,32,33,41,43,45,48,62,83,93,94,103,105,113,117,125,130,131,134,139,155,169,170,180],connectionist:77,connor:137,conquer:149,consciou:7,consecut:[14,32,40,49,153],consent:[113,174],consequ:[28,103,113,128,174],conserv:[110,176],conservationstatu:[110,176],consid:[1,3,7,8,11,14,18,22,24,29,36,39,40,41,45,46,49,50,53,56,62,64,74,75,76,77,94,102,104,105,107,109,114,115,116,117,118,124,125,128,130,132,135,138,139,140,141,143,145,146,148,149,150,153,154,155,156,157,163,165,168,169,170,182,184,191,192],consider:[59,102,107,113,115,116,121,149,152,156,174,175],consist:[0,1,3,8,15,30,33,34,36,41,45,49,50,52,54,59,89,107,113,118,120,124,130,131,137,138,139,140,141,146,155,163,164,166,170,174,175,178,189],consol:121,consolid:75,conspiraci:62,constant:[50,63,65,75,77,120,125,126,128,130,134,139,149,153],constant_initi:125,constantli:115,constrain:[30,128,143],constraint:[30,102,107,120,130,138,139,143,155,161,184],construct:[30,50,120,121,124,126,130,135,137,143,145,148,149,150,153,169,170,192],constructor:[121,130,169,170,191,192],consult:[7,121],consum:[10,20,41,45,99,101,102,104,109,112,113,114,137,139,141,154,157,163,173],consumpt:[109,157,173,177],cont:54,cont_num_var:54,contact:[119,178,185],contagi:165,contain:[1,3,6,7,12,14,15,17,22,29,31,33,36,37,39,40,41,45,46,47,48,49,50,51,52,54,57,59,60,62,68,79,81,84,87,92,93,94,101,102,104,107,114,117,118,119,121,122,127,130,131,134,137,138,139,143,145,148,150,153,156,157,160,163,164,165,166,168,169,170,177,191,192],container:138,content:[1,2,3,4,5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,22,23,24,25,26,27,28,29,30,31,32,34,35,36,37,38,39,40,41,42,43,44,46,49,50,51,52,53,54,55,56,59,62,63,64,65,66,67,68,69,71,72,76,79,81,83,84,85,86,87,89,90,91,92,100,101,102,103,104,105,107,109,110,111,112,113,114,115,117,118,119,120,121,122,124,125,126,128,129,130,131,132,133,134,135,136,139,141,143,144,145,146,148,149,150,152,153,157,160,161,162,163,164,165,166,168,169,170,171,177,184,185,186,187,190,192],contest:145,context:[9,28,31,50,59,75,77,101,103,105,107,113,121,125,131,139,141,143,144,155,156,157,167,169,170,172,174,175,192],contigu:120,contin:[57,58],continu:[0,1,17,18,31,33,34,40,47,48,50,54,55,58,59,62,75,76,77,100,101,103,110,112,117,120,122,126,131,138,139,140,141,143,144,148,149,150,155,157,163,165,171,189],contour:[77,154,156,182],contourf:[148,156,187,188],contract:[93,94,102,138,169,170],contradictori:139,contrari:[121,141],contrarili:169,contrast:[7,50,77,91,104,120,125,129,139,148,154,180],contrib:134,contribut:[46,52,53,57,58,66,76,103,120,1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class=\"section-number\">37. </span>Self-paced assignments","<span class=\"section-number\">37.22. </span>Analyzing COVID-19 papers","<span class=\"section-number\">37.27. </span>Analyzing data","<span class=\"section-number\">37.9. </span>Analyzing text about Data Science","<span class=\"section-number\">37.13. </span>Apply your skills","<span class=\"section-number\">37.16. </span>Build your own custom vis","<span class=\"section-number\">37.17. </span>Classifying datasets","<span class=\"section-number\">37.26. </span>Data preparation","<span class=\"section-number\">37.24. </span>Data processing in Python","<span class=\"section-number\">37.41. </span>Data Science in the cloud: The \u201cAzure ML SDK\u201d way","<span class=\"section-number\">37.40. </span>Data Science project using Azure ML SDK","<span class=\"section-number\">37.10. </span>Data Science scenarios","<span class=\"section-number\">37.20. </span>Displaying airport data","<span class=\"section-number\">37.15. </span>Dive into the beehive","<span class=\"section-number\">37.23. </span>Estimation of COVID-19 pandemic","<span class=\"section-number\">37.25. </span>Evaluating data from a form","<span class=\"section-number\">37.36. </span>Explore a planetary computer dataset","<span class=\"section-number\">37.37. </span>Exploring for answers","<span class=\"section-number\">37.19. </span>Introduction to probability and statistics","<span class=\"section-number\">37.12. </span>Lines, scatters and bars","<span class=\"section-number\">37.39. </span>Low code/no code Data Science project on Azure ML","<span class=\"section-number\">37.38. </span>Market research","<span class=\"section-number\">37.29. </span>Matplotlib applied","<span class=\"section-number\">37.28. </span>NYC taxi data in winter and summer","<span class=\"section-number\">37.18. </span>Small diabetes study","<span class=\"section-number\">37.21. </span>Soda profits","<span class=\"section-number\">37.35. </span>Tell a story","<span class=\"section-number\">37.14. </span>Try it in Excel","<span class=\"section-number\">37.11. </span>Write a data ethics case study","<span class=\"section-number\">37.103. </span>Intro to Autoencoders","<span class=\"section-number\">37.104. </span>Base/Denoising Autoencoder &amp; Dimension Reduction","<span class=\"section-number\">37.105. </span>Fun with Variational Autoencoders","<span class=\"section-number\">37.92. </span>How to choose cnn architecture mnist","<span class=\"section-number\">37.96. </span>Object Recognition in Images using CNN","<span class=\"section-number\">37.94. </span>Sign Language Digits Classification with CNN","<span class=\"section-number\">37.114. </span>DQN On Foreign Exchange Market","<span class=\"section-number\">37.115. </span>Art by gan","<span class=\"section-number\">37.117. </span>Generative Adversarial Networks (GANs)","<span class=\"section-number\">37.99. </span>Bitcoin LSTM Model with Tweet Volume and Sentiment","<span class=\"section-number\">37.109. </span>NN Classify 15 Fruits Assignment","<span class=\"section-number\">37.108. </span>Neural Networks for Classification with TensorFlow","<span class=\"section-number\">37.118. </span>Basic classification: Classify images of clothing","<span class=\"section-number\">37.101. </span>Google Stock Price Prediction RNN","<span class=\"section-number\">37.97. </span>Intro to TensorFlow for Deep Learning","<span class=\"section-number\">37.106. </span>Time Series Forecasting Assignment","<span class=\"section-number\">37.85. </span>Counterintuitive Challenges in ML Debugging","<span class=\"section-number\">37.84. </span>Data engineering","<span class=\"section-number\">37.86. </span>Case Study: Debugging in Classification","<span class=\"section-number\">37.87. </span>Case Study: Debugging in Regression","<span class=\"section-number\">37.77. </span>Beyond random forests: more ensemble models","<span class=\"section-number\">37.78. </span>Decision trees","<span class=\"section-number\">37.82. </span>Random Forest Classifier with Feature Importance","<span class=\"section-number\">37.76. </span>Random forests for classification","<span class=\"section-number\">37.75. </span>Random forests intro and regression","<span class=\"section-number\">37.81. </span>Boosting with tuning","<span class=\"section-number\">37.80. </span>Gradient boosting","<span class=\"section-number\">37.79. </span>Hyperparameter tuning gradient boosting","<span class=\"section-number\">37.69. </span>Decision Trees - Classification","<span class=\"section-number\">37.68. </span>Decision Trees - Intro and Regression","<span class=\"section-number\">37.65. </span>Kernel method assignment 1","<span class=\"section-number\">37.67. </span>Support Vector Machines (SVM) - Classification","<span class=\"section-number\">37.66. </span>Support Vector Machines (SVM) - Intro and SVM for Regression","<span class=\"section-number\">37.72. </span>Dropout and Batch Normalization","<span class=\"section-number\">37.73. </span>Lasso and Ridge Regression","<span class=\"section-number\">37.71. </span>Learning Curve To Identify Overfit &amp; Underfit","<span class=\"section-number\">37.70. </span>Model selection assignment 1","<span class=\"section-number\">37.74. </span>Regularized Linear Models","<span class=\"section-number\">37.90. </span>Build Classification Model","<span class=\"section-number\">37.89. </span>Build classification models","<span class=\"section-number\">37.58. </span>Create a regression model","<span class=\"section-number\">37.62. </span>Delicious asian and indian cuisines","<span class=\"section-number\">37.63. </span>Explore classification methods","<span class=\"section-number\">37.56. </span>Exploring visualizations","<span class=\"section-number\">37.59. </span>Linear and polynomial regression","<span class=\"section-number\">37.64. </span>Linear Regression Implementation from Scratch","<span class=\"section-number\">37.49. </span>Gradient descent","<span class=\"section-number\">37.47. </span>Linear Regression Metrics","<span class=\"section-number\">37.48. </span>Loss Function","<span class=\"section-number\">37.55. </span>Managing data","<span class=\"section-number\">37.45. </span>ML linear regression - assignment 1","<span class=\"section-number\">37.46. </span>ML linear regression - assignment 2","<span class=\"section-number\">37.51. </span>ML logistic regression - assignment 1","<span class=\"section-number\">37.52. </span>ML logistic regression - assignment 2","<span class=\"section-number\">37.53. </span>ML neural network - Assignment 1","<span class=\"section-number\">37.42. </span>Machine Learning overview - assignment 1","<span class=\"section-number\">37.43. </span>Machine Learning overview - assignment 2","<span class=\"section-number\">37.91. </span>Parameter play","<span class=\"section-number\">37.61. </span>Pumpkin varieties and color","<span class=\"section-number\">37.54. </span>Regression tools","<span class=\"section-number\">37.44. </span>Regression with Scikit-learn","<span class=\"section-number\">37.60. </span>Retrying some regression","<span class=\"section-number\">37.88. </span>Study the solvers","<span class=\"section-number\">37.57. </span>Try a different model","<span class=\"section-number\">37.8. </span>Python programming advanced","<span class=\"section-number\">37.7. </span>Python programming basics","<span class=\"section-number\">37.6. </span>Python programming introduction","<span class=\"section-number\">37.5. </span>Project Plan\u200b Template","<span class=\"section-number\">37.3. </span>First assignment","<span class=\"section-number\">37.4. </span>Second assignment","<span class=\"section-number\">8. </span>Data Science in the cloud","<span class=\"section-number\">8.1. </span>Introduction","<span class=\"section-number\">8.3. </span>Data Science in the cloud: The \u201cAzure ML SDK\u201d way","<span class=\"section-number\">8.2. </span>The \u201clow code/no code\u201d way","<span class=\"section-number\">9. </span>Data Science in the real world","<span class=\"section-number\">7.2. </span>Analyzing","<span class=\"section-number\">7.3. </span>Communication","<span class=\"section-number\">7. </span>Data Science lifecycle","<span class=\"section-number\">7.1. </span>Introduction to the Data Science lifecycle","<span class=\"section-number\">6. </span>Data visualization","<span class=\"section-number\">6.4. </span>Making meaningful visualizations","<span class=\"section-number\">6.1. </span>Visualizing distributions","<span class=\"section-number\">6.2. </span>Visualizing proportions","<span class=\"section-number\">6.3. </span>Visualizing relationships: all about honey \ud83c\udf6f","<span class=\"section-number\">4.2. </span>Data Science ethics","<span class=\"section-number\">4.3. </span>Defining data","<span class=\"section-number\">4.1. </span>Defining data science","<span class=\"section-number\">4. </span>Introduction","<span class=\"section-number\">4.4. </span>Introduction to statistics and probability","<span class=\"section-number\">5.5. </span>Data preparation","<span class=\"section-number\">5.2. </span>Non-relational data","<span class=\"section-number\">5.3. </span>NumPy","<span class=\"section-number\">5.4. </span>Pandas","<span class=\"section-number\">5.1. </span>Relational databases","<span class=\"section-number\">5. </span>Working with data","<span class=\"section-number\">24. </span>Autoencoder","<span class=\"section-number\">21. </span>Convolutional Neural Networks","<span class=\"section-number\">30. </span>Diffusion Model","<span class=\"section-number\">20. </span>Intro to Deep Learning","<span class=\"section-number\">27. </span>Deep Q-learning","<span class=\"section-number\">22. </span>Generative adversarial networks","<span class=\"section-number\">28. </span>Image classification","<span class=\"section-number\">29. </span>Image segmentation","<span class=\"section-number\">25. </span>Long-short term memory","<span class=\"section-number\">31. </span>Object detection","<span class=\"section-number\">23. </span>Recurrent Neural Networks","<span class=\"section-number\">26. </span>Time series","Learn AI together, for free","<span class=\"section-number\">34. </span>Data engineering","<span class=\"section-number\">36. </span>Model deployment","<span class=\"section-number\">35. </span>Model training &amp; evaluation","<span class=\"section-number\">32. </span>Overview","<span class=\"section-number\">33. </span>Problem framing","<span class=\"section-number\">14. </span>Clustering models for Machine Learning","<span class=\"section-number\">14.1. </span>Introduction to clustering","<span class=\"section-number\">14.2. </span>K-Means clustering","<span class=\"section-number\">15.1. </span>Bagging","<span class=\"section-number\">15.3. </span>Feature importance","<span class=\"section-number\">15. </span>Getting started with ensemble learning","<span class=\"section-number\">15.2. </span>Random forest","<span class=\"section-number\">16.1. </span>Gradient Boosting","<span class=\"section-number\">16.2. </span>Gradient boosting example","<span class=\"section-number\">16. </span>Introduction to Gradient Boosting","<span class=\"section-number\">16.3. </span>XGBoost","<span class=\"section-number\">16.4. </span>XGBoost + k-fold CV + Feature Importance","<span class=\"section-number\">18. </span>Kernel method","<span class=\"section-number\">19. </span>Model selection","<span class=\"section-number\">17. </span>Unsupervised learning","<span class=\"section-number\">12. </span>Build a web app to use a Machine Learning model","<span class=\"section-number\">13.4. </span>Applied Machine Learning : build a web app","<span class=\"section-number\">13. </span>Getting started with classification","<span class=\"section-number\">13.1. </span>Introduction to classification","<span class=\"section-number\">13.2. </span>More classifiers","<span class=\"section-number\">13.3. </span>Yet other classifiers","<span class=\"section-number\">10. </span>Machine Learning overview","<span class=\"section-number\">11.3. </span>Linear and polynomial regression","<span class=\"section-number\">11.4. </span>Logistic regression","<span class=\"section-number\">11.2. </span>Managing data","<span class=\"section-number\">11. </span>Regression models for Machine Learning","<span class=\"section-number\">11.1. </span>Tools of the trade","<span class=\"section-number\">3. </span>Python programming advanced","<span class=\"section-number\">2. </span>Python programming basics","<span class=\"section-number\">1. </span>Python programming introduction","<span class=\"section-number\">38.10. </span>Data Science in real world","<span class=\"section-number\">38.9. </span>Data Science in the cloud","<span class=\"section-number\">38.4. </span>Data Science introduction","<span class=\"section-number\">38.8. </span>Data Science lifecycle","<span class=\"section-number\">38.7. </span>Data visualization","<span class=\"section-number\">38.6. </span>NumPy and Pandas","<span class=\"section-number\">38.5. </span>Relational vs. non-relational database","<span class=\"section-number\">38.20. </span>Convolutional Neural Network","<span class=\"section-number\">38.21. </span>Generative Adversarial Network","<span class=\"section-number\">38. </span>Slides","<span class=\"section-number\">38.18. </span>Kernel method","<span class=\"section-number\">38.19. </span>Model Selection","<span class=\"section-number\">38.17. </span>Unsupervised learning","<span class=\"section-number\">38.16. </span>Build an machine learning web application","<span class=\"section-number\">38.12. </span>Linear Regression","<span class=\"section-number\">38.13. </span>Logistic Regression","<span class=\"section-number\">38.14. </span>Logistic Regression","<span class=\"section-number\">38.11. </span>Machine Learning overview","<span class=\"section-number\">38.15. </span>Neural Network","<span class=\"section-number\">38.3. </span>Python programming advanced","<span class=\"section-number\">38.2. </span>Python programming basics","<span class=\"section-number\">38.1. </span>Python programming introduction"],titleterms:{"0":59,"1":[3,24,30,32,43,49,50,52,53,54,56,57,58,59,60,61,65,68,79,81,83,84,105,114,120,130,172,173,174,175,176,177,178,179,180,182,186,188,192,193],"10":[40,56,125,130],"100":[51,59,130],"1000":[59,130],"11":56,"12":56,"13":56,"15":39,"19":[1,8,14],"1d":120,"2":[3,24,30,32,43,44,49,50,51,52,53,54,56,57,58,60,61,68,80,81,82,85,105,114,120,172,173,174,175,176,177,178,179,180,182,186,188,192,193],"2d":[30,120,182],"3":[3,24,32,39,43,49,50,52,53,54,56,57,58,60,61,68,79,81,105,120,172,173,174,175,176,177,179,180,186,192,193],"3d":[30,84,109,182],"4":[3,24,32,43,49,50,51,52,53,54,56,57,58,60,61,68,81,105,172,173,174,175,176,177,178,192,193],"5":[24,32,43,49,50,53,54,56,57,58,60,61,68,81,84,105,130,172,173,174,175,176,178,192,193],"50":56,"500":56,"6":[43,50,52,53,54,56,57,58,60,61,68,81,172,173,174,175,176],"7":[43,50,52,53,56,57,58,60,61,172,175,176],"8":[52,56,175],"9":56,"boolean":[120,170,171,192,193],"break":[93,169,191],"case":[28,45,47,48,50,105,113,166,182],"class":[35,39,47,50,51,63,65,93,169,191],"default":[51,59,169],"do":[47,115,165,166,170,174,182],"final":[49,75,79],"float":[7,170,192],"function":[43,51,59,75,77,83,93,94,120,127,128,149,169,171,177,187,188,190,191],"import":[9,29,33,37,41,44,49,51,52,53,54,56,57,58,59,60,61,64,66,84,118,126,139,146,153,168,169,186,187,188,190],"long":132,"new":[56,95,120,121,187,188],"null":[7,59,177],"public":37,"return":[95,128,169],"short":132,"true":59,"try":[0,27,48,68,81,92,169,191],"while":[93,169,191],A:[31,77,127,155,161,162,164],And:163,At:46,But:166,By:149,For:93,Is:141,It:[122,165,178],NOT:165,Not:163,On:35,One:[79,80,190],That:187,The:[9,36,43,51,53,75,76,83,101,102,118,122,155,156,162,166,168,169,170,173,177,178,179,182,191,192],There:166,To:[64,155],With:[34,75,135],about:[3,33,112,160,165,176],absolut:76,acceler:156,access:[94,120,121],accuraci:[32,41,47,59,130,153,190],acknowledg:[1,2,3,4,5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,71,72,74,79,81,83,84,85,86,87,89,90,91,92,93,94,100,101,102,103,104,105,107,109,110,111,112,113,114,115,117,118,119,120,121,122,124,125,126,128,129,130,131,132,133,134,135,136,139,143,144,145,146,148,149,150,152,153,154,157,160,161,162,163,164,165,166,168,169,170,171,179,182,184,185,186,187,190],ackowledg:180,action:128,activ:127,actual:105,ad:[30,31,47,62,66,120,155],adaboost:49,adam:190,add:[48,120,139],addit:121,adjust:76,advanc:[32,93,120,169,191],adversari:[37,129,180],after:[30,155,183],ag:24,against:48,agent:[35,128],agglom:156,aggreg:[120,121,174,177],ahead:83,ai:[36,41,136,184],airport:12,aka:64,algorithm:[50,54,128,140,143,148,149,153,156],align:121,all:[24,47,79,112,122,164,176,178],alpha:126,an:[9,31,64,66,101,102,105,119,120,157,179,185],anagram:170,analysi:[8,38,49,51,52,53,54,56,57,58,59,60,61,76,79,100,104,163,184,189],analyz:[1,2,3,18,104,120,166,175],anchor:[39,51],anim:109,ann:44,annot:[169,191],anomal:47,anomali:29,anoth:37,answer:17,api:[34,43],app:[157,158],append:[94,177],appli:[4,22,47,113,158,161,162,174],applic:[50,124,163,185,189],approach:[51,105,122,161,178],ar:170,arbitrari:169,architectur:[32,125,127,179,190],argument:[169,191],arithmet:[121,170,192],arrai:[120,121,177],art:36,artifici:[163,189],artwork:36,ascend:[94,95],ascent:129,asian:70,ask:166,assert:51,assign:[0,12,25,39,44,59,65,79,80,81,82,83,84,85,97,98,120,121,188],assist:131,attribut:[59,120,121,177],auc:[56,59],audienc:105,augment:[32,34,126],author:168,autoencod:[29,30,31,124],automl:[9,101,102,139],avail:120,averag:[93,166],avoid:[109,155],axi:120,azur:[9,10,20,101,102,119],b:75,babylonian:94,background:126,backprop:83,bag:[49,145,148,153],balanc:160,bar:[19,22,176],base:[30,93,121,153],basebal:18,baselin:[47,48,163,189],basi:59,basic:[29,32,41,43,94,113,120,121,128,170,171,174,177,192,193],batch:[33,62,156],beehiv:13,begin:105,behind:55,best:[9,32,37,101,141],beta:126,better:[161,165],between:[24,54,56,68,77,79,81,120,148,174,177],beyond:[40,49,154,182],bi:54,bia:[64,148,155,183],bibliographi:25,big:190,binai:54,binari:[40,165],binder:0,bird:[110,176],bit:[31,120],bitcoin:38,blend:54,bmi:24,boost:[49,54,55,56,149,150,151,153],bootstrap:145,bound:126,boundari:[154,156,182],boxplot:[24,84],bp:24,brain:179,broadcast:[120,177],bug:48,build:[5,29,31,32,36,41,50,51,67,68,81,109,144,157,158,164,165,166,176,185],c:59,cach:185,calcul:[47,93,94,170],call:191,callabl:121,callback:40,can:[50,56,115,174],candid:56,capac:155,captur:[107,175],cardin:51,cast:[170,192],catalog:137,catboost:54,categor:[7,51,54,57,68,79,81,164,186],categori:94,categorical_crossentropi:190,caus:45,central:[18,117],centroid:156,chain:121,challeng:[1,14,22,45,113,120,127,130,133,174],chang:[34,54],changin:34,channel:105,chart:[109,112,176,185],check:[30,47,48,51,53,57,59,68,79,81,139,153,166,170,193],checkbox:185,checklist:113,choic:[121,141],choos:[32,50,79,100,102,109,161,173],churn:50,cifar:[125,130],cit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00,104,109,112,124,127,129,131,137,139,140,141,143,146,150,151,152,154,156,157,158,165,167,170,171,172,173,184,188],By:[7,18,29,40,41,46,49,52,53,54,57,59,68,74,77,78,80,99,109,114,117,119,123,124,128,137,139,140,142,145,146,147,148,155,158,162,165,166,167,171,182],FOR:[92,93,171,172],For:[7,19,29,30,31,32,35,36,38,39,40,41,43,45,46,47,48,49,50,51,54,59,60,61,64,66,67,68,69,71,72,74,75,78,84,85,86,88,89,90,91,100,101,102,111,112,113,114,116,117,119,121,122,123,126,129,130,131,133,135,137,139,140,141,142,143,145,146,147,148,150,151,152,155,156,157,158,159,162,163,164,165,166,167,168,170,171,172,173,174,184,188,191,192,194],IN:[25,82,92,93,171,172],IS:[22,45,47,48,54,92,93,97,145,171,172],IT:[54,99,140],If:[1,7,14,16,18,29,33,34,35,39,40,41,43,45,48,49,50,51,52,53,54,57,58,59,60,62,64,66,68,69,74,76,77,78,80,82,92,93,95,100,101,103,104,106,108,109,110,111,112,113,114,116,117,118,119,121,122,123,124,126,128,129,130,131,132,134,136,139,140,141,143,145,147,150,151,152,154,156,157,158,159,162,164,165,166,167,168,170,171,172,173,180,186,188,191,193,194,195],In:[1,3,7,8,9,11,12,13,14,16,18,19,21,24,28,29,30,31,32,33,36,39,40,41,43,45,46,47,48,49,50,52,53,54,57,58,59,60,61,62,64,66,68,69,71,74,75,76,77,78,80,82,83,88,89,90,92,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,121,122,123,124,125,126,127,128,129,130,131,132,133,135,137,139,140,141,142,143,145,146,147,148,150,151,152,153,154,155,156,157,158,159,161,162,163,164,165,166,167,168,169,170,171,172,173,174,177,179,180,181,186,188,189,190,191,192,194,195],Is:[50,93,97,99,103,106,111,112,113,128,140,141,145,159,165,167,168,195],It:[0,1,2,3,4,5,7,8,9,10,11,13,14,15,16,17,18,19,20,23,24,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,71,72,74,76,77,78,80,82,83,84,85,86,88,89,90,91,99,100,101,102,103,104,106,108,109,110,111,112,113,114,116,117,118,119,121,122,123,127,128,129,130,131,132,133,134,135,136,137,139,140,141,142,143,144,145,146,147,148,150,151,152,153,154,155,156,157,158,159,162,163,164,165,166,168,170,171,172,173,175,176,182,186,187,188,189,191,192,194],Its:[129,150,155,167],NEAR:[61,74],NO:[92,93,171,172],NOT:[82,92,93,119,171,172],Near:[109,178],No:[7,20,29,33,50,54,56,64,82,88,93,98,100,101,110,130,143,147,156,158,163,171,172,175,178],Not:[7,40,43,49,52,54,56,68,80,101,111,117,118,134,151,163,166,167,172,187,194],OF:[22,45,47,48,92,93,171,172],ON:[124,180],ONE:7,OR:[22,45,47,48,92,93,119,171,172],Of:[50,101,102,104,114,158,173,174],On:[49,50,52,57,58,59,60,61,66,68,74,80,101,104,106,141,147,151,154,155,158,159,165,166,170,171,177,187],One:[1,7,11,28,40,43,49,50,52,53,54,55,57,58,59,66,83,98,103,104,106,108,110,114,116,119,127,129,135,141,146,148,150,156,165,166,168,171,172,173,177,178,179,184,186,191,194],Or:[32,40,50,58,74,102,104,119,129,130,141,145,148,165,171,172,187,191,194],Such:[1,7,30,40,43,49,50,54,116,141,142,166,171,193],THAT:82,THE:[92,93,171,172],TO:[54,92,93,136,171,172],That:[31,32,40,43,48,49,50,52,57,61,62,68,74,80,104,109,116,119,124,130,146,148,151,152,158,159,165,167,172,173,191],The:[0,3,5,6,7,8,12,13,14,15,16,18,19,24,25,26,28,29,30,31,32,33,34,35,37,39,40,41,45,46,47,48,49,50,52,54,55,56,57,58,59,60,61,62,63,64,65,66,68,71,74,76,78,80,83,84,90,91,92,93,99,102,103,104,106,108,109,110,112,113,114,115,116,118,119,121,122,123,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,145,146,147,148,150,151,152,153,154,155,156,159,162,163,165,166,167,173,174,176,177,178,182,183,185,186,187,188,189,190,191,192,195],Then:[7,31,45,47,50,56,66,76,77,82,101,104,116,119,127,128,131,134,136,141,142,143,146,147,148,150,151,155,159,166,170,171,172,173,187],There:[0,1,3,7,18,28,29,30,32,34,36,37,39,40,41,43,46,47,49,50,51,54,57,59,60,62,64,68,72,74,80,85,100,101,104,106,108,109,110,113,114,116,117,118,119,121,124,126,127,129,132,134,136,137,139,140,141,142,143,145,146,148,150,151,154,158,159,161,162,164,165,166,167,169,170,171,172,173,191,192,193,194],These:[7,30,31,39,41,45,49,54,56,59,60,74,75,77,78,99,101,104,106,109,112,113,118,119,122,123,132,140,146,150,151,155,157,158,165,171,173,175,177,179,180,182,193],To:[0,1,7,14,18,22,29,33,34,36,40,41,45,46,48,49,50,52,54,58,61,62,66,68,74,75,76,82,92,99,100,101,102,104,109,110,111,112,114,116,117,119,121,123,124,127,128,129,131,132,133,134,137,138,139,140,141,142,143,147,148,151,152,154,155,156,158,159,162,165,166,167,168,170,171,172,173,177,178,180,183,186,187,191,193,194,195],WITH:[92,93,171,172],Will:[143,167,173],With:[7,40,41,43,47,50,56,60,61,62,84,99,103,104,108,109,111,112,113,114,118,119,122,123,124,129,140,141,148,150,151,156,158,165,171,180,187,192],_0:151,_1:148,_2:148,_:[18,29,31,33,37,41,51,56,81,126,127,128,130,131,132,133,134,136,137,148,151,158,171,172,182,188,189,194],____:[3,12,22,24,25,46,92,93,97],_____:[24,92],______:[12,14,25],_______:14,________:14,_________:14,_________________________________________________________________:29,____i:93,__abs__: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ompon:[75,101,102,108,118,126,130,137,139,140,141,142,147,151,154,156,165,173,174],components_:[158,186],compos:[36,37,61,74,82,126,127,131,139,140,151,188],compose_greet_func:171,compose_greet_func_with_closur:171,composit:[119,150],compound:[172,179,194],compound_stmt:171,comprehend:49,comprehens:[93,108,123,141,166],compress:[29,30,31,106,126,129,132],compris:[39,78,101,140],compromis:[7,117],comput:[3,7,18,22,29,32,33,36,40,41,43,46,47,49,50,53,54,58,59,66,75,76,77,81,82,84,99,102,103,106,114,116,117,118,121,122,123,126,127,128,129,131,132,135,136,138,139,140,141,143,147,148,150,151,155,156,157,162,165,167,170,172,174,175,176,187,188,189,191,194,195],computation:[33,36,50,56,119,122,129,132,137,153],computationn:33,compute_reciproc:179,compute_target:[9,100],con:[7,47,56,101,112,159],concat:[22,30,36,38,42,54,56,66,128,132,133,134,135,137,162,171,172],concat_axi:121,concat_index:121,concaten:[34,38,55,76,119,121,128,133,135,168,172,182,188,189,190,194],concatenated_str:171,concav:128,conceiv:[142,171],concentr:145,concept:[3,18,29,31,47,50,59,75,76,77,101,102,114,116,118,119,123,127,131,132,138,139,140,141,142,148,151,155,156,163,167,170,172,181,194],conceptu:151,concern:[7,47,54,58,59,74,106,109,112,117,139,140,143,151,165,166,178,191],concis:[119,147,165,171,172,194],conclud:[56,59,75,102,108,116,141,148],conclus:[24,50,103,112,114,166],concret:[143,165,186],concurr:[84,100,101],conda:0,condens:130,condit:[3,22,31,39,40,45,47,48,50,54,93,102,112,119,128,132,141,148,150,151,166,167,171,172,173,193,194],condition2:54,condorcet:147,conduct:[56,100,112,176],conf:18,conf_conv:135,conf_matrix:[52,57],confer:[104,108,121,139,143],confid:[40,41,48,68,80,111,132,141,142,145,147,151],config:[9,38,50,66,134,137,147,150,186,187],configur:[10,41,45,47,99,101,136,139,140,141,165,168,169,171],confirm:[14,30,45,47,48,59,103,106,112,158,166,176,177],conflict:[93,104,112,123],conform:[113,123,139,141],confus:[7,34,40,50,52,57,60,68,80,83,104,117,119,148,150,155,157,162,171],confusingli:158,confusion_matrix:[34,39,40,51,52,57,59,60,68,80,83,84,163,164,167,189,190],confusion_mtx:34,congratul:[100,101,166,167,170,173],conjug:92,conjunct:114,connect:[6,30,32,33,41,43,45,48,62,82,92,93,102,104,112,116,127,132,133,136,141,157,171,172,182],connectionist:78,connor:139,conquer:151,consciou:7,consecut:[14,32,40,49,155],consent:[112,176],consequ:[28,102,112,130,176],conserv:[109,178],conservationstatu:[109,178],consid:[1,3,7,8,11,14,18,22,24,29,36,39,40,41,45,46,49,50,53,56,62,64,75,76,78,93,101,103,104,106,108,113,114,115,116,117,126,127,130,132,134,137,140,141,142,143,145,147,148,150,151,152,155,156,157,158,159,165,167,170,171,172,184,186,193,194],consider:[59,101,106,112,114,115,123,151,154,158,176,177],consist:[0,1,3,8,15,30,33,34,36,41,45,49,50,52,54,59,88,106,112,117,119,126,132,133,139,140,141,142,143,148,157,165,166,168,172,176,177,180,191],consol:123,consolid:75,conspiraci:62,constant:[50,63,65,75,78,119,127,128,130,132,136,141,151,155],constant_initi:127,constantli:114,constrain:[30,130,145],constraint:[30,101,106,119,132,140,141,145,157,163,186],construct:[30,50,119,123,126,128,132,137,139,145,147,150,151,152,155,171,172,194],constructor:[123,132,171,172,193,194],consult:[7,121],consum:[10,20,41,45,98,100,101,103,108,111,112,113,139,141,143,156,159,165,175],consumpt:[108,159,175,179],cont:54,cont_num_var:54,contact:[118,180,187],contagi:167,contain:[1,3,6,7,12,14,15,17,22,29,31,33,36,37,39,40,41,45,46,47,48,49,50,51,52,54,57,59,60,62,68,74,80,83,86,91,92,93,100,101,103,106,113,116,117,118,121,123,124,129,132,133,136,139,140,141,145,147,150,152,155,158,159,162,165,166,167,168,170,171,172,179,193,194],container:140,content:[1,2,3,4,5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,22,23,24,25,26,27,28,29,30,31,32,34,35,36,37,38,39,40,41,42,43,44,46,49,50,51,52,53,54,55,56,59,62,63,64,65,66,67,68,69,71,72,74,80,82,83,84,85,86,88,89,90,91,99,100,101,102,103,104,106,108,109,110,111,112,113,114,116,117,118,119,121,122,123,124,126,127,128,130,131,132,133,134,135,136,137,138,141,143,145,146,147,148,150,151,152,154,155,159,162,163,164,165,166,167,168,170,171,172,173,179,186,187,188,189,192,194],contest:147,context:[9,28,31,50,59,75,78,100,102,104,106,112,122,123,127,133,141,143,145,146,157,158,159,169,171,172,174,176,177,194],contigu:119,contin:[57,58],continu:[0,1,17,18,31,33,34,40,47,48,50,54,55,58,59,62,75,77,78,99,100,102,109,111,116,119,121,124,128,133,140,141,142,143,145,146,150,151,152,157,159,165,167,173,191],contour:[78,156,158,184],contourf:[150,158,189,190],contract:[92,93,101,140,171,172],contradictori:141,contrari:[122,123,143],contrarili:171,contrast:[7,50,78,90,103,119,127,131,141,150,156,182],contrib:136,contribut:[46,52,53,57,58,66,102,119,121,122,123,138,141,147,148,150,171,172,173,174],contributor:138,control:[7,11,30,43,48,56,59,61,62,63,64,65,99,103,104,106,112,114,117,119,129,130,132,134,139,140,142,150,151,157,172,177,187,194],controlflow:171,conv0:135,conv1:[127,133],conv1_1:127,conv1_2:127,conv1_add_bia:127,conv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class=\"section-number\">37. </span>Self-paced assignments","<span class=\"section-number\">37.22. </span>Analyzing COVID-19 papers","<span class=\"section-number\">37.27. </span>Analyzing data","<span class=\"section-number\">37.9. </span>Analyzing text about Data Science","<span class=\"section-number\">37.13. </span>Apply your skills","<span class=\"section-number\">37.16. </span>Build your own custom vis","<span class=\"section-number\">37.17. </span>Classifying datasets","<span class=\"section-number\">37.26. </span>Data preparation","<span class=\"section-number\">37.24. </span>Data processing in Python","<span class=\"section-number\">37.41. </span>Data Science in the cloud: The \u201cAzure ML SDK\u201d way","<span class=\"section-number\">37.40. </span>Data Science project using Azure ML SDK","<span class=\"section-number\">37.10. </span>Data Science scenarios","<span class=\"section-number\">37.20. </span>Displaying airport data","<span class=\"section-number\">37.15. </span>Dive into the beehive","<span class=\"section-number\">37.23. </span>Estimation of COVID-19 pandemic","<span class=\"section-number\">37.25. </span>Evaluating data from a form","<span class=\"section-number\">37.36. </span>Explore a planetary computer dataset","<span class=\"section-number\">37.37. </span>Exploring for answers","<span class=\"section-number\">37.19. </span>Introduction to probability and statistics","<span class=\"section-number\">37.12. </span>Lines, scatters and bars","<span class=\"section-number\">37.39. </span>Low code/no code Data Science project on Azure ML","<span class=\"section-number\">37.38. </span>Market research","<span class=\"section-number\">37.29. </span>Matplotlib applied","<span class=\"section-number\">37.28. </span>NYC taxi data in winter and summer","<span class=\"section-number\">37.18. </span>Small diabetes study","<span class=\"section-number\">37.21. </span>Soda profits","<span class=\"section-number\">37.35. </span>Tell a story","<span class=\"section-number\">37.14. </span>Try it in Excel","<span class=\"section-number\">37.11. </span>Write a data ethics case study","<span class=\"section-number\">37.101. </span>Intro to Autoencoders","<span class=\"section-number\">37.102. </span>Base/Denoising Autoencoder &amp; Dimension Reduction","<span class=\"section-number\">37.103. </span>Fun with Variational Autoencoders","<span class=\"section-number\">37.90. </span>How to choose cnn architecture mnist","<span class=\"section-number\">37.94. </span>Object Recognition in Images using CNN","<span class=\"section-number\">37.92. </span>Sign Language Digits Classification with CNN","<span class=\"section-number\">37.112. </span>DQN On Foreign Exchange Market","<span class=\"section-number\">37.113. </span>Art by gan","<span class=\"section-number\">37.115. </span>Generative Adversarial Networks (GANs)","<span class=\"section-number\">37.97. </span>Bitcoin LSTM Model with Tweet Volume and Sentiment","<span class=\"section-number\">37.107. </span>NN Classify 15 Fruits Assignment","<span class=\"section-number\">37.106. </span>Neural Networks for Classification with TensorFlow","<span class=\"section-number\">37.116. </span>Basic classification: Classify images of clothing","<span class=\"section-number\">37.99. </span>Google Stock Price Prediction RNN","<span class=\"section-number\">37.95. </span>Intro to TensorFlow for Deep Learning","<span class=\"section-number\">37.104. </span>Time Series Forecasting Assignment","<span class=\"section-number\">37.83. </span>Counterintuitive Challenges in ML Debugging","<span class=\"section-number\">37.82. </span>Data engineering","<span class=\"section-number\">37.84. </span>Case Study: Debugging in Classification","<span class=\"section-number\">37.85. </span>Case Study: Debugging in Regression","<span class=\"section-number\">37.75. </span>Beyond random forests: more ensemble models","<span class=\"section-number\">37.76. </span>Decision trees","<span class=\"section-number\">37.80. </span>Random Forest Classifier with Feature Importance","<span class=\"section-number\">37.74. </span>Random forests for classification","<span class=\"section-number\">37.73. </span>Random forests intro and regression","<span class=\"section-number\">37.79. </span>Boosting with tuning","<span class=\"section-number\">37.78. </span>Gradient boosting","<span class=\"section-number\">37.77. </span>Hyperparameter tuning gradient boosting","<span class=\"section-number\">37.67. </span>Decision Trees - Classification","<span class=\"section-number\">37.66. </span>Decision Trees - Intro and Regression","<span class=\"section-number\">37.63. </span>Kernel method assignment 1","<span class=\"section-number\">37.65. </span>Support Vector Machines (SVM) - Classification","<span class=\"section-number\">37.64. </span>Support Vector Machines (SVM) - Intro and SVM for Regression","<span class=\"section-number\">37.70. </span>Dropout and Batch Normalization","<span class=\"section-number\">37.71. </span>Lasso and Ridge Regression","<span class=\"section-number\">37.69. </span>Learning Curve To Identify Overfit &amp; Underfit","<span class=\"section-number\">37.68. </span>Model selection assignment 1","<span class=\"section-number\">37.72. </span>Regularized Linear Models","<span class=\"section-number\">37.88. </span>Build Classification Model","<span class=\"section-number\">37.87. </span>Build classification models","<span class=\"section-number\">37.57. </span>Create a regression model","<span class=\"section-number\">37.61. </span>Delicious asian and indian cuisines","<span class=\"section-number\">37.62. </span>Explore classification methods","<span class=\"section-number\">37.55. </span>Exploring visualizations","<span class=\"section-number\">37.58. </span>Linear and polynomial regression","<span class=\"section-number\">37.45. </span>Linear regression - California Housing","<span class=\"section-number\">37.48. </span>Gradient descent","<span class=\"section-number\">37.49. </span>Linear Regression Implementation from Scratch","<span class=\"section-number\">37.46. </span>Linear Regression Metrics","<span class=\"section-number\">37.47. </span>Loss Function","<span class=\"section-number\">37.54. </span>Managing data","<span class=\"section-number\">37.50. </span>ML logistic regression - assignment 1","<span class=\"section-number\">37.51. </span>ML logistic regression - assignment 2","<span class=\"section-number\">37.52. </span>ML neural network - Assignment 1","<span class=\"section-number\">37.42. </span>Machine Learning overview - assignment 1","<span class=\"section-number\">37.43. </span>Machine Learning overview - assignment 2","<span class=\"section-number\">37.89. </span>Parameter play","<span class=\"section-number\">37.60. </span>Pumpkin varieties and color","<span class=\"section-number\">37.53. </span>Regression tools","<span class=\"section-number\">37.44. </span>Regression with Scikit-learn","<span class=\"section-number\">37.59. </span>Retrying some regression","<span class=\"section-number\">37.86. </span>Study the solvers","<span class=\"section-number\">37.56. </span>Try a different model","<span class=\"section-number\">37.8. </span>Python programming advanced","<span class=\"section-number\">37.7. </span>Python programming basics","<span class=\"section-number\">37.6. </span>Python programming introduction","<span class=\"section-number\">37.5. </span>Project Plan\u200b Template","<span class=\"section-number\">37.3. </span>First assignment","<span class=\"section-number\">37.4. </span>Second assignment","<span class=\"section-number\">8. </span>Data Science in the cloud","<span class=\"section-number\">8.1. </span>Introduction","<span class=\"section-number\">8.3. </span>Data Science in the cloud: The \u201cAzure ML SDK\u201d way","<span class=\"section-number\">8.2. </span>The \u201clow code/no code\u201d way","<span class=\"section-number\">9. </span>Data Science in the real world","<span class=\"section-number\">7.2. </span>Analyzing","<span class=\"section-number\">7.3. </span>Communication","<span class=\"section-number\">7. </span>Data Science lifecycle","<span class=\"section-number\">7.1. </span>Introduction to the Data Science lifecycle","<span class=\"section-number\">6. </span>Data visualization","<span class=\"section-number\">6.4. </span>Making meaningful visualizations","<span class=\"section-number\">6.1. </span>Visualizing distributions","<span class=\"section-number\">6.2. </span>Visualizing proportions","<span class=\"section-number\">6.3. </span>Visualizing relationships: all about honey \ud83c\udf6f","<span class=\"section-number\">4.2. </span>Data Science ethics","<span class=\"section-number\">4.3. </span>Defining data","<span class=\"section-number\">4.1. </span>Defining data science","<span class=\"section-number\">4. </span>Introduction","<span class=\"section-number\">4.4. </span>Introduction to statistics and probability","<span class=\"section-number\">5.5. </span>Data preparation","<span class=\"section-number\">5.2. </span>Non-relational data","<span class=\"section-number\">5.3. </span>NumPy","<span class=\"section-number\">5.4. </span>Pandas","<span class=\"section-number\">5.4.3. </span>Advanced Pandas Techniques","<span class=\"section-number\">5.4.2. </span>Data Selection","<span class=\"section-number\">5.4.1. </span>Introduction and Data Structures","<span class=\"section-number\">5.1. </span>Relational databases","<span class=\"section-number\">5. </span>Working with data","<span class=\"section-number\">24. </span>Autoencoder","<span class=\"section-number\">21. </span>Convolutional Neural Networks","<span class=\"section-number\">30. </span>Diffusion Model","<span class=\"section-number\">20. </span>Intro to Deep Learning","<span class=\"section-number\">27. </span>Deep Q-learning","<span class=\"section-number\">22. </span>Generative adversarial networks","<span class=\"section-number\">28. </span>Image classification","<span class=\"section-number\">29. </span>Image segmentation","<span class=\"section-number\">25. </span>Long-short term memory","<span class=\"section-number\">31. </span>Object detection","<span class=\"section-number\">23. </span>Recurrent Neural Networks","<span class=\"section-number\">26. </span>Time series","Learn AI together, for free","<span class=\"section-number\">34. </span>Data engineering","<span class=\"section-number\">36. </span>Model deployment","<span class=\"section-number\">35. </span>Model training &amp; evaluation","<span class=\"section-number\">32. </span>Overview","<span class=\"section-number\">33. </span>Problem framing","<span class=\"section-number\">14. </span>Clustering models for Machine Learning","<span class=\"section-number\">14.1. </span>Introduction to clustering","<span class=\"section-number\">14.2. </span>K-Means clustering","<span class=\"section-number\">15.1. </span>Bagging","<span class=\"section-number\">15.3. </span>Feature importance","<span class=\"section-number\">15. </span>Getting started with ensemble learning","<span class=\"section-number\">15.2. </span>Random forest","<span class=\"section-number\">16.1. </span>Gradient Boosting","<span class=\"section-number\">16.2. </span>Gradient boosting example","<span class=\"section-number\">16. </span>Introduction to Gradient Boosting","<span class=\"section-number\">16.3. </span>XGBoost","<span class=\"section-number\">16.4. </span>XGBoost + k-fold CV + Feature Importance","<span class=\"section-number\">18. </span>Kernel method","<span class=\"section-number\">19. </span>Model selection","<span class=\"section-number\">17. </span>Unsupervised learning","<span class=\"section-number\">12. </span>Build a web app to use a Machine Learning model","<span class=\"section-number\">13.4. </span>Applied Machine Learning : build a web app","<span class=\"section-number\">13. </span>Getting started with classification","<span class=\"section-number\">13.1. </span>Introduction to classification","<span class=\"section-number\">13.2. </span>More classifiers","<span class=\"section-number\">13.3. </span>Yet other classifiers","<span class=\"section-number\">10. </span>Machine Learning overview","<span class=\"section-number\">11.3. </span>Linear and polynomial regression","<span class=\"section-number\">11.4. </span>Logistic regression","<span class=\"section-number\">11.2. </span>Managing data","<span class=\"section-number\">11. </span>Regression models for Machine Learning","<span class=\"section-number\">11.1. </span>Tools of the trade","<span class=\"section-number\">3. </span>Python programming advanced","<span class=\"section-number\">2. </span>Python programming basics","<span class=\"section-number\">1. </span>Python programming introduction","<span class=\"section-number\">38.10. </span>Data Science in real world","<span class=\"section-number\">38.9. </span>Data Science in the cloud","<span class=\"section-number\">38.4. </span>Data Science introduction","<span class=\"section-number\">38.8. </span>Data Science lifecycle","<span class=\"section-number\">38.7. </span>Data visualization","<span class=\"section-number\">38.6. </span>NumPy and Pandas","<span class=\"section-number\">38.5. </span>Relational vs. non-relational database","<span class=\"section-number\">38.20. </span>Convolutional Neural Network","<span class=\"section-number\">38.21. </span>Generative Adversarial Network","<span class=\"section-number\">38. </span>Slides","<span class=\"section-number\">38.18. </span>Kernel method","<span class=\"section-number\">38.19. </span>Model Selection","<span class=\"section-number\">38.17. </span>Unsupervised learning","<span class=\"section-number\">38.16. </span>Build an machine learning web application","<span class=\"section-number\">38.12. </span>Linear Regression","<span class=\"section-number\">38.13. </span>Logistic Regression","<span class=\"section-number\">38.14. </span>Logistic Regression","<span class=\"section-number\">38.11. </span>Machine Learning overview","<span class=\"section-number\">38.15. </span>Neural Network","<span class=\"section-number\">38.3. </span>Python programming advanced","<span class=\"section-number\">38.2. </span>Python programming basics","<span class=\"section-number\">38.1. </span>Python programming 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diff --git a/slides/data-science/data-science-in-real-world.html b/slides/data-science/data-science-in-real-world.html
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--- a/slides/data-science/data-science-in-real-world.html
+++ b/slides/data-science/data-science-in-real-world.html
@@ -236,10 +236,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
      5.3. NumPy
     </a>
    </li>
-   <li class="toctree-l2">
+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
     </a>
+    <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+    <label for="toctree-checkbox-3">
+     <i class="fas fa-chevron-down">
+     </i>
+    </label>
+    <ul>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
+     </li>
+    </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
@@ -252,8 +274,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../data-science/data-visualization/data-visualization.html">
    6. Data visualization
   </a>
-  <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
-  <label for="toctree-checkbox-3">
+  <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
+  <label for="toctree-checkbox-4">
    <i class="fas fa-chevron-down">
    </i>
   </label>
@@ -284,8 +306,8 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <a class="reference internal" href="../../data-science/data-science-lifecycle/data-science-lifecycle.html">
    7. Data Science lifecycle
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+    <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
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      5.3. NumPy
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+   <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas.html">
      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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-            <p class="prev-next-title"><span class="section-number">37.118. </span>Basic classification: Classify images of clothing</p>
+            <p class="prev-next-title"><span class="section-number">37.116. </span>Basic classification: Classify images of clothing</p>
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+   <li class="toctree-l2 has-children">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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+   <li class="toctree-l2 has-children">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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+   <li class="toctree-l2 has-children">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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+   <li class="toctree-l2 has-children">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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+   <li class="toctree-l2 has-children">
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      5.4. Pandas
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
+      </a>
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+    </ul>
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    <li class="toctree-l2">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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+   <li class="toctree-l2 has-children">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
+       5.4.1. Introduction and Data Structures
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+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
+      </a>
+     </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
+       5.4.2. Data Selection
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+      <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
+       5.4.3. Advanced Pandas Techniques
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