Intro to Matplotlib 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib is the \"grandfather\" library of data visualization with Python. It was created by John Hunter. \n",
    "\n",
    "He created it to try to replicate MatLab's (another programming language) plotting capabilities in Python. So if you happen to be familiar with matlab, matplotlib will feel natural to you.\n",
    "\n",
    "It is an excellent 2D and 3D graphics library for generating scientific figures. \n",
    "\n",
    "Some of the major Pros of Matplotlib are:\n",
    "\n",
    "* Generally easy to get started for simple plots\n",
    "* Support for custom labels and texts\n",
    "* Great control of every element in a figure\n",
    "* High-quality output in many formats\n",
    "* Very customizable in general"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to see plots inline\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That line is only for Jupyter Notebooks. \n",
    "\n",
    "Use **plt.show()** at the end of all your plotting commands if you are using another Integrated Development Environment (IDE) to have the figure pop up in another window."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's work with basic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "#x axis\n",
    "x = np.linspace(0, 5, 11)\n",
    "\n",
    "#y axis\n",
    "y = x ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dump x\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.  ,  0.25,  1.  ,  2.25,  4.  ,  6.25,  9.  , 12.25, 16.  ,\n",
       "       20.25, 25.  ])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dump y\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic Matplotlib Commands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, 'r') # 'r' is the color red\n",
    "plt.xlabel('X Axis Title Here')\n",
    "plt.ylabel('Y Axis Title Here')\n",
    "plt.title('String Title Here')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using hex for color\n",
    "plt.plot(x, y, '#FF0000')\n",
    "plt.xlabel('X Axis Title Here')\n",
    "plt.ylabel('Y Axis Title Here')\n",
    "plt.title('String Title Here')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating multiple plots on the same canvas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa0ede0b950>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.subplot(nrows, ncols, plot_number)\n",
    "plt.subplot(1,2,1) #1 row, 2 columns, which subplot you want to create your graph on\n",
    "plt.plot(x, y, 'r--') # dashed red\n",
    "plt.subplot(1,2,2) #1 row, 2 columns, create your second graph in the second column\n",
    "plt.plot(y, x, 'g*-') # asterisk green\n",
    "# plt.plot(y, x, 'bo-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib Object Oriented Method\n",
    "Now that we've seen the basics, let's break it all down with a more formal introduction of Matplotlib's Object Oriented API. This means we will instantiate figure objects and then call methods or attributes from that object.\n",
    "\n",
    "The main idea in using the more formal Object Oriented method is to create figure objects and then just call methods or attributes off of that object. This approach is nicer when dealing with a canvas that has multiple plots on it.\n",
    "\n",
    "To begin we create a figure instance. Then we can add axes to that figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Set Title')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create Figure (empty canvas)\n",
    "fig = plt.figure()\n",
    "\n",
    "# Add set of axes to figure\n",
    "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # left, bottom, width, height (range 0 to 1)\n",
    "\n",
    "# Plot on that set of axes\n",
    "axes.plot(x, y, 'b')\n",
    "axes.set_xlabel('Set X Label')\n",
    "axes.set_ylabel('Set y Label')\n",
    "axes.set_title('Set Title')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code is a little more complicated, but the advantage is that we now have full control of where the plot axes are placed, and we can easily add more than one axis to the figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAd0AAAFDCAYAAAB/UdRdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAcrUlEQVR4nO3dbWxc9Z328etXjIlguyFAohLGaeJOamJHDmmPiyniIc1u83BTpy+C66BCsyBMW3erzValvbU3WcqCmrsVrAQBukNTKIvW3jQv1tY2NiqUpCtECAO5l3VMsQMJeExVnD4ECg0mye9+YZP6KZlj1/Mfe+b7kSzNmfOf46s/pb44Z8bH5u4CAAC596F8BwAAoFhQugAABELpAgAQCKULAEAglC4AAIFQugAABJK1dM3sR2b2ppl1nmK/mdm9ZnbAzF40s09MfUwAAGa+OGe6j0hafZr9ayQtHvpqlPTgnx8LAIDCk7V03f0Xkn57miXrJD3qg/ZIOtfMLpyqgAAAFIqpeE/3Ikm9w7YzQ88BAIBhSqbgGDbOc+PeW9LMGjV4CVrnnHPOJy+++OIp+PYAAITz/PPPH3b3uZN57VSUbkZS2bDthKQ3xlvo7ilJKUmKosjT6fQUfHsAAMIxs9cm+9qpuLzcJumGoU8x10o64u6/moLjAgBQULKe6ZpZs6SrJV1gZhlJ/yjpTEly9x9I2ilpraQDkt6V9De5CgsAwEyWtXTdfUOW/S6pacoSAQBQoLgjFQAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgcQqXTNbbWYvm9kBM/v2OPsXmNlTZrbPzF40s7VTHxUAgJkta+ma2RmS7pe0RlKlpA1mVjlq2f+RtN3dl0tqkPTAVAcFAGCmi3Om+ylJB9z9VXcfkNQiad2oNS7pL4cez5b0xtRFBACgMMQp3Ysk9Q7bzgw9N9ztkr5oZhlJOyX97XgHMrNGM0ubWbq/v38ScQEAmLnilK6N85yP2t4g6RF3T0haK+lfzWzMsd095e6Ru0dz586deFoAAGawOKWbkVQ2bDuhsZePb5K0XZLc/RlJsyRdMBUBAQAoFHFK9zlJi81skZmVavCDUm2j1rwuaaUkmdkSDZYu148BABgma+m6+zFJX5P0uKSXNPgp5f1mdoeZ1Q0t+4akm83svyU1S9ro7qMvQQMAUNRK4ixy950a/IDU8Oc2D3vcJenyqY0GAEBh4Y5UAAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgcQqXTNbbWYvm9kBM/v2KdbUm1mXme03s3+b2pjx3XjjjZo3b56WLl067n5319e//nUlk0lVV1frhRdeCJwQAFCsspaumZ0h6X5JayRVStpgZpWj1iyW9L8lXe7uVZL+LgdZY9m4caM6OjpOub+9vV09PT3q6elRKpXSV77ylYDpAADFLM6Z7qckHXD3V919QFKLpHWj1tws6X53/50kufubUxszviuvvFLnnXfeKfe3trbqhhtukJmptrZWv//97/WrX/0qYEIAQLGKU7oXSeodtp0Zem64j0v6uJk9bWZ7zGz1VAWcan19fSorKzu5nUgk1NfXl8dEAIBiURJjjY3znI9znMWSrpaUkPRfZrbU3X8/4kBmjZIaJWnBggUTDjsV3EdHl8zG/k9MpVJKpVKSpF/+8pe6+OKLc54NADAjLJvsC+OUbkZS2bDthKQ3xlmzx93fl3TQzF7WYAk/N3yRu6ckpSQpiqKx7RdAIpFQb++fTtwzmYzmz58/Zl1jY6MaGxslSVEUKZ1OB8sIAJi+zGxgsq+Nc3n5OUmLzWyRmZVKapDUNmrNf0haMRTmAg1ebn51sqFyqa6uTo8++qjcXXv27NHs2bN14YUX5jsWAKAIZD3TdfdjZvY1SY9LOkPSj9x9v5ndISnt7m1D+z5rZl2Sjkv6prv/JpfBT2XDhg3atWuXDh8+rEQioe985zt6//33JUlf/vKXtXbtWu3cuVPJZFJnn322Hn744XzEBAAUIRvvPc4QoijymXLJlsvLAIAPmNm77n7OZF7LHakAAAiE0gUAIBBKFwCAQChdAAACoXQBAAiE0gUAIBBKFwCAQChdAAACoXQBAAiE0gUAIBBKFwCAQChdAAACoXQBAAiE0gUAIBBKFwCAQChdAAACoXQBAAiE0gUAIBBKFwCAQChdAAACKbjS7ejoUEVFhZLJpLZs2TJm/+uvv64VK1Zo+fLlqq6u1s6dO/OQEgBQjAqqdI8fP66mpia1t7erq6tLzc3N6urqGrHmzjvvVH19vfbt26eWlhZ99atfzVNaAECxKajS3bt3r5LJpMrLy1VaWqqGhga1traOWGNmeuuttyRJR44c0fz58/MRFQBQhEryHWAq9fX1qays7OR2IpHQs88+O2LN7bffrs9+9rO677779M477+iJJ54IHRMAUKQK6kzX3cc8Z2Yjtpubm7Vx40ZlMhnt3LlT119/vU6cODHmdalUSlEUKYoi9ff35ywzAKB4FFTpJhIJ9fb2ntzOZDJjLh9v27ZN9fX1kqTLLrtMR48e1eHDh8ccq7GxUel0Wul0WnPnzs1tcABAUSio0q2pqVFPT48OHjyogYEBtbS0qK6ubsSaBQsW6Mknn5QkvfTSSzp69CilCgAIoqBKt6SkRFu3btWqVau0ZMkS1dfXq6qqSps3b1ZbW5sk6e6779ZDDz2kZcuWacOGDXrkkUfGXIIGACAXbLz3QUOIosjT6XRevvdERVGkmZIVAJBbZvauu58zmdcW1JkuAADTGaULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIFQugAABBKrdM1stZm9bGYHzOzbp1m33szczKKpizgxHR0dqqioUDKZ1JYtW8Zds337dlVWVqqqqkrXXXdd4IQAgGJVkm2BmZ0h6X5Jfy0pI+k5M2tz965R6z4s6euSns1F0DiOHz+upqYm/exnP1MikVBNTY3q6upUWVl5ck1PT4+++93v6umnn9acOXP05ptv5isuAKDIxDnT/ZSkA+7+qrsPSGqRtG6cdf8k6XuSjk5hvgnZu3evksmkysvLVVpaqoaGBrW2to5Y89BDD6mpqUlz5syRJM2bNy8fUQEARShO6V4kqXfYdmbouZPMbLmkMnf/zynMNmF9fX0qKys7uZ1IJNTX1zdiTXd3t7q7u3X55ZertrZWHR0doWMCAIpU1svLkmyc5/zkTrMPSfpnSRuzHsisUVKjJC1YsCBewglw9zHPmY2Mf+zYMfX09GjXrl3KZDK64oor1NnZqXPPPXfEulQqpVQqJUnq7++f8qwAgOIT50w3I6ls2HZC0hvDtj8saamkXWZ2SFKtpLbxPkzl7il3j9w9mjt37uRTn0IikVBv759OyjOZjObPnz9mzbp163TmmWdq0aJFqqioUE9Pz5hjNTY2Kp1OK51OKxdZAQDFJ07pPidpsZktMrNSSQ2S2j7Y6e5H3P0Cd1/o7gsl7ZFU5+7pnCQ+jZqaGvX09OjgwYMaGBhQS0uL6urqRqz5/Oc/r6eeekqSdPjwYXV3d6u8vDx0VABAEcpauu5+TNLXJD0u6SVJ2919v5ndYWZ1p391WCUlJdq6datWrVqlJUuWqL6+XlVVVdq8ebPa2gb/O2HVqlU6//zzVVlZqRUrVuj73/++zj///DwnBwAUAxvvfdAQoijydDr4yfCkRFGkmZIVAJBbZvauu58zmddyRyoAAAKhdAEACITSBQAgEEoXAIBAKF0AAAKhdAEACITSBQAgEEoXAIBAKF0AAAKhdAEACITSBQAgEEoXAIBAKF0AAAKhdAEACITSBQAgEEoXAIBAKF0AAAKhdAEACITSBQAgEEoXAIBACq50Ozo6VFFRoWQyqS1btpxy3Y4dO2RmSqfTAdMBAIpZQZXu8ePH1dTUpPb2dnV1dam5uVldXV1j1r399tu69957demll+YhJQCgWBVU6e7du1fJZFLl5eUqLS1VQ0ODWltbx6y77bbbdOutt2rWrFl5SAkAKFYFVbp9fX0qKys7uZ1IJNTX1zdizb59+9Tb26trrrnmtMdKpVKKokhRFKm/vz8neQEAxaWgStfdxzxnZicfnzhxQps2bdLdd9+d9ViNjY1Kp9NKp9OaO3fulOYEABSngirdRCKh3t7ek9uZTEbz588/uf3222+rs7NTV199tRYuXKg9e/aorq6OD1MBAIIoqNKtqalRT0+PDh48qIGBAbW0tKiuru7k/tmzZ+vw4cM6dOiQDh06pNraWrW1tSmKojymBgAUi4Iq3ZKSEm3dulWrVq3SkiVLVF9fr6qqKm3evFltbW35jgcAKHI23vugIURR5DPlsm4URVyCBgBIkszsXXc/ZzKvLagzXQAApjNKFwCAQChdAAACoXQBAAiE0gUAIBBKFwCAQChdAAACoXQBAAiE0gUAIBBKFwCAQChdAAACoXQBAAiE0gUAIBBKFwCAQChdAAACoXQBAAiE0gUAIBBKFwCAQChdAAACoXQBAAgkVuma2Woze9nMDpjZt8fZ//dm1mVmL5rZk2b20amPGk9HR4cqKiqUTCa1ZcuWMfvvueceVVZWqrq6WitXrtRrr72Wh5QAgGKUtXTN7AxJ90taI6lS0gYzqxy1bJ+kyN2rJe2Q9L2pDhrH8ePH1dTUpPb2dnV1dam5uVldXV0j1ixfvlzpdFovvvii1q9fr1tvvTUfUQEARSjOme6nJB1w91fdfUBSi6R1wxe4+1Pu/u7Q5h5JiamNGc/evXuVTCZVXl6u0tJSNTQ0qLW1dcSaFStW6Oyzz5Yk1dbWKpPJ5CMqAKAIxSndiyT1DtvODD13KjdJah9vh5k1mlnazNL9/f3xU8bU19ensrKyk9uJREJ9fX2nXL9t2zatWbNm3H2pVEpRFCmKIuUiKwCg+JTEWGPjPOfjLjT7oqRI0lXj7Xf3lKSUJEVRNO4x/hzuYw9pNl586bHHHlM6ndbu3bvH3d/Y2KjGxkZJUhRFUxcSAFC04pRuRlLZsO2EpDdGLzKzv5L0D5Kucvf3pibexCQSCfX2/umkPJPJaP78+WPWPfHEE7rrrru0e/dunXXWWSEjAgCKWJzLy89JWmxmi8ysVFKDpLbhC8xsuaR/kVTn7m9Ofcx4ampq1NPTo4MHD2pgYEAtLS2qq6sbsWbfvn265ZZb1NbWpnnz5uUpKQCgGGUtXXc/Julrkh6X9JKk7e6+38zuMLMPGu37kv5C0k/M7P+ZWdspDpdTJSUl2rp1q1atWqUlS5aovr5eVVVV2rx5s9raBiN985vf1B/+8Adde+21uuSSS8aUMgAAuWLjvQ8aQhRFnk6n8/K9JyqKIs2UrACA3DKzd939nMm8ljtSAQAQCKULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIEUXOl2dHSooqJCyWRSW7ZsGbP/vffe0xe+8AUlk0ldeumlOnToUPiQAICiVFCle/z4cTU1Nam9vV1dXV1qbm5WV1fXiDXbtm3TnDlzdODAAW3atEnf+ta38pQWAFBsCqp09+7dq2QyqfLycpWWlqqhoUGtra0j1rS2tupLX/qSJGn9+vV68skn5e75iAsAKDIFVbp9fX0qKys7uZ1IJNTX13fKNSUlJZo9e7Z+85vfBM0JAChOJfkOMJXGO2M1swmvkaRUKqVUKiVJ6uzsVBRFU5SyePT392vu3Ln5jjHjMLfJYW4Tx8wmbdZkX1hQpZtIJNTb23tyO5PJaP78+eOuSSQSOnbsmI4cOaLzzjtvzLEaGxvV2NgoSYqiSOl0OrfhCxBzmxzmNjnMbeKY2eSY2dHJvragLi/X1NSop6dHBw8e1MDAgFpaWlRXVzdiTV1dnX784x9Lknbs2KHPfOYz457pAgAw1QrqTLekpERbt27VqlWrdPz4cd14442qqqrS5s2bFUWR6urqdNNNN+n6669XMpnUeeedp5aWlnzHBgAUiYIqXUlau3at1q5dO+K5O+644+TjWbNm6Sc/+cmEjvnBZWZMDHObHOY2Ocxt4pjZpPVP9oWWr1+XiaLIeS8BADDTmNnz7j6pT9cW1Hu6AABMZ5TuMNxCcnKyze2ee+5RZWWlqqurtXLlSr322mt5SDn9ZJvbB3bs2CEz41Omijez7du3q7KyUlVVVbruuusCJ5yess3t9ddf14oVK7R8+XJVV1dr586deUg5vdx4442aN2+eli5dOu5+G3SvmR0wsxfN7BOxDuzuefn65Cc/6dPJsWPHvLy83F955RV/7733vLq62vfv3z9izf333++33HKLu7s3Nzd7fX19PqJOK3Hm9vOf/9zfeecdd3d/4IEHmJvHm5u7+1tvveVXXHGFX3rppf7cc8/lIen0EWdm3d3dfskll/hvf/tbd3f/9a9/nY+o00qcud18883+wAMPuLv7/v37/aMf/Wgekk4vu3fv9ueff96rqqrG7JOUlrRWUrskk1Qr6VmP0X2c6Q7hFpKTE2duK1as0Nlnny1Jqq2tVSaTyUfUaSXO3CTptttu06233qpZsyb9u/gFI87MHnroITU1NWnOnDmSpHnz5uUj6rQSZ25mprfeekuSdOTIkTH3NyhGV1555bj3cBhmnaRHh3p4j6RzzezCbMeldIdwC8nJiTO34bZt26Y1a9aEiDatxZnbvn371Nvbq2uuuSZ0vGkpzsy6u7vV3d2tyy+/XLW1tero6Agdc9qJM7fbb79djz32mBKJhNauXav77rsvdMyZ6CJJvcO2M0PPnVbB/crQZI13xjrZW0gWk4nM5LHHHlM6ndbu3btzHWvayza3EydOaNOmTXrkkUcCppre4vxbO3bsmHp6erRr1y5lMhldccUV6uzs1Lnnnhsq5rQTZ27Nzc3auHGjvvGNb+iZZ57R9ddfr87OTn3oQ5yXncZ4P+iyXvpkokMmcgtJSae9hWQxiTM3SXriiSd01113qa2tTWeddVbIiNNStrm9/fbb6uzs1NVXX62FCxdqz549qqurK+oPU8X9/+i6det05plnatGiRaqoqFBPT0/oqNNKnLlt27ZN9fX1kqTLLrtMR48e1eHDh4PmnIEyksqGbSckvZH1VXHe+M3F13T7INX777/vixYt8ldfffXkhw06OztHrNm6deuID1Jde+21+Yg6rcSZ2wsvvODl5eXe3d2dp5TTT5y5DXfVVVcV/Qep4sysvb3db7jhBnd37+/v90Qi4YcPH85H3GkjztxWr17tDz/8sLu7d3V1+YUXXugnTpzIQ9rp5eDBg6f7INX/0sgPUu31GN1H6Q7z05/+1BcvXuzl5eV+5513urv7bbfd5q2tre7u/sc//tHXr1/vH/vYx7ympsZfeeWVfMadNrLNbeXKlT5v3jxftmyZL1u2zD/3uc/lM+60kW1uw1G6g7LN7MSJE75p0yZfsmSJL1261Jubm/MZd9rINrf9+/f7pz/9aa+urvZly5b5448/ns+400JDQ4N/5CMf8ZKSEr/ooov8hz/8oT/44IP+4IMPflC6Jul+Sa9I+h9JkcfoPu5IBQDABHBHKgAAZgBKFwCAQChdAAACoXQBAAiE0gUAIBBKFwCAQChdAAACoXQBAAiE0gUAIJBYpWtmq83sZTM7YGbfHmf/WWb270P7nzWzhVMdFACAmS5r6ZrZGRq8v+QaSZWSNphZ5ahlN0n6nbsnJf2zpP871UEBAJjp4pzpfkrSAXd/1d0HJLVIWjdqzTpJPx56vEPSSiv2PzQLAMAocUr3Ikm9w7YzQ8+Nu8bdj0k6Iun8qQgIAEChKImxZrwz1tF/mijOGplZo6TGoc33zKwzxvfHxFwgib8+nRvMNjeYa+4w29yomOwL45RuRlLZsO2EpDdOsSZjZiWSZkv67egDuXtKUkqSzCw92T+NhFNjrrnDbHODueYOs80NM5v036WNc3n5OUmLzWyRmZVKapDUNmpNm6QvDT1eL+nnnq8/1AsAwDSV9UzX3Y+Z2dckPS7pDEk/cvf9ZnaHpLS7t0naJulfzeyABs9wG3IZGgCAmSjO5WW5+05JO0c9t3nY46OSrp3g905NcD3iYa65w2xzg7nmDrPNjUnP1bgKDABAGNwGEgCAQHJeutxCMjdizPXvzazLzF40syfN7KP5yDkTZZvtsHXrzczNjE+HxhBnrmZWP/Tvdr+Z/VvojDNRjJ8FC8zsKTPbN/TzYG0+cs40ZvYjM3vzVL/aaoPuHZr7i2b2iVgHdvecfWnwg1evSCqXVCrpvyVVjlrzVUk/GHrcIOnfc5mpEL5iznWFpLOHHn+FuU7dbIfWfVjSLyTtkRTlO/d0/4r5b3axpH2S5gxtz8t37un+FXOuKUlfGXpcKelQvnPPhC9JV0r6hKTOU+xfK6ldg/epqJX0bJzj5vpMl1tI5kbWubr7U+7+7tDmHg3+fjWyi/NvVpL+SdL3JB0NGW4GizPXmyXd7+6/kyR3fzNwxpkozlxd0l8OPZ6tsfdZwDjc/Rca534Tw6yT9KgP2iPpXDO7MNtxc1263EIyN+LMdbibNPhfZMgu62zNbLmkMnf/z5DBZrg4/2Y/LunjZva0me0xs9XB0s1cceZ6u6QvmllGg7+F8rdhohW8if4clhTzV4b+DFN2C0mMEHtmZvZFSZGkq3KaqHCcdrZm9iEN/iWtjaECFYg4/2ZLNHiJ+WoNXpn5LzNb6u6/z3G2mSzOXDdIesTd7zazyzR4T4Wl7n4i9/EK2qS6K9dnuhO5haROdwtJjBBnrjKzv5L0D5Lq3P29QNlmumyz/bCkpZJ2mdkhDb6X08aHqbKK+7Og1d3fd/eDkl7WYAnj1OLM9SZJ2yXJ3Z+RNEuD92TGnyfWz+HRcl263EIyN7LOdegS6L9osHB5byy+087W3Y+4+wXuvtDdF2rw/fI6d5/0vViLRJyfBf+hwQ8Ayswu0ODl5leDppx54sz1dUkrJcnMlmiwdPuDpixMbZJuGPoUc62kI+7+q2wvyunlZecWkjkRc67fl/QXkn4y9Lm01929Lm+hZ4iYs8UExZzr45I+a2Zdko5L+qa7/yZ/qae/mHP9hqSHzGyTBi9/buTEJjsza9bgWx0XDL0f/o+SzpQkd/+BBt8fXyvpgKR3Jf1NrOMyewAAwuCOVAAABELpAgAQCKULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIH8f7VWnXF7N6TgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "axes1 = fig.add_axes([0.0, 0.0, 1, 1])\n",
    "axes2 = fig.add_axes([0.1, 0.1, 0.9, 0.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creates blank canvas\n",
    "fig = plt.figure()\n",
    "\n",
    "axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # main axes\n",
    "axes2 = fig.add_axes([0.2, 0.5, 0.4, 0.3]) # inset axes\n",
    "\n",
    "# Larger Figure Axes 1\n",
    "axes1.plot(x, y, 'b')\n",
    "axes1.set_xlabel('X_label_axes2')\n",
    "axes1.set_ylabel('Y_label_axes2')\n",
    "axes1.set_title('Axes 2 Title')\n",
    "\n",
    "# Insert Figure Axes 2\n",
    "axes2.plot(y, x, 'r')\n",
    "axes2.set_xlabel('X_label_axes2')\n",
    "axes2.set_ylabel('Y_label_axes2')\n",
    "axes2.set_title('Axes 2 Title');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## subplots()\n",
    "\n",
    "The plt.subplots() object will act as a more automatic axis manager.\n",
    "\n",
    "Basic use cases:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use similar to plt.figure() except use tuple unpacking to grab fig and axes\n",
    "fig, axes = plt.subplots()\n",
    "\n",
    "# Now use the axes object to add stuff to plot\n",
    "axes.plot(x, y, 'r')\n",
    "axes.set_xlabel('x')\n",
    "axes.set_ylabel('y')\n",
    "axes.set_title('title');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then you can specify the number of rows and columns when creating the subplots() object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/iJ8BVoGvA6/cprr/Avw38I3pn+Xteszr1o75i7HZYw7wt8Bp4FvAoW38Oe8DvjL9pfkG8Acj1PwU8D3gZ0zOeO4E3ga8bebxHp329K2x/p0XOdeLnO1FzfUiZ3sRcz3P2farFSSpCa+0laQmDHxJasLAl6QmDHxJasLAl6QmDHxJasLAl6Qm/g9yAKZwvIhC+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Empty canvas of 1 by 2 subplots\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fa0ec4c1950>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fa0ec4f07d0>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Axes is an array of axes to plot on\n",
    "axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can iterate through this array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# just create the same graph twice\n",
    "\n",
    "for ax in axes:\n",
    "    ax.plot(x, y, 'b')\n",
    "    ax.set_xlabel('x')\n",
    "    ax.set_ylabel('y')\n",
    "    ax.set_title('title')\n",
    "\n",
    "# Display the figure object    \n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A common issue with matplolib is overlapping subplots or figures. We can use fig.tight_layout() or plt.tight_layout() method, which automatically adjusts the positions of the axes on the figure canvas so that there is no overlapping content:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
    "\n",
    "for ax in axes:\n",
    "    ax.plot(x, y, 'g')\n",
    "    ax.set_xlabel('x')\n",
    "    ax.set_ylabel('y')\n",
    "    ax.set_title('title')\n",
    "\n",
    "# tight_layout() automatically adjusts your graphs\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure size, aspect ratio and DPI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib allows the aspect ratio, DPI and figure size to be specified when the Figure object is created. You can use the `figsize` and `dpi` keyword arguments. \n",
    "* `figsize` is a tuple of the width and height of the figure in inches\n",
    "* `dpi` is the dots-per-inch (pixel per inch). \n",
    "\n",
    "For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,4), dpi=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(12,3))\n",
    "\n",
    "axes.plot(x, y, 'r')\n",
    "axes.set_xlabel('x')\n",
    "axes.set_ylabel('y')\n",
    "axes.set_title('title');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving figures\n",
    "Matplotlib can generate high-quality output in a number formats, including PNG, JPG, EPS, SVG, PGF and PDF. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To save a figure to a file we can use the `savefig` method in the `Figure` class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(\"filename.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can also optionally specify the DPI and choose between different output formats:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(\"filename.png\", dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(\"filename.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Legends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use the **label=\"label text\"** keyword argument when plots or other objects are added to the figure, and then using the **legend** method without arguments to add the legend to the figure: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa0ebdc0750>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "\n",
    "ax.plot(x, x**2, label=\"x**2\")\n",
    "ax.plot(x, x**3, label=\"x**3\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how are legend overlaps some of the actual plot.\n",
    "\n",
    "The **legend** function takes an optional keyword argument **loc** that can be used to specify where in the figure the legend is to be drawn. The allowed values of **loc** are numerical codes for the various places the legend can be drawn. See the [documentation page](http://matplotlib.org/users/legend_guide.html#legend-location) for details. Some of the most common **loc** values are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lots of options....\n",
    "\n",
    "ax.legend(loc=1) # upper right corner\n",
    "ax.legend(loc=2) # upper left corner\n",
    "ax.legend(loc=3) # lower left corner\n",
    "ax.legend(loc=4) # lower right corner\n",
    "\n",
    "# .. many more options are available\n",
    "\n",
    "# Most common to choose\n",
    "ax.legend(loc=0) # let matplotlib decide the optimal location\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting colors, linewidths, linetypes\n",
    "\n",
    "Matplotlib gives you *a lot* of options for customizing colors, linewidths, and linetypes. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Colors with MatLab like syntax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With matplotlib, we can define the colors of lines and other graphical elements in a number of ways. First of all, we can use the MATLAB-like syntax where `'b'` means blue, `'g'` means green, etc. The MATLAB API for selecting line styles are also supported: where, for example, 'b.-' means a blue line with dots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa0ec4a3e90>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# MATLAB style line color and style \n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, x**2, 'b.-') # blue line with dots\n",
    "ax.plot(x, x**3, 'g--') # green dashed line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Colors with the color= parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also define colors by their names or RGB hex codes and optionally provide an alpha value using the `color` and `alpha` keyword arguments. Alpha indicates opacity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa0ebe64150>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(x, x+1, color=\"blue\", alpha=0.5) # half-transparant\n",
    "ax.plot(x, x+2, color=\"#8B008B\")        # RGB hex code\n",
    "ax.plot(x, x+3, color=\"#FF8C00\")        # RGB hex code "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line and marker styles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To change the line width, we can use the `linewidth` or `lw` keyword argument. The line style can be selected using the `linestyle` or `ls` keyword arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "\n",
    "ax.plot(x, x+1, color=\"red\", linewidth=0.25)\n",
    "ax.plot(x, x+2, color=\"red\", linewidth=0.50)\n",
    "ax.plot(x, x+3, color=\"red\", linewidth=1.00)\n",
    "ax.plot(x, x+4, color=\"red\", linewidth=2.00)\n",
    "\n",
    "# possible linestype options ‘-‘, ‘–’, ‘-.’, ‘:’, ‘steps’\n",
    "ax.plot(x, x+5, color=\"green\", lw=3, linestyle='-')\n",
    "ax.plot(x, x+6, color=\"green\", lw=3, ls='-.')\n",
    "ax.plot(x, x+7, color=\"green\", lw=3, ls=':')\n",
    "\n",
    "# custom dash\n",
    "line, = ax.plot(x, x+8, color=\"black\", lw=1.50)\n",
    "line.set_dashes([5, 10, 15, 10]) # format: line length, space length, ...\n",
    "\n",
    "# possible marker symbols: marker = '+', 'o', '*', 's', ',', '.', '1', '2', '3', '4', ...\n",
    "ax.plot(x, x+ 9, color=\"blue\", lw=3, ls='-', marker='+')\n",
    "ax.plot(x, x+10, color=\"blue\", lw=3, ls='--', marker='o')\n",
    "ax.plot(x, x+11, color=\"blue\", lw=3, ls='-', marker='s')\n",
    "ax.plot(x, x+12, color=\"blue\", lw=3, ls='--', marker='1')\n",
    "\n",
    "# marker size and color\n",
    "ax.plot(x, x+13, color=\"purple\", lw=1, ls='-', marker='o', markersize=2)\n",
    "ax.plot(x, x+14, color=\"purple\", lw=1, ls='-', marker='o', markersize=4)\n",
    "ax.plot(x, x+15, color=\"purple\", lw=1, ls='-', marker='o', markersize=8, markerfacecolor=\"red\")\n",
    "ax.plot(x, x+16, color=\"purple\", lw=1, ls='-', marker='s', markersize=8, \n",
    "        markerfacecolor=\"yellow\", markeredgewidth=3, markeredgecolor=\"green\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Control over axis appearance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we will look at controlling axis sizing properties in a matplotlib figure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot range\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can configure the ranges of the axes using the `set_ylim` and `set_xlim` methods in the axis object, or `axis('tight')` for automatically getting \"tightly fitted\" axes ranges:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(12, 4))\n",
    "\n",
    "axes[0].plot(x, x**2, x, x**3)\n",
    "axes[0].set_title(\"default axes ranges\")\n",
    "\n",
    "axes[1].plot(x, x**2, x, x**3)\n",
    "axes[1].axis('tight')\n",
    "axes[1].set_title(\"tight axes\")\n",
    "\n",
    "axes[2].plot(x, x**2, x, x**3)\n",
    "axes[2].set_ylim([0, 60])\n",
    "axes[2].set_xlim([2, 5])\n",
    "axes[2].set_title(\"custom axes range\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Special Plot Types\n",
    "\n",
    "There are many specialized plots we can create, such as barplots, histograms, scatter plots, and much more. Most of these type of plots we will actually create using seaborn, a statistical plotting library for Python. But here are a few examples of these type of plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fa0ed53e0d0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([10.,  5., 15., 14.,  9.,  8.,  7., 11., 11., 10.]),\n",
       " array([  7. , 105.7, 204.4, 303.1, 401.8, 500.5, 599.2, 697.9, 796.6,\n",
       "        895.3, 994. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from random import sample\n",
    "data = sample(range(1, 1000), 100)\n",
    "plt.hist(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nNTRXqQQ5sILtXZJukXS43EqwHrZHbB+T9IqkRyNiaM4fQQ5cwPZbJD0k6d6IeK3setC9iDjT+dnJ7ZJutT007U2CHOjo9FYfkvRARDxcdj24OhHxX5Iel3RHyaX0DEEO6PzNsnlJSxHxhbLrwfrYHrN9U+f9m3X2l8qeKbeq3iHIE7K9KOlfJb3L9gnbU2XXhK7dJuljknbbPtZ5faTsotC1rZIatr8t6Zs62yN/pOSaeobphwCQOa7IASBzBDkAZI4gB4DMEeQAkDmCHAAyR5ADQOYIcgDI3P8DEz0+VwrEvx4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [np.random.normal(0, std, 100) for std in range(1, 4)]\n",
    "\n",
    "# rectangular box plot\n",
    "plt.boxplot(data,vert=True,patch_artist=True);   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}