4.GroupBy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Handeling: Groupby, Merge, Split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic = pd.read_csv('data/titanic.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting Name column as index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_df1 = titanic.copy(deep =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Braund, Mr. Owen Harris</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    PassengerId  Survived  \\\n",
       "Name                                                                        \n",
       "Braund, Mr. Owen Harris                                       1         0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...            2         1   \n",
       "\n",
       "                                                    Pclass     Sex   Age  \\\n",
       "Name                                                                       \n",
       "Braund, Mr. Owen Harris                                  3    male  22.0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...       1  female  38.0   \n",
       "\n",
       "                                                    SibSp  Parch     Ticket  \\\n",
       "Name                                                                          \n",
       "Braund, Mr. Owen Harris                                 1      0  A/5 21171   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...      1      0   PC 17599   \n",
       "\n",
       "                                                       Fare Cabin Embarked  \n",
       "Name                                                                        \n",
       "Braund, Mr. Owen Harris                              7.2500   NaN        S  \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...  71.2833   C85        C  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df1 = titanic.set_index('Name')\n",
    "titanic_df1.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group By : ```groupby.aggregate()```, ```groupby.size()```,```groupby.mean()```,"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The groupby operation (split-apply-combine)\n",
    "\n",
    "The \"group by\" concept: we want to **apply the same function on subsets of your dataframe, based on some key to split the dataframe in subsets**\n",
    "\n",
    "This operation is also referred to as the \"split-apply-combine\" operation, involving the following steps:\n",
    "\n",
    "* **Splitting** the data into groups based on some criteria\n",
    "* **Applying** a function to each group independently\n",
    "* **Combining** the results into a data structure\n",
    "\n",
    "<img src=\"plots/splitApplyCombine.png\">\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data\n",
       "0   A     0\n",
       "1   B     5\n",
       "2   C    10\n",
       "3   A     5\n",
       "4   B    10\n",
       "5   C    15\n",
       "6   A    10\n",
       "7   B    15\n",
       "8   C    20"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'key':['A','B','C','A','B','C','A','B','C'],\n",
    "                   'data': [0, 5, 10, 5, 10, 15, 10, 15, 20]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data\n",
       "key      \n",
       "A      15\n",
       "B      30\n",
       "C      45"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').aggregate(np.sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise with Titanic Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    314\n",
       "male      577\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df1.groupby('Sex').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>: Using groupby(), calculate the average age for each sex.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    27.915709\n",
       "male      30.726645\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df1.groupby('Sex')['Age'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>: Calculate the average survival ratio for all passengers.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3838383838383838"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df1['Survived'].sum() / len(titanic_df1['Survived'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>: Calculate this survival ratio for all passengers younger that 25 (remember: filtering/boolean indexing).\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4119601328903654"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df25 = titanic_df1[titanic_df1['Age'] <= 25]\n",
    "df25['Survived'].sum() / len(df25['Survived'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>: Is there a difference in this survival ratio between the sexes? (tip: write the above calculation of the survival ratio as a function)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def survival_ratio(survived):\n",
    "    return survived.sum() / len(survived)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    0.742038\n",
       "male      0.188908\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df1.groupby('Sex')['Survived'].aggregate(survival_ratio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>: Make a bar plot of the survival ratio for the different classes ('Pclass' column).\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb321d163c8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAECCAYAAADjBlzIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAEmBJREFUeJzt3X9sVfX9x/HXvffcEsqtoR1XMSSXkOoFHZrbRp2JVsWmwtjEX6TtwOpwNRi/OBNrmgVNbbqmXnFfY2YqjJCVZQO5RBD8sY2koVqsUZLLrls3C+4HjZpNqzTKLbG3l3O+f/Dd/dov0MNK7718bp+PxIRzP/fHu/ckz54ee3o9juM4AgAYy5vvAQAA54eQA4DhCDkAGI6QA4DhCDkAGI6QA4DhrFy/4NDQ8Vy/ZE6VlhZrePhEvsfAJLDvzFbo+y8YLDnrGkfkU8yyfPkeAZPEvjPbdN5/hBwADEfIAcBwhBwADEfIAcBwhBwADEfIAcBwhBwADEfIAcBwOb+yM9ceiO7P9whZ9cuf3JrvEQDkGUfkAGA4Qg4AhiPkAGA4Qg4AhiPkAGA4Qg4AhnP99UPbttXa2qrDhw+rqKhI7e3tmj9/fmb9rbfeUmdnpyTpyiuv1FNPPSWPx5O9iQEA47gekXd3dyuVSikWi6mpqUnRaDSzlkwm9eyzz2rTpk3auXOn5s2bp+Hh4awODAAYzzXk8XhcVVVVkqRIJKL+/v7M2h/+8AeFw2E988wzWrVqlebMmaOysrLsTQsAOI3rqZVkMqlAIJDZ9vl8SqfTsixLw8PDeu+997Rnzx4VFxdr9erVikQiWrBgwVmfr7S0eFp/JNNUm+hz/PCf4/0023Tdf64hDwQCGhkZyWzbti3LOvWw2bNn66qrrlIwGJQkXXPNNfrggw8mDHkhfzhqPhT6h1nnUjBYwvtpsELff+f14cuVlZXq7e2VJCUSCYXD4cza4sWLdeTIER07dkzpdFrvv/++LrvssikYGQBwrlyPyGtqatTX16f6+no5jqOOjg51dXUpFAqpurpaTU1NamxslCQtW7ZsXOgBANnncRzHyeUL5vpHH/76Ic5Vof9oXugKff+d16kVAMCFjZADgOEIOQAYjpADgOEIOQAYjpADgOEIOQAYjpADgOEIOQAYjpADgOEIOQAYjpADgOEIOQAYjpADgOEIOQAYjpADgOEIOQAYjpADgOEIOQAYjpADgOEIOQAYjpADgOEIOQAYjpADgOEIOQAYznK7g23bam1t1eHDh1VUVKT29nbNnz8/s97e3q5Dhw5p1qxZkqQXX3xRJSUl2ZsYADCOa8i7u7uVSqUUi8WUSCQUjUa1cePGzPqf//xnbdmyRWVlZVkdFABwZq6nVuLxuKqqqiRJkUhE/f39mTXbtjU4OKiWlhbV19fr5Zdfzt6kAIAzcj0iTyaTCgQCmW2fz6d0Oi3LsnTixAnde++9WrNmjU6ePKn77rtPixcv1qJFi876fKWlxbIs39RMDwWDnMaaSryfZpuu+8815IFAQCMjI5lt27ZlWaceNnPmTN13332aOXOmJOn666/XwMDAhCEfHj5xvjPjG4aGjud7hIIRDJbwfhqs0PffRN+kXE+tVFZWqre3V5KUSCQUDocza0ePHtWqVat08uRJjY2N6dChQ/r2t789BSMDAM6V6xF5TU2N+vr6VF9fL8dx1NHRoa6uLoVCIVVXV+v2229XbW2t/H6/7rjjDl1++eW5mBsA8L88juM4uXzBXP/o80B0f05fL9d++ZNb8z1CwSj0H80LXaHvv/M6tQIAuLARcgAwHCEHAMMRcgAwnOtvrQD59F/7m/M9QtZ03roh3yOgQHBEDgCGI+QAYDhCDgCGI+QAYDhCDgCGI+QAYDhCDgCGI+QAYDhCDgCGI+QAYDhCDgCGI+QAYDhCDgCGI+QAYDhCDgCGI+QAYDhCDgCGI+QAYDhCDgCGI+QAYDjXkNu2rZaWFtXV1amhoUGDg4NnvE9jY6NeeumlrAwJADg715B3d3crlUopFoupqalJ0Wj0tPs8//zz+vLLL7MyIABgYq4hj8fjqqqqkiRFIhH19/ePW//9738vj8ejm266KTsTAgAmZLndIZlMKhAIZLZ9Pp/S6bQsy9KRI0f0+uuv6+c//7k6OzvP6QVLS4tlWb7JT4xxgsGSfI+ASWLfTb3p+p66hjwQCGhkZCSzbdu2LOvUw/bs2aNPP/1U999/vz755BP5/X7NmzdvwqPz4eETUzA2/m1o6Hi+R8Akse+mVjBYUtDv6UTfpFxDXllZqZ6eHi1fvlyJRELhcDiz1tzcnPn3Cy+8oDlz5nCKBQByzDXkNTU16uvrU319vRzHUUdHh7q6uhQKhVRdXZ2LGQEAE3ANudfrVVtb27jbysvLT7vfI488MnVTAQDOGRcEAYDhCDkAGI6QA4DhCDkAGI6QA4DhCDkAGI6QA4DhCDkAGI6QA4DhCDkAGI6QA4DhCDkAGI6QA4DhCDkAGI6QA4DhCDkAGI6QA4DhCDkAGI6QA4DhCDkAGI6QA4DhCDkAGI6QA4DhCDkAGM415LZtq6WlRXV1dWpoaNDg4OC49W3btumee+7RypUr1dPTk7VBAQBnZrndobu7W6lUSrFYTIlEQtFoVBs3bpQkHTt2TNu3b9eePXs0Ojqq733ve7rlllvk8XiyPjgA4BTXI/J4PK6qqipJUiQSUX9/f2atrKxMe/fuld/v1+eff66LLrqIiANAjrmGPJlMKhAIZLZ9Pp/S6XRm27Is/eY3v1FdXZ2WLl2anSkBAGflemolEAhoZGQks23btixr/MPuvfde1dbW6sEHH9S7776r66+//qzPV1paLMvyncfI+KZgsCTfI2CS2HdTb7q+p64hr6ysVE9Pj5YvX65EIqFwOJxZ+/vf/67nnntOL7zwgvx+v4qKiuT1TnyQPzx84vynRsbQ0PF8j4BJYt9NrWCwpKDf04m+SbmGvKamRn19faqvr5fjOOro6FBXV5dCoZCqq6u1aNEi1dXVyePxqKqqStddd92UDg8AmJhryL1er9ra2sbdVl5envn3unXrtG7duqmfDABwTrggCAAMR8gBwHCEHAAMR8gBwHCEHAAMR8gBwHCEHAAMR8gBwHCEHAAMR8gBwHCEHAAMR8gBwHCEHAAMR8gBwHCuf8YWACbjSOMPc/t6OX01Kbxla45f8ew4IgcAwxFyADAcIQcAwxFyADAcIQcAwxFyADAcIQcAwxFyADAcIQcAwxFyADCc6yX6tm2rtbVVhw8fVlFRkdrb2zV//vzM+tatW/XGG29Ikm6++WatW7cue9MCAE7jekTe3d2tVCqlWCympqYmRaPRzNpHH32kV199VTt27FAsFtPbb7+tgYGBrA4MABjP9Yg8Ho+rqqpKkhSJRNTf359Zmzt3rrZs2SKfzydJSqfTmjFjRpZGBQCciWvIk8mkAoFAZtvn8ymdTsuyLPn9fpWVlclxHG3YsEFXXnmlFixYMOHzlZYWy7J85z85JEnBYEm+R8AkFfq+y/VfI8y1C2n/uYY8EAhoZGQks23btizr/x42Ojqq9evXa9asWXrqqadcX3B4+MQkR8WZDA0dz/cImCT2ndlyvf8m+sbheo68srJSvb29kqREIqFwOJxZcxxHDz/8sBYuXKi2trbMKRYAQO64HpHX1NSor69P9fX1chxHHR0d6urqUigUkm3bOnjwoFKplA4cOCBJeuyxx1RRUZH1wQEAp7iG3Ov1qq2tbdxt5eXlmX//6U9/mvqpAADnjAuCAMBwhBwADEfIAcBwhBwADEfIAcBwhBwADEfIAcBwhBwADEfIAcBwhBwADEfIAcBwhBwADEfIAcBwhBwADEfIAcBwhBwADEfIAcBwhBwADEfIAcBwhBwADEfIAcBwhBwADEfIAcBwhBwADEfIAcBwriG3bVstLS2qq6tTQ0ODBgcHT7vPsWPHdNttt2l0dDQrQwIAzs415N3d3UqlUorFYmpqalI0Gh23fuDAAT3wwAP6/PPPszYkAODsXEMej8dVVVUlSYpEIurv7x//BF6vurq6NHv27OxMCACYkOV2h2QyqUAgkNn2+XxKp9OyrFMPveGGG/6jFywtLZZl+f7DMXE2wWBJvkfAJBX6vjuS7wGy7ELaf64hDwQCGhkZyWzbtp2J+GQMD5+Y9GNxuqGh4/keAZPEvjNbrvffRN84XE+tVFZWqre3V5KUSCQUDoenbjIAwHlzPbSuqalRX1+f6uvr5TiOOjo61NXVpVAopOrq6lzMCACYgGvIvV6v2traxt1WXl5+2v32798/dVMBAM4ZFwQBgOEIOQAYjpADgOEIOQAYjpADgOEIOQAYjpADgOEIOQAYjpADgOEIOQAYjpADgOEIOQAYjpADgOEIOQAYjpADgOEIOQAYjpADgOEIOQAYjpADgOEIOQAYjpADgOEIOQAYjpADgOEIOQAYjpADgOFcQ27btlpaWlRXV6eGhgYNDg6OW9+5c6fuvvtu1dbWqqenJ2uDAgDOzHK7Q3d3t1KplGKxmBKJhKLRqDZu3ChJGhoa0q9//Wvt2rVLo6OjWrVqlW644QYVFRVlfXAAwCmuR+TxeFxVVVWSpEgkov7+/szaH//4R1VUVKioqEglJSUKhUIaGBjI3rQAgNO4HpEnk0kFAoHMts/nUzqdlmVZSiaTKikpyazNmjVLyWRywucLBksmXJ9qr/33HTl9PUytnXUb8z0CJim4d1e+R5g2XI/IA4GARkZGMtu2bcuyrDOujYyMjAs7ACD7XENeWVmp3t5eSVIikVA4HM6sXX311YrH4xodHdXx48f1t7/9bdw6ACD7PI7jOBPdwbZttba26siRI3IcRx0dHert7VUoFFJ1dbV27typWCwmx3G0du1aLV26NFezAwB0DiEHAFzYuCAIAAxHyAHAcIQcAAxHyIFvSKVS+R4Bk/D1119P631HyDEt7d+/X0uWLFFNTY1++9vfZm5vbGzM41Q4Vx999JEefvhhtbS06J133tHy5cu1fPnyafv3nlyv7AQK0aZNm/TKK6/IcRw9+uijGh0d1V133SV+icsM69ev1yOPPKJPPvlEP/7xj7Vv3z7NmDFDjY2NWrJkSb7HyzlCfp4aGho0NjY27jbHceTxeLRjx448TQU3fr9fs2fPliS9+OKLuv/++3XppZfK4/HkeTKci3Q6reuuu06S9N577+lb3/qWJGWuOp9u+D3y8/T+++/rySefVGdnp3w+37i1efPm5WkquGlublZpaakeffRRFRcX65///Kd+9KMf6auvvtLbb7+d7/HgYv369fJ4PPrpT38qr/fUGeLNmzfrL3/5i55//vk8T5d7vtbW1tZ8D2GyuXPn6sSJE0qn04pEIrrooosy/+HCtWTJEn3xxRe6/PLL5ff7VVJSoqVLl+rLL7/UTTfdlO/x4OLfp0/Ky8szt3388cdau3at/H5/vsbKG47IAcBw/NYKABiOkAOA4abn/+JFwfr444+1bNkylZeXy+PxaGxsTBdffLGefvppzZ0797T77969WwcPHlQ0Gs3DtMDU4IgcBefiiy/W3r17tWfPHr3xxhtauHChNmzYkO+xgKwh5Ch43/nOd/Thhx/qnXfe0YoVK3T77bdr7dq1p30s4e9+9zvV1tZqxYoVWrZsmQ4dOiRJ6urq0ooVK3TnnXeqpaVFkjQwMKDa2lrdfffd+sEPfqCjR4/m+ssCMgg5CtrY2Jj27dunxYsX6/HHH9czzzyj1157TeFwWK+88krmfrZta8eOHdq0aZNeffVVNTY2avPmzTp58qR+8YtfaNeuXdq9e7fGxsb06aef6le/+pXWrFmj3bt3q7a2VolEIo9fJaY7zpGj4Hz22We6445TH7qdSqV09dVXa9WqVRoYGNAVV1whSWpqapJ06hy5JHm9XnV2dmr//v36xz/+oYMHD8rr9crn86miokIrV65UdXW11qxZo0suuUQ333yz2tradODAAd16663T8rJwXDgIOQrOv8+Rf9PAwMC4y++PHz9+2geHr1y5UitWrNC1116rhQsXatu2bZJOXcKfSCTU29urxsZG/exnP9OyZctUUVGhnp4ebd26VW+++aba29tz8wUC/w8hx7SwYMECffHFF/rrX/+qyy67TFu2bJEkzZ8/X5J09OhReTwePfTQQ3IcR83NzTp58qSOHTum1atX6+WXX1ZFRYX+9a9/6fDhw9q+fbu+//3vq76+XuXl5Xr66afz+eVhmiPkmBZmzJihZ599Vs3NzRobG1MoFNKGDRu0b98+SdKiRYt0xRVX6Lvf/a48Ho9uvPFGxeNxlZWVqa6uTitXrtTMmTO1YMEC3XPPPbr22mv1xBNPqLOzU36/X/ylC+QTl+gDgOH4rRUAMBwhBwDDEXIAMBwhBwDDEXIAMBwhBwDDEXIAMBwhBwDD/Q/DKNiybWttuAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb3630b6828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_df1.groupby('Pclass')['Survived'].aggregate(survival_ratio).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced Groupby Operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>: Find data for age distribution.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "11//2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(10//2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = titanic_df1.copy(deep =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb31d63f2b0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb31d62e9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(df.Age//10 * 10).size().plot(kind='bar',figsize = [10,10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>: Find data for male age distribution.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb31d524dd8>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb31d4e30f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Male = df[df['Sex'] == 'male']\n",
    "Male.groupby(Male.Age // 10 * 10).size().plot(kind='bar',figsize = [10,10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "    <b>EXERCISE</b>: List data with Fare size greater then 50.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    87\n",
       "male      73\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fare50 = df[df.Fare>50]\n",
    "Fare50.groupby([ 'Sex']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age    Sex     Survived\n",
       "0.92   male    1           1\n",
       "2.00   female  0           1\n",
       "4.00   male    1           1\n",
       "11.00  male    1           1\n",
       "14.00  female  1           1\n",
       "15.00  female  1           1\n",
       "16.00  female  1           2\n",
       "17.00  female  1           2\n",
       "       male    1           1\n",
       "18.00  female  1           3\n",
       "       male    0           2\n",
       "19.00  female  1           1\n",
       "       male    0           2\n",
       "21.00  female  1           2\n",
       "       male    0           3\n",
       "22.00  female  1           3\n",
       "       male    0           1\n",
       "23.00  female  1           2\n",
       "       male    1           1\n",
       "24.00  female  1           5\n",
       "       male    0           3\n",
       "25.00  female  0           1\n",
       "       male    1           2\n",
       "26.00  female  1           1\n",
       "       male    1           1\n",
       "27.00  male    0           1\n",
       "               1           2\n",
       "28.00  male    0           2\n",
       "29.00  female  1           1\n",
       "       male    0           1\n",
       "                          ..\n",
       "45.00  female  1           1\n",
       "       male    0           1\n",
       "46.00  male    0           2\n",
       "47.00  female  1           1\n",
       "       male    0           1\n",
       "48.00  female  1           1\n",
       "       male    1           2\n",
       "49.00  female  1           1\n",
       "       male    0           1\n",
       "               1           2\n",
       "50.00  female  1           1\n",
       "       male    0           2\n",
       "               1           1\n",
       "51.00  female  1           1\n",
       "       male    0           1\n",
       "52.00  female  1           2\n",
       "       male    0           1\n",
       "53.00  female  1           1\n",
       "54.00  female  1           2\n",
       "       male    0           2\n",
       "56.00  female  1           1\n",
       "58.00  female  1           2\n",
       "       male    0           1\n",
       "60.00  female  1           1\n",
       "       male    1           1\n",
       "62.00  female  1           1\n",
       "63.00  female  1           1\n",
       "64.00  male    0           1\n",
       "65.00  male    0           1\n",
       "70.00  male    0           1\n",
       "Length: 87, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fare50.groupby(['Age', 'Sex','Survived']).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group by followed by Transformation: ```groupby.transform()```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>data</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data key\n",
       "0     0   A\n",
       "1     5   B\n",
       "2    10   C\n",
       "3     5   A\n",
       "4    10   B\n",
       "5    15   C\n",
       "6    10   A\n",
       "7    15   B\n",
       "8    20   C"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'key':['A','B','C','A','B','C','A','B','C'],\n",
    "                   'data': [0, 5, 10, 5, 10, 15, 10, 15, 20]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
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