{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Groupby\n",
    "\n",
    "La méthode de regroupement par groupe vous permet de regrouper des lignes de données et d'appeler des fonctions d'agrégation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "# Création dataframe\n",
    "data = {'Company':['GOOG','GOOG','MSFT','MSFT','FB','FB'],\n",
    "       'Person':['Sam','Charlie','Amy','Vanessa','Carl','Sarah'],\n",
    "       'Sales':[200,120,340,124,243,350]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "  Company   Person  Sales\n",
       "0    GOOG      Sam    200\n",
       "1    GOOG  Charlie    120\n",
       "2    MSFT      Amy    340\n",
       "3    MSFT  Vanessa    124\n",
       "4      FB     Carl    243\n",
       "5      FB    Sarah    350"
      ],
      "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>Company</th>\n      <th>Person</th>\n      <th>Sales</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>GOOG</td>\n      <td>Sam</td>\n      <td>200</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>GOOG</td>\n      <td>Charlie</td>\n      <td>120</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>MSFT</td>\n      <td>Amy</td>\n      <td>340</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>MSFT</td>\n      <td>Vanessa</td>\n      <td>124</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>FB</td>\n      <td>Carl</td>\n      <td>243</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>FB</td>\n      <td>Sarah</td>\n      <td>350</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Maintenant vous pouvez utiliser la méthode .groupby() pour regrouper les lignes en fonction d'un nom de colonne. Par exemple, groupons les personnes par société. Ceci créera un objet DataFrameGroupBy :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x00000166211DA848>"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "df.groupby('Company')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vous pouvez sauvegarder cet objet comme nouvelle variable :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "by_comp = df.groupby(\"Company\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Et ensuite, appelez les méthodes d'agrégation sur l'objet :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "         Sales\n",
       "Company       \n",
       "FB       296.5\n",
       "GOOG     160.0\n",
       "MSFT     232.0"
      ],
      "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>Sales</th>\n    </tr>\n    <tr>\n      <th>Company</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>FB</th>\n      <td>296.5</td>\n    </tr>\n    <tr>\n      <th>GOOG</th>\n      <td>160.0</td>\n    </tr>\n    <tr>\n      <th>MSFT</th>\n      <td>232.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "by_comp.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "         Sales\n",
       "Company       \n",
       "FB       296.5\n",
       "GOOG     160.0\n",
       "MSFT     232.0"
      ],
      "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>Sales</th>\n    </tr>\n    <tr>\n      <th>Company</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>FB</th>\n      <td>296.5</td>\n    </tr>\n    <tr>\n      <th>GOOG</th>\n      <td>160.0</td>\n    </tr>\n    <tr>\n      <th>MSFT</th>\n      <td>232.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "df.groupby('Company').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Autres exemples de méthodes d'agrégation :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "              Sales\n",
       "Company            \n",
       "FB        75.660426\n",
       "GOOG      56.568542\n",
       "MSFT     152.735065"
      ],
      "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>Sales</th>\n    </tr>\n    <tr>\n      <th>Company</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>FB</th>\n      <td>75.660426</td>\n    </tr>\n    <tr>\n      <th>GOOG</th>\n      <td>56.568542</td>\n    </tr>\n    <tr>\n      <th>MSFT</th>\n      <td>152.735065</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "by_comp.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          Person  Sales\n",
       "Company                \n",
       "FB          Carl    243\n",
       "GOOG     Charlie    120\n",
       "MSFT         Amy    124"
      ],
      "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>Person</th>\n      <th>Sales</th>\n    </tr>\n    <tr>\n      <th>Company</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>FB</th>\n      <td>Carl</td>\n      <td>243</td>\n    </tr>\n    <tr>\n      <th>GOOG</th>\n      <td>Charlie</td>\n      <td>120</td>\n    </tr>\n    <tr>\n      <th>MSFT</th>\n      <td>Amy</td>\n      <td>124</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "by_comp.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          Person  Sales\n",
       "Company                \n",
       "FB         Sarah    350\n",
       "GOOG         Sam    200\n",
       "MSFT     Vanessa    340"
      ],
      "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>Person</th>\n      <th>Sales</th>\n    </tr>\n    <tr>\n      <th>Company</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>FB</th>\n      <td>Sarah</td>\n      <td>350</td>\n    </tr>\n    <tr>\n      <th>GOOG</th>\n      <td>Sam</td>\n      <td>200</td>\n    </tr>\n    <tr>\n      <th>MSFT</th>\n      <td>Vanessa</td>\n      <td>340</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "by_comp.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "         Person  Sales\n",
       "Company               \n",
       "FB            2      2\n",
       "GOOG          2      2\n",
       "MSFT          2      2"
      ],
      "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>Person</th>\n      <th>Sales</th>\n    </tr>\n    <tr>\n      <th>Company</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>FB</th>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>GOOG</th>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>MSFT</th>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "by_comp.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "        Sales                                                        \n",
       "        count   mean         std    min     25%    50%     75%    max\n",
       "Company                                                              \n",
       "FB        2.0  296.5   75.660426  243.0  269.75  296.5  323.25  350.0\n",
       "GOOG      2.0  160.0   56.568542  120.0  140.00  160.0  180.00  200.0\n",
       "MSFT      2.0  232.0  152.735065  124.0  178.00  232.0  286.00  340.0"
      ],
      "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 colspan=\"8\" halign=\"left\">Sales</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n    <tr>\n      <th>Company</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>FB</th>\n      <td>2.0</td>\n      <td>296.5</td>\n      <td>75.660426</td>\n      <td>243.0</td>\n      <td>269.75</td>\n      <td>296.5</td>\n      <td>323.25</td>\n      <td>350.0</td>\n    </tr>\n    <tr>\n      <th>GOOG</th>\n      <td>2.0</td>\n      <td>160.0</td>\n      <td>56.568542</td>\n      <td>120.0</td>\n      <td>140.00</td>\n      <td>160.0</td>\n      <td>180.00</td>\n      <td>200.0</td>\n    </tr>\n    <tr>\n      <th>MSFT</th>\n      <td>2.0</td>\n      <td>232.0</td>\n      <td>152.735065</td>\n      <td>124.0</td>\n      <td>178.00</td>\n      <td>232.0</td>\n      <td>286.00</td>\n      <td>340.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "by_comp.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Company              FB        GOOG        MSFT\n",
       "Sales count    2.000000    2.000000    2.000000\n",
       "      mean   296.500000  160.000000  232.000000\n",
       "      std     75.660426   56.568542  152.735065\n",
       "      min    243.000000  120.000000  124.000000\n",
       "      25%    269.750000  140.000000  178.000000\n",
       "      50%    296.500000  160.000000  232.000000\n",
       "      75%    323.250000  180.000000  286.000000\n",
       "      max    350.000000  200.000000  340.000000"
      ],
      "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>Company</th>\n      <th>FB</th>\n      <th>GOOG</th>\n      <th>MSFT</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"8\" valign=\"top\">Sales</th>\n      <th>count</th>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>296.500000</td>\n      <td>160.000000</td>\n      <td>232.000000</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>75.660426</td>\n      <td>56.568542</td>\n      <td>152.735065</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>243.000000</td>\n      <td>120.000000</td>\n      <td>124.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>269.750000</td>\n      <td>140.000000</td>\n      <td>178.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>296.500000</td>\n      <td>160.000000</td>\n      <td>232.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>323.250000</td>\n      <td>180.000000</td>\n      <td>286.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>350.000000</td>\n      <td>200.000000</td>\n      <td>340.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "by_comp.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Sales  count      2.000000\n",
       "       mean     160.000000\n",
       "       std       56.568542\n",
       "       min      120.000000\n",
       "       25%      140.000000\n",
       "       50%      160.000000\n",
       "       75%      180.000000\n",
       "       max      200.000000\n",
       "Name: GOOG, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "by_comp.describe().transpose()['GOOG']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bon travail!"
   ]
  }
 ],
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