{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "123bcceb",
   "metadata": {},
   "source": [
    "#### Importation des modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "31fbfc15",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as  np\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "import scipy "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50e9eaac",
   "metadata": {},
   "source": [
    "### Partie 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d7a6b8ac",
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size\n",
       "0         16.99  1.01  Female     No   Sun  Dinner     2\n",
       "1         10.34  1.66    Male     No   Sun  Dinner     3\n",
       "2         21.01  3.50    Male     No   Sun  Dinner     3\n",
       "3         23.68  3.31    Male     No   Sun  Dinner     2\n",
       "4         24.59  3.61  Female     No   Sun  Dinner     4\n",
       "..          ...   ...     ...    ...   ...     ...   ...\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
       "\n",
       "[244 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1. Importer le jeu de données sous forme de DataFrame à l’aide de l’instruction\n",
    "url=\"https://fxjollois.github.io/cours-2016-2017/donnees/tips.csv\"\n",
    "data=pd.read_csv(url)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "21619d7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
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       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
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       "      <td>22.67</td>\n",
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       "    <tr>\n",
       "      <th>242</th>\n",
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       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
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       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()#Afficher les 5 derniéres lignes pr avoir une idée sur le contenu du tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "c61e26fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>tip</th>\n",
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       "      <th>smoker</th>\n",
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       "      <td>10.34</td>\n",
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       "      <td>21.01</td>\n",
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       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
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       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()#Afficher les 5 derniéres lignes pr avoir une idée sur le contenu du tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6c0f65c0",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mdata\u001b[49m\u001b[38;5;241m.\u001b[39mcolumns)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "print(data.columns)#Afficher les columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "7a235fa1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total_bill    False\n",
      "tip           False\n",
      "sex           False\n",
      "smoker        False\n",
      "day           False\n",
      "time          False\n",
      "size          False\n",
      "dtype: bool\n"
     ]
    },
    {
     "data": {
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       "      <th>smoker</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill    tip    sex  smoker    day   time   size\n",
       "0         False  False  False   False  False  False  False\n",
       "1         False  False  False   False  False  False  False\n",
       "2         False  False  False   False  False  False  False\n",
       "3         False  False  False   False  False  False  False\n",
       "4         False  False  False   False  False  False  False\n",
       "..          ...    ...    ...     ...    ...    ...    ...\n",
       "239       False  False  False   False  False  False  False\n",
       "240       False  False  False   False  False  False  False\n",
       "241       False  False  False   False  False  False  False\n",
       "242       False  False  False   False  False  False  False\n",
       "243       False  False  False   False  False  False  False\n",
       "\n",
       "[244 rows x 7 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2. Vérifier qu’il n’y a pas de valeurs manquantes dans le DataFrame\n",
    "print(data.isna().any())\n",
    "#ou bien \n",
    "data.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "fc27a9b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# observation 244 # variable 7\n"
     ]
    }
   ],
   "source": [
    "#3. Quel est le nombre de variables et le nombre d’observations (dimension du DataFrame) ?\n",
    "[n,p]=data.shape;\n",
    "print(\"# observation\",n,'# variable',p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "a080cbbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total_bill    float64\n",
       "tip           float64\n",
       "sex            object\n",
       "smoker         object\n",
       "day            object\n",
       "time           object\n",
       "size            int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#4. Donner le type de chaque variable.\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4336bee",
   "metadata": {},
   "source": [
    "### Partie 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "66cd3c91",
   "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",
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       "    }\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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>244.000000</td>\n",
       "      <td>244.000000</td>\n",
       "      <td>244.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19.785943</td>\n",
       "      <td>2.998279</td>\n",
       "      <td>2.569672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.902412</td>\n",
       "      <td>1.383638</td>\n",
       "      <td>0.951100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.070000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13.347500</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.795000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24.127500</td>\n",
       "      <td>3.562500</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.810000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_bill         tip        size\n",
       "count  244.000000  244.000000  244.000000\n",
       "mean    19.785943    2.998279    2.569672\n",
       "std      8.902412    1.383638    0.951100\n",
       "min      3.070000    1.000000    1.000000\n",
       "25%     13.347500    2.000000    2.000000\n",
       "50%     17.795000    2.900000    2.000000\n",
       "75%     24.127500    3.562500    3.000000\n",
       "max     50.810000   10.000000    6.000000"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1. Décriver toutes les variables quantitatives de ce jeu de données sous forme de tableau (effectif, min,max, moyenne, médiane,. . .).\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "2953b88b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    244.000000\n",
       "mean       2.998279\n",
       "std        1.383638\n",
       "min        1.000000\n",
       "25%        2.000000\n",
       "50%        2.900000\n",
       "75%        3.562500\n",
       "max       10.000000\n",
       "Name: tip, dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['tip'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "cc2aa74d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_257001/911922866.py:2: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  data.drop(['size'],1).describe()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>244.000000</td>\n",
       "      <td>244.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19.785943</td>\n",
       "      <td>2.998279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.902412</td>\n",
       "      <td>1.383638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.070000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13.347500</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.795000</td>\n",
       "      <td>2.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24.127500</td>\n",
       "      <td>3.562500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.810000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_bill         tip\n",
       "count  244.000000  244.000000\n",
       "mean    19.785943    2.998279\n",
       "std      8.902412    1.383638\n",
       "min      3.070000    1.000000\n",
       "25%     13.347500    2.000000\n",
       "50%     17.795000    2.900000\n",
       "75%     24.127500    3.562500\n",
       "max     50.810000   10.000000"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#seulement les var. totall_bill et tip \n",
    "data.drop(['size'],1).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "aac54ac0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_257001/2900822370.py:2: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  data.drop(['size'],1).describe().round(3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>244.000</td>\n",
       "      <td>244.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19.786</td>\n",
       "      <td>2.998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.902</td>\n",
       "      <td>1.384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.070</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13.348</td>\n",
       "      <td>2.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.795</td>\n",
       "      <td>2.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24.127</td>\n",
       "      <td>3.562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.810</td>\n",
       "      <td>10.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_bill      tip\n",
       "count     244.000  244.000\n",
       "mean       19.786    2.998\n",
       "std         8.902    1.384\n",
       "min         3.070    1.000\n",
       "25%        13.348    2.000\n",
       "50%        17.795    2.900\n",
       "75%        24.127    3.562\n",
       "max        50.810   10.000"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Arrondi \n",
    "data.drop(['size'],1).describe().round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "336d5173",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tip</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>1.00</th>\n",
       "      <td>12.60</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.01</th>\n",
       "      <td>16.99</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.10</th>\n",
       "      <td>12.90</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.17</th>\n",
       "      <td>32.83</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.25</th>\n",
       "      <td>10.51</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.70</th>\n",
       "      <td>34.30</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.73</th>\n",
       "      <td>48.27</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.58</th>\n",
       "      <td>39.42</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.00</th>\n",
       "      <td>48.33</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.00</th>\n",
       "      <td>50.81</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>123 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_bill     sex smoker   day    time  size\n",
       "tip                                                 \n",
       "1.00        12.60    Male    Yes   Sat  Dinner     2\n",
       "1.01        16.99  Female     No   Sun  Dinner     2\n",
       "1.10        12.90  Female    Yes   Sat  Dinner     2\n",
       "1.17        32.83    Male    Yes   Sat  Dinner     2\n",
       "1.25        10.51    Male     No  Thur   Lunch     2\n",
       "...           ...     ...    ...   ...     ...   ...\n",
       "6.70        34.30    Male     No  Thur   Lunch     6\n",
       "6.73        48.27    Male     No   Sat  Dinner     4\n",
       "7.58        39.42    Male     No   Sat  Dinner     4\n",
       "9.00        48.33    Male     No   Sat  Dinner     4\n",
       "10.00       50.81    Male    Yes   Sat  Dinner     3\n",
       "\n",
       "[123 rows x 6 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2. Quel est le montant maximal du pourboire donné au serveur ?\n",
    "data.groupby(['tip']).max()# regrouper une variable suivant la moyenne \n",
    "#print('le montant maximal du pourboir est de ',10.00,\"dollar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "bda16c41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>n_i</th>\n",
       "      <th>N_i</th>\n",
       "      <th>f_i</th>\n",
       "      <th>F_i</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>0.618852</td>\n",
       "      <td>0.618852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>93</td>\n",
       "      <td>244</td>\n",
       "      <td>0.381148</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0   n_i  N_i       f_i       F_i\n",
       "smoker                              \n",
       "No      151  151  0.618852  0.618852\n",
       "Yes      93  244  0.381148  1.000000"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3. Construiser le tableau Statistique de la variable Smoker qui contient en colonnes les : effectifs (ni )et les fréquences (fi )\n",
    "tab=pd.crosstab(data['smoker'], \"n_i\")#Effectifs\n",
    "tab=tab.assign(N_i=np.zeros(2), f_i=np.zeros(2), F_i=np.zeros(2))\n",
    "tab.N_i=(np.cumsum(tab.n_i))\n",
    "tab.f_i=((tab.n_i)/n)\n",
    "tab.F_i=np.cumsum(tab.f_i)#Effectifs commulés \n",
    "tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bf61c834",
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoker</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>No</th>\n",
       "      <td>48.33</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>50.81</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_bill   tip   sex   day   time  size\n",
       "smoker                                           \n",
       "No           48.33   9.0  Male  Thur  Lunch     6\n",
       "Yes          50.81  10.0  Male  Thur  Lunch     5"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#4. Entre les fumeurs est les non–fumeur, qui donnent le plus de pourboires (en terme de nombre pas de montant laissés) ?\n",
    "data.groupby(['smoker']).max()\n",
    "#data.groupby(['smoker']).tip.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "723dd815",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "smoker   No  Yes  All\n",
      "row_0                \n",
      "10.0    151   93  244\n",
      "All     151   93  244\n"
     ]
    }
   ],
   "source": [
    "print(pd.crosstab(data.tip.max(), data.smoker, margins=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3893ac7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "smoker   No  Yes  All\n",
      "sex                  \n",
      "Female   54   33   87\n",
      "Male     97   60  157\n",
      "All     151   93  244\n"
     ]
    }
   ],
   "source": [
    "#5. Quel est le nombre des pourboires donnés par les femmes non fumeuses ?\n",
    "print(pd.crosstab(data.sex, data.smoker, margins=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "79b1a2ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "smoker   No  Yes  All\n",
      "row_0                \n",
      "10.0    151   93  244\n",
      "All     151   93  244\n"
     ]
    }
   ],
   "source": [
    "print(pd.crosstab(data.tip.max(), data.smoker, margins=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46eed7be",
   "metadata": {},
   "source": [
    "### Partie 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0baa0cc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ezougar/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='total_bill', ylabel='Density'>"
      ]
     },
     "execution_count": 60,
     "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": [
    "#1. Tracer la courbe de densité de la variable total_bill.\n",
    "sns.distplot(data.total_bill)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e5315ede",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'total_bill distribution')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BSFncBXyA6GKqFoOJirI3ZhyHSK1SgpCyuAvYD+iadSB5jGhF3Am8kXEsIrVICUI6bQXwGFFJtdqMJXaa+1vWgYjUICUI6bQHiSJ9+2UdSAEfA7ZG3UwiHaEEIZ12P/GLtHfWgRTQhdiX4haiJSEixVOCkE67n9gPukfWgbRiLNENdk/WgYjUGCUI6ZS1wENUZ/dSziHApqibSaRUShDSKY8Bb1HdCaIbUR9qOjFWIiLFUYKQTrk/ua3mBAHRzbQQeCTrQERqiBKEdMr9QL/kqGafIX7Zb8w4DpFaogQhHeZEgqj21gNALyLOGzOOQ6SWpJogzGy0mT1jZvPNbFKBx83MJiePP25mw1s83sXMHjGzv6QZp3TMi8AiaiNBQBTv+wfwXMZxiNSK1BKEmXUBLgbGEGVxjjazwS1OGwMMTI4JwK9aPH4a2vOlatXK+ENOrnjfnzONQqR2pNmC2AuY7+4L3P0d4Dre/xvNGQtc7eFBYAsz6w1gZn2BTwOXpxijdML9wGbArlkHUqSdgCGom0mkWGkmiD7ExJGcZjbcy76tcy4Evk1UkW6VmU0ws9lmNnvp0qWdClhKcz9RvXXjrAMpwThia9RlGcchUgvSTBBW4L6W09ALnmNmnwGWuPuc9l7E3S9z9xHuPqJXr14diVM6YCXRn/+xrAMp0TjiHcfNGcchUgvSTBDNrD/7sS8bbu7V2jn7AYeZ2fNE19RBZnZNeqFKqeYS/2j3yjqQEg0nfsluzDgOkVqQZoKYBQw0swFmtglwFBtu7jUdOC6ZzbQPsMLdX3L377h7X3fvnzzvTnc/JsVYpUQPJ7d7ZhpF6XJ7RNwKrMo4FpFql1qCcPe1wKnE3+I8YKq7P2lmE81sYnLaLcACYD7wG+DktOKR8poF7ABsk3UgHTAOeBv4e8ZxiFQ7c6+f6jQjRozw2bNnZx1GQxhAtB6mZh1IB6whFs59Hrgi41hEsmZmc9x9RKHHtJJaSrYUeJ7a617KaSLmT98EvJtxLCLVTAlCSjYrua21Aep844iprg9kHIdINVOCkJLNIgZ7h7d3YhUbDWyCZjOJtEUJQkr2MFE7pVp3kCtGD+ATRIKon1E4kfJSgpCSONGCqNXxh3xjiSl0T2YdiEiVUoKQkrxADFLX8vhDzmFEV9mNGcchUq2UIKQkuQHqemhBbEvUkrox4zhEqpUShJTkYWJwd2jWgZTJOGAO61eMFJGgBCElmQUMI5JEPcjVn29ZA0ZElCCkBO8S77broXspZ2fgo6ibSaQQJQgp2rNEme+Ca/Jr2DjgbuDVbMMQqTpKEFK0ucltLS+QK+RwYC3ailSkJSUIKdojwAeAQVkHUmZ7AP2BP2Ych0i1UYKQos0l9p9uyjqQMjPgSKL892sZxyJSTZQgpChOtCB2zzqQlBxJlAFXN5PI+5QgpCgvEO+u6238IWcE6mYSaUkJQorySHJbry0IA44AbgdezzYUkaqhBCFFmQt0oX5WUBeibiaR9SlBSFEeIRaUbZp1ICnak9hnW91MIkEJQooyl/odf8jJdTPdhmYziYAShBThZeAl6nf8Id9RRDfTn7IORKQKKEFIu3ID1PXegoBYNPcR4NqsAxGpAkoQ0q5cghiWZRAVYsB44B6gOeNYRLKmBCHtmgvsBGyedSAV8iViYeB1WQcikjElCGlXIwxQ5/swsaWqupmk0RWVIMzsT2b2aTNTQmkwrwHP0RgD1PnGA48CT2Uch0iWiv2H/yui5f2smf3EzD6aYkxSRR5NbhupBQHwRWJhoFoR0siKShDu/nd3H0/8n3geuN3MHjCzE82s3op7Sp56L7HRmg8BnwD+jxiPEGlERXcZmVlP4ATgJOL/xkVEwrg9lcikKswF+gDbZB1IBsYT74buyzgOkawUOwZxA3Av0A34rLsf5u5/cPevA5ulGaBkq55LfLfn80AP4HdZByKSkWJbEJe7+2B3/7G7vwRgZh8AcPdWtyg2s9Fm9oyZzTezSQUeNzObnDz+uJkNT+7vamYPm9ljZvakmZ3bga9NOmkV8DSNN/6Q050Yi5gKvJlxLCJZKDZB/LDAfTPbeoKZdQEuBsYAg4GjzWxwi9PGAAOTYwIxGA7wb+Agd9+NWJ812sz2KTJWKZPHgXU0bgsC4MvAW6iAnzSmjdt60My2JbqgNzWz3YmFpgAfJLqb2rIXMN/dFyTXug4Yy/ozB8cCV7u7Aw+a2RZm1jtppaxMzmlKDo0VVtjc5LZRWxAA+wA7A1cQyUKkkbSZIIBPEQPTfYEL8u5/Ezirnef2ARbmfd4M7F3EOX2Al5IWyBxi3dLF7v5QoRcxswlE64Ptt9++nZCkFI8APYF+WQeSISMSw5nAP4k6TSKNos0uJne/yt1HASe4+6i84zB3v6Gda1uB+1q2Alo9x93fdfdhRHLay8yGtBLjZe4+wt1H9OrVq52QpBRzie6lQj+kRnIssSZCg9XSaNrrYjrG3a8B+pvZN1s+7u4XFHhaTjPrv/nsCywu9Rx3f93M7gZGA0+0Fa+UzzvEN/v0jOOoBr2JwbKrgB/QfrNbpF60N0jdPbndjJjx1/JoyyxgoJkNMLNNiFL701ucMx04LpnNtA+wwt1fMrNeZrYFgJltSqxZerrIr0nK4CkiSTTyAHW+LxN7YtySdSAiFdTmmyF3/3VyW/I0U3dfa2anArcSLfQr3P1JM5uYPH4p8fd2KDCfmFV5YvL03sBVyTjERsBUd/9LqTFIxzXSHhDF+AywHTHN7rCMYxGplKJay2b2U2Kq69vA34DdgNOT7qdWufsttHjTlSSG3McOnFLgeY+jN6+Zmks0Gz+cdSBVoomYCXEO8C+i/LlIvSt2HcQn3f0N4o1UMzGZ479Si0oy9wixAEXle9/3FaIp/OusAxGpkGL//nMF+Q4Fprj7qynFI1XgXaKKq5pw69sOGEesiVidbSgiFVFsgrjJzJ4GRgB3mFkv9DdSt+YTq4eVIDZ0MrAcrayWxlBsue9JwL7ACHdfQ/z/GJtmYJIdraBu3ShiZfUlWQciUgGlTOkeRKyHyH/O1WWOR6rAXGATooCWrM+IVsRpxDL/PbINRyRVxZb7/j3wM2B/YM/kaLWKq9S2R4ChvD/wJOs7npjhdWHGcYikrdgWxAhgcDItVeqYEy2II7IOpIptTuya9UvgJ0TxMJF6VOwg9RPAtmkGItXhReA1NP7Qnm8QpdB/mXUgIikqtgWxNfCUmT1M7NUAgLtrUWmd0QB1cQYQO85dCvw32lZR6lOxCeKcNIOQ6jGXWAy2a9aB1IBvAtcTRfw2KAcgUgeKneZ6D7F/e1Py8Szef7MpdeQRYrraplkHUgP2JTYUupBYXChSb4qdxfQV4s1SrspAH+DGlGKSDOX2gJDifJNYWPjnrAMRSUGxg9SnAPsBbwC4+7PANmkFJdl4mShprfGH4n2OKNx3HtoTV+pPsQni3+7+Tu6TZLGc/h7qjEp8l25jYBKxaO62jGMRKbdiE8Q9ZnYWsKmZHUKUorkpvbAkC7lBpWFZBlGDjiO2QvxR1oGIlFmxCWISsBT4B/BVYo+H76YVlGTjEWL/hw9mHUiN2YSofX9vcojUi6Kmubr7OjO7EbjR3ZemG5JkZS6qn9JRJxE7av2I2FFLpB602YJI9oo+x8yWEXtCP2NmS83se5UJTyrlNeA5NP7QUd2IGU23ArMzjkWkXNrrYjqdmL20p7v3dPetgL2B/czsP9MOTirn0eRWCaLjTga2BL6fdSAiZdJegjgOONrdn8vd4e4LgGOSx6RO5AaotQai4z4IfJsYoHsg41hEyqG9BNHk7sta3pmMQ6gadB15hJiJ0yvrQGrc14kFQprBIfWgvQTxTgcfkxozF3UvlUN34CzgLuDOjGMR6az2EsRuZvZGgeNNVM+tbrwFPIO6l8rlq0Qtmu+i1aRS29pMEO7exd0/WODo4e7qYqoTjxN7G6gFUR5dgbOBmcDNGcci0hnFLpSTOqYB6vL7MrHo8ExgbcaxiHSUEoTwCLEjVN+sA6kjTcD/Ak8BV2Qci0hHKUEIs4nuJcs6kDrzOWIR0feAlRnHItIRShANbhWx4fieWQdShwz4OfAKcH7GsYh0hBJEg3uU2A1NCSIdewNfJBLEooxjESmVEkSDm5XcKkGk58dEEv5O1oGIlCjVBGFmo83sGTObb2aTCjxuZjY5efxxMxue3N/PzO4ys3lm9qSZnZZmnI1sFrBdckg6BgBnAL8H7ss4FpFSpJYgzKwLcDEwBhgMHG1mg1ucNgYYmBwTgF8l968FvuXug4h94U8p8Fwpg1mo9VAJZwH9gFOJ1oRILUizBbEXMN/dFyTblV4HjG1xzljgag8PAluYWW93f8nd5wK4+5vAPGJxqpTR68A/UYKohO7ABcBjwK8zjkWkWGkmiD7AwrzPm9nwn3y755hZf2IN10OFXsTMJpjZbDObvXSp9jIqxZzkVgmiMg4HDgb+m9ieUaTapZkgCk2rb1maps1zzGwz4E/A6e7+RqEXcffL3H2Eu4/o1Uu1SEuRG6DWLnKVYcAviDUR3844FpFipJkgmolu15y+wOJizzGzJiI5XOvuN6QYZ8OaBewEbJV1IA1kELF/9ZXAHdmGItKuNBPELGCgmQ0ws02Ao4DpLc6ZDhyXzGbaB1jh7i+ZmQG/Bea5+wUpxtjQNECdjbOJOk1fBd7OOBaRtqSWINx9LTFp41ZikHmquz9pZhPNbGJy2i3AAmA+8Bti10aICgXHAgeZ2aPJcWhasTaiV4jBHyWIytsUuAz4F3BuxrGItGXjNC/u7rcQSSD/vkvzPnbglALPuw+VBkqVFshlaxRR8fVnRNN6WKbRiBSmldQNahbxw9ceENk5n6iieyLanlGqkxJEg5pFrF7snnUgDWwr4FKiHtYPsw1FpCAliAa0jlhUslfWgQjjgOOA83i/20+kWihBNKB/Aq8SMwEkexcBvYHj0awmqS5KEA3ogeT2Y5lGITlbELvOzSNqNolUCyWIBvQA0f+9c9aByHsOAb4OXAjcnG0oIu9RgmhADxCtB80jri4/Jaa7Hk+UGBDJmhJEg3mV6MpQ91L16UqUPF4NjEdlwSV7ShANZmZyqwRRnXYGLgFmAD/IOBYRJYgG8wDQBa2grmbHJccPgLuzDUUanBJEg3mA2FyjW9aBSJsuJgr6fQlYknEs0riUIBrIGuBh1L1UCzYD/gC8BnyB+NmJVJoSRAN5HFiFEkStGEZUfb2H2ENCpNJSreYq1UUL5GrPscTWsBcBeySfi1SKWhAN5AFiy75+7Z0oVeV8YCQwgff3ERepBCWIBuHAvaj1UIuagKlAL+BzaNBaKkcJokHMBxYBB2UdiHRIL2AasBQ4Eu0fIZWhBNEg7kxulSBq1x7ERu0zgK8QrUKRNGmQukHcCfQh5tZL7foS0Rr8PvGzPDvbcKTOKUE0gHXAXcBoVKCvHpxNJInvATsRSUMkDUoQDeBJou9a3Uv1wYDfAC8S+1lvD+yfaURSrzQG0QBy4w+jMo1CyukDwA1Af2Lb0mezDEbqlhJEA7iT6IrYIetApKy2Am4hWhSfBBZnG47UISWIOreWqAiq7qX6tBORJJYSY0yvZxqN1BsliDr3CPAGShD1bE9ijcTTwGHA29mGI3VECaLOafyhMRwCXAPcBxxFtBxFOksJos7dCewCfCjrQCR1XwB+AUwn6jZpIZ10lqa51rG3ifpLJ2UdiFTMKUStpv8h9pS4CK19kY5Tgqhj9xBJ4tCsA5GKOgdYCVxATIf9KUoS0jFKEHXsFmBT4MCsA5GKMuBnwOrkdlOiRSFSqlTHIMxstJk9Y2bzzWxSgcfNzCYnjz9uZsPzHrvCzJaY2RNpxlivHLiZmL20acaxSOUZMR7xH8APgPOyDUdqVGoJwsy6EHuvjwEGA0eb2eAWp40BBibHBOBXeY9dSUztlg54FliAupca2UbAr4FjgP8mNh4SKUWaLYi9gPnuvsDd3wGuA8a2OGcscLWHB4EtzKw3gLvPAF5NMb669pfkdkymUUjWugC/A74IfJtoTWh2kxQrzTGIPsDCvM+bgb2LOKcP8FKxL2JmE4jWB9tvv32HAq1H04ChwICsA5HMbQxcC3QlKsCuIrqcNHAt7UmzBVHo96/lm5dizmmTu1/m7iPcfUSvXr1KeWrdWgLcTxRxE4FoSVwBTAR+ApxOlIEXaUuaLYhmoF/e533ZsJ5YMedIiaYTWfZzWQciVWUj4BJi0sL/I1oSlxLJQ6SQNFsQs4CBZjbAzDYhKgBMb3HOdOC4ZDbTPsAKdy+6e0kKm0aUgd4t4zik+hjwc2LQ+nJif2vVbpLWpJYg3H0tcCpwKzAPmOruT5rZRDObmJx2CzHZZj6xB8rJueeb2RRgJrCzmTWb2X+kFWs9eR34O9F6UB+zFGLAD4ELgRuBTwDLM4xHqleqC+Xc/RYiCeTfd2nex05UByj03KPTjK1e3QC8QzTXRNpyGjEj5BhgP2Lmm/Ysl3wq1ldnphB7BOyZdSBSE44Abif2k9gr+VgkRwmijrxMVG89GnUvSfE+TgwY9iFWpv4/tFZCghJEHZlKTF1U35yUakdiwG8s8E1iYd3rWQYkVUEJoo78DhhO1DURKdVmwPXEOokbgN2BhzKNSLKmBFEn5gKPEsXZRDpqI+BMYh8RB/YHziUmPkjjUYKoE78lSil8KetApC7sS7zh+AKxv8QIYE6G8Ug2lCDqwCrg/4DDgS2yDUXqyBZEDafpxDqJvYkSHa9nFpFUmhJEHbiG+KOd2M55Ih3xWeBJ4CvAZKI2/2XAu1kGJRWhBFHjnPijHU4sdhJJwxbEZi1zgUHAV4lqwdejon/1TAmixt1BvLv7Blr7IOkbRux1/kfizcmRxJuTXIFIqS9KEDXufGAbVFpDKseIFdj/AH4PvEWsn9iLaFGo66l+KEHUsIeB24AzgA9kHIs0ni5EHad5xCy614gWxc5EWfFV2YUmZaIEUcN+CGyFBqclWxsDXwaeIVoQWxMVOHcAziY2fZHapARRox4GbiKmHfbINhQRIFoUhxMlO2YQayl+ROxN8nmiDL3GKWqLEkQNcqJbaRsiQYhUEyMKAE4H/kX8rt4LHAJ8FLiI6I6S6qcEUYOmE39w56LWg1S3AURtp4XEgHZP4k1Nb2K8Yjoq41HNlCBqzFvEH9gg4KRsQxEpWldiQPsBYi3FV4npsmOB7YitJ+9EyaLaKEHUmHOB54nN5lPdDlAkJbsT3UyLiF3sPkHMgjoY6EXUf7oaWJJVgPIe/Y+pIQ8BFxAVWw/IOBaRzmoCPp0cK4lFn39Jjj8m53yEqBCwLzCEKGW/ecUjbVxKEDXiDaJSax/gZxnHIlJumxHdTWOJ0h2PELOe7ifGKX6Xd25fYBcieeyYHAOSY7PKhdwQlCBqgBOF0p4npg9ukWUwIinbCNgjOSB+/58jSsrkH/cTLY982/B+wtiR9RNIX2IqrhRPCaIG/JDYTvR/UUE+aTzG+//oP5t3vxNlyBcQCWRB3vEg8TeTX/ajiVi8NwL4WHIMQ0mjLUoQVe53wPeA44D/yjgWkWpixKrtrYk6UC2tIabX5ieQfxJTxK9LztkS+CQwhhgL2TrdkGuOEkQVu4YYkP4kUX9f1VpFitfE+y2PlhYC9xG1zP4G/IFoSRxCFL4chwbDQdNcq5ITs5WOBUYC01AxPpFy6gccTbTQFwGziRXf84ATiLGMzxF/e428NkMJosq8BZwIfIsoqXwL0C3TiETqW25Q/CdEV9RM4GvJ7eeJhXzfIPbkbrRaUkoQVeQBYgDtamLc4Q/EClQRqQwD9gEuJKrQ3kws4LuM+NvcldiD5aWM4qs0JYgqsJBo1u5P1NC/jVgxrR+OSHY2Bg4l3qi9RGy52gP4NjFlNvfY6qwCrAD9D8rQQ8Titx2BKUQf6BNE6QERqR5bEvuuzASeBiYRO+odBWxLTCa5kQ3XZdQ6JYgKcmKBz4+IPs99iCbsqcT0u5+i6qwi1W5n4m/4eWK192HAn4hB7Z7AaOCXxN/6umxCLJtUp7ma2WiiLlcX4HJ3/0mLxy15/FCid+UEd59bzHNrwQri3cbDxMKd+4EXksf2BiYTXUtKCiK1pwsxPnEwsebifqKO1E3A15NztiQW5A0nyoMMIUqENFU62A5KLUGYWRfgYmJqcTMwy8ymu/tTeaeNAQYmx95EN9/eRT63YpxYkbmaaEK+mdzmjuVEH2XuWAw8y/oDWdsRLYZJxDuO7SoUu4ikr4mYkj6SqJX2L2KdxX1E4vgr77cmmoCdiHGMPsnRl1ikt3ne8UFiksomydFE5ddCpdmC2AuY7+4LAMzsOqIWV/4/+bHA1e7uwINmtoWZ9SZ2KWzvuWWzM/GPfm2L492822J0IzZC6Q18itg9a2eiO6kvWugm0ih2So7jk89XE3t2P0F0PT1DrL+4g3gjWez/mCZiTdQmxD/vjZLjQ8Q+G+WWZoLoQ0zQyWkmWgntndOnlftbPhcAM5sATEg+XWlmz3Qi5k5ZRbxzyL17aMPWwLL0Iyq7Wo0bajf2Wo0bajf2qo17TXK0tJj33oB2JPYdWnsgzQRR6A1zy3UmrZ1TzHPjTvfLiGnKNcPMZrv7iKzjKFWtxg21G3utxg21G3utxg3ljz3NBNFMrGjP6UskumLO2aSI54qISIrSnOY6CxhoZgPMbBNiyvD0FudMB46zsA+wwt1fKvK5IiKSotRaEO6+1sxOBW4lZoRd4e5PmtnE5PFLiVJDhwLziS78E9t6blqxZqCmusTy1GrcULux12rcULux12rcUObYLSYQiYiIrE8rqUVEpCAlCBERKUgJooLMbLSZPWNm881sUtbxtMXMrjCzJWb2RN59W5nZ7Wb2bHK7ZZYxFmJm/czsLjObZ2ZPmtlpyf21EHtXM3vYzB5LYj83ub/qY4eonmBmj5jZX5LPayXu583sH2b2qJnNTu6r+tiThcXXm9nTye/7vuWOWwmiQvLKh4wBBgNHm9ngbKNq05VE3bF8k4A73H0gsQi0GpPcWuBb7j6IqG5ySvJ9roXY/w0c5O67AcOA0cnsvlqIHeA0YlO2nFqJG2CUuw/LW0NQC7FfBPzN3T8K7EZ878sbt7vrqMAB7Avcmvf5d4DvZB1XOzH3B57I+/wZoHfycW/gmaxjLOJr+DNR06umYicqt8wlKghUfezEWqU7gIOAv9TS7wtRmHXrFvdVdexEqabnSCYapRW3WhCV01pZkVryIY91KiS322QcT5vMrD+wO7H1Rk3EnnTTPAosAW5391qJ/UJiL538Cte1EDdElYbbzGxOUroHqj/2HYGlwO+Sbr3Lzaw7ZY5bCaJyii4fIp1nZpsRZfpPd/c3so6nWO7+rrsPI96R72VmQzIOqV1m9hlgibvPyTqWDtrP3YcT3b+nmNkBWQdUhI2JKuK/cvfdie3sy94NpgRROcWUHql2ryTVdklul2QcT0Fm1kQkh2vd/Ybk7pqIPcfdXwfuJsaBqj32/YDDzOx54DrgIDO7huqPGwB3X5zcLgGmEZWoqz32ZqA5aWECXE8kjLLGrQRROfVQPmQ671cwPp7o368qySZUvwXmufsFeQ/VQuy9zGyL5ONNid1nn6bKY3f377h7X3fvT/xe3+nux1DlcQOYWXcz65H7GPgkUZW7qmN395eBhWa2c3LXwcR2CGWNWyupK8jMDiX6anPlQ36UbUStM7MpxP4nWwOvAN8ntt2dCmwPvAgc6e6vZhRiQWa2P3AvsWVwrj/8LGIcotpjHwpcRfx+bARMdff/MbOeVHnsOWY2EjjD3T9TC3Gb2Y5EqwGi2+b/3P1HNRL7MOByorjpAqJU0UaUMW4lCBERKUhdTCIiUpAShIiIFKQEISIiBSlBiIhIQUoQIiJSkBKEiIgUpAQh0kJSRvnkds7pb2ZfKuJa/fNLphd4/AQz+2Urjz3Q8hpmNjJXTlskbUoQIhvaAmgzQRCVbttNEJ3h7h9L8/oi7VGCENnQT4Cdkg1kzk+OJ5JNZb6Yd87Hk3P+M3mXf6+ZzU2OUv659zOzv1lsJvX93J1mtrKcX5RIqTbOOgCRKjQJGOLuw8zscGAisSHL1sAsM5uRnHOGu38GwMy6AYe4+2ozGwhMAUYUvvwG9gKGAKuS69/s7rPL+yWJlE4JQqRt+wNT3P1dolLmPcCeQMsS4k3AL5P6OO8CHynhNW539+UAZnZD8ppKEJI5JQiRthXax6OQ/ySKGu5GdN2uLuE1WhZEU4E0qQoagxDZ0JtAj+TjGcAXk53eegEHAA+3OAdgc+Ald18HHEtUZC3WIclm85sC44D7Oxm/SFmoBSHSgrsvN7P7k6mlfwUeBx4j3tl/291fNrPlwFozewy4ErgE+JOZHQncRezwVaz7gN8DHybKTat7SaqCyn2LiEhB6mISEZGC1MUkUgFm9ingf1vc/Zy7fy6LeESKoS4mEREpSF1MIiJSkBKEiIgUpAQhIiIFKUGIiEhB/x8W6JxZgxV1pwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data['total_bill'] , color='cyan', label = 'var total_bill ')\n",
    "#sns.distplot(df_3['Sex'] , color='yellow', label = 'Skewness: %2.f'%(df_3['Sex'].skew())) avec histogram\n",
    "plt.legend(loc ='best')\n",
    "plt.title('total_bill distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "67a014a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a piori non car elle est asymetrique. On doit faire test d'hypothese\n"
     ]
    }
   ],
   "source": [
    "#2\n",
    "print(\"a piori non car elle est asymetrique. On doit faire test d'hypothese\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "8e22838f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non car la variable time est qualitative\n"
     ]
    }
   ],
   "source": [
    "#3. Le graphique précédent est-il adapté pour représenter la variable time ? Justifier votre réponse.\n",
    "print('Non car la variable time est qualitative')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b151f915",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#4. Donner une représentation graphique, de votre choix, de cette dernière variable.\n",
    "labels = 'Dinner', 'Lunch'\n",
    "sizes = [np.sum(data.time==\"Dinner\"), np.sum(data.time==\"Lunch\")]\n",
    "explode = (0, 0.1,)  # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "abee4a43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='time', ylabel='count'>"
      ]
     },
     "execution_count": 73,
     "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": [
    "#Histogramme de la variable Time\n",
    "sns.countplot(x= data['time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "646ffa14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f6b786cbbe0>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"time\", y=\"tip\", data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a038aa8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 76,
     "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": [
    "data['time'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "523602c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogramme de la var. Sex')"
      ]
     },
     "execution_count": 92,
     "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": [
    "#5. Tracer le diagramme en secteur de la variable sex.\n",
    "sns.countplot(x= data['sex'], label = 'Female ')\n",
    "plt.legend(loc ='best')\n",
    "plt.title('Diagramme de la var. Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "86e62732",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f6b77753880>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#6. Tracer et commentez la boite à moustache de la variable tip. \n",
    "sns.catplot(y = 'tip', x = 'sex', kind ='box', data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "eb19cf09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f6b77772190>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(y = 'tip', x = 'time', kind ='box', data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "34dabc68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f6b773e1af0>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(y = 'tip', x = 'size', kind ='box', data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "55028d2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f6b7729c370>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSOCKyZ1hQpVKJWq2WPcYp2bZtm+r1esf3e2yfAwMDHd/3sf1u3LgxZd8rCc+vruOT3Vjq9BRorzPPPDN7BBQYz6+lxQoYANrvpCtgjgEDQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQpCtOxmN7WtK92XN0kfMkPZg9BAqL59epezAihk68sSsCjFNjuxYRlew5UEw8v5YOhyAAIAkBBoAkBLiYtmcPgELj+bVEOAYMAElYAQNAEgIMAEkIcBezHbY/fNz2n9p+T+JIKAA33WF7/XG3vd72WOZcRUSAu9tjkl5n+7zsQVAc0fzF0NskfcT2GbZXS3qvpD/Onax4CHB3m1HzN9LvOPEDtp9v+zbbe1pvz+/8eOhWEbFX0hck/bmkayXdJOndtr9h+27bV0uS7RfY/rrte1rPtQsTx+46vAqii9l+RNJzJe2R9CJJfyjp7Ih4j+0vSLo5Iqq23yLpNRHx2rxp0W1aK9/dkh6XdIukfRFxk+1nSvq6pEskvV/Sf0fEp2w/TVJvRBzJmrnbEOAuZvuRiDjb9l9LOirpiP4/wA9Kek5EHLW9StKBiOBQBU5J67n1iKTXSzpDzZ+6JOlcSb+uZoTfLWmHpM9GxHcz5uxWpewBsCQ+puZK5ZPz3If/0+J0zLX+WdJvRcR3Tvj4ftt3SrpK0pds/0FEfLnTQ3YrjgEXQEQ8JOkzkt563M3/KekNrfd/T9IdnZ4LhfIlSRttW5JsX9J6e4Gk70fE9ZI+L+nivBG7DwEujg+reZrAY0Yl/b7tPZLeJGlTylQoir+RtErSHtt7W9uSdI2kvbbvkXSRmocisEgcAwaAJKyAASAJAQaAJAQYAJIQYABIQoABIAkBBo5j+1W2b8meAysDAQaWiG3+shSnhACjq9lebfvfbH/T9l7b19ietH2d7f+yXbN9qe0v2f6e7be1Ps+2P9j6nAnb15zksV/aOvPXBbZfYvsrtu9qPdZzWve5vbWvr4g/dsEp4v/Y6HZDku6PiKskyfbPSfqApB9ExGW2PyrpRkkvV/NkMvsk/b2k10l6sZpnkTtP0jdsf/XYg9p+maRtkq6WdEDN0zFeHRHTrVi/V9JbWnd/ZkT8Wpu/ThQQAUa3m5D0IdsfkHRLRPxH63QFnz/u42dHxCFJh2z/tHU6xVdI+nREzEo62FrBvlTSTyT9sprnWb4iIu63/UJJL5Q03nrsXjWjfMw/t/uLRDERYHS1iPgf2y+RdKWk99m+tfWhx1pv5457/9h2Sc2zez2ZA2quli+RdH/rvvsi4rInuf+jpzk+VjiOAaOr2X6upMMRcZOkD0m6dJGf+lVJ19jutd0n6ZVqnmRckn6s5ukVr7P9KknfkdRn+7LWPlfZfsGSfRFYsVgBo9v9iqQP2p5T86T0b5d08yI+73OSLpP0TTXPlfzOiHjA9kWSFBEHbf+mpJ1qHuv9bUnXt44xl9Q8B/O+Jf5asMJwNjQASMIhCABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCT/B/zspozaXCG/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(y = 'tip', x = 'smoker', kind ='box', data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f6bd3db",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}