03A - Preparation base equipements 2019.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\olivier\\anaconda\\envs\\Geo\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3165: DtypeWarning: Columns (0,1,7) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "#import de la base\n",
    "df = pd.read_csv(\"./databases/bpe/bpe_with_gps_2.csv\", sep =\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2471192 entries, 0 to 2471191\n",
      "Data columns (total 8 columns):\n",
      " #   Column           Dtype  \n",
      "---  ------           -----  \n",
      " 0   DEP              object \n",
      " 1   DEPCOM           object \n",
      " 2   AN               int64  \n",
      " 3   TYPEQU           object \n",
      " 4   QUALITE_XY       object \n",
      " 5   lat              float64\n",
      " 6   lon              float64\n",
      " 7   code_iris_clean  object \n",
      "dtypes: float64(2), int64(1), object(5)\n",
      "memory usage: 150.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2471192, 8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DEP                0\n",
       "DEPCOM             0\n",
       "AN                 0\n",
       "TYPEQU             0\n",
       "QUALITE_XY         0\n",
       "lat                0\n",
       "lon                0\n",
       "code_iris_clean    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>DEP</th>\n",
       "      <th>DEPCOM</th>\n",
       "      <th>AN</th>\n",
       "      <th>TYPEQU</th>\n",
       "      <th>QUALITE_XY</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>code_iris_clean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01</td>\n",
       "      <td>01053</td>\n",
       "      <td>2019</td>\n",
       "      <td>D106</td>\n",
       "      <td>Mauvaise</td>\n",
       "      <td>46.202783</td>\n",
       "      <td>5.219245</td>\n",
       "      <td>010530402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01</td>\n",
       "      <td>01053</td>\n",
       "      <td>2019</td>\n",
       "      <td>D101</td>\n",
       "      <td>Mauvaise</td>\n",
       "      <td>46.202783</td>\n",
       "      <td>5.219245</td>\n",
       "      <td>010530402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01</td>\n",
       "      <td>01053</td>\n",
       "      <td>2019</td>\n",
       "      <td>D111</td>\n",
       "      <td>Mauvaise</td>\n",
       "      <td>46.202783</td>\n",
       "      <td>5.219245</td>\n",
       "      <td>010530402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01</td>\n",
       "      <td>01053</td>\n",
       "      <td>2019</td>\n",
       "      <td>D112</td>\n",
       "      <td>Mauvaise</td>\n",
       "      <td>46.202783</td>\n",
       "      <td>5.219245</td>\n",
       "      <td>010530402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01</td>\n",
       "      <td>01034</td>\n",
       "      <td>2019</td>\n",
       "      <td>D101</td>\n",
       "      <td>Bonne</td>\n",
       "      <td>45.761208</td>\n",
       "      <td>5.684976</td>\n",
       "      <td>010340102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  DEP DEPCOM    AN TYPEQU QUALITE_XY        lat       lon code_iris_clean\n",
       "0  01  01053  2019   D106   Mauvaise  46.202783  5.219245       010530402\n",
       "1  01  01053  2019   D101   Mauvaise  46.202783  5.219245       010530402\n",
       "2  01  01053  2019   D111   Mauvaise  46.202783  5.219245       010530402\n",
       "3  01  01053  2019   D112   Mauvaise  46.202783  5.219245       010530402\n",
       "4  01  01034  2019   D101      Bonne  45.761208  5.684976       010340102"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DEP                0.0\n",
       "DEPCOM             0.0\n",
       "AN                 0.0\n",
       "TYPEQU             0.0\n",
       "QUALITE_XY         0.0\n",
       "lat                0.0\n",
       "lon                0.0\n",
       "code_iris_clean    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# % de valeurs nulles par champ\n",
    "df.isnull().sum()/len(df)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2451145, 8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#On va transformer en int les num de départements. Je choisis de supprimer les infos correspondant à 2A et 2B soit la corse\n",
    "df = df[-((df[\"DEP\"] == \"2A\") | (df[\"DEP\"] == \"2B\"))]\n",
    "df[\"DEP\"] = df[\"DEP\"].astype(int)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75    135306\n",
       "13     86039\n",
       "59     76706\n",
       "69     69381\n",
       "33     65177\n",
       "       ...  \n",
       "52      6032\n",
       "55      5953\n",
       "23      4723\n",
       "90      4297\n",
       "48      3881\n",
       "Name: DEP, Length: 98, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# On compte le nombre d'équipement par département\n",
    "df[\"DEP\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEP', ylabel='count'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(30,10))\n",
    "sns.countplot(x=\"DEP\", data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='TYPEQU', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(30,10))\n",
    "sns.countplot(x=\"TYPEQU\", data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Je décide de ne conserver que certains équipements\n",
    "eqfin = [\"A101\", \"A104\", \"A203\", \"A504\", \"B101\", \"B102\", \"B201\", \"B202\", \"B203\", \"C101\", \"C102\", \"C104\", \"C105\", \"C201\", \"C301\", \"C302\", \"C304\", \"C305\", \"C501\", \"C502\", \"C503\", \"C504\", \"C701\", \"C702\",\"D101\", \"D102\", \"D103\", \"D106\", \"D107\", \"D301\",\"D502\", \"E102\", \"E107\", \"E108\", \"E109\", \"F109\",\"F121\", \"F201\", \"F202\", \"F303\", \"G102\", \"G103\", \"G104\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"EQFIN\"] = df[\"TYPEQU\"].apply(lambda row : row in eqfin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df[df[\"EQFIN\"]==True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dic = {\"A101\" : \"Police ou gendarmerie\",\n",
    "       \"A104\" : \"Police ou gendarmerie\",\n",
    "       \"A203\" : \"Banques\",\n",
    "       \"A504\" : \"Restaurants\",\n",
    "       \"B101\" : \"Alimentation\",\n",
    "       \"B102\" : \"Alimentation\",\n",
    "       \"B201\" : \"Alimentation\",\n",
    "       \"B202\" : \"Alimentation\",\n",
    "       \"B203\" : \"Boulangerie\",\n",
    "       \"C101\" : \"Ecole mat et primaire\",\n",
    "       \"C102\" : \"Ecole mat et primaire\",\n",
    "       \"C104\" : \"Ecole mat et primaire\",\n",
    "       \"C105\" : \"Ecole mat et primaire\",\n",
    "       \"C201\" : \"Collège ou lycée\",\n",
    "       \"C301\" : \"Collège ou lycée\",\n",
    "       \"C302\" : \"Collège ou lycée\",\n",
    "       \"C303\" : \"Collège ou lycée\",\n",
    "       \"C304\" : \"Collège ou lycée\",\n",
    "       \"C305\" : \"Collège ou lycée\",\n",
    "       \"C501\" : \"Ecole sup\",\n",
    "       \"C502\" : \"Ecole sup\",\n",
    "       \"C503\" : \"Ecole sup\",\n",
    "       \"C504\" : \"Ecole sup\",\n",
    "       \"C701\" : \"Résidence U\",\n",
    "       \"C702\" : \"Resto U\",\n",
    "       \"D101\" : \"Etab hospi\",\n",
    "       \"D102\" : \"Etab hospi\",\n",
    "       \"D103\" : \"Etab hospi\",\n",
    "       \"D106\" : \"Etab hospi\",\n",
    "       \"D107\" : \"Etab hospi\",\n",
    "       \"D301\" : \"Pharmacie\",\n",
    "       \"D502\" : \"Crèche\",\n",
    "       \"E102\" : \"Aéroport\",\n",
    "       \"E107\" : \"Gare nationale\",\n",
    "       \"E108\" : \"Gare\",\n",
    "       \"E109\" : \"Gare\",\n",
    "       \"F109\" : \"Parcours sportif/santé\",\n",
    "       \"F121\" : \"Salle multisport\",\n",
    "       \"F201\" : \"Baignade\",\n",
    "       \"F202\" : \"Port plaisance\",\n",
    "       \"F303\" : \"Cinéma\",\n",
    "       \"G102\" : \"Hotels\",\n",
    "       \"G103\" : \"Camping\",\n",
    "       \"G104\" : \"Info touristique\"       \n",
    "      }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-16-df7fe7adaefa>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1[\"EQ\"] = df1[\"TYPEQU\"].replace(dic)\n"
     ]
    }
   ],
   "source": [
    "df1[\"EQ\"] = df1[\"TYPEQU\"].replace(dic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='count', ylabel='EQ'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(25,8))\n",
    "sns.countplot(y = \"EQ\", data = df1, orient = \"v\", order = df1['EQ'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='QUALITE_XY', ylabel='count'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(25,8))\n",
    "sns.countplot(x=\"QUALITE_XY\", data = df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>DEP</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>971</th>\n",
       "      <th>972</th>\n",
       "      <th>973</th>\n",
       "      <th>974</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EQ</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",
       "      <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>Alimentation</th>\n",
       "      <td>341</td>\n",
       "      <td>293</td>\n",
       "      <td>277</td>\n",
       "      <td>221</td>\n",
       "      <td>177</td>\n",
       "      <td>1180</td>\n",
       "      <td>281</td>\n",
       "      <td>161</td>\n",
       "      <td>151</td>\n",
       "      <td>207</td>\n",
       "      <td>...</td>\n",
       "      <td>57</td>\n",
       "      <td>847</td>\n",
       "      <td>1103</td>\n",
       "      <td>1795</td>\n",
       "      <td>1119</td>\n",
       "      <td>780</td>\n",
       "      <td>1218</td>\n",
       "      <td>608</td>\n",
       "      <td>393</td>\n",
       "      <td>1004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aéroport</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baignade</th>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banques</th>\n",
       "      <td>277</td>\n",
       "      <td>167</td>\n",
       "      <td>173</td>\n",
       "      <td>90</td>\n",
       "      <td>77</td>\n",
       "      <td>607</td>\n",
       "      <td>154</td>\n",
       "      <td>105</td>\n",
       "      <td>78</td>\n",
       "      <td>127</td>\n",
       "      <td>...</td>\n",
       "      <td>69</td>\n",
       "      <td>518</td>\n",
       "      <td>773</td>\n",
       "      <td>408</td>\n",
       "      <td>513</td>\n",
       "      <td>381</td>\n",
       "      <td>159</td>\n",
       "      <td>148</td>\n",
       "      <td>38</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boulangerie</th>\n",
       "      <td>407</td>\n",
       "      <td>342</td>\n",
       "      <td>302</td>\n",
       "      <td>182</td>\n",
       "      <td>173</td>\n",
       "      <td>981</td>\n",
       "      <td>328</td>\n",
       "      <td>233</td>\n",
       "      <td>165</td>\n",
       "      <td>219</td>\n",
       "      <td>...</td>\n",
       "      <td>104</td>\n",
       "      <td>658</td>\n",
       "      <td>853</td>\n",
       "      <td>1001</td>\n",
       "      <td>739</td>\n",
       "      <td>637</td>\n",
       "      <td>537</td>\n",
       "      <td>366</td>\n",
       "      <td>102</td>\n",
       "      <td>665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Camping</th>\n",
       "      <td>65</td>\n",
       "      <td>25</td>\n",
       "      <td>71</td>\n",
       "      <td>128</td>\n",
       "      <td>126</td>\n",
       "      <td>76</td>\n",
       "      <td>290</td>\n",
       "      <td>35</td>\n",
       "      <td>72</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cinéma</th>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>41</td>\n",
       "      <td>30</td>\n",
       "      <td>36</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Collège ou lycée</th>\n",
       "      <td>102</td>\n",
       "      <td>123</td>\n",
       "      <td>74</td>\n",
       "      <td>39</td>\n",
       "      <td>26</td>\n",
       "      <td>179</td>\n",
       "      <td>64</td>\n",
       "      <td>62</td>\n",
       "      <td>34</td>\n",
       "      <td>57</td>\n",
       "      <td>...</td>\n",
       "      <td>27</td>\n",
       "      <td>205</td>\n",
       "      <td>241</td>\n",
       "      <td>299</td>\n",
       "      <td>253</td>\n",
       "      <td>237</td>\n",
       "      <td>100</td>\n",
       "      <td>99</td>\n",
       "      <td>61</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crèche</th>\n",
       "      <td>100</td>\n",
       "      <td>45</td>\n",
       "      <td>43</td>\n",
       "      <td>47</td>\n",
       "      <td>39</td>\n",
       "      <td>217</td>\n",
       "      <td>61</td>\n",
       "      <td>32</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>282</td>\n",
       "      <td>584</td>\n",
       "      <td>344</td>\n",
       "      <td>385</td>\n",
       "      <td>228</td>\n",
       "      <td>59</td>\n",
       "      <td>68</td>\n",
       "      <td>31</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ecole mat et primaire</th>\n",
       "      <td>512</td>\n",
       "      <td>505</td>\n",
       "      <td>357</td>\n",
       "      <td>162</td>\n",
       "      <td>143</td>\n",
       "      <td>622</td>\n",
       "      <td>378</td>\n",
       "      <td>214</td>\n",
       "      <td>174</td>\n",
       "      <td>275</td>\n",
       "      <td>...</td>\n",
       "      <td>149</td>\n",
       "      <td>864</td>\n",
       "      <td>671</td>\n",
       "      <td>898</td>\n",
       "      <td>674</td>\n",
       "      <td>823</td>\n",
       "      <td>299</td>\n",
       "      <td>257</td>\n",
       "      <td>175</td>\n",
       "      <td>538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ecole sup</th>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>42</td>\n",
       "      <td>32</td>\n",
       "      <td>28</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Etab hospi</th>\n",
       "      <td>50</td>\n",
       "      <td>45</td>\n",
       "      <td>28</td>\n",
       "      <td>26</td>\n",
       "      <td>29</td>\n",
       "      <td>95</td>\n",
       "      <td>48</td>\n",
       "      <td>29</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>80</td>\n",
       "      <td>96</td>\n",
       "      <td>73</td>\n",
       "      <td>61</td>\n",
       "      <td>65</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gare</th>\n",
       "      <td>36</td>\n",
       "      <td>43</td>\n",
       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>69</td>\n",
       "      <td>43</td>\n",
       "      <td>30</td>\n",
       "      <td>34</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gare nationale</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hotels</th>\n",
       "      <td>163</td>\n",
       "      <td>70</td>\n",
       "      <td>118</td>\n",
       "      <td>150</td>\n",
       "      <td>175</td>\n",
       "      <td>630</td>\n",
       "      <td>156</td>\n",
       "      <td>51</td>\n",
       "      <td>60</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>22</td>\n",
       "      <td>93</td>\n",
       "      <td>184</td>\n",
       "      <td>107</td>\n",
       "      <td>99</td>\n",
       "      <td>107</td>\n",
       "      <td>45</td>\n",
       "      <td>46</td>\n",
       "      <td>29</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Info touristique</th>\n",
       "      <td>82</td>\n",
       "      <td>37</td>\n",
       "      <td>34</td>\n",
       "      <td>66</td>\n",
       "      <td>65</td>\n",
       "      <td>254</td>\n",
       "      <td>91</td>\n",
       "      <td>33</td>\n",
       "      <td>39</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>119</td>\n",
       "      <td>436</td>\n",
       "      <td>223</td>\n",
       "      <td>324</td>\n",
       "      <td>91</td>\n",
       "      <td>127</td>\n",
       "      <td>108</td>\n",
       "      <td>49</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parcours sportif/santé</th>\n",
       "      <td>25</td>\n",
       "      <td>34</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pharmacie</th>\n",
       "      <td>182</td>\n",
       "      <td>193</td>\n",
       "      <td>158</td>\n",
       "      <td>71</td>\n",
       "      <td>68</td>\n",
       "      <td>535</td>\n",
       "      <td>113</td>\n",
       "      <td>123</td>\n",
       "      <td>58</td>\n",
       "      <td>114</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>395</td>\n",
       "      <td>571</td>\n",
       "      <td>461</td>\n",
       "      <td>467</td>\n",
       "      <td>398</td>\n",
       "      <td>178</td>\n",
       "      <td>181</td>\n",
       "      <td>58</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Police ou gendarmerie</th>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>31</td>\n",
       "      <td>29</td>\n",
       "      <td>21</td>\n",
       "      <td>44</td>\n",
       "      <td>37</td>\n",
       "      <td>34</td>\n",
       "      <td>22</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>41</td>\n",
       "      <td>32</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>17</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Port plaisance</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Restaurants</th>\n",
       "      <td>1702</td>\n",
       "      <td>983</td>\n",
       "      <td>958</td>\n",
       "      <td>994</td>\n",
       "      <td>948</td>\n",
       "      <td>7622</td>\n",
       "      <td>1453</td>\n",
       "      <td>665</td>\n",
       "      <td>653</td>\n",
       "      <td>801</td>\n",
       "      <td>...</td>\n",
       "      <td>395</td>\n",
       "      <td>3363</td>\n",
       "      <td>5819</td>\n",
       "      <td>6094</td>\n",
       "      <td>4207</td>\n",
       "      <td>3316</td>\n",
       "      <td>3331</td>\n",
       "      <td>2279</td>\n",
       "      <td>987</td>\n",
       "      <td>3934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Resto U</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Résidence U</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salle multisport</th>\n",
       "      <td>146</td>\n",
       "      <td>124</td>\n",
       "      <td>92</td>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "      <td>100</td>\n",
       "      <td>56</td>\n",
       "      <td>112</td>\n",
       "      <td>38</td>\n",
       "      <td>73</td>\n",
       "      <td>...</td>\n",
       "      <td>42</td>\n",
       "      <td>242</td>\n",
       "      <td>244</td>\n",
       "      <td>232</td>\n",
       "      <td>233</td>\n",
       "      <td>215</td>\n",
       "      <td>35</td>\n",
       "      <td>25</td>\n",
       "      <td>42</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24 rows × 98 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "DEP                      1    2    3    4    5     6     7    8    9    10   \\\n",
       "EQ                                                                            \n",
       "Alimentation             341  293  277  221  177  1180   281  161  151  207   \n",
       "Aéroport                   0    0    0    0    0     2     0    0    0    1   \n",
       "Baignade                  11    2    2   11   25     0    22    7    5    7   \n",
       "Banques                  277  167  173   90   77   607   154  105   78  127   \n",
       "Boulangerie              407  342  302  182  173   981   328  233  165  219   \n",
       "Camping                   65   25   71  128  126    76   290   35   72   20   \n",
       "Cinéma                    19   15    8   14   23    45    19    5   11    4   \n",
       "Collège ou lycée         102  123   74   39   26   179    64   62   34   57   \n",
       "Crèche                   100   45   43   47   39   217    61   32   35   31   \n",
       "Ecole mat et primaire    512  505  357  162  143   622   378  214  174  275   \n",
       "Ecole sup                  9    6   12    3    3    38     0    3    3    8   \n",
       "Etab hospi                50   45   28   26   29    95    48   29   12   28   \n",
       "Gare                      36   43   18   29   10    66     0   25   12    4   \n",
       "Gare nationale             2    0    1    0    0     3     0    1    0    1   \n",
       "Hotels                   163   70  118  150  175   630   156   51   60   75   \n",
       "Info touristique          82   37   34   66   65   254    91   33   39   34   \n",
       "Parcours sportif/santé    25   34   16   14   14    10    17   10   11   13   \n",
       "Pharmacie                182  193  158   71   68   535   113  123   58  114   \n",
       "Police ou gendarmerie     44   44   31   29   21    44    37   34   22   27   \n",
       "Port plaisance             4    4    0    0   16    18     1    0    0    2   \n",
       "Restaurants             1702  983  958  994  948  7622  1453  665  653  801   \n",
       "Resto U                    0    0    1    0    0     5     0    2    0    1   \n",
       "Résidence U                1    3    2    0    0     9     0    3    0    2   \n",
       "Salle multisport         146  124   92   27   37   100    56  112   38   73   \n",
       "\n",
       "DEP                     ...  90    91    92    93    94    95    971   972  \\\n",
       "EQ                      ...                                                  \n",
       "Alimentation            ...   57   847  1103  1795  1119   780  1218   608   \n",
       "Aéroport                ...    0     0     0     1     1     1     1     1   \n",
       "Baignade                ...    0     0     0     0     0     1     6     2   \n",
       "Banques                 ...   69   518   773   408   513   381   159   148   \n",
       "Boulangerie             ...  104   658   853  1001   739   637   537   366   \n",
       "Camping                 ...    3    19     0     1     1     7     0     0   \n",
       "Cinéma                  ...    2    31    41    30    36    27     6     4   \n",
       "Collège ou lycée        ...   27   205   241   299   253   237   100    99   \n",
       "Crèche                  ...   20   282   584   344   385   228    59    68   \n",
       "Ecole mat et primaire   ...  149   864   671   898   674   823   299   257   \n",
       "Ecole sup               ...    3    42    32    28    31    21     9     7   \n",
       "Etab hospi              ...    6    80    96    73    61    65    40    35   \n",
       "Gare                    ...   10    69    43    30    34    68     0     0   \n",
       "Gare nationale          ...    1     1     0     1     0     0     0     0   \n",
       "Hotels                  ...   22    93   184   107    99   107    45    46   \n",
       "Info touristique        ...    3   119   436   223   324    91   127   108   \n",
       "Parcours sportif/santé  ...    7    35     7     7    12    23    13     9   \n",
       "Pharmacie               ...   56   395   571   461   467   398   178   181   \n",
       "Police ou gendarmerie   ...    9    41    32     5    27    32    22    23   \n",
       "Port plaisance          ...    0     0     2     0     3     1     2     1   \n",
       "Restaurants             ...  395  3363  5819  6094  4207  3316  3331  2279   \n",
       "Resto U                 ...    2    10    10     6     5     6     1     1   \n",
       "Résidence U             ...    4    12    13    13     8     6     4     1   \n",
       "Salle multisport        ...   42   242   244   232   233   215    35    25   \n",
       "\n",
       "DEP                     973   974  \n",
       "EQ                                 \n",
       "Alimentation            393  1004  \n",
       "Aéroport                  4     2  \n",
       "Baignade                  2     8  \n",
       "Banques                  38   200  \n",
       "Boulangerie             102   665  \n",
       "Camping                   0     0  \n",
       "Cinéma                    4    11  \n",
       "Collège ou lycée         61   158  \n",
       "Crèche                   31   127  \n",
       "Ecole mat et primaire   175   538  \n",
       "Ecole sup                 5    17  \n",
       "Etab hospi               16    45  \n",
       "Gare                      0     0  \n",
       "Gare nationale            0     0  \n",
       "Hotels                   29   109  \n",
       "Info touristique         49   112  \n",
       "Parcours sportif/santé    2    43  \n",
       "Pharmacie                58   274  \n",
       "Police ou gendarmerie    17    35  \n",
       "Port plaisance            2     6  \n",
       "Restaurants             987  3934  \n",
       "Resto U                   2     4  \n",
       "Résidence U               3     7  \n",
       "Salle multisport         42    76  \n",
       "\n",
       "[24 rows x 98 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df1[\"EQ\"],df[\"DEP\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 536612 entries, 0 to 2371665\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Non-Null Count   Dtype  \n",
      "---  ------           --------------   -----  \n",
      " 0   DEP              536612 non-null  int32  \n",
      " 1   DEPCOM           536612 non-null  object \n",
      " 2   AN               536612 non-null  int64  \n",
      " 3   TYPEQU           536612 non-null  object \n",
      " 4   QUALITE_XY       536612 non-null  object \n",
      " 5   lat              536612 non-null  float64\n",
      " 6   lon              536612 non-null  float64\n",
      " 7   code_iris_clean  536612 non-null  object \n",
      " 8   EQFIN            536612 non-null  bool   \n",
      " 9   EQ               536612 non-null  object \n",
      "dtypes: bool(1), float64(2), int32(1), int64(1), object(5)\n",
      "memory usage: 55.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df1.dropna(axis = 0, how = \"any\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 536612 entries, 0 to 2371665\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Non-Null Count   Dtype  \n",
      "---  ------           --------------   -----  \n",
      " 0   DEP              536612 non-null  int32  \n",
      " 1   DEPCOM           536612 non-null  object \n",
      " 2   AN               536612 non-null  int64  \n",
      " 3   TYPEQU           536612 non-null  object \n",
      " 4   QUALITE_XY       536612 non-null  object \n",
      " 5   lat              536612 non-null  float64\n",
      " 6   lon              536612 non-null  float64\n",
      " 7   code_iris_clean  536612 non-null  object \n",
      " 8   EQFIN            536612 non-null  bool   \n",
      " 9   EQ               536612 non-null  object \n",
      "dtypes: bool(1), float64(2), int32(1), int64(1), object(5)\n",
      "memory usage: 39.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>56.141043</td>\n",
       "      <td>-2.726819</td>\n",
       "      <td>974110111</td>\n",
       "      <td>Crèche</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2371662</th>\n",
       "      <td>974</td>\n",
       "      <td>97415</td>\n",
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       "      <td>55.916643</td>\n",
       "      <td>-2.990195</td>\n",
       "      <td>974151501</td>\n",
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       "      <td>-2.940308</td>\n",
       "      <td>974151702</td>\n",
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       "      <td>97414</td>\n",
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       "      <td>55.754464</td>\n",
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       "      <td>974140107</td>\n",
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      ],
      "text/plain": [
       "         DEP DEPCOM    AN TYPEQU QUALITE_XY        lat       lon  \\\n",
       "0          1  01053  2019   D106   Mauvaise  46.202783  5.219245   \n",
       "1          1  01053  2019   D101   Mauvaise  46.202783  5.219245   \n",
       "4          1  01034  2019   D101      Bonne  45.761208  5.684976   \n",
       "5          1  01034  2019   D102      Bonne  45.761208  5.684976   \n",
       "6          1  01034  2019   D106      Bonne  45.761208  5.684976   \n",
       "...      ...    ...   ...    ...        ...        ...       ...   \n",
       "2371661  974  97411  2019   D502      Bonne  56.141043 -2.726819   \n",
       "2371662  974  97415  2019   D502   Mauvaise  55.916643 -2.990195   \n",
       "2371663  974  97415  2019   D502   Mauvaise  55.969413 -2.940308   \n",
       "2371664  974  97414  2019   D502   Mauvaise  55.754464 -2.701898   \n",
       "2371665  974  97411  2019   D502   Mauvaise  56.143694 -2.742757   \n",
       "\n",
       "        code_iris_clean          EQ  \n",
       "0             010530402  Etab hospi  \n",
       "1             010530402  Etab hospi  \n",
       "4             010340102  Etab hospi  \n",
       "5             010340102  Etab hospi  \n",
       "6             010340102  Etab hospi  \n",
       "...                 ...         ...  \n",
       "2371661       974110111      Crèche  \n",
       "2371662       974151501      Crèche  \n",
       "2371663       974151702      Crèche  \n",
       "2371664       974140107      Crèche  \n",
       "2371665       974110102      Crèche  \n",
       "\n",
       "[536612 rows x 9 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.drop([\"EQFIN\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.to_csv(\"05 - equipements_gps_clean.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df1.groupby(['EQ', 'code_iris_clean'])['TYPEQU'].count().reset_index().rename(columns = {'TYPEQU': 'nb_equip'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "EQ               Alimentation  Aéroport  Baignade  Banques  Boulangerie  \\\n",
       "code_iris_clean                                                           \n",
       "010010000                   0         0         0        0            1   \n",
       "010040101                   6         0         0        1            3   \n",
       "010040102                   4         0         0        0            2   \n",
       "010040201                   2         0         0        5            4   \n",
       "010040202                   3         0         0        6            1   \n",
       "\n",
       "EQ               Camping  Cinéma  Collège ou lycée  Crèche  \\\n",
       "code_iris_clean                                              \n",
       "010010000              0       0                 0       0   \n",
       "010040101              0       0                 1       0   \n",
       "010040102              0       0                 0       0   \n",
       "010040201              0       1                 2       1   \n",
       "010040202              0       0                 1       1   \n",
       "\n",
       "EQ               Ecole mat et primaire  ...  Hotels  Info touristique  \\\n",
       "code_iris_clean                         ...                             \n",
       "010010000                            1  ...       0                 0   \n",
       "010040101                            0  ...       0                 0   \n",
       "010040102                            3  ...       0                 0   \n",
       "010040201                            4  ...       0                 0   \n",
       "010040202                            0  ...       1                 1   \n",
       "\n",
       "EQ               Parcours sportif/santé  Pharmacie  Police ou gendarmerie  \\\n",
       "code_iris_clean                                                             \n",
       "010010000                             0          0                      0   \n",
       "010040101                             0          1                      1   \n",
       "010040102                             0          1                      0   \n",
       "010040201                             0          4                      0   \n",
       "010040202                             1          0                      0   \n",
       "\n",
       "EQ               Port plaisance  Restaurants  Resto U  Résidence U  \\\n",
       "code_iris_clean                                                      \n",
       "010010000                     0            1        0            0   \n",
       "010040101                     0           10        0            0   \n",
       "010040102                     0           18        0            0   \n",
       "010040201                     0           12        0            0   \n",
       "010040202                     0            5        0            0   \n",
       "\n",
       "EQ               Salle multisport  \n",
       "code_iris_clean                    \n",
       "010010000                       0  \n",
       "010040101                       3  \n",
       "010040102                       0  \n",
       "010040201                       0  \n",
       "010040202                       2  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.crosstab(df2['code_iris_clean'], df2['EQ'], values = df2['nb_equip'], aggfunc = 'sum')\n",
    "df3 = df3.fillna(0)\n",
    "df3 = df3.astype(int)\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3.to_csv('./databases/inter/03A - equipements_nb_clean.csv', encoding = 'UTF-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
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