01_TIDE_session.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3dd2f27e",
   "metadata": {},
   "source": [
    "# Conférences Python Master TIDE #1\n",
    "\n",
    "## Librairie pandas\n",
    "\n",
    "Dataset des communes françaises : `correspondance-code-insee-code-postal.csv`\n",
    "\n",
    "0) Import\n",
    "- Importer la librairie pandas\n",
    "- Utiliser l'option suivante\n",
    "```python\n",
    "pd.set_option(\"display.min_rows\", 16)\n",
    "```\n",
    "\n",
    "1) Ecrire une fonction qui charge le fichier CSV\n",
    "- Uniquement les 11 premières colonnes\n",
    "- Utiliser la colonne \"Code INSEE\" comme index\n",
    "- Trier selon l'index\n",
    "- Pour vérifier, exécuter les tests unitaires\n",
    "\n",
    "```python\n",
    "def lecture_geo():\n",
    "    pass\n",
    "```\n",
    "\n",
    "2) Questions\n",
    "- Combien y a-t-il de départements ?\n",
    "- Quelle commune est la plus haute ?\n",
    "- Quelle commune est la plus peuplée ?\n",
    "- Quelle commune a le nom le plus court ?\n",
    "- Quelle commune a le nom le plus long ?\n",
    "- Quel est le nom de commune le plus fréquent ?\n",
    "- Ecrire une fonction qui recherche les communes se terminant par un motif du type \"-mot1-mot2\" (par exemple \"-SUR-MER\")\n",
    "```python\n",
    "def selection(mot1, mot2):\n",
    "    pass\n",
    "```\n",
    "- Quelles villes contiennent deux \"Z\" (accolés, séparés par une seule lettre, séparés par un nombre quelconque de lettres, séparés par au moins une lettre) ?\n",
    "- Quelles sont les dix terminaisons des noms de communes du type \"-mot1-mot2\" les plus fréquentes ?\n",
    "\n",
    "3) Transformations\n",
    "- Passer la colonne \"Superficie\" en km²\n",
    "- Passer la colonne \"Altitude Moyenne\" en entiers\n",
    "- Modifier la colonne \"Statut\" en categorie ordonnée\n",
    "\n",
    "4) Géographie\n",
    "- Créer une colonne \"CP Ville\" à partir du code postal et du nom de chaque commune\n",
    "- Extraire la latitude et la longitude de la colonne \"geo_point_2d\"\n",
    "- Ecrire une fonction qui retourne le \"CP Ville\" de la commune la plus proche d'une latitude et d'une longitude\n",
    "```python\n",
    "def ville(lat, long):\n",
    "    pass\n",
    "```\n",
    "- Ecrire une fonction qui transforme des coordonnées GPS (en degrés, minutes et secondes) en coordonnées décimales\n",
    "```python\n",
    "def gps2dec(deg, mn, sec):\n",
    "    pass\n",
    "```\n",
    "- Choisir une petite commune française sur Wikipédia et vérifier qu'elle est trouvée à partir de ces coordonnnées GPS en utilisant les 2 fonctions définies\n",
    "\n",
    "5) Géométrie\n",
    "- Décoder les données JSON de la colonne \"geo_shape\"\n",
    "- Donner le décompte des différentes valeurs associées à la clé \"type\"\n",
    "- Donner le décompte des longueurs des listes associées à la clé \"coordinates\"\n",
    "- Quelles sont les \"CP Ville\" des communes de type \"Polygon\" et dont la longueur des listes accédées avec la clé \"coordinates\" vaut 2 ?\n",
    "\n",
    "6) Graphiques\n",
    "- Afficher un histogramme avec les longueurs des noms des communes\n",
    "- Afficher un graphique de type scatterplot à partir des coordonnées des communes\n",
    "- Trouver une condition pour limiter le dataset à la France métropolitaine et afficher un graphique\n",
    "- Mettre toutes les communes à moins de 1000m en bleu et celle à plus de 1000m en rouge\n",
    "- Afficher un graphique de type scatterplot dans lequel les communes se terminant par un motif du type \"-mot1-mot2\" sont mis dans une autre couleur\n",
    "- Afficher un graphique de type scatterplot dans lequel la couleur utilisée dépend de la colonne \"Altitude moyenne\" selon la colormap \"Spectral_r\"\n",
    "- Afficher une carte de France métropolitaine avec les préfectures de région et leur nom, en corrigeant celles qui sont des arrondissements de Paris, Lyon ou Marseille.\n",
    "\n",
    "&copy; Francis Wolinski 2024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3e510141-7b75-42d1-be08-06c81442f881",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f5735be-7039-4fd6-85d0-f43aca12d827",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lecture_geo():\n",
    "    df = pd.read_csv(\"data/correspondance-code-insee-code-postal.csv\",\n",
    "                     sep=\";\",\n",
    "                     index_col=\"Code INSEE\",\n",
    "                     usecols=range(11)\n",
    "                    )\n",
    "    df = df.sort_index()\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "baf50956-a074-40f9-83c9-ed77f0e3b920",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lecture_geo():\n",
    "    df = (pd.read_csv(\"data/correspondance-code-insee-code-postal.csv\",\n",
    "                     sep=\";\",\n",
    "                     index_col=\"Code INSEE\",\n",
    "                     usecols=range(11)\n",
    "                    )\n",
    "          .sort_index()\n",
    "         )\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5a01a9d1-2de5-4ca8-99f4-e684daab2baa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = lecture_geo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "27005016-4b44-4c19-92da-36510a909215",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 36742 entries, 01001 to 97617\n",
      "Data columns (total 10 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Code Postal       36742 non-null  object \n",
      " 1   Commune           36742 non-null  object \n",
      " 2   Département       36742 non-null  object \n",
      " 3   Région            36742 non-null  object \n",
      " 4   Statut            36742 non-null  object \n",
      " 5   Altitude Moyenne  36742 non-null  float64\n",
      " 6   Superficie        36742 non-null  float64\n",
      " 7   Population        36742 non-null  float64\n",
      " 8   geo_point_2d      36742 non-null  object \n",
      " 9   geo_shape         36742 non-null  object \n",
      "dtypes: float64(3), object(7)\n",
      "memory usage: 3.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "04aa985d-73f0-4de1-83b7-f44ff7693668",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Département\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5b971321-d97a-4bcf-bd59-a7332552c3ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Code Postal          6101\n",
       "Commune             34130\n",
       "Département           101\n",
       "Région                 27\n",
       "Statut                  6\n",
       "Altitude Moyenne     1652\n",
       "Superficie           5246\n",
       "Population            509\n",
       "geo_point_2d        36742\n",
       "geo_shape           36742\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "79222afa-e946-4cb3-b70a-a445380a50fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Code INSEE\n",
       "73047    BONNEVAL-SUR-ARC\n",
       "Name: Commune, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"Altitude Moyenne\"]==df[\"Altitude Moyenne\"].max(), \"Commune\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "edbb4530-9d24-4c7e-bfde-16397a445406",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'BONNEVAL-SUR-ARC'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"Altitude Moyenne\"].idxmax(), \"Commune\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "213b1b76-e9bf-4dfd-b82e-9cc12048ce83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'TOULOUSE'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"Population\"].idxmax(), \"Commune\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a87c6778-9598-47f3-b182-24e0ba03b785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Y'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"Commune\"].str.len().idxmin(), \"Commune\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fc1f1a4c-ad12-41f3-a8a9-886d061b0802",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'SAINT-REMY-EN-BOUZEMONT-SAINT-GENEST-ET-ISSON'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"Commune\"].str.len().idxmax(), \"Commune\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "96ece45e-1070-4ecf-824c-33237ec97fa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'SAINTE-COLOMBE'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Commune\"].value_counts().index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "699ede05-1eab-4882-937b-9d273cbd1c8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.13 ms ± 432 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit df[\"Commune\"].value_counts().index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "91056a33-0c03-4e2c-97ce-bf4925e34c32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.32 ms ± 736 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit df[\"Commune\"].value_counts().idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "334c3402-6cef-44b2-baa9-e936280ec04a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def selection(mot1, mot2):\n",
    "    pattern = f\"-{mot1}-{mot2}\"\n",
    "    selection = df.loc[df[\"Commune\"].str.endswith(pattern)]\n",
    "    return selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fabdfd99-c65b-4078-9905-a5db6969772a",
   "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>Code Postal</th>\n",
       "      <th>Commune</th>\n",
       "      <th>Département</th>\n",
       "      <th>Région</th>\n",
       "      <th>Statut</th>\n",
       "      <th>Altitude Moyenne</th>\n",
       "      <th>Superficie</th>\n",
       "      <th>Population</th>\n",
       "      <th>geo_point_2d</th>\n",
       "      <th>geo_shape</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Code INSEE</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>06011</th>\n",
       "      <td>06310</td>\n",
       "      <td>BEAULIEU-SUR-MER</td>\n",
       "      <td>ALPES-MARITIMES</td>\n",
       "      <td>PROVENCE-ALPES-COTE D'AZUR</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>36.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>43.7079039397, 7.33256934881</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[7.326789...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06027</th>\n",
       "      <td>06800</td>\n",
       "      <td>CAGNES-SUR-MER</td>\n",
       "      <td>ALPES-MARITIMES</td>\n",
       "      <td>PROVENCE-ALPES-COTE D'AZUR</td>\n",
       "      <td>Chef-lieu canton</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>43.6715162078, 7.15275703379</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[7.145493...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06138</th>\n",
       "      <td>06590</td>\n",
       "      <td>THEOULE-SUR-MER</td>\n",
       "      <td>ALPES-MARITIMES</td>\n",
       "      <td>PROVENCE-ALPES-COTE D'AZUR</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>130.0</td>\n",
       "      <td>1072.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>43.4985345439, 6.92965248383</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[6.933726...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06159</th>\n",
       "      <td>06230</td>\n",
       "      <td>VILLEFRANCHE-SUR-MER</td>\n",
       "      <td>ALPES-MARITIMES</td>\n",
       "      <td>PROVENCE-ALPES-COTE D'AZUR</td>\n",
       "      <td>Chef-lieu canton</td>\n",
       "      <td>231.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>43.7122153121, 7.32225410533</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[7.308745...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13039</th>\n",
       "      <td>13270</td>\n",
       "      <td>FOS-SUR-MER</td>\n",
       "      <td>BOUCHES-DU-RHONE</td>\n",
       "      <td>PROVENCE-ALPES-COTE D'AZUR</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8868.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>43.4556812107, 4.90452133912</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[4.972215...</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85166</th>\n",
       "      <td>85340</td>\n",
       "      <td>OLONNE-SUR-MER</td>\n",
       "      <td>VENDEE</td>\n",
       "      <td>PAYS DE LA LOIRE</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4608.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>46.5413300559, -1.78813984621</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[-1.77321...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85201</th>\n",
       "      <td>85540</td>\n",
       "      <td>SAINT-BENOIST-SUR-MER</td>\n",
       "      <td>VENDEE</td>\n",
       "      <td>PAYS DE LA LOIRE</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1556.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>46.4266286403, -1.3399129604</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[-1.31773...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85243</th>\n",
       "      <td>85470</td>\n",
       "      <td>BREM-SUR-MER</td>\n",
       "      <td>VENDEE</td>\n",
       "      <td>PAYS DE LA LOIRE</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1616.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>46.6118566989, -1.81003917923</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[-1.81551...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85294</th>\n",
       "      <td>85360</td>\n",
       "      <td>LA TRANCHE-SUR-MER</td>\n",
       "      <td>VENDEE</td>\n",
       "      <td>PAYS DE LA LOIRE</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2103.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>46.3564601605, -1.43136322126</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[-1.35516...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85307</th>\n",
       "      <td>85460</td>\n",
       "      <td>LA FAUTE-SUR-MER</td>\n",
       "      <td>VENDEE</td>\n",
       "      <td>PAYS DE LA LOIRE</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>4.0</td>\n",
       "      <td>726.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>46.3199919131, -1.31487049579</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[-1.30340...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Code Postal                Commune       Département  \\\n",
       "Code INSEE                                                        \n",
       "06011            06310       BEAULIEU-SUR-MER   ALPES-MARITIMES   \n",
       "06027            06800         CAGNES-SUR-MER   ALPES-MARITIMES   \n",
       "06138            06590        THEOULE-SUR-MER   ALPES-MARITIMES   \n",
       "06159            06230   VILLEFRANCHE-SUR-MER   ALPES-MARITIMES   \n",
       "13039            13270            FOS-SUR-MER  BOUCHES-DU-RHONE   \n",
       "...                ...                    ...               ...   \n",
       "85166            85340         OLONNE-SUR-MER            VENDEE   \n",
       "85201            85540  SAINT-BENOIST-SUR-MER            VENDEE   \n",
       "85243            85470           BREM-SUR-MER            VENDEE   \n",
       "85294            85360     LA TRANCHE-SUR-MER            VENDEE   \n",
       "85307            85460       LA FAUTE-SUR-MER            VENDEE   \n",
       "\n",
       "                                Région            Statut  Altitude Moyenne  \\\n",
       "Code INSEE                                                                   \n",
       "06011       PROVENCE-ALPES-COTE D'AZUR    Commune simple              36.0   \n",
       "06027       PROVENCE-ALPES-COTE D'AZUR  Chef-lieu canton              64.0   \n",
       "06138       PROVENCE-ALPES-COTE D'AZUR    Commune simple             130.0   \n",
       "06159       PROVENCE-ALPES-COTE D'AZUR  Chef-lieu canton             231.0   \n",
       "13039       PROVENCE-ALPES-COTE D'AZUR    Commune simple               5.0   \n",
       "...                                ...               ...               ...   \n",
       "85166                 PAYS DE LA LOIRE    Commune simple              12.0   \n",
       "85201                 PAYS DE LA LOIRE    Commune simple               9.0   \n",
       "85243                 PAYS DE LA LOIRE    Commune simple              27.0   \n",
       "85294                 PAYS DE LA LOIRE    Commune simple               5.0   \n",
       "85307                 PAYS DE LA LOIRE    Commune simple               4.0   \n",
       "\n",
       "            Superficie  Population                   geo_point_2d  \\\n",
       "Code INSEE                                                          \n",
       "06011            108.0         3.7   43.7079039397, 7.33256934881   \n",
       "06027           1821.0        48.0   43.6715162078, 7.15275703379   \n",
       "06138           1072.0         1.6   43.4985345439, 6.92965248383   \n",
       "06159            492.0         5.8   43.7122153121, 7.32225410533   \n",
       "13039           8868.0        15.5   43.4556812107, 4.90452133912   \n",
       "...                ...         ...                            ...   \n",
       "85166           4608.0        13.3  46.5413300559, -1.78813984621   \n",
       "85201           1556.0         0.3   46.4266286403, -1.3399129604   \n",
       "85243           1616.0         2.6  46.6118566989, -1.81003917923   \n",
       "85294           2103.0         2.7  46.3564601605, -1.43136322126   \n",
       "85307            726.0         0.9  46.3199919131, -1.31487049579   \n",
       "\n",
       "                                                    geo_shape  \n",
       "Code INSEE                                                     \n",
       "06011       {\"type\": \"Polygon\", \"coordinates\": [[[7.326789...  \n",
       "06027       {\"type\": \"Polygon\", \"coordinates\": [[[7.145493...  \n",
       "06138       {\"type\": \"Polygon\", \"coordinates\": [[[6.933726...  \n",
       "06159       {\"type\": \"Polygon\", \"coordinates\": [[[7.308745...  \n",
       "13039       {\"type\": \"Polygon\", \"coordinates\": [[[4.972215...  \n",
       "...                                                       ...  \n",
       "85166       {\"type\": \"Polygon\", \"coordinates\": [[[-1.77321...  \n",
       "85201       {\"type\": \"Polygon\", \"coordinates\": [[[-1.31773...  \n",
       "85243       {\"type\": \"Polygon\", \"coordinates\": [[[-1.81551...  \n",
       "85294       {\"type\": \"Polygon\", \"coordinates\": [[[-1.35516...  \n",
       "85307       {\"type\": \"Polygon\", \"coordinates\": [[[-1.30340...  \n",
       "\n",
       "[100 rows x 10 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selection(\"SUR\", \"MER\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e9ea5213-90a6-4a8c-a3bb-3988d0764fe5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Code INSEE\n",
       "2A027              AZZANA\n",
       "2A099             COZZANO\n",
       "2A118             FOZZANO\n",
       "2A259               REZZA\n",
       "2A336    VALLE-DI-MEZZANA\n",
       "2B143        LINGUIZZETTA\n",
       "2B147               LOZZI\n",
       "2B149       LUGO-DI-NAZZA\n",
       "2B157             MAZZOLA\n",
       "2B164    MONACIA-D'OREZZA\n",
       "2B216            PIAZZALI\n",
       "2B217            PIAZZOLE\n",
       "2B222        PIE-D'OREZZA\n",
       "2B236     POGGIO-DI-NAZZA\n",
       "2B242      POGGIO-MEZZANA\n",
       "2B291            STAZZONA\n",
       "2B338      VALLE-D'OREZZA\n",
       "2B347             VEZZANI\n",
       "68030       BERGHOLTZZELL\n",
       "Name: Commune, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"Commune\"].str.contains(\"ZZ\"), \"Commune\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bc41fdb6-b911-4386-9e1e-a2d6daae4296",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Code INSEE\n",
       "2A363                   ZOZA\n",
       "43261               VEZEZOUX\n",
       "71174    DEZIZE-LES-MARANGES\n",
       "Name: Commune, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"Commune\"].str.contains(\"Z.Z\"), \"Commune\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "24645d87-7844-4c28-ac73-6a3b1f573424",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Code INSEE\n",
       "07161    MONTPEZAT-SOUS-BAUZON\n",
       "09105       DAUMAZAN-SUR-ARIZE\n",
       "09167           LEZAT-SUR-LEZE\n",
       "11034         BELVEZE-DU-RAZES\n",
       "11228      MAZEROLLES-DU-RAZES\n",
       "                 ...          \n",
       "74311           VIUZ-EN-SALLAZ\n",
       "78337            LIMETZ-VILLEZ\n",
       "88404      ROZIERES-SUR-MOUZON\n",
       "95282               GOUZANGREZ\n",
       "97608                 DZAOUDZI\n",
       "Name: Commune, Length: 67, dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"Commune\"].str.contains(\"Z.*Z\"), \"Commune\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9c6dce41-ccf1-4582-9979-baab4ec23923",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Code INSEE\n",
       "07161       MONTPEZAT-SOUS-BAUZON\n",
       "09105          DAUMAZAN-SUR-ARIZE\n",
       "09167              LEZAT-SUR-LEZE\n",
       "11034            BELVEZE-DU-RAZES\n",
       "11228         MAZEROLLES-DU-RAZES\n",
       "11417          VILLARZEL-DU-RAZES\n",
       "12294           VEZINS-DE-LEVEZOU\n",
       "16378                 TAIZE-AIZIE\n",
       "29008            BEUZEC-CAP-SIZUN\n",
       "29176             PLONEVEZ-PORZAY\n",
       "2A026              AZILONE-AMPAZA\n",
       "2A362                       ZONZA\n",
       "2A363                        ZOZA\n",
       "31245               JUZET-D'IZAUT\n",
       "34069         CAZOULS-LES-BEZIERS\n",
       "34279    SAINT-NAZAIRE-DE-LADAREZ\n",
       "34280      SAINT-NAZAIRE-DE-PEZAN\n",
       "34310          THEZAN-LES-BEZIERS\n",
       "42193             ROZIER-EN-DONZY\n",
       "43131             MAZERAT-AUROUZE\n",
       "43261                    VEZEZOUX\n",
       "45026        BAZOCHES-SUR-LE-BETZ\n",
       "45204          MEZIERES-LEZ-CLERY\n",
       "45245          OUZOUER-SUR-TREZEE\n",
       "47006          ALLEZ-ET-CAZENEUVE\n",
       "54129           CIREY-SUR-VEZOUZE\n",
       "54161         DOMEVRE-SUR-VEZOUZE\n",
       "55024      AZANNES-ET-SOUMAZANNES\n",
       "55178          ERIZE-SAINT-DIZIER\n",
       "56090           INZINZAC-LOCHRIST\n",
       "57433          MAIZIERES-LES-METZ\n",
       "58116                   FLEZ-CUZY\n",
       "59667                  ZERMEZEELE\n",
       "63435           TOURZEL-RONZIERES\n",
       "64063           ARZACQ-ARRAZIGUET\n",
       "64373              MAZERES-LEZONS\n",
       "65020           ARCIZAC-EZ-ANGLES\n",
       "65203               GEZ-EZ-ANGLES\n",
       "67347       OBERMODERN-ZUTZENDORF\n",
       "69052           CHAZAY-D'AZERGUES\n",
       "69229     SAINT-NIZIER-D'AZERGUES\n",
       "71174         DEZIZE-LES-MARANGES\n",
       "74310             VIUZ-LA-CHIESAZ\n",
       "74311              VIUZ-EN-SALLAZ\n",
       "78337               LIMETZ-VILLEZ\n",
       "88404         ROZIERES-SUR-MOUZON\n",
       "95282                  GOUZANGREZ\n",
       "97608                    DZAOUDZI\n",
       "Name: Commune, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"Commune\"].str.contains(\"Z.+Z\"), \"Commune\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d330a885-257f-466d-a32e-1067d23387e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Commune\n",
       "-SUR-MER         88\n",
       "-SUR-SEINE       67\n",
       "-SUR-MARNE       55\n",
       "-LES-BAINS       53\n",
       "-LE-CHATEAU      52\n",
       "-SUR-LOIRE       52\n",
       "-LE-SEC          45\n",
       "-LE-GRAND        43\n",
       "-SUR-MEUSE       42\n",
       "-SAINT-MARTIN    41\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Commune\"].str.extract(\"(-.+-.+)$\", expand=False).value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9d654b4e-2c15-4dde-9747-63c31870c4e4",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Code INSEE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>01001</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01002</th>\n",
       "      <td>DE</td>\n",
       "      <td>VAREY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01004</th>\n",
       "      <td>EN</td>\n",
       "      <td>BUGEY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01005</th>\n",
       "      <td>EN</td>\n",
       "      <td>DOMBES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01006</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97613</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97614</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97615</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97616</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97617</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36742 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0       1\n",
       "Code INSEE             \n",
       "01001       NaN     NaN\n",
       "01002        DE   VAREY\n",
       "01004        EN   BUGEY\n",
       "01005        EN  DOMBES\n",
       "01006       NaN     NaN\n",
       "...         ...     ...\n",
       "97613       NaN     NaN\n",
       "97614       NaN     NaN\n",
       "97615       NaN     NaN\n",
       "97616       NaN     NaN\n",
       "97617       NaN     NaN\n",
       "\n",
       "[36742 rows x 2 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Commune\"].str.extract(\"-(.+)-(.+)$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "89b45228-fe6c-4bdb-bad5-1398282101ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Superficie\"] /= 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "28b32d3a-19b5-40a8-8c5d-98f75668beeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Code INSEE\n",
       "01001    15.65\n",
       "01002     9.12\n",
       "01004    24.48\n",
       "01005    16.05\n",
       "01006     6.02\n",
       "         ...  \n",
       "97613    21.55\n",
       "97614    18.28\n",
       "97615     4.26\n",
       "97616    10.85\n",
       "97617    34.26\n",
       "Name: Superficie, Length: 36742, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Superficie\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7e3d0f60-5c1b-4a14-af53-640a3b2db8e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Code INSEE\n",
       "01001    242\n",
       "01002    483\n",
       "01004    379\n",
       "01005    290\n",
       "01006    589\n",
       "        ... \n",
       "97613     96\n",
       "97614    175\n",
       "97615     52\n",
       "97616    130\n",
       "97617    147\n",
       "Name: Altitude Moyenne, Length: 36742, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Altitude Moyenne\"] = df[\"Altitude Moyenne\"].astype(int)\n",
    "df[\"Altitude Moyenne\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f3ba2571-a57f-400a-b917-a04dc35df6dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Commune simple', 'Chef-lieu canton', 'Sous-préfecture',\n",
       "       'Préfecture', 'Préfecture de région', \"Capitale d'état\"],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Statut\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4d81a863-85a0-4303-92bd-61c440857876",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.79 ms ± 435 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit df.loc[df[\"Statut\"] == 'Commune simple']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "71d7915a-7ba0-424b-8fca-f356913ef47d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 36742 entries, 01001 to 97617\n",
      "Data columns (total 10 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Code Postal       36742 non-null  object \n",
      " 1   Commune           36742 non-null  object \n",
      " 2   Département       36742 non-null  object \n",
      " 3   Région            36742 non-null  object \n",
      " 4   Statut            36742 non-null  object \n",
      " 5   Altitude Moyenne  36742 non-null  int64  \n",
      " 6   Superficie        36742 non-null  float64\n",
      " 7   Population        36742 non-null  float64\n",
      " 8   geo_point_2d      36742 non-null  object \n",
      " 9   geo_shape         36742 non-null  object \n",
      "dtypes: float64(2), int64(1), object(7)\n",
      "memory usage: 4.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ffe0f003-54c9-4907-a695-a6e4e8d9ecf8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 36742 entries, 01001 to 97617\n",
      "Data columns (total 10 columns):\n",
      " #   Column            Non-Null Count  Dtype   \n",
      "---  ------            --------------  -----   \n",
      " 0   Code Postal       36742 non-null  object  \n",
      " 1   Commune           36742 non-null  object  \n",
      " 2   Département       36742 non-null  object  \n",
      " 3   Région            36742 non-null  object  \n",
      " 4   Statut            36742 non-null  category\n",
      " 5   Altitude Moyenne  36742 non-null  int64   \n",
      " 6   Superficie        36742 non-null  float64 \n",
      " 7   Population        36742 non-null  float64 \n",
      " 8   geo_point_2d      36742 non-null  object  \n",
      " 9   geo_shape         36742 non-null  object  \n",
      "dtypes: category(1), float64(2), int64(1), object(6)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "statut_cat = pd.CategoricalDtype(categories=['Commune simple', 'Chef-lieu canton', 'Sous-préfecture',\n",
    "                                             'Préfecture', 'Préfecture de région', \"Capitale d'état\"],\n",
    "                                 ordered=True)\n",
    "df[\"Statut\"] = df[\"Statut\"].astype(statut_cat)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "de6e4ac4-5788-4059-aadc-e47f5fa32c84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Code INSEE\n",
       "01001    0\n",
       "01002    0\n",
       "01004    1\n",
       "01005    0\n",
       "01006    0\n",
       "        ..\n",
       "97613    1\n",
       "97614    1\n",
       "97615    1\n",
       "97616    1\n",
       "97617    1\n",
       "Length: 36742, dtype: int8"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Statut\"].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "558f6e79-68a0-4c02-9f61-4e322fbc59ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.96 ms ± 102 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit df.loc[df[\"Statut\"] == 'Commune simple']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7931f37b-6f64-4443-86f0-99071ba03337",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5036/3093096956.py:2: 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",
      "  var[\"Population\"] = var[\"Population\"].astype(int)\n"
     ]
    }
   ],
   "source": [
    "var = df.loc[df[\"Commune\"].str.startswith(\"A\")]\n",
    "var[\"Population\"] = var[\"Population\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "91e5f9ec-2766-45dd-b062-40f4f5a70880",
   "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>Code Postal</th>\n",
       "      <th>Commune</th>\n",
       "      <th>Département</th>\n",
       "      <th>Région</th>\n",
       "      <th>Statut</th>\n",
       "      <th>Altitude Moyenne</th>\n",
       "      <th>Superficie</th>\n",
       "      <th>Population</th>\n",
       "      <th>geo_point_2d</th>\n",
       "      <th>geo_shape</th>\n",
       "      <th>CP Ville</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Code INSEE</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>01001</th>\n",
       "      <td>01400</td>\n",
       "      <td>L'ABERGEMENT-CLEMENCIAT</td>\n",
       "      <td>AIN</td>\n",
       "      <td>RHONE-ALPES</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>242</td>\n",
       "      <td>15.65</td>\n",
       "      <td>0.8</td>\n",
       "      <td>46.1534255214, 4.92611354223</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[4.926273...</td>\n",
       "      <td>01400 L'ABERGEMENT-CLEMENCIAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01002</th>\n",
       "      <td>01640</td>\n",
       "      <td>L'ABERGEMENT-DE-VAREY</td>\n",
       "      <td>AIN</td>\n",
       "      <td>RHONE-ALPES</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>483</td>\n",
       "      <td>9.12</td>\n",
       "      <td>0.2</td>\n",
       "      <td>46.0091878776, 5.42801696363</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[5.430089...</td>\n",
       "      <td>01640 L'ABERGEMENT-DE-VAREY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01004</th>\n",
       "      <td>01500</td>\n",
       "      <td>AMBERIEU-EN-BUGEY</td>\n",
       "      <td>AIN</td>\n",
       "      <td>RHONE-ALPES</td>\n",
       "      <td>Chef-lieu canton</td>\n",
       "      <td>379</td>\n",
       "      <td>24.48</td>\n",
       "      <td>13.4</td>\n",
       "      <td>45.9608475114, 5.3729257777</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[5.386190...</td>\n",
       "      <td>01500 AMBERIEU-EN-BUGEY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01005</th>\n",
       "      <td>01330</td>\n",
       "      <td>AMBERIEUX-EN-DOMBES</td>\n",
       "      <td>AIN</td>\n",
       "      <td>RHONE-ALPES</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>290</td>\n",
       "      <td>16.05</td>\n",
       "      <td>1.6</td>\n",
       "      <td>45.9961799872, 4.91227250796</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[4.895580...</td>\n",
       "      <td>01330 AMBERIEUX-EN-DOMBES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01006</th>\n",
       "      <td>01300</td>\n",
       "      <td>AMBLEON</td>\n",
       "      <td>AIN</td>\n",
       "      <td>RHONE-ALPES</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>589</td>\n",
       "      <td>6.02</td>\n",
       "      <td>0.1</td>\n",
       "      <td>45.7494989044, 5.59432017366</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[5.614854...</td>\n",
       "      <td>01300 AMBLEON</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97613</th>\n",
       "      <td>97650</td>\n",
       "      <td>M'TSANGAMOUJI</td>\n",
       "      <td>MAYOTTE</td>\n",
       "      <td>MAYOTTE</td>\n",
       "      <td>Chef-lieu canton</td>\n",
       "      <td>96</td>\n",
       "      <td>21.55</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-12.7513099309, 45.0871696871</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[45.10168...</td>\n",
       "      <td>97650 M'TSANGAMOUJI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97614</th>\n",
       "      <td>97670</td>\n",
       "      <td>OUANGANI</td>\n",
       "      <td>MAYOTTE</td>\n",
       "      <td>MAYOTTE</td>\n",
       "      <td>Chef-lieu canton</td>\n",
       "      <td>175</td>\n",
       "      <td>18.28</td>\n",
       "      <td>6.6</td>\n",
       "      <td>-12.8370955196, 45.1379095497</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[45.15401...</td>\n",
       "      <td>97670 OUANGANI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97615</th>\n",
       "      <td>97610</td>\n",
       "      <td>PAMANDZI</td>\n",
       "      <td>MAYOTTE</td>\n",
       "      <td>MAYOTTE</td>\n",
       "      <td>Chef-lieu canton</td>\n",
       "      <td>52</td>\n",
       "      <td>4.26</td>\n",
       "      <td>9.1</td>\n",
       "      <td>-12.7961353309, 45.2842063102</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[45.29645...</td>\n",
       "      <td>97610 PAMANDZI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97616</th>\n",
       "      <td>97640</td>\n",
       "      <td>SADA</td>\n",
       "      <td>MAYOTTE</td>\n",
       "      <td>MAYOTTE</td>\n",
       "      <td>Chef-lieu canton</td>\n",
       "      <td>130</td>\n",
       "      <td>10.85</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-12.8611649609, 45.1185503145</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[45.13226...</td>\n",
       "      <td>97640 SADA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97617</th>\n",
       "      <td>97680</td>\n",
       "      <td>TSINGONI</td>\n",
       "      <td>MAYOTTE</td>\n",
       "      <td>MAYOTTE</td>\n",
       "      <td>Chef-lieu canton</td>\n",
       "      <td>147</td>\n",
       "      <td>34.26</td>\n",
       "      <td>9.2</td>\n",
       "      <td>-12.7821666736, 45.1344279083</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[45.15256...</td>\n",
       "      <td>97680 TSINGONI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36742 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Code Postal                  Commune Département       Région  \\\n",
       "Code INSEE                                                                 \n",
       "01001            01400  L'ABERGEMENT-CLEMENCIAT         AIN  RHONE-ALPES   \n",
       "01002            01640    L'ABERGEMENT-DE-VAREY         AIN  RHONE-ALPES   \n",
       "01004            01500        AMBERIEU-EN-BUGEY         AIN  RHONE-ALPES   \n",
       "01005            01330      AMBERIEUX-EN-DOMBES         AIN  RHONE-ALPES   \n",
       "01006            01300                  AMBLEON         AIN  RHONE-ALPES   \n",
       "...                ...                      ...         ...          ...   \n",
       "97613            97650            M'TSANGAMOUJI     MAYOTTE      MAYOTTE   \n",
       "97614            97670                 OUANGANI     MAYOTTE      MAYOTTE   \n",
       "97615            97610                 PAMANDZI     MAYOTTE      MAYOTTE   \n",
       "97616            97640                     SADA     MAYOTTE      MAYOTTE   \n",
       "97617            97680                 TSINGONI     MAYOTTE      MAYOTTE   \n",
       "\n",
       "                      Statut  Altitude Moyenne  Superficie  Population  \\\n",
       "Code INSEE                                                               \n",
       "01001         Commune simple               242       15.65         0.8   \n",
       "01002         Commune simple               483        9.12         0.2   \n",
       "01004       Chef-lieu canton               379       24.48        13.4   \n",
       "01005         Commune simple               290       16.05         1.6   \n",
       "01006         Commune simple               589        6.02         0.1   \n",
       "...                      ...               ...         ...         ...   \n",
       "97613       Chef-lieu canton                96       21.55         5.0   \n",
       "97614       Chef-lieu canton               175       18.28         6.6   \n",
       "97615       Chef-lieu canton                52        4.26         9.1   \n",
       "97616       Chef-lieu canton               130       10.85         8.0   \n",
       "97617       Chef-lieu canton               147       34.26         9.2   \n",
       "\n",
       "                             geo_point_2d  \\\n",
       "Code INSEE                                  \n",
       "01001        46.1534255214, 4.92611354223   \n",
       "01002        46.0091878776, 5.42801696363   \n",
       "01004         45.9608475114, 5.3729257777   \n",
       "01005        45.9961799872, 4.91227250796   \n",
       "01006        45.7494989044, 5.59432017366   \n",
       "...                                   ...   \n",
       "97613       -12.7513099309, 45.0871696871   \n",
       "97614       -12.8370955196, 45.1379095497   \n",
       "97615       -12.7961353309, 45.2842063102   \n",
       "97616       -12.8611649609, 45.1185503145   \n",
       "97617       -12.7821666736, 45.1344279083   \n",
       "\n",
       "                                                    geo_shape  \\\n",
       "Code INSEE                                                      \n",
       "01001       {\"type\": \"Polygon\", \"coordinates\": [[[4.926273...   \n",
       "01002       {\"type\": \"Polygon\", \"coordinates\": [[[5.430089...   \n",
       "01004       {\"type\": \"Polygon\", \"coordinates\": [[[5.386190...   \n",
       "01005       {\"type\": \"Polygon\", \"coordinates\": [[[4.895580...   \n",
       "01006       {\"type\": \"Polygon\", \"coordinates\": [[[5.614854...   \n",
       "...                                                       ...   \n",
       "97613       {\"type\": \"Polygon\", \"coordinates\": [[[45.10168...   \n",
       "97614       {\"type\": \"Polygon\", \"coordinates\": [[[45.15401...   \n",
       "97615       {\"type\": \"Polygon\", \"coordinates\": [[[45.29645...   \n",
       "97616       {\"type\": \"Polygon\", \"coordinates\": [[[45.13226...   \n",
       "97617       {\"type\": \"Polygon\", \"coordinates\": [[[45.15256...   \n",
       "\n",
       "                                 CP Ville  \n",
       "Code INSEE                                 \n",
       "01001       01400 L'ABERGEMENT-CLEMENCIAT  \n",
       "01002         01640 L'ABERGEMENT-DE-VAREY  \n",
       "01004             01500 AMBERIEU-EN-BUGEY  \n",
       "01005           01330 AMBERIEUX-EN-DOMBES  \n",
       "01006                       01300 AMBLEON  \n",
       "...                                   ...  \n",
       "97613                 97650 M'TSANGAMOUJI  \n",
       "97614                      97670 OUANGANI  \n",
       "97615                      97610 PAMANDZI  \n",
       "97616                          97640 SADA  \n",
       "97617                      97680 TSINGONI  \n",
       "\n",
       "[36742 rows x 11 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"CP Ville\"] = df[\"Code Postal\"] + \" \" + df[\"Commune\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8dadecdc-f209-4cc4-94c5-4a72f6ab0d37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df[\"geo_point_2d\"].iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "9f1333ca-b638-4b7f-8149-dbd098497c3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46.1534255214"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float(df[\"geo_point_2d\"].iloc[0].split(', ')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "d97a3020-7b72-4706-b491-091d7aaf84f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Code INSEE\n",
       "01001    46.153426\n",
       "01002    46.009188\n",
       "01004    45.960848\n",
       "01005    45.996180\n",
       "01006    45.749499\n",
       "           ...    \n",
       "97613   -12.751310\n",
       "97614   -12.837096\n",
       "97615   -12.796135\n",
       "97616   -12.861165\n",
       "97617   -12.782167\n",
       "Name: geo_point_2d, Length: 36742, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"geo_point_2d\"].apply(lambda x: float(x.split(', ')[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0a11617a-2a3f-41f0-a66c-5224c681bdf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Code INSEE\n",
       "01001     4.926114\n",
       "01002     5.428017\n",
       "01004     5.372926\n",
       "01005     4.912273\n",
       "01006     5.594320\n",
       "           ...    \n",
       "97613    45.087170\n",
       "97614    45.137910\n",
       "97615    45.284206\n",
       "97616    45.118550\n",
       "97617    45.134428\n",
       "Name: geo_point_2d, Length: 36742, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"geo_point_2d\"].apply(lambda x: float(x.split(', ')[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6b5dd16d-3e62-4ff6-b679-aba5abe4e7cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 36742 entries, 01001 to 97617\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Code Postal       36742 non-null  object \n",
      " 1   Commune           36742 non-null  object \n",
      " 2   Département       36742 non-null  object \n",
      " 3   Région            36742 non-null  object \n",
      " 4   Statut            36742 non-null  object \n",
      " 5   Altitude Moyenne  36742 non-null  float64\n",
      " 6   Superficie        36742 non-null  float64\n",
      " 7   Population        36742 non-null  float64\n",
      " 8   geo_point_2d      36742 non-null  object \n",
      " 9   geo_shape         36742 non-null  object \n",
      " 10  Latitude          36742 non-null  float64\n",
      " 11  Longitude         36742 non-null  float64\n",
      "dtypes: float64(5), object(7)\n",
      "memory usage: 4.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df[[\"Latitude\", \"Longitude\"]] = df[\"geo_point_2d\"].str.extract(\"(.+), (.+)\").astype(float)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "93bf6088-c51a-4703-83bf-c5ac2c8ffb63",
   "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>Code Postal</th>\n",
       "      <th>Commune</th>\n",
       "      <th>Département</th>\n",
       "      <th>Région</th>\n",
       "      <th>Statut</th>\n",
       "      <th>Altitude Moyenne</th>\n",
       "      <th>Superficie</th>\n",
       "      <th>Population</th>\n",
       "      <th>geo_point_2d</th>\n",
       "      <th>geo_shape</th>\n",
       "      <th>CP Ville</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Code INSEE</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>01001</th>\n",
       "      <td>01400</td>\n",
       "      <td>L'ABERGEMENT-CLEMENCIAT</td>\n",
       "      <td>AIN</td>\n",
       "      <td>RHONE-ALPES</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>242</td>\n",
       "      <td>15.65</td>\n",
       "      <td>0.8</td>\n",
       "      <td>46.1534255214, 4.92611354223</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[4.926273...</td>\n",
       "      <td>01400 L'ABERGEMENT-CLEMENCIAT</td>\n",
       "      <td>46.153426</td>\n",
       "      <td>4.926114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01002</th>\n",
       "      <td>01640</td>\n",
       "      <td>L'ABERGEMENT-DE-VAREY</td>\n",
       "      <td>AIN</td>\n",
       "      <td>RHONE-ALPES</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>483</td>\n",
       "      <td>9.12</td>\n",
       "      <td>0.2</td>\n",
       "      <td>46.0091878776, 5.42801696363</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[5.430089...</td>\n",
       "      <td>01640 L'ABERGEMENT-DE-VAREY</td>\n",
       "      <td>46.009188</td>\n",
       "      <td>5.428017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01004</th>\n",
       "      <td>01500</td>\n",
       "      <td>AMBERIEU-EN-BUGEY</td>\n",
       "      <td>AIN</td>\n",
       "      <td>RHONE-ALPES</td>\n",
       "      <td>Chef-lieu canton</td>\n",
       "      <td>379</td>\n",
       "      <td>24.48</td>\n",
       "      <td>13.4</td>\n",
       "      <td>45.9608475114, 5.3729257777</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[5.386190...</td>\n",
       "      <td>01500 AMBERIEU-EN-BUGEY</td>\n",
       "      <td>45.960848</td>\n",
       "      <td>5.372926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01005</th>\n",
       "      <td>01330</td>\n",
       "      <td>AMBERIEUX-EN-DOMBES</td>\n",
       "      <td>AIN</td>\n",
       "      <td>RHONE-ALPES</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>290</td>\n",
       "      <td>16.05</td>\n",
       "      <td>1.6</td>\n",
       "      <td>45.9961799872, 4.91227250796</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[4.895580...</td>\n",
       "      <td>01330 AMBERIEUX-EN-DOMBES</td>\n",
       "      <td>45.996180</td>\n",
       "      <td>4.912273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01006</th>\n",
       "      <td>01300</td>\n",
       "      <td>AMBLEON</td>\n",
       "      <td>AIN</td>\n",
       "      <td>RHONE-ALPES</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>589</td>\n",
       "      <td>6.02</td>\n",
       "      <td>0.1</td>\n",
       "      <td>45.7494989044, 5.59432017366</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[5.614854...</td>\n",
       "      <td>01300 AMBLEON</td>\n",
       "      <td>45.749499</td>\n",
       "      <td>5.594320</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97613</th>\n",
       "      <td>97650</td>\n",
       "      <td>M'TSANGAMOUJI</td>\n",
       "      <td>MAYOTTE</td>\n",
       "      <td>MAYOTTE</td>\n",
       "      <td>Chef-lieu canton</td>\n",
       "      <td>96</td>\n",
       "      <td>21.55</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-12.7513099309, 45.0871696871</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[45.10168...</td>\n",
       "      <td>97650 M'TSANGAMOUJI</td>\n",
       "      <td>-12.751310</td>\n",
       "      <td>45.087170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97614</th>\n",
       "      <td>97670</td>\n",
       "      <td>OUANGANI</td>\n",
       "      <td>MAYOTTE</td>\n",
       "      <td>MAYOTTE</td>\n",
       "      <td>Chef-lieu canton</td>\n",
       "      <td>175</td>\n",
       "      <td>18.28</td>\n",
       "      <td>6.6</td>\n",
       "      <td>-12.8370955196, 45.1379095497</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[45.15401...</td>\n",
       "      <td>97670 OUANGANI</td>\n",
       "      <td>-12.837096</td>\n",
       "      <td>45.137910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97615</th>\n",
       "      <td>97610</td>\n",
       "      <td>PAMANDZI</td>\n",
       "      <td>MAYOTTE</td>\n",
       "      <td>MAYOTTE</td>\n",
       "      <td>Chef-lieu canton</td>\n",
       "      <td>52</td>\n",
       "      <td>4.26</td>\n",
       "      <td>9.1</td>\n",
       "      <td>-12.7961353309, 45.2842063102</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[45.29645...</td>\n",
       "      <td>97610 PAMANDZI</td>\n",
       "      <td>-12.796135</td>\n",
       "      <td>45.284206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97616</th>\n",
       "      <td>97640</td>\n",
       "      <td>SADA</td>\n",
       "      <td>MAYOTTE</td>\n",
       "      <td>MAYOTTE</td>\n",
       "      <td>Chef-lieu canton</td>\n",
       "      <td>130</td>\n",
       "      <td>10.85</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-12.8611649609, 45.1185503145</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[45.13226...</td>\n",
       "      <td>97640 SADA</td>\n",
       "      <td>-12.861165</td>\n",
       "      <td>45.118550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97617</th>\n",
       "      <td>97680</td>\n",
       "      <td>TSINGONI</td>\n",
       "      <td>MAYOTTE</td>\n",
       "      <td>MAYOTTE</td>\n",
       "      <td>Chef-lieu canton</td>\n",
       "      <td>147</td>\n",
       "      <td>34.26</td>\n",
       "      <td>9.2</td>\n",
       "      <td>-12.7821666736, 45.1344279083</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[45.15256...</td>\n",
       "      <td>97680 TSINGONI</td>\n",
       "      <td>-12.782167</td>\n",
       "      <td>45.134428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36742 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Code Postal                  Commune Département       Région  \\\n",
       "Code INSEE                                                                 \n",
       "01001            01400  L'ABERGEMENT-CLEMENCIAT         AIN  RHONE-ALPES   \n",
       "01002            01640    L'ABERGEMENT-DE-VAREY         AIN  RHONE-ALPES   \n",
       "01004            01500        AMBERIEU-EN-BUGEY         AIN  RHONE-ALPES   \n",
       "01005            01330      AMBERIEUX-EN-DOMBES         AIN  RHONE-ALPES   \n",
       "01006            01300                  AMBLEON         AIN  RHONE-ALPES   \n",
       "...                ...                      ...         ...          ...   \n",
       "97613            97650            M'TSANGAMOUJI     MAYOTTE      MAYOTTE   \n",
       "97614            97670                 OUANGANI     MAYOTTE      MAYOTTE   \n",
       "97615            97610                 PAMANDZI     MAYOTTE      MAYOTTE   \n",
       "97616            97640                     SADA     MAYOTTE      MAYOTTE   \n",
       "97617            97680                 TSINGONI     MAYOTTE      MAYOTTE   \n",
       "\n",
       "                      Statut  Altitude Moyenne  Superficie  Population  \\\n",
       "Code INSEE                                                               \n",
       "01001         Commune simple               242       15.65         0.8   \n",
       "01002         Commune simple               483        9.12         0.2   \n",
       "01004       Chef-lieu canton               379       24.48        13.4   \n",
       "01005         Commune simple               290       16.05         1.6   \n",
       "01006         Commune simple               589        6.02         0.1   \n",
       "...                      ...               ...         ...         ...   \n",
       "97613       Chef-lieu canton                96       21.55         5.0   \n",
       "97614       Chef-lieu canton               175       18.28         6.6   \n",
       "97615       Chef-lieu canton                52        4.26         9.1   \n",
       "97616       Chef-lieu canton               130       10.85         8.0   \n",
       "97617       Chef-lieu canton               147       34.26         9.2   \n",
       "\n",
       "                             geo_point_2d  \\\n",
       "Code INSEE                                  \n",
       "01001        46.1534255214, 4.92611354223   \n",
       "01002        46.0091878776, 5.42801696363   \n",
       "01004         45.9608475114, 5.3729257777   \n",
       "01005        45.9961799872, 4.91227250796   \n",
       "01006        45.7494989044, 5.59432017366   \n",
       "...                                   ...   \n",
       "97613       -12.7513099309, 45.0871696871   \n",
       "97614       -12.8370955196, 45.1379095497   \n",
       "97615       -12.7961353309, 45.2842063102   \n",
       "97616       -12.8611649609, 45.1185503145   \n",
       "97617       -12.7821666736, 45.1344279083   \n",
       "\n",
       "                                                    geo_shape  \\\n",
       "Code INSEE                                                      \n",
       "01001       {\"type\": \"Polygon\", \"coordinates\": [[[4.926273...   \n",
       "01002       {\"type\": \"Polygon\", \"coordinates\": [[[5.430089...   \n",
       "01004       {\"type\": \"Polygon\", \"coordinates\": [[[5.386190...   \n",
       "01005       {\"type\": \"Polygon\", \"coordinates\": [[[4.895580...   \n",
       "01006       {\"type\": \"Polygon\", \"coordinates\": [[[5.614854...   \n",
       "...                                                       ...   \n",
       "97613       {\"type\": \"Polygon\", \"coordinates\": [[[45.10168...   \n",
       "97614       {\"type\": \"Polygon\", \"coordinates\": [[[45.15401...   \n",
       "97615       {\"type\": \"Polygon\", \"coordinates\": [[[45.29645...   \n",
       "97616       {\"type\": \"Polygon\", \"coordinates\": [[[45.13226...   \n",
       "97617       {\"type\": \"Polygon\", \"coordinates\": [[[45.15256...   \n",
       "\n",
       "                                 CP Ville   Latitude  Longitude  \n",
       "Code INSEE                                                       \n",
       "01001       01400 L'ABERGEMENT-CLEMENCIAT  46.153426   4.926114  \n",
       "01002         01640 L'ABERGEMENT-DE-VAREY  46.009188   5.428017  \n",
       "01004             01500 AMBERIEU-EN-BUGEY  45.960848   5.372926  \n",
       "01005           01330 AMBERIEUX-EN-DOMBES  45.996180   4.912273  \n",
       "01006                       01300 AMBLEON  45.749499   5.594320  \n",
       "...                                   ...        ...        ...  \n",
       "97613                 97650 M'TSANGAMOUJI -12.751310  45.087170  \n",
       "97614                      97670 OUANGANI -12.837096  45.137910  \n",
       "97615                      97610 PAMANDZI -12.796135  45.284206  \n",
       "97616                          97640 SADA -12.861165  45.118550  \n",
       "97617                      97680 TSINGONI -12.782167  45.134428  \n",
       "\n",
       "[36742 rows x 13 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "5054caa5-e3e9-474a-96de-1b4c2c4869b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def ville(lat, long):\n",
    "    dist = np.sqrt((df[\"Latitude\"]-lat)**2 + (df[\"Longitude\"]-long)**2)\n",
    "    return df.loc[dist.idxmin(), \"CP Ville\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "e4728085-8f43-4189-aad4-94185b1870ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ville2(lat, long):\n",
    "    dist2 = (df[\"Latitude\"]-lat)**2 + (df[\"Longitude\"]-long)**2\n",
    "    return df.loc[dist2.idxmin(), \"CP Ville\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "ebc4909b-7114-4626-8b39-86d33a3ee9e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gps2dec(deg, mn, sec):\n",
    "    return deg + mn / 60 + sec / 3600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "68db2935-b68f-4ec4-b687-d5c0ca11cf2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'67510 OBERSTEINBACH'"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 49° 02′ 12″ nord, 7° 41′ 18″ est\n",
    "ville(gps2dec(49, 2, 12), gps2dec(7, 41, 18))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "c5689330-db78-4765-a430-2dc9743b7aa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'29660 CARANTEC'"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 48° 40′ 07″ nord, 3° 54′ 45″ ouest\n",
    "\n",
    "ville2(gps2dec(48, 40, 7), -gps2dec(3, 54, 45))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "31ec3b2f-b31f-47b1-b369-17c797f50a56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Code INSEE\n",
       "01001    {\"type\": \"Polygon\", \"coordinates\": [[[4.926273...\n",
       "01002    {\"type\": \"Polygon\", \"coordinates\": [[[5.430089...\n",
       "01004    {\"type\": \"Polygon\", \"coordinates\": [[[5.386190...\n",
       "01005    {\"type\": \"Polygon\", \"coordinates\": [[[4.895580...\n",
       "01006    {\"type\": \"Polygon\", \"coordinates\": [[[5.614854...\n",
       "                               ...                        \n",
       "97613    {\"type\": \"Polygon\", \"coordinates\": [[[45.10168...\n",
       "97614    {\"type\": \"Polygon\", \"coordinates\": [[[45.15401...\n",
       "97615    {\"type\": \"Polygon\", \"coordinates\": [[[45.29645...\n",
       "97616    {\"type\": \"Polygon\", \"coordinates\": [[[45.13226...\n",
       "97617    {\"type\": \"Polygon\", \"coordinates\": [[[45.15256...\n",
       "Name: geo_shape, Length: 36742, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"geo_shape\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "31e06e9c-73b2-49cf-b632-1ab071164629",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"type\": \"Polygon\", \"coordinates\": [[[4.92627342692396, 46.1200517234555], [4.92199538031264, 46.12069170563269], [4.921996260482057, 46.12663667307488], [4.90936269187648, 46.129164756530656], [4.916016986429157, 46.14461766984426], [4.903080786126749, 46.157977074275074], [4.904571300671208, 46.16096059498421], [4.910603091203733, 46.180432597608316], [4.914119126867876, 46.18271538441311], [4.932895432675545, 46.18304616431434], [4.938729478686701, 46.17383114520867], [4.933038591703602, 46.16429901579509], [4.958286230120842, 46.153246666893935], [4.943330383388636, 46.145964148557894], [4.939154593056283, 46.12938048470421], [4.931056072485923, 46.12087123131487], [4.92627342692396, 46.1200517234555]]]}'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"geo_shape\"].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e02c345f-744f-4f69-a40e-92509120b18c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df[\"geo_shape\"].values[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b7fc8c5-1d2e-4d69-8ac4-09fe78af4bd0",
   "metadata": {},
   "source": [
    "JSON : JavaScript Object Notation\n",
    "\n",
    "https://fr.wikipedia.org/wiki/JavaScript_Object_Notation\n",
    "\n",
    "https://www.json.org/json-en.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ecdd878f-6bb5-4dec-a791-d96ab4d1ee84",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "df7a38db-20db-4a29-9c74-b0e68dedf5b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 'Polygon',\n",
       " 'coordinates': [[[4.92627342692396, 46.1200517234555],\n",
       "   [4.92199538031264, 46.12069170563269],\n",
       "   [4.921996260482057, 46.12663667307488],\n",
       "   [4.90936269187648, 46.129164756530656],\n",
       "   [4.916016986429157, 46.14461766984426],\n",
       "   [4.903080786126749, 46.157977074275074],\n",
       "   [4.904571300671208, 46.16096059498421],\n",
       "   [4.910603091203733, 46.180432597608316],\n",
       "   [4.914119126867876, 46.18271538441311],\n",
       "   [4.932895432675545, 46.18304616431434],\n",
       "   [4.938729478686701, 46.17383114520867],\n",
       "   [4.933038591703602, 46.16429901579509],\n",
       "   [4.958286230120842, 46.153246666893935],\n",
       "   [4.943330383388636, 46.145964148557894],\n",
       "   [4.939154593056283, 46.12938048470421],\n",
       "   [4.931056072485923, 46.12087123131487],\n",
       "   [4.92627342692396, 46.1200517234555]]]}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df[\"geo_shape\"].values[0]\n",
    "d = json.loads(x)\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "72777624-20b4-4885-8045-2fa99569c63d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0aeab652-68c0-4272-9e79-0a2d5fc0b4ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Polygon'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[\"type\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "da04a0e1-882b-45f8-a9c8-c39c99da39f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[[4.92627342692396, 46.1200517234555],\n",
       "  [4.92199538031264, 46.12069170563269],\n",
       "  [4.921996260482057, 46.12663667307488],\n",
       "  [4.90936269187648, 46.129164756530656],\n",
       "  [4.916016986429157, 46.14461766984426],\n",
       "  [4.903080786126749, 46.157977074275074],\n",
       "  [4.904571300671208, 46.16096059498421],\n",
       "  [4.910603091203733, 46.180432597608316],\n",
       "  [4.914119126867876, 46.18271538441311],\n",
       "  [4.932895432675545, 46.18304616431434],\n",
       "  [4.938729478686701, 46.17383114520867],\n",
       "  [4.933038591703602, 46.16429901579509],\n",
       "  [4.958286230120842, 46.153246666893935],\n",
       "  [4.943330383388636, 46.145964148557894],\n",
       "  [4.939154593056283, 46.12938048470421],\n",
       "  [4.931056072485923, 46.12087123131487],\n",
       "  [4.92627342692396, 46.1200517234555]]]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[\"coordinates\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eff96fcb-81a5-48ba-ac64-b80c8761f0ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Code INSEE\n",
       "01001    {'type': 'Polygon', 'coordinates': [[[4.926273...\n",
       "01002    {'type': 'Polygon', 'coordinates': [[[5.430089...\n",
       "01004    {'type': 'Polygon', 'coordinates': [[[5.386190...\n",
       "01005    {'type': 'Polygon', 'coordinates': [[[4.895580...\n",
       "01006    {'type': 'Polygon', 'coordinates': [[[5.614854...\n",
       "                               ...                        \n",
       "97613    {'type': 'Polygon', 'coordinates': [[[45.10168...\n",
       "97614    {'type': 'Polygon', 'coordinates': [[[45.15401...\n",
       "97615    {'type': 'Polygon', 'coordinates': [[[45.29645...\n",
       "97616    {'type': 'Polygon', 'coordinates': [[[45.13226...\n",
       "97617    {'type': 'Polygon', 'coordinates': [[[45.15256...\n",
       "Name: geo_shape, Length: 36742, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"geo_shape\"].apply(lambda x: json.loads(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "14394101-f7db-46fe-ad62-0558ccc8506f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function json.loads(s, *, cls=None, object_hook=None, parse_float=None, parse_int=None, parse_constant=None, object_pairs_hook=None, **kw)>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json.loads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0621ec82-63ee-40ca-ac81-7feca96a1be1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 'Polygon',\n",
       " 'coordinates': [[[4.92627342692396, 46.1200517234555],\n",
       "   [4.92199538031264, 46.12069170563269],\n",
       "   [4.921996260482057, 46.12663667307488],\n",
       "   [4.90936269187648, 46.129164756530656],\n",
       "   [4.916016986429157, 46.14461766984426],\n",
       "   [4.903080786126749, 46.157977074275074],\n",
       "   [4.904571300671208, 46.16096059498421],\n",
       "   [4.910603091203733, 46.180432597608316],\n",
       "   [4.914119126867876, 46.18271538441311],\n",
       "   [4.932895432675545, 46.18304616431434],\n",
       "   [4.938729478686701, 46.17383114520867],\n",
       "   [4.933038591703602, 46.16429901579509],\n",
       "   [4.958286230120842, 46.153246666893935],\n",
       "   [4.943330383388636, 46.145964148557894],\n",
       "   [4.939154593056283, 46.12938048470421],\n",
       "   [4.931056072485923, 46.12087123131487],\n",
       "   [4.92627342692396, 46.1200517234555]]]}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_shape = df[\"geo_shape\"].apply(json.loads)\n",
    "geo_shape.values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "86057c89-3ca0-412f-9c6b-8b7fb3feabca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geo_shape\n",
       "Polygon         36670\n",
       "MultiPolygon       72\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_shape.apply(lambda d: d[\"type\"]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b868e9b5-fec9-475a-a20b-940d9007789a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geo_shape\n",
       "1    36660\n",
       "2       80\n",
       "3        1\n",
       "4        1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_shape.apply(lambda d: len(d[\"coordinates\"])).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2a68ca30-845c-4687-9a3b-42b4d9939c37",
   "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>Code Postal</th>\n",
       "      <th>Commune</th>\n",
       "      <th>Département</th>\n",
       "      <th>Région</th>\n",
       "      <th>Statut</th>\n",
       "      <th>Altitude Moyenne</th>\n",
       "      <th>Superficie</th>\n",
       "      <th>Population</th>\n",
       "      <th>geo_point_2d</th>\n",
       "      <th>geo_shape</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Code INSEE</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>02232</th>\n",
       "      <td>02600</td>\n",
       "      <td>COYOLLES</td>\n",
       "      <td>AISNE</td>\n",
       "      <td>PICARDIE</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>126.0</td>\n",
       "      <td>2489.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>49.2133089693, 3.01306173751</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[3.037897...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05052</th>\n",
       "      <td>05600</td>\n",
       "      <td>EYGLIERS</td>\n",
       "      <td>HAUTES-ALPES</td>\n",
       "      <td>PROVENCE-ALPES-COTE D'AZUR</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>1615.0</td>\n",
       "      <td>2987.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>44.6925244966, 6.66688741371</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[6.719912...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09042</th>\n",
       "      <td>09240</td>\n",
       "      <td>LA BASTIDE-DE-SEROU</td>\n",
       "      <td>ARIEGE</td>\n",
       "      <td>MIDI-PYRENEES</td>\n",
       "      <td>Chef-lieu canton</td>\n",
       "      <td>479.0</td>\n",
       "      <td>4138.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43.0242795037, 1.42677885373</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[1.423960...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22209</th>\n",
       "      <td>22650</td>\n",
       "      <td>PLOUBALAY</td>\n",
       "      <td>COTES-D'ARMOR</td>\n",
       "      <td>BRETAGNE</td>\n",
       "      <td>Chef-lieu canton</td>\n",
       "      <td>36.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>48.5596688192, -2.12838362694</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[-2.11237...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2A272</th>\n",
       "      <td>20100</td>\n",
       "      <td>SARTENE</td>\n",
       "      <td>CORSE-DU-SUD</td>\n",
       "      <td>CORSE</td>\n",
       "      <td>Sous-préfecture</td>\n",
       "      <td>190.0</td>\n",
       "      <td>20182.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>41.5718443328, 8.93538107497</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[8.940487...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2B049</th>\n",
       "      <td>20214</td>\n",
       "      <td>CALENZANA</td>\n",
       "      <td>HAUTE-CORSE</td>\n",
       "      <td>CORSE</td>\n",
       "      <td>Chef-lieu canton</td>\n",
       "      <td>596.0</td>\n",
       "      <td>18465.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>42.4784267166, 8.80369225001</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[8.911220...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48080</th>\n",
       "      <td>48300</td>\n",
       "      <td>LANGOGNE</td>\n",
       "      <td>LOZERE</td>\n",
       "      <td>LANGUEDOC-ROUSSILLON</td>\n",
       "      <td>Chef-lieu canton</td>\n",
       "      <td>973.0</td>\n",
       "      <td>3124.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>44.7237405653, 3.83363110341</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[3.850531...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67486</th>\n",
       "      <td>67920</td>\n",
       "      <td>SUNDHOUSE</td>\n",
       "      <td>BAS-RHIN</td>\n",
       "      <td>ALSACE</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>48.2550723499, 7.63031906582</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[7.594939...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68078</th>\n",
       "      <td>68420</td>\n",
       "      <td>EGUISHEIM</td>\n",
       "      <td>HAUT-RHIN</td>\n",
       "      <td>ALSACE</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>320.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>48.0371183049, 7.30052876111</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[7.281156...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71028</th>\n",
       "      <td>71270</td>\n",
       "      <td>BEAUVERNOIS</td>\n",
       "      <td>SAONE-ET-LOIRE</td>\n",
       "      <td>BOURGOGNE</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>204.0</td>\n",
       "      <td>882.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>46.8413806403, 5.4403170068</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[5.416652...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Code Postal              Commune     Département  \\\n",
       "Code INSEE                                                    \n",
       "02232            02600             COYOLLES           AISNE   \n",
       "05052            05600             EYGLIERS    HAUTES-ALPES   \n",
       "09042            09240  LA BASTIDE-DE-SEROU          ARIEGE   \n",
       "22209            22650            PLOUBALAY   COTES-D'ARMOR   \n",
       "2A272            20100              SARTENE    CORSE-DU-SUD   \n",
       "2B049            20214            CALENZANA     HAUTE-CORSE   \n",
       "48080            48300             LANGOGNE          LOZERE   \n",
       "67486            67920            SUNDHOUSE        BAS-RHIN   \n",
       "68078            68420            EGUISHEIM       HAUT-RHIN   \n",
       "71028            71270          BEAUVERNOIS  SAONE-ET-LOIRE   \n",
       "\n",
       "                                Région            Statut  Altitude Moyenne  \\\n",
       "Code INSEE                                                                   \n",
       "02232                         PICARDIE    Commune simple             126.0   \n",
       "05052       PROVENCE-ALPES-COTE D'AZUR    Commune simple            1615.0   \n",
       "09042                    MIDI-PYRENEES  Chef-lieu canton             479.0   \n",
       "22209                         BRETAGNE  Chef-lieu canton              36.0   \n",
       "2A272                            CORSE   Sous-préfecture             190.0   \n",
       "2B049                            CORSE  Chef-lieu canton             596.0   \n",
       "48080             LANGUEDOC-ROUSSILLON  Chef-lieu canton             973.0   \n",
       "67486                           ALSACE    Commune simple             166.0   \n",
       "68078                           ALSACE    Commune simple             320.0   \n",
       "71028                        BOURGOGNE    Commune simple             204.0   \n",
       "\n",
       "            Superficie  Population                   geo_point_2d  \\\n",
       "Code INSEE                                                          \n",
       "02232           2489.0         0.4   49.2133089693, 3.01306173751   \n",
       "05052           2987.0         0.7   44.6925244966, 6.66688741371   \n",
       "09042           4138.0         1.0   43.0242795037, 1.42677885373   \n",
       "22209           3600.0         2.7  48.5596688192, -2.12838362694   \n",
       "2A272          20182.0         3.1   41.5718443328, 8.93538107497   \n",
       "2B049          18465.0         2.1   42.4784267166, 8.80369225001   \n",
       "48080           3124.0         3.1   44.7237405653, 3.83363110341   \n",
       "67486           1590.0         1.5   48.2550723499, 7.63031906582   \n",
       "68078           1400.0         1.6   48.0371183049, 7.30052876111   \n",
       "71028            882.0         0.1    46.8413806403, 5.4403170068   \n",
       "\n",
       "                                                    geo_shape  \n",
       "Code INSEE                                                     \n",
       "02232       {\"type\": \"Polygon\", \"coordinates\": [[[3.037897...  \n",
       "05052       {\"type\": \"Polygon\", \"coordinates\": [[[6.719912...  \n",
       "09042       {\"type\": \"Polygon\", \"coordinates\": [[[1.423960...  \n",
       "22209       {\"type\": \"Polygon\", \"coordinates\": [[[-2.11237...  \n",
       "2A272       {\"type\": \"Polygon\", \"coordinates\": [[[8.940487...  \n",
       "2B049       {\"type\": \"Polygon\", \"coordinates\": [[[8.911220...  \n",
       "48080       {\"type\": \"Polygon\", \"coordinates\": [[[3.850531...  \n",
       "67486       {\"type\": \"Polygon\", \"coordinates\": [[[7.594939...  \n",
       "68078       {\"type\": \"Polygon\", \"coordinates\": [[[7.281156...  \n",
       "71028       {\"type\": \"Polygon\", \"coordinates\": [[[5.416652...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(geo_shape.apply(lambda d: d[\"type\"])==\"Polygon\") & (geo_shape.apply(lambda d: len(d[\"coordinates\"])) > 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fbe9edb1-0351-4cd6-9da9-7e5a3d067b0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Code Postal                                                     83400\n",
       "Commune                                                        HYERES\n",
       "Département                                                       VAR\n",
       "Région                                     PROVENCE-ALPES-COTE D'AZUR\n",
       "Statut                                               Chef-lieu canton\n",
       "Altitude Moyenne                                                 60.0\n",
       "Superficie                                                    13356.0\n",
       "Population                                                       54.7\n",
       "geo_point_2d                             43.1018713534, 6.18898508469\n",
       "geo_shape           {\"type\": \"MultiPolygon\", \"coordinates\": [[[[6....\n",
       "Name: 83069, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[geo_shape.apply(lambda d: len(d[\"coordinates\"])).idxmax()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "02084d73-56c1-42d4-b482-f50a03687ccc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Commune\"].str.len().plot.hist(bins=45);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "083acead-31a9-4489-bcab-55b4f88890f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x=\"Longitude\",y=\"Latitude\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "393ef266-823b-4d47-a6a2-5ee57a88b404",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metro = df.loc[df[\"Latitude\"]>40]\n",
    "metro.plot.scatter(x=\"Longitude\",y=\"Latitude\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c753249a-b89d-4fab-a70e-963c92f5218b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 36613 entries, 01001 to 95690\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Code Postal       36613 non-null  object \n",
      " 1   Commune           36613 non-null  object \n",
      " 2   Département       36613 non-null  object \n",
      " 3   Région            36613 non-null  object \n",
      " 4   Statut            36613 non-null  object \n",
      " 5   Altitude Moyenne  36613 non-null  float64\n",
      " 6   Superficie        36613 non-null  float64\n",
      " 7   Population        36613 non-null  float64\n",
      " 8   geo_point_2d      36613 non-null  object \n",
      " 9   geo_shape         36613 non-null  object \n",
      " 10  Latitude          36613 non-null  float64\n",
      " 11  Longitude         36613 non-null  float64\n",
      "dtypes: float64(5), object(7)\n",
      "memory usage: 4.6+ MB\n"
     ]
    }
   ],
   "source": [
    "metro.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3559bc55-1e4a-4725-8b0b-ca629adbfafe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metro1 = metro.loc[metro[\"Altitude Moyenne\"] < 1000]\n",
    "metro2 = metro.loc[metro[\"Altitude Moyenne\"] >= 1000]\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots()\n",
    "metro1.plot.scatter(x=\"Longitude\",y=\"Latitude\", c=\"b\", ax=ax)\n",
    "metro2.plot.scatter(x=\"Longitude\",y=\"Latitude\", c=\"r\", ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a549897e-f070-4551-8f74-0997107a4fe1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metro = metro.sort_values(\"Altitude Moyenne\")\n",
    "metro.plot.scatter(x=\"Longitude\",y=\"Latitude\", c=\"Altitude Moyenne\", s=10, cmap=\"Spectral_r\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a06b04e2-604e-4ec7-a2da-cfa254ba0b87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mmetro\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Hashable'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Hashable'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Hashable | Sequence[Hashable] | None'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mc\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Hashable | Sequence[Hashable] | None'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;34m'PlotAccessor'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Create a scatter plot with varying marker point size and color.\n",
       "\n",
       "The coordinates of each point are defined by two dataframe columns and\n",
       "filled circles are used to represent each point. This kind of plot is\n",
       "useful to see complex correlations between two variables. Points could\n",
       "be for instance natural 2D coordinates like longitude and latitude in\n",
       "a map or, in general, any pair of metrics that can be plotted against\n",
       "each other.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "x : int or str\n",
       "    The column name or column position to be used as horizontal\n",
       "    coordinates for each point.\n",
       "y : int or str\n",
       "    The column name or column position to be used as vertical\n",
       "    coordinates for each point.\n",
       "s : str, scalar or array-like, optional\n",
       "    The size of each point. Possible values are:\n",
       "\n",
       "    - A string with the name of the column to be used for marker's size.\n",
       "\n",
       "    - A single scalar so all points have the same size.\n",
       "\n",
       "    - A sequence of scalars, which will be used for each point's size\n",
       "      recursively. For instance, when passing [2,14] all points size\n",
       "      will be either 2 or 14, alternatively.\n",
       "\n",
       "c : str, int or array-like, optional\n",
       "    The color of each point. Possible values are:\n",
       "\n",
       "    - A single color string referred to by name, RGB or RGBA code,\n",
       "      for instance 'red' or '#a98d19'.\n",
       "\n",
       "    - A sequence of color strings referred to by name, RGB or RGBA\n",
       "      code, which will be used for each point's color recursively. For\n",
       "      instance ['green','yellow'] all points will be filled in green or\n",
       "      yellow, alternatively.\n",
       "\n",
       "    - A column name or position whose values will be used to color the\n",
       "      marker points according to a colormap.\n",
       "\n",
       "**kwargs\n",
       "    Keyword arguments to pass on to :meth:`DataFrame.plot`.\n",
       "\n",
       "Returns\n",
       "-------\n",
       ":class:`matplotlib.axes.Axes` or numpy.ndarray of them\n",
       "\n",
       "See Also\n",
       "--------\n",
       "matplotlib.pyplot.scatter : Scatter plot using multiple input data\n",
       "    formats.\n",
       "\n",
       "Examples\n",
       "--------\n",
       "Let's see how to draw a scatter plot using coordinates from the values\n",
       "in a DataFrame's columns.\n",
       "\n",
       ".. plot::\n",
       "    :context: close-figs\n",
       "\n",
       "    >>> df = pd.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1],\n",
       "    ...                    [6.4, 3.2, 1], [5.9, 3.0, 2]],\n",
       "    ...                   columns=['length', 'width', 'species'])\n",
       "    >>> ax1 = df.plot.scatter(x='length',\n",
       "    ...                       y='width',\n",
       "    ...                       c='DarkBlue')\n",
       "\n",
       "And now with the color determined by a column as well.\n",
       "\n",
       ".. plot::\n",
       "    :context: close-figs\n",
       "\n",
       "    >>> ax2 = df.plot.scatter(x='length',\n",
       "    ...                       y='width',\n",
       "    ...                       c='species',\n",
       "    ...                       colormap='viridis')\n",
       "\u001b[0;31mFile:\u001b[0m      ~/anaconda3/envs/pydata/lib/python3.11/site-packages/pandas/plotting/_core.py\n",
       "\u001b[0;31mType:\u001b[0m      method"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metro.plot.scatter?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ca62f9c-386a-4b46-acf3-a85ec4f18e30",
   "metadata": {},
   "source": [
    "### Tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3b7abafc-d0b3-4238-b71b-b1a7f44c1363",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tests unitaires de la fonction lecture_geo\n",
    "\n",
    "import unittest\n",
    "\n",
    "class Session1Test(unittest.TestCase):\n",
    "    \"\"\"\n",
    "    Tests unitaires du chargement du fichier :\n",
    "    correspondance-code-insee-code-postal.csv\n",
    "    \"\"\"\n",
    "        \n",
    "    def test_1_dataframe(self):\n",
    "        df = lecture_geo()\n",
    "        self.assertTrue(isinstance(df, pd.DataFrame))\n",
    "        \n",
    "    def test_2_columns(self):\n",
    "        df = lecture_geo()\n",
    "        self.assertEqual(len(df.columns), 10)\n",
    "        \n",
    "    def test_3_index(self):\n",
    "        df = lecture_geo()\n",
    "        self.assertTrue(isinstance(df.index, pd.Index))\n",
    "        \n",
    "    def test_4_index_name(self):\n",
    "        df = lecture_geo()\n",
    "        self.assertEqual(df.index.name, \"Code INSEE\")\n",
    "        \n",
    "    def test_5_index_sort(self):\n",
    "        df = lecture_geo()\n",
    "        self.assertEqual(df.index.min(), df.index[0])\n",
    "        self.assertEqual(df.index.max(), df.index[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d331653b-bbbe-4dd6-9ab8-14c3ff00038c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fonction run_tests\n",
    "def run_tests():\n",
    "    test_suite = unittest.TestLoader().loadTestsFromTestCase(Session1Test)\n",
    "    runner = unittest.TextTestRunner(verbosity=2)\n",
    "    runner.run(test_suite)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8cf0dc25-4baa-4fa4-824f-5dff90486b3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "test_1_dataframe (__main__.Session1Test.test_1_dataframe) ... ok\n",
      "test_2_columns (__main__.Session1Test.test_2_columns) ... ok\n",
      "test_3_index (__main__.Session1Test.test_3_index) ... ok\n",
      "test_4_index_name (__main__.Session1Test.test_4_index_name) ... ok\n",
      "test_5_index_sort (__main__.Session1Test.test_5_index_sort) ... ok\n",
      "\n",
      "----------------------------------------------------------------------\n",
      "Ran 5 tests in 1.486s\n",
      "\n",
      "OK\n"
     ]
    }
   ],
   "source": [
    "# run tests\n",
    "run_tests()"
   ]
  }
 ],
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    "version": 3
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