03_TIDE_session.ipynb

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    "# Conférences Python Master TIDE #3\n",
    "\n",
    "## A) Taux de change\n",
    "\n",
    "On va s'intéresser au dataset des cours des devises de la Banque de France :\n",
    "- https://webstat.banque-france.fr/fr/#/node/5385566\n",
    "\n",
    "Les données sont dans le fichier `Webstat_Export.csv`.\n",
    "\n",
    "L'idée est de charger les données, de les nettoyer et de pouvoir accéder aux cours de certaines devises à partir de leur code ISO3. On retiendra uniquement les colonnes se terminant effectivement par un code ISO3 entre parenthèses. Par ex., \"Dollar des Etats-Unis (USD)\".\n",
    "\n",
    "1) Charger le fichier dans un DataFrame dont l'index doit correspondre aux dates, triées dans l'ordre chronologique et les colonnes doivent être les codes ISO3 des devises.\n",
    "\n",
    "2) Sélectionner les devises : ['USD', 'CHF', 'GBP', 'JPY', 'TRY', 'CNY']\n",
    "\n",
    "3) Calculer la moyenne des taux de change par année. Voir les codes de resampling : https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects\n",
    "\n",
    "4) Faire des graphiques avec :\n",
    "- Les taux de change divisés par leur moyenne.\n",
    "- Les taux de change divisés par leur moyenne avec une moyenne glissante de 30 jours ou bien un maximum glissant de 100 jours.\n",
    "- Les taux de change divisés par la dernière valeur connue avec une moyenne glissante de 30 jours.\n",
    "\n",
    "5) Réindexer le DataFrame en prenant toutes les dates et compléter les valeurs manquantes par interpolation linéaire.\n",
    "\n",
    "6) Avec la librairie **sparklines** afficher un DataFrame avec la moyenne annuelle des taux de change pour une monnaie donnée ainsi que la tendance par trimestre.\n",
    "\n",
    "```python\n",
    "import sparklines\n",
    "\n",
    "print(sparklines.sparklines(pd.Series(range(8)))[0])\n",
    "```\n",
    "\n",
    "## B) Données télécom\n",
    "\n",
    "Dataset des opérateurs télécom : `couverture-2g-3g-4g-en-france-par-operateur-juillet-2015.csv`\n",
    "\n",
    "1) Chargez le fichier dans un DataFrame.\n",
    "\n",
    "2) Transformez ce DataFrame large en un DataFrame long en isolant les colonnes d'identification d'une part et les colonnes de valeurs de couverture d'autre part.\n",
    "\n",
    "3) Rajoutez 3 colonnes \"Opérateur\", \"Type Couverture\" et \"Réseau\" calculées à partir de la colonne \"variable\" obtenue en 2.\n",
    "\n",
    "4) Chargez le DataFrame des communes déjà utilisé et effectuez une jointure sur le code INSEE.\n",
    "\n",
    "6) Pour le réseau 4G et la couverture surfacique, calculez la moyenne des couvertures par statut de commune et par opérateur. Pour quel couple (statut, opérateur) a-t-on la moyenne de la couverture la plus faible ?\n",
    "\n",
    "## C) Données Formations\n",
    "\n",
    "Dataset : `ListeOF.zip`\n",
    "\n",
    "1) Charger le fichier dans un DataFrame.\n",
    "\n",
    "2) A partir du nombre de valeurs non nulles des colonnes, styliser un DataFrame avec une barre de couleur.\n",
    "\n",
    "3) A partir du pourcentage de remplissage des colonnes arrondi à un chiffre, styliser un DataFrame avec une colormap.\n",
    "\n",
    "\n",
    "&copy; Francis Wolinski 2024"
   ]
  },
  {
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   "execution_count": 1,
   "id": "7e64fe78-adeb-4810-82c3-1682251c5500",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "05552986-f411-4796-bf83-dbfb8dd4919a",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Code série :</th>\n",
       "      <th>EXR.A.FRF.I36.ERC0.T</th>\n",
       "      <th>EXR.A.FRF.Z32.ERC0.T</th>\n",
       "      <th>EXR.D.AUD.EUR.SP00.A</th>\n",
       "      <th>EXR.D.BGN.EUR.SP00.A</th>\n",
       "      <th>EXR.D.BRL.EUR.SP00.A</th>\n",
       "      <th>EXR.D.CAD.EUR.SP00.A</th>\n",
       "      <th>EXR.D.CHF.EUR.SP00.A</th>\n",
       "      <th>EXR.D.CNY.EUR.SP00.A</th>\n",
       "      <th>EXR.D.CYP.EUR.SP00.A</th>\n",
       "      <th>...</th>\n",
       "      <th>EXR.M.SGD.EUR.SP00.A</th>\n",
       "      <th>EXR.M.SGD.EUR.SP00.E</th>\n",
       "      <th>EXR.M.THB.EUR.SP00.A</th>\n",
       "      <th>EXR.M.THB.EUR.SP00.E</th>\n",
       "      <th>EXR.M.TRY.EUR.SP00.A</th>\n",
       "      <th>EXR.M.TRY.EUR.SP00.E</th>\n",
       "      <th>EXR.M.USD.EUR.SP00.A</th>\n",
       "      <th>EXR.M.USD.EUR.SP00.E</th>\n",
       "      <th>EXR.M.ZAR.EUR.SP00.A</th>\n",
       "      <th>EXR.M.ZAR.EUR.SP00.E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15/05/2024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.6308</td>\n",
       "      <td>1.9558</td>\n",
       "      <td>5.5827</td>\n",
       "      <td>1.4763</td>\n",
       "      <td>0.9800</td>\n",
       "      <td>7.8212</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14/05/2024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.6329</td>\n",
       "      <td>1.9558</td>\n",
       "      <td>5.5419</td>\n",
       "      <td>1.4753</td>\n",
       "      <td>0.9801</td>\n",
       "      <td>7.8103</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13/05/2024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.6305</td>\n",
       "      <td>1.9558</td>\n",
       "      <td>5.5486</td>\n",
       "      <td>1.4757</td>\n",
       "      <td>0.9784</td>\n",
       "      <td>7.8095</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12/05/2024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11/05/2024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 108 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Code série :  EXR.A.FRF.I36.ERC0.T  EXR.A.FRF.Z32.ERC0.T  \\\n",
       "0   15/05/2024                   NaN                   NaN   \n",
       "1   14/05/2024                   NaN                   NaN   \n",
       "2   13/05/2024                   NaN                   NaN   \n",
       "3   12/05/2024                   NaN                   NaN   \n",
       "4   11/05/2024                   NaN                   NaN   \n",
       "\n",
       "   EXR.D.AUD.EUR.SP00.A  EXR.D.BGN.EUR.SP00.A  EXR.D.BRL.EUR.SP00.A  \\\n",
       "0                1.6308                1.9558                5.5827   \n",
       "1                1.6329                1.9558                5.5419   \n",
       "2                1.6305                1.9558                5.5486   \n",
       "3                   NaN                   NaN                   NaN   \n",
       "4                   NaN                   NaN                   NaN   \n",
       "\n",
       "   EXR.D.CAD.EUR.SP00.A  EXR.D.CHF.EUR.SP00.A  EXR.D.CNY.EUR.SP00.A  \\\n",
       "0                1.4763                0.9800                7.8212   \n",
       "1                1.4753                0.9801                7.8103   \n",
       "2                1.4757                0.9784                7.8095   \n",
       "3                   NaN                   NaN                   NaN   \n",
       "4                   NaN                   NaN                   NaN   \n",
       "\n",
       "   EXR.D.CYP.EUR.SP00.A  ...  EXR.M.SGD.EUR.SP00.A  EXR.M.SGD.EUR.SP00.E  \\\n",
       "0                   NaN  ...                   NaN                   NaN   \n",
       "1                   NaN  ...                   NaN                   NaN   \n",
       "2                   NaN  ...                   NaN                   NaN   \n",
       "3                   NaN  ...                   NaN                   NaN   \n",
       "4                   NaN  ...                   NaN                   NaN   \n",
       "\n",
       "   EXR.M.THB.EUR.SP00.A  EXR.M.THB.EUR.SP00.E  EXR.M.TRY.EUR.SP00.A  \\\n",
       "0                   NaN                   NaN                   NaN   \n",
       "1                   NaN                   NaN                   NaN   \n",
       "2                   NaN                   NaN                   NaN   \n",
       "3                   NaN                   NaN                   NaN   \n",
       "4                   NaN                   NaN                   NaN   \n",
       "\n",
       "   EXR.M.TRY.EUR.SP00.E  EXR.M.USD.EUR.SP00.A  EXR.M.USD.EUR.SP00.E  \\\n",
       "0                   NaN                   NaN                   NaN   \n",
       "1                   NaN                   NaN                   NaN   \n",
       "2                   NaN                   NaN                   NaN   \n",
       "3                   NaN                   NaN                   NaN   \n",
       "4                   NaN                   NaN                   NaN   \n",
       "\n",
       "   EXR.M.ZAR.EUR.SP00.A  EXR.M.ZAR.EUR.SP00.E  \n",
       "0                   NaN                   NaN  \n",
       "1                   NaN                   NaN  \n",
       "2                   NaN                   NaN  \n",
       "3                   NaN                   NaN  \n",
       "4                   NaN                   NaN  \n",
       "\n",
       "[5 rows x 108 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/Webstat_Export.csv\",\n",
    "                 sep=\";\",\n",
    "                 header=2,\n",
    "                 na_values='-',\n",
    "                 decimal=','\n",
    "                )\n",
    "df.head()"
   ]
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   "cell_type": "code",
   "execution_count": 3,
   "id": "2d6baa41-6e55-4dbc-9b18-6d8b5543b11f",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 6229 entries, 2000-01-13 to 2024-05-15\n",
      "Data columns (total 6 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   USD     6229 non-null   float64\n",
      " 1   CHF     6229 non-null   float64\n",
      " 2   GBP     6229 non-null   float64\n",
      " 3   JPY     6229 non-null   float64\n",
      " 4   CNY     6229 non-null   float64\n",
      " 5   BRL     6229 non-null   float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 340.6 KB\n"
     ]
    }
   ],
   "source": [
    "# extraction des codes monnaies\n",
    "cols = pd.Series(df.columns.tolist()).str.extract('EXR\\.D\\.([A-Z]{3})\\.EUR\\.SP00\\.A', expand=True)\n",
    "cols.iloc[0] = 'Date'\n",
    "df.columns = cols[0]\n",
    "\n",
    "# conversion des dates\n",
    "df[\"Date\"] = pd.to_datetime(df[\"Date\"], format=\"%d/%m/%Y\")\n",
    "\n",
    "# sélection de quelques colonnes monnaies\n",
    "currencies = ['USD', 'CHF', 'GBP', 'JPY', 'CNY', 'BRL']\n",
    "df = df[['Date'] + currencies]\n",
    "\n",
    "# drop na\n",
    "df = df.dropna()\n",
    "\n",
    "# reset & sort index\n",
    "df = df.set_index('Date').sort_index()\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a5fe1696-c582-4131-bae3-9113db1973ae",
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    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USD</th>\n",
       "      <th>CHF</th>\n",
       "      <th>GBP</th>\n",
       "      <th>JPY</th>\n",
       "      <th>CNY</th>\n",
       "      <th>BRL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2000-01-13</th>\n",
       "      <td>1.0276</td>\n",
       "      <td>1.6111</td>\n",
       "      <td>0.62550</td>\n",
       "      <td>108.89</td>\n",
       "      <td>8.5054</td>\n",
       "      <td>1.8718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-14</th>\n",
       "      <td>1.0225</td>\n",
       "      <td>1.6130</td>\n",
       "      <td>0.62210</td>\n",
       "      <td>108.05</td>\n",
       "      <td>8.4632</td>\n",
       "      <td>1.8425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-17</th>\n",
       "      <td>1.0094</td>\n",
       "      <td>1.6136</td>\n",
       "      <td>0.61800</td>\n",
       "      <td>105.81</td>\n",
       "      <td>8.3548</td>\n",
       "      <td>1.8129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-18</th>\n",
       "      <td>1.0093</td>\n",
       "      <td>1.6131</td>\n",
       "      <td>0.61740</td>\n",
       "      <td>106.42</td>\n",
       "      <td>8.3540</td>\n",
       "      <td>1.8066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-19</th>\n",
       "      <td>1.0105</td>\n",
       "      <td>1.6120</td>\n",
       "      <td>0.61720</td>\n",
       "      <td>106.99</td>\n",
       "      <td>8.3639</td>\n",
       "      <td>1.8179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-09</th>\n",
       "      <td>1.0732</td>\n",
       "      <td>0.9760</td>\n",
       "      <td>0.85995</td>\n",
       "      <td>167.32</td>\n",
       "      <td>7.7554</td>\n",
       "      <td>5.5189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-10</th>\n",
       "      <td>1.0779</td>\n",
       "      <td>0.9779</td>\n",
       "      <td>0.86055</td>\n",
       "      <td>167.87</td>\n",
       "      <td>7.7877</td>\n",
       "      <td>5.5332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-13</th>\n",
       "      <td>1.0795</td>\n",
       "      <td>0.9784</td>\n",
       "      <td>0.86023</td>\n",
       "      <td>168.25</td>\n",
       "      <td>7.8095</td>\n",
       "      <td>5.5486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-14</th>\n",
       "      <td>1.0796</td>\n",
       "      <td>0.9801</td>\n",
       "      <td>0.85983</td>\n",
       "      <td>168.89</td>\n",
       "      <td>7.8103</td>\n",
       "      <td>5.5419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-15</th>\n",
       "      <td>1.0832</td>\n",
       "      <td>0.9800</td>\n",
       "      <td>0.85840</td>\n",
       "      <td>168.43</td>\n",
       "      <td>7.8212</td>\n",
       "      <td>5.5827</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6229 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0              USD     CHF      GBP     JPY     CNY     BRL\n",
       "Date                                                       \n",
       "2000-01-13  1.0276  1.6111  0.62550  108.89  8.5054  1.8718\n",
       "2000-01-14  1.0225  1.6130  0.62210  108.05  8.4632  1.8425\n",
       "2000-01-17  1.0094  1.6136  0.61800  105.81  8.3548  1.8129\n",
       "2000-01-18  1.0093  1.6131  0.61740  106.42  8.3540  1.8066\n",
       "2000-01-19  1.0105  1.6120  0.61720  106.99  8.3639  1.8179\n",
       "...            ...     ...      ...     ...     ...     ...\n",
       "2024-05-09  1.0732  0.9760  0.85995  167.32  7.7554  5.5189\n",
       "2024-05-10  1.0779  0.9779  0.86055  167.87  7.7877  5.5332\n",
       "2024-05-13  1.0795  0.9784  0.86023  168.25  7.8095  5.5486\n",
       "2024-05-14  1.0796  0.9801  0.85983  168.89  7.8103  5.5419\n",
       "2024-05-15  1.0832  0.9800  0.85840  168.43  7.8212  5.5827\n",
       "\n",
       "[6229 rows x 6 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
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  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ecdeb430-8235-478a-b34a-0bdefa7ed4fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USD</th>\n",
       "      <th>CHF</th>\n",
       "      <th>GBP</th>\n",
       "      <th>JPY</th>\n",
       "      <th>CNY</th>\n",
       "      <th>BRL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2020-10-01</th>\n",
       "      <td>1.1752</td>\n",
       "      <td>1.0787</td>\n",
       "      <td>0.90723</td>\n",
       "      <td>123.98</td>\n",
       "      <td>7.9805</td>\n",
       "      <td>6.5821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-02</th>\n",
       "      <td>1.1730</td>\n",
       "      <td>1.0790</td>\n",
       "      <td>0.90673</td>\n",
       "      <td>123.40</td>\n",
       "      <td>7.9656</td>\n",
       "      <td>6.6039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-05</th>\n",
       "      <td>1.1768</td>\n",
       "      <td>1.0781</td>\n",
       "      <td>0.90810</td>\n",
       "      <td>124.25</td>\n",
       "      <td>8.0128</td>\n",
       "      <td>6.6568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-06</th>\n",
       "      <td>1.1795</td>\n",
       "      <td>1.0781</td>\n",
       "      <td>0.91058</td>\n",
       "      <td>124.58</td>\n",
       "      <td>8.0111</td>\n",
       "      <td>6.5363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-07</th>\n",
       "      <td>1.1770</td>\n",
       "      <td>1.0787</td>\n",
       "      <td>0.91413</td>\n",
       "      <td>124.73</td>\n",
       "      <td>7.9934</td>\n",
       "      <td>6.5448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-25</th>\n",
       "      <td>1.2152</td>\n",
       "      <td>1.0772</td>\n",
       "      <td>0.88800</td>\n",
       "      <td>126.06</td>\n",
       "      <td>7.8712</td>\n",
       "      <td>6.6434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-26</th>\n",
       "      <td>1.2143</td>\n",
       "      <td>1.0789</td>\n",
       "      <td>0.88698</td>\n",
       "      <td>125.93</td>\n",
       "      <td>7.8537</td>\n",
       "      <td>6.5816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-27</th>\n",
       "      <td>1.2114</td>\n",
       "      <td>1.0759</td>\n",
       "      <td>0.88395</td>\n",
       "      <td>125.87</td>\n",
       "      <td>7.8411</td>\n",
       "      <td>6.4967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-28</th>\n",
       "      <td>1.2091</td>\n",
       "      <td>1.0783</td>\n",
       "      <td>0.88603</td>\n",
       "      <td>126.20</td>\n",
       "      <td>7.8226</td>\n",
       "      <td>6.5857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-29</th>\n",
       "      <td>1.2136</td>\n",
       "      <td>1.0798</td>\n",
       "      <td>0.88383</td>\n",
       "      <td>127.05</td>\n",
       "      <td>7.8047</td>\n",
       "      <td>6.6568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0              USD     CHF      GBP     JPY     CNY     BRL\n",
       "Date                                                       \n",
       "2020-10-01  1.1752  1.0787  0.90723  123.98  7.9805  6.5821\n",
       "2020-10-02  1.1730  1.0790  0.90673  123.40  7.9656  6.6039\n",
       "2020-10-05  1.1768  1.0781  0.90810  124.25  8.0128  6.6568\n",
       "2020-10-06  1.1795  1.0781  0.91058  124.58  8.0111  6.5363\n",
       "2020-10-07  1.1770  1.0787  0.91413  124.73  7.9934  6.5448\n",
       "...            ...     ...      ...     ...     ...     ...\n",
       "2021-01-25  1.2152  1.0772  0.88800  126.06  7.8712  6.6434\n",
       "2021-01-26  1.2143  1.0789  0.88698  125.93  7.8537  6.5816\n",
       "2021-01-27  1.2114  1.0759  0.88395  125.87  7.8411  6.4967\n",
       "2021-01-28  1.2091  1.0783  0.88603  126.20  7.8226  6.5857\n",
       "2021-01-29  1.2136  1.0798  0.88383  127.05  7.8047  6.6568\n",
       "\n",
       "[85 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"10/2020\":\"1/2021\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e16ccfe0-eec9-4c94-9563-fab8e538f71a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>2000-01-13</th>\n",
       "      <td>1.0276</td>\n",
       "      <td>1.6111</td>\n",
       "      <td>0.6255</td>\n",
       "      <td>108.89</td>\n",
       "      <td>8.5054</td>\n",
       "      <td>1.8718</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-14</th>\n",
       "      <td>1.0225</td>\n",
       "      <td>1.6130</td>\n",
       "      <td>0.6221</td>\n",
       "      <td>108.05</td>\n",
       "      <td>8.4632</td>\n",
       "      <td>1.8425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-17</th>\n",
       "      <td>1.0094</td>\n",
       "      <td>1.6136</td>\n",
       "      <td>0.6180</td>\n",
       "      <td>105.81</td>\n",
       "      <td>8.3548</td>\n",
       "      <td>1.8129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-18</th>\n",
       "      <td>1.0093</td>\n",
       "      <td>1.6131</td>\n",
       "      <td>0.6174</td>\n",
       "      <td>106.42</td>\n",
       "      <td>8.3540</td>\n",
       "      <td>1.8066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-19</th>\n",
       "      <td>1.0105</td>\n",
       "      <td>1.6120</td>\n",
       "      <td>0.6172</td>\n",
       "      <td>106.99</td>\n",
       "      <td>8.3639</td>\n",
       "      <td>1.8179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0              USD     CHF     GBP     JPY     CNY     BRL\n",
       "Date                                                      \n",
       "2000-01-13  1.0276  1.6111  0.6255  108.89  8.5054  1.8718\n",
       "2000-01-14  1.0225  1.6130  0.6221  108.05  8.4632  1.8425\n",
       "2000-01-17  1.0094  1.6136  0.6180  105.81  8.3548  1.8129\n",
       "2000-01-18  1.0093  1.6131  0.6174  106.42  8.3540  1.8066\n",
       "2000-01-19  1.0105  1.6120  0.6172  106.99  8.3639  1.8179"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d5180b1d-e4d5-4194-999b-b9333dbccf3a",
   "metadata": {},
   "outputs": [
    {
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       "      <th>JPY</th>\n",
       "      <th>CNY</th>\n",
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       "  <tbody>\n",
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       "      <th>2000-01-13</th>\n",
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       "      <td>1.6111</td>\n",
       "      <td>0.6255</td>\n",
       "      <td>108.89</td>\n",
       "      <td>8.5054</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-14</th>\n",
       "      <td>1.0225</td>\n",
       "      <td>1.6130</td>\n",
       "      <td>0.6221</td>\n",
       "      <td>108.05</td>\n",
       "      <td>8.4632</td>\n",
       "      <td>1.8425</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-17</th>\n",
       "      <td>1.0094</td>\n",
       "      <td>1.6136</td>\n",
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       "      <td>105.81</td>\n",
       "      <td>8.3548</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0              USD     CHF     GBP     JPY     CNY     BRL\n",
       "2000-01-13  1.0276  1.6111  0.6255  108.89  8.5054  1.8718\n",
       "2000-01-14  1.0225  1.6130  0.6221  108.05  8.4632  1.8425\n",
       "2000-01-15     NaN     NaN     NaN     NaN     NaN     NaN\n",
       "2000-01-16     NaN     NaN     NaN     NaN     NaN     NaN\n",
       "2000-01-17  1.0094  1.6136  0.6180  105.81  8.3548  1.8129"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_index = pd.date_range(df.index[0], df.index[-1], freq=\"1D\")\n",
    "df = df.reindex(new_index)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f707f7b7-4051-4639-8c79-2e5dddb1b5dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2000-01-15</th>\n",
       "      <td>1.0225</td>\n",
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       "      <td>108.05</td>\n",
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       "      <th>2000-01-16</th>\n",
       "      <td>1.0225</td>\n",
       "      <td>1.6130</td>\n",
       "      <td>0.6221</td>\n",
       "      <td>108.05</td>\n",
       "      <td>8.4632</td>\n",
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       "      <td>1.0094</td>\n",
       "      <td>1.6136</td>\n",
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       "      <td>105.81</td>\n",
       "      <td>8.3548</td>\n",
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      "text/plain": [
       "0              USD     CHF     GBP     JPY     CNY     BRL\n",
       "2000-01-13  1.0276  1.6111  0.6255  108.89  8.5054  1.8718\n",
       "2000-01-14  1.0225  1.6130  0.6221  108.05  8.4632  1.8425\n",
       "2000-01-15  1.0225  1.6130  0.6221  108.05  8.4632  1.8425\n",
       "2000-01-16  1.0225  1.6130  0.6221  108.05  8.4632  1.8425\n",
       "2000-01-17  1.0094  1.6136  0.6180  105.81  8.3548  1.8129"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ffill().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e82856c0-3ad7-4775-aee9-6f34d55a10db",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.0225</td>\n",
       "      <td>1.6130</td>\n",
       "      <td>0.6221</td>\n",
       "      <td>108.05</td>\n",
       "      <td>8.4632</td>\n",
       "      <td>1.8425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-15</th>\n",
       "      <td>1.0094</td>\n",
       "      <td>1.6136</td>\n",
       "      <td>0.6180</td>\n",
       "      <td>105.81</td>\n",
       "      <td>8.3548</td>\n",
       "      <td>1.8129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-16</th>\n",
       "      <td>1.0094</td>\n",
       "      <td>1.6136</td>\n",
       "      <td>0.6180</td>\n",
       "      <td>105.81</td>\n",
       "      <td>8.3548</td>\n",
       "      <td>1.8129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-17</th>\n",
       "      <td>1.0094</td>\n",
       "      <td>1.6136</td>\n",
       "      <td>0.6180</td>\n",
       "      <td>105.81</td>\n",
       "      <td>8.3548</td>\n",
       "      <td>1.8129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0              USD     CHF     GBP     JPY     CNY     BRL\n",
       "2000-01-13  1.0276  1.6111  0.6255  108.89  8.5054  1.8718\n",
       "2000-01-14  1.0225  1.6130  0.6221  108.05  8.4632  1.8425\n",
       "2000-01-15  1.0094  1.6136  0.6180  105.81  8.3548  1.8129\n",
       "2000-01-16  1.0094  1.6136  0.6180  105.81  8.3548  1.8129\n",
       "2000-01-17  1.0094  1.6136  0.6180  105.81  8.3548  1.8129"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.bfill().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4296fe1c-92ad-42e1-b62b-e412c6eadc75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USD</th>\n",
       "      <th>CHF</th>\n",
       "      <th>GBP</th>\n",
       "      <th>JPY</th>\n",
       "      <th>CNY</th>\n",
       "      <th>BRL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-13</th>\n",
       "      <td>1.027600</td>\n",
       "      <td>1.6111</td>\n",
       "      <td>0.625500</td>\n",
       "      <td>108.890000</td>\n",
       "      <td>8.505400</td>\n",
       "      <td>1.871800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-14</th>\n",
       "      <td>1.022500</td>\n",
       "      <td>1.6130</td>\n",
       "      <td>0.622100</td>\n",
       "      <td>108.050000</td>\n",
       "      <td>8.463200</td>\n",
       "      <td>1.842500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-15</th>\n",
       "      <td>1.018133</td>\n",
       "      <td>1.6132</td>\n",
       "      <td>0.620733</td>\n",
       "      <td>107.303333</td>\n",
       "      <td>8.427067</td>\n",
       "      <td>1.832633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-16</th>\n",
       "      <td>1.013767</td>\n",
       "      <td>1.6134</td>\n",
       "      <td>0.619367</td>\n",
       "      <td>106.556667</td>\n",
       "      <td>8.390933</td>\n",
       "      <td>1.822767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-17</th>\n",
       "      <td>1.009400</td>\n",
       "      <td>1.6136</td>\n",
       "      <td>0.618000</td>\n",
       "      <td>105.810000</td>\n",
       "      <td>8.354800</td>\n",
       "      <td>1.812900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0                USD     CHF       GBP         JPY       CNY       BRL\n",
       "2000-01-13  1.027600  1.6111  0.625500  108.890000  8.505400  1.871800\n",
       "2000-01-14  1.022500  1.6130  0.622100  108.050000  8.463200  1.842500\n",
       "2000-01-15  1.018133  1.6132  0.620733  107.303333  8.427067  1.832633\n",
       "2000-01-16  1.013767  1.6134  0.619367  106.556667  8.390933  1.822767\n",
       "2000-01-17  1.009400  1.6136  0.618000  105.810000  8.354800  1.812900"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.interpolate().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c81242ee-65f9-4411-b88c-bdd68c868623",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>USD</th>\n",
       "      <th>CHF</th>\n",
       "      <th>GBP</th>\n",
       "      <th>JPY</th>\n",
       "      <th>CNY</th>\n",
       "      <th>BRL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-12-31</th>\n",
       "      <td>0.920236</td>\n",
       "      <td>1.556258</td>\n",
       "      <td>0.608901</td>\n",
       "      <td>99.223036</td>\n",
       "      <td>7.616797</td>\n",
       "      <td>1.682124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-31</th>\n",
       "      <td>0.895630</td>\n",
       "      <td>1.510518</td>\n",
       "      <td>0.621874</td>\n",
       "      <td>108.682362</td>\n",
       "      <td>7.413124</td>\n",
       "      <td>2.106945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-12-31</th>\n",
       "      <td>0.945574</td>\n",
       "      <td>1.467025</td>\n",
       "      <td>0.628831</td>\n",
       "      <td>118.062549</td>\n",
       "      <td>7.826513</td>\n",
       "      <td>2.789636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-12-31</th>\n",
       "      <td>1.131160</td>\n",
       "      <td>1.521201</td>\n",
       "      <td>0.691993</td>\n",
       "      <td>130.971216</td>\n",
       "      <td>9.362613</td>\n",
       "      <td>3.470141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-12-31</th>\n",
       "      <td>1.243902</td>\n",
       "      <td>1.543822</td>\n",
       "      <td>0.678659</td>\n",
       "      <td>134.444595</td>\n",
       "      <td>10.296699</td>\n",
       "      <td>3.636177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-31</th>\n",
       "      <td>1.244090</td>\n",
       "      <td>1.548282</td>\n",
       "      <td>0.683796</td>\n",
       "      <td>136.849183</td>\n",
       "      <td>10.195502</td>\n",
       "      <td>3.036002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-31</th>\n",
       "      <td>1.255599</td>\n",
       "      <td>1.572878</td>\n",
       "      <td>0.681730</td>\n",
       "      <td>146.015333</td>\n",
       "      <td>10.009558</td>\n",
       "      <td>2.733316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-12-31</th>\n",
       "      <td>1.370478</td>\n",
       "      <td>1.642720</td>\n",
       "      <td>0.684337</td>\n",
       "      <td>161.252627</td>\n",
       "      <td>10.417824</td>\n",
       "      <td>2.663338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-31</th>\n",
       "      <td>1.470755</td>\n",
       "      <td>1.587395</td>\n",
       "      <td>0.796285</td>\n",
       "      <td>152.454062</td>\n",
       "      <td>10.223613</td>\n",
       "      <td>2.673726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>1.394782</td>\n",
       "      <td>1.510019</td>\n",
       "      <td>0.890940</td>\n",
       "      <td>130.336602</td>\n",
       "      <td>9.527708</td>\n",
       "      <td>2.767423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-31</th>\n",
       "      <td>1.325717</td>\n",
       "      <td>1.380344</td>\n",
       "      <td>0.857844</td>\n",
       "      <td>116.238566</td>\n",
       "      <td>8.971232</td>\n",
       "      <td>2.331428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-31</th>\n",
       "      <td>1.391955</td>\n",
       "      <td>1.232605</td>\n",
       "      <td>0.867884</td>\n",
       "      <td>110.958599</td>\n",
       "      <td>8.995997</td>\n",
       "      <td>2.326505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>1.284789</td>\n",
       "      <td>1.205275</td>\n",
       "      <td>0.810871</td>\n",
       "      <td>102.491875</td>\n",
       "      <td>8.105231</td>\n",
       "      <td>2.508436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>1.328118</td>\n",
       "      <td>1.231059</td>\n",
       "      <td>0.849255</td>\n",
       "      <td>129.662667</td>\n",
       "      <td>8.164629</td>\n",
       "      <td>2.868656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>1.328501</td>\n",
       "      <td>1.214622</td>\n",
       "      <td>0.806120</td>\n",
       "      <td>140.306118</td>\n",
       "      <td>8.185747</td>\n",
       "      <td>3.121129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>1.109513</td>\n",
       "      <td>1.067857</td>\n",
       "      <td>0.725835</td>\n",
       "      <td>134.314023</td>\n",
       "      <td>6.973325</td>\n",
       "      <td>3.700436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>1.106903</td>\n",
       "      <td>1.090155</td>\n",
       "      <td>0.819483</td>\n",
       "      <td>120.196654</td>\n",
       "      <td>7.352221</td>\n",
       "      <td>3.856144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>1.129681</td>\n",
       "      <td>1.111674</td>\n",
       "      <td>0.876674</td>\n",
       "      <td>126.711176</td>\n",
       "      <td>7.629002</td>\n",
       "      <td>3.605432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>1.180955</td>\n",
       "      <td>1.154958</td>\n",
       "      <td>0.884706</td>\n",
       "      <td>130.395882</td>\n",
       "      <td>7.808079</td>\n",
       "      <td>4.308493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>1.119475</td>\n",
       "      <td>1.112449</td>\n",
       "      <td>0.877771</td>\n",
       "      <td>122.005765</td>\n",
       "      <td>7.735491</td>\n",
       "      <td>4.413433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>1.142196</td>\n",
       "      <td>1.070523</td>\n",
       "      <td>0.889704</td>\n",
       "      <td>121.845759</td>\n",
       "      <td>7.874697</td>\n",
       "      <td>5.894258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>1.182740</td>\n",
       "      <td>1.081147</td>\n",
       "      <td>0.859604</td>\n",
       "      <td>129.876705</td>\n",
       "      <td>7.628231</td>\n",
       "      <td>6.377890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>1.053049</td>\n",
       "      <td>1.004709</td>\n",
       "      <td>0.852761</td>\n",
       "      <td>138.027393</td>\n",
       "      <td>7.078800</td>\n",
       "      <td>5.439904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31</th>\n",
       "      <td>1.081269</td>\n",
       "      <td>0.971797</td>\n",
       "      <td>0.869787</td>\n",
       "      <td>151.990275</td>\n",
       "      <td>7.660022</td>\n",
       "      <td>5.401009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12-31</th>\n",
       "      <td>1.081904</td>\n",
       "      <td>0.958101</td>\n",
       "      <td>0.856579</td>\n",
       "      <td>162.642340</td>\n",
       "      <td>7.793584</td>\n",
       "      <td>5.417083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0                USD       CHF       GBP         JPY        CNY       BRL\n",
       "2000-12-31  0.920236  1.556258  0.608901   99.223036   7.616797  1.682124\n",
       "2001-12-31  0.895630  1.510518  0.621874  108.682362   7.413124  2.106945\n",
       "2002-12-31  0.945574  1.467025  0.628831  118.062549   7.826513  2.789636\n",
       "2003-12-31  1.131160  1.521201  0.691993  130.971216   9.362613  3.470141\n",
       "2004-12-31  1.243902  1.543822  0.678659  134.444595  10.296699  3.636177\n",
       "2005-12-31  1.244090  1.548282  0.683796  136.849183  10.195502  3.036002\n",
       "2006-12-31  1.255599  1.572878  0.681730  146.015333  10.009558  2.733316\n",
       "2007-12-31  1.370478  1.642720  0.684337  161.252627  10.417824  2.663338\n",
       "2008-12-31  1.470755  1.587395  0.796285  152.454062  10.223613  2.673726\n",
       "2009-12-31  1.394782  1.510019  0.890940  130.336602   9.527708  2.767423\n",
       "2010-12-31  1.325717  1.380344  0.857844  116.238566   8.971232  2.331428\n",
       "2011-12-31  1.391955  1.232605  0.867884  110.958599   8.995997  2.326505\n",
       "2012-12-31  1.284789  1.205275  0.810871  102.491875   8.105231  2.508436\n",
       "2013-12-31  1.328118  1.231059  0.849255  129.662667   8.164629  2.868656\n",
       "2014-12-31  1.328501  1.214622  0.806120  140.306118   8.185747  3.121129\n",
       "2015-12-31  1.109513  1.067857  0.725835  134.314023   6.973325  3.700436\n",
       "2016-12-31  1.106903  1.090155  0.819483  120.196654   7.352221  3.856144\n",
       "2017-12-31  1.129681  1.111674  0.876674  126.711176   7.629002  3.605432\n",
       "2018-12-31  1.180955  1.154958  0.884706  130.395882   7.808079  4.308493\n",
       "2019-12-31  1.119475  1.112449  0.877771  122.005765   7.735491  4.413433\n",
       "2020-12-31  1.142196  1.070523  0.889704  121.845759   7.874697  5.894258\n",
       "2021-12-31  1.182740  1.081147  0.859604  129.876705   7.628231  6.377890\n",
       "2022-12-31  1.053049  1.004709  0.852761  138.027393   7.078800  5.439904\n",
       "2023-12-31  1.081269  0.971797  0.869787  151.990275   7.660022  5.401009\n",
       "2024-12-31  1.081904  0.958101  0.856579  162.642340   7.793584  5.417083"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.resample('YE').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4be37738-e4aa-4f88-a03f-b98d6dfd0117",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USD</th>\n",
       "      <th>CHF</th>\n",
       "      <th>GBP</th>\n",
       "      <th>JPY</th>\n",
       "      <th>CNY</th>\n",
       "      <th>BRL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-31</th>\n",
       "      <td>1.0276</td>\n",
       "      <td>1.6147</td>\n",
       "      <td>0.62550</td>\n",
       "      <td>108.89</td>\n",
       "      <td>8.5054</td>\n",
       "      <td>1.8718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02-29</th>\n",
       "      <td>1.0068</td>\n",
       "      <td>1.6105</td>\n",
       "      <td>0.62520</td>\n",
       "      <td>111.88</td>\n",
       "      <td>8.3333</td>\n",
       "      <td>2.0141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-03-31</th>\n",
       "      <td>0.9725</td>\n",
       "      <td>1.6126</td>\n",
       "      <td>0.62010</td>\n",
       "      <td>104.65</td>\n",
       "      <td>8.0494</td>\n",
       "      <td>1.7121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-30</th>\n",
       "      <td>0.9673</td>\n",
       "      <td>1.5886</td>\n",
       "      <td>0.60880</td>\n",
       "      <td>102.61</td>\n",
       "      <td>8.0063</td>\n",
       "      <td>1.7102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-05-31</th>\n",
       "      <td>0.9359</td>\n",
       "      <td>1.5718</td>\n",
       "      <td>0.62260</td>\n",
       "      <td>100.07</td>\n",
       "      <td>7.7464</td>\n",
       "      <td>1.7197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-31</th>\n",
       "      <td>1.0987</td>\n",
       "      <td>0.9459</td>\n",
       "      <td>0.86645</td>\n",
       "      <td>161.17</td>\n",
       "      <td>7.8649</td>\n",
       "      <td>5.3981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-29</th>\n",
       "      <td>1.0883</td>\n",
       "      <td>0.9546</td>\n",
       "      <td>0.85660</td>\n",
       "      <td>163.38</td>\n",
       "      <td>7.8140</td>\n",
       "      <td>5.4111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-31</th>\n",
       "      <td>1.0939</td>\n",
       "      <td>0.9811</td>\n",
       "      <td>0.85846</td>\n",
       "      <td>164.96</td>\n",
       "      <td>7.8678</td>\n",
       "      <td>5.4766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-30</th>\n",
       "      <td>1.0867</td>\n",
       "      <td>0.9846</td>\n",
       "      <td>0.86328</td>\n",
       "      <td>168.27</td>\n",
       "      <td>7.8603</td>\n",
       "      <td>5.6165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-31</th>\n",
       "      <td>1.0832</td>\n",
       "      <td>0.9801</td>\n",
       "      <td>0.86083</td>\n",
       "      <td>168.89</td>\n",
       "      <td>7.8212</td>\n",
       "      <td>5.5827</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>293 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0              USD     CHF      GBP     JPY     CNY     BRL\n",
       "2000-01-31  1.0276  1.6147  0.62550  108.89  8.5054  1.8718\n",
       "2000-02-29  1.0068  1.6105  0.62520  111.88  8.3333  2.0141\n",
       "2000-03-31  0.9725  1.6126  0.62010  104.65  8.0494  1.7121\n",
       "2000-04-30  0.9673  1.5886  0.60880  102.61  8.0063  1.7102\n",
       "2000-05-31  0.9359  1.5718  0.62260  100.07  7.7464  1.7197\n",
       "...            ...     ...      ...     ...     ...     ...\n",
       "2024-01-31  1.0987  0.9459  0.86645  161.17  7.8649  5.3981\n",
       "2024-02-29  1.0883  0.9546  0.85660  163.38  7.8140  5.4111\n",
       "2024-03-31  1.0939  0.9811  0.85846  164.96  7.8678  5.4766\n",
       "2024-04-30  1.0867  0.9846  0.86328  168.27  7.8603  5.6165\n",
       "2024-05-31  1.0832  0.9801  0.86083  168.89  7.8212  5.5827\n",
       "\n",
       "[293 rows x 6 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.resample('ME').max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "29ceadf7-241d-498c-8e38-b25f98a94138",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3f6859f3-c52b-453c-bc78-5e6394b3dc98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df/df.mean()).rolling(30).max().plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e70d89dd-fbab-475e-90c7-49d7ee0f4267",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df/df.iloc[-1]).rolling(30).mean().plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "753c6455-d1f3-477b-a237-e8d52dbb1193",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.concat([df[\"USD\"], df[\"USD\"].rolling(360).mean()], axis=1).plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "926a87f2-42a2-4f5f-89b9-85a1c4992d03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "df[\"USD\"].plot(ax=ax, label=\"USD\")\n",
    "df[\"USD\"].rolling(360, center=True).mean().plot(ax=ax, label=\"Moyenne glissante USD\")\n",
    "ax.legend(bbox_to_anchor=(1.5, 1.0));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e35b4bdd-1770-4441-8411-539a3be3d620",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sparklines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a2b53250-a5fa-4547-889c-481b63475c2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'▁▂▃▃▄▅▆▆▇█'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparklines.sparklines(range(10))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4aa7c7c7-360d-4318-9bf7-95ed6aa9a578",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'█▇▃▃▄▃▃▁▁▁'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparklines.sparklines(df[\"USD\"].head(10))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "3a959102-69a3-4fef-b08a-c60b14b7f68f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USD</th>\n",
       "      <th>trend_USD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-12-31</th>\n",
       "      <td>0.920</td>\n",
       "      <td>█▅▃▁</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-31</th>\n",
       "      <td>0.896</td>\n",
       "      <td>█▁▃▄</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-12-31</th>\n",
       "      <td>0.946</td>\n",
       "      <td>▁▃▇█</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-12-31</th>\n",
       "      <td>1.131</td>\n",
       "      <td>▁▅▄█</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-12-31</th>\n",
       "      <td>1.244</td>\n",
       "      <td>▄▁▂█</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-31</th>\n",
       "      <td>1.244</td>\n",
       "      <td>█▅▃▁</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-31</th>\n",
       "      <td>1.256</td>\n",
       "      <td>▁▆▇█</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-12-31</th>\n",
       "      <td>1.370</td>\n",
       "      <td>▁▃▄█</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-31</th>\n",
       "      <td>1.471</td>\n",
       "      <td>▆█▆▁</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>1.395</td>\n",
       "      <td>▁▃▆█</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-31</th>\n",
       "      <td>1.326</td>\n",
       "      <td>█▁▂▆</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-31</th>\n",
       "      <td>1.392</td>\n",
       "      <td>▃█▆▁</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>1.285</td>\n",
       "      <td>█▅▁▆</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>1.328</td>\n",
       "      <td>▃▁▃█</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>1.329</td>\n",
       "      <td>██▅▁</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>1.110</td>\n",
       "      <td>█▃▅▁</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>1.107</td>\n",
       "      <td>▄█▆▁</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>1.130</td>\n",
       "      <td>▁▃██</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>1.181</td>\n",
       "      <td>█▅▃▁</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>1.119</td>\n",
       "      <td>█▅▂▁</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>1.142</td>\n",
       "      <td>▁▁▆█</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>1.183</td>\n",
       "      <td>██▅▁</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>1.053</td>\n",
       "      <td>█▅▁▂</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31</th>\n",
       "      <td>1.081</td>\n",
       "      <td>▁██▂</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12-31</th>\n",
       "      <td>1.082</td>\n",
       "      <td>█▁</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              USD trend_USD\n",
       "2000-12-31  0.920      █▅▃▁\n",
       "2001-12-31  0.896      █▁▃▄\n",
       "2002-12-31  0.946      ▁▃▇█\n",
       "2003-12-31  1.131      ▁▅▄█\n",
       "2004-12-31  1.244      ▄▁▂█\n",
       "2005-12-31  1.244      █▅▃▁\n",
       "2006-12-31  1.256      ▁▆▇█\n",
       "2007-12-31  1.370      ▁▃▄█\n",
       "2008-12-31  1.471      ▆█▆▁\n",
       "2009-12-31  1.395      ▁▃▆█\n",
       "2010-12-31  1.326      █▁▂▆\n",
       "2011-12-31  1.392      ▃█▆▁\n",
       "2012-12-31  1.285      █▅▁▆\n",
       "2013-12-31  1.328      ▃▁▃█\n",
       "2014-12-31  1.329      ██▅▁\n",
       "2015-12-31  1.110      █▃▅▁\n",
       "2016-12-31  1.107      ▄█▆▁\n",
       "2017-12-31  1.130      ▁▃██\n",
       "2018-12-31  1.181      █▅▃▁\n",
       "2019-12-31  1.119      █▅▂▁\n",
       "2020-12-31  1.142      ▁▁▆█\n",
       "2021-12-31  1.183      ██▅▁\n",
       "2022-12-31  1.053      █▅▁▂\n",
       "2023-12-31  1.081      ▁██▂\n",
       "2024-12-31  1.082        █▁"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df\n",
    "# .groupby(pd.Grouper(freq='A'))\n",
    " .resample(\"YE\")\n",
    " .agg(USD=('USD', lambda s_: s_.mean().round(3)),\n",
    "      trend_USD=('USD', lambda s_: sparklines.sparklines(s_.resample('QE').mean())[0]))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a5dad34-5016-49ff-ba32-45b4d13fad12",
   "metadata": {},
   "source": [
    "à tester sktime : https://www.sktime.net/en/stable/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "47644778-f692-4bb6-85ac-4e00e6542947",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CODE POSTAL</th>\n",
       "      <th>CODE INSEE</th>\n",
       "      <th>NOM COMMUNE</th>\n",
       "      <th>CODE DEPARTEMENT</th>\n",
       "      <th>SURFACE COMMUNE (km²)</th>\n",
       "      <th>POPULATION COMMUNE</th>\n",
       "      <th>Orange France Couverture population 4G</th>\n",
       "      <th>Bouygues Telecom Couverture population 4G</th>\n",
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       "      <th>Free Mobile Couverture population 4G</th>\n",
       "      <th>...</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>17510</td>\n",
       "      <td>17105</td>\n",
       "      <td>CHIVES</td>\n",
       "      <td>17</td>\n",
       "      <td>20.642005</td>\n",
       "      <td>355.32</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>45.9508835378, -0.121918052323</td>\n",
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       "      <td>17600</td>\n",
       "      <td>17112</td>\n",
       "      <td>LA CLISSE</td>\n",
       "      <td>17</td>\n",
       "      <td>4.896771</td>\n",
       "      <td>591.78</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.00</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.7320429784, -0.75493581331</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17150</td>\n",
       "      <td>17116</td>\n",
       "      <td>CONSAC</td>\n",
       "      <td>17</td>\n",
       "      <td>8.915911</td>\n",
       "      <td>239.74</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.4182495786, -0.588917528415</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17130</td>\n",
       "      <td>17118</td>\n",
       "      <td>CORIGNAC</td>\n",
       "      <td>17</td>\n",
       "      <td>10.850210</td>\n",
       "      <td>358.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.2448604983, -0.39280322021</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17330</td>\n",
       "      <td>17124</td>\n",
       "      <td>COURANT</td>\n",
       "      <td>17</td>\n",
       "      <td>15.586269</td>\n",
       "      <td>371.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>46.0375312829, -0.587834905531</td>\n",
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       "    <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>36589</th>\n",
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       "      <td>NAVENNE</td>\n",
       "      <td>70</td>\n",
       "      <td>3.826265</td>\n",
       "      <td>1847.69</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>47.6030208472, 6.1618556725</td>\n",
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       "    <tr>\n",
       "      <th>36590</th>\n",
       "      <td>70000</td>\n",
       "      <td>70388</td>\n",
       "      <td>NOIDANS-LES-VESOUL</td>\n",
       "      <td>70</td>\n",
       "      <td>8.634594</td>\n",
       "      <td>2159.19</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>47.6097514089, 6.11842989879</td>\n",
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       "    <tr>\n",
       "      <th>36591</th>\n",
       "      <td>70100</td>\n",
       "      <td>70389</td>\n",
       "      <td>NOIRON</td>\n",
       "      <td>70</td>\n",
       "      <td>5.768295</td>\n",
       "      <td>64.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>47.3828510502, 5.62597938451</td>\n",
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       "    <tr>\n",
       "      <th>36592</th>\n",
       "      <td>70500</td>\n",
       "      <td>70399</td>\n",
       "      <td>ORMOY</td>\n",
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       "      <td>19.577164</td>\n",
       "      <td>224.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>47.8850612988, 5.99402771513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36593</th>\n",
       "      <td>70130</td>\n",
       "      <td>70418</td>\n",
       "      <td>LE PONT-DE-PLANCHES</td>\n",
       "      <td>70</td>\n",
       "      <td>6.379713</td>\n",
       "      <td>213.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>47.5374721676, 5.92263244824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36594 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CODE POSTAL CODE INSEE          NOM COMMUNE CODE DEPARTEMENT  \\\n",
       "0           17510      17105               CHIVES               17   \n",
       "1           17600      17112            LA CLISSE               17   \n",
       "2           17150      17116               CONSAC               17   \n",
       "3           17130      17118             CORIGNAC               17   \n",
       "4           17330      17124              COURANT               17   \n",
       "...           ...        ...                  ...              ...   \n",
       "36589       70000      70378              NAVENNE               70   \n",
       "36590       70000      70388   NOIDANS-LES-VESOUL               70   \n",
       "36591       70100      70389               NOIRON               70   \n",
       "36592       70500      70399                ORMOY               70   \n",
       "36593       70130      70418  LE PONT-DE-PLANCHES               70   \n",
       "\n",
       "       SURFACE COMMUNE (km²)  POPULATION COMMUNE  \\\n",
       "0                  20.642005              355.32   \n",
       "1                   4.896771              591.78   \n",
       "2                   8.915911              239.74   \n",
       "3                  10.850210              358.69   \n",
       "4                  15.586269              371.00   \n",
       "...                      ...                 ...   \n",
       "36589               3.826265             1847.69   \n",
       "36590               8.634594             2159.19   \n",
       "36591               5.768295               64.01   \n",
       "36592              19.577164              224.76   \n",
       "36593               6.379713              213.99   \n",
       "\n",
       "       Orange France Couverture population 4G  \\\n",
       "0                                        0.00   \n",
       "1                                        0.09   \n",
       "2                                        0.00   \n",
       "3                                        0.00   \n",
       "4                                        0.00   \n",
       "...                                       ...   \n",
       "36589                                    1.00   \n",
       "36590                                    0.99   \n",
       "36591                                    0.00   \n",
       "36592                                    0.00   \n",
       "36593                                    0.00   \n",
       "\n",
       "       Bouygues Telecom Couverture population 4G  \\\n",
       "0                                           0.00   \n",
       "1                                           0.00   \n",
       "2                                           0.00   \n",
       "3                                           0.00   \n",
       "4                                           0.00   \n",
       "...                                          ...   \n",
       "36589                                       1.00   \n",
       "36590                                       0.95   \n",
       "36591                                       0.00   \n",
       "36592                                       0.00   \n",
       "36593                                       0.00   \n",
       "\n",
       "       SFR Couverture population 4G  Free Mobile Couverture population 4G  \\\n",
       "0                              0.00                                   0.0   \n",
       "1                              0.00                                   0.0   \n",
       "2                              0.00                                   0.0   \n",
       "3                              0.00                                   0.0   \n",
       "4                              0.00                                   0.0   \n",
       "...                             ...                                   ...   \n",
       "36589                          0.99                                   0.0   \n",
       "36590                          0.36                                   0.0   \n",
       "36591                          0.00                                   0.0   \n",
       "36592                          0.00                                   0.0   \n",
       "36593                          0.00                                   0.0   \n",
       "\n",
       "       ...  Bouygues Telecom Couverture population 2G  \\\n",
       "0      ...                                       1.00   \n",
       "1      ...                                       1.00   \n",
       "2      ...                                       1.00   \n",
       "3      ...                                       0.63   \n",
       "4      ...                                       1.00   \n",
       "...    ...                                        ...   \n",
       "36589  ...                                       1.00   \n",
       "36590  ...                                       1.00   \n",
       "36591  ...                                       1.00   \n",
       "36592  ...                                       1.00   \n",
       "36593  ...                                       1.00   \n",
       "\n",
       "       SFR Couverture population 2G  Free Mobile Couverture population 2G  \\\n",
       "0                               1.0                                   1.0   \n",
       "1                               1.0                                   1.0   \n",
       "2                               1.0                                   1.0   \n",
       "3                               1.0                                   1.0   \n",
       "4                               1.0                                   1.0   \n",
       "...                             ...                                   ...   \n",
       "36589                           1.0                                   1.0   \n",
       "36590                           1.0                                   1.0   \n",
       "36591                           1.0                                   1.0   \n",
       "36592                           1.0                                   1.0   \n",
       "36593                           1.0                                   1.0   \n",
       "\n",
       "       Par au moins un opérateur Couverture population 2G  \\\n",
       "0                                                    1.0    \n",
       "1                                                    1.0    \n",
       "2                                                    1.0    \n",
       "3                                                    1.0    \n",
       "4                                                    1.0    \n",
       "...                                                  ...    \n",
       "36589                                                1.0    \n",
       "36590                                                1.0    \n",
       "36591                                                1.0    \n",
       "36592                                                1.0    \n",
       "36593                                                1.0    \n",
       "\n",
       "       Orange France Couverture surfacique 2G  \\\n",
       "0                                        1.00   \n",
       "1                                        1.00   \n",
       "2                                        1.00   \n",
       "3                                        0.99   \n",
       "4                                        1.00   \n",
       "...                                       ...   \n",
       "36589                                    1.00   \n",
       "36590                                    1.00   \n",
       "36591                                    1.00   \n",
       "36592                                    1.00   \n",
       "36593                                    1.00   \n",
       "\n",
       "       Bouygues Telecom Couverture surfacique 2G  \\\n",
       "0                                           0.99   \n",
       "1                                           1.00   \n",
       "2                                           1.00   \n",
       "3                                           0.85   \n",
       "4                                           1.00   \n",
       "...                                          ...   \n",
       "36589                                       1.00   \n",
       "36590                                       1.00   \n",
       "36591                                       0.98   \n",
       "36592                                       0.99   \n",
       "36593                                       0.98   \n",
       "\n",
       "       SFR Couverture surfacique 2G  Free Mobile Couverture surfacique 2G  \\\n",
       "0                              1.00                                  1.00   \n",
       "1                              1.00                                  1.00   \n",
       "2                              1.00                                  1.00   \n",
       "3                              1.00                                  0.99   \n",
       "4                              1.00                                  1.00   \n",
       "...                             ...                                   ...   \n",
       "36589                          1.00                                  1.00   \n",
       "36590                          1.00                                  1.00   \n",
       "36591                          0.96                                  1.00   \n",
       "36592                          0.98                                  1.00   \n",
       "36593                          0.98                                  1.00   \n",
       "\n",
       "       Par au moins un opérateur Couverture surfacique 2G  \\\n",
       "0                                                    1.0    \n",
       "1                                                    1.0    \n",
       "2                                                    1.0    \n",
       "3                                                    1.0    \n",
       "4                                                    1.0    \n",
       "...                                                  ...    \n",
       "36589                                                1.0    \n",
       "36590                                                1.0    \n",
       "36591                                                1.0    \n",
       "36592                                                1.0    \n",
       "36593                                                1.0    \n",
       "\n",
       "                          coordonnees  \n",
       "0      45.9508835378, -0.121918052323  \n",
       "1       45.7320429784, -0.75493581331  \n",
       "2      45.4182495786, -0.588917528415  \n",
       "3       45.2448604983, -0.39280322021  \n",
       "4      46.0375312829, -0.587834905531  \n",
       "...                               ...  \n",
       "36589     47.6030208472, 6.1618556725  \n",
       "36590    47.6097514089, 6.11842989879  \n",
       "36591    47.3828510502, 5.62597938451  \n",
       "36592    47.8850612988, 5.99402771513  \n",
       "36593    47.5374721676, 5.92263244824  \n",
       "\n",
       "[36594 rows x 37 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/couverture-2g-3g-4g-en-france-par-operateur-juillet-2015.csv\",\n",
    "             sep=\";\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "0de92bf8-ea00-476f-ad9f-df973dcf00e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CODE POSTAL', 'CODE INSEE', 'NOM COMMUNE', 'CODE DEPARTEMENT',\n",
       "       'SURFACE COMMUNE (km²)', 'POPULATION COMMUNE',\n",
       "       'Orange France Couverture population 4G',\n",
       "       'Bouygues Telecom Couverture population 4G',\n",
       "       'SFR Couverture population 4G', 'Free Mobile Couverture population 4G',\n",
       "       'Par au moins un opérateur Couverture population 4G',\n",
       "       'Orange France Couverture surfacique 4G',\n",
       "       'Bouygues Telecom Couverture surfacique 4G',\n",
       "       'SFR Couverture surfacique 4G', 'Free Mobile Couverture surfacique 4G',\n",
       "       'Par au moins un opérateur Couverture surfacique 4G',\n",
       "       'Orange France Couverture population 3G',\n",
       "       'Bouygues Telecom Couverture population 3G',\n",
       "       'SFR Couverture population 3G', 'Free Mobile Couverture population 3G',\n",
       "       'Par au moins un opérateur Couverture population 3G',\n",
       "       'Orange France Couverture surfacique 3G',\n",
       "       'Bouygues Telecom Couverture surfacique 3G',\n",
       "       'SFR Couverture surfacique 3G', 'Free Mobile Couverture surfacique 3G',\n",
       "       'Par au moins un opérateur Couverture surfacique 3G',\n",
       "       'Orange France Couverture population 2G',\n",
       "       'Bouygues Telecom Couverture population 2G',\n",
       "       'SFR Couverture population 2G', 'Free Mobile Couverture population 2G',\n",
       "       'Par au moins un opérateur Couverture population 2G',\n",
       "       'Orange France Couverture surfacique 2G',\n",
       "       'Bouygues Telecom Couverture surfacique 2G',\n",
       "       'SFR Couverture surfacique 2G', 'Free Mobile Couverture surfacique 2G',\n",
       "       'Par au moins un opérateur Couverture surfacique 2G', 'coordonnees'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "cb2aa8f7-87b2-49cd-bcd5-fc0c450e829d",
   "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>CODE INSEE</th>\n",
       "      <th>NOM COMMUNE</th>\n",
       "      <th>CODE DEPARTEMENT</th>\n",
       "      <th>SURFACE COMMUNE (km²)</th>\n",
       "      <th>POPULATION COMMUNE</th>\n",
       "      <th>coordonnees</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17510</td>\n",
       "      <td>17105</td>\n",
       "      <td>CHIVES</td>\n",
       "      <td>17</td>\n",
       "      <td>20.642005</td>\n",
       "      <td>355.32</td>\n",
       "      <td>45.9508835378, -0.121918052323</td>\n",
       "      <td>Orange France Couverture population 4G</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17600</td>\n",
       "      <td>17112</td>\n",
       "      <td>LA CLISSE</td>\n",
       "      <td>17</td>\n",
       "      <td>4.896771</td>\n",
       "      <td>591.78</td>\n",
       "      <td>45.7320429784, -0.75493581331</td>\n",
       "      <td>Orange France Couverture population 4G</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17150</td>\n",
       "      <td>17116</td>\n",
       "      <td>CONSAC</td>\n",
       "      <td>17</td>\n",
       "      <td>8.915911</td>\n",
       "      <td>239.74</td>\n",
       "      <td>45.4182495786, -0.588917528415</td>\n",
       "      <td>Orange France Couverture population 4G</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17130</td>\n",
       "      <td>17118</td>\n",
       "      <td>CORIGNAC</td>\n",
       "      <td>17</td>\n",
       "      <td>10.850210</td>\n",
       "      <td>358.69</td>\n",
       "      <td>45.2448604983, -0.39280322021</td>\n",
       "      <td>Orange France Couverture population 4G</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17330</td>\n",
       "      <td>17124</td>\n",
       "      <td>COURANT</td>\n",
       "      <td>17</td>\n",
       "      <td>15.586269</td>\n",
       "      <td>371.00</td>\n",
       "      <td>46.0375312829, -0.587834905531</td>\n",
       "      <td>Orange France Couverture population 4G</td>\n",
       "      <td>0.00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097815</th>\n",
       "      <td>70000</td>\n",
       "      <td>70378</td>\n",
       "      <td>NAVENNE</td>\n",
       "      <td>70</td>\n",
       "      <td>3.826265</td>\n",
       "      <td>1847.69</td>\n",
       "      <td>47.6030208472, 6.1618556725</td>\n",
       "      <td>Par au moins un opérateur Couverture surfaciqu...</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097816</th>\n",
       "      <td>70000</td>\n",
       "      <td>70388</td>\n",
       "      <td>NOIDANS-LES-VESOUL</td>\n",
       "      <td>70</td>\n",
       "      <td>8.634594</td>\n",
       "      <td>2159.19</td>\n",
       "      <td>47.6097514089, 6.11842989879</td>\n",
       "      <td>Par au moins un opérateur Couverture surfaciqu...</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097817</th>\n",
       "      <td>70100</td>\n",
       "      <td>70389</td>\n",
       "      <td>NOIRON</td>\n",
       "      <td>70</td>\n",
       "      <td>5.768295</td>\n",
       "      <td>64.01</td>\n",
       "      <td>47.3828510502, 5.62597938451</td>\n",
       "      <td>Par au moins un opérateur Couverture surfaciqu...</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097818</th>\n",
       "      <td>70500</td>\n",
       "      <td>70399</td>\n",
       "      <td>ORMOY</td>\n",
       "      <td>70</td>\n",
       "      <td>19.577164</td>\n",
       "      <td>224.76</td>\n",
       "      <td>47.8850612988, 5.99402771513</td>\n",
       "      <td>Par au moins un opérateur Couverture surfaciqu...</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097819</th>\n",
       "      <td>70130</td>\n",
       "      <td>70418</td>\n",
       "      <td>LE PONT-DE-PLANCHES</td>\n",
       "      <td>70</td>\n",
       "      <td>6.379713</td>\n",
       "      <td>213.99</td>\n",
       "      <td>47.5374721676, 5.92263244824</td>\n",
       "      <td>Par au moins un opérateur Couverture surfaciqu...</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1097820 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CODE POSTAL CODE INSEE          NOM COMMUNE CODE DEPARTEMENT  \\\n",
       "0             17510      17105               CHIVES               17   \n",
       "1             17600      17112            LA CLISSE               17   \n",
       "2             17150      17116               CONSAC               17   \n",
       "3             17130      17118             CORIGNAC               17   \n",
       "4             17330      17124              COURANT               17   \n",
       "...             ...        ...                  ...              ...   \n",
       "1097815       70000      70378              NAVENNE               70   \n",
       "1097816       70000      70388   NOIDANS-LES-VESOUL               70   \n",
       "1097817       70100      70389               NOIRON               70   \n",
       "1097818       70500      70399                ORMOY               70   \n",
       "1097819       70130      70418  LE PONT-DE-PLANCHES               70   \n",
       "\n",
       "         SURFACE COMMUNE (km²)  POPULATION COMMUNE  \\\n",
       "0                    20.642005              355.32   \n",
       "1                     4.896771              591.78   \n",
       "2                     8.915911              239.74   \n",
       "3                    10.850210              358.69   \n",
       "4                    15.586269              371.00   \n",
       "...                        ...                 ...   \n",
       "1097815               3.826265             1847.69   \n",
       "1097816               8.634594             2159.19   \n",
       "1097817               5.768295               64.01   \n",
       "1097818              19.577164              224.76   \n",
       "1097819               6.379713              213.99   \n",
       "\n",
       "                            coordonnees  \\\n",
       "0        45.9508835378, -0.121918052323   \n",
       "1         45.7320429784, -0.75493581331   \n",
       "2        45.4182495786, -0.588917528415   \n",
       "3         45.2448604983, -0.39280322021   \n",
       "4        46.0375312829, -0.587834905531   \n",
       "...                                 ...   \n",
       "1097815     47.6030208472, 6.1618556725   \n",
       "1097816    47.6097514089, 6.11842989879   \n",
       "1097817    47.3828510502, 5.62597938451   \n",
       "1097818    47.8850612988, 5.99402771513   \n",
       "1097819    47.5374721676, 5.92263244824   \n",
       "\n",
       "                                                  variable  value  \n",
       "0                   Orange France Couverture population 4G   0.00  \n",
       "1                   Orange France Couverture population 4G   0.09  \n",
       "2                   Orange France Couverture population 4G   0.00  \n",
       "3                   Orange France Couverture population 4G   0.00  \n",
       "4                   Orange France Couverture population 4G   0.00  \n",
       "...                                                    ...    ...  \n",
       "1097815  Par au moins un opérateur Couverture surfaciqu...   1.00  \n",
       "1097816  Par au moins un opérateur Couverture surfaciqu...   1.00  \n",
       "1097817  Par au moins un opérateur Couverture surfaciqu...   1.00  \n",
       "1097818  Par au moins un opérateur Couverture surfaciqu...   1.00  \n",
       "1097819  Par au moins un opérateur Couverture surfaciqu...   1.00  \n",
       "\n",
       "[1097820 rows x 9 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_long = pd.melt(df, id_vars=['CODE POSTAL', 'CODE INSEE', 'NOM COMMUNE', 'CODE DEPARTEMENT',\n",
    "                     'SURFACE COMMUNE (km²)', 'POPULATION COMMUNE', 'coordonnees'])\n",
    "df_long"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "1d38d1ba-4e7e-4a42-beb7-9ad02fbda6a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_long[[\"Opérateur\", \"Couverture\", \"Techno\"]] = df_long[\"variable\"].str.extract(r\"(.+) (Couverture .+) (\\dG)$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "ccd690f5-2132-45c0-92b9-87dc29f65b2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CODE POSTAL</th>\n",
       "      <th>CODE INSEE</th>\n",
       "      <th>NOM COMMUNE</th>\n",
       "      <th>CODE DEPARTEMENT</th>\n",
       "      <th>SURFACE COMMUNE (km²)</th>\n",
       "      <th>POPULATION COMMUNE</th>\n",
       "      <th>coordonnees</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>Opérateur</th>\n",
       "      <th>Couverture</th>\n",
       "      <th>Techno</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17510</td>\n",
       "      <td>17105</td>\n",
       "      <td>CHIVES</td>\n",
       "      <td>17</td>\n",
       "      <td>20.642005</td>\n",
       "      <td>355.32</td>\n",
       "      <td>45.9508835378, -0.121918052323</td>\n",
       "      <td>Orange France Couverture population 4G</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Orange France</td>\n",
       "      <td>Couverture population</td>\n",
       "      <td>4G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17600</td>\n",
       "      <td>17112</td>\n",
       "      <td>LA CLISSE</td>\n",
       "      <td>17</td>\n",
       "      <td>4.896771</td>\n",
       "      <td>591.78</td>\n",
       "      <td>45.7320429784, -0.75493581331</td>\n",
       "      <td>Orange France Couverture population 4G</td>\n",
       "      <td>0.09</td>\n",
       "      <td>Orange France</td>\n",
       "      <td>Couverture population</td>\n",
       "      <td>4G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17150</td>\n",
       "      <td>17116</td>\n",
       "      <td>CONSAC</td>\n",
       "      <td>17</td>\n",
       "      <td>8.915911</td>\n",
       "      <td>239.74</td>\n",
       "      <td>45.4182495786, -0.588917528415</td>\n",
       "      <td>Orange France Couverture population 4G</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Orange France</td>\n",
       "      <td>Couverture population</td>\n",
       "      <td>4G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17130</td>\n",
       "      <td>17118</td>\n",
       "      <td>CORIGNAC</td>\n",
       "      <td>17</td>\n",
       "      <td>10.850210</td>\n",
       "      <td>358.69</td>\n",
       "      <td>45.2448604983, -0.39280322021</td>\n",
       "      <td>Orange France Couverture population 4G</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Orange France</td>\n",
       "      <td>Couverture population</td>\n",
       "      <td>4G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17330</td>\n",
       "      <td>17124</td>\n",
       "      <td>COURANT</td>\n",
       "      <td>17</td>\n",
       "      <td>15.586269</td>\n",
       "      <td>371.00</td>\n",
       "      <td>46.0375312829, -0.587834905531</td>\n",
       "      <td>Orange France Couverture population 4G</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Orange France</td>\n",
       "      <td>Couverture population</td>\n",
       "      <td>4G</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097815</th>\n",
       "      <td>70000</td>\n",
       "      <td>70378</td>\n",
       "      <td>NAVENNE</td>\n",
       "      <td>70</td>\n",
       "      <td>3.826265</td>\n",
       "      <td>1847.69</td>\n",
       "      <td>47.6030208472, 6.1618556725</td>\n",
       "      <td>Par au moins un opérateur Couverture surfaciqu...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Par au moins un opérateur</td>\n",
       "      <td>Couverture surfacique</td>\n",
       "      <td>2G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097816</th>\n",
       "      <td>70000</td>\n",
       "      <td>70388</td>\n",
       "      <td>NOIDANS-LES-VESOUL</td>\n",
       "      <td>70</td>\n",
       "      <td>8.634594</td>\n",
       "      <td>2159.19</td>\n",
       "      <td>47.6097514089, 6.11842989879</td>\n",
       "      <td>Par au moins un opérateur Couverture surfaciqu...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Par au moins un opérateur</td>\n",
       "      <td>Couverture surfacique</td>\n",
       "      <td>2G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097817</th>\n",
       "      <td>70100</td>\n",
       "      <td>70389</td>\n",
       "      <td>NOIRON</td>\n",
       "      <td>70</td>\n",
       "      <td>5.768295</td>\n",
       "      <td>64.01</td>\n",
       "      <td>47.3828510502, 5.62597938451</td>\n",
       "      <td>Par au moins un opérateur Couverture surfaciqu...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Par au moins un opérateur</td>\n",
       "      <td>Couverture surfacique</td>\n",
       "      <td>2G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097818</th>\n",
       "      <td>70500</td>\n",
       "      <td>70399</td>\n",
       "      <td>ORMOY</td>\n",
       "      <td>70</td>\n",
       "      <td>19.577164</td>\n",
       "      <td>224.76</td>\n",
       "      <td>47.8850612988, 5.99402771513</td>\n",
       "      <td>Par au moins un opérateur Couverture surfaciqu...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Par au moins un opérateur</td>\n",
       "      <td>Couverture surfacique</td>\n",
       "      <td>2G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097819</th>\n",
       "      <td>70130</td>\n",
       "      <td>70418</td>\n",
       "      <td>LE PONT-DE-PLANCHES</td>\n",
       "      <td>70</td>\n",
       "      <td>6.379713</td>\n",
       "      <td>213.99</td>\n",
       "      <td>47.5374721676, 5.92263244824</td>\n",
       "      <td>Par au moins un opérateur Couverture surfaciqu...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Par au moins un opérateur</td>\n",
       "      <td>Couverture surfacique</td>\n",
       "      <td>2G</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1097820 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CODE POSTAL CODE INSEE          NOM COMMUNE CODE DEPARTEMENT  \\\n",
       "0             17510      17105               CHIVES               17   \n",
       "1             17600      17112            LA CLISSE               17   \n",
       "2             17150      17116               CONSAC               17   \n",
       "3             17130      17118             CORIGNAC               17   \n",
       "4             17330      17124              COURANT               17   \n",
       "...             ...        ...                  ...              ...   \n",
       "1097815       70000      70378              NAVENNE               70   \n",
       "1097816       70000      70388   NOIDANS-LES-VESOUL               70   \n",
       "1097817       70100      70389               NOIRON               70   \n",
       "1097818       70500      70399                ORMOY               70   \n",
       "1097819       70130      70418  LE PONT-DE-PLANCHES               70   \n",
       "\n",
       "         SURFACE COMMUNE (km²)  POPULATION COMMUNE  \\\n",
       "0                    20.642005              355.32   \n",
       "1                     4.896771              591.78   \n",
       "2                     8.915911              239.74   \n",
       "3                    10.850210              358.69   \n",
       "4                    15.586269              371.00   \n",
       "...                        ...                 ...   \n",
       "1097815               3.826265             1847.69   \n",
       "1097816               8.634594             2159.19   \n",
       "1097817               5.768295               64.01   \n",
       "1097818              19.577164              224.76   \n",
       "1097819               6.379713              213.99   \n",
       "\n",
       "                            coordonnees  \\\n",
       "0        45.9508835378, -0.121918052323   \n",
       "1         45.7320429784, -0.75493581331   \n",
       "2        45.4182495786, -0.588917528415   \n",
       "3         45.2448604983, -0.39280322021   \n",
       "4        46.0375312829, -0.587834905531   \n",
       "...                                 ...   \n",
       "1097815     47.6030208472, 6.1618556725   \n",
       "1097816    47.6097514089, 6.11842989879   \n",
       "1097817    47.3828510502, 5.62597938451   \n",
       "1097818    47.8850612988, 5.99402771513   \n",
       "1097819    47.5374721676, 5.92263244824   \n",
       "\n",
       "                                                  variable  value  \\\n",
       "0                   Orange France Couverture population 4G   0.00   \n",
       "1                   Orange France Couverture population 4G   0.09   \n",
       "2                   Orange France Couverture population 4G   0.00   \n",
       "3                   Orange France Couverture population 4G   0.00   \n",
       "4                   Orange France Couverture population 4G   0.00   \n",
       "...                                                    ...    ...   \n",
       "1097815  Par au moins un opérateur Couverture surfaciqu...   1.00   \n",
       "1097816  Par au moins un opérateur Couverture surfaciqu...   1.00   \n",
       "1097817  Par au moins un opérateur Couverture surfaciqu...   1.00   \n",
       "1097818  Par au moins un opérateur Couverture surfaciqu...   1.00   \n",
       "1097819  Par au moins un opérateur Couverture surfaciqu...   1.00   \n",
       "\n",
       "                         Opérateur             Couverture Techno  \n",
       "0                    Orange France  Couverture population     4G  \n",
       "1                    Orange France  Couverture population     4G  \n",
       "2                    Orange France  Couverture population     4G  \n",
       "3                    Orange France  Couverture population     4G  \n",
       "4                    Orange France  Couverture population     4G  \n",
       "...                            ...                    ...    ...  \n",
       "1097815  Par au moins un opérateur  Couverture surfacique     2G  \n",
       "1097816  Par au moins un opérateur  Couverture surfacique     2G  \n",
       "1097817  Par au moins un opérateur  Couverture surfacique     2G  \n",
       "1097818  Par au moins un opérateur  Couverture surfacique     2G  \n",
       "1097819  Par au moins un opérateur  Couverture surfacique     2G  \n",
       "\n",
       "[1097820 rows x 12 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_long"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "265983f5-9af9-4691-bc5e-10711b850458",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Code INSEE</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31080</td>\n",
       "      <td>31350</td>\n",
       "      <td>BOULOGNE-SUR-GESSE</td>\n",
       "      <td>HAUTE-GARONNE</td>\n",
       "      <td>MIDI-PYRENEES</td>\n",
       "      <td>Chef-lieu canton</td>\n",
       "      <td>301.0</td>\n",
       "      <td>2470.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>43.2904403081, 0.650641474176</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11143</td>\n",
       "      <td>11510</td>\n",
       "      <td>FEUILLA</td>\n",
       "      <td>AUDE</td>\n",
       "      <td>LANGUEDOC-ROUSSILLON</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>314.0</td>\n",
       "      <td>2426.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>42.9291375888, 2.90138923544</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43028</td>\n",
       "      <td>43200</td>\n",
       "      <td>BESSAMOREL</td>\n",
       "      <td>HAUTE-LOIRE</td>\n",
       "      <td>AUVERGNE</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>888.0</td>\n",
       "      <td>743.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>45.1306448726, 4.07952494849</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[4.091674...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78506</td>\n",
       "      <td>78660</td>\n",
       "      <td>PRUNAY-EN-YVELINES</td>\n",
       "      <td>YVELINES</td>\n",
       "      <td>ILE-DE-FRANCE</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>155.0</td>\n",
       "      <td>2717.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>48.5267627187, 1.80513972814</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[1.829610...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84081</td>\n",
       "      <td>84310</td>\n",
       "      <td>MORIERES-LES-AVIGNON</td>\n",
       "      <td>VAUCLUSE</td>\n",
       "      <td>PROVENCE-ALPES-COTE D'AZUR</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1042.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>43.9337788848, 4.90875878315</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[4.927176...</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>36737</th>\n",
       "      <td>74066</td>\n",
       "      <td>74270</td>\n",
       "      <td>CHAVANNAZ</td>\n",
       "      <td>HAUTE-SAVOIE</td>\n",
       "      <td>RHONE-ALPES</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>564.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>46.0500384099, 6.01652644319</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[6.026228...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36738</th>\n",
       "      <td>67315</td>\n",
       "      <td>67630</td>\n",
       "      <td>NEEWILLER-PRES-LAUTERBOURG</td>\n",
       "      <td>BAS-RHIN</td>\n",
       "      <td>ALSACE</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>157.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>48.9516989257, 8.11931601006</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[8.106826...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36739</th>\n",
       "      <td>07122</td>\n",
       "      <td>07200</td>\n",
       "      <td>LACHAPELLE-SOUS-AUBENAS</td>\n",
       "      <td>ARDECHE</td>\n",
       "      <td>RHONE-ALPES</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>257.0</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>44.5622616562, 4.35906947518</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[4.356584...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36740</th>\n",
       "      <td>01026</td>\n",
       "      <td>01380</td>\n",
       "      <td>BAGE-LE-CHATEL</td>\n",
       "      <td>AIN</td>\n",
       "      <td>RHONE-ALPES</td>\n",
       "      <td>Chef-lieu canton</td>\n",
       "      <td>202.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>46.3077069231, 4.92862084189</td>\n",
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       "    <tr>\n",
       "      <th>36741</th>\n",
       "      <td>50224</td>\n",
       "      <td>50160</td>\n",
       "      <td>GUILBERVILLE</td>\n",
       "      <td>MANCHE</td>\n",
       "      <td>BASSE-NORMANDIE</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>165.0</td>\n",
       "      <td>2224.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.9866701737, -0.949588175705</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[-0.99753...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36742 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Code INSEE Code Postal                     Commune    Département  \\\n",
       "0          31080       31350          BOULOGNE-SUR-GESSE  HAUTE-GARONNE   \n",
       "1          11143       11510                     FEUILLA           AUDE   \n",
       "2          43028       43200                  BESSAMOREL    HAUTE-LOIRE   \n",
       "3          78506       78660          PRUNAY-EN-YVELINES       YVELINES   \n",
       "4          84081       84310        MORIERES-LES-AVIGNON       VAUCLUSE   \n",
       "...          ...         ...                         ...            ...   \n",
       "36737      74066       74270                   CHAVANNAZ   HAUTE-SAVOIE   \n",
       "36738      67315       67630  NEEWILLER-PRES-LAUTERBOURG       BAS-RHIN   \n",
       "36739      07122       07200     LACHAPELLE-SOUS-AUBENAS        ARDECHE   \n",
       "36740      01026       01380              BAGE-LE-CHATEL            AIN   \n",
       "36741      50224       50160                GUILBERVILLE         MANCHE   \n",
       "\n",
       "                           Région            Statut  Altitude Moyenne  \\\n",
       "0                   MIDI-PYRENEES  Chef-lieu canton             301.0   \n",
       "1            LANGUEDOC-ROUSSILLON    Commune simple             314.0   \n",
       "2                        AUVERGNE    Commune simple             888.0   \n",
       "3                   ILE-DE-FRANCE    Commune simple             155.0   \n",
       "4      PROVENCE-ALPES-COTE D'AZUR    Commune simple              49.0   \n",
       "...                           ...               ...               ...   \n",
       "36737                 RHONE-ALPES    Commune simple             564.0   \n",
       "36738                      ALSACE    Commune simple             157.0   \n",
       "36739                 RHONE-ALPES    Commune simple             257.0   \n",
       "36740                 RHONE-ALPES  Chef-lieu canton             202.0   \n",
       "36741             BASSE-NORMANDIE    Commune simple             165.0   \n",
       "\n",
       "       Superficie  Population                    geo_point_2d  \\\n",
       "0          2470.0         1.6   43.2904403081, 0.650641474176   \n",
       "1          2426.0         0.1    42.9291375888, 2.90138923544   \n",
       "2           743.0         0.4    45.1306448726, 4.07952494849   \n",
       "3          2717.0         0.8    48.5267627187, 1.80513972814   \n",
       "4          1042.0         7.6    43.9337788848, 4.90875878315   \n",
       "...           ...         ...                             ...   \n",
       "36737       321.0         0.2    46.0500384099, 6.01652644319   \n",
       "36738       725.0         0.7    48.9516989257, 8.11931601006   \n",
       "36739      1018.0         1.5    44.5622616562, 4.35906947518   \n",
       "36740        86.0         0.8    46.3077069231, 4.92862084189   \n",
       "36741      2224.0         1.0  48.9866701737, -0.949588175705   \n",
       "\n",
       "                                               geo_shape  \n",
       "0      {\"type\": \"Polygon\", \"coordinates\": [[[0.671852...  \n",
       "1      {\"type\": \"Polygon\", \"coordinates\": [[[2.906996...  \n",
       "2      {\"type\": \"Polygon\", \"coordinates\": [[[4.091674...  \n",
       "3      {\"type\": \"Polygon\", \"coordinates\": [[[1.829610...  \n",
       "4      {\"type\": \"Polygon\", \"coordinates\": [[[4.927176...  \n",
       "...                                                  ...  \n",
       "36737  {\"type\": \"Polygon\", \"coordinates\": [[[6.026228...  \n",
       "36738  {\"type\": \"Polygon\", \"coordinates\": [[[8.106826...  \n",
       "36739  {\"type\": \"Polygon\", \"coordinates\": [[[4.356584...  \n",
       "36740  {\"type\": \"Polygon\", \"coordinates\": [[[4.926593...  \n",
       "36741  {\"type\": \"Polygon\", \"coordinates\": [[[-0.99753...  \n",
       "\n",
       "[36742 rows x 11 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo = (pd.read_csv(\"data/correspondance-code-insee-code-postal.csv\",\n",
    "                 sep=\";\",\n",
    "                 usecols=range(11)\n",
    "                )\n",
    "     )\n",
    "geo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "eb190e0a-af9e-42bd-9724-f2309a0746ee",
   "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>CODE INSEE</th>\n",
       "      <th>NOM COMMUNE</th>\n",
       "      <th>CODE DEPARTEMENT</th>\n",
       "      <th>SURFACE COMMUNE (km²)</th>\n",
       "      <th>POPULATION COMMUNE</th>\n",
       "      <th>coordonnees</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>Opérateur</th>\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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17510</td>\n",
       "      <td>17105</td>\n",
       "      <td>CHIVES</td>\n",
       "      <td>17</td>\n",
       "      <td>20.642005</td>\n",
       "      <td>355.32</td>\n",
       "      <td>45.9508835378, -0.121918052323</td>\n",
       "      <td>Orange France Couverture population 4G</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Orange France</td>\n",
       "      <td>...</td>\n",
       "      <td>17510</td>\n",
       "      <td>CHIVES</td>\n",
       "      <td>CHARENTE-MARITIME</td>\n",
       "      <td>POITOU-CHARENTES</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>108.0</td>\n",
       "      <td>2062.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>45.9508835378, -0.121918052323</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[-0.10484...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17600</td>\n",
       "      <td>17112</td>\n",
       "      <td>LA CLISSE</td>\n",
       "      <td>17</td>\n",
       "      <td>4.896771</td>\n",
       "      <td>591.78</td>\n",
       "      <td>45.7320429784, -0.75493581331</td>\n",
       "      <td>Orange France Couverture population 4G</td>\n",
       "      <td>0.09</td>\n",
       "      <td>Orange France</td>\n",
       "      <td>...</td>\n",
       "      <td>17600</td>\n",
       "      <td>LA CLISSE</td>\n",
       "      <td>CHARENTE-MARITIME</td>\n",
       "      <td>POITOU-CHARENTES</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>34.0</td>\n",
       "      <td>524.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>45.7320429784, -0.75493581331</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[-0.74528...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17150</td>\n",
       "      <td>17116</td>\n",
       "      <td>CONSAC</td>\n",
       "      <td>17</td>\n",
       "      <td>8.915911</td>\n",
       "      <td>239.74</td>\n",
       "      <td>45.4182495786, -0.588917528415</td>\n",
       "      <td>Orange France Couverture population 4G</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Orange France</td>\n",
       "      <td>...</td>\n",
       "      <td>17150</td>\n",
       "      <td>CONSAC</td>\n",
       "      <td>CHARENTE-MARITIME</td>\n",
       "      <td>POITOU-CHARENTES</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>58.0</td>\n",
       "      <td>905.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>45.4182495786, -0.588917528415</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[-0.60200...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17130</td>\n",
       "      <td>17118</td>\n",
       "      <td>CORIGNAC</td>\n",
       "      <td>17</td>\n",
       "      <td>10.850210</td>\n",
       "      <td>358.69</td>\n",
       "      <td>45.2448604983, -0.39280322021</td>\n",
       "      <td>Orange France Couverture population 4G</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Orange France</td>\n",
       "      <td>...</td>\n",
       "      <td>17130</td>\n",
       "      <td>CORIGNAC</td>\n",
       "      <td>CHARENTE-MARITIME</td>\n",
       "      <td>POITOU-CHARENTES</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1112.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>45.2448604983, -0.39280322021</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[-0.41318...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17330</td>\n",
       "      <td>17124</td>\n",
       "      <td>COURANT</td>\n",
       "      <td>17</td>\n",
       "      <td>15.586269</td>\n",
       "      <td>371.00</td>\n",
       "      <td>46.0375312829, -0.587834905531</td>\n",
       "      <td>Orange France Couverture population 4G</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Orange France</td>\n",
       "      <td>...</td>\n",
       "      <td>17330</td>\n",
       "      <td>COURANT</td>\n",
       "      <td>CHARENTE-MARITIME</td>\n",
       "      <td>POITOU-CHARENTES</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1574.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>46.0375312829, -0.587834905531</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[-0.58528...</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",
       "      <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>1097815</th>\n",
       "      <td>70000</td>\n",
       "      <td>70378</td>\n",
       "      <td>NAVENNE</td>\n",
       "      <td>70</td>\n",
       "      <td>3.826265</td>\n",
       "      <td>1847.69</td>\n",
       "      <td>47.6030208472, 6.1618556725</td>\n",
       "      <td>Par au moins un opérateur Couverture surfaciqu...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Par au moins un opérateur</td>\n",
       "      <td>...</td>\n",
       "      <td>70000</td>\n",
       "      <td>NAVENNE</td>\n",
       "      <td>HAUTE-SAONE</td>\n",
       "      <td>FRANCHE-COMTE</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>321.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>47.6030208472, 6.1618556725</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[6.157643...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097816</th>\n",
       "      <td>70000</td>\n",
       "      <td>70388</td>\n",
       "      <td>NOIDANS-LES-VESOUL</td>\n",
       "      <td>70</td>\n",
       "      <td>8.634594</td>\n",
       "      <td>2159.19</td>\n",
       "      <td>47.6097514089, 6.11842989879</td>\n",
       "      <td>Par au moins un opérateur Couverture surfaciqu...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Par au moins un opérateur</td>\n",
       "      <td>...</td>\n",
       "      <td>70000</td>\n",
       "      <td>NOIDANS-LES-VESOUL</td>\n",
       "      <td>HAUTE-SAONE</td>\n",
       "      <td>FRANCHE-COMTE</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>306.0</td>\n",
       "      <td>856.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>47.6097514089, 6.11842989879</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[6.119037...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097817</th>\n",
       "      <td>70100</td>\n",
       "      <td>70389</td>\n",
       "      <td>NOIRON</td>\n",
       "      <td>70</td>\n",
       "      <td>5.768295</td>\n",
       "      <td>64.01</td>\n",
       "      <td>47.3828510502, 5.62597938451</td>\n",
       "      <td>Par au moins un opérateur Couverture surfaciqu...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Par au moins un opérateur</td>\n",
       "      <td>...</td>\n",
       "      <td>70100</td>\n",
       "      <td>NOIRON</td>\n",
       "      <td>HAUTE-SAONE</td>\n",
       "      <td>FRANCHE-COMTE</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>219.0</td>\n",
       "      <td>575.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>47.3828510502, 5.62597938451</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[5.640740...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097818</th>\n",
       "      <td>70500</td>\n",
       "      <td>70399</td>\n",
       "      <td>ORMOY</td>\n",
       "      <td>70</td>\n",
       "      <td>19.577164</td>\n",
       "      <td>224.76</td>\n",
       "      <td>47.8850612988, 5.99402771513</td>\n",
       "      <td>Par au moins un opérateur Couverture surfaciqu...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Par au moins un opérateur</td>\n",
       "      <td>...</td>\n",
       "      <td>70500</td>\n",
       "      <td>ORMOY</td>\n",
       "      <td>HAUTE-SAONE</td>\n",
       "      <td>FRANCHE-COMTE</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>267.0</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>47.8850612988, 5.99402771513</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[5.945622...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097819</th>\n",
       "      <td>70130</td>\n",
       "      <td>70418</td>\n",
       "      <td>LE PONT-DE-PLANCHES</td>\n",
       "      <td>70</td>\n",
       "      <td>6.379713</td>\n",
       "      <td>213.99</td>\n",
       "      <td>47.5374721676, 5.92263244824</td>\n",
       "      <td>Par au moins un opérateur Couverture surfaciqu...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Par au moins un opérateur</td>\n",
       "      <td>...</td>\n",
       "      <td>70130</td>\n",
       "      <td>LE PONT-DE-PLANCHES</td>\n",
       "      <td>HAUTE-SAONE</td>\n",
       "      <td>FRANCHE-COMTE</td>\n",
       "      <td>Commune simple</td>\n",
       "      <td>234.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>47.5374721676, 5.92263244824</td>\n",
       "      <td>{\"type\": \"Polygon\", \"coordinates\": [[[5.938235...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1097820 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CODE POSTAL CODE INSEE          NOM COMMUNE CODE DEPARTEMENT  \\\n",
       "0             17510      17105               CHIVES               17   \n",
       "1             17600      17112            LA CLISSE               17   \n",
       "2             17150      17116               CONSAC               17   \n",
       "3             17130      17118             CORIGNAC               17   \n",
       "4             17330      17124              COURANT               17   \n",
       "...             ...        ...                  ...              ...   \n",
       "1097815       70000      70378              NAVENNE               70   \n",
       "1097816       70000      70388   NOIDANS-LES-VESOUL               70   \n",
       "1097817       70100      70389               NOIRON               70   \n",
       "1097818       70500      70399                ORMOY               70   \n",
       "1097819       70130      70418  LE PONT-DE-PLANCHES               70   \n",
       "\n",
       "         SURFACE COMMUNE (km²)  POPULATION COMMUNE  \\\n",
       "0                    20.642005              355.32   \n",
       "1                     4.896771              591.78   \n",
       "2                     8.915911              239.74   \n",
       "3                    10.850210              358.69   \n",
       "4                    15.586269              371.00   \n",
       "...                        ...                 ...   \n",
       "1097815               3.826265             1847.69   \n",
       "1097816               8.634594             2159.19   \n",
       "1097817               5.768295               64.01   \n",
       "1097818              19.577164              224.76   \n",
       "1097819               6.379713              213.99   \n",
       "\n",
       "                            coordonnees  \\\n",
       "0        45.9508835378, -0.121918052323   \n",
       "1         45.7320429784, -0.75493581331   \n",
       "2        45.4182495786, -0.588917528415   \n",
       "3         45.2448604983, -0.39280322021   \n",
       "4        46.0375312829, -0.587834905531   \n",
       "...                                 ...   \n",
       "1097815     47.6030208472, 6.1618556725   \n",
       "1097816    47.6097514089, 6.11842989879   \n",
       "1097817    47.3828510502, 5.62597938451   \n",
       "1097818    47.8850612988, 5.99402771513   \n",
       "1097819    47.5374721676, 5.92263244824   \n",
       "\n",
       "                                                  variable  value  \\\n",
       "0                   Orange France Couverture population 4G   0.00   \n",
       "1                   Orange France Couverture population 4G   0.09   \n",
       "2                   Orange France Couverture population 4G   0.00   \n",
       "3                   Orange France Couverture population 4G   0.00   \n",
       "4                   Orange France Couverture population 4G   0.00   \n",
       "...                                                    ...    ...   \n",
       "1097815  Par au moins un opérateur Couverture surfaciqu...   1.00   \n",
       "1097816  Par au moins un opérateur Couverture surfaciqu...   1.00   \n",
       "1097817  Par au moins un opérateur Couverture surfaciqu...   1.00   \n",
       "1097818  Par au moins un opérateur Couverture surfaciqu...   1.00   \n",
       "1097819  Par au moins un opérateur Couverture surfaciqu...   1.00   \n",
       "\n",
       "                         Opérateur  ... Code Postal              Commune  \\\n",
       "0                    Orange France  ...       17510               CHIVES   \n",
       "1                    Orange France  ...       17600            LA CLISSE   \n",
       "2                    Orange France  ...       17150               CONSAC   \n",
       "3                    Orange France  ...       17130             CORIGNAC   \n",
       "4                    Orange France  ...       17330              COURANT   \n",
       "...                            ...  ...         ...                  ...   \n",
       "1097815  Par au moins un opérateur  ...       70000              NAVENNE   \n",
       "1097816  Par au moins un opérateur  ...       70000   NOIDANS-LES-VESOUL   \n",
       "1097817  Par au moins un opérateur  ...       70100               NOIRON   \n",
       "1097818  Par au moins un opérateur  ...       70500                ORMOY   \n",
       "1097819  Par au moins un opérateur  ...       70130  LE PONT-DE-PLANCHES   \n",
       "\n",
       "               Département            Région          Statut Altitude Moyenne  \\\n",
       "0        CHARENTE-MARITIME  POITOU-CHARENTES  Commune simple            108.0   \n",
       "1        CHARENTE-MARITIME  POITOU-CHARENTES  Commune simple             34.0   \n",
       "2        CHARENTE-MARITIME  POITOU-CHARENTES  Commune simple             58.0   \n",
       "3        CHARENTE-MARITIME  POITOU-CHARENTES  Commune simple             69.0   \n",
       "4        CHARENTE-MARITIME  POITOU-CHARENTES  Commune simple             55.0   \n",
       "...                    ...               ...             ...              ...   \n",
       "1097815        HAUTE-SAONE     FRANCHE-COMTE  Commune simple            321.0   \n",
       "1097816        HAUTE-SAONE     FRANCHE-COMTE  Commune simple            306.0   \n",
       "1097817        HAUTE-SAONE     FRANCHE-COMTE  Commune simple            219.0   \n",
       "1097818        HAUTE-SAONE     FRANCHE-COMTE  Commune simple            267.0   \n",
       "1097819        HAUTE-SAONE     FRANCHE-COMTE  Commune simple            234.0   \n",
       "\n",
       "        Superficie Population                    geo_point_2d  \\\n",
       "0           2062.0        0.4  45.9508835378, -0.121918052323   \n",
       "1            524.0        0.6   45.7320429784, -0.75493581331   \n",
       "2            905.0        0.2  45.4182495786, -0.588917528415   \n",
       "3           1112.0        0.3   45.2448604983, -0.39280322021   \n",
       "4           1574.0        0.4  46.0375312829, -0.587834905531   \n",
       "...            ...        ...                             ...   \n",
       "1097815      400.0        1.8     47.6030208472, 6.1618556725   \n",
       "1097816      856.0        2.1    47.6097514089, 6.11842989879   \n",
       "1097817      575.0        0.1    47.3828510502, 5.62597938451   \n",
       "1097818     1962.0        0.2    47.8850612988, 5.99402771513   \n",
       "1097819      690.0        0.2    47.5374721676, 5.92263244824   \n",
       "\n",
       "                                                 geo_shape  \n",
       "0        {\"type\": \"Polygon\", \"coordinates\": [[[-0.10484...  \n",
       "1        {\"type\": \"Polygon\", \"coordinates\": [[[-0.74528...  \n",
       "2        {\"type\": \"Polygon\", \"coordinates\": [[[-0.60200...  \n",
       "3        {\"type\": \"Polygon\", \"coordinates\": [[[-0.41318...  \n",
       "4        {\"type\": \"Polygon\", \"coordinates\": [[[-0.58528...  \n",
       "...                                                    ...  \n",
       "1097815  {\"type\": \"Polygon\", \"coordinates\": [[[6.157643...  \n",
       "1097816  {\"type\": \"Polygon\", \"coordinates\": [[[6.119037...  \n",
       "1097817  {\"type\": \"Polygon\", \"coordinates\": [[[5.640740...  \n",
       "1097818  {\"type\": \"Polygon\", \"coordinates\": [[[5.945622...  \n",
       "1097819  {\"type\": \"Polygon\", \"coordinates\": [[[5.938235...  \n",
       "\n",
       "[1097820 rows x 23 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statut = pd.CategoricalDtype(categories=['Commune simple', 'Chef-lieu canton', 'Sous-préfecture',\n",
    "                                         'Préfecture', 'Préfecture de région', \"Capitale d'état\"],\n",
    "                             ordered=True)\n",
    "\n",
    "df_all = pd.merge(df_long, geo, left_on=\"CODE INSEE\", right_on=\"Code INSEE\", how=\"left\")\n",
    "df_all[\"Statut\"] = df_all[\"Statut\"].astype(statut)\n",
    "df_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e2ad8b91-cb59-4ee0-bee5-1cfe229c7e33",
   "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": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all[\"Statut\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "c46adab7-65bc-4658-9316-85c22149e31a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_8727/1321047617.py:3: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  .groupby(\"Statut\")[\"value\"]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Statut\n",
       "Commune simple          0.201893\n",
       "Chef-lieu canton        0.302156\n",
       "Sous-préfecture         0.449766\n",
       "Préfecture              0.724730\n",
       "Préfecture de région    0.893238\n",
       "Capitale d'état         0.996000\n",
       "Name: value, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_all\n",
    " .loc[(df_all[\"Couverture\"]==\"Couverture surfacique\") & (df_all[\"Techno\"]==\"4G\")]\n",
    " .groupby(\"Statut\")[\"value\"]\n",
    " .mean()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "03c0daa6-28fd-4866-b46a-cc7767a8310d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Opérateur\n",
       "Free Mobile                  0.033767\n",
       "SFR                          0.156458\n",
       "Orange France                0.256066\n",
       "Bouygues Telecom             0.256999\n",
       "Par au moins un opérateur    0.360874\n",
       "Name: value, dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_all\n",
    " .loc[(df_all[\"Couverture\"]==\"Couverture surfacique\") & (df_all[\"Techno\"]==\"4G\")]\n",
    " .groupby(\"Opérateur\")[\"value\"]\n",
    " .mean()\n",
    " .sort_values()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "cdec8371-b590-4645-8b40-99c643881072",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_8727/261650443.py:3: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  .groupby([\"Statut\", \"Opérateur\"])[\"value\"]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('Commune simple', 'Free Mobile')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_all\n",
    " .loc[(df_all[\"Couverture\"]==\"Couverture surfacique\") & (df_all[\"Techno\"]==\"4G\")]\n",
    " .groupby([\"Statut\", \"Opérateur\"])[\"value\"]\n",
    " .mean()\n",
    " .idxmin()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4ff7bd3c-521e-4452-a501-dbf47789c928",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 86948 entries, 0 to 86947\n",
      "Data columns (total 58 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   numero_de_da                 86948 non-null  object \n",
      " 1   form_total                   71492 non-null  float64\n",
      " 2   da_siren                     86948 non-null  int64  \n",
      " 3   da_no_etab                   80379 non-null  float64\n",
      " 4   da_raison_sociale            86948 non-null  object \n",
      " 5   sf1                          74274 non-null  object \n",
      " 6   nsf1                         74264 non-null  object \n",
      " 7   nhsf1                        74264 non-null  object \n",
      " 8   sf2                          66285 non-null  float64\n",
      " 9   nsf2                         66279 non-null  float64\n",
      " 10  nhsf2                        66279 non-null  float64\n",
      " 11  sf3                          59004 non-null  float64\n",
      " 12  nsf3                         58995 non-null  float64\n",
      " 13  nhsf3                        58994 non-null  float64\n",
      " 14  sf4                          52693 non-null  float64\n",
      " 15  nsf4                         52687 non-null  float64\n",
      " 16  nhsf4                        52686 non-null  float64\n",
      " 17  sf5                          46736 non-null  float64\n",
      " 18  nsf5                         46734 non-null  float64\n",
      " 19  nhsf5                        46734 non-null  float64\n",
      " 20  sf6                          42457 non-null  float64\n",
      " 21  nsf6                         42457 non-null  float64\n",
      " 22  nhsf6                        42457 non-null  float64\n",
      " 23  sf7                          38147 non-null  float64\n",
      " 24  nsf7                         38147 non-null  float64\n",
      " 25  nhsf7                        38147 non-null  float64\n",
      " 26  sf8                          34943 non-null  float64\n",
      " 27  nsf8                         34942 non-null  float64\n",
      " 28  nhsf8                        34942 non-null  float64\n",
      " 29  sf9                          31787 non-null  float64\n",
      " 30  nsf9                         31786 non-null  float64\n",
      " 31  nhsf9                        31786 non-null  float64\n",
      " 32  sf10                         29080 non-null  float64\n",
      " 33  nsf10                        29077 non-null  float64\n",
      " 34  nhsf10                       29077 non-null  float64\n",
      " 35  sf11                         26679 non-null  float64\n",
      " 36  nsf11                        26679 non-null  float64\n",
      " 37  nhsf11                       26679 non-null  float64\n",
      " 38  sf12                         24506 non-null  float64\n",
      " 39  nsf12                        24506 non-null  float64\n",
      " 40  nhsf12                       24505 non-null  float64\n",
      " 41  sf13                         22167 non-null  float64\n",
      " 42  nsf13                        22167 non-null  float64\n",
      " 43  nhsf13                       22167 non-null  float64\n",
      " 44  sf14                         19945 non-null  float64\n",
      " 45  nsf14                        19945 non-null  float64\n",
      " 46  nhsf14                       19945 non-null  float64\n",
      " 47  sf15                         17381 non-null  float64\n",
      " 48  nsf15                        17381 non-null  float64\n",
      " 49  nhsf15                       17380 non-null  float64\n",
      " 50  adr_rue_physique             86904 non-null  object \n",
      " 51  adr_rue_complement_physique  17597 non-null  object \n",
      " 52  adr_code_postal_physique     86914 non-null  object \n",
      " 53  adr_ville_physique           86943 non-null  object \n",
      " 54  adr_rue_postale              86935 non-null  object \n",
      " 55  adr_rue_complement_postale   18486 non-null  object \n",
      " 56  adr_code_postal_postale      86910 non-null  object \n",
      " 57  adr_ville_postale            86938 non-null  object \n",
      "dtypes: float64(44), int64(1), object(13)\n",
      "memory usage: 38.5+ MB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5258/459480866.py:1: DtypeWarning: Columns (5,6,7,52,56) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  of = pd.read_csv(\"data/ListeOF.zip\")\n"
     ]
    }
   ],
   "source": [
    "of = pd.read_csv(\"data/ListeOF.zip\")\n",
    "of.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c835fcbf-a1a6-4cf3-8c17-7bb72fb48e3f",
   "metadata": {},
   "outputs": [
    {
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       "      <td id=\"T_66c30_row12_col0\" class=\"data row12 col0\" >58995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row13\" class=\"row_heading level0 row13\" >nhsf3</th>\n",
       "      <td id=\"T_66c30_row13_col0\" class=\"data row13 col0\" >58994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row14\" class=\"row_heading level0 row14\" >sf4</th>\n",
       "      <td id=\"T_66c30_row14_col0\" class=\"data row14 col0\" >52693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row15\" class=\"row_heading level0 row15\" >nsf4</th>\n",
       "      <td id=\"T_66c30_row15_col0\" class=\"data row15 col0\" >52687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row16\" class=\"row_heading level0 row16\" >nhsf4</th>\n",
       "      <td id=\"T_66c30_row16_col0\" class=\"data row16 col0\" >52686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row17\" class=\"row_heading level0 row17\" >sf5</th>\n",
       "      <td id=\"T_66c30_row17_col0\" class=\"data row17 col0\" >46736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row18\" class=\"row_heading level0 row18\" >nsf5</th>\n",
       "      <td id=\"T_66c30_row18_col0\" class=\"data row18 col0\" >46734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row19\" class=\"row_heading level0 row19\" >nhsf5</th>\n",
       "      <td id=\"T_66c30_row19_col0\" class=\"data row19 col0\" >46734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row20\" class=\"row_heading level0 row20\" >sf6</th>\n",
       "      <td id=\"T_66c30_row20_col0\" class=\"data row20 col0\" >42457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row21\" class=\"row_heading level0 row21\" >nsf6</th>\n",
       "      <td id=\"T_66c30_row21_col0\" class=\"data row21 col0\" >42457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row22\" class=\"row_heading level0 row22\" >nhsf6</th>\n",
       "      <td id=\"T_66c30_row22_col0\" class=\"data row22 col0\" >42457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row23\" class=\"row_heading level0 row23\" >sf7</th>\n",
       "      <td id=\"T_66c30_row23_col0\" class=\"data row23 col0\" >38147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row24\" class=\"row_heading level0 row24\" >nsf7</th>\n",
       "      <td id=\"T_66c30_row24_col0\" class=\"data row24 col0\" >38147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row25\" class=\"row_heading level0 row25\" >nhsf7</th>\n",
       "      <td id=\"T_66c30_row25_col0\" class=\"data row25 col0\" >38147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row26\" class=\"row_heading level0 row26\" >sf8</th>\n",
       "      <td id=\"T_66c30_row26_col0\" class=\"data row26 col0\" >34943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row27\" class=\"row_heading level0 row27\" >nsf8</th>\n",
       "      <td id=\"T_66c30_row27_col0\" class=\"data row27 col0\" >34942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row28\" class=\"row_heading level0 row28\" >nhsf8</th>\n",
       "      <td id=\"T_66c30_row28_col0\" class=\"data row28 col0\" >34942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row29\" class=\"row_heading level0 row29\" >sf9</th>\n",
       "      <td id=\"T_66c30_row29_col0\" class=\"data row29 col0\" >31787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row30\" class=\"row_heading level0 row30\" >nsf9</th>\n",
       "      <td id=\"T_66c30_row30_col0\" class=\"data row30 col0\" >31786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row31\" class=\"row_heading level0 row31\" >nhsf9</th>\n",
       "      <td id=\"T_66c30_row31_col0\" class=\"data row31 col0\" >31786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row32\" class=\"row_heading level0 row32\" >sf10</th>\n",
       "      <td id=\"T_66c30_row32_col0\" class=\"data row32 col0\" >29080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row33\" class=\"row_heading level0 row33\" >nsf10</th>\n",
       "      <td id=\"T_66c30_row33_col0\" class=\"data row33 col0\" >29077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row34\" class=\"row_heading level0 row34\" >nhsf10</th>\n",
       "      <td id=\"T_66c30_row34_col0\" class=\"data row34 col0\" >29077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row35\" class=\"row_heading level0 row35\" >sf11</th>\n",
       "      <td id=\"T_66c30_row35_col0\" class=\"data row35 col0\" >26679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row36\" class=\"row_heading level0 row36\" >nsf11</th>\n",
       "      <td id=\"T_66c30_row36_col0\" class=\"data row36 col0\" >26679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row37\" class=\"row_heading level0 row37\" >nhsf11</th>\n",
       "      <td id=\"T_66c30_row37_col0\" class=\"data row37 col0\" >26679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row38\" class=\"row_heading level0 row38\" >sf12</th>\n",
       "      <td id=\"T_66c30_row38_col0\" class=\"data row38 col0\" >24506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row39\" class=\"row_heading level0 row39\" >nsf12</th>\n",
       "      <td id=\"T_66c30_row39_col0\" class=\"data row39 col0\" >24506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row40\" class=\"row_heading level0 row40\" >nhsf12</th>\n",
       "      <td id=\"T_66c30_row40_col0\" class=\"data row40 col0\" >24505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row41\" class=\"row_heading level0 row41\" >sf13</th>\n",
       "      <td id=\"T_66c30_row41_col0\" class=\"data row41 col0\" >22167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row42\" class=\"row_heading level0 row42\" >nsf13</th>\n",
       "      <td id=\"T_66c30_row42_col0\" class=\"data row42 col0\" >22167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row43\" class=\"row_heading level0 row43\" >nhsf13</th>\n",
       "      <td id=\"T_66c30_row43_col0\" class=\"data row43 col0\" >22167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row44\" class=\"row_heading level0 row44\" >sf14</th>\n",
       "      <td id=\"T_66c30_row44_col0\" class=\"data row44 col0\" >19945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row45\" class=\"row_heading level0 row45\" >nsf14</th>\n",
       "      <td id=\"T_66c30_row45_col0\" class=\"data row45 col0\" >19945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row46\" class=\"row_heading level0 row46\" >nhsf14</th>\n",
       "      <td id=\"T_66c30_row46_col0\" class=\"data row46 col0\" >19945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row47\" class=\"row_heading level0 row47\" >sf15</th>\n",
       "      <td id=\"T_66c30_row47_col0\" class=\"data row47 col0\" >17381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row48\" class=\"row_heading level0 row48\" >nsf15</th>\n",
       "      <td id=\"T_66c30_row48_col0\" class=\"data row48 col0\" >17381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row49\" class=\"row_heading level0 row49\" >nhsf15</th>\n",
       "      <td id=\"T_66c30_row49_col0\" class=\"data row49 col0\" >17380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row50\" class=\"row_heading level0 row50\" >adr_rue_physique</th>\n",
       "      <td id=\"T_66c30_row50_col0\" class=\"data row50 col0\" >86904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row51\" class=\"row_heading level0 row51\" >adr_rue_complement_physique</th>\n",
       "      <td id=\"T_66c30_row51_col0\" class=\"data row51 col0\" >17597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row52\" class=\"row_heading level0 row52\" >adr_code_postal_physique</th>\n",
       "      <td id=\"T_66c30_row52_col0\" class=\"data row52 col0\" >86914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row53\" class=\"row_heading level0 row53\" >adr_ville_physique</th>\n",
       "      <td id=\"T_66c30_row53_col0\" class=\"data row53 col0\" >86943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row54\" class=\"row_heading level0 row54\" >adr_rue_postale</th>\n",
       "      <td id=\"T_66c30_row54_col0\" class=\"data row54 col0\" >86935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row55\" class=\"row_heading level0 row55\" >adr_rue_complement_postale</th>\n",
       "      <td id=\"T_66c30_row55_col0\" class=\"data row55 col0\" >18486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row56\" class=\"row_heading level0 row56\" >adr_code_postal_postale</th>\n",
       "      <td id=\"T_66c30_row56_col0\" class=\"data row56 col0\" >86910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_66c30_level0_row57\" class=\"row_heading level0 row57\" >adr_ville_postale</th>\n",
       "      <td id=\"T_66c30_row57_col0\" class=\"data row57 col0\" >86938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f3f679c0e10>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(of\n",
    " .notna()\n",
    " .sum()\n",
    " .rename(\"complétion\")\n",
    " .to_frame()\n",
    " .style\n",
    " .bar(color=\"lightgreen\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "364dd6ae-de2e-4af5-b622-535a705ddcc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_9520c_row0_col0, #T_9520c_row2_col0, #T_9520c_row4_col0, #T_9520c_row50_col0, #T_9520c_row52_col0, #T_9520c_row53_col0, #T_9520c_row54_col0, #T_9520c_row56_col0, #T_9520c_row57_col0 {\n",
       "  background-color: #fde725;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9520c_row1_col0 {\n",
       "  background-color: #6ece58;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9520c_row3_col0 {\n",
       "  background-color: #c0df25;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9520c_row5_col0, #T_9520c_row6_col0, #T_9520c_row7_col0 {\n",
       "  background-color: #86d549;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9520c_row8_col0, #T_9520c_row9_col0, #T_9520c_row10_col0 {\n",
       "  background-color: #44bf70;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_9520c_row11_col0, #T_9520c_row12_col0, #T_9520c_row13_col0 {\n",
       "  background-color: #22a884;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_9520c_row14_col0, #T_9520c_row15_col0, #T_9520c_row16_col0 {\n",
       "  background-color: #20928c;\n",
       "  color: #f1f1f1;\n",
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       "#T_9520c_row17_col0, #T_9520c_row18_col0, #T_9520c_row19_col0 {\n",
       "  background-color: #277e8e;\n",
       "  color: #f1f1f1;\n",
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       "#T_9520c_row20_col0, #T_9520c_row21_col0, #T_9520c_row22_col0 {\n",
       "  background-color: #2e6f8e;\n",
       "  color: #f1f1f1;\n",
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       "#T_9520c_row23_col0, #T_9520c_row24_col0, #T_9520c_row25_col0 {\n",
       "  background-color: #355f8d;\n",
       "  color: #f1f1f1;\n",
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       "#T_9520c_row26_col0, #T_9520c_row27_col0, #T_9520c_row28_col0 {\n",
       "  background-color: #3b528b;\n",
       "  color: #f1f1f1;\n",
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       "#T_9520c_row29_col0, #T_9520c_row30_col0, #T_9520c_row31_col0 {\n",
       "  background-color: #404688;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_9520c_row32_col0, #T_9520c_row33_col0, #T_9520c_row34_col0 {\n",
       "  background-color: #443a83;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_9520c_row35_col0, #T_9520c_row36_col0, #T_9520c_row37_col0 {\n",
       "  background-color: #472f7d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_9520c_row38_col0, #T_9520c_row39_col0, #T_9520c_row40_col0 {\n",
       "  background-color: #482576;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_9520c_row41_col0, #T_9520c_row42_col0, #T_9520c_row43_col0 {\n",
       "  background-color: #481a6c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_9520c_row44_col0, #T_9520c_row45_col0, #T_9520c_row46_col0 {\n",
       "  background-color: #470e61;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_9520c_row47_col0, #T_9520c_row48_col0, #T_9520c_row49_col0, #T_9520c_row51_col0 {\n",
       "  background-color: #440154;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_9520c_row55_col0 {\n",
       "  background-color: #46075a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_9520c\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_9520c_level0_col0\" class=\"col_heading level0 col0\" >complétion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row0\" class=\"row_heading level0 row0\" >numero_de_da</th>\n",
       "      <td id=\"T_9520c_row0_col0\" class=\"data row0 col0\" >100.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row1\" class=\"row_heading level0 row1\" >form_total</th>\n",
       "      <td id=\"T_9520c_row1_col0\" class=\"data row1 col0\" >82.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row2\" class=\"row_heading level0 row2\" >da_siren</th>\n",
       "      <td id=\"T_9520c_row2_col0\" class=\"data row2 col0\" >100.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row3\" class=\"row_heading level0 row3\" >da_no_etab</th>\n",
       "      <td id=\"T_9520c_row3_col0\" class=\"data row3 col0\" >92.4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row4\" class=\"row_heading level0 row4\" >da_raison_sociale</th>\n",
       "      <td id=\"T_9520c_row4_col0\" class=\"data row4 col0\" >100.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row5\" class=\"row_heading level0 row5\" >sf1</th>\n",
       "      <td id=\"T_9520c_row5_col0\" class=\"data row5 col0\" >85.4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row6\" class=\"row_heading level0 row6\" >nsf1</th>\n",
       "      <td id=\"T_9520c_row6_col0\" class=\"data row6 col0\" >85.4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row7\" class=\"row_heading level0 row7\" >nhsf1</th>\n",
       "      <td id=\"T_9520c_row7_col0\" class=\"data row7 col0\" >85.4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row8\" class=\"row_heading level0 row8\" >sf2</th>\n",
       "      <td id=\"T_9520c_row8_col0\" class=\"data row8 col0\" >76.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row9\" class=\"row_heading level0 row9\" >nsf2</th>\n",
       "      <td id=\"T_9520c_row9_col0\" class=\"data row9 col0\" >76.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row10\" class=\"row_heading level0 row10\" >nhsf2</th>\n",
       "      <td id=\"T_9520c_row10_col0\" class=\"data row10 col0\" >76.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row11\" class=\"row_heading level0 row11\" >sf3</th>\n",
       "      <td id=\"T_9520c_row11_col0\" class=\"data row11 col0\" >67.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row12\" class=\"row_heading level0 row12\" >nsf3</th>\n",
       "      <td id=\"T_9520c_row12_col0\" class=\"data row12 col0\" >67.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row13\" class=\"row_heading level0 row13\" >nhsf3</th>\n",
       "      <td id=\"T_9520c_row13_col0\" class=\"data row13 col0\" >67.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row14\" class=\"row_heading level0 row14\" >sf4</th>\n",
       "      <td id=\"T_9520c_row14_col0\" class=\"data row14 col0\" >60.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row15\" class=\"row_heading level0 row15\" >nsf4</th>\n",
       "      <td id=\"T_9520c_row15_col0\" class=\"data row15 col0\" >60.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row16\" class=\"row_heading level0 row16\" >nhsf4</th>\n",
       "      <td id=\"T_9520c_row16_col0\" class=\"data row16 col0\" >60.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row17\" class=\"row_heading level0 row17\" >sf5</th>\n",
       "      <td id=\"T_9520c_row17_col0\" class=\"data row17 col0\" >53.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row18\" class=\"row_heading level0 row18\" >nsf5</th>\n",
       "      <td id=\"T_9520c_row18_col0\" class=\"data row18 col0\" >53.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row19\" class=\"row_heading level0 row19\" >nhsf5</th>\n",
       "      <td id=\"T_9520c_row19_col0\" class=\"data row19 col0\" >53.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row20\" class=\"row_heading level0 row20\" >sf6</th>\n",
       "      <td id=\"T_9520c_row20_col0\" class=\"data row20 col0\" >48.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row21\" class=\"row_heading level0 row21\" >nsf6</th>\n",
       "      <td id=\"T_9520c_row21_col0\" class=\"data row21 col0\" >48.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row22\" class=\"row_heading level0 row22\" >nhsf6</th>\n",
       "      <td id=\"T_9520c_row22_col0\" class=\"data row22 col0\" >48.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row23\" class=\"row_heading level0 row23\" >sf7</th>\n",
       "      <td id=\"T_9520c_row23_col0\" class=\"data row23 col0\" >43.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row24\" class=\"row_heading level0 row24\" >nsf7</th>\n",
       "      <td id=\"T_9520c_row24_col0\" class=\"data row24 col0\" >43.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row25\" class=\"row_heading level0 row25\" >nhsf7</th>\n",
       "      <td id=\"T_9520c_row25_col0\" class=\"data row25 col0\" >43.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row26\" class=\"row_heading level0 row26\" >sf8</th>\n",
       "      <td id=\"T_9520c_row26_col0\" class=\"data row26 col0\" >40.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row27\" class=\"row_heading level0 row27\" >nsf8</th>\n",
       "      <td id=\"T_9520c_row27_col0\" class=\"data row27 col0\" >40.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row28\" class=\"row_heading level0 row28\" >nhsf8</th>\n",
       "      <td id=\"T_9520c_row28_col0\" class=\"data row28 col0\" >40.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row29\" class=\"row_heading level0 row29\" >sf9</th>\n",
       "      <td id=\"T_9520c_row29_col0\" class=\"data row29 col0\" >36.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row30\" class=\"row_heading level0 row30\" >nsf9</th>\n",
       "      <td id=\"T_9520c_row30_col0\" class=\"data row30 col0\" >36.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row31\" class=\"row_heading level0 row31\" >nhsf9</th>\n",
       "      <td id=\"T_9520c_row31_col0\" class=\"data row31 col0\" >36.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row32\" class=\"row_heading level0 row32\" >sf10</th>\n",
       "      <td id=\"T_9520c_row32_col0\" class=\"data row32 col0\" >33.4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row33\" class=\"row_heading level0 row33\" >nsf10</th>\n",
       "      <td id=\"T_9520c_row33_col0\" class=\"data row33 col0\" >33.4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row34\" class=\"row_heading level0 row34\" >nhsf10</th>\n",
       "      <td id=\"T_9520c_row34_col0\" class=\"data row34 col0\" >33.4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row35\" class=\"row_heading level0 row35\" >sf11</th>\n",
       "      <td id=\"T_9520c_row35_col0\" class=\"data row35 col0\" >30.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row36\" class=\"row_heading level0 row36\" >nsf11</th>\n",
       "      <td id=\"T_9520c_row36_col0\" class=\"data row36 col0\" >30.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row37\" class=\"row_heading level0 row37\" >nhsf11</th>\n",
       "      <td id=\"T_9520c_row37_col0\" class=\"data row37 col0\" >30.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row38\" class=\"row_heading level0 row38\" >sf12</th>\n",
       "      <td id=\"T_9520c_row38_col0\" class=\"data row38 col0\" >28.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row39\" class=\"row_heading level0 row39\" >nsf12</th>\n",
       "      <td id=\"T_9520c_row39_col0\" class=\"data row39 col0\" >28.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row40\" class=\"row_heading level0 row40\" >nhsf12</th>\n",
       "      <td id=\"T_9520c_row40_col0\" class=\"data row40 col0\" >28.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row41\" class=\"row_heading level0 row41\" >sf13</th>\n",
       "      <td id=\"T_9520c_row41_col0\" class=\"data row41 col0\" >25.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row42\" class=\"row_heading level0 row42\" >nsf13</th>\n",
       "      <td id=\"T_9520c_row42_col0\" class=\"data row42 col0\" >25.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row43\" class=\"row_heading level0 row43\" >nhsf13</th>\n",
       "      <td id=\"T_9520c_row43_col0\" class=\"data row43 col0\" >25.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row44\" class=\"row_heading level0 row44\" >sf14</th>\n",
       "      <td id=\"T_9520c_row44_col0\" class=\"data row44 col0\" >22.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row45\" class=\"row_heading level0 row45\" >nsf14</th>\n",
       "      <td id=\"T_9520c_row45_col0\" class=\"data row45 col0\" >22.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row46\" class=\"row_heading level0 row46\" >nhsf14</th>\n",
       "      <td id=\"T_9520c_row46_col0\" class=\"data row46 col0\" >22.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row47\" class=\"row_heading level0 row47\" >sf15</th>\n",
       "      <td id=\"T_9520c_row47_col0\" class=\"data row47 col0\" >20.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row48\" class=\"row_heading level0 row48\" >nsf15</th>\n",
       "      <td id=\"T_9520c_row48_col0\" class=\"data row48 col0\" >20.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row49\" class=\"row_heading level0 row49\" >nhsf15</th>\n",
       "      <td id=\"T_9520c_row49_col0\" class=\"data row49 col0\" >20.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row50\" class=\"row_heading level0 row50\" >adr_rue_physique</th>\n",
       "      <td id=\"T_9520c_row50_col0\" class=\"data row50 col0\" >99.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row51\" class=\"row_heading level0 row51\" >adr_rue_complement_physique</th>\n",
       "      <td id=\"T_9520c_row51_col0\" class=\"data row51 col0\" >20.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row52\" class=\"row_heading level0 row52\" >adr_code_postal_physique</th>\n",
       "      <td id=\"T_9520c_row52_col0\" class=\"data row52 col0\" >100.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row53\" class=\"row_heading level0 row53\" >adr_ville_physique</th>\n",
       "      <td id=\"T_9520c_row53_col0\" class=\"data row53 col0\" >100.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row54\" class=\"row_heading level0 row54\" >adr_rue_postale</th>\n",
       "      <td id=\"T_9520c_row54_col0\" class=\"data row54 col0\" >100.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row55\" class=\"row_heading level0 row55\" >adr_rue_complement_postale</th>\n",
       "      <td id=\"T_9520c_row55_col0\" class=\"data row55 col0\" >21.3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row56\" class=\"row_heading level0 row56\" >adr_code_postal_postale</th>\n",
       "      <td id=\"T_9520c_row56_col0\" class=\"data row56 col0\" >100.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9520c_level0_row57\" class=\"row_heading level0 row57\" >adr_ville_postale</th>\n",
       "      <td id=\"T_9520c_row57_col0\" class=\"data row57 col0\" >100.0%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f3f6b178ad0>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(of\n",
    " .notna()\n",
    " .sum()\n",
    " .div(len(of)/100)\n",
    " .rename(\"complétion\")\n",
    " .to_frame()\n",
    " .style\n",
    " .format(\"{:.1f}%\")\n",
    " .background_gradient(cmap='viridis')\n",
    ")"
   ]
  }
 ],
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