diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb
index 1fa43e301d88bd845ac71e66283e4fa2183f76ff..20a1d333895106d4c56b692bc623c38c177469ef 100644
--- a/module3/exo1/analyse-syndrome-grippal.ipynb
+++ b/module3/exo1/analyse-syndrome-grippal.ipynb
@@ -1083,6 +1083,9 @@
     }
    ],
    "source": [
+    "# Note : Cette méthode crée des problèmes plus tard lors du plot des données pour des raisons qui m'échappent. \n",
+    "# La méthode utilisant os et urllib présentée dans la correction ne souffre pas de ce problème et est donc meilleure.\n",
+    "\n",
     "try:\n",
     "    data_path = \"incidence-PAY-3.csv\"\n",
     "    raw_data = pd.read_csv(data_path, encoding = 'iso-8859-1', skiprows=1, usecols=[i for i in range(11)])\n",
diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..4aeef35dcd21bc7d3bd99e85c2e8bb13850cf5df 100644
--- a/module3/exo2/exercice.ipynb
+++ b/module3/exo2/exercice.ipynb
@@ -1,5 +1,1589 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import isoweek"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Les données de l'incidence de la varicelle sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1991 et se termine avec une semaine récente."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nous vérifions si le fichier csv contenant les données est déjà téléchargé. Si ce n'est pas le cas nous le téléchargeons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_url = \"https://www.sentiweb.fr/datasets/all/inc-7-PAY.csv\"\n",
+    "data_file = \"varicelle.csv\"\n",
+    "\n",
+    "import os\n",
+    "import urllib.request\n",
+    "if not os.path.exists(data_file):\n",
+    "    urllib.request.urlretrieve(data_url, data_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n",
+    "\n",
+    "| Nom de colonne | Libellé de colonne                                                                                                                |\n",
+    "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n",
+    "| week           | Semaine calendaire (ISO 8601)                                                                                                     |\n",
+    "| indicator      | Code de l'indicateur de surveillance                                                                                              |\n",
+    "| inc            | Estimation de l'incidence de consultations en nombre de cas                                                                       |\n",
+    "| inc_low        | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation                                                     |\n",
+    "| inc_up         | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation                                                     |\n",
+    "| inc100         | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants)                                   |\n",
+    "| inc100_low     | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
+    "| inc100_up      | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
+    "| geo_insee      | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/                            |\n",
+    "| geo_name       | Libellé de la zone géographique (ce libellé peut être modifié sans préavis)                                                       |\n",
+    "\n",
+    "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`. Nous précisons aussi l'encodage des données."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>week</th>\n",
+       "      <th>indicator</th>\n",
+       "      <th>inc</th>\n",
+       "      <th>inc_low</th>\n",
+       "      <th>inc_up</th>\n",
+       "      <th>inc100</th>\n",
+       "      <th>inc100_low</th>\n",
+       "      <th>inc100_up</th>\n",
+       "      <th>geo_insee</th>\n",
+       "      <th>geo_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>202530</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9815</td>\n",
+       "      <td>4803</td>\n",
+       "      <td>14827</td>\n",
+       "      <td>15</td>\n",
+       "      <td>8</td>\n",
+       "      <td>22</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202529</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6436</td>\n",
+       "      <td>3413</td>\n",
+       "      <td>9459</td>\n",
+       "      <td>10</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202528</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5584</td>\n",
+       "      <td>3123</td>\n",
+       "      <td>8045</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>12</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202527</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5667</td>\n",
+       "      <td>2850</td>\n",
+       "      <td>8484</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>12</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202526</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5872</td>\n",
+       "      <td>3285</td>\n",
+       "      <td>8459</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>13</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>202525</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5953</td>\n",
+       "      <td>3698</td>\n",
+       "      <td>8208</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>12</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>202524</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4580</td>\n",
+       "      <td>2558</td>\n",
+       "      <td>6602</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>202523</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4911</td>\n",
+       "      <td>2663</td>\n",
+       "      <td>7159</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>202522</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6837</td>\n",
+       "      <td>3940</td>\n",
+       "      <td>9734</td>\n",
+       "      <td>10</td>\n",
+       "      <td>6</td>\n",
+       "      <td>14</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>202521</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4693</td>\n",
+       "      <td>2653</td>\n",
+       "      <td>6733</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>202520</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3083</td>\n",
+       "      <td>1535</td>\n",
+       "      <td>4631</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>202519</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5084</td>\n",
+       "      <td>1997</td>\n",
+       "      <td>8171</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>13</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>202518</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5003</td>\n",
+       "      <td>2718</td>\n",
+       "      <td>7288</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>202517</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6246</td>\n",
+       "      <td>3424</td>\n",
+       "      <td>9068</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>13</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>202516</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6151</td>\n",
+       "      <td>3193</td>\n",
+       "      <td>9109</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>13</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>202515</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5557</td>\n",
+       "      <td>3262</td>\n",
+       "      <td>7852</td>\n",
+       "      <td>8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>202514</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4984</td>\n",
+       "      <td>2858</td>\n",
+       "      <td>7110</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>202513</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5964</td>\n",
+       "      <td>3608</td>\n",
+       "      <td>8320</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>13</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>202512</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3855</td>\n",
+       "      <td>1995</td>\n",
+       "      <td>5715</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>202511</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5878</td>\n",
+       "      <td>2747</td>\n",
+       "      <td>9009</td>\n",
+       "      <td>9</td>\n",
+       "      <td>4</td>\n",
+       "      <td>14</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>202510</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2921</td>\n",
+       "      <td>1421</td>\n",
+       "      <td>4421</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>202509</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3381</td>\n",
+       "      <td>1468</td>\n",
+       "      <td>5294</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>202508</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2835</td>\n",
+       "      <td>1286</td>\n",
+       "      <td>4384</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>202507</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4502</td>\n",
+       "      <td>2382</td>\n",
+       "      <td>6622</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>202506</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3455</td>\n",
+       "      <td>1958</td>\n",
+       "      <td>4952</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>202505</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2087</td>\n",
+       "      <td>1056</td>\n",
+       "      <td>3118</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>202504</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6895</td>\n",
+       "      <td>4466</td>\n",
+       "      <td>9324</td>\n",
+       "      <td>10</td>\n",
+       "      <td>6</td>\n",
+       "      <td>14</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>202503</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2462</td>\n",
+       "      <td>1161</td>\n",
+       "      <td>3763</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>202502</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5966</td>\n",
+       "      <td>2757</td>\n",
+       "      <td>9175</td>\n",
+       "      <td>9</td>\n",
+       "      <td>4</td>\n",
+       "      <td>14</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>202501</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6059</td>\n",
+       "      <td>2451</td>\n",
+       "      <td>9667</td>\n",
+       "      <td>9</td>\n",
+       "      <td>4</td>\n",
+       "      <td>14</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1778</th>\n",
+       "      <td>199126</td>\n",
+       "      <td>7</td>\n",
+       "      <td>17608</td>\n",
+       "      <td>11304</td>\n",
+       "      <td>23912</td>\n",
+       "      <td>31</td>\n",
+       "      <td>20</td>\n",
+       "      <td>42</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1779</th>\n",
+       "      <td>199125</td>\n",
+       "      <td>7</td>\n",
+       "      <td>16169</td>\n",
+       "      <td>10700</td>\n",
+       "      <td>21638</td>\n",
+       "      <td>28</td>\n",
+       "      <td>18</td>\n",
+       "      <td>38</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1780</th>\n",
+       "      <td>199124</td>\n",
+       "      <td>7</td>\n",
+       "      <td>16171</td>\n",
+       "      <td>10071</td>\n",
+       "      <td>22271</td>\n",
+       "      <td>28</td>\n",
+       "      <td>17</td>\n",
+       "      <td>39</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1781</th>\n",
+       "      <td>199123</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11947</td>\n",
+       "      <td>7671</td>\n",
+       "      <td>16223</td>\n",
+       "      <td>21</td>\n",
+       "      <td>13</td>\n",
+       "      <td>29</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1782</th>\n",
+       "      <td>199122</td>\n",
+       "      <td>7</td>\n",
+       "      <td>15452</td>\n",
+       "      <td>9953</td>\n",
+       "      <td>20951</td>\n",
+       "      <td>27</td>\n",
+       "      <td>17</td>\n",
+       "      <td>37</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1783</th>\n",
+       "      <td>199121</td>\n",
+       "      <td>7</td>\n",
+       "      <td>14903</td>\n",
+       "      <td>8975</td>\n",
+       "      <td>20831</td>\n",
+       "      <td>26</td>\n",
+       "      <td>16</td>\n",
+       "      <td>36</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1784</th>\n",
+       "      <td>199120</td>\n",
+       "      <td>7</td>\n",
+       "      <td>19053</td>\n",
+       "      <td>12742</td>\n",
+       "      <td>25364</td>\n",
+       "      <td>34</td>\n",
+       "      <td>23</td>\n",
+       "      <td>45</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1785</th>\n",
+       "      <td>199119</td>\n",
+       "      <td>7</td>\n",
+       "      <td>16739</td>\n",
+       "      <td>11246</td>\n",
+       "      <td>22232</td>\n",
+       "      <td>29</td>\n",
+       "      <td>19</td>\n",
+       "      <td>39</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1786</th>\n",
+       "      <td>199118</td>\n",
+       "      <td>7</td>\n",
+       "      <td>21385</td>\n",
+       "      <td>13882</td>\n",
+       "      <td>28888</td>\n",
+       "      <td>38</td>\n",
+       "      <td>25</td>\n",
+       "      <td>51</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1787</th>\n",
+       "      <td>199117</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13462</td>\n",
+       "      <td>8877</td>\n",
+       "      <td>18047</td>\n",
+       "      <td>24</td>\n",
+       "      <td>16</td>\n",
+       "      <td>32</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1788</th>\n",
+       "      <td>199116</td>\n",
+       "      <td>7</td>\n",
+       "      <td>14857</td>\n",
+       "      <td>10068</td>\n",
+       "      <td>19646</td>\n",
+       "      <td>26</td>\n",
+       "      <td>18</td>\n",
+       "      <td>34</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1789</th>\n",
+       "      <td>199115</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13975</td>\n",
+       "      <td>9781</td>\n",
+       "      <td>18169</td>\n",
+       "      <td>25</td>\n",
+       "      <td>18</td>\n",
+       "      <td>32</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1790</th>\n",
+       "      <td>199114</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12265</td>\n",
+       "      <td>7684</td>\n",
+       "      <td>16846</td>\n",
+       "      <td>22</td>\n",
+       "      <td>14</td>\n",
+       "      <td>30</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1791</th>\n",
+       "      <td>199113</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9567</td>\n",
+       "      <td>6041</td>\n",
+       "      <td>13093</td>\n",
+       "      <td>17</td>\n",
+       "      <td>11</td>\n",
+       "      <td>23</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1792</th>\n",
+       "      <td>199112</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10864</td>\n",
+       "      <td>7331</td>\n",
+       "      <td>14397</td>\n",
+       "      <td>19</td>\n",
+       "      <td>13</td>\n",
+       "      <td>25</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1793</th>\n",
+       "      <td>199111</td>\n",
+       "      <td>7</td>\n",
+       "      <td>15574</td>\n",
+       "      <td>11184</td>\n",
+       "      <td>19964</td>\n",
+       "      <td>27</td>\n",
+       "      <td>19</td>\n",
+       "      <td>35</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1794</th>\n",
+       "      <td>199110</td>\n",
+       "      <td>7</td>\n",
+       "      <td>16643</td>\n",
+       "      <td>11372</td>\n",
+       "      <td>21914</td>\n",
+       "      <td>29</td>\n",
+       "      <td>20</td>\n",
+       "      <td>38</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1795</th>\n",
+       "      <td>199109</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13741</td>\n",
+       "      <td>8780</td>\n",
+       "      <td>18702</td>\n",
+       "      <td>24</td>\n",
+       "      <td>15</td>\n",
+       "      <td>33</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1796</th>\n",
+       "      <td>199108</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13289</td>\n",
+       "      <td>8813</td>\n",
+       "      <td>17765</td>\n",
+       "      <td>23</td>\n",
+       "      <td>15</td>\n",
+       "      <td>31</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1797</th>\n",
+       "      <td>199107</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12337</td>\n",
+       "      <td>8077</td>\n",
+       "      <td>16597</td>\n",
+       "      <td>22</td>\n",
+       "      <td>15</td>\n",
+       "      <td>29</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1798</th>\n",
+       "      <td>199106</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10877</td>\n",
+       "      <td>7013</td>\n",
+       "      <td>14741</td>\n",
+       "      <td>19</td>\n",
+       "      <td>12</td>\n",
+       "      <td>26</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1799</th>\n",
+       "      <td>199105</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10442</td>\n",
+       "      <td>6544</td>\n",
+       "      <td>14340</td>\n",
+       "      <td>18</td>\n",
+       "      <td>11</td>\n",
+       "      <td>25</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1800</th>\n",
+       "      <td>199104</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7913</td>\n",
+       "      <td>4563</td>\n",
+       "      <td>11263</td>\n",
+       "      <td>14</td>\n",
+       "      <td>8</td>\n",
+       "      <td>20</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1801</th>\n",
+       "      <td>199103</td>\n",
+       "      <td>7</td>\n",
+       "      <td>15387</td>\n",
+       "      <td>10484</td>\n",
+       "      <td>20290</td>\n",
+       "      <td>27</td>\n",
+       "      <td>18</td>\n",
+       "      <td>36</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1802</th>\n",
+       "      <td>199102</td>\n",
+       "      <td>7</td>\n",
+       "      <td>16277</td>\n",
+       "      <td>11046</td>\n",
+       "      <td>21508</td>\n",
+       "      <td>29</td>\n",
+       "      <td>20</td>\n",
+       "      <td>38</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1803</th>\n",
+       "      <td>199101</td>\n",
+       "      <td>7</td>\n",
+       "      <td>15565</td>\n",
+       "      <td>10271</td>\n",
+       "      <td>20859</td>\n",
+       "      <td>27</td>\n",
+       "      <td>18</td>\n",
+       "      <td>36</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1804</th>\n",
+       "      <td>199052</td>\n",
+       "      <td>7</td>\n",
+       "      <td>19375</td>\n",
+       "      <td>13295</td>\n",
+       "      <td>25455</td>\n",
+       "      <td>34</td>\n",
+       "      <td>23</td>\n",
+       "      <td>45</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1805</th>\n",
+       "      <td>199051</td>\n",
+       "      <td>7</td>\n",
+       "      <td>19080</td>\n",
+       "      <td>13807</td>\n",
+       "      <td>24353</td>\n",
+       "      <td>34</td>\n",
+       "      <td>25</td>\n",
+       "      <td>43</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1806</th>\n",
+       "      <td>199050</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11079</td>\n",
+       "      <td>6660</td>\n",
+       "      <td>15498</td>\n",
+       "      <td>20</td>\n",
+       "      <td>12</td>\n",
+       "      <td>28</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1807</th>\n",
+       "      <td>199049</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1143</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2610</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1808 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
+       "0     202530          7   9815     4803   14827      15           8   \n",
+       "1     202529          7   6436     3413    9459      10           5   \n",
+       "2     202528          7   5584     3123    8045       8           4   \n",
+       "3     202527          7   5667     2850    8484       8           4   \n",
+       "4     202526          7   5872     3285    8459       9           5   \n",
+       "5     202525          7   5953     3698    8208       9           6   \n",
+       "6     202524          7   4580     2558    6602       7           4   \n",
+       "7     202523          7   4911     2663    7159       7           4   \n",
+       "8     202522          7   6837     3940    9734      10           6   \n",
+       "9     202521          7   4693     2653    6733       7           4   \n",
+       "10    202520          7   3083     1535    4631       5           3   \n",
+       "11    202519          7   5084     1997    8171       8           3   \n",
+       "12    202518          7   5003     2718    7288       7           4   \n",
+       "13    202517          7   6246     3424    9068       9           5   \n",
+       "14    202516          7   6151     3193    9109       9           5   \n",
+       "15    202515          7   5557     3262    7852       8           5   \n",
+       "16    202514          7   4984     2858    7110       7           4   \n",
+       "17    202513          7   5964     3608    8320       9           5   \n",
+       "18    202512          7   3855     1995    5715       6           3   \n",
+       "19    202511          7   5878     2747    9009       9           4   \n",
+       "20    202510          7   2921     1421    4421       4           2   \n",
+       "21    202509          7   3381     1468    5294       5           2   \n",
+       "22    202508          7   2835     1286    4384       4           2   \n",
+       "23    202507          7   4502     2382    6622       7           4   \n",
+       "24    202506          7   3455     1958    4952       5           3   \n",
+       "25    202505          7   2087     1056    3118       3           1   \n",
+       "26    202504          7   6895     4466    9324      10           6   \n",
+       "27    202503          7   2462     1161    3763       4           2   \n",
+       "28    202502          7   5966     2757    9175       9           4   \n",
+       "29    202501          7   6059     2451    9667       9           4   \n",
+       "...      ...        ...    ...      ...     ...     ...         ...   \n",
+       "1778  199126          7  17608    11304   23912      31          20   \n",
+       "1779  199125          7  16169    10700   21638      28          18   \n",
+       "1780  199124          7  16171    10071   22271      28          17   \n",
+       "1781  199123          7  11947     7671   16223      21          13   \n",
+       "1782  199122          7  15452     9953   20951      27          17   \n",
+       "1783  199121          7  14903     8975   20831      26          16   \n",
+       "1784  199120          7  19053    12742   25364      34          23   \n",
+       "1785  199119          7  16739    11246   22232      29          19   \n",
+       "1786  199118          7  21385    13882   28888      38          25   \n",
+       "1787  199117          7  13462     8877   18047      24          16   \n",
+       "1788  199116          7  14857    10068   19646      26          18   \n",
+       "1789  199115          7  13975     9781   18169      25          18   \n",
+       "1790  199114          7  12265     7684   16846      22          14   \n",
+       "1791  199113          7   9567     6041   13093      17          11   \n",
+       "1792  199112          7  10864     7331   14397      19          13   \n",
+       "1793  199111          7  15574    11184   19964      27          19   \n",
+       "1794  199110          7  16643    11372   21914      29          20   \n",
+       "1795  199109          7  13741     8780   18702      24          15   \n",
+       "1796  199108          7  13289     8813   17765      23          15   \n",
+       "1797  199107          7  12337     8077   16597      22          15   \n",
+       "1798  199106          7  10877     7013   14741      19          12   \n",
+       "1799  199105          7  10442     6544   14340      18          11   \n",
+       "1800  199104          7   7913     4563   11263      14           8   \n",
+       "1801  199103          7  15387    10484   20290      27          18   \n",
+       "1802  199102          7  16277    11046   21508      29          20   \n",
+       "1803  199101          7  15565    10271   20859      27          18   \n",
+       "1804  199052          7  19375    13295   25455      34          23   \n",
+       "1805  199051          7  19080    13807   24353      34          25   \n",
+       "1806  199050          7  11079     6660   15498      20          12   \n",
+       "1807  199049          7   1143        0    2610       2           0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0            22        FR   France  \n",
+       "1            15        FR   France  \n",
+       "2            12        FR   France  \n",
+       "3            12        FR   France  \n",
+       "4            13        FR   France  \n",
+       "5            12        FR   France  \n",
+       "6            10        FR   France  \n",
+       "7            10        FR   France  \n",
+       "8            14        FR   France  \n",
+       "9            10        FR   France  \n",
+       "10            7        FR   France  \n",
+       "11           13        FR   France  \n",
+       "12           10        FR   France  \n",
+       "13           13        FR   France  \n",
+       "14           13        FR   France  \n",
+       "15           11        FR   France  \n",
+       "16           10        FR   France  \n",
+       "17           13        FR   France  \n",
+       "18            9        FR   France  \n",
+       "19           14        FR   France  \n",
+       "20            6        FR   France  \n",
+       "21            8        FR   France  \n",
+       "22            6        FR   France  \n",
+       "23           10        FR   France  \n",
+       "24            7        FR   France  \n",
+       "25            5        FR   France  \n",
+       "26           14        FR   France  \n",
+       "27            6        FR   France  \n",
+       "28           14        FR   France  \n",
+       "29           14        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1778         42        FR   France  \n",
+       "1779         38        FR   France  \n",
+       "1780         39        FR   France  \n",
+       "1781         29        FR   France  \n",
+       "1782         37        FR   France  \n",
+       "1783         36        FR   France  \n",
+       "1784         45        FR   France  \n",
+       "1785         39        FR   France  \n",
+       "1786         51        FR   France  \n",
+       "1787         32        FR   France  \n",
+       "1788         34        FR   France  \n",
+       "1789         32        FR   France  \n",
+       "1790         30        FR   France  \n",
+       "1791         23        FR   France  \n",
+       "1792         25        FR   France  \n",
+       "1793         35        FR   France  \n",
+       "1794         38        FR   France  \n",
+       "1795         33        FR   France  \n",
+       "1796         31        FR   France  \n",
+       "1797         29        FR   France  \n",
+       "1798         26        FR   France  \n",
+       "1799         25        FR   France  \n",
+       "1800         20        FR   France  \n",
+       "1801         36        FR   France  \n",
+       "1802         38        FR   France  \n",
+       "1803         36        FR   France  \n",
+       "1804         45        FR   France  \n",
+       "1805         43        FR   France  \n",
+       "1806         28        FR   France  \n",
+       "1807          5        FR   France  \n",
+       "\n",
+       "[1808 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv(data_file, encoding = 'iso-8859-1', skiprows=1)\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Y a-t-il des points manquants dans ce jeux de données ? Non. Pas besoin de modifier les données."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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>week</th>\n",
+       "      <th>indicator</th>\n",
+       "      <th>inc</th>\n",
+       "      <th>inc_low</th>\n",
+       "      <th>inc_up</th>\n",
+       "      <th>inc100</th>\n",
+       "      <th>inc100_low</th>\n",
+       "      <th>inc100_up</th>\n",
+       "      <th>geo_insee</th>\n",
+       "      <th>geo_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data[raw_data.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nos données utilisent une convention inhabituelle: le numéro de\n",
+    "semaine est collé à l'année, donnant l'impression qu'il s'agit\n",
+    "de nombre entier. C'est comme ça que Pandas les interprète.\n",
+    "  \n",
+    "Un deuxième problème est que Pandas ne comprend pas les numéros de\n",
+    "semaine.  Il faut lui fournir les dates de début et de fin de\n",
+    "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n",
+    "\n",
+    "Comme la conversion des semaines est devenu assez complexe, nous\n",
+    "écrivons une petite fonction Python pour cela. Ensuite, nous\n",
+    "l'appliquons à tous les points de nos donnés. Les résultats vont\n",
+    "dans une nouvelle colonne 'period'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def convert_week(year_and_week_int):\n",
+    "    year_and_week_str = str(year_and_week_int)\n",
+    "    year = int(year_and_week_str[:4])\n",
+    "    week = int(year_and_week_str[4:])\n",
+    "    w = isoweek.Week(year, week)\n",
+    "    return pd.Period(w.day(0), 'W')\n",
+    "\n",
+    "raw_data['period'] = [convert_week(yw) for yw in raw_data['week']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Il restent deux petites modifications à faire.\n",
+    "\n",
+    "Premièrement, nous définissons les périodes d'observation\n",
+    "comme nouvel index de notre jeux de données. Ceci en fait\n",
+    "une suite chronologique, ce qui sera pratique par la suite.\n",
+    "\n",
+    "Deuxièmement, nous trions les points par période, dans\n",
+    "le sens chronologique."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "period\n",
+       "1990-12-03/1990-12-09     1143\n",
+       "1990-12-10/1990-12-16    11079\n",
+       "1990-12-17/1990-12-23    19080\n",
+       "1990-12-24/1990-12-30    19375\n",
+       "1990-12-31/1991-01-06    15565\n",
+       "1991-01-07/1991-01-13    16277\n",
+       "1991-01-14/1991-01-20    15387\n",
+       "1991-01-21/1991-01-27     7913\n",
+       "1991-01-28/1991-02-03    10442\n",
+       "1991-02-04/1991-02-10    10877\n",
+       "1991-02-11/1991-02-17    12337\n",
+       "1991-02-18/1991-02-24    13289\n",
+       "1991-02-25/1991-03-03    13741\n",
+       "1991-03-04/1991-03-10    16643\n",
+       "1991-03-11/1991-03-17    15574\n",
+       "1991-03-18/1991-03-24    10864\n",
+       "1991-03-25/1991-03-31     9567\n",
+       "1991-04-01/1991-04-07    12265\n",
+       "1991-04-08/1991-04-14    13975\n",
+       "1991-04-15/1991-04-21    14857\n",
+       "1991-04-22/1991-04-28    13462\n",
+       "1991-04-29/1991-05-05    21385\n",
+       "1991-05-06/1991-05-12    16739\n",
+       "1991-05-13/1991-05-19    19053\n",
+       "1991-05-20/1991-05-26    14903\n",
+       "1991-05-27/1991-06-02    15452\n",
+       "1991-06-03/1991-06-09    11947\n",
+       "1991-06-10/1991-06-16    16171\n",
+       "1991-06-17/1991-06-23    16169\n",
+       "1991-06-24/1991-06-30    17608\n",
+       "                         ...  \n",
+       "2024-12-30/2025-01-05     6059\n",
+       "2025-01-06/2025-01-12     5966\n",
+       "2025-01-13/2025-01-19     2462\n",
+       "2025-01-20/2025-01-26     6895\n",
+       "2025-01-27/2025-02-02     2087\n",
+       "2025-02-03/2025-02-09     3455\n",
+       "2025-02-10/2025-02-16     4502\n",
+       "2025-02-17/2025-02-23     2835\n",
+       "2025-02-24/2025-03-02     3381\n",
+       "2025-03-03/2025-03-09     2921\n",
+       "2025-03-10/2025-03-16     5878\n",
+       "2025-03-17/2025-03-23     3855\n",
+       "2025-03-24/2025-03-30     5964\n",
+       "2025-03-31/2025-04-06     4984\n",
+       "2025-04-07/2025-04-13     5557\n",
+       "2025-04-14/2025-04-20     6151\n",
+       "2025-04-21/2025-04-27     6246\n",
+       "2025-04-28/2025-05-04     5003\n",
+       "2025-05-05/2025-05-11     5084\n",
+       "2025-05-12/2025-05-18     3083\n",
+       "2025-05-19/2025-05-25     4693\n",
+       "2025-05-26/2025-06-01     6837\n",
+       "2025-06-02/2025-06-08     4911\n",
+       "2025-06-09/2025-06-15     4580\n",
+       "2025-06-16/2025-06-22     5953\n",
+       "2025-06-23/2025-06-29     5872\n",
+       "2025-06-30/2025-07-06     5667\n",
+       "2025-07-07/2025-07-13     5584\n",
+       "2025-07-14/2025-07-20     6436\n",
+       "2025-07-21/2025-07-27     9815\n",
+       "Freq: W-SUN, Name: inc, Length: 1808, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted_data = raw_data.set_index('period').sort_index()\n",
+    "sorted_data['inc']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nous vérifions la cohérence des données. Entre la fin d'une période et\n",
+    "le début de la période qui suit, la différence temporelle doit être\n",
+    "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n",
+    "d'une seconde.\n",
+    "\n",
+    "Ceci s'avère tout à fait juste, nous pouvons donc continuer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "periods = sorted_data.index\n",
+    "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
+    "    delta = p2.to_timestamp() - p1.end_time\n",
+    "    if delta > pd.Timedelta('1s'):\n",
+    "        print(p1, p2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Un premier regard sur les données !"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7efbd38a97f0>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data['inc'].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Un zoom sur les dernières années montre mieux la situation des pics en hiver (voire au printemps?). Le creux des incidences se trouve en été (voire début automne?)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7efbd1770d30>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data['inc'][-200:].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Etude de l'incidence annuelle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n",
+    "entre deux années civiles, nous définissons la période de référence\n",
+    "entre deux minima de l'incidence, du 1er septembre de l'année $N$ au\n",
+    "1er septembre de l'année $N+1$.\n",
+    "\n",
+    "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n",
+    "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n",
+    "de référence: à la place du 1er septembre de chaque année, nous utilisons le\n",
+    "premier jour de la semaine qui contient le 1er septembre.\n",
+    "\n",
+    "Comme l'incidence de la varicelle est très faible en été, cette\n",
+    "modification ne risque pas de fausser nos conclusions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Period('1991-08-26/1991-09-01', 'W-SUN'),\n",
+       " Period('1992-08-31/1992-09-06', 'W-SUN'),\n",
+       " Period('1993-08-30/1993-09-05', 'W-SUN'),\n",
+       " Period('1994-08-29/1994-09-04', 'W-SUN'),\n",
+       " Period('1995-08-28/1995-09-03', 'W-SUN'),\n",
+       " Period('1996-08-26/1996-09-01', 'W-SUN'),\n",
+       " Period('1997-09-01/1997-09-07', 'W-SUN'),\n",
+       " Period('1998-08-31/1998-09-06', 'W-SUN'),\n",
+       " Period('1999-08-30/1999-09-05', 'W-SUN'),\n",
+       " Period('2000-08-28/2000-09-03', 'W-SUN'),\n",
+       " Period('2001-08-27/2001-09-02', 'W-SUN'),\n",
+       " Period('2002-08-26/2002-09-01', 'W-SUN'),\n",
+       " Period('2003-09-01/2003-09-07', 'W-SUN'),\n",
+       " Period('2004-08-30/2004-09-05', 'W-SUN'),\n",
+       " Period('2005-08-29/2005-09-04', 'W-SUN'),\n",
+       " Period('2006-08-28/2006-09-03', 'W-SUN'),\n",
+       " Period('2007-08-27/2007-09-02', 'W-SUN'),\n",
+       " Period('2008-09-01/2008-09-07', 'W-SUN'),\n",
+       " Period('2009-08-31/2009-09-06', 'W-SUN'),\n",
+       " Period('2010-08-30/2010-09-05', 'W-SUN'),\n",
+       " Period('2011-08-29/2011-09-04', 'W-SUN'),\n",
+       " Period('2012-08-27/2012-09-02', 'W-SUN'),\n",
+       " Period('2013-08-26/2013-09-01', 'W-SUN'),\n",
+       " Period('2014-09-01/2014-09-07', 'W-SUN'),\n",
+       " Period('2015-08-31/2015-09-06', 'W-SUN'),\n",
+       " Period('2016-08-29/2016-09-04', 'W-SUN'),\n",
+       " Period('2017-08-28/2017-09-03', 'W-SUN'),\n",
+       " Period('2018-08-27/2018-09-02', 'W-SUN'),\n",
+       " Period('2019-08-26/2019-09-01', 'W-SUN'),\n",
+       " Period('2020-08-31/2020-09-06', 'W-SUN'),\n",
+       " Period('2021-08-30/2021-09-05', 'W-SUN'),\n",
+       " Period('2022-08-29/2022-09-04', 'W-SUN'),\n",
+       " Period('2023-08-28/2023-09-03', 'W-SUN'),\n",
+       " Period('2024-08-26/2024-09-01', 'W-SUN')]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
+    "                        for y in range(1991,sorted_data.index[-1].year)]\n",
+    "first_september_week"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "En partant de cette liste des semaines qui contiennent un 1er septembre, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n",
+    "\n",
+    "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year = []\n",
+    "yearly_incidence = []\n",
+    "for week1, week2 in zip(first_september_week[:-1],\n",
+    "                        first_september_week[1:]):\n",
+    "    one_year = sorted_data['inc'][week1:week2-1]\n",
+    "    assert abs(len(one_year)-52) < 2\n",
+    "    yearly_incidence.append(one_year.sum())\n",
+    "    year.append(week2.year)\n",
+    "yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Voici les incidences annuelles."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7efbd1694dd8>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yearly_incidence.plot(style='*')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Une liste triée permet de plus facilement répérer les valeurs les plus faibles (au début) et les plus élevées (à la fin)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2020    221186\n",
+       "2023    366227\n",
+       "2021    376290\n",
+       "2024    479258\n",
+       "2002    516689\n",
+       "2018    542312\n",
+       "2017    551041\n",
+       "1996    564901\n",
+       "2019    584066\n",
+       "2015    604382\n",
+       "2000    617597\n",
+       "2001    619041\n",
+       "2012    624573\n",
+       "2005    628464\n",
+       "2006    632833\n",
+       "2022    641397\n",
+       "2011    642368\n",
+       "1993    643387\n",
+       "1995    652478\n",
+       "1994    661409\n",
+       "1998    677775\n",
+       "1997    683434\n",
+       "2014    685769\n",
+       "2013    698332\n",
+       "2007    717352\n",
+       "2008    749478\n",
+       "1999    756456\n",
+       "2003    758363\n",
+       "2004    777388\n",
+       "2016    782114\n",
+       "2010    829911\n",
+       "1992    832939\n",
+       "2009    842373\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7efbd166cf60>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yearly_incidence.hist(xrot=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +1600,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-
diff --git a/module3/exo2/varicelle.csv b/module3/exo2/varicelle.csv
new file mode 100644
index 0000000000000000000000000000000000000000..7dfa35594b43f90c454cb549521f49cfd4f61441
--- /dev/null
+++ b/module3/exo2/varicelle.csv
@@ -0,0 +1,1810 @@
+# {"source":"r&#233;seau Sentinelles, INSERM, Sorbonne Universit&#233;, https:\/\/www.sentiweb.fr","meta":{"period":[199049,202530],"geo":["PAY",0],"geo_ref":"insee","indicator":"7","type":"all","conf_int":true,"compact":false,"age_group":false,"span":"all"},"date":"2025-07-30T10:29:58+02:00","bundle":"1753864185"}
+week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name
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+202518,7,5003,2718,7288,7,4,10,FR,France
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+202516,7,6151,3193,9109,9,5,13,FR,France
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+202448,7,4189,1454,6924,6,2,10,FR,France
+202447,7,1931,726,3136,3,1,5,FR,France
+202446,7,2260,863,3657,3,1,5,FR,France
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+202444,7,2135,676,3594,3,1,5,FR,France
+202443,7,2124,641,3607,3,1,5,FR,France
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+202441,7,2035,381,3689,3,1,5,FR,France
+202440,7,2125,725,3525,3,1,5,FR,France
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+202438,7,751,0,1513,1,0,2,FR,France
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+202408,7,15899,11991,19807,24,18,30,FR,France
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+202406,7,12174,9020,15328,18,13,23,FR,France
+202405,7,8814,6110,11518,13,9,17,FR,France
+202404,7,9504,6566,12442,14,10,18,FR,France
+202403,7,6948,4633,9263,10,7,13,FR,France
+202402,7,7125,4852,9398,11,8,14,FR,France
+202401,7,13305,9214,17396,20,14,26,FR,France
+202352,7,11636,7354,15918,18,12,24,FR,France
+202351,7,6912,4227,9597,10,6,14,FR,France
+202350,7,8799,6215,11383,13,9,17,FR,France
+202349,7,7817,5362,10272,12,8,16,FR,France
+202348,7,7351,4749,9953,11,7,15,FR,France
+202347,7,6537,4277,8797,10,7,13,FR,France
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