diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..c4fa303ee1924b097ecbb5234034ddad65bd7baa 100644
--- a/module3/exo2/exercice.ipynb
+++ b/module3/exo2/exercice.ipynb
@@ -1,5 +1,1387 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Incidence de la varicelle"
+   ]
+  },
+  {
+   "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 du syndrome grippal 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 1984 et se termine avec une semaine récente.\n",
+    "Pour nous protéger contre une éventuelle disparition ou modification du serveur du Réseau Sentinelles, nous faisons une copie locale de ce jeux de données que nous préservons avec notre analyse. Il est inutile et même risqué de télécharger les données à chaque exécution, car dans le cas d'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"\n",
+    "\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)\n"
+   ]
+  },
+  {
+   "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`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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>202051</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9505</td>\n",
+       "      <td>6332</td>\n",
+       "      <td>12678</td>\n",
+       "      <td>14</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202050</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7116</td>\n",
+       "      <td>4766</td>\n",
+       "      <td>9466</td>\n",
+       "      <td>11</td>\n",
+       "      <td>7</td>\n",
+       "      <td>15</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202049</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5026</td>\n",
+       "      <td>3145</td>\n",
+       "      <td>6907</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>3</th>\n",
+       "      <td>202048</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6683</td>\n",
+       "      <td>4312</td>\n",
+       "      <td>9054</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>4</th>\n",
+       "      <td>202047</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4999</td>\n",
+       "      <td>2963</td>\n",
+       "      <td>7035</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>5</th>\n",
+       "      <td>202046</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3752</td>\n",
+       "      <td>1963</td>\n",
+       "      <td>5541</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>6</th>\n",
+       "      <td>202045</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3696</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>5376</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>7</th>\n",
+       "      <td>202044</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4391</td>\n",
+       "      <td>2375</td>\n",
+       "      <td>6407</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>202043</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4376</td>\n",
+       "      <td>2505</td>\n",
+       "      <td>6247</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>9</th>\n",
+       "      <td>202042</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4000</td>\n",
+       "      <td>1979</td>\n",
+       "      <td>6021</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>10</th>\n",
+       "      <td>202041</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3961</td>\n",
+       "      <td>2099</td>\n",
+       "      <td>5823</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>11</th>\n",
+       "      <td>202040</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2078</td>\n",
+       "      <td>675</td>\n",
+       "      <td>3481</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>12</th>\n",
+       "      <td>202039</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1049</td>\n",
+       "      <td>237</td>\n",
+       "      <td>1861</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>202038</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2253</td>\n",
+       "      <td>782</td>\n",
+       "      <td>3724</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>14</th>\n",
+       "      <td>202037</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1584</td>\n",
+       "      <td>405</td>\n",
+       "      <td>2763</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>202036</td>\n",
+       "      <td>7</td>\n",
+       "      <td>919</td>\n",
+       "      <td>100</td>\n",
+       "      <td>1738</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>202035</td>\n",
+       "      <td>7</td>\n",
+       "      <td>828</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1694</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>202034</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2272</td>\n",
+       "      <td>371</td>\n",
+       "      <td>4173</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>202033</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1284</td>\n",
+       "      <td>177</td>\n",
+       "      <td>2391</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>202032</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2650</td>\n",
+       "      <td>689</td>\n",
+       "      <td>4611</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>202031</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1303</td>\n",
+       "      <td>100</td>\n",
+       "      <td>2506</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>202030</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1385</td>\n",
+       "      <td>75</td>\n",
+       "      <td>2695</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>202029</td>\n",
+       "      <td>7</td>\n",
+       "      <td>841</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1672</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>202028</td>\n",
+       "      <td>7</td>\n",
+       "      <td>728</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1515</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>202027</td>\n",
+       "      <td>7</td>\n",
+       "      <td>986</td>\n",
+       "      <td>149</td>\n",
+       "      <td>1823</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>202026</td>\n",
+       "      <td>7</td>\n",
+       "      <td>694</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1454</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>202025</td>\n",
+       "      <td>7</td>\n",
+       "      <td>228</td>\n",
+       "      <td>0</td>\n",
+       "      <td>597</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>202024</td>\n",
+       "      <td>7</td>\n",
+       "      <td>388</td>\n",
+       "      <td>0</td>\n",
+       "      <td>959</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>202023</td>\n",
+       "      <td>7</td>\n",
+       "      <td>558</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1115</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>202022</td>\n",
+       "      <td>7</td>\n",
+       "      <td>277</td>\n",
+       "      <td>0</td>\n",
+       "      <td>633</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</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>1538</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>1539</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>1540</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>1541</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>1542</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>1543</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>1544</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>1545</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>1546</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>1547</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>1548</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>1549</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>1550</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>1551</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>1552</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>1553</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>1554</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>1555</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>1556</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>1557</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>1558</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>1559</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>1560</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>1561</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>1562</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>1563</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>1564</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>1565</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>1566</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>1567</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>1568 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
+       "0     202051          7   9505     6332   12678      14           9   \n",
+       "1     202050          7   7116     4766    9466      11           7   \n",
+       "2     202049          7   5026     3145    6907       8           5   \n",
+       "3     202048          7   6683     4312    9054      10           6   \n",
+       "4     202047          7   4999     2963    7035       8           5   \n",
+       "5     202046          7   3752     1963    5541       6           3   \n",
+       "6     202045          7   3696     2016    5376       6           3   \n",
+       "7     202044          7   4391     2375    6407       7           4   \n",
+       "8     202043          7   4376     2505    6247       7           4   \n",
+       "9     202042          7   4000     1979    6021       6           3   \n",
+       "10    202041          7   3961     2099    5823       6           3   \n",
+       "11    202040          7   2078      675    3481       3           1   \n",
+       "12    202039          7   1049      237    1861       2           1   \n",
+       "13    202038          7   2253      782    3724       3           1   \n",
+       "14    202037          7   1584      405    2763       2           0   \n",
+       "15    202036          7    919      100    1738       1           0   \n",
+       "16    202035          7    828        0    1694       1           0   \n",
+       "17    202034          7   2272      371    4173       3           0   \n",
+       "18    202033          7   1284      177    2391       2           0   \n",
+       "19    202032          7   2650      689    4611       4           1   \n",
+       "20    202031          7   1303      100    2506       2           0   \n",
+       "21    202030          7   1385       75    2695       2           0   \n",
+       "22    202029          7    841       10    1672       1           0   \n",
+       "23    202028          7    728        0    1515       1           0   \n",
+       "24    202027          7    986      149    1823       1           0   \n",
+       "25    202026          7    694        0    1454       1           0   \n",
+       "26    202025          7    228        0     597       0           0   \n",
+       "27    202024          7    388        0     959       1           0   \n",
+       "28    202023          7    558        1    1115       1           0   \n",
+       "29    202022          7    277        0     633       0           0   \n",
+       "...      ...        ...    ...      ...     ...     ...         ...   \n",
+       "1538  199126          7  17608    11304   23912      31          20   \n",
+       "1539  199125          7  16169    10700   21638      28          18   \n",
+       "1540  199124          7  16171    10071   22271      28          17   \n",
+       "1541  199123          7  11947     7671   16223      21          13   \n",
+       "1542  199122          7  15452     9953   20951      27          17   \n",
+       "1543  199121          7  14903     8975   20831      26          16   \n",
+       "1544  199120          7  19053    12742   25364      34          23   \n",
+       "1545  199119          7  16739    11246   22232      29          19   \n",
+       "1546  199118          7  21385    13882   28888      38          25   \n",
+       "1547  199117          7  13462     8877   18047      24          16   \n",
+       "1548  199116          7  14857    10068   19646      26          18   \n",
+       "1549  199115          7  13975     9781   18169      25          18   \n",
+       "1550  199114          7  12265     7684   16846      22          14   \n",
+       "1551  199113          7   9567     6041   13093      17          11   \n",
+       "1552  199112          7  10864     7331   14397      19          13   \n",
+       "1553  199111          7  15574    11184   19964      27          19   \n",
+       "1554  199110          7  16643    11372   21914      29          20   \n",
+       "1555  199109          7  13741     8780   18702      24          15   \n",
+       "1556  199108          7  13289     8813   17765      23          15   \n",
+       "1557  199107          7  12337     8077   16597      22          15   \n",
+       "1558  199106          7  10877     7013   14741      19          12   \n",
+       "1559  199105          7  10442     6544   14340      18          11   \n",
+       "1560  199104          7   7913     4563   11263      14           8   \n",
+       "1561  199103          7  15387    10484   20290      27          18   \n",
+       "1562  199102          7  16277    11046   21508      29          20   \n",
+       "1563  199101          7  15565    10271   20859      27          18   \n",
+       "1564  199052          7  19375    13295   25455      34          23   \n",
+       "1565  199051          7  19080    13807   24353      34          25   \n",
+       "1566  199050          7  11079     6660   15498      20          12   \n",
+       "1567  199049          7   1143        0    2610       2           0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0            19        FR   France  \n",
+       "1            15        FR   France  \n",
+       "2            11        FR   France  \n",
+       "3            14        FR   France  \n",
+       "4            11        FR   France  \n",
+       "5             9        FR   France  \n",
+       "6             9        FR   France  \n",
+       "7            10        FR   France  \n",
+       "8            10        FR   France  \n",
+       "9             9        FR   France  \n",
+       "10            9        FR   France  \n",
+       "11            5        FR   France  \n",
+       "12            3        FR   France  \n",
+       "13            5        FR   France  \n",
+       "14            4        FR   France  \n",
+       "15            2        FR   France  \n",
+       "16            2        FR   France  \n",
+       "17            6        FR   France  \n",
+       "18            4        FR   France  \n",
+       "19            7        FR   France  \n",
+       "20            4        FR   France  \n",
+       "21            4        FR   France  \n",
+       "22            2        FR   France  \n",
+       "23            2        FR   France  \n",
+       "24            2        FR   France  \n",
+       "25            2        FR   France  \n",
+       "26            1        FR   France  \n",
+       "27            2        FR   France  \n",
+       "28            2        FR   France  \n",
+       "29            1        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1538         42        FR   France  \n",
+       "1539         38        FR   France  \n",
+       "1540         39        FR   France  \n",
+       "1541         29        FR   France  \n",
+       "1542         37        FR   France  \n",
+       "1543         36        FR   France  \n",
+       "1544         45        FR   France  \n",
+       "1545         39        FR   France  \n",
+       "1546         51        FR   France  \n",
+       "1547         32        FR   France  \n",
+       "1548         34        FR   France  \n",
+       "1549         32        FR   France  \n",
+       "1550         30        FR   France  \n",
+       "1551         23        FR   France  \n",
+       "1552         25        FR   France  \n",
+       "1553         35        FR   France  \n",
+       "1554         38        FR   France  \n",
+       "1555         33        FR   France  \n",
+       "1556         31        FR   France  \n",
+       "1557         29        FR   France  \n",
+       "1558         26        FR   France  \n",
+       "1559         25        FR   France  \n",
+       "1560         20        FR   France  \n",
+       "1561         36        FR   France  \n",
+       "1562         38        FR   France  \n",
+       "1563         36        FR   France  \n",
+       "1564         45        FR   France  \n",
+       "1565         43        FR   France  \n",
+       "1566         28        FR   France  \n",
+       "1567          5        FR   France  \n",
+       "\n",
+       "[1568 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv(data_file, skiprows=1)\n",
+    "data=raw_data\n",
+    "data"
+   ]
+  },
+  {
+   "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": 9,
+   "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",
+    "data['period'] = [convert_week(yw) for yw in 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.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted_data = data.set_index('period').sort_index()"
+   ]
+  },
+  {
+   "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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Un premier regard sur les données !"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7c775aa278>"
+      ]
+     },
+     "execution_count": 12,
+     "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": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7c7547e278>"
+      ]
+     },
+     "execution_count": 14,
+     "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": [
+    "Nous définissons la période de référence\n",
+    "entre deux minima de l'incidence, du 1er septembre de l'année $N$ au\n",
+    "1er août de l'année $N+1$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first_sept_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
+    "                     for y in range(1991,\n",
+    "                                    sorted_data.index[-1].year)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year = []\n",
+    "yearly_incidence = []\n",
+    "for week1, week2 in zip(first_sept_week[:-1],\n",
+    "                        first_sept_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": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7c75462dd8>"
+      ]
+     },
+     "execution_count": 19,
+     "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 élevées (à la fin)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "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",
+       "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": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n",
+    "  française, sont assez rares: il y en eu trois au cours des 35 dernières années."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7c753c9278>"
+      ]
+     },
+     "execution_count": 21,
+     "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 +1398,9 @@
    "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..78eeb0a76b0679778d1dbc2363c5ce6c889c09e3
--- /dev/null
+++ b/module3/exo2/varicelle.csv
@@ -0,0 +1,1570 @@
+# @source="réseau Sentinelles, INSERM, Sorbonne Université, http://www.sentiweb.fr", @meta={"period":[199049,202051],"geo":["PAY","1"],"geo_ref":"insee","indicator":"7","type":"all","conf_int":true,"compact":false}, @date=2020-12-27T15:00:59+01:00
+week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name
+202051,7,9505,6332,12678,14,9,19,FR,France
+202050,7,7116,4766,9466,11,7,15,FR,France
+202049,7,5026,3145,6907,8,5,11,FR,France
+202048,7,6683,4312,9054,10,6,14,FR,France
+202047,7,4999,2963,7035,8,5,11,FR,France
+202046,7,3752,1963,5541,6,3,9,FR,France
+202045,7,3696,2016,5376,6,3,9,FR,France
+202044,7,4391,2375,6407,7,4,10,FR,France
+202043,7,4376,2505,6247,7,4,10,FR,France
+202042,7,4000,1979,6021,6,3,9,FR,France
+202041,7,3961,2099,5823,6,3,9,FR,France
+202040,7,2078,675,3481,3,1,5,FR,France
+202039,7,1049,237,1861,2,1,3,FR,France
+202038,7,2253,782,3724,3,1,5,FR,France
+202037,7,1584,405,2763,2,0,4,FR,France
+202036,7,919,100,1738,1,0,2,FR,France
+202035,7,828,0,1694,1,0,2,FR,France
+202034,7,2272,371,4173,3,0,6,FR,France
+202033,7,1284,177,2391,2,0,4,FR,France
+202032,7,2650,689,4611,4,1,7,FR,France
+202031,7,1303,100,2506,2,0,4,FR,France
+202030,7,1385,75,2695,2,0,4,FR,France
+202029,7,841,10,1672,1,0,2,FR,France
+202028,7,728,0,1515,1,0,2,FR,France
+202027,7,986,149,1823,1,0,2,FR,France
+202026,7,694,0,1454,1,0,2,FR,France
+202025,7,228,0,597,0,0,1,FR,France
+202024,7,388,0,959,1,0,2,FR,France
+202023,7,558,1,1115,1,0,2,FR,France
+202022,7,277,0,633,0,0,1,FR,France
+202021,7,602,36,1168,1,0,2,FR,France
+202020,7,824,20,1628,1,0,2,FR,France
+202019,7,310,0,753,0,0,1,FR,France
+202018,7,849,98,1600,1,0,2,FR,France
+202017,7,272,0,658,0,0,1,FR,France
+202016,7,758,78,1438,1,0,2,FR,France
+202015,7,1918,675,3161,3,1,5,FR,France
+202014,7,3879,2227,5531,6,3,9,FR,France
+202013,7,7326,5236,9416,11,8,14,FR,France
+202012,7,8123,5790,10456,12,8,16,FR,France
+202011,7,10198,7568,12828,15,11,19,FR,France
+202010,7,9011,6691,11331,14,10,18,FR,France
+202009,7,13631,10544,16718,21,16,26,FR,France
+202008,7,10424,7708,13140,16,12,20,FR,France
+202007,7,8959,6574,11344,14,10,18,FR,France
+202006,7,9264,6925,11603,14,10,18,FR,France
+202005,7,8505,6314,10696,13,10,16,FR,France
+202004,7,7991,5831,10151,12,9,15,FR,France
+202003,7,5968,4100,7836,9,6,12,FR,France
+202002,7,6534,4530,8538,10,7,13,FR,France
+202001,7,9835,7019,12651,15,11,19,FR,France
+201952,7,7941,5246,10636,12,8,16,FR,France
+201951,7,5823,3675,7971,9,6,12,FR,France
+201950,7,6424,4276,8572,10,7,13,FR,France
+201949,7,6621,4540,8702,10,7,13,FR,France
+201948,7,5542,3383,7701,8,5,11,FR,France
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+201946,7,2638,1316,3960,4,2,6,FR,France
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