diff --git a/module3/exo2/Analyse varicelle.ipynb b/module3/exo2/Analyse varicelle.ipynb
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index 0000000000000000000000000000000000000000..4b46742dbb9851afcfa9fef3c49a3bf3e8e4dead
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+++ b/module3/exo2/Analyse varicelle.ipynb	
@@ -0,0 +1,1543 @@
+{
+ "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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "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 risquée 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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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`."
+   ]
+  },
+  {
+   "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>202146</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9296</td>\n",
+       "      <td>6108</td>\n",
+       "      <td>12484</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>202145</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9178</td>\n",
+       "      <td>6625</td>\n",
+       "      <td>11731</td>\n",
+       "      <td>14</td>\n",
+       "      <td>10</td>\n",
+       "      <td>18</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202144</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8762</td>\n",
+       "      <td>5653</td>\n",
+       "      <td>11871</td>\n",
+       "      <td>13</td>\n",
+       "      <td>8</td>\n",
+       "      <td>18</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202143</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8145</td>\n",
+       "      <td>5164</td>\n",
+       "      <td>11126</td>\n",
+       "      <td>12</td>\n",
+       "      <td>7</td>\n",
+       "      <td>17</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202142</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9443</td>\n",
+       "      <td>6037</td>\n",
+       "      <td>12849</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>5</th>\n",
+       "      <td>202141</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4021</td>\n",
+       "      <td>2239</td>\n",
+       "      <td>5803</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>202140</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4441</td>\n",
+       "      <td>2454</td>\n",
+       "      <td>6428</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>202139</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2291</td>\n",
+       "      <td>1056</td>\n",
+       "      <td>3526</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>8</th>\n",
+       "      <td>202138</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4325</td>\n",
+       "      <td>2267</td>\n",
+       "      <td>6383</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>202137</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1964</td>\n",
+       "      <td>754</td>\n",
+       "      <td>3174</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>10</th>\n",
+       "      <td>202136</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3441</td>\n",
+       "      <td>1730</td>\n",
+       "      <td>5152</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>11</th>\n",
+       "      <td>202135</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2562</td>\n",
+       "      <td>1107</td>\n",
+       "      <td>4017</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>12</th>\n",
+       "      <td>202134</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1429</td>\n",
+       "      <td>378</td>\n",
+       "      <td>2480</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>13</th>\n",
+       "      <td>202133</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3829</td>\n",
+       "      <td>1830</td>\n",
+       "      <td>5828</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>14</th>\n",
+       "      <td>202132</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4108</td>\n",
+       "      <td>1895</td>\n",
+       "      <td>6321</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>15</th>\n",
+       "      <td>202131</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4793</td>\n",
+       "      <td>2301</td>\n",
+       "      <td>7285</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>11</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>202130</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7190</td>\n",
+       "      <td>4191</td>\n",
+       "      <td>10189</td>\n",
+       "      <td>11</td>\n",
+       "      <td>6</td>\n",
+       "      <td>16</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>202129</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6800</td>\n",
+       "      <td>4109</td>\n",
+       "      <td>9491</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>18</th>\n",
+       "      <td>202128</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9734</td>\n",
+       "      <td>0</td>\n",
+       "      <td>21731</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>202127</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9026</td>\n",
+       "      <td>4316</td>\n",
+       "      <td>13736</td>\n",
+       "      <td>14</td>\n",
+       "      <td>7</td>\n",
+       "      <td>21</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>202126</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7284</td>\n",
+       "      <td>4108</td>\n",
+       "      <td>10460</td>\n",
+       "      <td>11</td>\n",
+       "      <td>6</td>\n",
+       "      <td>16</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>202125</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9351</td>\n",
+       "      <td>6540</td>\n",
+       "      <td>12162</td>\n",
+       "      <td>14</td>\n",
+       "      <td>10</td>\n",
+       "      <td>18</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>202124</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12034</td>\n",
+       "      <td>8937</td>\n",
+       "      <td>15131</td>\n",
+       "      <td>18</td>\n",
+       "      <td>13</td>\n",
+       "      <td>23</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>202123</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9116</td>\n",
+       "      <td>6420</td>\n",
+       "      <td>11812</td>\n",
+       "      <td>14</td>\n",
+       "      <td>10</td>\n",
+       "      <td>18</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>202122</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4817</td>\n",
+       "      <td>2752</td>\n",
+       "      <td>6882</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>25</th>\n",
+       "      <td>202121</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6092</td>\n",
+       "      <td>3458</td>\n",
+       "      <td>8726</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>26</th>\n",
+       "      <td>202120</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7485</td>\n",
+       "      <td>4601</td>\n",
+       "      <td>10369</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>27</th>\n",
+       "      <td>202119</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6654</td>\n",
+       "      <td>4370</td>\n",
+       "      <td>8938</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>202118</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3912</td>\n",
+       "      <td>2110</td>\n",
+       "      <td>5714</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>29</th>\n",
+       "      <td>202117</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4686</td>\n",
+       "      <td>2878</td>\n",
+       "      <td>6494</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>...</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>1586</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>1587</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>1588</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>1589</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>1590</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>1591</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>1592</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>1593</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>1594</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>1595</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>1596</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>1597</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>1598</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>1599</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>1600</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>1601</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>1602</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>1603</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>1604</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>1605</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>1606</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>1607</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>1608</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>1609</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>1610</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>1611</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>1612</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>1613</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>1614</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>1615</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>1616 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
+       "0     202146          7   9296     6108   12484      14           9   \n",
+       "1     202145          7   9178     6625   11731      14          10   \n",
+       "2     202144          7   8762     5653   11871      13           8   \n",
+       "3     202143          7   8145     5164   11126      12           7   \n",
+       "4     202142          7   9443     6037   12849      14           9   \n",
+       "5     202141          7   4021     2239    5803       6           3   \n",
+       "6     202140          7   4441     2454    6428       7           4   \n",
+       "7     202139          7   2291     1056    3526       3           1   \n",
+       "8     202138          7   4325     2267    6383       7           4   \n",
+       "9     202137          7   1964      754    3174       3           1   \n",
+       "10    202136          7   3441     1730    5152       5           2   \n",
+       "11    202135          7   2562     1107    4017       4           2   \n",
+       "12    202134          7   1429      378    2480       2           0   \n",
+       "13    202133          7   3829     1830    5828       6           3   \n",
+       "14    202132          7   4108     1895    6321       6           3   \n",
+       "15    202131          7   4793     2301    7285       7           3   \n",
+       "16    202130          7   7190     4191   10189      11           6   \n",
+       "17    202129          7   6800     4109    9491      10           6   \n",
+       "18    202128          7   9734        0   21731      15           0   \n",
+       "19    202127          7   9026     4316   13736      14           7   \n",
+       "20    202126          7   7284     4108   10460      11           6   \n",
+       "21    202125          7   9351     6540   12162      14          10   \n",
+       "22    202124          7  12034     8937   15131      18          13   \n",
+       "23    202123          7   9116     6420   11812      14          10   \n",
+       "24    202122          7   4817     2752    6882       7           4   \n",
+       "25    202121          7   6092     3458    8726       9           5   \n",
+       "26    202120          7   7485     4601   10369      11           7   \n",
+       "27    202119          7   6654     4370    8938      10           7   \n",
+       "28    202118          7   3912     2110    5714       6           3   \n",
+       "29    202117          7   4686     2878    6494       7           4   \n",
+       "...      ...        ...    ...      ...     ...     ...         ...   \n",
+       "1586  199126          7  17608    11304   23912      31          20   \n",
+       "1587  199125          7  16169    10700   21638      28          18   \n",
+       "1588  199124          7  16171    10071   22271      28          17   \n",
+       "1589  199123          7  11947     7671   16223      21          13   \n",
+       "1590  199122          7  15452     9953   20951      27          17   \n",
+       "1591  199121          7  14903     8975   20831      26          16   \n",
+       "1592  199120          7  19053    12742   25364      34          23   \n",
+       "1593  199119          7  16739    11246   22232      29          19   \n",
+       "1594  199118          7  21385    13882   28888      38          25   \n",
+       "1595  199117          7  13462     8877   18047      24          16   \n",
+       "1596  199116          7  14857    10068   19646      26          18   \n",
+       "1597  199115          7  13975     9781   18169      25          18   \n",
+       "1598  199114          7  12265     7684   16846      22          14   \n",
+       "1599  199113          7   9567     6041   13093      17          11   \n",
+       "1600  199112          7  10864     7331   14397      19          13   \n",
+       "1601  199111          7  15574    11184   19964      27          19   \n",
+       "1602  199110          7  16643    11372   21914      29          20   \n",
+       "1603  199109          7  13741     8780   18702      24          15   \n",
+       "1604  199108          7  13289     8813   17765      23          15   \n",
+       "1605  199107          7  12337     8077   16597      22          15   \n",
+       "1606  199106          7  10877     7013   14741      19          12   \n",
+       "1607  199105          7  10442     6544   14340      18          11   \n",
+       "1608  199104          7   7913     4563   11263      14           8   \n",
+       "1609  199103          7  15387    10484   20290      27          18   \n",
+       "1610  199102          7  16277    11046   21508      29          20   \n",
+       "1611  199101          7  15565    10271   20859      27          18   \n",
+       "1612  199052          7  19375    13295   25455      34          23   \n",
+       "1613  199051          7  19080    13807   24353      34          25   \n",
+       "1614  199050          7  11079     6660   15498      20          12   \n",
+       "1615  199049          7   1143        0    2610       2           0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0            19        FR   France  \n",
+       "1            18        FR   France  \n",
+       "2            18        FR   France  \n",
+       "3            17        FR   France  \n",
+       "4            19        FR   France  \n",
+       "5             9        FR   France  \n",
+       "6            10        FR   France  \n",
+       "7             5        FR   France  \n",
+       "8            10        FR   France  \n",
+       "9             5        FR   France  \n",
+       "10            8        FR   France  \n",
+       "11            6        FR   France  \n",
+       "12            4        FR   France  \n",
+       "13            9        FR   France  \n",
+       "14            9        FR   France  \n",
+       "15           11        FR   France  \n",
+       "16           16        FR   France  \n",
+       "17           14        FR   France  \n",
+       "18           33        FR   France  \n",
+       "19           21        FR   France  \n",
+       "20           16        FR   France  \n",
+       "21           18        FR   France  \n",
+       "22           23        FR   France  \n",
+       "23           18        FR   France  \n",
+       "24           10        FR   France  \n",
+       "25           13        FR   France  \n",
+       "26           15        FR   France  \n",
+       "27           13        FR   France  \n",
+       "28            9        FR   France  \n",
+       "29           10        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1586         42        FR   France  \n",
+       "1587         38        FR   France  \n",
+       "1588         39        FR   France  \n",
+       "1589         29        FR   France  \n",
+       "1590         37        FR   France  \n",
+       "1591         36        FR   France  \n",
+       "1592         45        FR   France  \n",
+       "1593         39        FR   France  \n",
+       "1594         51        FR   France  \n",
+       "1595         32        FR   France  \n",
+       "1596         34        FR   France  \n",
+       "1597         32        FR   France  \n",
+       "1598         30        FR   France  \n",
+       "1599         23        FR   France  \n",
+       "1600         25        FR   France  \n",
+       "1601         35        FR   France  \n",
+       "1602         38        FR   France  \n",
+       "1603         33        FR   France  \n",
+       "1604         31        FR   France  \n",
+       "1605         29        FR   France  \n",
+       "1606         26        FR   France  \n",
+       "1607         25        FR   France  \n",
+       "1608         20        FR   France  \n",
+       "1609         36        FR   France  \n",
+       "1610         38        FR   France  \n",
+       "1611         36        FR   France  \n",
+       "1612         45        FR   France  \n",
+       "1613         43        FR   France  \n",
+       "1614         28        FR   France  \n",
+       "1615          5        FR   France  \n",
+       "\n",
+       "[1616 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv(data_url, skiprows=1)\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Y a-t-il des points manquants dans ce jeux de données ? Non !"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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": 6,
+     "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": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      2021-11-15/2021-11-21\n",
+       "1      2021-11-08/2021-11-14\n",
+       "2      2021-11-01/2021-11-07\n",
+       "3      2021-10-25/2021-10-31\n",
+       "4      2021-10-18/2021-10-24\n",
+       "5      2021-10-11/2021-10-17\n",
+       "6      2021-10-04/2021-10-10\n",
+       "7      2021-09-27/2021-10-03\n",
+       "8      2021-09-20/2021-09-26\n",
+       "9      2021-09-13/2021-09-19\n",
+       "10     2021-09-06/2021-09-12\n",
+       "11     2021-08-30/2021-09-05\n",
+       "12     2021-08-23/2021-08-29\n",
+       "13     2021-08-16/2021-08-22\n",
+       "14     2021-08-09/2021-08-15\n",
+       "15     2021-08-02/2021-08-08\n",
+       "16     2021-07-26/2021-08-01\n",
+       "17     2021-07-19/2021-07-25\n",
+       "18     2021-07-12/2021-07-18\n",
+       "19     2021-07-05/2021-07-11\n",
+       "20     2021-06-28/2021-07-04\n",
+       "21     2021-06-21/2021-06-27\n",
+       "22     2021-06-14/2021-06-20\n",
+       "23     2021-06-07/2021-06-13\n",
+       "24     2021-05-31/2021-06-06\n",
+       "25     2021-05-24/2021-05-30\n",
+       "26     2021-05-17/2021-05-23\n",
+       "27     2021-05-10/2021-05-16\n",
+       "28     2021-05-03/2021-05-09\n",
+       "29     2021-04-26/2021-05-02\n",
+       "                ...         \n",
+       "1586   1991-06-24/1991-06-30\n",
+       "1587   1991-06-17/1991-06-23\n",
+       "1588   1991-06-10/1991-06-16\n",
+       "1589   1991-06-03/1991-06-09\n",
+       "1590   1991-05-27/1991-06-02\n",
+       "1591   1991-05-20/1991-05-26\n",
+       "1592   1991-05-13/1991-05-19\n",
+       "1593   1991-05-06/1991-05-12\n",
+       "1594   1991-04-29/1991-05-05\n",
+       "1595   1991-04-22/1991-04-28\n",
+       "1596   1991-04-15/1991-04-21\n",
+       "1597   1991-04-08/1991-04-14\n",
+       "1598   1991-04-01/1991-04-07\n",
+       "1599   1991-03-25/1991-03-31\n",
+       "1600   1991-03-18/1991-03-24\n",
+       "1601   1991-03-11/1991-03-17\n",
+       "1602   1991-03-04/1991-03-10\n",
+       "1603   1991-02-25/1991-03-03\n",
+       "1604   1991-02-18/1991-02-24\n",
+       "1605   1991-02-11/1991-02-17\n",
+       "1606   1991-02-04/1991-02-10\n",
+       "1607   1991-01-28/1991-02-03\n",
+       "1608   1991-01-21/1991-01-27\n",
+       "1609   1991-01-14/1991-01-20\n",
+       "1610   1991-01-07/1991-01-13\n",
+       "1611   1990-12-31/1991-01-06\n",
+       "1612   1990-12-24/1990-12-30\n",
+       "1613   1990-12-17/1990-12-23\n",
+       "1614   1990-12-10/1990-12-16\n",
+       "1615   1990-12-03/1990-12-09\n",
+       "Name: period, Length: 1616, dtype: object"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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']]\n",
+    "raw_data['period']"
+   ]
+  },
+  {
+   "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": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted_raw_data = raw_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.\n",
+    "\n",
+    "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n",
+    "entre lesquelles il manque une semaine.\n",
+    "\n",
+    "Nous reconnaissons ces dates: c'est la semaine sans observations\n",
+    "que nous avions supprimées !"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "periods = sorted_raw_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": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4036a79400>"
+      ]
+     },
+     "execution_count": 26,
+     "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_raw_data['inc'].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4036685400>"
+      ]
+     },
+     "execution_count": 28,
+     "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_raw_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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
+    "                     for y in range(1991,\n",
+    "                                    sorted_raw_data.index[-1].year)]"
+   ]
+  },
+  {
+   "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": 30,
+   "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_raw_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": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f40368340f0>"
+      ]
+     },
+     "execution_count": 32,
+     "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": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2020    221186\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",
+       "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": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb
deleted file mode 100644
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..0000000000000000000000000000000000000000
--- a/module3/exo2/exercice.ipynb
+++ /dev/null
@@ -1,25 +0,0 @@
-{
- "cells": [],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
-
diff --git a/module3/exo2/varicelle.csv b/module3/exo2/varicelle.csv
new file mode 100644
index 0000000000000000000000000000000000000000..b7ecc01b03ce34aba64b590127c678e274b7dbbf
--- /dev/null
+++ b/module3/exo2/varicelle.csv
@@ -0,0 +1,1618 @@
+# @source="réseau Sentinelles, INSERM, Sorbonne Université, http://www.sentiweb.fr", @meta={"period":[199049,202146],"geo":["PAY","1"],"geo_ref":"insee","indicator":"7","type":"all","conf_int":true,"compact":false}, @date=2021-11-25T09:38:45+01:00
+week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name
+202146,7,9296,6108,12484,14,9,19,FR,France
+202145,7,9178,6625,11731,14,10,18,FR,France
+202144,7,8762,5653,11871,13,8,18,FR,France
+202143,7,8145,5164,11126,12,7,17,FR,France
+202142,7,9443,6037,12849,14,9,19,FR,France
+202141,7,4021,2239,5803,6,3,9,FR,France
+202140,7,4441,2454,6428,7,4,10,FR,France
+202139,7,2291,1056,3526,3,1,5,FR,France
+202138,7,4325,2267,6383,7,4,10,FR,France
+202137,7,1964,754,3174,3,1,5,FR,France
+202136,7,3441,1730,5152,5,2,8,FR,France
+202135,7,2562,1107,4017,4,2,6,FR,France
+202134,7,1429,378,2480,2,0,4,FR,France
+202133,7,3829,1830,5828,6,3,9,FR,France
+202132,7,4108,1895,6321,6,3,9,FR,France
+202131,7,4793,2301,7285,7,3,11,FR,France
+202130,7,7190,4191,10189,11,6,16,FR,France
+202129,7,6800,4109,9491,10,6,14,FR,France
+202128,7,9734,0,21731,15,0,33,FR,France
+202127,7,9026,4316,13736,14,7,21,FR,France
+202126,7,7284,4108,10460,11,6,16,FR,France
+202125,7,9351,6540,12162,14,10,18,FR,France
+202124,7,12034,8937,15131,18,13,23,FR,France
+202123,7,9116,6420,11812,14,10,18,FR,France
+202122,7,4817,2752,6882,7,4,10,FR,France
+202121,7,6092,3458,8726,9,5,13,FR,France
+202120,7,7485,4601,10369,11,7,15,FR,France
+202119,7,6654,4370,8938,10,7,13,FR,France
+202118,7,3912,2110,5714,6,3,9,FR,France
+202117,7,4686,2878,6494,7,4,10,FR,France
+202116,7,4780,2891,6669,7,4,10,FR,France
+202115,7,11215,7627,14803,17,12,22,FR,France
+202114,7,11197,7994,14400,17,12,22,FR,France
+202113,7,9714,6289,13139,15,10,20,FR,France
+202112,7,11520,8415,14625,17,12,22,FR,France
+202111,7,9386,6678,12094,14,10,18,FR,France
+202110,7,9056,6452,11660,14,10,18,FR,France
+202109,7,10988,7938,14038,17,12,22,FR,France
+202108,7,11281,8361,14201,17,13,21,FR,France
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+202106,7,13401,9810,16992,20,15,25,FR,France
+202105,7,12210,8988,15432,18,13,23,FR,France
+202104,7,12026,8826,15226,18,13,23,FR,France
+202103,7,8913,6375,11451,13,9,17,FR,France
+202102,7,7795,5430,10160,12,8,16,FR,France
+202101,7,10525,7750,13300,16,12,20,FR,France
+202053,7,11978,8406,15550,18,13,23,FR,France
+202052,7,12012,8285,15739,18,12,24,FR,France
+202051,7,10564,7574,13554,16,11,21,FR,France
+202050,7,7063,4744,9382,11,7,15,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
+202038,7,2251,781,3721,3,1,5,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
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+202023,7,558,1,1115,1,0,2,FR,France
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+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
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+201951,7,5823,3675,7971,9,6,12,FR,France
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+201949,7,6621,4540,8702,10,7,13,FR,France
+201948,7,5542,3383,7701,8,5,11,FR,France
+201947,7,7536,5058,10014,11,7,15,FR,France
+201946,7,2638,1316,3960,4,2,6,FR,France
+201945,7,4492,2615,6369,7,4,10,FR,France
+201944,7,5728,3627,7829,9,6,12,FR,France
+201943,7,4834,2751,6917,7,4,10,FR,France
+201942,7,6279,3989,8569,10,7,13,FR,France
+201941,7,4130,2030,6230,6,3,9,FR,France
+201940,7,4211,2218,6204,6,3,9,FR,France
+201939,7,3137,1310,4964,5,2,8,FR,France
+201938,7,3078,1416,4740,5,2,8,FR,France
+201937,7,970,162,1778,1,0,2,FR,France
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+201930,7,8875,5629,12121,13,8,18,FR,France
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+201927,7,16602,12349,20855,25,19,31,FR,France
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+201916,7,17127,12815,21439,26,19,33,FR,France
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+201914,7,16131,12254,20008,25,19,31,FR,France
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+201907,7,10206,7388,13024,16,12,20,FR,France
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+201846,7,7039,4453,9625,11,7,15,FR,France
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+199821,7,10741,6627,14855,18,11,25,FR,France
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+199818,7,15802,10374,21230,27,18,36,FR,France
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+199816,7,11744,7632,15856,20,13,27,FR,France
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+199814,7,20962,15699,26225,36,27,45,FR,France
+199813,7,17029,12181,21877,29,21,37,FR,France
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