diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..1c61593b4b3e1fcb51876a3bdd9870655f0a6910 100644
--- a/module3/exo2/exercice.ipynb
+++ b/module3/exo2/exercice.ipynb
@@ -1,25 +1,1478 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import isoweek"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Les données de l'incidence de la varicelle sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1991 et se termine avec une semaine récente."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_url = \"https://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 = \"incidence-PAY-7.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": [
+    "L'explication des colonnes est disponible [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json) :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>202015</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>609</td>\n",
+       "      <td>3397</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>1</th>\n",
+       "      <td>202014</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3881</td>\n",
+       "      <td>2223</td>\n",
+       "      <td>5539</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>2</th>\n",
+       "      <td>202013</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7341</td>\n",
+       "      <td>5247</td>\n",
+       "      <td>9435</td>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>14</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202012</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8123</td>\n",
+       "      <td>5790</td>\n",
+       "      <td>10456</td>\n",
+       "      <td>12</td>\n",
+       "      <td>8</td>\n",
+       "      <td>16</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202011</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10198</td>\n",
+       "      <td>7568</td>\n",
+       "      <td>12828</td>\n",
+       "      <td>15</td>\n",
+       "      <td>11</td>\n",
+       "      <td>19</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>202010</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9011</td>\n",
+       "      <td>6691</td>\n",
+       "      <td>11331</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>6</th>\n",
+       "      <td>202009</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13631</td>\n",
+       "      <td>10544</td>\n",
+       "      <td>16718</td>\n",
+       "      <td>21</td>\n",
+       "      <td>16</td>\n",
+       "      <td>26</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>202008</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10424</td>\n",
+       "      <td>7708</td>\n",
+       "      <td>13140</td>\n",
+       "      <td>16</td>\n",
+       "      <td>12</td>\n",
+       "      <td>20</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>202007</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8959</td>\n",
+       "      <td>6574</td>\n",
+       "      <td>11344</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>9</th>\n",
+       "      <td>202006</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9264</td>\n",
+       "      <td>6925</td>\n",
+       "      <td>11603</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>10</th>\n",
+       "      <td>202005</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8505</td>\n",
+       "      <td>6314</td>\n",
+       "      <td>10696</td>\n",
+       "      <td>13</td>\n",
+       "      <td>10</td>\n",
+       "      <td>16</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>202004</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7991</td>\n",
+       "      <td>5831</td>\n",
+       "      <td>10151</td>\n",
+       "      <td>12</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>202003</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5968</td>\n",
+       "      <td>4100</td>\n",
+       "      <td>7836</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>12</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>202002</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6534</td>\n",
+       "      <td>4530</td>\n",
+       "      <td>8538</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>14</th>\n",
+       "      <td>202001</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9835</td>\n",
+       "      <td>7019</td>\n",
+       "      <td>12651</td>\n",
+       "      <td>15</td>\n",
+       "      <td>11</td>\n",
+       "      <td>19</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>201952</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7941</td>\n",
+       "      <td>5246</td>\n",
+       "      <td>10636</td>\n",
+       "      <td>12</td>\n",
+       "      <td>8</td>\n",
+       "      <td>16</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>201951</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5823</td>\n",
+       "      <td>3675</td>\n",
+       "      <td>7971</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>12</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>201950</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6424</td>\n",
+       "      <td>4276</td>\n",
+       "      <td>8572</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>18</th>\n",
+       "      <td>201949</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6621</td>\n",
+       "      <td>4540</td>\n",
+       "      <td>8702</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>19</th>\n",
+       "      <td>201948</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5542</td>\n",
+       "      <td>3383</td>\n",
+       "      <td>7701</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>20</th>\n",
+       "      <td>201947</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7536</td>\n",
+       "      <td>5058</td>\n",
+       "      <td>10014</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>21</th>\n",
+       "      <td>201946</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2638</td>\n",
+       "      <td>1316</td>\n",
+       "      <td>3960</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>22</th>\n",
+       "      <td>201945</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4492</td>\n",
+       "      <td>2615</td>\n",
+       "      <td>6369</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>23</th>\n",
+       "      <td>201944</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5728</td>\n",
+       "      <td>3627</td>\n",
+       "      <td>7829</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>12</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>201943</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4834</td>\n",
+       "      <td>2751</td>\n",
+       "      <td>6917</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>201942</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6279</td>\n",
+       "      <td>3989</td>\n",
+       "      <td>8569</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>26</th>\n",
+       "      <td>201941</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4130</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>6230</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>27</th>\n",
+       "      <td>201940</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4211</td>\n",
+       "      <td>2218</td>\n",
+       "      <td>6204</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>28</th>\n",
+       "      <td>201939</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3137</td>\n",
+       "      <td>1310</td>\n",
+       "      <td>4964</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>29</th>\n",
+       "      <td>201938</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3078</td>\n",
+       "      <td>1416</td>\n",
+       "      <td>4740</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>...</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>1502</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>1503</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>1504</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>1505</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>1506</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>1507</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>1508</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>1509</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>1510</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>1511</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>1512</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>1513</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>1514</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>1515</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>1516</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>1517</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>1518</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>1519</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>1520</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>1521</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>1522</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>1523</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>1524</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>1525</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>1526</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>1527</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>1528</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>1529</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>1530</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>1531</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>1532 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
+       "0     202015          7   2003      609    3397       3           1   \n",
+       "1     202014          7   3881     2223    5539       6           3   \n",
+       "2     202013          7   7341     5247    9435      11           8   \n",
+       "3     202012          7   8123     5790   10456      12           8   \n",
+       "4     202011          7  10198     7568   12828      15          11   \n",
+       "5     202010          7   9011     6691   11331      14          10   \n",
+       "6     202009          7  13631    10544   16718      21          16   \n",
+       "7     202008          7  10424     7708   13140      16          12   \n",
+       "8     202007          7   8959     6574   11344      14          10   \n",
+       "9     202006          7   9264     6925   11603      14          10   \n",
+       "10    202005          7   8505     6314   10696      13          10   \n",
+       "11    202004          7   7991     5831   10151      12           9   \n",
+       "12    202003          7   5968     4100    7836       9           6   \n",
+       "13    202002          7   6534     4530    8538      10           7   \n",
+       "14    202001          7   9835     7019   12651      15          11   \n",
+       "15    201952          7   7941     5246   10636      12           8   \n",
+       "16    201951          7   5823     3675    7971       9           6   \n",
+       "17    201950          7   6424     4276    8572      10           7   \n",
+       "18    201949          7   6621     4540    8702      10           7   \n",
+       "19    201948          7   5542     3383    7701       8           5   \n",
+       "20    201947          7   7536     5058   10014      11           7   \n",
+       "21    201946          7   2638     1316    3960       4           2   \n",
+       "22    201945          7   4492     2615    6369       7           4   \n",
+       "23    201944          7   5728     3627    7829       9           6   \n",
+       "24    201943          7   4834     2751    6917       7           4   \n",
+       "25    201942          7   6279     3989    8569      10           7   \n",
+       "26    201941          7   4130     2030    6230       6           3   \n",
+       "27    201940          7   4211     2218    6204       6           3   \n",
+       "28    201939          7   3137     1310    4964       5           2   \n",
+       "29    201938          7   3078     1416    4740       5           2   \n",
+       "...      ...        ...    ...      ...     ...     ...         ...   \n",
+       "1502  199126          7  17608    11304   23912      31          20   \n",
+       "1503  199125          7  16169    10700   21638      28          18   \n",
+       "1504  199124          7  16171    10071   22271      28          17   \n",
+       "1505  199123          7  11947     7671   16223      21          13   \n",
+       "1506  199122          7  15452     9953   20951      27          17   \n",
+       "1507  199121          7  14903     8975   20831      26          16   \n",
+       "1508  199120          7  19053    12742   25364      34          23   \n",
+       "1509  199119          7  16739    11246   22232      29          19   \n",
+       "1510  199118          7  21385    13882   28888      38          25   \n",
+       "1511  199117          7  13462     8877   18047      24          16   \n",
+       "1512  199116          7  14857    10068   19646      26          18   \n",
+       "1513  199115          7  13975     9781   18169      25          18   \n",
+       "1514  199114          7  12265     7684   16846      22          14   \n",
+       "1515  199113          7   9567     6041   13093      17          11   \n",
+       "1516  199112          7  10864     7331   14397      19          13   \n",
+       "1517  199111          7  15574    11184   19964      27          19   \n",
+       "1518  199110          7  16643    11372   21914      29          20   \n",
+       "1519  199109          7  13741     8780   18702      24          15   \n",
+       "1520  199108          7  13289     8813   17765      23          15   \n",
+       "1521  199107          7  12337     8077   16597      22          15   \n",
+       "1522  199106          7  10877     7013   14741      19          12   \n",
+       "1523  199105          7  10442     6544   14340      18          11   \n",
+       "1524  199104          7   7913     4563   11263      14           8   \n",
+       "1525  199103          7  15387    10484   20290      27          18   \n",
+       "1526  199102          7  16277    11046   21508      29          20   \n",
+       "1527  199101          7  15565    10271   20859      27          18   \n",
+       "1528  199052          7  19375    13295   25455      34          23   \n",
+       "1529  199051          7  19080    13807   24353      34          25   \n",
+       "1530  199050          7  11079     6660   15498      20          12   \n",
+       "1531  199049          7   1143        0    2610       2           0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0             5        FR   France  \n",
+       "1             9        FR   France  \n",
+       "2            14        FR   France  \n",
+       "3            16        FR   France  \n",
+       "4            19        FR   France  \n",
+       "5            18        FR   France  \n",
+       "6            26        FR   France  \n",
+       "7            20        FR   France  \n",
+       "8            18        FR   France  \n",
+       "9            18        FR   France  \n",
+       "10           16        FR   France  \n",
+       "11           15        FR   France  \n",
+       "12           12        FR   France  \n",
+       "13           13        FR   France  \n",
+       "14           19        FR   France  \n",
+       "15           16        FR   France  \n",
+       "16           12        FR   France  \n",
+       "17           13        FR   France  \n",
+       "18           13        FR   France  \n",
+       "19           11        FR   France  \n",
+       "20           15        FR   France  \n",
+       "21            6        FR   France  \n",
+       "22           10        FR   France  \n",
+       "23           12        FR   France  \n",
+       "24           10        FR   France  \n",
+       "25           13        FR   France  \n",
+       "26            9        FR   France  \n",
+       "27            9        FR   France  \n",
+       "28            8        FR   France  \n",
+       "29            8        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1502         42        FR   France  \n",
+       "1503         38        FR   France  \n",
+       "1504         39        FR   France  \n",
+       "1505         29        FR   France  \n",
+       "1506         37        FR   France  \n",
+       "1507         36        FR   France  \n",
+       "1508         45        FR   France  \n",
+       "1509         39        FR   France  \n",
+       "1510         51        FR   France  \n",
+       "1511         32        FR   France  \n",
+       "1512         34        FR   France  \n",
+       "1513         32        FR   France  \n",
+       "1514         30        FR   France  \n",
+       "1515         23        FR   France  \n",
+       "1516         25        FR   France  \n",
+       "1517         35        FR   France  \n",
+       "1518         38        FR   France  \n",
+       "1519         33        FR   France  \n",
+       "1520         31        FR   France  \n",
+       "1521         29        FR   France  \n",
+       "1522         26        FR   France  \n",
+       "1523         25        FR   France  \n",
+       "1524         20        FR   France  \n",
+       "1525         36        FR   France  \n",
+       "1526         38        FR   France  \n",
+       "1527         36        FR   France  \n",
+       "1528         45        FR   France  \n",
+       "1529         43        FR   France  \n",
+       "1530         28        FR   France  \n",
+       "1531          5        FR   France  \n",
+       "\n",
+       "[1532 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv(data_file, skiprows=1)\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Y a-t-il des points manquants dans ce jeux de données ? "
+   ]
+  },
+  {
+   "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",
+       "  </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": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data[raw_data.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Non, il n'y en a pas. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nos données utilisent une convention inhabituelle: le numéro de semaine est collé à l'année, donnant l'impression qu'il s'agit 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 semaine. Il faut lui fournir les dates de début et de fin de 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": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def convert_week(year_and_week_int):\n",
+    "    year_and_week_str = str(year_and_week_int)\n",
+    "    year = int(year_and_week_str[:4])\n",
+    "    week = int(year_and_week_str[4:])\n",
+    "    w = isoweek.Week(year, week)\n",
+    "    return pd.Period(w.day(0), 'W')\n",
+    "\n",
+    "raw_data['period'] = [convert_week(yw) for yw in raw_data['week']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Il restent deux petites modifications à faire.\n",
+    "\n",
+    "Premièrement, nous définissons les périodes d'observation\n",
+    "comme nouvel index de notre jeux de données. Ceci en fait\n",
+    "une suite chronologique, ce qui sera pratique par la suite.\n",
+    "\n",
+    "Deuxièmement, nous trions les points par période, dans\n",
+    "le sens chronologique."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted_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 pour tout la plage de données\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "periods = sorted_data.index\n",
+    "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
+    "    delta = p2.to_timestamp() - p1.end_time\n",
+    "    if delta > pd.Timedelta('1s'):\n",
+    "        print(p1, p2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Première représentation visuelle des données"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd55d4219e8>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data['inc'].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Un zoom sur les dernières années montre mieux la situation des pics au début de l'année, entre mars et juin. Le creux des incidences se trouve en fin d'année."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd55b292e10>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data['inc'][-200:].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Etude de l'incidence annuelle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Etant donné que le pic de l'épidémie se situe au début de l'année, nous définissons la période de référence\n",
+    "entre deux minima de l'incidence, du 1er septembre de l'année $N$ au\n",
+    "1er septembre de l'année $N+1$.\n",
+    "\n",
+    "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n",
+    "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n",
+    "de référence: à la place du 1er septembre de chaque année, nous utilisons le\n",
+    "premier jour de la semaine qui contient le 1er septembre.\n",
+    "\n",
+    "Comme l'incidence de la varicelle est très faible durant la deuxieme partie de l'année, cette\n",
+    "modification ne risque pas de fausser nos conclusions.\n",
+    "\n",
+    "Encore un petit détail: les données commencent en décembre 1990, ce qui\n",
+    "rend la première année incomplète. Nous commençons donc l'analyse en 1991."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
+    "                     for y in range(1991,\n",
+    "                                    sorted_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": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year = []\n",
+    "yearly_incidence = []\n",
+    "for week1, week2 in zip(first_september_week[:-1],\n",
+    "                        first_september_week[1:]):\n",
+    "    one_year = sorted_data['inc'][week1:week2-1]\n",
+    "    assert abs(len(one_year)-52) < 2\n",
+    "    yearly_incidence.append(one_year.sum())\n",
+    "    year.append(week2.year)\n",
+    "yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Voici les incidences annuelles."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd55d47af28>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yearly_incidence.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": 21,
+   "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": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Enfin, un histogramme montre bien que les épidémies fortes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd55d3d20f0>"
+      ]
+     },
+     "execution_count": 22,
+     "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",
    "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/incidence-PAY-7.csv b/module3/exo2/incidence-PAY-7.csv
new file mode 100644
index 0000000000000000000000000000000000000000..bf9b56d8c6b093b27a7ee9a6ca4d4a2a1a5f5464
--- /dev/null
+++ b/module3/exo2/incidence-PAY-7.csv
@@ -0,0 +1,1534 @@
+# @source="réseau Sentinelles, INSERM, Sorbonne Université, http://www.sentiweb.fr", @meta={"period":[199049,202015],"geo":["PAY","1"],"geo_ref":"insee","indicator":"7","type":"all","conf_int":true,"compact":false}, @date=2020-04-17T00:40:34+02:00
+week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name
+202015,7,2003,609,3397,3,1,5,FR,France
+202014,7,3881,2223,5539,6,3,9,FR,France
+202013,7,7341,5247,9435,11,8,14,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
+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
+201936,7,1277,263,2291,2,0,4,FR,France
+201935,7,922,0,1857,1,0,2,FR,France
+201934,7,1997,605,3389,3,1,5,FR,France
+201933,7,3279,1103,5455,5,2,8,FR,France
+201932,7,3627,1576,5678,6,3,9,FR,France
+201931,7,7611,4605,10617,12,7,17,FR,France
+201930,7,8875,5629,12121,13,8,18,FR,France
+201929,7,10658,7151,14165,16,11,21,FR,France
+201928,7,11251,7604,14898,17,11,23,FR,France
+201927,7,16602,12349,20855,25,19,31,FR,France
+201926,7,18851,14491,23211,29,22,36,FR,France
+201925,7,18309,13972,22646,28,21,35,FR,France
+201924,7,13382,9659,17105,20,14,26,FR,France
+201923,7,19264,15027,23501,29,23,35,FR,France
+201922,7,16861,12783,20939,26,20,32,FR,France
+201921,7,17572,13140,22004,27,20,34,FR,France
+201920,7,16180,12310,20050,25,19,31,FR,France
+201919,7,16646,12721,20571,25,19,31,FR,France
+201918,7,13803,10159,17447,21,15,27,FR,France
+201917,7,15868,11815,19921,24,18,30,FR,France
+201916,7,17127,12815,21439,26,19,33,FR,France
+201915,7,21449,16525,26373,33,26,40,FR,France
+201914,7,16131,12254,20008,25,19,31,FR,France
+201913,7,17264,13409,21119,26,20,32,FR,France
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