diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..a18c64409ec588665a2ba7967e03f232d6b27a30 100644
--- a/module3/exo2/exercice.ipynb
+++ b/module3/exo2/exercice.ipynb
@@ -1,5 +1,1430 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Incidence de la varicelle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import isoweek\n",
+    "import os"
+   ]
+  },
+  {
+   "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": "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",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>202025</td>\n",
+       "      <td>7</td>\n",
+       "      <td>261</td>\n",
+       "      <td>0</td>\n",
+       "      <td>660</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202024</td>\n",
+       "      <td>7</td>\n",
+       "      <td>391</td>\n",
+       "      <td>0</td>\n",
+       "      <td>965</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202023</td>\n",
+       "      <td>7</td>\n",
+       "      <td>558</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1115</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202022</td>\n",
+       "      <td>7</td>\n",
+       "      <td>277</td>\n",
+       "      <td>0</td>\n",
+       "      <td>633</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202021</td>\n",
+       "      <td>7</td>\n",
+       "      <td>602</td>\n",
+       "      <td>36</td>\n",
+       "      <td>1168</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>202020</td>\n",
+       "      <td>7</td>\n",
+       "      <td>824</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1628</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>202019</td>\n",
+       "      <td>7</td>\n",
+       "      <td>310</td>\n",
+       "      <td>0</td>\n",
+       "      <td>753</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>202018</td>\n",
+       "      <td>7</td>\n",
+       "      <td>849</td>\n",
+       "      <td>98</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>202017</td>\n",
+       "      <td>7</td>\n",
+       "      <td>272</td>\n",
+       "      <td>0</td>\n",
+       "      <td>658</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>202016</td>\n",
+       "      <td>7</td>\n",
+       "      <td>758</td>\n",
+       "      <td>78</td>\n",
+       "      <td>1438</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>202015</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1918</td>\n",
+       "      <td>675</td>\n",
+       "      <td>3161</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>11</th>\n",
+       "      <td>202014</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3879</td>\n",
+       "      <td>2227</td>\n",
+       "      <td>5531</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>12</th>\n",
+       "      <td>202013</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7326</td>\n",
+       "      <td>5236</td>\n",
+       "      <td>9416</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>13</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>14</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>15</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>16</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>17</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>18</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>19</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>20</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>21</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>22</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>23</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>24</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>25</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>26</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>27</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>28</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>29</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>...</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>1512</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>1513</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>1514</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>1515</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>1516</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>1517</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>1518</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>1519</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>1520</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>1521</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>1522</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>1523</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>1524</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>1525</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>1526</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>1527</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>1528</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>1529</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>1530</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>1531</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>1532</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>1533</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>1534</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>1535</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>1536</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>1537</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>1538</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>1539</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>1540</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>1541</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>1542 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
+       "0     202025          7    261        0     660       0           0   \n",
+       "1     202024          7    391        0     965       1           0   \n",
+       "2     202023          7    558        1    1115       1           0   \n",
+       "3     202022          7    277        0     633       0           0   \n",
+       "4     202021          7    602       36    1168       1           0   \n",
+       "5     202020          7    824       20    1628       1           0   \n",
+       "6     202019          7    310        0     753       0           0   \n",
+       "7     202018          7    849       98    1600       1           0   \n",
+       "8     202017          7    272        0     658       0           0   \n",
+       "9     202016          7    758       78    1438       1           0   \n",
+       "10    202015          7   1918      675    3161       3           1   \n",
+       "11    202014          7   3879     2227    5531       6           3   \n",
+       "12    202013          7   7326     5236    9416      11           8   \n",
+       "13    202012          7   8123     5790   10456      12           8   \n",
+       "14    202011          7  10198     7568   12828      15          11   \n",
+       "15    202010          7   9011     6691   11331      14          10   \n",
+       "16    202009          7  13631    10544   16718      21          16   \n",
+       "17    202008          7  10424     7708   13140      16          12   \n",
+       "18    202007          7   8959     6574   11344      14          10   \n",
+       "19    202006          7   9264     6925   11603      14          10   \n",
+       "20    202005          7   8505     6314   10696      13          10   \n",
+       "21    202004          7   7991     5831   10151      12           9   \n",
+       "22    202003          7   5968     4100    7836       9           6   \n",
+       "23    202002          7   6534     4530    8538      10           7   \n",
+       "24    202001          7   9835     7019   12651      15          11   \n",
+       "25    201952          7   7941     5246   10636      12           8   \n",
+       "26    201951          7   5823     3675    7971       9           6   \n",
+       "27    201950          7   6424     4276    8572      10           7   \n",
+       "28    201949          7   6621     4540    8702      10           7   \n",
+       "29    201948          7   5542     3383    7701       8           5   \n",
+       "...      ...        ...    ...      ...     ...     ...         ...   \n",
+       "1512  199126          7  17608    11304   23912      31          20   \n",
+       "1513  199125          7  16169    10700   21638      28          18   \n",
+       "1514  199124          7  16171    10071   22271      28          17   \n",
+       "1515  199123          7  11947     7671   16223      21          13   \n",
+       "1516  199122          7  15452     9953   20951      27          17   \n",
+       "1517  199121          7  14903     8975   20831      26          16   \n",
+       "1518  199120          7  19053    12742   25364      34          23   \n",
+       "1519  199119          7  16739    11246   22232      29          19   \n",
+       "1520  199118          7  21385    13882   28888      38          25   \n",
+       "1521  199117          7  13462     8877   18047      24          16   \n",
+       "1522  199116          7  14857    10068   19646      26          18   \n",
+       "1523  199115          7  13975     9781   18169      25          18   \n",
+       "1524  199114          7  12265     7684   16846      22          14   \n",
+       "1525  199113          7   9567     6041   13093      17          11   \n",
+       "1526  199112          7  10864     7331   14397      19          13   \n",
+       "1527  199111          7  15574    11184   19964      27          19   \n",
+       "1528  199110          7  16643    11372   21914      29          20   \n",
+       "1529  199109          7  13741     8780   18702      24          15   \n",
+       "1530  199108          7  13289     8813   17765      23          15   \n",
+       "1531  199107          7  12337     8077   16597      22          15   \n",
+       "1532  199106          7  10877     7013   14741      19          12   \n",
+       "1533  199105          7  10442     6544   14340      18          11   \n",
+       "1534  199104          7   7913     4563   11263      14           8   \n",
+       "1535  199103          7  15387    10484   20290      27          18   \n",
+       "1536  199102          7  16277    11046   21508      29          20   \n",
+       "1537  199101          7  15565    10271   20859      27          18   \n",
+       "1538  199052          7  19375    13295   25455      34          23   \n",
+       "1539  199051          7  19080    13807   24353      34          25   \n",
+       "1540  199050          7  11079     6660   15498      20          12   \n",
+       "1541  199049          7   1143        0    2610       2           0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0             1        FR   France  \n",
+       "1             2        FR   France  \n",
+       "2             2        FR   France  \n",
+       "3             1        FR   France  \n",
+       "4             2        FR   France  \n",
+       "5             2        FR   France  \n",
+       "6             1        FR   France  \n",
+       "7             2        FR   France  \n",
+       "8             1        FR   France  \n",
+       "9             2        FR   France  \n",
+       "10            5        FR   France  \n",
+       "11            9        FR   France  \n",
+       "12           14        FR   France  \n",
+       "13           16        FR   France  \n",
+       "14           19        FR   France  \n",
+       "15           18        FR   France  \n",
+       "16           26        FR   France  \n",
+       "17           20        FR   France  \n",
+       "18           18        FR   France  \n",
+       "19           18        FR   France  \n",
+       "20           16        FR   France  \n",
+       "21           15        FR   France  \n",
+       "22           12        FR   France  \n",
+       "23           13        FR   France  \n",
+       "24           19        FR   France  \n",
+       "25           16        FR   France  \n",
+       "26           12        FR   France  \n",
+       "27           13        FR   France  \n",
+       "28           13        FR   France  \n",
+       "29           11        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1512         42        FR   France  \n",
+       "1513         38        FR   France  \n",
+       "1514         39        FR   France  \n",
+       "1515         29        FR   France  \n",
+       "1516         37        FR   France  \n",
+       "1517         36        FR   France  \n",
+       "1518         45        FR   France  \n",
+       "1519         39        FR   France  \n",
+       "1520         51        FR   France  \n",
+       "1521         32        FR   France  \n",
+       "1522         34        FR   France  \n",
+       "1523         32        FR   France  \n",
+       "1524         30        FR   France  \n",
+       "1525         23        FR   France  \n",
+       "1526         25        FR   France  \n",
+       "1527         35        FR   France  \n",
+       "1528         38        FR   France  \n",
+       "1529         33        FR   France  \n",
+       "1530         31        FR   France  \n",
+       "1531         29        FR   France  \n",
+       "1532         26        FR   France  \n",
+       "1533         25        FR   France  \n",
+       "1534         20        FR   France  \n",
+       "1535         36        FR   France  \n",
+       "1536         38        FR   France  \n",
+       "1537         36        FR   France  \n",
+       "1538         45        FR   France  \n",
+       "1539         43        FR   France  \n",
+       "1540         28        FR   France  \n",
+       "1541          5        FR   France  \n",
+       "\n",
+       "[1542 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "local_path = 'incidence_varicelle.csv'\n",
+    "if os.path.exists(local_path):\n",
+    "    raw_data = pd.read_csv(local_path)\n",
+    "else:\n",
+    "    raw_data = pd.read_csv(data_url, skiprows=1)\n",
+    "    raw_data.to_csv(local_path)  # Save the original CSV file without the comment in the first line\n",
+    "\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Point manquant ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data[raw_data.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = raw_data  # no data is excluded"
+   ]
+  },
+  {
+   "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": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def convert_week(year_and_week_int):\n",
+    "    year_and_week_str = str(year_and_week_int)\n",
+    "    year = int(year_and_week_str[:4])\n",
+    "    week = int(year_and_week_str[4:])\n",
+    "    w = isoweek.Week(year, week)\n",
+    "    return pd.Period(w.day(0), 'W')\n",
+    "\n",
+    "data['period'] = [convert_week(yw) for yw in data['week']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Il reste 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": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted_data = data.set_index('period').sort_index()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nous vérifions la cohérence des données. Entre la fin d'une période et\n",
+    "le début de la période qui suit, la différence temporelle doit être\n",
+    "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n",
+    "d'une seconde."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "periods = sorted_data.index\n",
+    "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
+    "    delta = p2.to_timestamp() - p1.end_time\n",
+    "    if delta > pd.Timedelta('1s'):\n",
+    "        print(p1, p2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3c5444de48>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data['inc'].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3c523158d0>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data['inc'][-200:].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Etude de l'incidence annuelle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nous définissons la période de référence 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",
+    "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": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W') for y in range(1991, 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": 36,
+   "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",
+    "    try:\n",
+    "        assert abs(len(one_year)-52) < 2\n",
+    "    except AssertionError:\n",
+    "        print(len(one_year), week1, week2)\n",
+    "    yearly_incidence.append(one_year.sum())\n",
+    "    year.append(week2.year)\n",
+    "yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3c50a0a400>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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pcjOrd04mw8ALLJpZvfPU4CHkp8nNbKRwy2QIeelrMxspnEyGkJ8mN7ORwslkiA106WsP1ptZLSoqmUj6qKSNkn4laY+kz0i6R9Lbkl5L2xdyx98pqVXSXkmLcvG5kl5P362RpBQfI+mZFG+RND1XZpmkfWlbVr6qD49Hbp7H6qWXM2vyOFYvvbzbUti98WC9mdWiopZTkbQOeDkivi/pI8A5wNeB9yLi2z2OnQWsBxqBycCLwJ9ExElJ24GvAf8EPAesiYgtkr4C/OuI+FtJTcB1EfGXkiYAO4F5QACvAHMj4uiZrrVWl1PpOVhf4MF6MxsOQ76ciqRxwGeBRwEi4g8R8bs+iiwBno6Izoh4E2gFGiVdBIyLiG2RZbDHgaW5MuvS/kZgQWq1LAKaI6IjJZBmYPGAa1kDPFhvZrWsmG6uPwbagR9KelXS9yWdm767XdIvJf1A0vgUmwK8lSt/MMWmpP2e8W5lIqILeBe4oI9z1R0P1ptZLSsmmTQAnwYejogrgN8DdwAPA58A5gCHge+k49XLOaKP+GDLfEDSCkk7Je1sb2/voyrVze+pNrNaVcxDiweBgxHRkn7eCNwREUcKB0j6HvA/c8dfnCs/FTiU4lN7iefLHJTUAJwPdKT453qU+VnPC4yItcBayMZMiqhTydqOneD29a/y4I1XlK314PdUm1mt6rdlEhHvAG9JuiyFFgBvpDGQguuAXWl/M9CUZmjNAGYC2yPiMHBc0pVpPOQW4NlcmcJMreuBl9K4yvPAQknjUzfawhSrOM+6MiuOp7uPDMUup/JV4EdpJte/AF8G1kiaQ9bttB+4FSAidkvaALwBdAErI+JkOs9twGPA2cCWtEE2uP+EpFayFklTOleHpPuAHem4eyOiY3BVLQ8vkWI2MPk/vFZf96lKX44NEb9pcYDajp1g9XN7eGH3O5x4/xRjR49i0eyPc9cXP+nBcrMcT3evLX7T4jDzrCuz4ni6+8jiVYMHoTDr6sbGaTy1/QDt7gs2+xD/4TWyOJkMgmddWbkMxazAauI/vEYOj5mYVdDdm17nR9sPcFPjNA9OW0WVOmbilolZBXhWoNUbD8CblVkxz1V4cNrqjZOJWZkV80CrB6et3riby6xMBtp15cHpD6v3CQn1zAPwZmXiB1pL5wkJleMBeLMq4a6rwfOEhNrnMROzMvJrBAbHExJqn1smVtVqrQ/dD7QOjlt1tc8tExt2A1mS3Ev9jxxu1dU2D8DbsCtmkNUrzpoNr1IH4J1MbNgMJEF4ZpTZ8PIS9FYzBjLI6j50s9riAXgbNgNNEH6oz6x2OJnYsBpIgvDMKLPa4TETMzPzmImZmVWek4mZWQ8DeRbKMk4mZmY9+GHZgSsqmUj6qKSNkn4laY+kz0iaIKlZ0r70OT53/J2SWiXtlbQoF58r6fX03RpJSvExkp5J8RZJ03NllqV/Y5+kZeWruplZd5fdvYXpd/yEJ1sOEJEtODn9jp9w2d1bKn1pVa/Ylsl3gZ9GxL8C/hTYA9wBbI2ImcDW9DOSZgFNwGxgMfCQpLPSeR4GVgAz07Y4xZcDRyPiUuAB4P50rgnAKmA+0AisyictM7Ny8oKTg9dvMpE0Dvgs8ChARPwhIn4HLAHWpcPWAUvT/hLg6YjojIg3gVagUdJFwLiI2BbZFLLHe5QpnGsjsCC1WhYBzRHRERFHgWZOJyAzs7Lyw7KDV0zL5I+BduCHkl6V9H1J5wIXRsRhgPQ5KR0/BXgrV/5gik1J+z3j3cpERBfwLnBBH+fqRtIKSTsl7Wxvby+iSmZmvfOCk4NTzEOLDcCnga9GRIuk75K6tM5AvcSij/hgy5wORKwF1kL2nEkf12Zm1ic/LDs4xbRMDgIHI6Il/byRLLkcSV1XpM+23PEX58pPBQ6l+NRe4t3KSGoAzgc6+jiXmZlVkX6TSUS8A7wl6bIUWgC8AWwGCrOrlgHPpv3NQFOaoTWDbKB9e+oKOy7pyjQeckuPMoVzXQ+8lMZVngcWShqfBt4XppiZmVWRYtfm+irwI0kfAf4F+DJZItogaTlwALgBICJ2S9pAlnC6gJURcTKd5zbgMeBsYEvaIBvcf0JSK1mLpCmdq0PSfcCOdNy9EdExyLqamdkQ8dpcZmbmtbnMzKzynEzMzOpApdcTczIxM6sDlV5PzC/HMjOrYZfdvYXOrlMf/PxkywGebDnAmIZR7F19zbBdh1smZmY1rFrWE3MyMTOrYdWynpi7uczMalxhPbEbG6fx1PYDtFdgEN7PmZiZmZ8zMTOzynMyMTOzkjmZmJlZyZxMzMysZE4mZmZWMieTGlfp9XjMzMDJpOZVej0eMzPwQ4s1q1rW4zEzA7dMala1rMdjZgZOJjWrWtbjMTMDd3PVtGpYj8fMDLw2l5mZ4bW5zMysChSVTCTtl/S6pNck7UyxeyS9nWKvSfpC7vg7JbVK2itpUS4+N52nVdIaSUrxMZKeSfEWSdNzZZZJ2pe2ZeWquJnZcKvn58IG0jK5OiLm9GgGPZBicyLiOQBJs4AmYDawGHhI0lnp+IeBFcDMtC1O8eXA0Yi4FHgAuD+dawKwCpgPNAKrJI0fRD3NzCqunp8LG4oB+CXA0xHRCbwpqRVolLQfGBcR2wAkPQ4sBbakMvek8huBB1OrZRHQHBEdqUwzWQJaPwTXbWY2JEbCc2HFtkwCeEHSK5JW5OK3S/qlpB/kWgxTgLdyxxxMsSlpv2e8W5mI6ALeBS7o41xmZjVjJDwXVmwyuSoiPg1cA6yU9FmyLqtPAHOAw8B30rHqpXz0ER9smQ9IWiFpp6Sd7e3tfVbEzGy4jYTnwopKJhFxKH22AZuAxog4EhEnI+IU8D2yMQ3IWg8X54pPBQ6l+NRe4t3KSGoAzgc6+jhXz+tbGxHzImLexIkTi6mSmdmwKjwXtukrV3HT/Etof6+z3zK1NGDfbzKRdK6k8wr7wEJgl6SLcoddB+xK+5uBpjRDawbZQPv2iDgMHJd0ZRoPuQV4NlemMFPreuClyB6AeR5YKGl86kZbmGJmZjXlkZvnsXrp5cyaPI7VSy/nkZv7f6SjlgbsixmAvxDYlGbxNgBPRcRPJT0haQ5Zt9N+4FaAiNgtaQPwBtAFrIyIk+lctwGPAWeTDbxvSfFHgSfSYH0H2WwwIqJD0n3AjnTcvYXBeDOzelWLA/Z+At7MrMq0HTvB6uf28MLudzjx/inGjh7Fotkf564vfnLIxln8BLyZWZ2pxQF7L/RoZlaFam0hV3dzmZmZu7nMzKzynEzMzKxkTiZmZlYyJxMzMyuZk4mZmZXMycTMzErmZGJmZiVzMjEzs5I5mZiZWcmcTKxu1NK7H8zqjZOJ1Y1aeveDWb3xQo9W82rx3Q9m9cYtE6t5L3/zaq6dM5mxo7P/nceOHsWSOZN5+VtXV/jKzEYOJxOrebX47gezeuNuLqsLtfbuB7N64/eZmJmZ32diZmaV52RiZmYlKyqZSNov6XVJr0namWITJDVL2pc+x+eOv1NSq6S9khbl4nPTeVolrZGkFB8j6ZkUb5E0PVdmWfo39klaVq6Km5lZ+QykZXJ1RMzJ9andAWyNiJnA1vQzkmYBTcBsYDHwkKSzUpmHgRXAzLQtTvHlwNGIuBR4ALg/nWsCsAqYDzQCq/JJy8zMqkMp3VxLgHVpfx2wNBd/OiI6I+JNoBVolHQRMC4itkU26v94jzKFc20EFqRWyyKgOSI6IuIo0MzpBGRmZlWi2GQSwAuSXpG0IsUujIjDAOlzUopPAd7KlT2YYlPSfs94tzIR0QW8C1zQx7nMzKyKFPucyVURcUjSJKBZ0q/6OFa9xKKP+GDLnP4HswRXSHLvSdrbx/XVgo8Bv630RQyxeq9jvdcP6r+OI61+l5RysqKSSUQcSp9tkjaRjV8ckXRRRBxOXVht6fCDwMW54lOBQyk+tZd4vsxBSQ3A+UBHin+uR5mf9XJ9a4G1xdSlFkjaWcp871pQ73Ws9/pB/dfR9RuYfru5JJ0r6bzCPrAQ2AVsBgqzq5YBz6b9zUBTmqE1g2ygfXvqCjsu6co0HnJLjzKFc10PvJTGVZ4HFkoanwbeF6aYmZlVkWJaJhcCm9Is3gbgqYj4qaQdwAZJy4EDwA0AEbFb0gbgDaALWBkRJ9O5bgMeA84GtqQN4FHgCUmtZC2SpnSuDkn3ATvScfdGREcJ9TUzsyFQd8up1ANJK1LXXd2q9zrWe/2g/uvo+g3wfE4mZmZWKi+nYmZmJXMyGSaSfiCpTdKuXOxPJW1LS8z8D0njUvwjkn6Y4r+Q9LlcmZ+lZWpeS9ukXv65YSfpYkn/S9IeSbslfS3Fy7bsTiWVuX51cQ8lXZCOf0/Sgz3OVfP3sJ/6Vd09HET9Pq/s2cHX0+e/z51r4PcvIrwNwwZ8Fvg0sCsX2wH8u7T/N8B9aX8l8MO0Pwl4BRiVfv4ZMK/S9emlfhcBn0775wH/DMwC/gG4I8XvAO5P+7OAXwBjgBnAr4Gz0nfbgc+QPWe0BbimzupXL/fwXODfAH8LPNjjXPVwD/uqX9Xdw0HU7wpgctq/HHi7lPvnlskwiYh/JJuplncZ8I9pvxn4i7Q/i2y9MyKiDfgdUNXz3SPicET8PO0fB/aQrVZQzmV3KqZc9Rveqx6YgdYxIn4fEf8b6PYmsnq5h2eqX7UaRP1ejfQMIbAbGKvskY5B3T8nk8raBVyb9m/g9MOevwCWSGpQ9qzOXLo/CPrD1LT+T9XQfdCTslWfrwBaKO+yO1WhxPoV1MM9PJN6uYf9qdp7OIj6/QXwakR0Msj752RSWX8DrJT0Clmz9A8p/gOyG7gT+C/A/yV7Zgfgpoj4FPBv03bzsF5xPyT9EfDfga9HxLG+Du0lVvQSOpVShvpB/dzDM56il1gt3sO+VO09HGj9JM0mW6n91kKol8P6vX9OJhUUEb+KiIURMRdYT9avTkR0RcQ3IlvyfwnwUWBf+u7t9HkceIoq6jqRNJrsf+IfRcSPU/hIajYXuj9KWXanospUv3q6h2dSL/fwjKr1Hg60fpKmApuAWyLi1yk8qPvnZFJBhRkgkkYBdwP/Nf18jrKla5D0eaArIt5I3V4fS/HRwH8g6yqruNTMfxTYExH/OfdVOZfdqZhy1a/O7mGv6ugenuk8VXkPB1o/SR8FfgLcGRH/p3DwoO9fpWYejLSNrOVxGHifLPMvB75GNuPin4G/5/RDpNOBvWQDaC8Cl6T4uWQzu35JNmD2XdIMoUpvZLNeIl3ba2n7AtmrBLaStay2AhNyZe4ia43tJTdbhGyywa703YOF/y71UL86vIf7ySaWvJf+v55VZ/fwQ/Wr1ns40PqR/QH7+9yxrwGTBnv//AS8mZmVzN1cZmZWMicTMzMrmZOJmZmVzMnEzMxK5mRiZmYlczIxM7OSOZmYmVnJnEzMzKxk/x+qftJYVEr3+gAAAABJRU5ErkJggg==\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 (au début)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2009    842373\n",
+       "1992    832939\n",
+       "2010    829911\n",
+       "2016    782114\n",
+       "2004    777388\n",
+       "2003    758363\n",
+       "1999    756456\n",
+       "2008    749478\n",
+       "2007    717352\n",
+       "2013    698332\n",
+       "2014    685769\n",
+       "1997    683434\n",
+       "1998    677775\n",
+       "1994    661409\n",
+       "1995    652478\n",
+       "1993    643387\n",
+       "2011    642368\n",
+       "2006    632833\n",
+       "2005    628464\n",
+       "2012    624573\n",
+       "2001    619041\n",
+       "2000    617597\n",
+       "2015    604382\n",
+       "2019    584066\n",
+       "1996    564901\n",
+       "2017    551041\n",
+       "2018    542312\n",
+       "2002    516689\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Enfin, un histogramme montre bien que les épidémies de varicelle ont une intensité assez régulière d'année en année"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3c4fffb5f8>"
+      ]
+     },
+     "execution_count": 43,
+     "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)"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +1441,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-
diff --git a/module3/exo2/incidence_varicelle.csv b/module3/exo2/incidence_varicelle.csv
new file mode 100644
index 0000000000000000000000000000000000000000..b019d6a3d4dc5b54f97de7d6d16aa48dabfa3375
--- /dev/null
+++ b/module3/exo2/incidence_varicelle.csv
@@ -0,0 +1,1543 @@
+,week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name
+0,202025,7,261,0,660,0,0,1,FR,France
+1,202024,7,391,0,965,1,0,2,FR,France
+2,202023,7,558,1,1115,1,0,2,FR,France
+3,202022,7,277,0,633,0,0,1,FR,France
+4,202021,7,602,36,1168,1,0,2,FR,France
+5,202020,7,824,20,1628,1,0,2,FR,France
+6,202019,7,310,0,753,0,0,1,FR,France
+7,202018,7,849,98,1600,1,0,2,FR,France
+8,202017,7,272,0,658,0,0,1,FR,France
+9,202016,7,758,78,1438,1,0,2,FR,France
+10,202015,7,1918,675,3161,3,1,5,FR,France
+11,202014,7,3879,2227,5531,6,3,9,FR,France
+12,202013,7,7326,5236,9416,11,8,14,FR,France
+13,202012,7,8123,5790,10456,12,8,16,FR,France
+14,202011,7,10198,7568,12828,15,11,19,FR,France
+15,202010,7,9011,6691,11331,14,10,18,FR,France
+16,202009,7,13631,10544,16718,21,16,26,FR,France
+17,202008,7,10424,7708,13140,16,12,20,FR,France
+18,202007,7,8959,6574,11344,14,10,18,FR,France
+19,202006,7,9264,6925,11603,14,10,18,FR,France
+20,202005,7,8505,6314,10696,13,10,16,FR,France
+21,202004,7,7991,5831,10151,12,9,15,FR,France
+22,202003,7,5968,4100,7836,9,6,12,FR,France
+23,202002,7,6534,4530,8538,10,7,13,FR,France
+24,202001,7,9835,7019,12651,15,11,19,FR,France
+25,201952,7,7941,5246,10636,12,8,16,FR,France
+26,201951,7,5823,3675,7971,9,6,12,FR,France
+27,201950,7,6424,4276,8572,10,7,13,FR,France
+28,201949,7,6621,4540,8702,10,7,13,FR,France
+29,201948,7,5542,3383,7701,8,5,11,FR,France
+30,201947,7,7536,5058,10014,11,7,15,FR,France
+31,201946,7,2638,1316,3960,4,2,6,FR,France
+32,201945,7,4492,2615,6369,7,4,10,FR,France
+33,201944,7,5728,3627,7829,9,6,12,FR,France
+34,201943,7,4834,2751,6917,7,4,10,FR,France
+35,201942,7,6279,3989,8569,10,7,13,FR,France
+36,201941,7,4130,2030,6230,6,3,9,FR,France
+37,201940,7,4211,2218,6204,6,3,9,FR,France
+38,201939,7,3137,1310,4964,5,2,8,FR,France
+39,201938,7,3078,1416,4740,5,2,8,FR,France
+40,201937,7,970,162,1778,1,0,2,FR,France
+41,201936,7,1277,263,2291,2,0,4,FR,France
+42,201935,7,922,0,1857,1,0,2,FR,France
+43,201934,7,1997,605,3389,3,1,5,FR,France
+44,201933,7,3279,1103,5455,5,2,8,FR,France
+45,201932,7,3627,1576,5678,6,3,9,FR,France
+46,201931,7,7611,4605,10617,12,7,17,FR,France
+47,201930,7,8875,5629,12121,13,8,18,FR,France
+48,201929,7,10658,7151,14165,16,11,21,FR,France
+49,201928,7,11251,7604,14898,17,11,23,FR,France
+50,201927,7,16602,12349,20855,25,19,31,FR,France
+51,201926,7,18851,14491,23211,29,22,36,FR,France
+52,201925,7,18309,13972,22646,28,21,35,FR,France
+53,201924,7,13382,9659,17105,20,14,26,FR,France
+54,201923,7,19264,15027,23501,29,23,35,FR,France
+55,201922,7,16861,12783,20939,26,20,32,FR,France
+56,201921,7,17572,13140,22004,27,20,34,FR,France
+57,201920,7,16180,12310,20050,25,19,31,FR,France
+58,201919,7,16646,12721,20571,25,19,31,FR,France
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