diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..385050c4f5462d07044c66885cbba88024390696 100644
--- a/module3/exo2/exercice.ipynb
+++ b/module3/exo2/exercice.ipynb
@@ -1,6 +1,1495 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Incidence de la varicelle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "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 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 = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n",
+    "\n",
+    "| Nom de colonne | Libellé de colonne                                                                                                                |\n",
+    "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n",
+    "| week           | Semaine calendaire (ISO 8601)                                                                                                     |\n",
+    "| indicator      | Code de l'indicateur de surveillance                                                                                              |\n",
+    "| inc            | Estimation de l'incidence de consultations en nombre de cas                                                                       |\n",
+    "| inc_low        | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation                                                     |\n",
+    "| inc_up         | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation                                                     |\n",
+    "| inc100         | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants)                                   |\n",
+    "| inc100_low     | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
+    "| inc100_up      | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
+    "| geo_insee      | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/                            |\n",
+    "| geo_name       | Libellé de la zone géographique (ce libellé peut être modifié sans préavis)                                                       |\n",
+    "\n",
+    "La première ligne du fichier CSV est potentiellement un commentaire, que nous ignorons en précisant `comment=1`. Si le fichier existe déjà en version locale, nous l'utilisons sinon nous téléchargeons l'url précisée et la sauvegardons en local pour pouvoir la réutiliser plus tard."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading local version\n"
+     ]
+    },
+    {
+     "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>Unnamed: 0</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>0</td>\n",
+       "      <td>202018</td>\n",
+       "      <td>7</td>\n",
+       "      <td>861</td>\n",
+       "      <td>88</td>\n",
+       "      <td>1634</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>1</th>\n",
+       "      <td>1</td>\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>2</th>\n",
+       "      <td>2</td>\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>3</th>\n",
+       "      <td>3</td>\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>4</th>\n",
+       "      <td>4</td>\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>5</th>\n",
+       "      <td>5</td>\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>6</th>\n",
+       "      <td>6</td>\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>7</th>\n",
+       "      <td>7</td>\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>8</th>\n",
+       "      <td>8</td>\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>9</th>\n",
+       "      <td>9</td>\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>10</th>\n",
+       "      <td>10</td>\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>11</th>\n",
+       "      <td>11</td>\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>12</th>\n",
+       "      <td>12</td>\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>13</th>\n",
+       "      <td>13</td>\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>14</th>\n",
+       "      <td>14</td>\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>15</th>\n",
+       "      <td>15</td>\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>16</th>\n",
+       "      <td>16</td>\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>17</th>\n",
+       "      <td>17</td>\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>18</th>\n",
+       "      <td>18</td>\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>19</th>\n",
+       "      <td>19</td>\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>20</th>\n",
+       "      <td>20</td>\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>21</th>\n",
+       "      <td>21</td>\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>22</th>\n",
+       "      <td>22</td>\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>23</th>\n",
+       "      <td>23</td>\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>24</th>\n",
+       "      <td>24</td>\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>25</th>\n",
+       "      <td>25</td>\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>26</th>\n",
+       "      <td>26</td>\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>27</th>\n",
+       "      <td>27</td>\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>28</th>\n",
+       "      <td>28</td>\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>29</th>\n",
+       "      <td>29</td>\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>...</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",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1505</th>\n",
+       "      <td>1505</td>\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>1506</th>\n",
+       "      <td>1506</td>\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>1507</th>\n",
+       "      <td>1507</td>\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>1508</th>\n",
+       "      <td>1508</td>\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>1509</th>\n",
+       "      <td>1509</td>\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>1510</th>\n",
+       "      <td>1510</td>\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>1511</th>\n",
+       "      <td>1511</td>\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>1512</th>\n",
+       "      <td>1512</td>\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>1513</th>\n",
+       "      <td>1513</td>\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>1514</th>\n",
+       "      <td>1514</td>\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>1515</th>\n",
+       "      <td>1515</td>\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>1516</th>\n",
+       "      <td>1516</td>\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>1517</th>\n",
+       "      <td>1517</td>\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>1518</th>\n",
+       "      <td>1518</td>\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>1519</th>\n",
+       "      <td>1519</td>\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>1520</th>\n",
+       "      <td>1520</td>\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>1521</th>\n",
+       "      <td>1521</td>\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>1522</th>\n",
+       "      <td>1522</td>\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>1523</th>\n",
+       "      <td>1523</td>\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>1524</th>\n",
+       "      <td>1524</td>\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>1525</th>\n",
+       "      <td>1525</td>\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>1526</th>\n",
+       "      <td>1526</td>\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>1527</th>\n",
+       "      <td>1527</td>\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>1528</th>\n",
+       "      <td>1528</td>\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>1529</th>\n",
+       "      <td>1529</td>\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>1530</th>\n",
+       "      <td>1530</td>\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>1531</th>\n",
+       "      <td>1531</td>\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>1532</th>\n",
+       "      <td>1532</td>\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>1533</th>\n",
+       "      <td>1533</td>\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>1534</th>\n",
+       "      <td>1534</td>\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>1535 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Unnamed: 0    week  indicator    inc  inc_low  inc_up  inc100  \\\n",
+       "0              0  202018          7    861       88    1634       1   \n",
+       "1              1  202017          7    272        0     658       0   \n",
+       "2              2  202016          7    758       78    1438       1   \n",
+       "3              3  202015          7   1918      675    3161       3   \n",
+       "4              4  202014          7   3879     2227    5531       6   \n",
+       "5              5  202013          7   7326     5236    9416      11   \n",
+       "6              6  202012          7   8123     5790   10456      12   \n",
+       "7              7  202011          7  10198     7568   12828      15   \n",
+       "8              8  202010          7   9011     6691   11331      14   \n",
+       "9              9  202009          7  13631    10544   16718      21   \n",
+       "10            10  202008          7  10424     7708   13140      16   \n",
+       "11            11  202007          7   8959     6574   11344      14   \n",
+       "12            12  202006          7   9264     6925   11603      14   \n",
+       "13            13  202005          7   8505     6314   10696      13   \n",
+       "14            14  202004          7   7991     5831   10151      12   \n",
+       "15            15  202003          7   5968     4100    7836       9   \n",
+       "16            16  202002          7   6534     4530    8538      10   \n",
+       "17            17  202001          7   9835     7019   12651      15   \n",
+       "18            18  201952          7   7941     5246   10636      12   \n",
+       "19            19  201951          7   5823     3675    7971       9   \n",
+       "20            20  201950          7   6424     4276    8572      10   \n",
+       "21            21  201949          7   6621     4540    8702      10   \n",
+       "22            22  201948          7   5542     3383    7701       8   \n",
+       "23            23  201947          7   7536     5058   10014      11   \n",
+       "24            24  201946          7   2638     1316    3960       4   \n",
+       "25            25  201945          7   4492     2615    6369       7   \n",
+       "26            26  201944          7   5728     3627    7829       9   \n",
+       "27            27  201943          7   4834     2751    6917       7   \n",
+       "28            28  201942          7   6279     3989    8569      10   \n",
+       "29            29  201941          7   4130     2030    6230       6   \n",
+       "...          ...     ...        ...    ...      ...     ...     ...   \n",
+       "1505        1505  199126          7  17608    11304   23912      31   \n",
+       "1506        1506  199125          7  16169    10700   21638      28   \n",
+       "1507        1507  199124          7  16171    10071   22271      28   \n",
+       "1508        1508  199123          7  11947     7671   16223      21   \n",
+       "1509        1509  199122          7  15452     9953   20951      27   \n",
+       "1510        1510  199121          7  14903     8975   20831      26   \n",
+       "1511        1511  199120          7  19053    12742   25364      34   \n",
+       "1512        1512  199119          7  16739    11246   22232      29   \n",
+       "1513        1513  199118          7  21385    13882   28888      38   \n",
+       "1514        1514  199117          7  13462     8877   18047      24   \n",
+       "1515        1515  199116          7  14857    10068   19646      26   \n",
+       "1516        1516  199115          7  13975     9781   18169      25   \n",
+       "1517        1517  199114          7  12265     7684   16846      22   \n",
+       "1518        1518  199113          7   9567     6041   13093      17   \n",
+       "1519        1519  199112          7  10864     7331   14397      19   \n",
+       "1520        1520  199111          7  15574    11184   19964      27   \n",
+       "1521        1521  199110          7  16643    11372   21914      29   \n",
+       "1522        1522  199109          7  13741     8780   18702      24   \n",
+       "1523        1523  199108          7  13289     8813   17765      23   \n",
+       "1524        1524  199107          7  12337     8077   16597      22   \n",
+       "1525        1525  199106          7  10877     7013   14741      19   \n",
+       "1526        1526  199105          7  10442     6544   14340      18   \n",
+       "1527        1527  199104          7   7913     4563   11263      14   \n",
+       "1528        1528  199103          7  15387    10484   20290      27   \n",
+       "1529        1529  199102          7  16277    11046   21508      29   \n",
+       "1530        1530  199101          7  15565    10271   20859      27   \n",
+       "1531        1531  199052          7  19375    13295   25455      34   \n",
+       "1532        1532  199051          7  19080    13807   24353      34   \n",
+       "1533        1533  199050          7  11079     6660   15498      20   \n",
+       "1534        1534  199049          7   1143        0    2610       2   \n",
+       "\n",
+       "      inc100_low  inc100_up geo_insee geo_name  \n",
+       "0              0          2        FR   France  \n",
+       "1              0          1        FR   France  \n",
+       "2              0          2        FR   France  \n",
+       "3              1          5        FR   France  \n",
+       "4              3          9        FR   France  \n",
+       "5              8         14        FR   France  \n",
+       "6              8         16        FR   France  \n",
+       "7             11         19        FR   France  \n",
+       "8             10         18        FR   France  \n",
+       "9             16         26        FR   France  \n",
+       "10            12         20        FR   France  \n",
+       "11            10         18        FR   France  \n",
+       "12            10         18        FR   France  \n",
+       "13            10         16        FR   France  \n",
+       "14             9         15        FR   France  \n",
+       "15             6         12        FR   France  \n",
+       "16             7         13        FR   France  \n",
+       "17            11         19        FR   France  \n",
+       "18             8         16        FR   France  \n",
+       "19             6         12        FR   France  \n",
+       "20             7         13        FR   France  \n",
+       "21             7         13        FR   France  \n",
+       "22             5         11        FR   France  \n",
+       "23             7         15        FR   France  \n",
+       "24             2          6        FR   France  \n",
+       "25             4         10        FR   France  \n",
+       "26             6         12        FR   France  \n",
+       "27             4         10        FR   France  \n",
+       "28             7         13        FR   France  \n",
+       "29             3          9        FR   France  \n",
+       "...          ...        ...       ...      ...  \n",
+       "1505          20         42        FR   France  \n",
+       "1506          18         38        FR   France  \n",
+       "1507          17         39        FR   France  \n",
+       "1508          13         29        FR   France  \n",
+       "1509          17         37        FR   France  \n",
+       "1510          16         36        FR   France  \n",
+       "1511          23         45        FR   France  \n",
+       "1512          19         39        FR   France  \n",
+       "1513          25         51        FR   France  \n",
+       "1514          16         32        FR   France  \n",
+       "1515          18         34        FR   France  \n",
+       "1516          18         32        FR   France  \n",
+       "1517          14         30        FR   France  \n",
+       "1518          11         23        FR   France  \n",
+       "1519          13         25        FR   France  \n",
+       "1520          19         35        FR   France  \n",
+       "1521          20         38        FR   France  \n",
+       "1522          15         33        FR   France  \n",
+       "1523          15         31        FR   France  \n",
+       "1524          15         29        FR   France  \n",
+       "1525          12         26        FR   France  \n",
+       "1526          11         25        FR   France  \n",
+       "1527           8         20        FR   France  \n",
+       "1528          18         36        FR   France  \n",
+       "1529          20         38        FR   France  \n",
+       "1530          18         36        FR   France  \n",
+       "1531          23         45        FR   France  \n",
+       "1532          25         43        FR   France  \n",
+       "1533          12         28        FR   France  \n",
+       "1534           0          5        FR   France  \n",
+       "\n",
+       "[1535 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fname = data_url.split('/')[-1]\n",
+    "if os.path.isfile(fname):\n",
+    "    print(\"Reading local version\")\n",
+    "    raw_data = pd.read_csv(fname, comment='#')\n",
+    "else:\n",
+    "    print(\"Downloading remote version at\", data_url)\n",
+    "    raw_data = pd.read_csv(data_url, comment='#')\n",
+    "    raw_data.to_csv(fname)\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Y a-t-il des points manquants dans ce jeux de données ? A priori, non."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</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: [Unnamed: 0, week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data[raw_data.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nous copions les données en éliminant les éventuelles lignes sans données."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = raw_data.dropna().copy()"
+   ]
+  },
+  {
+   "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 écrivons une petite fonction Python pour cela. Ensuite, nous l'appliquons à tous les points de nos donnés. Les résultats vont dans une nouvelle colonne 'period'.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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 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": 7,
+   "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 le début de la période qui suit, la différence temporelle doit être zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\" d'une seconde.\n",
+    "\n",
+    "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives entre lesquelles il manque une semaine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "periods = sorted_data.index\n",
+    "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
+    "    delta = p2.to_timestamp() - p1.end_time\n",
+    "    if delta > pd.Timedelta('1s'):\n",
+    "        print(p1, p2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Un premier regard sur les données !"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9eb8107320>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data['inc'].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9e7ffc3a20>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data['inc'][-200:].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Etude de l'incidence annuelle\n",
+    "\n",
+    "Etant donné que le pic de l'épidémie se situe en hiver, à cheval entre deux années civiles, nous définissons la période de référence entre deux minima de l'incidence, du 1er septembre de l'année $N$ au 1er septembre de l'année $N+1$.\n",
+    "\n",
+    "Notre tâche est un peu compliquée par le fait que l'année ne comporte pas un nombre entier de semaines. Nous modifions donc un peu nos périodes de référence: à la place du 1er septembre de chaque année, nous utilisons le premier jour de la semaine qui contient le 1er septembre.\n",
+    "\n",
+    "Comme l'incidence de syndrome grippal est très faible en été, cette modification ne risque pas de fausser nos conclusions.\n",
+    "\n",
+    "Encore un petit détail: les données commencent en décembre 1990, ce qui rend la première année incomplète. Nous commençons donc l'analyse à partir du 1er septembre 1991."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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 août, 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": 12,
+   "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": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9e7fed53c8>"
+      ]
+     },
+     "execution_count": 13,
+     "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": "code",
+   "execution_count": 14,
+   "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": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false
+   },
+   "source": [
+    "On ne souhaite que les années min et max d'incidence."
+   ]
+  }
+ ],
  "metadata": {
+  "hide_code_all_hidden": false,
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -16,10 +1505,9 @@
    "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-PAY-7.csv b/module3/exo2/incidence-PAY-7.csv
new file mode 100644
index 0000000000000000000000000000000000000000..dc6780cdbec9d1b9bed165b7c411e26be5ae4c14
--- /dev/null
+++ b/module3/exo2/incidence-PAY-7.csv
@@ -0,0 +1,1536 @@
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+1388,199338,7,2179,888,3470,4,2,6,FR,France
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