diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..c268532acec5942a3abbb39c82830c8abb40246b 100644
--- a/module3/exo2/exercice.ipynb
+++ b/module3/exo2/exercice.ipynb
@@ -1,5 +1,1356 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Analyse de l'incidence de la varicelle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import isoweek"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Les données de l'incidence de la varicelle sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/)\n",
+    "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": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv?v=8akq5\""
+   ]
+  },
+  {
+   "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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>week</th>\n",
+       "      <th>indicator</th>\n",
+       "      <th>inc</th>\n",
+       "      <th>inc_low</th>\n",
+       "      <th>inc_up</th>\n",
+       "      <th>inc100</th>\n",
+       "      <th>inc100_low</th>\n",
+       "      <th>inc100_up</th>\n",
+       "      <th>geo_insee</th>\n",
+       "      <th>geo_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>202345</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6118</td>\n",
+       "      <td>3049</td>\n",
+       "      <td>9187</td>\n",
+       "      <td>9</td>\n",
+       "      <td>4</td>\n",
+       "      <td>14</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202344</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3758</td>\n",
+       "      <td>1702</td>\n",
+       "      <td>5814</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202343</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3891</td>\n",
+       "      <td>1675</td>\n",
+       "      <td>6107</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>3</th>\n",
+       "      <td>202342</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3968</td>\n",
+       "      <td>1212</td>\n",
+       "      <td>6724</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>10</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202341</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3356</td>\n",
+       "      <td>1764</td>\n",
+       "      <td>4948</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>202340</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2845</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>4280</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>6</th>\n",
+       "      <td>202339</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1739</td>\n",
+       "      <td>629</td>\n",
+       "      <td>2849</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>7</th>\n",
+       "      <td>202338</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1663</td>\n",
+       "      <td>274</td>\n",
+       "      <td>3052</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>202337</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1122</td>\n",
+       "      <td>223</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>202336</td>\n",
+       "      <td>7</td>\n",
+       "      <td>726</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1442</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>202335</td>\n",
+       "      <td>7</td>\n",
+       "      <td>961</td>\n",
+       "      <td>96</td>\n",
+       "      <td>1826</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>11</th>\n",
+       "      <td>202334</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1168</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2327</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>202333</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3308</td>\n",
+       "      <td>1184</td>\n",
+       "      <td>5432</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>202332</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7996</td>\n",
+       "      <td>1120</td>\n",
+       "      <td>14872</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2</td>\n",
+       "      <td>22</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>202331</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3318</td>\n",
+       "      <td>1398</td>\n",
+       "      <td>5238</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>202330</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5821</td>\n",
+       "      <td>3269</td>\n",
+       "      <td>8373</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>13</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>202329</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13558</td>\n",
+       "      <td>8297</td>\n",
+       "      <td>18819</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>17</th>\n",
+       "      <td>202328</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6700</td>\n",
+       "      <td>4043</td>\n",
+       "      <td>9357</td>\n",
+       "      <td>10</td>\n",
+       "      <td>6</td>\n",
+       "      <td>14</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>202327</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7253</td>\n",
+       "      <td>4599</td>\n",
+       "      <td>9907</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>19</th>\n",
+       "      <td>202326</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9192</td>\n",
+       "      <td>6223</td>\n",
+       "      <td>12161</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>202325</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11498</td>\n",
+       "      <td>8257</td>\n",
+       "      <td>14739</td>\n",
+       "      <td>17</td>\n",
+       "      <td>12</td>\n",
+       "      <td>22</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>202324</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11115</td>\n",
+       "      <td>7968</td>\n",
+       "      <td>14262</td>\n",
+       "      <td>17</td>\n",
+       "      <td>12</td>\n",
+       "      <td>22</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>202323</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12563</td>\n",
+       "      <td>6134</td>\n",
+       "      <td>18992</td>\n",
+       "      <td>19</td>\n",
+       "      <td>9</td>\n",
+       "      <td>29</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>202322</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12184</td>\n",
+       "      <td>8125</td>\n",
+       "      <td>16243</td>\n",
+       "      <td>18</td>\n",
+       "      <td>12</td>\n",
+       "      <td>24</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>202321</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11349</td>\n",
+       "      <td>7598</td>\n",
+       "      <td>15100</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>25</th>\n",
+       "      <td>202320</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9000</td>\n",
+       "      <td>4615</td>\n",
+       "      <td>13385</td>\n",
+       "      <td>14</td>\n",
+       "      <td>7</td>\n",
+       "      <td>21</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>202319</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9344</td>\n",
+       "      <td>6091</td>\n",
+       "      <td>12597</td>\n",
+       "      <td>14</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>202318</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10671</td>\n",
+       "      <td>7291</td>\n",
+       "      <td>14051</td>\n",
+       "      <td>16</td>\n",
+       "      <td>11</td>\n",
+       "      <td>21</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>202317</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9184</td>\n",
+       "      <td>6162</td>\n",
+       "      <td>12206</td>\n",
+       "      <td>14</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>202316</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11387</td>\n",
+       "      <td>8014</td>\n",
+       "      <td>14760</td>\n",
+       "      <td>17</td>\n",
+       "      <td>12</td>\n",
+       "      <td>22</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>1689</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>1690</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>1691</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>1692</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>1693</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>1694</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>1695</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>1696</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>1697</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>1698</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>1699</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>1700</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>1701</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>1702</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>1703</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>1704</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>1705</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>1706</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>1707</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>1708</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>1709</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>1710</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>1711</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>1712</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>1713</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>1714</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>1715</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>1716</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>1717</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>1718</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>1719 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
+       "0     202345          7   6118     3049    9187       9           4   \n",
+       "1     202344          7   3758     1702    5814       6           3   \n",
+       "2     202343          7   3891     1675    6107       6           3   \n",
+       "3     202342          7   3968     1212    6724       6           2   \n",
+       "4     202341          7   3356     1764    4948       5           3   \n",
+       "5     202340          7   2845     1410    4280       4           2   \n",
+       "6     202339          7   1739      629    2849       3           1   \n",
+       "7     202338          7   1663      274    3052       3           1   \n",
+       "8     202337          7   1122      223    2021       2           1   \n",
+       "9     202336          7    726       10    1442       1           0   \n",
+       "10    202335          7    961       96    1826       1           0   \n",
+       "11    202334          7   1168        9    2327       2           0   \n",
+       "12    202333          7   3308     1184    5432       5           2   \n",
+       "13    202332          7   7996     1120   14872      12           2   \n",
+       "14    202331          7   3318     1398    5238       5           2   \n",
+       "15    202330          7   5821     3269    8373       9           5   \n",
+       "16    202329          7  13558     8297   18819      20          12   \n",
+       "17    202328          7   6700     4043    9357      10           6   \n",
+       "18    202327          7   7253     4599    9907      11           7   \n",
+       "19    202326          7   9192     6223   12161      14          10   \n",
+       "20    202325          7  11498     8257   14739      17          12   \n",
+       "21    202324          7  11115     7968   14262      17          12   \n",
+       "22    202323          7  12563     6134   18992      19           9   \n",
+       "23    202322          7  12184     8125   16243      18          12   \n",
+       "24    202321          7  11349     7598   15100      17          11   \n",
+       "25    202320          7   9000     4615   13385      14           7   \n",
+       "26    202319          7   9344     6091   12597      14           9   \n",
+       "27    202318          7  10671     7291   14051      16          11   \n",
+       "28    202317          7   9184     6162   12206      14           9   \n",
+       "29    202316          7  11387     8014   14760      17          12   \n",
+       "...      ...        ...    ...      ...     ...     ...         ...   \n",
+       "1689  199126          7  17608    11304   23912      31          20   \n",
+       "1690  199125          7  16169    10700   21638      28          18   \n",
+       "1691  199124          7  16171    10071   22271      28          17   \n",
+       "1692  199123          7  11947     7671   16223      21          13   \n",
+       "1693  199122          7  15452     9953   20951      27          17   \n",
+       "1694  199121          7  14903     8975   20831      26          16   \n",
+       "1695  199120          7  19053    12742   25364      34          23   \n",
+       "1696  199119          7  16739    11246   22232      29          19   \n",
+       "1697  199118          7  21385    13882   28888      38          25   \n",
+       "1698  199117          7  13462     8877   18047      24          16   \n",
+       "1699  199116          7  14857    10068   19646      26          18   \n",
+       "1700  199115          7  13975     9781   18169      25          18   \n",
+       "1701  199114          7  12265     7684   16846      22          14   \n",
+       "1702  199113          7   9567     6041   13093      17          11   \n",
+       "1703  199112          7  10864     7331   14397      19          13   \n",
+       "1704  199111          7  15574    11184   19964      27          19   \n",
+       "1705  199110          7  16643    11372   21914      29          20   \n",
+       "1706  199109          7  13741     8780   18702      24          15   \n",
+       "1707  199108          7  13289     8813   17765      23          15   \n",
+       "1708  199107          7  12337     8077   16597      22          15   \n",
+       "1709  199106          7  10877     7013   14741      19          12   \n",
+       "1710  199105          7  10442     6544   14340      18          11   \n",
+       "1711  199104          7   7913     4563   11263      14           8   \n",
+       "1712  199103          7  15387    10484   20290      27          18   \n",
+       "1713  199102          7  16277    11046   21508      29          20   \n",
+       "1714  199101          7  15565    10271   20859      27          18   \n",
+       "1715  199052          7  19375    13295   25455      34          23   \n",
+       "1716  199051          7  19080    13807   24353      34          25   \n",
+       "1717  199050          7  11079     6660   15498      20          12   \n",
+       "1718  199049          7   1143        0    2610       2           0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0            14        FR   France  \n",
+       "1             9        FR   France  \n",
+       "2             9        FR   France  \n",
+       "3            10        FR   France  \n",
+       "4             7        FR   France  \n",
+       "5             6        FR   France  \n",
+       "6             5        FR   France  \n",
+       "7             5        FR   France  \n",
+       "8             3        FR   France  \n",
+       "9             2        FR   France  \n",
+       "10            2        FR   France  \n",
+       "11            4        FR   France  \n",
+       "12            8        FR   France  \n",
+       "13           22        FR   France  \n",
+       "14            8        FR   France  \n",
+       "15           13        FR   France  \n",
+       "16           28        FR   France  \n",
+       "17           14        FR   France  \n",
+       "18           15        FR   France  \n",
+       "19           18        FR   France  \n",
+       "20           22        FR   France  \n",
+       "21           22        FR   France  \n",
+       "22           29        FR   France  \n",
+       "23           24        FR   France  \n",
+       "24           23        FR   France  \n",
+       "25           21        FR   France  \n",
+       "26           19        FR   France  \n",
+       "27           21        FR   France  \n",
+       "28           19        FR   France  \n",
+       "29           22        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1689         42        FR   France  \n",
+       "1690         38        FR   France  \n",
+       "1691         39        FR   France  \n",
+       "1692         29        FR   France  \n",
+       "1693         37        FR   France  \n",
+       "1694         36        FR   France  \n",
+       "1695         45        FR   France  \n",
+       "1696         39        FR   France  \n",
+       "1697         51        FR   France  \n",
+       "1698         32        FR   France  \n",
+       "1699         34        FR   France  \n",
+       "1700         32        FR   France  \n",
+       "1701         30        FR   France  \n",
+       "1702         23        FR   France  \n",
+       "1703         25        FR   France  \n",
+       "1704         35        FR   France  \n",
+       "1705         38        FR   France  \n",
+       "1706         33        FR   France  \n",
+       "1707         31        FR   France  \n",
+       "1708         29        FR   France  \n",
+       "1709         26        FR   France  \n",
+       "1710         25        FR   France  \n",
+       "1711         20        FR   France  \n",
+       "1712         36        FR   France  \n",
+       "1713         38        FR   France  \n",
+       "1714         36        FR   France  \n",
+       "1715         45        FR   France  \n",
+       "1716         43        FR   France  \n",
+       "1717         28        FR   France  \n",
+       "1718          5        FR   France  \n",
+       "\n",
+       "[1719 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv(data_url, encoding = 'iso-8859-1', skiprows=1)\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Y a-t-il des points manquants dans ce jeux de données ? Non."
+   ]
+  },
+  {
+   "cell_type": "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\n",
+    "de nombre entier. C'est comme ça que Pandas les interprète."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "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"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "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'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def convert_week(year_and_week_int):\n",
+    "    year_and_week_str = str(year_and_week_int)\n",
+    "    year = int(year_and_week_str[:4])\n",
+    "    week = int(year_and_week_str[4:])\n",
+    "    w = isoweek.Week(year, week)\n",
+    "    return pd.Period(w.day(0), 'W')\n",
+    "\n",
+    "raw_data['period'] = [convert_week(yw) for yw in raw_data['week']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Il restent deux petites modifications à faire."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Premièrement, nous définissons les périodes d'observation comme nouvel index de notre jeux de données. Ceci en fait\n",
+    "une suite chronologique, ce qui sera pratique par la suite."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Deuxièmement, nous trions les points par période, dans le sens chronologique."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted_data = raw_data.set_index('period').sort_index()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Un premier regard sur les données !"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6e5ee6d4e0>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data['inc'].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Un zoom sur les dernières années montre que "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6e5cce79b0>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data['inc'][-200:].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##Etude de l'incidence annuelle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Etant donné que le pic de l'épidémie se situe en hiver, à cheval entre deux années civiles, nous définissons la période de référence entre deux minima de l'incidence, du 1er août de l'année N au 1er août de l'année N+1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "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 août de chaque année, nous utilisons le premier jour de la semaine qui contient le 1er août."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Comme l'incidence de la varicelle est très faible en été, cette modification ne risque pas de fausser nos conclusions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "L'année 1990, la première année incomplète. Nous commençons donc l'analyse en 1991."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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. \n",
+    "\n",
+    "Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.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": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year = []\n",
+    "yearly_incidence = []\n",
+    "for week1, week2 in zip(first_september_week[:-1],\n",
+    "                        first_september_week[1:]):\n",
+    "    one_year = sorted_data['inc'][week1:week2-1]\n",
+    "    assert abs(len(one_year)-52) < 2\n",
+    "    yearly_incidence.append(one_year.sum())\n",
+    "    year.append(week2.year)\n",
+    "yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Voici les incidences annuelles."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6e5f35f9b0>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yearly_incidence.plot(style='*')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2020    221186\n",
+       "2021    376290\n",
+       "2002    516689\n",
+       "2018    542312\n",
+       "2017    551041\n",
+       "1996    564901\n",
+       "2019    584066\n",
+       "2015    604382\n",
+       "2000    617597\n",
+       "2001    619041\n",
+       "2012    624573\n",
+       "2005    628464\n",
+       "2006    632833\n",
+       "2022    641397\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": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +1367,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-