From dd93e4b77bc2ece7d1de3bfd61737c17f527bce0 Mon Sep 17 00:00:00 2001
From: 82e5a278bec86aaf9c3baaadd03ac681
 <82e5a278bec86aaf9c3baaadd03ac681@app-learninglab.inria.fr>
Date: Sun, 13 Dec 2020 15:03:41 +0000
Subject: [PATCH] module 3 exo 2

---
 module3/exo2/exercice.ipynb | 1430 +++++++++++++++++++++++++++++++-
 module3/exo2/varicelle.csv  | 1568 +++++++++++++++++++++++++++++++++++
 2 files changed, 2995 insertions(+), 3 deletions(-)
 create mode 100644 module3/exo2/varicelle.csv

diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb
index 0bbbe37..ccee356 100644
--- a/module3/exo2/exercice.ipynb
+++ b/module3/exo2/exercice.ipynb
@@ -1,5 +1,1430 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Incidence de la varicelle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import isoweek\n",
+    "import requests\n",
+    "import os\n",
+    "import urllib.request"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Si le fichier 'incidence-PAY-3.csv' n'existe pas dans le repertoire courant alors, le jeu de données complet, qui commence en 1990 et se termine avec une semaine récente, est directement téléchargé et stocké dans ce fichier pour en conserver une trace locale. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_file = \"varicelle.csv\"\n",
+    "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"\n",
+    "if not os.path.exists(data_file):\n",
+    "    urllib.request.urlretrieve(data_url, data_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n",
+    "\n",
+    "| Nom de colonne | Libellé de colonne                                                                                                                |\n",
+    "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n",
+    "| week           | Semaine calendaire (ISO 8601)                                                                                                     |\n",
+    "| indicator      | Code de l'indicateur de surveillance                                                                                              |\n",
+    "| inc            | Estimation de l'incidence de consultations en nombre de cas                                                                       |\n",
+    "| inc_low        | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation                                                     |\n",
+    "| inc_up         | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation                                                     |\n",
+    "| inc100         | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants)                                   |\n",
+    "| inc100_low     | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
+    "| inc100_up      | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
+    "| geo_insee      | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/                            |\n",
+    "| geo_name       | Libellé de la zone géographique (ce libellé peut être modifié sans préavis)                                                       |\n",
+    "\n",
+    "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>week</th>\n",
+       "      <th>indicator</th>\n",
+       "      <th>inc</th>\n",
+       "      <th>inc_low</th>\n",
+       "      <th>inc_up</th>\n",
+       "      <th>inc100</th>\n",
+       "      <th>inc100_low</th>\n",
+       "      <th>inc100_up</th>\n",
+       "      <th>geo_insee</th>\n",
+       "      <th>geo_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>202049</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5164</td>\n",
+       "      <td>2880</td>\n",
+       "      <td>7448</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>1</th>\n",
+       "      <td>202048</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6747</td>\n",
+       "      <td>4331</td>\n",
+       "      <td>9163</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>2</th>\n",
+       "      <td>202047</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4999</td>\n",
+       "      <td>2963</td>\n",
+       "      <td>7035</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>3</th>\n",
+       "      <td>202046</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3752</td>\n",
+       "      <td>1963</td>\n",
+       "      <td>5541</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>4</th>\n",
+       "      <td>202045</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3696</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>5376</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>202044</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4391</td>\n",
+       "      <td>2375</td>\n",
+       "      <td>6407</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>6</th>\n",
+       "      <td>202043</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4376</td>\n",
+       "      <td>2505</td>\n",
+       "      <td>6247</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>202042</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4000</td>\n",
+       "      <td>1979</td>\n",
+       "      <td>6021</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>8</th>\n",
+       "      <td>202041</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3961</td>\n",
+       "      <td>2099</td>\n",
+       "      <td>5823</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>9</th>\n",
+       "      <td>202040</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2078</td>\n",
+       "      <td>675</td>\n",
+       "      <td>3481</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>202039</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1049</td>\n",
+       "      <td>237</td>\n",
+       "      <td>1861</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>11</th>\n",
+       "      <td>202038</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2253</td>\n",
+       "      <td>782</td>\n",
+       "      <td>3724</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>12</th>\n",
+       "      <td>202037</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1584</td>\n",
+       "      <td>405</td>\n",
+       "      <td>2763</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>202036</td>\n",
+       "      <td>7</td>\n",
+       "      <td>919</td>\n",
+       "      <td>100</td>\n",
+       "      <td>1738</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>14</th>\n",
+       "      <td>202035</td>\n",
+       "      <td>7</td>\n",
+       "      <td>828</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1694</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>15</th>\n",
+       "      <td>202034</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2272</td>\n",
+       "      <td>371</td>\n",
+       "      <td>4173</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>202033</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1284</td>\n",
+       "      <td>177</td>\n",
+       "      <td>2391</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>17</th>\n",
+       "      <td>202032</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2650</td>\n",
+       "      <td>689</td>\n",
+       "      <td>4611</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>202031</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1303</td>\n",
+       "      <td>100</td>\n",
+       "      <td>2506</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>19</th>\n",
+       "      <td>202030</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1385</td>\n",
+       "      <td>75</td>\n",
+       "      <td>2695</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>20</th>\n",
+       "      <td>202029</td>\n",
+       "      <td>7</td>\n",
+       "      <td>841</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1672</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>21</th>\n",
+       "      <td>202028</td>\n",
+       "      <td>7</td>\n",
+       "      <td>728</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1515</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>22</th>\n",
+       "      <td>202027</td>\n",
+       "      <td>7</td>\n",
+       "      <td>986</td>\n",
+       "      <td>149</td>\n",
+       "      <td>1823</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>23</th>\n",
+       "      <td>202026</td>\n",
+       "      <td>7</td>\n",
+       "      <td>694</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1454</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>24</th>\n",
+       "      <td>202025</td>\n",
+       "      <td>7</td>\n",
+       "      <td>228</td>\n",
+       "      <td>0</td>\n",
+       "      <td>597</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>25</th>\n",
+       "      <td>202024</td>\n",
+       "      <td>7</td>\n",
+       "      <td>388</td>\n",
+       "      <td>0</td>\n",
+       "      <td>959</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>26</th>\n",
+       "      <td>202023</td>\n",
+       "      <td>7</td>\n",
+       "      <td>558</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1115</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>202022</td>\n",
+       "      <td>7</td>\n",
+       "      <td>277</td>\n",
+       "      <td>0</td>\n",
+       "      <td>633</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>202021</td>\n",
+       "      <td>7</td>\n",
+       "      <td>602</td>\n",
+       "      <td>36</td>\n",
+       "      <td>1168</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>202020</td>\n",
+       "      <td>7</td>\n",
+       "      <td>824</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1628</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</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>1536</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>1537</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>1538</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>1539</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>1540</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>1541</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>1542</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>1543</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>1544</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>1545</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>1546</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>1547</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>1548</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>1549</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>1550</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>1551</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>1552</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>1553</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>1554</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>1555</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>1556</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>1557</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>1558</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>1559</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>1560</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>1561</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>1562</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>1563</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>1564</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>1565</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>1566 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
+       "0     202049          7   5164     2880    7448       8           5   \n",
+       "1     202048          7   6747     4331    9163      10           6   \n",
+       "2     202047          7   4999     2963    7035       8           5   \n",
+       "3     202046          7   3752     1963    5541       6           3   \n",
+       "4     202045          7   3696     2016    5376       6           3   \n",
+       "5     202044          7   4391     2375    6407       7           4   \n",
+       "6     202043          7   4376     2505    6247       7           4   \n",
+       "7     202042          7   4000     1979    6021       6           3   \n",
+       "8     202041          7   3961     2099    5823       6           3   \n",
+       "9     202040          7   2078      675    3481       3           1   \n",
+       "10    202039          7   1049      237    1861       2           1   \n",
+       "11    202038          7   2253      782    3724       3           1   \n",
+       "12    202037          7   1584      405    2763       2           0   \n",
+       "13    202036          7    919      100    1738       1           0   \n",
+       "14    202035          7    828        0    1694       1           0   \n",
+       "15    202034          7   2272      371    4173       3           0   \n",
+       "16    202033          7   1284      177    2391       2           0   \n",
+       "17    202032          7   2650      689    4611       4           1   \n",
+       "18    202031          7   1303      100    2506       2           0   \n",
+       "19    202030          7   1385       75    2695       2           0   \n",
+       "20    202029          7    841       10    1672       1           0   \n",
+       "21    202028          7    728        0    1515       1           0   \n",
+       "22    202027          7    986      149    1823       1           0   \n",
+       "23    202026          7    694        0    1454       1           0   \n",
+       "24    202025          7    228        0     597       0           0   \n",
+       "25    202024          7    388        0     959       1           0   \n",
+       "26    202023          7    558        1    1115       1           0   \n",
+       "27    202022          7    277        0     633       0           0   \n",
+       "28    202021          7    602       36    1168       1           0   \n",
+       "29    202020          7    824       20    1628       1           0   \n",
+       "...      ...        ...    ...      ...     ...     ...         ...   \n",
+       "1536  199126          7  17608    11304   23912      31          20   \n",
+       "1537  199125          7  16169    10700   21638      28          18   \n",
+       "1538  199124          7  16171    10071   22271      28          17   \n",
+       "1539  199123          7  11947     7671   16223      21          13   \n",
+       "1540  199122          7  15452     9953   20951      27          17   \n",
+       "1541  199121          7  14903     8975   20831      26          16   \n",
+       "1542  199120          7  19053    12742   25364      34          23   \n",
+       "1543  199119          7  16739    11246   22232      29          19   \n",
+       "1544  199118          7  21385    13882   28888      38          25   \n",
+       "1545  199117          7  13462     8877   18047      24          16   \n",
+       "1546  199116          7  14857    10068   19646      26          18   \n",
+       "1547  199115          7  13975     9781   18169      25          18   \n",
+       "1548  199114          7  12265     7684   16846      22          14   \n",
+       "1549  199113          7   9567     6041   13093      17          11   \n",
+       "1550  199112          7  10864     7331   14397      19          13   \n",
+       "1551  199111          7  15574    11184   19964      27          19   \n",
+       "1552  199110          7  16643    11372   21914      29          20   \n",
+       "1553  199109          7  13741     8780   18702      24          15   \n",
+       "1554  199108          7  13289     8813   17765      23          15   \n",
+       "1555  199107          7  12337     8077   16597      22          15   \n",
+       "1556  199106          7  10877     7013   14741      19          12   \n",
+       "1557  199105          7  10442     6544   14340      18          11   \n",
+       "1558  199104          7   7913     4563   11263      14           8   \n",
+       "1559  199103          7  15387    10484   20290      27          18   \n",
+       "1560  199102          7  16277    11046   21508      29          20   \n",
+       "1561  199101          7  15565    10271   20859      27          18   \n",
+       "1562  199052          7  19375    13295   25455      34          23   \n",
+       "1563  199051          7  19080    13807   24353      34          25   \n",
+       "1564  199050          7  11079     6660   15498      20          12   \n",
+       "1565  199049          7   1143        0    2610       2           0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0            11        FR   France  \n",
+       "1            14        FR   France  \n",
+       "2            11        FR   France  \n",
+       "3             9        FR   France  \n",
+       "4             9        FR   France  \n",
+       "5            10        FR   France  \n",
+       "6            10        FR   France  \n",
+       "7             9        FR   France  \n",
+       "8             9        FR   France  \n",
+       "9             5        FR   France  \n",
+       "10            3        FR   France  \n",
+       "11            5        FR   France  \n",
+       "12            4        FR   France  \n",
+       "13            2        FR   France  \n",
+       "14            2        FR   France  \n",
+       "15            6        FR   France  \n",
+       "16            4        FR   France  \n",
+       "17            7        FR   France  \n",
+       "18            4        FR   France  \n",
+       "19            4        FR   France  \n",
+       "20            2        FR   France  \n",
+       "21            2        FR   France  \n",
+       "22            2        FR   France  \n",
+       "23            2        FR   France  \n",
+       "24            1        FR   France  \n",
+       "25            2        FR   France  \n",
+       "26            2        FR   France  \n",
+       "27            1        FR   France  \n",
+       "28            2        FR   France  \n",
+       "29            2        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1536         42        FR   France  \n",
+       "1537         38        FR   France  \n",
+       "1538         39        FR   France  \n",
+       "1539         29        FR   France  \n",
+       "1540         37        FR   France  \n",
+       "1541         36        FR   France  \n",
+       "1542         45        FR   France  \n",
+       "1543         39        FR   France  \n",
+       "1544         51        FR   France  \n",
+       "1545         32        FR   France  \n",
+       "1546         34        FR   France  \n",
+       "1547         32        FR   France  \n",
+       "1548         30        FR   France  \n",
+       "1549         23        FR   France  \n",
+       "1550         25        FR   France  \n",
+       "1551         35        FR   France  \n",
+       "1552         38        FR   France  \n",
+       "1553         33        FR   France  \n",
+       "1554         31        FR   France  \n",
+       "1555         29        FR   France  \n",
+       "1556         26        FR   France  \n",
+       "1557         25        FR   France  \n",
+       "1558         20        FR   France  \n",
+       "1559         36        FR   France  \n",
+       "1560         38        FR   France  \n",
+       "1561         36        FR   France  \n",
+       "1562         45        FR   France  \n",
+       "1563         43        FR   France  \n",
+       "1564         28        FR   France  \n",
+       "1565          5        FR   France  \n",
+       "\n",
+       "[1566 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('./varicelle.csv',skiprows=1)\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Y a-t-il des points manquants dans ce jeux de données ? Non, des données sont visiblement présentes pour toutes les semaines depuis fin 1990."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>week</th>\n",
+       "      <th>indicator</th>\n",
+       "      <th>inc</th>\n",
+       "      <th>inc_low</th>\n",
+       "      <th>inc_up</th>\n",
+       "      <th>inc100</th>\n",
+       "      <th>inc100_low</th>\n",
+       "      <th>inc100_up</th>\n",
+       "      <th>geo_insee</th>\n",
+       "      <th>geo_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data[raw_data.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nos données utilisent une convention inhabituelle: le numéro de\n",
+    "semaine est collé à l'année, donnant l'impression qu'il s'agit\n",
+    "de nombre entier. C'est comme ça que Pandas les interprète.\n",
+    "  \n",
+    "Un deuxième problème est que Pandas ne comprend pas les numéros de\n",
+    "semaine.  Il faut lui fournir les dates de début et de fin de\n",
+    "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n",
+    "\n",
+    "Comme la conversion des semaines est devenu assez complexe, nous\n",
+    "écrivons une petite fonction Python pour cela. Ensuite, nous\n",
+    "l'appliquons à tous les points de nos donnés. Les résultats vont\n",
+    "dans une nouvelle colonne 'period'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = raw_data\n",
+    "def convert_week(year_and_week_int):\n",
+    "    year_and_week_str = str(year_and_week_int)\n",
+    "    year = int(year_and_week_str[:4])\n",
+    "    week = int(year_and_week_str[4:])\n",
+    "    w = isoweek.Week(year, week)\n",
+    "    return pd.Period(w.day(0), 'W')\n",
+    "\n",
+    "data['period'] = [convert_week(yw) for yw in data['week']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Il 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\n",
+    "le début de la période qui suit, la différence temporelle doit être\n",
+    "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n",
+    "d'une seconde.\n",
+    "\n",
+    "Ceci s'avère tout à fait juste du fait qu'aucune donnée a été retiré"
+   ]
+  },
+  {
+   "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 0x7f695fb03588>"
+      ]
+     },
+     "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": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f695d23a208>"
+      ]
+     },
+     "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'][:54].plot()\n",
+    "#sorted_data['inc'][-200:-100].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Etude de l'incidence annuelle\n",
+    "Vraisemblablement, le pic a lieu vers se situe entre deux année civiles et est centré en avril. Nous définissons la période de référence entre deux minima qui ont lieux au début du mois septembre (de l'année $N$ et $N+1$).\n",
+    "\n",
+    "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n",
+    "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n",
+    "de référence: à la place du 1er septembre de chaque année, nous utilisons le\n",
+    "premier jour de la semaine qui contient le 1er septembre.\n",
+    "\n",
+    "Comme l'incidence de syndrome grippal est très faible à cette période, cette\n",
+    "modification ne risque pas de fausser nos conclusions (bien que la période de faible incidence est courte).\n",
+    "\n",
+    "Encore un petit détail: les données commencent en décembre 1990, ce qui\n",
+    "rend la première année incomplète. Nous commençons donc l'analyse en 1991."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
+    "                     for y in range(1991,\n",
+    "                                    sorted_data.index[-1].year)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "En partant de cette liste des semaines qui contiennent un 1er septembre, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n",
+    "\n",
+    "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f695d176710>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "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": 19,
+   "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": 19,
+     "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 +1441,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-
diff --git a/module3/exo2/varicelle.csv b/module3/exo2/varicelle.csv
new file mode 100644
index 0000000..2e0aee2
--- /dev/null
+++ b/module3/exo2/varicelle.csv
@@ -0,0 +1,1568 @@
+# @source="réseau Sentinelles, INSERM, Sorbonne Université, http://www.sentiweb.fr", @meta={"period":[199049,202049],"geo":["PAY","1"],"geo_ref":"insee","indicator":"7","type":"all","conf_int":true,"compact":false}, @date=2020-12-13T15:33:49+01:00
+week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name
+202049,7,5164,2880,7448,8,5,11,FR,France
+202048,7,6747,4331,9163,10,6,14,FR,France
+202047,7,4999,2963,7035,8,5,11,FR,France
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+202004,7,7991,5831,10151,12,9,15,FR,France
+202003,7,5968,4100,7836,9,6,12,FR,France
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+199126,7,17608,11304,23912,31,20,42,FR,France
+199125,7,16169,10700,21638,28,18,38,FR,France
+199124,7,16171,10071,22271,28,17,39,FR,France
+199123,7,11947,7671,16223,21,13,29,FR,France
+199122,7,15452,9953,20951,27,17,37,FR,France
+199121,7,14903,8975,20831,26,16,36,FR,France
+199120,7,19053,12742,25364,34,23,45,FR,France
+199119,7,16739,11246,22232,29,19,39,FR,France
+199118,7,21385,13882,28888,38,25,51,FR,France
+199117,7,13462,8877,18047,24,16,32,FR,France
+199116,7,14857,10068,19646,26,18,34,FR,France
+199115,7,13975,9781,18169,25,18,32,FR,France
+199114,7,12265,7684,16846,22,14,30,FR,France
+199113,7,9567,6041,13093,17,11,23,FR,France
+199112,7,10864,7331,14397,19,13,25,FR,France
+199111,7,15574,11184,19964,27,19,35,FR,France
+199110,7,16643,11372,21914,29,20,38,FR,France
+199109,7,13741,8780,18702,24,15,33,FR,France
+199108,7,13289,8813,17765,23,15,31,FR,France
+199107,7,12337,8077,16597,22,15,29,FR,France
+199106,7,10877,7013,14741,19,12,26,FR,France
+199105,7,10442,6544,14340,18,11,25,FR,France
+199104,7,7913,4563,11263,14,8,20,FR,France
+199103,7,15387,10484,20290,27,18,36,FR,France
+199102,7,16277,11046,21508,29,20,38,FR,France
+199101,7,15565,10271,20859,27,18,36,FR,France
+199052,7,19375,13295,25455,34,23,45,FR,France
+199051,7,19080,13807,24353,34,25,43,FR,France
+199050,7,11079,6660,15498,20,12,28,FR,France
+199049,7,1143,0,2610,2,0,5,FR,France
-- 
2.18.1