From 80ce5f28a3e597c83d9979918523e3f03c55bffb Mon Sep 17 00:00:00 2001
From: Renaud Marti <renaud.marti@gmail.com>
Date: Tue, 23 Feb 2021 09:53:19 +0100
Subject: [PATCH] Modif Windows Renaud

---
 .ipynb_checkpoints/readme-checkpoint.md       |   11 +
 .../.ipynb_checkpoints/README-checkpoint.md   |    1 +
 ...se-syndrome-grippal-Copy1-checkpoint.ipynb | 1038 +++++++++
 .../analyse-syndrome-grippal-checkpoint.ipynb |  372 ++++
 .../exo1/analyse-syndrome-grippal-Copy1.ipynb | 1038 +++++++++
 module3/exo1/analyse-syndrome-grippal.ipynb   |    2 +-
 module3/exo1/incidence.csv                    | 1896 +++++++++++++++++
 .../exercice_en-checkpoint.ipynb              |   25 +
 .../exercice_fr-checkpoint.ipynb              |   25 +
 .../exercice_fr-checkpoint.ipynb              |  930 ++++++++
 module3/exo3/exercice_fr.ipynb                |  913 +++++++-
 module3/exo3/incidence_varicelles.csv         | 1578 ++++++++++++++
 12 files changed, 7824 insertions(+), 5 deletions(-)
 create mode 100644 .ipynb_checkpoints/readme-checkpoint.md
 create mode 100644 module3/.ipynb_checkpoints/README-checkpoint.md
 create mode 100644 module3/exo1/.ipynb_checkpoints/analyse-syndrome-grippal-Copy1-checkpoint.ipynb
 create mode 100644 module3/exo1/.ipynb_checkpoints/analyse-syndrome-grippal-checkpoint.ipynb
 create mode 100644 module3/exo1/analyse-syndrome-grippal-Copy1.ipynb
 create mode 100644 module3/exo1/incidence.csv
 create mode 100644 module3/exo2/.ipynb_checkpoints/exercice_en-checkpoint.ipynb
 create mode 100644 module3/exo2/.ipynb_checkpoints/exercice_fr-checkpoint.ipynb
 create mode 100644 module3/exo3/.ipynb_checkpoints/exercice_fr-checkpoint.ipynb
 create mode 100644 module3/exo3/incidence_varicelles.csv

diff --git a/.ipynb_checkpoints/readme-checkpoint.md b/.ipynb_checkpoints/readme-checkpoint.md
new file mode 100644
index 0000000..9259ffa
--- /dev/null
+++ b/.ipynb_checkpoints/readme-checkpoint.md
@@ -0,0 +1,11 @@
+# MOOC Recherche Reproductible / Reproducibleesearch Mooc
+
+FR
+
+Ce dépôt contient les fichiers utiles au MOOC Recherche Reproductible.
+
+EN
+
+This repository contains useful files for the Reproducible Research MOOC.
+
+Ceci est un test. Un deuxième test depuis Windows ! Un de plus en mode graphique. Un troisième essai.
diff --git a/module3/.ipynb_checkpoints/README-checkpoint.md b/module3/.ipynb_checkpoints/README-checkpoint.md
new file mode 100644
index 0000000..7d100eb
--- /dev/null
+++ b/module3/.ipynb_checkpoints/README-checkpoint.md
@@ -0,0 +1 @@
+Module 3
diff --git a/module3/exo1/.ipynb_checkpoints/analyse-syndrome-grippal-Copy1-checkpoint.ipynb b/module3/exo1/.ipynb_checkpoints/analyse-syndrome-grippal-Copy1-checkpoint.ipynb
new file mode 100644
index 0000000..4cad743
--- /dev/null
+++ b/module3/exo1/.ipynb_checkpoints/analyse-syndrome-grippal-Copy1-checkpoint.ipynb
@@ -0,0 +1,1038 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Incidence du syndrome grippal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import isoweek\n",
+    "import requests\n",
+    "import csv\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r = requests.get(\"https://www.sentiweb.fr/datasets/incidence-PAY-3.csv\") # on envoie ne requête http à l'aide de la bibliothèque \"requests\" et on récupère le contenu dans l'objet 'r'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file = r\"C:\\Users\\renau\\mooc-rr\\module3\\exo1\\incidence.csv\" # on teste si le fichier des données existent\n",
+    "if (Path(file).is_file() == False):\n",
+    "    file_data = open(r\"C:\\Users\\renau\\mooc-rr\\module3\\exo1\\incidence.csv\",\"w\",encoding = \"ANSI\") # création d'un fichier vide accesible en écriture\n",
+    "    file_data.write(r.text) # écriture dans le fichier du contenu de la requête au format texte\n",
+    "    print(\"Ce fichier a été téléchargé depuis l'url du site du réseau **Sentinelles**\")\n",
+    "    file_data.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'C:\\\\Users\\\\renau\\\\mooc-rr\\\\module3\\\\exo1\\\\incidence.csv'"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "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>202106</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23816</td>\n",
+       "      <td>18867.0</td>\n",
+       "      <td>28765.0</td>\n",
+       "      <td>36</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202105</td>\n",
+       "      <td>3</td>\n",
+       "      <td>22491</td>\n",
+       "      <td>18436.0</td>\n",
+       "      <td>26546.0</td>\n",
+       "      <td>34</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202104</td>\n",
+       "      <td>3</td>\n",
+       "      <td>25804</td>\n",
+       "      <td>21491.0</td>\n",
+       "      <td>30117.0</td>\n",
+       "      <td>39</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202103</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21810</td>\n",
+       "      <td>17894.0</td>\n",
+       "      <td>25726.0</td>\n",
+       "      <td>33</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202102</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17320</td>\n",
+       "      <td>13906.0</td>\n",
+       "      <td>20734.0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>31.0</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>1889</th>\n",
+       "      <td>198448</td>\n",
+       "      <td>3</td>\n",
+       "      <td>78620</td>\n",
+       "      <td>60634.0</td>\n",
+       "      <td>96606.0</td>\n",
+       "      <td>143</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>176.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1890</th>\n",
+       "      <td>198447</td>\n",
+       "      <td>3</td>\n",
+       "      <td>72029</td>\n",
+       "      <td>54274.0</td>\n",
+       "      <td>89784.0</td>\n",
+       "      <td>131</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>163.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1891</th>\n",
+       "      <td>198446</td>\n",
+       "      <td>3</td>\n",
+       "      <td>87330</td>\n",
+       "      <td>67686.0</td>\n",
+       "      <td>106974.0</td>\n",
+       "      <td>159</td>\n",
+       "      <td>123.0</td>\n",
+       "      <td>195.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1892</th>\n",
+       "      <td>198445</td>\n",
+       "      <td>3</td>\n",
+       "      <td>135223</td>\n",
+       "      <td>101414.0</td>\n",
+       "      <td>169032.0</td>\n",
+       "      <td>246</td>\n",
+       "      <td>184.0</td>\n",
+       "      <td>308.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1893</th>\n",
+       "      <td>198444</td>\n",
+       "      <td>3</td>\n",
+       "      <td>68422</td>\n",
+       "      <td>20056.0</td>\n",
+       "      <td>116788.0</td>\n",
+       "      <td>125</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>213.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1894 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator     inc   inc_low    inc_up  inc100  inc100_low  \\\n",
+       "0     202106          3   23816   18867.0   28765.0      36        29.0   \n",
+       "1     202105          3   22491   18436.0   26546.0      34        28.0   \n",
+       "2     202104          3   25804   21491.0   30117.0      39        32.0   \n",
+       "3     202103          3   21810   17894.0   25726.0      33        27.0   \n",
+       "4     202102          3   17320   13906.0   20734.0      26        21.0   \n",
+       "...      ...        ...     ...       ...       ...     ...         ...   \n",
+       "1889  198448          3   78620   60634.0   96606.0     143       110.0   \n",
+       "1890  198447          3   72029   54274.0   89784.0     131        99.0   \n",
+       "1891  198446          3   87330   67686.0  106974.0     159       123.0   \n",
+       "1892  198445          3  135223  101414.0  169032.0     246       184.0   \n",
+       "1893  198444          3   68422   20056.0  116788.0     125        37.0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0          43.0        FR   France  \n",
+       "1          40.0        FR   France  \n",
+       "2          46.0        FR   France  \n",
+       "3          39.0        FR   France  \n",
+       "4          31.0        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1889      176.0        FR   France  \n",
+       "1890      163.0        FR   France  \n",
+       "1891      195.0        FR   France  \n",
+       "1892      308.0        FR   France  \n",
+       "1893      213.0        FR   France  \n",
+       "\n",
+       "[1894 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv(file, sep=',', encoding = 'ansi', skiprows=1) # on lit le fichier csv, en tant que panda data frame\n",
+    "raw_data"
+   ]
+  },
+  {
+   "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": "markdown",
+   "metadata": {},
+   "source": [
+    "Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "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>1657</th>\n",
+       "      <td>198919</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator  inc  inc_low  inc_up  inc100  inc100_low  inc100_up  \\\n",
+       "1657  198919          3    0      NaN     NaN       0         NaN        NaN   \n",
+       "\n",
+       "     geo_insee geo_name  \n",
+       "1657        FR   France  "
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data[raw_data.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "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>202106</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23816</td>\n",
+       "      <td>18867.0</td>\n",
+       "      <td>28765.0</td>\n",
+       "      <td>36</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202105</td>\n",
+       "      <td>3</td>\n",
+       "      <td>22491</td>\n",
+       "      <td>18436.0</td>\n",
+       "      <td>26546.0</td>\n",
+       "      <td>34</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202104</td>\n",
+       "      <td>3</td>\n",
+       "      <td>25804</td>\n",
+       "      <td>21491.0</td>\n",
+       "      <td>30117.0</td>\n",
+       "      <td>39</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202103</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21810</td>\n",
+       "      <td>17894.0</td>\n",
+       "      <td>25726.0</td>\n",
+       "      <td>33</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202102</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17320</td>\n",
+       "      <td>13906.0</td>\n",
+       "      <td>20734.0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>31.0</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>1889</th>\n",
+       "      <td>198448</td>\n",
+       "      <td>3</td>\n",
+       "      <td>78620</td>\n",
+       "      <td>60634.0</td>\n",
+       "      <td>96606.0</td>\n",
+       "      <td>143</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>176.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1890</th>\n",
+       "      <td>198447</td>\n",
+       "      <td>3</td>\n",
+       "      <td>72029</td>\n",
+       "      <td>54274.0</td>\n",
+       "      <td>89784.0</td>\n",
+       "      <td>131</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>163.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1891</th>\n",
+       "      <td>198446</td>\n",
+       "      <td>3</td>\n",
+       "      <td>87330</td>\n",
+       "      <td>67686.0</td>\n",
+       "      <td>106974.0</td>\n",
+       "      <td>159</td>\n",
+       "      <td>123.0</td>\n",
+       "      <td>195.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1892</th>\n",
+       "      <td>198445</td>\n",
+       "      <td>3</td>\n",
+       "      <td>135223</td>\n",
+       "      <td>101414.0</td>\n",
+       "      <td>169032.0</td>\n",
+       "      <td>246</td>\n",
+       "      <td>184.0</td>\n",
+       "      <td>308.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1893</th>\n",
+       "      <td>198444</td>\n",
+       "      <td>3</td>\n",
+       "      <td>68422</td>\n",
+       "      <td>20056.0</td>\n",
+       "      <td>116788.0</td>\n",
+       "      <td>125</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>213.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1893 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator     inc   inc_low    inc_up  inc100  inc100_low  \\\n",
+       "0     202106          3   23816   18867.0   28765.0      36        29.0   \n",
+       "1     202105          3   22491   18436.0   26546.0      34        28.0   \n",
+       "2     202104          3   25804   21491.0   30117.0      39        32.0   \n",
+       "3     202103          3   21810   17894.0   25726.0      33        27.0   \n",
+       "4     202102          3   17320   13906.0   20734.0      26        21.0   \n",
+       "...      ...        ...     ...       ...       ...     ...         ...   \n",
+       "1889  198448          3   78620   60634.0   96606.0     143       110.0   \n",
+       "1890  198447          3   72029   54274.0   89784.0     131        99.0   \n",
+       "1891  198446          3   87330   67686.0  106974.0     159       123.0   \n",
+       "1892  198445          3  135223  101414.0  169032.0     246       184.0   \n",
+       "1893  198444          3   68422   20056.0  116788.0     125        37.0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0          43.0        FR   France  \n",
+       "1          40.0        FR   France  \n",
+       "2          46.0        FR   France  \n",
+       "3          39.0        FR   France  \n",
+       "4          31.0        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1889      176.0        FR   France  \n",
+       "1890      163.0        FR   France  \n",
+       "1891      195.0        FR   France  \n",
+       "1892      308.0        FR   France  \n",
+       "1893      213.0        FR   France  \n",
+       "\n",
+       "[1893 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = raw_data.dropna().copy()\n",
+    "data"
+   ]
+  },
+  {
+   "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": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def convert_week(year_and_week_int):\n",
+    "    year_and_week_str = str(year_and_week_int)\n",
+    "    year = int(year_and_week_str[:4])\n",
+    "    week = int(year_and_week_str[4:])\n",
+    "    w = isoweek.Week(year, week)\n",
+    "    return pd.Period(w.day(0), 'W')\n",
+    "\n",
+    "data['period'] = [convert_week(yw) for yw in data['week']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Il 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": 52,
+   "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 sauf pour deux périodes consécutives\n",
+    "entre lesquelles il manque une semaine.\n",
+    "\n",
+    "Nous reconnaissons ces dates: c'est la semaine sans observations\n",
+    "que nous avions supprimées !"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n"
+     ]
+    }
+   ],
+   "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": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='period'>"
+      ]
+     },
+     "execution_count": 54,
+     "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": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='period'>"
+      ]
+     },
+     "execution_count": 55,
+     "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\n",
+    "entre deux années civiles, nous définissons la période de référence\n",
+    "entre deux minima de l'incidence, du 1er août de l'année $N$ au\n",
+    "1er août de l'année $N+1$.\n",
+    "\n",
+    "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n",
+    "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n",
+    "de référence: à la place du 1er août de chaque année, nous utilisons le\n",
+    "premier jour de la semaine qui contient le 1er août.\n",
+    "\n",
+    "Comme l'incidence de syndrome grippal est très faible en été, cette\n",
+    "modification ne risque pas de fausser nos conclusions.\n",
+    "\n",
+    "Encore un petit détail: les données commencent an octobre 1984, ce qui\n",
+    "rend la première année incomplète. Nous commençons donc l'analyse en 1985."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n",
+    "                     for y in range(1985,\n",
+    "                                    sorted_data.index[-1].year)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n",
+    "\n",
+    "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year = []\n",
+    "yearly_incidence = []\n",
+    "for week1, week2 in zip(first_august_week[:-1],\n",
+    "                        first_august_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": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 58,
+     "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": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2014    1600941\n",
+       "1991    1659249\n",
+       "1995    1840410\n",
+       "2020    2042389\n",
+       "2012    2175217\n",
+       "2003    2234584\n",
+       "2019    2254386\n",
+       "2006    2307352\n",
+       "2017    2321583\n",
+       "2001    2529279\n",
+       "1992    2574578\n",
+       "1993    2703886\n",
+       "2018    2705325\n",
+       "1988    2765617\n",
+       "2007    2780164\n",
+       "1987    2855570\n",
+       "2016    2856393\n",
+       "2011    2857040\n",
+       "2008    2973918\n",
+       "1998    3034904\n",
+       "2002    3125418\n",
+       "2009    3444020\n",
+       "1994    3514763\n",
+       "1996    3539413\n",
+       "2004    3567744\n",
+       "1997    3620066\n",
+       "2015    3654892\n",
+       "2000    3826372\n",
+       "2005    3835025\n",
+       "1999    3908112\n",
+       "2010    4111392\n",
+       "2013    4182691\n",
+       "1986    5115251\n",
+       "1990    5235827\n",
+       "1989    5466192\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n",
+    "  française, sont assez rares: il y en eu trois au cours des 35 dernières années."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yearly_incidence.hist(xrot=20)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/module3/exo1/.ipynb_checkpoints/analyse-syndrome-grippal-checkpoint.ipynb b/module3/exo1/.ipynb_checkpoints/analyse-syndrome-grippal-checkpoint.ipynb
new file mode 100644
index 0000000..fbcf11a
--- /dev/null
+++ b/module3/exo1/.ipynb_checkpoints/analyse-syndrome-grippal-checkpoint.ipynb
@@ -0,0 +1,372 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Incidence du syndrome grippal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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 du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n",
+    "\n",
+    "| Nom de colonne | Libellé de colonne                                                                                                                |\n",
+    "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n",
+    "| week           | Semaine calendaire (ISO 8601)                                                                                                     |\n",
+    "| indicator      | Code de l'indicateur de surveillance                                                                                              |\n",
+    "| inc            | Estimation de l'incidence de consultations en nombre de cas                                                                       |\n",
+    "| inc_low        | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation                                                     |\n",
+    "| inc_up         | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation                                                     |\n",
+    "| inc100         | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants)                                   |\n",
+    "| inc100_low     | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
+    "| inc100_up      | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
+    "| geo_insee      | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/                            |\n",
+    "| geo_name       | Libellé de la zone géographique (ce libellé peut être modifié sans préavis)                                                       |\n",
+    "\n",
+    "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_data = pd.read_csv(data_url, skiprows=1)\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_data[raw_data.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = raw_data.dropna().copy()\n",
+    "data"
+   ]
+  },
+  {
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def convert_week(year_and_week_int):\n",
+    "    year_and_week_str = str(year_and_week_int)\n",
+    "    year = int(year_and_week_str[:4])\n",
+    "    week = int(year_and_week_str[4:])\n",
+    "    w = isoweek.Week(year, week)\n",
+    "    return pd.Period(w.day(0), 'W')\n",
+    "\n",
+    "data['period'] = [convert_week(yw) for yw in data['week']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Il 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": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "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 sauf pour deux périodes consécutives\n",
+    "entre lesquelles il manque une semaine.\n",
+    "\n",
+    "Nous reconnaissons ces dates: c'est la semaine sans observations\n",
+    "que nous avions supprimées !"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "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\n",
+    "entre deux années civiles, nous définissons la période de référence\n",
+    "entre deux minima de l'incidence, du 1er août de l'année $N$ au\n",
+    "1er août de l'année $N+1$.\n",
+    "\n",
+    "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n",
+    "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n",
+    "de référence: à la place du 1er août de chaque année, nous utilisons le\n",
+    "premier jour de la semaine qui contient le 1er août.\n",
+    "\n",
+    "Comme l'incidence de syndrome grippal est très faible en été, cette\n",
+    "modification ne risque pas de fausser nos conclusions.\n",
+    "\n",
+    "Encore un petit détail: les données commencent an octobre 1984, ce qui\n",
+    "rend la première année incomplète. Nous commençons donc l'analyse en 1985."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n",
+    "                     for y in range(1985,\n",
+    "                                    sorted_data.index[-1].year)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n",
+    "\n",
+    "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year = []\n",
+    "yearly_incidence = []\n",
+    "for week1, week2 in zip(first_august_week[:-1],\n",
+    "                        first_august_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": null,
+   "metadata": {},
+   "outputs": [],
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yearly_incidence.sort_values()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n",
+    "  française, sont assez rares: il y en eu trois au cours des 35 dernières années."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yearly_incidence.hist(xrot=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/module3/exo1/analyse-syndrome-grippal-Copy1.ipynb b/module3/exo1/analyse-syndrome-grippal-Copy1.ipynb
new file mode 100644
index 0000000..8991855
--- /dev/null
+++ b/module3/exo1/analyse-syndrome-grippal-Copy1.ipynb
@@ -0,0 +1,1038 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Incidence du syndrome grippal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import isoweek\n",
+    "import requests\n",
+    "import csv\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r = requests.get(\"https://www.sentiweb.fr/datasets/incidence-PAY-3.csv\") # on envoie ne requête http à l'aide de la bibliothèque \"requests\" et on récupère le contenu dans l'objet 'r'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file = r\"C:\\Users\\renau\\mooc-rr\\module3\\exo1\\incidence.csv\" \n",
+    "if (Path(file).is_file() == False): # on teste si le fichier des données existent\n",
+    "    file_data = open(r\"C:\\Users\\renau\\mooc-rr\\module3\\exo1\\incidence.csv\",\"w\",encoding = \"ANSI\") # création d'un fichier vide accesible en écriture\n",
+    "    file_data.write(r.text) # écriture dans le fichier du contenu de la requête au format texte\n",
+    "    print(\"Ce fichier a été téléchargé depuis l'url du site du réseau **Sentinelles**\")\n",
+    "    file_data.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'C:\\\\Users\\\\renau\\\\mooc-rr\\\\module3\\\\exo1\\\\incidence.csv'"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "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>202106</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23816</td>\n",
+       "      <td>18867.0</td>\n",
+       "      <td>28765.0</td>\n",
+       "      <td>36</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202105</td>\n",
+       "      <td>3</td>\n",
+       "      <td>22491</td>\n",
+       "      <td>18436.0</td>\n",
+       "      <td>26546.0</td>\n",
+       "      <td>34</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202104</td>\n",
+       "      <td>3</td>\n",
+       "      <td>25804</td>\n",
+       "      <td>21491.0</td>\n",
+       "      <td>30117.0</td>\n",
+       "      <td>39</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202103</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21810</td>\n",
+       "      <td>17894.0</td>\n",
+       "      <td>25726.0</td>\n",
+       "      <td>33</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202102</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17320</td>\n",
+       "      <td>13906.0</td>\n",
+       "      <td>20734.0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>31.0</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>1889</th>\n",
+       "      <td>198448</td>\n",
+       "      <td>3</td>\n",
+       "      <td>78620</td>\n",
+       "      <td>60634.0</td>\n",
+       "      <td>96606.0</td>\n",
+       "      <td>143</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>176.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1890</th>\n",
+       "      <td>198447</td>\n",
+       "      <td>3</td>\n",
+       "      <td>72029</td>\n",
+       "      <td>54274.0</td>\n",
+       "      <td>89784.0</td>\n",
+       "      <td>131</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>163.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1891</th>\n",
+       "      <td>198446</td>\n",
+       "      <td>3</td>\n",
+       "      <td>87330</td>\n",
+       "      <td>67686.0</td>\n",
+       "      <td>106974.0</td>\n",
+       "      <td>159</td>\n",
+       "      <td>123.0</td>\n",
+       "      <td>195.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1892</th>\n",
+       "      <td>198445</td>\n",
+       "      <td>3</td>\n",
+       "      <td>135223</td>\n",
+       "      <td>101414.0</td>\n",
+       "      <td>169032.0</td>\n",
+       "      <td>246</td>\n",
+       "      <td>184.0</td>\n",
+       "      <td>308.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1893</th>\n",
+       "      <td>198444</td>\n",
+       "      <td>3</td>\n",
+       "      <td>68422</td>\n",
+       "      <td>20056.0</td>\n",
+       "      <td>116788.0</td>\n",
+       "      <td>125</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>213.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1894 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator     inc   inc_low    inc_up  inc100  inc100_low  \\\n",
+       "0     202106          3   23816   18867.0   28765.0      36        29.0   \n",
+       "1     202105          3   22491   18436.0   26546.0      34        28.0   \n",
+       "2     202104          3   25804   21491.0   30117.0      39        32.0   \n",
+       "3     202103          3   21810   17894.0   25726.0      33        27.0   \n",
+       "4     202102          3   17320   13906.0   20734.0      26        21.0   \n",
+       "...      ...        ...     ...       ...       ...     ...         ...   \n",
+       "1889  198448          3   78620   60634.0   96606.0     143       110.0   \n",
+       "1890  198447          3   72029   54274.0   89784.0     131        99.0   \n",
+       "1891  198446          3   87330   67686.0  106974.0     159       123.0   \n",
+       "1892  198445          3  135223  101414.0  169032.0     246       184.0   \n",
+       "1893  198444          3   68422   20056.0  116788.0     125        37.0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0          43.0        FR   France  \n",
+       "1          40.0        FR   France  \n",
+       "2          46.0        FR   France  \n",
+       "3          39.0        FR   France  \n",
+       "4          31.0        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1889      176.0        FR   France  \n",
+       "1890      163.0        FR   France  \n",
+       "1891      195.0        FR   France  \n",
+       "1892      308.0        FR   France  \n",
+       "1893      213.0        FR   France  \n",
+       "\n",
+       "[1894 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv(file, sep=',', encoding = 'ansi', skiprows=1) # on lit le fichier csv, en tant que panda data frame\n",
+    "raw_data"
+   ]
+  },
+  {
+   "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": "markdown",
+   "metadata": {},
+   "source": [
+    "Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "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>1657</th>\n",
+       "      <td>198919</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator  inc  inc_low  inc_up  inc100  inc100_low  inc100_up  \\\n",
+       "1657  198919          3    0      NaN     NaN       0         NaN        NaN   \n",
+       "\n",
+       "     geo_insee geo_name  \n",
+       "1657        FR   France  "
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data[raw_data.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "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>202106</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23816</td>\n",
+       "      <td>18867.0</td>\n",
+       "      <td>28765.0</td>\n",
+       "      <td>36</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202105</td>\n",
+       "      <td>3</td>\n",
+       "      <td>22491</td>\n",
+       "      <td>18436.0</td>\n",
+       "      <td>26546.0</td>\n",
+       "      <td>34</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202104</td>\n",
+       "      <td>3</td>\n",
+       "      <td>25804</td>\n",
+       "      <td>21491.0</td>\n",
+       "      <td>30117.0</td>\n",
+       "      <td>39</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202103</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21810</td>\n",
+       "      <td>17894.0</td>\n",
+       "      <td>25726.0</td>\n",
+       "      <td>33</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202102</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17320</td>\n",
+       "      <td>13906.0</td>\n",
+       "      <td>20734.0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>31.0</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>1889</th>\n",
+       "      <td>198448</td>\n",
+       "      <td>3</td>\n",
+       "      <td>78620</td>\n",
+       "      <td>60634.0</td>\n",
+       "      <td>96606.0</td>\n",
+       "      <td>143</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>176.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1890</th>\n",
+       "      <td>198447</td>\n",
+       "      <td>3</td>\n",
+       "      <td>72029</td>\n",
+       "      <td>54274.0</td>\n",
+       "      <td>89784.0</td>\n",
+       "      <td>131</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>163.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1891</th>\n",
+       "      <td>198446</td>\n",
+       "      <td>3</td>\n",
+       "      <td>87330</td>\n",
+       "      <td>67686.0</td>\n",
+       "      <td>106974.0</td>\n",
+       "      <td>159</td>\n",
+       "      <td>123.0</td>\n",
+       "      <td>195.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1892</th>\n",
+       "      <td>198445</td>\n",
+       "      <td>3</td>\n",
+       "      <td>135223</td>\n",
+       "      <td>101414.0</td>\n",
+       "      <td>169032.0</td>\n",
+       "      <td>246</td>\n",
+       "      <td>184.0</td>\n",
+       "      <td>308.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1893</th>\n",
+       "      <td>198444</td>\n",
+       "      <td>3</td>\n",
+       "      <td>68422</td>\n",
+       "      <td>20056.0</td>\n",
+       "      <td>116788.0</td>\n",
+       "      <td>125</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>213.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1893 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator     inc   inc_low    inc_up  inc100  inc100_low  \\\n",
+       "0     202106          3   23816   18867.0   28765.0      36        29.0   \n",
+       "1     202105          3   22491   18436.0   26546.0      34        28.0   \n",
+       "2     202104          3   25804   21491.0   30117.0      39        32.0   \n",
+       "3     202103          3   21810   17894.0   25726.0      33        27.0   \n",
+       "4     202102          3   17320   13906.0   20734.0      26        21.0   \n",
+       "...      ...        ...     ...       ...       ...     ...         ...   \n",
+       "1889  198448          3   78620   60634.0   96606.0     143       110.0   \n",
+       "1890  198447          3   72029   54274.0   89784.0     131        99.0   \n",
+       "1891  198446          3   87330   67686.0  106974.0     159       123.0   \n",
+       "1892  198445          3  135223  101414.0  169032.0     246       184.0   \n",
+       "1893  198444          3   68422   20056.0  116788.0     125        37.0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0          43.0        FR   France  \n",
+       "1          40.0        FR   France  \n",
+       "2          46.0        FR   France  \n",
+       "3          39.0        FR   France  \n",
+       "4          31.0        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1889      176.0        FR   France  \n",
+       "1890      163.0        FR   France  \n",
+       "1891      195.0        FR   France  \n",
+       "1892      308.0        FR   France  \n",
+       "1893      213.0        FR   France  \n",
+       "\n",
+       "[1893 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = raw_data.dropna().copy()\n",
+    "data"
+   ]
+  },
+  {
+   "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": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def convert_week(year_and_week_int):\n",
+    "    year_and_week_str = str(year_and_week_int)\n",
+    "    year = int(year_and_week_str[:4])\n",
+    "    week = int(year_and_week_str[4:])\n",
+    "    w = isoweek.Week(year, week)\n",
+    "    return pd.Period(w.day(0), 'W')\n",
+    "\n",
+    "data['period'] = [convert_week(yw) for yw in data['week']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Il 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": 52,
+   "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 sauf pour deux périodes consécutives\n",
+    "entre lesquelles il manque une semaine.\n",
+    "\n",
+    "Nous reconnaissons ces dates: c'est la semaine sans observations\n",
+    "que nous avions supprimées !"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n"
+     ]
+    }
+   ],
+   "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": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='period'>"
+      ]
+     },
+     "execution_count": 54,
+     "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": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='period'>"
+      ]
+     },
+     "execution_count": 55,
+     "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\n",
+    "entre deux années civiles, nous définissons la période de référence\n",
+    "entre deux minima de l'incidence, du 1er août de l'année $N$ au\n",
+    "1er août de l'année $N+1$.\n",
+    "\n",
+    "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n",
+    "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n",
+    "de référence: à la place du 1er août de chaque année, nous utilisons le\n",
+    "premier jour de la semaine qui contient le 1er août.\n",
+    "\n",
+    "Comme l'incidence de syndrome grippal est très faible en été, cette\n",
+    "modification ne risque pas de fausser nos conclusions.\n",
+    "\n",
+    "Encore un petit détail: les données commencent an octobre 1984, ce qui\n",
+    "rend la première année incomplète. Nous commençons donc l'analyse en 1985."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n",
+    "                     for y in range(1985,\n",
+    "                                    sorted_data.index[-1].year)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n",
+    "\n",
+    "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year = []\n",
+    "yearly_incidence = []\n",
+    "for week1, week2 in zip(first_august_week[:-1],\n",
+    "                        first_august_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": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 58,
+     "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": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2014    1600941\n",
+       "1991    1659249\n",
+       "1995    1840410\n",
+       "2020    2042389\n",
+       "2012    2175217\n",
+       "2003    2234584\n",
+       "2019    2254386\n",
+       "2006    2307352\n",
+       "2017    2321583\n",
+       "2001    2529279\n",
+       "1992    2574578\n",
+       "1993    2703886\n",
+       "2018    2705325\n",
+       "1988    2765617\n",
+       "2007    2780164\n",
+       "1987    2855570\n",
+       "2016    2856393\n",
+       "2011    2857040\n",
+       "2008    2973918\n",
+       "1998    3034904\n",
+       "2002    3125418\n",
+       "2009    3444020\n",
+       "1994    3514763\n",
+       "1996    3539413\n",
+       "2004    3567744\n",
+       "1997    3620066\n",
+       "2015    3654892\n",
+       "2000    3826372\n",
+       "2005    3835025\n",
+       "1999    3908112\n",
+       "2010    4111392\n",
+       "2013    4182691\n",
+       "1986    5115251\n",
+       "1990    5235827\n",
+       "1989    5466192\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n",
+    "  française, sont assez rares: il y en eu trois au cours des 35 dernières années."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yearly_incidence.hist(xrot=20)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb
index 59d72b5..fbcf11a 100644
--- a/module3/exo1/analyse-syndrome-grippal.ipynb
+++ b/module3/exo1/analyse-syndrome-grippal.ipynb
@@ -364,7 +364,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/module3/exo1/incidence.csv b/module3/exo1/incidence.csv
new file mode 100644
index 0000000..19a2bb3
--- /dev/null
+++ b/module3/exo1/incidence.csv
@@ -0,0 +1,1896 @@
+# @source="réseau Sentinelles, INSERM, Sorbonne Université, http://www.sentiweb.fr", @meta={"period":[198444,202106],"geo":["PAY","1"],"geo_ref":"insee","indicator":"3","type":"all","conf_int":true,"compact":false}, @date=2021-02-21T18:53:33+01:00
+week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name
+202106,3,23816,18867,28765,36,29,43,FR,France
+202105,3,22491,18436,26546,34,28,40,FR,France
+202104,3,25804,21491,30117,39,32,46,FR,France
+202103,3,21810,17894,25726,33,27,39,FR,France
+202102,3,17320,13906,20734,26,21,31,FR,France
+202101,3,21799,17778,25820,33,27,39,FR,France
+202053,3,21220,16498,25942,32,25,39,FR,France
+202052,3,16428,12285,20571,25,19,31,FR,France
+202051,3,21619,17370,25868,33,27,39,FR,France
+202050,3,16845,13220,20470,26,20,32,FR,France
+202049,3,12939,9923,15955,20,15,25,FR,France
+202048,3,13804,10641,16967,21,16,26,FR,France
+202047,3,19085,15285,22885,29,23,35,FR,France
+202046,3,24801,20503,29099,38,31,45,FR,France
+202045,3,42516,36857,48175,65,56,74,FR,France
+202044,3,44567,38521,50613,68,59,77,FR,France
+202043,3,43737,37523,49951,66,57,75,FR,France
+202042,3,35145,29812,40478,53,45,61,FR,France
+202041,3,27877,23206,32548,42,35,49,FR,France
+202040,3,20443,16381,24505,31,25,37,FR,France
+202039,3,19810,15900,23720,30,24,36,FR,France
+202038,3,25562,21142,29982,39,32,46,FR,France
+202037,3,18485,14649,22321,28,22,34,FR,France
+202036,3,10390,7646,13134,16,12,20,FR,France
+202035,3,9918,6842,12994,15,10,20,FR,France
+202034,3,6084,3090,9078,9,4,14,FR,France
+202033,3,6106,3411,8801,9,5,13,FR,France
+202032,3,5918,3330,8506,9,5,13,FR,France
+202031,3,4351,2269,6433,7,4,10,FR,France
+202030,3,8179,5442,10916,12,8,16,FR,France
+202029,3,8687,5860,11514,13,9,17,FR,France
+202028,3,8340,5701,10979,13,9,17,FR,France
+202027,3,4066,2406,5726,6,3,9,FR,France
+202026,3,4039,2389,5689,6,3,9,FR,France
+202025,3,2853,1488,4218,4,2,6,FR,France
+202024,3,3058,1690,4426,5,3,7,FR,France
+202023,3,4168,2468,5868,6,3,9,FR,France
+202022,3,3580,1947,5213,5,3,7,FR,France
+202021,3,6114,4026,8202,9,6,12,FR,France
+202020,3,9315,6775,11855,14,10,18,FR,France
+202019,3,11679,8722,14636,18,14,22,FR,France
+202018,3,16398,12851,19945,25,20,30,FR,France
+202017,3,18082,14454,21710,27,21,33,FR,France
+202016,3,24165,19893,28437,37,31,43,FR,France
+202015,3,41049,35377,46721,62,53,71,FR,France
+202014,3,71666,64531,78801,109,98,120,FR,France
+202013,3,107742,99187,116297,164,151,177,FR,France
+202012,3,107283,98610,115956,163,150,176,FR,France
+202011,3,101704,93652,109756,154,142,166,FR,France
+202010,3,104977,96650,113304,159,146,172,FR,France
+202009,3,110696,102066,119326,168,155,181,FR,France
+202008,3,143753,133984,153522,218,203,233,FR,France
+202007,3,183610,172812,194408,279,263,295,FR,France
+202006,3,206669,195481,217857,314,297,331,FR,France
+202005,3,187957,177445,198469,285,269,301,FR,France
+202004,3,122331,113492,131170,186,173,199,FR,France
+202003,3,78413,71330,85496,119,108,130,FR,France
+202002,3,53614,47654,59574,81,72,90,FR,France
+202001,3,36850,31608,42092,56,48,64,FR,France
+201952,3,28135,23220,33050,43,36,50,FR,France
+201951,3,29786,25042,34530,45,38,52,FR,France
+201950,3,34223,29156,39290,52,44,60,FR,France
+201949,3,25662,21414,29910,39,33,45,FR,France
+201948,3,22367,18055,26679,34,27,41,FR,France
+201947,3,18669,14759,22579,28,22,34,FR,France
+201946,3,16030,12567,19493,24,19,29,FR,France
+201945,3,10138,7160,13116,15,10,20,FR,France
+201944,3,7822,5010,10634,12,8,16,FR,France
+201943,3,9487,6448,12526,14,9,19,FR,France
+201942,3,7747,5243,10251,12,8,16,FR,France
+201941,3,7122,4720,9524,11,7,15,FR,France
+201940,3,8505,5784,11226,13,9,17,FR,France
+201939,3,7091,4462,9720,11,7,15,FR,France
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+200146,3,21127,14294,27960,36,24,48,FR,France
+200145,3,20766,13979,27553,35,24,46,FR,France
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+199545,3,29791,24523,35059,52,43,61,FR,France
+199544,3,13168,9820,16516,23,17,29,FR,France
+199543,3,16188,12389,19987,28,21,35,FR,France
+199542,3,15178,11387,18969,26,19,33,FR,France
+199541,3,14980,10851,19109,26,19,33,FR,France
+199540,3,16245,11610,20880,28,20,36,FR,France
+199539,3,18499,13844,23154,32,24,40,FR,France
+199538,3,13123,9410,16836,23,17,29,FR,France
+199537,3,4895,2666,7124,8,4,12,FR,France
+199536,3,3659,1574,5744,6,2,10,FR,France
+199535,3,2496,644,4348,4,1,7,FR,France
+199534,3,1887,214,3560,3,0,6,FR,France
+199533,3,905,0,2007,2,0,4,FR,France
+199532,3,700,0,1699,1,0,3,FR,France
+199531,3,1946,92,3800,3,0,6,FR,France
+199530,3,1898,46,3750,3,0,6,FR,France
+199529,3,2148,687,3609,4,1,7,FR,France
+199528,3,3312,1430,5194,6,3,9,FR,France
+199527,3,4853,2683,7023,8,4,12,FR,France
+199526,3,4986,2911,7061,9,5,13,FR,France
+199525,3,4571,2520,6622,8,4,12,FR,France
+199524,3,8058,5054,11062,14,9,19,FR,France
+199523,3,13820,9801,17839,24,17,31,FR,France
+199522,3,14124,9999,18249,24,17,31,FR,France
+199521,3,11931,8655,15207,21,15,27,FR,France
+199520,3,18846,14311,23381,33,25,41,FR,France
+199519,3,26261,21488,31034,45,37,53,FR,France
+199518,3,39231,33258,45204,68,58,78,FR,France
+199517,3,88640,79343,97937,153,137,169,FR,France
+199516,3,140488,129439,151537,243,224,262,FR,France
+199515,3,181007,169110,192904,313,292,334,FR,France
+199514,3,248704,235144,262264,431,408,454,FR,France
+199513,3,209839,197191,222487,363,341,385,FR,France
+199512,3,122434,112497,132371,212,195,229,FR,France
+199511,3,81730,73346,90114,142,127,157,FR,France
+199510,3,49387,42703,56071,86,74,98,FR,France
+199509,3,39696,33259,46133,69,58,80,FR,France
+199508,3,38008,32225,43791,66,56,76,FR,France
+199507,3,30550,25479,35621,53,44,62,FR,France
+199506,3,37351,32007,42695,65,56,74,FR,France
+199505,3,27876,23218,32534,48,40,56,FR,France
+199504,3,27643,22818,32468,48,40,56,FR,France
+199503,3,24844,20391,29297,43,35,51,FR,France
+199502,3,23480,19249,27711,41,34,48,FR,France
+199501,3,26556,21916,31196,46,38,54,FR,France
+199452,3,22036,17495,26577,38,30,46,FR,France
+199451,3,26912,21750,32074,47,38,56,FR,France
+199450,3,28831,23470,34192,50,41,59,FR,France
+199449,3,21161,16559,25763,37,29,45,FR,France
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+199447,3,15206,11578,18834,26,20,32,FR,France
+199446,3,14252,10525,17979,25,19,31,FR,France
+199445,3,12152,8448,15856,21,15,27,FR,France
+199444,3,11307,7688,14926,20,14,26,FR,France
+199443,3,11254,8002,14506,20,14,26,FR,France
+199442,3,14406,10653,18159,25,18,32,FR,France
+199441,3,14073,10341,17805,24,18,30,FR,France
+199440,3,15962,11850,20074,28,21,35,FR,France
+199439,3,15878,11643,20113,28,21,35,FR,France
+199438,3,15366,11100,19632,27,20,34,FR,France
+199437,3,5949,3561,8337,10,6,14,FR,France
+199436,3,2806,1133,4479,5,2,8,FR,France
+199435,3,4556,2358,6754,8,4,12,FR,France
+199434,3,3459,1444,5474,6,2,10,FR,France
+199433,3,4272,977,7567,7,1,13,FR,France
+199432,3,4786,1613,7959,8,2,14,FR,France
+199431,3,5027,1580,8474,9,3,15,FR,France
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+199429,3,1188,0,2384,2,0,4,FR,France
+199428,3,1581,316,2846,3,1,5,FR,France
+199427,3,713,0,1459,1,0,2,FR,France
+199426,3,990,80,1900,2,0,4,FR,France
+199425,3,5431,3222,7640,9,5,13,FR,France
+199424,3,6288,3723,8853,11,7,15,FR,France
+199423,3,6440,1052,11828,11,2,20,FR,France
+199422,3,845,0,2449,1,0,4,FR,France
+199421,3,2197,395,3999,4,1,7,FR,France
+199420,3,2217,852,3582,4,2,6,FR,France
+199419,3,1483,406,2560,3,1,5,FR,France
+199418,3,3705,1940,5470,6,3,9,FR,France
+199417,3,2288,1064,3512,4,2,6,FR,France
+199416,3,5156,3176,7136,9,6,12,FR,France
+199415,3,6952,4678,9226,12,8,16,FR,France
+199414,3,4671,2784,6558,8,5,11,FR,France
+199413,3,3343,1641,5045,6,3,9,FR,France
+199412,3,4305,2412,6198,7,4,10,FR,France
+199411,3,5399,3327,7471,9,5,13,FR,France
+199410,3,4548,2529,6567,8,4,12,FR,France
+199409,3,5635,3251,8019,10,6,14,FR,France
+199408,3,9539,6652,12426,17,12,22,FR,France
+199407,3,14639,11188,18090,25,19,31,FR,France
+199406,3,17308,13395,21221,30,23,37,FR,France
+199405,3,18489,14290,22688,32,25,39,FR,France
+199404,3,23712,19048,28376,41,33,49,FR,France
+199403,3,29882,24817,34947,52,43,61,FR,France
+199402,3,47402,41415,53389,82,72,92,FR,France
+199401,3,118119,108182,128056,205,188,222,FR,France
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+199350,3,627639,607097,648181,1094,1058,1130,FR,France
+199349,3,898045,873419,922671,1565,1522,1608,FR,France
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+199346,3,76034,68003,84065,133,119,147,FR,France
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+199344,3,17832,14015,21649,31,24,38,FR,France
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+199342,3,19187,14893,23481,33,26,40,FR,France
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+199339,3,14783,11173,18393,26,20,32,FR,France
+199338,3,10492,7329,13655,18,12,24,FR,France
+199337,3,7023,4503,9543,12,8,16,FR,France
+199336,3,4361,2445,6277,8,5,11,FR,France
+199335,3,5589,3066,8112,10,6,14,FR,France
+199334,3,2984,1209,4759,5,2,8,FR,France
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+199330,3,4055,1907,6203,7,3,11,FR,France
+199329,3,3714,1668,5760,6,2,10,FR,France
+199328,3,1774,484,3064,3,1,5,FR,France
+199327,3,2574,918,4230,4,1,7,FR,France
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+199318,3,5027,3075,6979,9,6,12,FR,France
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+199316,3,24932,19468,30396,43,33,53,FR,France
+199315,3,22181,17147,27215,39,30,48,FR,France
+199314,3,38266,30865,45667,67,54,80,FR,France
+199313,3,62987,54506,71468,110,95,125,FR,France
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+199301,3,75288,67244,83332,131,117,145,FR,France
+199253,3,56622,49107,64137,99,86,112,FR,France
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+199251,3,41156,35024,47288,72,61,83,FR,France
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+199249,3,39146,33847,44445,69,60,78,FR,France
+199248,3,32392,27499,37285,57,48,66,FR,France
+199247,3,26276,21797,30755,46,38,54,FR,France
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+199245,3,22023,17804,26242,39,32,46,FR,France
+199244,3,27619,22945,32293,48,40,56,FR,France
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+199242,3,29980,25261,34699,52,44,60,FR,France
+199241,3,24091,19953,28229,42,35,49,FR,France
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+199239,3,13535,10301,16769,24,18,30,FR,France
+199238,3,9837,7106,12568,17,12,22,FR,France
+199237,3,7480,5130,9830,13,9,17,FR,France
+199236,3,5155,2958,7352,9,5,13,FR,France
+199235,3,6383,3687,9079,11,6,16,FR,France
+199234,3,2953,922,4984,5,1,9,FR,France
+199233,3,4046,1549,6543,7,3,11,FR,France
+199232,3,4963,2215,7711,9,4,14,FR,France
+199231,3,4233,1733,6733,7,3,11,FR,France
+199230,3,3091,1145,5037,5,2,8,FR,France
+199229,3,3586,1583,5589,6,2,10,FR,France
+199228,3,4179,2133,6225,7,3,11,FR,France
+199227,3,5568,3134,8002,10,6,14,FR,France
+199226,3,5886,3229,8543,10,5,15,FR,France
+199225,3,5304,2805,7803,9,5,13,FR,France
+199224,3,5928,3482,8374,10,6,14,FR,France
+199223,3,3914,1855,5973,7,3,11,FR,France
+199222,3,4899,2641,7157,9,5,13,FR,France
+199221,3,5059,2889,7229,9,5,13,FR,France
+199220,3,4308,2226,6390,8,4,12,FR,France
+199219,3,2982,1181,4783,5,2,8,FR,France
+199218,3,3887,1777,5997,7,3,11,FR,France
+199217,3,3614,1216,6012,6,2,10,FR,France
+199216,3,5460,2934,7986,10,6,14,FR,France
+199215,3,8642,5029,12255,15,9,21,FR,France
+199214,3,11346,7536,15156,20,13,27,FR,France
+199213,3,11693,8096,15290,20,14,26,FR,France
+199212,3,11752,7834,15670,21,14,28,FR,France
+199211,3,11689,7575,15803,20,13,27,FR,France
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+199209,3,26629,20275,32983,47,36,58,FR,France
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+199207,3,57312,48777,65847,100,85,115,FR,France
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+199143,3,28842,21958,35726,51,39,63,FR,France
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+199139,3,14373,8734,20012,25,15,35,FR,France
+199138,3,7460,3700,11220,13,6,20,FR,France
+199137,3,2429,434,4424,4,0,8,FR,France
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+199124,3,8223,4660,11786,14,8,20,FR,France
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+199116,3,15665,11184,20146,28,20,36,FR,France
+199115,3,15062,10715,19409,26,18,34,FR,France
+199114,3,27862,21583,34141,49,38,60,FR,France
+199113,3,32179,25829,38529,57,46,68,FR,France
+199112,3,27873,22312,33434,49,39,59,FR,France
+199111,3,46186,38876,53496,81,68,94,FR,France
+199110,3,88750,77784,99716,156,137,175,FR,France
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+199102,3,29759,22044,37474,52,38,66,FR,France
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+199052,3,41262,31607,50917,73,56,90,FR,France
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+199047,3,23358,15890,30826,41,28,54,FR,France
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+199028,3,959,0,2252,2,0,4,FR,France
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+199026,3,7236,2651,11821,13,5,21,FR,France
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+199024,3,5727,2460,8994,10,4,16,FR,France
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+199021,3,1004,0,2367,2,0,4,FR,France
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+199017,3,5278,2197,8359,9,4,14,FR,France
+199016,3,8129,3754,12504,14,6,22,FR,France
+199015,3,11707,5831,17583,21,11,31,FR,France
+199014,3,12862,8177,17547,23,15,31,FR,France
+199013,3,21879,15088,28670,39,27,51,FR,France
+199012,3,17035,11135,22935,30,20,40,FR,France
+199011,3,20018,14311,25725,35,25,45,FR,France
+199010,3,16294,11045,21543,29,20,38,FR,France
+199009,3,21793,15460,28126,39,28,50,FR,France
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+199006,3,399984,353345,446623,707,625,789,FR,France
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+199002,3,345060,326087,364033,610,576,644,FR,France
+199001,3,483600,460913,506287,855,815,895,FR,France
+198952,3,581149,556023,606275,1035,990,1080,FR,France
+198951,3,821540,793846,849234,1463,1414,1512,FR,France
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+198949,3,358043,340053,376033,638,606,670,FR,France
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+198947,3,40963,33897,48029,73,60,86,FR,France
+198946,3,23547,17952,29142,42,32,52,FR,France
+198945,3,22711,16999,28423,40,30,50,FR,France
+198944,3,23405,17475,29335,42,31,53,FR,France
+198943,3,29549,23287,35811,53,42,64,FR,France
+198942,3,39410,32172,46648,70,57,83,FR,France
+198941,3,43763,35245,52281,78,63,93,FR,France
+198940,3,31453,24288,38618,56,43,69,FR,France
+198939,3,24622,18425,30819,44,33,55,FR,France
+198938,3,18296,13483,23109,33,24,42,FR,France
+198937,3,12022,8082,15962,21,14,28,FR,France
+198936,3,4769,2299,7239,8,4,12,FR,France
+198935,3,4661,1696,7626,8,3,13,FR,France
+198934,3,6039,2185,9893,11,4,18,FR,France
+198933,3,7940,3858,12022,14,7,21,FR,France
+198932,3,7114,2361,11867,13,5,21,FR,France
+198931,3,2721,0,5478,5,0,10,FR,France
+198930,3,3168,561,5775,6,1,11,FR,France
+198929,3,3909,1504,6314,7,3,11,FR,France
+198928,3,1284,105,2463,2,0,4,FR,France
+198927,3,3641,1350,5932,6,2,10,FR,France
+198926,3,5753,2885,8621,10,5,15,FR,France
+198925,3,6299,3454,9144,11,6,16,FR,France
+198924,3,6337,3537,9137,11,6,16,FR,France
+198923,3,11779,7984,15574,21,14,28,FR,France
+198922,3,9104,4764,13444,16,8,24,FR,France
+198921,3,9554,4316,14792,17,8,26,FR,France
+198920,3,6093,1023,11163,11,2,20,FR,France
+198919,3,0,,,0,,,FR,France
+198918,3,13452,1365,25539,24,2,46,FR,France
+198917,3,10949,7147,14751,19,12,26,FR,France
+198916,3,10908,7494,14322,19,13,25,FR,France
+198915,3,9667,6209,13125,17,11,23,FR,France
+198914,3,15230,10469,19991,27,19,35,FR,France
+198913,3,10564,6767,14361,19,12,26,FR,France
+198912,3,13419,9542,17296,24,17,31,FR,France
+198911,3,16633,12140,21126,30,22,38,FR,France
+198910,3,12387,8750,16024,22,16,28,FR,France
+198909,3,12895,9183,16607,23,16,30,FR,France
+198908,3,19746,15258,24234,35,27,43,FR,France
+198907,3,27277,21760,32794,49,39,59,FR,France
+198906,3,47844,40320,55368,85,72,98,FR,France
+198905,3,52840,45665,60015,94,81,107,FR,France
+198904,3,54942,47750,62134,98,85,111,FR,France
+198903,3,66010,58065,73955,118,104,132,FR,France
+198902,3,112284,102003,122565,200,182,218,FR,France
+198901,3,202266,188305,216227,360,335,385,FR,France
+198852,3,467971,447033,488909,837,800,874,FR,France
+198851,3,872748,846468,899028,1562,1515,1609,FR,France
+198850,3,1001824,974799,1028849,1793,1745,1841,FR,France
+198849,3,966523,940068,992978,1729,1682,1776,FR,France
+198848,3,637811,614785,660837,1141,1100,1182,FR,France
+198847,3,246062,231234,260890,440,413,467,FR,France
+198846,3,92484,83160,101808,165,148,182,FR,France
+198845,3,53940,46148,61732,97,83,111,FR,France
+198844,3,42106,35336,48876,75,63,87,FR,France
+198843,3,38252,31909,44595,68,57,79,FR,France
+198842,3,45495,38467,52523,81,68,94,FR,France
+198841,3,46336,39344,53328,83,70,96,FR,France
+198840,3,39882,33507,46257,71,60,82,FR,France
+198839,3,43730,36755,50705,78,66,90,FR,France
+198838,3,35243,29367,41119,63,52,74,FR,France
+198837,3,15537,11911,19163,28,22,34,FR,France
+198836,3,7539,4840,10238,13,8,18,FR,France
+198835,3,7797,4762,10832,14,9,19,FR,France
+198834,3,4950,2005,7895,9,4,14,FR,France
+198833,3,7937,3861,12013,14,7,21,FR,France
+198832,3,7033,2595,11471,13,5,21,FR,France
+198831,3,8758,3591,13925,16,7,25,FR,France
+198830,3,4205,1224,7186,8,3,13,FR,France
+198829,3,6103,1013,11193,11,2,20,FR,France
+198828,3,8562,3483,13641,15,6,24,FR,France
+198827,3,4690,2196,7184,8,4,12,FR,France
+198826,3,7065,3905,10225,13,7,19,FR,France
+198825,3,7657,4664,10650,14,9,19,FR,France
+198824,3,6676,3978,9374,12,7,17,FR,France
+198823,3,8695,5755,11635,16,11,21,FR,France
+198822,3,11566,8282,14850,21,15,27,FR,France
+198821,3,11107,7742,14472,20,14,26,FR,France
+198820,3,14118,10223,18013,25,18,32,FR,France
+198819,3,18260,13847,22673,33,25,41,FR,France
+198818,3,20415,16281,24549,37,30,44,FR,France
+198817,3,21750,17436,26064,39,31,47,FR,France
+198816,3,33730,27496,39964,60,49,71,FR,France
+198815,3,70616,61754,79478,126,110,142,FR,France
+198814,3,123868,113211,134525,222,203,241,FR,France
+198813,3,193710,181519,205901,347,325,369,FR,France
+198812,3,283445,268834,298056,507,481,533,FR,France
+198811,3,316175,300858,331492,566,539,593,FR,France
+198810,3,235142,222004,248280,421,397,445,FR,France
+198809,3,135564,125263,145865,243,225,261,FR,France
+198808,3,87753,78449,97057,157,140,174,FR,France
+198807,3,77099,68423,85775,138,122,154,FR,France
+198806,3,69349,61502,77196,124,110,138,FR,France
+198805,3,72705,64952,80458,130,116,144,FR,France
+198804,3,72818,64944,80692,130,116,144,FR,France
+198803,3,68869,60922,76816,123,109,137,FR,France
+198802,3,59743,51907,67579,107,93,121,FR,France
+198801,3,60929,52573,69285,109,94,124,FR,France
+198753,3,47257,38170,56344,85,69,101,FR,France
+198752,3,11935,2353,21517,21,4,38,FR,France
+198751,3,47744,34266,61222,86,62,110,FR,France
+198750,3,57767,50181,65353,104,90,118,FR,France
+198749,3,49380,42461,56299,89,77,101,FR,France
+198748,3,41871,35115,48627,75,63,87,FR,France
+198747,3,29688,23465,35911,53,42,64,FR,France
+198746,3,37216,30199,44233,67,54,80,FR,France
+198745,3,35456,29024,41888,64,52,76,FR,France
+198744,3,41024,33829,48219,74,61,87,FR,France
+198743,3,46356,38370,54342,83,69,97,FR,France
+198742,3,38021,30236,45806,68,54,82,FR,France
+198741,3,42021,32016,52026,76,58,94,FR,France
+198740,3,24641,15930,33352,44,28,60,FR,France
+198739,3,32328,23548,41108,58,42,74,FR,France
+198738,3,16782,11870,21694,30,21,39,FR,France
+198737,3,11208,6542,15874,20,12,28,FR,France
+198736,3,7906,4014,11798,14,7,21,FR,France
+198735,3,6149,2970,9328,11,5,17,FR,France
+198734,3,6631,2734,10528,12,5,19,FR,France
+198733,3,6404,1834,10974,12,4,20,FR,France
+198732,3,7215,2353,12077,13,4,22,FR,France
+198731,3,8233,3655,12811,15,7,23,FR,France
+198730,3,4642,1301,7983,8,2,14,FR,France
+198729,3,4389,913,7865,8,2,14,FR,France
+198728,3,2702,138,5266,5,0,10,FR,France
+198727,3,10171,5904,14438,18,10,26,FR,France
+198726,3,12341,7824,16858,22,14,30,FR,France
+198725,3,23413,15490,31336,42,28,56,FR,France
+198724,3,11289,7282,15296,20,13,27,FR,France
+198723,3,15857,10943,20771,29,20,38,FR,France
+198722,3,14887,9743,20031,27,18,36,FR,France
+198721,3,17619,12649,22589,32,23,41,FR,France
+198720,3,10344,6454,14234,19,12,26,FR,France
+198719,3,12539,7943,17135,23,15,31,FR,France
+198718,3,16095,10330,21860,29,19,39,FR,France
+198717,3,21256,14850,27662,38,26,50,FR,France
+198716,3,32416,25100,39732,58,45,71,FR,France
+198715,3,44292,36456,52128,80,66,94,FR,France
+198714,3,50642,41866,59418,91,75,107,FR,France
+198713,3,62246,52749,71743,112,95,129,FR,France
+198712,3,59625,50653,68597,107,91,123,FR,France
+198711,3,53561,45035,62087,96,81,111,FR,France
+198710,3,93067,80519,105615,167,144,190,FR,France
+198709,3,147006,133519,160493,264,240,288,FR,France
+198708,3,217779,202179,233379,391,363,419,FR,France
+198707,3,288180,270718,305642,518,487,549,FR,France
+198706,3,296702,279051,314353,533,501,565,FR,France
+198705,3,236046,219483,252609,424,394,454,FR,France
+198704,3,158333,143625,173041,285,259,311,FR,France
+198703,3,103841,91699,115983,187,165,209,FR,France
+198702,3,74213,64014,84412,133,115,151,FR,France
+198701,3,69745,59207,80283,125,106,144,FR,France
+198652,3,76263,64958,87568,138,118,158,FR,France
+198651,3,72920,63047,82793,132,114,150,FR,France
+198650,3,73413,64019,82807,133,116,150,FR,France
+198649,3,56296,48229,64363,102,87,117,FR,France
+198648,3,46031,38578,53484,83,70,96,FR,France
+198647,3,36740,29710,43770,66,53,79,FR,France
+198646,3,32134,25327,38941,58,46,70,FR,France
+198645,3,38964,31382,46546,70,56,84,FR,France
+198644,3,28646,22048,35244,52,40,64,FR,France
+198643,3,27532,20842,34222,50,38,62,FR,France
+198642,3,31815,25124,38506,57,45,69,FR,France
+198641,3,32864,25939,39789,59,47,71,FR,France
+198640,3,33516,26776,40256,60,48,72,FR,France
+198639,3,28665,22477,34853,52,41,63,FR,France
+198638,3,26615,20479,32751,48,37,59,FR,France
+198637,3,15998,10802,21194,29,20,38,FR,France
+198636,3,9182,4982,13382,17,9,25,FR,France
+198635,3,10587,4470,16704,19,8,30,FR,France
+198634,3,5762,858,10666,10,1,19,FR,France
+198633,3,3115,554,5676,6,1,11,FR,France
+198632,3,1027,0,2311,2,0,4,FR,France
+198631,3,2247,0,4574,4,0,8,FR,France
+198630,3,2346,141,4551,4,0,8,FR,France
+198629,3,3654,931,6377,7,2,12,FR,France
+198628,3,2327,0,5318,4,0,9,FR,France
+198627,3,7529,3353,11705,14,6,22,FR,France
+198626,3,8778,5106,12450,16,9,23,FR,France
+198625,3,11075,6804,15346,20,12,28,FR,France
+198624,3,11160,7252,15068,20,13,27,FR,France
+198623,3,14372,9896,18848,26,18,34,FR,France
+198622,3,9717,5594,13840,18,11,25,FR,France
+198621,3,14460,9575,19345,26,17,35,FR,France
+198620,3,17133,12106,22160,31,22,40,FR,France
+198619,3,22177,16008,28346,40,29,51,FR,France
+198618,3,31736,24115,39357,57,43,71,FR,France
+198617,3,34874,27440,42308,63,50,76,FR,France
+198616,3,41196,32894,49498,74,59,89,FR,France
+198615,3,42659,33537,51781,77,61,93,FR,France
+198614,3,49792,39728,59856,90,72,108,FR,France
+198613,3,87249,63562,110936,157,114,200,FR,France
+198612,3,124777,97237,152317,225,175,275,FR,France
+198611,3,168146,151035,185257,303,272,334,FR,France
+198610,3,257305,231225,283385,464,417,511,FR,France
+198609,3,321140,296644,345636,580,536,624,FR,France
+198608,3,448786,419219,478353,810,757,863,FR,France
+198607,3,490723,460150,521296,886,831,941,FR,France
+198606,3,439300,411764,466836,793,743,843,FR,France
+198605,3,364727,339868,389586,658,613,703,FR,France
+198604,3,348509,322464,374554,629,582,676,FR,France
+198603,3,271539,248654,294424,490,449,531,FR,France
+198602,3,178138,159344,196932,322,288,356,FR,France
+198601,3,112614,96539,128689,203,174,232,FR,France
+198552,3,86452,72250,100654,157,131,183,FR,France
+198551,3,114666,99538,129794,208,181,235,FR,France
+198550,3,86031,73604,98458,156,133,179,FR,France
+198549,3,65931,55326,76536,120,101,139,FR,France
+198548,3,68291,57581,79001,124,105,143,FR,France
+198547,3,66177,55221,77133,120,100,140,FR,France
+198546,3,70534,58788,82280,128,107,149,FR,France
+198545,3,57514,47167,67861,104,85,123,FR,France
+198544,3,67356,55744,78968,122,101,143,FR,France
+198543,3,87402,74208,100596,158,134,182,FR,France
+198542,3,76218,64604,87832,138,117,159,FR,France
+198541,3,45712,36755,54669,83,67,99,FR,France
+198540,3,49739,40335,59143,90,73,107,FR,France
+198539,3,39819,31234,48404,72,56,88,FR,France
+198538,3,27605,20614,34596,50,37,63,FR,France
+198537,3,22219,15626,28812,40,28,52,FR,France
+198536,3,27986,18992,36980,51,35,67,FR,France
+198535,3,30215,18546,41884,55,34,76,FR,France
+198534,3,40389,24786,55992,73,45,101,FR,France
+198533,3,22611,8819,36403,41,16,66,FR,France
+198532,3,7488,1851,13125,14,4,24,FR,France
+198531,3,16958,8479,25437,31,16,46,FR,France
+198530,3,11598,5507,17689,21,10,32,FR,France
+198529,3,13054,6474,19634,24,12,36,FR,France
+198528,3,14588,7659,21517,26,13,39,FR,France
+198527,3,19670,11761,27579,36,22,50,FR,France
+198526,3,18609,12637,24581,34,23,45,FR,France
+198525,3,19362,12454,26270,35,22,48,FR,France
+198524,3,19855,13577,26133,36,25,47,FR,France
+198523,3,19373,10010,28736,35,18,52,FR,France
+198522,3,24099,17190,31008,44,31,57,FR,France
+198521,3,26096,19621,32571,47,35,59,FR,France
+198520,3,27896,20885,34907,51,38,64,FR,France
+198519,3,43154,32821,53487,78,59,97,FR,France
+198518,3,40555,29935,51175,74,55,93,FR,France
+198517,3,34053,24366,43740,62,44,80,FR,France
+198516,3,50362,36451,64273,91,66,116,FR,France
+198515,3,63881,45538,82224,116,83,149,FR,France
+198514,3,134545,114400,154690,244,207,281,FR,France
+198513,3,197206,176080,218332,357,319,395,FR,France
+198512,3,245240,223304,267176,445,405,485,FR,France
+198511,3,276205,252399,300011,501,458,544,FR,France
+198510,3,353231,326279,380183,640,591,689,FR,France
+198509,3,369895,341109,398681,670,618,722,FR,France
+198508,3,389886,359529,420243,707,652,762,FR,France
+198507,3,471852,432599,511105,855,784,926,FR,France
+198506,3,565825,518011,613639,1026,939,1113,FR,France
+198505,3,637302,592795,681809,1155,1074,1236,FR,France
+198504,3,424937,390794,459080,770,708,832,FR,France
+198503,3,213901,174689,253113,388,317,459,FR,France
+198502,3,97586,80949,114223,177,147,207,FR,France
+198501,3,85489,65918,105060,155,120,190,FR,France
+198452,3,84830,60602,109058,154,110,198,FR,France
+198451,3,101726,80242,123210,185,146,224,FR,France
+198450,3,123680,101401,145959,225,184,266,FR,France
+198449,3,101073,81684,120462,184,149,219,FR,France
+198448,3,78620,60634,96606,143,110,176,FR,France
+198447,3,72029,54274,89784,131,99,163,FR,France
+198446,3,87330,67686,106974,159,123,195,FR,France
+198445,3,135223,101414,169032,246,184,308,FR,France
+198444,3,68422,20056,116788,125,37,213,FR,France
diff --git a/module3/exo2/.ipynb_checkpoints/exercice_en-checkpoint.ipynb b/module3/exo2/.ipynb_checkpoints/exercice_en-checkpoint.ipynb
new file mode 100644
index 0000000..0bbbe37
--- /dev/null
+++ b/module3/exo2/.ipynb_checkpoints/exercice_en-checkpoint.ipynb
@@ -0,0 +1,25 @@
+{
+ "cells": [],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
+
diff --git a/module3/exo2/.ipynb_checkpoints/exercice_fr-checkpoint.ipynb b/module3/exo2/.ipynb_checkpoints/exercice_fr-checkpoint.ipynb
new file mode 100644
index 0000000..0bbbe37
--- /dev/null
+++ b/module3/exo2/.ipynb_checkpoints/exercice_fr-checkpoint.ipynb
@@ -0,0 +1,25 @@
+{
+ "cells": [],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
+
diff --git a/module3/exo3/.ipynb_checkpoints/exercice_fr-checkpoint.ipynb b/module3/exo3/.ipynb_checkpoints/exercice_fr-checkpoint.ipynb
new file mode 100644
index 0000000..cfbb200
--- /dev/null
+++ b/module3/exo3/.ipynb_checkpoints/exercice_fr-checkpoint.ipynb
@@ -0,0 +1,930 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analyse d'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 csv\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lecture des données"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "URL = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r = requests.get(URL)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file_data = r\"C:\\Users\\renau\\mooc-rr\\module3\\exo3\\incidence_varicelles.csv\" \n",
+    "if (Path(file_data).is_file() == False): \n",
+    "    file_data = open(file_data,\"w\",encoding = \"ANSI\")\n",
+    "    file_data.write(r.text)\n",
+    "    file_data.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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>202106</td>\n",
+       "      <td>7</td>\n",
+       "      <td>14719</td>\n",
+       "      <td>10462</td>\n",
+       "      <td>18976</td>\n",
+       "      <td>22</td>\n",
+       "      <td>16</td>\n",
+       "      <td>28</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202105</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12379</td>\n",
+       "      <td>9107</td>\n",
+       "      <td>15651</td>\n",
+       "      <td>19</td>\n",
+       "      <td>14</td>\n",
+       "      <td>24</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202104</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12026</td>\n",
+       "      <td>8826</td>\n",
+       "      <td>15226</td>\n",
+       "      <td>18</td>\n",
+       "      <td>13</td>\n",
+       "      <td>23</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202103</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8913</td>\n",
+       "      <td>6375</td>\n",
+       "      <td>11451</td>\n",
+       "      <td>13</td>\n",
+       "      <td>9</td>\n",
+       "      <td>17</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202102</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7795</td>\n",
+       "      <td>5430</td>\n",
+       "      <td>10160</td>\n",
+       "      <td>12</td>\n",
+       "      <td>8</td>\n",
+       "      <td>16</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</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>1571</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>1572</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>1573</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>1574</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>1575</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>1576 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
+       "0     202106          7  14719    10462   18976      22          16   \n",
+       "1     202105          7  12379     9107   15651      19          14   \n",
+       "2     202104          7  12026     8826   15226      18          13   \n",
+       "3     202103          7   8913     6375   11451      13           9   \n",
+       "4     202102          7   7795     5430   10160      12           8   \n",
+       "...      ...        ...    ...      ...     ...     ...         ...   \n",
+       "1571  199101          7  15565    10271   20859      27          18   \n",
+       "1572  199052          7  19375    13295   25455      34          23   \n",
+       "1573  199051          7  19080    13807   24353      34          25   \n",
+       "1574  199050          7  11079     6660   15498      20          12   \n",
+       "1575  199049          7   1143        0    2610       2           0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0            28        FR   France  \n",
+       "1            24        FR   France  \n",
+       "2            23        FR   France  \n",
+       "3            17        FR   France  \n",
+       "4            16        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1571         36        FR   France  \n",
+       "1572         45        FR   France  \n",
+       "1573         43        FR   France  \n",
+       "1574         28        FR   France  \n",
+       "1575          5        FR   France  \n",
+       "\n",
+       "[1576 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_raw = pd.read_csv(file_data, sep =',', encoding = 'ANSI', skiprows = 1)\n",
+    "data_raw"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_raw[data_raw.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Je ne constate pas la présence de données nulles. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = data_raw"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "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')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['period'] = [convert_week(yw) for yw in data_raw['week']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted_data = data.set_index('period').sort_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "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",
+       "    <tr>\n",
+       "      <th>period</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1990-12-03/1990-12-09</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",
+       "    <tr>\n",
+       "      <th>1990-12-10/1990-12-16</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>1990-12-17/1990-12-23</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>1990-12-24/1990-12-30</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>1990-12-31/1991-01-06</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>...</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>2021-01-11/2021-01-17</th>\n",
+       "      <td>202102</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7795</td>\n",
+       "      <td>5430</td>\n",
+       "      <td>10160</td>\n",
+       "      <td>12</td>\n",
+       "      <td>8</td>\n",
+       "      <td>16</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-01-18/2021-01-24</th>\n",
+       "      <td>202103</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8913</td>\n",
+       "      <td>6375</td>\n",
+       "      <td>11451</td>\n",
+       "      <td>13</td>\n",
+       "      <td>9</td>\n",
+       "      <td>17</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-01-25/2021-01-31</th>\n",
+       "      <td>202104</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12026</td>\n",
+       "      <td>8826</td>\n",
+       "      <td>15226</td>\n",
+       "      <td>18</td>\n",
+       "      <td>13</td>\n",
+       "      <td>23</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-02-01/2021-02-07</th>\n",
+       "      <td>202105</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12379</td>\n",
+       "      <td>9107</td>\n",
+       "      <td>15651</td>\n",
+       "      <td>19</td>\n",
+       "      <td>14</td>\n",
+       "      <td>24</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-02-08/2021-02-14</th>\n",
+       "      <td>202106</td>\n",
+       "      <td>7</td>\n",
+       "      <td>14719</td>\n",
+       "      <td>10462</td>\n",
+       "      <td>18976</td>\n",
+       "      <td>22</td>\n",
+       "      <td>16</td>\n",
+       "      <td>28</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1576 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         week  indicator    inc  inc_low  inc_up  inc100  \\\n",
+       "period                                                                     \n",
+       "1990-12-03/1990-12-09  199049          7   1143        0    2610       2   \n",
+       "1990-12-10/1990-12-16  199050          7  11079     6660   15498      20   \n",
+       "1990-12-17/1990-12-23  199051          7  19080    13807   24353      34   \n",
+       "1990-12-24/1990-12-30  199052          7  19375    13295   25455      34   \n",
+       "1990-12-31/1991-01-06  199101          7  15565    10271   20859      27   \n",
+       "...                       ...        ...    ...      ...     ...     ...   \n",
+       "2021-01-11/2021-01-17  202102          7   7795     5430   10160      12   \n",
+       "2021-01-18/2021-01-24  202103          7   8913     6375   11451      13   \n",
+       "2021-01-25/2021-01-31  202104          7  12026     8826   15226      18   \n",
+       "2021-02-01/2021-02-07  202105          7  12379     9107   15651      19   \n",
+       "2021-02-08/2021-02-14  202106          7  14719    10462   18976      22   \n",
+       "\n",
+       "                       inc100_low  inc100_up geo_insee geo_name  \n",
+       "period                                                           \n",
+       "1990-12-03/1990-12-09           0          5        FR   France  \n",
+       "1990-12-10/1990-12-16          12         28        FR   France  \n",
+       "1990-12-17/1990-12-23          25         43        FR   France  \n",
+       "1990-12-24/1990-12-30          23         45        FR   France  \n",
+       "1990-12-31/1991-01-06          18         36        FR   France  \n",
+       "...                           ...        ...       ...      ...  \n",
+       "2021-01-11/2021-01-17           8         16        FR   France  \n",
+       "2021-01-18/2021-01-24           9         17        FR   France  \n",
+       "2021-01-25/2021-01-31          13         23        FR   France  \n",
+       "2021-02-01/2021-02-07          14         24        FR   France  \n",
+       "2021-02-08/2021-02-14          16         28        FR   France  \n",
+       "\n",
+       "[1576 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "periods = sorted_data.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PeriodIndex(['1990-12-03/1990-12-09', '1990-12-10/1990-12-16',\n",
+       "             '1990-12-17/1990-12-23', '1990-12-24/1990-12-30',\n",
+       "             '1990-12-31/1991-01-06', '1991-01-07/1991-01-13',\n",
+       "             '1991-01-14/1991-01-20', '1991-01-21/1991-01-27',\n",
+       "             '1991-01-28/1991-02-03', '1991-02-04/1991-02-10',\n",
+       "             ...\n",
+       "             '2020-12-07/2020-12-13', '2020-12-14/2020-12-20',\n",
+       "             '2020-12-21/2020-12-27', '2020-12-28/2021-01-03',\n",
+       "             '2021-01-04/2021-01-10', '2021-01-11/2021-01-17',\n",
+       "             '2021-01-18/2021-01-24', '2021-01-25/2021-01-31',\n",
+       "             '2021-02-01/2021-02-07', '2021-02-08/2021-02-14'],\n",
+       "            dtype='period[W-SUN]', name='period', length=1576, freq='W-SUN')"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "periods"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
+    "    delta = p2.to_timestamp() - p1.end_time\n",
+    "    if delta > pd.Timedelta('1s'):\n",
+    "        print(p1, p2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='period'>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data['inc'].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='period'>"
+      ]
+     },
+     "execution_count": 31,
+     "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 gros de l'épidémie va de janvier à juillet, la période de référence entre deux minima de l'incidence, est fixée du 1er septembre de l'année $N$ au 1er septembre de l'année $N+1$. On comence en 1991."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "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": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year = []\n",
+    "yearly_incidence = []\n",
+    "for week1, week2 in zip(first_september_week[:-1],\n",
+    "                        first_september_week[1:]):\n",
+    "    one_year = sorted_data['inc'][week1:week2-1]\n",
+    "    assert abs(len(one_year)-52) < 2\n",
+    "    yearly_incidence.append(one_year.sum())\n",
+    "    year.append(week2.year)\n",
+    "yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yearly_incidence.plot(style='*')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2020    221186\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",
+       "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": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yearly_incidence.hist(xrot=20)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/module3/exo3/exercice_fr.ipynb b/module3/exo3/exercice_fr.ipynb
index 0bbbe37..cfbb200 100644
--- a/module3/exo3/exercice_fr.ipynb
+++ b/module3/exo3/exercice_fr.ipynb
@@ -1,5 +1,911 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analyse d'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 csv\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lecture des données"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "URL = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r = requests.get(URL)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file_data = r\"C:\\Users\\renau\\mooc-rr\\module3\\exo3\\incidence_varicelles.csv\" \n",
+    "if (Path(file_data).is_file() == False): \n",
+    "    file_data = open(file_data,\"w\",encoding = \"ANSI\")\n",
+    "    file_data.write(r.text)\n",
+    "    file_data.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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>202106</td>\n",
+       "      <td>7</td>\n",
+       "      <td>14719</td>\n",
+       "      <td>10462</td>\n",
+       "      <td>18976</td>\n",
+       "      <td>22</td>\n",
+       "      <td>16</td>\n",
+       "      <td>28</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202105</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12379</td>\n",
+       "      <td>9107</td>\n",
+       "      <td>15651</td>\n",
+       "      <td>19</td>\n",
+       "      <td>14</td>\n",
+       "      <td>24</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202104</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12026</td>\n",
+       "      <td>8826</td>\n",
+       "      <td>15226</td>\n",
+       "      <td>18</td>\n",
+       "      <td>13</td>\n",
+       "      <td>23</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202103</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8913</td>\n",
+       "      <td>6375</td>\n",
+       "      <td>11451</td>\n",
+       "      <td>13</td>\n",
+       "      <td>9</td>\n",
+       "      <td>17</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202102</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7795</td>\n",
+       "      <td>5430</td>\n",
+       "      <td>10160</td>\n",
+       "      <td>12</td>\n",
+       "      <td>8</td>\n",
+       "      <td>16</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</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>1571</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>1572</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>1573</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>1574</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>1575</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>1576 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
+       "0     202106          7  14719    10462   18976      22          16   \n",
+       "1     202105          7  12379     9107   15651      19          14   \n",
+       "2     202104          7  12026     8826   15226      18          13   \n",
+       "3     202103          7   8913     6375   11451      13           9   \n",
+       "4     202102          7   7795     5430   10160      12           8   \n",
+       "...      ...        ...    ...      ...     ...     ...         ...   \n",
+       "1571  199101          7  15565    10271   20859      27          18   \n",
+       "1572  199052          7  19375    13295   25455      34          23   \n",
+       "1573  199051          7  19080    13807   24353      34          25   \n",
+       "1574  199050          7  11079     6660   15498      20          12   \n",
+       "1575  199049          7   1143        0    2610       2           0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0            28        FR   France  \n",
+       "1            24        FR   France  \n",
+       "2            23        FR   France  \n",
+       "3            17        FR   France  \n",
+       "4            16        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1571         36        FR   France  \n",
+       "1572         45        FR   France  \n",
+       "1573         43        FR   France  \n",
+       "1574         28        FR   France  \n",
+       "1575          5        FR   France  \n",
+       "\n",
+       "[1576 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_raw = pd.read_csv(file_data, sep =',', encoding = 'ANSI', skiprows = 1)\n",
+    "data_raw"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_raw[data_raw.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Je ne constate pas la présence de données nulles. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = data_raw"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "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')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['period'] = [convert_week(yw) for yw in data_raw['week']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted_data = data.set_index('period').sort_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "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",
+       "    <tr>\n",
+       "      <th>period</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1990-12-03/1990-12-09</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",
+       "    <tr>\n",
+       "      <th>1990-12-10/1990-12-16</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>1990-12-17/1990-12-23</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>1990-12-24/1990-12-30</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>1990-12-31/1991-01-06</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>...</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>2021-01-11/2021-01-17</th>\n",
+       "      <td>202102</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7795</td>\n",
+       "      <td>5430</td>\n",
+       "      <td>10160</td>\n",
+       "      <td>12</td>\n",
+       "      <td>8</td>\n",
+       "      <td>16</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-01-18/2021-01-24</th>\n",
+       "      <td>202103</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8913</td>\n",
+       "      <td>6375</td>\n",
+       "      <td>11451</td>\n",
+       "      <td>13</td>\n",
+       "      <td>9</td>\n",
+       "      <td>17</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-01-25/2021-01-31</th>\n",
+       "      <td>202104</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12026</td>\n",
+       "      <td>8826</td>\n",
+       "      <td>15226</td>\n",
+       "      <td>18</td>\n",
+       "      <td>13</td>\n",
+       "      <td>23</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-02-01/2021-02-07</th>\n",
+       "      <td>202105</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12379</td>\n",
+       "      <td>9107</td>\n",
+       "      <td>15651</td>\n",
+       "      <td>19</td>\n",
+       "      <td>14</td>\n",
+       "      <td>24</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-02-08/2021-02-14</th>\n",
+       "      <td>202106</td>\n",
+       "      <td>7</td>\n",
+       "      <td>14719</td>\n",
+       "      <td>10462</td>\n",
+       "      <td>18976</td>\n",
+       "      <td>22</td>\n",
+       "      <td>16</td>\n",
+       "      <td>28</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1576 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         week  indicator    inc  inc_low  inc_up  inc100  \\\n",
+       "period                                                                     \n",
+       "1990-12-03/1990-12-09  199049          7   1143        0    2610       2   \n",
+       "1990-12-10/1990-12-16  199050          7  11079     6660   15498      20   \n",
+       "1990-12-17/1990-12-23  199051          7  19080    13807   24353      34   \n",
+       "1990-12-24/1990-12-30  199052          7  19375    13295   25455      34   \n",
+       "1990-12-31/1991-01-06  199101          7  15565    10271   20859      27   \n",
+       "...                       ...        ...    ...      ...     ...     ...   \n",
+       "2021-01-11/2021-01-17  202102          7   7795     5430   10160      12   \n",
+       "2021-01-18/2021-01-24  202103          7   8913     6375   11451      13   \n",
+       "2021-01-25/2021-01-31  202104          7  12026     8826   15226      18   \n",
+       "2021-02-01/2021-02-07  202105          7  12379     9107   15651      19   \n",
+       "2021-02-08/2021-02-14  202106          7  14719    10462   18976      22   \n",
+       "\n",
+       "                       inc100_low  inc100_up geo_insee geo_name  \n",
+       "period                                                           \n",
+       "1990-12-03/1990-12-09           0          5        FR   France  \n",
+       "1990-12-10/1990-12-16          12         28        FR   France  \n",
+       "1990-12-17/1990-12-23          25         43        FR   France  \n",
+       "1990-12-24/1990-12-30          23         45        FR   France  \n",
+       "1990-12-31/1991-01-06          18         36        FR   France  \n",
+       "...                           ...        ...       ...      ...  \n",
+       "2021-01-11/2021-01-17           8         16        FR   France  \n",
+       "2021-01-18/2021-01-24           9         17        FR   France  \n",
+       "2021-01-25/2021-01-31          13         23        FR   France  \n",
+       "2021-02-01/2021-02-07          14         24        FR   France  \n",
+       "2021-02-08/2021-02-14          16         28        FR   France  \n",
+       "\n",
+       "[1576 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "periods = sorted_data.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PeriodIndex(['1990-12-03/1990-12-09', '1990-12-10/1990-12-16',\n",
+       "             '1990-12-17/1990-12-23', '1990-12-24/1990-12-30',\n",
+       "             '1990-12-31/1991-01-06', '1991-01-07/1991-01-13',\n",
+       "             '1991-01-14/1991-01-20', '1991-01-21/1991-01-27',\n",
+       "             '1991-01-28/1991-02-03', '1991-02-04/1991-02-10',\n",
+       "             ...\n",
+       "             '2020-12-07/2020-12-13', '2020-12-14/2020-12-20',\n",
+       "             '2020-12-21/2020-12-27', '2020-12-28/2021-01-03',\n",
+       "             '2021-01-04/2021-01-10', '2021-01-11/2021-01-17',\n",
+       "             '2021-01-18/2021-01-24', '2021-01-25/2021-01-31',\n",
+       "             '2021-02-01/2021-02-07', '2021-02-08/2021-02-14'],\n",
+       "            dtype='period[W-SUN]', name='period', length=1576, freq='W-SUN')"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "periods"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
+    "    delta = p2.to_timestamp() - p1.end_time\n",
+    "    if delta > pd.Timedelta('1s'):\n",
+    "        print(p1, p2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='period'>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data['inc'].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='period'>"
+      ]
+     },
+     "execution_count": 31,
+     "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 gros de l'épidémie va de janvier à juillet, la période de référence entre deux minima de l'incidence, est fixée du 1er septembre de l'année $N$ au 1er septembre de l'année $N+1$. On comence en 1991."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "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": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year = []\n",
+    "yearly_incidence = []\n",
+    "for week1, week2 in zip(first_september_week[:-1],\n",
+    "                        first_september_week[1:]):\n",
+    "    one_year = sorted_data['inc'][week1:week2-1]\n",
+    "    assert abs(len(one_year)-52) < 2\n",
+    "    yearly_incidence.append(one_year.sum())\n",
+    "    year.append(week2.year)\n",
+    "yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yearly_incidence.plot(style='*')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2020    221186\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",
+       "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": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yearly_incidence.hist(xrot=20)"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +922,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
-
diff --git a/module3/exo3/incidence_varicelles.csv b/module3/exo3/incidence_varicelles.csv
new file mode 100644
index 0000000..7e8247a
--- /dev/null
+++ b/module3/exo3/incidence_varicelles.csv
@@ -0,0 +1,1578 @@
+# @source="réseau Sentinelles, INSERM, Sorbonne Université, http://www.sentiweb.fr", @meta={"period":[199049,202106],"geo":["PAY","1"],"geo_ref":"insee","indicator":"7","type":"all","conf_int":true,"compact":false}, @date=2021-02-21T19:07:07+01:00
+week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name
+202106,7,14719,10462,18976,22,16,28,FR,France
+202105,7,12379,9107,15651,19,14,24,FR,France
+202104,7,12026,8826,15226,18,13,23,FR,France
+202103,7,8913,6375,11451,13,9,17,FR,France
+202102,7,7795,5430,10160,12,8,16,FR,France
+202101,7,10525,7750,13300,16,12,20,FR,France
+202053,7,11978,8406,15550,18,13,23,FR,France
+202052,7,12012,8285,15739,18,12,24,FR,France
+202051,7,10564,7574,13554,16,11,21,FR,France
+202050,7,7063,4744,9382,11,7,15,FR,France
+202049,7,5026,3145,6907,8,5,11,FR,France
+202048,7,6683,4312,9054,10,6,14,FR,France
+202047,7,4999,2963,7035,8,5,11,FR,France
+202046,7,3752,1963,5541,6,3,9,FR,France
+202045,7,3696,2016,5376,6,3,9,FR,France
+202044,7,4391,2375,6407,7,4,10,FR,France
+202043,7,4376,2505,6247,7,4,10,FR,France
+202042,7,4000,1979,6021,6,3,9,FR,France
+202041,7,3961,2099,5823,6,3,9,FR,France
+202040,7,2078,675,3481,3,1,5,FR,France
+202039,7,1049,237,1861,2,1,3,FR,France
+202038,7,2253,782,3724,3,1,5,FR,France
+202037,7,1584,405,2763,2,0,4,FR,France
+202036,7,919,100,1738,1,0,2,FR,France
+202035,7,828,0,1694,1,0,2,FR,France
+202034,7,2272,371,4173,3,0,6,FR,France
+202033,7,1284,177,2391,2,0,4,FR,France
+202032,7,2650,689,4611,4,1,7,FR,France
+202031,7,1303,100,2506,2,0,4,FR,France
+202030,7,1385,75,2695,2,0,4,FR,France
+202029,7,841,10,1672,1,0,2,FR,France
+202028,7,728,0,1515,1,0,2,FR,France
+202027,7,986,149,1823,1,0,2,FR,France
+202026,7,694,0,1454,1,0,2,FR,France
+202025,7,228,0,597,0,0,1,FR,France
+202024,7,388,0,959,1,0,2,FR,France
+202023,7,558,1,1115,1,0,2,FR,France
+202022,7,277,0,633,0,0,1,FR,France
+202021,7,602,36,1168,1,0,2,FR,France
+202020,7,824,20,1628,1,0,2,FR,France
+202019,7,310,0,753,0,0,1,FR,France
+202018,7,849,98,1600,1,0,2,FR,France
+202017,7,272,0,658,0,0,1,FR,France
+202016,7,758,78,1438,1,0,2,FR,France
+202015,7,1918,675,3161,3,1,5,FR,France
+202014,7,3879,2227,5531,6,3,9,FR,France
+202013,7,7326,5236,9416,11,8,14,FR,France
+202012,7,8123,5790,10456,12,8,16,FR,France
+202011,7,10198,7568,12828,15,11,19,FR,France
+202010,7,9011,6691,11331,14,10,18,FR,France
+202009,7,13631,10544,16718,21,16,26,FR,France
+202008,7,10424,7708,13140,16,12,20,FR,France
+202007,7,8959,6574,11344,14,10,18,FR,France
+202006,7,9264,6925,11603,14,10,18,FR,France
+202005,7,8505,6314,10696,13,10,16,FR,France
+202004,7,7991,5831,10151,12,9,15,FR,France
+202003,7,5968,4100,7836,9,6,12,FR,France
+202002,7,6534,4530,8538,10,7,13,FR,France
+202001,7,9835,7019,12651,15,11,19,FR,France
+201952,7,7941,5246,10636,12,8,16,FR,France
+201951,7,5823,3675,7971,9,6,12,FR,France
+201950,7,6424,4276,8572,10,7,13,FR,France
+201949,7,6621,4540,8702,10,7,13,FR,France
+201948,7,5542,3383,7701,8,5,11,FR,France
+201947,7,7536,5058,10014,11,7,15,FR,France
+201946,7,2638,1316,3960,4,2,6,FR,France
+201945,7,4492,2615,6369,7,4,10,FR,France
+201944,7,5728,3627,7829,9,6,12,FR,France
+201943,7,4834,2751,6917,7,4,10,FR,France
+201942,7,6279,3989,8569,10,7,13,FR,France
+201941,7,4130,2030,6230,6,3,9,FR,France
+201940,7,4211,2218,6204,6,3,9,FR,France
+201939,7,3137,1310,4964,5,2,8,FR,France
+201938,7,3078,1416,4740,5,2,8,FR,France
+201937,7,970,162,1778,1,0,2,FR,France
+201936,7,1277,263,2291,2,0,4,FR,France
+201935,7,922,0,1857,1,0,2,FR,France
+201934,7,1997,605,3389,3,1,5,FR,France
+201933,7,3279,1103,5455,5,2,8,FR,France
+201932,7,3627,1576,5678,6,3,9,FR,France
+201931,7,7611,4605,10617,12,7,17,FR,France
+201930,7,8875,5629,12121,13,8,18,FR,France
+201929,7,10658,7151,14165,16,11,21,FR,France
+201928,7,11251,7604,14898,17,11,23,FR,France
+201927,7,16602,12349,20855,25,19,31,FR,France
+201926,7,18851,14491,23211,29,22,36,FR,France
+201925,7,18309,13972,22646,28,21,35,FR,France
+201924,7,13382,9659,17105,20,14,26,FR,France
+201923,7,19264,15027,23501,29,23,35,FR,France
+201922,7,16861,12783,20939,26,20,32,FR,France
+201921,7,17572,13140,22004,27,20,34,FR,France
+201920,7,16180,12310,20050,25,19,31,FR,France
+201919,7,16646,12721,20571,25,19,31,FR,France
+201918,7,13803,10159,17447,21,15,27,FR,France
+201917,7,15868,11815,19921,24,18,30,FR,France
+201916,7,17127,12815,21439,26,19,33,FR,France
+201915,7,21449,16525,26373,33,26,40,FR,France
+201914,7,16131,12254,20008,25,19,31,FR,France
+201913,7,17264,13409,21119,26,20,32,FR,France
+201912,7,14133,10499,17767,21,15,27,FR,France
+201911,7,12655,9528,15782,19,14,24,FR,France
+201910,7,14062,10547,17577,21,16,26,FR,France
+201909,7,13725,10221,17229,21,16,26,FR,France
+201908,7,17186,13156,21216,26,20,32,FR,France
+201907,7,10206,7388,13024,16,12,20,FR,France
+201906,7,13504,10402,16606,21,16,26,FR,France
+201905,7,12799,9843,15755,19,15,23,FR,France
+201904,7,11842,9041,14643,18,14,22,FR,France
+201903,7,8616,6114,11118,13,9,17,FR,France
+201902,7,12005,9035,14975,18,13,23,FR,France
+201901,7,15951,12224,19678,24,18,30,FR,France
+201852,7,16234,11915,20553,25,18,32,FR,France
+201851,7,16859,12787,20931,26,20,32,FR,France
+201850,7,11554,8405,14703,18,13,23,FR,France
+201849,7,12632,9322,15942,19,14,24,FR,France
+201848,7,8093,5509,10677,12,8,16,FR,France
+201847,7,8670,5920,11420,13,9,17,FR,France
+201846,7,7039,4453,9625,11,7,15,FR,France
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+199441,7,4921,2796,7046,9,5,13,FR,France
+199440,7,5936,3694,8178,10,6,14,FR,France
+199439,7,4090,2080,6100,7,4,10,FR,France
+199438,7,2734,1164,4304,5,2,8,FR,France
+199437,7,4364,1957,6771,8,4,12,FR,France
+199436,7,1991,509,3473,3,0,6,FR,France
+199435,7,3676,1494,5858,6,2,10,FR,France
+199434,7,6830,3728,9932,12,7,17,FR,France
+199433,7,6933,3534,10332,12,6,18,FR,France
+199432,7,4851,2299,7403,8,4,12,FR,France
+199431,7,7346,3935,10757,13,7,19,FR,France
+199430,7,17674,12807,22541,31,23,39,FR,France
+199429,7,17175,12461,21889,30,22,38,FR,France
+199428,7,16743,12021,21465,29,21,37,FR,France
+199427,7,22335,17343,27327,39,30,48,FR,France
+199426,7,23679,18832,28526,41,33,49,FR,France
+199425,7,24951,20263,29639,43,35,51,FR,France
+199424,7,29139,22549,35729,51,40,62,FR,France
+199423,7,13583,3641,23525,24,7,41,FR,France
+199422,7,16336,4640,28032,28,8,48,FR,France
+199421,7,16477,11025,21929,29,20,38,FR,France
+199420,7,12872,9458,16286,22,16,28,FR,France
+199419,7,13769,10440,17098,24,18,30,FR,France
+199418,7,13066,9917,16215,23,18,28,FR,France
+199417,7,18648,14838,22458,32,25,39,FR,France
+199416,7,17629,13660,21598,31,24,38,FR,France
+199415,7,23128,18550,27706,40,32,48,FR,France
+199414,7,20128,15971,24285,35,28,42,FR,France
+199413,7,16259,12721,19797,28,22,34,FR,France
+199412,7,15215,11581,18849,26,20,32,FR,France
+199411,7,16439,12812,20066,29,23,35,FR,France
+199410,7,20681,16433,24929,36,29,43,FR,France
+199409,7,22652,17519,27785,39,30,48,FR,France
+199408,7,22067,17044,27090,38,29,47,FR,France
+199407,7,15332,11563,19101,27,20,34,FR,France
+199406,7,15321,11710,18932,27,21,33,FR,France
+199405,7,15264,11523,19005,27,21,33,FR,France
+199404,7,10871,7697,14045,19,13,25,FR,France
+199403,7,10882,7808,13956,19,14,24,FR,France
+199402,7,11834,8348,15320,21,15,27,FR,France
+199401,7,11104,8142,14066,19,14,24,FR,France
+199352,7,9874,6746,13002,17,12,22,FR,France
+199351,7,9565,6818,12312,17,12,22,FR,France
+199350,7,6195,4181,8209,11,7,15,FR,France
+199349,7,10095,7443,12747,18,13,23,FR,France
+199348,7,6413,4261,8565,11,7,15,FR,France
+199347,7,7543,4934,10152,13,8,18,FR,France
+199346,7,7862,5175,10549,14,9,19,FR,France
+199345,7,5648,3511,7785,10,6,14,FR,France
+199344,7,6964,4509,9419,12,8,16,FR,France
+199343,7,5976,3632,8320,10,6,14,FR,France
+199342,7,7669,4853,10485,13,8,18,FR,France
+199341,7,9657,6648,12666,17,12,22,FR,France
+199340,7,5725,3082,8368,10,5,15,FR,France
+199339,7,5520,3207,7833,10,6,14,FR,France
+199338,7,2179,888,3470,4,2,6,FR,France
+199337,7,1762,557,2967,3,1,5,FR,France
+199336,7,3214,1526,4902,6,3,9,FR,France
+199335,7,2335,910,3760,4,2,6,FR,France
+199334,7,3770,1712,5828,7,3,11,FR,France
+199333,7,5925,3250,8600,10,5,15,FR,France
+199332,7,12711,8079,17343,22,14,30,FR,France
+199331,7,12481,7874,17088,22,14,30,FR,France
+199330,7,12584,8619,16549,22,15,29,FR,France
+199329,7,13179,8991,17367,23,16,30,FR,France
+199328,7,15293,11335,19251,27,20,34,FR,France
+199327,7,14134,9791,18477,25,17,33,FR,France
+199326,7,17937,13577,22297,31,23,39,FR,France
+199325,7,19581,14916,24246,34,26,42,FR,France
+199324,7,18285,13450,23120,32,24,40,FR,France
+199323,7,16003,11927,20079,28,21,35,FR,France
+199322,7,12652,8597,16707,22,15,29,FR,France
+199321,7,15218,10970,19466,27,20,34,FR,France
+199320,7,11343,7655,15031,20,14,26,FR,France
+199319,7,12086,8856,15316,21,15,27,FR,France
+199318,7,18898,14305,23491,33,25,41,FR,France
+199317,7,14969,10565,19373,26,18,34,FR,France
+199316,7,15102,11226,18978,26,19,33,FR,France
+199315,7,15058,11183,18933,26,19,33,FR,France
+199314,7,12425,9045,15805,22,16,28,FR,France
+199313,7,12266,8927,15605,21,15,27,FR,France
+199312,7,10821,7884,13758,19,14,24,FR,France
+199311,7,11021,8006,14036,19,14,24,FR,France
+199310,7,15847,12158,19536,28,22,34,FR,France
+199309,7,15084,11059,19109,26,19,33,FR,France
+199308,7,16967,12936,20998,30,23,37,FR,France
+199307,7,16057,12350,19764,28,22,34,FR,France
+199306,7,15887,12275,19499,28,22,34,FR,France
+199305,7,15954,12443,19465,28,22,34,FR,France
+199304,7,12374,9426,15322,22,17,27,FR,France
+199303,7,15283,11831,18735,27,21,33,FR,France
+199302,7,13366,10262,16470,23,18,28,FR,France
+199301,7,18678,14977,22379,33,27,39,FR,France
+199253,7,17829,13827,21831,31,24,38,FR,France
+199252,7,14330,10582,18078,25,18,32,FR,France
+199251,7,14635,11262,18008,26,20,32,FR,France
+199250,7,15252,11566,18938,27,21,33,FR,France
+199249,7,14411,11187,17635,25,19,31,FR,France
+199248,7,10048,7396,12700,18,13,23,FR,France
+199247,7,13069,9752,16386,23,17,29,FR,France
+199246,7,6900,4727,9073,12,8,16,FR,France
+199245,7,10791,7794,13788,19,14,24,FR,France
+199244,7,10205,7482,12928,18,13,23,FR,France
+199243,7,7196,4908,9484,13,9,17,FR,France
+199242,7,5748,3738,7758,10,6,14,FR,France
+199241,7,3587,2045,5129,6,3,9,FR,France
+199240,7,5654,3442,7866,10,6,14,FR,France
+199239,7,4747,2751,6743,8,5,11,FR,France
+199238,7,2842,1306,4378,5,2,8,FR,France
+199237,7,4103,2203,6003,7,4,10,FR,France
+199236,7,2801,1200,4402,5,2,8,FR,France
+199235,7,6428,3717,9139,11,6,16,FR,France
+199234,7,7517,4190,10844,13,7,19,FR,France
+199233,7,9951,5955,13947,17,10,24,FR,France
+199232,7,16090,11337,20843,28,20,36,FR,France
+199231,7,17156,12441,21871,30,22,38,FR,France
+199230,7,21780,16301,27259,38,28,48,FR,France
+199229,7,18926,13397,24455,33,23,43,FR,France
+199228,7,28144,20384,35904,49,35,63,FR,France
+199227,7,29388,22331,36445,51,39,63,FR,France
+199226,7,27933,21686,34180,49,38,60,FR,France
+199225,7,26446,21153,31739,46,37,55,FR,France
+199224,7,19614,14769,24459,34,26,42,FR,France
+199223,7,23628,17852,29404,41,31,51,FR,France
+199222,7,20438,14868,26008,36,26,46,FR,France
+199221,7,22595,17395,27795,40,31,49,FR,France
+199220,7,21092,15963,26221,37,28,46,FR,France
+199219,7,19210,13934,24486,34,25,43,FR,France
+199218,7,30273,22605,37941,53,40,66,FR,France
+199217,7,19162,14251,24073,34,25,43,FR,France
+199216,7,15583,11158,20008,27,19,35,FR,France
+199215,7,15473,10723,20223,27,19,35,FR,France
+199214,7,14455,10040,18870,25,17,33,FR,France
+199213,7,17146,12105,22187,30,21,39,FR,France
+199212,7,15838,10923,20753,28,19,37,FR,France
+199211,7,20003,13824,26182,35,24,46,FR,France
+199210,7,22906,16060,29752,40,28,52,FR,France
+199209,7,21824,15687,27961,38,27,49,FR,France
+199208,7,15243,10265,20221,27,18,36,FR,France
+199207,7,22633,16160,29106,40,29,51,FR,France
+199206,7,17009,12219,21799,30,22,38,FR,France
+199205,7,15997,11590,20404,28,20,36,FR,France
+199204,7,20690,15075,26305,36,26,46,FR,France
+199203,7,18586,12684,24488,33,23,43,FR,France
+199202,7,16588,10590,22586,29,18,40,FR,France
+199201,7,23607,16552,30662,41,29,53,FR,France
+199152,7,15184,10392,19976,27,19,35,FR,France
+199151,7,15043,10624,19462,26,18,34,FR,France
+199150,7,14115,9737,18493,25,17,33,FR,France
+199149,7,13304,8548,18060,23,15,31,FR,France
+199148,7,15370,10164,20576,27,18,36,FR,France
+199147,7,12813,8139,17487,23,15,31,FR,France
+199146,7,6453,3123,9783,11,5,17,FR,France
+199145,7,8048,4083,12013,14,7,21,FR,France
+199144,7,9882,5623,14141,17,10,24,FR,France
+199143,7,14307,8645,19969,25,15,35,FR,France
+199142,7,6419,3334,9504,11,6,16,FR,France
+199141,7,6168,3113,9223,11,6,16,FR,France
+199140,7,3615,1159,6071,6,2,10,FR,France
+199139,7,3105,472,5738,5,0,10,FR,France
+199138,7,4446,1435,7457,8,3,13,FR,France
+199137,7,1457,0,3262,3,0,6,FR,France
+199136,7,3053,29,6077,5,0,10,FR,France
+199135,7,805,0,2556,1,0,4,FR,France
+199134,7,5512,856,10168,10,2,18,FR,France
+199133,7,7347,2903,11791,13,5,21,FR,France
+199132,7,16099,7334,24864,28,13,43,FR,France
+199131,7,16803,8145,25461,30,15,45,FR,France
+199130,7,15915,6368,25462,28,11,45,FR,France
+199129,7,23744,14703,32785,42,26,58,FR,France
+199128,7,24222,15617,32827,43,28,58,FR,France
+199127,7,20309,12868,27750,36,23,49,FR,France
+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