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 0000000000000000000000000000000000000000..59d72b5b58a3ae26346460dd39e62a39c55243d7
--- /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": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb
index 59d72b5b58a3ae26346460dd39e62a39c55243d7..7ffe3656f1405b9d0e19ccc103873ecca7a67d02 100644
--- a/module3/exo1/analyse-syndrome-grippal.ipynb
+++ b/module3/exo1/analyse-syndrome-grippal.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -28,10 +28,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [],
    "source": [
     "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\""
@@ -61,9 +59,226 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "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>202036</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10888</td>\n",
+       "      <td>8050.0</td>\n",
+       "      <td>13726.0</td>\n",
+       "      <td>17</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202035</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9918</td>\n",
+       "      <td>6842.0</td>\n",
+       "      <td>12994.0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202034</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6084</td>\n",
+       "      <td>3090.0</td>\n",
+       "      <td>9078.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202033</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6106</td>\n",
+       "      <td>3411.0</td>\n",
+       "      <td>8801.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202032</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5918</td>\n",
+       "      <td>3330.0</td>\n",
+       "      <td>8506.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>13.0</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>1866</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>1867</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>1868</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>1869</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>1870</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>1871 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator     inc   inc_low    inc_up  inc100  inc100_low  \\\n",
+       "0     202036          3   10888    8050.0   13726.0      17        13.0   \n",
+       "1     202035          3    9918    6842.0   12994.0      15        10.0   \n",
+       "2     202034          3    6084    3090.0    9078.0       9         4.0   \n",
+       "3     202033          3    6106    3411.0    8801.0       9         5.0   \n",
+       "4     202032          3    5918    3330.0    8506.0       9         5.0   \n",
+       "...      ...        ...     ...       ...       ...     ...         ...   \n",
+       "1866  198448          3   78620   60634.0   96606.0     143       110.0   \n",
+       "1867  198447          3   72029   54274.0   89784.0     131        99.0   \n",
+       "1868  198446          3   87330   67686.0  106974.0     159       123.0   \n",
+       "1869  198445          3  135223  101414.0  169032.0     246       184.0   \n",
+       "1870  198444          3   68422   20056.0  116788.0     125        37.0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0          21.0        FR   France  \n",
+       "1          20.0        FR   France  \n",
+       "2          14.0        FR   France  \n",
+       "3          13.0        FR   France  \n",
+       "4          13.0        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1866      176.0        FR   France  \n",
+       "1867      163.0        FR   France  \n",
+       "1868      195.0        FR   France  \n",
+       "1869      308.0        FR   France  \n",
+       "1870      213.0        FR   France  \n",
+       "\n",
+       "[1871 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "raw_data = pd.read_csv(data_url, skiprows=1)\n",
     "raw_data"
@@ -78,9 +293,73 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "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>1634</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",
+       "1634  198919          3    0      NaN     NaN       0         NaN        NaN   \n",
+       "\n",
+       "     geo_insee geo_name  \n",
+       "1634        FR   France  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "raw_data[raw_data.isnull().any(axis=1)]"
    ]
@@ -94,9 +373,226 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "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>202036</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10888</td>\n",
+       "      <td>8050.0</td>\n",
+       "      <td>13726.0</td>\n",
+       "      <td>17</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202035</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9918</td>\n",
+       "      <td>6842.0</td>\n",
+       "      <td>12994.0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202034</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6084</td>\n",
+       "      <td>3090.0</td>\n",
+       "      <td>9078.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202033</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6106</td>\n",
+       "      <td>3411.0</td>\n",
+       "      <td>8801.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202032</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5918</td>\n",
+       "      <td>3330.0</td>\n",
+       "      <td>8506.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>13.0</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>1866</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>1867</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>1868</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>1869</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>1870</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>1870 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator     inc   inc_low    inc_up  inc100  inc100_low  \\\n",
+       "0     202036          3   10888    8050.0   13726.0      17        13.0   \n",
+       "1     202035          3    9918    6842.0   12994.0      15        10.0   \n",
+       "2     202034          3    6084    3090.0    9078.0       9         4.0   \n",
+       "3     202033          3    6106    3411.0    8801.0       9         5.0   \n",
+       "4     202032          3    5918    3330.0    8506.0       9         5.0   \n",
+       "...      ...        ...     ...       ...       ...     ...         ...   \n",
+       "1866  198448          3   78620   60634.0   96606.0     143       110.0   \n",
+       "1867  198447          3   72029   54274.0   89784.0     131        99.0   \n",
+       "1868  198446          3   87330   67686.0  106974.0     159       123.0   \n",
+       "1869  198445          3  135223  101414.0  169032.0     246       184.0   \n",
+       "1870  198444          3   68422   20056.0  116788.0     125        37.0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0          21.0        FR   France  \n",
+       "1          20.0        FR   France  \n",
+       "2          14.0        FR   France  \n",
+       "3          13.0        FR   France  \n",
+       "4          13.0        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1866      176.0        FR   France  \n",
+       "1867      163.0        FR   France  \n",
+       "1868      195.0        FR   France  \n",
+       "1869      308.0        FR   France  \n",
+       "1870      213.0        FR   France  \n",
+       "\n",
+       "[1870 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "data = raw_data.dropna().copy()\n",
     "data"
@@ -122,7 +618,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -152,10 +648,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [],
    "source": [
     "sorted_data = data.set_index('period').sort_index()"
@@ -179,9 +673,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "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",
@@ -199,9 +701,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='period'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "sorted_data['inc'].plot()"
    ]
@@ -215,9 +740,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='period'>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "sorted_data['inc'][-200:].plot()"
    ]
@@ -252,10 +800,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [],
    "source": [
     "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n",
@@ -274,7 +820,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -298,9 +844,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "yearly_incidence.plot(style='*')"
    ]
@@ -314,9 +883,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2014    1600941\n",
+       "1991    1659249\n",
+       "1995    1840410\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": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "yearly_incidence.sort_values()"
    ]
@@ -331,9 +945,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "yearly_incidence.hist(xrot=20)"
    ]
@@ -342,7 +979,10 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "outputs": [],
    "source": []
@@ -364,9 +1004,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.7.7"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/module3/exo2/.ipynb_checkpoints/Analyse Incidence de la Varicelle-checkpoint.ipynb b/module3/exo2/.ipynb_checkpoints/Analyse Incidence de la Varicelle-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..24e27c56439cb2b776cdea1705307bbc28b94560
--- /dev/null
+++ b/module3/exo2/.ipynb_checkpoints/Analyse Incidence de la Varicelle-checkpoint.ipynb	
@@ -0,0 +1,726 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analyse Incidence de la Varicelle\n",
+    "\n",
+    "Première chose à faire c'est importer et inspecter les données avant de faire les questions et réponses. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import os \n",
+    "import isoweek"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Analyse Incidence de la Varicelle.ipynb',\n",
+       " '.DS_Store',\n",
+       " 'exercice_en.ipynb',\n",
+       " 'incidence-PAY-7.csv',\n",
+       " 'exercice.ipynb',\n",
+       " 'exercice_python_en.org',\n",
+       " 'exercice_python_fr.org',\n",
+       " 'exercice_R_en.org',\n",
+       " 'exercice_R_fr.org',\n",
+       " '.ipynb_checkpoints',\n",
+       " 'exercice_fr.Rmd',\n",
+       " 'exercice_en.Rmd',\n",
+       " 'exercice_fr.ipynb']"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "os.getcwd()\n",
+    "os.listdir()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>202036</td>\n",
+       "      <td>7</td>\n",
+       "      <td>904</td>\n",
+       "      <td>77</td>\n",
+       "      <td>1731</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202035</td>\n",
+       "      <td>7</td>\n",
+       "      <td>828</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1694</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202034</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2272</td>\n",
+       "      <td>371</td>\n",
+       "      <td>4173</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202033</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1284</td>\n",
+       "      <td>177</td>\n",
+       "      <td>2391</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202032</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2650</td>\n",
+       "      <td>689</td>\n",
+       "      <td>4611</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</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>1548</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>1549</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>1550</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>1551</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>1552</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>1553 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
+       "0     202036          7    904       77    1731       1           0   \n",
+       "1     202035          7    828        0    1694       1           0   \n",
+       "2     202034          7   2272      371    4173       3           0   \n",
+       "3     202033          7   1284      177    2391       2           0   \n",
+       "4     202032          7   2650      689    4611       4           1   \n",
+       "...      ...        ...    ...      ...     ...     ...         ...   \n",
+       "1548  199101          7  15565    10271   20859      27          18   \n",
+       "1549  199052          7  19375    13295   25455      34          23   \n",
+       "1550  199051          7  19080    13807   24353      34          25   \n",
+       "1551  199050          7  11079     6660   15498      20          12   \n",
+       "1552  199049          7   1143        0    2610       2           0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0             2        FR   France  \n",
+       "1             2        FR   France  \n",
+       "2             6        FR   France  \n",
+       "3             4        FR   France  \n",
+       "4             7        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1548         36        FR   France  \n",
+       "1549         45        FR   France  \n",
+       "1550         43        FR   France  \n",
+       "1551         28        FR   France  \n",
+       "1552          5        FR   France  \n",
+       "\n",
+       "[1553 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Since there is no missing line in the data we shall proceed\n",
+    "data = pd.read_csv(\"incidence-PAY-7.csv\", skiprows=1)\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>week</th>\n",
+       "      <th>indicator</th>\n",
+       "      <th>inc</th>\n",
+       "      <th>inc_low</th>\n",
+       "      <th>inc_up</th>\n",
+       "      <th>inc100</th>\n",
+       "      <th>inc100_low</th>\n",
+       "      <th>inc100_up</th>\n",
+       "      <th>geo_insee</th>\n",
+       "      <th>geo_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </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": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Checkin everything is good\n",
+    "data[data.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Wrangling to adapt to isoweek format\n",
+    "\n",
+    "Since everything is good no data missing we can proceed to the wrangling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted_data = data.set_index('period').sort_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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": [
+    "## Data Analysis "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='period'>"
+      ]
+     },
+     "execution_count": 8,
+     "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": [
+    "##Visual\n",
+    "sorted_data['inc'].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='period'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data['inc'][-200:].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Period('1991-01-07/1991-01-13', 'W-SUN')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted_data.index[5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Determine the annual frequency\n",
+    "\n",
+    "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
+    "                     for y in range(sorted_data.index[0].year,\n",
+    "                                    sorted_data.index[-1].year)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Period('1990-08-27/1990-09-02', 'W-SUN'),\n",
+       " Period('1991-08-26/1991-09-01', 'W-SUN'),\n",
+       " Period('1992-08-31/1992-09-06', 'W-SUN'),\n",
+       " Period('1993-08-30/1993-09-05', 'W-SUN'),\n",
+       " Period('1994-08-29/1994-09-04', 'W-SUN'),\n",
+       " Period('1995-08-28/1995-09-03', 'W-SUN'),\n",
+       " Period('1996-08-26/1996-09-01', 'W-SUN'),\n",
+       " Period('1997-09-01/1997-09-07', 'W-SUN'),\n",
+       " Period('1998-08-31/1998-09-06', 'W-SUN'),\n",
+       " Period('1999-08-30/1999-09-05', 'W-SUN'),\n",
+       " Period('2000-08-28/2000-09-03', 'W-SUN'),\n",
+       " Period('2001-08-27/2001-09-02', 'W-SUN'),\n",
+       " Period('2002-08-26/2002-09-01', 'W-SUN'),\n",
+       " Period('2003-09-01/2003-09-07', 'W-SUN'),\n",
+       " Period('2004-08-30/2004-09-05', 'W-SUN'),\n",
+       " Period('2005-08-29/2005-09-04', 'W-SUN'),\n",
+       " Period('2006-08-28/2006-09-03', 'W-SUN'),\n",
+       " Period('2007-08-27/2007-09-02', 'W-SUN'),\n",
+       " Period('2008-09-01/2008-09-07', 'W-SUN'),\n",
+       " Period('2009-08-31/2009-09-06', 'W-SUN'),\n",
+       " Period('2010-08-30/2010-09-05', 'W-SUN'),\n",
+       " Period('2011-08-29/2011-09-04', 'W-SUN'),\n",
+       " Period('2012-08-27/2012-09-02', 'W-SUN'),\n",
+       " Period('2013-08-26/2013-09-01', 'W-SUN'),\n",
+       " Period('2014-09-01/2014-09-07', 'W-SUN'),\n",
+       " Period('2015-08-31/2015-09-06', 'W-SUN'),\n",
+       " Period('2016-08-29/2016-09-04', 'W-SUN'),\n",
+       " Period('2017-08-28/2017-09-03', 'W-SUN'),\n",
+       " Period('2018-08-27/2018-09-02', 'W-SUN'),\n",
+       " Period('2019-08-26/2019-09-01', 'W-SUN')]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "first_september_week"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "38\n",
+      "53\n",
+      "52\n",
+      "52\n",
+      "52\n",
+      "52\n",
+      "53\n",
+      "52\n",
+      "52\n",
+      "52\n",
+      "52\n",
+      "52\n",
+      "53\n",
+      "52\n",
+      "52\n",
+      "52\n",
+      "52\n",
+      "53\n",
+      "52\n",
+      "52\n",
+      "52\n",
+      "52\n",
+      "52\n",
+      "53\n",
+      "52\n",
+      "52\n",
+      "52\n",
+      "52\n",
+      "52\n"
+     ]
+    }
+   ],
+   "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",
+    "    #print (one_year)\n",
+    "    print (len(one_year))\n",
+    "    #assert abs(len(one_year)-52) < 1\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": [
+    "We can see that for the first year we have a biased estimate since we only have 38 weeks approximately...\n",
+    "\n",
+    "So we shall not interpret the first annual incidence as \"fiable\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yearly_incidence.plot(style='*')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2002    516689\n",
+       "2018    542312\n",
+       "2017    551041\n",
+       "1991    553090\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": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/module3/exo2/Analyse Incidence de la Varicelle.ipynb b/module3/exo2/Analyse Incidence de la Varicelle.ipynb
index 88d33b0f3fa9d5d67bd9904093f74a8bf5f71c3e..24e27c56439cb2b776cdea1705307bbc28b94560 100644
--- a/module3/exo2/Analyse Incidence de la Varicelle.ipynb	
+++ b/module3/exo2/Analyse Incidence de la Varicelle.ipynb	
@@ -9,6 +9,691 @@
     "Première chose à faire c'est importer et inspecter les données avant de faire les questions et réponses. "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import os \n",
+    "import isoweek"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Analyse Incidence de la Varicelle.ipynb',\n",
+       " '.DS_Store',\n",
+       " 'exercice_en.ipynb',\n",
+       " 'incidence-PAY-7.csv',\n",
+       " 'exercice.ipynb',\n",
+       " 'exercice_python_en.org',\n",
+       " 'exercice_python_fr.org',\n",
+       " 'exercice_R_en.org',\n",
+       " 'exercice_R_fr.org',\n",
+       " '.ipynb_checkpoints',\n",
+       " 'exercice_fr.Rmd',\n",
+       " 'exercice_en.Rmd',\n",
+       " 'exercice_fr.ipynb']"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "os.getcwd()\n",
+    "os.listdir()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>202036</td>\n",
+       "      <td>7</td>\n",
+       "      <td>904</td>\n",
+       "      <td>77</td>\n",
+       "      <td>1731</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202035</td>\n",
+       "      <td>7</td>\n",
+       "      <td>828</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1694</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202034</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2272</td>\n",
+       "      <td>371</td>\n",
+       "      <td>4173</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202033</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1284</td>\n",
+       "      <td>177</td>\n",
+       "      <td>2391</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202032</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2650</td>\n",
+       "      <td>689</td>\n",
+       "      <td>4611</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</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>1548</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>1549</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>1550</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>1551</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>1552</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>1553 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
+       "0     202036          7    904       77    1731       1           0   \n",
+       "1     202035          7    828        0    1694       1           0   \n",
+       "2     202034          7   2272      371    4173       3           0   \n",
+       "3     202033          7   1284      177    2391       2           0   \n",
+       "4     202032          7   2650      689    4611       4           1   \n",
+       "...      ...        ...    ...      ...     ...     ...         ...   \n",
+       "1548  199101          7  15565    10271   20859      27          18   \n",
+       "1549  199052          7  19375    13295   25455      34          23   \n",
+       "1550  199051          7  19080    13807   24353      34          25   \n",
+       "1551  199050          7  11079     6660   15498      20          12   \n",
+       "1552  199049          7   1143        0    2610       2           0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0             2        FR   France  \n",
+       "1             2        FR   France  \n",
+       "2             6        FR   France  \n",
+       "3             4        FR   France  \n",
+       "4             7        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1548         36        FR   France  \n",
+       "1549         45        FR   France  \n",
+       "1550         43        FR   France  \n",
+       "1551         28        FR   France  \n",
+       "1552          5        FR   France  \n",
+       "\n",
+       "[1553 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Since there is no missing line in the data we shall proceed\n",
+    "data = pd.read_csv(\"incidence-PAY-7.csv\", skiprows=1)\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>week</th>\n",
+       "      <th>indicator</th>\n",
+       "      <th>inc</th>\n",
+       "      <th>inc_low</th>\n",
+       "      <th>inc_up</th>\n",
+       "      <th>inc100</th>\n",
+       "      <th>inc100_low</th>\n",
+       "      <th>inc100_up</th>\n",
+       "      <th>geo_insee</th>\n",
+       "      <th>geo_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </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": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Checkin everything is good\n",
+    "data[data.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Wrangling to adapt to isoweek format\n",
+    "\n",
+    "Since everything is good no data missing we can proceed to the wrangling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted_data = data.set_index('period').sort_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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": [
+    "## Data Analysis "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='period'>"
+      ]
+     },
+     "execution_count": 8,
+     "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": [
+    "##Visual\n",
+    "sorted_data['inc'].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='period'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data['inc'][-200:].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Period('1991-01-07/1991-01-13', 'W-SUN')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted_data.index[5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Determine the annual frequency\n",
+    "\n",
+    "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
+    "                     for y in range(sorted_data.index[0].year,\n",
+    "                                    sorted_data.index[-1].year)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Period('1990-08-27/1990-09-02', 'W-SUN'),\n",
+       " Period('1991-08-26/1991-09-01', 'W-SUN'),\n",
+       " Period('1992-08-31/1992-09-06', 'W-SUN'),\n",
+       " Period('1993-08-30/1993-09-05', 'W-SUN'),\n",
+       " Period('1994-08-29/1994-09-04', 'W-SUN'),\n",
+       " Period('1995-08-28/1995-09-03', 'W-SUN'),\n",
+       " Period('1996-08-26/1996-09-01', 'W-SUN'),\n",
+       " Period('1997-09-01/1997-09-07', 'W-SUN'),\n",
+       " Period('1998-08-31/1998-09-06', 'W-SUN'),\n",
+       " Period('1999-08-30/1999-09-05', 'W-SUN'),\n",
+       " Period('2000-08-28/2000-09-03', 'W-SUN'),\n",
+       " Period('2001-08-27/2001-09-02', 'W-SUN'),\n",
+       " Period('2002-08-26/2002-09-01', 'W-SUN'),\n",
+       " Period('2003-09-01/2003-09-07', 'W-SUN'),\n",
+       " Period('2004-08-30/2004-09-05', 'W-SUN'),\n",
+       " Period('2005-08-29/2005-09-04', 'W-SUN'),\n",
+       " Period('2006-08-28/2006-09-03', 'W-SUN'),\n",
+       " Period('2007-08-27/2007-09-02', 'W-SUN'),\n",
+       " Period('2008-09-01/2008-09-07', 'W-SUN'),\n",
+       " Period('2009-08-31/2009-09-06', 'W-SUN'),\n",
+       " Period('2010-08-30/2010-09-05', 'W-SUN'),\n",
+       " Period('2011-08-29/2011-09-04', 'W-SUN'),\n",
+       " Period('2012-08-27/2012-09-02', 'W-SUN'),\n",
+       " Period('2013-08-26/2013-09-01', 'W-SUN'),\n",
+       " Period('2014-09-01/2014-09-07', 'W-SUN'),\n",
+       " Period('2015-08-31/2015-09-06', 'W-SUN'),\n",
+       " Period('2016-08-29/2016-09-04', 'W-SUN'),\n",
+       " Period('2017-08-28/2017-09-03', 'W-SUN'),\n",
+       " Period('2018-08-27/2018-09-02', 'W-SUN'),\n",
+       " Period('2019-08-26/2019-09-01', 'W-SUN')]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "first_september_week"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "38\n",
+      "53\n",
+      "52\n",
+      "52\n",
+      "52\n",
+      "52\n",
+      "53\n",
+      "52\n",
+      "52\n",
+      "52\n",
+      "52\n",
+      "52\n",
+      "53\n",
+      "52\n",
+      "52\n",
+      "52\n",
+      "52\n",
+      "53\n",
+      "52\n",
+      "52\n",
+      "52\n",
+      "52\n",
+      "52\n",
+      "53\n",
+      "52\n",
+      "52\n",
+      "52\n",
+      "52\n",
+      "52\n"
+     ]
+    }
+   ],
+   "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",
+    "    #print (one_year)\n",
+    "    print (len(one_year))\n",
+    "    #assert abs(len(one_year)-52) < 1\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": [
+    "We can see that for the first year we have a biased estimate since we only have 38 weeks approximately...\n",
+    "\n",
+    "So we shall not interpret the first annual incidence as \"fiable\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yearly_incidence.plot(style='*')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2002    516689\n",
+       "2018    542312\n",
+       "2017    551041\n",
+       "1991    553090\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": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -33,9 +718,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.7.7"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }