From 222da9dbbf737632737b668019a7798904f49814 Mon Sep 17 00:00:00 2001
From: 39781cc7cca0dc30af9d6060ede9947c
 <39781cc7cca0dc30af9d6060ede9947c@app-learninglab.inria.fr>
Date: Sun, 19 Apr 2020 15:16:20 +0000
Subject: [PATCH] Modification mineure.

---
 module3/exo1/analyse-syndrome-grippal.ipynb | 1346 ++-----------------
 1 file changed, 85 insertions(+), 1261 deletions(-)

diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb
index fbd81f0..ff8cb28 100644
--- a/module3/exo1/analyse-syndrome-grippal.ipynb
+++ b/module3/exo1/analyse-syndrome-grippal.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -28,1042 +28,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fichier non trouvé\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os.path\n",
-    "# Vérifier si le fichier existe ou non\n",
-    "if os.path.isfile('incidence-PAY-3.csv'):\n",
-    "    print(\"Fichier trouvé\")\n",
-    "    raw_data = pd.read_csv(incidence-PAY-3.csv, skiprows=1)\n",
-    "else:\n",
-    "    print(\"Fichier non trouvé\")\n",
-    "    print(\"Téléchargement du fichier sur le site Web\")\n",
-    "    raw_data = pd.read_csv(data_url, skiprows=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "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": 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>202015</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>202014</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>202013</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>202012</td>\n",
-       "      <td>3</td>\n",
-       "      <td>8321</td>\n",
-       "      <td>5873.0</td>\n",
-       "      <td>10769.0</td>\n",
-       "      <td>13</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>202011</td>\n",
-       "      <td>3</td>\n",
-       "      <td>101704</td>\n",
-       "      <td>93652.0</td>\n",
-       "      <td>109756.0</td>\n",
-       "      <td>154</td>\n",
-       "      <td>142.0</td>\n",
-       "      <td>166.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>202010</td>\n",
-       "      <td>3</td>\n",
-       "      <td>104977</td>\n",
-       "      <td>96650.0</td>\n",
-       "      <td>113304.0</td>\n",
-       "      <td>159</td>\n",
-       "      <td>146.0</td>\n",
-       "      <td>172.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>202009</td>\n",
-       "      <td>3</td>\n",
-       "      <td>110696</td>\n",
-       "      <td>102066.0</td>\n",
-       "      <td>119326.0</td>\n",
-       "      <td>168</td>\n",
-       "      <td>155.0</td>\n",
-       "      <td>181.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>202008</td>\n",
-       "      <td>3</td>\n",
-       "      <td>143753</td>\n",
-       "      <td>133984.0</td>\n",
-       "      <td>153522.0</td>\n",
-       "      <td>218</td>\n",
-       "      <td>203.0</td>\n",
-       "      <td>233.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>202007</td>\n",
-       "      <td>3</td>\n",
-       "      <td>183610</td>\n",
-       "      <td>172812.0</td>\n",
-       "      <td>194408.0</td>\n",
-       "      <td>279</td>\n",
-       "      <td>263.0</td>\n",
-       "      <td>295.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>202006</td>\n",
-       "      <td>3</td>\n",
-       "      <td>206669</td>\n",
-       "      <td>195481.0</td>\n",
-       "      <td>217857.0</td>\n",
-       "      <td>314</td>\n",
-       "      <td>297.0</td>\n",
-       "      <td>331.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>202005</td>\n",
-       "      <td>3</td>\n",
-       "      <td>187957</td>\n",
-       "      <td>177445.0</td>\n",
-       "      <td>198469.0</td>\n",
-       "      <td>285</td>\n",
-       "      <td>269.0</td>\n",
-       "      <td>301.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>202004</td>\n",
-       "      <td>3</td>\n",
-       "      <td>122331</td>\n",
-       "      <td>113492.0</td>\n",
-       "      <td>131170.0</td>\n",
-       "      <td>186</td>\n",
-       "      <td>173.0</td>\n",
-       "      <td>199.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>202003</td>\n",
-       "      <td>3</td>\n",
-       "      <td>78413</td>\n",
-       "      <td>71330.0</td>\n",
-       "      <td>85496.0</td>\n",
-       "      <td>119</td>\n",
-       "      <td>108.0</td>\n",
-       "      <td>130.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>202002</td>\n",
-       "      <td>3</td>\n",
-       "      <td>53614</td>\n",
-       "      <td>47654.0</td>\n",
-       "      <td>59574.0</td>\n",
-       "      <td>81</td>\n",
-       "      <td>72.0</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>202001</td>\n",
-       "      <td>3</td>\n",
-       "      <td>36850</td>\n",
-       "      <td>31608.0</td>\n",
-       "      <td>42092.0</td>\n",
-       "      <td>56</td>\n",
-       "      <td>48.0</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>201952</td>\n",
-       "      <td>3</td>\n",
-       "      <td>28135</td>\n",
-       "      <td>23220.0</td>\n",
-       "      <td>33050.0</td>\n",
-       "      <td>43</td>\n",
-       "      <td>36.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>201951</td>\n",
-       "      <td>3</td>\n",
-       "      <td>29786</td>\n",
-       "      <td>25042.0</td>\n",
-       "      <td>34530.0</td>\n",
-       "      <td>45</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>201950</td>\n",
-       "      <td>3</td>\n",
-       "      <td>34223</td>\n",
-       "      <td>29156.0</td>\n",
-       "      <td>39290.0</td>\n",
-       "      <td>52</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>201949</td>\n",
-       "      <td>3</td>\n",
-       "      <td>25662</td>\n",
-       "      <td>21414.0</td>\n",
-       "      <td>29910.0</td>\n",
-       "      <td>39</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>201948</td>\n",
-       "      <td>3</td>\n",
-       "      <td>22367</td>\n",
-       "      <td>18055.0</td>\n",
-       "      <td>26679.0</td>\n",
-       "      <td>34</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>201947</td>\n",
-       "      <td>3</td>\n",
-       "      <td>18669</td>\n",
-       "      <td>14759.0</td>\n",
-       "      <td>22579.0</td>\n",
-       "      <td>28</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>201946</td>\n",
-       "      <td>3</td>\n",
-       "      <td>16030</td>\n",
-       "      <td>12567.0</td>\n",
-       "      <td>19493.0</td>\n",
-       "      <td>24</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>201945</td>\n",
-       "      <td>3</td>\n",
-       "      <td>10138</td>\n",
-       "      <td>7160.0</td>\n",
-       "      <td>13116.0</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>23</th>\n",
-       "      <td>201944</td>\n",
-       "      <td>3</td>\n",
-       "      <td>7822</td>\n",
-       "      <td>5010.0</td>\n",
-       "      <td>10634.0</td>\n",
-       "      <td>12</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>201943</td>\n",
-       "      <td>3</td>\n",
-       "      <td>9487</td>\n",
-       "      <td>6448.0</td>\n",
-       "      <td>12526.0</td>\n",
-       "      <td>14</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>201942</td>\n",
-       "      <td>3</td>\n",
-       "      <td>7747</td>\n",
-       "      <td>5243.0</td>\n",
-       "      <td>10251.0</td>\n",
-       "      <td>12</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>201941</td>\n",
-       "      <td>3</td>\n",
-       "      <td>7122</td>\n",
-       "      <td>4720.0</td>\n",
-       "      <td>9524.0</td>\n",
-       "      <td>11</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>201940</td>\n",
-       "      <td>3</td>\n",
-       "      <td>8505</td>\n",
-       "      <td>5784.0</td>\n",
-       "      <td>11226.0</td>\n",
-       "      <td>13</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>201939</td>\n",
-       "      <td>3</td>\n",
-       "      <td>7091</td>\n",
-       "      <td>4462.0</td>\n",
-       "      <td>9720.0</td>\n",
-       "      <td>11</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>201938</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4897</td>\n",
-       "      <td>2891.0</td>\n",
-       "      <td>6903.0</td>\n",
-       "      <td>7</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>10.0</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>1820</th>\n",
-       "      <td>198521</td>\n",
-       "      <td>3</td>\n",
-       "      <td>26096</td>\n",
-       "      <td>19621.0</td>\n",
-       "      <td>32571.0</td>\n",
-       "      <td>47</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1821</th>\n",
-       "      <td>198520</td>\n",
-       "      <td>3</td>\n",
-       "      <td>27896</td>\n",
-       "      <td>20885.0</td>\n",
-       "      <td>34907.0</td>\n",
-       "      <td>51</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1822</th>\n",
-       "      <td>198519</td>\n",
-       "      <td>3</td>\n",
-       "      <td>43154</td>\n",
-       "      <td>32821.0</td>\n",
-       "      <td>53487.0</td>\n",
-       "      <td>78</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>97.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1823</th>\n",
-       "      <td>198518</td>\n",
-       "      <td>3</td>\n",
-       "      <td>40555</td>\n",
-       "      <td>29935.0</td>\n",
-       "      <td>51175.0</td>\n",
-       "      <td>74</td>\n",
-       "      <td>55.0</td>\n",
-       "      <td>93.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1824</th>\n",
-       "      <td>198517</td>\n",
-       "      <td>3</td>\n",
-       "      <td>34053</td>\n",
-       "      <td>24366.0</td>\n",
-       "      <td>43740.0</td>\n",
-       "      <td>62</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1825</th>\n",
-       "      <td>198516</td>\n",
-       "      <td>3</td>\n",
-       "      <td>50362</td>\n",
-       "      <td>36451.0</td>\n",
-       "      <td>64273.0</td>\n",
-       "      <td>91</td>\n",
-       "      <td>66.0</td>\n",
-       "      <td>116.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1826</th>\n",
-       "      <td>198515</td>\n",
-       "      <td>3</td>\n",
-       "      <td>63881</td>\n",
-       "      <td>45538.0</td>\n",
-       "      <td>82224.0</td>\n",
-       "      <td>116</td>\n",
-       "      <td>83.0</td>\n",
-       "      <td>149.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1827</th>\n",
-       "      <td>198514</td>\n",
-       "      <td>3</td>\n",
-       "      <td>134545</td>\n",
-       "      <td>114400.0</td>\n",
-       "      <td>154690.0</td>\n",
-       "      <td>244</td>\n",
-       "      <td>207.0</td>\n",
-       "      <td>281.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1828</th>\n",
-       "      <td>198513</td>\n",
-       "      <td>3</td>\n",
-       "      <td>197206</td>\n",
-       "      <td>176080.0</td>\n",
-       "      <td>218332.0</td>\n",
-       "      <td>357</td>\n",
-       "      <td>319.0</td>\n",
-       "      <td>395.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1829</th>\n",
-       "      <td>198512</td>\n",
-       "      <td>3</td>\n",
-       "      <td>245240</td>\n",
-       "      <td>223304.0</td>\n",
-       "      <td>267176.0</td>\n",
-       "      <td>445</td>\n",
-       "      <td>405.0</td>\n",
-       "      <td>485.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1830</th>\n",
-       "      <td>198511</td>\n",
-       "      <td>3</td>\n",
-       "      <td>276205</td>\n",
-       "      <td>252399.0</td>\n",
-       "      <td>300011.0</td>\n",
-       "      <td>501</td>\n",
-       "      <td>458.0</td>\n",
-       "      <td>544.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1831</th>\n",
-       "      <td>198510</td>\n",
-       "      <td>3</td>\n",
-       "      <td>353231</td>\n",
-       "      <td>326279.0</td>\n",
-       "      <td>380183.0</td>\n",
-       "      <td>640</td>\n",
-       "      <td>591.0</td>\n",
-       "      <td>689.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1832</th>\n",
-       "      <td>198509</td>\n",
-       "      <td>3</td>\n",
-       "      <td>369895</td>\n",
-       "      <td>341109.0</td>\n",
-       "      <td>398681.0</td>\n",
-       "      <td>670</td>\n",
-       "      <td>618.0</td>\n",
-       "      <td>722.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1833</th>\n",
-       "      <td>198508</td>\n",
-       "      <td>3</td>\n",
-       "      <td>389886</td>\n",
-       "      <td>359529.0</td>\n",
-       "      <td>420243.0</td>\n",
-       "      <td>707</td>\n",
-       "      <td>652.0</td>\n",
-       "      <td>762.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1834</th>\n",
-       "      <td>198507</td>\n",
-       "      <td>3</td>\n",
-       "      <td>471852</td>\n",
-       "      <td>432599.0</td>\n",
-       "      <td>511105.0</td>\n",
-       "      <td>855</td>\n",
-       "      <td>784.0</td>\n",
-       "      <td>926.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1835</th>\n",
-       "      <td>198506</td>\n",
-       "      <td>3</td>\n",
-       "      <td>565825</td>\n",
-       "      <td>518011.0</td>\n",
-       "      <td>613639.0</td>\n",
-       "      <td>1026</td>\n",
-       "      <td>939.0</td>\n",
-       "      <td>1113.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1836</th>\n",
-       "      <td>198505</td>\n",
-       "      <td>3</td>\n",
-       "      <td>637302</td>\n",
-       "      <td>592795.0</td>\n",
-       "      <td>681809.0</td>\n",
-       "      <td>1155</td>\n",
-       "      <td>1074.0</td>\n",
-       "      <td>1236.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1837</th>\n",
-       "      <td>198504</td>\n",
-       "      <td>3</td>\n",
-       "      <td>424937</td>\n",
-       "      <td>390794.0</td>\n",
-       "      <td>459080.0</td>\n",
-       "      <td>770</td>\n",
-       "      <td>708.0</td>\n",
-       "      <td>832.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1838</th>\n",
-       "      <td>198503</td>\n",
-       "      <td>3</td>\n",
-       "      <td>213901</td>\n",
-       "      <td>174689.0</td>\n",
-       "      <td>253113.0</td>\n",
-       "      <td>388</td>\n",
-       "      <td>317.0</td>\n",
-       "      <td>459.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1839</th>\n",
-       "      <td>198502</td>\n",
-       "      <td>3</td>\n",
-       "      <td>97586</td>\n",
-       "      <td>80949.0</td>\n",
-       "      <td>114223.0</td>\n",
-       "      <td>177</td>\n",
-       "      <td>147.0</td>\n",
-       "      <td>207.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1840</th>\n",
-       "      <td>198501</td>\n",
-       "      <td>3</td>\n",
-       "      <td>85489</td>\n",
-       "      <td>65918.0</td>\n",
-       "      <td>105060.0</td>\n",
-       "      <td>155</td>\n",
-       "      <td>120.0</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1841</th>\n",
-       "      <td>198452</td>\n",
-       "      <td>3</td>\n",
-       "      <td>84830</td>\n",
-       "      <td>60602.0</td>\n",
-       "      <td>109058.0</td>\n",
-       "      <td>154</td>\n",
-       "      <td>110.0</td>\n",
-       "      <td>198.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1842</th>\n",
-       "      <td>198451</td>\n",
-       "      <td>3</td>\n",
-       "      <td>101726</td>\n",
-       "      <td>80242.0</td>\n",
-       "      <td>123210.0</td>\n",
-       "      <td>185</td>\n",
-       "      <td>146.0</td>\n",
-       "      <td>224.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1843</th>\n",
-       "      <td>198450</td>\n",
-       "      <td>3</td>\n",
-       "      <td>123680</td>\n",
-       "      <td>101401.0</td>\n",
-       "      <td>145959.0</td>\n",
-       "      <td>225</td>\n",
-       "      <td>184.0</td>\n",
-       "      <td>266.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1844</th>\n",
-       "      <td>198449</td>\n",
-       "      <td>3</td>\n",
-       "      <td>101073</td>\n",
-       "      <td>81684.0</td>\n",
-       "      <td>120462.0</td>\n",
-       "      <td>184</td>\n",
-       "      <td>149.0</td>\n",
-       "      <td>219.0</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1845</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>1846</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>1847</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>1848</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>1849</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>1850 rows × 10 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        week  indicator     inc   inc_low    inc_up  inc100  inc100_low  \\\n",
-       "0     202015          3       0       0.0       0.0       0         0.0   \n",
-       "1     202014          3       0       0.0       0.0       0         0.0   \n",
-       "2     202013          3       0       0.0       0.0       0         0.0   \n",
-       "3     202012          3    8321    5873.0   10769.0      13         9.0   \n",
-       "4     202011          3  101704   93652.0  109756.0     154       142.0   \n",
-       "5     202010          3  104977   96650.0  113304.0     159       146.0   \n",
-       "6     202009          3  110696  102066.0  119326.0     168       155.0   \n",
-       "7     202008          3  143753  133984.0  153522.0     218       203.0   \n",
-       "8     202007          3  183610  172812.0  194408.0     279       263.0   \n",
-       "9     202006          3  206669  195481.0  217857.0     314       297.0   \n",
-       "10    202005          3  187957  177445.0  198469.0     285       269.0   \n",
-       "11    202004          3  122331  113492.0  131170.0     186       173.0   \n",
-       "12    202003          3   78413   71330.0   85496.0     119       108.0   \n",
-       "13    202002          3   53614   47654.0   59574.0      81        72.0   \n",
-       "14    202001          3   36850   31608.0   42092.0      56        48.0   \n",
-       "15    201952          3   28135   23220.0   33050.0      43        36.0   \n",
-       "16    201951          3   29786   25042.0   34530.0      45        38.0   \n",
-       "17    201950          3   34223   29156.0   39290.0      52        44.0   \n",
-       "18    201949          3   25662   21414.0   29910.0      39        33.0   \n",
-       "19    201948          3   22367   18055.0   26679.0      34        27.0   \n",
-       "20    201947          3   18669   14759.0   22579.0      28        22.0   \n",
-       "21    201946          3   16030   12567.0   19493.0      24        19.0   \n",
-       "22    201945          3   10138    7160.0   13116.0      15        10.0   \n",
-       "23    201944          3    7822    5010.0   10634.0      12         8.0   \n",
-       "24    201943          3    9487    6448.0   12526.0      14         9.0   \n",
-       "25    201942          3    7747    5243.0   10251.0      12         8.0   \n",
-       "26    201941          3    7122    4720.0    9524.0      11         7.0   \n",
-       "27    201940          3    8505    5784.0   11226.0      13         9.0   \n",
-       "28    201939          3    7091    4462.0    9720.0      11         7.0   \n",
-       "29    201938          3    4897    2891.0    6903.0       7         4.0   \n",
-       "...      ...        ...     ...       ...       ...     ...         ...   \n",
-       "1820  198521          3   26096   19621.0   32571.0      47        35.0   \n",
-       "1821  198520          3   27896   20885.0   34907.0      51        38.0   \n",
-       "1822  198519          3   43154   32821.0   53487.0      78        59.0   \n",
-       "1823  198518          3   40555   29935.0   51175.0      74        55.0   \n",
-       "1824  198517          3   34053   24366.0   43740.0      62        44.0   \n",
-       "1825  198516          3   50362   36451.0   64273.0      91        66.0   \n",
-       "1826  198515          3   63881   45538.0   82224.0     116        83.0   \n",
-       "1827  198514          3  134545  114400.0  154690.0     244       207.0   \n",
-       "1828  198513          3  197206  176080.0  218332.0     357       319.0   \n",
-       "1829  198512          3  245240  223304.0  267176.0     445       405.0   \n",
-       "1830  198511          3  276205  252399.0  300011.0     501       458.0   \n",
-       "1831  198510          3  353231  326279.0  380183.0     640       591.0   \n",
-       "1832  198509          3  369895  341109.0  398681.0     670       618.0   \n",
-       "1833  198508          3  389886  359529.0  420243.0     707       652.0   \n",
-       "1834  198507          3  471852  432599.0  511105.0     855       784.0   \n",
-       "1835  198506          3  565825  518011.0  613639.0    1026       939.0   \n",
-       "1836  198505          3  637302  592795.0  681809.0    1155      1074.0   \n",
-       "1837  198504          3  424937  390794.0  459080.0     770       708.0   \n",
-       "1838  198503          3  213901  174689.0  253113.0     388       317.0   \n",
-       "1839  198502          3   97586   80949.0  114223.0     177       147.0   \n",
-       "1840  198501          3   85489   65918.0  105060.0     155       120.0   \n",
-       "1841  198452          3   84830   60602.0  109058.0     154       110.0   \n",
-       "1842  198451          3  101726   80242.0  123210.0     185       146.0   \n",
-       "1843  198450          3  123680  101401.0  145959.0     225       184.0   \n",
-       "1844  198449          3  101073   81684.0  120462.0     184       149.0   \n",
-       "1845  198448          3   78620   60634.0   96606.0     143       110.0   \n",
-       "1846  198447          3   72029   54274.0   89784.0     131        99.0   \n",
-       "1847  198446          3   87330   67686.0  106974.0     159       123.0   \n",
-       "1848  198445          3  135223  101414.0  169032.0     246       184.0   \n",
-       "1849  198444          3   68422   20056.0  116788.0     125        37.0   \n",
-       "\n",
-       "      inc100_up geo_insee geo_name  \n",
-       "0           0.0        FR   France  \n",
-       "1           0.0        FR   France  \n",
-       "2           0.0        FR   France  \n",
-       "3          17.0        FR   France  \n",
-       "4         166.0        FR   France  \n",
-       "5         172.0        FR   France  \n",
-       "6         181.0        FR   France  \n",
-       "7         233.0        FR   France  \n",
-       "8         295.0        FR   France  \n",
-       "9         331.0        FR   France  \n",
-       "10        301.0        FR   France  \n",
-       "11        199.0        FR   France  \n",
-       "12        130.0        FR   France  \n",
-       "13         90.0        FR   France  \n",
-       "14         64.0        FR   France  \n",
-       "15         50.0        FR   France  \n",
-       "16         52.0        FR   France  \n",
-       "17         60.0        FR   France  \n",
-       "18         45.0        FR   France  \n",
-       "19         41.0        FR   France  \n",
-       "20         34.0        FR   France  \n",
-       "21         29.0        FR   France  \n",
-       "22         20.0        FR   France  \n",
-       "23         16.0        FR   France  \n",
-       "24         19.0        FR   France  \n",
-       "25         16.0        FR   France  \n",
-       "26         15.0        FR   France  \n",
-       "27         17.0        FR   France  \n",
-       "28         15.0        FR   France  \n",
-       "29         10.0        FR   France  \n",
-       "...         ...       ...      ...  \n",
-       "1820       59.0        FR   France  \n",
-       "1821       64.0        FR   France  \n",
-       "1822       97.0        FR   France  \n",
-       "1823       93.0        FR   France  \n",
-       "1824       80.0        FR   France  \n",
-       "1825      116.0        FR   France  \n",
-       "1826      149.0        FR   France  \n",
-       "1827      281.0        FR   France  \n",
-       "1828      395.0        FR   France  \n",
-       "1829      485.0        FR   France  \n",
-       "1830      544.0        FR   France  \n",
-       "1831      689.0        FR   France  \n",
-       "1832      722.0        FR   France  \n",
-       "1833      762.0        FR   France  \n",
-       "1834      926.0        FR   France  \n",
-       "1835     1113.0        FR   France  \n",
-       "1836     1236.0        FR   France  \n",
-       "1837      832.0        FR   France  \n",
-       "1838      459.0        FR   France  \n",
-       "1839      207.0        FR   France  \n",
-       "1840      190.0        FR   France  \n",
-       "1841      198.0        FR   France  \n",
-       "1842      224.0        FR   France  \n",
-       "1843      266.0        FR   France  \n",
-       "1844      219.0        FR   France  \n",
-       "1845      176.0        FR   France  \n",
-       "1846      163.0        FR   France  \n",
-       "1847      195.0        FR   France  \n",
-       "1848      308.0        FR   France  \n",
-       "1849      213.0        FR   France  \n",
-       "\n",
-       "[1850 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"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": 3,
    "metadata": {},
+   "outputs": [],
    "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."
+    "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\""
    ]
   },
   {
@@ -1072,78 +41,46 @@
    "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>1613</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",
-       "1613  198919          3    0      NaN     NaN       0         NaN        NaN   \n",
-       "\n",
-       "     geo_insee geo_name  \n",
-       "1613        FR   France  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fichier non trouvé\n",
+      "Téléchargement du fichier sur le site Web\n"
+     ]
     }
    ],
    "source": [
-    "raw_data[raw_data.isnull().any(axis=1)]"
+    "import os.path\n",
+    "# Vérifier si le fichier existe ou non\n",
+    "if os.path.isfile('incidence-PAY-3.csv'):\n",
+    "    print(\"Fichier trouvé\")\n",
+    "    raw_data = pd.read_csv('incidence-PAY-3.csv', skiprows=1)\n",
+    "else:\n",
+    "    print(\"Fichier non trouvé\")\n",
+    "    print(\"Téléchargement du fichier sur le site Web\")\n",
+    "    raw_data = pd.read_csv(data_url, skiprows=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."
+    "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`."
    ]
   },
   {
@@ -1980,7 +917,7 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>1849 rows × 10 columns</p>\n",
+       "<p>1850 rows × 10 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -2110,7 +1047,7 @@
        "1848      308.0        FR   France  \n",
        "1849      213.0        FR   France  \n",
        "\n",
-       "[1849 rows x 10 columns]"
+       "[1850 rows x 10 columns]"
       ]
      },
      "execution_count": 5,
@@ -2118,6 +1055,38 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "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"
@@ -2143,7 +1112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2173,7 +1142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2198,17 +1167,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "periods = sorted_data.index\n",
     "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
@@ -2226,32 +1187,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fabe83e0b38>"
-      ]
-     },
-     "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sorted_data['inc'].plot()"
    ]
@@ -2265,32 +1203,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fabe62d76a0>"
-      ]
-     },
-     "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sorted_data['inc'][-200:].plot()"
    ]
@@ -2325,7 +1240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2345,7 +1260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2369,32 +1284,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fabe61db080>"
-      ]
-     },
-     "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "yearly_incidence.plot(style='*')"
    ]
@@ -2408,54 +1300,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "yearly_incidence.sort_values()"
    ]
@@ -2470,32 +1317,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fabe61b4ac8>"
-      ]
-     },
-     "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "yearly_incidence.hist(xrot=20)"
    ]
-- 
2.18.1