diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb
index 1b28c996b81638edc043e7a4a27e8be6c3f07ae1..543f32f147c2ea46f7d6635fefc07673bfaa6eb1 100644
--- a/module3/exo1/analyse-syndrome-grippal.ipynb
+++ b/module3/exo1/analyse-syndrome-grippal.ipynb
@@ -9,58 +9,1126 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import isoweek\n",
+    "import pathlib"
+   ]
+  },
+  {
+   "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": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "import isoweek\n",
-    "import pathlib"
+    "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>202121</td>\n",
+       "      <td>3</td>\n",
+       "      <td>11442</td>\n",
+       "      <td>8496.0</td>\n",
+       "      <td>14388.0</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>202120</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10303</td>\n",
+       "      <td>7556.0</td>\n",
+       "      <td>13050.0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202119</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9539</td>\n",
+       "      <td>6860.0</td>\n",
+       "      <td>12218.0</td>\n",
+       "      <td>14</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202118</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12135</td>\n",
+       "      <td>9165.0</td>\n",
+       "      <td>15105.0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202117</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12058</td>\n",
+       "      <td>8891.0</td>\n",
+       "      <td>15225.0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>202116</td>\n",
+       "      <td>3</td>\n",
+       "      <td>16505</td>\n",
+       "      <td>12735.0</td>\n",
+       "      <td>20275.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>202115</td>\n",
+       "      <td>3</td>\n",
+       "      <td>19306</td>\n",
+       "      <td>15398.0</td>\n",
+       "      <td>23214.0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>202114</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21073</td>\n",
+       "      <td>17099.0</td>\n",
+       "      <td>25047.0</td>\n",
+       "      <td>32</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>202113</td>\n",
+       "      <td>3</td>\n",
+       "      <td>26413</td>\n",
+       "      <td>22094.0</td>\n",
+       "      <td>30732.0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>202112</td>\n",
+       "      <td>3</td>\n",
+       "      <td>30658</td>\n",
+       "      <td>25919.0</td>\n",
+       "      <td>35397.0</td>\n",
+       "      <td>46</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>202111</td>\n",
+       "      <td>3</td>\n",
+       "      <td>24988</td>\n",
+       "      <td>20718.0</td>\n",
+       "      <td>29258.0</td>\n",
+       "      <td>38</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>202110</td>\n",
+       "      <td>3</td>\n",
+       "      <td>19539</td>\n",
+       "      <td>15951.0</td>\n",
+       "      <td>23127.0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>202109</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17572</td>\n",
+       "      <td>13926.0</td>\n",
+       "      <td>21218.0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>202108</td>\n",
+       "      <td>3</td>\n",
+       "      <td>20882</td>\n",
+       "      <td>16907.0</td>\n",
+       "      <td>24857.0</td>\n",
+       "      <td>32</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>202107</td>\n",
+       "      <td>3</td>\n",
+       "      <td>22393</td>\n",
+       "      <td>18303.0</td>\n",
+       "      <td>26483.0</td>\n",
+       "      <td>34</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>202106</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23183</td>\n",
+       "      <td>19134.0</td>\n",
+       "      <td>27232.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>202105</td>\n",
+       "      <td>3</td>\n",
+       "      <td>22426</td>\n",
+       "      <td>18445.0</td>\n",
+       "      <td>26407.0</td>\n",
+       "      <td>34</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>202104</td>\n",
+       "      <td>3</td>\n",
+       "      <td>25804</td>\n",
+       "      <td>21491.0</td>\n",
+       "      <td>30117.0</td>\n",
+       "      <td>39</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>202103</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21810</td>\n",
+       "      <td>17894.0</td>\n",
+       "      <td>25726.0</td>\n",
+       "      <td>33</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>202102</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17320</td>\n",
+       "      <td>13906.0</td>\n",
+       "      <td>20734.0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>202101</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21799</td>\n",
+       "      <td>17778.0</td>\n",
+       "      <td>25820.0</td>\n",
+       "      <td>33</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>202053</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21220</td>\n",
+       "      <td>16498.0</td>\n",
+       "      <td>25942.0</td>\n",
+       "      <td>32</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>202052</td>\n",
+       "      <td>3</td>\n",
+       "      <td>16428</td>\n",
+       "      <td>12285.0</td>\n",
+       "      <td>20571.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>202051</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21619</td>\n",
+       "      <td>17370.0</td>\n",
+       "      <td>25868.0</td>\n",
+       "      <td>33</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>202050</td>\n",
+       "      <td>3</td>\n",
+       "      <td>16845</td>\n",
+       "      <td>13220.0</td>\n",
+       "      <td>20470.0</td>\n",
+       "      <td>26</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>202049</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12939</td>\n",
+       "      <td>9923.0</td>\n",
+       "      <td>15955.0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>202048</td>\n",
+       "      <td>3</td>\n",
+       "      <td>13804</td>\n",
+       "      <td>10641.0</td>\n",
+       "      <td>16967.0</td>\n",
+       "      <td>21</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>202047</td>\n",
+       "      <td>3</td>\n",
+       "      <td>19085</td>\n",
+       "      <td>15285.0</td>\n",
+       "      <td>22885.0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>202046</td>\n",
+       "      <td>3</td>\n",
+       "      <td>24801</td>\n",
+       "      <td>20503.0</td>\n",
+       "      <td>29099.0</td>\n",
+       "      <td>38</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>202045</td>\n",
+       "      <td>3</td>\n",
+       "      <td>42516</td>\n",
+       "      <td>36857.0</td>\n",
+       "      <td>48175.0</td>\n",
+       "      <td>65</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>74.0</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>1879</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>1880</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>1881</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>1882</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>1883</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>1884</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>1885</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>1886</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>1887</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>1888</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>1889</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>1890</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>1891</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>1892</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>1893</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>1894</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>1895</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>1896</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>1897</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>1898</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>1899</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>1900</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>1901</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>1902</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>1903</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>1904</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>1905</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>1906</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>1907</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>1908</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>1909 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator     inc   inc_low    inc_up  inc100  inc100_low  \\\n",
+       "0     202121          3   11442    8496.0   14388.0      17        13.0   \n",
+       "1     202120          3   10303    7556.0   13050.0      16        12.0   \n",
+       "2     202119          3    9539    6860.0   12218.0      14        10.0   \n",
+       "3     202118          3   12135    9165.0   15105.0      18        14.0   \n",
+       "4     202117          3   12058    8891.0   15225.0      18        13.0   \n",
+       "5     202116          3   16505   12735.0   20275.0      25        19.0   \n",
+       "6     202115          3   19306   15398.0   23214.0      29        23.0   \n",
+       "7     202114          3   21073   17099.0   25047.0      32        26.0   \n",
+       "8     202113          3   26413   22094.0   30732.0      40        33.0   \n",
+       "9     202112          3   30658   25919.0   35397.0      46        39.0   \n",
+       "10    202111          3   24988   20718.0   29258.0      38        32.0   \n",
+       "11    202110          3   19539   15951.0   23127.0      30        25.0   \n",
+       "12    202109          3   17572   13926.0   21218.0      27        21.0   \n",
+       "13    202108          3   20882   16907.0   24857.0      32        26.0   \n",
+       "14    202107          3   22393   18303.0   26483.0      34        28.0   \n",
+       "15    202106          3   23183   19134.0   27232.0      35        29.0   \n",
+       "16    202105          3   22426   18445.0   26407.0      34        28.0   \n",
+       "17    202104          3   25804   21491.0   30117.0      39        32.0   \n",
+       "18    202103          3   21810   17894.0   25726.0      33        27.0   \n",
+       "19    202102          3   17320   13906.0   20734.0      26        21.0   \n",
+       "20    202101          3   21799   17778.0   25820.0      33        27.0   \n",
+       "21    202053          3   21220   16498.0   25942.0      32        25.0   \n",
+       "22    202052          3   16428   12285.0   20571.0      25        19.0   \n",
+       "23    202051          3   21619   17370.0   25868.0      33        27.0   \n",
+       "24    202050          3   16845   13220.0   20470.0      26        20.0   \n",
+       "25    202049          3   12939    9923.0   15955.0      20        15.0   \n",
+       "26    202048          3   13804   10641.0   16967.0      21        16.0   \n",
+       "27    202047          3   19085   15285.0   22885.0      29        23.0   \n",
+       "28    202046          3   24801   20503.0   29099.0      38        31.0   \n",
+       "29    202045          3   42516   36857.0   48175.0      65        56.0   \n",
+       "...      ...        ...     ...       ...       ...     ...         ...   \n",
+       "1879  198521          3   26096   19621.0   32571.0      47        35.0   \n",
+       "1880  198520          3   27896   20885.0   34907.0      51        38.0   \n",
+       "1881  198519          3   43154   32821.0   53487.0      78        59.0   \n",
+       "1882  198518          3   40555   29935.0   51175.0      74        55.0   \n",
+       "1883  198517          3   34053   24366.0   43740.0      62        44.0   \n",
+       "1884  198516          3   50362   36451.0   64273.0      91        66.0   \n",
+       "1885  198515          3   63881   45538.0   82224.0     116        83.0   \n",
+       "1886  198514          3  134545  114400.0  154690.0     244       207.0   \n",
+       "1887  198513          3  197206  176080.0  218332.0     357       319.0   \n",
+       "1888  198512          3  245240  223304.0  267176.0     445       405.0   \n",
+       "1889  198511          3  276205  252399.0  300011.0     501       458.0   \n",
+       "1890  198510          3  353231  326279.0  380183.0     640       591.0   \n",
+       "1891  198509          3  369895  341109.0  398681.0     670       618.0   \n",
+       "1892  198508          3  389886  359529.0  420243.0     707       652.0   \n",
+       "1893  198507          3  471852  432599.0  511105.0     855       784.0   \n",
+       "1894  198506          3  565825  518011.0  613639.0    1026       939.0   \n",
+       "1895  198505          3  637302  592795.0  681809.0    1155      1074.0   \n",
+       "1896  198504          3  424937  390794.0  459080.0     770       708.0   \n",
+       "1897  198503          3  213901  174689.0  253113.0     388       317.0   \n",
+       "1898  198502          3   97586   80949.0  114223.0     177       147.0   \n",
+       "1899  198501          3   85489   65918.0  105060.0     155       120.0   \n",
+       "1900  198452          3   84830   60602.0  109058.0     154       110.0   \n",
+       "1901  198451          3  101726   80242.0  123210.0     185       146.0   \n",
+       "1902  198450          3  123680  101401.0  145959.0     225       184.0   \n",
+       "1903  198449          3  101073   81684.0  120462.0     184       149.0   \n",
+       "1904  198448          3   78620   60634.0   96606.0     143       110.0   \n",
+       "1905  198447          3   72029   54274.0   89784.0     131        99.0   \n",
+       "1906  198446          3   87330   67686.0  106974.0     159       123.0   \n",
+       "1907  198445          3  135223  101414.0  169032.0     246       184.0   \n",
+       "1908  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          18.0        FR   France  \n",
+       "3          22.0        FR   France  \n",
+       "4          23.0        FR   France  \n",
+       "5          31.0        FR   France  \n",
+       "6          35.0        FR   France  \n",
+       "7          38.0        FR   France  \n",
+       "8          47.0        FR   France  \n",
+       "9          53.0        FR   France  \n",
+       "10         44.0        FR   France  \n",
+       "11         35.0        FR   France  \n",
+       "12         33.0        FR   France  \n",
+       "13         38.0        FR   France  \n",
+       "14         40.0        FR   France  \n",
+       "15         41.0        FR   France  \n",
+       "16         40.0        FR   France  \n",
+       "17         46.0        FR   France  \n",
+       "18         39.0        FR   France  \n",
+       "19         31.0        FR   France  \n",
+       "20         39.0        FR   France  \n",
+       "21         39.0        FR   France  \n",
+       "22         31.0        FR   France  \n",
+       "23         39.0        FR   France  \n",
+       "24         32.0        FR   France  \n",
+       "25         25.0        FR   France  \n",
+       "26         26.0        FR   France  \n",
+       "27         35.0        FR   France  \n",
+       "28         45.0        FR   France  \n",
+       "29         74.0        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1879       59.0        FR   France  \n",
+       "1880       64.0        FR   France  \n",
+       "1881       97.0        FR   France  \n",
+       "1882       93.0        FR   France  \n",
+       "1883       80.0        FR   France  \n",
+       "1884      116.0        FR   France  \n",
+       "1885      149.0        FR   France  \n",
+       "1886      281.0        FR   France  \n",
+       "1887      395.0        FR   France  \n",
+       "1888      485.0        FR   France  \n",
+       "1889      544.0        FR   France  \n",
+       "1890      689.0        FR   France  \n",
+       "1891      722.0        FR   France  \n",
+       "1892      762.0        FR   France  \n",
+       "1893      926.0        FR   France  \n",
+       "1894     1113.0        FR   France  \n",
+       "1895     1236.0        FR   France  \n",
+       "1896      832.0        FR   France  \n",
+       "1897      459.0        FR   France  \n",
+       "1898      207.0        FR   France  \n",
+       "1899      190.0        FR   France  \n",
+       "1900      198.0        FR   France  \n",
+       "1901      224.0        FR   France  \n",
+       "1902      266.0        FR   France  \n",
+       "1903      219.0        FR   France  \n",
+       "1904      176.0        FR   France  \n",
+       "1905      163.0        FR   France  \n",
+       "1906      195.0        FR   France  \n",
+       "1907      308.0        FR   France  \n",
+       "1908      213.0        FR   France  \n",
+       "\n",
+       "[1909 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "if pathlib.Path(\"incidence-PAY-3.csv\").exists():\n",
+    "    raw_data = pd.read_csv(\"incidence-PAY-3.csv\")\n",
+    "else:\n",
+    "    raw_data = pd.read_csv(data_url, skiprows=1)\n",
+    "    raw_data.to_csv(\"incidence-PAY-3.csv\", index=False)\n",
+    "raw_data"
    ]
   },
   {
    "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."
+    "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": 2,
+   "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>1672</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",
+       "1672  198919          3    0      NaN     NaN       0         NaN        NaN   \n",
+       "\n",
+       "     geo_insee geo_name  \n",
+       "1672        FR   France  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\""
+    "raw_data[raw_data.isnull().any(axis=1)]"
    ]
   },
   {
    "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`."
+    "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -892,7 +1960,7 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>1909 rows × 10 columns</p>\n",
+       "<p>1908 rows × 10 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -1022,51 +2090,14 @@
        "1907      308.0        FR   France  \n",
        "1908      213.0        FR   France  \n",
        "\n",
-       "[1909 rows x 10 columns]"
+       "[1908 rows x 10 columns]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "if pathlib.Path(\"incidence-PAY-3.csv\").exists():\n",
-    "    raw_data = pd.read_csv(\"incidence-PAY-3.csv\")\n",
-    "else:\n",
-    "    raw_data = pd.read_csv(data_url, skiprows=1)\n",
-    "    raw_data.to_csv(\"incidence-PAY-3.csv\", index=False)\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"
@@ -1092,7 +2123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1122,10 +2153,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [],
    "source": [
     "sorted_data = data.set_index('period').sort_index()"
@@ -1149,9 +2178,17 @@
   },
   {
    "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",
@@ -1169,9 +2206,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd075a82cf8>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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H05mCc4wfJVAaRoFA2b1/CLv376l2M+oSFiglUC2Tl80UVAsaSqTb14+AV/6Er/4ZQuijqJSGMsgaipWhzOgRKEzp8N0vgWprKDUgNyrKSF5fkPxLKQ0lwwLFhnonYhyad0DDAqUEqu6UN0V5jWBbTETjlC+/jChIxJz+zrBAYZhQsEApgVodX2plIC6XWonyikuBki5DI81kBR5/c2dFr6kW+quau28ztQMLlBKousnLMICMlhXmtXIVybjzepQjUG59aj0++YulWPLGzqiaVUAUj+PAcAb3vbK1JoQTU7+wU74EqmXyUtTybsOjyeSlNJRyJhBbuvoAADv3DRSdN+zgHoUQuOGBVbjz+Y1ob03htMMnFZ2f9RMGYA2lJIJmrLc+tQ7rOvaPYGty1MpAXC61omkpH0o6U3p7yhFKYe9nFBrK9m5H4PUMlLd1SrWfwcVPv42LfvRMdRtxAMMCpQRMg0P/UAbfePBN/P1Pnqto/WanfPUH4ii/2Fhts3wi5rwemTK+V6CinkqRSWGzRNHnUu6VLBDUvar2M/i1P72BVzfvrWobDmRYoJSAyeSlXqaeKm2QV+3ZYVRtqIXrAADpQinLh6I0lFLMUuFNXkUXXUBOeJdWmPp2Ta3cO/YFVQcWKCVg0lDc4xV+lkf7q6IEc6U1FJsZyrZ3WhjUzL8Uv1toDSXCB6Lcskz5N3f2obt/uLzCi2CQN/SsCixQSiBrGIiUZaTSTvtann3V0we2hkPGf5fTHtfkVcL4Ft6HUn6Hlft1TJup64xv/6XipmAvIym8mBwsUErA9MVGdbx6W7NUpdrIGanLsPVXFBpSTKoopWko4fJEIlBcH0hpCFc5LyxhYNjZC231zp4SSw/PlLYUAN71uFqwQCkCZb4wmUrSUkWp1rhebYcoEO1eXpUm7EBcTr/GqfJRXlH0FpXplA/KNpLmp8aks/9atUP7D1RYoBSBMl+YHlZl8qrUs6wmzOYPbFWm3mKIxOQl/5ZrhrExEoNObATChkWE43WpwlNNAnRtHkkTre0dYSoLC5QisNnDTaawqLCVPlreodxuw5WtxzbGV1qg2Qg7uEehmZYbpRWUrRqDO2so1YEFShGQxeSVKWMBXDGYBpBacNbXVdiwpR63n6vUrSO5sBFl+lCCumokB3dyrQgjViXjgQVKEdhMXpXWUGzUxjsU3cLGSmMb6EQE8qQcHSd82HAUGkp5ZQVpSdUY3FlDqQ4sUIpAOeVNC91GatPIWvahRMFIXcZIDjql1BR2cI/isSt3t2C3qTXjQxklL0OdwQKlCFwNxbQOpcIPsfuyVLSW8ojG5DVSUV6W81nlaC69PeWM06E1lBp6InTvQDVaV8ZuOUwZsEApAlvEjtpEsHp7UFV/YIk2yquyWD+p7KarcENM9YeN8opCQymzrKB8I2p+KmNnAqZ8WKAUgbsOxRQ2PEIP8ag3eY2YD8V2PsKGlFLWSAqUMjd3DLB4VcmHMvJ1MhEIFCKKE9FyIvqT/P8EIlpCRGvk3/GetNcS0VoiWk1ECz3H5xHRCnnuZpIGXSJKEdHd8vgLRDTTk2eRrGMNES0q9zrCYDN5Kc2lWgpKLbxDUZq8THb9qExiI+GULwdv+4KuOZqtV1Q9peUPal81/BmsoVSHKDSUzwBY5fn/NQAeE0LMBvCY/D+IaA6ASwEcC+ACAD8morjMcwuAKwHMlv8ukMevANAlhDgCwPcA3CjLmgDgOgAnA1gA4Dqv4KoU5G5FHhzlFauwzcscNlzRakcM63qbiK7TKlAirq9YwvtQyqdsp7z6q/OhjLzFiwVKlShLoBDRdADvBfAzz+GLANwuf98O4GLP8buEEINCiLcBrAWwgIimAmgTQjwnnKfxjrw8qqx7AZwjtZeFAJYIITqFEF0AliAnhCqGu3OsTUOpsIpiNnlV/yWK9HsohvNRDRa2YlR/llNfOYsjhU9DMaczPY+l1Rl9vnpdh1IL71O9Ua6GchOAfwPgjamYIoTYDgDy72R5fBqAzZ50W+SxafJ3/nFfHiFEGkA3gIkBZVUU29f3ciav6hi9vK16+PUd1WlDFC+yRShF9ZqHNnlVa7NPw+9KUH4EoTDmH0l/RpRhwyxPiqdkgUJE7wOwSwixLGwWzTERcLzUPP5Kia4koqVEtLSjoyNUQ03Yvr7nzhSr5ETxvgBX/TLsbak97JpDNPXYBjol2CKZ7ZaSJ2SmSDQAd3PIEp3yAdnq1YdSTbPZ27t7cfuzG6pWf6mUo6G8C8AHiGgDgLsAnE1EvwSwU5qxIP/ukum3ADjEk386gG3y+HTNcV8eIkoAGAugM6CsAoQQtwoh5gsh5re3t5d2pRKymLzSI+SUr2VVPBoNxXZ+ZJzy6jaXZfIqax1KOKd8NPIkKh9K4bkR1VDcd7S0/N5+rmak2CU/eRbX3b8Sg+lM9RpRAiULFCHEtUKI6UKImXCc7Y8LIf4BwP0AVNTVIgD3yd/3A7hURm7NguN8f1GaxXqI6BTpH7k8L48q60OyDgHgEQDnE9F46Yw/Xx6rKLGQTvlqrUOphQVu0fhQwpmiyq8n3PmqDSwjqaEUV2VR+etJQ/He62pqKOoDYTU8d9RSiXUo3wJwHhGtAXCe/D+EECsB3APgDQAPA7haCKHE76fgOPbXAlgH4CF5/OcAJhLRWgD/ChkxJoToBHA9gJfkv6/JYxXF9j2U7Aj5UIwPWQQPX+9gGve9srX8gqLA0I3RCRSb4BKh0lWKsD6UaKK87IXt6B4wfuUyt/WKJsqrvKYVRbm7JvtDtaNo0YFFIopChBBPAHhC/t4D4BxDuhsA3KA5vhTAXM3xAQCXGMpaDGBxqW0uhZjlY0nqeKxqGkr5fPWPK3HP0i2YPr4Z8w4tPhI7ipdQvdSmbsw3BZUa8hp2YWO1ZqqhPwAW5ToUw1PUMzCMU775GD568gzc8LfHFbYh0ClfPxqKN1sthB6P1P6AUcEr5YvAun29GzZc6XUohuMRPHu79w8BALr7h8ovrESKccqXc83hfSil16EoaaF8yOuMdKW8oax9A2kAwONv7tKeDw5rLqdlpVHqPfM+E9UUKDlzKwuUUY/pk6YZy8w6KozrUCLQURJSvRpKV/9lMuEzS5RRj12glK+hjMT29ZHsNmxpadbVvg27F6i/mraMpG/PdcpHoqFE0KAyqbdNLlmgFIF6vnoH09rzmRoKGy6VZMJ5JEy28pFogz3Ky1tf6RWG1oQiuabiC/EtbAzIH8kXGy0f2HIXmxr9WpWNQiuWKL7rUgvRlKyhjGbkve0dChYoFddQTFuvRFB2Q7w8gRIF9r28cr/LmUWOhIZSDmFNXlHMYm0ze6V9xy0OQt2zWR0fSvn5qqmhqKpZoIxi1K0dGNbHhlfch2Kxc0cxo0rGlcmrRA0lirDhIhKUU194p3zJVXhW6FZumhGlScl0raovjCavIIFXhTGx9LDh2vChKKr9FdhiYYFSBGrAThuWyrvRSVX7Hkr5uNvLVGDFdPhCbKe9ZonSqxmJhY0qb2kmr2jTBaPCbQ3PtmWfOjfKS+dDqaO9vGotyqsGmlAULFCKwKaGKitRxXYbFv52GE6XhXohq/kg28KGo5rxhl+HUk4d/r9F5Q15R6OM8jJFMA7LSVTc8mxXe6V8rh2lToiimaxEBYcNj2LUA2b+prwjUSqloBRjCiqVcjfXGwEFJbKX3v4JYJWuHMe/0lCKx9u+YJNSBE55TZ1e0rIzbCYvXfZqfFO+3tehRLHTdTVggVIEasZoW4dS+YaYnPKVj/axEeUur2bzivd3GeYoy/2KwilfjnM1bF9Go5kG15m2mbwCGlGNITGadSgRNaYM6kyesEApBldDMfhQMu5AWBkdxTbbjcT0UebWFVFg3b4+5Mzdht0pHy5dcBmlm83CCs4oZ7GmstQzb1+HUpg/yu+1hCWSvbxqQKKwyWsUo55Rs4YiTV5lypOB4Qy6+4aLzhfFuBKzzFStbSi/CSHWh3hnkRYtIyvwubtfwaub9waWE1RPOVqXK5RKGBhG0imvJhKmaPG0PGEKGw7+RHF5bSsG24p/G1EFfJRLOZptNWGBUgJpQ+C/ehnL1U/ec/NfccLX/lxwPDcL1OeL0ilfCzH4Yc7b0u7qGcQflm/FlXcuLTjn91FoZtZRhA0H+BZC5C4oR19HdKZO0wA2bNmnLqgFVdltuMSbVis+lFpqQzGwQCkC9WIYdxuOKGx4fUdvSflGYmCxt6HsJriFmNZuFGPyUt+TSCXiBedsO8tGETZcjnPVd51B6YouuXhy2newRNGGDYes4/P3vIpPlflhuNzzW1r+WluHUmcWLxYoxaDurfF7KBXevj43263cU1Z+yHP5bXN3sLGseQhTndp3LZUofNRte4JFubCxXB9KEFHa+k1luWHDJpNXBD6e3728BQ9F9OnqSu/l9bO/rscHfvh0SXUU045aEGrFEMn29QcKNqe8KZzYy1s7ezC+uQHtrani6w9YPOacL59ywy6joJgPbNmE6+CwFCjJQoGSb96I500EcmtIyvChZCPSUALyR3vf9edzTvngcvRbr5TRsCIp9575tVZzGV9/YFVJ5QPAd5e8heFMFl+84GhrWnbKj2JsYcNq8Aga5M7/3lM49ZuPRd84ROSUj5UX5RVFG2xFFPMRpCHp2ErELBqK1uRVujDIlVFYV/i84fJE+8VG02Qp5DoUnclrBCcn5UbmhdVQyuHmx9bglifWFd2eeoAFShFYo7xCmkjCaDJB9Vdyrhriw30AgDe27cO6jv0VaEGYKK/cb9tgqu5VQjO19jnlAzY1LGfzRVVuKbc8rA8lmt2QHWwaim1tkC57lINid/8wfv/yFuP5chcEFuuUr7SwrDcNhU1eRaBurXmlvDJJRfMQpDNZJOLBppowx4siZNjle27+KwBgw7fea0xT6tcUi7kMW1oV7hrTChSbhlKYrliCZu7WvCF7ItIPgBkaqjS9pOZ5dNpQvg8lDF+89zU8vHIHjpnahmOmthWcz/keSiu/WKd8JiuQiFdu875686GwhlIE9nUowSaxYskXXNaZewR1qoCCKGZ4pZvNivChWOpQ4a66PahsW7iE2cvr5U1d+OQvXnIFl7mMcn0oAekiCYQI1qTU7tM6TQ/ImXv1/VxcW7bu7Tee275vAADQb9jxO1tGf3vzO2XY0w9V+DMPdaagsEApjmCBoY5H9RCYBnXj91Ci8KGUOdnyfxSqMhQzi1y+qQuAPjrJbvIyn1P8y2+W4/E3d2Hb3gFDW8O1sxxs62nCkDNZGTSUtFrYqB8ygrZmKfban1+3p6j0XnKmu/L6IWwZwxX+silrKKMYN8rLsg4lqoegWE0nyr28oghFjWKWqC3X8zuoj4QQuOnRNQBKNXnZJwi5vc9sz4S5DBO+NgVpKD4BW3w93jKMGopr8tLPONR90DntixV4QR/xym1eqj9fbqh3sX2p01DW7tqP+1/dVloD8qiF7V+KgX0oRaBubcbgpVWCJqpJRX417sNewWdMDQilVhFyDAwuQ2Y0f7ExV3KQQPGe0o2D/q/zmTWUIAFn2/usnBmzbxuQoFCMvOvID38OVZfw/81HrefRCWbAI1A0U9T8Qdrmcig1aMWpTNVTvsm2VKf8ud99EgDw/uOnlr2vH39gaxTjfmDLEjYcmYZiNHnpiaLaKNehlOxDyWuL6TxgEyi5czpTjc08Z5u1A2G+xV76JCNsHq+wMd23dR378cJ6sykpJ1D0+d1PQhvapJ5VnYYS9n7l0pj9ErnigzXCUp+9YrWpoMvpG9L7eYqhzuQJC5RiyGko+rsctVM+vxxbqVHUWu4HtvyhruXPEm3ng2Zw3v7TBSeFNXkFDSy278fYBuogwjqIveOvKd05//0kPnzr89a6TAIpa3m2MyqsOKDsoPJ9ZQX4uW3z/XI0wvx8YV7joHp6BtIltSFs+bUIC5QisPlQMhYNpdhBpdhyotzLq3STV7hBUPHBW57FL55521iGto6QJi9v/dqFjb6BWGPyKuIDW6YUZflQSkhX8hon9deQ3SZwlGDXmXhspsWCskIs/LG1s9RJnc/kFUqbMqcxbSJbDPW2DoUFShHYNofM2M4X+XAUaCjC/7cSUNiFKBF9G7WoAAAgAElEQVSxbGMXvvLHN3zHrBqK53dYk1dZTvmgGbPandkYqFFYV1j82p4Zb9mm8GWFbWNTU3farsMWHJFfThBBQtG2G7Zw22mvR0exGkrQbY1CGNSZgsICpRi8Ji/djNYbNqw9X+TTUWjyss3ciyo+kJLfBZ8NOpKmFFbhNXkFNNTb3zpHsG3AFpZBFMiZYEyDYJhJwNpdPfjNi5t0uc2ZNHUA9nURw6bzqp0lRqsFLeoNe7+iSFOOidGbP2wZQc9GFAKl3jQUjvIqBp/qXjhI5c9uCs4XqQHnP6y5dRGm5kUwI3K3CinPdFJOe9SLHGa34cCZsae/bRqKPspLDZIBjaXgdoTZCuTiHz2L/YNpfHj+Ib52hh3cvOeGDRuX5s5n0Zg0b+VvqkYdt2nfurNhN1wkcuoJuqeqd8rp7yD8AQ729GF9eKXCPpRRjPfW6uyj3l2IdefL1lBspqAInj13hldi/mJXGuvLcP6aBIq3a4P61BflZV0fYc5floaiaUs++wcd521f3urvsN3nTTecDp61mHbKVkdNA77VhxLgP/R2TRhhEcYPZPJP5Exz1iIC8zu/7YXk95fX9FlW+LOmPfUAC5Qi8NmCNc9z1nI+Y5k9BpUnWyDbYWpfUcXryzDW7a0n6Fzud7lrAYzXGVJD8dZvW8EdtFI+UKDIgk2+izBaTqPcWr9nwP/Z51J8KEaTljpvGIhtvgfbdQStwSrWh1KOM7zcxcXFRqTlN8NrcrRpKOWa1GoRFihFYNNQvA+QbuZcvIbi/3+YrUDClSvwqxc26gcf1xEdNFB7k+dpUYZ02nKMs3rbi5j7HdaHYhvogs6HuW12Z7Y5b1JGoA0M+++HrX26czYfiklDcQWG0Yfi/C3FqV/sIB3slA9uR7mLi/1mRnv6/OvxjQGWF6DcsORapGSBQkSHENFfiGgVEa0kos/I4xOIaAkRrZF/x3vyXEtEa4loNREt9ByfR0Qr5LmbSU77iChFRHfL4y8Q0UxPnkWyjjVEtKjU6ygGIXKb4+kelrTlYSo/yssy0IYs97dLN+NLf3gdP/3remMZQU0dDpiF+bW44BYZF24WYbYIqsOvLWnyWgaPYiK0bDb9MLPRfC0n7OPibd+QxeRl0mBs993mm8gN5LrnXt/W/PJtQstfpj6Nur7SteNgrdXWDr/ZOzh/mLDienPKl6OhpAF8XghxDIBTAFxNRHMAXAPgMSHEbACPyf9DnrsUwLEALgDwYyJS3sFbAFwJYLb8d4E8fgWALiHEEQC+B+BGWdYEANcBOBnAAgDXeQVXpRAQ7vbduoclaxlMi33I89Or/5lNXuHK7+pzTCvdfcMF52zO1/xzQTsih/1WSUEb3LKCBY6ufi82Z3BYp3w5M8mccAwoRM668x3qYbde8WJ3ypcm+GxrctyPy1k0lDAh9UGavAprN913NaAH3bOZ1zyA7zyyWnvO50MxjPe9g7kFi/lN9QqJUp9/L0YTYyaLHzy2xvW/1QolCxQhxHYhxMvydw+AVQCmAbgIwO0y2e0ALpa/LwJwlxBiUAjxNoC1ABYQ0VQAbUKI54TzNN+Rl0eVdS+Ac6T2shDAEiFEpxCiC8AS5IRQxciK3OZ4OoFheym8L0Ep9lPbjD+suHIHpwC/QlD7vNeRb2IpJkrGtnDTOHiJcIOPzcEa9nwYf5JtZ+igvsg5o/M3bzPn8VKMD8XozFY7qxjqtAlXV0PR+qK8Ey19/kwIoeNLbzR5hdNQfviXtdrjfn+Pvox7lm42pvFNtizCPYzT3tSGB1Zsx38vecsoGKtFJD4UaYo6EcALAKYIIbYDjtABMFkmmwZgsyfbFnlsmvydf9yXRwiRBtANYGJAWbq2XUlES4loaUdHR2kXKBHCoqF4VXvt+fJempxZwqSiWIt0krnyxLzvUthwyPyXxm+DLm2GZvsMgPdwoK/HJ+A15VgGsWJ8KObPQsu/IRz7hRqK6T9+ovChZCyC0XY+G3DPvH0TZmFk4EAsH1nd+yeEcPuw1N0k/JMMfZogk6/N7O1LGyJIxyg4Zd7u/kIrQzUpW6AQUQuA3wH4rBBiX1BSzTERcLzUPP6DQtwqhJgvhJjf3t4e0Dw7QsD9Opveh+J50LS2ZM9LU8LsxGaOKtZZr4t8CuO/8Nr682fEPod5qQLFEhjgHRTCmryCTFqmcoqJGAqjTdnI96GEzeptui1s2OhDsQhP97mwOMOtYcMhBEqYrVd0aXyCy6QJWbX83HmT8GnyrOPJL84rJGzP/2DavnmkqQg1DkURmhwlZQkUIkrCESa/EkL8Xh7eKc1YkH93yeNbABziyT4dwDZ5fLrmuC8PESUAjAXQGVBWRcl6NBSt09374mjP22dqvvSG7ett9nob7sLBgHNBM3+fySud70j2XmNwO8yzestsWBSmtaXT+1CC21KcU15/3KpVIifYC/xRPh+KmeIWNpamFVpXyqs2aM5775FpkA7vQ3HQDaTeY7bgARN+H2BgUpk+X0Pxai/BgjHMbsSmvlB704URviNJOVFeBODnAFYJIb7rOXU/gEXy9yIA93mOXyojt2bBcb6/KM1iPUR0iizz8rw8qqwPAXhc+lkeAXA+EY2Xzvjz5bGKkhVAQ6DJK3iGVOyiJ5PJy+bMtuGavAJ8KEHtC3TKe8sqMcrLjfYxDizh+tE2Y/XvgWXWUILkiU1g5AS0uQxFkLYXhN/kFTxImdfLyLJKDBvOuM7w4ImUSfAWYyoypfGa+2y+HhNhQpz3eXYRzm+HX9MKrAr9IQSKqQ3qI2S2CcRIU87WK+8C8DEAK4joFXns3wF8C8A9RHQFgE0ALgEAIcRKIroHwBtwIsSuFkKoHv0UgF8AaALwkPwHOALrTiJaC0czuVSW1UlE1wN4Sab7mhCis4xrsaIGhmCTl3eg06yk9w1w9gehcOsVIesO0eAAVKk6H4o7Kw+poQQNgrYB0WaWMJ23OdNz9QcPDt5DuvsV5BcwpTXVEWq1fUZ/v/PbWlAHwg9iwyat0CI8rSvlVX7duRCag2+yFTBIutqcJs2AZ6cBkyZk2zxThHi2vJFVBSavIkx3+wbs/g/Ts+fuCl5b8qR0gSKEeBrmzxOcY8hzA4AbNMeXApirOT4AKZA05xYDWBy2veWibqxSNYMGICe9ZqZWroZiGZyKndGWqqEE+VC8Q4rNhmzePiN48Aob3GBzsNo0RneQDGPysvhQwtyb/P4Qvt8hr9PqCLat6LdoWhaTle78ht29BekK2hXS5BVUzsCQPWTXNqMPI8S9fVhg8ipiHcr6jl6cdvik4PZYJirh7RIjA6+UD4l6cJIJp8u0W6tYVHvbOpWg8oAwJq9wD5dqh+7bFarKP71mdkn5NZT8WXVhPcZ22DQU0+BmaEtB+b7BoTCdzYcSZMYpSGuc+Re2JR9TlFexUXuAfTAu2YfiRqvpz+d2Gy4899tluSBOU/OKWWEO6K9zwOPktrXTRBgNJUj4+X0oOq04d6xvyL6GxCbAa01DYYESEvVsNLjRFXoNpSHIaV9slFeBhuL837bCPCxBX9fLCmDTnj5tPn+IZ5DJK7hBtlm9yToRVjDbTC02H0rG0xdGbFpjQP0KdR8KzIchP1YWZuGgwhTlZRN8VpNXyAEuTJRX4NYrMJucvT4Js4ZiMXmFWEflf67M52xjQCinvFGjy8r21hYsUEKiHtBcdEXhrUxnBRqUBmMxeZW0DkW99CGc8kndB0A06fIJs2gwqoWN1q3QTaNTyBm5f+YeXL/+5Xf+hgobNkV5BfgW8gla0xOEN5mprTE3ksy0sDG4na5AKfWeWdpXrH9RNwEYTNtNXlanfIhtYoKiydIWweg9FEagmLrTtt6mWvD3UEKi7luQUz6TFe5AbpudhBEopq1XjHll+mOmtrk72OqTOelili3ddVu+O/Xn3rpCR7I3nUVDMQ5O8q9xthyuDpvJyysMdbvwqusM886Wsy+Z6ub8Nvi0vYC6w3wSOUaErBBGk5fNh2IzidnOu/VYggKA4EFfTVh0Dm8Vwh6jgJm9RUOxPTMAfLuG519POuBcfvnlmLxs11EtWEMJiXqQg0xavnUqOg0lxOzfl75gt+Fgs4M6atZNZDqZUL+wMVe2aatz76BUGOVlnr3lY925NsTgE1ag6NriXUOj+7SAzZcDAL1yULC1NdzmkPoJhI0wExX14S7zSnlZp6FSNcjbFqPa/Him2+VbEBjCjKkTOipkujEZLzlsOMicpSsjP41XA9QGehRr8jKZ7iwaZbVggRIS9RwErVBNZ3MCRb/1Su53KY5H9V/zzrbO31gsXJip1ofiaaNx8PE55c0zJdsYWqo9XmjS2srXyUaviSTIPGFq54tvd2LnvkGnHSX6HhyUU97fSO+1BQmkMGG5Sts0mbzCRnEZw3FdgVN4zjtxCbU5ZAiBokszKLf/b0rGzSv6i9hfy9wXZtOa7Tq86X//8tbAtgD2SLMas3ixQAnD5s4+PLZqJwAEaiDZrEAqEeRjCf/xHVWewmfWsGTVrS/xEqShhNloMCjKK0yUjMKmodgG6aAynHTBbfFpKAH3y1TFc+v2aOvykvN7GZvpqc88OAURZjGd8qHYorxsUVjGe+IO9P4GfPpXyyAEMKkl5aQzDdIh72mwhuLU7Wgoppl98I0oWkPJN3nZBEqI5yCMCdO9X/biRhT2oYTgPd//K3rkYiZXoBiiggIFThHROPnpfQO1UUORmgdZbO6BdeZ+mx9m8zqUYrZeMZkfrKa9kKZDv0AuPO/1oeij9grr8+K99rIWNrqL9fxt8LYpSDaH2dInFtNrQbl8/r8FdVh8JOpe5m/F8+CKHQDgTrTCzPqDvhOirlWroaSVQIkZJ13WDRtDaIVBQsdmuisIM85kkYj75/VhNM5adcqzhhKCHs/KWPcDW4YoroYADcX7Lhe7sFEYjntRR4kocATKmc5053L5TC+2d5YbFJlke3mti+SMkVOetAHqmqo+HiODhpJxgxeCXn7TZYT53GsxG3bmaw9hdqMF/MLMJNjirg8l2ORlmm7YFnmqek27HacC+tk57v0dpKE4f3WCUQmzpoZ4qEmAvnz7JCGdzS0PKIzyCt4gVqU/dGIzAKBX40fxa9b6drJTfpSQsHwPJViglKOh2GflrikrZLk2LcpmHgF0YcPe9gRfo21wCbM5ZPD3UJxziRhp0w2ls2hucJT0QsEorE75oRBhqjY/DOA145g1lCB8g7GhHvVMbN07oD2f2y04uI22dRHDGaF9N5QPx+z0d/In42QRKE46nbB1BUoyHhDlFV5DCeoL97tIBRpH8Duu+qYl5Tx3uv28wqyzcp3ytaWgsEApFncdikZ17RvKuKp90GaDQID/wGAP981aLBpKzGLyyg0OGjOPJ6Pp5fPF2gdGeQU0IqD8YnwowbsNO+ca4jFtfw9lsmhucLYiz9cY1X+VsqcTjmEi2nKLNM3tVH2Y3x9+84sxu9tPREHRZs7fXfsMAsXy6dwg34W3fECvpahjNp9AKmEWBt50QXUER3kFC2lf2G9AAELSYMLzRXlpxwDn7xglUIYtAsXUBnmtvSFCj0cSFihFYlqH8pMn1wEAnpWOWp1qHWZH1WGDyuzbANBidkjEYoEDUNp9uTVleDUUw8vn96Hkz+z1ZekwmR+8DmL9QJ77Hbz1ivO3IREzzmiVQMkXrrkZc6ygTredvglCcBsCBYo8F2TyCurLbFYgHiPESa+JqTSA33zrJWeT19fhDuSG7614B1LdYK8isGwCqyERC7WDtFagyLalEuYoL9UOE2GEeCab9URzmvMHLR0YI587nYaSsZThrWf5pr3GSUI1YIFSJEmD7XRLV7/v/7oHPsymhj6HnOHhtq15iFnuatDHkHyDtUlD8a5DKdjQMJz2AAQ5iIO1nGKd8s4gpR+AmpTJK19DkcmTMb1pAwjnPM14TEEmVN2FJq9cHjVhMeWPEyEWI6NgU/3UM6AXKLZP56Y9A7l2XzRPvTqho/bZsgoUgzbpppP5dR8SG0xnEI8RGhJmwerb4l5rlg6x2j4T4EPxmbzMY0BLYxIA0D9ceD+8zTKaCD313PdKxT8FFRoWKEWinJv5L+5g3gOuG4zD7Khq0mJ8jmhL6GU8RoHO4CB7uH9/qxBhw+kgDcXYBCevYVWxbaAOY/7z5jXNegfTWTQnlYbiP6/6MuEOHIXlh/GJBTmRFaqfCzWUXJ7Xt3Ub82eFQCzm+ClsA7bpK4FhdzUQwrQGy6OheN4FFa7cKzUjm48mlQwWKOq9MjnlG+Ixd1cAHd73VKeB+57JgLaafCjedgV9BbQlpTSU4ImnOTDAEwFYQ8HDLFCKxLRwUamurY3OjFf3wPu/RW0YrA1mjjDfvFBt0m2p4m9HsEBxP94TEOWSK8vslC/G5GUKfdW10dsXnb3mb0qorMm4weQV4ENR0WOmgQPITS5M5532K2Fh0Maywm1nf5493NumoI8xZZSGYthyRAjhzsxNJiv1TJiuw/utEV0ZmaxwP43rPX/oxDEAgP9zxmFuOtM1AHYNRbXTZPJqSEiBYjJ5eRezap4Jbz8b39Gsd8++wnMKU+AOAIxpCOdDMa6UzwiMbXK0HNM9rQYsUELgGTfQJgVG/o1WKn2bVGV1g7F/IZ2+LtPixzCzcnU4HqNgJ27W7CDNCs9XKU0+joA9sMI4FN28hrBbb5G6IlTStsYENnX2FibIq9/olE9n0aQESv4qdTdCzPyKJGIxN+zY9q1185YnHuHY5xeO3jz5GrCvjKxALKZMXvpBTFVjKse28tq7TYhJoCjh7B3sm5JxnH30ZHxonvOVb5tJrSERCzRjutFkac39zGSRSsRkmLg+/5BNoAxnMKYhjhgBAwZ/i2+LpbyKVD+mDFqxun7llNft5+XznRpNXrnJEAuUOsM7429qKDSR/Nu9r+KJ1R0AgIPHNQLQ23j9AsW+xsPn9DUc96LaFKdggZKz2esHHzVImpyjPrNBoMnLJlD0mo5tzzNlw5/c1oh9/eYoFzXIJxMxo4nEqKG4ec0aymA6g3FNDYgbwpK9bTCtz/AOap29g3lt8Jg1AicIAokYGdfbePt5UDMjBnITINM96x/KuJMp3bWkswKNGg1FmYfUO2SLhksZAijcdsqytY5/qaFQwOaQQxaTV99QBk0NCTQm4z6tzEs6k9PGChej5q7DNGEDgDHS5KWrw2feDpioJOKEVCKGwRpak8ICJQTeweadMycA8L8Y9yzNfUDoex9+h8xjDmsEzBpKV++Q+9s767U5E1WbiPQfzvISZIcezmQ9L4u+HrUYa2xTMvArg7ZlFP6V5rnjNh+KOjS+OYnufrPJSw2STQa7/K6eQaQSca2pyBUoMbMPRZlY4mR2hgcJb+d4LmPn/iHfOa+WGxTuOpxxoo6cdpg144Z4TDsQCyHcNLrxPp3JYiiTxfgxDb7yvGQ9Gkq+nyIh2+ak01+DetYaDANxrjxhbIO6H8mYPgjDaVtuADd9RripIeYIlAB/k5pY6iLzYmS+DnWsJSWd8pYoryCNLhGLoSERs0aujSQsUIrgfcdPRbvck0j3sBAB7a3OeV1Uz389str9bXrgN3o+auVfWa+fwXvJCpFbQGa6CJi3yXDqzM00TW3sG0yjuSGOBt3Mv0STV9oQXSMCQpvHNjUEfpdbzfAbk/GCdr4tP0t75/MbkYgVmidyTvkgDSVnYjFpnLk9rvQL/tyBNB7Dnt4h4z5OQT7zoYz0HZh2BJDXPiYVx2C6MErrl89vdH/rbpmy84+TNnudYz/tGWTzNZREjNztZWwBJQ2JeKjPTwc55Zsa4kZzlU9D0ZTRN5RGczKBxkTMWEY6m81pY3llKAEaMwj3nMnLyd+n0VDC3PfhdBbJOCGViBu132rAAqUI5hzc5u6JpHd+5ma0+Q/r5k7/1w9Ng+3Vv37Z/e19+bympaCIohiRdtNHL2rw1gmUdFYgldTPvhS9Q2mMSSXQEI8VztBCDoL55Zui2/QmL+fvuOYk+oYyRoe3Kr9Rs1iuxyOI4hrfg2suU+tQNFUMprNIJWNobogbtyL3BTBohI46396awmA66zPhea9LOWB1DMswVqOGIstpaUxAiML7umxjl/tb91yqWfTYZkdD0flhssLjlPdOFDLOTFoFMNg+0NUQjwVGqqnsunuuBHxjMq51dgN2gdI/nEVjQzzQ5DXsMXnll5HOCCRjhESM9D4UmVzlH9A8N2E+NjaQzqApGXdMXqyh1BdnHdkOAPinMw/PvRiGhz4mbdn5D9ru/X77uM6pmI/XPqsGo4ZELNDkpXzIQdueqBdFawvPZHM+FMNA3TvoOC4TcSpQ2cOo6wpTiKWtDCUQxzc7g+x+09oKJVCSsYIgCVXfTR9+BxKa+6XOJwzrDQDg8Td3YUf3IMakEm5YbD7ea9EJaHUts6e0AABWbs+FB2eyAq3SeTv34DZt+YCarSpNSX8eAFqlmSX/vs+XZtyWVEKr2SphOS4gqsikoaSzWZ8PxRYu7/hQTBMErzDQm/YaEjE0JeMYSme1wtX2yYL+oTSak3GkksFaTmMypn9u5GaP8bg+0syr+TYZBJ9XAwzyaTVKgcIaSp3xjb87Dk9+4W8QlzMPIHihWjJOBefVS3mT9LH82+9eK8i3ekcPAOCyk2egJZXwPdBqcBzblMR+w+CVleGjtr28+ofNGkomK9CY0Duqc9eSRnNDAi2pBDbs8UdZ+eP4wwuUjCGf7qVUdYyTM2bT9hPKDNXUUKihKKE6pa0Rcc3+UTkfin4CoQTI7v2OQDHdE7+WqRPgzvlDJ8jNAgdzg8lwRqCxIY4jp7QYBzcnXRbJBMnv4Oj6K6ehAIWOeXV+5qRm7T1Tz64S4PkaSlZGkemijtIZIRcbmrckUmUAZmc24Pc36MxuyvTX1ODUpdMwvMf0GkoGTQ1xNCZjxjU7qp6GRKzgHRqWQQhx0mso6pmMERk1W6/GYRLAA7Kdjg/F/qGukYIFSgimjWty4+lTiRiScdLOSH902UkAHLNX/sOq0qtN4XQsvOkpAMDk1pSjyqYLH/72lhT2DaS1s7iMEIgROesuAuxN6gE0zTSVhmIyJe0fTGNMKo7D21sKBlJvu4ozeenDOXVlqHYpM5B3ENaVr/aH8g626qV1ZpqF98u70M77/1y7cv9vDRAow9IMAwSbvNrktXjDSDPZLBIxQmMybhzcAGeAc53yBl8PkHv28gWCmiU3NyQMPhSnTZPbnAjG/NX2qs6cX8Hj+M46IbaqD0zXESZsWPkbWvMmWwrXhyLboZv9e++TTrg5UV5xNCYCTF5SI0xqTL4ZKUBNkX+5LfbjRtOc9/6Y5mT9w47Jq4E1lPqGiNDiGUC8g9R7j58KQB+muleuMZg+oclax5lHtstBJFeGerjnSNPHym37CvJl5XoER0PSP2RCCHdWpHsQ1Y7JRObZZEfPICa1pDAmFS8weXlNS7bdhk3rUAbTWdcP9P3H1mDmNQ/4BFXa1VCkycswmKc9Gorz/1wdqj8bk3G0pOIFQkkNfGoQLjCZefpm695+PL++s2CwzGQFeocymCCjo3RabaeM6lMLYr0z1t5BZ3BzJhfmQaN/KIPmhrhxHYqrochryZ9I9A9lQOTY9XX3TG0rNHWsI1D29vmj0fIX6/k16yziMcoJFIOmpe5HSyqBIU3gAOAEgwDAhJYG7WA/lM4ilYi7gk0XQeUVhrqgk4EhZ6BuTJqd8oNSQ0lqouaGs1kkYs4kRfdpBXUfHU2q8P1x0oQweQ3nfCi8DqXOSSXieHrtbgDAnt6hgvMtqUTB+giVbsaEZlxx+ixXCwCcgfeo/3jIU74zo1MvjRACH/rJcwCAdx0xEQCwfvd+X/nb9vbjr2t2O2a5eExrXgGARbe9hE0yQED3IA5nnBciqYl8UuzaN4gpbY3ODCvvhUhb7NxevLPx/DUpk2W03G9e3OSk9Zoq5EAwrkmavCwbHromPE8dKiS0KRnX+kDUYKIWoOX3p1fb2LrXGXBvfGi1L43qZyVQdFrlR376PABg2rhmJGLkC97Y1TMgtdW4UaBs2N2LpRu7sKN7wLj1iuoHJbQKNBQ5iOpMtQDwmbteAQAcJAXK9x9bU5AfAKa0OffMG/AwLKO8EtLHY7qOfQPDaIjHMH5MA7JCP9l5ao3zzrW3pJDOioJJ0/7BNJoa4u4EQid0egbTgWbrvmFHODuRYro1Ik6IdSoeQ0OcCp6LdEauuzE45ZVATSVivompF++zaBLA/UNO8EBD3mRjYDiD7y55CzurtGEkC5QS2LFvAOs7erGjewCf/pUTlbXo1EPd8zMmNGNl3t5Ld7/kDIzNDQlMaklhYDjrvogPvb7D91A0SqegOrZ881733ExpeuvOW1X9kZ8+j/W7e5GW5o8hw2D+1Fsd7m/dS6tU/kSctAPg5s4+9AymMaWtEc0NhSq7d4bcExDSCwCrtvdo8w1lspjc2uhL63258zUU3WpjwDHtEcG1qXtnpGoPpUYpUPJ34VWzROUUzx+8Lv/5i+7vX3zinU7aRr858wJpwpw61tFK8wMzgJxQb2tKoK0p6QuD3tUziMmtjXKCou/Lu17aDABY19HrTCQ09/1nf10PwGvy8t+zPjnbbWpIGKOjAOe5Bgo3QlU+rPbWFGKUmzylM1nfbgSpRMxYfs9AGm1NCVe7GNDscXX9n94AkLvv3rK6+4exdW8/jpjc4k4CdCHlPQPD7nqafA18z/5B7O0bxvTxTY7JS2OeU2H9k9satZaItAwbThhCuFXfpxJxtDbqBYrasXzq2Eb0DOrve/9QWmoocd/E8InVu3DzY2vwg8fXaPNVGhYoZdDRM4gX3+4EAJx9zBT3+EmHjsf63b3uw/P61m5s8KwvmTDGeSE6pengtS1+4ZOvoXhnQcdMdUxe+XZs9aDvG0g7M6cQdlXdLKarbwjjm5MFM63hdWcAABsgSURBVB/Fn9/YCQA4b85kNCXjBTNFb0hl0KLD5Zu68IpHUPq0lXTWne0qBvPMKEBuAN+v8aG8vrUbNz++Fs3JuBv669NQXJOXM1PM11BUfa2NhZFRg+kM3pQBFN+55AT8zVGT0RAv7C/1/2OmtgLwC9B8mhukhiCj/4QQ2LVvEO2tKbS3prTCSKVTtKTi2og3dc/UGql8zXRATiKak3GjcAaAg9oatceVmW5MKoEjJre416nMiKoPZ0xoxls79X2wr38YbY1J17Hfl7cLrxACR0x2IuHOmO1EXXonGUqzO7y9BQdLAa77mFjPQNoNLsj3aW2T6Q+dOMYY5dUl39lp45q0YfMDw47PLLSGorlf9y7bItvRrN0denNnH3qlVuk8d7l+2LnPeU527dM/L5WGBUoJzJSf79zVk3tg1UOqzguRm8l94IdP+/KPl9FJamV0/tbkjUkVZeI8fGoWc+9Vp7qhgia/AQDpLCx8GfIH+J6BtGvDB4CfPrUeA8NZjGtuwMQxDdiTtxXIso1duP5Pb6C5wXHIN2qcn8MZZ21Gayrh+o3y2d7dj6/J2aaiy5N2OCPQHqChDGYc56uaiepMXv94x1Ln3FDGDf319okaOJsa4gUCZXNnH/66xtHkWtzNPnODg/e6lPmkOWUejGdMaEZjMoYNu/Mj4nLtGZNK+BaKvrmjB/3DGSRihPbWFLr6hrUmyl+/4Gi+l508A62NSd+MVgjh6zc1GckXfH1DSkOJo08jnI8+qBXnHjMZiXgM7z/hYBw2aYzvvNrdYVxTA6aObXJ9LDfLWbIS/HOnjcVfpdkqn30DabQ2JnIO9TxT6oMrdmDtLsfMqzQtrxajNLhxzUmMlxO27jxfT3ffMDp6BnHIeOf9zddQ1ATLMefGtGtEnl/vTCCdCUDhJKJvKI0xDQnnK6EaH416B8c2J40ayrxDx2P6+Ca0tzZqBYp6tvf2DyGV9PtQdshrUGHa33lkNX44gtoKC5QSuPkjJwIAXvXMsJWQAHKmAWVDVxOV/3zfHAA5m/pfVu/Cxj2Fmxs6Gkq8QKAoVb21MYl9hrUXgBMU0DeUKbABb9rTV5B2XYfzknb2DuGGB1e57WtvTaGjxy9QPnjLswCcAYg8YZHecnsH02hJJTC2OWk003z5f1/H8k1O3/1A9mWn10ySybqzacVuz7Yk+/qH0daUdAcW3UvpHazV+gkltJ58qwO/e3krWlMJpBJxGfab66tzvvskbn/OWT2u6vCa/7o8A5WaHe7tG8Ydz22EEE402cdvy5nE/u6k6Zg5cYy7Ol+hBpdjprZh9uRWn5NXhZAfOaXV9V0s3djp5hVC4B9+9oJrqrv+orlobUz4BqCfPLkeR3/5YQDA3544zd24NL+/+qXfoL01hZ7BdIFg3D+YdvOOaYgX5N8pn5MpbSkc1NaIdbv2YziTxc+ffhtATrN54LXtAICnNUKlZ8C5p8o8lh9O++qW3Lumm8jslX3Z1ph025r/jvx+uTPzP/kwZ91NOm+C8dKGTvc6xjU1oGcwXTAxu/HhN51+SCWQ1ERY9UttryGuX3DY3T+MGAEtDQm0pJJaDaWrbwizJ7egtVFv6lQC+ozZ7c52OnKcGExncMsTzuR0Z88AjvzSQ/jhX9biO39+q6CMSsECpQSOmzYWDfEYbn58rXtMRcAAwAypwWza0+facT968gx88vRZAHIC5btL3sJZ//WEa6dXOHb9OHbtG0A2K9wXQz1IbY0J7PGYQLz+h4vecbD7AqvBBABWbOnG+6WmtORzZ+L3nz4NQM4X4zV/jW9uwKSWQoGizFBz5Ex3S5cjSJRJJZ3J4vfLt2L3/kFMbEm5s6V81nXkBtbz5jimQjVIP7Biu3uNXrzrXTp7hzBhTNKNHPqTHKgUf3ptmyuAzpg9yRVOv3zeGfAXLX4Rb+/uxcQW5z60pOLY75nZe2d86l55fSxdni3z882V6zp6cdOja9zNQudOa0M8RpgztQ3LNnX51tWol//z5x3prNXwaJZKwJ599GSceMg4AMBlP33Bzbu5s98NDAGc1f6tjX4TiteO/sULjnafy/We/r988Yt48q0ONCbjmD5emoo8PpIHXtuOLV39rjbY3JAoGOzVFwMntzVi3qHj0TuUwba9uTJOmO60X5n+8tcu/fL5jVi+aS/aGpO5FeR55qxbn3L8QNe9f47rE1MCZemGTteXedDYRje0P392/9U/OlrxWUdOdvrBI+BvenQN/kfWMakl5T4b3r317pH+KgCY2NKAsU2Fe8l19g2htTHhBHpoNJxuORmKxQhtTY7vLn+rmte37sOEMSmMlT61/LVYA8NZzDt0PBYeexDampLo6huGEALrduWuZ/mmvT5h57WmVJK6FihEdAERrSaitUR0zQjWi388c5b7/+svnuuaVQC4+31dd/9Kd/Z++hGT3PPTpcqtOKx9DM6YnTufiBHefdRkbO8ewKtb9rrObbXSef3uXvz5jZ2Y//Ul2LN/0BUu/+/8I/GdS07wCTfF/zyVM6sdMbkFE+VAqcxt6sVobojjtCMmor01hQ17+nw2+jgRZk9uwT1XnQoAuOJ05xsXh8iBSEX/DAxncfy0sVi6ocvnGH15UxdmXfsA3t7di9mTW7D+G+9xZ5vff3QNslnhRhTt6R3Cx0+b6ZppvOaiR1bulFvMEKa0pbBq+z48/qYj1HoH0/jnXy8H4Ni577ziZEyS9+PO5zdi8TMb3HKUsBgj1zWoGes7Z4530yht856XNuO3S50BxRs2+28LjwYA3Hf1uwAAH7/tRV8U1KxJjt1/3szx2Ns3jAXfeBRbuvpwwU1PuTP446aPBQDfuoa/rN6FZJwwrjnpe16ekw7bzV05rVAJgpZUEj0DafeeJT3P5EFjGzG2KYnJrSmsl1ppNivcII2Dxja69ShT7cBwxt0KSC1MbEnF0TuU9j0XSttsa0zgUCm0nl/vtPPkWRMwVpqDF3/8nQX9J4TAf/zv6wCcQU9pKN7B+Ixv/8X9fdnJMzBhjHM/1YTnWw+96Z4f35wEEaG1MYmfPLnO1ap/9UJuv7KjDmrF0Qe1uv5PALjbIyyS8Zj7fqiJyZqdPb7FyAe1NWJSSwN2eyZdNz36FjZ39uMdh4zDmFS8wBT74yfW4s7nN7qC7uBxzn3b0Z0b7JVmelj7GMyY0IzhjMC27pxwHhjO4I3t+3DyLEfLmj25Bf3DGaza3lNgovbyC89zX0nqVqAQURzAjwBcCGAOgI8Q0ZyRqv8LciAB4Ia4etrm/r7oR88AAA6ZkBsU1MupeHVLNw5vb8EJcmAhIpwuBczf/vhZfPthJxxVhRorDWH3/iHM+/qj+N9XtgJwzCPJeAznzZniftPkuXV7sGF3rzvj/dgph4KI3MFj6cYuPP7mTldD+cOn34W2xqTr2PyxnEXv2jeAbd0D+OC86a4ZSGkRv3phE5Zt7HIHlls+ehLOmzMFQ5ksHl+1C0PpLAbTGSzd0Oku1PrP989x90UDHKf32o5cKPSCWRPwlQ8ci4c+cwYObx/jagJ/L8OnlVP88+cfBQD45C8cu/Ib23Prc5RGpwQ8kIsUAoBzZCCFchp39Q3jqbc68NKG3N5Walb/0Os78IV7X8ML6/e4prPvffgEd7A8btpYNCZjBRFQ6p6qqLXd+4fw7YdXu+0HHJs94IQwP/7mLjy3bg86egYxYUwDiMgdZAEnmu9Tv1zmCgUA+Mr7jwUATBvXiKFMFk++1QEhBPqHM2hNJfDl9+Vei3RW4LfLtiCTFVixNaddffy0me7E4InVu7Btbz+2ewa6j582E4CzAFMIJ1S6q3cIj6zc4WqVRIRZ0r/yxd+tAODXAsY1N2DmxGY89PoOpDNZ/HnlDl9/7d4/hEMnNiNGwMtyfzHv7PzvTpyGVCKOaXIg3ra3H+lMFks9e5Gpd0/d+2Ubu7Bn/yC+9AdHaH36bw4HAMyaNAbrOvajq3cIw5msO6G65kLnvVZa7XY5mH/9gVVuHc9dezaIyDULCyHQP5TBTY86E4l3Hz0ZU9oasamzD2t3Ofe5fyjjvsfKojBJakEqOGcwncE/SwF+4dyD3L5UplIhBC7+0TPIZAXmTnOeq7OOcgIU7nhuA74sBfM/neVM9L79oePx1BfeDcB5j5dvyvVTpTAv2659FgBYK4RYDwBEdBeAiwC8EZgrQv71vCPx3SVvuaG8Xr5/6TvwmbtecR8eFaGi+PU/nuwzYRw/fSz+38KjXG1j6timgq0Z1Mvyh6tPwzn//aT7Mn7jQWeGpmztRIR7rjoVF//oGXedw+HtY3Dmke24/uK5ABwTyWfPnY2bHl3jDsZAbkb+kZNn4GdPr8d/PbLat0vymTLCBsgNxK9s3uvOBBfMnIALj5vqzh4/e/cr+Ozdr/iu/UeXneTT2L75d8fh2t+vcENxf3b5fLz7qMnu+XOOmYJbn1qP93z/r67AuOvKUwA4L96/3evMHGde84Cvni9e4AwObU2Fj/kdn1yAM+UebYe1O/fvnTc86kvT1phAS4M/74dvfd79feHcqe7vWIwKooI+e+5sfOJdjibrjZC6/9XcN8BXfnWh+/vEGePw4oZO955ddvIM99ydVyzAx2T/PPT6Djz0+g4k44Q3vnaBq4moQebjt73k5vvye4/Bx06d6f5fTSwO//cHcbkn1P0dh4xzhf3tz210fUgA8NPL57sTonmHOtrb6TfmtAZ1rQAKfF8P/ssZvv+fdWQ7bn9uI4740kO+4zECbvzg8Zjc2ojjpo3F9x9bU7De5T+kYFQD8XX3r8R19690z7/9zfe4vw9vb3EjH+d9PXdfr5ICpbUxgS1d/Tjx+iXuuXfOHI+rznLOz57smOeuuH0pvvl3x+FJqcm99pXzXR/N1DZHgM+69kG3jNZUArMmjsHCYw/CrU+tx7nffQqXnTwD9y3fmruO9x4DwNktGwAu/tEzuOqsw7F8UxfW7+7FuOYkDmtvcSdu1/xuBT5//pHYP5h2JyLqMxpTxzZhwpgGN3wcAK654Gh87twjXe3/2guPxt7+Ydf0WEnqVkMBMA3AZs//t8hjI8a/nDMbL3/5PBx1UGvBuYveMQ1zpzmaxHuPm+reXMVph0/CLR91tmq5ZN50vP+Eg9GSSrhbvADAs9ec7f6+84oF7u9UIo6nv3g2fvOPp7jHxjcnceSUXDvyNxNc19GLBR5TDgB85pzZ7owMcISJmg23pBK+cwplBwccTeuDJ033nb9kvvP/9tYUzvWEUismtaTw3uOn+rS4D5xwMCZJn0tzQ9wdtBTvPc4ZuJUw+dfzjsQphzkLPFsbk/jvS04oqGfdN96Ddx/tCCUiwhcWHoX3n3Cwe94r0HQv2h//+XS89pWFiMUIK75yvi+KT5F/T//2RP/j95lzZrubic6d1oaTZvjr+dFlJ7m+CQD4+3ce4js/y/MsnDG7HddceLTvs8MnzRjvM2sp86CXk/L68nPnHun+vkMKjXXfeA+InMV4ymTl5fD2XDuOk0LLy5FTWvBZWS6R01/vOmIi7rxiQYGA+c/3H1vgHwOAt75+IRZIM47SOhUNiRge/uwZromSiNyJj+J3nzrV90zp7unLXz7PFQbeyQDgaIfKJAfA1TwB4NrfO9rWtRce7eYHgPOPPcjXNwDw4pfORSxGmHfoeFdL+vULm9A7lEGMnInMFdKXekR7bpL5kyfX4YW3O3H6EZNwrzQpT25rxMFjG7F1bz/+9Z5X8Z/3rURLKoHnrz3H16+ffNdM9/dtH38niMj3bP7TWYfjixcc7bMIVAqybY9RqxDRJQAWCiH+j/z/xwAsEEL837x0VwK4EgBmzJgxb+PGjQVlVYr+oQzuXbYZF8ydWvBiAXDNEs0NpSuKW/f2Y/vefnfHWC8vb+rC61u78fSa3WhtTOLrF8/1mU8Umzv78PDrO3DxidMK2ql2Tx2S31/Qfbwrncli1fYeHOvZ3l+xa98A/uuR1RhIZ/GJd83E8dPG+vxNiqF0FoufeRvvPmqyVkDv3DeA+1/Zho79g/jsubML+uzNHfuwaU8fVm7bhyvPPMw3UCuEEHhwxQ6cdvhEN2JOMTCcQSoRw4tvd+KYg9t8A4cimxX4+/95Du2tKXz94rmY2FJ4T7v7hvGv97yCT7/7iALBqM4/uaYD6UwWf3vitIL+3L1/EJs6+/Dyxi78wymHFggtxcubujBnalvB+YHhDLr6hnDfK9twymET8Y5DCgfWdCaLj9/2Ep5euxu/+9RpvnYKIbCtewA3LXkLT77VgS8sPAqXzPcLuoHhDO5+aTNmTGhGQyKG46aP1faXiUxW4NUte5GIEX67dAv+38KjCrbn39s3hFc278WWrn5cMn86Ugn/dXb3DWPL3j4I4fjKdPfzidW7kErE0dEziJMPm+CbrKl2rO/Yj8mtzgLCfN8m4DjlV2ztxjPrduOqMw8vqAdwfCDPrtuN0w6f5FoJAMeP9NCK7ZjYksL7jp+KllTC3Q9NsWxjF/746jacc8xk9Aykcd6cKb5Jwq59A3h45Q6s2t7jWhm8E0eF2mm5UhDRMiHEfGu6OhYopwL4ihBiofz/tQAghPimKc/8+fPF0qVLTacZhmEYDWEFSj2bvF4CMJuIZhFRA4BLAdxf5TYxDMMcsNStU14IkSaifwbwCIA4gMVCiJWWbAzDMEyFqFuBAgBCiAcBPGhNyDAMw1ScejZ5MQzDMDUECxSGYRgmEligMAzDMJHAAoVhGIaJBBYoDMMwTCTU7cLGUiCifgBBocVjAXQHnJ8BYJOlGlsZ5Z4Pk8bWzijqiOI6ym3nSFwH3/Pozo9EX0ZRRj30JTCy789RQojCJfr5qA8CHQj/AHRYzt9aTv6QZZR1Pop2RlRHFNdR7v0Yievge15HfTkS7ayFvoyincVcB4CltrRCiAPO5LXXcv6PZeYPU0a558OkKfc6w6SJ4jrKbedIXAff8+jOj0RfRlFGPfQlUBvvj48DzeS1VITYj6ZS+UcKbmd01EMbgfpoZz20EeB2llPXgaah3Frl/CMFtzM66qGNQH20sx7aCHA7S67rgNJQGIZhmMpxoGkoDMMwTIU44AUKES0mol1E9Lrn2AlE9BwRrSCiPxJRmzyeJKLb5fFV6hss8twTRLSaiF6R/ybr6huBNjYQ0W3y+KtE9DeePPPk8bVEdDPpvpZVG+2sZF8eQkR/kfdvJRF9Rh6fQERLiGiN/Dvek+da2WeriWih53jF+jPidlakP4ttIxFNlOn3E9EP88qqmb60tLNmnk0iOo+Ilsl+W0ZEZ3vKqui7biRMKNho/gfgTAAnAXjdc+wlAGfJ358EcL38fRmAu+TvZgAbAMyU/38CwPwaaOPVAG6TvycDWAYgJv//IoBTARCAhwBcWKPtrGRfTgVwkvzdCuAtAHMAfBvANfL4NQBulL/nAHgVQArALADrAMQr3Z8Rt7Mi/VlCG8cAOB3AVQB+mFdWLfVlUDtr6dk8EcDB8vdcAFtHoj+D/h3wGooQ4ikAnXmHjwLwlPy9BMAHVXIAY4goAaAJwBCAfTXWxjkAHpP5dsEJLZxPRFMBtAkhnhPOE3cHgItrrZ1RtsfQxu1CiJfl7x4AqwBMA3ARgNtlstuR65uL4EwiBoUQbwNYC2BBpfszqnZG1Z4o2iiE6BVCPA1gwFtOrfWlqZ2VpoR2LhdCbJPHVwJoJKLUSLzrJg54gWLgdQAfkL8vAaA+qn0vgF4A2+GsUP2OEMI7gN4m1eAvj4CKaWrjqwAuIqIEEc0CME+emwZgiyf/Fnms0hTbTkXF+5KIZsKZ5b0AYIoQYjvgvNhwtCbA6aPNnmyq30asP8tsp6Ki/RmyjSZqrS9t1Mqz6eWDAJYLIQZRvXedBYqBTwK4moiWwVE9h+TxBQAyAA6GY1b4PBEdJs99VAhxHIAz5L+PVamNi+E8QEsB3ATgWQBpOKpvPiMR4ldsO4ER6EsiagHwOwCfFUIEaZmmfhuR/oygnUCF+7OINhqL0ByrZl8GUUvPpkp/LIAbAfyTOqRJNiLhvCxQNAgh3hRCnC+EmAfgN3Ds0YDjQ3lYCDEszTTPQJpphBBb5d8eAL9G5c0N2jYKIdJCiM8JId4hhLgIwDgAa+AM3tM9RUwHsC2/3BpoZ8X7koiScF7YXwkhfi8P75SmAmWC2SWPb4Ffc1L9VvH+jKidFe3PIttootb60kiNPZsgoukA/gDgciGEGqeq8q4DLFC0qMgNIooB+A8AP5GnNgE4mxzGADgFwJvSbDNJ5kkCeB8cU8+It5GImmXbQETnAUgLId6QqnIPEZ0i1fTLAdxXyTaW0s5K96W89p8DWCWE+K7n1P0AFsnfi5Drm/sBXCpt07MAzAbwYqX7M6p2VrI/S2ijlhrsS1M5NfVsEtE4AA8AuFYI8YxKXK13XVV+QP+DM2veDmAYjmS/AsBn4ERYvAXgW8gtAG0B8Fs4DrA3AHxB5KJClgF4TZ77PmSETRXaOBPAajgOvUcBHOopZz6cF2AdgB+qPLXUzhHoy9PhqP+vAXhF/nsPgIlwggTWyL8TPHm+JPtsNTzRMpXsz6jaWcn+LLGNG+AEbuyXz8icGu3LgnbW2rMJZ4LW60n7CoDJI/Gum/7xSnmGYRgmEtjkxTAMw0QCCxSGYRgmEligMAzDMJHAAoVhGIaJBBYoDMMwTCSwQGGYGoGIriKiy4tIP5M8OzszTLVJVLsBDMM4i+aEED+xp2SY2oUFCsNEhNzQ72E4G/qdCGcx5+UAjgHwXTgLY3cD+LgQYjsRPQFnD7N3AbifiFoB7BdCfIeI3gFnV4FmOIvTPimE6CKieXD2QesD8PTIXR3D2GGTF8NEy1EAbhVCHA/n0wZXA/gBgA8JZz+zxQBu8KQfJ4Q4Swjx33nl3AHgi7KcFQCuk8dvA/AvQohTK3kRDFMKrKEwTLRsFrl9lX4J4N/hfPxoidzpPA5nexrF3fkFENFYOILmSXnodgC/1Ry/E8CF0V8Cw5QGCxSGiZb8vYx6AKwM0Ch6iyibNOUzTM3AJi+GiZYZRKSEx0cAPA+gXR0joqT8foURIUQ3gC4iOkMe+hiAJ4UQewF0E9Hp8vhHo28+w5QOaygMEy2rACwiov+BszvsDwA8AuBmabJKwPmg2EpLOYsA/ISImgGsB/AJefwTABYTUZ8sl2FqBt5tmGEiQkZ5/UkIMbfKTWGYqsAmL4ZhGCYSWENhGIZhIoE1lP/fXh0LAAAAAAzyt57EzpIIgIVQAFgIBYCFUABYCAWAhVAAWAQlNWrTCLq7HQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "sorted_data['inc'].plot()"
    ]
@@ -1185,9 +2245,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd0738df3c8>"
+      ]
+     },
+     "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()"
    ]
@@ -1222,10 +2305,8 @@
   },
   {
    "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",
@@ -1244,7 +2325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1268,9 +2349,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd07387e208>"
+      ]
+     },
+     "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='*')"
    ]
@@ -1284,9 +2388,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2014    1600941\n",
+       "1991    1659249\n",
+       "1995    1840410\n",
+       "2020    2053781\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()"
    ]
@@ -1301,9 +2451,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd0737daa58>"
+      ]
+     },
+     "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)"
    ]
@@ -1311,9 +2484,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }