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varicelle

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Incidence du syndrome grippal"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import isoweek"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-3.csv?v=8nhxe\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pour ne pas à avoir à télécharger le fichier à chaque éxécution "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"data_file = \"syndrome-grippal.csv\"\n",
"\n",
"import os\n",
"import urllib.request\n",
"if not os.path.exists(data_file):\n",
" urllib.request.urlretrieve(data_url, data_file)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n",
"\n",
"| Nom de colonne | Libellé de colonne |\n",
"|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n",
"| week | Semaine calendaire (ISO 8601) |\n",
"| indicator | Code de l'indicateur de surveillance |\n",
"| inc | Estimation de l'incidence de consultations en nombre de cas |\n",
"| inc_low | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation |\n",
"| inc_up | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation |\n",
"| inc100 | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
"| inc100_low | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
"| inc100_up | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
"| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n",
"| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n",
"\n",
"La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`."
]
},
{
"cell_type": "code",
"execution_count": 14,
"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>202346</td>\n",
" <td>3</td>\n",
" <td>92934</td>\n",
" <td>81854.0</td>\n",
" <td>104014.0</td>\n",
" <td>140</td>\n",
" <td>123.0</td>\n",
" <td>157.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>202345</td>\n",
" <td>3</td>\n",
" <td>71845</td>\n",
" <td>62813.0</td>\n",
" <td>80877.0</td>\n",
" <td>108</td>\n",
" <td>94.0</td>\n",
" <td>122.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>202344</td>\n",
" <td>3</td>\n",
" <td>49952</td>\n",
" <td>42813.0</td>\n",
" <td>57091.0</td>\n",
" <td>75</td>\n",
" <td>64.0</td>\n",
" <td>86.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>202343</td>\n",
" <td>3</td>\n",
" <td>44982</td>\n",
" <td>38170.0</td>\n",
" <td>51794.0</td>\n",
" <td>68</td>\n",
" <td>58.0</td>\n",
" <td>78.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>202342</td>\n",
" <td>3</td>\n",
" <td>56842</td>\n",
" <td>49277.0</td>\n",
" <td>64407.0</td>\n",
" <td>86</td>\n",
" <td>75.0</td>\n",
" <td>97.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>202341</td>\n",
" <td>3</td>\n",
" <td>58357</td>\n",
" <td>51032.0</td>\n",
" <td>65682.0</td>\n",
" <td>88</td>\n",
" <td>77.0</td>\n",
" <td>99.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>202340</td>\n",
" <td>3</td>\n",
" <td>68894</td>\n",
" <td>60069.0</td>\n",
" <td>77719.0</td>\n",
" <td>104</td>\n",
" <td>91.0</td>\n",
" <td>117.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>202339</td>\n",
" <td>3</td>\n",
" <td>72003</td>\n",
" <td>63452.0</td>\n",
" <td>80554.0</td>\n",
" <td>108</td>\n",
" <td>95.0</td>\n",
" <td>121.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>202338</td>\n",
" <td>3</td>\n",
" <td>63218</td>\n",
" <td>55227.0</td>\n",
" <td>71209.0</td>\n",
" <td>95</td>\n",
" <td>83.0</td>\n",
" <td>107.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>202337</td>\n",
" <td>3</td>\n",
" <td>49085</td>\n",
" <td>42079.0</td>\n",
" <td>56091.0</td>\n",
" <td>74</td>\n",
" <td>63.0</td>\n",
" <td>85.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>202336</td>\n",
" <td>3</td>\n",
" <td>38247</td>\n",
" <td>32237.0</td>\n",
" <td>44257.0</td>\n",
" <td>58</td>\n",
" <td>49.0</td>\n",
" <td>67.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>202335</td>\n",
" <td>3</td>\n",
" <td>31695</td>\n",
" <td>26013.0</td>\n",
" <td>37377.0</td>\n",
" <td>48</td>\n",
" <td>39.0</td>\n",
" <td>57.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>202334</td>\n",
" <td>3</td>\n",
" <td>26663</td>\n",
" <td>21057.0</td>\n",
" <td>32269.0</td>\n",
" <td>40</td>\n",
" <td>32.0</td>\n",
" <td>48.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>202333</td>\n",
" <td>3</td>\n",
" <td>19144</td>\n",
" <td>13161.0</td>\n",
" <td>25127.0</td>\n",
" <td>29</td>\n",
" <td>20.0</td>\n",
" <td>38.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>202332</td>\n",
" <td>3</td>\n",
" <td>14641</td>\n",
" <td>10285.0</td>\n",
" <td>18997.0</td>\n",
" <td>22</td>\n",
" <td>15.0</td>\n",
" <td>29.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>202331</td>\n",
" <td>3</td>\n",
" <td>15286</td>\n",
" <td>10705.0</td>\n",
" <td>19867.0</td>\n",
" <td>23</td>\n",
" <td>16.0</td>\n",
" <td>30.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>202330</td>\n",
" <td>3</td>\n",
" <td>13205</td>\n",
" <td>8647.0</td>\n",
" <td>17763.0</td>\n",
" <td>20</td>\n",
" <td>13.0</td>\n",
" <td>27.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>202329</td>\n",
" <td>3</td>\n",
" <td>11122</td>\n",
" <td>7113.0</td>\n",
" <td>15131.0</td>\n",
" <td>17</td>\n",
" <td>11.0</td>\n",
" <td>23.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>202328</td>\n",
" <td>3</td>\n",
" <td>9179</td>\n",
" <td>5703.0</td>\n",
" <td>12655.0</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>19</th>\n",
" <td>202327</td>\n",
" <td>3</td>\n",
" <td>8999</td>\n",
" <td>5763.0</td>\n",
" <td>12235.0</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>20</th>\n",
" <td>202326</td>\n",
" <td>3</td>\n",
" <td>9023</td>\n",
" <td>5934.0</td>\n",
" <td>12112.0</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>21</th>\n",
" <td>202325</td>\n",
" <td>3</td>\n",
" <td>10090</td>\n",
" <td>6739.0</td>\n",
" <td>13441.0</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>22</th>\n",
" <td>202324</td>\n",
" <td>3</td>\n",
" <td>11308</td>\n",
" <td>7639.0</td>\n",
" <td>14977.0</td>\n",
" <td>17</td>\n",
" <td>11.0</td>\n",
" <td>23.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>202323</td>\n",
" <td>3</td>\n",
" <td>14300</td>\n",
" <td>10661.0</td>\n",
" <td>17939.0</td>\n",
" <td>22</td>\n",
" <td>17.0</td>\n",
" <td>27.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>202322</td>\n",
" <td>3</td>\n",
" <td>18303</td>\n",
" <td>13822.0</td>\n",
" <td>22784.0</td>\n",
" <td>28</td>\n",
" <td>21.0</td>\n",
" <td>35.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>202321</td>\n",
" <td>3</td>\n",
" <td>16460</td>\n",
" <td>12188.0</td>\n",
" <td>20732.0</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>26</th>\n",
" <td>202320</td>\n",
" <td>3</td>\n",
" <td>16162</td>\n",
" <td>11963.0</td>\n",
" <td>20361.0</td>\n",
" <td>24</td>\n",
" <td>18.0</td>\n",
" <td>30.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>202319</td>\n",
" <td>3</td>\n",
" <td>16901</td>\n",
" <td>12577.0</td>\n",
" <td>21225.0</td>\n",
" <td>25</td>\n",
" <td>18.0</td>\n",
" <td>32.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>202318</td>\n",
" <td>3</td>\n",
" <td>19929</td>\n",
" <td>15402.0</td>\n",
" <td>24456.0</td>\n",
" <td>30</td>\n",
" <td>23.0</td>\n",
" <td>37.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>202317</td>\n",
" <td>3</td>\n",
" <td>27007</td>\n",
" <td>21779.0</td>\n",
" <td>32235.0</td>\n",
" <td>41</td>\n",
" <td>33.0</td>\n",
" <td>49.0</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>2008</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>2009</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>2010</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>2011</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>2012</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>2013</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>2014</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>2015</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>2016</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>2017</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>2018</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>2019</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>2020</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>2021</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>2022</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>2023</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>2024</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>2025</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>2026</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>2027</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>2028</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>2029</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>2030</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>2031</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>2032</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>2033</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>2034</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>2035</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>2036</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>2037</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>2038 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 202346 3 92934 81854.0 104014.0 140 123.0 \n",
"1 202345 3 71845 62813.0 80877.0 108 94.0 \n",
"2 202344 3 49952 42813.0 57091.0 75 64.0 \n",
"3 202343 3 44982 38170.0 51794.0 68 58.0 \n",
"4 202342 3 56842 49277.0 64407.0 86 75.0 \n",
"5 202341 3 58357 51032.0 65682.0 88 77.0 \n",
"6 202340 3 68894 60069.0 77719.0 104 91.0 \n",
"7 202339 3 72003 63452.0 80554.0 108 95.0 \n",
"8 202338 3 63218 55227.0 71209.0 95 83.0 \n",
"9 202337 3 49085 42079.0 56091.0 74 63.0 \n",
"10 202336 3 38247 32237.0 44257.0 58 49.0 \n",
"11 202335 3 31695 26013.0 37377.0 48 39.0 \n",
"12 202334 3 26663 21057.0 32269.0 40 32.0 \n",
"13 202333 3 19144 13161.0 25127.0 29 20.0 \n",
"14 202332 3 14641 10285.0 18997.0 22 15.0 \n",
"15 202331 3 15286 10705.0 19867.0 23 16.0 \n",
"16 202330 3 13205 8647.0 17763.0 20 13.0 \n",
"17 202329 3 11122 7113.0 15131.0 17 11.0 \n",
"18 202328 3 9179 5703.0 12655.0 14 9.0 \n",
"19 202327 3 8999 5763.0 12235.0 14 9.0 \n",
"20 202326 3 9023 5934.0 12112.0 14 9.0 \n",
"21 202325 3 10090 6739.0 13441.0 15 10.0 \n",
"22 202324 3 11308 7639.0 14977.0 17 11.0 \n",
"23 202323 3 14300 10661.0 17939.0 22 17.0 \n",
"24 202322 3 18303 13822.0 22784.0 28 21.0 \n",
"25 202321 3 16460 12188.0 20732.0 25 19.0 \n",
"26 202320 3 16162 11963.0 20361.0 24 18.0 \n",
"27 202319 3 16901 12577.0 21225.0 25 18.0 \n",
"28 202318 3 19929 15402.0 24456.0 30 23.0 \n",
"29 202317 3 27007 21779.0 32235.0 41 33.0 \n",
"... ... ... ... ... ... ... ... \n",
"2008 198521 3 26096 19621.0 32571.0 47 35.0 \n",
"2009 198520 3 27896 20885.0 34907.0 51 38.0 \n",
"2010 198519 3 43154 32821.0 53487.0 78 59.0 \n",
"2011 198518 3 40555 29935.0 51175.0 74 55.0 \n",
"2012 198517 3 34053 24366.0 43740.0 62 44.0 \n",
"2013 198516 3 50362 36451.0 64273.0 91 66.0 \n",
"2014 198515 3 63881 45538.0 82224.0 116 83.0 \n",
"2015 198514 3 134545 114400.0 154690.0 244 207.0 \n",
"2016 198513 3 197206 176080.0 218332.0 357 319.0 \n",
"2017 198512 3 245240 223304.0 267176.0 445 405.0 \n",
"2018 198511 3 276205 252399.0 300011.0 501 458.0 \n",
"2019 198510 3 353231 326279.0 380183.0 640 591.0 \n",
"2020 198509 3 369895 341109.0 398681.0 670 618.0 \n",
"2021 198508 3 389886 359529.0 420243.0 707 652.0 \n",
"2022 198507 3 471852 432599.0 511105.0 855 784.0 \n",
"2023 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
"2024 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
"2025 198504 3 424937 390794.0 459080.0 770 708.0 \n",
"2026 198503 3 213901 174689.0 253113.0 388 317.0 \n",
"2027 198502 3 97586 80949.0 114223.0 177 147.0 \n",
"2028 198501 3 85489 65918.0 105060.0 155 120.0 \n",
"2029 198452 3 84830 60602.0 109058.0 154 110.0 \n",
"2030 198451 3 101726 80242.0 123210.0 185 146.0 \n",
"2031 198450 3 123680 101401.0 145959.0 225 184.0 \n",
"2032 198449 3 101073 81684.0 120462.0 184 149.0 \n",
"2033 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"2034 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"2035 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"2036 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"2037 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 157.0 FR France \n",
"1 122.0 FR France \n",
"2 86.0 FR France \n",
"3 78.0 FR France \n",
"4 97.0 FR France \n",
"5 99.0 FR France \n",
"6 117.0 FR France \n",
"7 121.0 FR France \n",
"8 107.0 FR France \n",
"9 85.0 FR France \n",
"10 67.0 FR France \n",
"11 57.0 FR France \n",
"12 48.0 FR France \n",
"13 38.0 FR France \n",
"14 29.0 FR France \n",
"15 30.0 FR France \n",
"16 27.0 FR France \n",
"17 23.0 FR France \n",
"18 19.0 FR France \n",
"19 19.0 FR France \n",
"20 19.0 FR France \n",
"21 20.0 FR France \n",
"22 23.0 FR France \n",
"23 27.0 FR France \n",
"24 35.0 FR France \n",
"25 31.0 FR France \n",
"26 30.0 FR France \n",
"27 32.0 FR France \n",
"28 37.0 FR France \n",
"29 49.0 FR France \n",
"... ... ... ... \n",
"2008 59.0 FR France \n",
"2009 64.0 FR France \n",
"2010 97.0 FR France \n",
"2011 93.0 FR France \n",
"2012 80.0 FR France \n",
"2013 116.0 FR France \n",
"2014 149.0 FR France \n",
"2015 281.0 FR France \n",
"2016 395.0 FR France \n",
"2017 485.0 FR France \n",
"2018 544.0 FR France \n",
"2019 689.0 FR France \n",
"2020 722.0 FR France \n",
"2021 762.0 FR France \n",
"2022 926.0 FR France \n",
"2023 1113.0 FR France \n",
"2024 1236.0 FR France \n",
"2025 832.0 FR France \n",
"2026 459.0 FR France \n",
"2027 207.0 FR France \n",
"2028 190.0 FR France \n",
"2029 198.0 FR France \n",
"2030 224.0 FR France \n",
"2031 266.0 FR France \n",
"2032 219.0 FR France \n",
"2033 176.0 FR France \n",
"2034 163.0 FR France \n",
"2035 195.0 FR France \n",
"2036 308.0 FR France \n",
"2037 213.0 FR France \n",
"\n",
"[2038 rows x 10 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data = pd.read_csv(data_file, skiprows=1)\n",
"raw_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"raw_data[raw_data.isnull().any(axis=1)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data = raw_data.dropna().copy()\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nos données utilisent une convention inhabituelle: le numéro de\n",
"semaine est collé à l'année, donnant l'impression qu'il s'agit\n",
"de nombre entier. C'est comme ça que Pandas les interprète.\n",
" \n",
"Un deuxième problème est que Pandas ne comprend pas les numéros de\n",
"semaine. Il faut lui fournir les dates de début et de fin de\n",
"semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n",
"\n",
"Comme la conversion des semaines est devenu assez complexe, nous\n",
"écrivons une petite fonction Python pour cela. Ensuite, nous\n",
"l'appliquons à tous les points de nos donnés. Les résultats vont\n",
"dans une nouvelle colonne 'period'."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def convert_week(year_and_week_int):\n",
" year_and_week_str = str(year_and_week_int)\n",
" year = int(year_and_week_str[:4])\n",
" week = int(year_and_week_str[4:])\n",
" w = isoweek.Week(year, week)\n",
" return pd.Period(w.day(0), 'W')\n",
"\n",
"data['period'] = [convert_week(yw) for yw in data['week']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Il restent deux petites modifications à faire.\n",
"\n",
"Premièrement, nous définissons les périodes d'observation\n",
"comme nouvel index de notre jeux de données. Ceci en fait\n",
"une suite chronologique, ce qui sera pratique par la suite.\n",
"\n",
"Deuxièmement, nous trions les points par période, dans\n",
"le sens chronologique."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sorted_data = data.set_index('period').sort_index()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous vérifions la cohérence des données. Entre la fin d'une période et\n",
"le début de la période qui suit, la différence temporelle doit être\n",
"zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n",
"d'une seconde.\n",
"\n",
"Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n",
"entre lesquelles il manque une semaine.\n",
"\n",
"Nous reconnaissons ces dates: c'est la semaine sans observations\n",
"que nous avions supprimées !"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"periods = sorted_data.index\n",
"for p1, p2 in zip(periods[:-1], periods[1:]):\n",
" delta = p2.to_timestamp() - p1.end_time\n",
" if delta > pd.Timedelta('1s'):\n",
" print(p1, p2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Un premier regard sur les données !"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sorted_data['inc'].plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sorted_data['inc'][-200:].plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Etude de l'incidence annuelle"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n",
"entre deux années civiles, nous définissons la période de référence\n",
"entre deux minima de l'incidence, du 1er août de l'année $N$ au\n",
"1er août de l'année $N+1$.\n",
"\n",
"Notre tâche est un peu compliquée par le fait que l'année ne comporte\n",
"pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n",
"de référence: à la place du 1er août de chaque année, nous utilisons le\n",
"premier jour de la semaine qui contient le 1er août.\n",
"\n",
"Comme l'incidence de syndrome grippal est très faible en été, cette\n",
"modification ne risque pas de fausser nos conclusions.\n",
"\n",
"Encore un petit détail: les données commencent an octobre 1984, ce qui\n",
"rend la première année incomplète. Nous commençons donc l'analyse en 1985."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n",
" for y in range(1985,\n",
" sorted_data.index[-1].year)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n",
"\n",
"Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"year = []\n",
"yearly_incidence = []\n",
"for week1, week2 in zip(first_august_week[:-1],\n",
" first_august_week[1:]):\n",
" one_year = sorted_data['inc'][week1:week2-1]\n",
" assert abs(len(one_year)-52) < 2\n",
" yearly_incidence.append(one_year.sum())\n",
" year.append(week2.year)\n",
"yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Voici les incidences annuelles."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"yearly_incidence.plot(style='*')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"yearly_incidence.sort_values()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n",
" française, sont assez rares: il y en eu trois au cours des 35 dernières années."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"yearly_incidence.hist(xrot=20)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
module3/exo2/.ipynb 0 → 100644
View file @ f30211d4
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Incidence du syndrome grippal"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import isoweek"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-3.csv?v=8nhxe\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pour ne pas à avoir à télécharger le fichier à chaque éxécution "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"data_file = \"syndrome-grippal.csv\"\n",
"\n",
"import os\n",
"import urllib.request\n",
"if not os.path.exists(data_file):\n",
" urllib.request.urlretrieve(data_url, data_file)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n",
"\n",
"| Nom de colonne | Libellé de colonne |\n",
"|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n",
"| week | Semaine calendaire (ISO 8601) |\n",
"| indicator | Code de l'indicateur de surveillance |\n",
"| inc | Estimation de l'incidence de consultations en nombre de cas |\n",
"| inc_low | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation |\n",
"| inc_up | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation |\n",
"| inc100 | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
"| inc100_low | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
"| inc100_up | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
"| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n",
"| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n",
"\n",
"La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</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",
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" <tr>\n",
" <th>0</th>\n",
" <td>202346</td>\n",
" <td>3</td>\n",
" <td>92934</td>\n",
" <td>81854.0</td>\n",
" <td>104014.0</td>\n",
" <td>140</td>\n",
" <td>123.0</td>\n",
" <td>157.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>202345</td>\n",
" <td>3</td>\n",
" <td>71845</td>\n",
" <td>62813.0</td>\n",
" <td>80877.0</td>\n",
" <td>108</td>\n",
" <td>94.0</td>\n",
" <td>122.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>202344</td>\n",
" <td>3</td>\n",
" <td>49952</td>\n",
" <td>42813.0</td>\n",
" <td>57091.0</td>\n",
" <td>75</td>\n",
" <td>64.0</td>\n",
" <td>86.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>202343</td>\n",
" <td>3</td>\n",
" <td>44982</td>\n",
" <td>38170.0</td>\n",
" <td>51794.0</td>\n",
" <td>68</td>\n",
" <td>58.0</td>\n",
" <td>78.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>202342</td>\n",
" <td>3</td>\n",
" <td>56842</td>\n",
" <td>49277.0</td>\n",
" <td>64407.0</td>\n",
" <td>86</td>\n",
" <td>75.0</td>\n",
" <td>97.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>202341</td>\n",
" <td>3</td>\n",
" <td>58357</td>\n",
" <td>51032.0</td>\n",
" <td>65682.0</td>\n",
" <td>88</td>\n",
" <td>77.0</td>\n",
" <td>99.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>202340</td>\n",
" <td>3</td>\n",
" <td>68894</td>\n",
" <td>60069.0</td>\n",
" <td>77719.0</td>\n",
" <td>104</td>\n",
" <td>91.0</td>\n",
" <td>117.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>202339</td>\n",
" <td>3</td>\n",
" <td>72003</td>\n",
" <td>63452.0</td>\n",
" <td>80554.0</td>\n",
" <td>108</td>\n",
" <td>95.0</td>\n",
" <td>121.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>202338</td>\n",
" <td>3</td>\n",
" <td>63218</td>\n",
" <td>55227.0</td>\n",
" <td>71209.0</td>\n",
" <td>95</td>\n",
" <td>83.0</td>\n",
" <td>107.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>202337</td>\n",
" <td>3</td>\n",
" <td>49085</td>\n",
" <td>42079.0</td>\n",
" <td>56091.0</td>\n",
" <td>74</td>\n",
" <td>63.0</td>\n",
" <td>85.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>202336</td>\n",
" <td>3</td>\n",
" <td>38247</td>\n",
" <td>32237.0</td>\n",
" <td>44257.0</td>\n",
" <td>58</td>\n",
" <td>49.0</td>\n",
" <td>67.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>202335</td>\n",
" <td>3</td>\n",
" <td>31695</td>\n",
" <td>26013.0</td>\n",
" <td>37377.0</td>\n",
" <td>48</td>\n",
" <td>39.0</td>\n",
" <td>57.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>202334</td>\n",
" <td>3</td>\n",
" <td>26663</td>\n",
" <td>21057.0</td>\n",
" <td>32269.0</td>\n",
" <td>40</td>\n",
" <td>32.0</td>\n",
" <td>48.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>202333</td>\n",
" <td>3</td>\n",
" <td>19144</td>\n",
" <td>13161.0</td>\n",
" <td>25127.0</td>\n",
" <td>29</td>\n",
" <td>20.0</td>\n",
" <td>38.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>202332</td>\n",
" <td>3</td>\n",
" <td>14641</td>\n",
" <td>10285.0</td>\n",
" <td>18997.0</td>\n",
" <td>22</td>\n",
" <td>15.0</td>\n",
" <td>29.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>202331</td>\n",
" <td>3</td>\n",
" <td>15286</td>\n",
" <td>10705.0</td>\n",
" <td>19867.0</td>\n",
" <td>23</td>\n",
" <td>16.0</td>\n",
" <td>30.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>202330</td>\n",
" <td>3</td>\n",
" <td>13205</td>\n",
" <td>8647.0</td>\n",
" <td>17763.0</td>\n",
" <td>20</td>\n",
" <td>13.0</td>\n",
" <td>27.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>202329</td>\n",
" <td>3</td>\n",
" <td>11122</td>\n",
" <td>7113.0</td>\n",
" <td>15131.0</td>\n",
" <td>17</td>\n",
" <td>11.0</td>\n",
" <td>23.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>202328</td>\n",
" <td>3</td>\n",
" <td>9179</td>\n",
" <td>5703.0</td>\n",
" <td>12655.0</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>19</th>\n",
" <td>202327</td>\n",
" <td>3</td>\n",
" <td>8999</td>\n",
" <td>5763.0</td>\n",
" <td>12235.0</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>20</th>\n",
" <td>202326</td>\n",
" <td>3</td>\n",
" <td>9023</td>\n",
" <td>5934.0</td>\n",
" <td>12112.0</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>21</th>\n",
" <td>202325</td>\n",
" <td>3</td>\n",
" <td>10090</td>\n",
" <td>6739.0</td>\n",
" <td>13441.0</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>22</th>\n",
" <td>202324</td>\n",
" <td>3</td>\n",
" <td>11308</td>\n",
" <td>7639.0</td>\n",
" <td>14977.0</td>\n",
" <td>17</td>\n",
" <td>11.0</td>\n",
" <td>23.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>202323</td>\n",
" <td>3</td>\n",
" <td>14300</td>\n",
" <td>10661.0</td>\n",
" <td>17939.0</td>\n",
" <td>22</td>\n",
" <td>17.0</td>\n",
" <td>27.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>202322</td>\n",
" <td>3</td>\n",
" <td>18303</td>\n",
" <td>13822.0</td>\n",
" <td>22784.0</td>\n",
" <td>28</td>\n",
" <td>21.0</td>\n",
" <td>35.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>202321</td>\n",
" <td>3</td>\n",
" <td>16460</td>\n",
" <td>12188.0</td>\n",
" <td>20732.0</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>26</th>\n",
" <td>202320</td>\n",
" <td>3</td>\n",
" <td>16162</td>\n",
" <td>11963.0</td>\n",
" <td>20361.0</td>\n",
" <td>24</td>\n",
" <td>18.0</td>\n",
" <td>30.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>202319</td>\n",
" <td>3</td>\n",
" <td>16901</td>\n",
" <td>12577.0</td>\n",
" <td>21225.0</td>\n",
" <td>25</td>\n",
" <td>18.0</td>\n",
" <td>32.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>202318</td>\n",
" <td>3</td>\n",
" <td>19929</td>\n",
" <td>15402.0</td>\n",
" <td>24456.0</td>\n",
" <td>30</td>\n",
" <td>23.0</td>\n",
" <td>37.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>202317</td>\n",
" <td>3</td>\n",
" <td>27007</td>\n",
" <td>21779.0</td>\n",
" <td>32235.0</td>\n",
" <td>41</td>\n",
" <td>33.0</td>\n",
" <td>49.0</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>2008</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>2009</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>2010</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>2011</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>2012</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>2013</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>2014</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>2015</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>2016</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>2017</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>2018</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>2019</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>2020</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>2021</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>2022</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>2023</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>2024</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>2025</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>2026</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>2027</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>2028</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>2029</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>2030</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>2031</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>2032</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>2033</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>2034</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>2035</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>2036</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>2037</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>2038 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 202346 3 92934 81854.0 104014.0 140 123.0 \n",
"1 202345 3 71845 62813.0 80877.0 108 94.0 \n",
"2 202344 3 49952 42813.0 57091.0 75 64.0 \n",
"3 202343 3 44982 38170.0 51794.0 68 58.0 \n",
"4 202342 3 56842 49277.0 64407.0 86 75.0 \n",
"5 202341 3 58357 51032.0 65682.0 88 77.0 \n",
"6 202340 3 68894 60069.0 77719.0 104 91.0 \n",
"7 202339 3 72003 63452.0 80554.0 108 95.0 \n",
"8 202338 3 63218 55227.0 71209.0 95 83.0 \n",
"9 202337 3 49085 42079.0 56091.0 74 63.0 \n",
"10 202336 3 38247 32237.0 44257.0 58 49.0 \n",
"11 202335 3 31695 26013.0 37377.0 48 39.0 \n",
"12 202334 3 26663 21057.0 32269.0 40 32.0 \n",
"13 202333 3 19144 13161.0 25127.0 29 20.0 \n",
"14 202332 3 14641 10285.0 18997.0 22 15.0 \n",
"15 202331 3 15286 10705.0 19867.0 23 16.0 \n",
"16 202330 3 13205 8647.0 17763.0 20 13.0 \n",
"17 202329 3 11122 7113.0 15131.0 17 11.0 \n",
"18 202328 3 9179 5703.0 12655.0 14 9.0 \n",
"19 202327 3 8999 5763.0 12235.0 14 9.0 \n",
"20 202326 3 9023 5934.0 12112.0 14 9.0 \n",
"21 202325 3 10090 6739.0 13441.0 15 10.0 \n",
"22 202324 3 11308 7639.0 14977.0 17 11.0 \n",
"23 202323 3 14300 10661.0 17939.0 22 17.0 \n",
"24 202322 3 18303 13822.0 22784.0 28 21.0 \n",
"25 202321 3 16460 12188.0 20732.0 25 19.0 \n",
"26 202320 3 16162 11963.0 20361.0 24 18.0 \n",
"27 202319 3 16901 12577.0 21225.0 25 18.0 \n",
"28 202318 3 19929 15402.0 24456.0 30 23.0 \n",
"29 202317 3 27007 21779.0 32235.0 41 33.0 \n",
"... ... ... ... ... ... ... ... \n",
"2008 198521 3 26096 19621.0 32571.0 47 35.0 \n",
"2009 198520 3 27896 20885.0 34907.0 51 38.0 \n",
"2010 198519 3 43154 32821.0 53487.0 78 59.0 \n",
"2011 198518 3 40555 29935.0 51175.0 74 55.0 \n",
"2012 198517 3 34053 24366.0 43740.0 62 44.0 \n",
"2013 198516 3 50362 36451.0 64273.0 91 66.0 \n",
"2014 198515 3 63881 45538.0 82224.0 116 83.0 \n",
"2015 198514 3 134545 114400.0 154690.0 244 207.0 \n",
"2016 198513 3 197206 176080.0 218332.0 357 319.0 \n",
"2017 198512 3 245240 223304.0 267176.0 445 405.0 \n",
"2018 198511 3 276205 252399.0 300011.0 501 458.0 \n",
"2019 198510 3 353231 326279.0 380183.0 640 591.0 \n",
"2020 198509 3 369895 341109.0 398681.0 670 618.0 \n",
"2021 198508 3 389886 359529.0 420243.0 707 652.0 \n",
"2022 198507 3 471852 432599.0 511105.0 855 784.0 \n",
"2023 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
"2024 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
"2025 198504 3 424937 390794.0 459080.0 770 708.0 \n",
"2026 198503 3 213901 174689.0 253113.0 388 317.0 \n",
"2027 198502 3 97586 80949.0 114223.0 177 147.0 \n",
"2028 198501 3 85489 65918.0 105060.0 155 120.0 \n",
"2029 198452 3 84830 60602.0 109058.0 154 110.0 \n",
"2030 198451 3 101726 80242.0 123210.0 185 146.0 \n",
"2031 198450 3 123680 101401.0 145959.0 225 184.0 \n",
"2032 198449 3 101073 81684.0 120462.0 184 149.0 \n",
"2033 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"2034 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"2035 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"2036 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"2037 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 157.0 FR France \n",
"1 122.0 FR France \n",
"2 86.0 FR France \n",
"3 78.0 FR France \n",
"4 97.0 FR France \n",
"5 99.0 FR France \n",
"6 117.0 FR France \n",
"7 121.0 FR France \n",
"8 107.0 FR France \n",
"9 85.0 FR France \n",
"10 67.0 FR France \n",
"11 57.0 FR France \n",
"12 48.0 FR France \n",
"13 38.0 FR France \n",
"14 29.0 FR France \n",
"15 30.0 FR France \n",
"16 27.0 FR France \n",
"17 23.0 FR France \n",
"18 19.0 FR France \n",
"19 19.0 FR France \n",
"20 19.0 FR France \n",
"21 20.0 FR France \n",
"22 23.0 FR France \n",
"23 27.0 FR France \n",
"24 35.0 FR France \n",
"25 31.0 FR France \n",
"26 30.0 FR France \n",
"27 32.0 FR France \n",
"28 37.0 FR France \n",
"29 49.0 FR France \n",
"... ... ... ... \n",
"2008 59.0 FR France \n",
"2009 64.0 FR France \n",
"2010 97.0 FR France \n",
"2011 93.0 FR France \n",
"2012 80.0 FR France \n",
"2013 116.0 FR France \n",
"2014 149.0 FR France \n",
"2015 281.0 FR France \n",
"2016 395.0 FR France \n",
"2017 485.0 FR France \n",
"2018 544.0 FR France \n",
"2019 689.0 FR France \n",
"2020 722.0 FR France \n",
"2021 762.0 FR France \n",
"2022 926.0 FR France \n",
"2023 1113.0 FR France \n",
"2024 1236.0 FR France \n",
"2025 832.0 FR France \n",
"2026 459.0 FR France \n",
"2027 207.0 FR France \n",
"2028 190.0 FR France \n",
"2029 198.0 FR France \n",
"2030 224.0 FR France \n",
"2031 266.0 FR France \n",
"2032 219.0 FR France \n",
"2033 176.0 FR France \n",
"2034 163.0 FR France \n",
"2035 195.0 FR France \n",
"2036 308.0 FR France \n",
"2037 213.0 FR France \n",
"\n",
"[2038 rows x 10 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data = pd.read_csv(data_file, skiprows=1)\n",
"raw_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"raw_data[raw_data.isnull().any(axis=1)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data = raw_data.dropna().copy()\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nos données utilisent une convention inhabituelle: le numéro de\n",
"semaine est collé à l'année, donnant l'impression qu'il s'agit\n",
"de nombre entier. C'est comme ça que Pandas les interprète.\n",
" \n",
"Un deuxième problème est que Pandas ne comprend pas les numéros de\n",
"semaine. Il faut lui fournir les dates de début et de fin de\n",
"semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n",
"\n",
"Comme la conversion des semaines est devenu assez complexe, nous\n",
"écrivons une petite fonction Python pour cela. Ensuite, nous\n",
"l'appliquons à tous les points de nos donnés. Les résultats vont\n",
"dans une nouvelle colonne 'period'."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def convert_week(year_and_week_int):\n",
" year_and_week_str = str(year_and_week_int)\n",
" year = int(year_and_week_str[:4])\n",
" week = int(year_and_week_str[4:])\n",
" w = isoweek.Week(year, week)\n",
" return pd.Period(w.day(0), 'W')\n",
"\n",
"data['period'] = [convert_week(yw) for yw in data['week']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Il restent deux petites modifications à faire.\n",
"\n",
"Premièrement, nous définissons les périodes d'observation\n",
"comme nouvel index de notre jeux de données. Ceci en fait\n",
"une suite chronologique, ce qui sera pratique par la suite.\n",
"\n",
"Deuxièmement, nous trions les points par période, dans\n",
"le sens chronologique."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sorted_data = data.set_index('period').sort_index()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous vérifions la cohérence des données. Entre la fin d'une période et\n",
"le début de la période qui suit, la différence temporelle doit être\n",
"zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n",
"d'une seconde.\n",
"\n",
"Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n",
"entre lesquelles il manque une semaine.\n",
"\n",
"Nous reconnaissons ces dates: c'est la semaine sans observations\n",
"que nous avions supprimées !"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"periods = sorted_data.index\n",
"for p1, p2 in zip(periods[:-1], periods[1:]):\n",
" delta = p2.to_timestamp() - p1.end_time\n",
" if delta > pd.Timedelta('1s'):\n",
" print(p1, p2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Un premier regard sur les données !"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sorted_data['inc'].plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sorted_data['inc'][-200:].plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Etude de l'incidence annuelle"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n",
"entre deux années civiles, nous définissons la période de référence\n",
"entre deux minima de l'incidence, du 1er août de l'année $N$ au\n",
"1er août de l'année $N+1$.\n",
"\n",
"Notre tâche est un peu compliquée par le fait que l'année ne comporte\n",
"pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n",
"de référence: à la place du 1er août de chaque année, nous utilisons le\n",
"premier jour de la semaine qui contient le 1er août.\n",
"\n",
"Comme l'incidence de syndrome grippal est très faible en été, cette\n",
"modification ne risque pas de fausser nos conclusions.\n",
"\n",
"Encore un petit détail: les données commencent an octobre 1984, ce qui\n",
"rend la première année incomplète. Nous commençons donc l'analyse en 1985."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n",
" for y in range(1985,\n",
" sorted_data.index[-1].year)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n",
"\n",
"Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"year = []\n",
"yearly_incidence = []\n",
"for week1, week2 in zip(first_august_week[:-1],\n",
" first_august_week[1:]):\n",
" one_year = sorted_data['inc'][week1:week2-1]\n",
" assert abs(len(one_year)-52) < 2\n",
" yearly_incidence.append(one_year.sum())\n",
" year.append(week2.year)\n",
"yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Voici les incidences annuelles."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"yearly_incidence.plot(style='*')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"yearly_incidence.sort_values()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n",
" française, sont assez rares: il y en eu trois au cours des 35 dernières années."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"yearly_incidence.hist(xrot=20)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
module3/exo2/exercice.ipynb
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