From 7138a75ca7b48701472fd526ae088d24685bf3d4 Mon Sep 17 00:00:00 2001 From: 55f88d13fde26eae718b7fd1cc4c4751 <55f88d13fde26eae718b7fd1cc4c4751@app-learninglab.inria.fr> Date: Thu, 9 Nov 2023 15:45:13 +0000 Subject: [PATCH] =?UTF-8?q?juste=20la=20copie=20de=20l'exo=20d'avant,=20?= =?UTF-8?q?=C3=A0=20modifier=20pour=20le=20nouveau=20jeu=20de=20donn=C3=A9?= =?UTF-8?q?es?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../exo1/analyse-syndrome-grippal-Copy1.ipynb | 2547 +++++++++++++++++ module3/exo2/exercice.ipynb | 2535 +++++++++++++++- 2 files changed, 5079 insertions(+), 3 deletions(-) create mode 100644 module3/exo1/analyse-syndrome-grippal-Copy1.ipynb diff --git a/module3/exo1/analyse-syndrome-grippal-Copy1.ipynb b/module3/exo1/analyse-syndrome-grippal-Copy1.ipynb new file mode 100644 index 0000000..039e144 --- /dev/null +++ b/module3/exo1/analyse-syndrome-grippal-Copy1.ipynb @@ -0,0 +1,2547 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence du syndrome grippal" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "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": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour nous protéger contre une éventuelle disparition ou modification du serveur du Réseau Sentinelles, nous faisons une copie locale de ce jeux de données que nous préservons avec notre analyse. Il est inutile et même risquée de télécharger les données à chaque exécution, car dans le cas d'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data_file = \"incidence-PAY-3.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": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020234435834048804.067876.08874.0102.0FRFrance
120234334620039090.053310.07059.081.0FRFrance
220234235684249277.064407.08675.097.0FRFrance
320234135835751032.065682.08877.099.0FRFrance
420234036889460069.077719.010491.0117.0FRFrance
520233937200363452.080554.010895.0121.0FRFrance
620233836321855227.071209.09583.0107.0FRFrance
720233734908542079.056091.07463.085.0FRFrance
820233633824732237.044257.05849.067.0FRFrance
920233533169526013.037377.04839.057.0FRFrance
1020233432666321057.032269.04032.048.0FRFrance
1120233331914413161.025127.02920.038.0FRFrance
1220233231464110285.018997.02215.029.0FRFrance
1320233131528610705.019867.02316.030.0FRFrance
142023303132058647.017763.02013.027.0FRFrance
152023293111227113.015131.01711.023.0FRFrance
16202328391795703.012655.0149.019.0FRFrance
17202327389995763.012235.0149.019.0FRFrance
18202326390235934.012112.0149.019.0FRFrance
192023253100906739.013441.01510.020.0FRFrance
202023243113087639.014977.01711.023.0FRFrance
2120232331430010661.017939.02217.027.0FRFrance
2220232231830313822.022784.02821.035.0FRFrance
2320232131646012188.020732.02519.031.0FRFrance
2420232031616211963.020361.02418.030.0FRFrance
2520231931690112577.021225.02518.032.0FRFrance
2620231831992915402.024456.03023.037.0FRFrance
2720231732700721779.032235.04133.049.0FRFrance
2820231632787522767.032983.04234.050.0FRFrance
2920231533745530993.043917.05646.066.0FRFrance
.................................
200619852132609619621.032571.04735.059.0FRFrance
200719852032789620885.034907.05138.064.0FRFrance
200819851934315432821.053487.07859.097.0FRFrance
200919851834055529935.051175.07455.093.0FRFrance
201019851733405324366.043740.06244.080.0FRFrance
201119851635036236451.064273.09166.0116.0FRFrance
201219851536388145538.082224.011683.0149.0FRFrance
20131985143134545114400.0154690.0244207.0281.0FRFrance
20141985133197206176080.0218332.0357319.0395.0FRFrance
20151985123245240223304.0267176.0445405.0485.0FRFrance
20161985113276205252399.0300011.0501458.0544.0FRFrance
20171985103353231326279.0380183.0640591.0689.0FRFrance
20181985093369895341109.0398681.0670618.0722.0FRFrance
20191985083389886359529.0420243.0707652.0762.0FRFrance
20201985073471852432599.0511105.0855784.0926.0FRFrance
20211985063565825518011.0613639.01026939.01113.0FRFrance
20221985053637302592795.0681809.011551074.01236.0FRFrance
20231985043424937390794.0459080.0770708.0832.0FRFrance
20241985033213901174689.0253113.0388317.0459.0FRFrance
202519850239758680949.0114223.0177147.0207.0FRFrance
202619850138548965918.0105060.0155120.0190.0FRFrance
202719845238483060602.0109058.0154110.0198.0FRFrance
2028198451310172680242.0123210.0185146.0224.0FRFrance
20291984503123680101401.0145959.0225184.0266.0FRFrance
2030198449310107381684.0120462.0184149.0219.0FRFrance
203119844837862060634.096606.0143110.0176.0FRFrance
203219844737202954274.089784.013199.0163.0FRFrance
203319844638733067686.0106974.0159123.0195.0FRFrance
20341984453135223101414.0169032.0246184.0308.0FRFrance
203519844436842220056.0116788.012537.0213.0FRFrance
\n", + "

2036 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202344 3 58340 48804.0 67876.0 88 74.0 \n", + "1 202343 3 46200 39090.0 53310.0 70 59.0 \n", + "2 202342 3 56842 49277.0 64407.0 86 75.0 \n", + "3 202341 3 58357 51032.0 65682.0 88 77.0 \n", + "4 202340 3 68894 60069.0 77719.0 104 91.0 \n", + "5 202339 3 72003 63452.0 80554.0 108 95.0 \n", + "6 202338 3 63218 55227.0 71209.0 95 83.0 \n", + "7 202337 3 49085 42079.0 56091.0 74 63.0 \n", + "8 202336 3 38247 32237.0 44257.0 58 49.0 \n", + "9 202335 3 31695 26013.0 37377.0 48 39.0 \n", + "10 202334 3 26663 21057.0 32269.0 40 32.0 \n", + "11 202333 3 19144 13161.0 25127.0 29 20.0 \n", + "12 202332 3 14641 10285.0 18997.0 22 15.0 \n", + "13 202331 3 15286 10705.0 19867.0 23 16.0 \n", + "14 202330 3 13205 8647.0 17763.0 20 13.0 \n", + "15 202329 3 11122 7113.0 15131.0 17 11.0 \n", + "16 202328 3 9179 5703.0 12655.0 14 9.0 \n", + "17 202327 3 8999 5763.0 12235.0 14 9.0 \n", + "18 202326 3 9023 5934.0 12112.0 14 9.0 \n", + "19 202325 3 10090 6739.0 13441.0 15 10.0 \n", + "20 202324 3 11308 7639.0 14977.0 17 11.0 \n", + "21 202323 3 14300 10661.0 17939.0 22 17.0 \n", + "22 202322 3 18303 13822.0 22784.0 28 21.0 \n", + "23 202321 3 16460 12188.0 20732.0 25 19.0 \n", + "24 202320 3 16162 11963.0 20361.0 24 18.0 \n", + "25 202319 3 16901 12577.0 21225.0 25 18.0 \n", + "26 202318 3 19929 15402.0 24456.0 30 23.0 \n", + "27 202317 3 27007 21779.0 32235.0 41 33.0 \n", + "28 202316 3 27875 22767.0 32983.0 42 34.0 \n", + "29 202315 3 37455 30993.0 43917.0 56 46.0 \n", + "... ... ... ... ... ... ... ... \n", + "2006 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2007 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2008 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2009 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2010 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2011 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2012 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2013 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2014 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2015 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2016 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2017 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2018 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2019 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2020 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2021 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2022 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2023 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2024 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2025 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2026 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2027 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2028 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2029 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2030 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2031 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2032 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2033 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2034 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2035 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 102.0 FR France \n", + "1 81.0 FR France \n", + "2 97.0 FR France \n", + "3 99.0 FR France \n", + "4 117.0 FR France \n", + "5 121.0 FR France \n", + "6 107.0 FR France \n", + "7 85.0 FR France \n", + "8 67.0 FR France \n", + "9 57.0 FR France \n", + "10 48.0 FR France \n", + "11 38.0 FR France \n", + "12 29.0 FR France \n", + "13 30.0 FR France \n", + "14 27.0 FR France \n", + "15 23.0 FR France \n", + "16 19.0 FR France \n", + "17 19.0 FR France \n", + "18 19.0 FR France \n", + "19 20.0 FR France \n", + "20 23.0 FR France \n", + "21 27.0 FR France \n", + "22 35.0 FR France \n", + "23 31.0 FR France \n", + "24 30.0 FR France \n", + "25 32.0 FR France \n", + "26 37.0 FR France \n", + "27 49.0 FR France \n", + "28 50.0 FR France \n", + "29 66.0 FR France \n", + "... ... ... ... \n", + "2006 59.0 FR France \n", + "2007 64.0 FR France \n", + "2008 97.0 FR France \n", + "2009 93.0 FR France \n", + "2010 80.0 FR France \n", + "2011 116.0 FR France \n", + "2012 149.0 FR France \n", + "2013 281.0 FR France \n", + "2014 395.0 FR France \n", + "2015 485.0 FR France \n", + "2016 544.0 FR France \n", + "2017 689.0 FR France \n", + "2018 722.0 FR France \n", + "2019 762.0 FR France \n", + "2020 926.0 FR France \n", + "2021 1113.0 FR France \n", + "2022 1236.0 FR France \n", + "2023 832.0 FR France \n", + "2024 459.0 FR France \n", + "2025 207.0 FR France \n", + "2026 190.0 FR France \n", + "2027 198.0 FR France \n", + "2028 224.0 FR France \n", + "2029 266.0 FR France \n", + "2030 219.0 FR France \n", + "2031 176.0 FR France \n", + "2032 163.0 FR France \n", + "2033 195.0 FR France \n", + "2034 308.0 FR France \n", + "2035 213.0 FR France \n", + "\n", + "[2036 rows x 10 columns]" + ] + }, + "execution_count": 4, + "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": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
17991989193-NaNNaN-NaNNaNFRFrance
\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", + "1799 198919 3 - NaN NaN - NaN NaN \n", + "\n", + " geo_insee geo_name \n", + "1799 FR France " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020234435834048804.067876.08874.0102.0FRFrance
120234334620039090.053310.07059.081.0FRFrance
220234235684249277.064407.08675.097.0FRFrance
320234135835751032.065682.08877.099.0FRFrance
420234036889460069.077719.010491.0117.0FRFrance
520233937200363452.080554.010895.0121.0FRFrance
620233836321855227.071209.09583.0107.0FRFrance
720233734908542079.056091.07463.085.0FRFrance
820233633824732237.044257.05849.067.0FRFrance
920233533169526013.037377.04839.057.0FRFrance
1020233432666321057.032269.04032.048.0FRFrance
1120233331914413161.025127.02920.038.0FRFrance
1220233231464110285.018997.02215.029.0FRFrance
1320233131528610705.019867.02316.030.0FRFrance
142023303132058647.017763.02013.027.0FRFrance
152023293111227113.015131.01711.023.0FRFrance
16202328391795703.012655.0149.019.0FRFrance
17202327389995763.012235.0149.019.0FRFrance
18202326390235934.012112.0149.019.0FRFrance
192023253100906739.013441.01510.020.0FRFrance
202023243113087639.014977.01711.023.0FRFrance
2120232331430010661.017939.02217.027.0FRFrance
2220232231830313822.022784.02821.035.0FRFrance
2320232131646012188.020732.02519.031.0FRFrance
2420232031616211963.020361.02418.030.0FRFrance
2520231931690112577.021225.02518.032.0FRFrance
2620231831992915402.024456.03023.037.0FRFrance
2720231732700721779.032235.04133.049.0FRFrance
2820231632787522767.032983.04234.050.0FRFrance
2920231533745530993.043917.05646.066.0FRFrance
.................................
200619852132609619621.032571.04735.059.0FRFrance
200719852032789620885.034907.05138.064.0FRFrance
200819851934315432821.053487.07859.097.0FRFrance
200919851834055529935.051175.07455.093.0FRFrance
201019851733405324366.043740.06244.080.0FRFrance
201119851635036236451.064273.09166.0116.0FRFrance
201219851536388145538.082224.011683.0149.0FRFrance
20131985143134545114400.0154690.0244207.0281.0FRFrance
20141985133197206176080.0218332.0357319.0395.0FRFrance
20151985123245240223304.0267176.0445405.0485.0FRFrance
20161985113276205252399.0300011.0501458.0544.0FRFrance
20171985103353231326279.0380183.0640591.0689.0FRFrance
20181985093369895341109.0398681.0670618.0722.0FRFrance
20191985083389886359529.0420243.0707652.0762.0FRFrance
20201985073471852432599.0511105.0855784.0926.0FRFrance
20211985063565825518011.0613639.01026939.01113.0FRFrance
20221985053637302592795.0681809.011551074.01236.0FRFrance
20231985043424937390794.0459080.0770708.0832.0FRFrance
20241985033213901174689.0253113.0388317.0459.0FRFrance
202519850239758680949.0114223.0177147.0207.0FRFrance
202619850138548965918.0105060.0155120.0190.0FRFrance
202719845238483060602.0109058.0154110.0198.0FRFrance
2028198451310172680242.0123210.0185146.0224.0FRFrance
20291984503123680101401.0145959.0225184.0266.0FRFrance
2030198449310107381684.0120462.0184149.0219.0FRFrance
203119844837862060634.096606.0143110.0176.0FRFrance
203219844737202954274.089784.013199.0163.0FRFrance
203319844638733067686.0106974.0159123.0195.0FRFrance
20341984453135223101414.0169032.0246184.0308.0FRFrance
203519844436842220056.0116788.012537.0213.0FRFrance
\n", + "

2035 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202344 3 58340 48804.0 67876.0 88 74.0 \n", + "1 202343 3 46200 39090.0 53310.0 70 59.0 \n", + "2 202342 3 56842 49277.0 64407.0 86 75.0 \n", + "3 202341 3 58357 51032.0 65682.0 88 77.0 \n", + "4 202340 3 68894 60069.0 77719.0 104 91.0 \n", + "5 202339 3 72003 63452.0 80554.0 108 95.0 \n", + "6 202338 3 63218 55227.0 71209.0 95 83.0 \n", + "7 202337 3 49085 42079.0 56091.0 74 63.0 \n", + "8 202336 3 38247 32237.0 44257.0 58 49.0 \n", + "9 202335 3 31695 26013.0 37377.0 48 39.0 \n", + "10 202334 3 26663 21057.0 32269.0 40 32.0 \n", + "11 202333 3 19144 13161.0 25127.0 29 20.0 \n", + "12 202332 3 14641 10285.0 18997.0 22 15.0 \n", + "13 202331 3 15286 10705.0 19867.0 23 16.0 \n", + "14 202330 3 13205 8647.0 17763.0 20 13.0 \n", + "15 202329 3 11122 7113.0 15131.0 17 11.0 \n", + "16 202328 3 9179 5703.0 12655.0 14 9.0 \n", + "17 202327 3 8999 5763.0 12235.0 14 9.0 \n", + "18 202326 3 9023 5934.0 12112.0 14 9.0 \n", + "19 202325 3 10090 6739.0 13441.0 15 10.0 \n", + "20 202324 3 11308 7639.0 14977.0 17 11.0 \n", + "21 202323 3 14300 10661.0 17939.0 22 17.0 \n", + "22 202322 3 18303 13822.0 22784.0 28 21.0 \n", + "23 202321 3 16460 12188.0 20732.0 25 19.0 \n", + "24 202320 3 16162 11963.0 20361.0 24 18.0 \n", + "25 202319 3 16901 12577.0 21225.0 25 18.0 \n", + "26 202318 3 19929 15402.0 24456.0 30 23.0 \n", + "27 202317 3 27007 21779.0 32235.0 41 33.0 \n", + "28 202316 3 27875 22767.0 32983.0 42 34.0 \n", + "29 202315 3 37455 30993.0 43917.0 56 46.0 \n", + "... ... ... ... ... ... ... ... \n", + "2006 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2007 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2008 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2009 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2010 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2011 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2012 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2013 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2014 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2015 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2016 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2017 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2018 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2019 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2020 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2021 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2022 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2023 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2024 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2025 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2026 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2027 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2028 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2029 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2030 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2031 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2032 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2033 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2034 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2035 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 102.0 FR France \n", + "1 81.0 FR France \n", + "2 97.0 FR France \n", + "3 99.0 FR France \n", + "4 117.0 FR France \n", + "5 121.0 FR France \n", + "6 107.0 FR France \n", + "7 85.0 FR France \n", + "8 67.0 FR France \n", + "9 57.0 FR France \n", + "10 48.0 FR France \n", + "11 38.0 FR France \n", + "12 29.0 FR France \n", + "13 30.0 FR France \n", + "14 27.0 FR France \n", + "15 23.0 FR France \n", + "16 19.0 FR France \n", + "17 19.0 FR France \n", + "18 19.0 FR France \n", + "19 20.0 FR France \n", + "20 23.0 FR France \n", + "21 27.0 FR France \n", + "22 35.0 FR France \n", + "23 31.0 FR France \n", + "24 30.0 FR France \n", + "25 32.0 FR France \n", + "26 37.0 FR France \n", + "27 49.0 FR France \n", + "28 50.0 FR France \n", + "29 66.0 FR France \n", + "... ... ... ... \n", + "2006 59.0 FR France \n", + "2007 64.0 FR France \n", + "2008 97.0 FR France \n", + "2009 93.0 FR France \n", + "2010 80.0 FR France \n", + "2011 116.0 FR France \n", + "2012 149.0 FR France \n", + "2013 281.0 FR France \n", + "2014 395.0 FR France \n", + "2015 485.0 FR France \n", + "2016 544.0 FR France \n", + "2017 689.0 FR France \n", + "2018 722.0 FR France \n", + "2019 762.0 FR France \n", + "2020 926.0 FR France \n", + "2021 1113.0 FR France \n", + "2022 1236.0 FR France \n", + "2023 832.0 FR France \n", + "2024 459.0 FR France \n", + "2025 207.0 FR France \n", + "2026 190.0 FR France \n", + "2027 198.0 FR France \n", + "2028 224.0 FR France \n", + "2029 266.0 FR France \n", + "2030 219.0 FR France \n", + "2031 176.0 FR France \n", + "2032 163.0 FR France \n", + "2033 195.0 FR France \n", + "2034 308.0 FR France \n", + "2035 213.0 FR France \n", + "\n", + "[2035 rows x 10 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = raw_data.dropna().copy()\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nos données utilisent une convention inhabituelle: le numéro de\n", + "semaine est collé à l'année, donnant l'impression qu'il s'agit\n", + "de nombre entier. C'est comme ça que Pandas les interprète.\n", + " \n", + "Un deuxième problème est que Pandas ne comprend pas les numéros de\n", + "semaine. Il faut lui fournir les dates de début et de fin de\n", + "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n", + "\n", + "Comme la conversion des semaines est devenu assez complexe, nous\n", + "écrivons une petite fonction Python pour cela. Ensuite, nous\n", + "l'appliquons à tous les points de nos donnés. Les résultats vont\n", + "dans une nouvelle colonne 'period'." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "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": 8, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous vérifions la cohérence des données. Entre la fin d'une période et\n", + "le début de la période qui suit, la différence temporelle doit être\n", + "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n", + "d'une seconde.\n", + "\n", + "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n", + "entre lesquelles il manque une semaine.\n", + "\n", + "Nous reconnaissons ces dates: c'est la semaine sans observations\n", + "que nous avions supprimées !" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" + ] + } + ], + "source": [ + "periods = sorted_data.index\n", + "for p1, p2 in zip(periods[:-1], periods[1:]):\n", + " delta = p2.to_timestamp() - p1.end_time\n", + " if delta > pd.Timedelta('1s'):\n", + " print(p1, p2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un premier regard sur les données !" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data['inc'] = sorted_data['inc'].astype(int)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "petite manip pour transformer la chaine de caractères en entier pour pouvoir génerer le plot" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEKCAYAAADenhiQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJztnXecHMWZ93/PhM0raZWFAhIgAyIZ0IlgsDmibHyHA/jwYSPb3HHmxdi+93w2OBx+4fDhBDa2webIDgQDNtiAsRA5WEIgQEhC0oLSKu1Ku5JWu9rdCfX+0VU91T3VYXpmdmZWz/fz2c/MdneFqa6up55Q1SSEAMMwDMOUklilK8AwDMOMPFi4MAzDMCWHhQvDMAxTcli4MAzDMCWHhQvDMAxTcli4MAzDMCWHhQvDMAxTcli4MAzDMCWHhQvDMAxTchKVrsBwMn78eDFz5sxKV4NhGKameO2113YIISYUkma/Ei4zZ87E0qVLK10NhmGYmoKINhSahs1iDMMwTMlh4cIwDMOUHBYuDMMwTMlh4cIwDMOUHBYuDMMwTMkJFC5EdAcRdRLR29qxsUS0kIjWys827dxVRNRORKuJ6Bzt+PFEtFyeu4mISB6vJ6L75fHFRDRTS7NAlrGWiBZox2fJa9fKtHXFNwXDMAxTKsJoLncBmO86diWARUKI2QAWyf9BRHMAXAjgCJnmZiKKyzS3ALgUwGz5p/K8BECPEOIQADcC+L7MayyAqwGcAGAegKs1IfZ9ADfK8ntkHgzDMEyVEChchBDPA+h2HT4PwN3y+90APqYdv08IMSiEWAegHcA8IpoCYJQQ4hVhvVf5HlcaldeDAM6QWs05ABYKIbqFED0AFgKYL8+dLq91lz8i6ejpxzOrOytdDYZhmNBE9blMEkJsBQD5OVEenwpgk3Zdhzw2VX53H3ekEUKkAewGMM4nr3EAdslr3XmNSM664Xl8/s5XK10NhmGY0JTaoU+GY8LneJQ0fnnlV4joUiJaSkRLu7q6vC6ravalMpWuAsMwTEFEFS7bpakL8lPZbDoATNeumwZgizw+zXDckYaIEgBGwzLDeeW1A8AYea07rzyEELcKIeYKIeZOmFDQ1jgMwzBMRKIKl0cBqOitBQAe0Y5fKCPAZsFy3C+RprNeIjpR+kwudqVReZ0P4Gnpl3kSwNlE1CYd+WcDeFKee0Ze6y6fYRiGqQICN64konsBnAZgPBF1wIrguh7AA0R0CYCNAC4AACHECiJ6AMBKAGkAlwshlE3nMliRZ40AnpB/AHA7gF8TUTssjeVCmVc3EV0LQDkbrhFCqMCCbwC4j4j+G8AymQfDMAxTJZClCOwfzJ07V9Tirsgzr3wMALD++nMrXBOGYfZHiOg1IcTcQtLwCn2GYRim5LBwYRiGYUoOCxeGYRim5LBwqSH2J/8YwzC1DQuXGoJlC8MwtQILlxoiy9KFYZgagYVLDcGihWGYWoGFSw3BmgvDMLUCC5cagmULwzC1AguXGoKFC8MwtQILlxpCsNeFYZgagYVLDZFl2cIwTI3AwqWG4EWUDMPUCixcaggWLQzD1AosXGoIka10DfK59J6lOPF7iypdDYZhqozAl4Ux1UM1OvT/unJ7pavAMEwVwppLDcEOfYZhagUWLjUEO/QZhqkVWLjUEKy5MAxTK7BwqSFYc2EYplZg4cIwDMOUHBYuNQTrLQzD1AosXBiGYZiSw8KlhmCXC8MwtQILlxqiGhdRMgzDmGDhwjAMw5QcFi41BJvFGIapFVi4MAzDMCWHhUsNwYoLwzC1AguXEcCPnlyNT97ycqWrwTAMY1OUcCGifyeiFUT0NhHdS0QNRDSWiBYS0Vr52aZdfxURtRPRaiI6Rzt+PBEtl+duIiKSx+uJ6H55fDERzdTSLJBlrCWiBcX8jlrBa/uXnz/Tjtc29AxzbRiGYbyJLFyIaCqALwOYK4Q4EkAcwIUArgSwSAgxG8Ai+T+IaI48fwSA+QBuJqK4zO4WAJcCmC3/5svjlwDoEUIcAuBGAN+XeY0FcDWAEwDMA3C1LsRGKuzQZximVijWLJYA0EhECQBNALYAOA/A3fL83QA+Jr+fB+A+IcSgEGIdgHYA84hoCoBRQohXhDU1v8eVRuX1IIAzpFZzDoCFQohuIUQPgIXICSSGYRimwkQWLkKIzQB+BGAjgK0Adgsh/gpgkhBiq7xmK4CJMslUAJu0LDrksanyu/u4I40QIg1gN4BxPnkxDMMwVUAxZrE2WJrFLAAHAGgmos/4JTEcEz7Ho6Zx1/NSIlpKREu7urp8qscwDMOUimLMYmcCWCeE6BJCpAA8DOBkANulqQvys1Ne3wFgupZ+GiwzWof87j7uSCNNb6MBdPvklYcQ4lYhxFwhxNwJEyZE/KnVAftcGIapFYoRLhsBnEhETdIPcgaAVQAeBaCitxYAeER+fxTAhTICbBYsx/0SaTrrJaITZT4Xu9KovM4H8LT0yzwJ4GwiapMa1Nny2IiG9xZjGKZWSERNKIRYTEQPAngdQBrAMgC3AmgB8AARXQJLAF0gr19BRA8AWCmvv1wIkZHZXQbgLgCNAJ6QfwBwO4BfE1E7LI3lQplXNxFdC+BVed01QojuqL+FYRiGKS2RhQsACCGuhhUSrDMIS4sxXX8dgOsMx5cCONJwfABSOBnO3QHgjgKrXNOwWYxhmFqBV+gzDMMwJYeFSw3BigvDMLUCCxeGYRim5LBwqSG89hZjGIapNli41BAjUbT8+K+rcc8r6ytdDYZhSkxR0WIMUyw/e7odAHDxSTMrWxGGYUoKay41BFvFGIapFVi4MAzDMCWHhUtNwaoLwzC1AQuXGoLNYgzD1AosXBiGYZiSw8KlhmDFhWGYWoGFC8MwDFNyWLjUEOxzYRimVmDhwjAMw5QcFi41BL+JkmGYWoGFSw3BZjGGYWoFFi4MwzBMyWHhUkOw5sIwTK3AwmUEwe97YRimWmDhUkMEOfRZtjAMUy2wcKkhgoQHyxaGYaoFFi4jiCyrLgzDVAksXEYQLFsYhqkWWLiMICq5yJKDCRiG0WHhUkME+lwqOL6Xq+zOPQNIZbLlyZxhmLLBwqUAXm7fgcXv7ax0NfYbBlIZzPveIlz18PJKV4VhmAJh4VIA/3zbYvzTrX+rWPnVHIpcjqIHUhkAwF9XbCtD7gzDlBMWLjVEcCjyyPK5qCyJqOR5MwxTXli4jCCyI0xzUXmybGGY2oOFSw0RNIBXMmKrHEWr38OyhWFqj6KECxGNIaIHiegdIlpFRCcR0VgiWkhEa+Vnm3b9VUTUTkSriegc7fjxRLRcnruJpB2EiOqJ6H55fDERzdTSLJBlrCWiBcX8jpFCJYOBy2GSy2kuLF4YptYoVnP5KYC/CCEOA3AMgFUArgSwSAgxG8Ai+T+IaA6ACwEcAWA+gJuJKC7zuQXApQBmy7/58vglAHqEEIcAuBHA92VeYwFcDeAEAPMAXK0LsZFKkGYy0kKRbZ9L6bNmGKbMRBYuRDQKwAcB3A4AQoghIcQuAOcBuFtedjeAj8nv5wG4TwgxKIRYB6AdwDwimgJglBDiFWGNnve40qi8HgRwhtRqzgGwUAjRLYToAbAQOYE0Ygkcv0foOkZWXBim9ihGczkIQBeAO4loGRHdRkTNACYJIbYCgPycKK+fCmCTlr5DHpsqv7uPO9IIIdIAdgMY55PXfs1Iew3ySPs9DLM/UYxwSQA4DsAtQohjAfRBmsA8MM0/hc/xqGmchRJdSkRLiWhpV1eXT/WqnyDTU0WjxcoZLsYwTM1RjHDpANAhhFgs/38QlrDZLk1dkJ+d2vXTtfTTAGyRx6cZjjvSEFECwGgA3T555SGEuFUIMVcIMXfChAkRfmbtUNFosTJIgpywZLsYw9QakYWLEGIbgE1EdKg8dAaAlQAeBaCitxYAeER+fxTAhTICbBYsx/0SaTrrJaITpT/lYlcaldf5AJ6WfpknAZxNRG3SkX+2PDbCCXDoD1MtjGWXoXD1CgH2uTBM7ZEoMv0VAH5LRHUA3gPweVgC6wEiugTARgAXAIAQYgURPQBLAKUBXC6EyMh8LgNwF4BGAE/IP8AKFvg1EbXD0lgulHl1E9G1AF6V110jhOgu8rfUPCNt+5csr3NhmJqlKOEihHgDwFzDqTM8rr8OwHWG40sBHGk4PgApnAzn7gBwRyH1rXX23+1fSp41wzBlhlfo1xDVHIpcXs1l/5Qut73wHmZe+Ri/coCpSVi4jCBGns/F+txfNZefPrUWANA/lAm4kmGqDxYuNURwKPLIit0dyT6X9s5erNne63/RSPzhzH5DsQ59poqoqGwp4/YvI5Ezb3geALD++nODLx7B7cCMXFhzqSEC9xYbpnqYyy6HQ1+FIvMUfrjZ1N2PtzfvrnQ1mBqGhUsNsb9tuV/uHQd270vhK/ctw+7+VHkLqkFO/cEz+OjPXqx0NZgahoXLCGLErnMpk+Jy98vr8cgbW3D7i++Vp4AiUT+b91hjahEWLjVENfsgyqE1lTtAoT5hdf+BdHWG+ipzYCX3jKtFrvnTSsy88rFKV2O/h4XLCKKS0WJl2beyRJlu6u5H1jBCK+EymKruUN+RFgVYbu54aR2AypqJGRYuNUWQeWSkvSysFLR37sWpP3gGtzz3bt65uoT1rrrBqtVcrE8WLtHYV+WThpEOC5cIVOuMqDpr5U1QO5bC57Kxuw8A8Or6/K3n6qTmMlSlwkVRpd2taqmLW/d172C6wjXZv2HhEoFMpYzgQXuL1diW+4F7pZXg56QyViaJWH5XT8QsqZWucqcGC5fCULe6Ys8pA4CFSySqVLZUVnOJUPhw/J60FC7JeL76E5fCJeogJITAolXbyybUVY3ZLFYYai86li2VhYVLBKr1Ya+1UOTARaEl+EHprGXySsT9NJdoZrEHlm7CJXcvxf2vbgq+uAii9reX2ndgU3d/iWtTO5iCOJjhg7d/iUCl1O3gMaa2FlEOh+aizGLJmJ/mEi3vrbsHAABbdu2LlkEAKhQ5qoy96DbrJbGhtpgZgVTpHHC/gTWXCGQ8eu0La7vwpzeNb1seFio5Uaten4vSXPKFS4yUcIkmXeIqfZlHsWrVlMvFhp19OPLqJ7F+R19R+fDi08rCwiUCXur2Z29fgivuXVa2ckdaKHLww1/8DxqQ4aiNybhn7lEd+rFYeW37OZ9LefKvVh59Ywv2Dqbx+9eimRtzIdwlrBRTMCxcIlCtUSgVfRNllDTDoLmotQ6NdfkWYKURRL2fSvMpt22/WkPfy0V9Ui1uLS5EfH/T+KoNFi4RKLcZxIvhGIyjUp7tX6zPYt5EOSBftNWQzO/qxVY5Pkwhr9U2l9nU3Y/egfJt9llf5OJWe082Fi4VhYVLBCKa6Ism6FGptZlasLAs/veksioU2dTVRah6eBErgc/F7zcq8041DZJCCJz6g2fw2duXlK0MNRHYU6QAqzahvL/BwiUCldJcgqg1n0uQMCz3zylecykumgsINwBW0yDZK1e9v7FpV9nKmNjaAABFh1HX2mRrpMHCJQKVip+vphlsKQh+P015y1e3MaqvKhdtFr2i/mlVwED13Pdh2SpHaWxFZlMpCwNjwcIlAtX0sOtUdFfkKNFigZqLcHxGwa8IUaxZLFa8WSzMPaum/jYsdSlRERyKXFlYuESgYosog87X2jqXoi8IU4aViWnzy2LbqxTvMAszWFeRbBmWdbqqTaL+7mIXnzKlgYVLBCo2kwwotvY0l4Dz0apixBRxVg0agd88JefQH566hGE45lWl+r3VcH/3Z1i4RCDqdiHlppKPUqSyQwrLYkKRw1Ss2HYrzqFfW2ax4TA12ZpLxPT76+LTaoOFSwQqZxar5hX6Ucxiw/d7fM1ikc0vkauTXwdT/vKzmoTLsGguJcqnmtptf4SFSwSqtdPWWjTZcJjF/PKododvNW5jMhyRknY/LrI/19rzMNJg4RKBat0VudbMYsEBCmVe+Z5V9ahoJETwJfvZIFmqn7ufNVvVwcIlAkGhp5UaDCr5/oryhCIXjyrDVJRtFSu6oDILwSoaJIdDay+6hCrU+PZHihYuRBQnomVE9Gf5/1giWkhEa+Vnm3btVUTUTkSriegc7fjxRLRcnruJZCwhEdUT0f3y+GIimqmlWSDLWEtEC4r9HYUQNIiXq1NXs+YSpfRhCUX2WShZDRpBGK0pkj+rTL9tOJqsWIe+Ox+mMpRCc/kKgFXa/1cCWCSEmA1gkfwfRDQHwIUAjgAwH8DNRKT2Qb8FwKUAZsu/+fL4JQB6hBCHALgRwPdlXmMBXA3gBADzAFytC7FyE2QWK1enrup1LmUJRS7+B/lpJ9U+9hTzut5y/bZh0VxKtP6IhUtlKUq4ENE0AOcCuE07fB6Au+X3uwF8TDt+nxBiUAixDkA7gHlENAXAKCHEK8Kabt3jSqPyehDAGVKrOQfAQiFEtxCiB8BC5ARS2Ql62CvVqSs5E4/mcwkQ0mUO+c7tABCNokKkVR1CrXMpg1YYkeHoYcUuolSwbKksxWouPwHwdQD6MDBJCLEVAOTnRHl8KgD97T8d8thU+d193JFGCJEGsBvAOJ+8hoXADRfLZhbzz/ini9aWp+AQRPrNIc18pQn5NZnFvM8VlndRyQOJprmUplLufKrBlBiEWqHPmktliSxciOijADqFEK+FTWI4JnyOR03jLJToUiJaSkRLu7q6QlU0iEqZxYJYvK67IuUC0UxYQYNmKQYylYWprGoYevzqUIx5p1R+P3fRw+Ekz/lciiusEg79/qE0bli4xn699v5MMZrLBwD8IxGtB3AfgNOJ6DcAtktTF+Rnp7y+A8B0Lf00AFvk8WmG4440RJQAMBpAt09eeQghbhVCzBVCzJ0wYUK0X+oiKFqsbA798mRbMQIXUZawDNMtK5XjuNxEES6lCq925zIs+1aWTDAO/5396aK1uGnRWvx+aUfwxSOcyMJFCHGVEGKaEGImLEf900KIzwB4FICK3loA4BH5/VEAF8oIsFmwHPdLpOmsl4hOlP6Ui11pVF7nyzIEgCcBnE1EbdKRf7Y8NiwER4vVbqROVMri0C97tFhxeZfLXJd3TaR8IyQy4O7Lw6GV269CqEGfyz755tOhdGb4C68yyrHO5XoAZxHRWgBnyf8hhFgB4AEAKwH8BcDlQgh1By6DFRTQDuBdAE/I47cDGEdE7QD+L2TkmRCiG8C1AF6Vf9fIY8NCkFlM1JhG/PjyrdjVP1RUHpGES8grSilknMeqWFprRAtFDr5my659gRMldz7Do7mU5r6zz6WyJEqRiRDiWQDPyu87AZzhcd11AK4zHF8K4EjD8QEAF3jkdQeAO6LWuRiCOm35OnXp8922ewD/57ev4+SDx+F3/3pi5Hwibbk/jIERRod+0XkWXxdfn4tyTEeYrATdj3e79uKMHz+Hb8w/DJeddrDndZXQXIotoRq2zaFSqLU1Dq/Qj0CQr66WZkxpOXKt39FXVD7VuuW+n1+l2B0NoobMFnp9JJ9LQBL1CuFX3ttZcN7lplQaZSWewxp69MsOC5cIDAbYUyu1Qj8KdXGrCwxlqu+psLfcL2IS6CcAir1PUZPr6cLc0yj1DBsuH9S0FdFcivWF2flUX5/en2DhEoH+IX/hUkudWqnvxYZOVqtD33YOG0RBsQNl1PtcaLpyLKJU7RELkC6VCUWWZZcoH6YysHCJQP9QOu+YbmKppVBkNXClixUukfYWG4ZQZPn7jOtcQs7evfOOlk6vS6i9xSKUEVZwB/kG8kORh8PnUqIw6goIF7/Xau9vsHApgLic5g2k8gdife1LLYUiqyxTFTCLBQ+AxdfJ3lbfaBYrkc+lwMEw7PU5x3T5giUKN4sVXJWCyYUil+b+MJWBhUsBxOXTbgpFzmTLL1zKQW4Fe7EmoghpiioxHH4CoFjzS277mGjpggovJuopqE5h/Vn5+QzDXSuyL/L2L9UBC5cCUAOUSbjoHblse4uV4cEu1Sr1aKabcLPrYhB5X/RztTEzLo/PxSLQLFZBzaVYKilb2CrGwqUgVGdNjyTNRX1WwLkd1ulcDH7Cs1K3SS/Xrwq5LfcLr2iposXyHPrD+JrjqPenklvu19CjX3ZYuBSA6jemTqsvdKulUGRRxZqLatNiZoG22c+kbRZ5oyIvoiywtSItogwsIpxZzN3Xh2PsLFUZHC1WWVi4RCBtcH4Pi0O/HHlG9Bt45VPKNKX4vb6LKIuMFot6nx2aS4gsIgnuoHfl2L+9sGixYd1brEbMlowZFi4FkAtrze+0aW16GdXElM0KdO4ZiFa5iJTu+SuDWSxk5YQQ+Nrv38Sr6/O3l/PbBLFYf1O50xUXLeZ/PqxDP6/sYfHnl8rpUppsIsGxyCxcCkH11bTBTlEKs9jNz7Zj3vcW2Vtz5JXv8dAl49E7ciXXFJRKcxlMZ/Hgax34zG2L8875RYsVa9uPKpyce4sFpy7HxpW5dS5BGTn/HQ5TU6m06Yr4XIa9xOqFhUsBqL5qDEUugVns+TU7AACbd+0rKF0iFv02VtIuHTiwCseHJ2p3gbhhubmfACl2PUV0n0s4co7pKGUEOPRVGYUuogxR+5fbd+DYa/5acD8upAw/KrlxJVvicrBwiYAxFFmPFou42F0NjsMRkaMo2etwI5UdlGe4XJUPzCxc1Kfhnvms3g9DKYRSmCzK8SZKVfdYgHCJsojywdc70NOfwlKDmTIMper+fu32z//7N9ywcE1pCmKMsHAJiT6QRA1Ffn1jj/0yIRMJad4y5V8uSlVSWcxiIfNUmkvCIFx8Q5Fd1xRKZLkcMl1uMWCEIkJWruBQ5BD5qslR0HuPgsqM3jdJ5uOdw8vv7sRNi9YWnPO/3P0q/vyW8aW3hhrs37BwCYneT4PMYqY+vXtfCp+4+WVcce/rnmWombfXQ+n1rBRjRiiZ5hJpnUs4000QQ7ZZLL87h3LoR/a5eOfth/67wyQth8+lnA59dR8iC5cSTXnKMUd7alUnvvS7ZYHXsT+fhUto9H4aaBYzroOxjj2/dodnGWrm7aW5eD10xciHsGmffmc7+gbzN+y08ylD2fYAGJCPMouZNBe/CD97DUxUzSXiIFiwMCqDVqjarByaSyJgkgRY7w96dnWnb5m1ubcYO10ULFxConf0KA59dX4o7e2QUZpLoTsUF9Odw8zu1u/owxfuWoqvP/iWdx3K8EwVahYz+Vz89g9TAr9YzaVQ9GR+A2gxK82D17mE87nkKS4hqhILmCQBwGk/ehafu/NVjzJLpU2XJJuaK7taYOESkiDNxelzyU8fxkmvFrR5XenZYYvRXEIk7pOvGHi3a29R+eSlCfK5uD69UGaxhCEkO8zLwvwG7+8+ugIPvtbhUUGVd2G/3RGKHCJpOTautAf+oI0rXa1fiObyyBubA681UeyGosWsD/KjkPvMsgVIVLoCtUKQz0WPEDN1wkyVTmXCVCtoFbeVUYSyA0ORw2kWb2/eDcA/WsxUwVy0mHcBd728HgBw/vHTDOmd+YQl9NXy50SZyQfVSU12gu6tu6uHEXRNdXEAwKvre4IvNlCyNZQlfuQKya+WXhhYLlhzCYk+EBqjxRxmsfz0pi1j3JA9mHjUwet4EfOkMAOj8pOXfiYYcB7hyv3GQ8sB+EeLmcLDi91XTbV7oZpF2GYszizmj+rDJquYU7Ny5xRcl1GNSQDASQeNC7zWhP17I96YoHbz8wUNpDL43+ffM5qmw0wQ/dbC7W+wcAmJ3q+MPpUgh36IjmkLFy/HfYi6FUohmovftVGqEJQm59gNl58pWiw3TuVnIlzXFEpU803BG1eWwSym+qvpNcd6X3ZnE6YuRW8I6lF2oXhVw++V3jcuXIPrHl+Fx5ZvNeQXvkYsW1i4RMKkhWSFv3Ap50ym3P1YDYZ+5UQZoIO3hQ82W+n4rnMx+lwKyz+/fs58wifMzyNMOW7e2bYHr280m56CzDIZH7OYM6zemU+Y+qr0Ube2KfS+e+GV3k+4bPTYegkobHE0m8XY5xIJk3rs0FwMnTCU5hKgIXh12GI6cph6KWHqV045HPqF+jTM0WLegjGqz0SR21qmTD4XiVf95v/kBQDA+uvPLbgM1YdNZjGn/zBcXZzXOD9965EVeYEYxYaIB+Fnot4rw+2jWh/KXfdagjWXkAQ59PVjZuETopCIC6+K6cahZqJZ7wE6bD5vbNqF465diF39Q3oq/7rJz7BKn1lz8a6fPUOOuF1PVLOafr2fUI75vFa7kDJMZNQ6F0Of0zdmdRcdpiaqXcPU2/Ss2JpyxI5tR4t5lO+nuahzQa/VCKLA1QRFk8pk8ZnbFuO1DdGCKMoBC5eQBDr0HZpLcWYx/UrnDrqlJ0ye6vfmr3kIX7efP92O7r4hLF6X228q0KFfoGbgv3GlYSaahee5MKj7XOgsNez1SrgUuu4pTBm5gdIgkHXNxXVnw7SV6uuhhIvxleGqrMDkvnj6XHzq5afNigJuw3BrLht29uPF9h34z9+/Oazl+sHCJSQOh35AtJj5gQnf2fT8i7HJhyqrgMHCz/4eNOgYo5KCqwegAM3FsM7FL/CoVO9z8avfPa+sx8adTju+c/Lgl791cihEpGFe2iDNxa60f192a3V6f/G65yrrMMLFNFErlWnJK72fsFbPnqlehdRnuH0uqrxq2naGhUtI9K5i7HiBZrHgzqb6hWfkWRn6a5hnQJlJ3JcKj+9hywvWXKzP8D4X095iPppLkYOYX96AFdb6X4+swAW/etlxPPxL0KzPKJpLkA9M9TGTSdDx4rs8zSX33atLF2QWMwjOYh36uSjDws1i6tkNmkAGMdzRYqq4oB0XhhMWLiERjtlcBLNYGIe+srF7mJv8XngVnUI0F5+yA7LJdfngmW/uyvCDFBDgczHlbw9iobI3pHd+ulH13r5n0JjOq14KNbj6DYaAub+F1VzM++B556MXFbSOJLLmYiirEIICClI+mmCxmkuhfbZUhN2IdDhh4RISp+ZieBOlbhYzPrDhNZdCtgcpl11ax/a5uGexjnz8M4piFgsavN34+VxMv1O4rimUoBl2sa9OUKmDzGIpQ38M+kmqbkYt26f/OSZ5Y/wJAAAgAElEQVRZXsJFeOedd62PFSD6xCngvvgJF+FXrxAlB2hN5UIVx5pLDRJkDtAnl0HRZEFkPByqxogn9/8enXrr7n34y9v5C8NCRYtlvE0oirA/ryCzmJ13CTQXn9DSIPNOcP3M573MWWF9VWHNYqbBMqxZzFS8bqry21vMqz8U8j4XkwBKZ/3vSxBBi1uHfNpT+AimUodhl5JqDH2OLFyIaDoRPUNEq4hoBRF9RR4fS0QLiWit/GzT0lxFRO1EtJqIztGOH09Ey+W5m0jah4ionojul8cXE9FMLc0CWcZaIloQ9XeERrt3Js0l7K7IYQh6N0wUzr/lFXzxN68bnPLRTBdW2tz3Ynfu9Rv8fZ3e2slk3MfnYkgb5HMJGhyDAgI838sT0kOlfluQWcy8qNc/bz+zmN9WRmHMYulChIuh7ur5Knb9URSHvi3QI04Qi12YGxU1JI0UzSUN4D+EEIcDOBHA5UQ0B8CVABYJIWYDWCT/hzx3IYAjAMwHcDMRxWVetwC4FMBs+TdfHr8EQI8Q4hAANwL4vsxrLICrAZwAYB6Aq3UhVg70AcG4SFJ36BvPB5dhis8Pss+HXUGt3mfufkDCLnQzllWA/8S4QWKANhjGoZ8NaJ8wmounzyRIcwkwgYQRyn4lqHN+PgLAyyzmf29sx7VRC/dOG+aNq+p4OJ9Lft3TtqZsTr951z7MvPIxvLC2y6N869Pr9r3VsTuwXlH8WFbZ8rd7XLxuRx/uXbIxOKMCUe1oiGmpGJGrIoTYKoR4XX7vBbAKwFQA5wG4W152N4CPye/nAbhPCDEohFgHoB3APCKaAmCUEOIVYfXke1xpVF4PAjhDajXnAFgohOgWQvQAWIicQCoLel8zai4BD10oh74hfaFmsSDc9Qgzi85t5+EqO0A4mNAvCzMLdpfjJmjbHb+FkkEz3KAJQVTNJ+w9C+vQD9JcjALEZwB3tqnznDOwxVyfYte5pOwdIcxplsktb7wG6dw9N2dw3eOrPOvjq7kUYBbzuvS8n7+Iqx5eXnKfjN92PpWiJHJOmquOBbAYwCQhxFbAEkAAJsrLpgLYpCXrkMemyu/u4440Qog0gN0AxvnkZarbpUS0lIiWdnWZZzphcL4sLP+8c22A4YEtyOfipbkEz6aCSnEPCKF8LioUOeQgb8K047MjCMI4U/SfBbrzMO9TBc88cosozXkHDybeggvw01z8Jwzuc0HCxXQ+vOaSf84prArXXPyCBbyudR7zN4sl5PR8KO2vGUbxe+R20fYXup7ps97pAWDPgLW9TJA2Wigpnx0XKkXRwoWIWgA8BOCrQog9fpcajgmf41HTOA8KcasQYq4QYu6ECRN8queP6itEXuGb2kBpepjDRIvJnuE1Gzeq6gXqLnmaS4jktpnCJ22UBznItxTGLOaog8+ebn4LWyP7XJRw8rgHng5931y162zh4p8icMcIk+biF4oc0ufiJTwKcuibhIvd38xpknH1pkv/9vW6r3UG35y7PkFr2bwIChJRBE0YCkW1RRXJluKECxElYQmW3wohHpaHt0tTF+SnelF2B4DpWvJpALbI49MMxx1piCgBYDSAbp+8yoaa/SVjscB3PURe56KudUSLaXmE6Nxhd8PN5R9+AHBfWZDPxfA6gWzALDjIxOBO5zdQ+q+nCNYwjOfhP5CE8bn4iZqwDv2MMTQ+v57ONN5199KcreuDNRclC6ObxdSiXXN6FbjhFVIctLj1rCMmAQDGNtd51seo6YaYFYR16BcjXG5/cR3ufGmd41ju/TzVI16KiRYjALcDWCWEuEE79SiABfL7AgCPaMcvlBFgs2A57pdI01kvEZ0o87zYlUbldT6Ap6Vf5kkAZxNRm3Tkny2PlQ3VVRJxMnYyp0M/2qzHvtZjRm96lgo3izmvKGidS8hB3oRpx+egzT7DCK+gGbptFvNxenuaxQKjxcLXzVWyb77uq/xCZwGzZhNkesut0DdoDj5C36tvOvPOOsrww2wW8xfage89UvfFo8w+n52P0z7tEk5YwjNvIFf3oHvqx7V/Xon/96eVjmNK0Jrez1Mpitly/wMAPgtgORG9IY99E8D1AB4goksAbARwAQAIIVYQ0QMAVsKKNLtcCJGR6S4DcBeARgBPyD/AEl6/JqJ2WBrLhTKvbiK6FsCr8rprhBC5HRHLgOosiRgZO0baMVDmpy9o6wiPEKhiX8JkqkchGxG6H1enoAhXvpfJxbQpoNspbdg6zJmHn+Zi2uU2QGiGjRYrRnMJI7CD7nvQDr6+wiXALOY+HbSeSz8eVXMZSGVk2V6aiX+eQdqDLVx8BEjQCv10JouEwbwWFCQSI0JGiJL7XJRArybNJbJwEUK8CG8T3xkeaa4DcJ3h+FIARxqOD0AKJ8O5OwDcEba+xaL6WjIew0AqIFos4qxHtabXmhlzNJT//27cdQvTxT13RdbzDSpY/TaPWbExvcvh77cC3ysPVZxxYastHDwGsQCBmdsa3muQLY3PJUjImUKRg8xiftqB802Uzgs2dvdpZRQvXEx+EyVcvJIHBZgECX2/aDQ/jU7/vZt69mHW+GbPazy1Lvk5lC6tzyVVhZpLFUVFVzdqAEnEKdr2LxE1Fz1VkOkoDH6ai3qo89J4RO8ERSTpqD7v0PBC+lzc13pdYzSLwWcm6jN7B0qguXjMTh2ai0/+Kt8gIWcMRQ4wF/rN7v125b53ySbtOnN9/PwWXtfqqMlblO1bgDBC3/u3q35i1lxy3/uH0ua87XvmrbkA5XToV490YeESEtUPk/EYsiK/4wZv/xK+DC9TT5iFXWG3/TCl/8lTa41pvGa5Ts3Ft9jcdR4CJUhwekYmBWkust2NUUm20Ayuq4mg9814msXC6y6yfv7XmwJMgkK00yEGWCutd7lhHPpBkw5TGw2klVnMI4w6pC8sKETcrLV5Cza93H1D5olY4I7OyudSYs0lPRJDkfcXVGdRkSr5K939hUAYf4lpxufQXAoQUF74rXPp6RuCiYxtRvAWTGE1My9nsSl5OM3FPw//0NLcdz9/jRe2Q9/jfLE+l1wwgn89TC+/CtLocusx8s8FbWUUdE43Bwb6RwwXDGpmZ2M0WUih723uDBasfuuuAGCfh5YfZBZTZqvSay5C5l890oWFS0hUv1KbI7pn0oGvOQ4x+JocuMGze4u2pmRg/kC+jVvPv7Eu7r5cphGOsvIKR/AgQoZX9urPV5A/yUs4Bwk49RCb/B9hnd5eqLNhBllHurBansifbJgwhsZ7BIXYaXxm6F5mWa/6+ZXttRYld977nnmdD/K52JqLx3m/3+43GdF/V7+H5uJncgN0s1hY7TUcuVDkkmZbFCxcQmJHi3loLqVw6BtnqgEDrJpNhZ2x5PlNtO/1SXN3sB84nwci7HYWXu0UNFOMahZT5ofghYaGtEHvUbEnA+bznj4XPcTaZ/gOimZTBIYiG8pQdQ4yi4Xd182Ut/u7CdN919vdb3sYc9nBbeu3xscvFFk/5GUWy5nkgoRLqc1iWUf+1QALl5CovqJWB5vMYkTWO0WiOvRNKrUwnNdRR8L2KXef1rOs91i5nHNyOhN7LYg0EfSWTbNZLDj/IPPPoPzBhe6hBQAX3PKKsUwbe4YcPIN3JAutufjn41dOULv4+ZucDn3nBQ3aBMR7+5es8bv5WvOEgAyRkwq/9gjjL/LzN4UNRX5+jXkrqaAX0JUrWox9LjXKpu5+PPOOtdGAbRYzaC5xIsSJzHuPhdBcTBE8ev83msXkIWV2Chq48h36uf/rk2azmDJDpDJOB60IGMBMONcD+c+Q9UNe/gu/gVAIYc8Qo2guvYPmiCC/NDpeddZnrX5ZhA3pDXoFhHlvMevTOMDqGqOW9dWPvO0Iw/dc5+LQtj2rLfPIvyCVyaJR9kXjlvw+K/hD7dqsaYTuiMew638eXrbZmHeQWQxl9rlUE8UsotxvOPOG5zAoZxqeZjEhEIsRYh57jxWyutdzLYgpDyVcAnPPz09LDgBo8BAuesdNZ4WtvQVpVTr26wQ8fk/Q7DuMQ999STorfJ3iftuchEGlCTOD9yrXD7/NJXWCzGKm4jI+moteP32GffcrGwBY2rs10TDXJ+voL4Xt6JzNWgsMRzfG0T+U8V3Bb8wvRDCBW7uxtSS93j6LbgHgg+8z71OoLvFqm7L5XNgsVpsMag+Yl0M/69Bc/Gc9QYvuHNc6znvXMWhLjFwernoL/YEKHgz1h66Q1zHb61wy5kHPLDdDzEIdeTivCXYMB+fvR1BkkJfPJXSob9As2C4nIBTZtIjSZ8t9vV38ZtheQtJLOzXh/m3K36ImOoVO1MLcU69rgnxN6lBjMo6BgFBkrzpSkZqLl6bODv0RgNJc3BOyTNbyt8RiZuHijIwy522yBQetQFcDRyysWSzPdJRfvle9AOdq8EI0F3VWN4NkPB4UU91CaS6ua/RZt3mFvlmIh0WlKXRvMYeA9nPo2wOVfz227RnwL8NQhN8iSi/NRaFm3UFmJ+u7R6VVPd33TP5YZRYzR5P5aS7BkzinEMkdd0Yyek8Qm+sTnqHIQdqm7XOJKFxSWfOESWlsYbXi4YCFS4EkPTSXpRu6sXcwjWQ8FryC38tWbbCxBw2wts8lXPV9t3/xmmnrdm+vQSuoT6t0Xn4Wsz8peBbqF1GmBsa6uHkn66BdmYMIct46BrECTEUq7yCzW33Cenw7ewfzzgWZ/OwgDeOLxjTh4jMIev9uf4e+8HkWUvKeNdWF8LkYys+EEGxe911vCz9Nt7k+7rmbRS4Kz1y28o0OeqQPIu3xLAYJ/ErAwqVA1P5W7kFavTo1EaNAe623jT5/puocwL1n37FYvh/EhJ9D39NHIPROrGsu4Qdnk2M9aCBwzirN+erXuNs9Z2KJBWsuQfuIGR28stwQvhWv3+0p0ANm0VadrM+9A/mBB6mA/qb672A6f5DT66prLk2udVChTJUB99X7nknhYijDT+A5NBev9+x4msWyxuPuejfVeWsuKp2X1qQWUX7nkRXG80E4BIpWXyWsSu3LKQYWLgWSiOe/0EsnGY8Zb7DXQGO6xmuNgl8Ei2lTR2MZEcxiYWzwgTv3KpOfh0nCz+RnpfcoVwlXMphY7FlwwjwTzfi3rbOc/GNB639MZgv38R8+udqYNswqeXXNXkNUW8YxCfCu26DB7KXfI30gb65PeF7nKDsrbNt/0CvB3YJzyK25GNKra4wTBg+fnrv8pP0c54477pePRtdc56O5KFOmp7aZE9B9AdGIJhxrgLQ6Dvq0SaVg4VIg6hWrXjfRiqSJZhYzvR42yCymnj0lXIIWM+Zt/6IP4B6Cw/OhcwgH32KNmksh2794mzisz2Q8XztRQr6xLh6ouQQLF+8Jg9dsUW/PlIc5441Nu8zlORYiGgSvyIXNBmkffvtzmQZJx2RCEz4tbuHiM3irtz2aF2l6B1qofqJ2izBNCtRAatoGRh98vX0uWfs51uunP7d+E7mm+oRxZ3TrGuenG93hHmWti952uuZiLxYucYhzMbBwKZAgzSXh4XPRO5L3jC9/9iEc5/PT2JoLhTSL5UW55b57ay5mG7p+dVizmNesNcjnsmufed8zVW6dod1Vmzck4x42dK0s39r7hzJ7aVV6mbqNXb8+yLwCmNvGy3RlOm+6NWqANg2Sjry1RpoyugEAcN77D8iro04mK1An/UFBDnl3HoPaPVN5uVF1Mg2kA452NveprMgthtbNoUGmSNvnUhfHvlTGdz86T8Hm8JMULghSafMkRbXbmx278eBrHQXnWw5YuBTIqAZrDy+9X+idPBEj40xWf0g9o7IMTrmg7U1Uhw9rFovi0Dc5DgHnoBW4+61y6HtoLkGLKDt69hnzVdckE7E85+9QxhpolIkl/y2cwrj+RjGqIeG41o2tuXjMQPXf2tOfMh73XohoHU/Gyai16QOTUbgELNRU25cMpPMHSS+HfiIewzHTRuOzJx6Yd5277irYwPT79Lrl+VzyzGKGZ8nHBKQLl0Gf9y4p4ef0J/rfF3Vpc30Cmaz5hV9B4eNpTaszmSSDcESLGYQLANy4cE3B+ZYDFi4F0tZkvXdbdZ49Aykc8q0n7PNJj8gkh+bi0fHUgJEtYDalsgobiuwWbBkfE4VC77jeDn3/cm2zmD5rDTDdZIVAqxzg9+xL5Z0HnIOw20wyJGd5XmGtmaxAUppHTO2WyujCx7tsr1169fK6tR2nw6ymVoLSesWDYfafNg8spjLc6bNZgX2pDOIxghD5Zj11i5NxcoVzZ5GMx4ybkDrT5wZQo3Bx9Gln3W1Tpm8octbxqaM0wYZkzNdcqHwf+rMapEnrmotelo7Kwtv0nbVNflE0F2dEm+bQ136r6XdXAhYuBTJrgvX2OdV5urQw0DMOm4hknDz3S1J4dbxdcnbrFZ3lFy0WVnNxd2hV13iMPO21+iZ9XppL2FXk+szLa5sRPf+W+gTiMUKvISLKSpdbF+FuV+UwbfSYBTsduy6NTggMpjO5bUh8Bknvxae54z39mnAJEdGj2qkhGQ+MmDJFT23fk+uX7tRKGKmdtAdcg5Fqi4Zk3NFfUhmBRJzsiCevW57JCiR9NBc9z0FX3dVzotrdZEL224xU9dUxjXW+b4xVe6TpE4OUj0YFOH0ugDmcOMzCWqWVRVnr4rUweCjtvE/VAAuXArjohBlo8lk5fNacSUjEY3kDuBACv9fsoF4P3E45u3WsiQiIaLKjpewn3v83uOtmP2yJmOeMum8onZtJekQhBU3G1SzLqcHp6U2/zdLIWuoT2DNg1lzUg1SfiOcN8lc+/JaxDopMVuQWxbqKT2ct27yfeSZnAjEPgnp76sLFayt+HfW7GhIx40uzgsxi9y7ZmKunK62acY+RWrjbqZ/WtAf3pCgRi+XC8f18Ln6ai9an3SvdlSmzsc4awP0maiYBMCDPjWlK5glNPT9bezBM+uoTZm1RjxYDPDQXmYeXmTiVydp9Stc+w+IU9rrmkvu+e18KvR7Py3DCwqUADp3caj9Ypmib+mTM3ndJ57UNPY7/TQ/czc+8mzvvEXvvN8DFQ66idA9E6gGt93B6A9ZscFRj/sMeZNYylePQxHxMN0DO7DaqMeFpFlP5mtay7NhrDeheAiIjNM3FdU49rKMbrdm9aSDx2rlAr5vKf/vuAWM699oROz9Zfr2H5qTasS4RC4w6cjet+i1Kc3H7JjKa5qLnnc4KaxeKMGYxP5+L1lbu96LkNBfv9LZD39DmSliNaUr6+lzUZMmpCcg295hoqUMqJNvUJ5QmZGoaSxvOYpTsU0qQFsIeTYPX28ZtGn1i+TYAVpj6zCsfw/2vbsRww8IlBGqTus+ccKCtIajBUFe9t+0eRCKW73PRnbmAeTa2bsfe3HkPB7o5Wsz6tEORDaqLPvC7VXGH5uKhpvcNpXOD7JA5GifILKbK9XKamibzanBurU96msVyA0I8z/fx+Q/MBADMPbAtr75WmcLTnLhttxVAoHxs/YY1CbpA8lrb1JiMY8roBqzZbr6/px1q3gBRDXrKMe7uMup8a30i0DHsFvz75PvfvTQX1S51iZjrfmWR0ISLn0Pf642tVt1zx9wD9JAWPg74hyKbNJec4Kwz+h6U8FICYshgCqtPxn33XGuWWpXJ7OYXbKDq3SqDgoYK1Fze2bYHC+5YYv+vt+NQOuMIc1ZWkO1ya6Drn3inoLJKAQuXEPzPJ47Cc/95GmIxsrd/UR1Dt7te+HfTjZpLv3yYrzj9EADAj//qXDiXymTxxze2AABOOmic46HQHyCVj447WszLMW1/d2su2dwD5aW59A9mMK2tCQCwobtfK9ts4jJh0lyC1pmks1kk4jG0NiS8hYvSXAxrWdIZgbamJOIea5MyQhjXOwDA/33gTQBAe5clFEwLFR3rNQyCOS0d4JNHN6BPu3eqnQ+a0OypdahBr97DDKv6X3N9wpjHmKYkxrfUA8i3lO4bcvpc3MIpnbU2Ya2Lxxzn0hkldPx39nVoLob7qgsU90u3bM1FDuAmE2JuNbq3Q99Lc1FplIBwrD/SzGJ+Dv2m+rix7kDOmW7S5JUQV1GIhfpc1moTFMDZ5wbTWXz82Kn2Pe3us3xuu6XG39Ofsr8PFyxcQjB1TCMOHGc58pUzTw30+oPS1lxn3FtMqf6d0sn6xNvbHOe/+fBy+3t90vlAK3PLuOa6PA0IyA06aqZoEhC67Tk/MkgzBRg6uxAC/akMZk9qAeDcasRvvYIbk88lyJ+UyggkYoRRjUlPn0ta07wyWef7ZgZSGTQk4/baJLf/QIjc2zfd7aaCK5SPzfRa23QIzSUeIzQm446BSLV5i4/WYQtND/OSGiSb6uLGQWrfUMY2uXmbxTw0FyEQi1nh3W4HcjIeQ13cyter7plsLhTZz+mu10UxZM/uvbUDJej98h7VaPa5DGntBpgd5PWGsHa9bmMaze2mzF6AebKlfqsyi3mFsHuxfkef43+3Q78+EcOTX/0gAGDvoFWWLlB27M3fg66csHApkBY5a1EdXK2w/s0lJwCQiyhdHVPd4DMOnwgAOGxyq+P8I1Jr+chRk1HvsqGr2dehk1vR3tmbN0tVg7IdWuljhwbyZ0tBmsuu/hQyWYGJrQ1IxsnxwAZtjqij6u3lkLznlQ2YeeVjDg0hnbFm/r6aixKOBt/EgHzgmgwOWFUftercfc9UPsqEcedL6/PK7htM25FTeW/pFALvdvUhHiM01cUdwmnvYAbJuHXcNLsGcsJMmW/cM2nVdi1yzYXTxGgNcvb6Hlfazbss7VOZxdwDfEZqLvWJmGMAVcJSCWSTxqRm7qpdTQOoEgBtTck8oa1+1/hmS+sy7ZvmJ1ysCUUMjck4UhmRJ5TdZrGUY7KTM7H6RegpE7G77vr7g0yTJSUo1Vq5QjWXH7vWr6jfv233ADp7B1GfiGPiqAbMGt9st5Huq1T1Hi5YuBSI6pQrt+4BAPzs6XYAwMRR1sPQUh/Pm2X39A2hPhHDWXMm4fApo3DAmEb73O0vrrM72b6hDOoTcXv2I4TAF3/zGgBg3qyxSGUEtuxyLib881tbAeRm4KYIlHnfW2R/dw8ImaxAjIC6uHnDTbXj7sTWejQk4p4roP1i9oUQ9oPlFUL7tHzT505tdpXOWqGvoxp8NBfXDF+vk9Jc1D3TBZc9yNjmEbPQbpEz6KdWbXec7xtMY8feIYxttgZod7uv2LIHS9Z1Y+vuAUwe3YDN2n3bsXcQ45rrrXvt0W4q0k2ZUNwba6q2s30H2n1VEwA1Q3ZrGP9+v2XyG99i1f2Py7Y4zu9LZdBYF0drfcLRZqlM1hIuCbUIMF8zUMJAmeQGDMLlT29Z5U1orc8zLSnz4fhWq26mt4H2yVm5SdPuH7LuuVrh766je5Gm7qfLTbTMG52qdhwjTU99LjP1Ls2yYNLqbLNYo7nPBXHWnEmO/9Xv/+YflsvyLT9LS30Ce+XzomsurQ3D+25IFi4Foh7mXz33HlZu2WMfP2SCZTaa1taEHXuHHOtffrt4I4YyWRCRNG/lwlKv/fNK+3tMzhaVTfmFtTvsczOlWU6fwa/e1otbn38PAFAfV3H7+TNoHXeH7h+y1nIkYuYHavX2XgDApFENaHBt2Kc/3CafhKKnP5Vz6Kd1gSQwobXeca1ehVQmi2TM0lz2DqaNTlY1eKj74thyJZ1FfTJuC5D+wfyFZvYMVvstW3fvw1YZ3fUl6Sdz8/UHrcFfObe7+53b03T05HxTbU11jtDQHXsHMb61Di31Cc+Q0Q07rfRqtum+r3dJTUoJP124qAF7omxbrzKmjLYmOQ+97twupH8wjaa6BFobEujRFn8OSKGj/Ckmk5Ua+G3hYjAnKk196pjGPK1pU3c/xrfUYWKrtdWMW3PJZoXd10wh4Ot39uGA0Y05Aeiq4yZ5X5QPMWUw09YZ9qkDrD5Tl4jZWpl748nOXqvPjGpIGDUu2yzWkC/0X2rfgeVyZ3Uv3OUpU+yLcpzYsssqv0WbFOyWAu9HFxzj2DRzOGDhUiBqoAKAL937OgCrM6oosvdPHwMAWNtpDcrLNvZg72DaVpfHNCUdMxwdInL4XPTuPVnu69Q7mEurCyk1yLiFR5fLzup26u3qT2FMUx2SCfOK5mdXd2JMUxJHTxuNhmTMMaDoA7lXqDAAnHXDcwCscGGHWSydxZjGpCNiyx3MoDQXIYC9rpli554BfOMha9amZum6rXwglUFDIoZmlynTKsdp29d/y+sbcptJnnTQOHz5jNkgcg5kKrx8xlhrkFqzrddRN72drFX2OVPbjr2DmNBSj3Etddi517xnmmK6zN89sPx1paVJTZCD+KAW1qq0hUmjrD6zZ59Z8B9xwCjj8X7przloQgu27B6whVPvQBqt9Qk0JuMY11yHd7v25qVV/VNNGtzCQ2/DyaMb80xLnXsGMbG1wTYnugWj0haU0HWbxrbtHsCMsU225uL2uyi/5fSxlmB1rhXJTTiMwiVlmVmbvYSL9KkePLHFONl6fk0XAM3nopV90W2L8Q8/fzEvjaK7bwgvv7vTcezaP6/Ev9y91J64qUhR3Yy8s28ILfUJnH/8NM+8ywULlwLRB0K1UEzNkgBgupwRqb2wPn7zy470bU05zUWf3Vp5w2EWUyGjj1z+AXu2pM+I9JdE2Q+by7S1qdtpRvvt4o3auX489HoHWuoTGNuUzJt9P7liGx5+fTMOmdCChmQ8zzGtr7XY4+ETeW1Djx0WOWNsk8MUlspkUZeIYYxmC9YH5VQmFy0GIM/v8svn3sv7/Xr+/UNpNNZpmotsz4FUBg+/vhkAbMGTcqVTJOQgJ4RzoGqT5rAjp45GPEbY0O10tupCXAUUpDJZDKQyeHvzHuzsG8L4lnrs3pfKM1Xq2uG0tsa8364L4PdNsvx3+gxd3aNJ0lTrNikePW00Tp09HmiehoYAABhuSURBVG3NdXjfpBbMP2Ky4/yufSmMakzaE5pd/Sk8+uYWDKazaK5PgIhw8MQWPPqG05wG5DQXJVzcTu9f/20DACtSrsmwdb2l1SkTcyJvkG7vtASaMi27fV2dvYOY0FpvP5Nu7eqJ5ZYZ2dxfcv4ik3DpHUhjVEMSdYkY6hKxPJOd0lwOGt9i1LR/8tRaADmNUt13r+37dZ5b05l3bPOufXhq1XZ7t4HvffwoANYYo7Yb6ujpt/vQcMPCJQL/cdb7QJQTKnonmzKmATECOrqdguMHnzwagOXE3NWfwp/e3JL3BkFlz1aDh4r4aGuqywkXraxO7fW26mFxayZKgCk7sY6KmV+9vRfjW+qxo9cpXP7t15a/R9ml12zfi7+syEW6qYHkgNENnmGOn7wlJ1yPm9HmMAUMSoe7/nCpEErAWjA2ujFpO9Xds1glGICc/0DV43eLN+LtzXswtqkub6b586fbceNTa2Qe+SGp+m+JxwgJOaFQfqEbFq7BKulz+8b8wzCtrdE2Y9l1l3k8+MWT7ElIKpO1Jx3T25owWWoWb3bkNKVVW/fgsO/8xf5fmVBU3R97aysO/bZ1/tTZ422NVdcQVHsq05JbU947mLbzbapL5PkOdvQOYmJrvT0ItnftxZfvXSbztI4tWdeNoUzWYf4FcvdoXIs5WODFdsuEs25HHxqTcfQPpR2m267eQftetjQk8gbwPy6zJgUnHjQWgPO+rdyyB7v3pTCxtd7+fe5+qWb/tlNd91XJuo5uTBp9JnsGUvZEp6U+kae5qC13DpJbRLnbVXHgOGmSk4LtOanRAMB7XXtx1cPL7fZWLJSa6ieOm5qX30Aqi6/PPxSz5URjypgGdPYOorN3ABu7+20T4HDDwiUCf3/YRAhhbW8NAH+vLYRLxmOYMroRHT377Bnkp+ZOw6f+bjqAXITOFfcuw/9Kf8lUOQsjsmbJqYxAT9+Q7ZRraUjYg4gejqxMXnWJGI6eZpnjLrptsf2wPvNOJ75y3xsAgFeuPAOfPfFAe6AEnGa3cS312JfKGNfSqAfZzR+WWbb6GeOaHYLOzl8bNL7z0TkY21yHnr4hZLMC63b04cX2HWiuT6BP04b0QXpX/xDGNCbtB/rdzpx2sH5Hnx1MURePYZyMLnpg6Sb0DqRsJ+fY5jpbCKlydO1ThePqA4U+GBMRjp1hte0jb2zBg6914KZFa+3zjXVxHDKhxQ7wACxflIrsmTtzrB0mnsoIezC+6IQZOHLqaADABb98xU7734/lfHB3fG5uXjDCA0s32ee/de7htllunQxT3bxrHz76sxft397WlMRGTat6bk0X3uvqs9ukqS6e51RXs39l4n23M2f+OungcY5r9QCT/qE0Lrl7KQDLXFeXiDmEy4adffYgedvFc9FYF0dW5EyU5/3iJWzZPWBv7d9a7/RdCCFw9yuW5nOUbLsNO63f1t03hI/c9IJVdms9xkoB1aNtqXSDFm2lJlu6xt3TP4QYWf6idFbkaZR7pEYHWKan3S5z49INPWhrSmKc1Gr1frRvKINEjPCvp87K+aOkVqUmcQBw+o+fw71LNuLRN7fYwuf5NV14XK64/9H5x+Cda+fDjWozAJg303pe71uyCR09++w+MtzUtHAhovlEtJqI2onoyuEq91BXKPHNFx3v+L+1IYGHl222VfiTDx5vn5s5PnejV8iAgNMPs0KU40T2bgB/XbnNHlCa6+O2ZnLtn1fiF8+0YyidRdeeQUwe1YDFV52Bo6eNtvNVg6g+UDXWxTGx1Xpo3pTh00rQ3POFefZsUQ1+Vhgw4cSDxuJrZx8KAA677b6hDJ5cYQ0UpxwyDnsG0nhLm4E/vnwrZl31OABgwUkH4pJTZmGcfGjf7NiFL9z1KgAraOG6jx9pm1E2So0vncmip9+aKR480QqWuPx3r9tBBKf96Fm7rDXXfdieKd/6/Hv4hbaVztiWOjQm4yDKCRBd41GLzn79ygb86U3LzON+d8zxB47FMdPHYMfeQXzt92/CzXEHtuG9rj7M/8nz6HXtkg1oa5AyWSxeZ82cJ46qx1TNXPHOtj2O9geAA8c125MKZRbTTWKHTR5lz5LVGogntTVUk0c3YGpbox2cAOS01UE7asop3PuH0tg7mMaE1npLa6xPOHwrar3X7794EoCcgxwAHng1J/jGyXbXBdeHfvis/f2MwyfZEVsDqQwG0xm7XyoNYFRjEs+t6cLR330SABzC4ZRDrGfqden70k3Mk0Y32AO8MskuXtdtTwrOOGwiRjUkkYiRHZ24qbsfv3jmXbxvUqs9mdEnHP/zxCosXtdtm7omj2qwd3FQvNu5FweOa7Y1Bb1tVm3bg3RW2JONyaMa7AmBF6ulH09pzIC1h6DyJ+no2skhck3aDQvXoH8ogwPGNORdPxzUrHAhojiAXwD4MIA5AD5NRHOGo+xkPIZnv3aa/X+ja3+od2SnUA46fRCZMyUnBNRA+gH5oBw9bTSOOMA6/42HluNHf7UepvpE3B6gAOvVuB/+6fP4wxubMXl0A9qa69CQjOPDR1q288fe2oL2zl77wb72vCMAAKdKwXXeL15C554BdO0dxEUnzMAH3zfBtq//92OrAFimslRG4FNzp9tlK3/S9x5fhSXruwFYb9b7x2MsVf2pldvtgeIZ7YH48hmzAeTs8D98crUd6nvm4RNx0QkH4tVvnYnDJrfaAlcN0Ft3D9iaHQC8tXm3cfWzEi4A8MvncsLllEPGg4jQUpdAT/8QuvuG8L3Hc1thqBX6r7y3E1fcuwztnXvtGecz2j0+fkYblm10vjVSTQTU73pnWy8+c9ti+/w/HGO9VEvZxO98eb0tBGaMbXa8L2b+T17Adx9dYQ8S45rrMHVMo206+530lam6ffVMq01bpWbz28UbIYTAMu3Nlu+b1IoJLfV4t2sv3t6822F+vOJ0K/2ohgQ2dfcjmxXYsLMPH73J6rOZjLDarSGBe5fkhIbiqKmjkYwTnl7Via279+H1jT1Yr2mdTXUJHDyhGYvf6867X5+eN11eYz03vQNpvLAmFxmp2k1plXsG0ti8a5+tqV7/iaMwobUeDckYlm/eg8F0Brc8m7vnJ8wai3FSO1Dbn/xI2xXj1ovnIhYjjG3OBVRcJ/t9Ik52IMSfpX+mbzCNX0n/nhLKY5vrHK9R+MOyDmzetQ9nzZlkm72UFj6UzuIT0vd6+GQriOKwKa14e7Nl+RjfUocjp+YHV9z/6iace9MLuOvl9QCAOz/3d/a5meOc2ojuV5nY2uCwpnhtMVRuhjfwubTMA9AuhHgPAIjoPgDnAVjpm6pEzBzf7HnusS+fgnNvykV+HD4l13Emj27ANz9ymGOAm3/kZDz+5VNx+JRWEBGOP7Atb7NLAHjlqtNx0v88DQB4t8sapHTb8PfPPxpPvL3NjqCa2FqPc4+ags+eNBNALpINyK19UdFIJx00Du+fPgYLV27HzCsfs69Tgg+AbWq49fn37BDoN/7rbHuQvOnpdtwkBwDFvf96ov2gf/SoKfjKfcvw8rs7UReP4R+OOQA/+/Sx9rUffN8E3Pr8ezjth8/Yx775kcMBAJ87eSbuenm9/ZAqLv3gQVbdmurgZum3z7RNEL2Dafzmbxvxm785N/Bzm3nOlJFtRMAs7R63uNYInD1nEn78qWMA5KKygJypFAB+8k/vBwAcI9tdDYAzxzXZ4bw3ffpY276uBhF3uwDAkvXd9n0596gp+OqZ75P1tLTPjd39+Kdf/c3WMi477WAA1iLQTd05UxlgTTYOkdrgsTPG4OFlm3HQNx93lHfxyTMBWFrZY3It1UOXnWyfb0jGcdyMNjy8bDMeln4QxT9K4fDJ46fhW39429ZgFf95zmEAcn3v1B/k7vdDl52M4+VecIdObgWkoviB65+2r/nYsVNBRBhIZfHQ6x2OUOq3/985aNIiOn/45GrUJ2L287TsO2fZZtGBVAb3L92E+zVT4/WfONqezHznj2/j7Y7d+MMbud/3tXMsLT4Zj+Hdrj586IfP4CNHTbHv7QdnT7CDDf7rkbeRzgqs3Z6LJFRC4O9mjsWzq1fb9/TT82bgcyfPQkt9HOccMRmzv/WEHfwAWD7bv5cWDgB45PJT0Nk7gP96ZAVWbdtj+9cUP/30sfj8na9i3qyxOGSi09IyXNSs5gJgKgB9StUhjw0bq66Zj1XX5Ns/jzhgND55nGVCOnbGmLx3j1/6wYPtgf6Ryz8AAJhzwCh7oLjnC/Psa+/6fG62MmV0I9qv+zAuPulA+5iawQI5J6Wis3cQx8kHVfHC1//enlkBwGw5yCTiMVz14cPyfos+cH7kyMl550c3JkFE+LcPHZR37sSDxjoG71iM8O1zLeVyKJPNWxQ2X+avZsC3XHScrRVc9ZHD8qJe/nzFKXadE/EYvnrmbHtgmDSq3hYsABxtBgBf/NDBWH/9uThgTCOWf/fsvLqfMMvpZ/rEsc6udd3Hj7IDDU45ZLytQShWXnOOPYgdLNdAKdTCS8AaiP/5hBmO8x92tfPBE5wTmWOmj3b8f9JBVhsvWd+NnX1D+NzJM/F1OQieOns83Mwan6vPOYZ7+r8Xz7XNsD//9LG46IQZuPLDh9mDvuLWz87NS/vjC47BTVIwKg1E54+Xf8D+/WoWr3PcjNwEyOTrW/ads2yz0AcOcU4Mrjj9EMezdow0FStt/DsfnWNH+QHAtz/qNHR8/5NH4cipo9HWXGeXff/STRhKWybi33/xJHxIaqsqjHvDzn7c8uy7qEvEcPNFx+GoaVb04PEHtiGVEfjOH9/GPa9swORRDVj+3bPtJQvzXe1+5uGTcP7x0zD/yCkgIvzuX0+02+Ohy062fbaK0U1JzJ7UitsWzMXT/3Fa3gasoxqSeOiyk/GN+fnP9HBBQVulVytEdAGAc4QQ/yL//yyAeUKIK1zXXQrgUgCYMWPG8Rs2bMjLqxwMpbN4+PUOnDJ7vDFaI5XJQojc61YL5fk1XThKPgg663b04fHlW7Fs4y6Makzgex8/ymijvW/JRkwf24STDx5nCzXA8gsk4jH7gdLPKTp6+tEg1zro53f1D+GaP61E31AaX/r72ZhzwCjjrsPPr+nCjr2D+MRx+bH3HT39uPOl9WhtSOCK02c70u/cO4gN3f14auV2fOSoKbZDXGcwbfmCzp4zyfG7hRAYymTRuWcQ/UOZPL8ZYJk/PviDZ/Cpv5uOK04/xDEDBqyZ7o1PrcG0tib7Vb8623YP4PHlW3H0tNGYO3NsXtr2zr14+d0dOGvOZIdWpEhlsnirYxeOP9CZNpO13hz58OsdmDSqAWfPmZR3X7bvGcAJ31uEgyY049EvneIYZDt7B/DIsi342dNr8bmTZ+KrZ74v9/4fWe/bXngPHzp0AprqEjhuxhjjfTfR2TuAnXuH8PyaLrQ11+FTc52DYN9gGqu392Lxe91YcPKBeW26qbsf/UMZDKQyOHraaEe5Qgg8uWIbGpJxbNk1gJMOHudoNyEE9uxLY18qg72DaUwf25i3UHDPQApvbdqNF9q7cNmHDrYDavQ8Hn1zC9qa6mwzJ2D5O+5+ZT3Gt9Tjo0dPwdjmOsdkRQiBGxeuwZQxjWhrskK6D9ImEbv7U/jDsg6s3LoHsye2Yv6Rk21NTfFS+w6kswLT2hrzJiCAFYQQMzw/lYCIXhNC5M8m/NLUsHA5CcB3hRDnyP+vAgAhxP94pZk7d65YunTpMNWQYRhmZBBFuNSyWexVALOJaBYR1QG4EMCjFa4TwzAMgxp26Ash0kT0JQBPAogDuEMIsaLC1WIYhmFQw8IFAIQQjwN4PPBChmEYZlipZbMYwzAMU6WwcGEYhmFKDgsXhmEYpuSwcGEYhmFKDgsXhmEYpuTU7CLKKBBRL4BtALzeJzra5xwAzACw0ed8UPpizldz3YLOc924bly36q8b4F2/Q4UQhW1SJoTYb/4ALAVwq895z3PyfFfA+aD0kc9Xc91C1J3rxnXjulV53fzqB2CpXzrT3/5oFvtTxHMAsCvgfFD6Ys5Xc92CznPdop3nukU7z3WLfj6ofqHZ38xiS0WB++OUMn054bpFg+sWDa5bNKq5boB3/aLUe3/TXG6tcPpywnWLBtctGly3aFRz3QDv+hVc7/1Kc2EYhmGGh/1Nc2EYhmGGgf1euBDRHUTUSURva8eOIaJXiGg5Ef2JiEbJ40kiulseX6XeISPPPUtEq4noDfk30VReGetWR0R3yuNvEtFpWprj5fF2IrqJwr4JanjqVtJ2I6LpRPSMvD8riOgr8vhYIlpIRGvlZ5uW5irZNquJ6BzteEnbrcR1q2i7EdE4ef1eIvq5K6+KtltA3SrdbmcR0WuyfV4jotO1vMrxnJayfoW1XaHhZSPtD8AHARwH4G3t2KsAPiS/fwHAtfL7PwO4T35vArAewEz5/7MA5lawbpcDuFN+nwjgNQAx+f8SACcBIABPAPhwFdWtpO0GYAqA4+T3VgBrAMwB8AMAV8rjVwL4vvw+B9ab2usBzALwLoB4OdqtxHWrdLs1AzgFwBcB/NyVV6Xbza9ulW63YwEcIL8fCWBzudqtDPUrqO1K0sC1/gdgJpyD5B7k/FHTAayU3z8NK5QvAWCcvFFjy9FpI9TtFwA+o123CMA82bne0Y5/GsCvqqFu5Ww3raxHAJwFYDWAKfLYFACr5ferAFylXf+kfMDL1m7F1q0a2k277nPQBvBqaDevulVTu8njBGAnrMlD2dutmPpFabv93izmwdsA/lF+vwDWQAkADwLoA7AV1irWHwkhurV0d0p18TulUGkLrNubAM4jogQRzQJwvDw3FUCHlr5DHquGuinK0m5ENBPWTGwxgElCiK0AID+VSj8VwCYtmWqfsrZbkXVTVLLdvKiGdguiWtrtkwCWCSEGMQzPaZH1U4RuOxYuZr4A4HIieg2WKjkkj88DkAFwACwzxX8Q0UHy3EVCiKMAnCr/PjvMdbsDVodcCuAnAF4GkIY1+3BTrhDBQusGlKndiKgFwEMAviqE2ON3qeGY8DleDXUDKt9unlkYjg13u/lRFe1GREcA+D6Af1OHDJeV7DktQf2AAtuOhYsBIcQ7QoizhRDHA7gXlq0bsHwufxFCpIQQnQBeAjBXptksP3sB/A6WIBq2ugkh0kKIfxdCvF8IcR6AMQDWwhrUp2lZTAOwpUrqVpZ2I6IkrAfpt0KIh+Xh7UQ0RZ6fAqBTHu+AU4tS7VOWditR3aqh3byohnbzpBrajYimAfgDgIuFEGpsKdtzWqL6Fdx2LFwMqCgIIooB+DaAX8pTGwGcThbNAE4E8I4094yXaZIAPgrLRDRsdSOiJlknENFZANJCiJVS5e0lohOlGnsxLLtrxetWjnaTv/F2AKuEEDdopx4FsEB+X4BcGzwK4EIiqpcmu9kAlpSj3UpVtyppNyNV0m5e+VS83YhoDIDHYPnSXlIXl+s5LVX9IrVdqR1GtfYHa4a9FUAK1uzhEgBfgeWsXwPgeuSc1C0Afg9gBYCVAP5THm+GFQH1ljz3U8ionmGs20xYTrpVAJ4CcKCWz1zZEd4F8HOVptJ1K0e7wYoSEjLPN+TfR2AFYCyCpTEtggzEkGm+JdtmNbQInVK3W6nqVkXtth5AN4C9sg/MqaJ2y6tbNbQbrElXn3btGwAmlvE5LUn9orQdr9BnGIZhSg6bxRiGYZiSw8KFYRiGKTksXBiGYZiSw8KFYRiGKTksXBiGYZiSw8KFYaoEIvoiEV1cwPUzSduVmmGqiUSlK8AwjLVITQjxy+ArGaY2YOHCMCVCbgz4F1gbAx4LazHpxQAOB3ADrEW4OwB8TgixlYiehbXP2gcAPEpErQD2CiF+RETvh7XDQROsRXVfEEL0ENHxsPZq6wfw4vD9OoYpDDaLMUxpORTArUKIo2G9guByAD8DcL6w9ly7A8B12vVjhBAfEkL82JXPPQC+IfNZDuBqefxOAF8WQpxUzh/BMMXCmgvDlJZNIrcn028AfBPWS5cWyh3K47C2zVHc786AiEbDEjrPyUN3A/i94fivAXy49D+BYYqHhQvDlBb3fkq9AFb4aBp9BeRNhvwZpiphsxjDlJYZRKQEyacB/A3ABHWMiJLyXRmeCCF2A+gholPloc8CeE4IsQvAbiI6RR6/qPTVZ5jSwJoLw5SWVQAWENGvYO04+zNYrye+SZq1ErBemLYiIJ8FAH5JRE0A3gPweXn88wDuIKJ+mS/DVCW8KzLDlAgZLfZnIcSRFa4Kw1QcNosxDMMwJYc1F4ZhGKbksObCMAzDlBwWLgzDMEzJYeHCMAzDlBwWLgzDMEzJYeHCMAzDlBwWLgzDMEzJ+f/uvPwidk82QAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'][-200:].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Etude de l'incidence annuelle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n", + "entre deux années civiles, nous définissons la période de référence\n", + "entre deux minima de l'incidence, du 1er août de l'année $N$ au\n", + "1er août de l'année $N+1$.\n", + "\n", + "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n", + "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n", + "de référence: à la place du 1er août de chaque année, nous utilisons le\n", + "premier jour de la semaine qui contient le 1er août.\n", + "\n", + "Comme l'incidence de syndrome grippal est très faible en été, cette\n", + "modification ne risque pas de fausser nos conclusions.\n", + "\n", + "Encore un petit détail: les données commencent an octobre 1984, ce qui\n", + "rend la première année incomplète. Nous commençons donc l'analyse en 1985." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", + " for y in range(1985,\n", + " sorted_data.index[-1].year)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n", + "\n", + "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "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": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAD8CAYAAABQFVIjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAG2JJREFUeJzt3X2Q1dWd5/H3BxubjEEFBMOD2myFZaLJro5daMp9ELMCmUmJ2TFZRjdSNVbhGFPlrFOrUGHLbGR2ZGsrzrBWHK1oxsQHdDOxZMcgQYSKM8sCzaqjaJjGDYOMxG4WFEwVPWn57h/3tN6+dje3u899/ryqbt17zz3n3MPh3vvt8/D7/RQRmJmZjdeEWjfAzMyagwOKmZll4YBiZmZZOKCYmVkWDihmZpaFA4qZmWXhgGJmZlk4oJiZWRYOKGZmlkVbrRtQTeecc050dHTUuhlmZg1l9+7dhyNi+qnytVRA6ejooKurq9bNMDNrKJL+vpx8nvIyM7MsHFDMzCwLBxQzM8vCAcXMzLJwQDEzsywcUOpEz7ETfPWB7fQcP1HrppiZjYkDSp1Yt6WbXfuPsO757lo3xcxsTFrqOJR6NH/1Rvr6T374/NEdB3h0xwHa2yawd80Xa9gyM7PR8Qilxl68YyHXXDyLSRML/xWTJk5g6cWzePHOhTVumZnZ6Dig1NiMMycxub2Nvv6TtLdNoK//JJPb25gxeVKtm2ZmNiqe8qoDh9/v44bLLuD6Befz+M4D9Hph3swakCKi1m2oms7OzvC5vMzMRkfS7ojoPFU+T3mZmVkWDihmZpaFA4qZmWXhgNJAfDS9mdUzB5QG4qPpzayeedtwA/DR9GbWCDxCaQA+mt7MGoEDSgPw0fRm1ggcUMpQD4vhA0fTP/31K7jhsgvofb+vZm0xMxuKj5Qvw+qnX+WxnQe4YcH5rPny5yrQMjOz+lXukfJelB+BF8PNzMrnKa8ReDHczKx8ZQUUSfslvSrpZUldKW2qpM2SutP9lKL8qyTtk7RX0uKi9EtTPfskrZOklN4u6cmUvkNSR1GZ5ek9uiUtL0qfm/J2p7Knj787BvNiuJlZ+UYzQlkYERcXzaOtBLZExDxgS3qOpAuBZcBFwBLgu5JOS2XuB1YA89JtSUq/CTgaEZ8G7gXWprqmAncBlwELgLuKAtda4N70/kdTHdl5MdzMrDxlLcpL2g90RsThorS9wJURcUjSTGBbRMyXtAogIv4k5dsEfAvYD2yNiN9M6b+Xyt88kCcitktqA34JTKcQmK6MiJtTmQeAbcB6oBf4VET0S/p8Kv/haGgoPn29mdno5T59fQA/lbRb0oqUdm5EHAJI9zNS+mzgraKyB1Pa7PS4NH1QmYjoB94Dpo1Q1zTg3ZS3tC4zM6uBcnd5XRERb0uaAWyW9PMR8mqItBghfSxlRqprcGMKAXAFwPnnnz9UFjMzy6CsEUpEvJ3ue4CnKaxnvJOmukj3PSn7QeC8ouJzgLdT+pwh0geVSVNeZwFHRqjrMHB2yltaV2nbH4yIzojonD59ejn/XDMzG4NTBhRJZ0iaPPAYWAS8BmwABnZdLQeeSY83AMvSzq25FBbfd6ZpseOSLk+7u24sKTNQ13XAC1FY3NkELJI0JS3GLwI2pde2pryl729mZjVQzpTXucDTaYdvG/B4RDwnaRfwlKSbgAPAVwAiYo+kp4DXgX7g1oj4INV1C/AXwCeAjekG8BDwQ0n7KIxMlqW6jki6G9iV8n07Io6kx3cC6yWtAV5KdZiZWY341CtmZjai3Lu8zMzMRuSAYmZmWTigmJlZFg4oZmaWhQOKmZll4YBiZmZZOKCYmVkWDihmZpaFA4qZmWXhgGJmZlk4oJiZWRYOKGZmloUDipmZZeGAYmZmWTigmJlZFg4oZmaWhQOKmZll4YBiZmZZOKCYmVkWDihmZpaFA4qZmWXhgGJmZlk4oJiZWRYOKGZmloUDipmZZeGAYmZmWTigmFlD6Tl2gq8+sJ2e4ydq3RQr4YBiZg1l3ZZudu0/wrrnu2vdFCvRVusGmJmVY/7qjfT1n/zw+aM7DvDojgO0t01g75ov1rBlNsAjFDPLppLTUS/esZBrLp7FpImFn61JEyew9OJZvHjnwuzvZWPjgGJm2VRyOmrGmZOY3N5GX/9J2tsm0Nd/ksntbcyYPCn7e9nYeMrLzMatWtNRh9/v44bLLuD6Befz+M4D9Hphvq4oImrdhqrp7OyMrq6uWjfDrOn0HDvBmp+8wU/3/JITvz7JpIkTWHzRp/jm73zGI4gmIGl3RHSeKl/ZU16STpP0kqS/Ss+nStosqTvdTynKu0rSPkl7JS0uSr9U0qvptXWSlNLbJT2Z0ndI6igqszy9R7ek5UXpc1Pe7lT29HL/LWaWl6ejDEa3hnIb8EbR85XAloiYB2xJz5F0IbAMuAhYAnxX0mmpzP3ACmBeui1J6TcBRyPi08C9wNpU11TgLuAyYAFwV1HgWgvcm97/aKrDzGpkYDrq6a9fwQ2XXUDv+321bpJVWVlTXpLmAI8AfwzcHhFfkrQXuDIiDkmaCWyLiPmSVgFExJ+kspuAbwH7ga0R8Zsp/fdS+ZsH8kTEdkltwC+B6RQC05URcXMq8wCwDVgP9AKfioh+SZ9P5T8cDQ3FU15mZqOXe8rrT4E7gJNFaedGxCGAdD8jpc8G3irKdzClzU6PS9MHlYmIfuA9YNoIdU0D3k15S+syM7MaOGVAkfQloCcidpdZp4ZIixHSx1JmpLoGN0ZaIalLUldvb+9QWaxJ+RQdZtVVzgjlCuAaSfspTDVdJelR4J001UW670n5DwLnFZWfA7yd0ucMkT6oTJryOgs4MkJdh4GzU97SugaJiAcjojMiOqdPn17GP9eahU/RYVZdpwwoEbEqIuZERAeFNY0XIuLfAxuAgV1Xy4Fn0uMNwLK0c2suhcX3nWla7Liky9PurhtLygzUdV16jwA2AYskTUmL8YuATem1rSlv6ftbi5u/eiMdK5/l0R0HiCgcE9Gx8lnmr95Y66aZNbXxHCl/D3C1pG7g6vSciNgDPAW8DjwH3BoRH6QytwDfA/YBbwID3/CHgGmS9gG3k3aMRcQR4G5gV7p9O6UB3AncnspMS3WY+RQdZjUyqiPlI2IbhV1WRMT/A74wTL4/prAjrDS9C/jsEOkngK8MU9fDwMNDpP9fCluJzQYp95iInmMn+MYTL3Hf9ZfU/fESjdRWa10+l5c1pXKOiWikNZZGaqu1Lp96xVpO6XmnBtTjadAbqa3WvLKfesWsWTTSGksjtdXMAcVaTiOdd6qR2mrm09dbS2qk06A3UluttXkNxczMRuQ1FDOzYfi0PJXhgGJmLcfbsCvDayhWl3wgn1VCtS5V3Ko8QrG65L8grRK8DbuyPEKxutKKf0F6NFY93oZdWR6hWE0Mtyjain9BejRWXb5UceV4hGKjluMv6uIf0TVf/tyH6a30F2QrjsbqwQNf+2j365prP3auWhsHBxQbteGCQTnK+RFtlQP5XrxjIWt+8gY/3fNLTvz6JJMmTmDxRZ/im7/zmVo3zWxMHFCsbDn+oi7nR7RV/oJspdGYtQavoVjZcqxv+Ed0sEaaz/fBgHYqHqFY2XIFg1aZ0ipHI43GxjPVaa3B5/KyUbn5h11MnzxpUDAo/lG05uNrsli55/JyQDGzEfUcOzHsulerTlW2Gp8c0syy8LqXlctrKGZ2Sl73snJ4ysvMzEbkKS8zM6sqBxQzM8vCAcXMzLJwQDEzsywcUMzMLAsHFDMzy8IBxWwcfMJEs484oJiNg6+2aPYRHylvNga+2qLZx3mEYjYGOa4NY9ZsHFCsIdV67cInTDT7uFMGFEmTJO2U9IqkPZL+c0qfKmmzpO50P6WozCpJ+yTtlbS4KP1SSa+m19ZJUkpvl/RkSt8hqaOozPL0Ht2Slhelz015u1PZ0/N0iTWCeli7aKSrLZpVwylPDpl+9M+IiPclTQT+GrgN+LfAkYi4R9JKYEpE3CnpQuAJYAEwC3ge+KcR8YGknans/wZ+AqyLiI2Svg78s4j4A0nLgC9HxL+TNBXoAjqBAHYDl0bEUUlPAT+OiPWS/hx4JSLuH+nf4pNDNj5f7Mms+rKdHDIK3k9PJ6ZbAEuBR1L6I8C16fFSYH1E9EXEL4B9wAJJM4EzI2J7FKLYD0rKDNT1I+ALKZAtBjZHxJGIOApsBpak165KeUvf35qY1y4qp9bTiNb4ylpDkXSapJeBHgo/8DuAcyPiEEC6n5GyzwbeKip+MKXNTo9L0weViYh+4D1g2gh1TQPeTXlL6ypt+wpJXZK6ent7y/nnWh3z2kXl1MM0ojW2srYNR8QHwMWSzgaelvTZEbJrqCpGSB9LmZHqGpwY8SDwIBSmvIbKY43FF3vKy1ugLZdRHYcSEe9K2gYsAd6RNDMiDqXprJ6U7SBwXlGxOcDbKX3OEOnFZQ5KagPOAo6k9CtLymwDDgNnS2pLo5TiuqzJPfC1j6Zy11w70t82Vo4X71g47DXjzUajnF1e09PIBEmfAP4N8HNgAzCw62o58Ex6vAFYlnZuzQXmATvTtNhxSZenNZAbS8oM1HUd8EJaZ9kELJI0Je0iWwRsSq9tTXlL39/MRqGa04iNtE7TSG2tF+WsocwEtkr6W2AXhTWUvwLuAa6W1A1cnZ4TEXuAp4DXgeeAW9OUGcAtwPcoLNS/CWxM6Q8B0yTtA24HVqa6jgB3p/fdBXw7pQHcCdyeykxLddSEP3jW6Kq1BbqR1mkaqa31wteUz2D106/y2M4D3LDgfNZ8+XPZ6zdrdI203buR2lot5W4bdkAZB3/wzMrTc+zEsOs09bZDr5HaWi3ZjkOx4Y3mmAhPi1mt1fIz2EjbvRuprfXGAWUcRvPB83ys1VqtP4ONdKqaRmprPfGU1zjd/MMupk+eNOiYiOJtrZ4Ws1rzZ9DGy2soQ6jFubwabT6259gJvvHES9x3/SV12T4bvUb7DDaTZvk+eQ2lTjTafGytp0Usv0b7DDaTVvs++YqNVdAIpwrx6TeaWyN8BptJq36fPOVlgKdFzHJqtu+Tp7xsVDwtYpZPq36fHFBazEjHInirpFk+rfh98pRXi/FpYsxstMqd8vKifIto1UXC8WqWbZ9m1eAprxbhS+eOTatt+zQbD49QWkSrLhKOlUd0ZqPnEUoLacVFwrHyiM5s9DxCaSG+dG75PKIzGz0HFLNh+Ohys9HxtmEzMxuRj5Q3M7OqckAxM7MsHFDMmoAvMV197vOPc0AxawI+ALP63Ocf50V5swbmy/tWXyv2uRflzVqAD8Csvpx93mzTZg4oZhVWyR8NH4BZfTn7vNmmzXxgo1mFFf9oVOKSAT4As/rG2+fNeq44r6GYVUgrzrXn0AqXDGi0SwR7DcWsxry+MTbNNg00lGadqvSUl1mFNOuPRqU06zTQcJpxqtIBxayCmvFHo1JevGPhsNNAzagZz/7tgGJWQc34o1EpHtE1Pq+hmFnd8EXg8qvmsS7e5WVm1sRWP/0qj+08wA0Lzh/ztvVsu7wknSdpq6Q3JO2RdFtKnypps6TudD+lqMwqSfsk7ZW0uCj9UkmvptfWSVJKb5f0ZErfIamjqMzy9B7dkpYXpc9NebtT2dPL7Rwzs0YwntHF/NUb6Vj5LI/uOEBEYZNDx8pnmb96YwVaWlDOlFc/8EcR8RngcuBWSRcCK4EtETEP2JKek15bBlwELAG+K+m0VNf9wApgXrotSek3AUcj4tPAvcDaVNdU4C7gMmABcFdR4FoL3Jve/2iqw8ysaYxnC3Uttq2fclE+Ig4Bh9Lj45LeAGYDS4ErU7ZHgG3AnSl9fUT0Ab+QtA9YIGk/cGZEbAeQ9APgWmBjKvOtVNePgPvS6GUxsDkijqQym4ElktYDVwHXF73/tygELDOzhpZjC3UtNjmMalE+TUVdAuwAzk3BZiDozEjZZgNvFRU7mNJmp8el6YPKREQ/8B4wbYS6pgHvpryldZW2eYWkLkldvb29o/nnmpnVRK7RRbU3OZS9bVjSJ4G/BP4wIo6l5Y8hsw6RFiOkj6XMSHUNTox4EHgQCovyQ+UxM6snuUYX1d62XtYIRdJECsHksYj4cUp+R9LM9PpMoCelHwTOKyo+B3g7pc8ZIn1QGUltwFnAkRHqOgycnfKW1mVm1vAacQv1KUcoaS3jIeCNiPhO0UsbgOXAPen+maL0xyV9B5hFYfF9Z0R8IOm4pMspTJndCPz3krq2A9cBL0RESNoE/JeihfhFwKr02taUd33J+5uZNbxGPCi2nBHKFcDXgKskvZxuv00hkFwtqRu4Oj0nIvYATwGvA88Bt0bEB6muW4DvAfuANyksyEMhYE1LC/i3k3aMpcX4u4Fd6fbtgQV6ChsAbk9lpqU6rA4020WDzKw8PrDRsstxIJWZ1Y9yD2z0ubwsm1Y7W6yZDeZzeVk2vv6HWWtzQLFsfLZYs9bmKS/Lytf/MGtdXpQ3M7MR+ZryZmZWVQ4oTcTHf5hZLTmgNJHxnOrazGy8vCjfBHz8h9lgPcdO8I0nXuK+6y/xLsMq8gilCfj4D7PBPFqvDY9QmoCP/zAr8Gi9tjxCaRKNeKprs9w8Wq8tj1CaRCOe6tosN4/Wa8sBxcyais/WUDs+Ut7MzEbkI+XNzKyqHFDMzCwLBxQzM8vCAcXMzLJwQDEzsywcUMzMLAsHFDMzy8IBxczMsnBAMTOzLBxQzMwsCwcUMzPLwgHFzMyycEAxM7MsHFDMzCwLBxQzM8vCAcXMzLJwQDEzsywcUMzMLItTBhRJD0vqkfRaUdpUSZsldaf7KUWvrZK0T9JeSYuL0i+V9Gp6bZ0kpfR2SU+m9B2SOorKLE/v0S1peVH63JS3O5U9ffxdYWZm41HOCOUvgCUlaSuBLRExD9iSniPpQmAZcFEq811Jp6Uy9wMrgHnpNlDnTcDRiPg0cC+wNtU1FbgLuAxYANxVFLjWAvem9z+a6jAzsxo6ZUCJiJ8BR0qSlwKPpMePANcWpa+PiL6I+AWwD1ggaSZwZkRsj4gAflBSZqCuHwFfSKOXxcDmiDgSEUeBzcCS9NpVKW/p+5uZWY2MdQ3l3Ig4BJDuZ6T02cBbRfkOprTZ6XFp+qAyEdEPvAdMG6GuacC7KW9pXR8jaYWkLkldvb29o/xnmplZuXIvymuItBghfSxlRqrr4y9EPBgRnRHROX369OGymZnZOI01oLyTprFI9z0p/SBwXlG+OcDbKX3OEOmDykhqA86iMMU2XF2HgbNT3tK6zMysRsYaUDYAA7uulgPPFKUvSzu35lJYfN+ZpsWOS7o8rYHcWFJmoK7rgBfSOssmYJGkKWkxfhGwKb22NeUtfX8zM6uRtlNlkPQEcCVwjqSDFHZe3QM8Jekm4ADwFYCI2CPpKeB1oB+4NSI+SFXdQmHH2CeAjekG8BDwQ0n7KIxMlqW6jki6G9iV8n07IgY2B9wJrJe0Bngp1WFmZjWkwh/8raGzszO6urpq3Qwzs4YiaXdEdJ4qn4+UNzOzLBxQzMwsCwcUM7MG1nPsBF99YDs9x0/UuikOKGZmjWzdlm527T/Cuue7a92UU+/yMjOz+jN/9Ub6+k9++PzRHQd4dMcB2tsmsHfNF2vSJo9QzMwa0It3LOSai2cxaWLhZ3zSxAksvXgWL965sGZtckAxM2tAM86cxOT2Nvr6T9LeNoG+/pNMbm9jxuRJNWuTp7zMzBrU4ff7uOGyC7h+wfk8vvMAvTVemPeBjWZmNiIf2GhmZlXlgGJmZlk4oJiZWRYOKGZmloUDipmZZeGAYmZmWbTUtmFJvcDfD/PyORQuL1zv3M68GqWd0DhtdTvzqod2XhAR00+VqaUCykgkdZWzz7rW3M68GqWd0DhtdTvzapR2gqe8zMwsEwcUMzPLwgHlIw/WugFlcjvzapR2QuO01e3Mq1Ha6TUUMzPLwyMUMzPLomkDiqSHJfVIeq0o7Z9L2i7pVUn/U9KZKX2ipEdS+huSVhWV2SZpr6SX021GDdt5uqTvp/RXJF1ZVObSlL5P0jpJytnOzG2tWJ9KOk/S1vT/uEfSbSl9qqTNkrrT/ZSiMqtSv+2VtLgovaJ9mrmtddOnkqal/O9Luq+kror1aeZ21lN/Xi1pd+q33ZKuKqqr4t/7UYmIprwB/wr4LeC1orRdwL9Oj38fuDs9vh5Ynx7/BrAf6EjPtwGdddLOW4Hvp8czgN3AhPR8J/B5QMBG4It13NaK9SkwE/it9Hgy8HfAhcB/BVam9JXA2vT4QuAVoB2YC7wJnFaNPs3c1nrq0zOAfwH8AXBfSV0V69PM7ayn/rwEmJUefxb4h2r051huTTtCiYifAUdKkucDP0uPNwO/O5AdOENSG/AJ4B+BY3XYzguBLalcD/Au0ClpJnBmRGyPwqfsB8C19djW3G0aoo2HIuL/pMfHgTeA2cBS4JGU7RE+6p+lFP6Y6IuIXwD7gAXV6NNcbc3ZphztjIhfRcRfA4Ou9lTpPs3VzkobQztfioi3U/oeYJKk9mp970ejaQPKMF4DrkmPvwKclx7/CPgVcAg4APy3iCj+4fx+Gvb+pyoNKYdr5yvAUkltkuYCl6bXZgMHi8ofTGnVMNq2Dqh4n0rqoPDX3Q7g3Ig4BIUvNIVRExT66a2iYgN9V9U+HWdbB9RLnw6nan06znYOqMf+/F3gpYjoo7bf+yG1WkD5feBWSbspDDX/MaUvAD4AZlGYSvgjSf8kvXZDRHwO+Jfp9rUatvNhCh+aLuBPgf8F9FMY7paq1va90bYVqtCnkj4J/CXwhxEx0mhzuL6rWp9maCvUV58OW8UQadn7NEM7oQ77U9JFwFrg5oGkIbLVdNtuSwWUiPh5RCyKiEuBJyjMQUNhDeW5iPh1mp75G9L0TET8Q7o/DjxOdaYYhmxnRPRHxH+IiIsjYilwNtBN4Yd7TlEVc4C3S+utk7ZWvE8lTaTwRX0sIn6ckt9JUwQDUy89Kf0gg0dOA31XlT7N1NZ669PhVLxPM7Wz7vpT0hzgaeDGiBj43arZ9344LRVQBnZqSJoArAb+PL10ALhKBWcAlwM/T9M156QyE4EvUZjiqUk7Jf1Gah+Srgb6I+L1NDw+LunyNDS/EXim0u0cS1sr3afp3/8Q8EZEfKfopQ3A8vR4OR/1zwZgWZqTngvMA3ZWo09ztbUO+3RIle7TXO2st/6UdDbwLLAqIv5mIHMtv/fDyr3KXy83Cn8tHwJ+TSGS3wTcRmFHxd8B9/DRgZ2fBP4HhQWv14H/GB/tAtkN/G167c9Iu2pq1M4OYC+FRbznKZwBdKCeTgof+jeB+wbK1FtbK92nFHbtRKr/5XT7bWAahU0C3el+alGZb6Z+20vRLplK92muttZpn+6nsIHj/fRZubDSfZqrnfXWnxT+UPtVUd6XgRnV+t6P5uYj5c3MLIuWmvIyM7PKcUAxM7MsHFDMzCwLBxQzM8vCAcXMzLJwQDEzsywcUMzMLAsHFDMzy+L/A9t1yP3IuE0bAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "yearly_incidence.plot(style='*')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2021 743449\n", + "2014 1600941\n", + "1991 1659249\n", + "1995 1840410\n", + "2020 2010315\n", + "2022 2060304\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": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n", + " française, sont assez rares: il y en eu trois au cours des 35 dernières années." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "yearly_incidence.hist(xrot=20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe37..a316fed 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,2535 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence du syndrome grippal" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "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": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour nous protéger contre une éventuelle disparition ou modification du serveur du Réseau Sentinelles, nous faisons une copie locale de ce jeux de données que nous préservons avec notre analyse. Il est inutile et même risquée de télécharger les données à chaque exécution, car dans le cas d'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data_file = \"incidence-PAY-3.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": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020234435834048804.067876.08874.0102.0FRFrance
120234334620039090.053310.07059.081.0FRFrance
220234235684249277.064407.08675.097.0FRFrance
320234135835751032.065682.08877.099.0FRFrance
420234036889460069.077719.010491.0117.0FRFrance
520233937200363452.080554.010895.0121.0FRFrance
620233836321855227.071209.09583.0107.0FRFrance
720233734908542079.056091.07463.085.0FRFrance
820233633824732237.044257.05849.067.0FRFrance
920233533169526013.037377.04839.057.0FRFrance
1020233432666321057.032269.04032.048.0FRFrance
1120233331914413161.025127.02920.038.0FRFrance
1220233231464110285.018997.02215.029.0FRFrance
1320233131528610705.019867.02316.030.0FRFrance
142023303132058647.017763.02013.027.0FRFrance
152023293111227113.015131.01711.023.0FRFrance
16202328391795703.012655.0149.019.0FRFrance
17202327389995763.012235.0149.019.0FRFrance
18202326390235934.012112.0149.019.0FRFrance
192023253100906739.013441.01510.020.0FRFrance
202023243113087639.014977.01711.023.0FRFrance
2120232331430010661.017939.02217.027.0FRFrance
2220232231830313822.022784.02821.035.0FRFrance
2320232131646012188.020732.02519.031.0FRFrance
2420232031616211963.020361.02418.030.0FRFrance
2520231931690112577.021225.02518.032.0FRFrance
2620231831992915402.024456.03023.037.0FRFrance
2720231732700721779.032235.04133.049.0FRFrance
2820231632787522767.032983.04234.050.0FRFrance
2920231533745530993.043917.05646.066.0FRFrance
.................................
200619852132609619621.032571.04735.059.0FRFrance
200719852032789620885.034907.05138.064.0FRFrance
200819851934315432821.053487.07859.097.0FRFrance
200919851834055529935.051175.07455.093.0FRFrance
201019851733405324366.043740.06244.080.0FRFrance
201119851635036236451.064273.09166.0116.0FRFrance
201219851536388145538.082224.011683.0149.0FRFrance
20131985143134545114400.0154690.0244207.0281.0FRFrance
20141985133197206176080.0218332.0357319.0395.0FRFrance
20151985123245240223304.0267176.0445405.0485.0FRFrance
20161985113276205252399.0300011.0501458.0544.0FRFrance
20171985103353231326279.0380183.0640591.0689.0FRFrance
20181985093369895341109.0398681.0670618.0722.0FRFrance
20191985083389886359529.0420243.0707652.0762.0FRFrance
20201985073471852432599.0511105.0855784.0926.0FRFrance
20211985063565825518011.0613639.01026939.01113.0FRFrance
20221985053637302592795.0681809.011551074.01236.0FRFrance
20231985043424937390794.0459080.0770708.0832.0FRFrance
20241985033213901174689.0253113.0388317.0459.0FRFrance
202519850239758680949.0114223.0177147.0207.0FRFrance
202619850138548965918.0105060.0155120.0190.0FRFrance
202719845238483060602.0109058.0154110.0198.0FRFrance
2028198451310172680242.0123210.0185146.0224.0FRFrance
20291984503123680101401.0145959.0225184.0266.0FRFrance
2030198449310107381684.0120462.0184149.0219.0FRFrance
203119844837862060634.096606.0143110.0176.0FRFrance
203219844737202954274.089784.013199.0163.0FRFrance
203319844638733067686.0106974.0159123.0195.0FRFrance
20341984453135223101414.0169032.0246184.0308.0FRFrance
203519844436842220056.0116788.012537.0213.0FRFrance
\n", + "

2036 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202344 3 58340 48804.0 67876.0 88 74.0 \n", + "1 202343 3 46200 39090.0 53310.0 70 59.0 \n", + "2 202342 3 56842 49277.0 64407.0 86 75.0 \n", + "3 202341 3 58357 51032.0 65682.0 88 77.0 \n", + "4 202340 3 68894 60069.0 77719.0 104 91.0 \n", + "5 202339 3 72003 63452.0 80554.0 108 95.0 \n", + "6 202338 3 63218 55227.0 71209.0 95 83.0 \n", + "7 202337 3 49085 42079.0 56091.0 74 63.0 \n", + "8 202336 3 38247 32237.0 44257.0 58 49.0 \n", + "9 202335 3 31695 26013.0 37377.0 48 39.0 \n", + "10 202334 3 26663 21057.0 32269.0 40 32.0 \n", + "11 202333 3 19144 13161.0 25127.0 29 20.0 \n", + "12 202332 3 14641 10285.0 18997.0 22 15.0 \n", + "13 202331 3 15286 10705.0 19867.0 23 16.0 \n", + "14 202330 3 13205 8647.0 17763.0 20 13.0 \n", + "15 202329 3 11122 7113.0 15131.0 17 11.0 \n", + "16 202328 3 9179 5703.0 12655.0 14 9.0 \n", + "17 202327 3 8999 5763.0 12235.0 14 9.0 \n", + "18 202326 3 9023 5934.0 12112.0 14 9.0 \n", + "19 202325 3 10090 6739.0 13441.0 15 10.0 \n", + "20 202324 3 11308 7639.0 14977.0 17 11.0 \n", + "21 202323 3 14300 10661.0 17939.0 22 17.0 \n", + "22 202322 3 18303 13822.0 22784.0 28 21.0 \n", + "23 202321 3 16460 12188.0 20732.0 25 19.0 \n", + "24 202320 3 16162 11963.0 20361.0 24 18.0 \n", + "25 202319 3 16901 12577.0 21225.0 25 18.0 \n", + "26 202318 3 19929 15402.0 24456.0 30 23.0 \n", + "27 202317 3 27007 21779.0 32235.0 41 33.0 \n", + "28 202316 3 27875 22767.0 32983.0 42 34.0 \n", + "29 202315 3 37455 30993.0 43917.0 56 46.0 \n", + "... ... ... ... ... ... ... ... \n", + "2006 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2007 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2008 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2009 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2010 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2011 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2012 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2013 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2014 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2015 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2016 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2017 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2018 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2019 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2020 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2021 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2022 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2023 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2024 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2025 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2026 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2027 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2028 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2029 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2030 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2031 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2032 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2033 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2034 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2035 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 102.0 FR France \n", + "1 81.0 FR France \n", + "2 97.0 FR France \n", + "3 99.0 FR France \n", + "4 117.0 FR France \n", + "5 121.0 FR France \n", + "6 107.0 FR France \n", + "7 85.0 FR France \n", + "8 67.0 FR France \n", + "9 57.0 FR France \n", + "10 48.0 FR France \n", + "11 38.0 FR France \n", + "12 29.0 FR France \n", + "13 30.0 FR France \n", + "14 27.0 FR France \n", + "15 23.0 FR France \n", + "16 19.0 FR France \n", + "17 19.0 FR France \n", + "18 19.0 FR France \n", + "19 20.0 FR France \n", + "20 23.0 FR France \n", + "21 27.0 FR France \n", + "22 35.0 FR France \n", + "23 31.0 FR France \n", + "24 30.0 FR France \n", + "25 32.0 FR France \n", + "26 37.0 FR France \n", + "27 49.0 FR France \n", + "28 50.0 FR France \n", + "29 66.0 FR France \n", + "... ... ... ... \n", + "2006 59.0 FR France \n", + "2007 64.0 FR France \n", + "2008 97.0 FR France \n", + "2009 93.0 FR France \n", + "2010 80.0 FR France \n", + "2011 116.0 FR France \n", + "2012 149.0 FR France \n", + "2013 281.0 FR France \n", + "2014 395.0 FR France \n", + "2015 485.0 FR France \n", + "2016 544.0 FR France \n", + "2017 689.0 FR France \n", + "2018 722.0 FR France \n", + "2019 762.0 FR France \n", + "2020 926.0 FR France \n", + "2021 1113.0 FR France \n", + "2022 1236.0 FR France \n", + "2023 832.0 FR France \n", + "2024 459.0 FR France \n", + "2025 207.0 FR France \n", + "2026 190.0 FR France \n", + "2027 198.0 FR France \n", + "2028 224.0 FR France \n", + "2029 266.0 FR France \n", + "2030 219.0 FR France \n", + "2031 176.0 FR France \n", + "2032 163.0 FR France \n", + "2033 195.0 FR France \n", + "2034 308.0 FR France \n", + "2035 213.0 FR France \n", + "\n", + "[2036 rows x 10 columns]" + ] + }, + "execution_count": 4, + "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": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
17991989193-NaNNaN-NaNNaNFRFrance
\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", + "1799 198919 3 - NaN NaN - NaN NaN \n", + "\n", + " geo_insee geo_name \n", + "1799 FR France " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020234435834048804.067876.08874.0102.0FRFrance
120234334620039090.053310.07059.081.0FRFrance
220234235684249277.064407.08675.097.0FRFrance
320234135835751032.065682.08877.099.0FRFrance
420234036889460069.077719.010491.0117.0FRFrance
520233937200363452.080554.010895.0121.0FRFrance
620233836321855227.071209.09583.0107.0FRFrance
720233734908542079.056091.07463.085.0FRFrance
820233633824732237.044257.05849.067.0FRFrance
920233533169526013.037377.04839.057.0FRFrance
1020233432666321057.032269.04032.048.0FRFrance
1120233331914413161.025127.02920.038.0FRFrance
1220233231464110285.018997.02215.029.0FRFrance
1320233131528610705.019867.02316.030.0FRFrance
142023303132058647.017763.02013.027.0FRFrance
152023293111227113.015131.01711.023.0FRFrance
16202328391795703.012655.0149.019.0FRFrance
17202327389995763.012235.0149.019.0FRFrance
18202326390235934.012112.0149.019.0FRFrance
192023253100906739.013441.01510.020.0FRFrance
202023243113087639.014977.01711.023.0FRFrance
2120232331430010661.017939.02217.027.0FRFrance
2220232231830313822.022784.02821.035.0FRFrance
2320232131646012188.020732.02519.031.0FRFrance
2420232031616211963.020361.02418.030.0FRFrance
2520231931690112577.021225.02518.032.0FRFrance
2620231831992915402.024456.03023.037.0FRFrance
2720231732700721779.032235.04133.049.0FRFrance
2820231632787522767.032983.04234.050.0FRFrance
2920231533745530993.043917.05646.066.0FRFrance
.................................
200619852132609619621.032571.04735.059.0FRFrance
200719852032789620885.034907.05138.064.0FRFrance
200819851934315432821.053487.07859.097.0FRFrance
200919851834055529935.051175.07455.093.0FRFrance
201019851733405324366.043740.06244.080.0FRFrance
201119851635036236451.064273.09166.0116.0FRFrance
201219851536388145538.082224.011683.0149.0FRFrance
20131985143134545114400.0154690.0244207.0281.0FRFrance
20141985133197206176080.0218332.0357319.0395.0FRFrance
20151985123245240223304.0267176.0445405.0485.0FRFrance
20161985113276205252399.0300011.0501458.0544.0FRFrance
20171985103353231326279.0380183.0640591.0689.0FRFrance
20181985093369895341109.0398681.0670618.0722.0FRFrance
20191985083389886359529.0420243.0707652.0762.0FRFrance
20201985073471852432599.0511105.0855784.0926.0FRFrance
20211985063565825518011.0613639.01026939.01113.0FRFrance
20221985053637302592795.0681809.011551074.01236.0FRFrance
20231985043424937390794.0459080.0770708.0832.0FRFrance
20241985033213901174689.0253113.0388317.0459.0FRFrance
202519850239758680949.0114223.0177147.0207.0FRFrance
202619850138548965918.0105060.0155120.0190.0FRFrance
202719845238483060602.0109058.0154110.0198.0FRFrance
2028198451310172680242.0123210.0185146.0224.0FRFrance
20291984503123680101401.0145959.0225184.0266.0FRFrance
2030198449310107381684.0120462.0184149.0219.0FRFrance
203119844837862060634.096606.0143110.0176.0FRFrance
203219844737202954274.089784.013199.0163.0FRFrance
203319844638733067686.0106974.0159123.0195.0FRFrance
20341984453135223101414.0169032.0246184.0308.0FRFrance
203519844436842220056.0116788.012537.0213.0FRFrance
\n", + "

2035 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202344 3 58340 48804.0 67876.0 88 74.0 \n", + "1 202343 3 46200 39090.0 53310.0 70 59.0 \n", + "2 202342 3 56842 49277.0 64407.0 86 75.0 \n", + "3 202341 3 58357 51032.0 65682.0 88 77.0 \n", + "4 202340 3 68894 60069.0 77719.0 104 91.0 \n", + "5 202339 3 72003 63452.0 80554.0 108 95.0 \n", + "6 202338 3 63218 55227.0 71209.0 95 83.0 \n", + "7 202337 3 49085 42079.0 56091.0 74 63.0 \n", + "8 202336 3 38247 32237.0 44257.0 58 49.0 \n", + "9 202335 3 31695 26013.0 37377.0 48 39.0 \n", + "10 202334 3 26663 21057.0 32269.0 40 32.0 \n", + "11 202333 3 19144 13161.0 25127.0 29 20.0 \n", + "12 202332 3 14641 10285.0 18997.0 22 15.0 \n", + "13 202331 3 15286 10705.0 19867.0 23 16.0 \n", + "14 202330 3 13205 8647.0 17763.0 20 13.0 \n", + "15 202329 3 11122 7113.0 15131.0 17 11.0 \n", + "16 202328 3 9179 5703.0 12655.0 14 9.0 \n", + "17 202327 3 8999 5763.0 12235.0 14 9.0 \n", + "18 202326 3 9023 5934.0 12112.0 14 9.0 \n", + "19 202325 3 10090 6739.0 13441.0 15 10.0 \n", + "20 202324 3 11308 7639.0 14977.0 17 11.0 \n", + "21 202323 3 14300 10661.0 17939.0 22 17.0 \n", + "22 202322 3 18303 13822.0 22784.0 28 21.0 \n", + "23 202321 3 16460 12188.0 20732.0 25 19.0 \n", + "24 202320 3 16162 11963.0 20361.0 24 18.0 \n", + "25 202319 3 16901 12577.0 21225.0 25 18.0 \n", + "26 202318 3 19929 15402.0 24456.0 30 23.0 \n", + "27 202317 3 27007 21779.0 32235.0 41 33.0 \n", + "28 202316 3 27875 22767.0 32983.0 42 34.0 \n", + "29 202315 3 37455 30993.0 43917.0 56 46.0 \n", + "... ... ... ... ... ... ... ... \n", + "2006 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2007 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2008 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2009 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2010 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2011 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2012 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2013 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2014 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2015 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2016 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2017 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2018 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2019 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2020 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2021 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2022 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2023 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2024 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2025 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2026 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2027 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2028 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2029 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2030 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2031 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2032 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2033 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2034 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2035 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 102.0 FR France \n", + "1 81.0 FR France \n", + "2 97.0 FR France \n", + "3 99.0 FR France \n", + "4 117.0 FR France \n", + "5 121.0 FR France \n", + "6 107.0 FR France \n", + "7 85.0 FR France \n", + "8 67.0 FR France \n", + "9 57.0 FR France \n", + "10 48.0 FR France \n", + "11 38.0 FR France \n", + "12 29.0 FR France \n", + "13 30.0 FR France \n", + "14 27.0 FR France \n", + "15 23.0 FR France \n", + "16 19.0 FR France \n", + "17 19.0 FR France \n", + "18 19.0 FR France \n", + "19 20.0 FR France \n", + "20 23.0 FR France \n", + "21 27.0 FR France \n", + "22 35.0 FR France \n", + "23 31.0 FR France \n", + "24 30.0 FR France \n", + "25 32.0 FR France \n", + "26 37.0 FR France \n", + "27 49.0 FR France \n", + "28 50.0 FR France \n", + "29 66.0 FR France \n", + "... ... ... ... \n", + "2006 59.0 FR France \n", + "2007 64.0 FR France \n", + "2008 97.0 FR France \n", + "2009 93.0 FR France \n", + "2010 80.0 FR France \n", + "2011 116.0 FR France \n", + "2012 149.0 FR France \n", + "2013 281.0 FR France \n", + "2014 395.0 FR France \n", + "2015 485.0 FR France \n", + "2016 544.0 FR France \n", + "2017 689.0 FR France \n", + "2018 722.0 FR France \n", + "2019 762.0 FR France \n", + "2020 926.0 FR France \n", + "2021 1113.0 FR France \n", + "2022 1236.0 FR France \n", + "2023 832.0 FR France \n", + "2024 459.0 FR France \n", + "2025 207.0 FR France \n", + "2026 190.0 FR France \n", + "2027 198.0 FR France \n", + "2028 224.0 FR France \n", + "2029 266.0 FR France \n", + "2030 219.0 FR France \n", + "2031 176.0 FR France \n", + "2032 163.0 FR France \n", + "2033 195.0 FR France \n", + "2034 308.0 FR France \n", + "2035 213.0 FR France \n", + "\n", + "[2035 rows x 10 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = raw_data.dropna().copy()\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nos données utilisent une convention inhabituelle: le numéro de\n", + "semaine est collé à l'année, donnant l'impression qu'il s'agit\n", + "de nombre entier. C'est comme ça que Pandas les interprète.\n", + " \n", + "Un deuxième problème est que Pandas ne comprend pas les numéros de\n", + "semaine. Il faut lui fournir les dates de début et de fin de\n", + "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n", + "\n", + "Comme la conversion des semaines est devenu assez complexe, nous\n", + "écrivons une petite fonction Python pour cela. Ensuite, nous\n", + "l'appliquons à tous les points de nos donnés. Les résultats vont\n", + "dans une nouvelle colonne 'period'." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "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": 8, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous vérifions la cohérence des données. Entre la fin d'une période et\n", + "le début de la période qui suit, la différence temporelle doit être\n", + "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n", + "d'une seconde.\n", + "\n", + "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n", + "entre lesquelles il manque une semaine.\n", + "\n", + "Nous reconnaissons ces dates: c'est la semaine sans observations\n", + "que nous avions supprimées !" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" + ] + } + ], + "source": [ + "periods = sorted_data.index\n", + "for p1, p2 in zip(periods[:-1], periods[1:]):\n", + " delta = p2.to_timestamp() - p1.end_time\n", + " if delta > pd.Timedelta('1s'):\n", + " print(p1, p2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un premier regard sur les données !" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data['inc'] = sorted_data['inc'].astype(int)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "petite manip pour transformer la chaine de caractères en entier pour pouvoir génerer le plot" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'][-200:].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Etude de l'incidence annuelle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n", + "entre deux années civiles, nous définissons la période de référence\n", + "entre deux minima de l'incidence, du 1er août de l'année $N$ au\n", + "1er août de l'année $N+1$.\n", + "\n", + "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n", + "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n", + "de référence: à la place du 1er août de chaque année, nous utilisons le\n", + "premier jour de la semaine qui contient le 1er août.\n", + "\n", + "Comme l'incidence de syndrome grippal est très faible en été, cette\n", + "modification ne risque pas de fausser nos conclusions.\n", + "\n", + "Encore un petit détail: les données commencent an octobre 1984, ce qui\n", + "rend la première année incomplète. Nous commençons donc l'analyse en 1985." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", + " for y in range(1985,\n", + " sorted_data.index[-1].year)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n", + "\n", + "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "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": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAD8CAYAAABQFVIjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAG2JJREFUeJzt3X2Q1dWd5/H3BxubjEEFBMOD2myFZaLJro5daMp9ELMCmUmJ2TFZRjdSNVbhGFPlrFOrUGHLbGR2ZGsrzrBWHK1oxsQHdDOxZMcgQYSKM8sCzaqjaJjGDYOMxG4WFEwVPWn57h/3tN6+dje3u899/ryqbt17zz3n3MPh3vvt8/D7/RQRmJmZjdeEWjfAzMyagwOKmZll4YBiZmZZOKCYmVkWDihmZpaFA4qZmWXhgGJmZlk4oJiZWRYOKGZmlkVbrRtQTeecc050dHTUuhlmZg1l9+7dhyNi+qnytVRA6ejooKurq9bNMDNrKJL+vpx8nvIyM7MsHFDMzCwLBxQzM8vCAcXMzLJwQDEzsywcUOpEz7ETfPWB7fQcP1HrppiZjYkDSp1Yt6WbXfuPsO757lo3xcxsTFrqOJR6NH/1Rvr6T374/NEdB3h0xwHa2yawd80Xa9gyM7PR8Qilxl68YyHXXDyLSRML/xWTJk5g6cWzePHOhTVumZnZ6Dig1NiMMycxub2Nvv6TtLdNoK//JJPb25gxeVKtm2ZmNiqe8qoDh9/v44bLLuD6Befz+M4D9Hph3swakCKi1m2oms7OzvC5vMzMRkfS7ojoPFU+T3mZmVkWDihmZpaFA4qZmWXhgNJAfDS9mdUzB5QG4qPpzayeedtwA/DR9GbWCDxCaQA+mt7MGoEDSgPw0fRm1ggcUMpQD4vhA0fTP/31K7jhsgvofb+vZm0xMxuKj5Qvw+qnX+WxnQe4YcH5rPny5yrQMjOz+lXukfJelB+BF8PNzMrnKa8ReDHczKx8ZQUUSfslvSrpZUldKW2qpM2SutP9lKL8qyTtk7RX0uKi9EtTPfskrZOklN4u6cmUvkNSR1GZ5ek9uiUtL0qfm/J2p7Knj787BvNiuJlZ+UYzQlkYERcXzaOtBLZExDxgS3qOpAuBZcBFwBLgu5JOS2XuB1YA89JtSUq/CTgaEZ8G7gXWprqmAncBlwELgLuKAtda4N70/kdTHdl5MdzMrDxlLcpL2g90RsThorS9wJURcUjSTGBbRMyXtAogIv4k5dsEfAvYD2yNiN9M6b+Xyt88kCcitktqA34JTKcQmK6MiJtTmQeAbcB6oBf4VET0S/p8Kv/haGgoPn29mdno5T59fQA/lbRb0oqUdm5EHAJI9zNS+mzgraKyB1Pa7PS4NH1QmYjoB94Dpo1Q1zTg3ZS3tC4zM6uBcnd5XRERb0uaAWyW9PMR8mqItBghfSxlRqprcGMKAXAFwPnnnz9UFjMzy6CsEUpEvJ3ue4CnKaxnvJOmukj3PSn7QeC8ouJzgLdT+pwh0geVSVNeZwFHRqjrMHB2yltaV2nbH4yIzojonD59ejn/XDMzG4NTBhRJZ0iaPPAYWAS8BmwABnZdLQeeSY83AMvSzq25FBbfd6ZpseOSLk+7u24sKTNQ13XAC1FY3NkELJI0JS3GLwI2pde2pryl729mZjVQzpTXucDTaYdvG/B4RDwnaRfwlKSbgAPAVwAiYo+kp4DXgX7g1oj4INV1C/AXwCeAjekG8BDwQ0n7KIxMlqW6jki6G9iV8n07Io6kx3cC6yWtAV5KdZiZWY341CtmZjai3Lu8zMzMRuSAYmZmWTigmJlZFg4oZmaWhQOKmZll4YBiZmZZOKCYmVkWDihmZpaFA4qZmWXhgGJmZlk4oJiZWRYOKGZmloUDipmZZeGAYmZmWTigmJlZFg4oZmaWhQOKmZll4YBiZmZZOKCYmVkWDihmZpaFA4qZmWXhgGJmZlk4oJiZWRYOKGZmloUDipmZZeGAYmZmWTigmFlD6Tl2gq8+sJ2e4ydq3RQr4YBiZg1l3ZZudu0/wrrnu2vdFCvRVusGmJmVY/7qjfT1n/zw+aM7DvDojgO0t01g75ov1rBlNsAjFDPLppLTUS/esZBrLp7FpImFn61JEyew9OJZvHjnwuzvZWPjgGJm2VRyOmrGmZOY3N5GX/9J2tsm0Nd/ksntbcyYPCn7e9nYeMrLzMatWtNRh9/v44bLLuD6Befz+M4D9Hphvq4oImrdhqrp7OyMrq6uWjfDrOn0HDvBmp+8wU/3/JITvz7JpIkTWHzRp/jm73zGI4gmIGl3RHSeKl/ZU16STpP0kqS/Ss+nStosqTvdTynKu0rSPkl7JS0uSr9U0qvptXWSlNLbJT2Z0ndI6igqszy9R7ek5UXpc1Pe7lT29HL/LWaWl6ejDEa3hnIb8EbR85XAloiYB2xJz5F0IbAMuAhYAnxX0mmpzP3ACmBeui1J6TcBRyPi08C9wNpU11TgLuAyYAFwV1HgWgvcm97/aKrDzGpkYDrq6a9fwQ2XXUDv+321bpJVWVlTXpLmAI8AfwzcHhFfkrQXuDIiDkmaCWyLiPmSVgFExJ+kspuAbwH7ga0R8Zsp/fdS+ZsH8kTEdkltwC+B6RQC05URcXMq8wCwDVgP9AKfioh+SZ9P5T8cDQ3FU15mZqOXe8rrT4E7gJNFaedGxCGAdD8jpc8G3irKdzClzU6PS9MHlYmIfuA9YNoIdU0D3k15S+syM7MaOGVAkfQloCcidpdZp4ZIixHSx1JmpLoGN0ZaIalLUldvb+9QWaxJ+RQdZtVVzgjlCuAaSfspTDVdJelR4J001UW670n5DwLnFZWfA7yd0ucMkT6oTJryOgs4MkJdh4GzU97SugaJiAcjojMiOqdPn17GP9eahU/RYVZdpwwoEbEqIuZERAeFNY0XIuLfAxuAgV1Xy4Fn0uMNwLK0c2suhcX3nWla7Liky9PurhtLygzUdV16jwA2AYskTUmL8YuATem1rSlv6ftbi5u/eiMdK5/l0R0HiCgcE9Gx8lnmr95Y66aZNbXxHCl/D3C1pG7g6vSciNgDPAW8DjwH3BoRH6QytwDfA/YBbwID3/CHgGmS9gG3k3aMRcQR4G5gV7p9O6UB3AncnspMS3WY+RQdZjUyqiPlI2IbhV1WRMT/A74wTL4/prAjrDS9C/jsEOkngK8MU9fDwMNDpP9fCluJzQYp95iInmMn+MYTL3Hf9ZfU/fESjdRWa10+l5c1pXKOiWikNZZGaqu1Lp96xVpO6XmnBtTjadAbqa3WvLKfesWsWTTSGksjtdXMAcVaTiOdd6qR2mrm09dbS2qk06A3UluttXkNxczMRuQ1FDOzYfi0PJXhgGJmLcfbsCvDayhWl3wgn1VCtS5V3Ko8QrG65L8grRK8DbuyPEKxutKKf0F6NFY93oZdWR6hWE0Mtyjain9BejRWXb5UceV4hGKjluMv6uIf0TVf/tyH6a30F2QrjsbqwQNf+2j365prP3auWhsHBxQbteGCQTnK+RFtlQP5XrxjIWt+8gY/3fNLTvz6JJMmTmDxRZ/im7/zmVo3zWxMHFCsbDn+oi7nR7RV/oJspdGYtQavoVjZcqxv+Ed0sEaaz/fBgHYqHqFY2XIFg1aZ0ipHI43GxjPVaa3B5/KyUbn5h11MnzxpUDAo/lG05uNrsli55/JyQDGzEfUcOzHsulerTlW2Gp8c0syy8LqXlctrKGZ2Sl73snJ4ysvMzEbkKS8zM6sqBxQzM8vCAcXMzLJwQDEzsywcUMzMLAsHFDMzy8IBxWwcfMJEs484oJiNg6+2aPYRHylvNga+2qLZx3mEYjYGOa4NY9ZsHFCsIdV67cInTDT7uFMGFEmTJO2U9IqkPZL+c0qfKmmzpO50P6WozCpJ+yTtlbS4KP1SSa+m19ZJUkpvl/RkSt8hqaOozPL0Ht2Slhelz015u1PZ0/N0iTWCeli7aKSrLZpVwylPDpl+9M+IiPclTQT+GrgN+LfAkYi4R9JKYEpE3CnpQuAJYAEwC3ge+KcR8YGknans/wZ+AqyLiI2Svg78s4j4A0nLgC9HxL+TNBXoAjqBAHYDl0bEUUlPAT+OiPWS/hx4JSLuH+nf4pNDNj5f7Mms+rKdHDIK3k9PJ6ZbAEuBR1L6I8C16fFSYH1E9EXEL4B9wAJJM4EzI2J7FKLYD0rKDNT1I+ALKZAtBjZHxJGIOApsBpak165KeUvf35qY1y4qp9bTiNb4ylpDkXSapJeBHgo/8DuAcyPiEEC6n5GyzwbeKip+MKXNTo9L0weViYh+4D1g2gh1TQPeTXlL6ypt+wpJXZK6ent7y/nnWh3z2kXl1MM0ojW2srYNR8QHwMWSzgaelvTZEbJrqCpGSB9LmZHqGpwY8SDwIBSmvIbKY43FF3vKy1ugLZdRHYcSEe9K2gYsAd6RNDMiDqXprJ6U7SBwXlGxOcDbKX3OEOnFZQ5KagPOAo6k9CtLymwDDgNnS2pLo5TiuqzJPfC1j6Zy11w70t82Vo4X71g47DXjzUajnF1e09PIBEmfAP4N8HNgAzCw62o58Ex6vAFYlnZuzQXmATvTtNhxSZenNZAbS8oM1HUd8EJaZ9kELJI0Je0iWwRsSq9tTXlL39/MRqGa04iNtE7TSG2tF+WsocwEtkr6W2AXhTWUvwLuAa6W1A1cnZ4TEXuAp4DXgeeAW9OUGcAtwPcoLNS/CWxM6Q8B0yTtA24HVqa6jgB3p/fdBXw7pQHcCdyeykxLddSEP3jW6Kq1BbqR1mkaqa31wteUz2D106/y2M4D3LDgfNZ8+XPZ6zdrdI203buR2lot5W4bdkAZB3/wzMrTc+zEsOs09bZDr5HaWi3ZjkOx4Y3mmAhPi1mt1fIz2EjbvRuprfXGAWUcRvPB83ys1VqtP4ONdKqaRmprPfGU1zjd/MMupk+eNOiYiOJtrZ4Ws1rzZ9DGy2soQ6jFubwabT6259gJvvHES9x3/SV12T4bvUb7DDaTZvk+eQ2lTjTafGytp0Usv0b7DDaTVvs++YqNVdAIpwrx6TeaWyN8BptJq36fPOVlgKdFzHJqtu+Tp7xsVDwtYpZPq36fHFBazEjHInirpFk+rfh98pRXi/FpYsxstMqd8vKifIto1UXC8WqWbZ9m1eAprxbhS+eOTatt+zQbD49QWkSrLhKOlUd0ZqPnEUoLacVFwrHyiM5s9DxCaSG+dG75PKIzGz0HFLNh+Ohys9HxtmEzMxuRj5Q3M7OqckAxM7MsHFDMmoAvMV197vOPc0AxawI+ALP63Ocf50V5swbmy/tWXyv2uRflzVqAD8Csvpx93mzTZg4oZhVWyR8NH4BZfTn7vNmmzXxgo1mFFf9oVOKSAT4As/rG2+fNeq44r6GYVUgrzrXn0AqXDGi0SwR7DcWsxry+MTbNNg00lGadqvSUl1mFNOuPRqU06zTQcJpxqtIBxayCmvFHo1JevGPhsNNAzagZz/7tgGJWQc34o1EpHtE1Pq+hmFnd8EXg8qvmsS7e5WVm1sRWP/0qj+08wA0Lzh/ztvVsu7wknSdpq6Q3JO2RdFtKnypps6TudD+lqMwqSfsk7ZW0uCj9UkmvptfWSVJKb5f0ZErfIamjqMzy9B7dkpYXpc9NebtT2dPL7Rwzs0YwntHF/NUb6Vj5LI/uOEBEYZNDx8pnmb96YwVaWlDOlFc/8EcR8RngcuBWSRcCK4EtETEP2JKek15bBlwELAG+K+m0VNf9wApgXrotSek3AUcj4tPAvcDaVNdU4C7gMmABcFdR4FoL3Jve/2iqw8ysaYxnC3Uttq2fclE+Ig4Bh9Lj45LeAGYDS4ErU7ZHgG3AnSl9fUT0Ab+QtA9YIGk/cGZEbAeQ9APgWmBjKvOtVNePgPvS6GUxsDkijqQym4ElktYDVwHXF73/tygELDOzhpZjC3UtNjmMalE+TUVdAuwAzk3BZiDozEjZZgNvFRU7mNJmp8el6YPKREQ/8B4wbYS6pgHvpryldZW2eYWkLkldvb29o/nnmpnVRK7RRbU3OZS9bVjSJ4G/BP4wIo6l5Y8hsw6RFiOkj6XMSHUNTox4EHgQCovyQ+UxM6snuUYX1d62XtYIRdJECsHksYj4cUp+R9LM9PpMoCelHwTOKyo+B3g7pc8ZIn1QGUltwFnAkRHqOgycnfKW1mVm1vAacQv1KUcoaS3jIeCNiPhO0UsbgOXAPen+maL0xyV9B5hFYfF9Z0R8IOm4pMspTJndCPz3krq2A9cBL0RESNoE/JeihfhFwKr02taUd33J+5uZNbxGPCi2nBHKFcDXgKskvZxuv00hkFwtqRu4Oj0nIvYATwGvA88Bt0bEB6muW4DvAfuANyksyEMhYE1LC/i3k3aMpcX4u4Fd6fbtgQV6ChsAbk9lpqU6rA4020WDzKw8PrDRsstxIJWZ1Y9yD2z0ubwsm1Y7W6yZDeZzeVk2vv6HWWtzQLFsfLZYs9bmKS/Lytf/MGtdXpQ3M7MR+ZryZmZWVQ4oTcTHf5hZLTmgNJHxnOrazGy8vCjfBHz8h9lgPcdO8I0nXuK+6y/xLsMq8gilCfj4D7PBPFqvDY9QmoCP/zAr8Gi9tjxCaRKNeKprs9w8Wq8tj1CaRCOe6tosN4/Wa8sBxcyais/WUDs+Ut7MzEbkI+XNzKyqHFDMzCwLBxQzM8vCAcXMzLJwQDEzsywcUMzMLAsHFDMzy8IBxczMsnBAMTOzLBxQzMwsCwcUMzPLwgHFzMyycEAxM7MsHFDMzCwLBxQzM8vCAcXMzLJwQDEzsywcUMzMLItTBhRJD0vqkfRaUdpUSZsldaf7KUWvrZK0T9JeSYuL0i+V9Gp6bZ0kpfR2SU+m9B2SOorKLE/v0S1peVH63JS3O5U9ffxdYWZm41HOCOUvgCUlaSuBLRExD9iSniPpQmAZcFEq811Jp6Uy9wMrgHnpNlDnTcDRiPg0cC+wNtU1FbgLuAxYANxVFLjWAvem9z+a6jAzsxo6ZUCJiJ8BR0qSlwKPpMePANcWpa+PiL6I+AWwD1ggaSZwZkRsj4gAflBSZqCuHwFfSKOXxcDmiDgSEUeBzcCS9NpVKW/p+5uZWY2MdQ3l3Ig4BJDuZ6T02cBbRfkOprTZ6XFp+qAyEdEPvAdMG6GuacC7KW9pXR8jaYWkLkldvb29o/xnmplZuXIvymuItBghfSxlRqrr4y9EPBgRnRHROX369OGymZnZOI01oLyTprFI9z0p/SBwXlG+OcDbKX3OEOmDykhqA86iMMU2XF2HgbNT3tK6zMysRsYaUDYAA7uulgPPFKUvSzu35lJYfN+ZpsWOS7o8rYHcWFJmoK7rgBfSOssmYJGkKWkxfhGwKb22NeUtfX8zM6uRtlNlkPQEcCVwjqSDFHZe3QM8Jekm4ADwFYCI2CPpKeB1oB+4NSI+SFXdQmHH2CeAjekG8BDwQ0n7KIxMlqW6jki6G9iV8n07IgY2B9wJrJe0Bngp1WFmZjWkwh/8raGzszO6urpq3Qwzs4YiaXdEdJ4qn4+UNzOzLBxQzMwsCwcUM7MG1nPsBF99YDs9x0/UuikOKGZmjWzdlm527T/Cuue7a92UU+/yMjOz+jN/9Ub6+k9++PzRHQd4dMcB2tsmsHfNF2vSJo9QzMwa0It3LOSai2cxaWLhZ3zSxAksvXgWL965sGZtckAxM2tAM86cxOT2Nvr6T9LeNoG+/pNMbm9jxuRJNWuTp7zMzBrU4ff7uOGyC7h+wfk8vvMAvTVemPeBjWZmNiIf2GhmZlXlgGJmZlk4oJiZWRYOKGZmloUDipmZZeGAYmZmWbTUtmFJvcDfD/PyORQuL1zv3M68GqWd0DhtdTvzqod2XhAR00+VqaUCykgkdZWzz7rW3M68GqWd0DhtdTvzapR2gqe8zMwsEwcUMzPLwgHlIw/WugFlcjvzapR2QuO01e3Mq1Ha6TUUMzPLwyMUMzPLomkDiqSHJfVIeq0o7Z9L2i7pVUn/U9KZKX2ipEdS+huSVhWV2SZpr6SX021GDdt5uqTvp/RXJF1ZVObSlL5P0jpJytnOzG2tWJ9KOk/S1vT/uEfSbSl9qqTNkrrT/ZSiMqtSv+2VtLgovaJ9mrmtddOnkqal/O9Luq+kror1aeZ21lN/Xi1pd+q33ZKuKqqr4t/7UYmIprwB/wr4LeC1orRdwL9Oj38fuDs9vh5Ynx7/BrAf6EjPtwGdddLOW4Hvp8czgN3AhPR8J/B5QMBG4It13NaK9SkwE/it9Hgy8HfAhcB/BVam9JXA2vT4QuAVoB2YC7wJnFaNPs3c1nrq0zOAfwH8AXBfSV0V69PM7ayn/rwEmJUefxb4h2r051huTTtCiYifAUdKkucDP0uPNwO/O5AdOENSG/AJ4B+BY3XYzguBLalcD/Au0ClpJnBmRGyPwqfsB8C19djW3G0aoo2HIuL/pMfHgTeA2cBS4JGU7RE+6p+lFP6Y6IuIXwD7gAXV6NNcbc3ZphztjIhfRcRfA4Ou9lTpPs3VzkobQztfioi3U/oeYJKk9mp970ejaQPKMF4DrkmPvwKclx7/CPgVcAg4APy3iCj+4fx+Gvb+pyoNKYdr5yvAUkltkuYCl6bXZgMHi8ofTGnVMNq2Dqh4n0rqoPDX3Q7g3Ig4BIUvNIVRExT66a2iYgN9V9U+HWdbB9RLnw6nan06znYOqMf+/F3gpYjoo7bf+yG1WkD5feBWSbspDDX/MaUvAD4AZlGYSvgjSf8kvXZDRHwO+Jfp9rUatvNhCh+aLuBPgf8F9FMY7paq1va90bYVqtCnkj4J/CXwhxEx0mhzuL6rWp9maCvUV58OW8UQadn7NEM7oQ77U9JFwFrg5oGkIbLVdNtuSwWUiPh5RCyKiEuBJyjMQUNhDeW5iPh1mp75G9L0TET8Q7o/DjxOdaYYhmxnRPRHxH+IiIsjYilwNtBN4Yd7TlEVc4C3S+utk7ZWvE8lTaTwRX0sIn6ckt9JUwQDUy89Kf0gg0dOA31XlT7N1NZ669PhVLxPM7Wz7vpT0hzgaeDGiBj43arZ9344LRVQBnZqSJoArAb+PL10ALhKBWcAlwM/T9M156QyE4EvUZjiqUk7Jf1Gah+Srgb6I+L1NDw+LunyNDS/EXim0u0cS1sr3afp3/8Q8EZEfKfopQ3A8vR4OR/1zwZgWZqTngvMA3ZWo09ztbUO+3RIle7TXO2st/6UdDbwLLAqIv5mIHMtv/fDyr3KXy83Cn8tHwJ+TSGS3wTcRmFHxd8B9/DRgZ2fBP4HhQWv14H/GB/tAtkN/G167c9Iu2pq1M4OYC+FRbznKZwBdKCeTgof+jeB+wbK1FtbK92nFHbtRKr/5XT7bWAahU0C3el+alGZb6Z+20vRLplK92muttZpn+6nsIHj/fRZubDSfZqrnfXWnxT+UPtVUd6XgRnV+t6P5uYj5c3MLIuWmvIyM7PKcUAxM7MsHFDMzCwLBxQzM8vCAcXMzLJwQDEzsywcUMzMLAsHFDMzy+L/A9t1yP3IuE0bAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "yearly_incidence.plot(style='*')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2021 743449\n", + "2014 1600941\n", + "1991 1659249\n", + "1995 1840410\n", + "2020 2010315\n", + "2022 2060304\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": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n", + " française, sont assez rares: il y en eu trois au cours des 35 dernières années." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "yearly_incidence.hist(xrot=20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +2546,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } - -- 2.18.1