diff --git a/module3/exo2/exercice_fr.ipynb b/module3/exo2/exercice_fr.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..594afc614fd2e8532d4bf3f25c39aeaeb27684a0 100644 --- a/module3/exo2/exercice_fr.ipynb +++ b/module3/exo2/exercice_fr.ipynb @@ -1,5 +1,1776 @@ { - "cells": [], + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence du syndrome grippal" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek" + ] + }, + { + "attachments": {}, + "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": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "data_file = \"incidence-PAY-3.csv\"" + ] + }, + { + "attachments": {}, + "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": 5, + "metadata": { + "collapsed": false + }, + "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", + "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02023273101145977.014251.0159.021.0FRFrance
1202326390925970.012214.0149.019.0FRFrance
22023253100906739.013441.01510.020.0FRFrance
32023243113087639.014977.01711.023.0FRFrance
420232331430010661.017939.02217.027.0FRFrance
.................................
201419844837862060634.096606.0143110.0176.0FRFrance
201519844737202954274.089784.013199.0163.0FRFrance
201619844638733067686.0106974.0159123.0195.0FRFrance
20171984453135223101414.0169032.0246184.0308.0FRFrance
201819844436842220056.0116788.012537.0213.0FRFrance
\n", + "

2019 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202327 3 10114 5977.0 14251.0 15 9.0 \n", + "1 202326 3 9092 5970.0 12214.0 14 9.0 \n", + "2 202325 3 10090 6739.0 13441.0 15 10.0 \n", + "3 202324 3 11308 7639.0 14977.0 17 11.0 \n", + "4 202323 3 14300 10661.0 17939.0 22 17.0 \n", + "... ... ... ... ... ... ... ... \n", + "2014 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2015 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2016 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2017 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2018 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 21.0 FR France \n", + "1 19.0 FR France \n", + "2 20.0 FR France \n", + "3 23.0 FR France \n", + "4 27.0 FR France \n", + "... ... ... ... \n", + "2014 176.0 FR France \n", + "2015 163.0 FR France \n", + "2016 195.0 FR France \n", + "2017 308.0 FR France \n", + "2018 213.0 FR France \n", + "\n", + "[2019 rows x 10 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(data_file, encoding = 'iso-8859-1', sep=\",\")\n", + "raw_data" + ] + }, + { + "attachments": {}, + "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": 4, + "metadata": { + "collapsed": false + }, + "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
153619891930NaNNaN0NaNNaNFRFrance
\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", + "1536 198919 3 0 NaN NaN 0 NaN NaN \n", + "\n", + " geo_insee geo_name \n", + "1536 FR France " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "attachments": {}, + "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": 5, + "metadata": { + "collapsed": false + }, + "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
0201842378325145.010519.0128.016.0FRFrance
1201841380485098.010998.0128.016.0FRFrance
2201840374094717.010101.0117.015.0FRFrance
3201839371744235.010113.0117.015.0FRFrance
4201838361273482.08772.095.013.0FRFrance
5201837346442200.07088.073.011.0FRFrance
6201836332151349.05081.052.08.0FRFrance
720183531506239.02773.020.04.0FRFrance
820183431368116.02620.020.04.0FRFrance
9201833319625.03919.030.06.0FRFrance
1020183231839183.03495.030.06.0FRFrance
1120183132048242.03854.030.06.0FRFrance
1220183031951202.03700.030.06.0FRFrance
1320182931951252.03650.030.06.0FRFrance
142018283165452.03256.031.05.0FRFrance
15201827332691145.05393.052.08.0FRFrance
16201826337581493.06023.063.09.0FRFrance
17201825345802220.06940.073.011.0FRFrance
18201824332231351.05095.052.08.0FRFrance
1920182331207136.02278.020.04.0FRFrance
20201822332021330.05074.052.08.0FRFrance
2120182132537763.04311.041.07.0FRFrance
2220182032694967.04421.041.07.0FRFrance
23201819310250.02098.020.04.0FRFrance
24201818335411416.05666.052.08.0FRFrance
25201817325731003.04143.042.06.0FRFrance
26201816348182724.06912.074.010.0FRFrance
2720181531631112168.020454.02519.031.0FRFrance
2820181432266618092.027240.03528.042.0FRFrance
2920181333268025536.039824.05039.061.0FRFrance
.................................
174319852132609619621.032571.04735.059.0FRFrance
174419852032789620885.034907.05138.064.0FRFrance
174519851934315432821.053487.07859.097.0FRFrance
174619851834055529935.051175.07455.093.0FRFrance
174719851733405324366.043740.06244.080.0FRFrance
174819851635036236451.064273.09166.0116.0FRFrance
174919851536388145538.082224.011683.0149.0FRFrance
17501985143134545114400.0154690.0244207.0281.0FRFrance
17511985133197206176080.0218332.0357319.0395.0FRFrance
17521985123245240223304.0267176.0445405.0485.0FRFrance
17531985113276205252399.0300011.0501458.0544.0FRFrance
17541985103353231326279.0380183.0640591.0689.0FRFrance
17551985093369895341109.0398681.0670618.0722.0FRFrance
17561985083389886359529.0420243.0707652.0762.0FRFrance
17571985073471852432599.0511105.0855784.0926.0FRFrance
17581985063565825518011.0613639.01026939.01113.0FRFrance
17591985053637302592795.0681809.011551074.01236.0FRFrance
17601985043424937390794.0459080.0770708.0832.0FRFrance
17611985033213901174689.0253113.0388317.0459.0FRFrance
176219850239758680949.0114223.0177147.0207.0FRFrance
176319850138548965918.0105060.0155120.0190.0FRFrance
176419845238483060602.0109058.0154110.0198.0FRFrance
1765198451310172680242.0123210.0185146.0224.0FRFrance
17661984503123680101401.0145959.0225184.0266.0FRFrance
1767198449310107381684.0120462.0184149.0219.0FRFrance
176819844837862060634.096606.0143110.0176.0FRFrance
176919844737202954274.089784.013199.0163.0FRFrance
177019844638733067686.0106974.0159123.0195.0FRFrance
17711984453135223101414.0169032.0246184.0308.0FRFrance
177219844436842220056.0116788.012537.0213.0FRFrance
\n", + "

1772 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 201842 3 7832 5145.0 10519.0 12 8.0 \n", + "1 201841 3 8048 5098.0 10998.0 12 8.0 \n", + "2 201840 3 7409 4717.0 10101.0 11 7.0 \n", + "3 201839 3 7174 4235.0 10113.0 11 7.0 \n", + "4 201838 3 6127 3482.0 8772.0 9 5.0 \n", + "5 201837 3 4644 2200.0 7088.0 7 3.0 \n", + "6 201836 3 3215 1349.0 5081.0 5 2.0 \n", + "7 201835 3 1506 239.0 2773.0 2 0.0 \n", + "8 201834 3 1368 116.0 2620.0 2 0.0 \n", + "9 201833 3 1962 5.0 3919.0 3 0.0 \n", + "10 201832 3 1839 183.0 3495.0 3 0.0 \n", + "11 201831 3 2048 242.0 3854.0 3 0.0 \n", + "12 201830 3 1951 202.0 3700.0 3 0.0 \n", + "13 201829 3 1951 252.0 3650.0 3 0.0 \n", + "14 201828 3 1654 52.0 3256.0 3 1.0 \n", + "15 201827 3 3269 1145.0 5393.0 5 2.0 \n", + "16 201826 3 3758 1493.0 6023.0 6 3.0 \n", + "17 201825 3 4580 2220.0 6940.0 7 3.0 \n", + "18 201824 3 3223 1351.0 5095.0 5 2.0 \n", + "19 201823 3 1207 136.0 2278.0 2 0.0 \n", + "20 201822 3 3202 1330.0 5074.0 5 2.0 \n", + "21 201821 3 2537 763.0 4311.0 4 1.0 \n", + "22 201820 3 2694 967.0 4421.0 4 1.0 \n", + "23 201819 3 1025 0.0 2098.0 2 0.0 \n", + "24 201818 3 3541 1416.0 5666.0 5 2.0 \n", + "25 201817 3 2573 1003.0 4143.0 4 2.0 \n", + "26 201816 3 4818 2724.0 6912.0 7 4.0 \n", + "27 201815 3 16311 12168.0 20454.0 25 19.0 \n", + "28 201814 3 22666 18092.0 27240.0 35 28.0 \n", + "29 201813 3 32680 25536.0 39824.0 50 39.0 \n", + "... ... ... ... ... ... ... ... \n", + "1743 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1744 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1745 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1746 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1747 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1748 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1749 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1750 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1751 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1752 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1753 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1754 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1755 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1756 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1757 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1758 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1759 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1760 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1761 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1762 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1763 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1764 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1765 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1766 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1767 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1768 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1769 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1770 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1771 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1772 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 16.0 FR France \n", + "1 16.0 FR France \n", + "2 15.0 FR France \n", + "3 15.0 FR France \n", + "4 13.0 FR France \n", + "5 11.0 FR France \n", + "6 8.0 FR France \n", + "7 4.0 FR France \n", + "8 4.0 FR France \n", + "9 6.0 FR France \n", + "10 6.0 FR France \n", + "11 6.0 FR France \n", + "12 6.0 FR France \n", + "13 6.0 FR France \n", + "14 5.0 FR France \n", + "15 8.0 FR France \n", + "16 9.0 FR France \n", + "17 11.0 FR France \n", + "18 8.0 FR France \n", + "19 4.0 FR France \n", + "20 8.0 FR France \n", + "21 7.0 FR France \n", + "22 7.0 FR France \n", + "23 4.0 FR France \n", + "24 8.0 FR France \n", + "25 6.0 FR France \n", + "26 10.0 FR France \n", + "27 31.0 FR France \n", + "28 42.0 FR France \n", + "29 61.0 FR France \n", + "... ... ... ... \n", + "1743 59.0 FR France \n", + "1744 64.0 FR France \n", + "1745 97.0 FR France \n", + "1746 93.0 FR France \n", + "1747 80.0 FR France \n", + "1748 116.0 FR France \n", + "1749 149.0 FR France \n", + "1750 281.0 FR France \n", + "1751 395.0 FR France \n", + "1752 485.0 FR France \n", + "1753 544.0 FR France \n", + "1754 689.0 FR France \n", + "1755 722.0 FR France \n", + "1756 762.0 FR France \n", + "1757 926.0 FR France \n", + "1758 1113.0 FR France \n", + "1759 1236.0 FR France \n", + "1760 832.0 FR France \n", + "1761 459.0 FR France \n", + "1762 207.0 FR France \n", + "1763 190.0 FR France \n", + "1764 198.0 FR France \n", + "1765 224.0 FR France \n", + "1766 266.0 FR France \n", + "1767 219.0 FR France \n", + "1768 176.0 FR France \n", + "1769 163.0 FR France \n", + "1770 195.0 FR France \n", + "1771 308.0 FR France \n", + "1772 213.0 FR France \n", + "\n", + "[1772 rows x 10 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = raw_data.dropna().copy()\n", + "data" + ] + }, + { + "attachments": {}, + "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": 6, + "metadata": { + "collapsed": false + }, + "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']]" + ] + }, + { + "attachments": {}, + "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": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "attachments": {}, + "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": 8, + "metadata": { + "collapsed": false + }, + "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)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un premier regard sur les données !" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'].plot()" + ] + }, + { + "attachments": {}, + "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": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'][-200:].plot()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Etude de l'incidence annuelle" + ] + }, + { + "attachments": {}, + "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": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", + " for y in range(1985,\n", + " sorted_data.index[-1].year)]" + ] + }, + { + "attachments": {}, + "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": 12, + "metadata": { + "collapsed": false + }, + "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)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voici les incidences annuelles." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "yearly_incidence.plot(style='*')" + ] + }, + { + "attachments": {}, + "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": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2014 1600941\n", + "1991 1659249\n", + "1995 1840410\n", + "2012 2175217\n", + "2003 2234584\n", + "2006 2307352\n", + "2017 2321583\n", + "2001 2529279\n", + "1992 2574578\n", + "1993 2703886\n", + "1988 2765617\n", + "2007 2780164\n", + "1987 2855570\n", + "2016 2856393\n", + "2011 2857040\n", + "2008 2973918\n", + "1998 3034904\n", + "2002 3125418\n", + "2009 3444020\n", + "1994 3514763\n", + "1996 3539413\n", + "2004 3567744\n", + "1997 3620066\n", + "2015 3654892\n", + "2000 3826372\n", + "2005 3835025\n", + "1999 3908112\n", + "2010 4111392\n", + "2013 4182691\n", + "1986 5115251\n", + "1990 5235827\n", + "1989 5466192\n", + "dtype: int64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "attachments": {}, + "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": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "yearly_incidence.hist(xrot=20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +1787,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.8.5" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 0 } -