{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 34, "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": 49, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n", "file = \"./incidence-PAY-3.csv\" \n", "\n", "#Je verifie si le fichier contenant les données appelé \"incidence-PAY-3.csv\" existe. \n", "\n", "try: #Si y'a existence, je lis les données\n", " raw_data = pd.read_csv(file) \n", " \n", "except FileNotFoundError: #Dans le cas contraire\n", " raw_data = pd.read_csv(data_url, skiprows=1) #je lis les données à partir de l'url en ignorant la premiére ligne représentant un commentaire\n", " raw_data.to_csv(file, index=False) # je je télécharge les données dans un fichier en interdisant l'ajout d'une colonne contenant le numéro de ligne i.e. index = False" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "scrolled": 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", "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020214432613620345.031927.04031.049.0FRFrance
120214332730222077.032527.04133.049.0FRFrance
220214232834323382.033304.04335.051.0FRFrance
320214132504320586.029500.03831.045.0FRFrance
420214032628621842.030730.04033.047.0FRFrance
520213932215518014.026296.03428.040.0FRFrance
620213831561412310.018918.02419.029.0FRFrance
720213731367310404.016942.02116.026.0FRFrance
82021363102897505.013073.01612.020.0FRFrance
92021353126099282.015936.01914.024.0FRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "0 202144 3 26136 20345.0 31927.0 40 31.0 49.0 \n", "1 202143 3 27302 22077.0 32527.0 41 33.0 49.0 \n", "2 202142 3 28343 23382.0 33304.0 43 35.0 51.0 \n", "3 202141 3 25043 20586.0 29500.0 38 31.0 45.0 \n", "4 202140 3 26286 21842.0 30730.0 40 33.0 47.0 \n", "5 202139 3 22155 18014.0 26296.0 34 28.0 40.0 \n", "6 202138 3 15614 12310.0 18918.0 24 19.0 29.0 \n", "7 202137 3 13673 10404.0 16942.0 21 16.0 26.0 \n", "8 202136 3 10289 7505.0 13073.0 16 12.0 20.0 \n", "9 202135 3 12609 9282.0 15936.0 19 14.0 24.0 \n", "\n", " geo_insee geo_name \n", "0 FR France \n", "1 FR France \n", "2 FR France \n", "3 FR France \n", "4 FR France \n", "5 FR France \n", "6 FR France \n", "7 FR France \n", "8 FR France \n", "9 FR France " ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[:10]" ] }, { "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": "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": 52, "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
169519891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1695 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1695 FR France " ] }, "execution_count": 52, "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": 53, "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
020214432613620345.031927.04031.049.0FRFrance
120214332730222077.032527.04133.049.0FRFrance
220214232834323382.033304.04335.051.0FRFrance
320214132504320586.029500.03831.045.0FRFrance
420214032628621842.030730.04033.047.0FRFrance
520213932215518014.026296.03428.040.0FRFrance
620213831561412310.018918.02419.029.0FRFrance
720213731367310404.016942.02116.026.0FRFrance
82021363102897505.013073.01612.020.0FRFrance
92021353126099282.015936.01914.024.0FRFrance
102021343130159485.016545.02015.025.0FRFrance
112021333103927042.013742.01611.021.0FRFrance
1220213231558611009.020163.02417.031.0FRFrance
1320213131885513664.024046.02921.037.0FRFrance
142021303139919695.018287.02114.028.0FRFrance
152021293136269618.017634.02115.027.0FRFrance
16202128386365430.011842.0138.018.0FRFrance
172021273106936838.014548.01610.022.0FRFrance
18202126370864109.010063.0116.016.0FRFrance
19202125379425540.010344.0128.016.0FRFrance
20202124348553011.06699.074.010.0FRFrance
21202123367104455.08965.0107.013.0FRFrance
22202122378795495.010263.0128.016.0FRFrance
23202121378275403.010251.0128.016.0FRFrance
242021203102787540.013016.01612.020.0FRFrance
25202119395396860.012218.01410.018.0FRFrance
262021183121359165.015105.01814.022.0FRFrance
272021173120588891.015225.01813.023.0FRFrance
2820211631650512735.020275.02519.031.0FRFrance
2920211531930615398.023214.02923.035.0FRFrance
.................................
190219852132609619621.032571.04735.059.0FRFrance
190319852032789620885.034907.05138.064.0FRFrance
190419851934315432821.053487.07859.097.0FRFrance
190519851834055529935.051175.07455.093.0FRFrance
190619851733405324366.043740.06244.080.0FRFrance
190719851635036236451.064273.09166.0116.0FRFrance
190819851536388145538.082224.011683.0149.0FRFrance
19091985143134545114400.0154690.0244207.0281.0FRFrance
19101985133197206176080.0218332.0357319.0395.0FRFrance
19111985123245240223304.0267176.0445405.0485.0FRFrance
19121985113276205252399.0300011.0501458.0544.0FRFrance
19131985103353231326279.0380183.0640591.0689.0FRFrance
19141985093369895341109.0398681.0670618.0722.0FRFrance
19151985083389886359529.0420243.0707652.0762.0FRFrance
19161985073471852432599.0511105.0855784.0926.0FRFrance
19171985063565825518011.0613639.01026939.01113.0FRFrance
19181985053637302592795.0681809.011551074.01236.0FRFrance
19191985043424937390794.0459080.0770708.0832.0FRFrance
19201985033213901174689.0253113.0388317.0459.0FRFrance
192119850239758680949.0114223.0177147.0207.0FRFrance
192219850138548965918.0105060.0155120.0190.0FRFrance
192319845238483060602.0109058.0154110.0198.0FRFrance
1924198451310172680242.0123210.0185146.0224.0FRFrance
19251984503123680101401.0145959.0225184.0266.0FRFrance
1926198449310107381684.0120462.0184149.0219.0FRFrance
192719844837862060634.096606.0143110.0176.0FRFrance
192819844737202954274.089784.013199.0163.0FRFrance
192919844638733067686.0106974.0159123.0195.0FRFrance
19301984453135223101414.0169032.0246184.0308.0FRFrance
193119844436842220056.0116788.012537.0213.0FRFrance
\n", "

1931 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202144 3 26136 20345.0 31927.0 40 31.0 \n", "1 202143 3 27302 22077.0 32527.0 41 33.0 \n", "2 202142 3 28343 23382.0 33304.0 43 35.0 \n", "3 202141 3 25043 20586.0 29500.0 38 31.0 \n", "4 202140 3 26286 21842.0 30730.0 40 33.0 \n", "5 202139 3 22155 18014.0 26296.0 34 28.0 \n", "6 202138 3 15614 12310.0 18918.0 24 19.0 \n", "7 202137 3 13673 10404.0 16942.0 21 16.0 \n", "8 202136 3 10289 7505.0 13073.0 16 12.0 \n", "9 202135 3 12609 9282.0 15936.0 19 14.0 \n", "10 202134 3 13015 9485.0 16545.0 20 15.0 \n", "11 202133 3 10392 7042.0 13742.0 16 11.0 \n", "12 202132 3 15586 11009.0 20163.0 24 17.0 \n", "13 202131 3 18855 13664.0 24046.0 29 21.0 \n", "14 202130 3 13991 9695.0 18287.0 21 14.0 \n", "15 202129 3 13626 9618.0 17634.0 21 15.0 \n", "16 202128 3 8636 5430.0 11842.0 13 8.0 \n", "17 202127 3 10693 6838.0 14548.0 16 10.0 \n", "18 202126 3 7086 4109.0 10063.0 11 6.0 \n", "19 202125 3 7942 5540.0 10344.0 12 8.0 \n", "20 202124 3 4855 3011.0 6699.0 7 4.0 \n", "21 202123 3 6710 4455.0 8965.0 10 7.0 \n", "22 202122 3 7879 5495.0 10263.0 12 8.0 \n", "23 202121 3 7827 5403.0 10251.0 12 8.0 \n", "24 202120 3 10278 7540.0 13016.0 16 12.0 \n", "25 202119 3 9539 6860.0 12218.0 14 10.0 \n", "26 202118 3 12135 9165.0 15105.0 18 14.0 \n", "27 202117 3 12058 8891.0 15225.0 18 13.0 \n", "28 202116 3 16505 12735.0 20275.0 25 19.0 \n", "29 202115 3 19306 15398.0 23214.0 29 23.0 \n", "... ... ... ... ... ... ... ... \n", "1902 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1903 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1904 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1905 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1906 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1907 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1908 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1909 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1910 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1911 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1912 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1913 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1914 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1915 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1916 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1917 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1918 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1919 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1920 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1921 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1922 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1923 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1924 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1925 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1926 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1927 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1928 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1929 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1930 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1931 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 49.0 FR France \n", "1 49.0 FR France \n", "2 51.0 FR France \n", "3 45.0 FR France \n", "4 47.0 FR France \n", "5 40.0 FR France \n", "6 29.0 FR France \n", "7 26.0 FR France \n", "8 20.0 FR France \n", "9 24.0 FR France \n", "10 25.0 FR France \n", "11 21.0 FR France \n", "12 31.0 FR France \n", "13 37.0 FR France \n", "14 28.0 FR France \n", "15 27.0 FR France \n", "16 18.0 FR France \n", "17 22.0 FR France \n", "18 16.0 FR France \n", "19 16.0 FR France \n", "20 10.0 FR France \n", "21 13.0 FR France \n", "22 16.0 FR France \n", "23 16.0 FR France \n", "24 20.0 FR France \n", "25 18.0 FR France \n", "26 22.0 FR France \n", "27 23.0 FR France \n", "28 31.0 FR France \n", "29 35.0 FR France \n", "... ... ... ... \n", "1902 59.0 FR France \n", "1903 64.0 FR France \n", "1904 97.0 FR France \n", "1905 93.0 FR France \n", "1906 80.0 FR France \n", "1907 116.0 FR France \n", "1908 149.0 FR France \n", "1909 281.0 FR France \n", "1910 395.0 FR France \n", "1911 485.0 FR France \n", "1912 544.0 FR France \n", "1913 689.0 FR France \n", "1914 722.0 FR France \n", "1915 762.0 FR France \n", "1916 926.0 FR France \n", "1917 1113.0 FR France \n", "1918 1236.0 FR France \n", "1919 832.0 FR France \n", "1920 459.0 FR France \n", "1921 207.0 FR France \n", "1922 190.0 FR France \n", "1923 198.0 FR France \n", "1924 224.0 FR France \n", "1925 266.0 FR France \n", "1926 219.0 FR France \n", "1927 176.0 FR France \n", "1928 163.0 FR France \n", "1929 195.0 FR France \n", "1930 308.0 FR France \n", "1931 213.0 FR France \n", "\n", "[1931 rows x 10 columns]" ] }, "execution_count": 53, "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": 54, "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": 55, "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": null, "metadata": {}, "outputs": [], "source": [ "periods = sorted_data.index\n", "for p1, p2 in zip(periods[:-1], periods[1:]):\n", " delta = p2.to_timestamp() - p1.end_time\n", " if delta > pd.Timedelta('1s'):\n", " print(p1, p2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un premier regard sur les données !" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 56, "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": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 57, "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": 58, "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": 59, "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": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 60, "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.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": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2014 1600941\n", "1991 1659249\n", "1995 1840410\n", "2020 2053781\n", "2012 2175217\n", "2003 2234584\n", "2019 2254386\n", "2006 2307352\n", "2017 2321583\n", "2001 2529279\n", "1992 2574578\n", "1993 2703886\n", "2018 2705325\n", "1988 2765617\n", "2007 2780164\n", "1987 2855570\n", "2016 2856393\n", "2011 2857040\n", "2008 2973918\n", "1998 3034904\n", "2002 3125418\n", "2009 3444020\n", "1994 3514763\n", "1996 3539413\n", "2004 3567744\n", "1997 3620066\n", "2015 3654892\n", "2000 3826372\n", "2005 3835025\n", "1999 3908112\n", "2010 4111392\n", "2013 4182691\n", "1986 5115251\n", "1990 5235827\n", "1989 5466192\n", "dtype: int64" ] }, "execution_count": 61, "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": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 62, "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": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 }