{ "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": [ "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", "\n", "| Nom de colonne | Libellé de colonne |\n", "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n", "| week | Semaine calendaire (ISO 8601) |\n", "| indicator | Code de l'indicateur de surveillance |\n", "| inc | Estimation de l'incidence de consultations en nombre de cas |\n", "| inc_low | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation |\n", "| inc_up | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation |\n", "| inc100 | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", "| inc100_low | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", "| inc100_up | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", "| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n", "| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n", "\n", "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020233938211270891.093333.0124107.0141.0FRFrance
120233836356755525.071609.09684.0108.0FRFrance
220233734908542079.056091.07463.085.0FRFrance
320233633824732237.044257.05849.067.0FRFrance
420233533169526013.037377.04839.057.0FRFrance
520233432666321057.032269.04032.048.0FRFrance
620233331914413161.025127.02920.038.0FRFrance
720233231464110285.018997.02215.029.0FRFrance
820233131528610705.019867.02316.030.0FRFrance
92023303132058647.017763.02013.027.0FRFrance
102023293111227113.015131.01711.023.0FRFrance
11202328391795703.012655.0149.019.0FRFrance
12202327389995763.012235.0149.019.0FRFrance
13202326390235934.012112.0149.019.0FRFrance
142023253100906739.013441.01510.020.0FRFrance
152023243113087639.014977.01711.023.0FRFrance
1620232331430010661.017939.02217.027.0FRFrance
1720232231830313822.022784.02821.035.0FRFrance
1820232131646012188.020732.02519.031.0FRFrance
1920232031616211963.020361.02418.030.0FRFrance
2020231931690112577.021225.02518.032.0FRFrance
2120231831992915402.024456.03023.037.0FRFrance
2220231732700721779.032235.04133.049.0FRFrance
2320231632787522767.032983.04234.050.0FRFrance
2420231533745530993.043917.05646.066.0FRFrance
2520231434806040671.055449.07261.083.0FRFrance
2620231336485956800.072918.09886.0110.0FRFrance
2720231237275064499.081001.010997.0121.0FRFrance
2820231137463866420.082856.0112100.0124.0FRFrance
2920231037636868243.084493.0115103.0127.0FRFrance
.................................
200119852132609619621.032571.04735.059.0FRFrance
200219852032789620885.034907.05138.064.0FRFrance
200319851934315432821.053487.07859.097.0FRFrance
200419851834055529935.051175.07455.093.0FRFrance
200519851733405324366.043740.06244.080.0FRFrance
200619851635036236451.064273.09166.0116.0FRFrance
200719851536388145538.082224.011683.0149.0FRFrance
20081985143134545114400.0154690.0244207.0281.0FRFrance
20091985133197206176080.0218332.0357319.0395.0FRFrance
20101985123245240223304.0267176.0445405.0485.0FRFrance
20111985113276205252399.0300011.0501458.0544.0FRFrance
20121985103353231326279.0380183.0640591.0689.0FRFrance
20131985093369895341109.0398681.0670618.0722.0FRFrance
20141985083389886359529.0420243.0707652.0762.0FRFrance
20151985073471852432599.0511105.0855784.0926.0FRFrance
20161985063565825518011.0613639.01026939.01113.0FRFrance
20171985053637302592795.0681809.011551074.01236.0FRFrance
20181985043424937390794.0459080.0770708.0832.0FRFrance
20191985033213901174689.0253113.0388317.0459.0FRFrance
202019850239758680949.0114223.0177147.0207.0FRFrance
202119850138548965918.0105060.0155120.0190.0FRFrance
202219845238483060602.0109058.0154110.0198.0FRFrance
2023198451310172680242.0123210.0185146.0224.0FRFrance
20241984503123680101401.0145959.0225184.0266.0FRFrance
2025198449310107381684.0120462.0184149.0219.0FRFrance
202619844837862060634.096606.0143110.0176.0FRFrance
202719844737202954274.089784.013199.0163.0FRFrance
202819844638733067686.0106974.0159123.0195.0FRFrance
20291984453135223101414.0169032.0246184.0308.0FRFrance
203019844436842220056.0116788.012537.0213.0FRFrance
\n", "

2031 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202339 3 82112 70891.0 93333.0 124 107.0 \n", "1 202338 3 63567 55525.0 71609.0 96 84.0 \n", "2 202337 3 49085 42079.0 56091.0 74 63.0 \n", "3 202336 3 38247 32237.0 44257.0 58 49.0 \n", "4 202335 3 31695 26013.0 37377.0 48 39.0 \n", "5 202334 3 26663 21057.0 32269.0 40 32.0 \n", "6 202333 3 19144 13161.0 25127.0 29 20.0 \n", "7 202332 3 14641 10285.0 18997.0 22 15.0 \n", "8 202331 3 15286 10705.0 19867.0 23 16.0 \n", "9 202330 3 13205 8647.0 17763.0 20 13.0 \n", "10 202329 3 11122 7113.0 15131.0 17 11.0 \n", "11 202328 3 9179 5703.0 12655.0 14 9.0 \n", "12 202327 3 8999 5763.0 12235.0 14 9.0 \n", "13 202326 3 9023 5934.0 12112.0 14 9.0 \n", "14 202325 3 10090 6739.0 13441.0 15 10.0 \n", "15 202324 3 11308 7639.0 14977.0 17 11.0 \n", "16 202323 3 14300 10661.0 17939.0 22 17.0 \n", "17 202322 3 18303 13822.0 22784.0 28 21.0 \n", "18 202321 3 16460 12188.0 20732.0 25 19.0 \n", "19 202320 3 16162 11963.0 20361.0 24 18.0 \n", "20 202319 3 16901 12577.0 21225.0 25 18.0 \n", "21 202318 3 19929 15402.0 24456.0 30 23.0 \n", "22 202317 3 27007 21779.0 32235.0 41 33.0 \n", "23 202316 3 27875 22767.0 32983.0 42 34.0 \n", "24 202315 3 37455 30993.0 43917.0 56 46.0 \n", "25 202314 3 48060 40671.0 55449.0 72 61.0 \n", "26 202313 3 64859 56800.0 72918.0 98 86.0 \n", "27 202312 3 72750 64499.0 81001.0 109 97.0 \n", "28 202311 3 74638 66420.0 82856.0 112 100.0 \n", "29 202310 3 76368 68243.0 84493.0 115 103.0 \n", "... ... ... ... ... ... ... ... \n", "2001 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2002 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2003 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2004 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2005 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2006 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2007 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2008 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2009 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2010 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2011 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2012 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2013 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2014 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2015 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2016 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2017 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2018 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2019 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2020 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2021 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2022 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2023 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2024 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2025 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2026 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2027 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2028 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2029 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2030 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 141.0 FR France \n", "1 108.0 FR France \n", "2 85.0 FR France \n", "3 67.0 FR France \n", "4 57.0 FR France \n", "5 48.0 FR France \n", "6 38.0 FR France \n", "7 29.0 FR France \n", "8 30.0 FR France \n", "9 27.0 FR France \n", "10 23.0 FR France \n", "11 19.0 FR France \n", "12 19.0 FR France \n", "13 19.0 FR France \n", "14 20.0 FR France \n", "15 23.0 FR France \n", "16 27.0 FR France \n", "17 35.0 FR France \n", "18 31.0 FR France \n", "19 30.0 FR France \n", "20 32.0 FR France \n", "21 37.0 FR France \n", "22 49.0 FR France \n", "23 50.0 FR France \n", "24 66.0 FR France \n", "25 83.0 FR France \n", "26 110.0 FR France \n", "27 121.0 FR France \n", "28 124.0 FR France \n", "29 127.0 FR France \n", "... ... ... ... \n", "2001 59.0 FR France \n", "2002 64.0 FR France \n", "2003 97.0 FR France \n", "2004 93.0 FR France \n", "2005 80.0 FR France \n", "2006 116.0 FR France \n", "2007 149.0 FR France \n", "2008 281.0 FR France \n", "2009 395.0 FR France \n", "2010 485.0 FR France \n", "2011 544.0 FR France \n", "2012 689.0 FR France \n", "2013 722.0 FR France \n", "2014 762.0 FR France \n", "2015 926.0 FR France \n", "2016 1113.0 FR France \n", "2017 1236.0 FR France \n", "2018 832.0 FR France \n", "2019 459.0 FR France \n", "2020 207.0 FR France \n", "2021 190.0 FR France \n", "2022 198.0 FR France \n", "2023 224.0 FR France \n", "2024 266.0 FR France \n", "2025 219.0 FR France \n", "2026 176.0 FR France \n", "2027 163.0 FR France \n", "2028 195.0 FR France \n", "2029 308.0 FR France \n", "2030 213.0 FR France \n", "\n", "[2031 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, 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": 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", "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
17941989193-NaNNaN-NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1794 198919 3 - NaN NaN - NaN NaN \n", "\n", " geo_insee geo_name \n", "1794 FR France " ] }, "execution_count": 4, "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": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020233938211270891.093333.0124107.0141.0FRFrance
120233836356755525.071609.09684.0108.0FRFrance
220233734908542079.056091.07463.085.0FRFrance
320233633824732237.044257.05849.067.0FRFrance
420233533169526013.037377.04839.057.0FRFrance
520233432666321057.032269.04032.048.0FRFrance
620233331914413161.025127.02920.038.0FRFrance
720233231464110285.018997.02215.029.0FRFrance
820233131528610705.019867.02316.030.0FRFrance
92023303132058647.017763.02013.027.0FRFrance
102023293111227113.015131.01711.023.0FRFrance
11202328391795703.012655.0149.019.0FRFrance
12202327389995763.012235.0149.019.0FRFrance
13202326390235934.012112.0149.019.0FRFrance
142023253100906739.013441.01510.020.0FRFrance
152023243113087639.014977.01711.023.0FRFrance
1620232331430010661.017939.02217.027.0FRFrance
1720232231830313822.022784.02821.035.0FRFrance
1820232131646012188.020732.02519.031.0FRFrance
1920232031616211963.020361.02418.030.0FRFrance
2020231931690112577.021225.02518.032.0FRFrance
2120231831992915402.024456.03023.037.0FRFrance
2220231732700721779.032235.04133.049.0FRFrance
2320231632787522767.032983.04234.050.0FRFrance
2420231533745530993.043917.05646.066.0FRFrance
2520231434806040671.055449.07261.083.0FRFrance
2620231336485956800.072918.09886.0110.0FRFrance
2720231237275064499.081001.010997.0121.0FRFrance
2820231137463866420.082856.0112100.0124.0FRFrance
2920231037636868243.084493.0115103.0127.0FRFrance
.................................
200119852132609619621.032571.04735.059.0FRFrance
200219852032789620885.034907.05138.064.0FRFrance
200319851934315432821.053487.07859.097.0FRFrance
200419851834055529935.051175.07455.093.0FRFrance
200519851733405324366.043740.06244.080.0FRFrance
200619851635036236451.064273.09166.0116.0FRFrance
200719851536388145538.082224.011683.0149.0FRFrance
20081985143134545114400.0154690.0244207.0281.0FRFrance
20091985133197206176080.0218332.0357319.0395.0FRFrance
20101985123245240223304.0267176.0445405.0485.0FRFrance
20111985113276205252399.0300011.0501458.0544.0FRFrance
20121985103353231326279.0380183.0640591.0689.0FRFrance
20131985093369895341109.0398681.0670618.0722.0FRFrance
20141985083389886359529.0420243.0707652.0762.0FRFrance
20151985073471852432599.0511105.0855784.0926.0FRFrance
20161985063565825518011.0613639.01026939.01113.0FRFrance
20171985053637302592795.0681809.011551074.01236.0FRFrance
20181985043424937390794.0459080.0770708.0832.0FRFrance
20191985033213901174689.0253113.0388317.0459.0FRFrance
202019850239758680949.0114223.0177147.0207.0FRFrance
202119850138548965918.0105060.0155120.0190.0FRFrance
202219845238483060602.0109058.0154110.0198.0FRFrance
2023198451310172680242.0123210.0185146.0224.0FRFrance
20241984503123680101401.0145959.0225184.0266.0FRFrance
2025198449310107381684.0120462.0184149.0219.0FRFrance
202619844837862060634.096606.0143110.0176.0FRFrance
202719844737202954274.089784.013199.0163.0FRFrance
202819844638733067686.0106974.0159123.0195.0FRFrance
20291984453135223101414.0169032.0246184.0308.0FRFrance
203019844436842220056.0116788.012537.0213.0FRFrance
\n", "

2030 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202339 3 82112 70891.0 93333.0 124 107.0 \n", "1 202338 3 63567 55525.0 71609.0 96 84.0 \n", "2 202337 3 49085 42079.0 56091.0 74 63.0 \n", "3 202336 3 38247 32237.0 44257.0 58 49.0 \n", "4 202335 3 31695 26013.0 37377.0 48 39.0 \n", "5 202334 3 26663 21057.0 32269.0 40 32.0 \n", "6 202333 3 19144 13161.0 25127.0 29 20.0 \n", "7 202332 3 14641 10285.0 18997.0 22 15.0 \n", "8 202331 3 15286 10705.0 19867.0 23 16.0 \n", "9 202330 3 13205 8647.0 17763.0 20 13.0 \n", "10 202329 3 11122 7113.0 15131.0 17 11.0 \n", "11 202328 3 9179 5703.0 12655.0 14 9.0 \n", "12 202327 3 8999 5763.0 12235.0 14 9.0 \n", "13 202326 3 9023 5934.0 12112.0 14 9.0 \n", "14 202325 3 10090 6739.0 13441.0 15 10.0 \n", "15 202324 3 11308 7639.0 14977.0 17 11.0 \n", "16 202323 3 14300 10661.0 17939.0 22 17.0 \n", "17 202322 3 18303 13822.0 22784.0 28 21.0 \n", "18 202321 3 16460 12188.0 20732.0 25 19.0 \n", "19 202320 3 16162 11963.0 20361.0 24 18.0 \n", "20 202319 3 16901 12577.0 21225.0 25 18.0 \n", "21 202318 3 19929 15402.0 24456.0 30 23.0 \n", "22 202317 3 27007 21779.0 32235.0 41 33.0 \n", "23 202316 3 27875 22767.0 32983.0 42 34.0 \n", "24 202315 3 37455 30993.0 43917.0 56 46.0 \n", "25 202314 3 48060 40671.0 55449.0 72 61.0 \n", "26 202313 3 64859 56800.0 72918.0 98 86.0 \n", "27 202312 3 72750 64499.0 81001.0 109 97.0 \n", "28 202311 3 74638 66420.0 82856.0 112 100.0 \n", "29 202310 3 76368 68243.0 84493.0 115 103.0 \n", "... ... ... ... ... ... ... ... \n", "2001 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2002 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2003 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2004 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2005 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2006 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2007 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2008 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2009 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2010 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2011 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2012 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2013 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2014 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2015 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2016 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2017 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2018 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2019 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2020 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2021 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2022 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2023 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2024 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2025 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2026 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2027 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2028 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2029 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2030 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 141.0 FR France \n", "1 108.0 FR France \n", "2 85.0 FR France \n", "3 67.0 FR France \n", "4 57.0 FR France \n", "5 48.0 FR France \n", "6 38.0 FR France \n", "7 29.0 FR France \n", "8 30.0 FR France \n", "9 27.0 FR France \n", "10 23.0 FR France \n", "11 19.0 FR France \n", "12 19.0 FR France \n", "13 19.0 FR France \n", "14 20.0 FR France \n", "15 23.0 FR France \n", "16 27.0 FR France \n", "17 35.0 FR France \n", "18 31.0 FR France \n", "19 30.0 FR France \n", "20 32.0 FR France \n", "21 37.0 FR France \n", "22 49.0 FR France \n", "23 50.0 FR France \n", "24 66.0 FR France \n", "25 83.0 FR France \n", "26 110.0 FR France \n", "27 121.0 FR France \n", "28 124.0 FR France \n", "29 127.0 FR France \n", "... ... ... ... \n", "2001 59.0 FR France \n", "2002 64.0 FR France \n", "2003 97.0 FR France \n", "2004 93.0 FR France \n", "2005 80.0 FR France \n", "2006 116.0 FR France \n", "2007 149.0 FR France \n", "2008 281.0 FR France \n", "2009 395.0 FR France \n", "2010 485.0 FR France \n", "2011 544.0 FR France \n", "2012 689.0 FR France \n", "2013 722.0 FR France \n", "2014 762.0 FR France \n", "2015 926.0 FR France \n", "2016 1113.0 FR France \n", "2017 1236.0 FR France \n", "2018 832.0 FR France \n", "2019 459.0 FR France \n", "2020 207.0 FR France \n", "2021 190.0 FR France \n", "2022 198.0 FR France \n", "2023 224.0 FR France \n", "2024 266.0 FR France \n", "2025 219.0 FR France \n", "2026 176.0 FR France \n", "2027 163.0 FR France \n", "2028 195.0 FR France \n", "2029 308.0 FR France \n", "2030 213.0 FR France \n", "\n", "[2030 rows x 10 columns]" ] }, "execution_count": 5, "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": 6, "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": 7, "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": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" ] } ], "source": [ "# verifier si la différence temporelle est supérieure à 1 seconde\n", "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", "# afficher les paires de périodes consécutives qui ont une difference temporelle\n", " print(p1, p2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un premier regard sur les données !" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "int64\n" ] } ], "source": [ "sorted_data['inc']=pd.to_numeric(sorted_data['inc'],errors='coerce')\n", "print(sorted_data['inc'].dtypes)" ] }, { "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": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "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": 11, "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": 12, "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": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "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": 15, "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": 15, "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": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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