{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek\n", "import os" ] }, { "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": 17, "metadata": {}, "outputs": [], "source": [ "remote_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n", "local_path = r\"\\module3\\exo3\\incidence-PAY-3.csv\"\n", "if os.path.exists(local_path):\n", " data_url = local_path\n", "else:\n", " data = pd.read_csv(remote_url)\n", " data.to_csv(local_path)\n", " data_url = local_path" ] }, { "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": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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2027 rows × 17 columns

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" ], "text/plain": [ " 0 week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 1 202335 3 40131 29724.0 50538.0 60 44.0 \n", "1 2 202334 3 26848 21168.0 32528.0 40 31.0 \n", "2 3 202333 3 19144 13161.0 25127.0 29 20.0 \n", "3 4 202332 3 14641 10285.0 18997.0 22 15.0 \n", "4 5 202331 3 15286 10705.0 19867.0 23 16.0 \n", "5 6 202330 3 13205 8647.0 17763.0 20 13.0 \n", "6 7 202329 3 11122 7113.0 15131.0 17 11.0 \n", "7 8 202328 3 9179 5703.0 12655.0 14 9.0 \n", "8 9 202327 3 8999 5763.0 12235.0 14 9.0 \n", "9 10 202326 3 9023 5934.0 12112.0 14 9.0 \n", "10 11 202325 3 10090 6739.0 13441.0 15 10.0 \n", "11 12 202324 3 11308 7639.0 14977.0 17 11.0 \n", "12 13 202323 3 14300 10661.0 17939.0 22 17.0 \n", "13 14 202322 3 18303 13822.0 22784.0 28 21.0 \n", "14 15 202321 3 16460 12188.0 20732.0 25 19.0 \n", "15 16 202320 3 16162 11963.0 20361.0 24 18.0 \n", "16 17 202319 3 16901 12577.0 21225.0 25 18.0 \n", "17 18 202318 3 19929 15402.0 24456.0 30 23.0 \n", "18 19 202317 3 27007 21779.0 32235.0 41 33.0 \n", "19 20 202316 3 27875 22767.0 32983.0 42 34.0 \n", "20 21 202315 3 37455 30993.0 43917.0 56 46.0 \n", "21 22 202314 3 48060 40671.0 55449.0 72 61.0 \n", "22 23 202313 3 64859 56800.0 72918.0 98 86.0 \n", "23 24 202312 3 72750 64499.0 81001.0 109 97.0 \n", "24 25 202311 3 74638 66420.0 82856.0 112 100.0 \n", "25 26 202310 3 76368 68243.0 84493.0 115 103.0 \n", "26 27 202309 3 62062 54778.0 69346.0 93 82.0 \n", "27 28 202308 3 76391 68065.0 84717.0 115 102.0 \n", "28 29 202307 3 89851 80397.0 99305.0 135 121.0 \n", "29 30 202306 3 97368 87636.0 107100.0 146 131.0 \n", "... ... ... ... ... ... ... ... ... \n", "1997 1998 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1998 1999 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1999 2000 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2000 2001 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2001 2002 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2002 2003 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2003 2004 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2004 2005 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2005 2006 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2006 2007 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2007 2008 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2008 2009 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2009 2010 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2010 2011 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2011 2012 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2012 2013 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2013 2014 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2014 2015 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2015 2016 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2016 2017 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2017 2018 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2018 2019 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2019 2020 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2020 2021 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2021 2022 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2022 2023 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2023 2024 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2024 2025 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2025 2026 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2026 2027 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name Unnamed: 11 Unnamed: 12 Unnamed: 13 \\\n", "0 76.0 FR France NaN NaN NaN \n", "1 49.0 FR France NaN NaN NaN \n", "2 38.0 FR France NaN NaN NaN \n", "3 29.0 FR France NaN NaN NaN \n", "4 30.0 FR France NaN NaN NaN \n", "5 27.0 FR France NaN NaN NaN \n", "6 23.0 FR France NaN NaN NaN \n", "7 19.0 FR France NaN NaN NaN \n", "8 19.0 FR France NaN NaN NaN \n", "9 19.0 FR France NaN NaN NaN \n", "10 20.0 FR France NaN NaN NaN \n", "11 23.0 FR France NaN NaN NaN \n", "12 27.0 FR France NaN NaN NaN \n", "13 35.0 FR France NaN NaN NaN \n", "14 31.0 FR France NaN NaN NaN \n", "15 30.0 FR France NaN NaN NaN \n", "16 32.0 FR France NaN NaN NaN \n", "17 37.0 FR France NaN NaN NaN \n", "18 49.0 FR France NaN NaN NaN \n", "19 50.0 FR France NaN NaN NaN \n", "20 66.0 FR France NaN NaN NaN \n", "21 83.0 FR France NaN NaN NaN \n", "22 110.0 FR France NaN NaN NaN \n", "23 121.0 FR France NaN NaN NaN \n", "24 124.0 FR France NaN NaN NaN \n", "25 127.0 FR France NaN NaN NaN \n", "26 104.0 FR France NaN NaN NaN \n", "27 128.0 FR France NaN NaN NaN \n", "28 149.0 FR France NaN NaN NaN \n", "29 161.0 FR France NaN NaN NaN \n", "... ... ... ... ... ... ... \n", "1997 59.0 FR France NaN NaN NaN \n", "1998 64.0 FR France NaN NaN NaN \n", "1999 97.0 FR France NaN NaN NaN \n", "2000 93.0 FR France NaN NaN NaN \n", "2001 80.0 FR France NaN NaN NaN \n", "2002 116.0 FR France NaN NaN NaN \n", "2003 149.0 FR France NaN NaN NaN \n", "2004 281.0 FR France NaN NaN NaN \n", "2005 395.0 FR France NaN NaN NaN \n", "2006 485.0 FR France NaN NaN NaN \n", "2007 544.0 FR France NaN NaN NaN \n", "2008 689.0 FR France NaN NaN NaN \n", "2009 722.0 FR France NaN NaN NaN \n", "2010 762.0 FR France NaN NaN NaN \n", "2011 926.0 FR France NaN NaN NaN \n", "2012 1113.0 FR France NaN NaN NaN \n", "2013 1236.0 FR France NaN NaN NaN \n", "2014 832.0 FR France NaN NaN NaN \n", "2015 459.0 FR France NaN NaN NaN \n", "2016 207.0 FR France NaN NaN NaN \n", "2017 190.0 FR France NaN NaN NaN \n", "2018 198.0 FR France NaN NaN NaN \n", "2019 224.0 FR France NaN NaN NaN \n", "2020 266.0 FR France NaN NaN NaN \n", "2021 219.0 FR France NaN NaN NaN \n", "2022 176.0 FR France NaN NaN NaN \n", "2023 163.0 FR France NaN NaN NaN \n", "2024 195.0 FR France NaN NaN NaN \n", "2025 308.0 FR France NaN NaN NaN \n", "2026 213.0 FR France NaN NaN NaN \n", "\n", " Unnamed: 14 Unnamed: 15 Unnamed: 16 \n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "5 NaN NaN NaN \n", "6 NaN NaN NaN \n", "7 NaN NaN NaN \n", "8 NaN NaN NaN \n", "9 NaN NaN NaN \n", "10 NaN NaN NaN \n", "11 NaN NaN NaN \n", "12 NaN NaN NaN \n", "13 NaN NaN NaN \n", "14 NaN NaN NaN \n", "15 NaN NaN NaN \n", "16 NaN NaN NaN \n", "17 NaN NaN NaN \n", "18 NaN NaN NaN \n", "19 NaN NaN NaN \n", "20 NaN NaN NaN \n", "21 NaN NaN NaN \n", "22 NaN NaN NaN \n", "23 NaN NaN NaN \n", "24 NaN NaN NaN \n", "25 NaN NaN NaN \n", "26 NaN NaN NaN \n", "27 NaN NaN NaN \n", "28 NaN NaN NaN \n", "29 NaN NaN NaN \n", "... ... ... ... \n", "1997 NaN NaN NaN \n", "1998 NaN NaN NaN \n", "1999 NaN NaN NaN \n", "2000 NaN NaN NaN \n", "2001 NaN NaN NaN \n", "2002 NaN NaN NaN \n", "2003 NaN NaN NaN \n", "2004 NaN NaN NaN \n", "2005 NaN NaN NaN \n", "2006 NaN NaN NaN \n", "2007 NaN NaN NaN \n", "2008 NaN NaN NaN \n", "2009 NaN NaN NaN \n", "2010 NaN NaN NaN \n", "2011 NaN NaN NaN \n", "2012 NaN NaN NaN \n", "2013 NaN NaN NaN \n", "2014 NaN NaN NaN \n", "2015 NaN NaN NaN \n", "2016 NaN NaN NaN \n", "2017 NaN NaN NaN \n", "2018 NaN NaN NaN \n", "2019 NaN NaN NaN \n", "2020 NaN NaN NaN \n", "2021 NaN NaN NaN \n", "2022 NaN NaN NaN \n", "2023 NaN NaN NaN \n", "2024 NaN NaN NaN \n", "2025 NaN NaN NaN \n", "2026 NaN NaN NaN \n", "\n", "[2027 rows x 17 columns]" ] }, "execution_count": 18, "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": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
17901989193-NaNNaN-NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1790 198919 3 - NaN NaN - NaN NaN \n", "\n", " geo_insee geo_name \n", "1790 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
020233534013129724.050538.06044.076.0FRFrance
120233432684821168.032528.04031.049.0FRFrance
220233331914413161.025127.02920.038.0FRFrance
320233231464110285.018997.02215.029.0FRFrance
420233131528610705.019867.02316.030.0FRFrance
52023303132058647.017763.02013.027.0FRFrance
62023293111227113.015131.01711.023.0FRFrance
7202328391795703.012655.0149.019.0FRFrance
8202327389995763.012235.0149.019.0FRFrance
9202326390235934.012112.0149.019.0FRFrance
102023253100906739.013441.01510.020.0FRFrance
112023243113087639.014977.01711.023.0FRFrance
1220232331430010661.017939.02217.027.0FRFrance
1320232231830313822.022784.02821.035.0FRFrance
1420232131646012188.020732.02519.031.0FRFrance
1520232031616211963.020361.02418.030.0FRFrance
1620231931690112577.021225.02518.032.0FRFrance
1720231831992915402.024456.03023.037.0FRFrance
1820231732700721779.032235.04133.049.0FRFrance
1920231632787522767.032983.04234.050.0FRFrance
2020231533745530993.043917.05646.066.0FRFrance
2120231434806040671.055449.07261.083.0FRFrance
2220231336485956800.072918.09886.0110.0FRFrance
2320231237275064499.081001.010997.0121.0FRFrance
2420231137463866420.082856.0112100.0124.0FRFrance
2520231037636868243.084493.0115103.0127.0FRFrance
2620230936206254778.069346.09382.0104.0FRFrance
2720230837639168065.084717.0115102.0128.0FRFrance
2820230738985180397.099305.0135121.0149.0FRFrance
2920230639736887636.0107100.0146131.0161.0FRFrance
.................................
199719852132609619621.032571.04735.059.0FRFrance
199819852032789620885.034907.05138.064.0FRFrance
199919851934315432821.053487.07859.097.0FRFrance
200019851834055529935.051175.07455.093.0FRFrance
200119851733405324366.043740.06244.080.0FRFrance
200219851635036236451.064273.09166.0116.0FRFrance
200319851536388145538.082224.011683.0149.0FRFrance
20041985143134545114400.0154690.0244207.0281.0FRFrance
20051985133197206176080.0218332.0357319.0395.0FRFrance
20061985123245240223304.0267176.0445405.0485.0FRFrance
20071985113276205252399.0300011.0501458.0544.0FRFrance
20081985103353231326279.0380183.0640591.0689.0FRFrance
20091985093369895341109.0398681.0670618.0722.0FRFrance
20101985083389886359529.0420243.0707652.0762.0FRFrance
20111985073471852432599.0511105.0855784.0926.0FRFrance
20121985063565825518011.0613639.01026939.01113.0FRFrance
20131985053637302592795.0681809.011551074.01236.0FRFrance
20141985043424937390794.0459080.0770708.0832.0FRFrance
20151985033213901174689.0253113.0388317.0459.0FRFrance
201619850239758680949.0114223.0177147.0207.0FRFrance
201719850138548965918.0105060.0155120.0190.0FRFrance
201819845238483060602.0109058.0154110.0198.0FRFrance
2019198451310172680242.0123210.0185146.0224.0FRFrance
20201984503123680101401.0145959.0225184.0266.0FRFrance
2021198449310107381684.0120462.0184149.0219.0FRFrance
202219844837862060634.096606.0143110.0176.0FRFrance
202319844737202954274.089784.013199.0163.0FRFrance
202419844638733067686.0106974.0159123.0195.0FRFrance
20251984453135223101414.0169032.0246184.0308.0FRFrance
202619844436842220056.0116788.012537.0213.0FRFrance
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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202335 3 40131 29724.0 50538.0 60 44.0 \n", "1 202334 3 26848 21168.0 32528.0 40 31.0 \n", "2 202333 3 19144 13161.0 25127.0 29 20.0 \n", "3 202332 3 14641 10285.0 18997.0 22 15.0 \n", "4 202331 3 15286 10705.0 19867.0 23 16.0 \n", "5 202330 3 13205 8647.0 17763.0 20 13.0 \n", "6 202329 3 11122 7113.0 15131.0 17 11.0 \n", "7 202328 3 9179 5703.0 12655.0 14 9.0 \n", "8 202327 3 8999 5763.0 12235.0 14 9.0 \n", "9 202326 3 9023 5934.0 12112.0 14 9.0 \n", "10 202325 3 10090 6739.0 13441.0 15 10.0 \n", "11 202324 3 11308 7639.0 14977.0 17 11.0 \n", "12 202323 3 14300 10661.0 17939.0 22 17.0 \n", "13 202322 3 18303 13822.0 22784.0 28 21.0 \n", "14 202321 3 16460 12188.0 20732.0 25 19.0 \n", "15 202320 3 16162 11963.0 20361.0 24 18.0 \n", "16 202319 3 16901 12577.0 21225.0 25 18.0 \n", "17 202318 3 19929 15402.0 24456.0 30 23.0 \n", "18 202317 3 27007 21779.0 32235.0 41 33.0 \n", "19 202316 3 27875 22767.0 32983.0 42 34.0 \n", "20 202315 3 37455 30993.0 43917.0 56 46.0 \n", "21 202314 3 48060 40671.0 55449.0 72 61.0 \n", "22 202313 3 64859 56800.0 72918.0 98 86.0 \n", "23 202312 3 72750 64499.0 81001.0 109 97.0 \n", "24 202311 3 74638 66420.0 82856.0 112 100.0 \n", "25 202310 3 76368 68243.0 84493.0 115 103.0 \n", "26 202309 3 62062 54778.0 69346.0 93 82.0 \n", "27 202308 3 76391 68065.0 84717.0 115 102.0 \n", "28 202307 3 89851 80397.0 99305.0 135 121.0 \n", "29 202306 3 97368 87636.0 107100.0 146 131.0 \n", "... ... ... ... ... ... ... ... \n", "1997 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1998 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1999 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2000 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2001 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2002 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2003 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2004 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2005 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2006 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2007 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2008 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2009 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2010 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2011 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2012 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2013 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2014 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2015 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2016 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2017 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2018 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2019 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2020 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2021 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2022 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2023 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2024 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2025 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2026 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 76.0 FR France \n", "1 49.0 FR France \n", "2 38.0 FR France \n", "3 29.0 FR France \n", "4 30.0 FR France \n", "5 27.0 FR France \n", "6 23.0 FR France \n", "7 19.0 FR France \n", "8 19.0 FR France \n", "9 19.0 FR France \n", "10 20.0 FR France \n", "11 23.0 FR France \n", "12 27.0 FR France \n", "13 35.0 FR France \n", "14 31.0 FR France \n", "15 30.0 FR France \n", "16 32.0 FR France \n", "17 37.0 FR France \n", "18 49.0 FR France \n", "19 50.0 FR France \n", "20 66.0 FR France \n", "21 83.0 FR France \n", "22 110.0 FR France \n", "23 121.0 FR France \n", "24 124.0 FR France \n", "25 127.0 FR France \n", "26 104.0 FR France \n", "27 128.0 FR France \n", "28 149.0 FR France \n", "29 161.0 FR France \n", "... ... ... ... \n", "1997 59.0 FR France \n", "1998 64.0 FR France \n", "1999 97.0 FR France \n", "2000 93.0 FR France \n", "2001 80.0 FR France \n", "2002 116.0 FR France \n", "2003 149.0 FR France \n", "2004 281.0 FR France \n", "2005 395.0 FR France \n", "2006 485.0 FR France \n", "2007 544.0 FR France \n", "2008 689.0 FR France \n", "2009 722.0 FR France \n", "2010 762.0 FR France \n", "2011 926.0 FR France \n", "2012 1113.0 FR France \n", "2013 1236.0 FR France \n", "2014 832.0 FR France \n", "2015 459.0 FR France \n", "2016 207.0 FR France \n", "2017 190.0 FR France \n", "2018 198.0 FR France \n", "2019 224.0 FR France \n", "2020 266.0 FR France \n", "2021 219.0 FR France \n", "2022 176.0 FR France \n", "2023 163.0 FR France \n", "2024 195.0 FR France \n", "2025 308.0 FR France \n", "2026 213.0 FR France \n", "\n", "[2026 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": [ "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": 9, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "Empty 'DataFrame': no numeric data to plot", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msorted_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 2501\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 1925\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1926\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 1928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m 1727\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1729\u001b[0;31m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1730\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 365\u001b[0;31m 'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot" ] } ], "source": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sorted_data['inc'][-200:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Etude de l'incidence annuelle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n", "entre deux années civiles, nous définissons la période de référence\n", "entre deux minima de l'incidence, du 1er août de l'année $N$ au\n", "1er août de l'année $N+1$.\n", "\n", "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n", "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n", "de référence: à la place du 1er août de chaque année, nous utilisons le\n", "premier jour de la semaine qui contient le 1er août.\n", "\n", "Comme l'incidence de syndrome grippal est très faible en été, cette\n", "modification ne risque pas de fausser nos conclusions.\n", "\n", "Encore un petit détail: les données commencent an octobre 1984, ce qui\n", "rend la première année incomplète. Nous commençons donc l'analyse en 1985." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", " for y in range(1985,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n", "\n", "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_august_week[:-1],\n", " first_august_week[1:]):\n", " one_year = sorted_data['inc'][week1:week2-1]\n", " assert abs(len(one_year)-52) < 2\n", " yearly_incidence.append(one_year.sum())\n", " year.append(week2.year)\n", "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voici les incidences annuelles." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "yearly_incidence.plot(style='*')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n", " française, sont assez rares: il y en eu trois au cours des 35 dernières années." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "yearly_incidence.hist(xrot=20)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "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 }