{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek\n", "import os.path" ] }, { "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": 7, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://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": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202307311088298145.0123619.0167148.0186.0FRFrance
120230639807188232.0107910.0148133.0163.0FRFrance
220230539546986268.0104670.0144130.0158.0FRFrance
320230437490166916.082886.0113101.0125.0FRFrance
420230336957061893.077247.010593.0117.0FRFrance
520230237826070090.086430.0118106.0130.0FRFrance
62023013121773111024.0132522.0183167.0199.0FRFrance
72022523155383142015.0168751.0234214.0254.0FRFrance
82022513248311232120.0264502.0374350.0398.0FRFrance
92022503234279219533.0249025.0353331.0375.0FRFrance
102022493163421151727.0175115.0246228.0264.0FRFrance
112022483121884111932.0131836.0184169.0199.0FRFrance
1220224739644787259.0105635.0145131.0159.0FRFrance
1320224636773560075.075395.010290.0114.0FRFrance
1420224534530638909.051703.06858.078.0FRFrance
1520224433471328880.040546.05243.061.0FRFrance
1620224334476936884.052654.06856.080.0FRFrance
1720224234746240773.054151.07262.082.0FRFrance
1820224134858342388.054778.07364.082.0FRFrance
1920224034192736115.047739.06354.072.0FRFrance
2020223933990234168.045636.06051.069.0FRFrance
2120223832878123733.033829.04335.051.0FRFrance
2220223732139517076.025714.03225.039.0FRFrance
2320223631412010487.017753.02116.026.0FRFrance
24202235392836485.012081.01410.018.0FRFrance
25202234374984731.010265.0117.015.0FRFrance
26202233375864442.010730.0116.016.0FRFrance
272022323122227749.016695.01811.025.0FRFrance
282022313132578905.017609.02013.027.0FRFrance
2920223031500610738.019274.02317.029.0FRFrance
.................................
196919852132609619621.032571.04735.059.0FRFrance
197019852032789620885.034907.05138.064.0FRFrance
197119851934315432821.053487.07859.097.0FRFrance
197219851834055529935.051175.07455.093.0FRFrance
197319851733405324366.043740.06244.080.0FRFrance
197419851635036236451.064273.09166.0116.0FRFrance
197519851536388145538.082224.011683.0149.0FRFrance
19761985143134545114400.0154690.0244207.0281.0FRFrance
19771985133197206176080.0218332.0357319.0395.0FRFrance
19781985123245240223304.0267176.0445405.0485.0FRFrance
19791985113276205252399.0300011.0501458.0544.0FRFrance
19801985103353231326279.0380183.0640591.0689.0FRFrance
19811985093369895341109.0398681.0670618.0722.0FRFrance
19821985083389886359529.0420243.0707652.0762.0FRFrance
19831985073471852432599.0511105.0855784.0926.0FRFrance
19841985063565825518011.0613639.01026939.01113.0FRFrance
19851985053637302592795.0681809.011551074.01236.0FRFrance
19861985043424937390794.0459080.0770708.0832.0FRFrance
19871985033213901174689.0253113.0388317.0459.0FRFrance
198819850239758680949.0114223.0177147.0207.0FRFrance
198919850138548965918.0105060.0155120.0190.0FRFrance
199019845238483060602.0109058.0154110.0198.0FRFrance
1991198451310172680242.0123210.0185146.0224.0FRFrance
19921984503123680101401.0145959.0225184.0266.0FRFrance
1993198449310107381684.0120462.0184149.0219.0FRFrance
199419844837862060634.096606.0143110.0176.0FRFrance
199519844737202954274.089784.013199.0163.0FRFrance
199619844638733067686.0106974.0159123.0195.0FRFrance
19971984453135223101414.0169032.0246184.0308.0FRFrance
199819844436842220056.0116788.012537.0213.0FRFrance
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1999 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202307 3 110882 98145.0 123619.0 167 148.0 \n", "1 202306 3 98071 88232.0 107910.0 148 133.0 \n", "2 202305 3 95469 86268.0 104670.0 144 130.0 \n", "3 202304 3 74901 66916.0 82886.0 113 101.0 \n", "4 202303 3 69570 61893.0 77247.0 105 93.0 \n", "5 202302 3 78260 70090.0 86430.0 118 106.0 \n", "6 202301 3 121773 111024.0 132522.0 183 167.0 \n", "7 202252 3 155383 142015.0 168751.0 234 214.0 \n", "8 202251 3 248311 232120.0 264502.0 374 350.0 \n", "9 202250 3 234279 219533.0 249025.0 353 331.0 \n", "10 202249 3 163421 151727.0 175115.0 246 228.0 \n", "11 202248 3 121884 111932.0 131836.0 184 169.0 \n", "12 202247 3 96447 87259.0 105635.0 145 131.0 \n", "13 202246 3 67735 60075.0 75395.0 102 90.0 \n", "14 202245 3 45306 38909.0 51703.0 68 58.0 \n", "15 202244 3 34713 28880.0 40546.0 52 43.0 \n", "16 202243 3 44769 36884.0 52654.0 68 56.0 \n", "17 202242 3 47462 40773.0 54151.0 72 62.0 \n", "18 202241 3 48583 42388.0 54778.0 73 64.0 \n", "19 202240 3 41927 36115.0 47739.0 63 54.0 \n", "20 202239 3 39902 34168.0 45636.0 60 51.0 \n", "21 202238 3 28781 23733.0 33829.0 43 35.0 \n", "22 202237 3 21395 17076.0 25714.0 32 25.0 \n", "23 202236 3 14120 10487.0 17753.0 21 16.0 \n", "24 202235 3 9283 6485.0 12081.0 14 10.0 \n", "25 202234 3 7498 4731.0 10265.0 11 7.0 \n", "26 202233 3 7586 4442.0 10730.0 11 6.0 \n", "27 202232 3 12222 7749.0 16695.0 18 11.0 \n", "28 202231 3 13257 8905.0 17609.0 20 13.0 \n", "29 202230 3 15006 10738.0 19274.0 23 17.0 \n", "... ... ... ... ... ... ... ... \n", "1969 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1970 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1971 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1972 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1973 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1974 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1975 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1976 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1977 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1978 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1979 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1980 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1981 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1982 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1983 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1984 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1985 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1986 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1987 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1988 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1989 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1990 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1991 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1992 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1993 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1994 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1995 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1996 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1997 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1998 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 186.0 FR France \n", "1 163.0 FR France \n", "2 158.0 FR France \n", "3 125.0 FR France \n", "4 117.0 FR France \n", "5 130.0 FR France \n", "6 199.0 FR France \n", "7 254.0 FR France \n", "8 398.0 FR France \n", "9 375.0 FR France \n", "10 264.0 FR France \n", "11 199.0 FR France \n", "12 159.0 FR France \n", "13 114.0 FR France \n", "14 78.0 FR France \n", "15 61.0 FR France \n", "16 80.0 FR France \n", "17 82.0 FR France \n", "18 82.0 FR France \n", "19 72.0 FR France \n", "20 69.0 FR France \n", "21 51.0 FR France \n", "22 39.0 FR France \n", "23 26.0 FR France \n", "24 18.0 FR France \n", "25 15.0 FR France \n", "26 16.0 FR France \n", "27 25.0 FR France \n", "28 27.0 FR France \n", "29 29.0 FR France \n", "... ... ... ... \n", "1969 59.0 FR France \n", "1970 64.0 FR France \n", "1971 97.0 FR France \n", "1972 93.0 FR France \n", "1973 80.0 FR France \n", "1974 116.0 FR France \n", "1975 149.0 FR France \n", "1976 281.0 FR France \n", "1977 395.0 FR France \n", "1978 485.0 FR France \n", "1979 544.0 FR France \n", "1980 689.0 FR France \n", "1981 722.0 FR France \n", "1982 762.0 FR France \n", "1983 926.0 FR France \n", "1984 1113.0 FR France \n", "1985 1236.0 FR France \n", "1986 832.0 FR France \n", "1987 459.0 FR France \n", "1988 207.0 FR France \n", "1989 190.0 FR France \n", "1990 198.0 FR France \n", "1991 224.0 FR France \n", "1992 266.0 FR France \n", "1993 219.0 FR France \n", "1994 176.0 FR France \n", "1995 163.0 FR France \n", "1996 195.0 FR France \n", "1997 308.0 FR France \n", "1998 213.0 FR France \n", "\n", "[1999 rows x 10 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path='/home/mhashan/Documents/Formation/Recherche_reproductible/Module3/incidence-PAY-3.csv'\n", "if os.path.exists(path)==False:\n", " raw_data = pd.read_csv(data_url, skiprows=1)\n", "else:\n", " raw_data=pd.read_csv(path, skiprow=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": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
176219891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1762 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1762 FR France " ] }, "execution_count": 9, "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": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202307311088298145.0123619.0167148.0186.0FRFrance
120230639807188232.0107910.0148133.0163.0FRFrance
220230539546986268.0104670.0144130.0158.0FRFrance
320230437490166916.082886.0113101.0125.0FRFrance
420230336957061893.077247.010593.0117.0FRFrance
520230237826070090.086430.0118106.0130.0FRFrance
62023013121773111024.0132522.0183167.0199.0FRFrance
72022523155383142015.0168751.0234214.0254.0FRFrance
82022513248311232120.0264502.0374350.0398.0FRFrance
92022503234279219533.0249025.0353331.0375.0FRFrance
102022493163421151727.0175115.0246228.0264.0FRFrance
112022483121884111932.0131836.0184169.0199.0FRFrance
1220224739644787259.0105635.0145131.0159.0FRFrance
1320224636773560075.075395.010290.0114.0FRFrance
1420224534530638909.051703.06858.078.0FRFrance
1520224433471328880.040546.05243.061.0FRFrance
1620224334476936884.052654.06856.080.0FRFrance
1720224234746240773.054151.07262.082.0FRFrance
1820224134858342388.054778.07364.082.0FRFrance
1920224034192736115.047739.06354.072.0FRFrance
2020223933990234168.045636.06051.069.0FRFrance
2120223832878123733.033829.04335.051.0FRFrance
2220223732139517076.025714.03225.039.0FRFrance
2320223631412010487.017753.02116.026.0FRFrance
24202235392836485.012081.01410.018.0FRFrance
25202234374984731.010265.0117.015.0FRFrance
26202233375864442.010730.0116.016.0FRFrance
272022323122227749.016695.01811.025.0FRFrance
282022313132578905.017609.02013.027.0FRFrance
2920223031500610738.019274.02317.029.0FRFrance
.................................
196919852132609619621.032571.04735.059.0FRFrance
197019852032789620885.034907.05138.064.0FRFrance
197119851934315432821.053487.07859.097.0FRFrance
197219851834055529935.051175.07455.093.0FRFrance
197319851733405324366.043740.06244.080.0FRFrance
197419851635036236451.064273.09166.0116.0FRFrance
197519851536388145538.082224.011683.0149.0FRFrance
19761985143134545114400.0154690.0244207.0281.0FRFrance
19771985133197206176080.0218332.0357319.0395.0FRFrance
19781985123245240223304.0267176.0445405.0485.0FRFrance
19791985113276205252399.0300011.0501458.0544.0FRFrance
19801985103353231326279.0380183.0640591.0689.0FRFrance
19811985093369895341109.0398681.0670618.0722.0FRFrance
19821985083389886359529.0420243.0707652.0762.0FRFrance
19831985073471852432599.0511105.0855784.0926.0FRFrance
19841985063565825518011.0613639.01026939.01113.0FRFrance
19851985053637302592795.0681809.011551074.01236.0FRFrance
19861985043424937390794.0459080.0770708.0832.0FRFrance
19871985033213901174689.0253113.0388317.0459.0FRFrance
198819850239758680949.0114223.0177147.0207.0FRFrance
198919850138548965918.0105060.0155120.0190.0FRFrance
199019845238483060602.0109058.0154110.0198.0FRFrance
1991198451310172680242.0123210.0185146.0224.0FRFrance
19921984503123680101401.0145959.0225184.0266.0FRFrance
1993198449310107381684.0120462.0184149.0219.0FRFrance
199419844837862060634.096606.0143110.0176.0FRFrance
199519844737202954274.089784.013199.0163.0FRFrance
199619844638733067686.0106974.0159123.0195.0FRFrance
19971984453135223101414.0169032.0246184.0308.0FRFrance
199819844436842220056.0116788.012537.0213.0FRFrance
\n", "

1998 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202307 3 110882 98145.0 123619.0 167 148.0 \n", "1 202306 3 98071 88232.0 107910.0 148 133.0 \n", "2 202305 3 95469 86268.0 104670.0 144 130.0 \n", "3 202304 3 74901 66916.0 82886.0 113 101.0 \n", "4 202303 3 69570 61893.0 77247.0 105 93.0 \n", "5 202302 3 78260 70090.0 86430.0 118 106.0 \n", "6 202301 3 121773 111024.0 132522.0 183 167.0 \n", "7 202252 3 155383 142015.0 168751.0 234 214.0 \n", "8 202251 3 248311 232120.0 264502.0 374 350.0 \n", "9 202250 3 234279 219533.0 249025.0 353 331.0 \n", "10 202249 3 163421 151727.0 175115.0 246 228.0 \n", "11 202248 3 121884 111932.0 131836.0 184 169.0 \n", "12 202247 3 96447 87259.0 105635.0 145 131.0 \n", "13 202246 3 67735 60075.0 75395.0 102 90.0 \n", "14 202245 3 45306 38909.0 51703.0 68 58.0 \n", "15 202244 3 34713 28880.0 40546.0 52 43.0 \n", "16 202243 3 44769 36884.0 52654.0 68 56.0 \n", "17 202242 3 47462 40773.0 54151.0 72 62.0 \n", "18 202241 3 48583 42388.0 54778.0 73 64.0 \n", "19 202240 3 41927 36115.0 47739.0 63 54.0 \n", "20 202239 3 39902 34168.0 45636.0 60 51.0 \n", "21 202238 3 28781 23733.0 33829.0 43 35.0 \n", "22 202237 3 21395 17076.0 25714.0 32 25.0 \n", "23 202236 3 14120 10487.0 17753.0 21 16.0 \n", "24 202235 3 9283 6485.0 12081.0 14 10.0 \n", "25 202234 3 7498 4731.0 10265.0 11 7.0 \n", "26 202233 3 7586 4442.0 10730.0 11 6.0 \n", "27 202232 3 12222 7749.0 16695.0 18 11.0 \n", "28 202231 3 13257 8905.0 17609.0 20 13.0 \n", "29 202230 3 15006 10738.0 19274.0 23 17.0 \n", "... ... ... ... ... ... ... ... \n", "1969 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1970 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1971 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1972 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1973 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1974 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1975 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1976 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1977 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1978 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1979 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1980 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1981 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1982 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1983 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1984 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1985 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1986 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1987 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1988 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1989 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1990 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1991 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1992 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1993 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1994 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1995 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1996 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1997 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1998 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 186.0 FR France \n", "1 163.0 FR France \n", "2 158.0 FR France \n", "3 125.0 FR France \n", "4 117.0 FR France \n", "5 130.0 FR France \n", "6 199.0 FR France \n", "7 254.0 FR France \n", "8 398.0 FR France \n", "9 375.0 FR France \n", "10 264.0 FR France \n", "11 199.0 FR France \n", "12 159.0 FR France \n", "13 114.0 FR France \n", "14 78.0 FR France \n", "15 61.0 FR France \n", "16 80.0 FR France \n", "17 82.0 FR France \n", "18 82.0 FR France \n", "19 72.0 FR France \n", "20 69.0 FR France \n", "21 51.0 FR France \n", "22 39.0 FR France \n", "23 26.0 FR France \n", "24 18.0 FR France \n", "25 15.0 FR France \n", "26 16.0 FR France \n", "27 25.0 FR France \n", "28 27.0 FR France \n", "29 29.0 FR France \n", "... ... ... ... \n", "1969 59.0 FR France \n", "1970 64.0 FR France \n", "1971 97.0 FR France \n", "1972 93.0 FR France \n", "1973 80.0 FR France \n", "1974 116.0 FR France \n", "1975 149.0 FR France \n", "1976 281.0 FR France \n", "1977 395.0 FR France \n", "1978 485.0 FR France \n", "1979 544.0 FR France \n", "1980 689.0 FR France \n", "1981 722.0 FR France \n", "1982 762.0 FR France \n", "1983 926.0 FR France \n", "1984 1113.0 FR France \n", "1985 1236.0 FR France \n", "1986 832.0 FR France \n", "1987 459.0 FR France \n", "1988 207.0 FR France \n", "1989 190.0 FR France \n", "1990 198.0 FR France \n", "1991 224.0 FR France \n", "1992 266.0 FR France \n", "1993 219.0 FR France \n", "1994 176.0 FR France \n", "1995 163.0 FR France \n", "1996 195.0 FR France \n", "1997 308.0 FR France \n", "1998 213.0 FR France \n", "\n", "[1998 rows x 10 columns]" ] }, "execution_count": 10, "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": 11, "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": 12, "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": 13, "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": 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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" ] }, "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": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "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": 16, "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": 17, "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": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "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": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2021 743449\n", "2014 1600941\n", "1991 1659249\n", "1995 1840410\n", "2020 2010315\n", "2022 2060360\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": 19, "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": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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