{ "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
020231832342517651.029199.03526.044.0FRFrance
120231732707821826.032330.04133.049.0FRFrance
220231632787522767.032983.04234.050.0FRFrance
320231533745530993.043917.05646.066.0FRFrance
420231434806040671.055449.07261.083.0FRFrance
520231336485956800.072918.09886.0110.0FRFrance
620231237275064499.081001.010997.0121.0FRFrance
720231137463866420.082856.0112100.0124.0FRFrance
820231037636868243.084493.0115103.0127.0FRFrance
920230936206254778.069346.09382.0104.0FRFrance
1020230837639168065.084717.0115102.0128.0FRFrance
1120230738985180397.099305.0135121.0149.0FRFrance
1220230639736887636.0107100.0146131.0161.0FRFrance
1320230539546986268.0104670.0144130.0158.0FRFrance
1420230437490166916.082886.0113101.0125.0FRFrance
1520230336957061893.077247.010593.0117.0FRFrance
1620230237826070090.086430.0118106.0130.0FRFrance
172023013121773111024.0132522.0183167.0199.0FRFrance
182022523155371142004.0168738.0234214.0254.0FRFrance
192022513248319232128.0264510.0374350.0398.0FRFrance
202022503234143219402.0248884.0353331.0375.0FRFrance
212022493163384151691.0175077.0246228.0264.0FRFrance
222022483121691111744.0131638.0184169.0199.0FRFrance
2320224739641687230.0105602.0145131.0159.0FRFrance
2420224636773560075.075395.010290.0114.0FRFrance
2520224534530638909.051703.06858.078.0FRFrance
2620224433471328880.040546.05243.061.0FRFrance
2720224334476936884.052654.06856.080.0FRFrance
2820224234746240773.054151.07262.082.0FRFrance
2920224134858342388.054778.07364.082.0FRFrance
.................................
198019852132609619621.032571.04735.059.0FRFrance
198119852032789620885.034907.05138.064.0FRFrance
198219851934315432821.053487.07859.097.0FRFrance
198319851834055529935.051175.07455.093.0FRFrance
198419851733405324366.043740.06244.080.0FRFrance
198519851635036236451.064273.09166.0116.0FRFrance
198619851536388145538.082224.011683.0149.0FRFrance
19871985143134545114400.0154690.0244207.0281.0FRFrance
19881985133197206176080.0218332.0357319.0395.0FRFrance
19891985123245240223304.0267176.0445405.0485.0FRFrance
19901985113276205252399.0300011.0501458.0544.0FRFrance
19911985103353231326279.0380183.0640591.0689.0FRFrance
19921985093369895341109.0398681.0670618.0722.0FRFrance
19931985083389886359529.0420243.0707652.0762.0FRFrance
19941985073471852432599.0511105.0855784.0926.0FRFrance
19951985063565825518011.0613639.01026939.01113.0FRFrance
19961985053637302592795.0681809.011551074.01236.0FRFrance
19971985043424937390794.0459080.0770708.0832.0FRFrance
19981985033213901174689.0253113.0388317.0459.0FRFrance
199919850239758680949.0114223.0177147.0207.0FRFrance
200019850138548965918.0105060.0155120.0190.0FRFrance
200119845238483060602.0109058.0154110.0198.0FRFrance
2002198451310172680242.0123210.0185146.0224.0FRFrance
20031984503123680101401.0145959.0225184.0266.0FRFrance
2004198449310107381684.0120462.0184149.0219.0FRFrance
200519844837862060634.096606.0143110.0176.0FRFrance
200619844737202954274.089784.013199.0163.0FRFrance
200719844638733067686.0106974.0159123.0195.0FRFrance
20081984453135223101414.0169032.0246184.0308.0FRFrance
200919844436842220056.0116788.012537.0213.0FRFrance
\n", "

2010 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202318 3 23425 17651.0 29199.0 35 26.0 \n", "1 202317 3 27078 21826.0 32330.0 41 33.0 \n", "2 202316 3 27875 22767.0 32983.0 42 34.0 \n", "3 202315 3 37455 30993.0 43917.0 56 46.0 \n", "4 202314 3 48060 40671.0 55449.0 72 61.0 \n", "5 202313 3 64859 56800.0 72918.0 98 86.0 \n", "6 202312 3 72750 64499.0 81001.0 109 97.0 \n", "7 202311 3 74638 66420.0 82856.0 112 100.0 \n", "8 202310 3 76368 68243.0 84493.0 115 103.0 \n", "9 202309 3 62062 54778.0 69346.0 93 82.0 \n", "10 202308 3 76391 68065.0 84717.0 115 102.0 \n", "11 202307 3 89851 80397.0 99305.0 135 121.0 \n", "12 202306 3 97368 87636.0 107100.0 146 131.0 \n", "13 202305 3 95469 86268.0 104670.0 144 130.0 \n", "14 202304 3 74901 66916.0 82886.0 113 101.0 \n", "15 202303 3 69570 61893.0 77247.0 105 93.0 \n", "16 202302 3 78260 70090.0 86430.0 118 106.0 \n", "17 202301 3 121773 111024.0 132522.0 183 167.0 \n", "18 202252 3 155371 142004.0 168738.0 234 214.0 \n", "19 202251 3 248319 232128.0 264510.0 374 350.0 \n", "20 202250 3 234143 219402.0 248884.0 353 331.0 \n", "21 202249 3 163384 151691.0 175077.0 246 228.0 \n", "22 202248 3 121691 111744.0 131638.0 184 169.0 \n", "23 202247 3 96416 87230.0 105602.0 145 131.0 \n", "24 202246 3 67735 60075.0 75395.0 102 90.0 \n", "25 202245 3 45306 38909.0 51703.0 68 58.0 \n", "26 202244 3 34713 28880.0 40546.0 52 43.0 \n", "27 202243 3 44769 36884.0 52654.0 68 56.0 \n", "28 202242 3 47462 40773.0 54151.0 72 62.0 \n", "29 202241 3 48583 42388.0 54778.0 73 64.0 \n", "... ... ... ... ... ... ... ... \n", "1980 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1981 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1982 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1983 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1984 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1985 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1986 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1987 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1988 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1989 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1990 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1991 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1992 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1993 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1994 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1995 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1996 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1997 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1998 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1999 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2000 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2001 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2002 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2003 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2004 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2005 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2006 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2007 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2008 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2009 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 44.0 FR France \n", "1 49.0 FR France \n", "2 50.0 FR France \n", "3 66.0 FR France \n", "4 83.0 FR France \n", "5 110.0 FR France \n", "6 121.0 FR France \n", "7 124.0 FR France \n", "8 127.0 FR France \n", "9 104.0 FR France \n", "10 128.0 FR France \n", "11 149.0 FR France \n", "12 161.0 FR France \n", "13 158.0 FR France \n", "14 125.0 FR France \n", "15 117.0 FR France \n", "16 130.0 FR France \n", "17 199.0 FR France \n", "18 254.0 FR France \n", "19 398.0 FR France \n", "20 375.0 FR France \n", "21 264.0 FR France \n", "22 199.0 FR France \n", "23 159.0 FR France \n", "24 114.0 FR France \n", "25 78.0 FR France \n", "26 61.0 FR France \n", "27 80.0 FR France \n", "28 82.0 FR France \n", "29 82.0 FR France \n", "... ... ... ... \n", "1980 59.0 FR France \n", "1981 64.0 FR France \n", "1982 97.0 FR France \n", "1983 93.0 FR France \n", "1984 80.0 FR France \n", "1985 116.0 FR France \n", "1986 149.0 FR France \n", "1987 281.0 FR France \n", "1988 395.0 FR France \n", "1989 485.0 FR France \n", "1990 544.0 FR France \n", "1991 689.0 FR France \n", "1992 722.0 FR France \n", "1993 762.0 FR France \n", "1994 926.0 FR France \n", "1995 1113.0 FR France \n", "1996 1236.0 FR France \n", "1997 832.0 FR France \n", "1998 459.0 FR France \n", "1999 207.0 FR France \n", "2000 190.0 FR France \n", "2001 198.0 FR France \n", "2002 224.0 FR France \n", "2003 266.0 FR France \n", "2004 219.0 FR France \n", "2005 176.0 FR France \n", "2006 163.0 FR France \n", "2007 195.0 FR France \n", "2008 308.0 FR France \n", "2009 213.0 FR France \n", "\n", "[2010 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": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
177319891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1773 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1773 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
020231832342517651.029199.03526.044.0FRFrance
120231732707821826.032330.04133.049.0FRFrance
220231632787522767.032983.04234.050.0FRFrance
320231533745530993.043917.05646.066.0FRFrance
420231434806040671.055449.07261.083.0FRFrance
520231336485956800.072918.09886.0110.0FRFrance
620231237275064499.081001.010997.0121.0FRFrance
720231137463866420.082856.0112100.0124.0FRFrance
820231037636868243.084493.0115103.0127.0FRFrance
920230936206254778.069346.09382.0104.0FRFrance
1020230837639168065.084717.0115102.0128.0FRFrance
1120230738985180397.099305.0135121.0149.0FRFrance
1220230639736887636.0107100.0146131.0161.0FRFrance
1320230539546986268.0104670.0144130.0158.0FRFrance
1420230437490166916.082886.0113101.0125.0FRFrance
1520230336957061893.077247.010593.0117.0FRFrance
1620230237826070090.086430.0118106.0130.0FRFrance
172023013121773111024.0132522.0183167.0199.0FRFrance
182022523155371142004.0168738.0234214.0254.0FRFrance
192022513248319232128.0264510.0374350.0398.0FRFrance
202022503234143219402.0248884.0353331.0375.0FRFrance
212022493163384151691.0175077.0246228.0264.0FRFrance
222022483121691111744.0131638.0184169.0199.0FRFrance
2320224739641687230.0105602.0145131.0159.0FRFrance
2420224636773560075.075395.010290.0114.0FRFrance
2520224534530638909.051703.06858.078.0FRFrance
2620224433471328880.040546.05243.061.0FRFrance
2720224334476936884.052654.06856.080.0FRFrance
2820224234746240773.054151.07262.082.0FRFrance
2920224134858342388.054778.07364.082.0FRFrance
.................................
198019852132609619621.032571.04735.059.0FRFrance
198119852032789620885.034907.05138.064.0FRFrance
198219851934315432821.053487.07859.097.0FRFrance
198319851834055529935.051175.07455.093.0FRFrance
198419851733405324366.043740.06244.080.0FRFrance
198519851635036236451.064273.09166.0116.0FRFrance
198619851536388145538.082224.011683.0149.0FRFrance
19871985143134545114400.0154690.0244207.0281.0FRFrance
19881985133197206176080.0218332.0357319.0395.0FRFrance
19891985123245240223304.0267176.0445405.0485.0FRFrance
19901985113276205252399.0300011.0501458.0544.0FRFrance
19911985103353231326279.0380183.0640591.0689.0FRFrance
19921985093369895341109.0398681.0670618.0722.0FRFrance
19931985083389886359529.0420243.0707652.0762.0FRFrance
19941985073471852432599.0511105.0855784.0926.0FRFrance
19951985063565825518011.0613639.01026939.01113.0FRFrance
19961985053637302592795.0681809.011551074.01236.0FRFrance
19971985043424937390794.0459080.0770708.0832.0FRFrance
19981985033213901174689.0253113.0388317.0459.0FRFrance
199919850239758680949.0114223.0177147.0207.0FRFrance
200019850138548965918.0105060.0155120.0190.0FRFrance
200119845238483060602.0109058.0154110.0198.0FRFrance
2002198451310172680242.0123210.0185146.0224.0FRFrance
20031984503123680101401.0145959.0225184.0266.0FRFrance
2004198449310107381684.0120462.0184149.0219.0FRFrance
200519844837862060634.096606.0143110.0176.0FRFrance
200619844737202954274.089784.013199.0163.0FRFrance
200719844638733067686.0106974.0159123.0195.0FRFrance
20081984453135223101414.0169032.0246184.0308.0FRFrance
200919844436842220056.0116788.012537.0213.0FRFrance
\n", "

2009 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202318 3 23425 17651.0 29199.0 35 26.0 \n", "1 202317 3 27078 21826.0 32330.0 41 33.0 \n", "2 202316 3 27875 22767.0 32983.0 42 34.0 \n", "3 202315 3 37455 30993.0 43917.0 56 46.0 \n", "4 202314 3 48060 40671.0 55449.0 72 61.0 \n", "5 202313 3 64859 56800.0 72918.0 98 86.0 \n", "6 202312 3 72750 64499.0 81001.0 109 97.0 \n", "7 202311 3 74638 66420.0 82856.0 112 100.0 \n", "8 202310 3 76368 68243.0 84493.0 115 103.0 \n", "9 202309 3 62062 54778.0 69346.0 93 82.0 \n", "10 202308 3 76391 68065.0 84717.0 115 102.0 \n", "11 202307 3 89851 80397.0 99305.0 135 121.0 \n", "12 202306 3 97368 87636.0 107100.0 146 131.0 \n", "13 202305 3 95469 86268.0 104670.0 144 130.0 \n", "14 202304 3 74901 66916.0 82886.0 113 101.0 \n", "15 202303 3 69570 61893.0 77247.0 105 93.0 \n", "16 202302 3 78260 70090.0 86430.0 118 106.0 \n", "17 202301 3 121773 111024.0 132522.0 183 167.0 \n", "18 202252 3 155371 142004.0 168738.0 234 214.0 \n", "19 202251 3 248319 232128.0 264510.0 374 350.0 \n", "20 202250 3 234143 219402.0 248884.0 353 331.0 \n", "21 202249 3 163384 151691.0 175077.0 246 228.0 \n", "22 202248 3 121691 111744.0 131638.0 184 169.0 \n", "23 202247 3 96416 87230.0 105602.0 145 131.0 \n", "24 202246 3 67735 60075.0 75395.0 102 90.0 \n", "25 202245 3 45306 38909.0 51703.0 68 58.0 \n", "26 202244 3 34713 28880.0 40546.0 52 43.0 \n", "27 202243 3 44769 36884.0 52654.0 68 56.0 \n", "28 202242 3 47462 40773.0 54151.0 72 62.0 \n", "29 202241 3 48583 42388.0 54778.0 73 64.0 \n", "... ... ... ... ... ... ... ... \n", "1980 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1981 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1982 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1983 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1984 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1985 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1986 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1987 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1988 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1989 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1990 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1991 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1992 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1993 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1994 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1995 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1996 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1997 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1998 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1999 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2000 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2001 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2002 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2003 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2004 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2005 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2006 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2007 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2008 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2009 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 44.0 FR France \n", "1 49.0 FR France \n", "2 50.0 FR France \n", "3 66.0 FR France \n", "4 83.0 FR France \n", "5 110.0 FR France \n", "6 121.0 FR France \n", "7 124.0 FR France \n", "8 127.0 FR France \n", "9 104.0 FR France \n", "10 128.0 FR France \n", "11 149.0 FR France \n", "12 161.0 FR France \n", "13 158.0 FR France \n", "14 125.0 FR France \n", "15 117.0 FR France \n", "16 130.0 FR France \n", "17 199.0 FR France \n", "18 254.0 FR France \n", "19 398.0 FR France \n", "20 375.0 FR France \n", "21 264.0 FR France \n", "22 199.0 FR France \n", "23 159.0 FR France \n", "24 114.0 FR France \n", "25 78.0 FR France \n", "26 61.0 FR France \n", "27 80.0 FR France \n", "28 82.0 FR France \n", "29 82.0 FR France \n", "... ... ... ... \n", "1980 59.0 FR France \n", "1981 64.0 FR France \n", "1982 97.0 FR France \n", "1983 93.0 FR France \n", "1984 80.0 FR France \n", "1985 116.0 FR France \n", "1986 149.0 FR France \n", "1987 281.0 FR France \n", "1988 395.0 FR France \n", "1989 485.0 FR France \n", "1990 544.0 FR France \n", "1991 689.0 FR France \n", "1992 722.0 FR France \n", "1993 762.0 FR France \n", "1994 926.0 FR France \n", "1995 1113.0 FR France \n", "1996 1236.0 FR France \n", "1997 832.0 FR France \n", "1998 459.0 FR France \n", "1999 207.0 FR France \n", "2000 190.0 FR France \n", "2001 198.0 FR France \n", "2002 224.0 FR France \n", "2003 266.0 FR France \n", "2004 219.0 FR France \n", "2005 176.0 FR France \n", "2006 163.0 FR France \n", "2007 195.0 FR France \n", "2008 308.0 FR France \n", "2009 213.0 FR France \n", "\n", "[2009 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": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "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": 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": 14, "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": 14, "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": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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