{ "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
020251533881331411.046215.05847.069.0FRFrance
120251433787031403.044337.05747.067.0FRFrance
220251333967333686.045660.05950.068.0FRFrance
320251235254345627.059459.07868.088.0FRFrance
420251135946952154.066784.08978.0100.0FRFrance
520251036033453048.067620.09079.0101.0FRFrance
620250938453174994.094068.0126112.0140.0FRFrance
72025083136020124824.0147216.0203186.0220.0FRFrance
82025073208952195988.0221916.0312293.0331.0FRFrance
92025063273519258159.0288879.0408385.0431.0FRFrance
102025053334395318416.0350374.0499475.0523.0FRFrance
112025043350043332885.0367201.0522496.0548.0FRFrance
122025033252772238917.0266627.0377356.0398.0FRFrance
132025023257247242991.0271503.0384363.0405.0FRFrance
142025013231549214627.0248471.0345320.0370.0FRFrance
152024523201726185870.0217582.0302278.0326.0FRFrance
162024513201697187843.0215551.0302281.0323.0FRFrance
172024503136694126369.0147019.0205190.0220.0FRFrance
18202449310848799037.0117937.0163149.0177.0FRFrance
1920244838738178687.096075.0131118.0144.0FRFrance
2020244737628667626.084946.0114101.0127.0FRFrance
2120244635639949006.063792.08574.096.0FRFrance
2220244534734740843.053851.07161.081.0FRFrance
2320244433603930122.041956.05445.063.0FRFrance
2420244334657239928.053216.07060.080.0FRFrance
2520244236778560009.075561.010290.0114.0FRFrance
2620244137943571386.087484.0119107.0131.0FRFrance
2720244038496576555.093375.0127114.0140.0FRFrance
2820243939166082937.0100383.0137124.0150.0FRFrance
2920243839178682903.0100669.0138125.0151.0FRFrance
.................................
208119852132609619621.032571.04735.059.0FRFrance
208219852032789620885.034907.05138.064.0FRFrance
208319851934315432821.053487.07859.097.0FRFrance
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208619851635036236451.064273.09166.0116.0FRFrance
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20881985143134545114400.0154690.0244207.0281.0FRFrance
20891985133197206176080.0218332.0357319.0395.0FRFrance
20901985123245240223304.0267176.0445405.0485.0FRFrance
20911985113276205252399.0300011.0501458.0544.0FRFrance
20921985103353231326279.0380183.0640591.0689.0FRFrance
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20961985063565825518011.0613639.01026939.01113.0FRFrance
20971985053637302592795.0681809.011551074.01236.0FRFrance
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210019850239758680949.0114223.0177147.0207.0FRFrance
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210219845238483060602.0109058.0154110.0198.0FRFrance
2103198451310172680242.0123210.0185146.0224.0FRFrance
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211019844436842220056.0116788.012537.0213.0FRFrance
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2111 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202515 3 38813 31411.0 46215.0 58 47.0 \n", "1 202514 3 37870 31403.0 44337.0 57 47.0 \n", "2 202513 3 39673 33686.0 45660.0 59 50.0 \n", "3 202512 3 52543 45627.0 59459.0 78 68.0 \n", "4 202511 3 59469 52154.0 66784.0 89 78.0 \n", "5 202510 3 60334 53048.0 67620.0 90 79.0 \n", "6 202509 3 84531 74994.0 94068.0 126 112.0 \n", "7 202508 3 136020 124824.0 147216.0 203 186.0 \n", "8 202507 3 208952 195988.0 221916.0 312 293.0 \n", "9 202506 3 273519 258159.0 288879.0 408 385.0 \n", "10 202505 3 334395 318416.0 350374.0 499 475.0 \n", "11 202504 3 350043 332885.0 367201.0 522 496.0 \n", "12 202503 3 252772 238917.0 266627.0 377 356.0 \n", "13 202502 3 257247 242991.0 271503.0 384 363.0 \n", "14 202501 3 231549 214627.0 248471.0 345 320.0 \n", "15 202452 3 201726 185870.0 217582.0 302 278.0 \n", "16 202451 3 201697 187843.0 215551.0 302 281.0 \n", "17 202450 3 136694 126369.0 147019.0 205 190.0 \n", "18 202449 3 108487 99037.0 117937.0 163 149.0 \n", "19 202448 3 87381 78687.0 96075.0 131 118.0 \n", "20 202447 3 76286 67626.0 84946.0 114 101.0 \n", "21 202446 3 56399 49006.0 63792.0 85 74.0 \n", "22 202445 3 47347 40843.0 53851.0 71 61.0 \n", "23 202444 3 36039 30122.0 41956.0 54 45.0 \n", "24 202443 3 46572 39928.0 53216.0 70 60.0 \n", "25 202442 3 67785 60009.0 75561.0 102 90.0 \n", "26 202441 3 79435 71386.0 87484.0 119 107.0 \n", "27 202440 3 84965 76555.0 93375.0 127 114.0 \n", "28 202439 3 91660 82937.0 100383.0 137 124.0 \n", "29 202438 3 91786 82903.0 100669.0 138 125.0 \n", "... ... ... ... ... ... ... ... \n", "2081 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2082 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2083 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2084 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2085 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2086 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2087 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2088 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2089 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2090 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2091 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2092 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2093 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2094 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2095 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2096 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2097 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2098 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2099 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2100 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2101 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2102 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2103 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2104 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2105 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2106 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2107 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2108 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2109 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2110 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 69.0 FR France \n", "1 67.0 FR France \n", "2 68.0 FR France \n", "3 88.0 FR France \n", "4 100.0 FR France \n", "5 101.0 FR France \n", "6 140.0 FR France \n", "7 220.0 FR France \n", "8 331.0 FR France \n", "9 431.0 FR France \n", "10 523.0 FR France \n", "11 548.0 FR France \n", "12 398.0 FR France \n", "13 405.0 FR France \n", "14 370.0 FR France \n", "15 326.0 FR France \n", "16 323.0 FR France \n", "17 220.0 FR France \n", "18 177.0 FR France \n", "19 144.0 FR France \n", "20 127.0 FR France \n", "21 96.0 FR France \n", "22 81.0 FR France \n", "23 63.0 FR France \n", "24 80.0 FR France \n", "25 114.0 FR France \n", "26 131.0 FR France \n", "27 140.0 FR France \n", "28 150.0 FR France \n", "29 151.0 FR France \n", "... ... ... ... \n", "2081 59.0 FR France \n", "2082 64.0 FR France \n", "2083 97.0 FR France \n", "2084 93.0 FR France \n", "2085 80.0 FR France \n", "2086 116.0 FR France \n", "2087 149.0 FR France \n", "2088 281.0 FR France \n", "2089 395.0 FR France \n", "2090 485.0 FR France \n", "2091 544.0 FR France \n", "2092 689.0 FR France \n", "2093 722.0 FR France \n", "2094 762.0 FR France \n", "2095 926.0 FR France \n", "2096 1113.0 FR France \n", "2097 1236.0 FR France \n", "2098 832.0 FR France \n", "2099 459.0 FR France \n", "2100 207.0 FR France \n", "2101 190.0 FR France \n", "2102 198.0 FR France \n", "2103 224.0 FR France \n", "2104 266.0 FR France \n", "2105 219.0 FR France \n", "2106 176.0 FR France \n", "2107 163.0 FR France \n", "2108 195.0 FR France \n", "2109 308.0 FR France \n", "2110 213.0 FR France \n", "\n", "[2111 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
18741989193-NaNNaN-NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1874 198919 3 - NaN NaN - NaN NaN \n", "\n", " geo_insee geo_name \n", "1874 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
020251533881331411.046215.05847.069.0FRFrance
120251433787031403.044337.05747.067.0FRFrance
220251333967333686.045660.05950.068.0FRFrance
320251235254345627.059459.07868.088.0FRFrance
420251135946952154.066784.08978.0100.0FRFrance
520251036033453048.067620.09079.0101.0FRFrance
620250938453174994.094068.0126112.0140.0FRFrance
72025083136020124824.0147216.0203186.0220.0FRFrance
82025073208952195988.0221916.0312293.0331.0FRFrance
92025063273519258159.0288879.0408385.0431.0FRFrance
102025053334395318416.0350374.0499475.0523.0FRFrance
112025043350043332885.0367201.0522496.0548.0FRFrance
122025033252772238917.0266627.0377356.0398.0FRFrance
132025023257247242991.0271503.0384363.0405.0FRFrance
142025013231549214627.0248471.0345320.0370.0FRFrance
152024523201726185870.0217582.0302278.0326.0FRFrance
162024513201697187843.0215551.0302281.0323.0FRFrance
172024503136694126369.0147019.0205190.0220.0FRFrance
18202449310848799037.0117937.0163149.0177.0FRFrance
1920244838738178687.096075.0131118.0144.0FRFrance
2020244737628667626.084946.0114101.0127.0FRFrance
2120244635639949006.063792.08574.096.0FRFrance
2220244534734740843.053851.07161.081.0FRFrance
2320244433603930122.041956.05445.063.0FRFrance
2420244334657239928.053216.07060.080.0FRFrance
2520244236778560009.075561.010290.0114.0FRFrance
2620244137943571386.087484.0119107.0131.0FRFrance
2720244038496576555.093375.0127114.0140.0FRFrance
2820243939166082937.0100383.0137124.0150.0FRFrance
2920243839178682903.0100669.0138125.0151.0FRFrance
.................................
208119852132609619621.032571.04735.059.0FRFrance
208219852032789620885.034907.05138.064.0FRFrance
208319851934315432821.053487.07859.097.0FRFrance
208419851834055529935.051175.07455.093.0FRFrance
208519851733405324366.043740.06244.080.0FRFrance
208619851635036236451.064273.09166.0116.0FRFrance
208719851536388145538.082224.011683.0149.0FRFrance
20881985143134545114400.0154690.0244207.0281.0FRFrance
20891985133197206176080.0218332.0357319.0395.0FRFrance
20901985123245240223304.0267176.0445405.0485.0FRFrance
20911985113276205252399.0300011.0501458.0544.0FRFrance
20921985103353231326279.0380183.0640591.0689.0FRFrance
20931985093369895341109.0398681.0670618.0722.0FRFrance
20941985083389886359529.0420243.0707652.0762.0FRFrance
20951985073471852432599.0511105.0855784.0926.0FRFrance
20961985063565825518011.0613639.01026939.01113.0FRFrance
20971985053637302592795.0681809.011551074.01236.0FRFrance
20981985043424937390794.0459080.0770708.0832.0FRFrance
20991985033213901174689.0253113.0388317.0459.0FRFrance
210019850239758680949.0114223.0177147.0207.0FRFrance
210119850138548965918.0105060.0155120.0190.0FRFrance
210219845238483060602.0109058.0154110.0198.0FRFrance
2103198451310172680242.0123210.0185146.0224.0FRFrance
21041984503123680101401.0145959.0225184.0266.0FRFrance
2105198449310107381684.0120462.0184149.0219.0FRFrance
210619844837862060634.096606.0143110.0176.0FRFrance
210719844737202954274.089784.013199.0163.0FRFrance
210819844638733067686.0106974.0159123.0195.0FRFrance
21091984453135223101414.0169032.0246184.0308.0FRFrance
211019844436842220056.0116788.012537.0213.0FRFrance
\n", "

2110 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202515 3 38813 31411.0 46215.0 58 47.0 \n", "1 202514 3 37870 31403.0 44337.0 57 47.0 \n", "2 202513 3 39673 33686.0 45660.0 59 50.0 \n", "3 202512 3 52543 45627.0 59459.0 78 68.0 \n", "4 202511 3 59469 52154.0 66784.0 89 78.0 \n", "5 202510 3 60334 53048.0 67620.0 90 79.0 \n", "6 202509 3 84531 74994.0 94068.0 126 112.0 \n", "7 202508 3 136020 124824.0 147216.0 203 186.0 \n", "8 202507 3 208952 195988.0 221916.0 312 293.0 \n", "9 202506 3 273519 258159.0 288879.0 408 385.0 \n", "10 202505 3 334395 318416.0 350374.0 499 475.0 \n", "11 202504 3 350043 332885.0 367201.0 522 496.0 \n", "12 202503 3 252772 238917.0 266627.0 377 356.0 \n", "13 202502 3 257247 242991.0 271503.0 384 363.0 \n", "14 202501 3 231549 214627.0 248471.0 345 320.0 \n", "15 202452 3 201726 185870.0 217582.0 302 278.0 \n", "16 202451 3 201697 187843.0 215551.0 302 281.0 \n", "17 202450 3 136694 126369.0 147019.0 205 190.0 \n", "18 202449 3 108487 99037.0 117937.0 163 149.0 \n", "19 202448 3 87381 78687.0 96075.0 131 118.0 \n", "20 202447 3 76286 67626.0 84946.0 114 101.0 \n", "21 202446 3 56399 49006.0 63792.0 85 74.0 \n", "22 202445 3 47347 40843.0 53851.0 71 61.0 \n", "23 202444 3 36039 30122.0 41956.0 54 45.0 \n", "24 202443 3 46572 39928.0 53216.0 70 60.0 \n", "25 202442 3 67785 60009.0 75561.0 102 90.0 \n", "26 202441 3 79435 71386.0 87484.0 119 107.0 \n", "27 202440 3 84965 76555.0 93375.0 127 114.0 \n", "28 202439 3 91660 82937.0 100383.0 137 124.0 \n", "29 202438 3 91786 82903.0 100669.0 138 125.0 \n", "... ... ... ... ... ... ... ... \n", "2081 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2082 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2083 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2084 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2085 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2086 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2087 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2088 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2089 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2090 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2091 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2092 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2093 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2094 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2095 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2096 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2097 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2098 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2099 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2100 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2101 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2102 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2103 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2104 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2105 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2106 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2107 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2108 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2109 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2110 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 69.0 FR France \n", "1 67.0 FR France \n", "2 68.0 FR France \n", "3 88.0 FR France \n", "4 100.0 FR France \n", "5 101.0 FR France \n", "6 140.0 FR France \n", "7 220.0 FR France \n", "8 331.0 FR France \n", "9 431.0 FR France \n", "10 523.0 FR France \n", "11 548.0 FR France \n", "12 398.0 FR France \n", "13 405.0 FR France \n", "14 370.0 FR France \n", "15 326.0 FR France \n", "16 323.0 FR France \n", "17 220.0 FR France \n", "18 177.0 FR France \n", "19 144.0 FR France \n", "20 127.0 FR France \n", "21 96.0 FR France \n", "22 81.0 FR France \n", "23 63.0 FR France \n", "24 80.0 FR France \n", "25 114.0 FR France \n", "26 131.0 FR France \n", "27 140.0 FR France \n", "28 150.0 FR France \n", "29 151.0 FR France \n", "... ... ... ... \n", "2081 59.0 FR France \n", "2082 64.0 FR France \n", "2083 97.0 FR France \n", "2084 93.0 FR France \n", "2085 80.0 FR France \n", "2086 116.0 FR France \n", "2087 149.0 FR France \n", "2088 281.0 FR France \n", "2089 395.0 FR France \n", "2090 485.0 FR France \n", "2091 544.0 FR France \n", "2092 689.0 FR France \n", "2093 722.0 FR France \n", "2094 762.0 FR France \n", "2095 926.0 FR France \n", "2096 1113.0 FR France \n", "2097 1236.0 FR France \n", "2098 832.0 FR France \n", "2099 459.0 FR France \n", "2100 207.0 FR France \n", "2101 190.0 FR France \n", "2102 198.0 FR France \n", "2103 224.0 FR France \n", "2104 266.0 FR France \n", "2105 219.0 FR France \n", "2106 176.0 FR France \n", "2107 163.0 FR France \n", "2108 195.0 FR France \n", "2109 308.0 FR France \n", "2110 213.0 FR France \n", "\n", "[2110 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": 14, "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": 15, "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": 16, "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": null, "metadata": {}, "outputs": [], "source": [ "\n", "sorted_data['inc'] = sorted_data['inc'].astype(int)\n", "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": 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": [ { "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[0myearly_incidence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m=\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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1773001572217011071786253289451195013920166732...\n", "2002 1899900299043994663802597362540718113202411896...\n", "1996 1946700905188724963659489513123184991624514980...\n", "2008 1965132418933531177110014494769159851351712987...\n", "2019 2048183919621368150632154915734971747409804877...\n", "2001 2159140516905188559029902243741719338272374002...\n", "1987 2247102731155762105879182159982661528665335163...\n", "2014 2270470947401889129146395027932453719794123911...\n", "2020 2337159215931672101022953172489770918505712277...\n", "2012 2409259024213312417846068365140071702114053748...\n", "1992 2464625165420992372238024297460143732674028185...\n", "2013 2696638164923858827763847775147152025717873186...\n", "1990 2721711479406039466147691202218296246223145343...\n", "2016 2845159415377602291922881784028595131001358518...\n", "2005 2949349616974501440877539103174832268025639158...\n", "1997 3010145354012731674112229259470837710765991811...\n", "2010 3025523151244352498531660460241013321379241212...\n", "2021 3144207319003098396053641402823252186071819424...\n", "2018 3256266724062497279428159716114631494912725124...\n", "2017 3691210015672233342023724065596910598133791390...\n", "2006 3767157228155281244870821034721881239942664726...\n", "2009 3972437872292552190125122429367921957869992590...\n", "2011 3989273267129504926427949545318893611978183311...\n", "1994 4055416337553217298455894361702310492147831380...\n", "2015 4143322923582367226850187714105678583165262248...\n", "1993 4233496340462953638351557480983713535175932409...\n", "1995 5027478642723459455628065949153661587815962140...\n", "2004 5164161915042194150410951147351481126592308622...\n", "2003 5455108726702843212616791240970671598981371103...\n", "1988 8233721564046631614979061120816782323282464142...\n", "2022 8567106658699673786038055781795301141915052179...\n", "1989 8758703379374950779775391553735243437303988246...\n", "1991 8811399020193425424051111461561820433197231301...\n", "dtype: object" ] }, "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": null, "metadata": {}, "outputs": [], "source": [ "yearly_incidence.hist(xrot=20)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "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 }