{ "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": 10, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "data_file = \"syndrome-grippal.csv\"\n", "\n", "import os\n", "import urllib.request\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "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": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020251931860213410.023794.02820.036.0FRFrance
120251831789313718.022068.02721.033.0FRFrance
220251732215017291.027009.03326.040.0FRFrance
320251632856422550.034578.04334.052.0FRFrance
420251533572129592.041850.05344.062.0FRFrance
520251433757931232.043926.05647.065.0FRFrance
620251333967333686.045660.05950.068.0FRFrance
720251235254345627.059459.07868.088.0FRFrance
820251135946952154.066784.08978.0100.0FRFrance
920251036033453048.067620.09079.0101.0FRFrance
1020250938453174994.094068.0126112.0140.0FRFrance
112025083136020124824.0147216.0203186.0220.0FRFrance
122025073208952195988.0221916.0312293.0331.0FRFrance
132025063273519258159.0288879.0408385.0431.0FRFrance
142025053334395318416.0350374.0499475.0523.0FRFrance
152025043350043332885.0367201.0522496.0548.0FRFrance
162025033252772238917.0266627.0377356.0398.0FRFrance
172025023257247242991.0271503.0384363.0405.0FRFrance
182025013231549214627.0248471.0345320.0370.0FRFrance
192024523201726185870.0217582.0302278.0326.0FRFrance
202024513201697187843.0215551.0302281.0323.0FRFrance
212024503136694126369.0147019.0205190.0220.0FRFrance
22202449310848799037.0117937.0163149.0177.0FRFrance
2320244838738178687.096075.0131118.0144.0FRFrance
2420244737628667626.084946.0114101.0127.0FRFrance
2520244635639949006.063792.08574.096.0FRFrance
2620244534734740843.053851.07161.081.0FRFrance
2720244433603930122.041956.05445.063.0FRFrance
2820244334657239928.053216.07060.080.0FRFrance
2920244236778560009.075561.010290.0114.0FRFrance
.................................
208519852132609619621.032571.04735.059.0FRFrance
208619852032789620885.034907.05138.064.0FRFrance
208719851934315432821.053487.07859.097.0FRFrance
208819851834055529935.051175.07455.093.0FRFrance
208919851733405324366.043740.06244.080.0FRFrance
209019851635036236451.064273.09166.0116.0FRFrance
209119851536388145538.082224.011683.0149.0FRFrance
20921985143134545114400.0154690.0244207.0281.0FRFrance
20931985133197206176080.0218332.0357319.0395.0FRFrance
20941985123245240223304.0267176.0445405.0485.0FRFrance
20951985113276205252399.0300011.0501458.0544.0FRFrance
20961985103353231326279.0380183.0640591.0689.0FRFrance
20971985093369895341109.0398681.0670618.0722.0FRFrance
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21001985063565825518011.0613639.01026939.01113.0FRFrance
21011985053637302592795.0681809.011551074.01236.0FRFrance
21021985043424937390794.0459080.0770708.0832.0FRFrance
21031985033213901174689.0253113.0388317.0459.0FRFrance
210419850239758680949.0114223.0177147.0207.0FRFrance
210519850138548965918.0105060.0155120.0190.0FRFrance
210619845238483060602.0109058.0154110.0198.0FRFrance
2107198451310172680242.0123210.0185146.0224.0FRFrance
21081984503123680101401.0145959.0225184.0266.0FRFrance
2109198449310107381684.0120462.0184149.0219.0FRFrance
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\n", "

2115 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202519 3 18602 13410.0 23794.0 28 20.0 \n", "1 202518 3 17893 13718.0 22068.0 27 21.0 \n", "2 202517 3 22150 17291.0 27009.0 33 26.0 \n", "3 202516 3 28564 22550.0 34578.0 43 34.0 \n", "4 202515 3 35721 29592.0 41850.0 53 44.0 \n", "5 202514 3 37579 31232.0 43926.0 56 47.0 \n", "6 202513 3 39673 33686.0 45660.0 59 50.0 \n", "7 202512 3 52543 45627.0 59459.0 78 68.0 \n", "8 202511 3 59469 52154.0 66784.0 89 78.0 \n", "9 202510 3 60334 53048.0 67620.0 90 79.0 \n", "10 202509 3 84531 74994.0 94068.0 126 112.0 \n", "11 202508 3 136020 124824.0 147216.0 203 186.0 \n", "12 202507 3 208952 195988.0 221916.0 312 293.0 \n", "13 202506 3 273519 258159.0 288879.0 408 385.0 \n", "14 202505 3 334395 318416.0 350374.0 499 475.0 \n", "15 202504 3 350043 332885.0 367201.0 522 496.0 \n", "16 202503 3 252772 238917.0 266627.0 377 356.0 \n", "17 202502 3 257247 242991.0 271503.0 384 363.0 \n", "18 202501 3 231549 214627.0 248471.0 345 320.0 \n", "19 202452 3 201726 185870.0 217582.0 302 278.0 \n", "20 202451 3 201697 187843.0 215551.0 302 281.0 \n", "21 202450 3 136694 126369.0 147019.0 205 190.0 \n", "22 202449 3 108487 99037.0 117937.0 163 149.0 \n", "23 202448 3 87381 78687.0 96075.0 131 118.0 \n", "24 202447 3 76286 67626.0 84946.0 114 101.0 \n", "25 202446 3 56399 49006.0 63792.0 85 74.0 \n", "26 202445 3 47347 40843.0 53851.0 71 61.0 \n", "27 202444 3 36039 30122.0 41956.0 54 45.0 \n", "28 202443 3 46572 39928.0 53216.0 70 60.0 \n", "29 202442 3 67785 60009.0 75561.0 102 90.0 \n", "... ... ... ... ... ... ... ... \n", "2085 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2086 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2087 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2088 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2089 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2090 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2091 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2092 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2093 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2094 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2095 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2096 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2097 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2098 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2099 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2100 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2101 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2102 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2103 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2104 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2105 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2106 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2107 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2108 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2109 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2110 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2111 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2112 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2113 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2114 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 36.0 FR France \n", "1 33.0 FR France \n", "2 40.0 FR France \n", "3 52.0 FR France \n", "4 62.0 FR France \n", "5 65.0 FR France \n", "6 68.0 FR France \n", "7 88.0 FR France \n", "8 100.0 FR France \n", "9 101.0 FR France \n", "10 140.0 FR France \n", "11 220.0 FR France \n", "12 331.0 FR France \n", "13 431.0 FR France \n", "14 523.0 FR France \n", "15 548.0 FR France \n", "16 398.0 FR France \n", "17 405.0 FR France \n", "18 370.0 FR France \n", "19 326.0 FR France \n", "20 323.0 FR France \n", "21 220.0 FR France \n", "22 177.0 FR France \n", "23 144.0 FR France \n", "24 127.0 FR France \n", "25 96.0 FR France \n", "26 81.0 FR France \n", "27 63.0 FR France \n", "28 80.0 FR France \n", "29 114.0 FR France \n", "... ... ... ... \n", "2085 59.0 FR France \n", "2086 64.0 FR France \n", "2087 97.0 FR France \n", "2088 93.0 FR France \n", "2089 80.0 FR France \n", "2090 116.0 FR France \n", "2091 149.0 FR France \n", "2092 281.0 FR France \n", "2093 395.0 FR France \n", "2094 485.0 FR France \n", "2095 544.0 FR France \n", "2096 689.0 FR France \n", "2097 722.0 FR France \n", "2098 762.0 FR France \n", "2099 926.0 FR France \n", "2100 1113.0 FR France \n", "2101 1236.0 FR France \n", "2102 832.0 FR France \n", "2103 459.0 FR France \n", "2104 207.0 FR France \n", "2105 190.0 FR France \n", "2106 198.0 FR France \n", "2107 224.0 FR France \n", "2108 266.0 FR France \n", "2109 219.0 FR France \n", "2110 176.0 FR France \n", "2111 163.0 FR France \n", "2112 195.0 FR France \n", "2113 308.0 FR France \n", "2114 213.0 FR France \n", "\n", "[2115 rows x 10 columns]" ] }, "execution_count": 12, "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": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
18781989193-NaNNaN-NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1878 198919 3 - NaN NaN - NaN NaN \n", "\n", " geo_insee geo_name \n", "1878 FR France " ] }, "execution_count": 13, "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": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020251931860213410.023794.02820.036.0FRFrance
120251831789313718.022068.02721.033.0FRFrance
220251732215017291.027009.03326.040.0FRFrance
320251632856422550.034578.04334.052.0FRFrance
420251533572129592.041850.05344.062.0FRFrance
520251433757931232.043926.05647.065.0FRFrance
620251333967333686.045660.05950.068.0FRFrance
720251235254345627.059459.07868.088.0FRFrance
820251135946952154.066784.08978.0100.0FRFrance
920251036033453048.067620.09079.0101.0FRFrance
1020250938453174994.094068.0126112.0140.0FRFrance
112025083136020124824.0147216.0203186.0220.0FRFrance
122025073208952195988.0221916.0312293.0331.0FRFrance
132025063273519258159.0288879.0408385.0431.0FRFrance
142025053334395318416.0350374.0499475.0523.0FRFrance
152025043350043332885.0367201.0522496.0548.0FRFrance
162025033252772238917.0266627.0377356.0398.0FRFrance
172025023257247242991.0271503.0384363.0405.0FRFrance
182025013231549214627.0248471.0345320.0370.0FRFrance
192024523201726185870.0217582.0302278.0326.0FRFrance
202024513201697187843.0215551.0302281.0323.0FRFrance
212024503136694126369.0147019.0205190.0220.0FRFrance
22202449310848799037.0117937.0163149.0177.0FRFrance
2320244838738178687.096075.0131118.0144.0FRFrance
2420244737628667626.084946.0114101.0127.0FRFrance
2520244635639949006.063792.08574.096.0FRFrance
2620244534734740843.053851.07161.081.0FRFrance
2720244433603930122.041956.05445.063.0FRFrance
2820244334657239928.053216.07060.080.0FRFrance
2920244236778560009.075561.010290.0114.0FRFrance
.................................
208519852132609619621.032571.04735.059.0FRFrance
208619852032789620885.034907.05138.064.0FRFrance
208719851934315432821.053487.07859.097.0FRFrance
208819851834055529935.051175.07455.093.0FRFrance
208919851733405324366.043740.06244.080.0FRFrance
209019851635036236451.064273.09166.0116.0FRFrance
209119851536388145538.082224.011683.0149.0FRFrance
20921985143134545114400.0154690.0244207.0281.0FRFrance
20931985133197206176080.0218332.0357319.0395.0FRFrance
20941985123245240223304.0267176.0445405.0485.0FRFrance
20951985113276205252399.0300011.0501458.0544.0FRFrance
20961985103353231326279.0380183.0640591.0689.0FRFrance
20971985093369895341109.0398681.0670618.0722.0FRFrance
20981985083389886359529.0420243.0707652.0762.0FRFrance
20991985073471852432599.0511105.0855784.0926.0FRFrance
21001985063565825518011.0613639.01026939.01113.0FRFrance
21011985053637302592795.0681809.011551074.01236.0FRFrance
21021985043424937390794.0459080.0770708.0832.0FRFrance
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210419850239758680949.0114223.0177147.0207.0FRFrance
210519850138548965918.0105060.0155120.0190.0FRFrance
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2107198451310172680242.0123210.0185146.0224.0FRFrance
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\n", "

2114 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202519 3 18602 13410.0 23794.0 28 20.0 \n", "1 202518 3 17893 13718.0 22068.0 27 21.0 \n", "2 202517 3 22150 17291.0 27009.0 33 26.0 \n", "3 202516 3 28564 22550.0 34578.0 43 34.0 \n", "4 202515 3 35721 29592.0 41850.0 53 44.0 \n", "5 202514 3 37579 31232.0 43926.0 56 47.0 \n", "6 202513 3 39673 33686.0 45660.0 59 50.0 \n", "7 202512 3 52543 45627.0 59459.0 78 68.0 \n", "8 202511 3 59469 52154.0 66784.0 89 78.0 \n", "9 202510 3 60334 53048.0 67620.0 90 79.0 \n", "10 202509 3 84531 74994.0 94068.0 126 112.0 \n", "11 202508 3 136020 124824.0 147216.0 203 186.0 \n", "12 202507 3 208952 195988.0 221916.0 312 293.0 \n", "13 202506 3 273519 258159.0 288879.0 408 385.0 \n", "14 202505 3 334395 318416.0 350374.0 499 475.0 \n", "15 202504 3 350043 332885.0 367201.0 522 496.0 \n", "16 202503 3 252772 238917.0 266627.0 377 356.0 \n", "17 202502 3 257247 242991.0 271503.0 384 363.0 \n", "18 202501 3 231549 214627.0 248471.0 345 320.0 \n", "19 202452 3 201726 185870.0 217582.0 302 278.0 \n", "20 202451 3 201697 187843.0 215551.0 302 281.0 \n", "21 202450 3 136694 126369.0 147019.0 205 190.0 \n", "22 202449 3 108487 99037.0 117937.0 163 149.0 \n", "23 202448 3 87381 78687.0 96075.0 131 118.0 \n", "24 202447 3 76286 67626.0 84946.0 114 101.0 \n", "25 202446 3 56399 49006.0 63792.0 85 74.0 \n", "26 202445 3 47347 40843.0 53851.0 71 61.0 \n", "27 202444 3 36039 30122.0 41956.0 54 45.0 \n", "28 202443 3 46572 39928.0 53216.0 70 60.0 \n", "29 202442 3 67785 60009.0 75561.0 102 90.0 \n", "... ... ... ... ... ... ... ... \n", "2085 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2086 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2087 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2088 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2089 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2090 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2091 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2092 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2093 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2094 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2095 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2096 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2097 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2098 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2099 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2100 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2101 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2102 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2103 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2104 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2105 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2106 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2107 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2108 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2109 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2110 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2111 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2112 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2113 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2114 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 36.0 FR France \n", "1 33.0 FR France \n", "2 40.0 FR France \n", "3 52.0 FR France \n", "4 62.0 FR France \n", "5 65.0 FR France \n", "6 68.0 FR France \n", "7 88.0 FR France \n", "8 100.0 FR France \n", "9 101.0 FR France \n", "10 140.0 FR France \n", "11 220.0 FR France \n", "12 331.0 FR France \n", "13 431.0 FR France \n", "14 523.0 FR France \n", "15 548.0 FR France \n", "16 398.0 FR France \n", "17 405.0 FR France \n", "18 370.0 FR France \n", "19 326.0 FR France \n", "20 323.0 FR France \n", "21 220.0 FR France \n", "22 177.0 FR France \n", "23 144.0 FR France \n", "24 127.0 FR France \n", "25 96.0 FR France \n", "26 81.0 FR France \n", "27 63.0 FR France \n", "28 80.0 FR France \n", "29 114.0 FR France \n", "... ... ... ... \n", "2085 59.0 FR France \n", "2086 64.0 FR France \n", "2087 97.0 FR France \n", "2088 93.0 FR France \n", "2089 80.0 FR France \n", "2090 116.0 FR France \n", "2091 149.0 FR France \n", "2092 281.0 FR France \n", "2093 395.0 FR France \n", "2094 485.0 FR France \n", "2095 544.0 FR France \n", "2096 689.0 FR France \n", "2097 722.0 FR France \n", "2098 762.0 FR France \n", "2099 926.0 FR France \n", "2100 1113.0 FR France \n", "2101 1236.0 FR France \n", "2102 832.0 FR France \n", "2103 459.0 FR France \n", "2104 207.0 FR France \n", "2105 190.0 FR France \n", "2106 198.0 FR France \n", "2107 224.0 FR France \n", "2108 266.0 FR France \n", "2109 219.0 FR France \n", "2110 176.0 FR France \n", "2111 163.0 FR France \n", "2112 195.0 FR France \n", "2113 308.0 FR France \n", "2114 213.0 FR France \n", "\n", "[2114 rows x 10 columns]" ] }, "execution_count": 14, "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": 15, "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": 16, "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": 17, "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": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.series.Series" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(sorted_data['inc'])" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 31, "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'].astype(float).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": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 32, "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:].astype(float).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": 33, "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": 43, "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).astype(float)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voici les incidences annuelles." ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "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": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2007 1.773002e+233\n", "2019 2.048184e+234\n", "2017 3.691210e+235\n", "1994 4.055416e+237\n", "1991 8.811399e+238\n", "2014 2.270471e+239\n", "2021 3.144207e+240\n", "2002 1.899900e+242\n", "1997 3.010145e+242\n", "2020 2.337159e+243\n", "1992 2.464625e+243\n", "2012 2.409259e+244\n", "2004 5.164162e+244\n", "2008 1.965132e+245\n", "2009 3.972438e+245\n", "2011 3.989273e+245\n", "2003 5.455109e+245\n", "2018 3.256267e+246\n", "2015 4.143323e+246\n", "1993 4.233496e+246\n", "1989 8.758703e+246\n", "2000 1.478264e+247\n", "1990 2.721711e+247\n", "2006 3.767157e+247\n", "1996 1.946701e+248\n", "2001 2.159141e+249\n", "2013 2.696638e+249\n", "1999 1.214713e+250\n", "2016 2.845159e+250\n", "1995 5.027479e+250\n", "2005 2.949350e+251\n", "1998 1.386209e+254\n", "2010 3.025523e+254\n", "1988 8.233722e+256\n", "1987 2.247103e+258\n", "2023 1.325712e+259\n", "2022 8.567107e+262\n", "1986 1.695875e+265\n", "2024 1.528615e+273\n", "dtype: float64" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.astype(float).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": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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