{ "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\"\n", "data_csv = \"\\\\lurs\\_comptes\\AB283490\\personnel\\documents\\MOOC\\inc-25-PAY.csv\"\n", "\n", "import os\n", "import urllib.request\n", "if not os.path.exists(data_csv):\n", " urllib.request.urlretrieve(data_url, data_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
020251434476636591.052941.06755.079.0FRFrance
120251333987333848.045898.05950.068.0FRFrance
220251235254345627.059459.07868.088.0FRFrance
320251135946952154.066784.08978.0100.0FRFrance
420251036033453048.067620.09079.0101.0FRFrance
520250938453174994.094068.0126112.0140.0FRFrance
62025083136020124824.0147216.0203186.0220.0FRFrance
72025073208952195988.0221916.0312293.0331.0FRFrance
82025063273519258159.0288879.0408385.0431.0FRFrance
92025053334395318416.0350374.0499475.0523.0FRFrance
102025043350043332885.0367201.0522496.0548.0FRFrance
112025033252772238917.0266627.0377356.0398.0FRFrance
122025023257247242991.0271503.0384363.0405.0FRFrance
132025013231549214627.0248471.0345320.0370.0FRFrance
142024523201726185870.0217582.0302278.0326.0FRFrance
152024513201697187843.0215551.0302281.0323.0FRFrance
162024503136694126369.0147019.0205190.0220.0FRFrance
17202449310848799037.0117937.0163149.0177.0FRFrance
1820244838738178687.096075.0131118.0144.0FRFrance
1920244737628667626.084946.0114101.0127.0FRFrance
2020244635639949006.063792.08574.096.0FRFrance
2120244534734740843.053851.07161.081.0FRFrance
2220244433603930122.041956.05445.063.0FRFrance
2320244334657239928.053216.07060.080.0FRFrance
2420244236778560009.075561.010290.0114.0FRFrance
2520244137943571386.087484.0119107.0131.0FRFrance
2620244038496576555.093375.0127114.0140.0FRFrance
2720243939166082937.0100383.0137124.0150.0FRFrance
2820243839178682903.0100669.0138125.0151.0FRFrance
2920243735646049319.063601.08574.096.0FRFrance
.................................
208019852132609619621.032571.04735.059.0FRFrance
208119852032789620885.034907.05138.064.0FRFrance
208219851934315432821.053487.07859.097.0FRFrance
208319851834055529935.051175.07455.093.0FRFrance
208419851733405324366.043740.06244.080.0FRFrance
208519851635036236451.064273.09166.0116.0FRFrance
208619851536388145538.082224.011683.0149.0FRFrance
20871985143134545114400.0154690.0244207.0281.0FRFrance
20881985133197206176080.0218332.0357319.0395.0FRFrance
20891985123245240223304.0267176.0445405.0485.0FRFrance
20901985113276205252399.0300011.0501458.0544.0FRFrance
20911985103353231326279.0380183.0640591.0689.0FRFrance
20921985093369895341109.0398681.0670618.0722.0FRFrance
20931985083389886359529.0420243.0707652.0762.0FRFrance
20941985073471852432599.0511105.0855784.0926.0FRFrance
20951985063565825518011.0613639.01026939.01113.0FRFrance
20961985053637302592795.0681809.011551074.01236.0FRFrance
20971985043424937390794.0459080.0770708.0832.0FRFrance
20981985033213901174689.0253113.0388317.0459.0FRFrance
209919850239758680949.0114223.0177147.0207.0FRFrance
210019850138548965918.0105060.0155120.0190.0FRFrance
210119845238483060602.0109058.0154110.0198.0FRFrance
2102198451310172680242.0123210.0185146.0224.0FRFrance
21031984503123680101401.0145959.0225184.0266.0FRFrance
2104198449310107381684.0120462.0184149.0219.0FRFrance
210519844837862060634.096606.0143110.0176.0FRFrance
210619844737202954274.089784.013199.0163.0FRFrance
210719844638733067686.0106974.0159123.0195.0FRFrance
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210919844436842220056.0116788.012537.0213.0FRFrance
\n", "

2110 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202514 3 44766 36591.0 52941.0 67 55.0 \n", "1 202513 3 39873 33848.0 45898.0 59 50.0 \n", "2 202512 3 52543 45627.0 59459.0 78 68.0 \n", "3 202511 3 59469 52154.0 66784.0 89 78.0 \n", "4 202510 3 60334 53048.0 67620.0 90 79.0 \n", "5 202509 3 84531 74994.0 94068.0 126 112.0 \n", "6 202508 3 136020 124824.0 147216.0 203 186.0 \n", "7 202507 3 208952 195988.0 221916.0 312 293.0 \n", "8 202506 3 273519 258159.0 288879.0 408 385.0 \n", "9 202505 3 334395 318416.0 350374.0 499 475.0 \n", "10 202504 3 350043 332885.0 367201.0 522 496.0 \n", "11 202503 3 252772 238917.0 266627.0 377 356.0 \n", "12 202502 3 257247 242991.0 271503.0 384 363.0 \n", "13 202501 3 231549 214627.0 248471.0 345 320.0 \n", "14 202452 3 201726 185870.0 217582.0 302 278.0 \n", "15 202451 3 201697 187843.0 215551.0 302 281.0 \n", "16 202450 3 136694 126369.0 147019.0 205 190.0 \n", "17 202449 3 108487 99037.0 117937.0 163 149.0 \n", "18 202448 3 87381 78687.0 96075.0 131 118.0 \n", "19 202447 3 76286 67626.0 84946.0 114 101.0 \n", "20 202446 3 56399 49006.0 63792.0 85 74.0 \n", "21 202445 3 47347 40843.0 53851.0 71 61.0 \n", "22 202444 3 36039 30122.0 41956.0 54 45.0 \n", "23 202443 3 46572 39928.0 53216.0 70 60.0 \n", "24 202442 3 67785 60009.0 75561.0 102 90.0 \n", "25 202441 3 79435 71386.0 87484.0 119 107.0 \n", "26 202440 3 84965 76555.0 93375.0 127 114.0 \n", "27 202439 3 91660 82937.0 100383.0 137 124.0 \n", "28 202438 3 91786 82903.0 100669.0 138 125.0 \n", "29 202437 3 56460 49319.0 63601.0 85 74.0 \n", "... ... ... ... ... ... ... ... \n", "2080 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2081 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2082 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2083 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2084 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2085 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2086 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2087 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2088 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2089 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2090 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2091 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2092 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2093 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2094 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2095 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2096 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2097 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2098 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2099 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2100 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2101 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2102 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2103 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2104 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2105 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2106 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2107 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2108 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2109 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 79.0 FR France \n", "1 68.0 FR France \n", "2 88.0 FR France \n", "3 100.0 FR France \n", "4 101.0 FR France \n", "5 140.0 FR France \n", "6 220.0 FR France \n", "7 331.0 FR France \n", "8 431.0 FR France \n", "9 523.0 FR France \n", "10 548.0 FR France \n", "11 398.0 FR France \n", "12 405.0 FR France \n", "13 370.0 FR France \n", "14 326.0 FR France \n", "15 323.0 FR France \n", "16 220.0 FR France \n", "17 177.0 FR France \n", "18 144.0 FR France \n", "19 127.0 FR France \n", "20 96.0 FR France \n", "21 81.0 FR France \n", "22 63.0 FR France \n", "23 80.0 FR France \n", "24 114.0 FR France \n", "25 131.0 FR France \n", "26 140.0 FR France \n", "27 150.0 FR France \n", "28 151.0 FR France \n", "29 96.0 FR France \n", "... ... ... ... \n", "2080 59.0 FR France \n", "2081 64.0 FR France \n", "2082 97.0 FR France \n", "2083 93.0 FR France \n", "2084 80.0 FR France \n", "2085 116.0 FR France \n", "2086 149.0 FR France \n", "2087 281.0 FR France \n", "2088 395.0 FR France \n", "2089 485.0 FR France \n", "2090 544.0 FR France \n", "2091 689.0 FR France \n", "2092 722.0 FR France \n", "2093 762.0 FR France \n", "2094 926.0 FR France \n", "2095 1113.0 FR France \n", "2096 1236.0 FR France \n", "2097 832.0 FR France \n", "2098 459.0 FR France \n", "2099 207.0 FR France \n", "2100 190.0 FR France \n", "2101 198.0 FR France \n", "2102 224.0 FR France \n", "2103 266.0 FR France \n", "2104 219.0 FR France \n", "2105 176.0 FR France \n", "2106 163.0 FR France \n", "2107 195.0 FR France \n", "2108 308.0 FR France \n", "2109 213.0 FR France \n", "\n", "[2110 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_csv, 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
18731989193-NaNNaN-NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1873 198919 3 - NaN NaN - NaN NaN \n", "\n", " geo_insee geo_name \n", "1873 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
020251434476636591.052941.06755.079.0FRFrance
120251333987333848.045898.05950.068.0FRFrance
220251235254345627.059459.07868.088.0FRFrance
320251135946952154.066784.08978.0100.0FRFrance
420251036033453048.067620.09079.0101.0FRFrance
520250938453174994.094068.0126112.0140.0FRFrance
62025083136020124824.0147216.0203186.0220.0FRFrance
72025073208952195988.0221916.0312293.0331.0FRFrance
82025063273519258159.0288879.0408385.0431.0FRFrance
92025053334395318416.0350374.0499475.0523.0FRFrance
102025043350043332885.0367201.0522496.0548.0FRFrance
112025033252772238917.0266627.0377356.0398.0FRFrance
122025023257247242991.0271503.0384363.0405.0FRFrance
132025013231549214627.0248471.0345320.0370.0FRFrance
142024523201726185870.0217582.0302278.0326.0FRFrance
152024513201697187843.0215551.0302281.0323.0FRFrance
162024503136694126369.0147019.0205190.0220.0FRFrance
17202449310848799037.0117937.0163149.0177.0FRFrance
1820244838738178687.096075.0131118.0144.0FRFrance
1920244737628667626.084946.0114101.0127.0FRFrance
2020244635639949006.063792.08574.096.0FRFrance
2120244534734740843.053851.07161.081.0FRFrance
2220244433603930122.041956.05445.063.0FRFrance
2320244334657239928.053216.07060.080.0FRFrance
2420244236778560009.075561.010290.0114.0FRFrance
2520244137943571386.087484.0119107.0131.0FRFrance
2620244038496576555.093375.0127114.0140.0FRFrance
2720243939166082937.0100383.0137124.0150.0FRFrance
2820243839178682903.0100669.0138125.0151.0FRFrance
2920243735646049319.063601.08574.096.0FRFrance
.................................
208019852132609619621.032571.04735.059.0FRFrance
208119852032789620885.034907.05138.064.0FRFrance
208219851934315432821.053487.07859.097.0FRFrance
208319851834055529935.051175.07455.093.0FRFrance
208419851733405324366.043740.06244.080.0FRFrance
208519851635036236451.064273.09166.0116.0FRFrance
208619851536388145538.082224.011683.0149.0FRFrance
20871985143134545114400.0154690.0244207.0281.0FRFrance
20881985133197206176080.0218332.0357319.0395.0FRFrance
20891985123245240223304.0267176.0445405.0485.0FRFrance
20901985113276205252399.0300011.0501458.0544.0FRFrance
20911985103353231326279.0380183.0640591.0689.0FRFrance
20921985093369895341109.0398681.0670618.0722.0FRFrance
20931985083389886359529.0420243.0707652.0762.0FRFrance
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210019850138548965918.0105060.0155120.0190.0FRFrance
210119845238483060602.0109058.0154110.0198.0FRFrance
2102198451310172680242.0123210.0185146.0224.0FRFrance
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\n", "

2109 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202514 3 44766 36591.0 52941.0 67 55.0 \n", "1 202513 3 39873 33848.0 45898.0 59 50.0 \n", "2 202512 3 52543 45627.0 59459.0 78 68.0 \n", "3 202511 3 59469 52154.0 66784.0 89 78.0 \n", "4 202510 3 60334 53048.0 67620.0 90 79.0 \n", "5 202509 3 84531 74994.0 94068.0 126 112.0 \n", "6 202508 3 136020 124824.0 147216.0 203 186.0 \n", "7 202507 3 208952 195988.0 221916.0 312 293.0 \n", "8 202506 3 273519 258159.0 288879.0 408 385.0 \n", "9 202505 3 334395 318416.0 350374.0 499 475.0 \n", "10 202504 3 350043 332885.0 367201.0 522 496.0 \n", "11 202503 3 252772 238917.0 266627.0 377 356.0 \n", "12 202502 3 257247 242991.0 271503.0 384 363.0 \n", "13 202501 3 231549 214627.0 248471.0 345 320.0 \n", "14 202452 3 201726 185870.0 217582.0 302 278.0 \n", "15 202451 3 201697 187843.0 215551.0 302 281.0 \n", "16 202450 3 136694 126369.0 147019.0 205 190.0 \n", "17 202449 3 108487 99037.0 117937.0 163 149.0 \n", "18 202448 3 87381 78687.0 96075.0 131 118.0 \n", "19 202447 3 76286 67626.0 84946.0 114 101.0 \n", "20 202446 3 56399 49006.0 63792.0 85 74.0 \n", "21 202445 3 47347 40843.0 53851.0 71 61.0 \n", "22 202444 3 36039 30122.0 41956.0 54 45.0 \n", "23 202443 3 46572 39928.0 53216.0 70 60.0 \n", "24 202442 3 67785 60009.0 75561.0 102 90.0 \n", "25 202441 3 79435 71386.0 87484.0 119 107.0 \n", "26 202440 3 84965 76555.0 93375.0 127 114.0 \n", "27 202439 3 91660 82937.0 100383.0 137 124.0 \n", "28 202438 3 91786 82903.0 100669.0 138 125.0 \n", "29 202437 3 56460 49319.0 63601.0 85 74.0 \n", "... ... ... ... ... ... ... ... \n", "2080 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2081 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2082 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2083 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2084 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2085 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2086 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2087 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2088 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2089 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2090 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2091 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2092 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2093 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2094 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2095 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2096 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2097 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2098 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2099 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2100 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2101 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2102 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2103 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2104 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2105 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2106 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2107 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2108 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2109 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 79.0 FR France \n", "1 68.0 FR France \n", "2 88.0 FR France \n", "3 100.0 FR France \n", "4 101.0 FR France \n", "5 140.0 FR France \n", "6 220.0 FR France \n", "7 331.0 FR France \n", "8 431.0 FR France \n", "9 523.0 FR France \n", "10 548.0 FR France \n", "11 398.0 FR France \n", "12 405.0 FR France \n", "13 370.0 FR France \n", "14 326.0 FR France \n", "15 323.0 FR France \n", "16 220.0 FR France \n", "17 177.0 FR France \n", "18 144.0 FR France \n", "19 127.0 FR France \n", "20 96.0 FR France \n", "21 81.0 FR France \n", "22 63.0 FR France \n", "23 80.0 FR France \n", "24 114.0 FR France \n", "25 131.0 FR France \n", "26 140.0 FR France \n", "27 150.0 FR France \n", "28 151.0 FR France \n", "29 96.0 FR France \n", "... ... ... ... \n", "2080 59.0 FR France \n", "2081 64.0 FR France \n", "2082 97.0 FR France \n", "2083 93.0 FR France \n", "2084 80.0 FR France \n", "2085 116.0 FR France \n", "2086 149.0 FR France \n", "2087 281.0 FR France \n", "2088 395.0 FR France \n", "2089 485.0 FR France \n", "2090 544.0 FR France \n", "2091 689.0 FR France \n", "2092 722.0 FR France \n", "2093 762.0 FR France \n", "2094 926.0 FR France \n", "2095 1113.0 FR France \n", "2096 1236.0 FR France \n", "2097 832.0 FR France \n", "2098 459.0 FR France \n", "2099 207.0 FR France \n", "2100 190.0 FR France \n", "2101 198.0 FR France \n", "2102 224.0 FR France \n", "2103 266.0 FR France \n", "2104 219.0 FR France \n", "2105 176.0 FR France \n", "2106 163.0 FR France \n", "2107 195.0 FR France \n", "2108 308.0 FR France \n", "2109 213.0 FR France \n", "\n", "[2109 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": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
period
1984-10-29/1984-11-0419844436842220056.0116788.012537.0213.0FRFrance
1984-11-05/1984-11-111984453135223101414.0169032.0246184.0308.0FRFrance
1984-11-12/1984-11-1819844638733067686.0106974.0159123.0195.0FRFrance
1984-11-19/1984-11-2519844737202954274.089784.013199.0163.0FRFrance
1984-11-26/1984-12-0219844837862060634.096606.0143110.0176.0FRFrance
1984-12-03/1984-12-09198449310107381684.0120462.0184149.0219.0FRFrance
1984-12-10/1984-12-161984503123680101401.0145959.0225184.0266.0FRFrance
1984-12-17/1984-12-23198451310172680242.0123210.0185146.0224.0FRFrance
1984-12-24/1984-12-3019845238483060602.0109058.0154110.0198.0FRFrance
1984-12-31/1985-01-0619850138548965918.0105060.0155120.0190.0FRFrance
1985-01-07/1985-01-1319850239758680949.0114223.0177147.0207.0FRFrance
1985-01-14/1985-01-201985033213901174689.0253113.0388317.0459.0FRFrance
1985-01-21/1985-01-271985043424937390794.0459080.0770708.0832.0FRFrance
1985-01-28/1985-02-031985053637302592795.0681809.011551074.01236.0FRFrance
1985-02-04/1985-02-101985063565825518011.0613639.01026939.01113.0FRFrance
1985-02-11/1985-02-171985073471852432599.0511105.0855784.0926.0FRFrance
1985-02-18/1985-02-241985083389886359529.0420243.0707652.0762.0FRFrance
1985-02-25/1985-03-031985093369895341109.0398681.0670618.0722.0FRFrance
1985-03-04/1985-03-101985103353231326279.0380183.0640591.0689.0FRFrance
1985-03-11/1985-03-171985113276205252399.0300011.0501458.0544.0FRFrance
1985-03-18/1985-03-241985123245240223304.0267176.0445405.0485.0FRFrance
1985-03-25/1985-03-311985133197206176080.0218332.0357319.0395.0FRFrance
1985-04-01/1985-04-071985143134545114400.0154690.0244207.0281.0FRFrance
1985-04-08/1985-04-1419851536388145538.082224.011683.0149.0FRFrance
1985-04-15/1985-04-2119851635036236451.064273.09166.0116.0FRFrance
1985-04-22/1985-04-2819851733405324366.043740.06244.080.0FRFrance
1985-04-29/1985-05-0519851834055529935.051175.07455.093.0FRFrance
1985-05-06/1985-05-1219851934315432821.053487.07859.097.0FRFrance
1985-05-13/1985-05-1919852032789620885.034907.05138.064.0FRFrance
1985-05-20/1985-05-2619852132609619621.032571.04735.059.0FRFrance
.................................
2024-09-09/2024-09-1520243735646049319.063601.08574.096.0FRFrance
2024-09-16/2024-09-2220243839178682903.0100669.0138125.0151.0FRFrance
2024-09-23/2024-09-2920243939166082937.0100383.0137124.0150.0FRFrance
2024-09-30/2024-10-0620244038496576555.093375.0127114.0140.0FRFrance
2024-10-07/2024-10-1320244137943571386.087484.0119107.0131.0FRFrance
2024-10-14/2024-10-2020244236778560009.075561.010290.0114.0FRFrance
2024-10-21/2024-10-2720244334657239928.053216.07060.080.0FRFrance
2024-10-28/2024-11-0320244433603930122.041956.05445.063.0FRFrance
2024-11-04/2024-11-1020244534734740843.053851.07161.081.0FRFrance
2024-11-11/2024-11-1720244635639949006.063792.08574.096.0FRFrance
2024-11-18/2024-11-2420244737628667626.084946.0114101.0127.0FRFrance
2024-11-25/2024-12-0120244838738178687.096075.0131118.0144.0FRFrance
2024-12-02/2024-12-08202449310848799037.0117937.0163149.0177.0FRFrance
2024-12-09/2024-12-152024503136694126369.0147019.0205190.0220.0FRFrance
2024-12-16/2024-12-222024513201697187843.0215551.0302281.0323.0FRFrance
2024-12-23/2024-12-292024523201726185870.0217582.0302278.0326.0FRFrance
2024-12-30/2025-01-052025013231549214627.0248471.0345320.0370.0FRFrance
2025-01-06/2025-01-122025023257247242991.0271503.0384363.0405.0FRFrance
2025-01-13/2025-01-192025033252772238917.0266627.0377356.0398.0FRFrance
2025-01-20/2025-01-262025043350043332885.0367201.0522496.0548.0FRFrance
2025-01-27/2025-02-022025053334395318416.0350374.0499475.0523.0FRFrance
2025-02-03/2025-02-092025063273519258159.0288879.0408385.0431.0FRFrance
2025-02-10/2025-02-162025073208952195988.0221916.0312293.0331.0FRFrance
2025-02-17/2025-02-232025083136020124824.0147216.0203186.0220.0FRFrance
2025-02-24/2025-03-0220250938453174994.094068.0126112.0140.0FRFrance
2025-03-03/2025-03-0920251036033453048.067620.09079.0101.0FRFrance
2025-03-10/2025-03-1620251135946952154.066784.08978.0100.0FRFrance
2025-03-17/2025-03-2320251235254345627.059459.07868.088.0FRFrance
2025-03-24/2025-03-3020251333987333848.045898.05950.068.0FRFrance
2025-03-31/2025-04-0620251434476636591.052941.06755.079.0FRFrance
\n", "

2109 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 \\\n", "period \n", "1984-10-29/1984-11-04 198444 3 68422 20056.0 116788.0 125 \n", "1984-11-05/1984-11-11 198445 3 135223 101414.0 169032.0 246 \n", "1984-11-12/1984-11-18 198446 3 87330 67686.0 106974.0 159 \n", "1984-11-19/1984-11-25 198447 3 72029 54274.0 89784.0 131 \n", "1984-11-26/1984-12-02 198448 3 78620 60634.0 96606.0 143 \n", "1984-12-03/1984-12-09 198449 3 101073 81684.0 120462.0 184 \n", "1984-12-10/1984-12-16 198450 3 123680 101401.0 145959.0 225 \n", "1984-12-17/1984-12-23 198451 3 101726 80242.0 123210.0 185 \n", "1984-12-24/1984-12-30 198452 3 84830 60602.0 109058.0 154 \n", "1984-12-31/1985-01-06 198501 3 85489 65918.0 105060.0 155 \n", "1985-01-07/1985-01-13 198502 3 97586 80949.0 114223.0 177 \n", "1985-01-14/1985-01-20 198503 3 213901 174689.0 253113.0 388 \n", "1985-01-21/1985-01-27 198504 3 424937 390794.0 459080.0 770 \n", "1985-01-28/1985-02-03 198505 3 637302 592795.0 681809.0 1155 \n", "1985-02-04/1985-02-10 198506 3 565825 518011.0 613639.0 1026 \n", "1985-02-11/1985-02-17 198507 3 471852 432599.0 511105.0 855 \n", "1985-02-18/1985-02-24 198508 3 389886 359529.0 420243.0 707 \n", "1985-02-25/1985-03-03 198509 3 369895 341109.0 398681.0 670 \n", "1985-03-04/1985-03-10 198510 3 353231 326279.0 380183.0 640 \n", "1985-03-11/1985-03-17 198511 3 276205 252399.0 300011.0 501 \n", "1985-03-18/1985-03-24 198512 3 245240 223304.0 267176.0 445 \n", "1985-03-25/1985-03-31 198513 3 197206 176080.0 218332.0 357 \n", "1985-04-01/1985-04-07 198514 3 134545 114400.0 154690.0 244 \n", "1985-04-08/1985-04-14 198515 3 63881 45538.0 82224.0 116 \n", "1985-04-15/1985-04-21 198516 3 50362 36451.0 64273.0 91 \n", "1985-04-22/1985-04-28 198517 3 34053 24366.0 43740.0 62 \n", "1985-04-29/1985-05-05 198518 3 40555 29935.0 51175.0 74 \n", "1985-05-06/1985-05-12 198519 3 43154 32821.0 53487.0 78 \n", "1985-05-13/1985-05-19 198520 3 27896 20885.0 34907.0 51 \n", "1985-05-20/1985-05-26 198521 3 26096 19621.0 32571.0 47 \n", "... ... ... ... ... ... ... \n", "2024-09-09/2024-09-15 202437 3 56460 49319.0 63601.0 85 \n", "2024-09-16/2024-09-22 202438 3 91786 82903.0 100669.0 138 \n", "2024-09-23/2024-09-29 202439 3 91660 82937.0 100383.0 137 \n", "2024-09-30/2024-10-06 202440 3 84965 76555.0 93375.0 127 \n", "2024-10-07/2024-10-13 202441 3 79435 71386.0 87484.0 119 \n", "2024-10-14/2024-10-20 202442 3 67785 60009.0 75561.0 102 \n", "2024-10-21/2024-10-27 202443 3 46572 39928.0 53216.0 70 \n", "2024-10-28/2024-11-03 202444 3 36039 30122.0 41956.0 54 \n", "2024-11-04/2024-11-10 202445 3 47347 40843.0 53851.0 71 \n", "2024-11-11/2024-11-17 202446 3 56399 49006.0 63792.0 85 \n", "2024-11-18/2024-11-24 202447 3 76286 67626.0 84946.0 114 \n", "2024-11-25/2024-12-01 202448 3 87381 78687.0 96075.0 131 \n", "2024-12-02/2024-12-08 202449 3 108487 99037.0 117937.0 163 \n", "2024-12-09/2024-12-15 202450 3 136694 126369.0 147019.0 205 \n", "2024-12-16/2024-12-22 202451 3 201697 187843.0 215551.0 302 \n", "2024-12-23/2024-12-29 202452 3 201726 185870.0 217582.0 302 \n", "2024-12-30/2025-01-05 202501 3 231549 214627.0 248471.0 345 \n", "2025-01-06/2025-01-12 202502 3 257247 242991.0 271503.0 384 \n", "2025-01-13/2025-01-19 202503 3 252772 238917.0 266627.0 377 \n", "2025-01-20/2025-01-26 202504 3 350043 332885.0 367201.0 522 \n", "2025-01-27/2025-02-02 202505 3 334395 318416.0 350374.0 499 \n", "2025-02-03/2025-02-09 202506 3 273519 258159.0 288879.0 408 \n", "2025-02-10/2025-02-16 202507 3 208952 195988.0 221916.0 312 \n", "2025-02-17/2025-02-23 202508 3 136020 124824.0 147216.0 203 \n", "2025-02-24/2025-03-02 202509 3 84531 74994.0 94068.0 126 \n", "2025-03-03/2025-03-09 202510 3 60334 53048.0 67620.0 90 \n", "2025-03-10/2025-03-16 202511 3 59469 52154.0 66784.0 89 \n", "2025-03-17/2025-03-23 202512 3 52543 45627.0 59459.0 78 \n", "2025-03-24/2025-03-30 202513 3 39873 33848.0 45898.0 59 \n", "2025-03-31/2025-04-06 202514 3 44766 36591.0 52941.0 67 \n", "\n", " inc100_low inc100_up geo_insee geo_name \n", "period \n", "1984-10-29/1984-11-04 37.0 213.0 FR France \n", "1984-11-05/1984-11-11 184.0 308.0 FR France \n", "1984-11-12/1984-11-18 123.0 195.0 FR France \n", "1984-11-19/1984-11-25 99.0 163.0 FR France \n", "1984-11-26/1984-12-02 110.0 176.0 FR France \n", "1984-12-03/1984-12-09 149.0 219.0 FR France \n", "1984-12-10/1984-12-16 184.0 266.0 FR France \n", "1984-12-17/1984-12-23 146.0 224.0 FR France \n", "1984-12-24/1984-12-30 110.0 198.0 FR France \n", "1984-12-31/1985-01-06 120.0 190.0 FR France \n", "1985-01-07/1985-01-13 147.0 207.0 FR France \n", "1985-01-14/1985-01-20 317.0 459.0 FR France \n", "1985-01-21/1985-01-27 708.0 832.0 FR France \n", "1985-01-28/1985-02-03 1074.0 1236.0 FR France \n", "1985-02-04/1985-02-10 939.0 1113.0 FR France \n", "1985-02-11/1985-02-17 784.0 926.0 FR France \n", "1985-02-18/1985-02-24 652.0 762.0 FR France \n", "1985-02-25/1985-03-03 618.0 722.0 FR France \n", "1985-03-04/1985-03-10 591.0 689.0 FR France \n", "1985-03-11/1985-03-17 458.0 544.0 FR France \n", "1985-03-18/1985-03-24 405.0 485.0 FR France \n", "1985-03-25/1985-03-31 319.0 395.0 FR France \n", "1985-04-01/1985-04-07 207.0 281.0 FR France \n", "1985-04-08/1985-04-14 83.0 149.0 FR France \n", "1985-04-15/1985-04-21 66.0 116.0 FR France \n", "1985-04-22/1985-04-28 44.0 80.0 FR France \n", "1985-04-29/1985-05-05 55.0 93.0 FR France \n", "1985-05-06/1985-05-12 59.0 97.0 FR France \n", "1985-05-13/1985-05-19 38.0 64.0 FR France \n", "1985-05-20/1985-05-26 35.0 59.0 FR France \n", "... ... ... ... ... \n", "2024-09-09/2024-09-15 74.0 96.0 FR France \n", "2024-09-16/2024-09-22 125.0 151.0 FR France \n", "2024-09-23/2024-09-29 124.0 150.0 FR France \n", "2024-09-30/2024-10-06 114.0 140.0 FR France \n", "2024-10-07/2024-10-13 107.0 131.0 FR France \n", "2024-10-14/2024-10-20 90.0 114.0 FR France \n", "2024-10-21/2024-10-27 60.0 80.0 FR France \n", "2024-10-28/2024-11-03 45.0 63.0 FR France \n", "2024-11-04/2024-11-10 61.0 81.0 FR France \n", "2024-11-11/2024-11-17 74.0 96.0 FR France \n", "2024-11-18/2024-11-24 101.0 127.0 FR France \n", "2024-11-25/2024-12-01 118.0 144.0 FR France \n", "2024-12-02/2024-12-08 149.0 177.0 FR France \n", "2024-12-09/2024-12-15 190.0 220.0 FR France \n", "2024-12-16/2024-12-22 281.0 323.0 FR France \n", "2024-12-23/2024-12-29 278.0 326.0 FR France \n", "2024-12-30/2025-01-05 320.0 370.0 FR France \n", "2025-01-06/2025-01-12 363.0 405.0 FR France \n", "2025-01-13/2025-01-19 356.0 398.0 FR France \n", "2025-01-20/2025-01-26 496.0 548.0 FR France \n", "2025-01-27/2025-02-02 475.0 523.0 FR France \n", "2025-02-03/2025-02-09 385.0 431.0 FR France \n", "2025-02-10/2025-02-16 293.0 331.0 FR France \n", "2025-02-17/2025-02-23 186.0 220.0 FR France \n", "2025-02-24/2025-03-02 112.0 140.0 FR France \n", "2025-03-03/2025-03-09 79.0 101.0 FR France \n", "2025-03-10/2025-03-16 78.0 100.0 FR France \n", "2025-03-17/2025-03-23 68.0 88.0 FR France \n", "2025-03-24/2025-03-30 50.0 68.0 FR France \n", "2025-03-31/2025-04-06 55.0 79.0 FR France \n", "\n", "[2109 rows x 10 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data = data.set_index('period').sort_index()\n", "sorted_data" ] }, { "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": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "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'] = sorted_data['inc'].astype(int)\n", "\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": 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'][-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": 15, "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": 16, "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": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "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": 18, "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", "2023 2873501\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", "2024 3670417\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": 18, "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": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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