{ "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": [ "Pour garder une trace des données utilisées, nous faisons une copie locale des données au cas où par exemple, l'URL ou le format des données pourraient changer." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import os\n", "import urllib.request\n", "\n", "data_file = \"incidence-PAY-3.csv\"\n", "\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": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020204032266717728.027606.03426.042.0FRFrance
120203932004016074.024006.03024.036.0FRFrance
220203832556221142.029982.03932.046.0FRFrance
320203731848514649.022321.02822.034.0FRFrance
42020363103907646.013134.01612.020.0FRFrance
5202035399186842.012994.01510.020.0FRFrance
6202034360843090.09078.094.014.0FRFrance
7202033361063411.08801.095.013.0FRFrance
8202032359183330.08506.095.013.0FRFrance
9202031343512269.06433.074.010.0FRFrance
10202030381795442.010916.0128.016.0FRFrance
11202029386875860.011514.0139.017.0FRFrance
12202028383405701.010979.0139.017.0FRFrance
13202027340662406.05726.063.09.0FRFrance
14202026340392389.05689.063.09.0FRFrance
15202025328531488.04218.042.06.0FRFrance
16202024330581690.04426.053.07.0FRFrance
17202023341682468.05868.063.09.0FRFrance
18202022335801947.05213.053.07.0FRFrance
19202021361144026.08202.096.012.0FRFrance
20202020393156775.011855.01410.018.0FRFrance
212020193116798722.014636.01814.022.0FRFrance
2220201831639812851.019945.02520.030.0FRFrance
2320201731808214454.021710.02721.033.0FRFrance
2420201632416519893.028437.03731.043.0FRFrance
2520201534104935377.046721.06253.071.0FRFrance
2620201437166664531.078801.010998.0120.0FRFrance
27202013310774299187.0116297.0164151.0177.0FRFrance
28202012310728398610.0115956.0163150.0176.0FRFrance
29202011310170493652.0109756.0154142.0166.0FRFrance
.................................
184519852132609619621.032571.04735.059.0FRFrance
184619852032789620885.034907.05138.064.0FRFrance
184719851934315432821.053487.07859.097.0FRFrance
184819851834055529935.051175.07455.093.0FRFrance
184919851733405324366.043740.06244.080.0FRFrance
185019851635036236451.064273.09166.0116.0FRFrance
185119851536388145538.082224.011683.0149.0FRFrance
18521985143134545114400.0154690.0244207.0281.0FRFrance
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18561985103353231326279.0380183.0640591.0689.0FRFrance
18571985093369895341109.0398681.0670618.0722.0FRFrance
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18601985063565825518011.0613639.01026939.01113.0FRFrance
18611985053637302592795.0681809.011551074.01236.0FRFrance
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186419850239758680949.0114223.0177147.0207.0FRFrance
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187019844837862060634.096606.0143110.0176.0FRFrance
187119844737202954274.089784.013199.0163.0FRFrance
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187419844436842220056.0116788.012537.0213.0FRFrance
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1875 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202040 3 22667 17728.0 27606.0 34 26.0 \n", "1 202039 3 20040 16074.0 24006.0 30 24.0 \n", "2 202038 3 25562 21142.0 29982.0 39 32.0 \n", "3 202037 3 18485 14649.0 22321.0 28 22.0 \n", "4 202036 3 10390 7646.0 13134.0 16 12.0 \n", "5 202035 3 9918 6842.0 12994.0 15 10.0 \n", "6 202034 3 6084 3090.0 9078.0 9 4.0 \n", "7 202033 3 6106 3411.0 8801.0 9 5.0 \n", "8 202032 3 5918 3330.0 8506.0 9 5.0 \n", "9 202031 3 4351 2269.0 6433.0 7 4.0 \n", "10 202030 3 8179 5442.0 10916.0 12 8.0 \n", "11 202029 3 8687 5860.0 11514.0 13 9.0 \n", "12 202028 3 8340 5701.0 10979.0 13 9.0 \n", "13 202027 3 4066 2406.0 5726.0 6 3.0 \n", "14 202026 3 4039 2389.0 5689.0 6 3.0 \n", "15 202025 3 2853 1488.0 4218.0 4 2.0 \n", "16 202024 3 3058 1690.0 4426.0 5 3.0 \n", "17 202023 3 4168 2468.0 5868.0 6 3.0 \n", "18 202022 3 3580 1947.0 5213.0 5 3.0 \n", "19 202021 3 6114 4026.0 8202.0 9 6.0 \n", "20 202020 3 9315 6775.0 11855.0 14 10.0 \n", "21 202019 3 11679 8722.0 14636.0 18 14.0 \n", "22 202018 3 16398 12851.0 19945.0 25 20.0 \n", "23 202017 3 18082 14454.0 21710.0 27 21.0 \n", "24 202016 3 24165 19893.0 28437.0 37 31.0 \n", "25 202015 3 41049 35377.0 46721.0 62 53.0 \n", "26 202014 3 71666 64531.0 78801.0 109 98.0 \n", "27 202013 3 107742 99187.0 116297.0 164 151.0 \n", "28 202012 3 107283 98610.0 115956.0 163 150.0 \n", "29 202011 3 101704 93652.0 109756.0 154 142.0 \n", "... ... ... ... ... ... ... ... \n", "1845 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1846 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1847 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1848 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1849 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1850 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1851 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1852 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1853 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1854 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1855 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1856 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1857 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1858 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1859 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1860 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1861 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1862 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1863 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1864 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1865 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1866 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1867 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1868 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1869 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1870 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1871 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1872 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1873 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1874 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 42.0 FR France \n", "1 36.0 FR France \n", "2 46.0 FR France \n", "3 34.0 FR France \n", "4 20.0 FR France \n", "5 20.0 FR France \n", "6 14.0 FR France \n", "7 13.0 FR France \n", "8 13.0 FR France \n", "9 10.0 FR France \n", "10 16.0 FR France \n", "11 17.0 FR France \n", "12 17.0 FR France \n", "13 9.0 FR France \n", "14 9.0 FR France \n", "15 6.0 FR France \n", "16 7.0 FR France \n", "17 9.0 FR France \n", "18 7.0 FR France \n", "19 12.0 FR France \n", "20 18.0 FR France \n", "21 22.0 FR France \n", "22 30.0 FR France \n", "23 33.0 FR France \n", "24 43.0 FR France \n", "25 71.0 FR France \n", "26 120.0 FR France \n", "27 177.0 FR France \n", "28 176.0 FR France \n", "29 166.0 FR France \n", "... ... ... ... \n", "1845 59.0 FR France \n", "1846 64.0 FR France \n", "1847 97.0 FR France \n", "1848 93.0 FR France \n", "1849 80.0 FR France \n", "1850 116.0 FR France \n", "1851 149.0 FR France \n", "1852 281.0 FR France \n", "1853 395.0 FR France \n", "1854 485.0 FR France \n", "1855 544.0 FR France \n", "1856 689.0 FR France \n", "1857 722.0 FR France \n", "1858 762.0 FR France \n", "1859 926.0 FR France \n", "1860 1113.0 FR France \n", "1861 1236.0 FR France \n", "1862 832.0 FR France \n", "1863 459.0 FR France \n", "1864 207.0 FR France \n", "1865 190.0 FR France \n", "1866 198.0 FR France \n", "1867 224.0 FR France \n", "1868 266.0 FR France \n", "1869 219.0 FR France \n", "1870 176.0 FR France \n", "1871 163.0 FR France \n", "1872 195.0 FR France \n", "1873 308.0 FR France \n", "1874 213.0 FR France \n", "\n", "[1875 rows x 10 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, 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": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
163819891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1638 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1638 FR France " ] }, "execution_count": 5, "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": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020204032266717728.027606.03426.042.0FRFrance
120203932004016074.024006.03024.036.0FRFrance
220203832556221142.029982.03932.046.0FRFrance
320203731848514649.022321.02822.034.0FRFrance
42020363103907646.013134.01612.020.0FRFrance
5202035399186842.012994.01510.020.0FRFrance
6202034360843090.09078.094.014.0FRFrance
7202033361063411.08801.095.013.0FRFrance
8202032359183330.08506.095.013.0FRFrance
9202031343512269.06433.074.010.0FRFrance
10202030381795442.010916.0128.016.0FRFrance
11202029386875860.011514.0139.017.0FRFrance
12202028383405701.010979.0139.017.0FRFrance
13202027340662406.05726.063.09.0FRFrance
14202026340392389.05689.063.09.0FRFrance
15202025328531488.04218.042.06.0FRFrance
16202024330581690.04426.053.07.0FRFrance
17202023341682468.05868.063.09.0FRFrance
18202022335801947.05213.053.07.0FRFrance
19202021361144026.08202.096.012.0FRFrance
20202020393156775.011855.01410.018.0FRFrance
212020193116798722.014636.01814.022.0FRFrance
2220201831639812851.019945.02520.030.0FRFrance
2320201731808214454.021710.02721.033.0FRFrance
2420201632416519893.028437.03731.043.0FRFrance
2520201534104935377.046721.06253.071.0FRFrance
2620201437166664531.078801.010998.0120.0FRFrance
27202013310774299187.0116297.0164151.0177.0FRFrance
28202012310728398610.0115956.0163150.0176.0FRFrance
29202011310170493652.0109756.0154142.0166.0FRFrance
.................................
184519852132609619621.032571.04735.059.0FRFrance
184619852032789620885.034907.05138.064.0FRFrance
184719851934315432821.053487.07859.097.0FRFrance
184819851834055529935.051175.07455.093.0FRFrance
184919851733405324366.043740.06244.080.0FRFrance
185019851635036236451.064273.09166.0116.0FRFrance
185119851536388145538.082224.011683.0149.0FRFrance
18521985143134545114400.0154690.0244207.0281.0FRFrance
18531985133197206176080.0218332.0357319.0395.0FRFrance
18541985123245240223304.0267176.0445405.0485.0FRFrance
18551985113276205252399.0300011.0501458.0544.0FRFrance
18561985103353231326279.0380183.0640591.0689.0FRFrance
18571985093369895341109.0398681.0670618.0722.0FRFrance
18581985083389886359529.0420243.0707652.0762.0FRFrance
18591985073471852432599.0511105.0855784.0926.0FRFrance
18601985063565825518011.0613639.01026939.01113.0FRFrance
18611985053637302592795.0681809.011551074.01236.0FRFrance
18621985043424937390794.0459080.0770708.0832.0FRFrance
18631985033213901174689.0253113.0388317.0459.0FRFrance
186419850239758680949.0114223.0177147.0207.0FRFrance
186519850138548965918.0105060.0155120.0190.0FRFrance
186619845238483060602.0109058.0154110.0198.0FRFrance
1867198451310172680242.0123210.0185146.0224.0FRFrance
18681984503123680101401.0145959.0225184.0266.0FRFrance
1869198449310107381684.0120462.0184149.0219.0FRFrance
187019844837862060634.096606.0143110.0176.0FRFrance
187119844737202954274.089784.013199.0163.0FRFrance
187219844638733067686.0106974.0159123.0195.0FRFrance
18731984453135223101414.0169032.0246184.0308.0FRFrance
187419844436842220056.0116788.012537.0213.0FRFrance
\n", "

1874 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202040 3 22667 17728.0 27606.0 34 26.0 \n", "1 202039 3 20040 16074.0 24006.0 30 24.0 \n", "2 202038 3 25562 21142.0 29982.0 39 32.0 \n", "3 202037 3 18485 14649.0 22321.0 28 22.0 \n", "4 202036 3 10390 7646.0 13134.0 16 12.0 \n", "5 202035 3 9918 6842.0 12994.0 15 10.0 \n", "6 202034 3 6084 3090.0 9078.0 9 4.0 \n", "7 202033 3 6106 3411.0 8801.0 9 5.0 \n", "8 202032 3 5918 3330.0 8506.0 9 5.0 \n", "9 202031 3 4351 2269.0 6433.0 7 4.0 \n", "10 202030 3 8179 5442.0 10916.0 12 8.0 \n", "11 202029 3 8687 5860.0 11514.0 13 9.0 \n", "12 202028 3 8340 5701.0 10979.0 13 9.0 \n", "13 202027 3 4066 2406.0 5726.0 6 3.0 \n", "14 202026 3 4039 2389.0 5689.0 6 3.0 \n", "15 202025 3 2853 1488.0 4218.0 4 2.0 \n", "16 202024 3 3058 1690.0 4426.0 5 3.0 \n", "17 202023 3 4168 2468.0 5868.0 6 3.0 \n", "18 202022 3 3580 1947.0 5213.0 5 3.0 \n", "19 202021 3 6114 4026.0 8202.0 9 6.0 \n", "20 202020 3 9315 6775.0 11855.0 14 10.0 \n", "21 202019 3 11679 8722.0 14636.0 18 14.0 \n", "22 202018 3 16398 12851.0 19945.0 25 20.0 \n", "23 202017 3 18082 14454.0 21710.0 27 21.0 \n", "24 202016 3 24165 19893.0 28437.0 37 31.0 \n", "25 202015 3 41049 35377.0 46721.0 62 53.0 \n", "26 202014 3 71666 64531.0 78801.0 109 98.0 \n", "27 202013 3 107742 99187.0 116297.0 164 151.0 \n", "28 202012 3 107283 98610.0 115956.0 163 150.0 \n", "29 202011 3 101704 93652.0 109756.0 154 142.0 \n", "... ... ... ... ... ... ... ... \n", "1845 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1846 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1847 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1848 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1849 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1850 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1851 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1852 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1853 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1854 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1855 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1856 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1857 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1858 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1859 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1860 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1861 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1862 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1863 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1864 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1865 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1866 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1867 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1868 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1869 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1870 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1871 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1872 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1873 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1874 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 42.0 FR France \n", "1 36.0 FR France \n", "2 46.0 FR France \n", "3 34.0 FR France \n", "4 20.0 FR France \n", "5 20.0 FR France \n", "6 14.0 FR France \n", "7 13.0 FR France \n", "8 13.0 FR France \n", "9 10.0 FR France \n", "10 16.0 FR France \n", "11 17.0 FR France \n", "12 17.0 FR France \n", "13 9.0 FR France \n", "14 9.0 FR France \n", "15 6.0 FR France \n", "16 7.0 FR France \n", "17 9.0 FR France \n", "18 7.0 FR France \n", "19 12.0 FR France \n", "20 18.0 FR France \n", "21 22.0 FR France \n", "22 30.0 FR France \n", "23 33.0 FR France \n", "24 43.0 FR France \n", "25 71.0 FR France \n", "26 120.0 FR France \n", "27 177.0 FR France \n", "28 176.0 FR France \n", "29 166.0 FR France \n", "... ... ... ... \n", "1845 59.0 FR France \n", "1846 64.0 FR France \n", "1847 97.0 FR France \n", "1848 93.0 FR France \n", "1849 80.0 FR France \n", "1850 116.0 FR France \n", "1851 149.0 FR France \n", "1852 281.0 FR France \n", "1853 395.0 FR France \n", "1854 485.0 FR France \n", "1855 544.0 FR France \n", "1856 689.0 FR France \n", "1857 722.0 FR France \n", "1858 762.0 FR France \n", "1859 926.0 FR France \n", "1860 1113.0 FR France \n", "1861 1236.0 FR France \n", "1862 832.0 FR France \n", "1863 459.0 FR France \n", "1864 207.0 FR France \n", "1865 190.0 FR France \n", "1866 198.0 FR France \n", "1867 224.0 FR France \n", "1868 266.0 FR France \n", "1869 219.0 FR France \n", "1870 176.0 FR France \n", "1871 163.0 FR France \n", "1872 195.0 FR France \n", "1873 308.0 FR France \n", "1874 213.0 FR France \n", "\n", "[1874 rows x 10 columns]" ] }, "execution_count": 6, "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": 7, "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": 8, "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": 9, "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": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'].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": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'][-200:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Etude de l'incidence annuelle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n", "entre deux années civiles, nous définissons la période de référence\n", "entre deux minima de l'incidence, du 1er août de l'année $N$ au\n", "1er août de l'année $N+1$.\n", "\n", "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n", "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n", "de référence: à la place du 1er août de chaque année, nous utilisons le\n", "premier jour de la semaine qui contient le 1er août.\n", "\n", "Comme l'incidence de syndrome grippal est très faible en été, cette\n", "modification ne risque pas de fausser nos conclusions.\n", "\n", "Encore un petit détail: les données commencent an octobre 1984, ce qui\n", "rend la première année incomplète. Nous commençons donc l'analyse en 1985." ] }, { "cell_type": "code", "execution_count": 12, "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": 13, "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": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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UJWm3pHm59FmSXkmvrUzbHZO2RH48pW+RNDVXZrGkzvRYnEuflvJ2prJnF3NKbKCKWoJ5qm/kQ/3boXtv1gqq6bkcA+ZExHuSRgB/L6m0PfF9EfGn+cySZpBtU3wpMAl4RtLH01bHq4AlwM+ADcB8sq2ObwYOR8QlkhYB9wJfkjQWuAtoAwLYLml9RBxOee6LiLWSvpfqWDXwU2GnKx8UVlz7qZrLV/uNfKh/O3TvzVrBKXsukXkv/TgiPSrdkGwBsDYijkXE60AXMFvSRGB0RGyO7IZmDwELc2XWpOMngGtSr2Ye0B4RPSmgtAPz02tzUl5S2VJdVmef+PpGpt75NzyyZS8RWVCYeuff8Imvbzx14Rx/I6/eUO+9WfOras5F0lnAduAS4H9HxBZJnwFuk3QT0AF8NQWAyWQ9k5J9Ke39dFyeTnp+AyAieiW9A4zLp5eVGQe8HRG9/dRldfbCHVefdC/6WvgbefWGeu/Nml9Vq8Ui4nhEzASmkPVCLiMbgvoYMBM4AHw7ZVd/VVRIH0iZSnX1IWmJpA5JHd3d3f1lsdNUZFDwN/LBz7dHGRpqWi0WEW9Leh6Yn59rkfQD4Cfpx33AhbliU4D9KX1KP+n5MvskDQfOA3pS+qfLyjwPHALOlzQ89V7ydZW3eTWwGrJb7tfy+1r1ito7xN/IB7/TnZuz1nDK/VwkjQfeT4HlHOBpssn07RFxIOX5Q+CKiFgk6VLgUWA22YT+T4HpEXFc0jbgvwFbyCb0/1dEbJC0FPhURHwlTej/dkT8bprQ3w78RmrOz4FZEdEj6a+BH+Um9F+OiO9W+l28n4tZ45Qv2CjxEurmN5D9XKrpuUwE1qR5l2HAuoj4iaSHJc0kG47aA3wZICJ2SloHvAb0AkvTSjGAW4AHgXPIVomVZnwfAB6W1EXWY1mU6uqRdA+wLeW7OyJ60vEyYK2kFcCOVIeZNami5uasNZwyuETEy8Dl/aTfWKHMnwB/0k96B/ArYx0RcRT44knq+iHww37S/x9Z78jMWoAXbAwtvkLfzOqmqLk5a36nnHMZTDznYmZWu4HMufjGlWZmVjgHFzMzK5yDi5mZFc7BxczMCufgYmZmhXNwMTOzwjm4mJlZ4RxczMyscA4uZmZWOAcXszrzfiY2FDi4mNVZfj8Ts8HKN640q5Py/Uwe2bKXR7bs9X4mNii552JWJy/ccTVfmDmJUSOy/3ajRgxjwcxJvLDs6ga3zKx4Di5mdeL9TGwocXCxQa3ZJs9L+5k8eetV3HDFxXS/d6zRTTI7I04ZXCSNkrRV0kuSdkr645Q+VlK7pM70PCZXZrmkLkm7Jc3Lpc+S9Ep6baUkpfSRkh5P6VskTc2VWZzeo1PS4lz6tJS3M5U9u5hTYoNJs02ef//GNlYsvIwZk0azYuFlfP/GmrbIMGsZp9wsLAWAD0XEe5JGAH8P3A78NtATEd+UdCcwJiKWSZoBPEa2BfEk4Bng4xFxXNLWVPZnwAZgZURslHQr8OsR8RVJi4BrI+JLksYCHUAbEMB2YFZEHJa0DvhxRKyV9D3gpYhYVel38WZhQ0f55HmJJ8/NandGNguLzHvpxxHpEcACYE1KXwMsTMcLgLURcSwiXge6gNmSJgKjI2JzZBHtobIypbqeAK5JQW0e0B4RPRFxGGgH5qfX5qS85e9v5snzBmm2YUhrnKrmXCSdJelF4CDZH/stwAURcQAgPU9I2ScDb+SK70tpk9NxeXqfMhHRC7wDjKtQ1zjg7ZS3vK7yti+R1CGpo7u7u5pf1wYBT543RrMNQ1rjVHWdS0QcB2ZKOh94UtJlFbKrvyoqpA+kTKW6+iZGrAZWQzYs1l8eG5xKk+fXz76IR7fupdvfps8YX8Nj5Wq6iDIi3pb0PDAfeEvSxIg4kIa8DqZs+4ALc8WmAPtT+pR+0vNl9kkaDpwH9KT0T5eVeR44BJwvaXjqveTrMgPoM1m+YmGl70N2ul6442pWbNjF0zt/ydH3TzBqxDDmXfpRvva5Tza6adYg1awWG596LEg6B/gvwC+A9UBp9dZi4Kl0vB5YlFaATQOmA1vT0NkRSVemOZObysqU6roOeDbNy2wC5koak1ajzQU2pdeeS3nL39/M6szDkFaump7LRGCNpLPIgtG6iPiJpM3AOkk3A3uBLwJExM60kus1oBdYmobVAG4BHgTOATamB8ADwMOSush6LItSXT2S7gG2pXx3R0RPOl4GrJW0AtiR6jCzBvEwpOWdcinyYHImlyIffPcotz22g/uvv9zf1szqpFX/37Vau8/IUmSrjlfJmNVfq/6/a9V218I9l9Pki/XM6q9V/9+1arvdc2kAX6xnVn+t+v+uVds9EA4up6mWVTK+etlaWTN9flt1dVqrtnsgHFwKUO2dbofCOKsNXs32+W3VO0y3artr5TmXOmjVcVYz8OfXPOfStIbSOKsNPv78Nq9mGqos5+BSB4NhnLWZP8R2Zg2Gz+9g1WxDlXk13VvMBq7Vr17Of4hXXPupRjfH6qzVP7+DTSvcKNRzLlaRx9vNms/Bd4+e9EahZ6JH6TkXK5zH282aTysMVXpYzCpqhQ+x2VDU7EOVDi52Ss3+ITYbipp9vyLPuQxxrXZ3VjOrP8+5WM2aeSmjmbUuD4sNUa2wlLHVuBdo9oFqtjm+UNJzknZJ2inp9pT+DUlvSnoxPT6bK7NcUpek3ZLm5dJnSXolvbYybXdM2hL58ZS+RdLUXJnFkjrTY3EufVrK25nKnl3MKRkavAqseO4Fmn2gmp5LL/DViPi5pHOB7ZLa02v3RcSf5jNLmkG2TfGlwCTgGUkfT1sdrwKWAD8DNgDzybY6vhk4HBGXSFoE3At8SdJY4C6gDYj03usj4nDKc19ErJX0vVTHqoGfiqHFq8CK416g2a86Zc8lIg5ExM/T8RFgFzC5QpEFwNqIOBYRrwNdwGxJE4HREbE5slUEDwELc2XWpOMngGtSr2Ye0B4RPSmgtAPz02tzUl5S2VJdVqWhcnfWM829QLNfVdOcSxquuhzYAlwF3CbpJqCDrHdzmCzw/CxXbF9Kez8dl6eTnt8AiIheSe8A4/LpZWXGAW9HRG8/dVmVmn0pY6twL9DsV1W9WkzSh4EfAX8QEe+SDUF9DJgJHAC+XcraT/GokD6QMpXqKm/3Ekkdkjq6u7v7y2J22twLNOurqp6LpBFkgeWvIuLHABHxVu71HwA/ST/uAy7MFZ8C7E/pU/pJz5fZJ2k4cB7Qk9I/XVbmeeAQcL6k4an3kq+rj4hYDayG7DqXan5fs1q5F2jWVzWrxQQ8AOyKiO/k0ifmsl0LvJqO1wOL0gqwacB0YGtEHACOSLoy1XkT8FSuTGkl2HXAs2leZhMwV9IYSWOAucCm9NpzKS+pbKkuMzNrsGp6LlcBNwKvSHoxpf0R8F8lzSQbjtoDfBkgInZKWge8RrbSbGlaKQZwC/AgcA7ZKrGNKf0B4GFJXWQ9lkWprh5J9wDbUr67I6InHS8D1kpaAexIdZiZWRPw7V/MzKwi3/7FzMyagoOLmVkTavWtxR1czKzl/5ANRq1+OyHfuNLM+vwhW3HtpxrdnCFtsNxOyBP6ZkNY+R+yklb7QzaYHHz3KCs27OLpnb/k6PsnGDViGPMu/Shf+9wnB3TXhyLu1u0JfTOrie+L1nyKvp1Qo4bXPCxm1oTqtTeM74vWnIrYWrzRw2sOLmZNqJ5zIEX8IbNiFXE7oRfuuPqkw2v14OBi1kQa8W3T90UbnBrdK/Wci1kT8RxI8YbyMutG3q3bPRezJtLob5uD0VBeZt3IXqmDi1mT8RxIMRo9oT3U+ToXMxuUir5eZCjzdS5mZomHGBvLw2JmNmh5iLFxPCxmZmYVeVjMzMyawimDi6QLJT0naZeknZJuT+ljJbVL6kzPY3JllkvqkrRb0rxc+ixJr6TXVkpSSh8p6fGUvkXS1FyZxek9OiUtzqVPS3k7U9mzizklZmZ2uqrpufQCX42ITwJXAkslzQDuBH4aEdOBn6afSa8tAi4F5gPflXRWqmsVsASYnh7zU/rNwOGIuAS4D7g31TUWuAu4ApgN3JULYvcC96X3P5zqMDOzJnDK4BIRByLi5+n4CLALmAwsANakbGuAhel4AbA2Io5FxOtAFzBb0kRgdERsjmyi56GyMqW6ngCuSb2aeUB7RPRExGGgHZifXpuT8pa/v5mZNVhNcy5puOpyYAtwQUQcgCwAARNStsnAG7li+1La5HRcnt6nTET0Au8A4yrUNQ54O+Utr8vMzBqs6uAi6cPAj4A/iIh3K2XtJy0qpA+kTKW6+jZGWiKpQ1JHd3d3f1nMzKxgVQUXSSPIAstfRcSPU/JbaaiL9Hwwpe8DLswVnwLsT+lT+knvU0bScOA8oKdCXYeA81Pe8rr6iIjVEdEWEW3jx4+v5tc1M7PTVM1qMQEPALsi4ju5l9YDpdVbi4GncumL0gqwaWQT91vT0NkRSVemOm8qK1Oq6zrg2TQvswmYK2lMmsifC2xKrz2X8pa/v5mZNVg1V+hfBdwIvCLpxZT2R8A3gXWSbgb2Al8EiIidktYBr5GtNFsaEcdTuVuAB4FzgI3pAVnwelhSF1mPZVGqq0fSPcC2lO/uiOhJx8uAtZJWADtSHWZm1gR8hb6ZmVXkK/St6Q3ljZvMhhIHF6ur/MZNZjZ4+a7IVhfeuMlsaHHPxerCe8ObDS0OLlYX3rjJbGjxsJjVjTduMhs6vBTZzMwq8lJkMzNrCg4uZmZWOAeXQcwXLJpZozi4DGK+YNHMGsWrxQYhX7BoZo3mnssg5AsWzYrnYebaOLgMQr5g0ax4HmaujYfFBilfsGhWDA8zD4wvojQzq+Dgu0dZsWEXT+/8JUffP8GoEcOYd+lH+drnPjlkRgPOyEWUkn4o6aCkV3Np35D0pqQX0+OzudeWS+qStFvSvFz6LEmvpNdWpq2OSdshP57St0iamiuzWFJneizOpU9LeTtT2bNr+aXNzKrlYeaBqWbO5UFgfj/p90XEzPTYACBpBtkWxZemMt+VdFbKvwpYAkxPj1KdNwOHI+IS4D7g3lTXWOAu4ApgNnCXpDGpzL3p/acDh1MdZmZnRGmY+clbr+KGKy6m+71jjW5S0zvlnEtE/F2+N3EKC4C1EXEMeF1SFzBb0h5gdERsBpD0ELAQ2JjKfCOVfwK4P/Vq5gHtEdGTyrQD8yWtBeYA16cya1L5VVW20cysJt+/8YMRoRULL2tgS1rH6awWu03Sy2nYrNSjmAy8kcuzL6VNTsfl6X3KREQv8A4wrkJd44C3U97yuszMrAkMNLisAj4GzAQOAN9O6eonb1RIH0iZSnX9CklLJHVI6uju7j5ZNjMzK9CAgktEvBURxyPiBPADsjkRyHoRF+ayTgH2p/Qp/aT3KSNpOHAe0FOhrkPA+SlveV39tXV1RLRFRNv48eNr/VXNzGwABhRcJE3M/XgtUFpJth5YlFaATSObuN8aEQeAI5KuTPMpNwFP5cqUVoJdBzwb2froTcBcSWPSsNtcYFN67bmUl1S2VJeZmTWBU07oS3oM+DTwEUn7yFZwfVrSTLLhqD3AlwEiYqekdcBrQC+wNCKOp6puIVt5dg7ZRP7GlP4A8HCa/O8hW21GRPRIugfYlvLdXZrcB5YBayWtAHakOszMrEn4IkozM6toIBdRDqngIqkb+Od+XvoI2VxOq3G768vtrq9WbTe0bttP1u6LI6KmSeshFVxORlJHrVG5Gbjd9eV211erthtat+1Fttt3RTYzs8I5uJiZWeEcXDKrG92AAXK768vtrq9WbTe0btsLa7fnXMzMrHDuuZiZWeEGZXA5yR40/17S5rSnzP+RNDqlj5C0JqXvkrQ8V+b5tC9Nad+aCU3U7rMl/WVKf0nSp3Nl+t07pwXaXe/zfaGk59K/+05Jt6f0sZLa035B7bkbs9a8X1ELtLtu57zWdksal/K/J+n+srrq/Rkvsu3NfM5/S9L2dG63S5qTq6u2cx4Rg+4B/CfgN4BXc2nbgP+cjn8fuCcdX0+2TQDAr5HdcWBq+vl5oK1J270U+Mt0PAHYDgxLP28F/gPZTT43Ap9pkXbX+3xPBH4jHZ8L/CMwA/jPDIkDAAADj0lEQVQWcGdKvxO4Nx3PAF4CRgLTgH8Czqr3OS+43XU75wNo94eA3wS+AtxfVle9P+NFtr2Zz/nlwKR0fBnw5kDP+aDsuUTE35HdSibvE8DfpeN24HdK2YEPKbsR5jnAvwLv1qOd5Wps9wzgp6ncQeBtoE3Zfd9GR8TmyD4Rpb1zmrrdZ7J9JxMRByLi5+n4CLCLbPuGBWT7BJGeS+fv3/YriojXgdJ+RXU950W1+0y172RqbXdE/EtE/D1wNF9Pgz7jhbS93gbQ7h0RUboR8E5glLJ7RdZ8zgdlcDmJV4EvpOMv8sEdl58A/oVs64C9wJ/GB/cwA/jL1HX9n2e6630SJ2v3S8ACScOV3SR0Vnqt0t459VRru0sacr6VbYh3ObAFuCCym62SnkvDFgPZr+iMOs12l9T9nFfZ7pNp6Gf8NNte0grn/HeAHZFt/ljzOR9KweX3gaWStpN1D/81pc8GjgOTyIYMvirp36XXboiITwH/MT1urG+TgZO3+4dk/8AdwJ8B/5fsZqE17XdzBtXabmjQ+Zb0YeBHwB9ERKVeayF7DBWlgHZDA855De0+aRX9pNXlM15A26EFzrmkS8m2k/9yKamfbBXP+ZAJLhHxi4iYGxGzgMfIxp0hm3P524h4Pw3T/ANpmCYi3kzPR4BHacxQQr/tjojeiPjDiJgZEQuA84FOKu+d08ztbsj5ljSC7D/dX0XEj1PyW2kYoDQEczClD2S/omZud93PeY3tPpmGfMYLanvTn3NJU4AngZsiovR3suZzPmSCS2lFhqRhwNeB76WX9gJzlPkQcCXwizRs85FUZgTweT7Yt6bh7Zb0a6m9SPotoDciXovKe+c0bbsbcb7T+XkA2BUR38m9lN9jKL9f0ED2K2radtf7nA+g3f1qxGe8qLY3+zmXdD7wN8DyiPiHUuYBnfNKs/2t+iD7pnwAeJ8s4t4M3E62UuIfgW/ywQWkHwb+mmzy6jXgf8QHqz22Ay+n1/6ctMKmSdo9FdhNNkH3DNldS0v1tJF9YP8JuL9Uppnb3aDz/ZtkXfuXgRfT47PAOLJFB53peWyuzNfSed1NbrVMPc95Ue2u9zkfYLv3kC0WeS99tmY06DNeSNub/ZyTfRH8l1zeF4EJAznnvkLfzMwKN2SGxczMrH4cXMzMrHAOLmZmVjgHFzMzK5yDi5mZFc7BxczMCufgYmZmhXNwMTOzwv1/q4MdzlEclD8AAAAASUVORK5CYII=\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": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2014 1600941\n", "1991 1659249\n", "1995 1840410\n", "2012 2175217\n", "2003 2234584\n", "2019 2254386\n", "2006 2307352\n", "2017 2321583\n", "2001 2529279\n", "1992 2574578\n", "1993 2703886\n", "2018 2705325\n", "1988 2765617\n", "2007 2780164\n", "1987 2855570\n", "2016 2856393\n", "2011 2857040\n", "2008 2973918\n", "1998 3034904\n", "2002 3125418\n", "2009 3444020\n", "1994 3514763\n", "1996 3539413\n", "2004 3567744\n", "1997 3620066\n", "2015 3654892\n", "2000 3826372\n", "2005 3835025\n", "1999 3908112\n", "2010 4111392\n", "2013 4182691\n", "1986 5115251\n", "1990 5235827\n", "1989 5466192\n", "dtype: int64" ] }, "execution_count": 15, "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": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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