{ "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": [ "On copie les données dans un fichier local au cas ou il y ait un problème avec le serveur" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import os\n", "import urllib.request\n", "data_file = \"data_grippe.csv\"\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "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
020203732279918087.027511.03528.042.0FRFrance
12020363108478019.013675.01612.020.0FRFrance
2202035399186842.012994.01510.020.0FRFrance
3202034360843090.09078.094.014.0FRFrance
4202033361063411.08801.095.013.0FRFrance
5202032359183330.08506.095.013.0FRFrance
6202031343512269.06433.074.010.0FRFrance
7202030381795442.010916.0128.016.0FRFrance
8202029386875860.011514.0139.017.0FRFrance
9202028383405701.010979.0139.017.0FRFrance
10202027340662406.05726.063.09.0FRFrance
11202026340392389.05689.063.09.0FRFrance
12202025328531488.04218.042.06.0FRFrance
13202024330581690.04426.053.07.0FRFrance
14202023341682468.05868.063.09.0FRFrance
15202022335801947.05213.053.07.0FRFrance
16202021361144026.08202.096.012.0FRFrance
17202020393156775.011855.01410.018.0FRFrance
182020193116798722.014636.01814.022.0FRFrance
1920201831639812851.019945.02520.030.0FRFrance
2020201731808214454.021710.02721.033.0FRFrance
2120201632416519893.028437.03731.043.0FRFrance
2220201534104935377.046721.06253.071.0FRFrance
2320201437166664531.078801.010998.0120.0FRFrance
24202013310774299187.0116297.0164151.0177.0FRFrance
25202012310728398610.0115956.0163150.0176.0FRFrance
26202011310170493652.0109756.0154142.0166.0FRFrance
27202010310497796650.0113304.0159146.0172.0FRFrance
282020093110696102066.0119326.0168155.0181.0FRFrance
292020083143753133984.0153522.0218203.0233.0FRFrance
.................................
184219852132609619621.032571.04735.059.0FRFrance
184319852032789620885.034907.05138.064.0FRFrance
184419851934315432821.053487.07859.097.0FRFrance
184519851834055529935.051175.07455.093.0FRFrance
184619851733405324366.043740.06244.080.0FRFrance
184719851635036236451.064273.09166.0116.0FRFrance
184819851536388145538.082224.011683.0149.0FRFrance
18491985143134545114400.0154690.0244207.0281.0FRFrance
18501985133197206176080.0218332.0357319.0395.0FRFrance
18511985123245240223304.0267176.0445405.0485.0FRFrance
18521985113276205252399.0300011.0501458.0544.0FRFrance
18531985103353231326279.0380183.0640591.0689.0FRFrance
18541985093369895341109.0398681.0670618.0722.0FRFrance
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18571985063565825518011.0613639.01026939.01113.0FRFrance
18581985053637302592795.0681809.011551074.01236.0FRFrance
18591985043424937390794.0459080.0770708.0832.0FRFrance
18601985033213901174689.0253113.0388317.0459.0FRFrance
186119850239758680949.0114223.0177147.0207.0FRFrance
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1864198451310172680242.0123210.0185146.0224.0FRFrance
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1866198449310107381684.0120462.0184149.0219.0FRFrance
186719844837862060634.096606.0143110.0176.0FRFrance
186819844737202954274.089784.013199.0163.0FRFrance
186919844638733067686.0106974.0159123.0195.0FRFrance
18701984453135223101414.0169032.0246184.0308.0FRFrance
187119844436842220056.0116788.012537.0213.0FRFrance
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1872 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202037 3 22799 18087.0 27511.0 35 28.0 \n", "1 202036 3 10847 8019.0 13675.0 16 12.0 \n", "2 202035 3 9918 6842.0 12994.0 15 10.0 \n", "3 202034 3 6084 3090.0 9078.0 9 4.0 \n", "4 202033 3 6106 3411.0 8801.0 9 5.0 \n", "5 202032 3 5918 3330.0 8506.0 9 5.0 \n", "6 202031 3 4351 2269.0 6433.0 7 4.0 \n", "7 202030 3 8179 5442.0 10916.0 12 8.0 \n", "8 202029 3 8687 5860.0 11514.0 13 9.0 \n", "9 202028 3 8340 5701.0 10979.0 13 9.0 \n", "10 202027 3 4066 2406.0 5726.0 6 3.0 \n", "11 202026 3 4039 2389.0 5689.0 6 3.0 \n", "12 202025 3 2853 1488.0 4218.0 4 2.0 \n", "13 202024 3 3058 1690.0 4426.0 5 3.0 \n", "14 202023 3 4168 2468.0 5868.0 6 3.0 \n", "15 202022 3 3580 1947.0 5213.0 5 3.0 \n", "16 202021 3 6114 4026.0 8202.0 9 6.0 \n", "17 202020 3 9315 6775.0 11855.0 14 10.0 \n", "18 202019 3 11679 8722.0 14636.0 18 14.0 \n", "19 202018 3 16398 12851.0 19945.0 25 20.0 \n", "20 202017 3 18082 14454.0 21710.0 27 21.0 \n", "21 202016 3 24165 19893.0 28437.0 37 31.0 \n", "22 202015 3 41049 35377.0 46721.0 62 53.0 \n", "23 202014 3 71666 64531.0 78801.0 109 98.0 \n", "24 202013 3 107742 99187.0 116297.0 164 151.0 \n", "25 202012 3 107283 98610.0 115956.0 163 150.0 \n", "26 202011 3 101704 93652.0 109756.0 154 142.0 \n", "27 202010 3 104977 96650.0 113304.0 159 146.0 \n", "28 202009 3 110696 102066.0 119326.0 168 155.0 \n", "29 202008 3 143753 133984.0 153522.0 218 203.0 \n", "... ... ... ... ... ... ... ... \n", "1842 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1843 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1844 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1845 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1846 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1847 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1848 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1849 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1850 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1851 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1852 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1853 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1854 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1855 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1856 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1857 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1858 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1859 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1860 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1861 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1862 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1863 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1864 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1865 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1866 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1867 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1868 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1869 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1870 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1871 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 20.0 FR France \n", "2 20.0 FR France \n", "3 14.0 FR France \n", "4 13.0 FR France \n", "5 13.0 FR France \n", "6 10.0 FR France \n", "7 16.0 FR France \n", "8 17.0 FR France \n", "9 17.0 FR France \n", "10 9.0 FR France \n", "11 9.0 FR France \n", "12 6.0 FR France \n", "13 7.0 FR France \n", "14 9.0 FR France \n", "15 7.0 FR France \n", "16 12.0 FR France \n", "17 18.0 FR France \n", "18 22.0 FR France \n", "19 30.0 FR France \n", "20 33.0 FR France \n", "21 43.0 FR France \n", "22 71.0 FR France \n", "23 120.0 FR France \n", "24 177.0 FR France \n", "25 176.0 FR France \n", "26 166.0 FR France \n", "27 172.0 FR France \n", "28 181.0 FR France \n", "29 233.0 FR France \n", "... ... ... ... \n", "1842 59.0 FR France \n", "1843 64.0 FR France \n", "1844 97.0 FR France \n", "1845 93.0 FR France \n", "1846 80.0 FR France \n", "1847 116.0 FR France \n", "1848 149.0 FR France \n", "1849 281.0 FR France \n", "1850 395.0 FR France \n", "1851 485.0 FR France \n", "1852 544.0 FR France \n", "1853 689.0 FR France \n", "1854 722.0 FR France \n", "1855 762.0 FR France \n", "1856 926.0 FR France \n", "1857 1113.0 FR France \n", "1858 1236.0 FR France \n", "1859 832.0 FR France \n", "1860 459.0 FR France \n", "1861 207.0 FR France \n", "1862 190.0 FR France \n", "1863 198.0 FR France \n", "1864 224.0 FR France \n", "1865 266.0 FR France \n", "1866 219.0 FR France \n", "1867 176.0 FR France \n", "1868 163.0 FR France \n", "1869 195.0 FR France \n", "1870 308.0 FR France \n", "1871 213.0 FR France \n", "\n", "[1872 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
163519891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1635 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1635 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
020203732279918087.027511.03528.042.0FRFrance
12020363108478019.013675.01612.020.0FRFrance
2202035399186842.012994.01510.020.0FRFrance
3202034360843090.09078.094.014.0FRFrance
4202033361063411.08801.095.013.0FRFrance
5202032359183330.08506.095.013.0FRFrance
6202031343512269.06433.074.010.0FRFrance
7202030381795442.010916.0128.016.0FRFrance
8202029386875860.011514.0139.017.0FRFrance
9202028383405701.010979.0139.017.0FRFrance
10202027340662406.05726.063.09.0FRFrance
11202026340392389.05689.063.09.0FRFrance
12202025328531488.04218.042.06.0FRFrance
13202024330581690.04426.053.07.0FRFrance
14202023341682468.05868.063.09.0FRFrance
15202022335801947.05213.053.07.0FRFrance
16202021361144026.08202.096.012.0FRFrance
17202020393156775.011855.01410.018.0FRFrance
182020193116798722.014636.01814.022.0FRFrance
1920201831639812851.019945.02520.030.0FRFrance
2020201731808214454.021710.02721.033.0FRFrance
2120201632416519893.028437.03731.043.0FRFrance
2220201534104935377.046721.06253.071.0FRFrance
2320201437166664531.078801.010998.0120.0FRFrance
24202013310774299187.0116297.0164151.0177.0FRFrance
25202012310728398610.0115956.0163150.0176.0FRFrance
26202011310170493652.0109756.0154142.0166.0FRFrance
27202010310497796650.0113304.0159146.0172.0FRFrance
282020093110696102066.0119326.0168155.0181.0FRFrance
292020083143753133984.0153522.0218203.0233.0FRFrance
.................................
184219852132609619621.032571.04735.059.0FRFrance
184319852032789620885.034907.05138.064.0FRFrance
184419851934315432821.053487.07859.097.0FRFrance
184519851834055529935.051175.07455.093.0FRFrance
184619851733405324366.043740.06244.080.0FRFrance
184719851635036236451.064273.09166.0116.0FRFrance
184819851536388145538.082224.011683.0149.0FRFrance
18491985143134545114400.0154690.0244207.0281.0FRFrance
18501985133197206176080.0218332.0357319.0395.0FRFrance
18511985123245240223304.0267176.0445405.0485.0FRFrance
18521985113276205252399.0300011.0501458.0544.0FRFrance
18531985103353231326279.0380183.0640591.0689.0FRFrance
18541985093369895341109.0398681.0670618.0722.0FRFrance
18551985083389886359529.0420243.0707652.0762.0FRFrance
18561985073471852432599.0511105.0855784.0926.0FRFrance
18571985063565825518011.0613639.01026939.01113.0FRFrance
18581985053637302592795.0681809.011551074.01236.0FRFrance
18591985043424937390794.0459080.0770708.0832.0FRFrance
18601985033213901174689.0253113.0388317.0459.0FRFrance
186119850239758680949.0114223.0177147.0207.0FRFrance
186219850138548965918.0105060.0155120.0190.0FRFrance
186319845238483060602.0109058.0154110.0198.0FRFrance
1864198451310172680242.0123210.0185146.0224.0FRFrance
18651984503123680101401.0145959.0225184.0266.0FRFrance
1866198449310107381684.0120462.0184149.0219.0FRFrance
186719844837862060634.096606.0143110.0176.0FRFrance
186819844737202954274.089784.013199.0163.0FRFrance
186919844638733067686.0106974.0159123.0195.0FRFrance
18701984453135223101414.0169032.0246184.0308.0FRFrance
187119844436842220056.0116788.012537.0213.0FRFrance
\n", "

1871 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202037 3 22799 18087.0 27511.0 35 28.0 \n", "1 202036 3 10847 8019.0 13675.0 16 12.0 \n", "2 202035 3 9918 6842.0 12994.0 15 10.0 \n", "3 202034 3 6084 3090.0 9078.0 9 4.0 \n", "4 202033 3 6106 3411.0 8801.0 9 5.0 \n", "5 202032 3 5918 3330.0 8506.0 9 5.0 \n", "6 202031 3 4351 2269.0 6433.0 7 4.0 \n", "7 202030 3 8179 5442.0 10916.0 12 8.0 \n", "8 202029 3 8687 5860.0 11514.0 13 9.0 \n", "9 202028 3 8340 5701.0 10979.0 13 9.0 \n", "10 202027 3 4066 2406.0 5726.0 6 3.0 \n", "11 202026 3 4039 2389.0 5689.0 6 3.0 \n", "12 202025 3 2853 1488.0 4218.0 4 2.0 \n", "13 202024 3 3058 1690.0 4426.0 5 3.0 \n", "14 202023 3 4168 2468.0 5868.0 6 3.0 \n", "15 202022 3 3580 1947.0 5213.0 5 3.0 \n", "16 202021 3 6114 4026.0 8202.0 9 6.0 \n", "17 202020 3 9315 6775.0 11855.0 14 10.0 \n", "18 202019 3 11679 8722.0 14636.0 18 14.0 \n", "19 202018 3 16398 12851.0 19945.0 25 20.0 \n", "20 202017 3 18082 14454.0 21710.0 27 21.0 \n", "21 202016 3 24165 19893.0 28437.0 37 31.0 \n", "22 202015 3 41049 35377.0 46721.0 62 53.0 \n", "23 202014 3 71666 64531.0 78801.0 109 98.0 \n", "24 202013 3 107742 99187.0 116297.0 164 151.0 \n", "25 202012 3 107283 98610.0 115956.0 163 150.0 \n", "26 202011 3 101704 93652.0 109756.0 154 142.0 \n", "27 202010 3 104977 96650.0 113304.0 159 146.0 \n", "28 202009 3 110696 102066.0 119326.0 168 155.0 \n", "29 202008 3 143753 133984.0 153522.0 218 203.0 \n", "... ... ... ... ... ... ... ... \n", "1842 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1843 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1844 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1845 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1846 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1847 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1848 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1849 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1850 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1851 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1852 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1853 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1854 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1855 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1856 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1857 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1858 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1859 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1860 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1861 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1862 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1863 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1864 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1865 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1866 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1867 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1868 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1869 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1870 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1871 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 20.0 FR France \n", "2 20.0 FR France \n", "3 14.0 FR France \n", "4 13.0 FR France \n", "5 13.0 FR France \n", "6 10.0 FR France \n", "7 16.0 FR France \n", "8 17.0 FR France \n", "9 17.0 FR France \n", "10 9.0 FR France \n", "11 9.0 FR France \n", "12 6.0 FR France \n", "13 7.0 FR France \n", "14 9.0 FR France \n", "15 7.0 FR France \n", "16 12.0 FR France \n", "17 18.0 FR France \n", "18 22.0 FR France \n", "19 30.0 FR France \n", "20 33.0 FR France \n", "21 43.0 FR France \n", "22 71.0 FR France \n", "23 120.0 FR France \n", "24 177.0 FR France \n", "25 176.0 FR France \n", "26 166.0 FR France \n", "27 172.0 FR France \n", "28 181.0 FR France \n", "29 233.0 FR France \n", "... ... ... ... \n", "1842 59.0 FR France \n", "1843 64.0 FR France \n", "1844 97.0 FR France \n", "1845 93.0 FR France \n", "1846 80.0 FR France \n", "1847 116.0 FR France \n", "1848 149.0 FR France \n", "1849 281.0 FR France \n", "1850 395.0 FR France \n", "1851 485.0 FR France \n", "1852 544.0 FR France \n", "1853 689.0 FR France \n", "1854 722.0 FR France \n", "1855 762.0 FR France \n", "1856 926.0 FR France \n", "1857 1113.0 FR France \n", "1858 1236.0 FR France \n", "1859 832.0 FR France \n", "1860 459.0 FR France \n", "1861 207.0 FR France \n", "1862 190.0 FR France \n", "1863 198.0 FR France \n", "1864 224.0 FR France \n", "1865 266.0 FR France \n", "1866 219.0 FR France \n", "1867 176.0 FR France \n", "1868 163.0 FR France \n", "1869 195.0 FR France \n", "1870 308.0 FR France \n", "1871 213.0 FR France \n", "\n", "[1871 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": null, "metadata": {}, "outputs": [], "source": [ "periods = sorted_data.index\n", "for p1, p2 in zip(periods[:-1], periods[1:]):\n", " delta = p2.to_timestamp() - p1.end_time\n", " if delta > pd.Timedelta('1s'):\n", " print(p1, p2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un premier regard sur les données !" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sorted_data['inc'][-200:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Etude de l'incidence annuelle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n", "entre deux années civiles, nous définissons la période de référence\n", "entre deux minima de l'incidence, du 1er août de l'année $N$ au\n", "1er août de l'année $N+1$.\n", "\n", "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n", "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n", "de référence: à la place du 1er août de chaque année, nous utilisons le\n", "premier jour de la semaine qui contient le 1er août.\n", "\n", "Comme l'incidence de syndrome grippal est très faible en été, cette\n", "modification ne risque pas de fausser nos conclusions.\n", "\n", "Encore un petit détail: les données commencent an octobre 1984, ce qui\n", "rend la première année incomplète. Nous commençons donc l'analyse en 1985." ] }, { "cell_type": "code", "execution_count": null, "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": null, "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": null, "metadata": {}, "outputs": [], "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": null, "metadata": {}, "outputs": [], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n", " française, sont assez rares: il y en eu trois au cours des 35 dernières années." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "yearly_incidence.hist(xrot=20)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 }