{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 8, "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": 23, "metadata": { "scrolled": true }, "outputs": [], "source": [ "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-3.csv\" " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", "\n", "| Nom de colonne | Libellé de colonne |\n", "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n", "| week | Semaine calendaire (ISO 8601) |\n", "| indicator | Code de l'indicateur de surveillance |\n", "| inc | Estimation de l'incidence de consultations en nombre de cas |\n", "| inc_low | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation |\n", "| inc_up | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation |\n", "| inc100 | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", "| inc100_low | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", "| inc100_up | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", "| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n", "| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n", "\n", "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020214532105217090.025014.03226.038.0FRFrance
120214431901415044.022984.02923.035.0FRFrance
220214332704021935.032145.04133.049.0FRFrance
320214232834323382.033304.04335.051.0FRFrance
420214132504320586.029500.03831.045.0FRFrance
520214032628621842.030730.04033.047.0FRFrance
620213932215518014.026296.03428.040.0FRFrance
720213831561412310.018918.02419.029.0FRFrance
820213731367310404.016942.02116.026.0FRFrance
92021363102897505.013073.01612.020.0FRFrance
102021353126099282.015936.01914.024.0FRFrance
112021343130159485.016545.02015.025.0FRFrance
122021333103927042.013742.01611.021.0FRFrance
1320213231558611009.020163.02417.031.0FRFrance
1420213131885513664.024046.02921.037.0FRFrance
152021303139919695.018287.02114.028.0FRFrance
162021293136269618.017634.02115.027.0FRFrance
17202128386365430.011842.0138.018.0FRFrance
182021273106936838.014548.01610.022.0FRFrance
19202126370864109.010063.0116.016.0FRFrance
20202125379425540.010344.0128.016.0FRFrance
21202124348553011.06699.074.010.0FRFrance
22202123367104455.08965.0107.013.0FRFrance
23202122378795495.010263.0128.016.0FRFrance
24202121378275403.010251.0128.016.0FRFrance
252021203102787540.013016.01612.020.0FRFrance
26202119395396860.012218.01410.018.0FRFrance
272021183121359165.015105.01814.022.0FRFrance
282021173120588891.015225.01813.023.0FRFrance
2920211631650512735.020275.02519.031.0FRFrance
.................................
190319852132609619621.032571.04735.059.0FRFrance
190419852032789620885.034907.05138.064.0FRFrance
190519851934315432821.053487.07859.097.0FRFrance
190619851834055529935.051175.07455.093.0FRFrance
190719851733405324366.043740.06244.080.0FRFrance
190819851635036236451.064273.09166.0116.0FRFrance
190919851536388145538.082224.011683.0149.0FRFrance
19101985143134545114400.0154690.0244207.0281.0FRFrance
19111985133197206176080.0218332.0357319.0395.0FRFrance
19121985123245240223304.0267176.0445405.0485.0FRFrance
19131985113276205252399.0300011.0501458.0544.0FRFrance
19141985103353231326279.0380183.0640591.0689.0FRFrance
19151985093369895341109.0398681.0670618.0722.0FRFrance
19161985083389886359529.0420243.0707652.0762.0FRFrance
19171985073471852432599.0511105.0855784.0926.0FRFrance
19181985063565825518011.0613639.01026939.01113.0FRFrance
19191985053637302592795.0681809.011551074.01236.0FRFrance
19201985043424937390794.0459080.0770708.0832.0FRFrance
19211985033213901174689.0253113.0388317.0459.0FRFrance
192219850239758680949.0114223.0177147.0207.0FRFrance
192319850138548965918.0105060.0155120.0190.0FRFrance
192419845238483060602.0109058.0154110.0198.0FRFrance
1925198451310172680242.0123210.0185146.0224.0FRFrance
19261984503123680101401.0145959.0225184.0266.0FRFrance
1927198449310107381684.0120462.0184149.0219.0FRFrance
192819844837862060634.096606.0143110.0176.0FRFrance
192919844737202954274.089784.013199.0163.0FRFrance
193019844638733067686.0106974.0159123.0195.0FRFrance
19311984453135223101414.0169032.0246184.0308.0FRFrance
193219844436842220056.0116788.012537.0213.0FRFrance
\n", "

1933 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202145 3 21052 17090.0 25014.0 32 26.0 \n", "1 202144 3 19014 15044.0 22984.0 29 23.0 \n", "2 202143 3 27040 21935.0 32145.0 41 33.0 \n", "3 202142 3 28343 23382.0 33304.0 43 35.0 \n", "4 202141 3 25043 20586.0 29500.0 38 31.0 \n", "5 202140 3 26286 21842.0 30730.0 40 33.0 \n", "6 202139 3 22155 18014.0 26296.0 34 28.0 \n", "7 202138 3 15614 12310.0 18918.0 24 19.0 \n", "8 202137 3 13673 10404.0 16942.0 21 16.0 \n", "9 202136 3 10289 7505.0 13073.0 16 12.0 \n", "10 202135 3 12609 9282.0 15936.0 19 14.0 \n", "11 202134 3 13015 9485.0 16545.0 20 15.0 \n", "12 202133 3 10392 7042.0 13742.0 16 11.0 \n", "13 202132 3 15586 11009.0 20163.0 24 17.0 \n", "14 202131 3 18855 13664.0 24046.0 29 21.0 \n", "15 202130 3 13991 9695.0 18287.0 21 14.0 \n", "16 202129 3 13626 9618.0 17634.0 21 15.0 \n", "17 202128 3 8636 5430.0 11842.0 13 8.0 \n", "18 202127 3 10693 6838.0 14548.0 16 10.0 \n", "19 202126 3 7086 4109.0 10063.0 11 6.0 \n", "20 202125 3 7942 5540.0 10344.0 12 8.0 \n", "21 202124 3 4855 3011.0 6699.0 7 4.0 \n", "22 202123 3 6710 4455.0 8965.0 10 7.0 \n", "23 202122 3 7879 5495.0 10263.0 12 8.0 \n", "24 202121 3 7827 5403.0 10251.0 12 8.0 \n", "25 202120 3 10278 7540.0 13016.0 16 12.0 \n", "26 202119 3 9539 6860.0 12218.0 14 10.0 \n", "27 202118 3 12135 9165.0 15105.0 18 14.0 \n", "28 202117 3 12058 8891.0 15225.0 18 13.0 \n", "29 202116 3 16505 12735.0 20275.0 25 19.0 \n", "... ... ... ... ... ... ... ... \n", "1903 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1904 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1905 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1906 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1907 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1908 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1909 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1910 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1911 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1912 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1913 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1914 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1915 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1916 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1917 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1918 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1919 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1920 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1921 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1922 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1923 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1924 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1925 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1926 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1927 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1928 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1929 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1930 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1931 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1932 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 38.0 FR France \n", "1 35.0 FR France \n", "2 49.0 FR France \n", "3 51.0 FR France \n", "4 45.0 FR France \n", "5 47.0 FR France \n", "6 40.0 FR France \n", "7 29.0 FR France \n", "8 26.0 FR France \n", "9 20.0 FR France \n", "10 24.0 FR France \n", "11 25.0 FR France \n", "12 21.0 FR France \n", "13 31.0 FR France \n", "14 37.0 FR France \n", "15 28.0 FR France \n", "16 27.0 FR France \n", "17 18.0 FR France \n", "18 22.0 FR France \n", "19 16.0 FR France \n", "20 16.0 FR France \n", "21 10.0 FR France \n", "22 13.0 FR France \n", "23 16.0 FR France \n", "24 16.0 FR France \n", "25 20.0 FR France \n", "26 18.0 FR France \n", "27 22.0 FR France \n", "28 23.0 FR France \n", "29 31.0 FR France \n", "... ... ... ... \n", "1903 59.0 FR France \n", "1904 64.0 FR France \n", "1905 97.0 FR France \n", "1906 93.0 FR France \n", "1907 80.0 FR France \n", "1908 116.0 FR France \n", "1909 149.0 FR France \n", "1910 281.0 FR France \n", "1911 395.0 FR France \n", "1912 485.0 FR France \n", "1913 544.0 FR France \n", "1914 689.0 FR France \n", "1915 722.0 FR France \n", "1916 762.0 FR France \n", "1917 926.0 FR France \n", "1918 1113.0 FR France \n", "1919 1236.0 FR France \n", "1920 832.0 FR France \n", "1921 459.0 FR France \n", "1922 207.0 FR France \n", "1923 190.0 FR France \n", "1924 198.0 FR France \n", "1925 224.0 FR France \n", "1926 266.0 FR France \n", "1927 219.0 FR France \n", "1928 176.0 FR France \n", "1929 163.0 FR France \n", "1930 195.0 FR France \n", "1931 308.0 FR France \n", "1932 213.0 FR France \n", "\n", "[1933 rows x 10 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url,encoding = 'iso-8859-1', 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": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
169619891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1696 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1696 FR France " ] }, "execution_count": 26, "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": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020214532105217090.025014.03226.038.0FRFrance
120214431901415044.022984.02923.035.0FRFrance
220214332704021935.032145.04133.049.0FRFrance
320214232834323382.033304.04335.051.0FRFrance
420214132504320586.029500.03831.045.0FRFrance
520214032628621842.030730.04033.047.0FRFrance
620213932215518014.026296.03428.040.0FRFrance
720213831561412310.018918.02419.029.0FRFrance
820213731367310404.016942.02116.026.0FRFrance
92021363102897505.013073.01612.020.0FRFrance
102021353126099282.015936.01914.024.0FRFrance
112021343130159485.016545.02015.025.0FRFrance
122021333103927042.013742.01611.021.0FRFrance
1320213231558611009.020163.02417.031.0FRFrance
1420213131885513664.024046.02921.037.0FRFrance
152021303139919695.018287.02114.028.0FRFrance
162021293136269618.017634.02115.027.0FRFrance
17202128386365430.011842.0138.018.0FRFrance
182021273106936838.014548.01610.022.0FRFrance
19202126370864109.010063.0116.016.0FRFrance
20202125379425540.010344.0128.016.0FRFrance
21202124348553011.06699.074.010.0FRFrance
22202123367104455.08965.0107.013.0FRFrance
23202122378795495.010263.0128.016.0FRFrance
24202121378275403.010251.0128.016.0FRFrance
252021203102787540.013016.01612.020.0FRFrance
26202119395396860.012218.01410.018.0FRFrance
272021183121359165.015105.01814.022.0FRFrance
282021173120588891.015225.01813.023.0FRFrance
2920211631650512735.020275.02519.031.0FRFrance
.................................
190319852132609619621.032571.04735.059.0FRFrance
190419852032789620885.034907.05138.064.0FRFrance
190519851934315432821.053487.07859.097.0FRFrance
190619851834055529935.051175.07455.093.0FRFrance
190719851733405324366.043740.06244.080.0FRFrance
190819851635036236451.064273.09166.0116.0FRFrance
190919851536388145538.082224.011683.0149.0FRFrance
19101985143134545114400.0154690.0244207.0281.0FRFrance
19111985133197206176080.0218332.0357319.0395.0FRFrance
19121985123245240223304.0267176.0445405.0485.0FRFrance
19131985113276205252399.0300011.0501458.0544.0FRFrance
19141985103353231326279.0380183.0640591.0689.0FRFrance
19151985093369895341109.0398681.0670618.0722.0FRFrance
19161985083389886359529.0420243.0707652.0762.0FRFrance
19171985073471852432599.0511105.0855784.0926.0FRFrance
19181985063565825518011.0613639.01026939.01113.0FRFrance
19191985053637302592795.0681809.011551074.01236.0FRFrance
19201985043424937390794.0459080.0770708.0832.0FRFrance
19211985033213901174689.0253113.0388317.0459.0FRFrance
192219850239758680949.0114223.0177147.0207.0FRFrance
192319850138548965918.0105060.0155120.0190.0FRFrance
192419845238483060602.0109058.0154110.0198.0FRFrance
1925198451310172680242.0123210.0185146.0224.0FRFrance
19261984503123680101401.0145959.0225184.0266.0FRFrance
1927198449310107381684.0120462.0184149.0219.0FRFrance
192819844837862060634.096606.0143110.0176.0FRFrance
192919844737202954274.089784.013199.0163.0FRFrance
193019844638733067686.0106974.0159123.0195.0FRFrance
19311984453135223101414.0169032.0246184.0308.0FRFrance
193219844436842220056.0116788.012537.0213.0FRFrance
\n", "

1932 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202145 3 21052 17090.0 25014.0 32 26.0 \n", "1 202144 3 19014 15044.0 22984.0 29 23.0 \n", "2 202143 3 27040 21935.0 32145.0 41 33.0 \n", "3 202142 3 28343 23382.0 33304.0 43 35.0 \n", "4 202141 3 25043 20586.0 29500.0 38 31.0 \n", "5 202140 3 26286 21842.0 30730.0 40 33.0 \n", "6 202139 3 22155 18014.0 26296.0 34 28.0 \n", "7 202138 3 15614 12310.0 18918.0 24 19.0 \n", "8 202137 3 13673 10404.0 16942.0 21 16.0 \n", "9 202136 3 10289 7505.0 13073.0 16 12.0 \n", "10 202135 3 12609 9282.0 15936.0 19 14.0 \n", "11 202134 3 13015 9485.0 16545.0 20 15.0 \n", "12 202133 3 10392 7042.0 13742.0 16 11.0 \n", "13 202132 3 15586 11009.0 20163.0 24 17.0 \n", "14 202131 3 18855 13664.0 24046.0 29 21.0 \n", "15 202130 3 13991 9695.0 18287.0 21 14.0 \n", "16 202129 3 13626 9618.0 17634.0 21 15.0 \n", "17 202128 3 8636 5430.0 11842.0 13 8.0 \n", "18 202127 3 10693 6838.0 14548.0 16 10.0 \n", "19 202126 3 7086 4109.0 10063.0 11 6.0 \n", "20 202125 3 7942 5540.0 10344.0 12 8.0 \n", "21 202124 3 4855 3011.0 6699.0 7 4.0 \n", "22 202123 3 6710 4455.0 8965.0 10 7.0 \n", "23 202122 3 7879 5495.0 10263.0 12 8.0 \n", "24 202121 3 7827 5403.0 10251.0 12 8.0 \n", "25 202120 3 10278 7540.0 13016.0 16 12.0 \n", "26 202119 3 9539 6860.0 12218.0 14 10.0 \n", "27 202118 3 12135 9165.0 15105.0 18 14.0 \n", "28 202117 3 12058 8891.0 15225.0 18 13.0 \n", "29 202116 3 16505 12735.0 20275.0 25 19.0 \n", "... ... ... ... ... ... ... ... \n", "1903 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1904 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1905 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1906 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1907 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1908 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1909 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1910 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1911 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1912 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1913 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1914 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1915 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1916 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1917 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1918 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1919 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1920 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1921 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1922 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1923 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1924 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1925 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1926 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1927 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1928 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1929 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1930 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1931 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1932 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 38.0 FR France \n", "1 35.0 FR France \n", "2 49.0 FR France \n", "3 51.0 FR France \n", "4 45.0 FR France \n", "5 47.0 FR France \n", "6 40.0 FR France \n", "7 29.0 FR France \n", "8 26.0 FR France \n", "9 20.0 FR France \n", "10 24.0 FR France \n", "11 25.0 FR France \n", "12 21.0 FR France \n", "13 31.0 FR France \n", "14 37.0 FR France \n", "15 28.0 FR France \n", "16 27.0 FR France \n", "17 18.0 FR France \n", "18 22.0 FR France \n", "19 16.0 FR France \n", "20 16.0 FR France \n", "21 10.0 FR France \n", "22 13.0 FR France \n", "23 16.0 FR France \n", "24 16.0 FR France \n", "25 20.0 FR France \n", "26 18.0 FR France \n", "27 22.0 FR France \n", "28 23.0 FR France \n", "29 31.0 FR France \n", "... ... ... ... \n", "1903 59.0 FR France \n", "1904 64.0 FR France \n", "1905 97.0 FR France \n", "1906 93.0 FR France \n", "1907 80.0 FR France \n", "1908 116.0 FR France \n", "1909 149.0 FR France \n", "1910 281.0 FR France \n", "1911 395.0 FR France \n", "1912 485.0 FR France \n", "1913 544.0 FR France \n", "1914 689.0 FR France \n", "1915 722.0 FR France \n", "1916 762.0 FR France \n", "1917 926.0 FR France \n", "1918 1113.0 FR France \n", "1919 1236.0 FR France \n", "1920 832.0 FR France \n", "1921 459.0 FR France \n", "1922 207.0 FR France \n", "1923 190.0 FR France \n", "1924 198.0 FR France \n", "1925 224.0 FR France \n", "1926 266.0 FR France \n", "1927 219.0 FR France \n", "1928 176.0 FR France \n", "1929 163.0 FR France \n", "1930 195.0 FR France \n", "1931 308.0 FR France \n", "1932 213.0 FR France \n", "\n", "[1932 rows x 10 columns]" ] }, "execution_count": 12, "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": 30, "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": 31, "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": 32, "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": 29, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 29, "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": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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vEruvYvUA56wAVsQorwTmxyjvAK7p51qrgFWD1dMkXyTriNlHYk1bxqQtm9luEibo9hzJDvh7lQcyrLPdmHRmgcQkTLDLa8HMOi4jEesjMSaNWSAxCRPJSI4LJD4fIZuQaEzaskBiEiYY8jKS45u2bEKiMenMAolJmI6ufjISvw3/NSadWSAxCRPJSLL6ZCS2jLwx6c0CiUmYoMtIsmMukWJ9JMakKwskJmE6+slIMvxCd1gJhy2YGJOOLJCYhAkO0EcC0GWz241JSxZITML0N/w3smSKNW8Zk54skJiE6Xf4r99bYacrZBmJMenIAolJmP6G/2b0ZCQWSIxJRxZITMIMNPwXoMs6241JSxZITML0O/w3w5q2jElnFkhMwnSEuvH7pKcpKyJgTVvGpDULJCZhgl3h4/pH4FggsWVS4tcdVvY3tSe7GsbEJZ6tdqeLyLMiskVENonIF1x5sYisEZEd7ntR1Dm3ikiViGwTkSVR5QtEZIN77m635S5uW94HXflaEZkRdc4y9xo7RGQZJmUFQ+HjRmxB1KgtG/4bt4cq9/Geu56jua0r2VUxZlDxZCQh4IuqejqwGLhJROYBXwaeVtU5wNPuZ9xzS4EzgMuBH4hI5NPlHmA53j7uc9zzADcAjao6G/gOcIe7VjFwG3A+sAi4LTpgmdTS0dU9YEYSsowkbuurmwiGwuw53JrsqhgzqEEDiarWqurr7nELsAUoBz4M3OcOuw+4yj3+MPCAqgZVdTdQBSwSkTKgQFVfVlUF7u9zTuRaDwOXuGxlCbBGVRtUtRFYw7HgY1JMMGRNW4lSVXcUgL0NbUmuiTGDG1IfiWtyOhdYC0xR1Vrwgg0w2R1WDuyLOq3alZW7x33Le52jqiGgGSgZ4FomBQVD3WRlxGraspntQ6GqbD9ogcScOOIOJCKSD/wWuEVVjwx0aIwyHaB8uOdE1225iFSKSGV9ff0AVTOjyesjiZWR2PDfoTh0tJPmdq9vZJ8FEnMCiCuQiEgAL4j8UlV/54oPuuYq3Pc6V14NTI86vQLY78orYpT3OkdEMoBCoGGAa/Wiqveq6kJVXVhaWhrPWzKjwOsj6T8jCdmijXHZUdcCgN8n7Gu0QGJSXzyjtgRYCWxR1W9HPfUoEBlFtQx4JKp8qRuJNROvU/1V1/zVIiKL3TWv73NO5FpXA8+4fpQngctEpMh1sl/mykwKCobCZMXMSCJ9JNa0FY+drn9kwclF1rRlTggZcRxzAXAdsEFE3nRlXwG+CTwkIjcAe4FrAFR1k4g8BGzGG/F1k6p2u/NuBH4G5ACPuy/wAtXPRaQKLxNZ6q7VICK3A+vccV9X1YZhvlczyoJdYUryjs9IepZIsaatuOyoO8qErAzOm1HED59vpKs73BOMjUlFgwYSVX2R2H0VAJf0c84KYEWM8kpgfozyDlwgivHcKmDVYPU0ydcR6o6dkUSWSLFRW3HZcfAosybnc3JxHt1hpbapg5NKcpNdLWP6ZX/mmITpb2Z7hs+WSBmKqvqjzJ6cz/RiL3hYP4lJdRZITMJ480gGaNqyPpJBqSqHjwaZWpDdk4VYP4lJdRZITMIEu7pjD/+1pq24BUNhwgq5WX6mFmQT8IsFEpPyLJCYhOkvI7HVf+PXGgwBkJeZgd8nTCnI5kBzR5JrZczALJBEqTti/2GHKxxWOrv76yPxMhIb/ju4tk5vgGNupheQi3IzaWzrTGaVjBmUBRJn96FWLvn283z3qe14U1jMUETW0Yq1+q+IEPCLZSRxiASSvCxvQGVRXiaNtgKwSXEWSJzpRTksOWMq331qB3c8sS3Z1TnhdHS5bXZjZCTgNW/Z6r+Da+30mraOZSQBGlstIzGpzQKJk+H3cedHz+JDZ0/jx3/Z1fPBaOITdJMNY80jAS+Q2KitwbUF+2Qk1rRlTgAWSKL4fMKl86bQHVZ21ds+EENxbL/245u2wAskQZvZPqjjM5JMWjpC1ixoUpoFkj7mTp0AwLaDAy1wbPrqCLmmrX4ykqwMH8GQZXmDaesJJF5GUpwXAKDJ+klMCrNA0sfMSXkE/MK2A0eTXZUTSiQjiTX8FyA74Os5xvSvNdK05TKSibmZANa8ZVKaBZI+An4fs0rz2X6wJdlVOaFEso3+OtuzA37ard9pUD0ZSVYkI/ECSYN1uJsUZoEkhlOnTGDbAQskQ9HR1f/wX4CcgN8GMMQhkpHkBCIZSaRpywKJSV0WSGKYO3UCNU3ttHRYu3S84slILJAMrq0zRE7Aj99N4jyWkdjvokldFkhiOHWK1+Ee2TfbDG6w4b/ZAV9P1mL619bZTV7WsayuyPpIzAnAAkkMp0VGblnzVtwiGUl/w38tI4lPW2d3z4gt8O5bTsBvkxJNSotnq91VIlInIhujyr4mIjUi8qb7ujLquVtFpEpEtonIkqjyBSKywT13t9tuF7cl74OufK2IzIg6Z5mI7HBfka14R135xBxyAn6q6iwjiVck2+g/I7FAEo/WYKhnDklEsS2TYlJcPBnJz4DLY5R/R1XPcV+rAURkHt42uWe4c34gIpH/FfcAy/H2cJ8Tdc0bgEZVnQ18B7jDXasYuA04H1gE3Ob2bR91Pp9wSmkeVfUWSOIV7Fkipf/hvx02IXFQXkbS+x5OzA1Y05ZJaYMGElV9AW8f9Xh8GHhAVYOquhuoAhaJSBlQoKovq7ci4v3AVVHn3OcePwxc4rKVJcAaVW1Q1UZgDbED2qiYPTmfnZaRxC3SRxJrPxLwmrwsIxlca2eoZ3mUCC8jsUBiUtdI+khuFpH1rukrkimUA/uijql2ZeXucd/yXueoaghoBkoGuNaYmF2aT01Te8/+EGZgkaatyG6IfeVkevNIbGXlgbUFY2UkmdZHYlLacAPJPcAs4BygFviWK5cYx+oA5cM9pxcRWS4ilSJSWV9fP1C94zZ7cj6ArbkVp2ComwyfkNFPIMkO+FE9tty8ia21M0ReZp+MJDdgExJNShtWIFHVg6raraph4Md4fRjgZQ3Tow6tAPa78ooY5b3OEZEMoBCvKa2/a8Wqz72qulBVF5aWlg7nLR0nEkiq6m3kVjw6umJvahURec6GAA+srbOb3KzjM5IjHSFbht+krGEFEtfnEfERIDKi61FgqRuJNROvU/1VVa0FWkRksev/uB54JOqcyIisq4FnXD/Kk8BlIlLkms4uc2Vj4uSSPPw+YWedZSTxaO8KkdPnL+lokRnvQesnGVBrMEZG4iYlNrXbyC2Tmvr/n++IyK+Bi4FJIlKNN5LqYhE5B6+paQ/wGQBV3SQiDwGbgRBwk6pGPjluxBsBlgM87r4AVgI/F5EqvExkqbtWg4jcDqxzx31dVePt9B+xzAwfJ5fkUlV3lB0HWygvyuk1vt/01h5jtFG0yJIflpH0rzusBEPh437PIoHk8NFOJuVnJaNqxgxo0E9GVb02RvHKAY5fAayIUV4JzI9R3gFc08+1VgGrBqvjaJldms9z2+t4YtMBrlt8MrdfdVz1jRNr2Gq0SEZiCzf2r63PXiQRkeBx6GiQuUwY83oZMxib2T6A08sK6OgKU5KXyeoNtXSHbcRRf9q7uskZMJBE+kgskPQnsl973z6S0gnHAokxqcgCyQCWv/sU/nDTBdx+1XwOt3by6u4xa1k74cSbkVgg6V9kqHnfPpJSl5HUt1ggManJAskA8rIyOGf6RC6eW0p2wMfjG2uTXaWU1dbZTU5g8M52m93ev56MpE9ALsjJINPvo94yEpOiLJDEITczg/fMnczjGw8QtuatmNo7Q3E1bbV3WkbSn56MpM/MdhGhdEKWZSQmZVkgidNFc0qpbwlS09Se7KqkpLbObnL72dQKoob/2r7t/eovIwGYlJ/JoaM2KdGkJgskcTq5JBeAfY1tSa5JamrvHKyz3fpIBtPaGTsjASwjMSnNAkmcTir2Akl1g2UkfakqbV02j2Sk2oIDZSRZNmrLpCwLJHEqK8zG7xP2NlhG0ldXt9Id1kFGbbk+EstI+tXaM48kdkZy+GjQhqCblGSBJE4Zfh/TJmZb01YMkQ70AZdIybCmrcEM1EdSOiGLsNqWuyY1WSAZgulFuZaRxNDWFXtGdjSfT8j0277tAznS0UWm3xdz8ctJNpfEpDALJEMwvSiXfdZHcpyB/pKOlh3wWUYygOa2LgpzA7hdqHux2e0mlVkgGYKTSnI5dDTYsyaS8fQ0bQ0w/Bds3/bBNLV1MTEnEPM5y0hMKrNAMgQVRTkAVDdaVhLtWEYy8BqgFkgG1tzexcTc2IGkb0bS0dVtc3JMyrBAMgSRIcD7rJ+kl0iGlpM58K+T17RlfST9aWrvorCfjCQv009OwN+TkXz8J2v5xKp1tnWxSQkWSIZgugsk1uHe27GmrYEzkpyAnw77K7pfzW2dFOZkxnwuskzK7kOtbKxppvLtRl7edZhnttaNcS2NOZ7t1DQEJXmZ5Gb6LZD0EW9ne1bAb2ttDaBpgKYtgCvPLONHL+ykNdhNZoaPqQXZ/NfjW9lRd5SzKybyzlklY1hbY44ZNCMRkVUiUiciG6PKikVkjYjscN+Lop67VUSqRGSbiCyJKl8gIhvcc3e7LXdx2/I+6MrXisiMqHOWudfYISKR7XiTRkSYM2UCW2qPJLsqKSUyyXDwUVt+W/23H52hMG2d3f12tgPcePEsJuYEeHnXYa6cP5WvXHkaVXVH+ebjW/nG6i1jWFtjeounaetnwOV9yr4MPK2qc4Cn3c+IyDy8rXLPcOf8QEQiny73AMvx9nGfE3XNG4BGVZ0NfAe4w12rGG9b3/OBRcBt0QErWc4qL2RjzRFbBTjKsQmJgwSSDJ/t2d6PZrcfe+EAGUlhToDPXzIHgGsXncTl88t49v9ezAfOKrNhwSapBg0kqvoC3l7q0T4M3Oce3wdcFVX+gKoGVXU3UAUsEpEyoEBVX1avd/D+PudErvUwcInLVpYAa1S1QVUbgTUcH9DG3JkVhRwNhth9uDXZVUkZ8Y7aysm0UVv9aW73Zqz319ke8Yl3zeCJWy7i/FO8ZqyZk/IoL8rhcGundbybpBluZ/sUVa0FcN8nu/JyYF/UcdWurNw97lve6xxVDQHNQMkA10qqsyoKAdhQ3ZzkmqSOtq4QmRk+/L7jJ9JFy87w21pb/Whq8zKSibmxO9sjRITTphb0KivJy6QzFOZo0OY3meRI9KitWJ8kOkD5cM/p/aIiy0WkUkQq6+vr46rocM0uzSc74GO9BZIe7YNssxthw3/7F2naGqiPpD8led4ck8O2X4lJkuEGkoOuuQr3PTIGsRqYHnVcBbDflVfEKO91johkAIV4TWn9Xes4qnqvqi5U1YWlpaXDfEvxyfD7OGNaIRtrLJBEDLapVYRNSOxfJCMZrGkrlpJ8L4s53GqBxCTHcAPJo0BkFNUy4JGo8qVuJNZMvE71V13zV4uILHb9H9f3OSdyrauBZ1w/ypPAZSJS5DrZL3NlSXdmeSEb9zfbkt7OYJtaRWQH/ARDYRuoEENTJCMZoLO9P8cyEutwN8kx6DwSEfk1cDEwSUSq8UZSfRN4SERuAPYC1wCo6iYReQjYDISAm1Q18ifojXgjwHKAx90XwErg5yJShZeJLHXXahCR24F17rivq2rfTv+kmDetgLbObvY1tDFjUl6yq5N0bYPs1x5xbLvdcFzHjyfNbZ2IwIRsy0jMiWfQQKKq1/bz1CX9HL8CWBGjvBKYH6O8AxeIYjy3Clg1WB3HWvlEb82t2uYOCyREmrYGn9sa2dyqoyu+DGY8aW7voiA7MOiAhViK87xA0mCBxCSJLZEyDFMLswE4eKQjyTVJDfEGhp59222ZlOMMtM7WYLIDfvKzMmwuiUkaCyTDMLXACyS1zRZIwGUkcQSSCdle1tLSYcNU+2pqG3h5lMGU5GdaRmKSxgLJMORlZTAhK8MyEqctzs72SBOMDVM93kgyEvDurd1XkywWSIZpamE2tc22Lwl4a23Fk5FEAontO368I+1dg05GHEhJXpY1bZmksUAyTFMLszlwxP7jgjdqa7DlUQCKc61TuD9NbZ0U5gx/Me6SPGvaMsljgWSYphZkc8AyEsJhpaMrPOg2u3Bs+Y9G+8DrJRxWb3fEfvZgimd+AAAdkElEQVQiiUekj8Tm6JhksEAyTFMLs6lvCRLqHt9LfsS7hDxAZoaPCVkZNFjTVi+HWoOE9dh2usNRnJdJKKwc6ehKYM2MiY8FkmGaWphNWKF+nLdLR0Zg5WXF1yxTZE0wx6lp9DLbiqKcYV9jUr6b3W731iSBBZJhigwBPjDOhwDXNHkfgtMmZsd1fLEFkuNUu0BSPoJA0jO73UZumSSwQDJMkUmJFkjch+DE3LiOL87LtFFbfRy7hyMIJG69LRu5ZZLBAskw9WQk43wuSc0Q/5ouys2ksdXa8aNVN7ZRmBMY1jpbEZMLvEBSN85/H01yWCAZpuK8TDL9vnGfkexvaqcwJ0B+nH0kxXkBa9rqo6axfUTZCHhDqzN8Ql2LZSRm7FkgGSYRYWphdk/79nhV0zS0D8GivEzau7p79nk33j0cSUc7gM8nlE7I4qDNbTJJYIFkBM4sL+TNfU3JrkZS1TS2M20IgaRnUqL1kwCgqlQ3to+ooz1i8oQs6lrGd4ZsksMCyQgsOLmImqZ29jeNz6xEVYf813RRnk1KjNbU1kVbZzcVRfENVhjI5AJvbpMxY80CyQgsnFEEQOXbjUmuSXIcaQ9xNBgaUtNWie2d0UvP0N8R9pGAl5HYQqImGSyQjMC8sgJyM/28ticlNm4cc9VNbcDQ5j8U2cKNvdS4ezjSPhKAKQXZNLZ1EbT9XswYG1EgEZE9IrJBRN4UkUpXViwia0Rkh/teFHX8rSJSJSLbRGRJVPkCd50qEbnb7euO2/v9QVe+VkRmjKS+iZbh93HO9Ims2zM+M5L9Td5fv0P5azrSR2IT5zzVCZjVHjHZLbFizVtmrCUiI3mPqp6jqgvdz18GnlbVOcDT7mdEZB7efuxnAJcDPxCRyAJN9wDLgTnu63JXfgPQqKqzge8AdySgvgm1cEYxWw8coWUcrnFU0+j9NT2UzvaCnAA+sYwk4u3DbeRnZYxoL5KIKW5ukw0BNmNtNJq2Pgzc5x7fB1wVVf6AqgZVdTdQBSwSkTKgQFVfVlUF7u9zTuRaDwOXRLKVVHHu9ImEFTbvP5Lsqoy5mqZ2sjJ8TMqPf9Vav0+YmGvLpERsO9DC3KkTSMSvdWTRR5uUaMbaSAOJAn8WkddEZLkrm6KqtQDu+2RXXg7sizq32pWVu8d9y3udo6ohoBko6VsJEVkuIpUiUllfXz/CtzQ0syfnA7DrUOuYvm4q2LT/CKeU5g/5Q7A03zqFwRv1tuXAEU4vm5CQ61lGYpJlpIHkAlV9B3AFcJOIvHuAY2N92ugA5QOd07tA9V5VXaiqC0tLSwerc0KVT8whK8PHzrqjY/q6ydYZCvP63kbOn1k85HNnTc5jxzi7X7HUNLXT0hHitKkFCbleSV4mfp9QZ5MSzRgbUSBR1f3uex3we2ARcNA1V+G+17nDq4HpUadXAPtdeUWM8l7niEgGUAik1BApn084pTSfqvrx9cG4oaaJjq4wi08ZeiCZM3kCexva6Oga36OLttS2AHB6WWICic8nJ1S2t7GmmX9/ZCOf/OmrvLo7pf5bmyEadiARkTwRmRB5DFwGbAQeBZa5w5YBj7jHjwJL3UismXid6q+65q8WEVns+j+u73NO5FpXA8+4fpSUMntyPjvHWSB5ZZf3H3/RzONaGgd16pQJqELVOM9KttZ6/WpzpyamaQu8xRtPhKatru4w1696ld9UVrOh5ggfu/dl7ntpT7KrZYZpJBnJFOBFEXkLeBX4k6o+AXwTuFREdgCXup9R1U3AQ8Bm4AngJlWN/El6I/ATvA74ncDjrnwlUCIiVcA/40aApZpZpXlUN7aPi7+wQ91hgqFu1u5u4NQp+RTnDX172DlTvH6lHXUtia7eCWXLgSOcXJIb94KX8Zg8IfuEWEj0he31NLR28v2/P5fn/+Viziov5Nev7k12tcwwDfs3WFV3AWfHKD8MXNLPOSuAFTHKK4H5Mco7gGuGW8exMqs0H1XYfag1Yc0Uqeqrf9jIH9/aT1dY+djC6YOfEMOMkjwyfML2g+M9I2nh9AT1j0TMKs3jhe31hLrDZPhTd77xI2/uZ2JugIvmlJKZ4WPxrBJ++uIeurrDBFK43iY2+xdLgMjIrXRv3gqHlSc3HejZN+OyM6YM6zqZGT5mTspjx8Hxm5EcDYbYfbiV0xI0YivitLIJdHaH2Z3CowhbgyHWbD7I+88sIzPD+wiaO8Wr99uHU7fepn8WSBJg5qQ8RNK/zX9z7REa27r40hVz2f6fV3DRnOGPkDt1yoRxlZG8suswj7xZ0/PzU5sPogrvmjUpoa8TGQG25UDqBuknNx2gvaubD509rafs1CleQN12YPz8TqQTCyQJkB3wU1GUk/ZDWl/aeQhIzIffnCn57GtsGxf7kqgqX/3DRm558M2e0UmPvrWfssJsFp5cNMjZQzOrNJ+AX9hSm7oTZH/xytucMimP82YcG/E3e3I+PoFt4zhLPZFZIEmQc6YXsXbXYcLhlBtUljAvVh1mzuT8nolvI3HaVG/k1sb9zQmoWWrbtP8IVXVH8Yvwxd+8ye5DrbywvZ4PnT0Nny+xCzVkZviYVZrfMyIs1Wza38zre5v4h8Un93rv2QE/M0ry2D7GmVR9S5CbfvU6Ww+k5v06UVggSZD3nT6ZQ0c7ebM6PTe6Coa6eXX3YS6YnZimmHfNnkTALzy58UBCrpfKHnmzhgyfcM/HF7C/qYNLv/08obDyoXOmDX7yMJxeVsDWFG3a+sUre8kO+Lj6HRXHPec1d45tvb+9Zjt/Wl/LTb98/YTKjqsb2/iPP27i5Z2HSYUZERZIEuTiUyeT4ROe2nww2VUZFS9VHaajK8xFcxITSAqyA1w4exKPbzyQEv8RRkt3WHn0rf1cPLeUS+dN4ZGbLuCC2ZN496mlzBulEX6nTZ1AbXMHTUleGLMzFKYmatO3Ix1d/OGNGj509jQKc49fpPLUqRPYc7h1RMPo2zu7ee3t+CY3VtW18OC6vZw/s5id9a2sWL152K87Fmqa2rnjia38ZUc91618lZ/+dQ/X/vgVvvL7jcmumgWSRCnMDXDejGKe2pKegeQ3r+2jOC9zRB3sfV0xv4yapnY21qRvs8L3nt7BwSNB/tb9BT6/vJD7PrWI+z+1KCELNcZymgtQkZnzyfKtNdt4z13Psa/BWyX6d69V097VzXWLZ8Q8fu6UCd4CqMNslguHlZt+9TofvedlVr24G/ACeX+++fg28jIz+ME/vINPXTCTX67dy4bq1Gxq7QyF+dwvXuOe53Zy3cpXqW1u55efPp+/Pbech1/bl/RFUC2QJND75k1h+8Gj7EnhoZfD0djayVOb67jqnPKe4ZqJcOm8Kfh9wuMbaxN2zVRy30t7uPvpHfzdwgqumD91zF73zPJCMv0+Hn1r/+AHj5LWYIhfrd1LZyjMd9ZsR1X5xdq9nD19ImdWFMY8512zSsjPyuAHz+6M+XxHV3evPen3NbTxzNaDHHFbOPzwhZ08s7WOU0rz+Ppjm/mb/36WU7/6OFf9719Z+eLuXk1Xa3cd5qktB/nsxbMoyc/ilkvnUJybye2PbU7JDPmuP2/jrepmvnXN2fzX357JL244nwtmT+If330KXd3KI2/W0B1WmtuSs52FBZIEev+ZZQT8wqq/7k52VRKmqq6FH76wk87uMNcsPL5deySK8jL5m1NLeWDdvrTbz+X57fX8xx83cem8KXzjI2eOWvYRS3FeJh87bzq/qdxHtdszZqz97o0aWjpCXDh7Er9/s4av/H4DVXVH+fj5J/V7TlFeJjdePIunthzklV2Hez2nqnzyp+u49NsvcOhokDuf2MpFdz7Lp35WyRd+/Qbrq5v41p+38/6zylj9+Yu4ZkEFs0rz+eS7ZqCq3P7YZt7938+yaX8zqso3Vm9hakE2n7pgJuA1tX7xsrm8uqeBr/x+Q9L/wo+2r6GNlS/uZul50/noggquXXQSC92It9PLCphfXsAvXnmbpfe+zGd+UZmUAT8WSBJoamE2f3tuBQ+s29frL6cT1U/+sov3ffsFfvT8Ls6uKByVWfu3vG8ODa2d/PiFXQm/drKsr27i5l+9ztypBXxv6TlJmWF+48Wz8InwrT9vH7B5ZzSoKve9tIezKgr5/t+fy8ScAA9VVnPh7El88OyBBxjccOFMygqz+cbqLb0+EB+q3MfLuw7T3N7Fzb96nR8+v5MPnj2Nm98zm2e31fPxn6ylND+Lb3zkTLIDfv77mrNZ9Ynz+OoH5vHIzRfym8++k4BPWLZqHct+uo63qpv54mWnkpPp73mNj503nRsunMlDldUs+e4Lo7bHUF1LB//wk1d4Os5m8JUv7kaAz18yJ+bzf7dwOjvrW9l2oIVrFkwnGTs2WSBJsBsvnkWoO8zKv5zYWcmTmw6wYvUWlpwxhYc/+07uv+H8UXmdsyom8oGzyvjxX3ZT29w++Akp7tmtdXzsR69QkB3gx9cvIDczcetoDcW0iTl84oIZ/P6NGq743gtUjeG6Zq+93ehlH4tPZmJuJs/9y3vY+LUl/OLT55Md8A94bnbAz78smcv66mb+uN5rmtvf1M6KP21h0cxibrhwJq/samDyhGy+8ZH5/POlp/LOU0o40hHijqvP6nenyfNmFHP/DefTHQ5TuaeB2z44j6sX9M6w/T7h3z4wjz/efCEZPuFj9748Kn0mtz+2hb9WHebGX77eMzerPw2tnTywbi9XnVve706kf7dwOl99/+n8+f/8DR9dUDGm2W+EpGJ74EgsXLhQKysrk1qH5fdXsqGmmZe+/N6k/KOO1MEjHbzv289zyqQ8HvzMOwf9zz9Sbx9u5Yrv/YW5UyfwwPLFZGWM7uuNliMdXVx0x7OUT8zhZ586j8kTRj7fZiRUlSc2HuBf/7CRaROz+cPnLhiT7OhLD6/nj+v3s+5f30feMBakDIeVD37/RZraunhg+WJu/vUb7Kw7yh//6UIm5Wfyfx58i09eMKNnKHpzexfbDrSwKI69cQ40d+DzMei/TU1TO9fc8xLZAT9/+vxFvTKX4dq0v5m/Vh3iG6u38qkLZvJiVT0HjwR59v9eHHPx09f3NvKlh9ezs/4oT97ybuZMSexyOn2JyGtRW6YPiWUko+DiuZOpbe5I6fWO+hMOe7OwO0Nhvrv03FEPIgAnl+Rx1zVn88beJv5r9dZRf73RsvIvu2lu7+KOj56V9CACICJccWYZ/3nVfDbWHOEnL45+ltzWGeJPG2p5/5llwwoi4O2r8q/vP53a5nYuuvNZ3trXxF3XnMXMSXlMyA7wk2ULe81nKswJxBVEwGt+juffpnxiDnddcza7DrXyhQfe4H+frRrROmB/3nSA99/9It9YvZUzphXwpSvm8j/XvoOjwRD//eRWvrF6C+/6r6e5+p6XeGz9fl6qOsTSH71CW2c3Kz9x3qgHkZFKTt6d5i6Y7e3R8deqQ5xSmp/k2sQn1B3m0/dX8vz2elThK1eexsxJeWP2+leeWca1i07il2vf5saLZyVk9vxoU1WOBkNMyA5Q19LByhd3c/kZU/sdlZQsV8yfypIzpnDnE16Q/sy7TxmVTPnZrXU8tr6Wo8HQcc1GQ/WuWZN48pZ3s2bLQSblZXH5/LIE1XIIdZg9ic9dPIsfPLeTP28+yL0v7OJfrzyd+qNBLpw9ibOnTxzw/N2HWrnzia1cvaCCrz26idOmTuB//+EdnFScS8DvY+7UCVy3+GR+5vZhuXhuKfub2rn5V2+Q6fcxY1Iuv/nMu2LOuUk11rQ1ClSVi+58ljOmFfCj64aVKSa0Lkc6QuRm+mnr7Ka6sY1Nbt5GRXEO588swe8Tvv/MDu7683auf+fJnF0xkavOLcef4OU7BrP3cBsX3/Usn/mbWXzp8tPG9LUHU1V3lAfX7WV6cS7vP7OMLbUtfHvNNl7f28QV86fyxt4mGts6eeyfLkzJvx6PBkP8v4ffYvWGA1y76CS+8ZH5cQWTJzcd4E/razncGmR6US4fPHtazNUNHly3ly/9dgN+n/CeuZO597oFCV/+JVk6uro50NzBp++v7FmYNeAXbnnfqVQU5XDGtMKeFcA7urp5eddhphZkc9MvX2dXVKvEw599Z89oq4jmti4++4vXuPKsMq5bfDJd3WHuenIbL+08zL3XL6CsMHa/yGgYSdOWBZJR8uXfrmf1hlre+PfLxvwDOaKxtZPPP/AGf9nRf4de+cQcTps6gRd21HP5/DL+59pzx7CGx/vcL1/jLzsO8dcvv5eC7OT/Jdbe2c2K1Zv55dq9+ER6jYCalJ/FpfOm8Ps3qplSkM3//v07mF+eWtlINFXlzie3cc9zO/nUBTP5tw+cPmAweXNfE1ff8xLFeZmUTcxhz6FW2ju7efAzi8nPyuCNvU0Eu8O0dHTx3TU7WDSzmJWfWHjC9nENpjUYYnPtEcon5nDr7zbw/Pb6nucWnlzEFy+byw+f39lTnun3sfITC/lr1WEKcjL43MWzk1X1uKR9IBGRy4HvAX7gJ6r6zf6OTZVA8se39vNPv36D7y09hw+dPY3Gti6KcgMD/sdt7+zmt69X8/reRt5xUhHnnjSRWaX5Q+6niHSy/ueftlDfEuQf3z2TrAw/uZl+Jhdkc2Z5IRk+YX11Mw9V7qO+JUhZYTZ3XXM2RcPY8TCR3trXxFU/+CszS/L4tw/OY9GM4p62dlUd08ELL7i5IDvrW/nkBTO46T2z2XOolbW7G5g7ZQLvnFVCXlYGTW2dZAf8Y9KfNFKqytcf28xP/7qH959VxpIzptLY2sn75k2hfGIOnaEwz2+vp7qxjZ+9tIdQt7L6CxdRmBOgqa2TD37/RRpbu2jtDBH90TGrNI+HP/uupP/+jJVwWNlzuJVQWHlhez0/+ctuDhzxhvz/65Wn4/MJcybn8+5TE7cSxGhL60AiIn5gO962vdXAOuBaVY25ME6qBJLWYIiP3fsyG2uOMK0wm/3NHbz/rDI+fv7J3PfSHoKhblqD3Ww9cIT3njaZi+dOZsVq74O/IDuDIx0hALIyfHziXTNYfEoJh1s7Ccf49/KLMGtyPlMLsnn7cCt3/Xkb6/Y0MmdyPv99zdmcM0hbbqp5qeoQ//Lwemqa2snwCUsXTWdGSR7/80wVYVWmFGQzeUJWz/fJBdmcP7OYM6YVcOBIBzWN7dQ0tbO+upmywmyWnDGVt6qbONQSJDPDz4HmdrICft5xUhE5mf6eLGN2aT5HOrp4cN0+ntpykK0HWjipOJf/vGr+CfWBMBhV5d4XdvHNJ7b2BAMRmDIhm45QN01udnRupp+ffuI8zj+lpOfczfuPcMuDb3DJ6VP4u4XTycv0k5nhY0J2IGmZdyo4Ggzx4xd2cUppHh8+pzzZ1RmWdA8k7wS+pqpL3M+3Aqjqf8U6PlUCCXgr5n5nzQ6q6loon5jD/a+8jSqU5GUybWIOAb9wUnEuf9pQS1e3Mq+sgH//4DzOn1nM7kOtbKlt4aktB/nDmzUM5Z9pUn4m/3zpXP5uYUVKb7c6kLbOEGt3N/DU5oM8sG4f3WHlojmTOGVSHnUtQQ4e6aCuJUjdkSCd3WGAXgEYvCXVO0Ph467tE+hvjp4I+EQ4b0YRV8wvY+mi6WnbVLPjYAtd3UpOpp/VG2p5+3ArYfVWaDj3pIknTJZlEiPdA8nVwOWq+mn383XA+ap6c9Qxy4HlACeddNKCt99+Oyl1HcwL2+vZUnuEjy8+udfQyG0HWnhjbyMfXVARc7/qPYdaOdzayaT8zJh/9XWGwmw/2MLh1k4KsgNcPLe0ZzvcdFBVd5SG1k7Om1F0XNOWqlJ/NMiTGw+woaaZeWUFzCzNZ/KELGZPzmfHwaO8tPMQ555UxIySXIKhMJPyszgaDLGhppnucBi/z0d3OMyW2hZUlasXTGdqYeqPGjMmkdI9kFwDLOkTSBap6j/FOj6VMhJjjDlRpPuExGpgetTPFUDyljU1xhjTy4kQSNYBc0RkpohkAkuBR5NcJ2OMMU7Kz2xX1ZCI3Aw8iTf8d5WqbkpytYwxxjgpH0gAVHU1sDrZ9TDGGHO8E6FpyxhjTAqzQGKMMWZELJAYY4wZEQskxhhjRiTlJyQOlYi0ANuGeNokYOA9L+NTCCRyb85EX280rpmoexeR6vdwPN0/u3fpfb3o+zcJyFPV4S0qp6pp9QVUjsU5/Vzn3gS/l4Reb5TqmJB7d6Lcw/F0/+zepf31KmM9Hs6XNW0l1h9T/Hqjdc1ESvV7OJ7un9279L5ewqRj01alDnG9mOGcYzx270bG7t/w2b0bmej7N9J7mY4Zyb1jdI7x2L0bGbt/w2f3bmTu7efxkKVdRmKMMWZspWNGYowxZgylZSARkeki8qyIbBGRTSLyBVdeLCJrRGSH+17kykvc8UdF5Pt9rnWtiGwQkfUi8oSITErGexorCb53H3P3bZOI3JmM9zPWhnH/LhWR19zv2Gsi8t6oay1w5VUicreM5Yb1SZDge7dCRPaJyNFkvZ+xlqj7JyK5IvInEdnqrvPNQV88kcPJUuULKAPe4R5PwNvzfR5wJ/BlV/5l4A73OA+4EPgs8P2o62QAdcAk9/OdeNv+Jv09ngD3rgTYC5S6n+8DLkn2+0vB+3cuMM09ng/URF3rVeCdgACPA1ck+/2dQPdusbve0WS/rxPt/gG5wHvc40zgL4P97iX9zY/RDX4EuBRvomJZ1E3f1ue4T/T5MAwA9cDJ7j/zD4HlyX4/J8i9Ow94Kurn64AfJPv9pOr9c+UCHAay3DFbo567FvhRst/PiXDv+pSPm0AyGvfPPfc94B8Heq20bNqKJiIz8CLvWmCKqtYCuO+TBzpXVbuAG4ENeLsyzgNWjmJ1U8pI7h1QBZwmIjNEJAO4it47Xaa9Ydy/jwJvqGoQKMfbHTSi2pWNCyO8d+Neou6fiEwEPgg8PdDrpXUgEZF84LfALap6ZBjnB/ACybnANGA9cGtCK5miRnrvVLUR7949iJca7wFCiaxjKhvq/RORM4A7gM9EimIcNi6GWCbg3o1ribp/7g/AXwN3q+quga6RtoHEBYHfAr9U1d+54oMiUuaeL8Pr/xjIOQCqulO9HO8h4F2jVOWUkaB7h6r+UVXPV9V34qXXO0arzqlkqPdPRCqA3wPXq+pOV1wNVERdtgIvK05rCbp341aC79+9wA5V/e5gr5uWgcSNblkJbFHVb0c99SiwzD1ehteGOJAaYJ6IRBYyuxTYksi6ppoE3jtEZLL7XgR8DvhJYmubeoZ6/1zTwZ+AW1X1r5GDXRNEi4gsdte8njju+YksUfduvErk/ROR/8RbJPKWuF482R1Co9TJdCFeM8B64E33dSXeSKKn8f4yfhoojjpnD9AAHMX7a3CeK/8sXvBYj7fWTUmy398JdO9+DWx2X0uT/d5S8f4BXwVao459E5jsnlsIbAR2At/HTSBO168E37s73e9i2H3/WrLf34ly//CyX3Wfe5HyTw/02jaz3RhjzIikZdOWMcaYsWOBxBhjzIhYIDHGGDMiFkiMMcaMiAUSY4wxI2KBxJgxJiKfFZHrh3D8DBHZOJp1MmYkMpJdAWPGExHJUNUfJrsexiSSBRJjhsgtiPcE3oJ45+It1309cDrwbSAfOAR8QlVrReQ54CXgAuBREZmAtyrtXSJyDt6q0rl4Ew8/paqNIrIAWAW0AS+O3bszZuisacuY4ZkL3KuqZwFHgJuA/wGuVtVIEFgRdfxEVf0bVf1Wn+vcD3zJXWcDcJsr/ynwefXWKTMmpVlGYszw7NNj6xP9AvgK3uZAa9xGhn6gNur4B/teQEQK8QLM867oPuA3Mcp/DlyR+LdgTGJYIDFmePquLdQCbBogg2gdwrUlxvWNSVnWtGXM8JwkIpGgcS3wClAaKRORgNvnoV+q2gw0ishFrug64HlVbQKaReRCV/4Pia++MYljGYkxw7MFWCYiP8JbVfV/gCeBu13TVAbwXWDTINdZBvxQRHKBXcAnXfkngVUi0uaua0zKstV/jRkiN2rrMVWdn+SqGJMSrGnLGGPMiFhGYowxZkQsIzHGGDMiFkiMMcaMiAUSY4wxI2KBxBhjzIhYIDHGGDMiFkiMMcaMyP8HOb0rYdupIpYAAAAASUVORK5CYII=\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": 36, "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": 37, "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": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.plot(style='*')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2014 1600941\n", "1991 1659249\n", "1995 1840410\n", "2020 2053781\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": 39, "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": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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