{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 2, "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": 3, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://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": "markdown", "metadata": {}, "source": [ "Si le fichier CSV existe je l'utilise sinon je télécharge les données à l'url correspondante" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "read from file\n" ] }, { "data": { "text/html": [ "
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592795.0 681809.0 1155 1074.0 1236.0 FR \n", "1977 1978 198504 3 424937 390794.0 459080.0 770 708.0 832.0 FR \n", "1978 1979 198503 3 213901 174689.0 253113.0 388 317.0 459.0 FR \n", "1979 1980 198502 3 97586 80949.0 114223.0 177 147.0 207.0 FR \n", "1980 1981 198501 3 85489 65918.0 105060.0 155 120.0 190.0 FR \n", "1981 1982 198452 3 84830 60602.0 109058.0 154 110.0 198.0 FR \n", "1982 1983 198451 3 101726 80242.0 123210.0 185 146.0 224.0 FR \n", "1983 1984 198450 3 123680 101401.0 145959.0 225 184.0 266.0 FR \n", "1984 1985 198449 3 101073 81684.0 120462.0 184 149.0 219.0 FR \n", "1985 1986 198448 3 78620 60634.0 96606.0 143 110.0 176.0 FR \n", "1986 1987 198447 3 72029 54274.0 89784.0 131 99.0 163.0 FR \n", "1987 1988 198446 3 87330 67686.0 106974.0 159 123.0 195.0 FR \n", "1988 1989 198445 3 135223 101414.0 169032.0 246 184.0 308.0 FR \n", "1989 1990 198444 3 68422 20056.0 116788.0 125 37.0 213.0 FR \n", "\n", " France \n", "0 France \n", "1 France \n", "2 France \n", "3 France \n", "4 France \n", "5 France \n", "6 France \n", "7 France \n", "8 France \n", "9 France \n", "10 France \n", "11 France \n", "12 France \n", "13 France \n", "14 France \n", "15 France \n", "16 France \n", "17 France \n", "18 France \n", "19 France \n", "20 France \n", "21 France \n", "22 France \n", "23 France \n", "24 France \n", "25 France \n", "26 France \n", "27 France \n", "28 France \n", "29 France \n", "... ... \n", "1960 France \n", "1961 France \n", "1962 France \n", "1963 France \n", "1964 France \n", "1965 France \n", "1966 France \n", "1967 France \n", "1968 France \n", "1969 France \n", "1970 France \n", "1971 France \n", "1972 France \n", "1973 France \n", "1974 France \n", "1975 France \n", "1976 France \n", "1977 France \n", "1978 France \n", "1979 France \n", "1980 France \n", "1981 France \n", "1982 France \n", "1983 France \n", "1984 France \n", "1985 France \n", "1986 France \n", "1987 France \n", "1988 France \n", "1989 France \n", "\n", "[1990 rows x 11 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "FILE_PATH = 'save.csv'\n", "try:\n", " raw_data = pd.read_csv(FILE_PATH, skiprows=1)\n", "except:\n", " raw_data = pd.read_csv(data_url, skiprows=1)\n", " raw_data.to_csv(FILE_PATH)\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": [ "
\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
175419891930NaNNaN0NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1754 198919 3 0 NaN NaN 0 NaN NaN \n", "\n", " geo_insee geo_name \n", "1754 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
02022513255228238612.0271844.0385360.0410.0FRFrance
12022503234279219533.0249025.0353331.0375.0FRFrance
22022493163421151727.0175115.0246228.0264.0FRFrance
32022483121884111932.0131836.0184169.0199.0FRFrance
420224739644787259.0105635.0145131.0159.0FRFrance
520224636773560075.075395.010290.0114.0FRFrance
620224534530638909.051703.06858.078.0FRFrance
720224433471328880.040546.05243.061.0FRFrance
820224334476936884.052654.06856.080.0FRFrance
920224234746240773.054151.07262.082.0FRFrance
1020224134858342388.054778.07364.082.0FRFrance
1120224034192736115.047739.06354.072.0FRFrance
1220223933990234168.045636.06051.069.0FRFrance
1320223832878123733.033829.04335.051.0FRFrance
1420223732139517076.025714.03225.039.0FRFrance
1520223631412010487.017753.02116.026.0FRFrance
16202235392836485.012081.01410.018.0FRFrance
17202234374984731.010265.0117.015.0FRFrance
18202233375864442.010730.0116.016.0FRFrance
192022323122227749.016695.01811.025.0FRFrance
202022313132578905.017609.02013.027.0FRFrance
2120223031500610738.019274.02317.029.0FRFrance
2220222932080115829.025773.03124.038.0FRFrance
2320222832338717970.028804.03527.043.0FRFrance
2420222733601529709.042321.05444.064.0FRFrance
2520222632942124314.034528.04436.052.0FRFrance
2620222532288718582.027192.03529.041.0FRFrance
2720222431929415406.023182.02923.035.0FRFrance
2820222331715913450.020868.02620.032.0FRFrance
2920222231423910930.017548.02116.026.0FRFrance
.................................
196119852132609619621.032571.04735.059.0FRFrance
196219852032789620885.034907.05138.064.0FRFrance
196319851934315432821.053487.07859.097.0FRFrance
196419851834055529935.051175.07455.093.0FRFrance
196519851733405324366.043740.06244.080.0FRFrance
196619851635036236451.064273.09166.0116.0FRFrance
196719851536388145538.082224.011683.0149.0FRFrance
19681985143134545114400.0154690.0244207.0281.0FRFrance
19691985133197206176080.0218332.0357319.0395.0FRFrance
19701985123245240223304.0267176.0445405.0485.0FRFrance
19711985113276205252399.0300011.0501458.0544.0FRFrance
19721985103353231326279.0380183.0640591.0689.0FRFrance
19731985093369895341109.0398681.0670618.0722.0FRFrance
19741985083389886359529.0420243.0707652.0762.0FRFrance
19751985073471852432599.0511105.0855784.0926.0FRFrance
19761985063565825518011.0613639.01026939.01113.0FRFrance
19771985053637302592795.0681809.011551074.01236.0FRFrance
19781985043424937390794.0459080.0770708.0832.0FRFrance
19791985033213901174689.0253113.0388317.0459.0FRFrance
198019850239758680949.0114223.0177147.0207.0FRFrance
198119850138548965918.0105060.0155120.0190.0FRFrance
198219845238483060602.0109058.0154110.0198.0FRFrance
1983198451310172680242.0123210.0185146.0224.0FRFrance
19841984503123680101401.0145959.0225184.0266.0FRFrance
1985198449310107381684.0120462.0184149.0219.0FRFrance
198619844837862060634.096606.0143110.0176.0FRFrance
198719844737202954274.089784.013199.0163.0FRFrance
198819844638733067686.0106974.0159123.0195.0FRFrance
19891984453135223101414.0169032.0246184.0308.0FRFrance
199019844436842220056.0116788.012537.0213.0FRFrance
\n", "

1990 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202251 3 255228 238612.0 271844.0 385 360.0 \n", "1 202250 3 234279 219533.0 249025.0 353 331.0 \n", "2 202249 3 163421 151727.0 175115.0 246 228.0 \n", "3 202248 3 121884 111932.0 131836.0 184 169.0 \n", "4 202247 3 96447 87259.0 105635.0 145 131.0 \n", "5 202246 3 67735 60075.0 75395.0 102 90.0 \n", "6 202245 3 45306 38909.0 51703.0 68 58.0 \n", "7 202244 3 34713 28880.0 40546.0 52 43.0 \n", "8 202243 3 44769 36884.0 52654.0 68 56.0 \n", "9 202242 3 47462 40773.0 54151.0 72 62.0 \n", "10 202241 3 48583 42388.0 54778.0 73 64.0 \n", "11 202240 3 41927 36115.0 47739.0 63 54.0 \n", "12 202239 3 39902 34168.0 45636.0 60 51.0 \n", "13 202238 3 28781 23733.0 33829.0 43 35.0 \n", "14 202237 3 21395 17076.0 25714.0 32 25.0 \n", "15 202236 3 14120 10487.0 17753.0 21 16.0 \n", "16 202235 3 9283 6485.0 12081.0 14 10.0 \n", "17 202234 3 7498 4731.0 10265.0 11 7.0 \n", "18 202233 3 7586 4442.0 10730.0 11 6.0 \n", "19 202232 3 12222 7749.0 16695.0 18 11.0 \n", "20 202231 3 13257 8905.0 17609.0 20 13.0 \n", "21 202230 3 15006 10738.0 19274.0 23 17.0 \n", "22 202229 3 20801 15829.0 25773.0 31 24.0 \n", "23 202228 3 23387 17970.0 28804.0 35 27.0 \n", "24 202227 3 36015 29709.0 42321.0 54 44.0 \n", "25 202226 3 29421 24314.0 34528.0 44 36.0 \n", "26 202225 3 22887 18582.0 27192.0 35 29.0 \n", "27 202224 3 19294 15406.0 23182.0 29 23.0 \n", "28 202223 3 17159 13450.0 20868.0 26 20.0 \n", "29 202222 3 14239 10930.0 17548.0 21 16.0 \n", "... ... ... ... ... ... ... ... \n", "1961 198521 3 26096 19621.0 32571.0 47 35.0 \n", "1962 198520 3 27896 20885.0 34907.0 51 38.0 \n", "1963 198519 3 43154 32821.0 53487.0 78 59.0 \n", "1964 198518 3 40555 29935.0 51175.0 74 55.0 \n", "1965 198517 3 34053 24366.0 43740.0 62 44.0 \n", "1966 198516 3 50362 36451.0 64273.0 91 66.0 \n", "1967 198515 3 63881 45538.0 82224.0 116 83.0 \n", "1968 198514 3 134545 114400.0 154690.0 244 207.0 \n", "1969 198513 3 197206 176080.0 218332.0 357 319.0 \n", "1970 198512 3 245240 223304.0 267176.0 445 405.0 \n", "1971 198511 3 276205 252399.0 300011.0 501 458.0 \n", "1972 198510 3 353231 326279.0 380183.0 640 591.0 \n", "1973 198509 3 369895 341109.0 398681.0 670 618.0 \n", "1974 198508 3 389886 359529.0 420243.0 707 652.0 \n", "1975 198507 3 471852 432599.0 511105.0 855 784.0 \n", "1976 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "1977 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "1978 198504 3 424937 390794.0 459080.0 770 708.0 \n", "1979 198503 3 213901 174689.0 253113.0 388 317.0 \n", "1980 198502 3 97586 80949.0 114223.0 177 147.0 \n", "1981 198501 3 85489 65918.0 105060.0 155 120.0 \n", "1982 198452 3 84830 60602.0 109058.0 154 110.0 \n", "1983 198451 3 101726 80242.0 123210.0 185 146.0 \n", "1984 198450 3 123680 101401.0 145959.0 225 184.0 \n", "1985 198449 3 101073 81684.0 120462.0 184 149.0 \n", "1986 198448 3 78620 60634.0 96606.0 143 110.0 \n", "1987 198447 3 72029 54274.0 89784.0 131 99.0 \n", "1988 198446 3 87330 67686.0 106974.0 159 123.0 \n", "1989 198445 3 135223 101414.0 169032.0 246 184.0 \n", "1990 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 410.0 FR France \n", "1 375.0 FR France \n", "2 264.0 FR France \n", "3 199.0 FR France \n", "4 159.0 FR France \n", "5 114.0 FR France \n", "6 78.0 FR France \n", "7 61.0 FR France \n", "8 80.0 FR France \n", "9 82.0 FR France \n", "10 82.0 FR France \n", "11 72.0 FR France \n", "12 69.0 FR France \n", "13 51.0 FR France \n", "14 39.0 FR France \n", "15 26.0 FR France \n", "16 18.0 FR France \n", "17 15.0 FR France \n", "18 16.0 FR France \n", "19 25.0 FR France \n", "20 27.0 FR France \n", "21 29.0 FR France \n", "22 38.0 FR France \n", "23 43.0 FR France \n", "24 64.0 FR France \n", "25 52.0 FR France \n", "26 41.0 FR France \n", "27 35.0 FR France \n", "28 32.0 FR France \n", "29 26.0 FR France \n", "... ... ... ... \n", "1961 59.0 FR France \n", "1962 64.0 FR France \n", "1963 97.0 FR France \n", "1964 93.0 FR France \n", "1965 80.0 FR France \n", "1966 116.0 FR France \n", "1967 149.0 FR France \n", "1968 281.0 FR France \n", "1969 395.0 FR France \n", "1970 485.0 FR France \n", "1971 544.0 FR France \n", "1972 689.0 FR France \n", "1973 722.0 FR France \n", "1974 762.0 FR France \n", "1975 926.0 FR France \n", "1976 1113.0 FR France \n", "1977 1236.0 FR France \n", "1978 832.0 FR France \n", "1979 459.0 FR France \n", "1980 207.0 FR France \n", "1981 190.0 FR France \n", "1982 198.0 FR France \n", "1983 224.0 FR France \n", "1984 266.0 FR France \n", "1985 219.0 FR France \n", "1986 176.0 FR France \n", "1987 163.0 FR France \n", "1988 195.0 FR France \n", "1989 308.0 FR France \n", "1990 213.0 FR France \n", "\n", "[1990 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", "text/plain": [ "
" ] }, "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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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.plot(style='*')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2021 743449\n", "2014 1600941\n", "1991 1659249\n", "1995 1840410\n", "2020 2010315\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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