{ "cells": [ { "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. 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-7.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Voici l'explication des colonnes données sur le site d'origine:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02023337366495663726210FRFrance
12023327806811781495812222FRFrance
22023317331813985238528FRFrance
320233075821326983739513FRFrance
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6202327772534599990711715FRFrance
720232679192622312161141018FRFrance
8202325711498825714739171222FRFrance
9202324711115796814262171222FRFrance
1020232371256361341899219929FRFrance
11202322712184812516243181224FRFrance
12202321711349759815100171123FRFrance
132023207900046151338514721FRFrance
142023197934460911259714919FRFrance
15202318710671729114051161121FRFrance
162023177918461621220614919FRFrance
17202316711387801414760171222FRFrance
18202315714040761320467211131FRFrance
192023147152471103219462231729FRFrance
20202313713322970016944201525FRFrance
21202312710374721813530161121FRFrance
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24202309770044548946011715FRFrance
252023087817553161103412816FRFrance
26202307765953782940810614FRFrance
272023067959560171317314919FRFrance
2820230576237390785679513FRFrance
2920230476299397386259612FRFrance
.................................
16771991267176081130423912312042FRFrance
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1680199123711947767116223211329FRFrance
1681199122715452995320951271737FRFrance
1682199121714903897520831261636FRFrance
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16851991187213851388228888382551FRFrance
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1705199050711079666015498201228FRFrance
17061990497114302610205FRFrance
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1707 rows × 10 columns

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" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202333 7 3664 956 6372 6 2 \n", "1 202332 7 8068 1178 14958 12 2 \n", "2 202331 7 3318 1398 5238 5 2 \n", "3 202330 7 5821 3269 8373 9 5 \n", "4 202329 7 13558 8297 18819 20 12 \n", "5 202328 7 6700 4043 9357 10 6 \n", "6 202327 7 7253 4599 9907 11 7 \n", "7 202326 7 9192 6223 12161 14 10 \n", "8 202325 7 11498 8257 14739 17 12 \n", "9 202324 7 11115 7968 14262 17 12 \n", "10 202323 7 12563 6134 18992 19 9 \n", "11 202322 7 12184 8125 16243 18 12 \n", "12 202321 7 11349 7598 15100 17 11 \n", "13 202320 7 9000 4615 13385 14 7 \n", "14 202319 7 9344 6091 12597 14 9 \n", "15 202318 7 10671 7291 14051 16 11 \n", "16 202317 7 9184 6162 12206 14 9 \n", "17 202316 7 11387 8014 14760 17 12 \n", "18 202315 7 14040 7613 20467 21 11 \n", "19 202314 7 15247 11032 19462 23 17 \n", "20 202313 7 13322 9700 16944 20 15 \n", "21 202312 7 10374 7218 13530 16 11 \n", "22 202311 7 4919 2880 6958 7 4 \n", "23 202310 7 4854 2731 6977 7 4 \n", "24 202309 7 7004 4548 9460 11 7 \n", "25 202308 7 8175 5316 11034 12 8 \n", "26 202307 7 6595 3782 9408 10 6 \n", "27 202306 7 9595 6017 13173 14 9 \n", "28 202305 7 6237 3907 8567 9 5 \n", "29 202304 7 6299 3973 8625 9 6 \n", "... ... ... ... ... ... ... ... \n", "1677 199126 7 17608 11304 23912 31 20 \n", "1678 199125 7 16169 10700 21638 28 18 \n", "1679 199124 7 16171 10071 22271 28 17 \n", "1680 199123 7 11947 7671 16223 21 13 \n", "1681 199122 7 15452 9953 20951 27 17 \n", "1682 199121 7 14903 8975 20831 26 16 \n", "1683 199120 7 19053 12742 25364 34 23 \n", "1684 199119 7 16739 11246 22232 29 19 \n", "1685 199118 7 21385 13882 28888 38 25 \n", "1686 199117 7 13462 8877 18047 24 16 \n", "1687 199116 7 14857 10068 19646 26 18 \n", "1688 199115 7 13975 9781 18169 25 18 \n", "1689 199114 7 12265 7684 16846 22 14 \n", "1690 199113 7 9567 6041 13093 17 11 \n", "1691 199112 7 10864 7331 14397 19 13 \n", "1692 199111 7 15574 11184 19964 27 19 \n", "1693 199110 7 16643 11372 21914 29 20 \n", "1694 199109 7 13741 8780 18702 24 15 \n", "1695 199108 7 13289 8813 17765 23 15 \n", "1696 199107 7 12337 8077 16597 22 15 \n", "1697 199106 7 10877 7013 14741 19 12 \n", "1698 199105 7 10442 6544 14340 18 11 \n", "1699 199104 7 7913 4563 11263 14 8 \n", "1700 199103 7 15387 10484 20290 27 18 \n", "1701 199102 7 16277 11046 21508 29 20 \n", "1702 199101 7 15565 10271 20859 27 18 \n", "1703 199052 7 19375 13295 25455 34 23 \n", "1704 199051 7 19080 13807 24353 34 25 \n", "1705 199050 7 11079 6660 15498 20 12 \n", "1706 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 10 FR France \n", "1 22 FR France \n", "2 8 FR France \n", "3 13 FR France \n", "4 28 FR France \n", "5 14 FR France \n", "6 15 FR France \n", "7 18 FR France \n", "8 22 FR France \n", "9 22 FR France \n", "10 29 FR France \n", "11 24 FR France \n", "12 23 FR France \n", "13 21 FR France \n", "14 19 FR France \n", "15 21 FR France \n", "16 19 FR France \n", "17 22 FR France \n", "18 31 FR France \n", "19 29 FR France \n", "20 25 FR France \n", "21 21 FR France \n", "22 10 FR France \n", "23 10 FR France \n", "24 15 FR France \n", "25 16 FR France \n", "26 14 FR France \n", "27 19 FR France \n", "28 13 FR France \n", "29 12 FR France \n", "... ... ... ... \n", "1677 42 FR France \n", "1678 38 FR France \n", "1679 39 FR France \n", "1680 29 FR France \n", "1681 37 FR France \n", "1682 36 FR France \n", "1683 45 FR France \n", "1684 39 FR France \n", "1685 51 FR France \n", "1686 32 FR France \n", "1687 34 FR France \n", "1688 32 FR France \n", "1689 30 FR France \n", "1690 23 FR France \n", "1691 25 FR France \n", "1692 35 FR France \n", "1693 38 FR France \n", "1694 33 FR France \n", "1695 31 FR France \n", "1696 29 FR France \n", "1697 26 FR France \n", "1698 25 FR France \n", "1699 20 FR France \n", "1700 36 FR France \n", "1701 38 FR France \n", "1702 36 FR France \n", "1703 45 FR France \n", "1704 43 FR France \n", "1705 28 FR France \n", "1706 5 FR France \n", "\n", "[1707 rows x 10 columns]" ] }, "execution_count": 3, "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 ? " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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" ], "text/plain": [ "Empty DataFrame\n", "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n", "Index: []" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "pas de points manquants" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02023337366495663726210FRFrance
12023327806811781495812222FRFrance
22023317331813985238528FRFrance
320233075821326983739513FRFrance
4202329713558829718819201228FRFrance
5202328767004043935710614FRFrance
6202327772534599990711715FRFrance
720232679192622312161141018FRFrance
8202325711498825714739171222FRFrance
9202324711115796814262171222FRFrance
1020232371256361341899219929FRFrance
11202322712184812516243181224FRFrance
12202321711349759815100171123FRFrance
132023207900046151338514721FRFrance
142023197934460911259714919FRFrance
15202318710671729114051161121FRFrance
162023177918461621220614919FRFrance
17202316711387801414760171222FRFrance
18202315714040761320467211131FRFrance
192023147152471103219462231729FRFrance
20202313713322970016944201525FRFrance
21202312710374721813530161121FRFrance
2220231174919288069587410FRFrance
2320231074854273169777410FRFrance
24202309770044548946011715FRFrance
252023087817553161103412816FRFrance
26202307765953782940810614FRFrance
272023067959560171317314919FRFrance
2820230576237390785679513FRFrance
2920230476299397386259612FRFrance
.................................
16771991267176081130423912312042FRFrance
16781991257161691070021638281838FRFrance
16791991247161711007122271281739FRFrance
1680199123711947767116223211329FRFrance
1681199122715452995320951271737FRFrance
1682199121714903897520831261636FRFrance
16831991207190531274225364342345FRFrance
16841991197167391124622232291939FRFrance
16851991187213851388228888382551FRFrance
1686199117713462887718047241632FRFrance
16871991167148571006819646261834FRFrance
1688199115713975978118169251832FRFrance
1689199114712265768416846221430FRFrance
169019911379567604113093171123FRFrance
1691199112710864733114397191325FRFrance
16921991117155741118419964271935FRFrance
16931991107166431137221914292038FRFrance
1694199109713741878018702241533FRFrance
1695199108713289881317765231531FRFrance
1696199107712337807716597221529FRFrance
1697199106710877701314741191226FRFrance
1698199105710442654414340181125FRFrance
16991991047791345631126314820FRFrance
17001991037153871048420290271836FRFrance
17011991027162771104621508292038FRFrance
17021991017155651027120859271836FRFrance
17031990527193751329525455342345FRFrance
17041990517190801380724353342543FRFrance
1705199050711079666015498201228FRFrance
17061990497114302610205FRFrance
\n", "

1707 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202333 7 3664 956 6372 6 2 \n", "1 202332 7 8068 1178 14958 12 2 \n", "2 202331 7 3318 1398 5238 5 2 \n", "3 202330 7 5821 3269 8373 9 5 \n", "4 202329 7 13558 8297 18819 20 12 \n", "5 202328 7 6700 4043 9357 10 6 \n", "6 202327 7 7253 4599 9907 11 7 \n", "7 202326 7 9192 6223 12161 14 10 \n", "8 202325 7 11498 8257 14739 17 12 \n", "9 202324 7 11115 7968 14262 17 12 \n", "10 202323 7 12563 6134 18992 19 9 \n", "11 202322 7 12184 8125 16243 18 12 \n", "12 202321 7 11349 7598 15100 17 11 \n", "13 202320 7 9000 4615 13385 14 7 \n", "14 202319 7 9344 6091 12597 14 9 \n", "15 202318 7 10671 7291 14051 16 11 \n", "16 202317 7 9184 6162 12206 14 9 \n", "17 202316 7 11387 8014 14760 17 12 \n", "18 202315 7 14040 7613 20467 21 11 \n", "19 202314 7 15247 11032 19462 23 17 \n", "20 202313 7 13322 9700 16944 20 15 \n", "21 202312 7 10374 7218 13530 16 11 \n", "22 202311 7 4919 2880 6958 7 4 \n", "23 202310 7 4854 2731 6977 7 4 \n", "24 202309 7 7004 4548 9460 11 7 \n", "25 202308 7 8175 5316 11034 12 8 \n", "26 202307 7 6595 3782 9408 10 6 \n", "27 202306 7 9595 6017 13173 14 9 \n", "28 202305 7 6237 3907 8567 9 5 \n", "29 202304 7 6299 3973 8625 9 6 \n", "... ... ... ... ... ... ... ... \n", "1677 199126 7 17608 11304 23912 31 20 \n", "1678 199125 7 16169 10700 21638 28 18 \n", "1679 199124 7 16171 10071 22271 28 17 \n", "1680 199123 7 11947 7671 16223 21 13 \n", "1681 199122 7 15452 9953 20951 27 17 \n", "1682 199121 7 14903 8975 20831 26 16 \n", "1683 199120 7 19053 12742 25364 34 23 \n", "1684 199119 7 16739 11246 22232 29 19 \n", "1685 199118 7 21385 13882 28888 38 25 \n", "1686 199117 7 13462 8877 18047 24 16 \n", "1687 199116 7 14857 10068 19646 26 18 \n", "1688 199115 7 13975 9781 18169 25 18 \n", "1689 199114 7 12265 7684 16846 22 14 \n", "1690 199113 7 9567 6041 13093 17 11 \n", "1691 199112 7 10864 7331 14397 19 13 \n", "1692 199111 7 15574 11184 19964 27 19 \n", "1693 199110 7 16643 11372 21914 29 20 \n", "1694 199109 7 13741 8780 18702 24 15 \n", "1695 199108 7 13289 8813 17765 23 15 \n", "1696 199107 7 12337 8077 16597 22 15 \n", "1697 199106 7 10877 7013 14741 19 12 \n", "1698 199105 7 10442 6544 14340 18 11 \n", "1699 199104 7 7913 4563 11263 14 8 \n", "1700 199103 7 15387 10484 20290 27 18 \n", "1701 199102 7 16277 11046 21508 29 20 \n", "1702 199101 7 15565 10271 20859 27 18 \n", "1703 199052 7 19375 13295 25455 34 23 \n", "1704 199051 7 19080 13807 24353 34 25 \n", "1705 199050 7 11079 6660 15498 20 12 \n", "1706 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 10 FR France \n", "1 22 FR France \n", "2 8 FR France \n", "3 13 FR France \n", "4 28 FR France \n", "5 14 FR France \n", "6 15 FR France \n", "7 18 FR France \n", "8 22 FR France \n", "9 22 FR France \n", "10 29 FR France \n", "11 24 FR France \n", "12 23 FR France \n", "13 21 FR France \n", "14 19 FR France \n", "15 21 FR France \n", "16 19 FR France \n", "17 22 FR France \n", "18 31 FR France \n", "19 29 FR France \n", "20 25 FR France \n", "21 21 FR France \n", "22 10 FR France \n", "23 10 FR France \n", "24 15 FR France \n", "25 16 FR France \n", "26 14 FR France \n", "27 19 FR France \n", "28 13 FR France \n", "29 12 FR France \n", "... ... ... ... \n", "1677 42 FR France \n", "1678 38 FR France \n", "1679 39 FR France \n", "1680 29 FR France \n", "1681 37 FR France \n", "1682 36 FR France \n", "1683 45 FR France \n", "1684 39 FR France \n", "1685 51 FR France \n", "1686 32 FR France \n", "1687 34 FR France \n", "1688 32 FR France \n", "1689 30 FR France \n", "1690 23 FR France \n", "1691 25 FR France \n", "1692 35 FR France \n", "1693 38 FR France \n", "1694 33 FR France \n", "1695 31 FR France \n", "1696 29 FR France \n", "1697 26 FR France \n", "1698 25 FR France \n", "1699 20 FR France \n", "1700 36 FR France \n", "1701 38 FR France \n", "1702 36 FR France \n", "1703 45 FR France \n", "1704 43 FR France \n", "1705 28 FR France \n", "1706 5 FR France \n", "\n", "[1707 rows x 10 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nos données utilisent une convention inhabituelle: le numéro de\n", "semaine est collé à l'année, donnant l'impression qu'il s'agit\n", "de nombre entier. C'est comme ça que Pandas les interprète.\n", " \n", "Un deuxième problème est que Pandas ne comprend pas les numéros de\n", "semaine. Il faut lui fournir les dates de début et de fin de\n", "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n", "\n", "Comme la conversion des semaines est devenu assez complexe, nous\n", "écrivons une petite fonction Python pour cela. Ensuite, nous\n", "l'appliquons à tous les points de nos donnés. Les résultats vont\n", "dans une nouvelle colonne 'period'." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def convert_week(year_and_week_int):\n", " year_and_week_str = str(year_and_week_int)\n", " year = int(year_and_week_str[:4])\n", " week = int(year_and_week_str[4:])\n", " w = isoweek.Week(year, week)\n", " return pd.Period(w.day(0), 'W')\n", "\n", "data['period'] = [convert_week(yw) for yw in data['week']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il restent deux petites modifications à faire.\n", "\n", "Premièrement, nous définissons les périodes d'observation comme nouvel index de notre jeux de données. Ceci en fait une suite chronologique, ce qui sera pratique par la suite.\n", "\n", "Deuxièmement, nous trions les points par période, dans le sens chronologique.\n" ] }, { "cell_type": "code", "execution_count": 7, "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 le début de la période qui suit, la différence temporelle doit être zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\" d'une seconde.\n", "\n", "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives entre lesquelles il manque une semaine.\n", "\n", "Nous reconnaissons ces dates: c'est la semaine sans observations que nous avions supprimées !\n" ] }, { "cell_type": "code", "execution_count": 8, "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": [ "ok premier regard sur les données sans incohérence" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "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()\n" ] }, { "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 entre deux années civiles, nous définissons la période de référence entre deux minima de l'incidence, du 1er septembre de l'année 𝑁 au 1er spetembre de l'année 𝑁+1\n", "\n", ".\n", "Notre tâche est un peu compliquée par le fait que l'année ne comporte pas un nombre entier de semaines. Nous modifions donc un peu nos périodes de référence: à la place du 1er septembre de chaque année, nous utilisons le premier jour de la semaine qui contient le 1er septembre.\n", "on commencera l'analyse en 1991\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1991,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En partant de cette liste des semaines qui contiennent un 1er septembre, 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_september_week[:-1],\n", " first_september_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": "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": [ "2020 221186\n", "2021 376290\n", "2002 516689\n", "2018 542312\n", "2017 551041\n", "1996 564901\n", "2019 584066\n", "2015 604382\n", "2000 617597\n", "2001 619041\n", "2012 624573\n", "2005 628464\n", "2006 632833\n", "2022 641397\n", "2011 642368\n", "1993 643387\n", "1995 652478\n", "1994 661409\n", "1998 677775\n", "1997 683434\n", "2014 685769\n", "2013 698332\n", "2007 717352\n", "2008 749478\n", "1999 756456\n", "2003 758363\n", "2004 777388\n", "2016 782114\n", "2010 829911\n", "1992 832939\n", "2009 842373\n", "dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "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": 2 }