{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome de la varicelle" ] }, { "cell_type": "code", "execution_count": 3, "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 de la varicelle 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": 4, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv?v=8nhxe\"" ] }, { "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):" ] }, { "cell_type": "code", "execution_count": 18, "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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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", " \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
020234676089291892609414FRFrance
120234575090271374678412FRFrance
22023447368816645712639FRFrance
32023437389116756107639FRFrance
420234273968121267246210FRFrance
52023417335617644948537FRFrance
62023407284514104280426FRFrance
7202339717396292849315FRFrance
8202338716632743052315FRFrance
9202337711222232021213FRFrance
102023367726101442102FRFrance
112023357961961826102FRFrance
122023347116892327204FRFrance
132023337330811845432528FRFrance
142023327799611201487212222FRFrance
152023317331813985238528FRFrance
1620233075821326983739513FRFrance
17202329713558829718819201228FRFrance
18202328767004043935710614FRFrance
19202327772534599990711715FRFrance
2020232679192622312161141018FRFrance
21202325711498825714739171222FRFrance
22202324711115796814262171222FRFrance
2320232371256361341899219929FRFrance
24202322712184812516243181224FRFrance
25202321711349759815100171123FRFrance
262023207900046151338514721FRFrance
272023197934460911259714919FRFrance
28202318710671729114051161121FRFrance
292023177918461621220614919FRFrance
.................................
16891991277203091286827750362349FRFrance
16901991267176081130423912312042FRFrance
16911991257161691070021638281838FRFrance
16921991247161711007122271281739FRFrance
1693199123711947767116223211329FRFrance
1694199122715452995320951271737FRFrance
1695199121714903897520831261636FRFrance
16961991207190531274225364342345FRFrance
16971991197167391124622232291939FRFrance
16981991187213851388228888382551FRFrance
1699199117713462887718047241632FRFrance
17001991167148571006819646261834FRFrance
1701199115713975978118169251832FRFrance
1702199114712265768416846221430FRFrance
170319911379567604113093171123FRFrance
1704199112710864733114397191325FRFrance
17051991117155741118419964271935FRFrance
17061991107166431137221914292038FRFrance
1707199109713741878018702241533FRFrance
1708199108713289881317765231531FRFrance
1709199107712337807716597221529FRFrance
1710199106710877701314741191226FRFrance
1711199105710442654414340181125FRFrance
17121991047791345631126314820FRFrance
17131991037153871048420290271836FRFrance
17141991027162771104621508292038FRFrance
17151991017155651027120859271836FRFrance
17161990527193751329525455342345FRFrance
17171990517190801380724353342543FRFrance
1718199050711079666015498201228FRFrance
\n", "

1719 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202346 7 6089 2918 9260 9 4 \n", "1 202345 7 5090 2713 7467 8 4 \n", "2 202344 7 3688 1664 5712 6 3 \n", "3 202343 7 3891 1675 6107 6 3 \n", "4 202342 7 3968 1212 6724 6 2 \n", "5 202341 7 3356 1764 4948 5 3 \n", "6 202340 7 2845 1410 4280 4 2 \n", "7 202339 7 1739 629 2849 3 1 \n", "8 202338 7 1663 274 3052 3 1 \n", "9 202337 7 1122 223 2021 2 1 \n", "10 202336 7 726 10 1442 1 0 \n", "11 202335 7 961 96 1826 1 0 \n", "12 202334 7 1168 9 2327 2 0 \n", "13 202333 7 3308 1184 5432 5 2 \n", "14 202332 7 7996 1120 14872 12 2 \n", "15 202331 7 3318 1398 5238 5 2 \n", "16 202330 7 5821 3269 8373 9 5 \n", "17 202329 7 13558 8297 18819 20 12 \n", "18 202328 7 6700 4043 9357 10 6 \n", "19 202327 7 7253 4599 9907 11 7 \n", "20 202326 7 9192 6223 12161 14 10 \n", "21 202325 7 11498 8257 14739 17 12 \n", "22 202324 7 11115 7968 14262 17 12 \n", "23 202323 7 12563 6134 18992 19 9 \n", "24 202322 7 12184 8125 16243 18 12 \n", "25 202321 7 11349 7598 15100 17 11 \n", "26 202320 7 9000 4615 13385 14 7 \n", "27 202319 7 9344 6091 12597 14 9 \n", "28 202318 7 10671 7291 14051 16 11 \n", "29 202317 7 9184 6162 12206 14 9 \n", "... ... ... ... ... ... ... ... \n", "1689 199127 7 20309 12868 27750 36 23 \n", "1690 199126 7 17608 11304 23912 31 20 \n", "1691 199125 7 16169 10700 21638 28 18 \n", "1692 199124 7 16171 10071 22271 28 17 \n", "1693 199123 7 11947 7671 16223 21 13 \n", "1694 199122 7 15452 9953 20951 27 17 \n", "1695 199121 7 14903 8975 20831 26 16 \n", "1696 199120 7 19053 12742 25364 34 23 \n", "1697 199119 7 16739 11246 22232 29 19 \n", "1698 199118 7 21385 13882 28888 38 25 \n", "1699 199117 7 13462 8877 18047 24 16 \n", "1700 199116 7 14857 10068 19646 26 18 \n", "1701 199115 7 13975 9781 18169 25 18 \n", "1702 199114 7 12265 7684 16846 22 14 \n", "1703 199113 7 9567 6041 13093 17 11 \n", "1704 199112 7 10864 7331 14397 19 13 \n", "1705 199111 7 15574 11184 19964 27 19 \n", "1706 199110 7 16643 11372 21914 29 20 \n", "1707 199109 7 13741 8780 18702 24 15 \n", "1708 199108 7 13289 8813 17765 23 15 \n", "1709 199107 7 12337 8077 16597 22 15 \n", "1710 199106 7 10877 7013 14741 19 12 \n", "1711 199105 7 10442 6544 14340 18 11 \n", "1712 199104 7 7913 4563 11263 14 8 \n", "1713 199103 7 15387 10484 20290 27 18 \n", "1714 199102 7 16277 11046 21508 29 20 \n", "1715 199101 7 15565 10271 20859 27 18 \n", "1716 199052 7 19375 13295 25455 34 23 \n", "1717 199051 7 19080 13807 24353 34 25 \n", "1718 199050 7 11079 6660 15498 20 12 \n", "\n", " inc100_up geo_insee geo_name \n", "0 14 FR France \n", "1 12 FR France \n", "2 9 FR France \n", "3 9 FR France \n", "4 10 FR France \n", "5 7 FR France \n", "6 6 FR France \n", "7 5 FR France \n", "8 5 FR France \n", "9 3 FR France \n", "10 2 FR France \n", "11 2 FR France \n", "12 4 FR France \n", "13 8 FR France \n", "14 22 FR France \n", "15 8 FR France \n", "16 13 FR France \n", "17 28 FR France \n", "18 14 FR France \n", "19 15 FR France \n", "20 18 FR France \n", "21 22 FR France \n", "22 22 FR France \n", "23 29 FR France \n", "24 24 FR France \n", "25 23 FR France \n", "26 21 FR France \n", "27 19 FR France \n", "28 21 FR France \n", "29 19 FR France \n", "... ... ... ... \n", "1689 49 FR France \n", "1690 42 FR France \n", "1691 38 FR France \n", "1692 39 FR France \n", "1693 29 FR France \n", "1694 37 FR France \n", "1695 36 FR France \n", "1696 45 FR France \n", "1697 39 FR France \n", "1698 51 FR France \n", "1699 32 FR France \n", "1700 34 FR France \n", "1701 32 FR France \n", "1702 30 FR France \n", "1703 23 FR France \n", "1704 25 FR France \n", "1705 35 FR France \n", "1706 38 FR France \n", "1707 33 FR France \n", "1708 31 FR France \n", "1709 29 FR France \n", "1710 26 FR France \n", "1711 25 FR France \n", "1712 20 FR France \n", "1713 36 FR France \n", "1714 38 FR France \n", "1715 36 FR France \n", "1716 45 FR France \n", "1717 43 FR France \n", "1718 28 FR France \n", "\n", "[1719 rows x 10 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, skiprows=1)\n", "raw_data.head(-1)" ] }, { "cell_type": "code", "execution_count": 8, "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", "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
\n", "
" ], "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": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "code", "execution_count": 10, "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", " \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", " \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
020234676089291892609414FRFrance
120234575090271374678412FRFrance
22023447368816645712639FRFrance
32023437389116756107639FRFrance
420234273968121267246210FRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "0 202346 7 6089 2918 9260 9 4 14 \n", "1 202345 7 5090 2713 7467 8 4 12 \n", "2 202344 7 3688 1664 5712 6 3 9 \n", "3 202343 7 3891 1675 6107 6 3 9 \n", "4 202342 7 3968 1212 6724 6 2 10 \n", "\n", " geo_insee geo_name \n", "0 FR France \n", "1 FR France \n", "2 FR France \n", "3 FR France \n", "4 FR France " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data.head()" ] }, { "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": 22, "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": "code", "execution_count": 24, "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", " \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", " \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
period
1990-12-03/1990-12-091990497114302610205FRFrance
1990-12-10/1990-12-16199050711079666015498201228FRFrance
1990-12-17/1990-12-231990517190801380724353342543FRFrance
1990-12-24/1990-12-301990527193751329525455342345FRFrance
1990-12-31/1991-01-061991017155651027120859271836FRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 \\\n", "period \n", "1990-12-03/1990-12-09 199049 7 1143 0 2610 2 \n", "1990-12-10/1990-12-16 199050 7 11079 6660 15498 20 \n", "1990-12-17/1990-12-23 199051 7 19080 13807 24353 34 \n", "1990-12-24/1990-12-30 199052 7 19375 13295 25455 34 \n", "1990-12-31/1991-01-06 199101 7 15565 10271 20859 27 \n", "\n", " inc100_low inc100_up geo_insee geo_name \n", "period \n", "1990-12-03/1990-12-09 0 5 FR France \n", "1990-12-10/1990-12-16 12 28 FR France \n", "1990-12-17/1990-12-23 25 43 FR France \n", "1990-12-24/1990-12-30 23 45 FR France \n", "1990-12-31/1991-01-06 18 36 FR France " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data = data.set_index('period').sort_index()\n", "sorted_data.head()" ] }, { "cell_type": "code", "execution_count": 13, "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": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 25, "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": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 29, "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": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1990,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "code", "execution_count": 35, "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) < 1\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": 37, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAD8CAYAAACLrvgBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAHpFJREFUeJzt3X+Q1PWd5/HnC4aAccGACbP8UPFK9IKmYsIUcpfc3rhsgGy2hK3VZC6uTl2oYs942WTrqhQ3XlGnkNKrq8uGSsWTWlHUTZTz1pLbhDUjXtd6d2T4kZgYNGRINEgkjDpEmVyYOPK+P/rT0g7NzLeHmf5297weVV3z7Xd/P9/5fvgO/e7Pj++nFRGYmZllMSnvEzAzs8bhpGFmZpk5aZiZWWZOGmZmlpmThpmZZeakYWZmmTlpmJlZZk4aZmaWmZOGmZll1pL3CYy197///bFgwYLT4r/5zW8499xza39CY8h1qA+uQ31wHcbWvn37XouID4y0X9MljQULFrB3797T4oVCgfb29tqf0BhyHeqD61AfXIexJekXWfZz95SZmWXmpGFmZpk5aZiZWWZOGmZmlpmThpmZZeakYdYket88wafv3UXv8RN5n4o1MScNsyaxaWcPe17qY9NTPXmfijWxprtPw2yiuez2HQwMnnzn+cPdh3i4+xBTWyZxYMMnczwza0ZuaVTJXQBWb5655WquuXIu06YU/ztPmzKJVVfO5Zlbr875zKwZOWlUyV0AVm9mz5jG9KktDAyeZGrLJAYGTzJ9aguzp0+ruH/vmyf4Svdv/cHHRsXdUxm5C8Dq2Wv9A1x/1UV8dsmFfHP3IV4dJiFs2tlDz7GTbHqqhw1/+qEanqU1AyeNjJ655Wo2fOcFvrv/V5x46yTTpkxixeW/z5c/9cG8T82Me29oe2d7w+orKu7jDz42Ftw9lVG1XQBm9cZjHzYW3NKoQjVdAGb1pvyDz5RJ+IOPjYqTRhWydAGY1bPSB59LJx3lpydb/cHHquakYTaBlD74FAqvcWO7P/hY9TymYWZmmTlpmJlZZpmShqS/krRf0o8lfUvSNEmzJHVJ6kk/Z5btf5ukg5IOSFpRFl8s6bn02iZJSvGpkh5N8W5JC8rKdKbf0SOpc+yqbmZm1RoxaUiaB/wl0BYRVwCTgQ5gHbAzIhYCO9NzJC1Kr18OrAS+IWlyOtw9wFpgYXqsTPE1wLGIuAT4KnB3OtYsYD1wFbAEWF+enMzMrLaydk+1AOdIagHeC7wCrAK2pte3AqvT9irgkYgYiIgXgYPAEklzgBkRsSsiAnhwSJnSsR4DlqVWyAqgKyL6IuIY0MWpRGNmZjU2YtKIiF8C/wU4BBwB3oiI7wKtEXEk7XMEmJ2KzANeLjvE4RSbl7aHxt9VJiIGgTeA84c5lpmZ5WDEKbepO2gVcDHwa+C/S/rz4YpUiMUw8dGWKT/HtRS7vWhtbaVQKJxWqL+/v2K8kbgO9cF1qA+uQz6y3KfxR8CLEfEqgKS/B/4lcFTSnIg4krqeetP+h4ELysrPp9iddThtD42XlzmcusDOA/pSvH1ImcLQE4yIzcBmgLa2tmhvbx+6C4VCgUrxRuI61AfXoT64DvnIMqZxCFgq6b1pnGEZ8AKwHSjNZuoEnkjb24GONCPqYooD3rtTF9ZxSUvTcW4cUqZ0rGuBp9O4x5PAckkzU4tneYqZmY3I338z9kZsaUREt6THgO8Dg8APKH6q/z1gm6Q1FBPLdWn//ZK2Ac+n/W+OiLfT4W4CHgDOAXakB8B9wEOSDlJsYXSkY/VJuhPYk/a7IyL6zqrGZjZhlH//jZeBHxuZlhGJiPUUp76WG6DY6qi0/0ZgY4X4XuC0tQsi4gQp6VR4bQuwJct5mo1W75sn+Pff+gFf/+xHvIBfE/Ay8OPHd4Sb4W9kbDZeBn78eMFCm9D8ibQ5+ftvxo9bGjah+RNp8yotA//45z/G9VddxKv9A3mfUlNwS8MmNH8ibV7+/pvx4aRhE56/kdEsOycNa2pZZkX5E6lZdh7TsLNWzzdQeVaU2dhyS8POWj3eQOVZUWbjwy0NG7XLbt/BgnXf5uHuQ0QU35gXrPs2l92+Y+TC46zZZkXVc2vOJhYnDRu1en5jbrZZUe5ms3rh7ikbtXp/Y26GWVHuZrN646RhZ6We35ibYVbUM7dczYbvvMB39/+KE2+dZNqUSay4/Pf58qc+mPep2QTlpGFnpRnemOtZvbfmbOJx0jCrc/XcmrOJx0nDrM65NWf1xLOnzMwsMycNMzPLzEnDzMwyc9Iwy5Hv9LZG46RhliPf6W2NxrOnzHLgO72tUbmlYZaDel63y2w4ThpmOfCd3taoRkwaki6T9GzZ401JX5I0S1KXpJ70c2ZZmdskHZR0QNKKsvhiSc+l1zZJUopPlfRoindLWlBWpjP9jh5JnWNbfbP8lO70fvzzH+P6qy7i1f6BvE/JbEQjjmlExAHgSgBJk4FfAo8D64CdEXGXpHXp+a2SFgEdwOXAXOApSZdGxNvAPcBa4HvAd4CVwA5gDXAsIi6R1AHcDXxG0ixgPdAGBLBP0vaIODZm/wJmOZlod3pn+epdq3/Vdk8tA34WEb8AVgFbU3wrsDptrwIeiYiBiHgROAgskTQHmBERuyIigAeHlCkd6zFgWWqFrAC6IqIvJYouionGzBqMZ4o1h2pnT3UA30rbrRFxBCAijkianeLzKLYkSg6n2Ftpe2i8VObldKxBSW8A55fHK5QxswbgmWLNJXPSkPQe4BrgtpF2rRCLYeKjLVN+bmspdnvR2tpKoVA4rVB/f3/FeCNxHeqD61Cduz8+lUcO/I7vH32b352E90yCj7ZOpuOfv+eszsHXIR/VtDQ+CXw/Io6m50clzUmtjDlAb4ofBi4oKzcfeCXF51eIl5c5LKkFOA/oS/H2IWUKQ08sIjYDmwHa2tqivb196C4UCgUqxRuJ61AfXIfq7fntc3T/qti6+N3bJ7nkwnmsXvGh0/arZtzD1yEf1Yxp/BtOdU0BbAdKs5k6gSfK4h1pRtTFwEJgd+rKOi5paRqvuHFImdKxrgWeTuMeTwLLJc1Ms7OWp5idJS9fYbWUdaaYxz3qX6aWhqT3Ap8A/qIsfBewTdIa4BBwHUBE7Je0DXgeGARuTjOnAG4CHgDOoThrakeK3wc8JOkgxRZGRzpWn6Q7gT1pvzsiom8U9bQhyv9zbvjT0z/xmY2lkWaKedyjcWRKGhHx/ygOTJfHXqc4m6rS/huBjRXie4HT/mIi4gQp6VR4bQuwJct52siq+c/pKZJWK/4u9MbhO8InmGqWr3BXgdWK75BvHF6wcILJ8p/TXQWWB38XemNw0piARvrP6a4Cy8NEu0O+UTlpTEAj/ed0V4GZnYnHNHJWr1NfvZiemVXilkbO6nXqq7sKzKwSJ42ceLDZzBqRu6dyUu03t/W+eYKvdP+27rqxzGxicdLISbWDzZt29tBz7KTvmTCzXLl7KkdZ5qW7G8vM6olbGuMky6yoe29oY8PqK1g0dwYbVl/xrsHnkmq7sczGSr3O7LPKanW9nDTGyVgtwVHejTVlEr5nwmrGy8g0llpdL3dPjbHx6E4qdWNdOukoPz3Z6uUVbFy5S7Sx1Pp6uaUxxsajO6nUjXXhjMln7May+tLIXTvuEm0stb5eThpjzEtwNLesU58buWvHf8ONpdbXy91T48CrdTav8qnPle7gb5auHf8NN5ZaXi8njXHgJTiaT9Zk0CwrBFfzN+wv68pfLd9z3D1ldacexwOy9htPxK6dRu6Ks+q5pWE1UxoPWLT4xLBvovW4iGM1U58nStdOs3TFWXWcNKxmGn08IOvU54nSPdksXXFWHScNG3fNMh5QSgaFwmvc2N68ySCridgVZx7TsBrweEDz8pd1TTxuadi483hA88qrKy7r+JiNvUxJQ9L7gL8FrgAC+BxwAHgUWAC8BHw6Io6l/W8D1gBvA38ZEU+m+GLgAeAc4DvAFyMiJE0FHgQWA68Dn4mIl1KZTuD2dCobImLr2VTY8uHxABtLI42P2fjJ2tL4GvCPEXGtpPcA7wX+GtgZEXdJWgesA26VtAjoAC4H5gJPSbo0It4G7gHWAt+jmDRWAjsoJphjEXGJpA7gbuAzkmYB64E2islqn6TtpeRkjcPjATYW6n2yxEQw4piGpBnAHwD3AUTE7yLi18AqoPSpfyuwOm2vAh6JiIGIeBE4CCyRNAeYERG7IiIotizKy5SO9RiwTJKAFUBXRPSlRNFFMdGY2QTkdbHyl6Wl8c+AV4H7JX0Y2Ad8EWiNiCMAEXFE0uy0/zyKLYmSwyn2VtoeGi+VeTkda1DSG8D55fEKZd4haS3FFgytra0UCoXTKtHf318x3khch/rgOuTr+OsDDLx1khYFA2+d5M3Xe3l+3xs8n/eJjUIjXocsSaMF+CjwhYjolvQ1il1RZ6IKsRgmPtoypwIRm4HNAG1tbdHe3n5aoUKhQKV4I3Ed6oPrkK9vvbyX65dOe9f4WHt7Y6783IjXIcuU28PA4YjoTs8fo5hEjqYuJ9LP3rL9LygrPx94JcXnV4i/q4ykFuA8oG+YY5nZBNUIXxVQj0vhjJURk0ZE/Ap4WdJlKbQMeB7YDnSmWCfwRNreDnRImirpYmAhsDt1ZR2XtDSNV9w4pEzpWNcCT6dxjyeB5ZJmSpoJLE8xM7O61czrcWWdPfUF4O/SzKmfA/+WYsLZJmkNcAi4DiAi9kvaRjGxDAI3p5lTADdxasrtjvSA4iD7Q5IOUmxhdKRj9Um6E9iT9rsjIvpGWVczs3E1EWZ3ZUoaEfEsxWmvQy07w/4bgY0V4nsp3usxNH6ClHQqvLYF2JLlPM3M8lTvS+GMBS8jYmY2RibCUjheRsTMbAw1+1I4ThpmZmOo2ZfCcfdUk2nmqX5mlj8njSbTzFP9zCx/7p5qEhNhqp+Z5c8tjTKN3LXjhdzMrBacNMo0ctfORJjqZzZeGvkDY625e4rm6dpp9ql+ZuOl/AOjv9RpeE4aNM9dnM0+1c9srDXLB8ZacvcU7toxm6iqHQt0N5aTxjtKXTuPf/5jXH/VRbzaP5D3KZnZOKv2A2Mjj3uOFXdPJe7aMZuYsowFuhvrFCcNM5vQsnxgbJZxz7Hg7ikzsxF43PMUtzTMzDLwlPYiJw0zsww87lnk7ikzM8vMScMalufMm9Wek4Y1LM+ZN6s9j2lYw/GcebP8uKVhDcfLwJvlx0nDGo7nzJvlJ1PSkPSSpOckPStpb4rNktQlqSf9nFm2/22SDko6IGlFWXxxOs5BSZskKcWnSno0xbslLSgr05l+R4+kzrGquDU2rxVmlo9qxjSujojXyp6vA3ZGxF2S1qXnt0paBHQAlwNzgackXRoRbwP3AGuB7wHfAVYCO4A1wLGIuERSB3A38BlJs4D1QBsQwD5J2yPi2FnU2ZqA58yb5eNsuqdWAVvT9lZgdVn8kYgYiIgXgYPAEklzgBkRsSsiAnhwSJnSsR4DlqVWyAqgKyL6UqLoophozMwsB1lbGgF8V1IA90bEZqA1Io4ARMQRSbPTvvMotiRKDqfYW2l7aLxU5uV0rEFJbwDnl8crlHmHpLUUWzC0trZSKBROq0B/f3/FeCNxHeqD61AfXId8ZE0aH4uIV1Ji6JL0k2H2VYVYDBMfbZlTgWIS2wzQ1tYW7e3tpxUqFApUijcS16E+uA71wXXIR6buqYh4Jf3sBR4HlgBHU5cT6Wdv2v0wcEFZ8fnAKyk+v0L8XWUktQDnAX3DHMvMzHIwYtKQdK6k6aVtYDnwY2A7UJrN1Ak8kba3Ax1pRtTFwEJgd+rKOi5paRqvuHFImdKxrgWeTuMeTwLLJc1Ms7OWp5iZmeUgS/dUK/B4mh3bAnwzIv5R0h5gm6Q1wCHgOoCI2C9pG/A8MAjcnGZOAdwEPACcQ3HW1I4Uvw94SNJBii2MjnSsPkl3AnvSfndERN9Z1NfMzM7CiEkjIn4OfLhC/HVg2RnKbAQ2VojvBU6bHxkRJ0hJp8JrW4AtI52nmZmNP98RbmZmmTlpmJlZZk4aZmaWmZOGmZll5qRhZmaZOWmYmVlmThpmZpaZk4aZmWXmpGFmZpk5aZiZWWZOGmZmlpmThpmZZeakYWZmmTlpmJlZZk4aZmaWmZOGmZll5qRhZmaZOWmYmVlmThpmZpaZk4aZmWXmpGFmZpk5aZiZWWaZk4akyZJ+IOkf0vNZkrok9aSfM8v2vU3SQUkHJK0oiy+W9Fx6bZMkpfhUSY+meLekBWVlOtPv6JHUORaVNjOz0ammpfFF4IWy5+uAnRGxENiZniNpEdABXA6sBL4haXIqcw+wFliYHitTfA1wLCIuAb4K3J2ONQtYD1wFLAHWlycnMzOrrUxJQ9J84FPA35aFVwFb0/ZWYHVZ/JGIGIiIF4GDwBJJc4AZEbErIgJ4cEiZ0rEeA5alVsgKoCsi+iLiGNDFqURjZmY1lrWl8TfALcDJslhrRBwBSD9np/g84OWy/Q6n2Ly0PTT+rjIRMQi8AZw/zLHMzCwHLSPtIOlPgN6I2CepPcMxVSEWw8RHW6b8HNdS7PaitbWVQqFwWqH+/v6K8UbiOtQH16E+uA75GDFpAB8DrpH0x8A0YIakh4GjkuZExJHU9dSb9j8MXFBWfj7wSorPrxAvL3NYUgtwHtCX4u1DyhSGnmBEbAY2A7S1tUV7e/vQXSgUClSKNxLXoT64DvXBdcjHiN1TEXFbRMyPiAUUB7ifjog/B7YDpdlMncATaXs70JFmRF1MccB7d+rCOi5paRqvuHFImdKxrk2/I4AngeWSZqYB8OUpZmZmOcjS0jiTu4BtktYAh4DrACJiv6RtwPPAIHBzRLydytwEPACcA+xID4D7gIckHaTYwuhIx+qTdCewJ+13R0T0ncU5m5nZWagqaUREgdQ9FBGvA8vOsN9GYGOF+F7gigrxE6SkU+G1LcCWas7TzMzGh+8INzOrY71vnuDT9+6i9/iJvE8FcNIwM6trm3b2sOelPjY91ZP3qQBnN6ZhZmbj5LLbdzAweOrWuIe7D/Fw9yGmtkziwIZP5nZebmmYmdWhZ265mmuunMu0KcW36WlTJrHqyrk8c+vVuZ6Xk4aZWR2aPWMa06e2MDB4kqktkxgYPMn0qS3Mnj4t1/Ny95SZWZ16rX+A66+6iM8uuZBv7j7Eq3UwGO6kYWZWp+69oe2d7Q2rT7tbIRfunjIzs8ycNMzMLDMnDTMzy8xJw8zMMnPSMDOzzJw0zMwsMycNMzPLzEnDzMwyc9IwM7PMnDTMzCwzJw0zM8vMScPMzDJz0jAzs8ycNMzMLDMnDTMzy8xJw8zMMhsxaUiaJmm3pB9K2i/pP6X4LEldknrSz5llZW6TdFDSAUkryuKLJT2XXtskSSk+VdKjKd4taUFZmc70O3okdY5l5c3MrDpZWhoDwB9GxIeBK4GVkpYC64CdEbEQ2JmeI2kR0AFcDqwEviFpcjrWPcBaYGF6rEzxNcCxiLgE+CpwdzrWLGA9cBWwBFhfnpzMzKy2RkwaUdSfnk5JjwBWAVtTfCuwOm2vAh6JiIGIeBE4CCyRNAeYERG7IiKAB4eUKR3rMWBZaoWsALoioi8ijgFdnEo0ZmZWY5nGNCRNlvQs0EvxTbwbaI2IIwDp5+y0+zzg5bLih1NsXtoeGn9XmYgYBN4Azh/mWGZmloOWLDtFxNvAlZLeBzwuabhvOFelQwwTH22ZU79QWkux24vW1lYKhcJphfr7+yvGG4nrUB9ch/rgOuQjU9IoiYhfSypQ7CI6KmlORBxJXU+9abfDwAVlxeYDr6T4/Arx8jKHJbUA5wF9Kd4+pEyhwnltBjYDtLW1RXt7+9BdKBQKVIo3EtehPrgO9cF1yEeW2VMfSC0MJJ0D/BHwE2A7UJrN1Ak8kba3Ax1pRtTFFAe8d6curOOSlqbxihuHlCkd61rg6TTu8SSwXNLMNAC+PMXMzCwHWVoac4CtaQbUJGBbRPyDpF3ANklrgEPAdQARsV/SNuB5YBC4OXVvAdwEPACcA+xID4D7gIckHaTYwuhIx+qTdCewJ+13R0T0nU2Fzcxs9EZMGhHxI+AjFeKvA8vOUGYjsLFCfC9w2nhIRJwgJZ0Kr20Btox0nmZmNv58R7iZmWXmpGFmZpk5aZiZWWZOGmZmlpmThpmZZeakYWZmmTlpmJlZZk4aZmaWmZOGmZll5qRhZmaZOWmYmVlmThpmZpaZk4aZmWXmpGFmZpk5aZiZWWZOGmZmlpmThpmZZeakYWaWg943T/CV7t/Se/xE3qdSFScNM7McbNrZQ8+xk2x6qifvU6nKiN8RbmZmY+ey23cwMHjynecPdx/i4e5DTG2ZxIENn8zxzLJxS8PMrIaeueVqrrlyLtOmFN9+p02ZxKor5/LMrVfnfGbZOGmYmdXQ7BnTmD61hYHBk0yZBAODJ5k+tYXZ06flfWqZuHvKzKzGXusf4PqrLuLSSUf56clWXm2gwfARWxqSLpD0vyS9IGm/pC+m+CxJXZJ60s+ZZWVuk3RQ0gFJK8riiyU9l17bJEkpPlXSoyneLWlBWZnO9Dt6JHWOZeXNzPJw7w1tbFh9BRfOmMyG1Vdw7w1teZ9SZlm6pwaB/xARHwSWAjdLWgSsA3ZGxEJgZ3pOeq0DuBxYCXxD0uR0rHuAtcDC9FiZ4muAYxFxCfBV4O50rFnAeuAqYAmwvjw5mZlZbY2YNCLiSER8P20fB14A5gGrgK1pt63A6rS9CngkIgYi4kXgILBE0hxgRkTsiogAHhxSpnSsx4BlqRWyAuiKiL6IOAZ0cSrRmJlZjVU1EJ66jT4CdAOtEXEEiokFmJ12mwe8XFbscIrNS9tD4+8qExGDwBvA+cMcy8zMcpB5IFzS7wH/A/hSRLyZhiMq7lohFsPER1um/NzWUuz2orW1lUKhcFqh/v7+ivFG4jrUB9ehPrgO+ciUNCRNoZgw/i4i/j6Fj0qaExFHUtdTb4ofBi4oKz4feCXF51eIl5c5LKkFOA/oS/H2IWUKQ88vIjYDmwHa2tqivb196C4UCgUqxRuJ61AfXIf64DrkI8vsKQH3AS9ExH8te2k7UJrN1Ak8URbvSDOiLqY44L07dWEdl7Q0HfPGIWVKx7oWeDqNezwJLJc0Mw2AL08xMzPLgYrvzcPsIH0ceAZ4Dijd+/7XFMc1tgEXAoeA6yKiL5X5MvA5ijOvvhQRO1K8DXgAOAfYAXwhIkLSNOAhiuMlfUBHRPw8lflc+n0AGyPi/hHO91XgFxVeej/w2rCVrX+uQ31wHeqD6zC2LoqID4y004hJo1lI2hsRjTMZugLXoT64DvXBdciHlxExM7PMnDTMzCyziZQ0Nud9AmPAdagPrkN9cB1yMGHGNMzM7OxNpJaGmZmdpYZNGpK2SOqV9OOy2Icl7Uor6f5PSTNS/D2S7k/xH0pqLytTceXdBqtDIa0o/Gx6zK7w68arDuO+CnKD1SGXa1FtHSSdn/bvl/T1IcdqiOswQh0a5Tp8QtK+9O+9T9Iflh0rt/emYUVEQz6APwA+Cvy4LLYH+Ndp+3PAnWn7ZuD+tD0b2AdMSs93A/+C4pIlO4BPNmAdCkBbTtdhDvDRtD0d+CmwCPjPwLoUXwfcnbYXAT8EpgIXAz8DJud5Lca4Drlci1HU4Vzg48C/A74+5FiNch2Gq0OjXIePAHPT9hXAL/O+DiM9GralERH/RPFGwHKXAf+UtruAP0vbiygu305E9AK/Bto0/Mq7424s6lCD0xxW1GYV5IaoQy3O9UyqrUNE/CYi/jfwrm//aaTrcKY65GkUdfhBRJSWU9oPTFNxNY1c35uG07BJ4wx+DFyTtq/j1BpYPwRWSWpRcWmTxem14VbezUu1dSi5PzXD/2NezViN3yrINXOWdSjJ9VpkrMOZNNJ1GEmjXYc/A34QEQPUyXWopNmSxucofknUPopNw9+l+BaK/+h7gb8B/i/FJU4yraJbY9XWAeD6iPgQ8K/S44aanjGnr4I83K4VYplXNB5PY1AHyPlaVFGHMx6iQqxer8NwGuo6SLqc4pfP/UUpVGG3vN+bgCZLGhHxk4hYHhGLgW9R7GsmIgYj4q8i4sqIWAW8D+hh+JV3czGKOhARv0w/jwPfpMZdJRpmFeT0+tmugjzuxqgOuV6LKutwJo10Hc6oka6DpPnA48CNEfGzFK6796aSpkoapRkSkiYBtwP/LT1/r6Rz0/YngMGIeD6GX3k3F9XWIXVXvT/FpwB/QrGLq1bnW4tVkBuiDnlei1HUoaIGuw5nOk7DXAdJ7wO+DdwWEf+ntHM9vje9I68R+LN9UPwUfgR4i2JWXgN8keJshZ8Cd3Hq5sUFwAGKg1JPUVzNsXScNop/UD8Dvl4q0yh1oDiDZB/wI4oDaV8jzeSpUR0+TrHZ/CPg2fT4Y4rfvLiTYmtoJzCrrMyX07/3AcpmhOR1LcaqDnlei1HW4SWKEzH609/foga8DqfVoZGuA8UPhr8p2/dZYHae12Gkh+8INzOzzJqqe8rMzMaXk4aZmWXmpGFmZpk5aZiZWWZOGmZmlpmThpmZZeakYWZmmTlpmJlZZv8f+51HisQz3y8AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.plot(style='*')\n", "plt.grid()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2020 221186\n", "2021 376290\n", "2002 516689\n", "2018 542312\n", "2017 551041\n", "1991 553090\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": 38, "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 }