{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# incidence de la varicelle\n" ] }, { "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": "raw", "metadata": {}, "source": [ "Les données de l'incidence du syndrome de la varicelle 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": "markdown", "metadata": {}, "source": [ "# Extraction des données localement\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"\n", "data_file = \"Varicelle.csv\"\n", "\n", "import os\n", "import urllib.request\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant skiprows=1.\n" ] }, { "cell_type": "code", "execution_count": 3, "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
02021367356517485382528FRFrance
12021357256211074017426FRFrance
2202134714293782480204FRFrance
32021337382918305828639FRFrance
42021327410818956321639FRFrance
520213174793230172857311FRFrance
62021307719041911018911616FRFrance
7202129768004109949110614FRFrance
82021287973402173115033FRFrance
92021277902643161373614721FRFrance
102021267728441081046011616FRFrance
1120212579351654012162141018FRFrance
12202124712034893715131181323FRFrance
1320212379116642011812141018FRFrance
1420212274817275268827410FRFrance
1520212176092345887269513FRFrance
162021207748546011036911715FRFrance
17202119766544370893810713FRFrance
182021187391221105714639FRFrance
1920211774686287864947410FRFrance
2020211674780289166697410FRFrance
21202115711215762714803171222FRFrance
22202114711197799414400171222FRFrance
2320211379714628913139151020FRFrance
24202112711520841514625171222FRFrance
2520211179386667812094141018FRFrance
2620211079056645211660141018FRFrance
27202109710988793814038171222FRFrance
28202108711281836114201171321FRFrance
292021077135611031516807211626FRFrance
.................................
15761991267176081130423912312042FRFrance
15771991257161691070021638281838FRFrance
15781991247161711007122271281739FRFrance
1579199123711947767116223211329FRFrance
1580199122715452995320951271737FRFrance
1581199121714903897520831261636FRFrance
15821991207190531274225364342345FRFrance
15831991197167391124622232291939FRFrance
15841991187213851388228888382551FRFrance
1585199117713462887718047241632FRFrance
15861991167148571006819646261834FRFrance
1587199115713975978118169251832FRFrance
1588199114712265768416846221430FRFrance
158919911379567604113093171123FRFrance
1590199112710864733114397191325FRFrance
15911991117155741118419964271935FRFrance
15921991107166431137221914292038FRFrance
1593199109713741878018702241533FRFrance
1594199108713289881317765231531FRFrance
1595199107712337807716597221529FRFrance
1596199106710877701314741191226FRFrance
1597199105710442654414340181125FRFrance
15981991047791345631126314820FRFrance
15991991037153871048420290271836FRFrance
16001991027162771104621508292038FRFrance
16011991017155651027120859271836FRFrance
16021990527193751329525455342345FRFrance
16031990517190801380724353342543FRFrance
1604199050711079666015498201228FRFrance
16051990497114302610205FRFrance
\n", "

1606 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202136 7 3565 1748 5382 5 2 \n", "1 202135 7 2562 1107 4017 4 2 \n", "2 202134 7 1429 378 2480 2 0 \n", "3 202133 7 3829 1830 5828 6 3 \n", "4 202132 7 4108 1895 6321 6 3 \n", "5 202131 7 4793 2301 7285 7 3 \n", "6 202130 7 7190 4191 10189 11 6 \n", "7 202129 7 6800 4109 9491 10 6 \n", "8 202128 7 9734 0 21731 15 0 \n", "9 202127 7 9026 4316 13736 14 7 \n", "10 202126 7 7284 4108 10460 11 6 \n", "11 202125 7 9351 6540 12162 14 10 \n", "12 202124 7 12034 8937 15131 18 13 \n", "13 202123 7 9116 6420 11812 14 10 \n", "14 202122 7 4817 2752 6882 7 4 \n", "15 202121 7 6092 3458 8726 9 5 \n", "16 202120 7 7485 4601 10369 11 7 \n", "17 202119 7 6654 4370 8938 10 7 \n", "18 202118 7 3912 2110 5714 6 3 \n", "19 202117 7 4686 2878 6494 7 4 \n", "20 202116 7 4780 2891 6669 7 4 \n", "21 202115 7 11215 7627 14803 17 12 \n", "22 202114 7 11197 7994 14400 17 12 \n", "23 202113 7 9714 6289 13139 15 10 \n", "24 202112 7 11520 8415 14625 17 12 \n", "25 202111 7 9386 6678 12094 14 10 \n", "26 202110 7 9056 6452 11660 14 10 \n", "27 202109 7 10988 7938 14038 17 12 \n", "28 202108 7 11281 8361 14201 17 13 \n", "29 202107 7 13561 10315 16807 21 16 \n", "... ... ... ... ... ... ... ... \n", "1576 199126 7 17608 11304 23912 31 20 \n", "1577 199125 7 16169 10700 21638 28 18 \n", "1578 199124 7 16171 10071 22271 28 17 \n", "1579 199123 7 11947 7671 16223 21 13 \n", "1580 199122 7 15452 9953 20951 27 17 \n", "1581 199121 7 14903 8975 20831 26 16 \n", "1582 199120 7 19053 12742 25364 34 23 \n", "1583 199119 7 16739 11246 22232 29 19 \n", "1584 199118 7 21385 13882 28888 38 25 \n", "1585 199117 7 13462 8877 18047 24 16 \n", "1586 199116 7 14857 10068 19646 26 18 \n", "1587 199115 7 13975 9781 18169 25 18 \n", "1588 199114 7 12265 7684 16846 22 14 \n", "1589 199113 7 9567 6041 13093 17 11 \n", "1590 199112 7 10864 7331 14397 19 13 \n", "1591 199111 7 15574 11184 19964 27 19 \n", "1592 199110 7 16643 11372 21914 29 20 \n", "1593 199109 7 13741 8780 18702 24 15 \n", "1594 199108 7 13289 8813 17765 23 15 \n", "1595 199107 7 12337 8077 16597 22 15 \n", "1596 199106 7 10877 7013 14741 19 12 \n", "1597 199105 7 10442 6544 14340 18 11 \n", "1598 199104 7 7913 4563 11263 14 8 \n", "1599 199103 7 15387 10484 20290 27 18 \n", "1600 199102 7 16277 11046 21508 29 20 \n", "1601 199101 7 15565 10271 20859 27 18 \n", "1602 199052 7 19375 13295 25455 34 23 \n", "1603 199051 7 19080 13807 24353 34 25 \n", "1604 199050 7 11079 6660 15498 20 12 \n", "1605 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 8 FR France \n", "1 6 FR France \n", "2 4 FR France \n", "3 9 FR France \n", "4 9 FR France \n", "5 11 FR France \n", "6 16 FR France \n", "7 14 FR France \n", "8 33 FR France \n", "9 21 FR France \n", "10 16 FR France \n", "11 18 FR France \n", "12 23 FR France \n", "13 18 FR France \n", "14 10 FR France \n", "15 13 FR France \n", "16 15 FR France \n", "17 13 FR France \n", "18 9 FR France \n", "19 10 FR France \n", "20 10 FR France \n", "21 22 FR France \n", "22 22 FR France \n", "23 20 FR France \n", "24 22 FR France \n", "25 18 FR France \n", "26 18 FR France \n", "27 22 FR France \n", "28 21 FR France \n", "29 26 FR France \n", "... ... ... ... \n", "1576 42 FR France \n", "1577 38 FR France \n", "1578 39 FR France \n", "1579 29 FR France \n", "1580 37 FR France \n", "1581 36 FR France \n", "1582 45 FR France \n", "1583 39 FR France \n", "1584 51 FR France \n", "1585 32 FR France \n", "1586 34 FR France \n", "1587 32 FR France \n", "1588 30 FR France \n", "1589 23 FR France \n", "1590 25 FR France \n", "1591 35 FR France \n", "1592 38 FR France \n", "1593 33 FR France \n", "1594 31 FR France \n", "1595 29 FR France \n", "1596 26 FR France \n", "1597 25 FR France \n", "1598 20 FR France \n", "1599 36 FR France \n", "1600 38 FR France \n", "1601 36 FR France \n", "1602 45 FR France \n", "1603 43 FR France \n", "1604 28 FR France \n", "1605 5 FR France \n", "\n", "[1606 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, skiprows=1)\n", "\n", "raw_data\n" ] }, { "cell_type": "code", "execution_count": 4, "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": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]\n" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Comme pour le cas de la grippe nous utilisons cette ligne pour sortir les potentiels manques de données\n" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Comme le cas précédent nous utilisons la bibliothèque isoweek." ] }, { "cell_type": "code", "execution_count": 5, "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", "raw_data['period'] = [convert_week(yw) for yw in raw_data['week']]" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Comme précédement, 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." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "sorted_data = raw_data.set_index('period').sort_index()" ] }, { "cell_type": "raw", "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." ] }, { "cell_type": "code", "execution_count": 7, "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)\n", " " ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Tous va bien!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# lecture data\n", " " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "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": "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'][-200:].plot()\n" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Comme demander, septembre sera le début de notre année" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "first_sept_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1985,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "raw", "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.\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_sept_week[:-1],\n", " first_sept_week[1:]):\n", " one_year = sorted_data['inc'][week1:week2-1]\n", " " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "period\n", "2019-08-26/2019-09-01 922\n", "2019-09-02/2019-09-08 1277\n", "2019-09-09/2019-09-15 970\n", "2019-09-16/2019-09-22 3078\n", "2019-09-23/2019-09-29 3137\n", "2019-09-30/2019-10-06 4211\n", "2019-10-07/2019-10-13 4130\n", "2019-10-14/2019-10-20 6279\n", "2019-10-21/2019-10-27 4834\n", "2019-10-28/2019-11-03 5728\n", "2019-11-04/2019-11-10 4492\n", "2019-11-11/2019-11-17 2638\n", "2019-11-18/2019-11-24 7536\n", "2019-11-25/2019-12-01 5542\n", "2019-12-02/2019-12-08 6621\n", "2019-12-09/2019-12-15 6424\n", "2019-12-16/2019-12-22 5823\n", "2019-12-23/2019-12-29 7941\n", "2019-12-30/2020-01-05 9835\n", "2020-01-06/2020-01-12 6534\n", "2020-01-13/2020-01-19 5968\n", "2020-01-20/2020-01-26 7991\n", "2020-01-27/2020-02-02 8505\n", "2020-02-03/2020-02-09 9264\n", "2020-02-10/2020-02-16 8959\n", "2020-02-17/2020-02-23 10424\n", "2020-02-24/2020-03-01 13631\n", "2020-03-02/2020-03-08 9011\n", "2020-03-09/2020-03-15 10198\n", "2020-03-16/2020-03-22 8123\n", "2020-03-23/2020-03-29 7326\n", "2020-03-30/2020-04-05 3879\n", "2020-04-06/2020-04-12 1918\n", "2020-04-13/2020-04-19 758\n", "2020-04-20/2020-04-26 272\n", "2020-04-27/2020-05-03 849\n", "2020-05-04/2020-05-10 310\n", "2020-05-11/2020-05-17 824\n", "2020-05-18/2020-05-24 602\n", "2020-05-25/2020-05-31 277\n", "2020-06-01/2020-06-07 558\n", "2020-06-08/2020-06-14 388\n", "2020-06-15/2020-06-21 228\n", "2020-06-22/2020-06-28 694\n", "2020-06-29/2020-07-05 986\n", "2020-07-06/2020-07-12 728\n", "2020-07-13/2020-07-19 841\n", "2020-07-20/2020-07-26 1385\n", "2020-07-27/2020-08-02 1303\n", "2020-08-03/2020-08-09 2650\n", "2020-08-10/2020-08-16 1284\n", "2020-08-17/2020-08-23 2272\n", "2020-08-24/2020-08-30 828\n", "Freq: W-SUN, Name: inc, dtype: int64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "one_year\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "assert abs(len(one_year)-52) < 2\n", " " ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "yearly_incidence.append(one_year.sum())\n", " " ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ " year.append(week2.year)\n", "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "faire sortir un plot des incidences annuelles" ] }, { "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.plot(style='*')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2020 221186\n", "dtype: int64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()\n" ] }, { "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 }