{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence de la varicelle" ] }, { "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 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": 3, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import os\n", "import urllib\n", "csv_file = \"fichier_varicelle.csv\"\n", "if not os.path.exists(csv_file):\n", " urllib.request.urlretrieve(data_url, csv_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Je cherche si le fichier csv contenant les données existe en local. S'il n'existe pas, alors les données sont téléchargées à partir de l'URL et sont enregistrées en local dans un fichier csv." ] }, { "cell_type": "code", "execution_count": 7, "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
020204876225351089409513FRFrance
120204774983294370238511FRFrance
22020467375219635541639FRFrance
32020457369620165376639FRFrance
420204474391237564077410FRFrance
520204374376250562477410FRFrance
62020427400019796021639FRFrance
72020417396120995823639FRFrance
8202040720786753481315FRFrance
9202039710492371861213FRFrance
10202038722537823724315FRFrance
11202037715844052763204FRFrance
1220203679191001738102FRFrance
13202035782801694102FRFrance
14202034722723714173306FRFrance
15202033712841772391204FRFrance
16202032726506894611417FRFrance
17202031713031002506204FRFrance
1820203071385752695204FRFrance
192020297841101672102FRFrance
20202028772801515102FRFrance
2120202779861491823102FRFrance
22202026769401454102FRFrance
2320202572280597001FRFrance
2420202473880959102FRFrance
25202023755811115102FRFrance
2620202272770633001FRFrance
272020217602361168102FRFrance
282020207824201628102FRFrance
2920201973100753001FRFrance
.................................
15351991267176081130423912312042FRFrance
15361991257161691070021638281838FRFrance
15371991247161711007122271281739FRFrance
1538199123711947767116223211329FRFrance
1539199122715452995320951271737FRFrance
1540199121714903897520831261636FRFrance
15411991207190531274225364342345FRFrance
15421991197167391124622232291939FRFrance
15431991187213851388228888382551FRFrance
1544199117713462887718047241632FRFrance
15451991167148571006819646261834FRFrance
1546199115713975978118169251832FRFrance
1547199114712265768416846221430FRFrance
154819911379567604113093171123FRFrance
1549199112710864733114397191325FRFrance
15501991117155741118419964271935FRFrance
15511991107166431137221914292038FRFrance
1552199109713741878018702241533FRFrance
1553199108713289881317765231531FRFrance
1554199107712337807716597221529FRFrance
1555199106710877701314741191226FRFrance
1556199105710442654414340181125FRFrance
15571991047791345631126314820FRFrance
15581991037153871048420290271836FRFrance
15591991027162771104621508292038FRFrance
15601991017155651027120859271836FRFrance
15611990527193751329525455342345FRFrance
15621990517190801380724353342543FRFrance
1563199050711079666015498201228FRFrance
15641990497114302610205FRFrance
\n", "

1565 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202048 7 6225 3510 8940 9 5 \n", "1 202047 7 4983 2943 7023 8 5 \n", "2 202046 7 3752 1963 5541 6 3 \n", "3 202045 7 3696 2016 5376 6 3 \n", "4 202044 7 4391 2375 6407 7 4 \n", "5 202043 7 4376 2505 6247 7 4 \n", "6 202042 7 4000 1979 6021 6 3 \n", "7 202041 7 3961 2099 5823 6 3 \n", "8 202040 7 2078 675 3481 3 1 \n", "9 202039 7 1049 237 1861 2 1 \n", "10 202038 7 2253 782 3724 3 1 \n", "11 202037 7 1584 405 2763 2 0 \n", "12 202036 7 919 100 1738 1 0 \n", "13 202035 7 828 0 1694 1 0 \n", "14 202034 7 2272 371 4173 3 0 \n", "15 202033 7 1284 177 2391 2 0 \n", "16 202032 7 2650 689 4611 4 1 \n", "17 202031 7 1303 100 2506 2 0 \n", "18 202030 7 1385 75 2695 2 0 \n", "19 202029 7 841 10 1672 1 0 \n", "20 202028 7 728 0 1515 1 0 \n", "21 202027 7 986 149 1823 1 0 \n", "22 202026 7 694 0 1454 1 0 \n", "23 202025 7 228 0 597 0 0 \n", "24 202024 7 388 0 959 1 0 \n", "25 202023 7 558 1 1115 1 0 \n", "26 202022 7 277 0 633 0 0 \n", "27 202021 7 602 36 1168 1 0 \n", "28 202020 7 824 20 1628 1 0 \n", "29 202019 7 310 0 753 0 0 \n", "... ... ... ... ... ... ... ... \n", "1535 199126 7 17608 11304 23912 31 20 \n", "1536 199125 7 16169 10700 21638 28 18 \n", "1537 199124 7 16171 10071 22271 28 17 \n", "1538 199123 7 11947 7671 16223 21 13 \n", "1539 199122 7 15452 9953 20951 27 17 \n", "1540 199121 7 14903 8975 20831 26 16 \n", "1541 199120 7 19053 12742 25364 34 23 \n", "1542 199119 7 16739 11246 22232 29 19 \n", "1543 199118 7 21385 13882 28888 38 25 \n", "1544 199117 7 13462 8877 18047 24 16 \n", "1545 199116 7 14857 10068 19646 26 18 \n", "1546 199115 7 13975 9781 18169 25 18 \n", "1547 199114 7 12265 7684 16846 22 14 \n", "1548 199113 7 9567 6041 13093 17 11 \n", "1549 199112 7 10864 7331 14397 19 13 \n", "1550 199111 7 15574 11184 19964 27 19 \n", "1551 199110 7 16643 11372 21914 29 20 \n", "1552 199109 7 13741 8780 18702 24 15 \n", "1553 199108 7 13289 8813 17765 23 15 \n", "1554 199107 7 12337 8077 16597 22 15 \n", "1555 199106 7 10877 7013 14741 19 12 \n", "1556 199105 7 10442 6544 14340 18 11 \n", "1557 199104 7 7913 4563 11263 14 8 \n", "1558 199103 7 15387 10484 20290 27 18 \n", "1559 199102 7 16277 11046 21508 29 20 \n", "1560 199101 7 15565 10271 20859 27 18 \n", "1561 199052 7 19375 13295 25455 34 23 \n", "1562 199051 7 19080 13807 24353 34 25 \n", "1563 199050 7 11079 6660 15498 20 12 \n", "1564 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 13 FR France \n", "1 11 FR France \n", "2 9 FR France \n", "3 9 FR France \n", "4 10 FR France \n", "5 10 FR France \n", "6 9 FR France \n", "7 9 FR France \n", "8 5 FR France \n", "9 3 FR France \n", "10 5 FR France \n", "11 4 FR France \n", "12 2 FR France \n", "13 2 FR France \n", "14 6 FR France \n", "15 4 FR France \n", "16 7 FR France \n", "17 4 FR France \n", "18 4 FR France \n", "19 2 FR France \n", "20 2 FR France \n", "21 2 FR France \n", "22 2 FR France \n", "23 1 FR France \n", "24 2 FR France \n", "25 2 FR France \n", "26 1 FR France \n", "27 2 FR France \n", "28 2 FR France \n", "29 1 FR France \n", "... ... ... ... \n", "1535 42 FR France \n", "1536 38 FR France \n", "1537 39 FR France \n", "1538 29 FR France \n", "1539 37 FR France \n", "1540 36 FR France \n", "1541 45 FR France \n", "1542 39 FR France \n", "1543 51 FR France \n", "1544 32 FR France \n", "1545 34 FR France \n", "1546 32 FR France \n", "1547 30 FR France \n", "1548 23 FR France \n", "1549 25 FR France \n", "1550 35 FR France \n", "1551 38 FR France \n", "1552 33 FR France \n", "1553 31 FR France \n", "1554 29 FR France \n", "1555 26 FR France \n", "1556 25 FR France \n", "1557 20 FR France \n", "1558 36 FR France \n", "1559 38 FR France \n", "1560 36 FR France \n", "1561 45 FR France \n", "1562 43 FR France \n", "1563 28 FR France \n", "1564 5 FR France \n", "\n", "[1565 rows x 10 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(csv_file, skiprows=1) \n", "raw_data" ] }, { "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \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
020204876225351089409513FRFrance
120204774983294370238511FRFrance
22020467375219635541639FRFrance
32020457369620165376639FRFrance
420204474391237564077410FRFrance
520204374376250562477410FRFrance
62020427400019796021639FRFrance
72020417396120995823639FRFrance
8202040720786753481315FRFrance
9202039710492371861213FRFrance
10202038722537823724315FRFrance
11202037715844052763204FRFrance
1220203679191001738102FRFrance
13202035782801694102FRFrance
14202034722723714173306FRFrance
15202033712841772391204FRFrance
16202032726506894611417FRFrance
17202031713031002506204FRFrance
1820203071385752695204FRFrance
192020297841101672102FRFrance
20202028772801515102FRFrance
2120202779861491823102FRFrance
22202026769401454102FRFrance
2320202572280597001FRFrance
2420202473880959102FRFrance
25202023755811115102FRFrance
2620202272770633001FRFrance
272020217602361168102FRFrance
282020207824201628102FRFrance
2920201973100753001FRFrance
.................................
15351991267176081130423912312042FRFrance
15361991257161691070021638281838FRFrance
15371991247161711007122271281739FRFrance
1538199123711947767116223211329FRFrance
1539199122715452995320951271737FRFrance
1540199121714903897520831261636FRFrance
15411991207190531274225364342345FRFrance
15421991197167391124622232291939FRFrance
15431991187213851388228888382551FRFrance
1544199117713462887718047241632FRFrance
15451991167148571006819646261834FRFrance
1546199115713975978118169251832FRFrance
1547199114712265768416846221430FRFrance
154819911379567604113093171123FRFrance
1549199112710864733114397191325FRFrance
15501991117155741118419964271935FRFrance
15511991107166431137221914292038FRFrance
1552199109713741878018702241533FRFrance
1553199108713289881317765231531FRFrance
1554199107712337807716597221529FRFrance
1555199106710877701314741191226FRFrance
1556199105710442654414340181125FRFrance
15571991047791345631126314820FRFrance
15581991037153871048420290271836FRFrance
15591991027162771104621508292038FRFrance
15601991017155651027120859271836FRFrance
15611990527193751329525455342345FRFrance
15621990517190801380724353342543FRFrance
1563199050711079666015498201228FRFrance
15641990497114302610205FRFrance
\n", "

1565 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202048 7 6225 3510 8940 9 5 \n", "1 202047 7 4983 2943 7023 8 5 \n", "2 202046 7 3752 1963 5541 6 3 \n", "3 202045 7 3696 2016 5376 6 3 \n", "4 202044 7 4391 2375 6407 7 4 \n", "5 202043 7 4376 2505 6247 7 4 \n", "6 202042 7 4000 1979 6021 6 3 \n", "7 202041 7 3961 2099 5823 6 3 \n", "8 202040 7 2078 675 3481 3 1 \n", "9 202039 7 1049 237 1861 2 1 \n", "10 202038 7 2253 782 3724 3 1 \n", "11 202037 7 1584 405 2763 2 0 \n", "12 202036 7 919 100 1738 1 0 \n", "13 202035 7 828 0 1694 1 0 \n", "14 202034 7 2272 371 4173 3 0 \n", "15 202033 7 1284 177 2391 2 0 \n", "16 202032 7 2650 689 4611 4 1 \n", "17 202031 7 1303 100 2506 2 0 \n", "18 202030 7 1385 75 2695 2 0 \n", "19 202029 7 841 10 1672 1 0 \n", "20 202028 7 728 0 1515 1 0 \n", "21 202027 7 986 149 1823 1 0 \n", "22 202026 7 694 0 1454 1 0 \n", "23 202025 7 228 0 597 0 0 \n", "24 202024 7 388 0 959 1 0 \n", "25 202023 7 558 1 1115 1 0 \n", "26 202022 7 277 0 633 0 0 \n", "27 202021 7 602 36 1168 1 0 \n", "28 202020 7 824 20 1628 1 0 \n", "29 202019 7 310 0 753 0 0 \n", "... ... ... ... ... ... ... ... \n", "1535 199126 7 17608 11304 23912 31 20 \n", "1536 199125 7 16169 10700 21638 28 18 \n", "1537 199124 7 16171 10071 22271 28 17 \n", "1538 199123 7 11947 7671 16223 21 13 \n", "1539 199122 7 15452 9953 20951 27 17 \n", "1540 199121 7 14903 8975 20831 26 16 \n", "1541 199120 7 19053 12742 25364 34 23 \n", "1542 199119 7 16739 11246 22232 29 19 \n", "1543 199118 7 21385 13882 28888 38 25 \n", "1544 199117 7 13462 8877 18047 24 16 \n", "1545 199116 7 14857 10068 19646 26 18 \n", "1546 199115 7 13975 9781 18169 25 18 \n", "1547 199114 7 12265 7684 16846 22 14 \n", "1548 199113 7 9567 6041 13093 17 11 \n", "1549 199112 7 10864 7331 14397 19 13 \n", "1550 199111 7 15574 11184 19964 27 19 \n", "1551 199110 7 16643 11372 21914 29 20 \n", "1552 199109 7 13741 8780 18702 24 15 \n", "1553 199108 7 13289 8813 17765 23 15 \n", "1554 199107 7 12337 8077 16597 22 15 \n", "1555 199106 7 10877 7013 14741 19 12 \n", "1556 199105 7 10442 6544 14340 18 11 \n", "1557 199104 7 7913 4563 11263 14 8 \n", "1558 199103 7 15387 10484 20290 27 18 \n", "1559 199102 7 16277 11046 21508 29 20 \n", "1560 199101 7 15565 10271 20859 27 18 \n", "1561 199052 7 19375 13295 25455 34 23 \n", "1562 199051 7 19080 13807 24353 34 25 \n", "1563 199050 7 11079 6660 15498 20 12 \n", "1564 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 13 FR France \n", "1 11 FR France \n", "2 9 FR France \n", "3 9 FR France \n", "4 10 FR France \n", "5 10 FR France \n", "6 9 FR France \n", "7 9 FR France \n", "8 5 FR France \n", "9 3 FR France \n", "10 5 FR France \n", "11 4 FR France \n", "12 2 FR France \n", "13 2 FR France \n", "14 6 FR France \n", "15 4 FR France \n", "16 7 FR France \n", "17 4 FR France \n", "18 4 FR France \n", "19 2 FR France \n", "20 2 FR France \n", "21 2 FR France \n", "22 2 FR France \n", "23 1 FR France \n", "24 2 FR France \n", "25 2 FR France \n", "26 1 FR France \n", "27 2 FR France \n", "28 2 FR France \n", "29 1 FR France \n", "... ... ... ... \n", "1535 42 FR France \n", "1536 38 FR France \n", "1537 39 FR France \n", "1538 29 FR France \n", "1539 37 FR France \n", "1540 36 FR France \n", "1541 45 FR France \n", "1542 39 FR France \n", "1543 51 FR France \n", "1544 32 FR France \n", "1545 34 FR France \n", "1546 32 FR France \n", "1547 30 FR France \n", "1548 23 FR France \n", "1549 25 FR France \n", "1550 35 FR France \n", "1551 38 FR France \n", "1552 33 FR France \n", "1553 31 FR France \n", "1554 29 FR France \n", "1555 26 FR France \n", "1556 25 FR France \n", "1557 20 FR France \n", "1558 36 FR France \n", "1559 38 FR France \n", "1560 36 FR France \n", "1561 45 FR France \n", "1562 43 FR France \n", "1563 28 FR France \n", "1564 5 FR France \n", "\n", "[1565 rows x 10 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y a-t-il des points manquants dans ce jeux de données ? Non" ] }, { "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": 25, "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": 26, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "code", "execution_count": 27, "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": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "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": 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": [ "sorted_data['inc'][-200:].plot()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "first_septembre_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1991,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_septembre_week[:-1],\n", " first_septembre_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": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "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": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "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", "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": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "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.hist(xrot=20)" ] }, { "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 }