diff --git a/module2/exo1/Incidence de la varicelle.ipynb b/module2/exo1/Incidence de la varicelle.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..aa56c8b3bb2bf3fc78a9275c71d36166c2ec8787 --- /dev/null +++ b/module2/exo1/Incidence de la varicelle.ipynb @@ -0,0 +1,3333 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "hideCode": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence des syndromes grippaux" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour nous protéger contre une éventuelle disparition ou modification du serveur du Réseau Sentinelles, nous faisons une copie locale de ce jeux de données que nous préservons avec notre analyse. Il est inutile et même risquée de télécharger les données à chaque exécution, car dans le cas d'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'data_url' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0murllib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0murllib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murlretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'data_url' is not defined" + ] + } + ], + "source": [ + "data_file = \"incidence-PAY-7.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": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "data_url=\"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "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
02021027775047851071512717FRFrance
1202101710678780413552161220FRFrance
2202053711978840615550181323FRFrance
3202052712012828515739181224FRFrance
4202051710564757413554161121FRFrance
5202050770634744938211715FRFrance
620204975026314569078511FRFrance
7202048766834312905410614FRFrance
820204774999296370358511FRFrance
92020467375219635541639FRFrance
102020457369620165376639FRFrance
1120204474391237564077410FRFrance
1220204374376250562477410FRFrance
132020427400019796021639FRFrance
142020417396120995823639FRFrance
15202040720786753481315FRFrance
16202039710492371861213FRFrance
17202038722537823724315FRFrance
18202037715844052763204FRFrance
1920203679191001738102FRFrance
20202035782801694102FRFrance
21202034722723714173306FRFrance
22202033712841772391204FRFrance
23202032726506894611417FRFrance
24202031713031002506204FRFrance
2520203071385752695204FRFrance
262020297841101672102FRFrance
27202028772801515102FRFrance
2820202779861491823102FRFrance
29202026769401454102FRFrance
.................................
15421991267176081130423912312042FRFrance
15431991257161691070021638281838FRFrance
15441991247161711007122271281739FRFrance
1545199123711947767116223211329FRFrance
1546199122715452995320951271737FRFrance
1547199121714903897520831261636FRFrance
15481991207190531274225364342345FRFrance
15491991197167391124622232291939FRFrance
15501991187213851388228888382551FRFrance
1551199117713462887718047241632FRFrance
15521991167148571006819646261834FRFrance
1553199115713975978118169251832FRFrance
1554199114712265768416846221430FRFrance
155519911379567604113093171123FRFrance
1556199112710864733114397191325FRFrance
15571991117155741118419964271935FRFrance
15581991107166431137221914292038FRFrance
1559199109713741878018702241533FRFrance
1560199108713289881317765231531FRFrance
1561199107712337807716597221529FRFrance
1562199106710877701314741191226FRFrance
1563199105710442654414340181125FRFrance
15641991047791345631126314820FRFrance
15651991037153871048420290271836FRFrance
15661991027162771104621508292038FRFrance
15671991017155651027120859271836FRFrance
15681990527193751329525455342345FRFrance
15691990517190801380724353342543FRFrance
1570199050711079666015498201228FRFrance
15711990497114302610205FRFrance
\n", + "

1572 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202102 7 7750 4785 10715 12 7 \n", + "1 202101 7 10678 7804 13552 16 12 \n", + "2 202053 7 11978 8406 15550 18 13 \n", + "3 202052 7 12012 8285 15739 18 12 \n", + "4 202051 7 10564 7574 13554 16 11 \n", + "5 202050 7 7063 4744 9382 11 7 \n", + "6 202049 7 5026 3145 6907 8 5 \n", + "7 202048 7 6683 4312 9054 10 6 \n", + "8 202047 7 4999 2963 7035 8 5 \n", + "9 202046 7 3752 1963 5541 6 3 \n", + "10 202045 7 3696 2016 5376 6 3 \n", + "11 202044 7 4391 2375 6407 7 4 \n", + "12 202043 7 4376 2505 6247 7 4 \n", + "13 202042 7 4000 1979 6021 6 3 \n", + "14 202041 7 3961 2099 5823 6 3 \n", + "15 202040 7 2078 675 3481 3 1 \n", + "16 202039 7 1049 237 1861 2 1 \n", + "17 202038 7 2253 782 3724 3 1 \n", + "18 202037 7 1584 405 2763 2 0 \n", + "19 202036 7 919 100 1738 1 0 \n", + "20 202035 7 828 0 1694 1 0 \n", + "21 202034 7 2272 371 4173 3 0 \n", + "22 202033 7 1284 177 2391 2 0 \n", + "23 202032 7 2650 689 4611 4 1 \n", + "24 202031 7 1303 100 2506 2 0 \n", + "25 202030 7 1385 75 2695 2 0 \n", + "26 202029 7 841 10 1672 1 0 \n", + "27 202028 7 728 0 1515 1 0 \n", + "28 202027 7 986 149 1823 1 0 \n", + "29 202026 7 694 0 1454 1 0 \n", + "... ... ... ... ... ... ... ... \n", + "1542 199126 7 17608 11304 23912 31 20 \n", + "1543 199125 7 16169 10700 21638 28 18 \n", + "1544 199124 7 16171 10071 22271 28 17 \n", + "1545 199123 7 11947 7671 16223 21 13 \n", + "1546 199122 7 15452 9953 20951 27 17 \n", + "1547 199121 7 14903 8975 20831 26 16 \n", + "1548 199120 7 19053 12742 25364 34 23 \n", + "1549 199119 7 16739 11246 22232 29 19 \n", + "1550 199118 7 21385 13882 28888 38 25 \n", + "1551 199117 7 13462 8877 18047 24 16 \n", + "1552 199116 7 14857 10068 19646 26 18 \n", + "1553 199115 7 13975 9781 18169 25 18 \n", + "1554 199114 7 12265 7684 16846 22 14 \n", + "1555 199113 7 9567 6041 13093 17 11 \n", + "1556 199112 7 10864 7331 14397 19 13 \n", + "1557 199111 7 15574 11184 19964 27 19 \n", + "1558 199110 7 16643 11372 21914 29 20 \n", + "1559 199109 7 13741 8780 18702 24 15 \n", + "1560 199108 7 13289 8813 17765 23 15 \n", + "1561 199107 7 12337 8077 16597 22 15 \n", + "1562 199106 7 10877 7013 14741 19 12 \n", + "1563 199105 7 10442 6544 14340 18 11 \n", + "1564 199104 7 7913 4563 11263 14 8 \n", + "1565 199103 7 15387 10484 20290 27 18 \n", + "1566 199102 7 16277 11046 21508 29 20 \n", + "1567 199101 7 15565 10271 20859 27 18 \n", + "1568 199052 7 19375 13295 25455 34 23 \n", + "1569 199051 7 19080 13807 24353 34 25 \n", + "1570 199050 7 11079 6660 15498 20 12 \n", + "1571 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 17 FR France \n", + "1 20 FR France \n", + "2 23 FR France \n", + "3 24 FR France \n", + "4 21 FR France \n", + "5 15 FR France \n", + "6 11 FR France \n", + "7 14 FR France \n", + "8 11 FR France \n", + "9 9 FR France \n", + "10 9 FR France \n", + "11 10 FR France \n", + "12 10 FR France \n", + "13 9 FR France \n", + "14 9 FR France \n", + "15 5 FR France \n", + "16 3 FR France \n", + "17 5 FR France \n", + "18 4 FR France \n", + "19 2 FR France \n", + "20 2 FR France \n", + "21 6 FR France \n", + "22 4 FR France \n", + "23 7 FR France \n", + "24 4 FR France \n", + "25 4 FR France \n", + "26 2 FR France \n", + "27 2 FR France \n", + "28 2 FR France \n", + "29 2 FR France \n", + "... ... ... ... \n", + "1542 42 FR France \n", + "1543 38 FR France \n", + "1544 39 FR France \n", + "1545 29 FR France \n", + "1546 37 FR France \n", + "1547 36 FR France \n", + "1548 45 FR France \n", + "1549 39 FR France \n", + "1550 51 FR France \n", + "1551 32 FR France \n", + "1552 34 FR France \n", + "1553 32 FR France \n", + "1554 30 FR France \n", + "1555 23 FR France \n", + "1556 25 FR France \n", + "1557 35 FR France \n", + "1558 38 FR France \n", + "1559 33 FR France \n", + "1560 31 FR France \n", + "1561 29 FR France \n", + "1562 26 FR France \n", + "1563 25 FR France \n", + "1564 20 FR France \n", + "1565 36 FR France \n", + "1566 38 FR France \n", + "1567 36 FR France \n", + "1568 45 FR France \n", + "1569 43 FR France \n", + "1570 28 FR France \n", + "1571 5 FR France \n", + "\n", + "[1572 rows x 10 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(data_url, skiprows=1)\n", + "raw_data" + ] + }, + { + "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", + "
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": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Aucune ligne ne sera exclue car elles contiennent toutes des données." + ] + }, + { + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \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
02021027775047851071512717FRFrance
1202101710678780413552161220FRFrance
2202053711978840615550181323FRFrance
3202052712012828515739181224FRFrance
4202051710564757413554161121FRFrance
5202050770634744938211715FRFrance
620204975026314569078511FRFrance
7202048766834312905410614FRFrance
820204774999296370358511FRFrance
92020467375219635541639FRFrance
102020457369620165376639FRFrance
1120204474391237564077410FRFrance
1220204374376250562477410FRFrance
132020427400019796021639FRFrance
142020417396120995823639FRFrance
15202040720786753481315FRFrance
16202039710492371861213FRFrance
17202038722537823724315FRFrance
18202037715844052763204FRFrance
1920203679191001738102FRFrance
20202035782801694102FRFrance
21202034722723714173306FRFrance
22202033712841772391204FRFrance
23202032726506894611417FRFrance
24202031713031002506204FRFrance
2520203071385752695204FRFrance
262020297841101672102FRFrance
27202028772801515102FRFrance
2820202779861491823102FRFrance
29202026769401454102FRFrance
.................................
15421991267176081130423912312042FRFrance
15431991257161691070021638281838FRFrance
15441991247161711007122271281739FRFrance
1545199123711947767116223211329FRFrance
1546199122715452995320951271737FRFrance
1547199121714903897520831261636FRFrance
15481991207190531274225364342345FRFrance
15491991197167391124622232291939FRFrance
15501991187213851388228888382551FRFrance
1551199117713462887718047241632FRFrance
15521991167148571006819646261834FRFrance
1553199115713975978118169251832FRFrance
1554199114712265768416846221430FRFrance
155519911379567604113093171123FRFrance
1556199112710864733114397191325FRFrance
15571991117155741118419964271935FRFrance
15581991107166431137221914292038FRFrance
1559199109713741878018702241533FRFrance
1560199108713289881317765231531FRFrance
1561199107712337807716597221529FRFrance
1562199106710877701314741191226FRFrance
1563199105710442654414340181125FRFrance
15641991047791345631126314820FRFrance
15651991037153871048420290271836FRFrance
15661991027162771104621508292038FRFrance
15671991017155651027120859271836FRFrance
15681990527193751329525455342345FRFrance
15691990517190801380724353342543FRFrance
1570199050711079666015498201228FRFrance
15711990497114302610205FRFrance
\n", + "

1572 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202102 7 7750 4785 10715 12 7 \n", + "1 202101 7 10678 7804 13552 16 12 \n", + "2 202053 7 11978 8406 15550 18 13 \n", + "3 202052 7 12012 8285 15739 18 12 \n", + "4 202051 7 10564 7574 13554 16 11 \n", + "5 202050 7 7063 4744 9382 11 7 \n", + "6 202049 7 5026 3145 6907 8 5 \n", + "7 202048 7 6683 4312 9054 10 6 \n", + "8 202047 7 4999 2963 7035 8 5 \n", + "9 202046 7 3752 1963 5541 6 3 \n", + "10 202045 7 3696 2016 5376 6 3 \n", + "11 202044 7 4391 2375 6407 7 4 \n", + "12 202043 7 4376 2505 6247 7 4 \n", + "13 202042 7 4000 1979 6021 6 3 \n", + "14 202041 7 3961 2099 5823 6 3 \n", + "15 202040 7 2078 675 3481 3 1 \n", + "16 202039 7 1049 237 1861 2 1 \n", + "17 202038 7 2253 782 3724 3 1 \n", + "18 202037 7 1584 405 2763 2 0 \n", + "19 202036 7 919 100 1738 1 0 \n", + "20 202035 7 828 0 1694 1 0 \n", + "21 202034 7 2272 371 4173 3 0 \n", + "22 202033 7 1284 177 2391 2 0 \n", + "23 202032 7 2650 689 4611 4 1 \n", + "24 202031 7 1303 100 2506 2 0 \n", + "25 202030 7 1385 75 2695 2 0 \n", + "26 202029 7 841 10 1672 1 0 \n", + "27 202028 7 728 0 1515 1 0 \n", + "28 202027 7 986 149 1823 1 0 \n", + "29 202026 7 694 0 1454 1 0 \n", + "... ... ... ... ... ... ... ... \n", + "1542 199126 7 17608 11304 23912 31 20 \n", + "1543 199125 7 16169 10700 21638 28 18 \n", + "1544 199124 7 16171 10071 22271 28 17 \n", + "1545 199123 7 11947 7671 16223 21 13 \n", + "1546 199122 7 15452 9953 20951 27 17 \n", + "1547 199121 7 14903 8975 20831 26 16 \n", + "1548 199120 7 19053 12742 25364 34 23 \n", + "1549 199119 7 16739 11246 22232 29 19 \n", + "1550 199118 7 21385 13882 28888 38 25 \n", + "1551 199117 7 13462 8877 18047 24 16 \n", + "1552 199116 7 14857 10068 19646 26 18 \n", + "1553 199115 7 13975 9781 18169 25 18 \n", + "1554 199114 7 12265 7684 16846 22 14 \n", + "1555 199113 7 9567 6041 13093 17 11 \n", + "1556 199112 7 10864 7331 14397 19 13 \n", + "1557 199111 7 15574 11184 19964 27 19 \n", + "1558 199110 7 16643 11372 21914 29 20 \n", + "1559 199109 7 13741 8780 18702 24 15 \n", + "1560 199108 7 13289 8813 17765 23 15 \n", + "1561 199107 7 12337 8077 16597 22 15 \n", + "1562 199106 7 10877 7013 14741 19 12 \n", + "1563 199105 7 10442 6544 14340 18 11 \n", + "1564 199104 7 7913 4563 11263 14 8 \n", + "1565 199103 7 15387 10484 20290 27 18 \n", + "1566 199102 7 16277 11046 21508 29 20 \n", + "1567 199101 7 15565 10271 20859 27 18 \n", + "1568 199052 7 19375 13295 25455 34 23 \n", + "1569 199051 7 19080 13807 24353 34 25 \n", + "1570 199050 7 11079 6660 15498 20 12 \n", + "1571 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 17 FR France \n", + "1 20 FR France \n", + "2 23 FR France \n", + "3 24 FR France \n", + "4 21 FR France \n", + "5 15 FR France \n", + "6 11 FR France \n", + "7 14 FR France \n", + "8 11 FR France \n", + "9 9 FR France \n", + "10 9 FR France \n", + "11 10 FR France \n", + "12 10 FR France \n", + "13 9 FR France \n", + "14 9 FR France \n", + "15 5 FR France \n", + "16 3 FR France \n", + "17 5 FR France \n", + "18 4 FR France \n", + "19 2 FR France \n", + "20 2 FR France \n", + "21 6 FR France \n", + "22 4 FR France \n", + "23 7 FR France \n", + "24 4 FR France \n", + "25 4 FR France \n", + "26 2 FR France \n", + "27 2 FR France \n", + "28 2 FR France \n", + "29 2 FR France \n", + "... ... ... ... \n", + "1542 42 FR France \n", + "1543 38 FR France \n", + "1544 39 FR France \n", + "1545 29 FR France \n", + "1546 37 FR France \n", + "1547 36 FR France \n", + "1548 45 FR France \n", + "1549 39 FR France \n", + "1550 51 FR France \n", + "1551 32 FR France \n", + "1552 34 FR France \n", + "1553 32 FR France \n", + "1554 30 FR France \n", + "1555 23 FR France \n", + "1556 25 FR France \n", + "1557 35 FR France \n", + "1558 38 FR France \n", + "1559 33 FR France \n", + "1560 31 FR France \n", + "1561 29 FR France \n", + "1562 26 FR France \n", + "1563 25 FR France \n", + "1564 20 FR France \n", + "1565 36 FR France \n", + "1566 38 FR France \n", + "1567 36 FR France \n", + "1568 45 FR France \n", + "1569 43 FR France \n", + "1570 28 FR France \n", + "1571 5 FR France \n", + "\n", + "[1572 rows x 10 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = raw_data.dropna().copy()\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "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": 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
02021027775047851071512717FRFrance
1202101710678780413552161220FRFrance
2202053711978840615550181323FRFrance
3202052712012828515739181224FRFrance
4202051710564757413554161121FRFrance
5202050770634744938211715FRFrance
620204975026314569078511FRFrance
7202048766834312905410614FRFrance
820204774999296370358511FRFrance
92020467375219635541639FRFrance
102020457369620165376639FRFrance
1120204474391237564077410FRFrance
1220204374376250562477410FRFrance
132020427400019796021639FRFrance
142020417396120995823639FRFrance
15202040720786753481315FRFrance
16202039710492371861213FRFrance
17202038722537823724315FRFrance
18202037715844052763204FRFrance
1920203679191001738102FRFrance
20202035782801694102FRFrance
21202034722723714173306FRFrance
22202033712841772391204FRFrance
23202032726506894611417FRFrance
24202031713031002506204FRFrance
2520203071385752695204FRFrance
262020297841101672102FRFrance
27202028772801515102FRFrance
2820202779861491823102FRFrance
29202026769401454102FRFrance
.................................
15421991267176081130423912312042FRFrance
15431991257161691070021638281838FRFrance
15441991247161711007122271281739FRFrance
1545199123711947767116223211329FRFrance
1546199122715452995320951271737FRFrance
1547199121714903897520831261636FRFrance
15481991207190531274225364342345FRFrance
15491991197167391124622232291939FRFrance
15501991187213851388228888382551FRFrance
1551199117713462887718047241632FRFrance
15521991167148571006819646261834FRFrance
1553199115713975978118169251832FRFrance
1554199114712265768416846221430FRFrance
155519911379567604113093171123FRFrance
1556199112710864733114397191325FRFrance
15571991117155741118419964271935FRFrance
15581991107166431137221914292038FRFrance
1559199109713741878018702241533FRFrance
1560199108713289881317765231531FRFrance
1561199107712337807716597221529FRFrance
1562199106710877701314741191226FRFrance
1563199105710442654414340181125FRFrance
15641991047791345631126314820FRFrance
15651991037153871048420290271836FRFrance
15661991027162771104621508292038FRFrance
15671991017155651027120859271836FRFrance
15681990527193751329525455342345FRFrance
15691990517190801380724353342543FRFrance
1570199050711079666015498201228FRFrance
15711990497114302610205FRFrance
\n", + "

1572 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202102 7 7750 4785 10715 12 7 \n", + "1 202101 7 10678 7804 13552 16 12 \n", + "2 202053 7 11978 8406 15550 18 13 \n", + "3 202052 7 12012 8285 15739 18 12 \n", + "4 202051 7 10564 7574 13554 16 11 \n", + "5 202050 7 7063 4744 9382 11 7 \n", + "6 202049 7 5026 3145 6907 8 5 \n", + "7 202048 7 6683 4312 9054 10 6 \n", + "8 202047 7 4999 2963 7035 8 5 \n", + "9 202046 7 3752 1963 5541 6 3 \n", + "10 202045 7 3696 2016 5376 6 3 \n", + "11 202044 7 4391 2375 6407 7 4 \n", + "12 202043 7 4376 2505 6247 7 4 \n", + "13 202042 7 4000 1979 6021 6 3 \n", + "14 202041 7 3961 2099 5823 6 3 \n", + "15 202040 7 2078 675 3481 3 1 \n", + "16 202039 7 1049 237 1861 2 1 \n", + "17 202038 7 2253 782 3724 3 1 \n", + "18 202037 7 1584 405 2763 2 0 \n", + "19 202036 7 919 100 1738 1 0 \n", + "20 202035 7 828 0 1694 1 0 \n", + "21 202034 7 2272 371 4173 3 0 \n", + "22 202033 7 1284 177 2391 2 0 \n", + "23 202032 7 2650 689 4611 4 1 \n", + "24 202031 7 1303 100 2506 2 0 \n", + "25 202030 7 1385 75 2695 2 0 \n", + "26 202029 7 841 10 1672 1 0 \n", + "27 202028 7 728 0 1515 1 0 \n", + "28 202027 7 986 149 1823 1 0 \n", + "29 202026 7 694 0 1454 1 0 \n", + "... ... ... ... ... ... ... ... \n", + "1542 199126 7 17608 11304 23912 31 20 \n", + "1543 199125 7 16169 10700 21638 28 18 \n", + "1544 199124 7 16171 10071 22271 28 17 \n", + "1545 199123 7 11947 7671 16223 21 13 \n", + "1546 199122 7 15452 9953 20951 27 17 \n", + "1547 199121 7 14903 8975 20831 26 16 \n", + "1548 199120 7 19053 12742 25364 34 23 \n", + "1549 199119 7 16739 11246 22232 29 19 \n", + "1550 199118 7 21385 13882 28888 38 25 \n", + "1551 199117 7 13462 8877 18047 24 16 \n", + "1552 199116 7 14857 10068 19646 26 18 \n", + "1553 199115 7 13975 9781 18169 25 18 \n", + "1554 199114 7 12265 7684 16846 22 14 \n", + "1555 199113 7 9567 6041 13093 17 11 \n", + "1556 199112 7 10864 7331 14397 19 13 \n", + "1557 199111 7 15574 11184 19964 27 19 \n", + "1558 199110 7 16643 11372 21914 29 20 \n", + "1559 199109 7 13741 8780 18702 24 15 \n", + "1560 199108 7 13289 8813 17765 23 15 \n", + "1561 199107 7 12337 8077 16597 22 15 \n", + "1562 199106 7 10877 7013 14741 19 12 \n", + "1563 199105 7 10442 6544 14340 18 11 \n", + "1564 199104 7 7913 4563 11263 14 8 \n", + "1565 199103 7 15387 10484 20290 27 18 \n", + "1566 199102 7 16277 11046 21508 29 20 \n", + "1567 199101 7 15565 10271 20859 27 18 \n", + "1568 199052 7 19375 13295 25455 34 23 \n", + "1569 199051 7 19080 13807 24353 34 25 \n", + "1570 199050 7 11079 6660 15498 20 12 \n", + "1571 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 17 FR France \n", + "1 20 FR France \n", + "2 23 FR France \n", + "3 24 FR France \n", + "4 21 FR France \n", + "5 15 FR France \n", + "6 11 FR France \n", + "7 14 FR France \n", + "8 11 FR France \n", + "9 9 FR France \n", + "10 9 FR France \n", + "11 10 FR France \n", + "12 10 FR France \n", + "13 9 FR France \n", + "14 9 FR France \n", + "15 5 FR France \n", + "16 3 FR France \n", + "17 5 FR France \n", + "18 4 FR France \n", + "19 2 FR France \n", + "20 2 FR France \n", + "21 6 FR France \n", + "22 4 FR France \n", + "23 7 FR France \n", + "24 4 FR France \n", + "25 4 FR France \n", + "26 2 FR France \n", + "27 2 FR France \n", + "28 2 FR France \n", + "29 2 FR France \n", + "... ... ... ... \n", + "1542 42 FR France \n", + "1543 38 FR France \n", + "1544 39 FR France \n", + "1545 29 FR France \n", + "1546 37 FR France \n", + "1547 36 FR France \n", + "1548 45 FR France \n", + "1549 39 FR France \n", + "1550 51 FR France \n", + "1551 32 FR France \n", + "1552 34 FR France \n", + "1553 32 FR France \n", + "1554 30 FR France \n", + "1555 23 FR France \n", + "1556 25 FR France \n", + "1557 35 FR France \n", + "1558 38 FR France \n", + "1559 33 FR France \n", + "1560 31 FR France \n", + "1561 29 FR France \n", + "1562 26 FR France \n", + "1563 25 FR France \n", + "1564 20 FR France \n", + "1565 36 FR France \n", + "1566 38 FR France \n", + "1567 36 FR France \n", + "1568 45 FR France \n", + "1569 43 FR France \n", + "1570 28 FR France \n", + "1571 5 FR France \n", + "\n", + "[1572 rows x 10 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "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": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "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": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "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": 25, + "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": "code", + "execution_count": 26, + "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": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "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": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2020 221186\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", + "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": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAEICAYAAACQzXX2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAEaNJREFUeJzt3XuQZHV5h/HnZRd0YWBFF4YKoEPQGImrImPwUsFZsCwV1BJNvCCBlGZNeSNmUxZJqWgSlIRgaSxIakWUEuOoYEoB46Wio+IFmVXLFQlqBBGIRGJAl6Cw8uaPc4bMrjs7Zy49fd6t51M1Nae7T/d53z7d33P616e7IzORJNWy17ALkCQtnOEtSQUZ3pJUkOEtSQUZ3pJUkOEtSQUZ3pJUkOEtSQUZ3pJU0OpB3fC6detybGxsh/Puuusu9ttvv0EtckXYQz/YQz/Yw/LbsmXL7Zl50HzzDSy8x8bGmJ6e3uG8qakpJiYmBrXIFWEP/WAP/WAPyy8ifthlPodNJKkgw1uSCjK8Jakgw1uSCjK8Jakgw1uSCjK8Jakgw1uSChrYh3Qk/bqxM6+8f3rT+u2cPuv0IN14zokrshytHPe8Jakgw1uSCjK8Jakgw1uSCjK8Jakgw1uSCjK8Jakgw1uSCjK8Jakgw1uSCjK8Jakgw1uSCjK8Jakgw1uSCjK8Jakgw1uSCjK8Jakgw1uSCjK8JamgzuEdEa+LiGsj4tsR8cGIeOAgC5Mkza1TeEfEocBrgfHMfDSwCnjRIAuTJM1tIcMmq4E1EbEa2Be4dTAlSZLmE5nZbcaIM4CzgbuBT2fmKbuYZyOwEWB0dPSYycnJHS7ftm0bIyMjS615qOyhH6r2sPWWO++fHl0Dt929Mstdf+jagdxu1fUwW9962LBhw5bMHJ9vvk7hHREHApcBLwTuAD4CXJqZl8x1nfHx8Zyent7hvKmpKSYmJuZdXp/ZQz9U7WHszCvvn960fjvnbV29Isu98ZwTB3K7VdfDbH3rISI6hXfXYZOnATdk5k8y817go8CTl1KgJGnxuob3TcATI2LfiAjgBOC6wZUlSdqdTuGdmVcDlwJfB7a219s8wLokSbvRecAtM88CzhpgLZKkjvyEpSQVZHhLUkGGtyQVZHhLUkGGtyQVZHhLUkGGtyQVZHhLUkGGtyQVZHhLUkGGtyQVZHhLUkGGtyQVZHhLUkGGtyQVZHhLUkGGtyQVZHhLUkGGtyQVZHhLUkGGtyQVZHhLUkGGtyQVZHhLUkGGtyQVZHhLUkGGtyQVZHhLUkGGtyQVZHhLUkGGtyQVZHhLUkGGtyQVZHhLUkGGtyQVZHhLUkGGtyQV1Dm8I+JBEXFpRPx7RFwXEU8aZGGSpLmtXsC87wQ+mZkviIh9gH0HVJMkaR6dwjsiDgCOA04HyMx7gHsGV5YkaXciM+efKeJxwGbgO8BjgS3AGZl5107zbQQ2AoyOjh4zOTm5w+1s27aNkZGR5al8SOyhH6r2sPWWO++fHl0Dt909xGKWQZce1h+6dmWKWaS+PZY2bNiwJTPH55uva3iPA18FnpKZV0fEO4GfZeYb57rO+Ph4Tk9P73De1NQUExMT8y6vz+yhH6r2MHbmlfdPb1q/nfO2LmTksn+69HDjOSeuUDWL07fHUkR0Cu+ub1jeDNycmVe3py8FHr/Y4iRJS9MpvDPzx8CPIuKR7Vkn0AyhSJKGYCGv2V4DfKA90uQHwB8NpiRJ0nw6h3dmfhOYdxxGkjR4fsJSkgoyvCWpIMNbkgoyvCWpIMNbkgoyvCWpIMNbkgoyvCWpIMNbkgoyvCWpIMNbkgoyvCWpIMNbkgoyvCWpIMNbkgoyvCWpIMNbkgoyvCWpIMNbkgoyvCWpIMNbkgoyvCWpIMNbkgoyvCWpIMNbkgoyvCWpIMNbkgoyvCWpIMNbkgoyvCWpIMNbkgoyvCWpIMNbkgoyvCWpIMNbkgoyvCWpIMNbkgpaUHhHxKqI+EZEXDGogiRJ81vonvcZwHWDKESS1F3n8I6Iw4ATgQsHV44kqYvIzG4zRlwKvA3YH/jzzDxpF/NsBDYCjI6OHjM5ObnD5du2bWNkZGSpNQ+VPfTDUnrYesudy1zN4oyugdvuHnYVS2MPu7b+0LWLvu6GDRu2ZOb4fPOt7nJjEXES8F+ZuSUiJuaaLzM3A5sBxsfHc2Jix1mnpqbY+bxq7KEfltLD6WdeubzFLNKm9ds5b2unp2Bv2cOu3XjKxLLe3q50HTZ5CvCciLgRmASOj4hLBlaVJGm3OoV3Zv5FZh6WmWPAi4DPZuZLB1qZJGlOHuctSQUteKAnM6eAqWWvRJLUmXveklSQ4S1JBRneklSQ4S1JBRneklSQ4S1JBRneklSQ4S1JBRneklSQ4S1JBRneklSQ4S1JBRneklSQ4S1JBRneklSQ4S1JBRneklSQ4S1JBRneklSQ4S1JBRneklSQ4S1JBRneklSQ4S1JBRneklSQ4S1JBRneklSQ4S1JBRneklSQ4S1JBRneklSQ4S1JBRneklSQ4S1JBRneklSQ4S1JBRneklRQp/COiMMj4nMRcV1EXBsRZwy6MEnS3FZ3nG87sCkzvx4R+wNbIuIzmfmdAdYmSZpDpz3vzPzPzPx6O/1z4Drg0EEWJkmaW2Tmwq4QMQZ8AXh0Zv5sp8s2AhsBRkdHj5mcnNzhutu2bWNkZGQJ5Q6fPfTDUnrYesudy1zN4oyugdvuHnYVS2MPu7b+0LWLvu6GDRu2ZOb4fPMtKLwjYgT4PHB2Zn50d/OOj4/n9PT0DudNTU0xMTHReXl9ZA/9sJQexs68cnmLWaRN67dz3tauI5f9ZA+7duM5Jy76uhHRKbw7H20SEXsDlwEfmC+4JUmD1fVokwDeA1yXmW8fbEmSpPl03fN+CnAqcHxEfLP9e9YA65Ik7UangZ7MvAqIAdciSerIT1hKUkGGtyQVZHhLUkGGtyQVZHhLUkGGtyQVZHhLUkGGtyQVZHhLUkGGtyQVZHhLUkGGtyQVZHhLUkGGtyQVZHhLUkGGtyQVZHhLUkG9/NnnYf6691J+9VkLs5T1vGn9dk7vya/AS8PgnrckFWR4S1JBhrckFWR4S1JBhrckFWR4S1JBhrckFWR4S1JBhrckFWR4S1JBhrckFWR4S1JBhrckFWR4S1JBhrckFWR4S1JBhrckFWR4S1JBhrckFdQ5vCPiGRFxfUR8PyLOHGRRkqTd6xTeEbEKOB94JnAU8OKIOGqQhUmS5tZ1z/t3ge9n5g8y8x5gEnju4MqSJO1OZOb8M0W8AHhGZr68PX0qcGxmvnqn+TYCG9uTjwSu3+mm1gG3L7XoIbOHfrCHfrCH5fewzDxovplWd7yx2MV5v5b6mbkZ2DznjURMZ+Z4x2X2kj30gz30gz0MT9dhk5uBw2edPgy4dfnLkSR10TW8rwEeERFHRMQ+wIuAjw+uLEnS7nQaNsnM7RHxauBTwCrgosy8dhHLm3NIpRB76Ad76Ad7GJJOb1hKkvrFT1hKUkGGtyQVZHhLUkG9D++IOD4ijhh2HUtRvYfq9c/YE/qwh37oQw+9fcOy/e6USeAO4D7grMz8/HCrWpjqPVSvf8ae0Ic99EOfeujNnndEHBYRB8w664XAZZl5HM2d9eKIeNJwquumeg8LqT8idvWp216ovh7AHvqizz0MPbwj4lER8QngKuCvImLmC69+AezbTn+Y5rsHntjH0Kjew2Lqzx6+ZKu+HsAe+qJCD0MJ74jYb9bJxwE3Z+YY8Fng79vzfwr8MiL2z8yfAt8FRoGxFSx1TtV72E39n+PX6x9p6/8ePal/RvX1APaAPSzKioV3RBwYEe+LiGuAcyLioHZr9RjgS+3e3MeBOyLiRJo7ZX9gfXsT36P59q97VqrmnVXvYTf1r59V/8d2Uf9j2pv47jDrn1F9PYA9tDdhD0uwknvexwHbgWfRfEvhXwIHtDUcMutl+MXAS4CvAT+n+QEIMvMrwPHAz1aw5p1V72Gu+ldRo/4Z1dcD2IM9LFVmLusfTRC8Avg8zXd7r2vP/zDw2nb6COCc9vIn0IwrrWovGwF+0t7OocB1wKuB9wIXAPsud817Wg/V69+T+rAHexjU3yD2vE8CngO8BXgS8Hft+Z8BntxO/wj4IvDMzLyGZou3ASAztwFXA0/IzFuAU2nGlH4MvCEz/3cANe9pPVSvf0/qwx7sYSC6/hjDDmaONoiIJ9C8lPgicGVm/hL4LeAHmfnZiLgBODcing5sAZ4XEesy8/aI+B5wV0Q8FHgX8NKIOJjmu8L/m+blCZk5DUwvsc89rofq9e9pfbS92IPP6RWz4D3vWXfQccBFNIfOPA14WzvLfcB3I2JNZt5A8/LiMTTjRLfSHCcJ8CualyB7AZfRfC3jKcAxwObMvG/RXc3fw6q2h6fSvOwp1UNbV0bEBEXXQdvHAdX7iIiHtP+fDLyvaA8HR8RDImKcZmy3Yg/7VH5OL0qXsRWa4xr/BPhn4I+BvYE/BV7VXn4g8C3gaJo74RxgrL3sJJo7YF07vRVYSzPI/wlgn1nL2WtQ40PAfsDLaVbIJpo3Jar1sD9wJc33qQO8rlL9sx5LpwH/RvNhh1J98P+fSv59mjHRKZrfa632WNoPOJ1m2OBO4MSCPewNvBK4HPhH4OHAGZV6WMrfvHveEXEIcAUwAbyfZjD/ZJpxo+0Amfk/wMeA17YPhoOBR7U38QXgqcA9mXkF8B7gUuB8mq38vTPLysHtqe5HExbHA+8Gng48n+ZNifsq9NBaAzwAODIi1gFH0uwllKg/IvYGrgVeAJybmc9vLzp61vJ73UdmZkSsBf4AeEdmTmTm9TR7ZiV6iIiH0wwpnAC8AbgFuInmDbsyjyfgVTTP6XfQ/Kbuye3pXxXqYfE6bN3W0PxS/Mzp02kG+08Dvjbr/N8Abm2nX0Xz0dED2+tfDjx01rzrVnorBTxo1vTraVboKcV6OA04F3gj8DLg2cA1Vepvl/tR4JSdznshcHWVPmj29v66nZ7ZEz+5Sg80Af2AWacvotmgPrdKD+0yLwf+sJ1+GfCa9rFU5jm9lL8uY96/AL7WHrgOzcuQYzPzYpo9wEMAMvNW4NqIODYzzwe+D3yI5seLr8rMm2ZuMDNv77DcZZWZd0TEARHxPpphk3U0K+/IiBjtcw+z7vu9gP+g2Xs9PjMvB47oe/07uQg4KyLOi4ipiHgT8FWa30g9uK2t733cDvxeRJwCbImIi2n29n67fUXU6x4y81fZvIk3M2YfNF+0dDnNeijxnAb+FTgtIj4CvBl4LPBtmh4Oauvqew+Lt4Ct3MwexsXAGe30+4G/bacfDFxIuyWjGY96NPDAYW+hdurjlTQvDzfTjIF/GXgTzQO41z0AH6HZa1pLc7zqG2gerG8stg4+RfNhiMNpnkhnAF8ptB4e0db7DzR7cS8B3k5zHPDraTayve5hp36+AZzcTl9S6TndPocvotkZezNwFnB9+/gqtR4W+tf5aJPMzIg4DDiEZotHe2cREVfQjCmvynZLlpn3Zua3M/MXXZexEjLzgmzebb6AZpzyn2heQn2cHvcQESM0e3zvBj5J8075scCLgQMj4nJ6XP9OnpeZb83MHwFvpTmM610UWA+tm2g+Dr06m3HVK9rzrqB5Q7ZCD0TEzPP/Kpo3+wDObi4q83g6CpjKZq/5vTQ7Nx+kzmNp8Ra4lXs2zRe07E2zxXsGzZ31EuDxw94SLbCXw4FPAw9pT78UOHrYde2m3gfSvOq5kOaNpgng07Mu73X9u+nrYTRvJj24Uh80H4/e0k4/iOYVxNGVemhr3Zdmw3nyTuef0vce2uz5M+DC9vQ6mh3LI6qth8X8LejHGCLiS8BvAjfSHBv5lsz8VucbGLL2KIETaDY2R9EMnZyfmffu9oo91H6I4GRgMjN/POx6FiIiHkCz4T8V+B2aw7wuyMztQy1sgSLibJrnw9E0OwJnZbMnXkpEXA+8KTM/NPM5jmHX1FVEHEnzPL6HZl38C/A32Xwico/WObzbw7zOAm4ALsn2DY9KImI1zfcb/JKmh3IvnSJiFXBfpSfYrkTEK2gO03x/xfUwIyIeCfywYg+zPnD3OJo3wbdXfFy1OzKPAL6cmXcPu56V0tufQZMkzW3ov6QjSVo4w1uSCjK8Jakgw1uSCjK8Jakgw1uSCjK8Jamg/wNI6CKPSu0/QAAAAABJRU5ErkJggg==\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": 4 +}