{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "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": "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \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
02022017161871190020474241830FRFrance
1202152713203956616840201426FRFrance
2202151713326962917023201426FRFrance
32021507141281031217944211527FRFrance
42021497136741036916979211626FRFrance
5202148711549850314595171222FRFrance
6202147711419837614462171222FRFrance
72021467821657241070812816FRFrance
820214578965646811462141018FRFrance
92021447873656361183613818FRFrance
102021437814551641112612717FRFrance
112021427944360371284914919FRFrance
122021417402122395803639FRFrance
1320214074441245464287410FRFrance
142021397229110563526315FRFrance
1520213874325226763837410FRFrance
16202137719647543174315FRFrance
172021367344117305152528FRFrance
182021357256211074017426FRFrance
19202134714293782480204FRFrance
202021337382918305828639FRFrance
212021327410818956321639FRFrance
2220213174793230172857311FRFrance
232021307719041911018911616FRFrance
24202129768004109949110614FRFrance
252021287973402173115033FRFrance
262021277902643161373614721FRFrance
272021267728441081046011616FRFrance
2820212579351654012162141018FRFrance
29202124712034893715131181323FRFrance
.................................
15931991267176081130423912312042FRFrance
15941991257161691070021638281838FRFrance
15951991247161711007122271281739FRFrance
1596199123711947767116223211329FRFrance
1597199122715452995320951271737FRFrance
1598199121714903897520831261636FRFrance
15991991207190531274225364342345FRFrance
16001991197167391124622232291939FRFrance
16011991187213851388228888382551FRFrance
1602199117713462887718047241632FRFrance
16031991167148571006819646261834FRFrance
1604199115713975978118169251832FRFrance
1605199114712265768416846221430FRFrance
160619911379567604113093171123FRFrance
1607199112710864733114397191325FRFrance
16081991117155741118419964271935FRFrance
16091991107166431137221914292038FRFrance
1610199109713741878018702241533FRFrance
1611199108713289881317765231531FRFrance
1612199107712337807716597221529FRFrance
1613199106710877701314741191226FRFrance
1614199105710442654414340181125FRFrance
16151991047791345631126314820FRFrance
16161991037153871048420290271836FRFrance
16171991027162771104621508292038FRFrance
16181991017155651027120859271836FRFrance
16191990527193751329525455342345FRFrance
16201990517190801380724353342543FRFrance
1621199050711079666015498201228FRFrance
16221990497114302610205FRFrance
\n", "

1623 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202201 7 16187 11900 20474 24 18 \n", "1 202152 7 13203 9566 16840 20 14 \n", "2 202151 7 13326 9629 17023 20 14 \n", "3 202150 7 14128 10312 17944 21 15 \n", "4 202149 7 13674 10369 16979 21 16 \n", "5 202148 7 11549 8503 14595 17 12 \n", "6 202147 7 11419 8376 14462 17 12 \n", "7 202146 7 8216 5724 10708 12 8 \n", "8 202145 7 8965 6468 11462 14 10 \n", "9 202144 7 8736 5636 11836 13 8 \n", "10 202143 7 8145 5164 11126 12 7 \n", "11 202142 7 9443 6037 12849 14 9 \n", "12 202141 7 4021 2239 5803 6 3 \n", "13 202140 7 4441 2454 6428 7 4 \n", "14 202139 7 2291 1056 3526 3 1 \n", "15 202138 7 4325 2267 6383 7 4 \n", "16 202137 7 1964 754 3174 3 1 \n", "17 202136 7 3441 1730 5152 5 2 \n", "18 202135 7 2562 1107 4017 4 2 \n", "19 202134 7 1429 378 2480 2 0 \n", "20 202133 7 3829 1830 5828 6 3 \n", "21 202132 7 4108 1895 6321 6 3 \n", "22 202131 7 4793 2301 7285 7 3 \n", "23 202130 7 7190 4191 10189 11 6 \n", "24 202129 7 6800 4109 9491 10 6 \n", "25 202128 7 9734 0 21731 15 0 \n", "26 202127 7 9026 4316 13736 14 7 \n", "27 202126 7 7284 4108 10460 11 6 \n", "28 202125 7 9351 6540 12162 14 10 \n", "29 202124 7 12034 8937 15131 18 13 \n", "... ... ... ... ... ... ... ... \n", "1593 199126 7 17608 11304 23912 31 20 \n", "1594 199125 7 16169 10700 21638 28 18 \n", "1595 199124 7 16171 10071 22271 28 17 \n", "1596 199123 7 11947 7671 16223 21 13 \n", "1597 199122 7 15452 9953 20951 27 17 \n", "1598 199121 7 14903 8975 20831 26 16 \n", "1599 199120 7 19053 12742 25364 34 23 \n", "1600 199119 7 16739 11246 22232 29 19 \n", "1601 199118 7 21385 13882 28888 38 25 \n", "1602 199117 7 13462 8877 18047 24 16 \n", "1603 199116 7 14857 10068 19646 26 18 \n", "1604 199115 7 13975 9781 18169 25 18 \n", "1605 199114 7 12265 7684 16846 22 14 \n", "1606 199113 7 9567 6041 13093 17 11 \n", "1607 199112 7 10864 7331 14397 19 13 \n", "1608 199111 7 15574 11184 19964 27 19 \n", "1609 199110 7 16643 11372 21914 29 20 \n", "1610 199109 7 13741 8780 18702 24 15 \n", "1611 199108 7 13289 8813 17765 23 15 \n", "1612 199107 7 12337 8077 16597 22 15 \n", "1613 199106 7 10877 7013 14741 19 12 \n", "1614 199105 7 10442 6544 14340 18 11 \n", "1615 199104 7 7913 4563 11263 14 8 \n", "1616 199103 7 15387 10484 20290 27 18 \n", "1617 199102 7 16277 11046 21508 29 20 \n", "1618 199101 7 15565 10271 20859 27 18 \n", "1619 199052 7 19375 13295 25455 34 23 \n", "1620 199051 7 19080 13807 24353 34 25 \n", "1621 199050 7 11079 6660 15498 20 12 \n", "1622 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 30 FR France \n", "1 26 FR France \n", "2 26 FR France \n", "3 27 FR France \n", "4 26 FR France \n", "5 22 FR France \n", "6 22 FR France \n", "7 16 FR France \n", "8 18 FR France \n", "9 18 FR France \n", "10 17 FR France \n", "11 19 FR France \n", "12 9 FR France \n", "13 10 FR France \n", "14 5 FR France \n", "15 10 FR France \n", "16 5 FR France \n", "17 8 FR France \n", "18 6 FR France \n", "19 4 FR France \n", "20 9 FR France \n", "21 9 FR France \n", "22 11 FR France \n", "23 16 FR France \n", "24 14 FR France \n", "25 33 FR France \n", "26 21 FR France \n", "27 16 FR France \n", "28 18 FR France \n", "29 23 FR France \n", "... ... ... ... \n", "1593 42 FR France \n", "1594 38 FR France \n", "1595 39 FR France \n", "1596 29 FR France \n", "1597 37 FR France \n", "1598 36 FR France \n", "1599 45 FR France \n", "1600 39 FR France \n", "1601 51 FR France \n", "1602 32 FR France \n", "1603 34 FR France \n", "1604 32 FR France \n", "1605 30 FR France \n", "1606 23 FR France \n", "1607 25 FR France \n", "1608 35 FR France \n", "1609 38 FR France \n", "1610 33 FR France \n", "1611 31 FR France \n", "1612 29 FR France \n", "1613 26 FR France \n", "1614 25 FR France \n", "1615 20 FR France \n", "1616 36 FR France \n", "1617 38 FR France \n", "1618 36 FR France \n", "1619 45 FR France \n", "1620 43 FR France \n", "1621 28 FR France \n", "1622 5 FR France \n", "\n", "[1623 rows x 10 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, skiprows=1)\n", "raw_data" ] }, { "cell_type": "code", "execution_count": 5, "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
02022017161871190020474241830FRFrance
1202152713203956616840201426FRFrance
2202151713326962917023201426FRFrance
32021507141281031217944211527FRFrance
42021497136741036916979211626FRFrance
5202148711549850314595171222FRFrance
6202147711419837614462171222FRFrance
72021467821657241070812816FRFrance
820214578965646811462141018FRFrance
92021447873656361183613818FRFrance
102021437814551641112612717FRFrance
112021427944360371284914919FRFrance
122021417402122395803639FRFrance
1320214074441245464287410FRFrance
142021397229110563526315FRFrance
1520213874325226763837410FRFrance
16202137719647543174315FRFrance
172021367344117305152528FRFrance
182021357256211074017426FRFrance
19202134714293782480204FRFrance
202021337382918305828639FRFrance
212021327410818956321639FRFrance
2220213174793230172857311FRFrance
232021307719041911018911616FRFrance
24202129768004109949110614FRFrance
252021287973402173115033FRFrance
262021277902643161373614721FRFrance
272021267728441081046011616FRFrance
2820212579351654012162141018FRFrance
29202124712034893715131181323FRFrance
.................................
15931991267176081130423912312042FRFrance
15941991257161691070021638281838FRFrance
15951991247161711007122271281739FRFrance
1596199123711947767116223211329FRFrance
1597199122715452995320951271737FRFrance
1598199121714903897520831261636FRFrance
15991991207190531274225364342345FRFrance
16001991197167391124622232291939FRFrance
16011991187213851388228888382551FRFrance
1602199117713462887718047241632FRFrance
16031991167148571006819646261834FRFrance
1604199115713975978118169251832FRFrance
1605199114712265768416846221430FRFrance
160619911379567604113093171123FRFrance
1607199112710864733114397191325FRFrance
16081991117155741118419964271935FRFrance
16091991107166431137221914292038FRFrance
1610199109713741878018702241533FRFrance
1611199108713289881317765231531FRFrance
1612199107712337807716597221529FRFrance
1613199106710877701314741191226FRFrance
1614199105710442654414340181125FRFrance
16151991047791345631126314820FRFrance
16161991037153871048420290271836FRFrance
16171991027162771104621508292038FRFrance
16181991017155651027120859271836FRFrance
16191990527193751329525455342345FRFrance
16201990517190801380724353342543FRFrance
1621199050711079666015498201228FRFrance
16221990497114302610205FRFrance
\n", "

1623 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202201 7 16187 11900 20474 24 18 \n", "1 202152 7 13203 9566 16840 20 14 \n", "2 202151 7 13326 9629 17023 20 14 \n", "3 202150 7 14128 10312 17944 21 15 \n", "4 202149 7 13674 10369 16979 21 16 \n", "5 202148 7 11549 8503 14595 17 12 \n", "6 202147 7 11419 8376 14462 17 12 \n", "7 202146 7 8216 5724 10708 12 8 \n", "8 202145 7 8965 6468 11462 14 10 \n", "9 202144 7 8736 5636 11836 13 8 \n", "10 202143 7 8145 5164 11126 12 7 \n", "11 202142 7 9443 6037 12849 14 9 \n", "12 202141 7 4021 2239 5803 6 3 \n", "13 202140 7 4441 2454 6428 7 4 \n", "14 202139 7 2291 1056 3526 3 1 \n", "15 202138 7 4325 2267 6383 7 4 \n", "16 202137 7 1964 754 3174 3 1 \n", "17 202136 7 3441 1730 5152 5 2 \n", "18 202135 7 2562 1107 4017 4 2 \n", "19 202134 7 1429 378 2480 2 0 \n", "20 202133 7 3829 1830 5828 6 3 \n", "21 202132 7 4108 1895 6321 6 3 \n", "22 202131 7 4793 2301 7285 7 3 \n", "23 202130 7 7190 4191 10189 11 6 \n", "24 202129 7 6800 4109 9491 10 6 \n", "25 202128 7 9734 0 21731 15 0 \n", "26 202127 7 9026 4316 13736 14 7 \n", "27 202126 7 7284 4108 10460 11 6 \n", "28 202125 7 9351 6540 12162 14 10 \n", "29 202124 7 12034 8937 15131 18 13 \n", "... ... ... ... ... ... ... ... \n", "1593 199126 7 17608 11304 23912 31 20 \n", "1594 199125 7 16169 10700 21638 28 18 \n", "1595 199124 7 16171 10071 22271 28 17 \n", "1596 199123 7 11947 7671 16223 21 13 \n", "1597 199122 7 15452 9953 20951 27 17 \n", "1598 199121 7 14903 8975 20831 26 16 \n", "1599 199120 7 19053 12742 25364 34 23 \n", "1600 199119 7 16739 11246 22232 29 19 \n", "1601 199118 7 21385 13882 28888 38 25 \n", "1602 199117 7 13462 8877 18047 24 16 \n", "1603 199116 7 14857 10068 19646 26 18 \n", "1604 199115 7 13975 9781 18169 25 18 \n", "1605 199114 7 12265 7684 16846 22 14 \n", "1606 199113 7 9567 6041 13093 17 11 \n", "1607 199112 7 10864 7331 14397 19 13 \n", "1608 199111 7 15574 11184 19964 27 19 \n", "1609 199110 7 16643 11372 21914 29 20 \n", "1610 199109 7 13741 8780 18702 24 15 \n", "1611 199108 7 13289 8813 17765 23 15 \n", "1612 199107 7 12337 8077 16597 22 15 \n", "1613 199106 7 10877 7013 14741 19 12 \n", "1614 199105 7 10442 6544 14340 18 11 \n", "1615 199104 7 7913 4563 11263 14 8 \n", "1616 199103 7 15387 10484 20290 27 18 \n", "1617 199102 7 16277 11046 21508 29 20 \n", "1618 199101 7 15565 10271 20859 27 18 \n", "1619 199052 7 19375 13295 25455 34 23 \n", "1620 199051 7 19080 13807 24353 34 25 \n", "1621 199050 7 11079 6660 15498 20 12 \n", "1622 199049 7 1143 0 2610 2 0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 30 FR France \n", "1 26 FR France \n", "2 26 FR France \n", "3 27 FR France \n", "4 26 FR France \n", "5 22 FR France \n", "6 22 FR France \n", "7 16 FR France \n", "8 18 FR France \n", "9 18 FR France \n", "10 17 FR France \n", "11 19 FR France \n", "12 9 FR France \n", "13 10 FR France \n", "14 5 FR France \n", "15 10 FR France \n", "16 5 FR France \n", "17 8 FR France \n", "18 6 FR France \n", "19 4 FR France \n", "20 9 FR France \n", "21 9 FR France \n", "22 11 FR France \n", "23 16 FR France \n", "24 14 FR France \n", "25 33 FR France \n", "26 21 FR France \n", "27 16 FR France \n", "28 18 FR France \n", "29 23 FR France \n", "... ... ... ... \n", "1593 42 FR France \n", "1594 38 FR France \n", "1595 39 FR France \n", "1596 29 FR France \n", "1597 37 FR France \n", "1598 36 FR France \n", "1599 45 FR France \n", "1600 39 FR France \n", "1601 51 FR France \n", "1602 32 FR France \n", "1603 34 FR France \n", "1604 32 FR France \n", "1605 30 FR France \n", "1606 23 FR France \n", "1607 25 FR France \n", "1608 35 FR France \n", "1609 38 FR France \n", "1610 33 FR France \n", "1611 31 FR France \n", "1612 29 FR France \n", "1613 26 FR France \n", "1614 25 FR France \n", "1615 20 FR France \n", "1616 36 FR France \n", "1617 38 FR France \n", "1618 36 FR France \n", "1619 45 FR France \n", "1620 43 FR France \n", "1621 28 FR France \n", "1622 5 FR France \n", "\n", "[1623 rows x 10 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]\n", "data = raw_data.dropna().copy()\n", "data\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def convert_week(year_and_week_int):\n", " year_and_week_str = str(year_and_week_int)\n", " year = int(year_and_week_str[:4])\n", " week = int(year_and_week_str[4:])\n", " w = isoweek.Week(year, week)\n", " return pd.Period(w.day(0), 'W')\n", "\n", "data['period'] = [convert_week(yw) for yw in data['week']]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "periods = sorted_data.index\n", "for p1, p2 in zip(periods[:-1], periods[1:]):\n", " delta = p2.to_timestamp() - p1.end_time\n", " if delta > pd.Timedelta('1s'):\n", " print(p1, p2)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "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": 11, "metadata": {}, "outputs": [], "source": [ "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1990,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "code", "execution_count": 12, "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", "\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": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2020 221186\n", "2021 376290\n", "2002 516689\n", "2018 542312\n", "2017 551041\n", "1991 553090\n", "1996 564901\n", "2019 584066\n", "2015 604382\n", "2000 617597\n", "2001 619041\n", "2012 624573\n", "2005 628464\n", "2006 632833\n", "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": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }