diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..45b372b91a542ff9779b1f798a632d6be8eeee25 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,3288 @@ { - "cells": [], + "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 = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv?v=bvzjp\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02024037736245771014711715FRFrance
1202402772274927952711814FRFrance
2202401713305921417396201426FRFrance
3202352711636735415918181224FRFrance
4202351769124227959710614FRFrance
52023507879962151138313917FRFrance
62023497781753621027212816FRFrance
7202348773514749995311715FRFrance
8202347765374277879710713FRFrance
920234675223296874788511FRFrance
1020234575007267573398412FRFrance
112023447368816645712639FRFrance
122023437389116756107639FRFrance
1320234273968121267246210FRFrance
142023417335617644948537FRFrance
152023407284514104280426FRFrance
16202339717396292849315FRFrance
17202338716632743052315FRFrance
18202337711222232021213FRFrance
192023367726101442102FRFrance
202023357961961826102FRFrance
212023347116892327204FRFrance
222023337330811845432528FRFrance
232023327799611201487212222FRFrance
242023317331813985238528FRFrance
2520233075821326983739513FRFrance
26202329713558829718819201228FRFrance
27202328767004043935710614FRFrance
28202327772534599990711715FRFrance
2920232679192622312161141018FRFrance
.................................
16991991267176081130423912312042FRFrance
17001991257161691070021638281838FRFrance
17011991247161711007122271281739FRFrance
1702199123711947767116223211329FRFrance
1703199122715452995320951271737FRFrance
1704199121714903897520831261636FRFrance
17051991207190531274225364342345FRFrance
17061991197167391124622232291939FRFrance
17071991187213851388228888382551FRFrance
1708199117713462887718047241632FRFrance
17091991167148571006819646261834FRFrance
1710199115713975978118169251832FRFrance
1711199114712265768416846221430FRFrance
171219911379567604113093171123FRFrance
1713199112710864733114397191325FRFrance
17141991117155741118419964271935FRFrance
17151991107166431137221914292038FRFrance
1716199109713741878018702241533FRFrance
1717199108713289881317765231531FRFrance
1718199107712337807716597221529FRFrance
1719199106710877701314741191226FRFrance
1720199105710442654414340181125FRFrance
17211991047791345631126314820FRFrance
17221991037153871048420290271836FRFrance
17231991027162771104621508292038FRFrance
17241991017155651027120859271836FRFrance
17251990527193751329525455342345FRFrance
17261990517190801380724353342543FRFrance
1727199050711079666015498201228FRFrance
17281990497114302610205FRFrance
\n", + "

1729 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202403 7 7362 4577 10147 11 7 \n", + "1 202402 7 7227 4927 9527 11 8 \n", + "2 202401 7 13305 9214 17396 20 14 \n", + "3 202352 7 11636 7354 15918 18 12 \n", + "4 202351 7 6912 4227 9597 10 6 \n", + "5 202350 7 8799 6215 11383 13 9 \n", + "6 202349 7 7817 5362 10272 12 8 \n", + "7 202348 7 7351 4749 9953 11 7 \n", + "8 202347 7 6537 4277 8797 10 7 \n", + "9 202346 7 5223 2968 7478 8 5 \n", + "10 202345 7 5007 2675 7339 8 4 \n", + "11 202344 7 3688 1664 5712 6 3 \n", + "12 202343 7 3891 1675 6107 6 3 \n", + "13 202342 7 3968 1212 6724 6 2 \n", + "14 202341 7 3356 1764 4948 5 3 \n", + "15 202340 7 2845 1410 4280 4 2 \n", + "16 202339 7 1739 629 2849 3 1 \n", + "17 202338 7 1663 274 3052 3 1 \n", + "18 202337 7 1122 223 2021 2 1 \n", + "19 202336 7 726 10 1442 1 0 \n", + "20 202335 7 961 96 1826 1 0 \n", + "21 202334 7 1168 9 2327 2 0 \n", + "22 202333 7 3308 1184 5432 5 2 \n", + "23 202332 7 7996 1120 14872 12 2 \n", + "24 202331 7 3318 1398 5238 5 2 \n", + "25 202330 7 5821 3269 8373 9 5 \n", + "26 202329 7 13558 8297 18819 20 12 \n", + "27 202328 7 6700 4043 9357 10 6 \n", + "28 202327 7 7253 4599 9907 11 7 \n", + "29 202326 7 9192 6223 12161 14 10 \n", + "... ... ... ... ... ... ... ... \n", + "1699 199126 7 17608 11304 23912 31 20 \n", + "1700 199125 7 16169 10700 21638 28 18 \n", + "1701 199124 7 16171 10071 22271 28 17 \n", + "1702 199123 7 11947 7671 16223 21 13 \n", + "1703 199122 7 15452 9953 20951 27 17 \n", + "1704 199121 7 14903 8975 20831 26 16 \n", + "1705 199120 7 19053 12742 25364 34 23 \n", + "1706 199119 7 16739 11246 22232 29 19 \n", + "1707 199118 7 21385 13882 28888 38 25 \n", + "1708 199117 7 13462 8877 18047 24 16 \n", + "1709 199116 7 14857 10068 19646 26 18 \n", + "1710 199115 7 13975 9781 18169 25 18 \n", + "1711 199114 7 12265 7684 16846 22 14 \n", + "1712 199113 7 9567 6041 13093 17 11 \n", + "1713 199112 7 10864 7331 14397 19 13 \n", + "1714 199111 7 15574 11184 19964 27 19 \n", + "1715 199110 7 16643 11372 21914 29 20 \n", + "1716 199109 7 13741 8780 18702 24 15 \n", + "1717 199108 7 13289 8813 17765 23 15 \n", + "1718 199107 7 12337 8077 16597 22 15 \n", + "1719 199106 7 10877 7013 14741 19 12 \n", + "1720 199105 7 10442 6544 14340 18 11 \n", + "1721 199104 7 7913 4563 11263 14 8 \n", + "1722 199103 7 15387 10484 20290 27 18 \n", + "1723 199102 7 16277 11046 21508 29 20 \n", + "1724 199101 7 15565 10271 20859 27 18 \n", + "1725 199052 7 19375 13295 25455 34 23 \n", + "1726 199051 7 19080 13807 24353 34 25 \n", + "1727 199050 7 11079 6660 15498 20 12 \n", + "1728 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 15 FR France \n", + "1 14 FR France \n", + "2 26 FR France \n", + "3 24 FR France \n", + "4 14 FR France \n", + "5 17 FR France \n", + "6 16 FR France \n", + "7 15 FR France \n", + "8 13 FR France \n", + "9 11 FR France \n", + "10 12 FR France \n", + "11 9 FR France \n", + "12 9 FR France \n", + "13 10 FR France \n", + "14 7 FR France \n", + "15 6 FR France \n", + "16 5 FR France \n", + "17 5 FR France \n", + "18 3 FR France \n", + "19 2 FR France \n", + "20 2 FR France \n", + "21 4 FR France \n", + "22 8 FR France \n", + "23 22 FR France \n", + "24 8 FR France \n", + "25 13 FR France \n", + "26 28 FR France \n", + "27 14 FR France \n", + "28 15 FR France \n", + "29 18 FR France \n", + "... ... ... ... \n", + "1699 42 FR France \n", + "1700 38 FR France \n", + "1701 39 FR France \n", + "1702 29 FR France \n", + "1703 37 FR France \n", + "1704 36 FR France \n", + "1705 45 FR France \n", + "1706 39 FR France \n", + "1707 51 FR France \n", + "1708 32 FR France \n", + "1709 34 FR France \n", + "1710 32 FR France \n", + "1711 30 FR France \n", + "1712 23 FR France \n", + "1713 25 FR France \n", + "1714 35 FR France \n", + "1715 38 FR France \n", + "1716 33 FR France \n", + "1717 31 FR France \n", + "1718 29 FR France \n", + "1719 26 FR France \n", + "1720 25 FR France \n", + "1721 20 FR France \n", + "1722 36 FR France \n", + "1723 38 FR France \n", + "1724 36 FR France \n", + "1725 45 FR France \n", + "1726 43 FR France \n", + "1727 28 FR France \n", + "1728 5 FR France \n", + "\n", + "[1729 rows x 10 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(data_url, encoding = 'iso-8859-1', skiprows=1)\n", + "raw_data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "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
3202352711636735415918181224FRFrance
4202351769124227959710614FRFrance
52023507879962151138313917FRFrance
62023497781753621027212816FRFrance
7202348773514749995311715FRFrance
8202347765374277879710713FRFrance
920234675223296874788511FRFrance
1020234575007267573398412FRFrance
112023447368816645712639FRFrance
122023437389116756107639FRFrance
1320234273968121267246210FRFrance
142023417335617644948537FRFrance
152023407284514104280426FRFrance
16202339717396292849315FRFrance
17202338716632743052315FRFrance
18202337711222232021213FRFrance
192023367726101442102FRFrance
202023357961961826102FRFrance
212023347116892327204FRFrance
222023337330811845432528FRFrance
232023327799611201487212222FRFrance
242023317331813985238528FRFrance
2520233075821326983739513FRFrance
26202329713558829718819201228FRFrance
27202328767004043935710614FRFrance
28202327772534599990711715FRFrance
2920232679192622312161141018FRFrance
30202325711498825714739171222FRFrance
31202324711115796814262171222FRFrance
3220232371256361341899219929FRFrance
.................................
16991991267176081130423912312042FRFrance
17001991257161691070021638281838FRFrance
17011991247161711007122271281739FRFrance
1702199123711947767116223211329FRFrance
1703199122715452995320951271737FRFrance
1704199121714903897520831261636FRFrance
17051991207190531274225364342345FRFrance
17061991197167391124622232291939FRFrance
17071991187213851388228888382551FRFrance
1708199117713462887718047241632FRFrance
17091991167148571006819646261834FRFrance
1710199115713975978118169251832FRFrance
1711199114712265768416846221430FRFrance
171219911379567604113093171123FRFrance
1713199112710864733114397191325FRFrance
17141991117155741118419964271935FRFrance
17151991107166431137221914292038FRFrance
1716199109713741878018702241533FRFrance
1717199108713289881317765231531FRFrance
1718199107712337807716597221529FRFrance
1719199106710877701314741191226FRFrance
1720199105710442654414340181125FRFrance
17211991047791345631126314820FRFrance
17221991037153871048420290271836FRFrance
17231991027162771104621508292038FRFrance
17241991017155651027120859271836FRFrance
17251990527193751329525455342345FRFrance
17261990517190801380724353342543FRFrance
1727199050711079666015498201228FRFrance
17281990497114302610205FRFrance
\n", + "

1726 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "3 202352 7 11636 7354 15918 18 12 \n", + "4 202351 7 6912 4227 9597 10 6 \n", + "5 202350 7 8799 6215 11383 13 9 \n", + "6 202349 7 7817 5362 10272 12 8 \n", + "7 202348 7 7351 4749 9953 11 7 \n", + "8 202347 7 6537 4277 8797 10 7 \n", + "9 202346 7 5223 2968 7478 8 5 \n", + "10 202345 7 5007 2675 7339 8 4 \n", + "11 202344 7 3688 1664 5712 6 3 \n", + "12 202343 7 3891 1675 6107 6 3 \n", + "13 202342 7 3968 1212 6724 6 2 \n", + "14 202341 7 3356 1764 4948 5 3 \n", + "15 202340 7 2845 1410 4280 4 2 \n", + "16 202339 7 1739 629 2849 3 1 \n", + "17 202338 7 1663 274 3052 3 1 \n", + "18 202337 7 1122 223 2021 2 1 \n", + "19 202336 7 726 10 1442 1 0 \n", + "20 202335 7 961 96 1826 1 0 \n", + "21 202334 7 1168 9 2327 2 0 \n", + "22 202333 7 3308 1184 5432 5 2 \n", + "23 202332 7 7996 1120 14872 12 2 \n", + "24 202331 7 3318 1398 5238 5 2 \n", + "25 202330 7 5821 3269 8373 9 5 \n", + "26 202329 7 13558 8297 18819 20 12 \n", + "27 202328 7 6700 4043 9357 10 6 \n", + "28 202327 7 7253 4599 9907 11 7 \n", + "29 202326 7 9192 6223 12161 14 10 \n", + "30 202325 7 11498 8257 14739 17 12 \n", + "31 202324 7 11115 7968 14262 17 12 \n", + "32 202323 7 12563 6134 18992 19 9 \n", + "... ... ... ... ... ... ... ... \n", + "1699 199126 7 17608 11304 23912 31 20 \n", + "1700 199125 7 16169 10700 21638 28 18 \n", + "1701 199124 7 16171 10071 22271 28 17 \n", + "1702 199123 7 11947 7671 16223 21 13 \n", + "1703 199122 7 15452 9953 20951 27 17 \n", + "1704 199121 7 14903 8975 20831 26 16 \n", + "1705 199120 7 19053 12742 25364 34 23 \n", + "1706 199119 7 16739 11246 22232 29 19 \n", + "1707 199118 7 21385 13882 28888 38 25 \n", + "1708 199117 7 13462 8877 18047 24 16 \n", + "1709 199116 7 14857 10068 19646 26 18 \n", + "1710 199115 7 13975 9781 18169 25 18 \n", + "1711 199114 7 12265 7684 16846 22 14 \n", + "1712 199113 7 9567 6041 13093 17 11 \n", + "1713 199112 7 10864 7331 14397 19 13 \n", + "1714 199111 7 15574 11184 19964 27 19 \n", + "1715 199110 7 16643 11372 21914 29 20 \n", + "1716 199109 7 13741 8780 18702 24 15 \n", + "1717 199108 7 13289 8813 17765 23 15 \n", + "1718 199107 7 12337 8077 16597 22 15 \n", + "1719 199106 7 10877 7013 14741 19 12 \n", + "1720 199105 7 10442 6544 14340 18 11 \n", + "1721 199104 7 7913 4563 11263 14 8 \n", + "1722 199103 7 15387 10484 20290 27 18 \n", + "1723 199102 7 16277 11046 21508 29 20 \n", + "1724 199101 7 15565 10271 20859 27 18 \n", + "1725 199052 7 19375 13295 25455 34 23 \n", + "1726 199051 7 19080 13807 24353 34 25 \n", + "1727 199050 7 11079 6660 15498 20 12 \n", + "1728 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "3 24 FR France \n", + "4 14 FR France \n", + "5 17 FR France \n", + "6 16 FR France \n", + "7 15 FR France \n", + "8 13 FR France \n", + "9 11 FR France \n", + "10 12 FR France \n", + "11 9 FR France \n", + "12 9 FR France \n", + "13 10 FR France \n", + "14 7 FR France \n", + "15 6 FR France \n", + "16 5 FR France \n", + "17 5 FR France \n", + "18 3 FR France \n", + "19 2 FR France \n", + "20 2 FR France \n", + "21 4 FR France \n", + "22 8 FR France \n", + "23 22 FR France \n", + "24 8 FR France \n", + "25 13 FR France \n", + "26 28 FR France \n", + "27 14 FR France \n", + "28 15 FR France \n", + "29 18 FR France \n", + "30 22 FR France \n", + "31 22 FR France \n", + "32 29 FR France \n", + "... ... ... ... \n", + "1699 42 FR France \n", + "1700 38 FR France \n", + "1701 39 FR France \n", + "1702 29 FR France \n", + "1703 37 FR France \n", + "1704 36 FR France \n", + "1705 45 FR France \n", + "1706 39 FR France \n", + "1707 51 FR France \n", + "1708 32 FR France \n", + "1709 34 FR France \n", + "1710 32 FR France \n", + "1711 30 FR France \n", + "1712 23 FR France \n", + "1713 25 FR France \n", + "1714 35 FR France \n", + "1715 38 FR France \n", + "1716 33 FR France \n", + "1717 31 FR France \n", + "1718 29 FR France \n", + "1719 26 FR France \n", + "1720 25 FR France \n", + "1721 20 FR France \n", + "1722 36 FR France \n", + "1723 38 FR France \n", + "1724 36 FR France \n", + "1725 45 FR France \n", + "1726 43 FR France \n", + "1727 28 FR France \n", + "1728 5 FR France \n", + "\n", + "[1726 rows x 10 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = raw_data.dropna().copy()\n", + "data = data.drop([0,1,2], axis=0)\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "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
02024037736245771014711715FRFrance
1202402772274927952711814FRFrance
2202401713305921417396201426FRFrance
3202352711636735415918181224FRFrance
4202351769124227959710614FRFrance
52023507879962151138313917FRFrance
62023497781753621027212816FRFrance
7202348773514749995311715FRFrance
8202347765374277879710713FRFrance
920234675223296874788511FRFrance
1020234575007267573398412FRFrance
112023447368816645712639FRFrance
122023437389116756107639FRFrance
1320234273968121267246210FRFrance
142023417335617644948537FRFrance
152023407284514104280426FRFrance
16202339717396292849315FRFrance
17202338716632743052315FRFrance
18202337711222232021213FRFrance
192023367726101442102FRFrance
202023357961961826102FRFrance
212023347116892327204FRFrance
222023337330811845432528FRFrance
232023327799611201487212222FRFrance
242023317331813985238528FRFrance
2520233075821326983739513FRFrance
26202329713558829718819201228FRFrance
27202328767004043935710614FRFrance
28202327772534599990711715FRFrance
2920232679192622312161141018FRFrance
.................................
16991991267176081130423912312042FRFrance
17001991257161691070021638281838FRFrance
17011991247161711007122271281739FRFrance
1702199123711947767116223211329FRFrance
1703199122715452995320951271737FRFrance
1704199121714903897520831261636FRFrance
17051991207190531274225364342345FRFrance
17061991197167391124622232291939FRFrance
17071991187213851388228888382551FRFrance
1708199117713462887718047241632FRFrance
17091991167148571006819646261834FRFrance
1710199115713975978118169251832FRFrance
1711199114712265768416846221430FRFrance
171219911379567604113093171123FRFrance
1713199112710864733114397191325FRFrance
17141991117155741118419964271935FRFrance
17151991107166431137221914292038FRFrance
1716199109713741878018702241533FRFrance
1717199108713289881317765231531FRFrance
1718199107712337807716597221529FRFrance
1719199106710877701314741191226FRFrance
1720199105710442654414340181125FRFrance
17211991047791345631126314820FRFrance
17221991037153871048420290271836FRFrance
17231991027162771104621508292038FRFrance
17241991017155651027120859271836FRFrance
17251990527193751329525455342345FRFrance
17261990517190801380724353342543FRFrance
1727199050711079666015498201228FRFrance
17281990497114302610205FRFrance
\n", + "

1729 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202403 7 7362 4577 10147 11 7 \n", + "1 202402 7 7227 4927 9527 11 8 \n", + "2 202401 7 13305 9214 17396 20 14 \n", + "3 202352 7 11636 7354 15918 18 12 \n", + "4 202351 7 6912 4227 9597 10 6 \n", + "5 202350 7 8799 6215 11383 13 9 \n", + "6 202349 7 7817 5362 10272 12 8 \n", + "7 202348 7 7351 4749 9953 11 7 \n", + "8 202347 7 6537 4277 8797 10 7 \n", + "9 202346 7 5223 2968 7478 8 5 \n", + "10 202345 7 5007 2675 7339 8 4 \n", + "11 202344 7 3688 1664 5712 6 3 \n", + "12 202343 7 3891 1675 6107 6 3 \n", + "13 202342 7 3968 1212 6724 6 2 \n", + "14 202341 7 3356 1764 4948 5 3 \n", + "15 202340 7 2845 1410 4280 4 2 \n", + "16 202339 7 1739 629 2849 3 1 \n", + "17 202338 7 1663 274 3052 3 1 \n", + "18 202337 7 1122 223 2021 2 1 \n", + "19 202336 7 726 10 1442 1 0 \n", + "20 202335 7 961 96 1826 1 0 \n", + "21 202334 7 1168 9 2327 2 0 \n", + "22 202333 7 3308 1184 5432 5 2 \n", + "23 202332 7 7996 1120 14872 12 2 \n", + "24 202331 7 3318 1398 5238 5 2 \n", + "25 202330 7 5821 3269 8373 9 5 \n", + "26 202329 7 13558 8297 18819 20 12 \n", + "27 202328 7 6700 4043 9357 10 6 \n", + "28 202327 7 7253 4599 9907 11 7 \n", + "29 202326 7 9192 6223 12161 14 10 \n", + "... ... ... ... ... ... ... ... \n", + "1699 199126 7 17608 11304 23912 31 20 \n", + "1700 199125 7 16169 10700 21638 28 18 \n", + "1701 199124 7 16171 10071 22271 28 17 \n", + "1702 199123 7 11947 7671 16223 21 13 \n", + "1703 199122 7 15452 9953 20951 27 17 \n", + "1704 199121 7 14903 8975 20831 26 16 \n", + "1705 199120 7 19053 12742 25364 34 23 \n", + "1706 199119 7 16739 11246 22232 29 19 \n", + "1707 199118 7 21385 13882 28888 38 25 \n", + "1708 199117 7 13462 8877 18047 24 16 \n", + "1709 199116 7 14857 10068 19646 26 18 \n", + "1710 199115 7 13975 9781 18169 25 18 \n", + "1711 199114 7 12265 7684 16846 22 14 \n", + "1712 199113 7 9567 6041 13093 17 11 \n", + "1713 199112 7 10864 7331 14397 19 13 \n", + "1714 199111 7 15574 11184 19964 27 19 \n", + "1715 199110 7 16643 11372 21914 29 20 \n", + "1716 199109 7 13741 8780 18702 24 15 \n", + "1717 199108 7 13289 8813 17765 23 15 \n", + "1718 199107 7 12337 8077 16597 22 15 \n", + "1719 199106 7 10877 7013 14741 19 12 \n", + "1720 199105 7 10442 6544 14340 18 11 \n", + "1721 199104 7 7913 4563 11263 14 8 \n", + "1722 199103 7 15387 10484 20290 27 18 \n", + "1723 199102 7 16277 11046 21508 29 20 \n", + "1724 199101 7 15565 10271 20859 27 18 \n", + "1725 199052 7 19375 13295 25455 34 23 \n", + "1726 199051 7 19080 13807 24353 34 25 \n", + "1727 199050 7 11079 6660 15498 20 12 \n", + "1728 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 15 FR France \n", + "1 14 FR France \n", + "2 26 FR France \n", + "3 24 FR France \n", + "4 14 FR France \n", + "5 17 FR France \n", + "6 16 FR France \n", + "7 15 FR France \n", + "8 13 FR France \n", + "9 11 FR France \n", + "10 12 FR France \n", + "11 9 FR France \n", + "12 9 FR France \n", + "13 10 FR France \n", + "14 7 FR France \n", + "15 6 FR France \n", + "16 5 FR France \n", + "17 5 FR France \n", + "18 3 FR France \n", + "19 2 FR France \n", + "20 2 FR France \n", + "21 4 FR France \n", + "22 8 FR France \n", + "23 22 FR France \n", + "24 8 FR France \n", + "25 13 FR France \n", + "26 28 FR France \n", + "27 14 FR France \n", + "28 15 FR France \n", + "29 18 FR France \n", + "... ... ... ... \n", + "1699 42 FR France \n", + "1700 38 FR France \n", + "1701 39 FR France \n", + "1702 29 FR France \n", + "1703 37 FR France \n", + "1704 36 FR France \n", + "1705 45 FR France \n", + "1706 39 FR France \n", + "1707 51 FR France \n", + "1708 32 FR France \n", + "1709 34 FR France \n", + "1710 32 FR France \n", + "1711 30 FR France \n", + "1712 23 FR France \n", + "1713 25 FR France \n", + "1714 35 FR France \n", + "1715 38 FR France \n", + "1716 33 FR France \n", + "1717 31 FR France \n", + "1718 29 FR France \n", + "1719 26 FR France \n", + "1720 25 FR France \n", + "1721 20 FR France \n", + "1722 36 FR France \n", + "1723 38 FR France \n", + "1724 36 FR France \n", + "1725 45 FR France \n", + "1726 43 FR France \n", + "1727 28 FR France \n", + "1728 5 FR France \n", + "\n", + "[1729 rows x 10 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + " raw_data[raw_data.isnull().any(axis=1)]\n", + "data = raw_data.dropna().copy()\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "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": 16, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "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": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "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'].astype(int).plot()" + ] + }, + { + "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": [ + "sorted_data['inc'][-300:-175].astype(int).plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", + " for y in range(1990,\n", + " sorted_data.index[-1].year)]" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Period('1990-07-30/1990-08-05', 'W-SUN'),\n", + " Period('1991-07-29/1991-08-04', 'W-SUN'),\n", + " Period('1992-07-27/1992-08-02', 'W-SUN'),\n", + " Period('1993-07-26/1993-08-01', 'W-SUN'),\n", + " Period('1994-08-01/1994-08-07', 'W-SUN'),\n", + " Period('1995-07-31/1995-08-06', 'W-SUN'),\n", + " Period('1996-07-29/1996-08-04', 'W-SUN'),\n", + " Period('1997-07-28/1997-08-03', 'W-SUN'),\n", + " Period('1998-07-27/1998-08-02', 'W-SUN'),\n", + " Period('1999-07-26/1999-08-01', 'W-SUN'),\n", + " Period('2000-07-31/2000-08-06', 'W-SUN'),\n", + " Period('2001-07-30/2001-08-05', 'W-SUN'),\n", + " Period('2002-07-29/2002-08-04', 'W-SUN'),\n", + " Period('2003-07-28/2003-08-03', 'W-SUN'),\n", + " Period('2004-07-26/2004-08-01', 'W-SUN'),\n", + " Period('2005-08-01/2005-08-07', 'W-SUN'),\n", + " Period('2006-07-31/2006-08-06', 'W-SUN'),\n", + " Period('2007-07-30/2007-08-05', 'W-SUN'),\n", + " Period('2008-07-28/2008-08-03', 'W-SUN'),\n", + " Period('2009-07-27/2009-08-02', 'W-SUN'),\n", + " Period('2010-07-26/2010-08-01', 'W-SUN'),\n", + " Period('2011-08-01/2011-08-07', 'W-SUN'),\n", + " Period('2012-07-30/2012-08-05', 'W-SUN'),\n", + " Period('2013-07-29/2013-08-04', 'W-SUN'),\n", + " Period('2014-07-28/2014-08-03', 'W-SUN'),\n", + " Period('2015-07-27/2015-08-02', 'W-SUN'),\n", + " Period('2016-08-01/2016-08-07', 'W-SUN'),\n", + " Period('2017-07-31/2017-08-06', 'W-SUN'),\n", + " Period('2018-07-30/2018-08-05', 'W-SUN'),\n", + " Period('2019-07-29/2019-08-04', 'W-SUN'),\n", + " Period('2020-07-27/2020-08-02', 'W-SUN'),\n", + " Period('2021-07-26/2021-08-01', 'W-SUN'),\n", + " Period('2022-08-01/2022-08-07', 'W-SUN'),\n", + " Period('2023-07-31/2023-08-06', 'W-SUN')]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "first_august_week" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_august_week[:-1],\n", + " first_august_week[1:]):\n", + " one_year = sorted_data['inc'][week1:week2-1].astype(int)\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": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1991 507329\n", + "1992 821558\n", + "1993 653058\n", + "1994 682920\n", + "1995 648598\n", + "1996 553859\n", + "1997 679308\n", + "1998 660316\n", + "1999 784963\n", + "2000 605096\n", + "2001 650660\n", + "2002 502271\n", + "2003 770211\n", + "2004 736266\n", + "2005 654308\n", + "2006 657482\n", + "2007 701566\n", + "2008 745701\n", + "2009 822819\n", + "2010 848236\n", + "2011 645042\n", + "2012 620315\n", + "2013 708874\n", + "2014 673458\n", + "2015 613286\n", + "2016 780645\n", + "2017 557449\n", + "2018 543281\n", + "2019 584926\n", + "2020 229363\n", + "2021 363278\n", + "2022 638443\n", + "2023 374740\n", + "dtype: int64" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "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.astype(int).plot(style='*')" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2020 229363\n", + "2021 363278\n", + "2023 374740\n", + "2002 502271\n", + "1991 507329\n", + "2018 543281\n", + "1996 553859\n", + "2017 557449\n", + "2019 584926\n", + "2000 605096\n", + "2015 613286\n", + "2012 620315\n", + "2022 638443\n", + "2011 645042\n", + "1995 648598\n", + "2001 650660\n", + "1993 653058\n", + "2005 654308\n", + "2006 657482\n", + "1998 660316\n", + "2014 673458\n", + "1997 679308\n", + "1994 682920\n", + "2007 701566\n", + "2013 708874\n", + "2004 736266\n", + "2008 745701\n", + "2003 770211\n", + "2016 780645\n", + "1999 784963\n", + "1992 821558\n", + "2009 822819\n", + "2010 848236\n", + "dtype: int64" + ] + }, + "execution_count": 37, + "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", @@ -16,10 +3299,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } -