{ "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-3.csv\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data_file = \"syndrome-grippal.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
020233938211270891.093333.0124107.0141.0FRFrance
120233836356755525.071609.09684.0108.0FRFrance
220233734908542079.056091.07463.085.0FRFrance
320233633824732237.044257.05849.067.0FRFrance
420233533169526013.037377.04839.057.0FRFrance
520233432666321057.032269.04032.048.0FRFrance
620233331914413161.025127.02920.038.0FRFrance
720233231464110285.018997.02215.029.0FRFrance
820233131528610705.019867.02316.030.0FRFrance
92023303132058647.017763.02013.027.0FRFrance
102023293111227113.015131.01711.023.0FRFrance
11202328391795703.012655.0149.019.0FRFrance
12202327389995763.012235.0149.019.0FRFrance
13202326390235934.012112.0149.019.0FRFrance
142023253100906739.013441.01510.020.0FRFrance
152023243113087639.014977.01711.023.0FRFrance
1620232331430010661.017939.02217.027.0FRFrance
1720232231830313822.022784.02821.035.0FRFrance
1820232131646012188.020732.02519.031.0FRFrance
1920232031616211963.020361.02418.030.0FRFrance
2020231931690112577.021225.02518.032.0FRFrance
2120231831992915402.024456.03023.037.0FRFrance
2220231732700721779.032235.04133.049.0FRFrance
2320231632787522767.032983.04234.050.0FRFrance
2420231533745530993.043917.05646.066.0FRFrance
2520231434806040671.055449.07261.083.0FRFrance
2620231336485956800.072918.09886.0110.0FRFrance
2720231237275064499.081001.010997.0121.0FRFrance
2820231137463866420.082856.0112100.0124.0FRFrance
2920231037636868243.084493.0115103.0127.0FRFrance
.................................
200119852132609619621.032571.04735.059.0FRFrance
200219852032789620885.034907.05138.064.0FRFrance
200319851934315432821.053487.07859.097.0FRFrance
200419851834055529935.051175.07455.093.0FRFrance
200519851733405324366.043740.06244.080.0FRFrance
200619851635036236451.064273.09166.0116.0FRFrance
200719851536388145538.082224.011683.0149.0FRFrance
20081985143134545114400.0154690.0244207.0281.0FRFrance
20091985133197206176080.0218332.0357319.0395.0FRFrance
20101985123245240223304.0267176.0445405.0485.0FRFrance
20111985113276205252399.0300011.0501458.0544.0FRFrance
20121985103353231326279.0380183.0640591.0689.0FRFrance
20131985093369895341109.0398681.0670618.0722.0FRFrance
20141985083389886359529.0420243.0707652.0762.0FRFrance
20151985073471852432599.0511105.0855784.0926.0FRFrance
20161985063565825518011.0613639.01026939.01113.0FRFrance
20171985053637302592795.0681809.011551074.01236.0FRFrance
20181985043424937390794.0459080.0770708.0832.0FRFrance
20191985033213901174689.0253113.0388317.0459.0FRFrance
202019850239758680949.0114223.0177147.0207.0FRFrance
202119850138548965918.0105060.0155120.0190.0FRFrance
202219845238483060602.0109058.0154110.0198.0FRFrance
2023198451310172680242.0123210.0185146.0224.0FRFrance
20241984503123680101401.0145959.0225184.0266.0FRFrance
2025198449310107381684.0120462.0184149.0219.0FRFrance
202619844837862060634.096606.0143110.0176.0FRFrance
202719844737202954274.089784.013199.0163.0FRFrance
202819844638733067686.0106974.0159123.0195.0FRFrance
20291984453135223101414.0169032.0246184.0308.0FRFrance
203019844436842220056.0116788.012537.0213.0FRFrance
\n", "

2031 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202339 3 82112 70891.0 93333.0 124 107.0 \n", "1 202338 3 63567 55525.0 71609.0 96 84.0 \n", "2 202337 3 49085 42079.0 56091.0 74 63.0 \n", "3 202336 3 38247 32237.0 44257.0 58 49.0 \n", "4 202335 3 31695 26013.0 37377.0 48 39.0 \n", "5 202334 3 26663 21057.0 32269.0 40 32.0 \n", "6 202333 3 19144 13161.0 25127.0 29 20.0 \n", "7 202332 3 14641 10285.0 18997.0 22 15.0 \n", "8 202331 3 15286 10705.0 19867.0 23 16.0 \n", "9 202330 3 13205 8647.0 17763.0 20 13.0 \n", "10 202329 3 11122 7113.0 15131.0 17 11.0 \n", "11 202328 3 9179 5703.0 12655.0 14 9.0 \n", "12 202327 3 8999 5763.0 12235.0 14 9.0 \n", "13 202326 3 9023 5934.0 12112.0 14 9.0 \n", "14 202325 3 10090 6739.0 13441.0 15 10.0 \n", "15 202324 3 11308 7639.0 14977.0 17 11.0 \n", "16 202323 3 14300 10661.0 17939.0 22 17.0 \n", "17 202322 3 18303 13822.0 22784.0 28 21.0 \n", "18 202321 3 16460 12188.0 20732.0 25 19.0 \n", "19 202320 3 16162 11963.0 20361.0 24 18.0 \n", "20 202319 3 16901 12577.0 21225.0 25 18.0 \n", "21 202318 3 19929 15402.0 24456.0 30 23.0 \n", "22 202317 3 27007 21779.0 32235.0 41 33.0 \n", "23 202316 3 27875 22767.0 32983.0 42 34.0 \n", "24 202315 3 37455 30993.0 43917.0 56 46.0 \n", "25 202314 3 48060 40671.0 55449.0 72 61.0 \n", "26 202313 3 64859 56800.0 72918.0 98 86.0 \n", "27 202312 3 72750 64499.0 81001.0 109 97.0 \n", "28 202311 3 74638 66420.0 82856.0 112 100.0 \n", "29 202310 3 76368 68243.0 84493.0 115 103.0 \n", "... ... ... ... ... ... ... ... \n", "2001 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2002 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2003 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2004 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2005 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2006 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2007 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2008 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2009 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2010 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2011 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2012 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2013 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2014 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2015 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2016 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2017 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2018 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2019 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2020 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2021 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2022 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2023 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2024 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2025 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2026 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2027 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2028 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2029 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2030 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 141.0 FR France \n", "1 108.0 FR France \n", "2 85.0 FR France \n", "3 67.0 FR France \n", "4 57.0 FR France \n", "5 48.0 FR France \n", "6 38.0 FR France \n", "7 29.0 FR France \n", "8 30.0 FR France \n", "9 27.0 FR France \n", "10 23.0 FR France \n", "11 19.0 FR France \n", "12 19.0 FR France \n", "13 19.0 FR France \n", "14 20.0 FR France \n", "15 23.0 FR France \n", "16 27.0 FR France \n", "17 35.0 FR France \n", "18 31.0 FR France \n", "19 30.0 FR France \n", "20 32.0 FR France \n", "21 37.0 FR France \n", "22 49.0 FR France \n", "23 50.0 FR France \n", "24 66.0 FR France \n", "25 83.0 FR France \n", "26 110.0 FR France \n", "27 121.0 FR France \n", "28 124.0 FR France \n", "29 127.0 FR France \n", "... ... ... ... \n", "2001 59.0 FR France \n", "2002 64.0 FR France \n", "2003 97.0 FR France \n", "2004 93.0 FR France \n", "2005 80.0 FR France \n", "2006 116.0 FR France \n", "2007 149.0 FR France \n", "2008 281.0 FR France \n", "2009 395.0 FR France \n", "2010 485.0 FR France \n", "2011 544.0 FR France \n", "2012 689.0 FR France \n", "2013 722.0 FR France \n", "2014 762.0 FR France \n", "2015 926.0 FR France \n", "2016 1113.0 FR France \n", "2017 1236.0 FR France \n", "2018 832.0 FR France \n", "2019 459.0 FR France \n", "2020 207.0 FR France \n", "2021 190.0 FR France \n", "2022 198.0 FR France \n", "2023 224.0 FR France \n", "2024 266.0 FR France \n", "2025 219.0 FR France \n", "2026 176.0 FR France \n", "2027 163.0 FR France \n", "2028 195.0 FR France \n", "2029 308.0 FR France \n", "2030 213.0 FR France \n", "\n", "[2031 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", "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
17941989193-NaNNaN-NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1794 198919 3 - NaN NaN - NaN NaN \n", "\n", " geo_insee geo_name \n", "1794 FR France " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "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
020233938211270891.093333.0124107.0141.0FRFrance
120233836356755525.071609.09684.0108.0FRFrance
220233734908542079.056091.07463.085.0FRFrance
320233633824732237.044257.05849.067.0FRFrance
420233533169526013.037377.04839.057.0FRFrance
520233432666321057.032269.04032.048.0FRFrance
620233331914413161.025127.02920.038.0FRFrance
720233231464110285.018997.02215.029.0FRFrance
820233131528610705.019867.02316.030.0FRFrance
92023303132058647.017763.02013.027.0FRFrance
102023293111227113.015131.01711.023.0FRFrance
11202328391795703.012655.0149.019.0FRFrance
12202327389995763.012235.0149.019.0FRFrance
13202326390235934.012112.0149.019.0FRFrance
142023253100906739.013441.01510.020.0FRFrance
152023243113087639.014977.01711.023.0FRFrance
1620232331430010661.017939.02217.027.0FRFrance
1720232231830313822.022784.02821.035.0FRFrance
1820232131646012188.020732.02519.031.0FRFrance
1920232031616211963.020361.02418.030.0FRFrance
2020231931690112577.021225.02518.032.0FRFrance
2120231831992915402.024456.03023.037.0FRFrance
2220231732700721779.032235.04133.049.0FRFrance
2320231632787522767.032983.04234.050.0FRFrance
2420231533745530993.043917.05646.066.0FRFrance
2520231434806040671.055449.07261.083.0FRFrance
2620231336485956800.072918.09886.0110.0FRFrance
2720231237275064499.081001.010997.0121.0FRFrance
2820231137463866420.082856.0112100.0124.0FRFrance
2920231037636868243.084493.0115103.0127.0FRFrance
.................................
200119852132609619621.032571.04735.059.0FRFrance
200219852032789620885.034907.05138.064.0FRFrance
200319851934315432821.053487.07859.097.0FRFrance
200419851834055529935.051175.07455.093.0FRFrance
200519851733405324366.043740.06244.080.0FRFrance
200619851635036236451.064273.09166.0116.0FRFrance
200719851536388145538.082224.011683.0149.0FRFrance
20081985143134545114400.0154690.0244207.0281.0FRFrance
20091985133197206176080.0218332.0357319.0395.0FRFrance
20101985123245240223304.0267176.0445405.0485.0FRFrance
20111985113276205252399.0300011.0501458.0544.0FRFrance
20121985103353231326279.0380183.0640591.0689.0FRFrance
20131985093369895341109.0398681.0670618.0722.0FRFrance
20141985083389886359529.0420243.0707652.0762.0FRFrance
20151985073471852432599.0511105.0855784.0926.0FRFrance
20161985063565825518011.0613639.01026939.01113.0FRFrance
20171985053637302592795.0681809.011551074.01236.0FRFrance
20181985043424937390794.0459080.0770708.0832.0FRFrance
20191985033213901174689.0253113.0388317.0459.0FRFrance
202019850239758680949.0114223.0177147.0207.0FRFrance
202119850138548965918.0105060.0155120.0190.0FRFrance
202219845238483060602.0109058.0154110.0198.0FRFrance
2023198451310172680242.0123210.0185146.0224.0FRFrance
20241984503123680101401.0145959.0225184.0266.0FRFrance
2025198449310107381684.0120462.0184149.0219.0FRFrance
202619844837862060634.096606.0143110.0176.0FRFrance
202719844737202954274.089784.013199.0163.0FRFrance
202819844638733067686.0106974.0159123.0195.0FRFrance
20291984453135223101414.0169032.0246184.0308.0FRFrance
203019844436842220056.0116788.012537.0213.0FRFrance
\n", "

2030 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202339 3 82112 70891.0 93333.0 124 107.0 \n", "1 202338 3 63567 55525.0 71609.0 96 84.0 \n", "2 202337 3 49085 42079.0 56091.0 74 63.0 \n", "3 202336 3 38247 32237.0 44257.0 58 49.0 \n", "4 202335 3 31695 26013.0 37377.0 48 39.0 \n", "5 202334 3 26663 21057.0 32269.0 40 32.0 \n", "6 202333 3 19144 13161.0 25127.0 29 20.0 \n", "7 202332 3 14641 10285.0 18997.0 22 15.0 \n", "8 202331 3 15286 10705.0 19867.0 23 16.0 \n", "9 202330 3 13205 8647.0 17763.0 20 13.0 \n", "10 202329 3 11122 7113.0 15131.0 17 11.0 \n", "11 202328 3 9179 5703.0 12655.0 14 9.0 \n", "12 202327 3 8999 5763.0 12235.0 14 9.0 \n", "13 202326 3 9023 5934.0 12112.0 14 9.0 \n", "14 202325 3 10090 6739.0 13441.0 15 10.0 \n", "15 202324 3 11308 7639.0 14977.0 17 11.0 \n", "16 202323 3 14300 10661.0 17939.0 22 17.0 \n", "17 202322 3 18303 13822.0 22784.0 28 21.0 \n", "18 202321 3 16460 12188.0 20732.0 25 19.0 \n", "19 202320 3 16162 11963.0 20361.0 24 18.0 \n", "20 202319 3 16901 12577.0 21225.0 25 18.0 \n", "21 202318 3 19929 15402.0 24456.0 30 23.0 \n", "22 202317 3 27007 21779.0 32235.0 41 33.0 \n", "23 202316 3 27875 22767.0 32983.0 42 34.0 \n", "24 202315 3 37455 30993.0 43917.0 56 46.0 \n", "25 202314 3 48060 40671.0 55449.0 72 61.0 \n", "26 202313 3 64859 56800.0 72918.0 98 86.0 \n", "27 202312 3 72750 64499.0 81001.0 109 97.0 \n", "28 202311 3 74638 66420.0 82856.0 112 100.0 \n", "29 202310 3 76368 68243.0 84493.0 115 103.0 \n", "... ... ... ... ... ... ... ... \n", "2001 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2002 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2003 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2004 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2005 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2006 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2007 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2008 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2009 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2010 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2011 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2012 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2013 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2014 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2015 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2016 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2017 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2018 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2019 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2020 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2021 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2022 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2023 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2024 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2025 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2026 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2027 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2028 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2029 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2030 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 141.0 FR France \n", "1 108.0 FR France \n", "2 85.0 FR France \n", "3 67.0 FR France \n", "4 57.0 FR France \n", "5 48.0 FR France \n", "6 38.0 FR France \n", "7 29.0 FR France \n", "8 30.0 FR France \n", "9 27.0 FR France \n", "10 23.0 FR France \n", "11 19.0 FR France \n", "12 19.0 FR France \n", "13 19.0 FR France \n", "14 20.0 FR France \n", "15 23.0 FR France \n", "16 27.0 FR France \n", "17 35.0 FR France \n", "18 31.0 FR France \n", "19 30.0 FR France \n", "20 32.0 FR France \n", "21 37.0 FR France \n", "22 49.0 FR France \n", "23 50.0 FR France \n", "24 66.0 FR France \n", "25 83.0 FR France \n", "26 110.0 FR France \n", "27 121.0 FR France \n", "28 124.0 FR France \n", "29 127.0 FR France \n", "... ... ... ... \n", "2001 59.0 FR France \n", "2002 64.0 FR France \n", "2003 97.0 FR France \n", "2004 93.0 FR France \n", "2005 80.0 FR France \n", "2006 116.0 FR France \n", "2007 149.0 FR France \n", "2008 281.0 FR France \n", "2009 395.0 FR France \n", "2010 485.0 FR France \n", "2011 544.0 FR France \n", "2012 689.0 FR France \n", "2013 722.0 FR France \n", "2014 762.0 FR France \n", "2015 926.0 FR France \n", "2016 1113.0 FR France \n", "2017 1236.0 FR France \n", "2018 832.0 FR France \n", "2019 459.0 FR France \n", "2020 207.0 FR France \n", "2021 190.0 FR France \n", "2022 198.0 FR France \n", "2023 224.0 FR France \n", "2024 266.0 FR France \n", "2025 219.0 FR France \n", "2026 176.0 FR France \n", "2027 163.0 FR France \n", "2028 195.0 FR France \n", "2029 308.0 FR France \n", "2030 213.0 FR France \n", "\n", "[2030 rows x 10 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "code", "execution_count": 7, "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": 11, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
period
1984-10-29/1984-11-0419844436842220056.0116788.012537.0213.0FRFrance
1984-11-05/1984-11-111984453135223101414.0169032.0246184.0308.0FRFrance
1984-11-12/1984-11-1819844638733067686.0106974.0159123.0195.0FRFrance
1984-11-19/1984-11-2519844737202954274.089784.013199.0163.0FRFrance
1984-11-26/1984-12-0219844837862060634.096606.0143110.0176.0FRFrance
1984-12-03/1984-12-09198449310107381684.0120462.0184149.0219.0FRFrance
1984-12-10/1984-12-161984503123680101401.0145959.0225184.0266.0FRFrance
1984-12-17/1984-12-23198451310172680242.0123210.0185146.0224.0FRFrance
1984-12-24/1984-12-3019845238483060602.0109058.0154110.0198.0FRFrance
1984-12-31/1985-01-0619850138548965918.0105060.0155120.0190.0FRFrance
1985-01-07/1985-01-1319850239758680949.0114223.0177147.0207.0FRFrance
1985-01-14/1985-01-201985033213901174689.0253113.0388317.0459.0FRFrance
1985-01-21/1985-01-271985043424937390794.0459080.0770708.0832.0FRFrance
1985-01-28/1985-02-031985053637302592795.0681809.011551074.01236.0FRFrance
1985-02-04/1985-02-101985063565825518011.0613639.01026939.01113.0FRFrance
1985-02-11/1985-02-171985073471852432599.0511105.0855784.0926.0FRFrance
1985-02-18/1985-02-241985083389886359529.0420243.0707652.0762.0FRFrance
1985-02-25/1985-03-031985093369895341109.0398681.0670618.0722.0FRFrance
1985-03-04/1985-03-101985103353231326279.0380183.0640591.0689.0FRFrance
1985-03-11/1985-03-171985113276205252399.0300011.0501458.0544.0FRFrance
1985-03-18/1985-03-241985123245240223304.0267176.0445405.0485.0FRFrance
1985-03-25/1985-03-311985133197206176080.0218332.0357319.0395.0FRFrance
1985-04-01/1985-04-071985143134545114400.0154690.0244207.0281.0FRFrance
1985-04-08/1985-04-1419851536388145538.082224.011683.0149.0FRFrance
1985-04-15/1985-04-2119851635036236451.064273.09166.0116.0FRFrance
1985-04-22/1985-04-2819851733405324366.043740.06244.080.0FRFrance
1985-04-29/1985-05-0519851834055529935.051175.07455.093.0FRFrance
1985-05-06/1985-05-1219851934315432821.053487.07859.097.0FRFrance
1985-05-13/1985-05-1919852032789620885.034907.05138.064.0FRFrance
1985-05-20/1985-05-2619852132609619621.032571.04735.059.0FRFrance
.................................
2023-03-06/2023-03-1220231037636868243.084493.0115103.0127.0FRFrance
2023-03-13/2023-03-1920231137463866420.082856.0112100.0124.0FRFrance
2023-03-20/2023-03-2620231237275064499.081001.010997.0121.0FRFrance
2023-03-27/2023-04-0220231336485956800.072918.09886.0110.0FRFrance
2023-04-03/2023-04-0920231434806040671.055449.07261.083.0FRFrance
2023-04-10/2023-04-1620231533745530993.043917.05646.066.0FRFrance
2023-04-17/2023-04-2320231632787522767.032983.04234.050.0FRFrance
2023-04-24/2023-04-3020231732700721779.032235.04133.049.0FRFrance
2023-05-01/2023-05-0720231831992915402.024456.03023.037.0FRFrance
2023-05-08/2023-05-1420231931690112577.021225.02518.032.0FRFrance
2023-05-15/2023-05-2120232031616211963.020361.02418.030.0FRFrance
2023-05-22/2023-05-2820232131646012188.020732.02519.031.0FRFrance
2023-05-29/2023-06-0420232231830313822.022784.02821.035.0FRFrance
2023-06-05/2023-06-1120232331430010661.017939.02217.027.0FRFrance
2023-06-12/2023-06-182023243113087639.014977.01711.023.0FRFrance
2023-06-19/2023-06-252023253100906739.013441.01510.020.0FRFrance
2023-06-26/2023-07-02202326390235934.012112.0149.019.0FRFrance
2023-07-03/2023-07-09202327389995763.012235.0149.019.0FRFrance
2023-07-10/2023-07-16202328391795703.012655.0149.019.0FRFrance
2023-07-17/2023-07-232023293111227113.015131.01711.023.0FRFrance
2023-07-24/2023-07-302023303132058647.017763.02013.027.0FRFrance
2023-07-31/2023-08-0620233131528610705.019867.02316.030.0FRFrance
2023-08-07/2023-08-1320233231464110285.018997.02215.029.0FRFrance
2023-08-14/2023-08-2020233331914413161.025127.02920.038.0FRFrance
2023-08-21/2023-08-2720233432666321057.032269.04032.048.0FRFrance
2023-08-28/2023-09-0320233533169526013.037377.04839.057.0FRFrance
2023-09-04/2023-09-1020233633824732237.044257.05849.067.0FRFrance
2023-09-11/2023-09-1720233734908542079.056091.07463.085.0FRFrance
2023-09-18/2023-09-2420233836356755525.071609.09684.0108.0FRFrance
2023-09-25/2023-10-0120233938211270891.093333.0124107.0141.0FRFrance
\n", "

2030 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 \\\n", "period \n", "1984-10-29/1984-11-04 198444 3 68422 20056.0 116788.0 125 \n", "1984-11-05/1984-11-11 198445 3 135223 101414.0 169032.0 246 \n", "1984-11-12/1984-11-18 198446 3 87330 67686.0 106974.0 159 \n", "1984-11-19/1984-11-25 198447 3 72029 54274.0 89784.0 131 \n", "1984-11-26/1984-12-02 198448 3 78620 60634.0 96606.0 143 \n", "1984-12-03/1984-12-09 198449 3 101073 81684.0 120462.0 184 \n", "1984-12-10/1984-12-16 198450 3 123680 101401.0 145959.0 225 \n", "1984-12-17/1984-12-23 198451 3 101726 80242.0 123210.0 185 \n", "1984-12-24/1984-12-30 198452 3 84830 60602.0 109058.0 154 \n", "1984-12-31/1985-01-06 198501 3 85489 65918.0 105060.0 155 \n", "1985-01-07/1985-01-13 198502 3 97586 80949.0 114223.0 177 \n", "1985-01-14/1985-01-20 198503 3 213901 174689.0 253113.0 388 \n", "1985-01-21/1985-01-27 198504 3 424937 390794.0 459080.0 770 \n", "1985-01-28/1985-02-03 198505 3 637302 592795.0 681809.0 1155 \n", "1985-02-04/1985-02-10 198506 3 565825 518011.0 613639.0 1026 \n", "1985-02-11/1985-02-17 198507 3 471852 432599.0 511105.0 855 \n", "1985-02-18/1985-02-24 198508 3 389886 359529.0 420243.0 707 \n", "1985-02-25/1985-03-03 198509 3 369895 341109.0 398681.0 670 \n", "1985-03-04/1985-03-10 198510 3 353231 326279.0 380183.0 640 \n", "1985-03-11/1985-03-17 198511 3 276205 252399.0 300011.0 501 \n", "1985-03-18/1985-03-24 198512 3 245240 223304.0 267176.0 445 \n", "1985-03-25/1985-03-31 198513 3 197206 176080.0 218332.0 357 \n", "1985-04-01/1985-04-07 198514 3 134545 114400.0 154690.0 244 \n", "1985-04-08/1985-04-14 198515 3 63881 45538.0 82224.0 116 \n", "1985-04-15/1985-04-21 198516 3 50362 36451.0 64273.0 91 \n", "1985-04-22/1985-04-28 198517 3 34053 24366.0 43740.0 62 \n", "1985-04-29/1985-05-05 198518 3 40555 29935.0 51175.0 74 \n", "1985-05-06/1985-05-12 198519 3 43154 32821.0 53487.0 78 \n", "1985-05-13/1985-05-19 198520 3 27896 20885.0 34907.0 51 \n", "1985-05-20/1985-05-26 198521 3 26096 19621.0 32571.0 47 \n", "... ... ... ... ... ... ... \n", "2023-03-06/2023-03-12 202310 3 76368 68243.0 84493.0 115 \n", "2023-03-13/2023-03-19 202311 3 74638 66420.0 82856.0 112 \n", "2023-03-20/2023-03-26 202312 3 72750 64499.0 81001.0 109 \n", "2023-03-27/2023-04-02 202313 3 64859 56800.0 72918.0 98 \n", "2023-04-03/2023-04-09 202314 3 48060 40671.0 55449.0 72 \n", "2023-04-10/2023-04-16 202315 3 37455 30993.0 43917.0 56 \n", "2023-04-17/2023-04-23 202316 3 27875 22767.0 32983.0 42 \n", "2023-04-24/2023-04-30 202317 3 27007 21779.0 32235.0 41 \n", "2023-05-01/2023-05-07 202318 3 19929 15402.0 24456.0 30 \n", "2023-05-08/2023-05-14 202319 3 16901 12577.0 21225.0 25 \n", "2023-05-15/2023-05-21 202320 3 16162 11963.0 20361.0 24 \n", "2023-05-22/2023-05-28 202321 3 16460 12188.0 20732.0 25 \n", "2023-05-29/2023-06-04 202322 3 18303 13822.0 22784.0 28 \n", "2023-06-05/2023-06-11 202323 3 14300 10661.0 17939.0 22 \n", "2023-06-12/2023-06-18 202324 3 11308 7639.0 14977.0 17 \n", "2023-06-19/2023-06-25 202325 3 10090 6739.0 13441.0 15 \n", "2023-06-26/2023-07-02 202326 3 9023 5934.0 12112.0 14 \n", "2023-07-03/2023-07-09 202327 3 8999 5763.0 12235.0 14 \n", "2023-07-10/2023-07-16 202328 3 9179 5703.0 12655.0 14 \n", "2023-07-17/2023-07-23 202329 3 11122 7113.0 15131.0 17 \n", "2023-07-24/2023-07-30 202330 3 13205 8647.0 17763.0 20 \n", "2023-07-31/2023-08-06 202331 3 15286 10705.0 19867.0 23 \n", "2023-08-07/2023-08-13 202332 3 14641 10285.0 18997.0 22 \n", "2023-08-14/2023-08-20 202333 3 19144 13161.0 25127.0 29 \n", "2023-08-21/2023-08-27 202334 3 26663 21057.0 32269.0 40 \n", "2023-08-28/2023-09-03 202335 3 31695 26013.0 37377.0 48 \n", "2023-09-04/2023-09-10 202336 3 38247 32237.0 44257.0 58 \n", "2023-09-11/2023-09-17 202337 3 49085 42079.0 56091.0 74 \n", "2023-09-18/2023-09-24 202338 3 63567 55525.0 71609.0 96 \n", "2023-09-25/2023-10-01 202339 3 82112 70891.0 93333.0 124 \n", "\n", " inc100_low inc100_up geo_insee geo_name \n", "period \n", "1984-10-29/1984-11-04 37.0 213.0 FR France \n", "1984-11-05/1984-11-11 184.0 308.0 FR France \n", "1984-11-12/1984-11-18 123.0 195.0 FR France \n", "1984-11-19/1984-11-25 99.0 163.0 FR France \n", "1984-11-26/1984-12-02 110.0 176.0 FR France \n", "1984-12-03/1984-12-09 149.0 219.0 FR France \n", "1984-12-10/1984-12-16 184.0 266.0 FR France \n", "1984-12-17/1984-12-23 146.0 224.0 FR France \n", "1984-12-24/1984-12-30 110.0 198.0 FR France \n", "1984-12-31/1985-01-06 120.0 190.0 FR France \n", "1985-01-07/1985-01-13 147.0 207.0 FR France \n", "1985-01-14/1985-01-20 317.0 459.0 FR France \n", "1985-01-21/1985-01-27 708.0 832.0 FR France \n", "1985-01-28/1985-02-03 1074.0 1236.0 FR France \n", "1985-02-04/1985-02-10 939.0 1113.0 FR France \n", "1985-02-11/1985-02-17 784.0 926.0 FR France \n", "1985-02-18/1985-02-24 652.0 762.0 FR France \n", "1985-02-25/1985-03-03 618.0 722.0 FR France \n", "1985-03-04/1985-03-10 591.0 689.0 FR France \n", "1985-03-11/1985-03-17 458.0 544.0 FR France \n", "1985-03-18/1985-03-24 405.0 485.0 FR France \n", "1985-03-25/1985-03-31 319.0 395.0 FR France \n", "1985-04-01/1985-04-07 207.0 281.0 FR France \n", "1985-04-08/1985-04-14 83.0 149.0 FR France \n", "1985-04-15/1985-04-21 66.0 116.0 FR France \n", "1985-04-22/1985-04-28 44.0 80.0 FR France \n", "1985-04-29/1985-05-05 55.0 93.0 FR France \n", "1985-05-06/1985-05-12 59.0 97.0 FR France \n", "1985-05-13/1985-05-19 38.0 64.0 FR France \n", "1985-05-20/1985-05-26 35.0 59.0 FR France \n", "... ... ... ... ... \n", "2023-03-06/2023-03-12 103.0 127.0 FR France \n", "2023-03-13/2023-03-19 100.0 124.0 FR France \n", "2023-03-20/2023-03-26 97.0 121.0 FR France \n", "2023-03-27/2023-04-02 86.0 110.0 FR France \n", "2023-04-03/2023-04-09 61.0 83.0 FR France \n", "2023-04-10/2023-04-16 46.0 66.0 FR France \n", "2023-04-17/2023-04-23 34.0 50.0 FR France \n", "2023-04-24/2023-04-30 33.0 49.0 FR France \n", "2023-05-01/2023-05-07 23.0 37.0 FR France \n", "2023-05-08/2023-05-14 18.0 32.0 FR France \n", "2023-05-15/2023-05-21 18.0 30.0 FR France \n", "2023-05-22/2023-05-28 19.0 31.0 FR France \n", "2023-05-29/2023-06-04 21.0 35.0 FR France \n", "2023-06-05/2023-06-11 17.0 27.0 FR France \n", "2023-06-12/2023-06-18 11.0 23.0 FR France \n", "2023-06-19/2023-06-25 10.0 20.0 FR France \n", "2023-06-26/2023-07-02 9.0 19.0 FR France \n", "2023-07-03/2023-07-09 9.0 19.0 FR France \n", "2023-07-10/2023-07-16 9.0 19.0 FR France \n", "2023-07-17/2023-07-23 11.0 23.0 FR France \n", "2023-07-24/2023-07-30 13.0 27.0 FR France \n", "2023-07-31/2023-08-06 16.0 30.0 FR France \n", "2023-08-07/2023-08-13 15.0 29.0 FR France \n", "2023-08-14/2023-08-20 20.0 38.0 FR France \n", "2023-08-21/2023-08-27 32.0 48.0 FR France \n", "2023-08-28/2023-09-03 39.0 57.0 FR France \n", "2023-09-04/2023-09-10 49.0 67.0 FR France \n", "2023-09-11/2023-09-17 63.0 85.0 FR France \n", "2023-09-18/2023-09-24 84.0 108.0 FR France \n", "2023-09-25/2023-10-01 107.0 141.0 FR France \n", "\n", "[2030 rows x 10 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data = data.set_index('period').sort_index()\n", "sorted_data " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" ] } ], "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": 10, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "Empty 'DataFrame': no numeric data to plot", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msorted_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 2501\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2502\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2503\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 2504\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 1925\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1926\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 1928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m 1727\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1729\u001b[0;31m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1730\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1731\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 365\u001b[0;31m 'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot" ] } ], "source": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'68422'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data['inc'][0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "tout la colonne \"inc\" est representer par des chaines de caractère a cause d'un trait dans une ligne de la semaine 19 de l'année 1989 trouver dans par la cellule \"5\" \n", "apres \"sorted_data['inc'][0]\" j'ai plus ce probléme de l'année 1989 \n", "du coup maintenant je vais convertire mes chaines de caractère en entier ." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "sorted_data['inc'] = sorted_data['inc'].astype(int)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "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": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "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": 19, "metadata": {}, "outputs": [], "source": [ "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", " for y in range(1985,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "code", "execution_count": 20, "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]\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": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "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": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2021 743449\n", "2014 1600941\n", "1991 1659249\n", "1995 1840410\n", "2020 2010315\n", "2022 2060304\n", "2012 2175217\n", "2003 2234584\n", "2019 2254386\n", "2006 2307352\n", "2017 2321583\n", "2001 2529279\n", "1992 2574578\n", "1993 2703886\n", "2018 2705325\n", "1988 2765617\n", "2007 2780164\n", "1987 2855570\n", "2016 2856393\n", "2011 2857040\n", "2008 2973918\n", "1998 3034904\n", "2002 3125418\n", "2009 3444020\n", "1994 3514763\n", "1996 3539413\n", "2004 3567744\n", "1997 3620066\n", "2015 3654892\n", "2000 3826372\n", "2005 3835025\n", "1999 3908112\n", "2010 4111392\n", "2013 4182691\n", "1986 5115251\n", "1990 5235827\n", "1989 5466192\n", "dtype: int64" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.hist(xrot=20)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 4 }