X-Git-Url: https://git.njae.me.uk/?p=covid19.git;a=blobdiff_plain;f=uk_deaths.ipynb;h=9cf1f8631f978f6f349150b70a72cd7581fcfcc1;hp=a5b6363c33eaadb831ee6a43a527e3e6e5b8fc0a;hb=HEAD;hpb=186f048121c3b9b88282ecb9b0decf928c4af1d6
diff --git a/uk_deaths.ipynb b/uk_deaths.ipynb
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-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Data from:\n",
- "\n",
- "* [Office of National Statistics](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/weeklyprovisionalfiguresondeathsregisteredinenglandandwales) (Endland and Wales) Weeks start on a Saturday.\n",
- "* [Northern Ireland Statistics and Research Agency](https://www.nisra.gov.uk/publications/weekly-deaths) (Northern Ireland). Weeks start on a Saturday. Note that the week numbers don't match the England and Wales data.\n",
- "* [National Records of Scotland](https://www.nrscotland.gov.uk/statistics-and-data/statistics/statistics-by-theme/vital-events/general-publications/weekly-and-monthly-data-on-births-and-deaths/weekly-data-on-births-and-deaths) (Scotland). Note that Scotland uses ISO8601 week numbers, which start on a Monday.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 204,
- "metadata": {},
- "outputs": [],
- "source": [
- "import itertools\n",
- "import collections\n",
- "import pandas as pd\n",
- "import numpy as np\n",
- "from scipy.stats import gmean\n",
- "\n",
- "import matplotlib as mpl\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 205,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " publishedweek1620201.xlsx publishedweek522018.ods\n",
- " publishedweek172020.csv publishedweek522018withupdatedrespiratoryrow.xls\n",
- " publishedweek172020.ods publishedweek522019.csv\n",
- " publishedweek172020.xlsx publishedweek522019.ods\n",
- " publishedweek182020.csv publishedweek522019.xls\n",
- " publishedweek182020.ods scotland-deaths-2020.csv\n",
- " publishedweek182020.xlsx weekly-deaths-april-20-scotland.csv\n",
- " publishedweek2015.csv\t weekly-deaths-april-20-scotland.xlsx\n",
- " publishedweek2015.ods\t 'Weekly_Deaths - Historical_NI.xls'\n",
- " publishedweek2015.xls\t Weekly_Deaths_NI_2015.csv\n",
- " publishedweek522016.csv Weekly_Deaths_NI_2016.csv\n",
- " publishedweek522016.ods Weekly_Deaths_NI_2017.csv\n",
- " publishedweek522016.xls Weekly_Deaths_NI_2018.csv\n",
- " publishedweek522017.csv Weekly_Deaths_NI_2019.csv\n",
- " publishedweek522017.ods Weekly_Deaths_NI_2020.csv\n",
- " publishedweek522017.xls Weekly_Deaths_NI_2020.xls\n",
- " publishedweek522018.csv weekly-march-20-scotland.zip\n"
- ]
- }
- ],
- "source": [
- "!ls uk-deaths-data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 206,
- "metadata": {},
- "outputs": [],
- "source": [
- "raw_data_2015 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2015.csv', \n",
- " parse_dates=[1, 2], dayfirst=True,\n",
- " index_col=0,\n",
- " header=[0, 1]\n",
- " )\n",
- "dh15i = raw_data_2015.iloc[:, [2]]\n",
- "dh15i.columns = ['total_2015']\n",
- "# dh15i.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 207,
- "metadata": {},
- "outputs": [],
- "source": [
- "raw_data_2016 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2016.csv', \n",
- " parse_dates=[1, 2], dayfirst=True,\n",
- " index_col=0,\n",
- " header=[0, 1]\n",
- " )\n",
- "dh16i = raw_data_2016.iloc[:, [2]]\n",
- "dh16i.columns = ['total_2016']\n",
- "# dh16i.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 208,
- "metadata": {},
- "outputs": [],
- "source": [
- "raw_data_2017 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2017.csv', \n",
- " parse_dates=[1, 2], dayfirst=True,\n",
- " index_col=0,\n",
- " header=[0, 1]\n",
- " )\n",
- "dh17i = raw_data_2017.iloc[:, [2]]\n",
- "dh17i.columns = ['total_2017']\n",
- "# dh17i.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 209,
- "metadata": {},
- "outputs": [],
- "source": [
- "raw_data_2018 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2018.csv', \n",
- " parse_dates=[1, 2], dayfirst=True,\n",
- " index_col=0,\n",
- " header=[0, 1]\n",
- " )\n",
- "dh18i = raw_data_2018.iloc[:, [2]]\n",
- "dh18i.columns = ['total_2018']\n",
- "# dh18i.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 210,
- "metadata": {},
- "outputs": [],
- "source": [
- "raw_data_2019 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2019.csv', \n",
- " parse_dates=[1, 2], dayfirst=True,\n",
- " index_col=0,\n",
- " header=[0, 1]\n",
- " )\n",
- "dh19i = raw_data_2019.iloc[:, [2]]\n",
- "dh19i.columns = ['total_2019']\n",
- "# dh19i.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 211,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
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- "execution_count": 211,
- "metadata": {},
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- "source": [
- "raw_data_2020_i = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2020.csv', \n",
- " parse_dates=[1], dayfirst=True,\n",
- " index_col=0,\n",
- " header=[0, 1]\n",
- " )\n",
- "deaths_headlines_i = raw_data_2020_i.iloc[:, [1]]\n",
- "deaths_headlines_i.columns = ['total_2020']\n",
- "deaths_headlines_i.head()"
- ]
- },
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- "cell_type": "code",
- "execution_count": 212,
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "raw_data_s = pd.read_csv('uk-deaths-data/weekly-deaths-april-20-scotland.csv', \n",
- " index_col=0,\n",
- " header=0,\n",
- " skiprows=2\n",
- " )\n",
- "# raw_data_s"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 213,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
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- " total_2020 total_2019 total_2018 total_2017 total_2016 total_2015\n",
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- "2 1567.0 1507.0 1899.0 1379.0 1305.0 1708\n",
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- "9 1171.0 1125.0 1022.0 1219.0 1150.0 1308\n",
- "10 1208.0 1156.0 1475.0 1146.0 1174.0 1192\n",
- "11 1156.0 1108.0 1220.0 1141.0 1175.0 1201\n",
- "12 1196.0 1101.0 1158.0 1152.0 1042.0 1149\n",
- "13 1079.0 1086.0 1050.0 1112.0 1172.0 1171\n",
- "14 1744.0 1032.0 1192.0 1060.0 1166.0 1042\n",
- "15 1978.0 1069.0 1192.0 998.0 1048.0 1192\n",
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- "17 1836.0 1121.0 1008.0 1121.0 1076.0 1108\n",
- "18 1679.0 1131.0 1093.0 1050.0 1006.0 1117\n",
- "19 1434.0 1018.0 967.0 1119.0 1047.0 1020\n",
- "20 NaN 1115.0 977.0 1115.0 1010.0 1103\n",
- "21 NaN 1061.0 1070.0 1063.0 994.0 1039\n",
- "22 NaN 1029.0 998.0 1015.0 999.0 1043\n",
- "23 NaN 1042.0 1033.0 1076.0 1023.0 1106\n",
- "24 NaN 1028.0 915.0 1031.0 988.0 1038\n",
- "25 NaN 1053.0 993.0 1032.0 994.0 1025\n",
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- "30 NaN 1041.0 933.0 978.0 979.0 956\n",
- "31 NaN 1020.0 969.0 1011.0 987.0 985\n",
- "32 NaN 1018.0 953.0 1002.0 997.0 1043\n",
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- "39 NaN 1142.0 1015.0 1056.0 1009.0 1010\n",
- "40 NaN 1051.0 1042.0 1016.0 1072.0 1008\n",
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- "42 NaN 1153.0 1031.0 1067.0 1070.0 989\n",
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- "44 NaN 1101.0 1085.0 1062.0 1032.0 1116\n",
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- "48 NaN 1163.0 1062.0 1153.0 1159.0 1115\n",
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- "50 NaN 1312.0 1212.0 1335.0 1219.0 1101\n",
- "51 NaN 1277.0 1216.0 1437.0 1284.0 1146\n",
- "52 NaN 1000.0 1058.0 1168.0 1133.0 944\n",
- "53 NaN NaN NaN NaN NaN 1018"
- ]
- },
- "execution_count": 213,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "deaths_headlines_s = raw_data_s[reversed('2015 2016 2017 2018 2019 2020'.split())]\n",
- "deaths_headlines_s.columns = ['total_' + c for c in deaths_headlines_s.columns]\n",
- "deaths_headlines_s.reset_index(drop=True, inplace=True)\n",
- "deaths_headlines_s.index = deaths_headlines_s.index + 1\n",
- "deaths_headlines_s"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 214,
- "metadata": {},
- "outputs": [],
- "source": [
- "raw_data_2020 = pd.read_csv('uk-deaths-data/publishedweek182020.csv', \n",
- " parse_dates=[1], dayfirst=True,\n",
- " index_col=0,\n",
- " header=[0, 1])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 215,
- "metadata": {},
- "outputs": [],
- "source": [
- "# raw_data_2020.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 216,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "1 787\n",
- "2 939\n",
- "3 767\n",
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- "18 929\n",
- "Name: (W92000004, Wales), dtype: int64"
- ]
- },
- "execution_count": 216,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "raw_data_2020['W92000004', 'Wales']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 217,
- "metadata": {},
- "outputs": [],
- "source": [
- "raw_data_2019 = pd.read_csv('uk-deaths-data/publishedweek522019.csv', \n",
- " parse_dates=[1], dayfirst=True,\n",
- " index_col=0,\n",
- " header=[0, 1])\n",
- "# raw_data_2019.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 218,
- "metadata": {},
- "outputs": [],
- "source": [
- "raw_data_2018 = pd.read_csv('uk-deaths-data/publishedweek522018.csv', \n",
- " parse_dates=[1], dayfirst=True,\n",
- " index_col=0,\n",
- " header=[0, 1])\n",
- "# raw_data_2018.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 219,
- "metadata": {},
- "outputs": [],
- "source": [
- "raw_data_2017 = pd.read_csv('uk-deaths-data/publishedweek522017.csv', \n",
- " parse_dates=[1], dayfirst=True,\n",
- " index_col=0,\n",
- " header=[0, 1])\n",
- "# raw_data_2017.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 220,
- "metadata": {},
- "outputs": [],
- "source": [
- "raw_data_2016 = pd.read_csv('uk-deaths-data/publishedweek522016.csv', \n",
- " parse_dates=[1], dayfirst=True,\n",
- " index_col=0,\n",
- " header=[0, 1])\n",
- "# raw_data_2016.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 221,
- "metadata": {},
- "outputs": [],
- "source": [
- "raw_data_2015 = pd.read_csv('uk-deaths-data/publishedweek2015.csv', \n",
- " parse_dates=[1], dayfirst=True,\n",
- " index_col=0,\n",
- " header=[0, 1])\n",
- "# raw_data_2015.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 222,
- "metadata": {},
- "outputs": [
- {
- "data": {
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- " total_2020\n",
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- ]
- },
- "execution_count": 222,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "deaths_headlines_e = raw_data_2020.iloc[:, [1]]\n",
- "deaths_headlines_e.columns = ['total_2020']\n",
- "deaths_headlines_w = raw_data_2020['W92000004']\n",
- "deaths_headlines_e.columns = ['total_2020']\n",
- "deaths_headlines_w.columns = ['total_2020']\n",
- "deaths_headlines_e.total_2020 -= deaths_headlines_w.total_2020\n",
- "deaths_headlines_e.head()\n",
- "deaths_headlines_e"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 223,
- "metadata": {},
- "outputs": [
- {
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- },
- "execution_count": 223,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "dh19e = raw_data_2019.iloc[:, [1]]\n",
- "dh19w = raw_data_2019['W92000004']\n",
- "dh19e.columns = ['total_2019']\n",
- "dh19w.columns = ['total_2019']\n",
- "dh19e.total_2019 -= dh19w.total_2019\n",
- "dh19e.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 224,
- "metadata": {},
- "outputs": [
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- "execution_count": 224,
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- "source": [
- "dh19w.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 225,
- "metadata": {},
- "outputs": [],
- "source": [
- "dh18e = raw_data_2018.iloc[:, [1]]\n",
- "dh18w = raw_data_2018['W92000004']\n",
- "dh18e.columns = ['total_2018']\n",
- "dh18w.columns = ['total_2018']\n",
- "dh18e.total_2018 -= dh18w.total_2018\n",
- "# dh18e.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 226,
- "metadata": {},
- "outputs": [],
- "source": [
- "dh17e = raw_data_2017.iloc[:, [1]]\n",
- "dh17w = raw_data_2017['W92000004']\n",
- "dh17e.columns = ['total_2017']\n",
- "dh17w.columns = ['total_2017']\n",
- "dh17e.total_2017 -= dh17w.total_2017\n",
- "# dh17e.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 227,
- "metadata": {},
- "outputs": [],
- "source": [
- "dh16e = raw_data_2016.iloc[:, [1]]\n",
- "dh16w = raw_data_2016['W92000004']\n",
- "dh16e.columns = ['total_2016']\n",
- "dh16w.columns = ['total_2016']\n",
- "dh16e.total_2016 -= dh16w.total_2016\n",
- "# dh16e.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 228,
- "metadata": {},
- "outputs": [],
- "source": [
- "dh15e = raw_data_2015.iloc[:, [1]]\n",
- "dh15w = raw_data_2015['W92000004']\n",
- "dh15e.columns = ['total_2015']\n",
- "dh15w.columns = ['total_2015']\n",
- "dh15e.total_2015 -= dh15w.total_2015\n",
- "# dh15e.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 229,
- "metadata": {},
- "outputs": [],
- "source": [
- "# dh18 = raw_data_2018.iloc[:, [1, 2]]\n",
- "# dh18.columns = ['total_2018', 'total_previous']\n",
- "# # dh18.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 230,
- "metadata": {},
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- "text/plain": [
- " total_2020 total_2019 total_2018 total_2017 total_2016 total_2015\n",
- "1 11467.0 10237 11940 11247 12236 11561\n",
- "2 13119.0 11800 14146 12890 10790 15206\n",
- "3 12223.0 11177 13371 12775 10753 13930\n",
- "4 11133.0 11006 13085 11996 10600 13106\n",
- "5 10885.0 10552 12470 11736 10362 12099\n",
- "6 10296.0 10959 11694 11546 10470 11319\n",
- "7 10216.0 11076 11443 10954 9933 11112\n",
- "8 10162.0 10600 11353 11093 10360 10695\n",
- "9 10165.0 10360 10220 10533 10564 10736\n",
- "10 10243.0 10276 12101 10443 10276 10757\n",
- "11 10344.0 9901 11870 10044 10284 10290\n",
- "12 9926.0 9774 11139 9690 8967 9888\n",
- "13 10422.0 9213 9308 9369 9574 9827\n",
- "14 15467.0 9484 10064 9297 10857 8482\n",
- "15 17588.0 9654 11558 7916 10679 9429\n",
- "16 21182.0 8445 10535 8990 10211 10968\n",
- "17 20873.0 9381 9692 10181 9784 9937\n",
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- "19 NaN 8455 8094 10006 9985 8273\n",
- "20 NaN 9614 9474 9673 9342 9668\n",
- "21 NaN 9657 9033 9438 9115 9391\n",
- "22 NaN 7742 7609 7746 7409 7625\n",
- "23 NaN 9514 9343 9182 9289 9509\n",
- "24 NaN 8847 8782 8768 8808 8942\n",
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- "27 NaN 8528 8633 8707 8614 8670\n",
- "28 NaN 8591 8710 8812 8789 8461\n",
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- "30 NaN 8569 8581 8296 8725 8262\n",
- "31 NaN 8701 8587 8351 8602 8071\n",
- "32 NaN 8580 8782 8466 8531 8298\n",
- "33 NaN 8504 8312 8707 8496 8600\n",
- "34 NaN 8441 8413 8812 8700 8564\n",
- "35 NaN 7684 7354 7594 7453 8423\n",
- "36 NaN 9113 8851 8955 8847 7389\n",
- "37 NaN 8946 8612 8845 8548 8704\n",
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- "41 NaN 9383 9043 9300 9132 9124\n",
- "42 NaN 9534 9224 9381 9144 8899\n",
- "43 NaN 9351 8970 9131 9100 9104\n",
- "44 NaN 9522 8925 9389 9508 9025\n",
- "45 NaN 10044 9509 9748 9844 9388\n",
- "46 NaN 9976 9537 9609 10013 9325\n",
- "47 NaN 10183 9300 9982 9942 9219\n",
- "48 NaN 10269 9360 9902 9796 9233\n",
- "49 NaN 10079 9613 10151 10530 9706\n",
- "50 NaN 10489 9842 10509 9870 9581\n",
- "51 NaN 11159 10392 11755 10802 10043\n",
- "52 NaN 7037 6670 7946 7445 8095"
- ]
- },
- "execution_count": 230,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "deaths_headlines_e = deaths_headlines_e.merge(dh19e['total_2019'], how='outer', left_index=True, right_index=True)\n",
- "deaths_headlines_e = deaths_headlines_e.merge(dh18e['total_2018'], how='outer', left_index=True, right_index=True)\n",
- "deaths_headlines_e = deaths_headlines_e.merge(dh17e['total_2017'], how='outer', left_index=True, right_index=True)\n",
- "deaths_headlines_e = deaths_headlines_e.merge(dh16e['total_2016'], how='outer', left_index=True, right_index=True)\n",
- "# deaths_headlines = deaths_headlines.merge(dh15['total_2015'], how='outer', left_index=True, right_index=True)\n",
- "deaths_headlines_e = deaths_headlines_e.merge(dh15e['total_2015'], how='left', left_index=True, right_index=True)\n",
- "deaths_headlines_e"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 231,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
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- ]
- },
- "execution_count": 231,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "deaths_headlines_s = raw_data_s[reversed('2015 2016 2017 2018 2019 2020'.split())]\n",
- "deaths_headlines_s.columns = ['total_' + c for c in deaths_headlines_s.columns]\n",
- "deaths_headlines_s.reset_index(drop=True, inplace=True)\n",
- "deaths_headlines_s.index = deaths_headlines_s.index + 1\n",
- "deaths_headlines_s = deaths_headlines_s.loc[1:52]\n",
- "deaths_headlines_s"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 232,
- "metadata": {},
- "outputs": [
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- "51 NaN 767 724 762 691 646\n",
- "52 NaN 496 461 541 558 535"
- ]
- },
- "execution_count": 232,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "deaths_headlines_w = deaths_headlines_w.merge(dh19w['total_2019'], how='outer', left_index=True, right_index=True)\n",
- "deaths_headlines_w = deaths_headlines_w.merge(dh18w['total_2018'], how='outer', left_index=True, right_index=True)\n",
- "deaths_headlines_w = deaths_headlines_w.merge(dh17w['total_2017'], how='outer', left_index=True, right_index=True)\n",
- "deaths_headlines_w = deaths_headlines_w.merge(dh16w['total_2016'], how='outer', left_index=True, right_index=True)\n",
- "# deaths_headlines = deaths_headlines.merge(dh15['total_2015'], how='outer', left_index=True, right_index=True)\n",
- "deaths_headlines_w = deaths_headlines_w.merge(dh15w['total_2015'], how='left', left_index=True, right_index=True)\n",
- "deaths_headlines_w"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 233,
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- " total_2020 total_2019 total_2018 total_2017 total_2016 total_2015\n",
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- "execution_count": 233,
- "metadata": {},
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- "source": [
- "deaths_headlines_i = deaths_headlines_i.merge(dh19i['total_2019'], how='outer', left_index=True, right_index=True)\n",
- "deaths_headlines_i = deaths_headlines_i.merge(dh18i['total_2018'], how='outer', left_index=True, right_index=True)\n",
- "deaths_headlines_i = deaths_headlines_i.merge(dh17i['total_2017'], how='outer', left_index=True, right_index=True)\n",
- "deaths_headlines_i = deaths_headlines_i.merge(dh16i['total_2016'], how='outer', left_index=True, right_index=True)\n",
- "deaths_headlines_i = deaths_headlines_i.merge(dh15i['total_2015'], how='left', left_index=True, right_index=True)\n",
- "deaths_headlines_i"
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- "cell_type": "code",
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- "metadata": {},
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- "text/plain": [
- " total_2020 total_2019 total_2018 total_2017 total_2016 total_2015\n",
- "1 13768.0 12424.0 14701.0 13612.0 14863.0 13751\n",
- "2 16020.0 14487.0 17430.0 15528.0 13154.0 18318\n",
- "3 14723.0 13545.0 16355.0 15231.0 13060.0 16738\n",
- "4 13429.0 13283.0 15971.0 14461.0 12859.0 15712\n",
- "5 13123.0 12799.0 15087.0 14188.0 12571.0 14560\n",
- "6 12534.0 13222.0 14111.0 13805.0 12697.0 13730\n",
- "7 12412.0 13347.0 13925.0 13212.0 12016.0 13510\n",
- "8 12300.0 12877.0 13753.0 13330.0 12718.0 13071\n",
- "9 12334.0 12479.0 12190.0 12819.0 12733.0 13181\n",
- "10 12415.0 12396.0 14859.0 12580.0 12493.0 13007\n",
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- "13 12507.0 11260.0 11310.0 11453.0 11738.0 11987\n",
- "14 18565.0 11445.0 12272.0 11305.0 13060.0 10325\n",
- "15 20929.0 11661.0 13843.0 9761.0 12757.0 11575\n",
- "16 24691.0 10243.0 12639.0 11000.0 12310.0 13061\n",
- "17 24303.0 11452.0 11596.0 12356.0 11795.0 12023\n",
- "18 20059.0 12695.0 11538.0 10372.0 10401.0 11586\n",
- "19 NaN 10361.0 9821.0 12114.0 12002.0 10138\n",
- "20 NaN 11717.0 11386.0 11718.0 11222.0 11692\n",
- "21 NaN 11653.0 10974.0 11431.0 11013.0 11334\n",
- "22 NaN 9534.0 9397.0 9603.0 9192.0 9514\n",
- "23 NaN 11461.0 11259.0 11134.0 11171.0 11603\n",
- "24 NaN 10754.0 10535.0 10698.0 10673.0 10858\n",
- "25 NaN 10807.0 10514.0 10930.0 10611.0 10629\n",
- "26 NaN 10824.0 10529.0 10624.0 10526.0 10525\n",
- "27 NaN 10328.0 10565.0 10565.0 10412.0 10545\n",
- "28 NaN 10512.0 10467.0 10643.0 10647.0 10278\n",
- "29 NaN 10324.0 10353.0 10426.0 10672.0 10028\n",
- "30 NaN 10422.0 10356.0 10147.0 10612.0 10021\n",
- "31 NaN 10564.0 10408.0 10239.0 10433.0 9893\n",
- "32 NaN 10406.0 10542.0 10278.0 10439.0 10153\n",
- "33 NaN 10405.0 10091.0 10569.0 10312.0 10352\n",
- "34 NaN 10279.0 10199.0 10698.0 10637.0 10354\n",
- "35 NaN 9478.0 9046.0 9372.0 9226.0 10239\n",
- "36 NaN 10918.0 10680.0 10781.0 10681.0 9092\n",
- "37 NaN 10892.0 10496.0 10692.0 10401.0 10573\n",
- "38 NaN 10792.0 10498.0 10875.0 10183.0 10381\n",
- "39 NaN 10954.0 10463.0 11027.0 10278.0 10826\n",
- "40 NaN 11113.0 10869.0 11101.0 10671.0 10700\n",
- "41 NaN 11403.0 11048.0 11357.0 11016.0 11108\n",
- "42 NaN 11625.0 11177.0 11389.0 11134.0 10799\n",
- "43 NaN 11415.0 10885.0 11152.0 11048.0 10966\n",
- "44 NaN 11567.0 10866.0 11366.0 11463.0 11026\n",
- "45 NaN 12177.0 11588.0 11767.0 11803.0 11312\n",
- "46 NaN 12146.0 11552.0 11773.0 12209.0 11338\n",
- "47 NaN 12472.0 11289.0 12102.0 12064.0 11178\n",
- "48 NaN 12455.0 11392.0 12046.0 11901.0 11216\n",
- "49 NaN 12275.0 11687.0 12342.0 12733.0 11748\n",
- "50 NaN 12853.0 12078.0 12924.0 12076.0 11713\n",
- "51 NaN 13566.0 12649.0 14308.0 13137.0 12136\n",
- "52 NaN 8727.0 8384.0 9904.0 9335.0 9806"
- ]
- },
- "execution_count": 234,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "deaths_headlines = deaths_headlines_e + deaths_headlines_w + deaths_headlines_i + deaths_headlines_s\n",
- "deaths_headlines"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 241,
- "metadata": {},
- "outputs": [
- {
- "data": {
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- " 9478.0 | \n",
- " 9046.0 | \n",
- " 9372.0 | \n",
- " 9226.0 | \n",
- " 10239 | \n",
- " 9472.2 | \n",
- "
\n",
- " \n",
- " 36 | \n",
- " NaN | \n",
- " 10918.0 | \n",
- " 10680.0 | \n",
- " 10781.0 | \n",
- " 10681.0 | \n",
- " 9092 | \n",
- " 10430.4 | \n",
- "
\n",
- " \n",
- " 37 | \n",
- " NaN | \n",
- " 10892.0 | \n",
- " 10496.0 | \n",
- " 10692.0 | \n",
- " 10401.0 | \n",
- " 10573 | \n",
- " 10610.8 | \n",
- "
\n",
- " \n",
- " 38 | \n",
- " NaN | \n",
- " 10792.0 | \n",
- " 10498.0 | \n",
- " 10875.0 | \n",
- " 10183.0 | \n",
- " 10381 | \n",
- " 10545.8 | \n",
- "
\n",
- " \n",
- " 39 | \n",
- " NaN | \n",
- " 10954.0 | \n",
- " 10463.0 | \n",
- " 11027.0 | \n",
- " 10278.0 | \n",
- " 10826 | \n",
- " 10709.6 | \n",
- "
\n",
- " \n",
- " 40 | \n",
- " NaN | \n",
- " 11113.0 | \n",
- " 10869.0 | \n",
- " 11101.0 | \n",
- " 10671.0 | \n",
- " 10700 | \n",
- " 10890.8 | \n",
- "
\n",
- " \n",
- " 41 | \n",
- " NaN | \n",
- " 11403.0 | \n",
- " 11048.0 | \n",
- " 11357.0 | \n",
- " 11016.0 | \n",
- " 11108 | \n",
- " 11186.4 | \n",
- "
\n",
- " \n",
- " 42 | \n",
- " NaN | \n",
- " 11625.0 | \n",
- " 11177.0 | \n",
- " 11389.0 | \n",
- " 11134.0 | \n",
- " 10799 | \n",
- " 11224.8 | \n",
- "
\n",
- " \n",
- " 43 | \n",
- " NaN | \n",
- " 11415.0 | \n",
- " 10885.0 | \n",
- " 11152.0 | \n",
- " 11048.0 | \n",
- " 10966 | \n",
- " 11093.2 | \n",
- "
\n",
- " \n",
- " 44 | \n",
- " NaN | \n",
- " 11567.0 | \n",
- " 10866.0 | \n",
- " 11366.0 | \n",
- " 11463.0 | \n",
- " 11026 | \n",
- " 11257.6 | \n",
- "
\n",
- " \n",
- " 45 | \n",
- " NaN | \n",
- " 12177.0 | \n",
- " 11588.0 | \n",
- " 11767.0 | \n",
- " 11803.0 | \n",
- " 11312 | \n",
- " 11729.4 | \n",
- "
\n",
- " \n",
- " 46 | \n",
- " NaN | \n",
- " 12146.0 | \n",
- " 11552.0 | \n",
- " 11773.0 | \n",
- " 12209.0 | \n",
- " 11338 | \n",
- " 11803.6 | \n",
- "
\n",
- " \n",
- " 47 | \n",
- " NaN | \n",
- " 12472.0 | \n",
- " 11289.0 | \n",
- " 12102.0 | \n",
- " 12064.0 | \n",
- " 11178 | \n",
- " 11821.0 | \n",
- "
\n",
- " \n",
- " 48 | \n",
- " NaN | \n",
- " 12455.0 | \n",
- " 11392.0 | \n",
- " 12046.0 | \n",
- " 11901.0 | \n",
- " 11216 | \n",
- " 11802.0 | \n",
- "
\n",
- " \n",
- " 49 | \n",
- " NaN | \n",
- " 12275.0 | \n",
- " 11687.0 | \n",
- " 12342.0 | \n",
- " 12733.0 | \n",
- " 11748 | \n",
- " 12157.0 | \n",
- "
\n",
- " \n",
- " 50 | \n",
- " NaN | \n",
- " 12853.0 | \n",
- " 12078.0 | \n",
- " 12924.0 | \n",
- " 12076.0 | \n",
- " 11713 | \n",
- " 12328.8 | \n",
- "
\n",
- " \n",
- " 51 | \n",
- " NaN | \n",
- " 13566.0 | \n",
- " 12649.0 | \n",
- " 14308.0 | \n",
- " 13137.0 | \n",
- " 12136 | \n",
- " 13159.2 | \n",
- "
\n",
- " \n",
- " 52 | \n",
- " NaN | \n",
- " 8727.0 | \n",
- " 8384.0 | \n",
- " 9904.0 | \n",
- " 9335.0 | \n",
- " 9806 | \n",
- " 9231.2 | \n",
- "
\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " total_2020 total_2019 total_2018 total_2017 total_2016 total_2015 \\\n",
- "1 13768.0 12424.0 14701.0 13612.0 14863.0 13751 \n",
- "2 16020.0 14487.0 17430.0 15528.0 13154.0 18318 \n",
- "3 14723.0 13545.0 16355.0 15231.0 13060.0 16738 \n",
- "4 13429.0 13283.0 15971.0 14461.0 12859.0 15712 \n",
- "5 13123.0 12799.0 15087.0 14188.0 12571.0 14560 \n",
- "6 12534.0 13222.0 14111.0 13805.0 12697.0 13730 \n",
- "7 12412.0 13347.0 13925.0 13212.0 12016.0 13510 \n",
- "8 12300.0 12877.0 13753.0 13330.0 12718.0 13071 \n",
- "9 12334.0 12479.0 12190.0 12819.0 12733.0 13181 \n",
- "10 12415.0 12396.0 14859.0 12580.0 12493.0 13007 \n",
- "11 12499.0 12018.0 14367.0 12089.0 12489.0 12475 \n",
- "12 12112.0 11797.0 13397.0 11833.0 10983.0 12027 \n",
- "13 12507.0 11260.0 11310.0 11453.0 11738.0 11987 \n",
- "14 18565.0 11445.0 12272.0 11305.0 13060.0 10325 \n",
- "15 20929.0 11661.0 13843.0 9761.0 12757.0 11575 \n",
- "16 24691.0 10243.0 12639.0 11000.0 12310.0 13061 \n",
- "17 24303.0 11452.0 11596.0 12356.0 11795.0 12023 \n",
- "18 20059.0 12695.0 11538.0 10372.0 10401.0 11586 \n",
- "19 NaN 10361.0 9821.0 12114.0 12002.0 10138 \n",
- "20 NaN 11717.0 11386.0 11718.0 11222.0 11692 \n",
- "21 NaN 11653.0 10974.0 11431.0 11013.0 11334 \n",
- "22 NaN 9534.0 9397.0 9603.0 9192.0 9514 \n",
- "23 NaN 11461.0 11259.0 11134.0 11171.0 11603 \n",
- "24 NaN 10754.0 10535.0 10698.0 10673.0 10858 \n",
- "25 NaN 10807.0 10514.0 10930.0 10611.0 10629 \n",
- "26 NaN 10824.0 10529.0 10624.0 10526.0 10525 \n",
- "27 NaN 10328.0 10565.0 10565.0 10412.0 10545 \n",
- "28 NaN 10512.0 10467.0 10643.0 10647.0 10278 \n",
- "29 NaN 10324.0 10353.0 10426.0 10672.0 10028 \n",
- "30 NaN 10422.0 10356.0 10147.0 10612.0 10021 \n",
- "31 NaN 10564.0 10408.0 10239.0 10433.0 9893 \n",
- "32 NaN 10406.0 10542.0 10278.0 10439.0 10153 \n",
- "33 NaN 10405.0 10091.0 10569.0 10312.0 10352 \n",
- "34 NaN 10279.0 10199.0 10698.0 10637.0 10354 \n",
- "35 NaN 9478.0 9046.0 9372.0 9226.0 10239 \n",
- "36 NaN 10918.0 10680.0 10781.0 10681.0 9092 \n",
- "37 NaN 10892.0 10496.0 10692.0 10401.0 10573 \n",
- "38 NaN 10792.0 10498.0 10875.0 10183.0 10381 \n",
- "39 NaN 10954.0 10463.0 11027.0 10278.0 10826 \n",
- "40 NaN 11113.0 10869.0 11101.0 10671.0 10700 \n",
- "41 NaN 11403.0 11048.0 11357.0 11016.0 11108 \n",
- "42 NaN 11625.0 11177.0 11389.0 11134.0 10799 \n",
- "43 NaN 11415.0 10885.0 11152.0 11048.0 10966 \n",
- "44 NaN 11567.0 10866.0 11366.0 11463.0 11026 \n",
- "45 NaN 12177.0 11588.0 11767.0 11803.0 11312 \n",
- "46 NaN 12146.0 11552.0 11773.0 12209.0 11338 \n",
- "47 NaN 12472.0 11289.0 12102.0 12064.0 11178 \n",
- "48 NaN 12455.0 11392.0 12046.0 11901.0 11216 \n",
- "49 NaN 12275.0 11687.0 12342.0 12733.0 11748 \n",
- "50 NaN 12853.0 12078.0 12924.0 12076.0 11713 \n",
- "51 NaN 13566.0 12649.0 14308.0 13137.0 12136 \n",
- "52 NaN 8727.0 8384.0 9904.0 9335.0 9806 \n",
- "\n",
- " previous_mean \n",
- "1 13870.2 \n",
- "2 15783.4 \n",
- "3 14985.8 \n",
- "4 14457.2 \n",
- "5 13841.0 \n",
- "6 13513.0 \n",
- "7 13202.0 \n",
- "8 13149.8 \n",
- "9 12680.4 \n",
- "10 13067.0 \n",
- "11 12687.6 \n",
- "12 12007.4 \n",
- "13 11549.6 \n",
- "14 11681.4 \n",
- "15 11919.4 \n",
- "16 11850.6 \n",
- "17 11844.4 \n",
- "18 11318.4 \n",
- "19 10887.2 \n",
- "20 11547.0 \n",
- "21 11281.0 \n",
- "22 9448.0 \n",
- "23 11325.6 \n",
- "24 10703.6 \n",
- "25 10698.2 \n",
- "26 10605.6 \n",
- "27 10483.0 \n",
- "28 10509.4 \n",
- "29 10360.6 \n",
- "30 10311.6 \n",
- "31 10307.4 \n",
- "32 10363.6 \n",
- "33 10345.8 \n",
- "34 10433.4 \n",
- "35 9472.2 \n",
- "36 10430.4 \n",
- "37 10610.8 \n",
- "38 10545.8 \n",
- "39 10709.6 \n",
- "40 10890.8 \n",
- "41 11186.4 \n",
- "42 11224.8 \n",
- "43 11093.2 \n",
- "44 11257.6 \n",
- "45 11729.4 \n",
- "46 11803.6 \n",
- "47 11821.0 \n",
- "48 11802.0 \n",
- "49 12157.0 \n",
- "50 12328.8 \n",
- "51 13159.2 \n",
- "52 9231.2 "
- ]
- },
- "execution_count": 241,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "deaths_headlines_e['previous_mean'] = deaths_headlines_e['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)\n",
- "deaths_headlines_w['previous_mean'] = deaths_headlines_w['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)\n",
- "deaths_headlines_s['previous_mean'] = deaths_headlines_s['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)\n",
- "deaths_headlines_i['previous_mean'] = deaths_headlines_i['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)\n",
- "deaths_headlines['previous_mean'] = deaths_headlines['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)\n",
- "deaths_headlines"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 242,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 242,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
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\n",
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