X-Git-Url: https://git.njae.me.uk/?p=covid19.git;a=blobdiff_plain;f=uk_deaths.ipynb;h=9cf1f8631f978f6f349150b70a72cd7581fcfcc1;hp=5f34a0e04ea8b04a3146507ac0d1e7cd2168ae59;hb=HEAD;hpb=a596078977bfdf8b1929a5e288cc70f745101f9d
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": 137,
- "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": 138,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " publishedweek1620201.xlsx publishedweek522018.csv\n",
- " publishedweek172020.csv publishedweek522018.ods\n",
- " publishedweek172020.ods publishedweek522018withupdatedrespiratoryrow.xls\n",
- " publishedweek172020.xlsx publishedweek522019.csv\n",
- " publishedweek182020.csv publishedweek522019.ods\n",
- " publishedweek182020.ods publishedweek522019.xls\n",
- " publishedweek182020.xlsx 'Weekly_Deaths - Historical_NI.xls'\n",
- " publishedweek2015.csv\t Weekly_Deaths_NI_2015.csv\n",
- " publishedweek2015.ods\t Weekly_Deaths_NI_2016.csv\n",
- " publishedweek2015.xls\t Weekly_Deaths_NI_2017.csv\n",
- " publishedweek522016.csv Weekly_Deaths_NI_2018.csv\n",
- " publishedweek522016.ods Weekly_Deaths_NI_2019.csv\n",
- " publishedweek522016.xls Weekly_Deaths_NI_2020.csv\n",
- " publishedweek522017.csv Weekly_Deaths_NI_2020.xls\n",
- " publishedweek522017.ods weekly-march-20-scotland.zip\n",
- " publishedweek522017.xls\n"
- ]
- }
- ],
- "source": [
- "!ls uk-deaths-data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 139,
- "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": 140,
- "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": 141,
- "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": 142,
- "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": 143,
- "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": 144,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
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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": null,
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- "cell_type": "code",
- "execution_count": 185,
- "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": 184,
- "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",
- "3 1322.0 1353.0 1629.0 1224.0 1215.0 1489\n",
- "4 1226.0 1208.0 1610.0 1197.0 1187.0 1381\n",
- "5 1188.0 1206.0 1369.0 1332.0 1205.0 1286\n",
- "6 1216.0 1243.0 1265.0 1200.0 1217.0 1344\n",
- "7 1162.0 1181.0 1315.0 1231.0 1209.0 1360\n",
- "8 1162.0 1245.0 1245.0 1185.0 1239.0 1320\n",
- "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",
- "16 1916.0 902.0 1136.0 1111.0 1092.0 1095\n",
- "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",
- "26 NaN 1051.0 1046.0 994.0 1007.0 1032\n",
- "27 NaN 981.0 1041.0 1040.0 988.0 1040\n",
- "28 NaN 1077.0 1002.0 1014.0 1022.0 1011\n",
- "29 NaN 964.0 928.0 1025.0 1041.0 1023\n",
- "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",
- "33 NaN 1028.0 978.0 1004.0 982.0 969\n",
- "34 NaN 1011.0 941.0 1045.0 1017.0 982\n",
- "35 NaN 1013.0 930.0 980.0 1039.0 954\n",
- "36 NaN 980.0 970.0 1006.0 1007.0 977\n",
- "37 NaN 1074.0 1020.0 972.0 983.0 991\n",
- "38 NaN 1071.0 946.0 1049.0 966.0 1001\n",
- "39 NaN 1142.0 1015.0 1056.0 1009.0 1010\n",
- "40 NaN 1051.0 1042.0 1016.0 1072.0 1008\n",
- "41 NaN 1143.0 1081.0 1133.0 1009.0 1028\n",
- "42 NaN 1153.0 1031.0 1067.0 1070.0 989\n",
- "43 NaN 1115.0 1019.0 1095.0 1052.0 981\n",
- "44 NaN 1101.0 1085.0 1062.0 1032.0 1116\n",
- "45 NaN 1184.0 1144.0 1126.0 1043.0 1028\n",
- "46 NaN 1160.0 1084.0 1175.0 1174.0 1103\n",
- "47 NaN 1229.0 1058.0 1178.0 1132.0 1054\n",
- "48 NaN 1163.0 1062.0 1153.0 1159.0 1115\n",
- "49 NaN 1108.0 1076.0 1237.0 1188.0 1089\n",
- "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": 184,
- "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": 145,
- "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": 146,
- "metadata": {},
- "outputs": [],
- "source": [
- "# raw_data_2020.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 147,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "1 787\n",
- "2 939\n",
- "3 767\n",
- "4 723\n",
- "5 727\n",
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- "17 1124\n",
- "18 929\n",
- "Name: (W92000004, Wales), dtype: int64"
- ]
- },
- "execution_count": 147,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "raw_data_2020['W92000004', 'Wales']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 148,
- "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": 149,
- "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": 150,
- "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": 151,
- "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": 152,
- "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": 153,
- "metadata": {},
- "outputs": [
- {
- "data": {
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- " total_2020\n",
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- ]
- },
- "execution_count": 153,
- "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": 154,
- "metadata": {},
- "outputs": [
- {
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- },
- "execution_count": 154,
- "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": 155,
- "metadata": {},
- "outputs": [
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- "execution_count": 155,
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- "source": [
- "dh19w.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 156,
- "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": 157,
- "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": 158,
- "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": 159,
- "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": 160,
- "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": 161,
- "metadata": {},
- "outputs": [
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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",
- "29 NaN 8527 8579 8562 8790 8228\n",
- "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",
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- "51 NaN 11159 10392 11755 10802 10043\n",
- "52 NaN 7037 6670 7946 7445 8095"
- ]
- },
- "execution_count": 161,
- "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": 182,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
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- },
- "execution_count": 182,
- "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": 162,
- "metadata": {},
- "outputs": [
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- "48 NaN 689 673 636 643 589\n",
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- ]
- },
- "execution_count": 162,
- "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": 163,
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- " total_2020 total_2019 total_2018 total_2017 total_2016 total_2015\n",
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- "execution_count": 163,
- "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",
- "execution_count": 186,
- "metadata": {},
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- ]
- },
- "execution_count": 186,
- "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": 187,
- "metadata": {},
- "outputs": [
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\n",
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\n",
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\n",
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\n",
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- "
\n",
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\n",
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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 13751.0 \n",
- "2 16020.0 14487.0 17430.0 15528.0 13154.0 18318.0 \n",
- "3 14723.0 13545.0 16355.0 15231.0 13060.0 16738.0 \n",
- "4 13429.0 13283.0 15971.0 14461.0 12859.0 15712.0 \n",
- "5 13123.0 12799.0 15087.0 14188.0 12571.0 14560.0 \n",
- "6 12534.0 13222.0 14111.0 13805.0 12697.0 13730.0 \n",
- "7 12412.0 13347.0 13925.0 13212.0 12016.0 13510.0 \n",
- "8 12300.0 12877.0 13753.0 13330.0 12718.0 13071.0 \n",
- "9 12334.0 12479.0 12190.0 12819.0 12733.0 13181.0 \n",
- "10 12415.0 12396.0 14859.0 12580.0 12493.0 13007.0 \n",
- "11 12499.0 12018.0 14367.0 12089.0 12489.0 12475.0 \n",
- "12 12112.0 11797.0 13397.0 11833.0 10983.0 12027.0 \n",
- "13 12507.0 11260.0 11310.0 11453.0 11738.0 11987.0 \n",
- "14 18565.0 11445.0 12272.0 11305.0 13060.0 10325.0 \n",
- "15 20929.0 11661.0 13843.0 9761.0 12757.0 11575.0 \n",
- "16 24691.0 10243.0 12639.0 11000.0 12310.0 13061.0 \n",
- "17 24303.0 11452.0 11596.0 12356.0 11795.0 12023.0 \n",
- "18 20059.0 12695.0 11538.0 10372.0 10401.0 11586.0 \n",
- "19 NaN 10361.0 9821.0 12114.0 12002.0 10138.0 \n",
- "20 NaN 11717.0 11386.0 11718.0 11222.0 11692.0 \n",
- "21 NaN 11653.0 10974.0 11431.0 11013.0 11334.0 \n",
- "22 NaN 9534.0 9397.0 9603.0 9192.0 9514.0 \n",
- "23 NaN 11461.0 11259.0 11134.0 11171.0 11603.0 \n",
- "24 NaN 10754.0 10535.0 10698.0 10673.0 10858.0 \n",
- "25 NaN 10807.0 10514.0 10930.0 10611.0 10629.0 \n",
- "26 NaN 10824.0 10529.0 10624.0 10526.0 10525.0 \n",
- "27 NaN 10328.0 10565.0 10565.0 10412.0 10545.0 \n",
- "28 NaN 10512.0 10467.0 10643.0 10647.0 10278.0 \n",
- "29 NaN 10324.0 10353.0 10426.0 10672.0 10028.0 \n",
- "30 NaN 10422.0 10356.0 10147.0 10612.0 10021.0 \n",
- "31 NaN 10564.0 10408.0 10239.0 10433.0 9893.0 \n",
- "32 NaN 10406.0 10542.0 10278.0 10439.0 10153.0 \n",
- "33 NaN 10405.0 10091.0 10569.0 10312.0 10352.0 \n",
- "34 NaN 10279.0 10199.0 10698.0 10637.0 10354.0 \n",
- "35 NaN 9478.0 9046.0 9372.0 9226.0 10239.0 \n",
- "36 NaN 10918.0 10680.0 10781.0 10681.0 9092.0 \n",
- "37 NaN 10892.0 10496.0 10692.0 10401.0 10573.0 \n",
- "38 NaN 10792.0 10498.0 10875.0 10183.0 10381.0 \n",
- "39 NaN 10954.0 10463.0 11027.0 10278.0 10826.0 \n",
- "40 NaN 11113.0 10869.0 11101.0 10671.0 10700.0 \n",
- "41 NaN 11403.0 11048.0 11357.0 11016.0 11108.0 \n",
- "42 NaN 11625.0 11177.0 11389.0 11134.0 10799.0 \n",
- "43 NaN 11415.0 10885.0 11152.0 11048.0 10966.0 \n",
- "44 NaN 11567.0 10866.0 11366.0 11463.0 11026.0 \n",
- "45 NaN 12177.0 11588.0 11767.0 11803.0 11312.0 \n",
- "46 NaN 12146.0 11552.0 11773.0 12209.0 11338.0 \n",
- "47 NaN 12472.0 11289.0 12102.0 12064.0 11178.0 \n",
- "48 NaN 12455.0 11392.0 12046.0 11901.0 11216.0 \n",
- "49 NaN 12275.0 11687.0 12342.0 12733.0 11748.0 \n",
- "50 NaN 12853.0 12078.0 12924.0 12076.0 11713.0 \n",
- "51 NaN 13566.0 12649.0 14308.0 13137.0 12136.0 \n",
- "52 NaN 8727.0 8384.0 9904.0 9335.0 9806.0 \n",
- "53 NaN NaN NaN NaN NaN NaN \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 \n",
- "53 NaN "
- ]
- },
- "execution_count": 187,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "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": 188,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 188,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
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