{
"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": 49,
"metadata": {},
"outputs": [],
"source": [
"import itertools\n",
"import collections\n",
"import json\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": 2,
"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 scotland-deaths-2020.csv\n",
" publishedweek192020.csv weekly-deaths-april-20-scotland.csv\n",
" publishedweek192020.ods weekly-deaths-april-20-scotland.xlsx\n",
" publishedweek192020.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": 3,
"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": 4,
"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": 5,
"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": 6,
"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": 7,
"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": 8,
"metadata": {},
"outputs": [],
"source": [
"raw_data_2020 = pd.read_csv('uk-deaths-data/publishedweek192020.csv', \n",
" parse_dates=[1], dayfirst=True,\n",
" index_col=0,\n",
" header=[0, 1])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
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"\n",
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" total_2020 | \n",
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"text/plain": [
" total_2020\n",
"1 353\n",
"2 395\n",
"3 411\n",
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"5 323"
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},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"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()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"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": 11,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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"
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],
"text/plain": [
" total_2020 total_2019 total_2018 total_2017 total_2016 total_2015\n",
"1 1161.0 1104.0 1531.0 1205.0 1394.0 1146\n",
"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",
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]
},
"execution_count": 11,
"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": []
},
{
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"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": 215,
"metadata": {},
"outputs": [],
"source": [
"# raw_data_2020.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1 787\n",
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"Name: (W92000004, Wales), dtype: int64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data_2020['W92000004', 'Wales']"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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": 14,
"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": 15,
"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": 16,
"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": 17,
"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": 20,
"metadata": {},
"outputs": [
{
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},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"deaths_headlines_e = raw_data_2020.iloc[:, [1]].copy()\n",
"deaths_headlines_e.columns = ['total_2020']\n",
"deaths_headlines_w = raw_data_2020['W92000004'].copy()\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": 21,
"metadata": {},
"outputs": [
{
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},
"execution_count": 21,
"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": 22,
"metadata": {},
"outputs": [
{
"data": {
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"metadata": {},
"output_type": "execute_result"
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"source": [
"dh19w.head()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"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": 24,
"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": 25,
"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": 26,
"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": 27,
"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": 28,
"metadata": {},
"outputs": [
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" 9132 | \n",
" 9124 | \n",
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" 42 | \n",
" NaN | \n",
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" NaN | \n",
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" 9300 | \n",
" 9982 | \n",
" 9942 | \n",
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" 48 | \n",
" NaN | \n",
" 10269 | \n",
" 9360 | \n",
" 9902 | \n",
" 9796 | \n",
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" 49 | \n",
" NaN | \n",
" 10079 | \n",
" 9613 | \n",
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" 10530 | \n",
" 9706 | \n",
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" 50 | \n",
" NaN | \n",
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" 9842 | \n",
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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",
"18 17024.0 10519 9520 8464 8568 9506\n",
"19 11965.0 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",
"25 NaN 8916 8694 9019 8807 8717\n",
"26 NaN 8947 8613 8788 8679 8593\n",
"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",
"38 NaN 8868 8707 8949 8382 8542\n",
"39 NaN 8906 8590 9096 8402 8865\n",
"40 NaN 9202 8908 9147 8729 8858\n",
"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": 28,
"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": 29,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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],
"text/plain": [
" total_2020 total_2019 total_2018 total_2017 total_2016 total_2015\n",
"1 1161.0 1104.0 1531.0 1205.0 1394.0 1146\n",
"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",
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"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",
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"37 NaN 1074.0 1020.0 972.0 983.0 991\n",
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"39 NaN 1142.0 1015.0 1056.0 1009.0 1010\n",
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"41 NaN 1143.0 1081.0 1133.0 1009.0 1028\n",
"42 NaN 1153.0 1031.0 1067.0 1070.0 989\n",
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"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"
]
},
"execution_count": 29,
"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": 30,
"metadata": {},
"outputs": [
{
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"text/plain": [
" total_2020 total_2019 total_2018 total_2017 total_2016 total_2015\n",
"1 787.0 718 783 744 809 725\n",
"2 939.0 809 904 825 711 1031\n",
"3 767.0 683 885 835 720 936\n",
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"9 651.0 684 634 715 721 736\n",
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"11 675.0 666 918 653 738 661\n",
"12 719.0 628 774 635 668 680\n",
"13 719.0 654 633 658 712 666\n",
"14 920.0 642 730 642 742 580\n",
"15 928.0 637 743 577 738 660\n",
"16 1169.0 580 688 654 714 671\n",
"17 1124.0 678 614 727 629 662\n",
"18 929.0 688 633 600 569 628\n",
"19 692.0 600 530 687 652 589\n",
"20 NaN 658 667 615 611 622\n",
"21 NaN 627 603 602 624 614\n",
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"23 NaN 626 607 584 584 648\n",
"24 NaN 598 561 599 578 606\n",
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"37 NaN 567 579 609 576 554\n",
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"39 NaN 611 560 593 592 664\n",
"40 NaN 597 595 631 562 552\n",
"41 NaN 590 606 640 587 652\n",
"42 NaN 622 640 650 624 612\n",
"43 NaN 670 633 608 624 607\n",
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"46 NaN 674 656 666 681 613\n",
"47 NaN 699 657 639 661 611\n",
"48 NaN 689 673 636 643 589\n",
"49 NaN 737 674 630 693 659\n",
"50 NaN 699 708 708 663 688\n",
"51 NaN 767 724 762 691 646\n",
"52 NaN 496 461 541 558 535"
]
},
"execution_count": 30,
"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": 31,
"metadata": {},
"outputs": [
{
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" 45 | \n",
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" 296 | \n",
" 293 | \n",
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" 290 | \n",
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" 46 | \n",
" NaN | \n",
" 336 | \n",
" 275 | \n",
" 323 | \n",
" 341 | \n",
" 297 | \n",
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" 47 | \n",
" NaN | \n",
" 361 | \n",
" 274 | \n",
" 303 | \n",
" 329 | \n",
" 294 | \n",
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" 48 | \n",
" NaN | \n",
" 334 | \n",
" 297 | \n",
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],
"text/plain": [
" total_2020 total_2019 total_2018 total_2017 total_2016 total_2015\n",
"1 353.0 365 447 416 424 319\n",
"2 395.0 371 481 434 348 373\n",
"3 411.0 332 470 397 372 383\n",
"4 347.0 335 426 387 355 397\n",
"5 323.0 296 433 371 314 374\n",
"6 332.0 319 351 336 310 347\n",
"7 306.0 342 364 337 217 328\n",
"8 297.0 337 366 351 423 317\n",
"9 347.0 310 314 352 298 401\n",
"10 312.0 342 387 357 309 346\n",
"11 324.0 343 359 251 292 323\n",
"12 271.0 294 326 356 306 310\n",
"13 287.0 307 319 314 280 323\n",
"14 434.0 287 286 306 295 221\n",
"15 435.0 301 350 270 292 294\n",
"16 424.0 316 280 245 293 327\n",
"17 470.0 272 282 327 306 316\n",
"18 427.0 357 292 258 258 335\n",
"19 336.0 288 230 302 318 256\n",
"20 NaN 330 268 315 259 299\n",
"21 NaN 308 268 328 280 290\n",
"22 NaN 245 252 256 284 258\n",
"23 NaN 279 276 292 275 340\n",
"24 NaN 281 277 300 299 272\n",
"25 NaN 296 265 271 252 292\n",
"26 NaN 262 271 296 291 303\n",
"27 NaN 285 266 262 286 300\n",
"28 NaN 256 172 253 237 252\n",
"29 NaN 280 298 288 281 203\n",
"30 NaN 269 282 287 298 274\n",
"31 NaN 273 278 287 264 291\n",
"32 NaN 266 270 238 270 248\n",
"33 NaN 284 283 266 260 235\n",
"34 NaN 274 280 271 301 251\n",
"35 NaN 223 251 243 264 259\n",
"36 NaN 243 265 278 275 237\n",
"37 NaN 305 285 266 294 324\n",
"38 NaN 281 247 292 272 283\n",
"39 NaN 295 298 282 275 287\n",
"40 NaN 263 324 307 308 282\n",
"41 NaN 287 318 284 288 304\n",
"42 NaN 316 282 291 296 299\n",
"43 NaN 279 263 318 272 274\n",
"44 NaN 302 252 320 279 292\n",
"45 NaN 296 293 295 290 290\n",
"46 NaN 336 275 323 341 297\n",
"47 NaN 361 274 303 329 294\n",
"48 NaN 334 297 355 303 279\n",
"49 NaN 351 324 324 322 294\n",
"50 NaN 353 316 372 324 343\n",
"51 NaN 363 317 354 360 301\n",
"52 NaN 194 195 249 199 232"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"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"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
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"
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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",
"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 14427.0 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",
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]
},
"execution_count": 32,
"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": 33,
"metadata": {},
"outputs": [
{
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"
"
],
"text/plain": [
" total_2020 total_2019 total_2018 total_2017 total_2016 total_2015 \\\n",
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"\n",
" previous_mean \n",
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"52 9231.2 "
]
},
"execution_count": 33,
"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": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
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