{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"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": 392,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The sql extension is already loaded. To reload it, use:\n",
" %reload_ext sql\n"
]
}
],
"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",
"import datetime\n",
"\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"from sqlalchemy.types import Integer, Text, String, DateTime, Float\n",
"from sqlalchemy import create_engine\n",
"%load_ext sql"
]
},
{
"cell_type": "code",
"execution_count": 393,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"connection_string = 'postgresql://covid:3NbjJTkT63@localhost/covid'"
]
},
{
"cell_type": "code",
"execution_count": 394,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/plain": [
"'Connected: covid@covid'"
]
},
"execution_count": 394,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql $connection_string"
]
},
{
"cell_type": "code",
"execution_count": 395,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"conn = create_engine(connection_string)"
]
},
{
"cell_type": "code",
"execution_count": 396,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"england_wales_filename = 'uk-deaths-data/publishedweek532020.xlsx'"
]
},
{
"cell_type": "code",
"execution_count": 397,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * postgresql://covid:***@localhost/covid\n",
"Done.\n",
"Done.\n"
]
},
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 397,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"drop table if exists all_causes_deaths;\n",
"create table all_causes_deaths (\n",
" week integer,\n",
" year integer,\n",
" date_up_to date,\n",
" nation varchar(20),\n",
" deaths integer,\n",
" CONSTRAINT week_nation PRIMARY KEY(year, week, nation)\n",
");"
]
},
{
"cell_type": "code",
"execution_count": 525,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" total_2015 | \n",
"
\n",
" \n",
" (Registration Week, Unnamed: 0_level_1) | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" 49 | \n",
" 294 | \n",
"
\n",
" \n",
" 50 | \n",
" 343 | \n",
"
\n",
" \n",
" 51 | \n",
" 301 | \n",
"
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" \n",
" 52 | \n",
" 232 | \n",
"
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" \n",
" 53 | \n",
" 232 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" total_2015\n",
"(Registration Week, Unnamed: 0_level_1) \n",
"49 294\n",
"50 343\n",
"51 301\n",
"52 232\n",
"53 232"
]
},
"execution_count": 525,
"metadata": {},
"output_type": "execute_result"
}
],
"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[:, [0, 3]]\n",
"dh15i.set_index(dh15i.columns[0], inplace=True)\n",
"dh15i.columns = ['total_2015']\n",
"dh15i.tail()"
]
},
{
"cell_type": "code",
"execution_count": 399,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Registration Week | \n",
" Week Starts (Saturday) | \n",
" Week Ends (Friday) | \n",
" Total Number of Deaths Registered in Week (2015P) | \n",
" Average number of deaths registered in corresponding week in previous 5 years (2010 to 2014P) | \n",
" Range | \n",
" Unnamed: 6_level_0 | \n",
"
\n",
" \n",
" | \n",
" Unnamed: 0_level_1 | \n",
" Unnamed: 1_level_1 | \n",
" Unnamed: 2_level_1 | \n",
" Unnamed: 3_level_1 | \n",
" Unnamed: 4_level_1 | \n",
" Minimum in Previous 5 years | \n",
" Maximum in Previous 5 years | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1 | \n",
" 2014-12-29 | \n",
" 2015-01-02 | \n",
" 319 | \n",
" 317.2 | \n",
" 248 | \n",
" 372 | \n",
"
\n",
" \n",
" 1 | \n",
" 2 | \n",
" 2015-01-03 | \n",
" 2015-01-09 | \n",
" 373 | \n",
" 384.0 | \n",
" 344 | \n",
" 421 | \n",
"
\n",
" \n",
" 2 | \n",
" 3 | \n",
" 2015-01-10 | \n",
" 2015-01-16 | \n",
" 383 | \n",
" 347.8 | \n",
" 319 | \n",
" 373 | \n",
"
\n",
" \n",
" 3 | \n",
" 4 | \n",
" 2015-01-17 | \n",
" 2015-01-23 | \n",
" 397 | \n",
" 319.0 | \n",
" 282 | \n",
" 353 | \n",
"
\n",
" \n",
" 4 | \n",
" 5 | \n",
" 2015-01-24 | \n",
" 2015-01-30 | \n",
" 374 | \n",
" 309.2 | \n",
" 284 | \n",
" 336 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Registration Week Week Starts (Saturday) Week Ends (Friday) \\\n",
" Unnamed: 0_level_1 Unnamed: 1_level_1 Unnamed: 2_level_1 \n",
"0 1 2014-12-29 2015-01-02 \n",
"1 2 2015-01-03 2015-01-09 \n",
"2 3 2015-01-10 2015-01-16 \n",
"3 4 2015-01-17 2015-01-23 \n",
"4 5 2015-01-24 2015-01-30 \n",
"\n",
" Total Number of Deaths Registered in Week (2015P) \\\n",
" Unnamed: 3_level_1 \n",
"0 319 \n",
"1 373 \n",
"2 383 \n",
"3 397 \n",
"4 374 \n",
"\n",
" Average number of deaths registered in corresponding week in previous 5 years (2010 to 2014P) \\\n",
" Unnamed: 4_level_1 \n",
"0 317.2 \n",
"1 384.0 \n",
"2 347.8 \n",
"3 319.0 \n",
"4 309.2 \n",
"\n",
" Range Unnamed: 6_level_0 \n",
" Minimum in Previous 5 years Maximum in Previous 5 years \n",
"0 248 372 \n",
"1 344 421 \n",
"2 319 373 \n",
"3 282 353 \n",
"4 284 336 "
]
},
"execution_count": 399,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data_2015.head()"
]
},
{
"cell_type": "code",
"execution_count": 400,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" week | \n",
" date_up_to | \n",
" deaths | \n",
" year | \n",
" nation | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1 | \n",
" 2015-01-02 | \n",
" 319 | \n",
" 2015 | \n",
" Northern Ireland | \n",
"
\n",
" \n",
" 1 | \n",
" 2 | \n",
" 2015-01-09 | \n",
" 373 | \n",
" 2015 | \n",
" Northern Ireland | \n",
"
\n",
" \n",
" 2 | \n",
" 3 | \n",
" 2015-01-16 | \n",
" 383 | \n",
" 2015 | \n",
" Northern Ireland | \n",
"
\n",
" \n",
" 3 | \n",
" 4 | \n",
" 2015-01-23 | \n",
" 397 | \n",
" 2015 | \n",
" Northern Ireland | \n",
"
\n",
" \n",
" 4 | \n",
" 5 | \n",
" 2015-01-30 | \n",
" 374 | \n",
" 2015 | \n",
" Northern Ireland | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" week date_up_to deaths year nation\n",
"0 1 2015-01-02 319 2015 Northern Ireland\n",
"1 2 2015-01-09 373 2015 Northern Ireland\n",
"2 3 2015-01-16 383 2015 Northern Ireland\n",
"3 4 2015-01-23 397 2015 Northern Ireland\n",
"4 5 2015-01-30 374 2015 Northern Ireland"
]
},
"execution_count": 400,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd = raw_data_2015.iloc[:, [0, 2, 3]].droplevel(1, axis=1).rename(\n",
" columns={'Week Ends (Friday)': 'date_up_to', 'Total Number of Deaths Registered in Week (2015P)': 'deaths',\n",
" 'Registration Week': 'week'}\n",
" )\n",
"rd['year'] = 2015\n",
"rd['nation'] = 'Northern Ireland'\n",
"rd.head()"
]
},
{
"cell_type": "code",
"execution_count": 401,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"rd.to_sql(\n",
" 'all_causes_deaths',\n",
" conn,\n",
" if_exists='append',\n",
" index=False)"
]
},
{
"cell_type": "code",
"execution_count": 402,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * postgresql://covid:***@localhost/covid\n",
"10 rows affected.\n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" week | \n",
" year | \n",
" date_up_to | \n",
" nation | \n",
" deaths | \n",
"
\n",
" \n",
" \n",
" \n",
" 1 | \n",
" 2015 | \n",
" 2015-01-02 | \n",
" Northern Ireland | \n",
" 319 | \n",
"
\n",
" \n",
" 2 | \n",
" 2015 | \n",
" 2015-01-09 | \n",
" Northern Ireland | \n",
" 373 | \n",
"
\n",
" \n",
" 3 | \n",
" 2015 | \n",
" 2015-01-16 | \n",
" Northern Ireland | \n",
" 383 | \n",
"
\n",
" \n",
" 4 | \n",
" 2015 | \n",
" 2015-01-23 | \n",
" Northern Ireland | \n",
" 397 | \n",
"
\n",
" \n",
" 5 | \n",
" 2015 | \n",
" 2015-01-30 | \n",
" Northern Ireland | \n",
" 374 | \n",
"
\n",
" \n",
" 6 | \n",
" 2015 | \n",
" 2015-02-06 | \n",
" Northern Ireland | \n",
" 347 | \n",
"
\n",
" \n",
" 7 | \n",
" 2015 | \n",
" 2015-02-13 | \n",
" Northern Ireland | \n",
" 328 | \n",
"
\n",
" \n",
" 8 | \n",
" 2015 | \n",
" 2015-02-20 | \n",
" Northern Ireland | \n",
" 317 | \n",
"
\n",
" \n",
" 9 | \n",
" 2015 | \n",
" 2015-02-27 | \n",
" Northern Ireland | \n",
" 401 | \n",
"
\n",
" \n",
" 10 | \n",
" 2015 | \n",
" 2015-03-06 | \n",
" Northern Ireland | \n",
" 346 | \n",
"
\n",
" \n",
"
"
],
"text/plain": [
"[(1, 2015, datetime.date(2015, 1, 2), 'Northern Ireland', 319),\n",
" (2, 2015, datetime.date(2015, 1, 9), 'Northern Ireland', 373),\n",
" (3, 2015, datetime.date(2015, 1, 16), 'Northern Ireland', 383),\n",
" (4, 2015, datetime.date(2015, 1, 23), 'Northern Ireland', 397),\n",
" (5, 2015, datetime.date(2015, 1, 30), 'Northern Ireland', 374),\n",
" (6, 2015, datetime.date(2015, 2, 6), 'Northern Ireland', 347),\n",
" (7, 2015, datetime.date(2015, 2, 13), 'Northern Ireland', 328),\n",
" (8, 2015, datetime.date(2015, 2, 20), 'Northern Ireland', 317),\n",
" (9, 2015, datetime.date(2015, 2, 27), 'Northern Ireland', 401),\n",
" (10, 2015, datetime.date(2015, 3, 6), 'Northern Ireland', 346)]"
]
},
"execution_count": 402,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql select * from all_causes_deaths limit 10"
]
},
{
"cell_type": "code",
"execution_count": 526,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" total_2016 | \n",
"
\n",
" \n",
" (Registration Week, Unnamed: 0_level_1) | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" 48 | \n",
" 303 | \n",
"
\n",
" \n",
" 49 | \n",
" 322 | \n",
"
\n",
" \n",
" 50 | \n",
" 324 | \n",
"
\n",
" \n",
" 51 | \n",
" 360 | \n",
"
\n",
" \n",
" 52 | \n",
" 199 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" total_2016\n",
"(Registration Week, Unnamed: 0_level_1) \n",
"48 303\n",
"49 322\n",
"50 324\n",
"51 360\n",
"52 199"
]
},
"execution_count": 526,
"metadata": {},
"output_type": "execute_result"
}
],
"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",
"raw_data_2016.head()\n",
"# dh16i = raw_data_2016.iloc[:, [2]]\n",
"# dh16i.columns = ['total_2016']\n",
"# # dh16i.head()\n",
"dh16i = raw_data_2016.iloc[:, [0, 3]]\n",
"dh16i.set_index(dh16i.columns[0], inplace=True)\n",
"dh16i.columns = ['total_2016']\n",
"dh16i.tail()"
]
},
{
"cell_type": "code",
"execution_count": 404,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" week | \n",
" date_up_to | \n",
" deaths | \n",
" year | \n",
" nation | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1 | \n",
" 2016-01-08 | \n",
" 424 | \n",
" 2016 | \n",
" Northern Ireland | \n",
"
\n",
" \n",
" 1 | \n",
" 2 | \n",
" 2016-01-15 | \n",
" 348 | \n",
" 2016 | \n",
" Northern Ireland | \n",
"
\n",
" \n",
" 2 | \n",
" 3 | \n",
" 2016-01-22 | \n",
" 372 | \n",
" 2016 | \n",
" Northern Ireland | \n",
"
\n",
" \n",
" 3 | \n",
" 4 | \n",
" 2016-01-29 | \n",
" 355 | \n",
" 2016 | \n",
" Northern Ireland | \n",
"
\n",
" \n",
" 4 | \n",
" 5 | \n",
" 2016-02-05 | \n",
" 314 | \n",
" 2016 | \n",
" Northern Ireland | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" week date_up_to deaths year nation\n",
"0 1 2016-01-08 424 2016 Northern Ireland\n",
"1 2 2016-01-15 348 2016 Northern Ireland\n",
"2 3 2016-01-22 372 2016 Northern Ireland\n",
"3 4 2016-01-29 355 2016 Northern Ireland\n",
"4 5 2016-02-05 314 2016 Northern Ireland"
]
},
"execution_count": 404,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd = raw_data_2016.iloc[:, [0, 2, 3]].droplevel(1, axis=1).rename(\n",
" columns={'Week Ends (Friday)': 'date_up_to', 'Total Number of Deaths Registered in Week (2016P)': 'deaths',\n",
" 'Registration Week': 'week'}\n",
" )\n",
"rd['year'] = 2016\n",
"rd['nation'] = 'Northern Ireland'\n",
"rd.head()"
]
},
{
"cell_type": "code",
"execution_count": 405,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"rd.to_sql(\n",
" 'all_causes_deaths',\n",
" conn,\n",
" if_exists='append',\n",
" index=False)"
]
},
{
"cell_type": "code",
"execution_count": 406,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * postgresql://covid:***@localhost/covid\n",
"2 rows affected.\n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" year | \n",
" nation | \n",
" count | \n",
"
\n",
" \n",
" \n",
" \n",
" 2015 | \n",
" Northern Ireland | \n",
" 53 | \n",
"
\n",
" \n",
" 2016 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
\n",
" \n",
"
"
],
"text/plain": [
"[(2015, 'Northern Ireland', 53), (2016, 'Northern Ireland', 52)]"
]
},
"execution_count": 406,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql select year, nation, count(date_up_to) from all_causes_deaths group by (year, nation)"
]
},
{
"cell_type": "code",
"execution_count": 527,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" total_2017 | \n",
"
\n",
" \n",
" (Registration Week, Unnamed: 0_level_1) | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" 48 | \n",
" 355 | \n",
"
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" \n",
" 49 | \n",
" 324 | \n",
"
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" \n",
" 50 | \n",
" 372 | \n",
"
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" \n",
" 51 | \n",
" 354 | \n",
"
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" \n",
" 52 | \n",
" 249 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" total_2017\n",
"(Registration Week, Unnamed: 0_level_1) \n",
"48 355\n",
"49 324\n",
"50 372\n",
"51 354\n",
"52 249"
]
},
"execution_count": 527,
"metadata": {},
"output_type": "execute_result"
}
],
"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",
"raw_data_2017.head()\n",
"dh17i = raw_data_2017.iloc[:, [0, 3]]\n",
"dh17i.set_index(dh17i.columns[0], inplace=True)\n",
"dh17i.columns = ['total_2017']\n",
"dh17i.tail()"
]
},
{
"cell_type": "code",
"execution_count": 408,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" week | \n",
" date_up_to | \n",
" deaths | \n",
" year | \n",
" nation | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1 | \n",
" 2017-01-06 | \n",
" 416 | \n",
" 2017 | \n",
" Northern Ireland | \n",
"
\n",
" \n",
" 1 | \n",
" 2 | \n",
" 2017-01-13 | \n",
" 434 | \n",
" 2017 | \n",
" Northern Ireland | \n",
"
\n",
" \n",
" 2 | \n",
" 3 | \n",
" 2017-01-20 | \n",
" 397 | \n",
" 2017 | \n",
" Northern Ireland | \n",
"
\n",
" \n",
" 3 | \n",
" 4 | \n",
" 2017-01-27 | \n",
" 387 | \n",
" 2017 | \n",
" Northern Ireland | \n",
"
\n",
" \n",
" 4 | \n",
" 5 | \n",
" 2017-02-03 | \n",
" 371 | \n",
" 2017 | \n",
" Northern Ireland | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" week date_up_to deaths year nation\n",
"0 1 2017-01-06 416 2017 Northern Ireland\n",
"1 2 2017-01-13 434 2017 Northern Ireland\n",
"2 3 2017-01-20 397 2017 Northern Ireland\n",
"3 4 2017-01-27 387 2017 Northern Ireland\n",
"4 5 2017-02-03 371 2017 Northern Ireland"
]
},
"execution_count": 408,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd = raw_data_2017.iloc[:, [0, 2, 3]].droplevel(1, axis=1).rename(\n",
" columns={'Week Ends (Friday)': 'date_up_to', 'Total Number of Deaths Registered in Week (2017P)': 'deaths',\n",
" 'Registration Week': 'week'}\n",
" )\n",
"rd['year'] = 2017\n",
"rd['nation'] = 'Northern Ireland'\n",
"rd.head()"
]
},
{
"cell_type": "code",
"execution_count": 409,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"rd.to_sql(\n",
" 'all_causes_deaths',\n",
" conn,\n",
" if_exists='append',\n",
" index=False)"
]
},
{
"cell_type": "code",
"execution_count": 410,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * postgresql://covid:***@localhost/covid\n",
"3 rows affected.\n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" year | \n",
" nation | \n",
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" \n",
" 2015 | \n",
" Northern Ireland | \n",
" 53 | \n",
"
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" \n",
" 2017 | \n",
" Northern Ireland | \n",
" 52 | \n",
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"text/plain": [
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"execution_count": 410,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql select year, nation, count(date_up_to) from all_causes_deaths group by (year, nation)"
]
},
{
"cell_type": "code",
"execution_count": 528,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
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" \n",
" \n",
" | \n",
" total_2018 | \n",
"
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" (Registration Week, Unnamed: 0_level_1) | \n",
" | \n",
"
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" \n",
" \n",
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" 48 | \n",
" 297 | \n",
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"text/plain": [
" total_2018\n",
"(Registration Week, Unnamed: 0_level_1) \n",
"48 297\n",
"49 324\n",
"50 316\n",
"51 317\n",
"52 195"
]
},
"execution_count": 528,
"metadata": {},
"output_type": "execute_result"
}
],
"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",
"raw_data_2018.head()\n",
"dh18i = raw_data_2018.iloc[:, [0, 3]]\n",
"dh18i.set_index(dh18i.columns[0], inplace=True)\n",
"dh18i.columns = ['total_2018']\n",
"dh18i.tail()"
]
},
{
"cell_type": "code",
"execution_count": 412,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
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" Northern Ireland | \n",
"
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" 1 | \n",
" 2 | \n",
" 2018-01-12 | \n",
" 481 | \n",
" 2018 | \n",
" Northern Ireland | \n",
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" 2 | \n",
" 3 | \n",
" 2018-01-19 | \n",
" 470 | \n",
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" 3 | \n",
" 4 | \n",
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" 4 | \n",
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"
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],
"text/plain": [
" week date_up_to deaths year nation\n",
"0 1 2018-01-05 447 2018 Northern Ireland\n",
"1 2 2018-01-12 481 2018 Northern Ireland\n",
"2 3 2018-01-19 470 2018 Northern Ireland\n",
"3 4 2018-01-26 426 2018 Northern Ireland\n",
"4 5 2018-02-02 433 2018 Northern Ireland"
]
},
"execution_count": 412,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd = raw_data_2018.iloc[:, [0, 2, 3]].droplevel(1, axis=1).rename(\n",
" columns={'Week Ends (Friday)': 'date_up_to', 'Total Number of Deaths Registered in Week (2018P)': 'deaths',\n",
" 'Registration Week': 'week'}\n",
" )\n",
"rd['year'] = 2018\n",
"rd['nation'] = 'Northern Ireland'\n",
"rd.head()"
]
},
{
"cell_type": "code",
"execution_count": 413,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"rd.to_sql(\n",
" 'all_causes_deaths',\n",
" conn,\n",
" if_exists='append',\n",
" index=False)"
]
},
{
"cell_type": "code",
"execution_count": 414,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * postgresql://covid:***@localhost/covid\n",
"4 rows affected.\n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" year | \n",
" nation | \n",
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" \n",
" 2015 | \n",
" Northern Ireland | \n",
" 53 | \n",
"
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" 52 | \n",
"
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" Northern Ireland | \n",
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"
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" 2016 | \n",
" Northern Ireland | \n",
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"[(2015, 'Northern Ireland', 53),\n",
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},
"execution_count": 414,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql select year, nation, count(date_up_to) from all_causes_deaths group by (year, nation)"
]
},
{
"cell_type": "code",
"execution_count": 529,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
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" \n",
" \n",
" | \n",
" total_2019 | \n",
"
\n",
" \n",
" (Registration Week, Unnamed: 0_level_1) | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" 48 | \n",
" 334 | \n",
"
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" \n",
" 49 | \n",
" 351 | \n",
"
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" 50 | \n",
" 353 | \n",
"
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" 51 | \n",
" 363 | \n",
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" 52 | \n",
" 194 | \n",
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"text/plain": [
" total_2019\n",
"(Registration Week, Unnamed: 0_level_1) \n",
"48 334\n",
"49 351\n",
"50 353\n",
"51 363\n",
"52 194"
]
},
"execution_count": 529,
"metadata": {},
"output_type": "execute_result"
}
],
"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",
"raw_data_2019.head()\n",
"dh19i = raw_data_2019.iloc[:, [0, 3]]\n",
"dh19i.set_index(dh19i.columns[0], inplace=True)\n",
"dh19i.columns = ['total_2019']\n",
"dh19i.tail()"
]
},
{
"cell_type": "code",
"execution_count": 416,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
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" nation | \n",
"
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" 2019 | \n",
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"
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" 1 | \n",
" 2 | \n",
" 2019-01-11 | \n",
" 371 | \n",
" 2019 | \n",
" Northern Ireland | \n",
"
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" 2 | \n",
" 3 | \n",
" 2019-01-18 | \n",
" 332 | \n",
" 2019 | \n",
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" 3 | \n",
" 4 | \n",
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" 335 | \n",
" 2019 | \n",
" Northern Ireland | \n",
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" \n",
" 4 | \n",
" 5 | \n",
" 2019-02-01 | \n",
" 296 | \n",
" 2019 | \n",
" Northern Ireland | \n",
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"text/plain": [
" week date_up_to deaths year nation\n",
"0 1 2019-01-04 365 2019 Northern Ireland\n",
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"3 4 2019-01-25 335 2019 Northern Ireland\n",
"4 5 2019-02-01 296 2019 Northern Ireland"
]
},
"execution_count": 416,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd = raw_data_2019.iloc[:, [0, 2, 3]].droplevel(1, axis=1).rename(\n",
" columns={'Week Ends (Friday)': 'date_up_to', 'Total Number of Deaths Registered in Week (2019P)': 'deaths',\n",
" 'Registration Week': 'week'}\n",
" )\n",
"rd['year'] = 2019\n",
"rd['nation'] = 'Northern Ireland'\n",
"rd.head()"
]
},
{
"cell_type": "code",
"execution_count": 417,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"rd.to_sql(\n",
" 'all_causes_deaths',\n",
" conn,\n",
" if_exists='append',\n",
" index=False)"
]
},
{
"cell_type": "code",
"execution_count": 418,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * postgresql://covid:***@localhost/covid\n",
"5 rows affected.\n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" year | \n",
" nation | \n",
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" \n",
" 2015 | \n",
" Northern Ireland | \n",
" 53 | \n",
"
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" \n",
" 2018 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
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" \n",
" 2019 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
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" \n",
" 2017 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
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" (2016, 'Northern Ireland', 52)]"
]
},
"execution_count": 418,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql select year, nation, count(date_up_to) from all_causes_deaths group by (year, nation)"
]
},
{
"cell_type": "code",
"execution_count": 419,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
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" Covid-193 deaths registered in week (2020P) | \n",
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" NaN | \n",
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" 116 | \n",
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\n",
" \n",
" 3 | \n",
" 4 | \n",
" 2020-01-24 | \n",
" 347 | \n",
" 382.6 | \n",
" 335 | \n",
" 426 | \n",
" 113 | \n",
" 123.8 | \n",
" 0 | \n",
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" NaN | \n",
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\n",
" \n",
" 4 | \n",
" 5 | \n",
" 2020-01-31 | \n",
" 323 | \n",
" 373.6 | \n",
" 296 | \n",
" 433 | \n",
" 78 | \n",
" 124 | \n",
" 0 | \n",
" NaN | \n",
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" Total Number of Deaths Registered in Week (2020P) \\\n",
" Unnamed: 2_level_1 \n",
"0 353 \n",
"1 395 \n",
"2 411 \n",
"3 347 \n",
"4 323 \n",
"\n",
" Average number of deaths registered in corresponding week over previous 5 years (2015 to 2019P) \\\n",
" Unnamed: 3_level_1 \n",
"0 250.0 \n",
"1 402.2 \n",
"2 391.4 \n",
"3 382.6 \n",
"4 373.6 \n",
"\n",
" Range Unnamed: 5_level_0 \\\n",
" Minimum in Previous 5 years Maximum in Previous 5 years \n",
"0 199 365 \n",
"1 319 481 \n",
"2 332 470 \n",
"3 335 426 \n",
"4 296 433 \n",
"\n",
" Respiratory2 deaths registered in week (2020P) \\\n",
" Unnamed: 6_level_1 \n",
"0 0 \n",
"1 131 \n",
"2 116 \n",
"3 113 \n",
"4 78 \n",
"\n",
" Average number of respiratory2 deaths registered in corresponding week over previous 5 years (2015 to 2019P) \\\n",
" Unnamed: 7_level_1 \n",
"0 0 \n",
"1 144 \n",
"2 127.6 \n",
"3 123.8 \n",
"4 124 \n",
"\n",
" Covid-193 deaths registered in week (2020P) Unnamed: 9_level_0 \\\n",
" Unnamed: 8_level_1 Unnamed: 9_level_1 \n",
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},
"execution_count": 419,
"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",
" header=[0, 1]\n",
" )\n",
"raw_data_2020_i.head()"
]
},
{
"cell_type": "code",
"execution_count": 420,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
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" 4 | \n",
" 5 | \n",
" 2020-01-31 | \n",
" 323 | \n",
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" Northern Ireland | \n",
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"text/plain": [
" week date_up_to deaths year nation\n",
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"3 4 2020-01-24 347 2020 Northern Ireland\n",
"4 5 2020-01-31 323 2020 Northern Ireland"
]
},
"execution_count": 420,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd = raw_data_2020_i.iloc[:, [0, 1, 2]].droplevel(1, axis=1).rename(\n",
" columns={'Week Ending (Friday)': 'date_up_to', 'Total Number of Deaths Registered in Week (2020P)': 'deaths',\n",
" 'Registration Week': 'week'}\n",
" )\n",
"rd['year'] = 2020\n",
"rd['nation'] = 'Northern Ireland'\n",
"rd.head()"
]
},
{
"cell_type": "code",
"execution_count": 421,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
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" \n",
" \n",
" | \n",
" week | \n",
" date_up_to | \n",
" deaths | \n",
" year | \n",
" nation | \n",
"
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" \n",
" \n",
" \n",
" 48 | \n",
" 49 | \n",
" 2020-12-04 | \n",
" 387 | \n",
" 2020 | \n",
" Northern Ireland | \n",
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" 50 | \n",
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" 366 | \n",
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" 350 | \n",
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" 53 | \n",
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"text/plain": [
" week date_up_to deaths year nation\n",
"48 49 2020-12-04 387 2020 Northern Ireland\n",
"49 50 2020-12-11 366 2020 Northern Ireland\n",
"50 51 2020-12-18 350 2020 Northern Ireland\n",
"51 52 2020-12-25 310 2020 Northern Ireland\n",
"52 53 2021-01-01 333 2020 Northern Ireland"
]
},
"execution_count": 421,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd.tail()"
]
},
{
"cell_type": "code",
"execution_count": 422,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"rd.to_sql(\n",
" 'all_causes_deaths',\n",
" conn,\n",
" if_exists='append',\n",
" index=False)"
]
},
{
"cell_type": "code",
"execution_count": 423,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * postgresql://covid:***@localhost/covid\n",
"6 rows affected.\n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" year | \n",
" nation | \n",
" count | \n",
"
\n",
" \n",
" \n",
" \n",
" 2015 | \n",
" Northern Ireland | \n",
" 53 | \n",
"
\n",
" \n",
" 2016 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
\n",
" \n",
" 2017 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
\n",
" \n",
" 2018 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
\n",
" \n",
" 2019 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
\n",
" \n",
" 2020 | \n",
" Northern Ireland | \n",
" 53 | \n",
"
\n",
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"
],
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"[(2015, 'Northern Ireland', 53),\n",
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" (2017, 'Northern Ireland', 52),\n",
" (2018, 'Northern Ireland', 52),\n",
" (2019, 'Northern Ireland', 52),\n",
" (2020, 'Northern Ireland', 53)]"
]
},
"execution_count": 423,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql select year, nation, count(date_up_to) from all_causes_deaths group by (year, nation) order by nation, year"
]
},
{
"cell_type": "code",
"execution_count": 521,
"metadata": {},
"outputs": [
{
"data": {
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\n",
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" Week Ending (Friday) | \n",
" Total Number of Deaths Registered in Week (2020P) | \n",
" Average number of deaths registered in corresponding week over previous 5 years (2015 to 2019P) | \n",
" Range | \n",
" Unnamed: 5_level_0 | \n",
" Respiratory2 deaths registered in week (2020P) | \n",
" Average number of respiratory2 deaths registered in corresponding week over previous 5 years (2015 to 2019P) | \n",
" Covid-193 deaths registered in week (2020P) | \n",
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" Unnamed: 11_level_1 | \n",
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"
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" \n",
" 50 | \n",
" 2020-12-11 | \n",
" 366 | \n",
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" 294 | \n",
" 353 | \n",
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" - | \n",
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" 102.0 | \n",
" - | \n",
" 82.0 | \n",
"
\n",
" \n",
" 52 | \n",
" 2020-12-25 | \n",
" 310 | \n",
" 281.0 | \n",
" 194 | \n",
" 360 | \n",
" - | \n",
" - | \n",
" - | \n",
" 132.0 | \n",
" 92.0 | \n",
" - | \n",
" 88.0 | \n",
"
\n",
" \n",
" 53 | \n",
" 2021-01-01 | \n",
" 333 | \n",
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" 199 | \n",
" 365 | \n",
" - | \n",
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" Week Ending (Friday) \\\n",
" Unnamed: 1_level_1 \n",
"(Registration Week, Unnamed: 0_level_1) \n",
"49 2020-12-04 \n",
"50 2020-12-11 \n",
"51 2020-12-18 \n",
"52 2020-12-25 \n",
"53 2021-01-01 \n",
"\n",
" Total Number of Deaths Registered in Week (2020P) \\\n",
" Unnamed: 2_level_1 \n",
"(Registration Week, Unnamed: 0_level_1) \n",
"49 387 \n",
"50 366 \n",
"51 350 \n",
"52 310 \n",
"53 333 \n",
"\n",
" Average number of deaths registered in corresponding week over previous 5 years (2015 to 2019P) \\\n",
" Unnamed: 3_level_1 \n",
"(Registration Week, Unnamed: 0_level_1) \n",
"49 322.0 \n",
"50 322.0 \n",
"51 344.0 \n",
"52 281.0 \n",
"53 280.0 \n",
"\n",
" Range \\\n",
" Minimum in Previous 5 years \n",
"(Registration Week, Unnamed: 0_level_1) \n",
"49 279 \n",
"50 294 \n",
"51 317 \n",
"52 194 \n",
"53 199 \n",
"\n",
" Unnamed: 5_level_0 \\\n",
" Maximum in Previous 5 years \n",
"(Registration Week, Unnamed: 0_level_1) \n",
"49 355 \n",
"50 353 \n",
"51 372 \n",
"52 360 \n",
"53 365 \n",
"\n",
" Respiratory2 deaths registered in week (2020P) \\\n",
" Unnamed: 6_level_1 \n",
"(Registration Week, Unnamed: 0_level_1) \n",
"49 - \n",
"50 - \n",
"51 - \n",
"52 - \n",
"53 - \n",
"\n",
" Average number of respiratory2 deaths registered in corresponding week over previous 5 years (2015 to 2019P) \\\n",
" Unnamed: 7_level_1 \n",
"(Registration Week, Unnamed: 0_level_1) \n",
"49 - \n",
"50 - \n",
"51 - \n",
"52 - \n",
"53 - \n",
"\n",
" Covid-193 deaths registered in week (2020P) \\\n",
" Unnamed: 8_level_1 \n",
"(Registration Week, Unnamed: 0_level_1) \n",
"49 - \n",
"50 - \n",
"51 - \n",
"52 - \n",
"53 - \n",
"\n",
" Unnamed: 9_level_0 \\\n",
" Unnamed: 9_level_1 \n",
"(Registration Week, Unnamed: 0_level_1) \n",
"49 155.0 \n",
"50 138.0 \n",
"51 140.0 \n",
"52 132.0 \n",
"53 146.0 \n",
"\n",
" Unnamed: 10_level_0 \\\n",
" Unnamed: 10_level_1 \n",
"(Registration Week, Unnamed: 0_level_1) \n",
"49 87.0 \n",
"50 93.0 \n",
"51 102.0 \n",
"52 92.0 \n",
"53 97.0 \n",
"\n",
" Unnamed: 11_level_0 \\\n",
" Unnamed: 11_level_1 \n",
"(Registration Week, Unnamed: 0_level_1) \n",
"49 - \n",
"50 - \n",
"51 - \n",
"52 - \n",
"53 - \n",
"\n",
" Unnamed: 12_level_0 \n",
" Unnamed: 12_level_1 \n",
"(Registration Week, Unnamed: 0_level_1) \n",
"49 98.0 \n",
"50 87.0 \n",
"51 82.0 \n",
"52 88.0 \n",
"53 94.0 "
]
},
"execution_count": 521,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data_2020_i.set_index(raw_data_2020_i.columns[0], inplace=True)\n",
"raw_data_2020_i.tail()"
]
},
{
"cell_type": "code",
"execution_count": 424,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/plain": [
"(2021, 3, 1)"
]
},
"execution_count": 424,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datetime.datetime.now().isocalendar()"
]
},
{
"cell_type": "code",
"execution_count": 425,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/plain": [
"datetime.datetime(2021, 1, 18, 0, 0)"
]
},
"execution_count": 425,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datetime.datetime.fromisocalendar(2021, 3, 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 426,
"metadata": {
"Collapsed": "false",
"scrolled": true
},
"outputs": [],
"source": [
"raw_data_s = pd.read_csv('uk-deaths-data/weekly-deaths-scotland.csv', \n",
" index_col=0,\n",
" header=0,\n",
" skiprows=2\n",
" )\n",
"# raw_data_s"
]
},
{
"cell_type": "code",
"execution_count": 427,
"metadata": {
"Collapsed": "false",
"scrolled": true
},
"outputs": [
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" 989 | \n",
"
\n",
" \n",
" 43 | \n",
" 1187 | \n",
" 1115.0 | \n",
" 1019.0 | \n",
" 1095.0 | \n",
" 1052.0 | \n",
" 981 | \n",
"
\n",
" \n",
" 44 | \n",
" 1262 | \n",
" 1101.0 | \n",
" 1085.0 | \n",
" 1062.0 | \n",
" 1032.0 | \n",
" 1116 | \n",
"
\n",
" \n",
" 45 | \n",
" 1250 | \n",
" 1184.0 | \n",
" 1144.0 | \n",
" 1126.0 | \n",
" 1043.0 | \n",
" 1028 | \n",
"
\n",
" \n",
" 46 | \n",
" 1138 | \n",
" 1160.0 | \n",
" 1084.0 | \n",
" 1175.0 | \n",
" 1174.0 | \n",
" 1103 | \n",
"
\n",
" \n",
" 47 | \n",
" 1360 | \n",
" 1229.0 | \n",
" 1058.0 | \n",
" 1178.0 | \n",
" 1132.0 | \n",
" 1054 | \n",
"
\n",
" \n",
" 48 | \n",
" 1328 | \n",
" 1163.0 | \n",
" 1062.0 | \n",
" 1153.0 | \n",
" 1159.0 | \n",
" 1115 | \n",
"
\n",
" \n",
" 49 | \n",
" 1296 | \n",
" 1108.0 | \n",
" 1076.0 | \n",
" 1237.0 | \n",
" 1188.0 | \n",
" 1089 | \n",
"
\n",
" \n",
" 50 | \n",
" 1284 | \n",
" 1312.0 | \n",
" 1212.0 | \n",
" 1335.0 | \n",
" 1219.0 | \n",
" 1101 | \n",
"
\n",
" \n",
" 51 | \n",
" 1297 | \n",
" 1277.0 | \n",
" 1216.0 | \n",
" 1437.0 | \n",
" 1284.0 | \n",
" 1146 | \n",
"
\n",
" \n",
" 52 | \n",
" 1205 | \n",
" 1000.0 | \n",
" 1058.0 | \n",
" 1168.0 | \n",
" 1133.0 | \n",
" 944 | \n",
"
\n",
" \n",
" 53 | \n",
" 1178 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 1018 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" total_2020 total_2019 total_2018 total_2017 total_2016 total_2015\n",
"1 1161 1104.0 1531.0 1205.0 1394.0 1146\n",
"2 1567 1507.0 1899.0 1379.0 1305.0 1708\n",
"3 1322 1353.0 1629.0 1224.0 1215.0 1489\n",
"4 1226 1208.0 1610.0 1197.0 1187.0 1381\n",
"5 1188 1206.0 1369.0 1332.0 1205.0 1286\n",
"6 1216 1243.0 1265.0 1200.0 1217.0 1344\n",
"7 1162 1181.0 1315.0 1231.0 1209.0 1360\n",
"8 1162 1245.0 1245.0 1185.0 1239.0 1320\n",
"9 1171 1125.0 1022.0 1219.0 1150.0 1308\n",
"10 1208 1156.0 1475.0 1146.0 1174.0 1192\n",
"11 1156 1108.0 1220.0 1141.0 1175.0 1201\n",
"12 1196 1101.0 1158.0 1152.0 1042.0 1149\n",
"13 1079 1086.0 1050.0 1112.0 1172.0 1171\n",
"14 1744 1032.0 1192.0 1060.0 1166.0 1042\n",
"15 1978 1069.0 1192.0 998.0 1048.0 1192\n",
"16 1916 902.0 1136.0 1111.0 1092.0 1095\n",
"17 1836 1121.0 1008.0 1121.0 1076.0 1108\n",
"18 1679 1131.0 1093.0 1050.0 1006.0 1117\n",
"19 1435 1018.0 967.0 1119.0 1047.0 1020\n",
"20 1421 1115.0 977.0 1115.0 1010.0 1103\n",
"21 1226 1061.0 1070.0 1063.0 994.0 1039\n",
"22 1125 1029.0 998.0 1015.0 999.0 1043\n",
"23 1093 1042.0 1033.0 1076.0 1023.0 1106\n",
"24 1034 1028.0 915.0 1031.0 988.0 1038\n",
"25 1065 1053.0 993.0 1032.0 994.0 1025\n",
"26 1008 1051.0 1046.0 994.0 1007.0 1032\n",
"27 983 981.0 1041.0 1040.0 988.0 1040\n",
"28 976 1077.0 1002.0 1014.0 1022.0 1011\n",
"29 1033 964.0 928.0 1025.0 1041.0 1023\n",
"30 961 1041.0 933.0 978.0 979.0 956\n",
"31 1043 1020.0 969.0 1011.0 987.0 985\n",
"32 1011 1018.0 953.0 1002.0 997.0 1043\n",
"33 922 1028.0 978.0 1004.0 982.0 969\n",
"34 1046 1011.0 941.0 1045.0 1017.0 982\n",
"35 1029 1013.0 930.0 980.0 1039.0 954\n",
"36 1050 980.0 970.0 1006.0 1007.0 977\n",
"37 1069 1074.0 1020.0 972.0 983.0 991\n",
"38 952 1071.0 946.0 1049.0 966.0 1001\n",
"39 933 1142.0 1015.0 1056.0 1009.0 1010\n",
"40 1195 1051.0 1042.0 1016.0 1072.0 1008\n",
"41 1071 1143.0 1081.0 1133.0 1009.0 1028\n",
"42 1131 1153.0 1031.0 1067.0 1070.0 989\n",
"43 1187 1115.0 1019.0 1095.0 1052.0 981\n",
"44 1262 1101.0 1085.0 1062.0 1032.0 1116\n",
"45 1250 1184.0 1144.0 1126.0 1043.0 1028\n",
"46 1138 1160.0 1084.0 1175.0 1174.0 1103\n",
"47 1360 1229.0 1058.0 1178.0 1132.0 1054\n",
"48 1328 1163.0 1062.0 1153.0 1159.0 1115\n",
"49 1296 1108.0 1076.0 1237.0 1188.0 1089\n",
"50 1284 1312.0 1212.0 1335.0 1219.0 1101\n",
"51 1297 1277.0 1216.0 1437.0 1284.0 1146\n",
"52 1205 1000.0 1058.0 1168.0 1133.0 944\n",
"53 1178 NaN NaN NaN NaN 1018"
]
},
"execution_count": 427,
"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": 428,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * postgresql://covid:***@localhost/covid\n",
"5 rows affected.\n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" week | \n",
" year | \n",
" date_up_to | \n",
" nation | \n",
" deaths | \n",
"
\n",
" \n",
" \n",
" \n",
" 1 | \n",
" 2015 | \n",
" 2015-01-02 | \n",
" Northern Ireland | \n",
" 319 | \n",
"
\n",
" \n",
" 2 | \n",
" 2015 | \n",
" 2015-01-09 | \n",
" Northern Ireland | \n",
" 373 | \n",
"
\n",
" \n",
" 3 | \n",
" 2015 | \n",
" 2015-01-16 | \n",
" Northern Ireland | \n",
" 383 | \n",
"
\n",
" \n",
" 4 | \n",
" 2015 | \n",
" 2015-01-23 | \n",
" Northern Ireland | \n",
" 397 | \n",
"
\n",
" \n",
" 5 | \n",
" 2015 | \n",
" 2015-01-30 | \n",
" Northern Ireland | \n",
" 374 | \n",
"
\n",
" \n",
"
"
],
"text/plain": [
"[(1, 2015, datetime.date(2015, 1, 2), 'Northern Ireland', 319),\n",
" (2, 2015, datetime.date(2015, 1, 9), 'Northern Ireland', 373),\n",
" (3, 2015, datetime.date(2015, 1, 16), 'Northern Ireland', 383),\n",
" (4, 2015, datetime.date(2015, 1, 23), 'Northern Ireland', 397),\n",
" (5, 2015, datetime.date(2015, 1, 30), 'Northern Ireland', 374)]"
]
},
"execution_count": 428,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql select * from all_causes_deaths limit 5"
]
},
{
"cell_type": "code",
"execution_count": 429,
"metadata": {
"Collapsed": "false",
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
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"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
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"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
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"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
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"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
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"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
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"1 rows affected.\n",
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"1 rows affected.\n",
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"1 rows affected.\n",
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"1 rows affected.\n",
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"1 rows affected.\n",
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"1 rows affected.\n",
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"1 rows affected.\n",
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"1 rows affected.\n",
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"1 rows affected.\n",
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"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
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"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
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"1 rows affected.\n",
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"1 rows affected.\n",
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"1 rows affected.\n",
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"1 rows affected.\n",
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"1 rows affected.\n",
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"1 rows affected.\n",
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"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
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"1 rows affected.\n",
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"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
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"1 rows affected.\n",
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"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n",
" * postgresql://covid:***@localhost/covid\n",
"1 rows affected.\n"
]
}
],
"source": [
"for year, ser in deaths_headlines_s.items():\n",
" year_i = int(year[-4:])\n",
"# print(year_i)\n",
" for week, deaths in ser.dropna().iteritems():\n",
"# print(datetime.date.fromisocalendar(year_i, week, 7), deaths)\n",
" dut = datetime.date.fromisocalendar(year_i, week, 7)\n",
" %sql insert into all_causes_deaths(week, year, date_up_to, nation, deaths) values ({week}, {year_i}, :dut, 'Scotland', {deaths})"
]
},
{
"cell_type": "code",
"execution_count": 430,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * postgresql://covid:***@localhost/covid\n",
"12 rows affected.\n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" year | \n",
" nation | \n",
" count | \n",
"
\n",
" \n",
" \n",
" \n",
" 2015 | \n",
" Northern Ireland | \n",
" 53 | \n",
"
\n",
" \n",
" 2016 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
\n",
" \n",
" 2017 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
\n",
" \n",
" 2018 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
\n",
" \n",
" 2019 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
\n",
" \n",
" 2020 | \n",
" Northern Ireland | \n",
" 53 | \n",
"
\n",
" \n",
" 2015 | \n",
" Scotland | \n",
" 53 | \n",
"
\n",
" \n",
" 2016 | \n",
" Scotland | \n",
" 52 | \n",
"
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" \n",
" 2017 | \n",
" Scotland | \n",
" 52 | \n",
"
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" \n",
" 2018 | \n",
" Scotland | \n",
" 52 | \n",
"
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" \n",
" 2019 | \n",
" Scotland | \n",
" 52 | \n",
"
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" \n",
" 2020 | \n",
" Scotland | \n",
" 53 | \n",
"
\n",
" \n",
"
"
],
"text/plain": [
"[(2015, 'Northern Ireland', 53),\n",
" (2016, 'Northern Ireland', 52),\n",
" (2017, 'Northern Ireland', 52),\n",
" (2018, 'Northern Ireland', 52),\n",
" (2019, 'Northern Ireland', 52),\n",
" (2020, 'Northern Ireland', 53),\n",
" (2015, 'Scotland', 53),\n",
" (2016, 'Scotland', 52),\n",
" (2017, 'Scotland', 52),\n",
" (2018, 'Scotland', 52),\n",
" (2019, 'Scotland', 52),\n",
" (2020, 'Scotland', 53)]"
]
},
"execution_count": 430,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql select year, nation, count(date_up_to) from all_causes_deaths group by (year, nation) order by nation, year"
]
},
{
"cell_type": "code",
"execution_count": 431,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * postgresql://covid:***@localhost/covid\n",
"12 rows affected.\n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" year | \n",
" nation | \n",
" date_up_to | \n",
"
\n",
" \n",
" \n",
" \n",
" 2015 | \n",
" Northern Ireland | \n",
" 2015-01-16 | \n",
"
\n",
" \n",
" 2015 | \n",
" Scotland | \n",
" 2015-01-18 | \n",
"
\n",
" \n",
" 2016 | \n",
" Northern Ireland | \n",
" 2016-01-22 | \n",
"
\n",
" \n",
" 2016 | \n",
" Scotland | \n",
" 2016-01-24 | \n",
"
\n",
" \n",
" 2017 | \n",
" Northern Ireland | \n",
" 2017-01-20 | \n",
"
\n",
" \n",
" 2017 | \n",
" Scotland | \n",
" 2017-01-22 | \n",
"
\n",
" \n",
" 2018 | \n",
" Northern Ireland | \n",
" 2018-01-19 | \n",
"
\n",
" \n",
" 2018 | \n",
" Scotland | \n",
" 2018-01-21 | \n",
"
\n",
" \n",
" 2019 | \n",
" Northern Ireland | \n",
" 2019-01-18 | \n",
"
\n",
" \n",
" 2019 | \n",
" Scotland | \n",
" 2019-01-20 | \n",
"
\n",
" \n",
" 2020 | \n",
" Northern Ireland | \n",
" 2020-01-17 | \n",
"
\n",
" \n",
" 2020 | \n",
" Scotland | \n",
" 2020-01-19 | \n",
"
\n",
" \n",
"
"
],
"text/plain": [
"[(2015, 'Northern Ireland', datetime.date(2015, 1, 16)),\n",
" (2015, 'Scotland', datetime.date(2015, 1, 18)),\n",
" (2016, 'Northern Ireland', datetime.date(2016, 1, 22)),\n",
" (2016, 'Scotland', datetime.date(2016, 1, 24)),\n",
" (2017, 'Northern Ireland', datetime.date(2017, 1, 20)),\n",
" (2017, 'Scotland', datetime.date(2017, 1, 22)),\n",
" (2018, 'Northern Ireland', datetime.date(2018, 1, 19)),\n",
" (2018, 'Scotland', datetime.date(2018, 1, 21)),\n",
" (2019, 'Northern Ireland', datetime.date(2019, 1, 18)),\n",
" (2019, 'Scotland', datetime.date(2019, 1, 20)),\n",
" (2020, 'Northern Ireland', datetime.date(2020, 1, 17)),\n",
" (2020, 'Scotland', datetime.date(2020, 1, 19))]"
]
},
"execution_count": 431,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql select year, nation, date_up_to from all_causes_deaths where week=3 order by year, nation"
]
},
{
"cell_type": "code",
"execution_count": 432,
"metadata": {
"Collapsed": "false",
"scrolled": true
},
"outputs": [
{
"data": {
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" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" ... | \n",
" North East | \n",
" North West | \n",
" Yorkshire and The Humber | \n",
" East Midlands | \n",
" West Midlands | \n",
" East | \n",
" London | \n",
" South East | \n",
" South West | \n",
" Wales | \n",
"
\n",
" \n",
" \n",
" \n",
" Week number | \n",
" Week ended | \n",
" NaN | \n",
" NaN | \n",
" Total deaths, all ages | \n",
" Total deaths: average of corresponding | \n",
" week over the previous 5 years 1, 10, 11 (Engl... | \n",
" Total deaths: average of corresponding | \n",
" week over the previous 5 years 1, 10, 11 (Engl... | \n",
" Total deaths: average of corresponding | \n",
" week over the previous 5 years 1, 10, 11 (Wales) | \n",
" ... | \n",
" E12000001 | \n",
" E12000002 | \n",
" E12000003 | \n",
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" 2020-01-10 00:00:00 | \n",
" NaN | \n",
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" ... | \n",
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" 2020-01-17 00:00:00 | \n",
" NaN | \n",
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" 2020-01-24 00:00:00 | \n",
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" 2020-01-31 00:00:00 | \n",
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"
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" 2020-02-07 00:00:00 | \n",
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\n",
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" 2020-07-10 00:00:00 | \n",
" NaN | \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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" 2020-08-07 00:00:00 | \n",
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\n",
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" 2020-08-14 00:00:00 | \n",
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\n",
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" 2020-08-21 00:00:00 | \n",
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\n",
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\n",
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\n",
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" 2020-09-11 00:00:00 | \n",
" NaN | \n",
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\n",
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" 1129 | \n",
" 712 | \n",
"
\n",
" \n",
" 45 | \n",
" 2020-11-06 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 11812 | \n",
" NaN | \n",
" 10331 | \n",
" NaN | \n",
" 9675 | \n",
" NaN | \n",
" 625 | \n",
" ... | \n",
" 675 | \n",
" 1900 | \n",
" 1294 | \n",
" 990 | \n",
" 1186 | \n",
" 1177 | \n",
" 952 | \n",
" 1614 | \n",
" 1174 | \n",
" 832 | \n",
"
\n",
" \n",
" 46 | \n",
" 2020-11-13 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 12254 | \n",
" NaN | \n",
" 10350 | \n",
" NaN | \n",
" 9662 | \n",
" NaN | \n",
" 658 | \n",
" ... | \n",
" 711 | \n",
" 1950 | \n",
" 1350 | \n",
" 1099 | \n",
" 1317 | \n",
" 1172 | \n",
" 1112 | \n",
" 1616 | \n",
" 1168 | \n",
" 742 | \n",
"
\n",
" \n",
" 47 | \n",
" 2020-11-20 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 12535 | \n",
" NaN | \n",
" 10380 | \n",
" NaN | \n",
" 9701 | \n",
" NaN | \n",
" 653 | \n",
" ... | \n",
" 691 | \n",
" 1935 | \n",
" 1441 | \n",
" 1105 | \n",
" 1385 | \n",
" 1186 | \n",
" 1086 | \n",
" 1687 | \n",
" 1159 | \n",
" 848 | \n",
"
\n",
" \n",
" 48 | \n",
" 2020-11-27 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 12456 | \n",
" NaN | \n",
" 10357 | \n",
" NaN | \n",
" 9690 | \n",
" NaN | \n",
" 646 | \n",
" ... | \n",
" 679 | \n",
" 1791 | \n",
" 1501 | \n",
" 1218 | \n",
" 1358 | \n",
" 1159 | \n",
" 1012 | \n",
" 1655 | \n",
" 1272 | \n",
" 797 | \n",
"
\n",
" \n",
" 49 | \n",
" 2020-12-04 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 12303 | \n",
" NaN | \n",
" 10695 | \n",
" NaN | \n",
" 9995 | \n",
" NaN | \n",
" 679 | \n",
" ... | \n",
" 645 | \n",
" 1679 | \n",
" 1403 | \n",
" 1121 | \n",
" 1340 | \n",
" 1229 | \n",
" 1029 | \n",
" 1720 | \n",
" 1284 | \n",
" 836 | \n",
"
\n",
" \n",
" 50 | \n",
" 2020-12-11 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 12292 | \n",
" NaN | \n",
" 10750 | \n",
" NaN | \n",
" 10034 | \n",
" NaN | \n",
" 693 | \n",
" ... | \n",
" 661 | \n",
" 1691 | \n",
" 1326 | \n",
" 1199 | \n",
" 1432 | \n",
" 1224 | \n",
" 1065 | \n",
" 1706 | \n",
" 1156 | \n",
" 814 | \n",
"
\n",
" \n",
" 51 | \n",
" 2020-12-18 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 13011 | \n",
" NaN | \n",
" 11548 | \n",
" NaN | \n",
" 10804 | \n",
" NaN | \n",
" 718 | \n",
" ... | \n",
" 689 | \n",
" 1718 | \n",
" 1380 | \n",
" 1199 | \n",
" 1385 | \n",
" 1317 | \n",
" 1167 | \n",
" 1947 | \n",
" 1311 | \n",
" 882 | \n",
"
\n",
" \n",
" 52 | \n",
" 2020-12-25 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 11520 | \n",
" NaN | \n",
" 7954 | \n",
" NaN | \n",
" 7421 | \n",
" NaN | \n",
" 518 | \n",
" ... | \n",
" 669 | \n",
" 1463 | \n",
" 1130 | \n",
" 1097 | \n",
" 1217 | \n",
" 1198 | \n",
" 1090 | \n",
" 1701 | \n",
" 1115 | \n",
" 825 | \n",
"
\n",
" \n",
" 53 | \n",
" 2021-01-01 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 10069 | \n",
" NaN | \n",
" 7954 | \n",
" NaN | \n",
" 7421 | \n",
" NaN | \n",
" 518 | \n",
" ... | \n",
" 547 | \n",
" 1346 | \n",
" 886 | \n",
" 842 | \n",
" 1024 | \n",
" 997 | \n",
" 1159 | \n",
" 1595 | \n",
" 929 | \n",
" 727 | \n",
"
\n",
" \n",
"
\n",
"
54 rows × 91 columns
\n",
"
"
],
"text/plain": [
" NaN NaN NaN NaN \\\n",
"Week number Week ended NaN NaN Total deaths, all ages \n",
"1 2020-01-03 00:00:00 NaN NaN 12254 \n",
"2 2020-01-10 00:00:00 NaN NaN 14058 \n",
"3 2020-01-17 00:00:00 NaN NaN 12990 \n",
"4 2020-01-24 00:00:00 NaN NaN 11856 \n",
"5 2020-01-31 00:00:00 NaN NaN 11612 \n",
"6 2020-02-07 00:00:00 NaN NaN 10986 \n",
"7 2020-02-14 00:00:00 NaN NaN 10944 \n",
"8 2020-02-21 00:00:00 NaN NaN 10841 \n",
"9 2020-02-28 00:00:00 NaN NaN 10816 \n",
"10 2020-03-06 00:00:00 NaN NaN 10895 \n",
"11 2020-03-13 00:00:00 NaN NaN 11019 \n",
"12 2020-03-20 00:00:00 NaN NaN 10645 \n",
"13 2020-03-27 00:00:00 NaN NaN 11141 \n",
"14 2020-04-03 00:00:00 NaN NaN 16387 \n",
"15 2020-04-10 00:00:00 NaN NaN 18516 \n",
"16 2020-04-17 00:00:00 NaN NaN 22351 \n",
"17 2020-04-24 00:00:00 NaN NaN 21997 \n",
"18 2020-05-01 00:00:00 NaN NaN 17953 \n",
"19 2020-05-08 00:00:00 NaN NaN 12657 \n",
"20 2020-05-15 00:00:00 NaN NaN 14573 \n",
"21 2020-05-22 00:00:00 NaN NaN 12288 \n",
"22 2020-05-29 00:00:00 NaN NaN 9824 \n",
"23 2020-06-05 00:00:00 NaN NaN 10709 \n",
"24 2020-06-12 00:00:00 NaN NaN 9976 \n",
"25 2020-06-19 00:00:00 NaN NaN 9339 \n",
"26 2020-06-26 00:00:00 NaN NaN 8979 \n",
"27 2020-07-03 00:00:00 NaN NaN 9140 \n",
"28 2020-07-10 00:00:00 NaN NaN 8690 \n",
"29 2020-07-17 00:00:00 NaN NaN 8823 \n",
"30 2020-07-24 00:00:00 NaN NaN 8891 \n",
"31 2020-07-31 00:00:00 NaN NaN 8946 \n",
"32 2020-08-07 00:00:00 NaN NaN 8945 \n",
"33 2020-08-14 00:00:00 NaN NaN 9392 \n",
"34 2020-08-21 00:00:00 NaN NaN 9631 \n",
"35 2020-08-28 00:00:00 NaN NaN 9032 \n",
"36 2020-09-04 00:00:00 NaN NaN 7739 \n",
"37 2020-09-11 00:00:00 NaN NaN 9811 \n",
"38 2020-09-18 00:00:00 NaN NaN 9523 \n",
"39 2020-09-25 00:00:00 NaN NaN 9634 \n",
"40 2020-10-02 00:00:00 NaN NaN 9945 \n",
"41 2020-10-09 00:00:00 NaN NaN 9954 \n",
"42 2020-10-16 00:00:00 NaN NaN 10534 \n",
"43 2020-10-23 00:00:00 NaN NaN 10739 \n",
"44 2020-10-30 00:00:00 NaN NaN 10887 \n",
"45 2020-11-06 00:00:00 NaN NaN 11812 \n",
"46 2020-11-13 00:00:00 NaN NaN 12254 \n",
"47 2020-11-20 00:00:00 NaN NaN 12535 \n",
"48 2020-11-27 00:00:00 NaN NaN 12456 \n",
"49 2020-12-04 00:00:00 NaN NaN 12303 \n",
"50 2020-12-11 00:00:00 NaN NaN 12292 \n",
"51 2020-12-18 00:00:00 NaN NaN 13011 \n",
"52 2020-12-25 00:00:00 NaN NaN 11520 \n",
"53 2021-01-01 00:00:00 NaN NaN 10069 \n",
"\n",
" NaN \\\n",
"Week number Total deaths: average of corresponding \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"5 NaN \n",
"6 NaN \n",
"7 NaN \n",
"8 NaN \n",
"9 NaN \n",
"10 NaN \n",
"11 NaN \n",
"12 NaN \n",
"13 NaN \n",
"14 NaN \n",
"15 NaN \n",
"16 NaN \n",
"17 NaN \n",
"18 NaN \n",
"19 NaN \n",
"20 NaN \n",
"21 NaN \n",
"22 NaN \n",
"23 NaN \n",
"24 NaN \n",
"25 NaN \n",
"26 NaN \n",
"27 NaN \n",
"28 NaN \n",
"29 NaN \n",
"30 NaN \n",
"31 NaN \n",
"32 NaN \n",
"33 NaN \n",
"34 NaN \n",
"35 NaN \n",
"36 NaN \n",
"37 NaN \n",
"38 NaN \n",
"39 NaN \n",
"40 NaN \n",
"41 NaN \n",
"42 NaN \n",
"43 NaN \n",
"44 NaN \n",
"45 NaN \n",
"46 NaN \n",
"47 NaN \n",
"48 NaN \n",
"49 NaN \n",
"50 NaN \n",
"51 NaN \n",
"52 NaN \n",
"53 NaN \n",
"\n",
" NaN \\\n",
"Week number week over the previous 5 years 1, 10, 11 (Engl... \n",
"1 12175 \n",
"2 13822 \n",
"3 13216 \n",
"4 12760 \n",
"5 12206 \n",
"6 11925 \n",
"7 11627 \n",
"8 11548 \n",
"9 11183 \n",
"10 11498 \n",
"11 11205 \n",
"12 10573 \n",
"13 10130 \n",
"14 10305 \n",
"15 10520 \n",
"16 10497 \n",
"17 10458 \n",
"18 9941 \n",
"19 9576 \n",
"20 10188 \n",
"21 9940 \n",
"22 8171 \n",
"23 9977 \n",
"24 9417 \n",
"25 9404 \n",
"26 9293 \n",
"27 9183 \n",
"28 9250 \n",
"29 9093 \n",
"30 9052 \n",
"31 9036 \n",
"32 9102 \n",
"33 9085 \n",
"34 9157 \n",
"35 8241 \n",
"36 9182 \n",
"37 9306 \n",
"38 9264 \n",
"39 9377 \n",
"40 9555 \n",
"41 9811 \n",
"42 9865 \n",
"43 9759 \n",
"44 9891 \n",
"45 10331 \n",
"46 10350 \n",
"47 10380 \n",
"48 10357 \n",
"49 10695 \n",
"50 10750 \n",
"51 11548 \n",
"52 7954 \n",
"53 7954 \n",
"\n",
" NaN \\\n",
"Week number Total deaths: average of corresponding \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"5 NaN \n",
"6 NaN \n",
"7 NaN \n",
"8 NaN \n",
"9 NaN \n",
"10 NaN \n",
"11 NaN \n",
"12 NaN \n",
"13 NaN \n",
"14 NaN \n",
"15 NaN \n",
"16 NaN \n",
"17 NaN \n",
"18 NaN \n",
"19 NaN \n",
"20 NaN \n",
"21 NaN \n",
"22 NaN \n",
"23 NaN \n",
"24 NaN \n",
"25 NaN \n",
"26 NaN \n",
"27 NaN \n",
"28 NaN \n",
"29 NaN \n",
"30 NaN \n",
"31 NaN \n",
"32 NaN \n",
"33 NaN \n",
"34 NaN \n",
"35 NaN \n",
"36 NaN \n",
"37 NaN \n",
"38 NaN \n",
"39 NaN \n",
"40 NaN \n",
"41 NaN \n",
"42 NaN \n",
"43 NaN \n",
"44 NaN \n",
"45 NaN \n",
"46 NaN \n",
"47 NaN \n",
"48 NaN \n",
"49 NaN \n",
"50 NaN \n",
"51 NaN \n",
"52 NaN \n",
"53 NaN \n",
"\n",
" NaN \\\n",
"Week number week over the previous 5 years 1, 10, 11 (Engl... \n",
"1 11412 \n",
"2 12933 \n",
"3 12370 \n",
"4 11933 \n",
"5 11419 \n",
"6 11154 \n",
"7 10876 \n",
"8 10790 \n",
"9 10448 \n",
"10 10745 \n",
"11 10447 \n",
"12 9841 \n",
"13 9414 \n",
"14 9601 \n",
"15 9807 \n",
"16 9787 \n",
"17 9768 \n",
"18 9289 \n",
"19 8937 \n",
"20 9526 \n",
"21 9299 \n",
"22 7607 \n",
"23 9346 \n",
"24 8803 \n",
"25 8810 \n",
"26 8695 \n",
"27 8606 \n",
"28 8648 \n",
"29 8502 \n",
"30 8452 \n",
"31 8436 \n",
"32 8502 \n",
"33 8494 \n",
"34 8560 \n",
"35 7674 \n",
"36 8604 \n",
"37 8708 \n",
"38 8663 \n",
"39 8744 \n",
"40 8942 \n",
"41 9168 \n",
"42 9215 \n",
"43 9104 \n",
"44 9248 \n",
"45 9675 \n",
"46 9662 \n",
"47 9701 \n",
"48 9690 \n",
"49 9995 \n",
"50 10034 \n",
"51 10804 \n",
"52 7421 \n",
"53 7421 \n",
"\n",
" NaN \\\n",
"Week number Total deaths: average of corresponding \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"5 NaN \n",
"6 NaN \n",
"7 NaN \n",
"8 NaN \n",
"9 NaN \n",
"10 NaN \n",
"11 NaN \n",
"12 NaN \n",
"13 NaN \n",
"14 NaN \n",
"15 NaN \n",
"16 NaN \n",
"17 NaN \n",
"18 NaN \n",
"19 NaN \n",
"20 NaN \n",
"21 NaN \n",
"22 NaN \n",
"23 NaN \n",
"24 NaN \n",
"25 NaN \n",
"26 NaN \n",
"27 NaN \n",
"28 NaN \n",
"29 NaN \n",
"30 NaN \n",
"31 NaN \n",
"32 NaN \n",
"33 NaN \n",
"34 NaN \n",
"35 NaN \n",
"36 NaN \n",
"37 NaN \n",
"38 NaN \n",
"39 NaN \n",
"40 NaN \n",
"41 NaN \n",
"42 NaN \n",
"43 NaN \n",
"44 NaN \n",
"45 NaN \n",
"46 NaN \n",
"47 NaN \n",
"48 NaN \n",
"49 NaN \n",
"50 NaN \n",
"51 NaN \n",
"52 NaN \n",
"53 NaN \n",
"\n",
" NaN ... North East \\\n",
"Week number week over the previous 5 years 1, 10, 11 (Wales) ... E12000001 \n",
"1 756 ... 673 \n",
"2 856 ... 707 \n",
"3 812 ... 647 \n",
"4 802 ... 612 \n",
"5 760 ... 561 \n",
"6 729 ... 564 \n",
"7 722 ... 573 \n",
"8 724 ... 539 \n",
"9 698 ... 572 \n",
"10 720 ... 568 \n",
"11 727 ... 590 \n",
"12 677 ... 522 \n",
"13 665 ... 542 \n",
"14 667 ... 770 \n",
"15 671 ... 849 \n",
"16 661 ... 1155 \n",
"17 662 ... 1103 \n",
"18 624 ... 922 \n",
"19 612 ... 769 \n",
"20 635 ... 845 \n",
"21 614 ... 718 \n",
"22 546 ... 550 \n",
"23 610 ... 576 \n",
"24 588 ... 478 \n",
"25 573 ... 498 \n",
"26 571 ... 485 \n",
"27 555 ... 515 \n",
"28 578 ... 468 \n",
"29 557 ... 445 \n",
"30 566 ... 493 \n",
"31 572 ... 507 \n",
"32 571 ... 486 \n",
"33 564 ... 520 \n",
"34 573 ... 479 \n",
"35 539 ... 455 \n",
"36 552 ... 431 \n",
"37 577 ... 502 \n",
"38 575 ... 465 \n",
"39 604 ... 514 \n",
"40 587 ... 558 \n",
"41 615 ... 544 \n",
"42 630 ... 606 \n",
"43 628 ... 600 \n",
"44 616 ... 591 \n",
"45 625 ... 675 \n",
"46 658 ... 711 \n",
"47 653 ... 691 \n",
"48 646 ... 679 \n",
"49 679 ... 645 \n",
"50 693 ... 661 \n",
"51 718 ... 689 \n",
"52 518 ... 669 \n",
"53 518 ... 547 \n",
"\n",
" North West Yorkshire and The Humber East Midlands West Midlands \\\n",
"Week number E12000002 E12000003 E12000004 E12000005 \n",
"1 1806 1240 1060 1349 \n",
"2 1932 1339 1195 1450 \n",
"3 1696 1278 1106 1407 \n",
"4 1529 1187 1024 1231 \n",
"5 1461 1136 1015 1262 \n",
"6 1529 1072 922 1052 \n",
"7 1427 1059 976 1159 \n",
"8 1477 1087 924 1116 \n",
"9 1476 1078 919 1174 \n",
"10 1490 1112 930 1098 \n",
"11 1472 1053 915 1187 \n",
"12 1443 1012 947 1115 \n",
"13 1538 982 922 1035 \n",
"14 2137 1436 1246 1812 \n",
"15 2597 1503 1452 2182 \n",
"16 3195 1960 1632 2536 \n",
"17 3109 2095 1711 2481 \n",
"18 2503 1844 1418 1975 \n",
"19 1790 1328 1094 1326 \n",
"20 1992 1589 1283 1502 \n",
"21 1636 1236 1041 1319 \n",
"22 1337 1046 863 970 \n",
"23 1478 1090 931 1172 \n",
"24 1374 980 967 1096 \n",
"25 1234 952 835 973 \n",
"26 1300 922 800 946 \n",
"27 1225 875 827 949 \n",
"28 1154 848 771 902 \n",
"29 1159 843 816 956 \n",
"30 1197 817 798 964 \n",
"31 1272 837 729 902 \n",
"32 1211 851 798 933 \n",
"33 1304 853 829 923 \n",
"34 1269 870 780 1028 \n",
"35 1148 922 724 945 \n",
"36 1057 780 640 770 \n",
"37 1229 952 867 1021 \n",
"38 1287 939 795 1051 \n",
"39 1271 965 825 1036 \n",
"40 1301 978 842 1045 \n",
"41 1367 1067 884 1053 \n",
"42 1553 1001 904 1154 \n",
"43 1714 1111 891 1124 \n",
"44 1754 1168 882 1102 \n",
"45 1900 1294 990 1186 \n",
"46 1950 1350 1099 1317 \n",
"47 1935 1441 1105 1385 \n",
"48 1791 1501 1218 1358 \n",
"49 1679 1403 1121 1340 \n",
"50 1691 1326 1199 1432 \n",
"51 1718 1380 1199 1385 \n",
"52 1463 1130 1097 1217 \n",
"53 1346 886 842 1024 \n",
"\n",
" East London South East South West Wales \n",
"Week number E12000006 E12000007 E12000008 E12000009 W92000004 \n",
"1 1162 1113 1814 1225 787 \n",
"2 1573 1272 2132 1487 939 \n",
"3 1457 1073 2064 1466 767 \n",
"4 1410 1028 1833 1253 723 \n",
"5 1286 1092 1820 1233 727 \n",
"6 1259 987 1729 1157 690 \n",
"7 1172 967 1688 1169 728 \n",
"8 1167 1032 1675 1118 679 \n",
"9 1115 1085 1587 1133 651 \n",
"10 1149 982 1726 1170 652 \n",
"11 1211 964 1751 1174 675 \n",
"12 1043 1008 1657 1156 719 \n",
"13 1182 1297 1822 1092 719 \n",
"14 1717 2511 2294 1520 920 \n",
"15 1984 2832 2604 1560 928 \n",
"16 2466 3275 3084 1854 1169 \n",
"17 2299 2785 3334 1924 1124 \n",
"18 1982 1953 2853 1554 929 \n",
"19 1321 1213 1887 1218 692 \n",
"20 1543 1329 2251 1449 772 \n",
"21 1397 1125 1937 1177 692 \n",
"22 1095 841 1515 1011 587 \n",
"23 1131 891 1610 1116 700 \n",
"24 1048 883 1530 1035 574 \n",
"25 927 896 1411 990 617 \n",
"26 880 791 1311 979 552 \n",
"27 922 837 1454 938 584 \n",
"28 999 803 1228 930 572 \n",
"29 945 806 1339 953 550 \n",
"30 951 816 1340 941 565 \n",
"31 949 773 1447 988 531 \n",
"32 955 832 1370 929 563 \n",
"33 1006 928 1395 1009 617 \n",
"34 1024 920 1608 1043 594 \n",
"35 951 810 1511 959 591 \n",
"36 806 737 1208 803 488 \n",
"37 1052 898 1610 1084 578 \n",
"38 1023 844 1530 1021 555 \n",
"39 963 869 1521 1041 617 \n",
"40 1054 899 1577 1003 671 \n",
"41 1019 902 1462 1010 638 \n",
"42 1056 923 1462 1174 688 \n",
"43 1154 922 1510 1044 661 \n",
"44 1089 888 1563 1129 712 \n",
"45 1177 952 1614 1174 832 \n",
"46 1172 1112 1616 1168 742 \n",
"47 1186 1086 1687 1159 848 \n",
"48 1159 1012 1655 1272 797 \n",
"49 1229 1029 1720 1284 836 \n",
"50 1224 1065 1706 1156 814 \n",
"51 1317 1167 1947 1311 882 \n",
"52 1198 1090 1701 1115 825 \n",
"53 997 1159 1595 929 727 \n",
"\n",
"[54 rows x 91 columns]"
]
},
"execution_count": 432,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eng_xls = pd.read_excel(england_wales_filename, \n",
" sheet_name=\"Weekly figures 2020\",\n",
" skiprows=[0, 1, 2, 3],\n",
" header=0,\n",
" index_col=[1]\n",
" ).iloc[:91].T\n",
"eng_xls"
]
},
{
"cell_type": "code",
"execution_count": 433,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"# eng_xls_columns"
]
},
{
"cell_type": "code",
"execution_count": 434,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"eng_xls_columns = list(eng_xls.columns)\n",
"\n",
"for i, c in enumerate(eng_xls_columns):\n",
"# print(i, c, type(c), isinstance(c, float))\n",
" if isinstance(c, float) and np.isnan(c):\n",
" if eng_xls.iloc[0].iloc[i] is not pd.NaT:\n",
" eng_xls_columns[i] = eng_xls.iloc[0].iloc[i]\n",
"\n",
"# np.isnan(eng_xls_columns[0])\n",
"# eng_xls_columns\n",
"\n",
"eng_xls.columns = eng_xls_columns\n",
"# eng_xls.columns"
]
},
{
"cell_type": "code",
"execution_count": 435,
"metadata": {
"scrolled": true
},
"outputs": [
{
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"
\n",
" \n",
" 36 | \n",
" 2020-09-04 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 7739 | \n",
" NaN | \n",
" 9182 | \n",
" NaN | \n",
" 8604 | \n",
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" 552 | \n",
" ... | \n",
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"
\n",
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" 2020-09-11 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
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" NaN | \n",
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" NaN | \n",
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" 577 | \n",
" ... | \n",
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"
\n",
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" 2020-09-18 00:00:00 | \n",
" NaN | \n",
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" NaN | \n",
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" 2020-09-25 00:00:00 | \n",
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" 2020-10-02 00:00:00 | \n",
" NaN | \n",
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" NaN | \n",
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" NaN | \n",
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" ... | \n",
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" NaN | \n",
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" ... | \n",
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" 2020-10-16 00:00:00 | \n",
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" 2020-10-23 00:00:00 | \n",
" NaN | \n",
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" 2020-10-30 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 10887 | \n",
" NaN | \n",
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" NaN | \n",
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" NaN | \n",
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" 2020-11-06 00:00:00 | \n",
" NaN | \n",
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" NaN | \n",
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" ... | \n",
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" 2020-12-04 00:00:00 | \n",
" NaN | \n",
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" NaN | \n",
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" NaN | \n",
" 9995 | \n",
" NaN | \n",
" 679 | \n",
" ... | \n",
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\n",
" \n",
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" 2020-12-11 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 12292 | \n",
" NaN | \n",
" 10750 | \n",
" NaN | \n",
" 10034 | \n",
" NaN | \n",
" 693 | \n",
" ... | \n",
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\n",
" \n",
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" 2020-12-18 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 13011 | \n",
" NaN | \n",
" 11548 | \n",
" NaN | \n",
" 10804 | \n",
" NaN | \n",
" 718 | \n",
" ... | \n",
" 689 | \n",
" 1718 | \n",
" 1380 | \n",
" 1199 | \n",
" 1385 | \n",
" 1317 | \n",
" 1167 | \n",
" 1947 | \n",
" 1311 | \n",
" 882 | \n",
"
\n",
" \n",
" 52 | \n",
" 2020-12-25 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 11520 | \n",
" NaN | \n",
" 7954 | \n",
" NaN | \n",
" 7421 | \n",
" NaN | \n",
" 518 | \n",
" ... | \n",
" 669 | \n",
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" 1130 | \n",
" 1097 | \n",
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" 1198 | \n",
" 1090 | \n",
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" 825 | \n",
"
\n",
" \n",
" 53 | \n",
" 2021-01-01 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 10069 | \n",
" NaN | \n",
" 7954 | \n",
" NaN | \n",
" 7421 | \n",
" NaN | \n",
" 518 | \n",
" ... | \n",
" 547 | \n",
" 1346 | \n",
" 886 | \n",
" 842 | \n",
" 1024 | \n",
" 997 | \n",
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" 929 | \n",
" 727 | \n",
"
\n",
" \n",
"
\n",
"
54 rows × 91 columns
\n",
"
"
],
"text/plain": [
" Week ended NaN NaN Total deaths, all ages \\\n",
"Week number Week ended NaN NaN Total deaths, all ages \n",
"1 2020-01-03 00:00:00 NaN NaN 12254 \n",
"2 2020-01-10 00:00:00 NaN NaN 14058 \n",
"3 2020-01-17 00:00:00 NaN NaN 12990 \n",
"4 2020-01-24 00:00:00 NaN NaN 11856 \n",
"5 2020-01-31 00:00:00 NaN NaN 11612 \n",
"6 2020-02-07 00:00:00 NaN NaN 10986 \n",
"7 2020-02-14 00:00:00 NaN NaN 10944 \n",
"8 2020-02-21 00:00:00 NaN NaN 10841 \n",
"9 2020-02-28 00:00:00 NaN NaN 10816 \n",
"10 2020-03-06 00:00:00 NaN NaN 10895 \n",
"11 2020-03-13 00:00:00 NaN NaN 11019 \n",
"12 2020-03-20 00:00:00 NaN NaN 10645 \n",
"13 2020-03-27 00:00:00 NaN NaN 11141 \n",
"14 2020-04-03 00:00:00 NaN NaN 16387 \n",
"15 2020-04-10 00:00:00 NaN NaN 18516 \n",
"16 2020-04-17 00:00:00 NaN NaN 22351 \n",
"17 2020-04-24 00:00:00 NaN NaN 21997 \n",
"18 2020-05-01 00:00:00 NaN NaN 17953 \n",
"19 2020-05-08 00:00:00 NaN NaN 12657 \n",
"20 2020-05-15 00:00:00 NaN NaN 14573 \n",
"21 2020-05-22 00:00:00 NaN NaN 12288 \n",
"22 2020-05-29 00:00:00 NaN NaN 9824 \n",
"23 2020-06-05 00:00:00 NaN NaN 10709 \n",
"24 2020-06-12 00:00:00 NaN NaN 9976 \n",
"25 2020-06-19 00:00:00 NaN NaN 9339 \n",
"26 2020-06-26 00:00:00 NaN NaN 8979 \n",
"27 2020-07-03 00:00:00 NaN NaN 9140 \n",
"28 2020-07-10 00:00:00 NaN NaN 8690 \n",
"29 2020-07-17 00:00:00 NaN NaN 8823 \n",
"30 2020-07-24 00:00:00 NaN NaN 8891 \n",
"31 2020-07-31 00:00:00 NaN NaN 8946 \n",
"32 2020-08-07 00:00:00 NaN NaN 8945 \n",
"33 2020-08-14 00:00:00 NaN NaN 9392 \n",
"34 2020-08-21 00:00:00 NaN NaN 9631 \n",
"35 2020-08-28 00:00:00 NaN NaN 9032 \n",
"36 2020-09-04 00:00:00 NaN NaN 7739 \n",
"37 2020-09-11 00:00:00 NaN NaN 9811 \n",
"38 2020-09-18 00:00:00 NaN NaN 9523 \n",
"39 2020-09-25 00:00:00 NaN NaN 9634 \n",
"40 2020-10-02 00:00:00 NaN NaN 9945 \n",
"41 2020-10-09 00:00:00 NaN NaN 9954 \n",
"42 2020-10-16 00:00:00 NaN NaN 10534 \n",
"43 2020-10-23 00:00:00 NaN NaN 10739 \n",
"44 2020-10-30 00:00:00 NaN NaN 10887 \n",
"45 2020-11-06 00:00:00 NaN NaN 11812 \n",
"46 2020-11-13 00:00:00 NaN NaN 12254 \n",
"47 2020-11-20 00:00:00 NaN NaN 12535 \n",
"48 2020-11-27 00:00:00 NaN NaN 12456 \n",
"49 2020-12-04 00:00:00 NaN NaN 12303 \n",
"50 2020-12-11 00:00:00 NaN NaN 12292 \n",
"51 2020-12-18 00:00:00 NaN NaN 13011 \n",
"52 2020-12-25 00:00:00 NaN NaN 11520 \n",
"53 2021-01-01 00:00:00 NaN NaN 10069 \n",
"\n",
" Total deaths: average of corresponding \\\n",
"Week number Total deaths: average of corresponding \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"5 NaN \n",
"6 NaN \n",
"7 NaN \n",
"8 NaN \n",
"9 NaN \n",
"10 NaN \n",
"11 NaN \n",
"12 NaN \n",
"13 NaN \n",
"14 NaN \n",
"15 NaN \n",
"16 NaN \n",
"17 NaN \n",
"18 NaN \n",
"19 NaN \n",
"20 NaN \n",
"21 NaN \n",
"22 NaN \n",
"23 NaN \n",
"24 NaN \n",
"25 NaN \n",
"26 NaN \n",
"27 NaN \n",
"28 NaN \n",
"29 NaN \n",
"30 NaN \n",
"31 NaN \n",
"32 NaN \n",
"33 NaN \n",
"34 NaN \n",
"35 NaN \n",
"36 NaN \n",
"37 NaN \n",
"38 NaN \n",
"39 NaN \n",
"40 NaN \n",
"41 NaN \n",
"42 NaN \n",
"43 NaN \n",
"44 NaN \n",
"45 NaN \n",
"46 NaN \n",
"47 NaN \n",
"48 NaN \n",
"49 NaN \n",
"50 NaN \n",
"51 NaN \n",
"52 NaN \n",
"53 NaN \n",
"\n",
" week over the previous 5 years 1, 10, 11 (England and Wales) \\\n",
"Week number week over the previous 5 years 1, 10, 11 (Engl... \n",
"1 12175 \n",
"2 13822 \n",
"3 13216 \n",
"4 12760 \n",
"5 12206 \n",
"6 11925 \n",
"7 11627 \n",
"8 11548 \n",
"9 11183 \n",
"10 11498 \n",
"11 11205 \n",
"12 10573 \n",
"13 10130 \n",
"14 10305 \n",
"15 10520 \n",
"16 10497 \n",
"17 10458 \n",
"18 9941 \n",
"19 9576 \n",
"20 10188 \n",
"21 9940 \n",
"22 8171 \n",
"23 9977 \n",
"24 9417 \n",
"25 9404 \n",
"26 9293 \n",
"27 9183 \n",
"28 9250 \n",
"29 9093 \n",
"30 9052 \n",
"31 9036 \n",
"32 9102 \n",
"33 9085 \n",
"34 9157 \n",
"35 8241 \n",
"36 9182 \n",
"37 9306 \n",
"38 9264 \n",
"39 9377 \n",
"40 9555 \n",
"41 9811 \n",
"42 9865 \n",
"43 9759 \n",
"44 9891 \n",
"45 10331 \n",
"46 10350 \n",
"47 10380 \n",
"48 10357 \n",
"49 10695 \n",
"50 10750 \n",
"51 11548 \n",
"52 7954 \n",
"53 7954 \n",
"\n",
" Total deaths: average of corresponding \\\n",
"Week number Total deaths: average of corresponding \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"5 NaN \n",
"6 NaN \n",
"7 NaN \n",
"8 NaN \n",
"9 NaN \n",
"10 NaN \n",
"11 NaN \n",
"12 NaN \n",
"13 NaN \n",
"14 NaN \n",
"15 NaN \n",
"16 NaN \n",
"17 NaN \n",
"18 NaN \n",
"19 NaN \n",
"20 NaN \n",
"21 NaN \n",
"22 NaN \n",
"23 NaN \n",
"24 NaN \n",
"25 NaN \n",
"26 NaN \n",
"27 NaN \n",
"28 NaN \n",
"29 NaN \n",
"30 NaN \n",
"31 NaN \n",
"32 NaN \n",
"33 NaN \n",
"34 NaN \n",
"35 NaN \n",
"36 NaN \n",
"37 NaN \n",
"38 NaN \n",
"39 NaN \n",
"40 NaN \n",
"41 NaN \n",
"42 NaN \n",
"43 NaN \n",
"44 NaN \n",
"45 NaN \n",
"46 NaN \n",
"47 NaN \n",
"48 NaN \n",
"49 NaN \n",
"50 NaN \n",
"51 NaN \n",
"52 NaN \n",
"53 NaN \n",
"\n",
" week over the previous 5 years 1, 10, 11 (England) \\\n",
"Week number week over the previous 5 years 1, 10, 11 (Engl... \n",
"1 11412 \n",
"2 12933 \n",
"3 12370 \n",
"4 11933 \n",
"5 11419 \n",
"6 11154 \n",
"7 10876 \n",
"8 10790 \n",
"9 10448 \n",
"10 10745 \n",
"11 10447 \n",
"12 9841 \n",
"13 9414 \n",
"14 9601 \n",
"15 9807 \n",
"16 9787 \n",
"17 9768 \n",
"18 9289 \n",
"19 8937 \n",
"20 9526 \n",
"21 9299 \n",
"22 7607 \n",
"23 9346 \n",
"24 8803 \n",
"25 8810 \n",
"26 8695 \n",
"27 8606 \n",
"28 8648 \n",
"29 8502 \n",
"30 8452 \n",
"31 8436 \n",
"32 8502 \n",
"33 8494 \n",
"34 8560 \n",
"35 7674 \n",
"36 8604 \n",
"37 8708 \n",
"38 8663 \n",
"39 8744 \n",
"40 8942 \n",
"41 9168 \n",
"42 9215 \n",
"43 9104 \n",
"44 9248 \n",
"45 9675 \n",
"46 9662 \n",
"47 9701 \n",
"48 9690 \n",
"49 9995 \n",
"50 10034 \n",
"51 10804 \n",
"52 7421 \n",
"53 7421 \n",
"\n",
" Total deaths: average of corresponding \\\n",
"Week number Total deaths: average of corresponding \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"5 NaN \n",
"6 NaN \n",
"7 NaN \n",
"8 NaN \n",
"9 NaN \n",
"10 NaN \n",
"11 NaN \n",
"12 NaN \n",
"13 NaN \n",
"14 NaN \n",
"15 NaN \n",
"16 NaN \n",
"17 NaN \n",
"18 NaN \n",
"19 NaN \n",
"20 NaN \n",
"21 NaN \n",
"22 NaN \n",
"23 NaN \n",
"24 NaN \n",
"25 NaN \n",
"26 NaN \n",
"27 NaN \n",
"28 NaN \n",
"29 NaN \n",
"30 NaN \n",
"31 NaN \n",
"32 NaN \n",
"33 NaN \n",
"34 NaN \n",
"35 NaN \n",
"36 NaN \n",
"37 NaN \n",
"38 NaN \n",
"39 NaN \n",
"40 NaN \n",
"41 NaN \n",
"42 NaN \n",
"43 NaN \n",
"44 NaN \n",
"45 NaN \n",
"46 NaN \n",
"47 NaN \n",
"48 NaN \n",
"49 NaN \n",
"50 NaN \n",
"51 NaN \n",
"52 NaN \n",
"53 NaN \n",
"\n",
" week over the previous 5 years 1, 10, 11 (Wales) ... North East \\\n",
"Week number week over the previous 5 years 1, 10, 11 (Wales) ... E12000001 \n",
"1 756 ... 673 \n",
"2 856 ... 707 \n",
"3 812 ... 647 \n",
"4 802 ... 612 \n",
"5 760 ... 561 \n",
"6 729 ... 564 \n",
"7 722 ... 573 \n",
"8 724 ... 539 \n",
"9 698 ... 572 \n",
"10 720 ... 568 \n",
"11 727 ... 590 \n",
"12 677 ... 522 \n",
"13 665 ... 542 \n",
"14 667 ... 770 \n",
"15 671 ... 849 \n",
"16 661 ... 1155 \n",
"17 662 ... 1103 \n",
"18 624 ... 922 \n",
"19 612 ... 769 \n",
"20 635 ... 845 \n",
"21 614 ... 718 \n",
"22 546 ... 550 \n",
"23 610 ... 576 \n",
"24 588 ... 478 \n",
"25 573 ... 498 \n",
"26 571 ... 485 \n",
"27 555 ... 515 \n",
"28 578 ... 468 \n",
"29 557 ... 445 \n",
"30 566 ... 493 \n",
"31 572 ... 507 \n",
"32 571 ... 486 \n",
"33 564 ... 520 \n",
"34 573 ... 479 \n",
"35 539 ... 455 \n",
"36 552 ... 431 \n",
"37 577 ... 502 \n",
"38 575 ... 465 \n",
"39 604 ... 514 \n",
"40 587 ... 558 \n",
"41 615 ... 544 \n",
"42 630 ... 606 \n",
"43 628 ... 600 \n",
"44 616 ... 591 \n",
"45 625 ... 675 \n",
"46 658 ... 711 \n",
"47 653 ... 691 \n",
"48 646 ... 679 \n",
"49 679 ... 645 \n",
"50 693 ... 661 \n",
"51 718 ... 689 \n",
"52 518 ... 669 \n",
"53 518 ... 547 \n",
"\n",
" North West Yorkshire and The Humber East Midlands West Midlands \\\n",
"Week number E12000002 E12000003 E12000004 E12000005 \n",
"1 1806 1240 1060 1349 \n",
"2 1932 1339 1195 1450 \n",
"3 1696 1278 1106 1407 \n",
"4 1529 1187 1024 1231 \n",
"5 1461 1136 1015 1262 \n",
"6 1529 1072 922 1052 \n",
"7 1427 1059 976 1159 \n",
"8 1477 1087 924 1116 \n",
"9 1476 1078 919 1174 \n",
"10 1490 1112 930 1098 \n",
"11 1472 1053 915 1187 \n",
"12 1443 1012 947 1115 \n",
"13 1538 982 922 1035 \n",
"14 2137 1436 1246 1812 \n",
"15 2597 1503 1452 2182 \n",
"16 3195 1960 1632 2536 \n",
"17 3109 2095 1711 2481 \n",
"18 2503 1844 1418 1975 \n",
"19 1790 1328 1094 1326 \n",
"20 1992 1589 1283 1502 \n",
"21 1636 1236 1041 1319 \n",
"22 1337 1046 863 970 \n",
"23 1478 1090 931 1172 \n",
"24 1374 980 967 1096 \n",
"25 1234 952 835 973 \n",
"26 1300 922 800 946 \n",
"27 1225 875 827 949 \n",
"28 1154 848 771 902 \n",
"29 1159 843 816 956 \n",
"30 1197 817 798 964 \n",
"31 1272 837 729 902 \n",
"32 1211 851 798 933 \n",
"33 1304 853 829 923 \n",
"34 1269 870 780 1028 \n",
"35 1148 922 724 945 \n",
"36 1057 780 640 770 \n",
"37 1229 952 867 1021 \n",
"38 1287 939 795 1051 \n",
"39 1271 965 825 1036 \n",
"40 1301 978 842 1045 \n",
"41 1367 1067 884 1053 \n",
"42 1553 1001 904 1154 \n",
"43 1714 1111 891 1124 \n",
"44 1754 1168 882 1102 \n",
"45 1900 1294 990 1186 \n",
"46 1950 1350 1099 1317 \n",
"47 1935 1441 1105 1385 \n",
"48 1791 1501 1218 1358 \n",
"49 1679 1403 1121 1340 \n",
"50 1691 1326 1199 1432 \n",
"51 1718 1380 1199 1385 \n",
"52 1463 1130 1097 1217 \n",
"53 1346 886 842 1024 \n",
"\n",
" East London South East South West Wales \n",
"Week number E12000006 E12000007 E12000008 E12000009 W92000004 \n",
"1 1162 1113 1814 1225 787 \n",
"2 1573 1272 2132 1487 939 \n",
"3 1457 1073 2064 1466 767 \n",
"4 1410 1028 1833 1253 723 \n",
"5 1286 1092 1820 1233 727 \n",
"6 1259 987 1729 1157 690 \n",
"7 1172 967 1688 1169 728 \n",
"8 1167 1032 1675 1118 679 \n",
"9 1115 1085 1587 1133 651 \n",
"10 1149 982 1726 1170 652 \n",
"11 1211 964 1751 1174 675 \n",
"12 1043 1008 1657 1156 719 \n",
"13 1182 1297 1822 1092 719 \n",
"14 1717 2511 2294 1520 920 \n",
"15 1984 2832 2604 1560 928 \n",
"16 2466 3275 3084 1854 1169 \n",
"17 2299 2785 3334 1924 1124 \n",
"18 1982 1953 2853 1554 929 \n",
"19 1321 1213 1887 1218 692 \n",
"20 1543 1329 2251 1449 772 \n",
"21 1397 1125 1937 1177 692 \n",
"22 1095 841 1515 1011 587 \n",
"23 1131 891 1610 1116 700 \n",
"24 1048 883 1530 1035 574 \n",
"25 927 896 1411 990 617 \n",
"26 880 791 1311 979 552 \n",
"27 922 837 1454 938 584 \n",
"28 999 803 1228 930 572 \n",
"29 945 806 1339 953 550 \n",
"30 951 816 1340 941 565 \n",
"31 949 773 1447 988 531 \n",
"32 955 832 1370 929 563 \n",
"33 1006 928 1395 1009 617 \n",
"34 1024 920 1608 1043 594 \n",
"35 951 810 1511 959 591 \n",
"36 806 737 1208 803 488 \n",
"37 1052 898 1610 1084 578 \n",
"38 1023 844 1530 1021 555 \n",
"39 963 869 1521 1041 617 \n",
"40 1054 899 1577 1003 671 \n",
"41 1019 902 1462 1010 638 \n",
"42 1056 923 1462 1174 688 \n",
"43 1154 922 1510 1044 661 \n",
"44 1089 888 1563 1129 712 \n",
"45 1177 952 1614 1174 832 \n",
"46 1172 1112 1616 1168 742 \n",
"47 1186 1086 1687 1159 848 \n",
"48 1159 1012 1655 1272 797 \n",
"49 1229 1029 1720 1284 836 \n",
"50 1224 1065 1706 1156 814 \n",
"51 1317 1167 1947 1311 882 \n",
"52 1198 1090 1701 1115 825 \n",
"53 997 1159 1595 929 727 \n",
"\n",
"[54 rows x 91 columns]"
]
},
"execution_count": 435,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eng_xls"
]
},
{
"cell_type": "code",
"execution_count": 436,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" week | \n",
" date_up_to | \n",
" deaths | \n",
" year | \n",
" nation | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1 | \n",
" 2020-01-03 00:00:00 | \n",
" 787 | \n",
" 2020 | \n",
" Wales | \n",
"
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" \n",
" 1 | \n",
" 2 | \n",
" 2020-01-10 00:00:00 | \n",
" 939 | \n",
" 2020 | \n",
" Wales | \n",
"
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" 3 | \n",
" 2020-01-17 00:00:00 | \n",
" 767 | \n",
" 2020 | \n",
" Wales | \n",
"
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" 3 | \n",
" 4 | \n",
" 2020-01-24 00:00:00 | \n",
" 723 | \n",
" 2020 | \n",
" Wales | \n",
"
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" \n",
" 4 | \n",
" 5 | \n",
" 2020-01-31 00:00:00 | \n",
" 727 | \n",
" 2020 | \n",
" Wales | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" week date_up_to deaths year nation\n",
"0 1 2020-01-03 00:00:00 787 2020 Wales\n",
"1 2 2020-01-10 00:00:00 939 2020 Wales\n",
"2 3 2020-01-17 00:00:00 767 2020 Wales\n",
"3 4 2020-01-24 00:00:00 723 2020 Wales\n",
"4 5 2020-01-31 00:00:00 727 2020 Wales"
]
},
"execution_count": 436,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd = eng_xls.iloc[1:][['Week ended', 'Wales']].reset_index(level=0).rename(\n",
" columns={'Week ended': 'date_up_to', 'Wales': 'deaths',\n",
" 'index': 'week'}\n",
" )\n",
"rd['year'] = 2020\n",
"rd['nation'] = 'Wales'\n",
"rd.head()"
]
},
{
"cell_type": "code",
"execution_count": 437,
"metadata": {},
"outputs": [],
"source": [
"rd.to_sql(\n",
" 'all_causes_deaths',\n",
" conn,\n",
" if_exists='append',\n",
" index=False)"
]
},
{
"cell_type": "code",
"execution_count": 438,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
":2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" eng_xls['England deaths'] = eng_xls.loc[:, 'Total deaths, all ages'] - eng_xls.loc[:, 'Wales']\n"
]
}
],
"source": [
"eng_xls = eng_xls.iloc[1:]\n",
"eng_xls['England deaths'] = eng_xls.loc[:, 'Total deaths, all ages'] - eng_xls.loc[:, 'Wales']"
]
},
{
"cell_type": "code",
"execution_count": 439,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
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" \n",
" \n",
" | \n",
" Week ended | \n",
" NaN | \n",
" NaN | \n",
" Total deaths, all ages | \n",
" Total deaths: average of corresponding | \n",
" week over the previous 5 years 1, 10, 11 (England and Wales) | \n",
" Total deaths: average of corresponding | \n",
" week over the previous 5 years 1, 10, 11 (England) | \n",
" Total deaths: average of corresponding | \n",
" week over the previous 5 years 1, 10, 11 (Wales) | \n",
" ... | \n",
" North West | \n",
" Yorkshire and The Humber | \n",
" East Midlands | \n",
" West Midlands | \n",
" East | \n",
" London | \n",
" South East | \n",
" South West | \n",
" Wales | \n",
" England deaths | \n",
"
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" 1 | \n",
" 2020-01-03 00:00:00 | \n",
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" 2020-01-10 00:00:00 | \n",
" NaN | \n",
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" 13822 | \n",
" NaN | \n",
" 12933 | \n",
" NaN | \n",
" 856 | \n",
" ... | \n",
" 1932 | \n",
" 1339 | \n",
" 1195 | \n",
" 1450 | \n",
" 1573 | \n",
" 1272 | \n",
" 2132 | \n",
" 1487 | \n",
" 939 | \n",
" 13119 | \n",
"
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" \n",
" 3 | \n",
" 2020-01-17 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 12990 | \n",
" NaN | \n",
" 13216 | \n",
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" 812 | \n",
" ... | \n",
" 1696 | \n",
" 1278 | \n",
" 1106 | \n",
" 1407 | \n",
" 1457 | \n",
" 1073 | \n",
" 2064 | \n",
" 1466 | \n",
" 767 | \n",
" 12223 | \n",
"
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" \n",
" 4 | \n",
" 2020-01-24 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 11856 | \n",
" NaN | \n",
" 12760 | \n",
" NaN | \n",
" 11933 | \n",
" NaN | \n",
" 802 | \n",
" ... | \n",
" 1529 | \n",
" 1187 | \n",
" 1024 | \n",
" 1231 | \n",
" 1410 | \n",
" 1028 | \n",
" 1833 | \n",
" 1253 | \n",
" 723 | \n",
" 11133 | \n",
"
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" \n",
" 5 | \n",
" 2020-01-31 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 11612 | \n",
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" 12206 | \n",
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" 11419 | \n",
" NaN | \n",
" 760 | \n",
" ... | \n",
" 1461 | \n",
" 1136 | \n",
" 1015 | \n",
" 1262 | \n",
" 1286 | \n",
" 1092 | \n",
" 1820 | \n",
" 1233 | \n",
" 727 | \n",
" 10885 | \n",
"
\n",
" \n",
"
\n",
"
5 rows × 92 columns
\n",
"
"
],
"text/plain": [
" Week ended NaN NaN Total deaths, all ages \\\n",
"1 2020-01-03 00:00:00 NaN NaN 12254 \n",
"2 2020-01-10 00:00:00 NaN NaN 14058 \n",
"3 2020-01-17 00:00:00 NaN NaN 12990 \n",
"4 2020-01-24 00:00:00 NaN NaN 11856 \n",
"5 2020-01-31 00:00:00 NaN NaN 11612 \n",
"\n",
" Total deaths: average of corresponding \\\n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"5 NaN \n",
"\n",
" week over the previous 5 years 1, 10, 11 (England and Wales) \\\n",
"1 12175 \n",
"2 13822 \n",
"3 13216 \n",
"4 12760 \n",
"5 12206 \n",
"\n",
" Total deaths: average of corresponding \\\n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"5 NaN \n",
"\n",
" week over the previous 5 years 1, 10, 11 (England) \\\n",
"1 11412 \n",
"2 12933 \n",
"3 12370 \n",
"4 11933 \n",
"5 11419 \n",
"\n",
" Total deaths: average of corresponding \\\n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"5 NaN \n",
"\n",
" week over the previous 5 years 1, 10, 11 (Wales) ... North West \\\n",
"1 756 ... 1806 \n",
"2 856 ... 1932 \n",
"3 812 ... 1696 \n",
"4 802 ... 1529 \n",
"5 760 ... 1461 \n",
"\n",
" Yorkshire and The Humber East Midlands West Midlands East London \\\n",
"1 1240 1060 1349 1162 1113 \n",
"2 1339 1195 1450 1573 1272 \n",
"3 1278 1106 1407 1457 1073 \n",
"4 1187 1024 1231 1410 1028 \n",
"5 1136 1015 1262 1286 1092 \n",
"\n",
" South East South West Wales England deaths \n",
"1 1814 1225 787 11467 \n",
"2 2132 1487 939 13119 \n",
"3 2064 1466 767 12223 \n",
"4 1833 1253 723 11133 \n",
"5 1820 1233 727 10885 \n",
"\n",
"[5 rows x 92 columns]"
]
},
"execution_count": 439,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eng_xls.head()"
]
},
{
"cell_type": "code",
"execution_count": 440,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" week | \n",
" date_up_to | \n",
" deaths | \n",
" year | \n",
" nation | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1 | \n",
" 2020-01-03 00:00:00 | \n",
" 11467 | \n",
" 2020 | \n",
" England | \n",
"
\n",
" \n",
" 1 | \n",
" 2 | \n",
" 2020-01-10 00:00:00 | \n",
" 13119 | \n",
" 2020 | \n",
" England | \n",
"
\n",
" \n",
" 2 | \n",
" 3 | \n",
" 2020-01-17 00:00:00 | \n",
" 12223 | \n",
" 2020 | \n",
" England | \n",
"
\n",
" \n",
" 3 | \n",
" 4 | \n",
" 2020-01-24 00:00:00 | \n",
" 11133 | \n",
" 2020 | \n",
" England | \n",
"
\n",
" \n",
" 4 | \n",
" 5 | \n",
" 2020-01-31 00:00:00 | \n",
" 10885 | \n",
" 2020 | \n",
" England | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" week date_up_to deaths year nation\n",
"0 1 2020-01-03 00:00:00 11467 2020 England\n",
"1 2 2020-01-10 00:00:00 13119 2020 England\n",
"2 3 2020-01-17 00:00:00 12223 2020 England\n",
"3 4 2020-01-24 00:00:00 11133 2020 England\n",
"4 5 2020-01-31 00:00:00 10885 2020 England"
]
},
"execution_count": 440,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd = eng_xls[['Week ended', 'England deaths']].reset_index(level=0).rename(\n",
" columns={'Week ended': 'date_up_to', 'England deaths': 'deaths',\n",
" 'index': 'week'}\n",
" )\n",
"rd['year'] = 2020\n",
"rd['nation'] = 'England'\n",
"rd.head()"
]
},
{
"cell_type": "code",
"execution_count": 441,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * postgresql://covid:***@localhost/covid\n",
"0 rows affected.\n"
]
},
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 441,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql delete from all_causes_deaths where nation = 'England'"
]
},
{
"cell_type": "code",
"execution_count": 442,
"metadata": {},
"outputs": [],
"source": [
"rd.to_sql(\n",
" 'all_causes_deaths',\n",
" conn,\n",
" if_exists='append',\n",
" index=False)"
]
},
{
"cell_type": "code",
"execution_count": 443,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
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" | \n",
" week | \n",
" date_up_to | \n",
" deaths | \n",
" year | \n",
" nation | \n",
"
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" \n",
" \n",
" \n",
" 48 | \n",
" 49 | \n",
" 2020-12-04 00:00:00 | \n",
" 11467 | \n",
" 2020 | \n",
" England | \n",
"
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" 49 | \n",
" 50 | \n",
" 2020-12-11 00:00:00 | \n",
" 11478 | \n",
" 2020 | \n",
" England | \n",
"
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" \n",
" 50 | \n",
" 51 | \n",
" 2020-12-18 00:00:00 | \n",
" 12129 | \n",
" 2020 | \n",
" England | \n",
"
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" \n",
" 51 | \n",
" 52 | \n",
" 2020-12-25 00:00:00 | \n",
" 10695 | \n",
" 2020 | \n",
" England | \n",
"
\n",
" \n",
" 52 | \n",
" 53 | \n",
" 2021-01-01 00:00:00 | \n",
" 9342 | \n",
" 2020 | \n",
" England | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" week date_up_to deaths year nation\n",
"48 49 2020-12-04 00:00:00 11467 2020 England\n",
"49 50 2020-12-11 00:00:00 11478 2020 England\n",
"50 51 2020-12-18 00:00:00 12129 2020 England\n",
"51 52 2020-12-25 00:00:00 10695 2020 England\n",
"52 53 2021-01-01 00:00:00 9342 2020 England"
]
},
"execution_count": 443,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd.tail()"
]
},
{
"cell_type": "code",
"execution_count": 444,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * postgresql://covid:***@localhost/covid\n",
"14 rows affected.\n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" year | \n",
" nation | \n",
" count | \n",
"
\n",
" \n",
" \n",
" \n",
" 2020 | \n",
" England | \n",
" 53 | \n",
"
\n",
" \n",
" 2015 | \n",
" Northern Ireland | \n",
" 53 | \n",
"
\n",
" \n",
" 2016 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
\n",
" \n",
" 2017 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
\n",
" \n",
" 2018 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
\n",
" \n",
" 2019 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
\n",
" \n",
" 2020 | \n",
" Northern Ireland | \n",
" 53 | \n",
"
\n",
" \n",
" 2015 | \n",
" Scotland | \n",
" 53 | \n",
"
\n",
" \n",
" 2016 | \n",
" Scotland | \n",
" 52 | \n",
"
\n",
" \n",
" 2017 | \n",
" Scotland | \n",
" 52 | \n",
"
\n",
" \n",
" 2018 | \n",
" Scotland | \n",
" 52 | \n",
"
\n",
" \n",
" 2019 | \n",
" Scotland | \n",
" 52 | \n",
"
\n",
" \n",
" 2020 | \n",
" Scotland | \n",
" 53 | \n",
"
\n",
" \n",
" 2020 | \n",
" Wales | \n",
" 53 | \n",
"
\n",
" \n",
"
"
],
"text/plain": [
"[(2020, 'England', 53),\n",
" (2015, 'Northern Ireland', 53),\n",
" (2016, 'Northern Ireland', 52),\n",
" (2017, 'Northern Ireland', 52),\n",
" (2018, 'Northern Ireland', 52),\n",
" (2019, 'Northern Ireland', 52),\n",
" (2020, 'Northern Ireland', 53),\n",
" (2015, 'Scotland', 53),\n",
" (2016, 'Scotland', 52),\n",
" (2017, 'Scotland', 52),\n",
" (2018, 'Scotland', 52),\n",
" (2019, 'Scotland', 52),\n",
" (2020, 'Scotland', 53),\n",
" (2020, 'Wales', 53)]"
]
},
"execution_count": 444,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql select year, nation, count(date_up_to) from all_causes_deaths group by (year, nation) order by nation, year"
]
},
{
"cell_type": "code",
"execution_count": 445,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"# raw_data_2020 = pd.read_csv('uk-deaths-data/publishedweek272020.csv', \n",
"# parse_dates=[1], dayfirst=True,\n",
"# index_col=0,\n",
"# header=[0, 1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 446,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"# raw_data_2020.head()"
]
},
{
"cell_type": "code",
"execution_count": 447,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"# raw_data_2020['W92000004', 'Wales']"
]
},
{
"cell_type": "code",
"execution_count": 448,
"metadata": {
"Collapsed": "false"
},
"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": 449,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Week number | \n",
" Week ended | \n",
" Total deaths, all ages | \n",
" W92000004 | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1 | \n",
" 2019-01-04 | \n",
" 10955 | \n",
" 718 | \n",
"
\n",
" \n",
" 1 | \n",
" 2 | \n",
" 2019-01-11 | \n",
" 12609 | \n",
" 809 | \n",
"
\n",
" \n",
" 2 | \n",
" 3 | \n",
" 2019-01-18 | \n",
" 11860 | \n",
" 683 | \n",
"
\n",
" \n",
" 3 | \n",
" 4 | \n",
" 2019-01-25 | \n",
" 11740 | \n",
" 734 | \n",
"
\n",
" \n",
" 4 | \n",
" 5 | \n",
" 2019-02-01 | \n",
" 11297 | \n",
" 745 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Week number Week ended Total deaths, all ages W92000004\n",
"0 1 2019-01-04 10955 718\n",
"1 2 2019-01-11 12609 809\n",
"2 3 2019-01-18 11860 683\n",
"3 4 2019-01-25 11740 734\n",
"4 5 2019-02-01 11297 745"
]
},
"execution_count": 449,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rdew = raw_data_2019.iloc[:, [0, 1, 2, -1]].droplevel(axis=1, level=1)\n",
"rdew.head()"
]
},
{
"cell_type": "code",
"execution_count": 450,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" week | \n",
" date_up_to | \n",
" deaths | \n",
" year | \n",
" nation | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1 | \n",
" 2019-01-04 | \n",
" 718 | \n",
" 2019 | \n",
" Wales | \n",
"
\n",
" \n",
" 1 | \n",
" 2 | \n",
" 2019-01-11 | \n",
" 809 | \n",
" 2019 | \n",
" Wales | \n",
"
\n",
" \n",
" 2 | \n",
" 3 | \n",
" 2019-01-18 | \n",
" 683 | \n",
" 2019 | \n",
" Wales | \n",
"
\n",
" \n",
" 3 | \n",
" 4 | \n",
" 2019-01-25 | \n",
" 734 | \n",
" 2019 | \n",
" Wales | \n",
"
\n",
" \n",
" 4 | \n",
" 5 | \n",
" 2019-02-01 | \n",
" 745 | \n",
" 2019 | \n",
" Wales | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" week date_up_to deaths year nation\n",
"0 1 2019-01-04 718 2019 Wales\n",
"1 2 2019-01-11 809 2019 Wales\n",
"2 3 2019-01-18 683 2019 Wales\n",
"3 4 2019-01-25 734 2019 Wales\n",
"4 5 2019-02-01 745 2019 Wales"
]
},
"execution_count": 450,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd = rdew.drop(columns=['Total deaths, all ages']).rename(\n",
" columns={'Week ended': 'date_up_to', 'W92000004': 'deaths',\n",
" 'Week number': 'week'}\n",
" )\n",
"rd['year'] = 2019\n",
"rd['nation'] = 'Wales'\n",
"rd.head()"
]
},
{
"cell_type": "code",
"execution_count": 451,
"metadata": {},
"outputs": [],
"source": [
"rd.to_sql(\n",
" 'all_causes_deaths',\n",
" conn,\n",
" if_exists='append',\n",
" index=False)"
]
},
{
"cell_type": "code",
"execution_count": 452,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" date_up_to | \n",
" week | \n",
" deaths | \n",
" year | \n",
" nation | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 2019-01-04 | \n",
" 1 | \n",
" 10237 | \n",
" 2019 | \n",
" England | \n",
"
\n",
" \n",
" 1 | \n",
" 2019-01-11 | \n",
" 2 | \n",
" 11800 | \n",
" 2019 | \n",
" England | \n",
"
\n",
" \n",
" 2 | \n",
" 2019-01-18 | \n",
" 3 | \n",
" 11177 | \n",
" 2019 | \n",
" England | \n",
"
\n",
" \n",
" 3 | \n",
" 2019-01-25 | \n",
" 4 | \n",
" 11006 | \n",
" 2019 | \n",
" England | \n",
"
\n",
" \n",
" 4 | \n",
" 2019-02-01 | \n",
" 5 | \n",
" 10552 | \n",
" 2019 | \n",
" England | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" date_up_to week deaths year nation\n",
"0 2019-01-04 1 10237 2019 England\n",
"1 2019-01-11 2 11800 2019 England\n",
"2 2019-01-18 3 11177 2019 England\n",
"3 2019-01-25 4 11006 2019 England\n",
"4 2019-02-01 5 10552 2019 England"
]
},
"execution_count": 452,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd = rdew.loc[:, ['Week ended','Week number']]\n",
"rd['deaths'] = rdew['Total deaths, all ages'] - rdew['W92000004']\n",
"rd = rd.rename(\n",
" columns={'Week ended': 'date_up_to', 'Week number': 'week'}\n",
" )\n",
"rd['year'] = 2019\n",
"rd['nation'] = 'England'\n",
"rd.head()"
]
},
{
"cell_type": "code",
"execution_count": 453,
"metadata": {},
"outputs": [],
"source": [
"rd.to_sql(\n",
" 'all_causes_deaths',\n",
" conn,\n",
" if_exists='append',\n",
" index=False)"
]
},
{
"cell_type": "code",
"execution_count": 454,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * postgresql://covid:***@localhost/covid\n",
"16 rows affected.\n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" year | \n",
" nation | \n",
" count | \n",
"
\n",
" \n",
" \n",
" \n",
" 2019 | \n",
" England | \n",
" 52 | \n",
"
\n",
" \n",
" 2020 | \n",
" England | \n",
" 53 | \n",
"
\n",
" \n",
" 2015 | \n",
" Northern Ireland | \n",
" 53 | \n",
"
\n",
" \n",
" 2016 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
\n",
" \n",
" 2017 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
\n",
" \n",
" 2018 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
\n",
" \n",
" 2019 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
\n",
" \n",
" 2020 | \n",
" Northern Ireland | \n",
" 53 | \n",
"
\n",
" \n",
" 2015 | \n",
" Scotland | \n",
" 53 | \n",
"
\n",
" \n",
" 2016 | \n",
" Scotland | \n",
" 52 | \n",
"
\n",
" \n",
" 2017 | \n",
" Scotland | \n",
" 52 | \n",
"
\n",
" \n",
" 2018 | \n",
" Scotland | \n",
" 52 | \n",
"
\n",
" \n",
" 2019 | \n",
" Scotland | \n",
" 52 | \n",
"
\n",
" \n",
" 2020 | \n",
" Scotland | \n",
" 53 | \n",
"
\n",
" \n",
" 2019 | \n",
" Wales | \n",
" 52 | \n",
"
\n",
" \n",
" 2020 | \n",
" Wales | \n",
" 53 | \n",
"
\n",
" \n",
"
"
],
"text/plain": [
"[(2019, 'England', 52),\n",
" (2020, 'England', 53),\n",
" (2015, 'Northern Ireland', 53),\n",
" (2016, 'Northern Ireland', 52),\n",
" (2017, 'Northern Ireland', 52),\n",
" (2018, 'Northern Ireland', 52),\n",
" (2019, 'Northern Ireland', 52),\n",
" (2020, 'Northern Ireland', 53),\n",
" (2015, 'Scotland', 53),\n",
" (2016, 'Scotland', 52),\n",
" (2017, 'Scotland', 52),\n",
" (2018, 'Scotland', 52),\n",
" (2019, 'Scotland', 52),\n",
" (2020, 'Scotland', 53),\n",
" (2019, 'Wales', 52),\n",
" (2020, 'Wales', 53)]"
]
},
"execution_count": 454,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql select year, nation, count(date_up_to) from all_causes_deaths group by (year, nation) order by nation, year"
]
},
{
"cell_type": "code",
"execution_count": 455,
"metadata": {
"Collapsed": "false"
},
"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": 456,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Week number | \n",
" Week ended | \n",
" Total deaths, all ages | \n",
" W92000004 | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1 | \n",
" 2018-01-05 | \n",
" 12723 | \n",
" 783 | \n",
"
\n",
" \n",
" 1 | \n",
" 2 | \n",
" 2018-01-12 | \n",
" 15050 | \n",
" 904 | \n",
"
\n",
" \n",
" 2 | \n",
" 3 | \n",
" 2018-01-19 | \n",
" 14256 | \n",
" 885 | \n",
"
\n",
" \n",
" 3 | \n",
" 4 | \n",
" 2018-01-26 | \n",
" 13935 | \n",
" 850 | \n",
"
\n",
" \n",
" 4 | \n",
" 5 | \n",
" 2018-02-02 | \n",
" 13285 | \n",
" 815 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Week number Week ended Total deaths, all ages W92000004\n",
"0 1 2018-01-05 12723 783\n",
"1 2 2018-01-12 15050 904\n",
"2 3 2018-01-19 14256 885\n",
"3 4 2018-01-26 13935 850\n",
"4 5 2018-02-02 13285 815"
]
},
"execution_count": 456,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rdew = raw_data_2018.iloc[:, [0, 1, 2, -1]].droplevel(axis=1, level=1)\n",
"rdew.head()"
]
},
{
"cell_type": "code",
"execution_count": 457,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" week | \n",
" date_up_to | \n",
" deaths | \n",
" year | \n",
" nation | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1 | \n",
" 2018-01-05 | \n",
" 783 | \n",
" 2018 | \n",
" Wales | \n",
"
\n",
" \n",
" 1 | \n",
" 2 | \n",
" 2018-01-12 | \n",
" 904 | \n",
" 2018 | \n",
" Wales | \n",
"
\n",
" \n",
" 2 | \n",
" 3 | \n",
" 2018-01-19 | \n",
" 885 | \n",
" 2018 | \n",
" Wales | \n",
"
\n",
" \n",
" 3 | \n",
" 4 | \n",
" 2018-01-26 | \n",
" 850 | \n",
" 2018 | \n",
" Wales | \n",
"
\n",
" \n",
" 4 | \n",
" 5 | \n",
" 2018-02-02 | \n",
" 815 | \n",
" 2018 | \n",
" Wales | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" week date_up_to deaths year nation\n",
"0 1 2018-01-05 783 2018 Wales\n",
"1 2 2018-01-12 904 2018 Wales\n",
"2 3 2018-01-19 885 2018 Wales\n",
"3 4 2018-01-26 850 2018 Wales\n",
"4 5 2018-02-02 815 2018 Wales"
]
},
"execution_count": 457,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd = rdew.drop(columns=['Total deaths, all ages']).rename(\n",
" columns={'Week ended': 'date_up_to', 'W92000004': 'deaths',\n",
" 'Week number': 'week'}\n",
" )\n",
"rd['year'] = 2018\n",
"rd['nation'] = 'Wales'\n",
"rd.head()"
]
},
{
"cell_type": "code",
"execution_count": 458,
"metadata": {},
"outputs": [],
"source": [
"rd.to_sql(\n",
" 'all_causes_deaths',\n",
" conn,\n",
" if_exists='append',\n",
" index=False)"
]
},
{
"cell_type": "code",
"execution_count": 459,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" date_up_to | \n",
" week | \n",
" deaths | \n",
" year | \n",
" nation | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 2018-01-05 | \n",
" 1 | \n",
" 11940 | \n",
" 2018 | \n",
" England | \n",
"
\n",
" \n",
" 1 | \n",
" 2018-01-12 | \n",
" 2 | \n",
" 14146 | \n",
" 2018 | \n",
" England | \n",
"
\n",
" \n",
" 2 | \n",
" 2018-01-19 | \n",
" 3 | \n",
" 13371 | \n",
" 2018 | \n",
" England | \n",
"
\n",
" \n",
" 3 | \n",
" 2018-01-26 | \n",
" 4 | \n",
" 13085 | \n",
" 2018 | \n",
" England | \n",
"
\n",
" \n",
" 4 | \n",
" 2018-02-02 | \n",
" 5 | \n",
" 12470 | \n",
" 2018 | \n",
" England | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" date_up_to week deaths year nation\n",
"0 2018-01-05 1 11940 2018 England\n",
"1 2018-01-12 2 14146 2018 England\n",
"2 2018-01-19 3 13371 2018 England\n",
"3 2018-01-26 4 13085 2018 England\n",
"4 2018-02-02 5 12470 2018 England"
]
},
"execution_count": 459,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd = rdew.loc[:, ['Week ended','Week number']]\n",
"rd['deaths'] = rdew['Total deaths, all ages'] - rdew['W92000004']\n",
"rd = rd.rename(\n",
" columns={'Week ended': 'date_up_to', 'Week number': 'week'}\n",
" )\n",
"rd['year'] = 2018\n",
"rd['nation'] = 'England'\n",
"rd.head()"
]
},
{
"cell_type": "code",
"execution_count": 460,
"metadata": {},
"outputs": [],
"source": [
"rd.to_sql(\n",
" 'all_causes_deaths',\n",
" conn,\n",
" if_exists='append',\n",
" index=False)"
]
},
{
"cell_type": "code",
"execution_count": 461,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * postgresql://covid:***@localhost/covid\n",
"18 rows affected.\n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" year | \n",
" nation | \n",
" count | \n",
"
\n",
" \n",
" \n",
" \n",
" 2018 | \n",
" England | \n",
" 52 | \n",
"
\n",
" \n",
" 2019 | \n",
" England | \n",
" 52 | \n",
"
\n",
" \n",
" 2020 | \n",
" England | \n",
" 53 | \n",
"
\n",
" \n",
" 2015 | \n",
" Northern Ireland | \n",
" 53 | \n",
"
\n",
" \n",
" 2016 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
\n",
" \n",
" 2017 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
\n",
" \n",
" 2018 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
\n",
" \n",
" 2019 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
\n",
" \n",
" 2020 | \n",
" Northern Ireland | \n",
" 53 | \n",
"
\n",
" \n",
" 2015 | \n",
" Scotland | \n",
" 53 | \n",
"
\n",
" \n",
" 2016 | \n",
" Scotland | \n",
" 52 | \n",
"
\n",
" \n",
" 2017 | \n",
" Scotland | \n",
" 52 | \n",
"
\n",
" \n",
" 2018 | \n",
" Scotland | \n",
" 52 | \n",
"
\n",
" \n",
" 2019 | \n",
" Scotland | \n",
" 52 | \n",
"
\n",
" \n",
" 2020 | \n",
" Scotland | \n",
" 53 | \n",
"
\n",
" \n",
" 2018 | \n",
" Wales | \n",
" 52 | \n",
"
\n",
" \n",
" 2019 | \n",
" Wales | \n",
" 52 | \n",
"
\n",
" \n",
" 2020 | \n",
" Wales | \n",
" 53 | \n",
"
\n",
" \n",
"
"
],
"text/plain": [
"[(2018, 'England', 52),\n",
" (2019, 'England', 52),\n",
" (2020, 'England', 53),\n",
" (2015, 'Northern Ireland', 53),\n",
" (2016, 'Northern Ireland', 52),\n",
" (2017, 'Northern Ireland', 52),\n",
" (2018, 'Northern Ireland', 52),\n",
" (2019, 'Northern Ireland', 52),\n",
" (2020, 'Northern Ireland', 53),\n",
" (2015, 'Scotland', 53),\n",
" (2016, 'Scotland', 52),\n",
" (2017, 'Scotland', 52),\n",
" (2018, 'Scotland', 52),\n",
" (2019, 'Scotland', 52),\n",
" (2020, 'Scotland', 53),\n",
" (2018, 'Wales', 52),\n",
" (2019, 'Wales', 52),\n",
" (2020, 'Wales', 53)]"
]
},
"execution_count": 461,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql select year, nation, count(date_up_to) from all_causes_deaths group by (year, nation) order by nation, year"
]
},
{
"cell_type": "code",
"execution_count": 462,
"metadata": {
"Collapsed": "false"
},
"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": 463,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Week number | \n",
" Week ended | \n",
" Total deaths, all ages | \n",
" W92000004 | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1 | \n",
" 2017-01-06 | \n",
" 11991 | \n",
" 744 | \n",
"
\n",
" \n",
" 1 | \n",
" 2 | \n",
" 2017-01-13 | \n",
" 13715 | \n",
" 825 | \n",
"
\n",
" \n",
" 2 | \n",
" 3 | \n",
" 2017-01-20 | \n",
" 13610 | \n",
" 835 | \n",
"
\n",
" \n",
" 3 | \n",
" 4 | \n",
" 2017-01-27 | \n",
" 12877 | \n",
" 881 | \n",
"
\n",
" \n",
" 4 | \n",
" 5 | \n",
" 2017-02-03 | \n",
" 12485 | \n",
" 749 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Week number Week ended Total deaths, all ages W92000004\n",
"0 1 2017-01-06 11991 744\n",
"1 2 2017-01-13 13715 825\n",
"2 3 2017-01-20 13610 835\n",
"3 4 2017-01-27 12877 881\n",
"4 5 2017-02-03 12485 749"
]
},
"execution_count": 463,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rdew = raw_data_2017.iloc[:, [0, 1, 2, -1]].droplevel(axis=1, level=1)\n",
"rdew.head()"
]
},
{
"cell_type": "code",
"execution_count": 464,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" week | \n",
" date_up_to | \n",
" deaths | \n",
" year | \n",
" nation | \n",
"
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" \n",
" \n",
" \n",
" 0 | \n",
" 1 | \n",
" 2017-01-06 | \n",
" 744 | \n",
" 2017 | \n",
" Wales | \n",
"
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" 2 | \n",
" 2017-01-13 | \n",
" 825 | \n",
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"
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" 3 | \n",
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" 881 | \n",
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" Wales | \n",
"
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" 4 | \n",
" 5 | \n",
" 2017-02-03 | \n",
" 749 | \n",
" 2017 | \n",
" Wales | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" week date_up_to deaths year nation\n",
"0 1 2017-01-06 744 2017 Wales\n",
"1 2 2017-01-13 825 2017 Wales\n",
"2 3 2017-01-20 835 2017 Wales\n",
"3 4 2017-01-27 881 2017 Wales\n",
"4 5 2017-02-03 749 2017 Wales"
]
},
"execution_count": 464,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd = rdew.drop(columns=['Total deaths, all ages']).rename(\n",
" columns={'Week ended': 'date_up_to', 'W92000004': 'deaths',\n",
" 'Week number': 'week'}\n",
" )\n",
"rd['year'] = 2017\n",
"rd['nation'] = 'Wales'\n",
"rd.head()"
]
},
{
"cell_type": "code",
"execution_count": 465,
"metadata": {},
"outputs": [],
"source": [
"rd.to_sql(\n",
" 'all_causes_deaths',\n",
" conn,\n",
" if_exists='append',\n",
" index=False)"
]
},
{
"cell_type": "code",
"execution_count": 466,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" date_up_to | \n",
" week | \n",
" deaths | \n",
" year | \n",
" nation | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 2017-01-06 | \n",
" 1 | \n",
" 11247 | \n",
" 2017 | \n",
" England | \n",
"
\n",
" \n",
" 1 | \n",
" 2017-01-13 | \n",
" 2 | \n",
" 12890 | \n",
" 2017 | \n",
" England | \n",
"
\n",
" \n",
" 2 | \n",
" 2017-01-20 | \n",
" 3 | \n",
" 12775 | \n",
" 2017 | \n",
" England | \n",
"
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" \n",
" 3 | \n",
" 2017-01-27 | \n",
" 4 | \n",
" 11996 | \n",
" 2017 | \n",
" England | \n",
"
\n",
" \n",
" 4 | \n",
" 2017-02-03 | \n",
" 5 | \n",
" 11736 | \n",
" 2017 | \n",
" England | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" date_up_to week deaths year nation\n",
"0 2017-01-06 1 11247 2017 England\n",
"1 2017-01-13 2 12890 2017 England\n",
"2 2017-01-20 3 12775 2017 England\n",
"3 2017-01-27 4 11996 2017 England\n",
"4 2017-02-03 5 11736 2017 England"
]
},
"execution_count": 466,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd = rdew.loc[:, ['Week ended','Week number']]\n",
"rd['deaths'] = rdew['Total deaths, all ages'] - rdew['W92000004']\n",
"rd = rd.rename(\n",
" columns={'Week ended': 'date_up_to', 'Week number': 'week'}\n",
" )\n",
"rd['year'] = 2017\n",
"rd['nation'] = 'England'\n",
"rd.head()"
]
},
{
"cell_type": "code",
"execution_count": 467,
"metadata": {},
"outputs": [],
"source": [
"rd.to_sql(\n",
" 'all_causes_deaths',\n",
" conn,\n",
" if_exists='append',\n",
" index=False)"
]
},
{
"cell_type": "code",
"execution_count": 468,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * postgresql://covid:***@localhost/covid\n",
"20 rows affected.\n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" year | \n",
" nation | \n",
" count | \n",
"
\n",
" \n",
" \n",
" \n",
" 2017 | \n",
" England | \n",
" 52 | \n",
"
\n",
" \n",
" 2018 | \n",
" England | \n",
" 52 | \n",
"
\n",
" \n",
" 2019 | \n",
" England | \n",
" 52 | \n",
"
\n",
" \n",
" 2020 | \n",
" England | \n",
" 53 | \n",
"
\n",
" \n",
" 2015 | \n",
" Northern Ireland | \n",
" 53 | \n",
"
\n",
" \n",
" 2016 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
\n",
" \n",
" 2017 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
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" \n",
" 2018 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
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" \n",
" 2019 | \n",
" Northern Ireland | \n",
" 52 | \n",
"
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" \n",
" 2020 | \n",
" Northern Ireland | \n",
" 53 | \n",
"
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" \n",
" 2015 | \n",
" Scotland | \n",
" 53 | \n",
"
\n",
" \n",
" 2016 | \n",
" Scotland | \n",
" 52 | \n",
"
\n",
" \n",
" 2017 | \n",
" Scotland | \n",
" 52 | \n",
"
\n",
" \n",
" 2018 | \n",
" Scotland | \n",
" 52 | \n",
"
\n",
" \n",
" 2019 | \n",
" Scotland | \n",
" 52 | \n",
"
\n",
" \n",
" 2020 | \n",
" Scotland | \n",
" 53 | \n",
"
\n",
" \n",
" 2017 | \n",
" Wales | \n",
" 52 | \n",
"
\n",
" \n",
" 2018 | \n",
" Wales | \n",
" 52 | \n",
"
\n",
" \n",
" 2019 | \n",
" Wales | \n",
" 52 | \n",
"
\n",
" \n",
" 2020 | \n",
" Wales | \n",
" 53 | \n",
"
\n",
" \n",
"
"
],
"text/plain": [
"[(2017, 'England', 52),\n",
" (2018, 'England', 52),\n",
" (2019, 'England', 52),\n",
" (2020, 'England', 53),\n",
" (2015, 'Northern Ireland', 53),\n",
" (2016, 'Northern Ireland', 52),\n",
" (2017, 'Northern Ireland', 52),\n",
" (2018, 'Northern Ireland', 52),\n",
" (2019, 'Northern Ireland', 52),\n",
" (2020, 'Northern Ireland', 53),\n",
" (2015, 'Scotland', 53),\n",
" (2016, 'Scotland', 52),\n",
" (2017, 'Scotland', 52),\n",
" (2018, 'Scotland', 52),\n",
" (2019, 'Scotland', 52),\n",
" (2020, 'Scotland', 53),\n",
" (2017, 'Wales', 52),\n",
" (2018, 'Wales', 52),\n",
" (2019, 'Wales', 52),\n",
" (2020, 'Wales', 53)]"
]
},
"execution_count": 468,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql select year, nation, count(date_up_to) from all_causes_deaths group by (year, nation) order by nation, year"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 469,
"metadata": {
"Collapsed": "false"
},
"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": 470,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Week number | \n",
" Week ended | \n",
" Total deaths, all ages | \n",
" Total deaths: average of corresponding | \n",
" Unnamed: 4_level_0 | \n",
" Unnamed: 5_level_0 | \n",
" Persons | \n",
" Unnamed: 7_level_0 | \n",
" Unnamed: 8_level_0 | \n",
" Unnamed: 9_level_0 | \n",
" ... | \n",
" E12000001 | \n",
" E12000002 | \n",
" E12000003 | \n",
" E12000004 | \n",
" E12000005 | \n",
" E12000006 | \n",
" E12000007 | \n",
" E12000008 | \n",
" E12000009 | \n",
" W92000004 | \n",
"
\n",
" \n",
" | \n",
" Unnamed: 0_level_1 | \n",
" Unnamed: 1_level_1 | \n",
" Unnamed: 2_level_1 | \n",
" Unnamed: 3_level_1 | \n",
" Deaths by underlying cause | \n",
" All respiratory diseases (ICD-10 J00-J99)\\nICD-10 v 2013 (IRIS) | \n",
" Deaths by age group | \n",
" Under 1 year | \n",
" 01-14 | \n",
" 15-44 | \n",
" ... | \n",
" North East | \n",
" North West | \n",
" Yorkshire and The Humber | \n",
" East Midlands | \n",
" West Midlands | \n",
" East | \n",
" London | \n",
" South East | \n",
" South West | \n",
" Wales | \n",
"
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" 0 | \n",
" 1 | \n",
" 2016-01-08 | \n",
" 13045 | \n",
" 11701.4 | \n",
" NaN | \n",
" 2046 | \n",
" NaN | \n",
" 49 | \n",
" 17 | \n",
" 299 | \n",
" ... | \n",
" 705 | \n",
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" 1401 | \n",
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" 1951 | \n",
" 1424 | \n",
" 809 | \n",
"
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" \n",
" 1 | \n",
" 2 | \n",
" 2016-01-15 | \n",
" 11501 | \n",
" 13016.4 | \n",
" NaN | \n",
" 1835 | \n",
" NaN | \n",
" 64 | \n",
" 24 | \n",
" 326 | \n",
" ... | \n",
" 600 | \n",
" 1532 | \n",
" 1148 | \n",
" 956 | \n",
" 1208 | \n",
" 1237 | \n",
" 1048 | \n",
" 1771 | \n",
" 1263 | \n",
" 711 | \n",
"
\n",
" \n",
" 2 | \n",
" 3 | \n",
" 2016-01-22 | \n",
" 11473 | \n",
" 11765.4 | \n",
" NaN | \n",
" 1775 | \n",
" NaN | \n",
" 50 | \n",
" 18 | \n",
" 345 | \n",
" ... | \n",
" 612 | \n",
" 1602 | \n",
" 1119 | \n",
" 929 | \n",
" 1209 | \n",
" 1253 | \n",
" 1068 | \n",
" 1710 | \n",
" 1219 | \n",
" 720 | \n",
"
\n",
" \n",
" 3 | \n",
" 4 | \n",
" 2016-01-29 | \n",
" 11317 | \n",
" 11289.0 | \n",
" NaN | \n",
" 1810 | \n",
" NaN | \n",
" 47 | \n",
" 14 | \n",
" 324 | \n",
" ... | \n",
" 631 | \n",
" 1516 | \n",
" 1131 | \n",
" 858 | \n",
" 1195 | \n",
" 1236 | \n",
" 1065 | \n",
" 1730 | \n",
" 1207 | \n",
" 717 | \n",
"
\n",
" \n",
" 4 | \n",
" 5 | \n",
" 2016-02-05 | \n",
" 11052 | \n",
" 10965.6 | \n",
" NaN | \n",
" 1748 | \n",
" NaN | \n",
" 60 | \n",
" 22 | \n",
" 314 | \n",
" ... | \n",
" 620 | \n",
" 1459 | \n",
" 1109 | \n",
" 954 | \n",
" 1197 | \n",
" 1160 | \n",
" 1059 | \n",
" 1604 | \n",
" 1169 | \n",
" 690 | \n",
"
\n",
" \n",
"
\n",
"
5 rows × 41 columns
\n",
"
"
],
"text/plain": [
" Week number Week ended Total deaths, all ages \\\n",
" Unnamed: 0_level_1 Unnamed: 1_level_1 Unnamed: 2_level_1 \n",
"0 1 2016-01-08 13045 \n",
"1 2 2016-01-15 11501 \n",
"2 3 2016-01-22 11473 \n",
"3 4 2016-01-29 11317 \n",
"4 5 2016-02-05 11052 \n",
"\n",
" Total deaths: average of corresponding Unnamed: 4_level_0 \\\n",
" Unnamed: 3_level_1 Deaths by underlying cause \n",
"0 11701.4 NaN \n",
"1 13016.4 NaN \n",
"2 11765.4 NaN \n",
"3 11289.0 NaN \n",
"4 10965.6 NaN \n",
"\n",
" Unnamed: 5_level_0 \\\n",
" All respiratory diseases (ICD-10 J00-J99)\\nICD-10 v 2013 (IRIS) \n",
"0 2046 \n",
"1 1835 \n",
"2 1775 \n",
"3 1810 \n",
"4 1748 \n",
"\n",
" Persons Unnamed: 7_level_0 Unnamed: 8_level_0 \\\n",
" Deaths by age group Under 1 year 01-14 \n",
"0 NaN 49 17 \n",
"1 NaN 64 24 \n",
"2 NaN 50 18 \n",
"3 NaN 47 14 \n",
"4 NaN 60 22 \n",
"\n",
" Unnamed: 9_level_0 ... E12000001 E12000002 E12000003 \\\n",
" 15-44 ... North East North West Yorkshire and The Humber \n",
"0 299 ... 705 1748 1284 \n",
"1 326 ... 600 1532 1148 \n",
"2 345 ... 612 1602 1119 \n",
"3 324 ... 631 1516 1131 \n",
"4 314 ... 620 1459 1109 \n",
"\n",
" E12000004 E12000005 E12000006 E12000007 E12000008 E12000009 \\\n",
" East Midlands West Midlands East London South East South West \n",
"0 1067 1396 1401 1226 1951 1424 \n",
"1 956 1208 1237 1048 1771 1263 \n",
"2 929 1209 1253 1068 1710 1219 \n",
"3 858 1195 1236 1065 1730 1207 \n",
"4 954 1197 1160 1059 1604 1169 \n",
"\n",
" W92000004 \n",
" Wales \n",
"0 809 \n",
"1 711 \n",
"2 720 \n",
"3 717 \n",
"4 690 \n",
"\n",
"[5 rows x 41 columns]"
]
},
"execution_count": 470,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data_2016.head()"
]
},
{
"cell_type": "code",
"execution_count": 471,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Week number | \n",
" Week ended | \n",
" Total deaths, all ages | \n",
" W92000004 | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1 | \n",
" 2016-01-08 | \n",
" 13045 | \n",
" 809 | \n",
"
\n",
" \n",
" 1 | \n",
" 2 | \n",
" 2016-01-15 | \n",
" 11501 | \n",
" 711 | \n",
"
\n",
" \n",
" 2 | \n",
" 3 | \n",
" 2016-01-22 | \n",
" 11473 | \n",
" 720 | \n",
"
\n",
" \n",
" 3 | \n",
" 4 | \n",
" 2016-01-29 | \n",
" 11317 | \n",
" 717 | \n",
"
\n",
" \n",
" 4 | \n",
" 5 | \n",
" 2016-02-05 | \n",
" 11052 | \n",
" 690 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Week number Week ended Total deaths, all ages W92000004\n",
"0 1 2016-01-08 13045 809\n",
"1 2 2016-01-15 11501 711\n",
"2 3 2016-01-22 11473 720\n",
"3 4 2016-01-29 11317 717\n",
"4 5 2016-02-05 11052 690"
]
},
"execution_count": 471,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rdew = raw_data_2016.iloc[:, [0, 1, 2, -1]].droplevel(axis=1, level=1)\n",
"rdew.head()"
]
},
{
"cell_type": "code",
"execution_count": 472,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" week | \n",
" date_up_to | \n",
" deaths | \n",
" year | \n",
" nation | \n",
"
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" \n",
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" 0 | \n",
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" 2016-01-08 | \n",
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"
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" 2 | \n",
" 2016-01-15 | \n",
" 711 | \n",
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" 2 | \n",
" 3 | \n",
" 2016-01-22 | \n",
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"
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" 4 | \n",
" 2016-01-29 | \n",
" 717 | \n",
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"
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" 4 | \n",
" 5 | \n",
" 2016-02-05 | \n",
" 690 | \n",
" 2016 | \n",
" Wales | \n",
"
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" \n",
"
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"
"
],
"text/plain": [
" week date_up_to deaths year nation\n",
"0 1 2016-01-08 809 2016 Wales\n",
"1 2 2016-01-15 711 2016 Wales\n",
"2 3 2016-01-22 720 2016 Wales\n",
"3 4 2016-01-29 717 2016 Wales\n",
"4 5 2016-02-05 690 2016 Wales"
]
},
"execution_count": 472,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd = rdew.drop(columns=['Total deaths, all ages']).rename(\n",
" columns={'Week ended': 'date_up_to', 'W92000004': 'deaths',\n",
" 'Week number': 'week'}\n",
" )\n",
"rd['year'] = 2016\n",
"rd['nation'] = 'Wales'\n",
"rd.head()"
]
},
{
"cell_type": "code",
"execution_count": 473,
"metadata": {},
"outputs": [],
"source": [
"rd.to_sql(\n",
" 'all_causes_deaths',\n",
" conn,\n",
" if_exists='append',\n",
" index=False)"
]
},
{
"cell_type": "code",
"execution_count": 474,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" date_up_to | \n",
" week | \n",
" deaths | \n",
" year | \n",
" nation | \n",
"
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" \n",
" \n",
" \n",
" 0 | \n",
" 2016-01-08 | \n",
" 1 | \n",
" 12236 | \n",
" 2016 | \n",
" England | \n",
"
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" 2016-01-15 | \n",
" 2 | \n",
" 10790 | \n",
" 2016 | \n",
" England | \n",
"
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" \n",
" 2 | \n",
" 2016-01-22 | \n",
" 3 | \n",
" 10753 | \n",
" 2016 | \n",
" England | \n",
"
\n",
" \n",
" 3 | \n",
" 2016-01-29 | \n",
" 4 | \n",
" 10600 | \n",
" 2016 | \n",
" England | \n",
"
\n",
" \n",
" 4 | \n",
" 2016-02-05 | \n",
" 5 | \n",
" 10362 | \n",
" 2016 | \n",
" England | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" date_up_to week deaths year nation\n",
"0 2016-01-08 1 12236 2016 England\n",
"1 2016-01-15 2 10790 2016 England\n",
"2 2016-01-22 3 10753 2016 England\n",
"3 2016-01-29 4 10600 2016 England\n",
"4 2016-02-05 5 10362 2016 England"
]
},
"execution_count": 474,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd = rdew.loc[:, ['Week ended','Week number']]\n",
"rd['deaths'] = rdew['Total deaths, all ages'] - rdew['W92000004']\n",
"rd = rd.rename(\n",
" columns={'Week ended': 'date_up_to', 'Week number': 'week'}\n",
" )\n",
"rd['year'] = 2016\n",
"rd['nation'] = 'England'\n",
"rd.head()"
]
},
{
"cell_type": "code",
"execution_count": 475,
"metadata": {},
"outputs": [],
"source": [
"rd.to_sql(\n",
" 'all_causes_deaths',\n",
" conn,\n",
" if_exists='append',\n",
" index=False)"
]
},
{
"cell_type": "code",
"execution_count": 476,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * postgresql://covid:***@localhost/covid\n",
"22 rows affected.\n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" year | \n",
" nation | \n",
" count | \n",
"
\n",
" \n",
" \n",
" \n",
" 2016 | \n",
" England | \n",
" 52 | \n",
"
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" 2017 | \n",
" England | \n",
" 52 | \n",
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" 2020 | \n",
" England | \n",
" 53 | \n",
"
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" Northern Ireland | \n",
" 53 | \n",
"
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" 52 | \n",
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"
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" Wales | \n",
" 52 | \n",
"
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" 2017 | \n",
" Wales | \n",
" 52 | \n",
"
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" 52 | \n",
"
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" Wales | \n",
" 52 | \n",
"
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" \n",
" 2020 | \n",
" Wales | \n",
" 53 | \n",
"
\n",
" \n",
"
"
],
"text/plain": [
"[(2016, 'England', 52),\n",
" (2017, 'England', 52),\n",
" (2018, 'England', 52),\n",
" (2019, 'England', 52),\n",
" (2020, 'England', 53),\n",
" (2015, 'Northern Ireland', 53),\n",
" (2016, 'Northern Ireland', 52),\n",
" (2017, 'Northern Ireland', 52),\n",
" (2018, 'Northern Ireland', 52),\n",
" (2019, 'Northern Ireland', 52),\n",
" (2020, 'Northern Ireland', 53),\n",
" (2015, 'Scotland', 53),\n",
" (2016, 'Scotland', 52),\n",
" (2017, 'Scotland', 52),\n",
" (2018, 'Scotland', 52),\n",
" (2019, 'Scotland', 52),\n",
" (2020, 'Scotland', 53),\n",
" (2016, 'Wales', 52),\n",
" (2017, 'Wales', 52),\n",
" (2018, 'Wales', 52),\n",
" (2019, 'Wales', 52),\n",
" (2020, 'Wales', 53)]"
]
},
"execution_count": 476,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
" %sql select year, nation, count(date_up_to) from all_causes_deaths group by (year, nation) order by nation, year"
]
},
{
"cell_type": "code",
"execution_count": 477,
"metadata": {
"Collapsed": "false"
},
"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": 478,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Week number | \n",
" Week ended | \n",
" Total deaths, all ages | \n",
" W92000004 | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1 | \n",
" 2015-01-02 | \n",
" 12286 | \n",
" 725 | \n",
"
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" \n",
" 1 | \n",
" 2 | \n",
" 2015-01-09 | \n",
" 16237 | \n",
" 1031 | \n",
"
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" 2 | \n",
" 3 | \n",
" 2015-01-16 | \n",
" 14866 | \n",
" 936 | \n",
"
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" \n",
" 3 | \n",
" 4 | \n",
" 2015-01-23 | \n",
" 13934 | \n",
" 828 | \n",
"
\n",
" \n",
" 4 | \n",
" 5 | \n",
" 2015-01-30 | \n",
" 12900 | \n",
" 801 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Week number Week ended Total deaths, all ages W92000004\n",
"0 1 2015-01-02 12286 725\n",
"1 2 2015-01-09 16237 1031\n",
"2 3 2015-01-16 14866 936\n",
"3 4 2015-01-23 13934 828\n",
"4 5 2015-01-30 12900 801"
]
},
"execution_count": 478,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rdew = raw_data_2015.iloc[:, [0, 1, 2, -1]].droplevel(axis=1, level=1)\n",
"rdew.head()"
]
},
{
"cell_type": "code",
"execution_count": 479,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" week | \n",
" date_up_to | \n",
" deaths | \n",
" year | \n",
" nation | \n",
"
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" 0 | \n",
" 1 | \n",
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" 2 | \n",
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" 4 | \n",
" 5 | \n",
" 2015-01-30 | \n",
" 801 | \n",
" 2015 | \n",
" Wales | \n",
"
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" \n",
"
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"
"
],
"text/plain": [
" week date_up_to deaths year nation\n",
"0 1 2015-01-02 725 2015 Wales\n",
"1 2 2015-01-09 1031 2015 Wales\n",
"2 3 2015-01-16 936 2015 Wales\n",
"3 4 2015-01-23 828 2015 Wales\n",
"4 5 2015-01-30 801 2015 Wales"
]
},
"execution_count": 479,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd = rdew.drop(columns=['Total deaths, all ages']).rename(\n",
" columns={'Week ended': 'date_up_to', 'W92000004': 'deaths',\n",
" 'Week number': 'week'}\n",
" )\n",
"rd['year'] = 2015\n",
"rd['nation'] = 'Wales'\n",
"rd.head()"
]
},
{
"cell_type": "code",
"execution_count": 480,
"metadata": {},
"outputs": [],
"source": [
"rd.to_sql(\n",
" 'all_causes_deaths',\n",
" conn,\n",
" if_exists='append',\n",
" index=False)"
]
},
{
"cell_type": "code",
"execution_count": 481,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" date_up_to | \n",
" week | \n",
" deaths | \n",
" year | \n",
" nation | \n",
"
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" \n",
" \n",
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" 0 | \n",
" 2015-01-02 | \n",
" 1 | \n",
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"
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" 2015-01-09 | \n",
" 2 | \n",
" 15206 | \n",
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" England | \n",
"
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" 2 | \n",
" 2015-01-16 | \n",
" 3 | \n",
" 13930 | \n",
" 2015 | \n",
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"
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" 3 | \n",
" 2015-01-23 | \n",
" 4 | \n",
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"
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" 4 | \n",
" 2015-01-30 | \n",
" 5 | \n",
" 12099 | \n",
" 2015 | \n",
" England | \n",
"
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" \n",
"
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"
"
],
"text/plain": [
" date_up_to week deaths year nation\n",
"0 2015-01-02 1 11561 2015 England\n",
"1 2015-01-09 2 15206 2015 England\n",
"2 2015-01-16 3 13930 2015 England\n",
"3 2015-01-23 4 13106 2015 England\n",
"4 2015-01-30 5 12099 2015 England"
]
},
"execution_count": 481,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd = rdew.loc[:, ['Week ended','Week number']]\n",
"rd['deaths'] = rdew['Total deaths, all ages'] - rdew['W92000004']\n",
"rd = rd.rename(\n",
" columns={'Week ended': 'date_up_to', 'Week number': 'week'}\n",
" )\n",
"rd['year'] = 2015\n",
"rd['nation'] = 'England'\n",
"rd.head()"
]
},
{
"cell_type": "code",
"execution_count": 482,
"metadata": {},
"outputs": [],
"source": [
"rd.to_sql(\n",
" 'all_causes_deaths',\n",
" conn,\n",
" if_exists='append',\n",
" index=False)"
]
},
{
"cell_type": "code",
"execution_count": 483,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * postgresql://covid:***@localhost/covid\n",
"24 rows affected.\n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" year | \n",
" nation | \n",
" count | \n",
"
\n",
" \n",
" \n",
" \n",
" 2015 | \n",
" England | \n",
" 53 | \n",
"
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" 2015 | \n",
" Northern Ireland | \n",
" 53 | \n",
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"
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" England | \n",
" 53 | \n",
"
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" 2020 | \n",
" Northern Ireland | \n",
" 53 | \n",
"
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" 2020 | \n",
" Scotland | \n",
" 53 | \n",
"
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" \n",
" 2020 | \n",
" Wales | \n",
" 53 | \n",
"
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" \n",
"
"
],
"text/plain": [
"[(2015, 'England', 53),\n",
" (2015, 'Northern Ireland', 53),\n",
" (2015, 'Scotland', 53),\n",
" (2015, 'Wales', 53),\n",
" (2016, 'England', 52),\n",
" (2016, 'Northern Ireland', 52),\n",
" (2016, 'Scotland', 52),\n",
" (2016, 'Wales', 52),\n",
" (2017, 'England', 52),\n",
" (2017, 'Northern Ireland', 52),\n",
" (2017, 'Scotland', 52),\n",
" (2017, 'Wales', 52),\n",
" (2018, 'England', 52),\n",
" (2018, 'Northern Ireland', 52),\n",
" (2018, 'Scotland', 52),\n",
" (2018, 'Wales', 52),\n",
" (2019, 'England', 52),\n",
" (2019, 'Northern Ireland', 52),\n",
" (2019, 'Scotland', 52),\n",
" (2019, 'Wales', 52),\n",
" (2020, 'England', 53),\n",
" (2020, 'Northern Ireland', 53),\n",
" (2020, 'Scotland', 53),\n",
" (2020, 'Wales', 53)]"
]
},
"execution_count": 483,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql select year, nation, count(date_up_to) from all_causes_deaths group by (year, nation) order by year, nation"
]
},
{
"cell_type": "code",
"execution_count": 568,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * postgresql://covid:***@localhost/covid\n",
"314 rows affected.\n",
"Returning data to local variable res\n"
]
}
],
"source": [
"%%sql res << select week, year, deaths\n",
"from all_causes_deaths\n",
"where nation = 'England'"
]
},
{
"cell_type": "code",
"execution_count": 569,
"metadata": {},
"outputs": [
{
"data": {
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],
"text/plain": [
"year 2015 2016 2017 2018 2019 2020\n",
"week \n",
"1 11561.0 12236.0 11247.0 11940.0 10237.0 11467.0\n",
"2 15206.0 10790.0 12890.0 14146.0 11800.0 13119.0\n",
"3 13930.0 10753.0 12775.0 13371.0 11177.0 12223.0\n",
"4 13106.0 10600.0 11996.0 13085.0 11006.0 11133.0\n",
"5 12099.0 10362.0 11736.0 12470.0 10552.0 10885.0\n",
"6 11319.0 10470.0 11546.0 11694.0 10959.0 10296.0\n",
"7 11112.0 9933.0 10954.0 11443.0 11076.0 10216.0\n",
"8 10695.0 10360.0 11093.0 11353.0 10600.0 10162.0\n",
"9 10736.0 10564.0 10533.0 10220.0 10360.0 10165.0\n",
"10 10757.0 10276.0 10443.0 12101.0 10276.0 10243.0\n",
"11 10290.0 10284.0 10044.0 11870.0 9901.0 10344.0\n",
"12 9888.0 8967.0 9690.0 11139.0 9774.0 9926.0\n",
"13 9827.0 9574.0 9369.0 9308.0 9213.0 10422.0\n",
"14 8482.0 10857.0 9297.0 10064.0 9484.0 15467.0\n",
"15 9429.0 10679.0 7916.0 11558.0 9654.0 17588.0\n",
"16 10968.0 10211.0 8990.0 10535.0 8445.0 21182.0\n",
"17 9937.0 9784.0 10181.0 9692.0 9381.0 20873.0\n",
"18 9506.0 8568.0 8464.0 9520.0 10519.0 17024.0\n",
"19 8273.0 9985.0 10006.0 8094.0 8455.0 11965.0\n",
"20 9668.0 9342.0 9673.0 9474.0 9614.0 13801.0\n",
"21 9391.0 9115.0 9438.0 9033.0 9657.0 11596.0\n",
"22 7625.0 7409.0 7746.0 7609.0 7742.0 9237.0\n",
"23 9509.0 9289.0 9182.0 9343.0 9514.0 10009.0\n",
"24 8942.0 8808.0 8768.0 8782.0 8847.0 9402.0\n",
"25 8717.0 8807.0 9019.0 8694.0 8916.0 8722.0\n",
"26 8593.0 8679.0 8788.0 8613.0 8947.0 8427.0\n",
"27 8670.0 8614.0 8707.0 8633.0 8528.0 8556.0\n",
"28 8461.0 8789.0 8812.0 8710.0 8591.0 8118.0\n",
"29 8228.0 8790.0 8562.0 8579.0 8527.0 8273.0\n",
"30 8262.0 8725.0 8296.0 8581.0 8569.0 8326.0\n",
"31 8071.0 8602.0 8351.0 8587.0 8701.0 8415.0\n",
"32 8298.0 8531.0 8466.0 8782.0 8580.0 8382.0\n",
"33 8600.0 8496.0 8707.0 8312.0 8504.0 8775.0\n",
"34 8564.0 8700.0 8812.0 8413.0 8441.0 9037.0\n",
"35 8423.0 7453.0 7594.0 7354.0 7684.0 8441.0\n",
"36 7389.0 8847.0 8955.0 8851.0 9113.0 7251.0\n",
"37 8704.0 8548.0 8845.0 8612.0 8946.0 9233.0\n",
"38 8542.0 8382.0 8949.0 8707.0 8868.0 8968.0\n",
"39 8865.0 8402.0 9096.0 8590.0 8906.0 9017.0\n",
"40 8858.0 8729.0 9147.0 8908.0 9202.0 9274.0\n",
"41 9124.0 9132.0 9300.0 9043.0 9383.0 9316.0\n",
"42 8899.0 9144.0 9381.0 9224.0 9534.0 9846.0\n",
"43 9104.0 9100.0 9131.0 8970.0 9351.0 10078.0\n",
"44 9025.0 9508.0 9389.0 8925.0 9522.0 10175.0\n",
"45 9388.0 9844.0 9748.0 9509.0 10044.0 10980.0\n",
"46 9325.0 10013.0 9609.0 9537.0 9976.0 11512.0\n",
"47 9219.0 9942.0 9982.0 9300.0 10183.0 11687.0\n",
"48 9233.0 9796.0 9902.0 9360.0 10269.0 11659.0\n",
"49 9706.0 10530.0 10151.0 9613.0 10079.0 11467.0\n",
"50 9581.0 9870.0 10509.0 9842.0 10489.0 11478.0\n",
"51 10043.0 10802.0 11755.0 10392.0 11159.0 12129.0\n",
"52 8095.0 7445.0 7946.0 6670.0 7037.0 10695.0\n",
"53 7008.0 NaN NaN NaN NaN 9342.0"
]
},
"execution_count": 569,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"deaths_headlines_e = res.DataFrame().pivot(index='week', columns='year', values='deaths')\n",
"deaths_headlines_e"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 570,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * postgresql://covid:***@localhost/covid\n",
"314 rows affected.\n",
"Returning data to local variable res\n"
]
}
],
"source": [
"%%sql res << select week, year, deaths\n",
"from all_causes_deaths\n",
"where nation = 'Scotland'"
]
},
{
"cell_type": "code",
"execution_count": 571,
"metadata": {},
"outputs": [
{
"data": {
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],
"text/plain": [
"year 2015 2016 2017 2018 2019 2020\n",
"week \n",
"1 1146.0 1394.0 1205.0 1531.0 1104.0 1161.0\n",
"2 1708.0 1305.0 1379.0 1899.0 1507.0 1567.0\n",
"3 1489.0 1215.0 1224.0 1629.0 1353.0 1322.0\n",
"4 1381.0 1187.0 1197.0 1610.0 1208.0 1226.0\n",
"5 1286.0 1205.0 1332.0 1369.0 1206.0 1188.0\n",
"6 1344.0 1217.0 1200.0 1265.0 1243.0 1216.0\n",
"7 1360.0 1209.0 1231.0 1315.0 1181.0 1162.0\n",
"8 1320.0 1239.0 1185.0 1245.0 1245.0 1162.0\n",
"9 1308.0 1150.0 1219.0 1022.0 1125.0 1171.0\n",
"10 1192.0 1174.0 1146.0 1475.0 1156.0 1208.0\n",
"11 1201.0 1175.0 1141.0 1220.0 1108.0 1156.0\n",
"12 1149.0 1042.0 1152.0 1158.0 1101.0 1196.0\n",
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"14 1042.0 1166.0 1060.0 1192.0 1032.0 1744.0\n",
"15 1192.0 1048.0 998.0 1192.0 1069.0 1978.0\n",
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"29 1023.0 1041.0 1025.0 928.0 964.0 1033.0\n",
"30 956.0 979.0 978.0 933.0 1041.0 961.0\n",
"31 985.0 987.0 1011.0 969.0 1020.0 1043.0\n",
"32 1043.0 997.0 1002.0 953.0 1018.0 1011.0\n",
"33 969.0 982.0 1004.0 978.0 1028.0 922.0\n",
"34 982.0 1017.0 1045.0 941.0 1011.0 1046.0\n",
"35 954.0 1039.0 980.0 930.0 1013.0 1029.0\n",
"36 977.0 1007.0 1006.0 970.0 980.0 1050.0\n",
"37 991.0 983.0 972.0 1020.0 1074.0 1069.0\n",
"38 1001.0 966.0 1049.0 946.0 1071.0 952.0\n",
"39 1010.0 1009.0 1056.0 1015.0 1142.0 933.0\n",
"40 1008.0 1072.0 1016.0 1042.0 1051.0 1195.0\n",
"41 1028.0 1009.0 1133.0 1081.0 1143.0 1071.0\n",
"42 989.0 1070.0 1067.0 1031.0 1153.0 1131.0\n",
"43 981.0 1052.0 1095.0 1019.0 1115.0 1187.0\n",
"44 1116.0 1032.0 1062.0 1085.0 1101.0 1262.0\n",
"45 1028.0 1043.0 1126.0 1144.0 1184.0 1250.0\n",
"46 1103.0 1174.0 1175.0 1084.0 1160.0 1138.0\n",
"47 1054.0 1132.0 1178.0 1058.0 1229.0 1360.0\n",
"48 1115.0 1159.0 1153.0 1062.0 1163.0 1328.0\n",
"49 1089.0 1188.0 1237.0 1076.0 1108.0 1296.0\n",
"50 1101.0 1219.0 1335.0 1212.0 1312.0 1284.0\n",
"51 1146.0 1284.0 1437.0 1216.0 1277.0 1297.0\n",
"52 944.0 1133.0 1168.0 1058.0 1000.0 1205.0\n",
"53 1018.0 NaN NaN NaN NaN 1178.0"
]
},
"execution_count": 571,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"deaths_headlines_s = res.DataFrame().pivot(index='week', columns='year', values='deaths')\n",
"deaths_headlines_s"
]
},
{
"cell_type": "code",
"execution_count": 572,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * postgresql://covid:***@localhost/covid\n",
"314 rows affected.\n",
"Returning data to local variable res\n"
]
}
],
"source": [
"%%sql res << select week, year, deaths\n",
"from all_causes_deaths\n",
"where nation = 'Wales'"
]
},
{
"cell_type": "code",
"execution_count": 573,
"metadata": {},
"outputs": [
{
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],
"text/plain": [
"year 2015 2016 2017 2018 2019 2020\n",
"week \n",
"1 725.0 809.0 744.0 783.0 718.0 787.0\n",
"2 1031.0 711.0 825.0 904.0 809.0 939.0\n",
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"46 613.0 681.0 666.0 656.0 674.0 742.0\n",
"47 611.0 661.0 639.0 657.0 699.0 848.0\n",
"48 589.0 643.0 636.0 673.0 689.0 797.0\n",
"49 659.0 693.0 630.0 674.0 737.0 836.0\n",
"50 688.0 663.0 708.0 708.0 699.0 814.0\n",
"51 646.0 691.0 762.0 724.0 767.0 882.0\n",
"52 535.0 558.0 541.0 461.0 496.0 825.0\n",
"53 516.0 NaN NaN NaN NaN 727.0"
]
},
"execution_count": 573,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"deaths_headlines_w = res.DataFrame().pivot(index='week', columns='year', values='deaths')\n",
"deaths_headlines_w"
]
},
{
"cell_type": "code",
"execution_count": 574,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * postgresql://covid:***@localhost/covid\n",
"314 rows affected.\n",
"Returning data to local variable res\n"
]
}
],
"source": [
"%%sql res << select week, year, deaths\n",
"from all_causes_deaths\n",
"where nation = 'Northern Ireland'"
]
},
{
"cell_type": "code",
"execution_count": 575,
"metadata": {},
"outputs": [
{
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],
"text/plain": [
"year 2015 2016 2017 2018 2019 2020\n",
"week \n",
"1 319.0 424.0 416.0 447.0 365.0 353.0\n",
"2 373.0 348.0 434.0 481.0 371.0 395.0\n",
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"52 232.0 199.0 249.0 195.0 194.0 310.0\n",
"53 232.0 NaN NaN NaN NaN 333.0"
]
},
"execution_count": 575,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"deaths_headlines_i = res.DataFrame().pivot(index='week', columns='year', values='deaths')\n",
"deaths_headlines_i"
]
},
{
"cell_type": "code",
"execution_count": 579,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
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" | \n",
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" 1 | \n",
" 13751.0 | \n",
" 14863.0 | \n",
" 13612.0 | \n",
" 14701.0 | \n",
" 12424.0 | \n",
" 13768.0 | \n",
" 13870.2 | \n",
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" 2 | \n",
" 18318.0 | \n",
" 13154.0 | \n",
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" 46 | \n",
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"
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" 48 | \n",
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" 12046.0 | \n",
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" 14132.0 | \n",
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"
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" 49 | \n",
" 11748.0 | \n",
" 12733.0 | \n",
" 12342.0 | \n",
" 11687.0 | \n",
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" 13986.0 | \n",
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"
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" 50 | \n",
" 11713.0 | \n",
" 12076.0 | \n",
" 12924.0 | \n",
" 12078.0 | \n",
" 12853.0 | \n",
" 13942.0 | \n",
" 12328.8 | \n",
"
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" 51 | \n",
" 12136.0 | \n",
" 13137.0 | \n",
" 14308.0 | \n",
" 12649.0 | \n",
" 13566.0 | \n",
" 14658.0 | \n",
" 13159.2 | \n",
"
\n",
" \n",
" 52 | \n",
" 9806.0 | \n",
" 9335.0 | \n",
" 9904.0 | \n",
" 8384.0 | \n",
" 8727.0 | \n",
" 13035.0 | \n",
" 9231.2 | \n",
"
\n",
" \n",
" 53 | \n",
" 8774.0 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 11580.0 | \n",
" 8774.0 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"year 2015 2016 2017 2018 2019 2020 previous_mean\n",
"week \n",
"1 13751.0 14863.0 13612.0 14701.0 12424.0 13768.0 13870.2\n",
"2 18318.0 13154.0 15528.0 17430.0 14487.0 16020.0 15783.4\n",
"3 16738.0 13060.0 15231.0 16355.0 13545.0 14723.0 14985.8\n",
"4 15712.0 12859.0 14461.0 15971.0 13283.0 13429.0 14457.2\n",
"5 14560.0 12571.0 14188.0 15087.0 12799.0 13123.0 13841.0\n",
"6 13730.0 12697.0 13805.0 14111.0 13222.0 12534.0 13513.0\n",
"7 13510.0 12016.0 13212.0 13925.0 13347.0 12412.0 13202.0\n",
"8 13071.0 12718.0 13330.0 13753.0 12877.0 12300.0 13149.8\n",
"9 13181.0 12733.0 12819.0 12190.0 12479.0 12334.0 12680.4\n",
"10 13007.0 12493.0 12580.0 14859.0 12396.0 12415.0 13067.0\n",
"11 12475.0 12489.0 12089.0 14367.0 12018.0 12499.0 12687.6\n",
"12 12027.0 10983.0 11833.0 13397.0 11797.0 12112.0 12007.4\n",
"13 11987.0 11738.0 11453.0 11310.0 11260.0 12507.0 11549.6\n",
"14 10325.0 13060.0 11305.0 12272.0 11445.0 18565.0 11681.4\n",
"15 11575.0 12757.0 9761.0 13843.0 11661.0 20929.0 11919.4\n",
"16 13061.0 12310.0 11000.0 12639.0 10243.0 24691.0 11850.6\n",
"17 12023.0 11795.0 12356.0 11596.0 11452.0 24303.0 11844.4\n",
"18 11586.0 10401.0 10372.0 11538.0 12695.0 20059.0 11318.4\n",
"19 10138.0 12002.0 12114.0 9821.0 10361.0 14428.0 10887.2\n",
"20 11692.0 11222.0 11718.0 11386.0 11717.0 16390.0 11547.0\n",
"21 11334.0 11013.0 11431.0 10974.0 11653.0 13839.0 11281.0\n",
"22 9514.0 9192.0 9603.0 9397.0 9534.0 11265.0 9448.0\n",
"23 11603.0 11171.0 11134.0 11259.0 11461.0 12106.0 11325.6\n",
"24 10858.0 10673.0 10698.0 10535.0 10754.0 11302.0 10703.6\n",
"25 10629.0 10611.0 10930.0 10514.0 10807.0 10694.0 10698.2\n",
"26 10525.0 10526.0 10624.0 10529.0 10824.0 10282.0 10605.6\n",
"27 10545.0 10412.0 10565.0 10565.0 10328.0 10412.0 10483.0\n",
"28 10278.0 10647.0 10643.0 10467.0 10512.0 9941.0 10509.4\n",
"29 10028.0 10672.0 10426.0 10353.0 10324.0 10096.0 10360.6\n",
"30 10021.0 10612.0 10147.0 10356.0 10422.0 10159.0 10311.6\n",
"31 9893.0 10433.0 10239.0 10408.0 10564.0 10262.0 10307.4\n",
"32 10153.0 10439.0 10278.0 10542.0 10406.0 10236.0 10363.6\n",
"33 10352.0 10312.0 10569.0 10091.0 10405.0 10592.0 10345.8\n",
"34 10354.0 10637.0 10698.0 10199.0 10279.0 10990.0 10433.4\n",
"35 10239.0 9226.0 9372.0 9046.0 9478.0 10364.0 9472.2\n",
"36 9092.0 10681.0 10781.0 10680.0 10918.0 9023.0 10430.4\n",
"37 10573.0 10401.0 10692.0 10496.0 10892.0 11176.0 10610.8\n",
"38 10381.0 10183.0 10875.0 10498.0 10792.0 10797.0 10545.8\n",
"39 10826.0 10278.0 11027.0 10463.0 10954.0 10890.0 10709.6\n",
"40 10700.0 10671.0 11101.0 10869.0 11113.0 11468.0 10890.8\n",
"41 11108.0 11016.0 11357.0 11048.0 11403.0 11373.0 11186.4\n",
"42 10799.0 11134.0 11389.0 11177.0 11625.0 11943.0 11224.8\n",
"43 10966.0 11048.0 11152.0 10885.0 11415.0 12317.0 11093.2\n",
"44 11026.0 11463.0 11366.0 10866.0 11567.0 12517.0 11257.6\n",
"45 11312.0 11803.0 11767.0 11588.0 12177.0 13448.0 11729.4\n",
"46 11338.0 12209.0 11773.0 11552.0 12146.0 13798.0 11803.6\n",
"47 11178.0 12064.0 12102.0 11289.0 12472.0 14291.0 11821.0\n",
"48 11216.0 11901.0 12046.0 11392.0 12455.0 14132.0 11802.0\n",
"49 11748.0 12733.0 12342.0 11687.0 12275.0 13986.0 12157.0\n",
"50 11713.0 12076.0 12924.0 12078.0 12853.0 13942.0 12328.8\n",
"51 12136.0 13137.0 14308.0 12649.0 13566.0 14658.0 13159.2\n",
"52 9806.0 9335.0 9904.0 8384.0 8727.0 13035.0 9231.2\n",
"53 8774.0 NaN NaN NaN NaN 11580.0 8774.0"
]
},
"execution_count": 579,
"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": 577,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Int64Index([2015, 2016, 2017, 2018, 2019, 2020], dtype='int64', name='year')"
]
},
"execution_count": 577,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"deaths_headlines_e.columns"
]
},
{
"cell_type": "code",
"execution_count": 578,
"metadata": {},
"outputs": [
{
"data": {
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" 13730.0 | \n",
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" 8 | \n",
" 13071.0 | \n",
" 12718.0 | \n",
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"
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" 9 | \n",
" 13181.0 | \n",
" 12733.0 | \n",
" 12819.0 | \n",
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" 12334.0 | \n",
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"
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" 10 | \n",
" 13007.0 | \n",
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" 11 | \n",
" 12475.0 | \n",
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" 12499.0 | \n",
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" \n",
" 12 | \n",
" 12027.0 | \n",
" 10983.0 | \n",
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" 13 | \n",
" 11987.0 | \n",
" 11738.0 | \n",
" 11453.0 | \n",
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" 14 | \n",
" 10325.0 | \n",
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" 11575.0 | \n",
" 12757.0 | \n",
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"
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" \n",
" 16 | \n",
" 13061.0 | \n",
" 12310.0 | \n",
" 11000.0 | \n",
" 12639.0 | \n",
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" 24691.0 | \n",
" 11850.6 | \n",
"
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" \n",
" 17 | \n",
" 12023.0 | \n",
" 11795.0 | \n",
" 12356.0 | \n",
" 11596.0 | \n",
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" 24303.0 | \n",
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"
\n",
" \n",
" 18 | \n",
" 11586.0 | \n",
" 10401.0 | \n",
" 10372.0 | \n",
" 11538.0 | \n",
" 12695.0 | \n",
" 20059.0 | \n",
" 11318.4 | \n",
"
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" \n",
" 19 | \n",
" 10138.0 | \n",
" 12002.0 | \n",
" 12114.0 | \n",
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" 10361.0 | \n",
" 14428.0 | \n",
" 10887.2 | \n",
"
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" \n",
" 20 | \n",
" 11692.0 | \n",
" 11222.0 | \n",
" 11718.0 | \n",
" 11386.0 | \n",
" 11717.0 | \n",
" 16390.0 | \n",
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"
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" \n",
" 21 | \n",
" 11334.0 | \n",
" 11013.0 | \n",
" 11431.0 | \n",
" 10974.0 | \n",
" 11653.0 | \n",
" 13839.0 | \n",
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"
\n",
" \n",
" 22 | \n",
" 9514.0 | \n",
" 9192.0 | \n",
" 9603.0 | \n",
" 9397.0 | \n",
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"
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" \n",
" 23 | \n",
" 11603.0 | \n",
" 11171.0 | \n",
" 11134.0 | \n",
" 11259.0 | \n",
" 11461.0 | \n",
" 12106.0 | \n",
" 11325.6 | \n",
"
\n",
" \n",
" 24 | \n",
" 10858.0 | \n",
" 10673.0 | \n",
" 10698.0 | \n",
" 10535.0 | \n",
" 10754.0 | \n",
" 11302.0 | \n",
" 10703.6 | \n",
"
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" \n",
" 25 | \n",
" 10629.0 | \n",
" 10611.0 | \n",
" 10930.0 | \n",
" 10514.0 | \n",
" 10807.0 | \n",
" 10694.0 | \n",
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"
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" 26 | \n",
" 10525.0 | \n",
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" 10624.0 | \n",
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" 10282.0 | \n",
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"
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" 27 | \n",
" 10545.0 | \n",
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" 10565.0 | \n",
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" 28 | \n",
" 10278.0 | \n",
" 10647.0 | \n",
" 10643.0 | \n",
" 10467.0 | \n",
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" 9941.0 | \n",
" 10509.4 | \n",
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" 29 | \n",
" 10028.0 | \n",
" 10672.0 | \n",
" 10426.0 | \n",
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" 10096.0 | \n",
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" 30 | \n",
" 10021.0 | \n",
" 10612.0 | \n",
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" 10159.0 | \n",
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" 31 | \n",
" 9893.0 | \n",
" 10433.0 | \n",
" 10239.0 | \n",
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"
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" \n",
" 32 | \n",
" 10153.0 | \n",
" 10439.0 | \n",
" 10278.0 | \n",
" 10542.0 | \n",
" 10406.0 | \n",
" 10236.0 | \n",
" 10363.6 | \n",
"
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" \n",
" 33 | \n",
" 10352.0 | \n",
" 10312.0 | \n",
" 10569.0 | \n",
" 10091.0 | \n",
" 10405.0 | \n",
" 10592.0 | \n",
" 10345.8 | \n",
"
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" \n",
" 34 | \n",
" 10354.0 | \n",
" 10637.0 | \n",
" 10698.0 | \n",
" 10199.0 | \n",
" 10279.0 | \n",
" 10990.0 | \n",
" 10433.4 | \n",
"
\n",
" \n",
" 35 | \n",
" 10239.0 | \n",
" 9226.0 | \n",
" 9372.0 | \n",
" 9046.0 | \n",
" 9478.0 | \n",
" 10364.0 | \n",
" 9472.2 | \n",
"
\n",
" \n",
" 36 | \n",
" 9092.0 | \n",
" 10681.0 | \n",
" 10781.0 | \n",
" 10680.0 | \n",
" 10918.0 | \n",
" 9023.0 | \n",
" 10430.4 | \n",
"
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" \n",
" 37 | \n",
" 10573.0 | \n",
" 10401.0 | \n",
" 10692.0 | \n",
" 10496.0 | \n",
" 10892.0 | \n",
" 11176.0 | \n",
" 10610.8 | \n",
"
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" \n",
" 38 | \n",
" 10381.0 | \n",
" 10183.0 | \n",
" 10875.0 | \n",
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" 10797.0 | \n",
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"
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" 39 | \n",
" 10826.0 | \n",
" 10278.0 | \n",
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" 10463.0 | \n",
" 10954.0 | \n",
" 10890.0 | \n",
" 10709.6 | \n",
"
\n",
" \n",
" 40 | \n",
" 10700.0 | \n",
" 10671.0 | \n",
" 11101.0 | \n",
" 10869.0 | \n",
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" 11468.0 | \n",
" 10890.8 | \n",
"
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" \n",
" 41 | \n",
" 11108.0 | \n",
" 11016.0 | \n",
" 11357.0 | \n",
" 11048.0 | \n",
" 11403.0 | \n",
" 11373.0 | \n",
" 11186.4 | \n",
"
\n",
" \n",
" 42 | \n",
" 10799.0 | \n",
" 11134.0 | \n",
" 11389.0 | \n",
" 11177.0 | \n",
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" 11943.0 | \n",
" 11224.8 | \n",
"
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" \n",
" 43 | \n",
" 10966.0 | \n",
" 11048.0 | \n",
" 11152.0 | \n",
" 10885.0 | \n",
" 11415.0 | \n",
" 12317.0 | \n",
" 11093.2 | \n",
"
\n",
" \n",
" 44 | \n",
" 11026.0 | \n",
" 11463.0 | \n",
" 11366.0 | \n",
" 10866.0 | \n",
" 11567.0 | \n",
" 12517.0 | \n",
" 11257.6 | \n",
"
\n",
" \n",
" 45 | \n",
" 11312.0 | \n",
" 11803.0 | \n",
" 11767.0 | \n",
" 11588.0 | \n",
" 12177.0 | \n",
" 13448.0 | \n",
" 11729.4 | \n",
"
\n",
" \n",
" 46 | \n",
" 11338.0 | \n",
" 12209.0 | \n",
" 11773.0 | \n",
" 11552.0 | \n",
" 12146.0 | \n",
" 13798.0 | \n",
" 11803.6 | \n",
"
\n",
" \n",
" 47 | \n",
" 11178.0 | \n",
" 12064.0 | \n",
" 12102.0 | \n",
" 11289.0 | \n",
" 12472.0 | \n",
" 14291.0 | \n",
" 11821.0 | \n",
"
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" \n",
" 48 | \n",
" 11216.0 | \n",
" 11901.0 | \n",
" 12046.0 | \n",
" 11392.0 | \n",
" 12455.0 | \n",
" 14132.0 | \n",
" 11802.0 | \n",
"
\n",
" \n",
" 49 | \n",
" 11748.0 | \n",
" 12733.0 | \n",
" 12342.0 | \n",
" 11687.0 | \n",
" 12275.0 | \n",
" 13986.0 | \n",
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"
\n",
" \n",
" 50 | \n",
" 11713.0 | \n",
" 12076.0 | \n",
" 12924.0 | \n",
" 12078.0 | \n",
" 12853.0 | \n",
" 13942.0 | \n",
" 12328.8 | \n",
"
\n",
" \n",
" 51 | \n",
" 12136.0 | \n",
" 13137.0 | \n",
" 14308.0 | \n",
" 12649.0 | \n",
" 13566.0 | \n",
" 14658.0 | \n",
" 13159.2 | \n",
"
\n",
" \n",
" 52 | \n",
" 9806.0 | \n",
" 9335.0 | \n",
" 9904.0 | \n",
" 8384.0 | \n",
" 8727.0 | \n",
" 13035.0 | \n",
" 9231.2 | \n",
"
\n",
" \n",
" 53 | \n",
" 8774.0 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 11580.0 | \n",
" 8774.0 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"year 2015 2016 2017 2018 2019 2020 previous_mean\n",
"week \n",
"1 13751.0 14863.0 13612.0 14701.0 12424.0 13768.0 13870.2\n",
"2 18318.0 13154.0 15528.0 17430.0 14487.0 16020.0 15783.4\n",
"3 16738.0 13060.0 15231.0 16355.0 13545.0 14723.0 14985.8\n",
"4 15712.0 12859.0 14461.0 15971.0 13283.0 13429.0 14457.2\n",
"5 14560.0 12571.0 14188.0 15087.0 12799.0 13123.0 13841.0\n",
"6 13730.0 12697.0 13805.0 14111.0 13222.0 12534.0 13513.0\n",
"7 13510.0 12016.0 13212.0 13925.0 13347.0 12412.0 13202.0\n",
"8 13071.0 12718.0 13330.0 13753.0 12877.0 12300.0 13149.8\n",
"9 13181.0 12733.0 12819.0 12190.0 12479.0 12334.0 12680.4\n",
"10 13007.0 12493.0 12580.0 14859.0 12396.0 12415.0 13067.0\n",
"11 12475.0 12489.0 12089.0 14367.0 12018.0 12499.0 12687.6\n",
"12 12027.0 10983.0 11833.0 13397.0 11797.0 12112.0 12007.4\n",
"13 11987.0 11738.0 11453.0 11310.0 11260.0 12507.0 11549.6\n",
"14 10325.0 13060.0 11305.0 12272.0 11445.0 18565.0 11681.4\n",
"15 11575.0 12757.0 9761.0 13843.0 11661.0 20929.0 11919.4\n",
"16 13061.0 12310.0 11000.0 12639.0 10243.0 24691.0 11850.6\n",
"17 12023.0 11795.0 12356.0 11596.0 11452.0 24303.0 11844.4\n",
"18 11586.0 10401.0 10372.0 11538.0 12695.0 20059.0 11318.4\n",
"19 10138.0 12002.0 12114.0 9821.0 10361.0 14428.0 10887.2\n",
"20 11692.0 11222.0 11718.0 11386.0 11717.0 16390.0 11547.0\n",
"21 11334.0 11013.0 11431.0 10974.0 11653.0 13839.0 11281.0\n",
"22 9514.0 9192.0 9603.0 9397.0 9534.0 11265.0 9448.0\n",
"23 11603.0 11171.0 11134.0 11259.0 11461.0 12106.0 11325.6\n",
"24 10858.0 10673.0 10698.0 10535.0 10754.0 11302.0 10703.6\n",
"25 10629.0 10611.0 10930.0 10514.0 10807.0 10694.0 10698.2\n",
"26 10525.0 10526.0 10624.0 10529.0 10824.0 10282.0 10605.6\n",
"27 10545.0 10412.0 10565.0 10565.0 10328.0 10412.0 10483.0\n",
"28 10278.0 10647.0 10643.0 10467.0 10512.0 9941.0 10509.4\n",
"29 10028.0 10672.0 10426.0 10353.0 10324.0 10096.0 10360.6\n",
"30 10021.0 10612.0 10147.0 10356.0 10422.0 10159.0 10311.6\n",
"31 9893.0 10433.0 10239.0 10408.0 10564.0 10262.0 10307.4\n",
"32 10153.0 10439.0 10278.0 10542.0 10406.0 10236.0 10363.6\n",
"33 10352.0 10312.0 10569.0 10091.0 10405.0 10592.0 10345.8\n",
"34 10354.0 10637.0 10698.0 10199.0 10279.0 10990.0 10433.4\n",
"35 10239.0 9226.0 9372.0 9046.0 9478.0 10364.0 9472.2\n",
"36 9092.0 10681.0 10781.0 10680.0 10918.0 9023.0 10430.4\n",
"37 10573.0 10401.0 10692.0 10496.0 10892.0 11176.0 10610.8\n",
"38 10381.0 10183.0 10875.0 10498.0 10792.0 10797.0 10545.8\n",
"39 10826.0 10278.0 11027.0 10463.0 10954.0 10890.0 10709.6\n",
"40 10700.0 10671.0 11101.0 10869.0 11113.0 11468.0 10890.8\n",
"41 11108.0 11016.0 11357.0 11048.0 11403.0 11373.0 11186.4\n",
"42 10799.0 11134.0 11389.0 11177.0 11625.0 11943.0 11224.8\n",
"43 10966.0 11048.0 11152.0 10885.0 11415.0 12317.0 11093.2\n",
"44 11026.0 11463.0 11366.0 10866.0 11567.0 12517.0 11257.6\n",
"45 11312.0 11803.0 11767.0 11588.0 12177.0 13448.0 11729.4\n",
"46 11338.0 12209.0 11773.0 11552.0 12146.0 13798.0 11803.6\n",
"47 11178.0 12064.0 12102.0 11289.0 12472.0 14291.0 11821.0\n",
"48 11216.0 11901.0 12046.0 11392.0 12455.0 14132.0 11802.0\n",
"49 11748.0 12733.0 12342.0 11687.0 12275.0 13986.0 12157.0\n",
"50 11713.0 12076.0 12924.0 12078.0 12853.0 13942.0 12328.8\n",
"51 12136.0 13137.0 14308.0 12649.0 13566.0 14658.0 13159.2\n",
"52 9806.0 9335.0 9904.0 8384.0 8727.0 13035.0 9231.2\n",
"53 8774.0 NaN NaN NaN NaN 11580.0 8774.0"
]
},
"execution_count": 578,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"deaths_headlines_e['previous_mean'] = deaths_headlines_e[[int(y) for y in '2019 2018 2017 2016 2015'.split()]].apply(np.mean, axis=1)\n",
"deaths_headlines_w['previous_mean'] = deaths_headlines_w[[int(y) for y in '2019 2018 2017 2016 2015'.split()]].apply(np.mean, axis=1)\n",
"deaths_headlines_s['previous_mean'] = deaths_headlines_s[[int(y) for y in '2019 2018 2017 2016 2015'.split()]].apply(np.mean, axis=1)\n",
"deaths_headlines_i['previous_mean'] = deaths_headlines_i[[int(y) for y in '2019 2018 2017 2016 2015'.split()]].apply(np.mean, axis=1)\n",
"deaths_headlines['previous_mean'] = deaths_headlines[[int(y) for y in '2019 2018 2017 2016 2015'.split()]].apply(np.mean, axis=1)\n",
"deaths_headlines"
]
},
{
"cell_type": "code",
"execution_count": 580,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 580,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
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