{
"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": 1,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"import itertools\n",
"import collections\n",
"import json\n",
"import pandas as pd\n",
"import numpy as np\n",
"from scipy.stats import gmean\n",
"\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"england_wales_filename = 'uk-deaths-data/publishedweek532020.xlsx'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"raw_data_2015 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2015.csv', \n",
" parse_dates=[1, 2], dayfirst=True,\n",
" index_col=0,\n",
" header=[0, 1]\n",
" )\n",
"dh15i = raw_data_2015.iloc[:, [2]]\n",
"dh15i.columns = ['total_2015']\n",
"# dh15i.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"raw_data_2016 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2016.csv', \n",
" parse_dates=[1, 2], dayfirst=True,\n",
" index_col=0,\n",
" header=[0, 1]\n",
" )\n",
"dh16i = raw_data_2016.iloc[:, [2]]\n",
"dh16i.columns = ['total_2016']\n",
"# dh16i.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"raw_data_2017 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2017.csv', \n",
" parse_dates=[1, 2], dayfirst=True,\n",
" index_col=0,\n",
" header=[0, 1]\n",
" )\n",
"dh17i = raw_data_2017.iloc[:, [2]]\n",
"dh17i.columns = ['total_2017']\n",
"# dh17i.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"raw_data_2018 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2018.csv', \n",
" parse_dates=[1, 2], dayfirst=True,\n",
" index_col=0,\n",
" header=[0, 1]\n",
" )\n",
"dh18i = raw_data_2018.iloc[:, [2]]\n",
"dh18i.columns = ['total_2018']\n",
"# dh18i.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"raw_data_2019 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2019.csv', \n",
" parse_dates=[1, 2], dayfirst=True,\n",
" index_col=0,\n",
" header=[0, 1]\n",
" )\n",
"dh19i = raw_data_2019.iloc[:, [2]]\n",
"dh19i.columns = ['total_2019']\n",
"# dh19i.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" total_2020 | \n",
"
\n",
" \n",
" \n",
" \n",
" 49 | \n",
" 387 | \n",
"
\n",
" \n",
" 50 | \n",
" 366 | \n",
"
\n",
" \n",
" 51 | \n",
" 350 | \n",
"
\n",
" \n",
" 52 | \n",
" 310 | \n",
"
\n",
" \n",
" 53 | \n",
" 333 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" total_2020\n",
"49 387\n",
"50 366\n",
"51 350\n",
"52 310\n",
"53 333"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data_2020_i = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2020.csv', \n",
" parse_dates=[1], dayfirst=True,\n",
" index_col=0,\n",
" header=[0, 1]\n",
" )\n",
"deaths_headlines_i = raw_data_2020_i.iloc[:, [1]]\n",
"deaths_headlines_i.columns = ['total_2020']\n",
"deaths_headlines_i.tail()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" Registration Week | \n",
" Week Starts (Saturday) | \n",
" Week Ends (Friday) | \n",
" Total Number of Deaths Registered in Week (2019P) | \n",
" Average number of deaths registered in corresponding week in previous 5 years (2014 to 2018P) | \n",
" Range | \n",
" Unnamed: 6_level_0 | \n",
"
\n",
" \n",
" | \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",
" 1 | \n",
" 2018-12-29 | \n",
" 2019-01-04 | \n",
" 365 | \n",
" 212 | \n",
" 178 | \n",
" 249 | \n",
"
\n",
" \n",
" 2 | \n",
" 2019-01-05 | \n",
" 2019-01-11 | \n",
" 371 | \n",
" 387 | \n",
" 294 | \n",
" 481 | \n",
"
\n",
" \n",
" 3 | \n",
" 2019-01-12 | \n",
" 2019-01-18 | \n",
" 332 | \n",
" 401 | \n",
" 348 | \n",
" 470 | \n",
"
\n",
" \n",
" 4 | \n",
" 2019-01-19 | \n",
" 2019-01-25 | \n",
" 335 | \n",
" 382 | \n",
" 331 | \n",
" 426 | \n",
"
\n",
" \n",
" 5 | \n",
" 2019-01-26 | \n",
" 2019-02-01 | \n",
" 296 | \n",
" 385 | \n",
" 353 | \n",
" 433 | \n",
"
\n",
" \n",
" 6 | \n",
" 2019-02-02 | \n",
" 2019-02-08 | \n",
" 319 | \n",
" 349 | \n",
" 314 | \n",
" 374 | \n",
"
\n",
" \n",
" 7 | \n",
" 2019-02-09 | \n",
" 2019-02-15 | \n",
" 342 | \n",
" 327 | \n",
" 280 | \n",
" 364 | \n",
"
\n",
" \n",
" 8 | \n",
" 2019-02-16 | \n",
" 2019-02-22 | \n",
" 337 | \n",
" 309 | \n",
" 217 | \n",
" 366 | \n",
"
\n",
" \n",
" 9 | \n",
" 2019-02-23 | \n",
" 2019-03-01 | \n",
" 310 | \n",
" 347 | \n",
" 314 | \n",
" 423 | \n",
"
\n",
" \n",
" 10 | \n",
" 2019-03-02 | \n",
" 2019-03-08 | \n",
" 342 | \n",
" 345 | \n",
" 285 | \n",
" 401 | \n",
"
\n",
" \n",
" 11 | \n",
" 2019-03-09 | \n",
" 2019-03-15 | \n",
" 343 | \n",
" 338 | \n",
" 309 | \n",
" 359 | \n",
"
\n",
" \n",
" 12 | \n",
" 2019-03-16 | \n",
" 2019-03-22 | \n",
" 294 | \n",
" 304 | \n",
" 251 | \n",
" 327 | \n",
"
\n",
" \n",
" 13 | \n",
" 2019-03-23 | \n",
" 2019-03-29 | \n",
" 307 | \n",
" 310 | \n",
" 261 | \n",
" 356 | \n",
"
\n",
" \n",
" 14 | \n",
" 2019-03-30 | \n",
" 2019-04-05 | \n",
" 287 | \n",
" 301 | \n",
" 280 | \n",
" 323 | \n",
"
\n",
" \n",
" 15 | \n",
" 2019-04-06 | \n",
" 2019-04-12 | \n",
" 301 | \n",
" 292 | \n",
" 221 | \n",
" 350 | \n",
"
\n",
" \n",
" 16 | \n",
" 2019-04-13 | \n",
" 2019-04-19 | \n",
" 316 | \n",
" 290 | \n",
" 270 | \n",
" 316 | \n",
"
\n",
" \n",
" 17 | \n",
" 2019-04-20 | \n",
" 2019-04-26 | \n",
" 272 | \n",
" 279 | \n",
" 245 | \n",
" 327 | \n",
"
\n",
" \n",
" 18 | \n",
" 2019-04-27 | \n",
" 2019-05-03 | \n",
" 357 | \n",
" 298 | \n",
" 251 | \n",
" 327 | \n",
"
\n",
" \n",
" 19 | \n",
" 2019-05-04 | \n",
" 2019-05-10 | \n",
" 288 | \n",
" 279 | \n",
" 230 | \n",
" 335 | \n",
"
\n",
" \n",
" 20 | \n",
" 2019-05-11 | \n",
" 2019-05-17 | \n",
" 330 | \n",
" 279 | \n",
" 249 | \n",
" 318 | \n",
"
\n",
" \n",
" 21 | \n",
" 2019-05-18 | \n",
" 2019-05-24 | \n",
" 308 | \n",
" 278 | \n",
" 250 | \n",
" 315 | \n",
"
\n",
" \n",
" 22 | \n",
" 2019-05-25 | \n",
" 2019-05-31 | \n",
" 245 | \n",
" 278 | \n",
" 241 | \n",
" 328 | \n",
"
\n",
" \n",
" 23 | \n",
" 2019-06-01 | \n",
" 2019-06-07 | \n",
" 279 | \n",
" 263 | \n",
" 241 | \n",
" 284 | \n",
"
\n",
" \n",
" 24 | \n",
" 2019-06-08 | \n",
" 2019-06-14 | \n",
" 281 | \n",
" 286 | \n",
" 246 | \n",
" 340 | \n",
"
\n",
" \n",
" 25 | \n",
" 2019-06-15 | \n",
" 2019-06-21 | \n",
" 296 | \n",
" 284 | \n",
" 265 | \n",
" 300 | \n",
"
\n",
" \n",
" 26 | \n",
" 2019-06-22 | \n",
" 2019-06-28 | \n",
" 262 | \n",
" 259 | \n",
" 211 | \n",
" 292 | \n",
"
\n",
" \n",
" 27 | \n",
" 2019-06-29 | \n",
" 2019-07-05 | \n",
" 285 | \n",
" 278 | \n",
" 235 | \n",
" 303 | \n",
"
\n",
" \n",
" 28 | \n",
" 2019-07-06 | \n",
" 2019-07-12 | \n",
" 256 | \n",
" 254 | \n",
" 172 | \n",
" 300 | \n",
"
\n",
" \n",
" 29 | \n",
" 2019-07-13 | \n",
" 2019-07-19 | \n",
" 280 | \n",
" 256 | \n",
" 237 | \n",
" 298 | \n",
"
\n",
" \n",
" 30 | \n",
" 2019-07-20 | \n",
" 2019-07-26 | \n",
" 269 | \n",
" 255 | \n",
" 203 | \n",
" 288 | \n",
"
\n",
" \n",
" 31 | \n",
" 2019-07-27 | \n",
" 2019-08-02 | \n",
" 273 | \n",
" 280 | \n",
" 261 | \n",
" 298 | \n",
"
\n",
" \n",
" 32 | \n",
" 2019-08-03 | \n",
" 2019-08-09 | \n",
" 266 | \n",
" 281 | \n",
" 264 | \n",
" 292 | \n",
"
\n",
" \n",
" 33 | \n",
" 2019-08-10 | \n",
" 2019-08-16 | \n",
" 284 | \n",
" 260 | \n",
" 238 | \n",
" 283 | \n",
"
\n",
" \n",
" 34 | \n",
" 2019-08-17 | \n",
" 2019-08-23 | \n",
" 274 | \n",
" 260 | \n",
" 235 | \n",
" 280 | \n",
"
\n",
" \n",
" 35 | \n",
" 2019-08-24 | \n",
" 2019-08-30 | \n",
" 223 | \n",
" 271 | \n",
" 251 | \n",
" 301 | \n",
"
\n",
" \n",
" 36 | \n",
" 2019-08-31 | \n",
" 2019-09-06 | \n",
" 243 | \n",
" 258 | \n",
" 243 | \n",
" 265 | \n",
"
\n",
" \n",
" 37 | \n",
" 2019-09-07 | \n",
" 2019-09-13 | \n",
" 305 | \n",
" 275 | \n",
" 237 | \n",
" 301 | \n",
"
\n",
" \n",
" 38 | \n",
" 2019-09-14 | \n",
" 2019-09-20 | \n",
" 281 | \n",
" 285 | \n",
" 247 | \n",
" 324 | \n",
"
\n",
" \n",
" 39 | \n",
" 2019-09-21 | \n",
" 2019-09-27 | \n",
" 295 | \n",
" 281 | \n",
" 259 | \n",
" 298 | \n",
"
\n",
" \n",
" 40 | \n",
" 2019-09-28 | \n",
" 2019-10-04 | \n",
" 263 | \n",
" 292 | \n",
" 275 | \n",
" 324 | \n",
"
\n",
" \n",
" 41 | \n",
" 2019-10-05 | \n",
" 2019-10-11 | \n",
" 287 | \n",
" 298 | \n",
" 277 | \n",
" 318 | \n",
"
\n",
" \n",
" 42 | \n",
" 2019-10-12 | \n",
" 2019-10-18 | \n",
" 316 | \n",
" 295 | \n",
" 282 | \n",
" 317 | \n",
"
\n",
" \n",
" 43 | \n",
" 2019-10-19 | \n",
" 2019-10-25 | \n",
" 279 | \n",
" 287 | \n",
" 263 | \n",
" 299 | \n",
"
\n",
" \n",
" 44 | \n",
" 2019-10-26 | \n",
" 2019-11-01 | \n",
" 302 | \n",
" 283 | \n",
" 252 | \n",
" 318 | \n",
"
\n",
" \n",
" 45 | \n",
" 2019-11-02 | \n",
" 2019-11-08 | \n",
" 296 | \n",
" 290 | \n",
" 267 | \n",
" 320 | \n",
"
\n",
" \n",
" 46 | \n",
" 2019-11-09 | \n",
" 2019-11-15 | \n",
" 336 | \n",
" 286 | \n",
" 275 | \n",
" 295 | \n",
"
\n",
" \n",
" 47 | \n",
" 2019-11-16 | \n",
" 2019-11-22 | \n",
" 361 | \n",
" 305 | \n",
" 274 | \n",
" 341 | \n",
"
\n",
" \n",
" 48 | \n",
" 2019-11-23 | \n",
" 2019-11-29 | \n",
" 334 | \n",
" 304 | \n",
" 294 | \n",
" 329 | \n",
"
\n",
" \n",
" 49 | \n",
" 2019-11-30 | \n",
" 2019-12-06 | \n",
" 351 | \n",
" 310 | \n",
" 279 | \n",
" 355 | \n",
"
\n",
" \n",
" 50 | \n",
" 2019-12-07 | \n",
" 2019-12-13 | \n",
" 353 | \n",
" 306 | \n",
" 275 | \n",
" 324 | \n",
"
\n",
" \n",
" 51 | \n",
" 2019-12-14 | \n",
" 2019-12-20 | \n",
" 363 | \n",
" 331 | \n",
" 298 | \n",
" 372 | \n",
"
\n",
" \n",
" 52 | \n",
" 2019-12-21 | \n",
" 2019-12-27 | \n",
" 194 | \n",
" 298 | \n",
" 195 | \n",
" 360 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"Registration Week Week Starts (Saturday) Week Ends (Friday) \\\n",
" Unnamed: 1_level_1 Unnamed: 2_level_1 \n",
"1 2018-12-29 2019-01-04 \n",
"2 2019-01-05 2019-01-11 \n",
"3 2019-01-12 2019-01-18 \n",
"4 2019-01-19 2019-01-25 \n",
"5 2019-01-26 2019-02-01 \n",
"6 2019-02-02 2019-02-08 \n",
"7 2019-02-09 2019-02-15 \n",
"8 2019-02-16 2019-02-22 \n",
"9 2019-02-23 2019-03-01 \n",
"10 2019-03-02 2019-03-08 \n",
"11 2019-03-09 2019-03-15 \n",
"12 2019-03-16 2019-03-22 \n",
"13 2019-03-23 2019-03-29 \n",
"14 2019-03-30 2019-04-05 \n",
"15 2019-04-06 2019-04-12 \n",
"16 2019-04-13 2019-04-19 \n",
"17 2019-04-20 2019-04-26 \n",
"18 2019-04-27 2019-05-03 \n",
"19 2019-05-04 2019-05-10 \n",
"20 2019-05-11 2019-05-17 \n",
"21 2019-05-18 2019-05-24 \n",
"22 2019-05-25 2019-05-31 \n",
"23 2019-06-01 2019-06-07 \n",
"24 2019-06-08 2019-06-14 \n",
"25 2019-06-15 2019-06-21 \n",
"26 2019-06-22 2019-06-28 \n",
"27 2019-06-29 2019-07-05 \n",
"28 2019-07-06 2019-07-12 \n",
"29 2019-07-13 2019-07-19 \n",
"30 2019-07-20 2019-07-26 \n",
"31 2019-07-27 2019-08-02 \n",
"32 2019-08-03 2019-08-09 \n",
"33 2019-08-10 2019-08-16 \n",
"34 2019-08-17 2019-08-23 \n",
"35 2019-08-24 2019-08-30 \n",
"36 2019-08-31 2019-09-06 \n",
"37 2019-09-07 2019-09-13 \n",
"38 2019-09-14 2019-09-20 \n",
"39 2019-09-21 2019-09-27 \n",
"40 2019-09-28 2019-10-04 \n",
"41 2019-10-05 2019-10-11 \n",
"42 2019-10-12 2019-10-18 \n",
"43 2019-10-19 2019-10-25 \n",
"44 2019-10-26 2019-11-01 \n",
"45 2019-11-02 2019-11-08 \n",
"46 2019-11-09 2019-11-15 \n",
"47 2019-11-16 2019-11-22 \n",
"48 2019-11-23 2019-11-29 \n",
"49 2019-11-30 2019-12-06 \n",
"50 2019-12-07 2019-12-13 \n",
"51 2019-12-14 2019-12-20 \n",
"52 2019-12-21 2019-12-27 \n",
"\n",
"Registration Week Total Number of Deaths Registered in Week (2019P) \\\n",
" Unnamed: 3_level_1 \n",
"1 365 \n",
"2 371 \n",
"3 332 \n",
"4 335 \n",
"5 296 \n",
"6 319 \n",
"7 342 \n",
"8 337 \n",
"9 310 \n",
"10 342 \n",
"11 343 \n",
"12 294 \n",
"13 307 \n",
"14 287 \n",
"15 301 \n",
"16 316 \n",
"17 272 \n",
"18 357 \n",
"19 288 \n",
"20 330 \n",
"21 308 \n",
"22 245 \n",
"23 279 \n",
"24 281 \n",
"25 296 \n",
"26 262 \n",
"27 285 \n",
"28 256 \n",
"29 280 \n",
"30 269 \n",
"31 273 \n",
"32 266 \n",
"33 284 \n",
"34 274 \n",
"35 223 \n",
"36 243 \n",
"37 305 \n",
"38 281 \n",
"39 295 \n",
"40 263 \n",
"41 287 \n",
"42 316 \n",
"43 279 \n",
"44 302 \n",
"45 296 \n",
"46 336 \n",
"47 361 \n",
"48 334 \n",
"49 351 \n",
"50 353 \n",
"51 363 \n",
"52 194 \n",
"\n",
"Registration Week Average number of deaths registered in corresponding week in previous 5 years (2014 to 2018P) \\\n",
" Unnamed: 4_level_1 \n",
"1 212 \n",
"2 387 \n",
"3 401 \n",
"4 382 \n",
"5 385 \n",
"6 349 \n",
"7 327 \n",
"8 309 \n",
"9 347 \n",
"10 345 \n",
"11 338 \n",
"12 304 \n",
"13 310 \n",
"14 301 \n",
"15 292 \n",
"16 290 \n",
"17 279 \n",
"18 298 \n",
"19 279 \n",
"20 279 \n",
"21 278 \n",
"22 278 \n",
"23 263 \n",
"24 286 \n",
"25 284 \n",
"26 259 \n",
"27 278 \n",
"28 254 \n",
"29 256 \n",
"30 255 \n",
"31 280 \n",
"32 281 \n",
"33 260 \n",
"34 260 \n",
"35 271 \n",
"36 258 \n",
"37 275 \n",
"38 285 \n",
"39 281 \n",
"40 292 \n",
"41 298 \n",
"42 295 \n",
"43 287 \n",
"44 283 \n",
"45 290 \n",
"46 286 \n",
"47 305 \n",
"48 304 \n",
"49 310 \n",
"50 306 \n",
"51 331 \n",
"52 298 \n",
"\n",
"Registration Week Range Unnamed: 6_level_0 \n",
" Minimum in Previous 5 years Maximum in Previous 5 years \n",
"1 178 249 \n",
"2 294 481 \n",
"3 348 470 \n",
"4 331 426 \n",
"5 353 433 \n",
"6 314 374 \n",
"7 280 364 \n",
"8 217 366 \n",
"9 314 423 \n",
"10 285 401 \n",
"11 309 359 \n",
"12 251 327 \n",
"13 261 356 \n",
"14 280 323 \n",
"15 221 350 \n",
"16 270 316 \n",
"17 245 327 \n",
"18 251 327 \n",
"19 230 335 \n",
"20 249 318 \n",
"21 250 315 \n",
"22 241 328 \n",
"23 241 284 \n",
"24 246 340 \n",
"25 265 300 \n",
"26 211 292 \n",
"27 235 303 \n",
"28 172 300 \n",
"29 237 298 \n",
"30 203 288 \n",
"31 261 298 \n",
"32 264 292 \n",
"33 238 283 \n",
"34 235 280 \n",
"35 251 301 \n",
"36 243 265 \n",
"37 237 301 \n",
"38 247 324 \n",
"39 259 298 \n",
"40 275 324 \n",
"41 277 318 \n",
"42 282 317 \n",
"43 263 299 \n",
"44 252 318 \n",
"45 267 320 \n",
"46 275 295 \n",
"47 274 341 \n",
"48 294 329 \n",
"49 279 355 \n",
"50 275 324 \n",
"51 298 372 \n",
"52 195 360 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data_2019"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"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": 11,
"metadata": {
"Collapsed": "false",
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" total_2020 | \n",
" total_2019 | \n",
" total_2018 | \n",
" total_2017 | \n",
" total_2016 | \n",
" total_2015 | \n",
"
\n",
" \n",
" \n",
" \n",
" 1 | \n",
" 1161 | \n",
" 1104.0 | \n",
" 1531.0 | \n",
" 1205.0 | \n",
" 1394.0 | \n",
" 1146 | \n",
"
\n",
" \n",
" 2 | \n",
" 1567 | \n",
" 1507.0 | \n",
" 1899.0 | \n",
" 1379.0 | \n",
" 1305.0 | \n",
" 1708 | \n",
"
\n",
" \n",
" 3 | \n",
" 1322 | \n",
" 1353.0 | \n",
" 1629.0 | \n",
" 1224.0 | \n",
" 1215.0 | \n",
" 1489 | \n",
"
\n",
" \n",
" 4 | \n",
" 1226 | \n",
" 1208.0 | \n",
" 1610.0 | \n",
" 1197.0 | \n",
" 1187.0 | \n",
" 1381 | \n",
"
\n",
" \n",
" 5 | \n",
" 1188 | \n",
" 1206.0 | \n",
" 1369.0 | \n",
" 1332.0 | \n",
" 1205.0 | \n",
" 1286 | \n",
"
\n",
" \n",
" 6 | \n",
" 1216 | \n",
" 1243.0 | \n",
" 1265.0 | \n",
" 1200.0 | \n",
" 1217.0 | \n",
" 1344 | \n",
"
\n",
" \n",
" 7 | \n",
" 1162 | \n",
" 1181.0 | \n",
" 1315.0 | \n",
" 1231.0 | \n",
" 1209.0 | \n",
" 1360 | \n",
"
\n",
" \n",
" 8 | \n",
" 1162 | \n",
" 1245.0 | \n",
" 1245.0 | \n",
" 1185.0 | \n",
" 1239.0 | \n",
" 1320 | \n",
"
\n",
" \n",
" 9 | \n",
" 1171 | \n",
" 1125.0 | \n",
" 1022.0 | \n",
" 1219.0 | \n",
" 1150.0 | \n",
" 1308 | \n",
"
\n",
" \n",
" 10 | \n",
" 1208 | \n",
" 1156.0 | \n",
" 1475.0 | \n",
" 1146.0 | \n",
" 1174.0 | \n",
" 1192 | \n",
"
\n",
" \n",
" 11 | \n",
" 1156 | \n",
" 1108.0 | \n",
" 1220.0 | \n",
" 1141.0 | \n",
" 1175.0 | \n",
" 1201 | \n",
"
\n",
" \n",
" 12 | \n",
" 1196 | \n",
" 1101.0 | \n",
" 1158.0 | \n",
" 1152.0 | \n",
" 1042.0 | \n",
" 1149 | \n",
"
\n",
" \n",
" 13 | \n",
" 1079 | \n",
" 1086.0 | \n",
" 1050.0 | \n",
" 1112.0 | \n",
" 1172.0 | \n",
" 1171 | \n",
"
\n",
" \n",
" 14 | \n",
" 1744 | \n",
" 1032.0 | \n",
" 1192.0 | \n",
" 1060.0 | \n",
" 1166.0 | \n",
" 1042 | \n",
"
\n",
" \n",
" 15 | \n",
" 1978 | \n",
" 1069.0 | \n",
" 1192.0 | \n",
" 998.0 | \n",
" 1048.0 | \n",
" 1192 | \n",
"
\n",
" \n",
" 16 | \n",
" 1916 | \n",
" 902.0 | \n",
" 1136.0 | \n",
" 1111.0 | \n",
" 1092.0 | \n",
" 1095 | \n",
"
\n",
" \n",
" 17 | \n",
" 1836 | \n",
" 1121.0 | \n",
" 1008.0 | \n",
" 1121.0 | \n",
" 1076.0 | \n",
" 1108 | \n",
"
\n",
" \n",
" 18 | \n",
" 1679 | \n",
" 1131.0 | \n",
" 1093.0 | \n",
" 1050.0 | \n",
" 1006.0 | \n",
" 1117 | \n",
"
\n",
" \n",
" 19 | \n",
" 1435 | \n",
" 1018.0 | \n",
" 967.0 | \n",
" 1119.0 | \n",
" 1047.0 | \n",
" 1020 | \n",
"
\n",
" \n",
" 20 | \n",
" 1421 | \n",
" 1115.0 | \n",
" 977.0 | \n",
" 1115.0 | \n",
" 1010.0 | \n",
" 1103 | \n",
"
\n",
" \n",
" 21 | \n",
" 1226 | \n",
" 1061.0 | \n",
" 1070.0 | \n",
" 1063.0 | \n",
" 994.0 | \n",
" 1039 | \n",
"
\n",
" \n",
" 22 | \n",
" 1125 | \n",
" 1029.0 | \n",
" 998.0 | \n",
" 1015.0 | \n",
" 999.0 | \n",
" 1043 | \n",
"
\n",
" \n",
" 23 | \n",
" 1093 | \n",
" 1042.0 | \n",
" 1033.0 | \n",
" 1076.0 | \n",
" 1023.0 | \n",
" 1106 | \n",
"
\n",
" \n",
" 24 | \n",
" 1034 | \n",
" 1028.0 | \n",
" 915.0 | \n",
" 1031.0 | \n",
" 988.0 | \n",
" 1038 | \n",
"
\n",
" \n",
" 25 | \n",
" 1065 | \n",
" 1053.0 | \n",
" 993.0 | \n",
" 1032.0 | \n",
" 994.0 | \n",
" 1025 | \n",
"
\n",
" \n",
" 26 | \n",
" 1008 | \n",
" 1051.0 | \n",
" 1046.0 | \n",
" 994.0 | \n",
" 1007.0 | \n",
" 1032 | \n",
"
\n",
" \n",
" 27 | \n",
" 983 | \n",
" 981.0 | \n",
" 1041.0 | \n",
" 1040.0 | \n",
" 988.0 | \n",
" 1040 | \n",
"
\n",
" \n",
" 28 | \n",
" 976 | \n",
" 1077.0 | \n",
" 1002.0 | \n",
" 1014.0 | \n",
" 1022.0 | \n",
" 1011 | \n",
"
\n",
" \n",
" 29 | \n",
" 1033 | \n",
" 964.0 | \n",
" 928.0 | \n",
" 1025.0 | \n",
" 1041.0 | \n",
" 1023 | \n",
"
\n",
" \n",
" 30 | \n",
" 961 | \n",
" 1041.0 | \n",
" 933.0 | \n",
" 978.0 | \n",
" 979.0 | \n",
" 956 | \n",
"
\n",
" \n",
" 31 | \n",
" 1043 | \n",
" 1020.0 | \n",
" 969.0 | \n",
" 1011.0 | \n",
" 987.0 | \n",
" 985 | \n",
"
\n",
" \n",
" 32 | \n",
" 1011 | \n",
" 1018.0 | \n",
" 953.0 | \n",
" 1002.0 | \n",
" 997.0 | \n",
" 1043 | \n",
"
\n",
" \n",
" 33 | \n",
" 922 | \n",
" 1028.0 | \n",
" 978.0 | \n",
" 1004.0 | \n",
" 982.0 | \n",
" 969 | \n",
"
\n",
" \n",
" 34 | \n",
" 1046 | \n",
" 1011.0 | \n",
" 941.0 | \n",
" 1045.0 | \n",
" 1017.0 | \n",
" 982 | \n",
"
\n",
" \n",
" 35 | \n",
" 1029 | \n",
" 1013.0 | \n",
" 930.0 | \n",
" 980.0 | \n",
" 1039.0 | \n",
" 954 | \n",
"
\n",
" \n",
" 36 | \n",
" 1050 | \n",
" 980.0 | \n",
" 970.0 | \n",
" 1006.0 | \n",
" 1007.0 | \n",
" 977 | \n",
"
\n",
" \n",
" 37 | \n",
" 1069 | \n",
" 1074.0 | \n",
" 1020.0 | \n",
" 972.0 | \n",
" 983.0 | \n",
" 991 | \n",
"
\n",
" \n",
" 38 | \n",
" 952 | \n",
" 1071.0 | \n",
" 946.0 | \n",
" 1049.0 | \n",
" 966.0 | \n",
" 1001 | \n",
"
\n",
" \n",
" 39 | \n",
" 933 | \n",
" 1142.0 | \n",
" 1015.0 | \n",
" 1056.0 | \n",
" 1009.0 | \n",
" 1010 | \n",
"
\n",
" \n",
" 40 | \n",
" 1195 | \n",
" 1051.0 | \n",
" 1042.0 | \n",
" 1016.0 | \n",
" 1072.0 | \n",
" 1008 | \n",
"
\n",
" \n",
" 41 | \n",
" 1071 | \n",
" 1143.0 | \n",
" 1081.0 | \n",
" 1133.0 | \n",
" 1009.0 | \n",
" 1028 | \n",
"
\n",
" \n",
" 42 | \n",
" 1131 | \n",
" 1153.0 | \n",
" 1031.0 | \n",
" 1067.0 | \n",
" 1070.0 | \n",
" 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": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"deaths_headlines_s = raw_data_s[reversed('2015 2016 2017 2018 2019 2020'.split())]\n",
"deaths_headlines_s.columns = ['total_' + c for c in deaths_headlines_s.columns]\n",
"deaths_headlines_s.reset_index(drop=True, inplace=True)\n",
"deaths_headlines_s.index = deaths_headlines_s.index + 1\n",
"deaths_headlines_s"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"Collapsed": "false",
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" NaN | \n",
" NaN | \n",
" 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",
" E12000004 | \n",
" E12000005 | \n",
" E12000006 | \n",
" E12000007 | \n",
" E12000008 | \n",
" E12000009 | \n",
" W92000004 | \n",
"
\n",
" \n",
" 1 | \n",
" 2020-01-03 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 12254 | \n",
" NaN | \n",
" 12175 | \n",
" NaN | \n",
" 11412 | \n",
" NaN | \n",
" 756 | \n",
" ... | \n",
" 673 | \n",
" 1806 | \n",
" 1240 | \n",
" 1060 | \n",
" 1349 | \n",
" 1162 | \n",
" 1113 | \n",
" 1814 | \n",
" 1225 | \n",
" 787 | \n",
"
\n",
" \n",
" 2 | \n",
" 2020-01-10 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 14058 | \n",
" NaN | \n",
" 13822 | \n",
" NaN | \n",
" 12933 | \n",
" NaN | \n",
" 856 | \n",
" ... | \n",
" 707 | \n",
" 1932 | \n",
" 1339 | \n",
" 1195 | \n",
" 1450 | \n",
" 1573 | \n",
" 1272 | \n",
" 2132 | \n",
" 1487 | \n",
" 939 | \n",
"
\n",
" \n",
" 3 | \n",
" 2020-01-17 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 12990 | \n",
" NaN | \n",
" 13216 | \n",
" NaN | \n",
" 12370 | \n",
" NaN | \n",
" 812 | \n",
" ... | \n",
" 647 | \n",
" 1696 | \n",
" 1278 | \n",
" 1106 | \n",
" 1407 | \n",
" 1457 | \n",
" 1073 | \n",
" 2064 | \n",
" 1466 | \n",
" 767 | \n",
"
\n",
" \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",
" 612 | \n",
" 1529 | \n",
" 1187 | \n",
" 1024 | \n",
" 1231 | \n",
" 1410 | \n",
" 1028 | \n",
" 1833 | \n",
" 1253 | \n",
" 723 | \n",
"
\n",
" \n",
" 5 | \n",
" 2020-01-31 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 11612 | \n",
" NaN | \n",
" 12206 | \n",
" NaN | \n",
" 11419 | \n",
" NaN | \n",
" 760 | \n",
" ... | \n",
" 561 | \n",
" 1461 | \n",
" 1136 | \n",
" 1015 | \n",
" 1262 | \n",
" 1286 | \n",
" 1092 | \n",
" 1820 | \n",
" 1233 | \n",
" 727 | \n",
"
\n",
" \n",
" 6 | \n",
" 2020-02-07 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 10986 | \n",
" NaN | \n",
" 11925 | \n",
" NaN | \n",
" 11154 | \n",
" NaN | \n",
" 729 | \n",
" ... | \n",
" 564 | \n",
" 1529 | \n",
" 1072 | \n",
" 922 | \n",
" 1052 | \n",
" 1259 | \n",
" 987 | \n",
" 1729 | \n",
" 1157 | \n",
" 690 | \n",
"
\n",
" \n",
" 7 | \n",
" 2020-02-14 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 10944 | \n",
" NaN | \n",
" 11627 | \n",
" NaN | \n",
" 10876 | \n",
" NaN | \n",
" 722 | \n",
" ... | \n",
" 573 | \n",
" 1427 | \n",
" 1059 | \n",
" 976 | \n",
" 1159 | \n",
" 1172 | \n",
" 967 | \n",
" 1688 | \n",
" 1169 | \n",
" 728 | \n",
"
\n",
" \n",
" 8 | \n",
" 2020-02-21 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 10841 | \n",
" NaN | \n",
" 11548 | \n",
" NaN | \n",
" 10790 | \n",
" NaN | \n",
" 724 | \n",
" ... | \n",
" 539 | \n",
" 1477 | \n",
" 1087 | \n",
" 924 | \n",
" 1116 | \n",
" 1167 | \n",
" 1032 | \n",
" 1675 | \n",
" 1118 | \n",
" 679 | \n",
"
\n",
" \n",
" 9 | \n",
" 2020-02-28 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 10816 | \n",
" NaN | \n",
" 11183 | \n",
" NaN | \n",
" 10448 | \n",
" NaN | \n",
" 698 | \n",
" ... | \n",
" 572 | \n",
" 1476 | \n",
" 1078 | \n",
" 919 | \n",
" 1174 | \n",
" 1115 | \n",
" 1085 | \n",
" 1587 | \n",
" 1133 | \n",
" 651 | \n",
"
\n",
" \n",
" 10 | \n",
" 2020-03-06 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 10895 | \n",
" NaN | \n",
" 11498 | \n",
" NaN | \n",
" 10745 | \n",
" NaN | \n",
" 720 | \n",
" ... | \n",
" 568 | \n",
" 1490 | \n",
" 1112 | \n",
" 930 | \n",
" 1098 | \n",
" 1149 | \n",
" 982 | \n",
" 1726 | \n",
" 1170 | \n",
" 652 | \n",
"
\n",
" \n",
" 11 | \n",
" 2020-03-13 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 11019 | \n",
" NaN | \n",
" 11205 | \n",
" NaN | \n",
" 10447 | \n",
" NaN | \n",
" 727 | \n",
" ... | \n",
" 590 | \n",
" 1472 | \n",
" 1053 | \n",
" 915 | \n",
" 1187 | \n",
" 1211 | \n",
" 964 | \n",
" 1751 | \n",
" 1174 | \n",
" 675 | \n",
"
\n",
" \n",
" 12 | \n",
" 2020-03-20 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 10645 | \n",
" NaN | \n",
" 10573 | \n",
" NaN | \n",
" 9841 | \n",
" NaN | \n",
" 677 | \n",
" ... | \n",
" 522 | \n",
" 1443 | \n",
" 1012 | \n",
" 947 | \n",
" 1115 | \n",
" 1043 | \n",
" 1008 | \n",
" 1657 | \n",
" 1156 | \n",
" 719 | \n",
"
\n",
" \n",
" 13 | \n",
" 2020-03-27 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 11141 | \n",
" NaN | \n",
" 10130 | \n",
" NaN | \n",
" 9414 | \n",
" NaN | \n",
" 665 | \n",
" ... | \n",
" 542 | \n",
" 1538 | \n",
" 982 | \n",
" 922 | \n",
" 1035 | \n",
" 1182 | \n",
" 1297 | \n",
" 1822 | \n",
" 1092 | \n",
" 719 | \n",
"
\n",
" \n",
" 14 | \n",
" 2020-04-03 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 16387 | \n",
" NaN | \n",
" 10305 | \n",
" NaN | \n",
" 9601 | \n",
" NaN | \n",
" 667 | \n",
" ... | \n",
" 770 | \n",
" 2137 | \n",
" 1436 | \n",
" 1246 | \n",
" 1812 | \n",
" 1717 | \n",
" 2511 | \n",
" 2294 | \n",
" 1520 | \n",
" 920 | \n",
"
\n",
" \n",
" 15 | \n",
" 2020-04-10 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 18516 | \n",
" NaN | \n",
" 10520 | \n",
" NaN | \n",
" 9807 | \n",
" NaN | \n",
" 671 | \n",
" ... | \n",
" 849 | \n",
" 2597 | \n",
" 1503 | \n",
" 1452 | \n",
" 2182 | \n",
" 1984 | \n",
" 2832 | \n",
" 2604 | \n",
" 1560 | \n",
" 928 | \n",
"
\n",
" \n",
" 16 | \n",
" 2020-04-17 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 22351 | \n",
" NaN | \n",
" 10497 | \n",
" NaN | \n",
" 9787 | \n",
" NaN | \n",
" 661 | \n",
" ... | \n",
" 1155 | \n",
" 3195 | \n",
" 1960 | \n",
" 1632 | \n",
" 2536 | \n",
" 2466 | \n",
" 3275 | \n",
" 3084 | \n",
" 1854 | \n",
" 1169 | \n",
"
\n",
" \n",
" 17 | \n",
" 2020-04-24 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 21997 | \n",
" NaN | \n",
" 10458 | \n",
" NaN | \n",
" 9768 | \n",
" NaN | \n",
" 662 | \n",
" ... | \n",
" 1103 | \n",
" 3109 | \n",
" 2095 | \n",
" 1711 | \n",
" 2481 | \n",
" 2299 | \n",
" 2785 | \n",
" 3334 | \n",
" 1924 | \n",
" 1124 | \n",
"
\n",
" \n",
" 18 | \n",
" 2020-05-01 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 17953 | \n",
" NaN | \n",
" 9941 | \n",
" NaN | \n",
" 9289 | \n",
" NaN | \n",
" 624 | \n",
" ... | \n",
" 922 | \n",
" 2503 | \n",
" 1844 | \n",
" 1418 | \n",
" 1975 | \n",
" 1982 | \n",
" 1953 | \n",
" 2853 | \n",
" 1554 | \n",
" 929 | \n",
"
\n",
" \n",
" 19 | \n",
" 2020-05-08 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 12657 | \n",
" NaN | \n",
" 9576 | \n",
" NaN | \n",
" 8937 | \n",
" NaN | \n",
" 612 | \n",
" ... | \n",
" 769 | \n",
" 1790 | \n",
" 1328 | \n",
" 1094 | \n",
" 1326 | \n",
" 1321 | \n",
" 1213 | \n",
" 1887 | \n",
" 1218 | \n",
" 692 | \n",
"
\n",
" \n",
" 20 | \n",
" 2020-05-15 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 14573 | \n",
" NaN | \n",
" 10188 | \n",
" NaN | \n",
" 9526 | \n",
" NaN | \n",
" 635 | \n",
" ... | \n",
" 845 | \n",
" 1992 | \n",
" 1589 | \n",
" 1283 | \n",
" 1502 | \n",
" 1543 | \n",
" 1329 | \n",
" 2251 | \n",
" 1449 | \n",
" 772 | \n",
"
\n",
" \n",
" 21 | \n",
" 2020-05-22 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 12288 | \n",
" NaN | \n",
" 9940 | \n",
" NaN | \n",
" 9299 | \n",
" NaN | \n",
" 614 | \n",
" ... | \n",
" 718 | \n",
" 1636 | \n",
" 1236 | \n",
" 1041 | \n",
" 1319 | \n",
" 1397 | \n",
" 1125 | \n",
" 1937 | \n",
" 1177 | \n",
" 692 | \n",
"
\n",
" \n",
" 22 | \n",
" 2020-05-29 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 9824 | \n",
" NaN | \n",
" 8171 | \n",
" NaN | \n",
" 7607 | \n",
" NaN | \n",
" 546 | \n",
" ... | \n",
" 550 | \n",
" 1337 | \n",
" 1046 | \n",
" 863 | \n",
" 970 | \n",
" 1095 | \n",
" 841 | \n",
" 1515 | \n",
" 1011 | \n",
" 587 | \n",
"
\n",
" \n",
" 23 | \n",
" 2020-06-05 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 10709 | \n",
" NaN | \n",
" 9977 | \n",
" NaN | \n",
" 9346 | \n",
" NaN | \n",
" 610 | \n",
" ... | \n",
" 576 | \n",
" 1478 | \n",
" 1090 | \n",
" 931 | \n",
" 1172 | \n",
" 1131 | \n",
" 891 | \n",
" 1610 | \n",
" 1116 | \n",
" 700 | \n",
"
\n",
" \n",
" 24 | \n",
" 2020-06-12 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 9976 | \n",
" NaN | \n",
" 9417 | \n",
" NaN | \n",
" 8803 | \n",
" NaN | \n",
" 588 | \n",
" ... | \n",
" 478 | \n",
" 1374 | \n",
" 980 | \n",
" 967 | \n",
" 1096 | \n",
" 1048 | \n",
" 883 | \n",
" 1530 | \n",
" 1035 | \n",
" 574 | \n",
"
\n",
" \n",
" 25 | \n",
" 2020-06-19 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 9339 | \n",
" NaN | \n",
" 9404 | \n",
" NaN | \n",
" 8810 | \n",
" NaN | \n",
" 573 | \n",
" ... | \n",
" 498 | \n",
" 1234 | \n",
" 952 | \n",
" 835 | \n",
" 973 | \n",
" 927 | \n",
" 896 | \n",
" 1411 | \n",
" 990 | \n",
" 617 | \n",
"
\n",
" \n",
" 26 | \n",
" 2020-06-26 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 8979 | \n",
" NaN | \n",
" 9293 | \n",
" NaN | \n",
" 8695 | \n",
" NaN | \n",
" 571 | \n",
" ... | \n",
" 485 | \n",
" 1300 | \n",
" 922 | \n",
" 800 | \n",
" 946 | \n",
" 880 | \n",
" 791 | \n",
" 1311 | \n",
" 979 | \n",
" 552 | \n",
"
\n",
" \n",
" 27 | \n",
" 2020-07-03 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 9140 | \n",
" NaN | \n",
" 9183 | \n",
" NaN | \n",
" 8606 | \n",
" NaN | \n",
" 555 | \n",
" ... | \n",
" 515 | \n",
" 1225 | \n",
" 875 | \n",
" 827 | \n",
" 949 | \n",
" 922 | \n",
" 837 | \n",
" 1454 | \n",
" 938 | \n",
" 584 | \n",
"
\n",
" \n",
" 28 | \n",
" 2020-07-10 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 8690 | \n",
" NaN | \n",
" 9250 | \n",
" NaN | \n",
" 8648 | \n",
" NaN | \n",
" 578 | \n",
" ... | \n",
" 468 | \n",
" 1154 | \n",
" 848 | \n",
" 771 | \n",
" 902 | \n",
" 999 | \n",
" 803 | \n",
" 1228 | \n",
" 930 | \n",
" 572 | \n",
"
\n",
" \n",
" 29 | \n",
" 2020-07-17 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 8823 | \n",
" NaN | \n",
" 9093 | \n",
" NaN | \n",
" 8502 | \n",
" NaN | \n",
" 557 | \n",
" ... | \n",
" 445 | \n",
" 1159 | \n",
" 843 | \n",
" 816 | \n",
" 956 | \n",
" 945 | \n",
" 806 | \n",
" 1339 | \n",
" 953 | \n",
" 550 | \n",
"
\n",
" \n",
" 30 | \n",
" 2020-07-24 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 8891 | \n",
" NaN | \n",
" 9052 | \n",
" NaN | \n",
" 8452 | \n",
" NaN | \n",
" 566 | \n",
" ... | \n",
" 493 | \n",
" 1197 | \n",
" 817 | \n",
" 798 | \n",
" 964 | \n",
" 951 | \n",
" 816 | \n",
" 1340 | \n",
" 941 | \n",
" 565 | \n",
"
\n",
" \n",
" 31 | \n",
" 2020-07-31 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 8946 | \n",
" NaN | \n",
" 9036 | \n",
" NaN | \n",
" 8436 | \n",
" NaN | \n",
" 572 | \n",
" ... | \n",
" 507 | \n",
" 1272 | \n",
" 837 | \n",
" 729 | \n",
" 902 | \n",
" 949 | \n",
" 773 | \n",
" 1447 | \n",
" 988 | \n",
" 531 | \n",
"
\n",
" \n",
" 32 | \n",
" 2020-08-07 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 8945 | \n",
" NaN | \n",
" 9102 | \n",
" NaN | \n",
" 8502 | \n",
" NaN | \n",
" 571 | \n",
" ... | \n",
" 486 | \n",
" 1211 | \n",
" 851 | \n",
" 798 | \n",
" 933 | \n",
" 955 | \n",
" 832 | \n",
" 1370 | \n",
" 929 | \n",
" 563 | \n",
"
\n",
" \n",
" 33 | \n",
" 2020-08-14 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 9392 | \n",
" NaN | \n",
" 9085 | \n",
" NaN | \n",
" 8494 | \n",
" NaN | \n",
" 564 | \n",
" ... | \n",
" 520 | \n",
" 1304 | \n",
" 853 | \n",
" 829 | \n",
" 923 | \n",
" 1006 | \n",
" 928 | \n",
" 1395 | \n",
" 1009 | \n",
" 617 | \n",
"
\n",
" \n",
" 34 | \n",
" 2020-08-21 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 9631 | \n",
" NaN | \n",
" 9157 | \n",
" NaN | \n",
" 8560 | \n",
" NaN | \n",
" 573 | \n",
" ... | \n",
" 479 | \n",
" 1269 | \n",
" 870 | \n",
" 780 | \n",
" 1028 | \n",
" 1024 | \n",
" 920 | \n",
" 1608 | \n",
" 1043 | \n",
" 594 | \n",
"
\n",
" \n",
" 35 | \n",
" 2020-08-28 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 9032 | \n",
" NaN | \n",
" 8241 | \n",
" NaN | \n",
" 7674 | \n",
" NaN | \n",
" 539 | \n",
" ... | \n",
" 455 | \n",
" 1148 | \n",
" 922 | \n",
" 724 | \n",
" 945 | \n",
" 951 | \n",
" 810 | \n",
" 1511 | \n",
" 959 | \n",
" 591 | \n",
"
\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",
" NaN | \n",
" 552 | \n",
" ... | \n",
" 431 | \n",
" 1057 | \n",
" 780 | \n",
" 640 | \n",
" 770 | \n",
" 806 | \n",
" 737 | \n",
" 1208 | \n",
" 803 | \n",
" 488 | \n",
"
\n",
" \n",
" 37 | \n",
" 2020-09-11 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 9811 | \n",
" NaN | \n",
" 9306 | \n",
" NaN | \n",
" 8708 | \n",
" NaN | \n",
" 577 | \n",
" ... | \n",
" 502 | \n",
" 1229 | \n",
" 952 | \n",
" 867 | \n",
" 1021 | \n",
" 1052 | \n",
" 898 | \n",
" 1610 | \n",
" 1084 | \n",
" 578 | \n",
"
\n",
" \n",
" 38 | \n",
" 2020-09-18 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 9523 | \n",
" NaN | \n",
" 9264 | \n",
" NaN | \n",
" 8663 | \n",
" NaN | \n",
" 575 | \n",
" ... | \n",
" 465 | \n",
" 1287 | \n",
" 939 | \n",
" 795 | \n",
" 1051 | \n",
" 1023 | \n",
" 844 | \n",
" 1530 | \n",
" 1021 | \n",
" 555 | \n",
"
\n",
" \n",
" 39 | \n",
" 2020-09-25 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 9634 | \n",
" NaN | \n",
" 9377 | \n",
" NaN | \n",
" 8744 | \n",
" NaN | \n",
" 604 | \n",
" ... | \n",
" 514 | \n",
" 1271 | \n",
" 965 | \n",
" 825 | \n",
" 1036 | \n",
" 963 | \n",
" 869 | \n",
" 1521 | \n",
" 1041 | \n",
" 617 | \n",
"
\n",
" \n",
" 40 | \n",
" 2020-10-02 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 9945 | \n",
" NaN | \n",
" 9555 | \n",
" NaN | \n",
" 8942 | \n",
" NaN | \n",
" 587 | \n",
" ... | \n",
" 558 | \n",
" 1301 | \n",
" 978 | \n",
" 842 | \n",
" 1045 | \n",
" 1054 | \n",
" 899 | \n",
" 1577 | \n",
" 1003 | \n",
" 671 | \n",
"
\n",
" \n",
" 41 | \n",
" 2020-10-09 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 9954 | \n",
" NaN | \n",
" 9811 | \n",
" NaN | \n",
" 9168 | \n",
" NaN | \n",
" 615 | \n",
" ... | \n",
" 544 | \n",
" 1367 | \n",
" 1067 | \n",
" 884 | \n",
" 1053 | \n",
" 1019 | \n",
" 902 | \n",
" 1462 | \n",
" 1010 | \n",
" 638 | \n",
"
\n",
" \n",
" 42 | \n",
" 2020-10-16 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 10534 | \n",
" NaN | \n",
" 9865 | \n",
" NaN | \n",
" 9215 | \n",
" NaN | \n",
" 630 | \n",
" ... | \n",
" 606 | \n",
" 1553 | \n",
" 1001 | \n",
" 904 | \n",
" 1154 | \n",
" 1056 | \n",
" 923 | \n",
" 1462 | \n",
" 1174 | \n",
" 688 | \n",
"
\n",
" \n",
" 43 | \n",
" 2020-10-23 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 10739 | \n",
" NaN | \n",
" 9759 | \n",
" NaN | \n",
" 9104 | \n",
" NaN | \n",
" 628 | \n",
" ... | \n",
" 600 | \n",
" 1714 | \n",
" 1111 | \n",
" 891 | \n",
" 1124 | \n",
" 1154 | \n",
" 922 | \n",
" 1510 | \n",
" 1044 | \n",
" 661 | \n",
"
\n",
" \n",
" 44 | \n",
" 2020-10-30 00:00:00 | \n",
" NaN | \n",
" NaN | \n",
" 10887 | \n",
" NaN | \n",
" 9891 | \n",
" NaN | \n",
" 9248 | \n",
" NaN | \n",
" 616 | \n",
" ... | \n",
" 591 | \n",
" 1754 | \n",
" 1168 | \n",
" 882 | \n",
" 1102 | \n",
" 1089 | \n",
" 888 | \n",
" 1563 | \n",
" 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": 12,
"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": 13,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"# eng_xls_columns"
]
},
{
"cell_type": "code",
"execution_count": 14,
"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": 15,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/plain": [
"Week number Total deaths, all ages\n",
"1 12254\n",
"2 14058\n",
"3 12990\n",
"4 11856\n",
"5 11612\n",
"6 10986\n",
"7 10944\n",
"8 10841\n",
"9 10816\n",
"10 10895\n",
"11 11019\n",
"12 10645\n",
"13 11141\n",
"14 16387\n",
"15 18516\n",
"16 22351\n",
"17 21997\n",
"18 17953\n",
"19 12657\n",
"20 14573\n",
"21 12288\n",
"22 9824\n",
"23 10709\n",
"24 9976\n",
"25 9339\n",
"26 8979\n",
"27 9140\n",
"28 8690\n",
"29 8823\n",
"30 8891\n",
"31 8946\n",
"32 8945\n",
"33 9392\n",
"34 9631\n",
"35 9032\n",
"36 7739\n",
"37 9811\n",
"38 9523\n",
"39 9634\n",
"40 9945\n",
"41 9954\n",
"42 10534\n",
"43 10739\n",
"44 10887\n",
"45 11812\n",
"46 12254\n",
"47 12535\n",
"48 12456\n",
"49 12303\n",
"50 12292\n",
"51 13011\n",
"52 11520\n",
"53 10069\n",
"Name: Total deaths, all ages, dtype: object"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eng_xls['Total deaths, all ages']"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/plain": [
"1 787\n",
"2 939\n",
"3 767\n",
"4 723\n",
"5 727\n",
"6 690\n",
"7 728\n",
"8 679\n",
"9 651\n",
"10 652\n",
"11 675\n",
"12 719\n",
"13 719\n",
"14 920\n",
"15 928\n",
"16 1169\n",
"17 1124\n",
"18 929\n",
"19 692\n",
"20 772\n",
"21 692\n",
"22 587\n",
"23 700\n",
"24 574\n",
"25 617\n",
"26 552\n",
"27 584\n",
"28 572\n",
"29 550\n",
"30 565\n",
"31 531\n",
"32 563\n",
"33 617\n",
"34 594\n",
"35 591\n",
"36 488\n",
"37 578\n",
"38 555\n",
"39 617\n",
"40 671\n",
"41 638\n",
"42 688\n",
"43 661\n",
"44 712\n",
"45 832\n",
"46 742\n",
"47 848\n",
"48 797\n",
"49 836\n",
"50 814\n",
"51 882\n",
"52 825\n",
"53 727\n",
"Name: Wales, dtype: object"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eng_xls['Wales'].iloc[1:]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"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": 18,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"# raw_data_2020.head()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"# raw_data_2020['W92000004', 'Wales']"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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": 21,
"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": 22,
"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": 23,
"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": 24,
"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": 25,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"dhw = eng_xls['Wales'].iloc[1:]\n",
"dhe = eng_xls['Total deaths, all ages'].iloc[1:] - dhw\n",
"deaths_headlines_e = pd.DataFrame({'total_2020': dhe.dropna()})\n",
"deaths_headlines_w = pd.DataFrame({'total_2020': dhw.dropna()})"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"# deaths_headlines_e = raw_data_2020.iloc[:, [1]].copy()\n",
"# deaths_headlines_e.columns = ['total_2020']\n",
"# deaths_headlines_w = raw_data_2020['W92000004'].copy()\n",
"# deaths_headlines_e.columns = ['total_2020']\n",
"# deaths_headlines_w.columns = ['total_2020']\n",
"# deaths_headlines_e.total_2020 -= deaths_headlines_w.total_2020\n",
"# deaths_headlines_e.head()\n",
"# deaths_headlines_e"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/neil/anaconda3/lib/python3.8/site-packages/pandas/core/generic.py:5489: 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",
" self[name] = value\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" total_2019 | \n",
"
\n",
" \n",
" \n",
" \n",
" 48 | \n",
" 10269 | \n",
"
\n",
" \n",
" 49 | \n",
" 10079 | \n",
"
\n",
" \n",
" 50 | \n",
" 10489 | \n",
"
\n",
" \n",
" 51 | \n",
" 11159 | \n",
"
\n",
" \n",
" 52 | \n",
" 7037 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" total_2019\n",
"48 10269\n",
"49 10079\n",
"50 10489\n",
"51 11159\n",
"52 7037"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dh19e = raw_data_2019.iloc[:, [1]]\n",
"dh19w = raw_data_2019['W92000004']\n",
"dh19e.columns = ['total_2019']\n",
"dh19w.columns = ['total_2019']\n",
"dh19e.total_2019 -= dh19w.total_2019\n",
"dh19e.tail()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" total_2019 | \n",
"
\n",
" \n",
" \n",
" \n",
" 1 | \n",
" 718 | \n",
"
\n",
" \n",
" 2 | \n",
" 809 | \n",
"
\n",
" \n",
" 3 | \n",
" 683 | \n",
"
\n",
" \n",
" 4 | \n",
" 734 | \n",
"
\n",
" \n",
" 5 | \n",
" 745 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" total_2019\n",
"1 718\n",
"2 809\n",
"3 683\n",
"4 734\n",
"5 745"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dh19w.head()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/neil/anaconda3/lib/python3.8/site-packages/pandas/core/generic.py:5489: 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",
" self[name] = value\n"
]
}
],
"source": [
"dh18e = raw_data_2018.iloc[:, [1]]\n",
"dh18w = raw_data_2018['W92000004']\n",
"dh18e.columns = ['total_2018']\n",
"dh18w.columns = ['total_2018']\n",
"dh18e.total_2018 -= dh18w.total_2018\n",
"# dh18e.head()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"dh17e = raw_data_2017.iloc[:, [1]]\n",
"dh17w = raw_data_2017['W92000004']\n",
"dh17e.columns = ['total_2017']\n",
"dh17w.columns = ['total_2017']\n",
"dh17e.total_2017 -= dh17w.total_2017\n",
"# dh17e.head()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"dh16e = raw_data_2016.iloc[:, [1]]\n",
"dh16w = raw_data_2016['W92000004']\n",
"dh16e.columns = ['total_2016']\n",
"dh16w.columns = ['total_2016']\n",
"dh16e.total_2016 -= dh16w.total_2016\n",
"# dh16e.head()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"dh15e = raw_data_2015.iloc[:, [1]]\n",
"dh15w = raw_data_2015['W92000004']\n",
"dh15e.columns = ['total_2015']\n",
"dh15w.columns = ['total_2015']\n",
"dh15e.total_2015 -= dh15w.total_2015\n",
"# dh15e.head()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"# dh18 = raw_data_2018.iloc[:, [1, 2]]\n",
"# dh18.columns = ['total_2018', 'total_previous']\n",
"# # dh18.head()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" total_2020 | \n",
" total_2019 | \n",
" total_2018 | \n",
" total_2017 | \n",
" total_2016 | \n",
" total_2015 | \n",
"
\n",
" \n",
" \n",
" \n",
" 1 | \n",
" 11467 | \n",
" 10237.0 | \n",
" 11940.0 | \n",
" 11247.0 | \n",
" 12236.0 | \n",
" 11561 | \n",
"
\n",
" \n",
" 2 | \n",
" 13119 | \n",
" 11800.0 | \n",
" 14146.0 | \n",
" 12890.0 | \n",
" 10790.0 | \n",
" 15206 | \n",
"
\n",
" \n",
" 3 | \n",
" 12223 | \n",
" 11177.0 | \n",
" 13371.0 | \n",
" 12775.0 | \n",
" 10753.0 | \n",
" 13930 | \n",
"
\n",
" \n",
" 4 | \n",
" 11133 | \n",
" 11006.0 | \n",
" 13085.0 | \n",
" 11996.0 | \n",
" 10600.0 | \n",
" 13106 | \n",
"
\n",
" \n",
" 5 | \n",
" 10885 | \n",
" 10552.0 | \n",
" 12470.0 | \n",
" 11736.0 | \n",
" 10362.0 | \n",
" 12099 | \n",
"
\n",
" \n",
" 6 | \n",
" 10296 | \n",
" 10959.0 | \n",
" 11694.0 | \n",
" 11546.0 | \n",
" 10470.0 | \n",
" 11319 | \n",
"
\n",
" \n",
" 7 | \n",
" 10216 | \n",
" 11076.0 | \n",
" 11443.0 | \n",
" 10954.0 | \n",
" 9933.0 | \n",
" 11112 | \n",
"
\n",
" \n",
" 8 | \n",
" 10162 | \n",
" 10600.0 | \n",
" 11353.0 | \n",
" 11093.0 | \n",
" 10360.0 | \n",
" 10695 | \n",
"
\n",
" \n",
" 9 | \n",
" 10165 | \n",
" 10360.0 | \n",
" 10220.0 | \n",
" 10533.0 | \n",
" 10564.0 | \n",
" 10736 | \n",
"
\n",
" \n",
" 10 | \n",
" 10243 | \n",
" 10276.0 | \n",
" 12101.0 | \n",
" 10443.0 | \n",
" 10276.0 | \n",
" 10757 | \n",
"
\n",
" \n",
" 11 | \n",
" 10344 | \n",
" 9901.0 | \n",
" 11870.0 | \n",
" 10044.0 | \n",
" 10284.0 | \n",
" 10290 | \n",
"
\n",
" \n",
" 12 | \n",
" 9926 | \n",
" 9774.0 | \n",
" 11139.0 | \n",
" 9690.0 | \n",
" 8967.0 | \n",
" 9888 | \n",
"
\n",
" \n",
" 13 | \n",
" 10422 | \n",
" 9213.0 | \n",
" 9308.0 | \n",
" 9369.0 | \n",
" 9574.0 | \n",
" 9827 | \n",
"
\n",
" \n",
" 14 | \n",
" 15467 | \n",
" 9484.0 | \n",
" 10064.0 | \n",
" 9297.0 | \n",
" 10857.0 | \n",
" 8482 | \n",
"
\n",
" \n",
" 15 | \n",
" 17588 | \n",
" 9654.0 | \n",
" 11558.0 | \n",
" 7916.0 | \n",
" 10679.0 | \n",
" 9429 | \n",
"
\n",
" \n",
" 16 | \n",
" 21182 | \n",
" 8445.0 | \n",
" 10535.0 | \n",
" 8990.0 | \n",
" 10211.0 | \n",
" 10968 | \n",
"
\n",
" \n",
" 17 | \n",
" 20873 | \n",
" 9381.0 | \n",
" 9692.0 | \n",
" 10181.0 | \n",
" 9784.0 | \n",
" 9937 | \n",
"
\n",
" \n",
" 18 | \n",
" 17024 | \n",
" 10519.0 | \n",
" 9520.0 | \n",
" 8464.0 | \n",
" 8568.0 | \n",
" 9506 | \n",
"
\n",
" \n",
" 19 | \n",
" 11965 | \n",
" 8455.0 | \n",
" 8094.0 | \n",
" 10006.0 | \n",
" 9985.0 | \n",
" 8273 | \n",
"
\n",
" \n",
" 20 | \n",
" 13801 | \n",
" 9614.0 | \n",
" 9474.0 | \n",
" 9673.0 | \n",
" 9342.0 | \n",
" 9668 | \n",
"
\n",
" \n",
" 21 | \n",
" 11596 | \n",
" 9657.0 | \n",
" 9033.0 | \n",
" 9438.0 | \n",
" 9115.0 | \n",
" 9391 | \n",
"
\n",
" \n",
" 22 | \n",
" 9237 | \n",
" 7742.0 | \n",
" 7609.0 | \n",
" 7746.0 | \n",
" 7409.0 | \n",
" 7625 | \n",
"
\n",
" \n",
" 23 | \n",
" 10009 | \n",
" 9514.0 | \n",
" 9343.0 | \n",
" 9182.0 | \n",
" 9289.0 | \n",
" 9509 | \n",
"
\n",
" \n",
" 24 | \n",
" 9402 | \n",
" 8847.0 | \n",
" 8782.0 | \n",
" 8768.0 | \n",
" 8808.0 | \n",
" 8942 | \n",
"
\n",
" \n",
" 25 | \n",
" 8722 | \n",
" 8916.0 | \n",
" 8694.0 | \n",
" 9019.0 | \n",
" 8807.0 | \n",
" 8717 | \n",
"
\n",
" \n",
" 26 | \n",
" 8427 | \n",
" 8947.0 | \n",
" 8613.0 | \n",
" 8788.0 | \n",
" 8679.0 | \n",
" 8593 | \n",
"
\n",
" \n",
" 27 | \n",
" 8556 | \n",
" 8528.0 | \n",
" 8633.0 | \n",
" 8707.0 | \n",
" 8614.0 | \n",
" 8670 | \n",
"
\n",
" \n",
" 28 | \n",
" 8118 | \n",
" 8591.0 | \n",
" 8710.0 | \n",
" 8812.0 | \n",
" 8789.0 | \n",
" 8461 | \n",
"
\n",
" \n",
" 29 | \n",
" 8273 | \n",
" 8527.0 | \n",
" 8579.0 | \n",
" 8562.0 | \n",
" 8790.0 | \n",
" 8228 | \n",
"
\n",
" \n",
" 30 | \n",
" 8326 | \n",
" 8569.0 | \n",
" 8581.0 | \n",
" 8296.0 | \n",
" 8725.0 | \n",
" 8262 | \n",
"
\n",
" \n",
" 31 | \n",
" 8415 | \n",
" 8701.0 | \n",
" 8587.0 | \n",
" 8351.0 | \n",
" 8602.0 | \n",
" 8071 | \n",
"
\n",
" \n",
" 32 | \n",
" 8382 | \n",
" 8580.0 | \n",
" 8782.0 | \n",
" 8466.0 | \n",
" 8531.0 | \n",
" 8298 | \n",
"
\n",
" \n",
" 33 | \n",
" 8775 | \n",
" 8504.0 | \n",
" 8312.0 | \n",
" 8707.0 | \n",
" 8496.0 | \n",
" 8600 | \n",
"
\n",
" \n",
" 34 | \n",
" 9037 | \n",
" 8441.0 | \n",
" 8413.0 | \n",
" 8812.0 | \n",
" 8700.0 | \n",
" 8564 | \n",
"
\n",
" \n",
" 35 | \n",
" 8441 | \n",
" 7684.0 | \n",
" 7354.0 | \n",
" 7594.0 | \n",
" 7453.0 | \n",
" 8423 | \n",
"
\n",
" \n",
" 36 | \n",
" 7251 | \n",
" 9113.0 | \n",
" 8851.0 | \n",
" 8955.0 | \n",
" 8847.0 | \n",
" 7389 | \n",
"
\n",
" \n",
" 37 | \n",
" 9233 | \n",
" 8946.0 | \n",
" 8612.0 | \n",
" 8845.0 | \n",
" 8548.0 | \n",
" 8704 | \n",
"
\n",
" \n",
" 38 | \n",
" 8968 | \n",
" 8868.0 | \n",
" 8707.0 | \n",
" 8949.0 | \n",
" 8382.0 | \n",
" 8542 | \n",
"
\n",
" \n",
" 39 | \n",
" 9017 | \n",
" 8906.0 | \n",
" 8590.0 | \n",
" 9096.0 | \n",
" 8402.0 | \n",
" 8865 | \n",
"
\n",
" \n",
" 40 | \n",
" 9274 | \n",
" 9202.0 | \n",
" 8908.0 | \n",
" 9147.0 | \n",
" 8729.0 | \n",
" 8858 | \n",
"
\n",
" \n",
" 41 | \n",
" 9316 | \n",
" 9383.0 | \n",
" 9043.0 | \n",
" 9300.0 | \n",
" 9132.0 | \n",
" 9124 | \n",
"
\n",
" \n",
" 42 | \n",
" 9846 | \n",
" 9534.0 | \n",
" 9224.0 | \n",
" 9381.0 | \n",
" 9144.0 | \n",
" 8899 | \n",
"
\n",
" \n",
" 43 | \n",
" 10078 | \n",
" 9351.0 | \n",
" 8970.0 | \n",
" 9131.0 | \n",
" 9100.0 | \n",
" 9104 | \n",
"
\n",
" \n",
" 44 | \n",
" 10175 | \n",
" 9522.0 | \n",
" 8925.0 | \n",
" 9389.0 | \n",
" 9508.0 | \n",
" 9025 | \n",
"
\n",
" \n",
" 45 | \n",
" 10980 | \n",
" 10044.0 | \n",
" 9509.0 | \n",
" 9748.0 | \n",
" 9844.0 | \n",
" 9388 | \n",
"
\n",
" \n",
" 46 | \n",
" 11512 | \n",
" 9976.0 | \n",
" 9537.0 | \n",
" 9609.0 | \n",
" 10013.0 | \n",
" 9325 | \n",
"
\n",
" \n",
" 47 | \n",
" 11687 | \n",
" 10183.0 | \n",
" 9300.0 | \n",
" 9982.0 | \n",
" 9942.0 | \n",
" 9219 | \n",
"
\n",
" \n",
" 48 | \n",
" 11659 | \n",
" 10269.0 | \n",
" 9360.0 | \n",
" 9902.0 | \n",
" 9796.0 | \n",
" 9233 | \n",
"
\n",
" \n",
" 49 | \n",
" 11467 | \n",
" 10079.0 | \n",
" 9613.0 | \n",
" 10151.0 | \n",
" 10530.0 | \n",
" 9706 | \n",
"
\n",
" \n",
" 50 | \n",
" 11478 | \n",
" 10489.0 | \n",
" 9842.0 | \n",
" 10509.0 | \n",
" 9870.0 | \n",
" 9581 | \n",
"
\n",
" \n",
" 51 | \n",
" 12129 | \n",
" 11159.0 | \n",
" 10392.0 | \n",
" 11755.0 | \n",
" 10802.0 | \n",
" 10043 | \n",
"
\n",
" \n",
" 52 | \n",
" 10695 | \n",
" 7037.0 | \n",
" 6670.0 | \n",
" 7946.0 | \n",
" 7445.0 | \n",
" 8095 | \n",
"
\n",
" \n",
" 53 | \n",
" 9342 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 7008 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" total_2020 total_2019 total_2018 total_2017 total_2016 total_2015\n",
"1 11467 10237.0 11940.0 11247.0 12236.0 11561\n",
"2 13119 11800.0 14146.0 12890.0 10790.0 15206\n",
"3 12223 11177.0 13371.0 12775.0 10753.0 13930\n",
"4 11133 11006.0 13085.0 11996.0 10600.0 13106\n",
"5 10885 10552.0 12470.0 11736.0 10362.0 12099\n",
"6 10296 10959.0 11694.0 11546.0 10470.0 11319\n",
"7 10216 11076.0 11443.0 10954.0 9933.0 11112\n",
"8 10162 10600.0 11353.0 11093.0 10360.0 10695\n",
"9 10165 10360.0 10220.0 10533.0 10564.0 10736\n",
"10 10243 10276.0 12101.0 10443.0 10276.0 10757\n",
"11 10344 9901.0 11870.0 10044.0 10284.0 10290\n",
"12 9926 9774.0 11139.0 9690.0 8967.0 9888\n",
"13 10422 9213.0 9308.0 9369.0 9574.0 9827\n",
"14 15467 9484.0 10064.0 9297.0 10857.0 8482\n",
"15 17588 9654.0 11558.0 7916.0 10679.0 9429\n",
"16 21182 8445.0 10535.0 8990.0 10211.0 10968\n",
"17 20873 9381.0 9692.0 10181.0 9784.0 9937\n",
"18 17024 10519.0 9520.0 8464.0 8568.0 9506\n",
"19 11965 8455.0 8094.0 10006.0 9985.0 8273\n",
"20 13801 9614.0 9474.0 9673.0 9342.0 9668\n",
"21 11596 9657.0 9033.0 9438.0 9115.0 9391\n",
"22 9237 7742.0 7609.0 7746.0 7409.0 7625\n",
"23 10009 9514.0 9343.0 9182.0 9289.0 9509\n",
"24 9402 8847.0 8782.0 8768.0 8808.0 8942\n",
"25 8722 8916.0 8694.0 9019.0 8807.0 8717\n",
"26 8427 8947.0 8613.0 8788.0 8679.0 8593\n",
"27 8556 8528.0 8633.0 8707.0 8614.0 8670\n",
"28 8118 8591.0 8710.0 8812.0 8789.0 8461\n",
"29 8273 8527.0 8579.0 8562.0 8790.0 8228\n",
"30 8326 8569.0 8581.0 8296.0 8725.0 8262\n",
"31 8415 8701.0 8587.0 8351.0 8602.0 8071\n",
"32 8382 8580.0 8782.0 8466.0 8531.0 8298\n",
"33 8775 8504.0 8312.0 8707.0 8496.0 8600\n",
"34 9037 8441.0 8413.0 8812.0 8700.0 8564\n",
"35 8441 7684.0 7354.0 7594.0 7453.0 8423\n",
"36 7251 9113.0 8851.0 8955.0 8847.0 7389\n",
"37 9233 8946.0 8612.0 8845.0 8548.0 8704\n",
"38 8968 8868.0 8707.0 8949.0 8382.0 8542\n",
"39 9017 8906.0 8590.0 9096.0 8402.0 8865\n",
"40 9274 9202.0 8908.0 9147.0 8729.0 8858\n",
"41 9316 9383.0 9043.0 9300.0 9132.0 9124\n",
"42 9846 9534.0 9224.0 9381.0 9144.0 8899\n",
"43 10078 9351.0 8970.0 9131.0 9100.0 9104\n",
"44 10175 9522.0 8925.0 9389.0 9508.0 9025\n",
"45 10980 10044.0 9509.0 9748.0 9844.0 9388\n",
"46 11512 9976.0 9537.0 9609.0 10013.0 9325\n",
"47 11687 10183.0 9300.0 9982.0 9942.0 9219\n",
"48 11659 10269.0 9360.0 9902.0 9796.0 9233\n",
"49 11467 10079.0 9613.0 10151.0 10530.0 9706\n",
"50 11478 10489.0 9842.0 10509.0 9870.0 9581\n",
"51 12129 11159.0 10392.0 11755.0 10802.0 10043\n",
"52 10695 7037.0 6670.0 7946.0 7445.0 8095\n",
"53 9342 NaN NaN NaN NaN 7008"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"deaths_headlines_e = deaths_headlines_e.merge(dh19e['total_2019'], how='outer', left_index=True, right_index=True)\n",
"deaths_headlines_e = deaths_headlines_e.merge(dh18e['total_2018'], how='outer', left_index=True, right_index=True)\n",
"deaths_headlines_e = deaths_headlines_e.merge(dh17e['total_2017'], how='outer', left_index=True, right_index=True)\n",
"deaths_headlines_e = deaths_headlines_e.merge(dh16e['total_2016'], how='outer', left_index=True, right_index=True)\n",
"# deaths_headlines = deaths_headlines.merge(dh15['total_2015'], how='outer', left_index=True, right_index=True)\n",
"deaths_headlines_e = deaths_headlines_e.merge(dh15e['total_2015'], how='left', left_index=True, right_index=True)\n",
"deaths_headlines_e"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"Collapsed": "false",
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" total_2020 | \n",
" total_2019 | \n",
" total_2018 | \n",
" total_2017 | \n",
" total_2016 | \n",
" total_2015 | \n",
"
\n",
" \n",
" \n",
" \n",
" 1 | \n",
" 1161 | \n",
" 1104.0 | \n",
" 1531.0 | \n",
" 1205.0 | \n",
" 1394.0 | \n",
" 1146 | \n",
"
\n",
" \n",
" 2 | \n",
" 1567 | \n",
" 1507.0 | \n",
" 1899.0 | \n",
" 1379.0 | \n",
" 1305.0 | \n",
" 1708 | \n",
"
\n",
" \n",
" 3 | \n",
" 1322 | \n",
" 1353.0 | \n",
" 1629.0 | \n",
" 1224.0 | \n",
" 1215.0 | \n",
" 1489 | \n",
"
\n",
" \n",
" 4 | \n",
" 1226 | \n",
" 1208.0 | \n",
" 1610.0 | \n",
" 1197.0 | \n",
" 1187.0 | \n",
" 1381 | \n",
"
\n",
" \n",
" 5 | \n",
" 1188 | \n",
" 1206.0 | \n",
" 1369.0 | \n",
" 1332.0 | \n",
" 1205.0 | \n",
" 1286 | \n",
"
\n",
" \n",
" 6 | \n",
" 1216 | \n",
" 1243.0 | \n",
" 1265.0 | \n",
" 1200.0 | \n",
" 1217.0 | \n",
" 1344 | \n",
"
\n",
" \n",
" 7 | \n",
" 1162 | \n",
" 1181.0 | \n",
" 1315.0 | \n",
" 1231.0 | \n",
" 1209.0 | \n",
" 1360 | \n",
"
\n",
" \n",
" 8 | \n",
" 1162 | \n",
" 1245.0 | \n",
" 1245.0 | \n",
" 1185.0 | \n",
" 1239.0 | \n",
" 1320 | \n",
"
\n",
" \n",
" 9 | \n",
" 1171 | \n",
" 1125.0 | \n",
" 1022.0 | \n",
" 1219.0 | \n",
" 1150.0 | \n",
" 1308 | \n",
"
\n",
" \n",
" 10 | \n",
" 1208 | \n",
" 1156.0 | \n",
" 1475.0 | \n",
" 1146.0 | \n",
" 1174.0 | \n",
" 1192 | \n",
"
\n",
" \n",
" 11 | \n",
" 1156 | \n",
" 1108.0 | \n",
" 1220.0 | \n",
" 1141.0 | \n",
" 1175.0 | \n",
" 1201 | \n",
"
\n",
" \n",
" 12 | \n",
" 1196 | \n",
" 1101.0 | \n",
" 1158.0 | \n",
" 1152.0 | \n",
" 1042.0 | \n",
" 1149 | \n",
"
\n",
" \n",
" 13 | \n",
" 1079 | \n",
" 1086.0 | \n",
" 1050.0 | \n",
" 1112.0 | \n",
" 1172.0 | \n",
" 1171 | \n",
"
\n",
" \n",
" 14 | \n",
" 1744 | \n",
" 1032.0 | \n",
" 1192.0 | \n",
" 1060.0 | \n",
" 1166.0 | \n",
" 1042 | \n",
"
\n",
" \n",
" 15 | \n",
" 1978 | \n",
" 1069.0 | \n",
" 1192.0 | \n",
" 998.0 | \n",
" 1048.0 | \n",
" 1192 | \n",
"
\n",
" \n",
" 16 | \n",
" 1916 | \n",
" 902.0 | \n",
" 1136.0 | \n",
" 1111.0 | \n",
" 1092.0 | \n",
" 1095 | \n",
"
\n",
" \n",
" 17 | \n",
" 1836 | \n",
" 1121.0 | \n",
" 1008.0 | \n",
" 1121.0 | \n",
" 1076.0 | \n",
" 1108 | \n",
"
\n",
" \n",
" 18 | \n",
" 1679 | \n",
" 1131.0 | \n",
" 1093.0 | \n",
" 1050.0 | \n",
" 1006.0 | \n",
" 1117 | \n",
"
\n",
" \n",
" 19 | \n",
" 1435 | \n",
" 1018.0 | \n",
" 967.0 | \n",
" 1119.0 | \n",
" 1047.0 | \n",
" 1020 | \n",
"
\n",
" \n",
" 20 | \n",
" 1421 | \n",
" 1115.0 | \n",
" 977.0 | \n",
" 1115.0 | \n",
" 1010.0 | \n",
" 1103 | \n",
"
\n",
" \n",
" 21 | \n",
" 1226 | \n",
" 1061.0 | \n",
" 1070.0 | \n",
" 1063.0 | \n",
" 994.0 | \n",
" 1039 | \n",
"
\n",
" \n",
" 22 | \n",
" 1125 | \n",
" 1029.0 | \n",
" 998.0 | \n",
" 1015.0 | \n",
" 999.0 | \n",
" 1043 | \n",
"
\n",
" \n",
" 23 | \n",
" 1093 | \n",
" 1042.0 | \n",
" 1033.0 | \n",
" 1076.0 | \n",
" 1023.0 | \n",
" 1106 | \n",
"
\n",
" \n",
" 24 | \n",
" 1034 | \n",
" 1028.0 | \n",
" 915.0 | \n",
" 1031.0 | \n",
" 988.0 | \n",
" 1038 | \n",
"
\n",
" \n",
" 25 | \n",
" 1065 | \n",
" 1053.0 | \n",
" 993.0 | \n",
" 1032.0 | \n",
" 994.0 | \n",
" 1025 | \n",
"
\n",
" \n",
" 26 | \n",
" 1008 | \n",
" 1051.0 | \n",
" 1046.0 | \n",
" 994.0 | \n",
" 1007.0 | \n",
" 1032 | \n",
"
\n",
" \n",
" 27 | \n",
" 983 | \n",
" 981.0 | \n",
" 1041.0 | \n",
" 1040.0 | \n",
" 988.0 | \n",
" 1040 | \n",
"
\n",
" \n",
" 28 | \n",
" 976 | \n",
" 1077.0 | \n",
" 1002.0 | \n",
" 1014.0 | \n",
" 1022.0 | \n",
" 1011 | \n",
"
\n",
" \n",
" 29 | \n",
" 1033 | \n",
" 964.0 | \n",
" 928.0 | \n",
" 1025.0 | \n",
" 1041.0 | \n",
" 1023 | \n",
"
\n",
" \n",
" 30 | \n",
" 961 | \n",
" 1041.0 | \n",
" 933.0 | \n",
" 978.0 | \n",
" 979.0 | \n",
" 956 | \n",
"
\n",
" \n",
" 31 | \n",
" 1043 | \n",
" 1020.0 | \n",
" 969.0 | \n",
" 1011.0 | \n",
" 987.0 | \n",
" 985 | \n",
"
\n",
" \n",
" 32 | \n",
" 1011 | \n",
" 1018.0 | \n",
" 953.0 | \n",
" 1002.0 | \n",
" 997.0 | \n",
" 1043 | \n",
"
\n",
" \n",
" 33 | \n",
" 922 | \n",
" 1028.0 | \n",
" 978.0 | \n",
" 1004.0 | \n",
" 982.0 | \n",
" 969 | \n",
"
\n",
" \n",
" 34 | \n",
" 1046 | \n",
" 1011.0 | \n",
" 941.0 | \n",
" 1045.0 | \n",
" 1017.0 | \n",
" 982 | \n",
"
\n",
" \n",
" 35 | \n",
" 1029 | \n",
" 1013.0 | \n",
" 930.0 | \n",
" 980.0 | \n",
" 1039.0 | \n",
" 954 | \n",
"
\n",
" \n",
" 36 | \n",
" 1050 | \n",
" 980.0 | \n",
" 970.0 | \n",
" 1006.0 | \n",
" 1007.0 | \n",
" 977 | \n",
"
\n",
" \n",
" 37 | \n",
" 1069 | \n",
" 1074.0 | \n",
" 1020.0 | \n",
" 972.0 | \n",
" 983.0 | \n",
" 991 | \n",
"
\n",
" \n",
" 38 | \n",
" 952 | \n",
" 1071.0 | \n",
" 946.0 | \n",
" 1049.0 | \n",
" 966.0 | \n",
" 1001 | \n",
"
\n",
" \n",
" 39 | \n",
" 933 | \n",
" 1142.0 | \n",
" 1015.0 | \n",
" 1056.0 | \n",
" 1009.0 | \n",
" 1010 | \n",
"
\n",
" \n",
" 40 | \n",
" 1195 | \n",
" 1051.0 | \n",
" 1042.0 | \n",
" 1016.0 | \n",
" 1072.0 | \n",
" 1008 | \n",
"
\n",
" \n",
" 41 | \n",
" 1071 | \n",
" 1143.0 | \n",
" 1081.0 | \n",
" 1133.0 | \n",
" 1009.0 | \n",
" 1028 | \n",
"
\n",
" \n",
" 42 | \n",
" 1131 | \n",
" 1153.0 | \n",
" 1031.0 | \n",
" 1067.0 | \n",
" 1070.0 | \n",
" 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",
"
\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"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"deaths_headlines_s = raw_data_s[reversed('2015 2016 2017 2018 2019 2020'.split())]\n",
"deaths_headlines_s.columns = ['total_' + c for c in deaths_headlines_s.columns]\n",
"deaths_headlines_s.reset_index(drop=True, inplace=True)\n",
"deaths_headlines_s.index = deaths_headlines_s.index + 1\n",
"deaths_headlines_s = deaths_headlines_s.loc[1:52]\n",
"deaths_headlines_s"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"# Correction for missing data"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"# deaths_headlines_s.loc[20, 'total_2020'] = 1000\n",
"# deaths_headlines_s"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" total_2020 | \n",
" total_2019 | \n",
" total_2018 | \n",
" total_2017 | \n",
" total_2016 | \n",
" total_2015 | \n",
"
\n",
" \n",
" \n",
" \n",
" 1 | \n",
" 787 | \n",
" 718.0 | \n",
" 783.0 | \n",
" 744.0 | \n",
" 809.0 | \n",
" 725 | \n",
"
\n",
" \n",
" 2 | \n",
" 939 | \n",
" 809.0 | \n",
" 904.0 | \n",
" 825.0 | \n",
" 711.0 | \n",
" 1031 | \n",
"
\n",
" \n",
" 3 | \n",
" 767 | \n",
" 683.0 | \n",
" 885.0 | \n",
" 835.0 | \n",
" 720.0 | \n",
" 936 | \n",
"
\n",
" \n",
" 4 | \n",
" 723 | \n",
" 734.0 | \n",
" 850.0 | \n",
" 881.0 | \n",
" 717.0 | \n",
" 828 | \n",
"
\n",
" \n",
" 5 | \n",
" 727 | \n",
" 745.0 | \n",
" 815.0 | \n",
" 749.0 | \n",
" 690.0 | \n",
" 801 | \n",
"
\n",
" \n",
" 6 | \n",
" 690 | \n",
" 701.0 | \n",
" 801.0 | \n",
" 723.0 | \n",
" 700.0 | \n",
" 720 | \n",
"
\n",
" \n",
" 7 | \n",
" 728 | \n",
" 748.0 | \n",
" 803.0 | \n",
" 690.0 | \n",
" 657.0 | \n",
" 710 | \n",
"
\n",
" \n",
" 8 | \n",
" 679 | \n",
" 695.0 | \n",
" 789.0 | \n",
" 701.0 | \n",
" 696.0 | \n",
" 739 | \n",
"
\n",
" \n",
" 9 | \n",
" 651 | \n",
" 684.0 | \n",
" 634.0 | \n",
" 715.0 | \n",
" 721.0 | \n",
" 736 | \n",
"
\n",
" \n",
" 10 | \n",
" 652 | \n",
" 622.0 | \n",
" 896.0 | \n",
" 634.0 | \n",
" 734.0 | \n",
" 712 | \n",
"
\n",
" \n",
" 11 | \n",
" 675 | \n",
" 666.0 | \n",
" 918.0 | \n",
" 653.0 | \n",
" 738.0 | \n",
" 661 | \n",
"
\n",
" \n",
" 12 | \n",
" 719 | \n",
" 628.0 | \n",
" 774.0 | \n",
" 635.0 | \n",
" 668.0 | \n",
" 680 | \n",
"
\n",
" \n",
" 13 | \n",
" 719 | \n",
" 654.0 | \n",
" 633.0 | \n",
" 658.0 | \n",
" 712.0 | \n",
" 666 | \n",
"
\n",
" \n",
" 14 | \n",
" 920 | \n",
" 642.0 | \n",
" 730.0 | \n",
" 642.0 | \n",
" 742.0 | \n",
" 580 | \n",
"
\n",
" \n",
" 15 | \n",
" 928 | \n",
" 637.0 | \n",
" 743.0 | \n",
" 577.0 | \n",
" 738.0 | \n",
" 660 | \n",
"
\n",
" \n",
" 16 | \n",
" 1169 | \n",
" 580.0 | \n",
" 688.0 | \n",
" 654.0 | \n",
" 714.0 | \n",
" 671 | \n",
"
\n",
" \n",
" 17 | \n",
" 1124 | \n",
" 678.0 | \n",
" 614.0 | \n",
" 727.0 | \n",
" 629.0 | \n",
" 662 | \n",
"
\n",
" \n",
" 18 | \n",
" 929 | \n",
" 688.0 | \n",
" 633.0 | \n",
" 600.0 | \n",
" 569.0 | \n",
" 628 | \n",
"
\n",
" \n",
" 19 | \n",
" 692 | \n",
" 600.0 | \n",
" 530.0 | \n",
" 687.0 | \n",
" 652.0 | \n",
" 589 | \n",
"
\n",
" \n",
" 20 | \n",
" 772 | \n",
" 658.0 | \n",
" 667.0 | \n",
" 615.0 | \n",
" 611.0 | \n",
" 622 | \n",
"
\n",
" \n",
" 21 | \n",
" 692 | \n",
" 627.0 | \n",
" 603.0 | \n",
" 602.0 | \n",
" 624.0 | \n",
" 614 | \n",
"
\n",
" \n",
" 22 | \n",
" 587 | \n",
" 518.0 | \n",
" 538.0 | \n",
" 586.0 | \n",
" 500.0 | \n",
" 588 | \n",
"
\n",
" \n",
" 23 | \n",
" 700 | \n",
" 626.0 | \n",
" 607.0 | \n",
" 584.0 | \n",
" 584.0 | \n",
" 648 | \n",
"
\n",
" \n",
" 24 | \n",
" 574 | \n",
" 598.0 | \n",
" 561.0 | \n",
" 599.0 | \n",
" 578.0 | \n",
" 606 | \n",
"
\n",
" \n",
" 25 | \n",
" 617 | \n",
" 542.0 | \n",
" 562.0 | \n",
" 608.0 | \n",
" 558.0 | \n",
" 595 | \n",
"
\n",
" \n",
" 26 | \n",
" 552 | \n",
" 564.0 | \n",
" 599.0 | \n",
" 546.0 | \n",
" 549.0 | \n",
" 597 | \n",
"
\n",
" \n",
" 27 | \n",
" 584 | \n",
" 534.0 | \n",
" 625.0 | \n",
" 556.0 | \n",
" 524.0 | \n",
" 535 | \n",
"
\n",
" \n",
" 28 | \n",
" 572 | \n",
" 588.0 | \n",
" 583.0 | \n",
" 564.0 | \n",
" 599.0 | \n",
" 554 | \n",
"
\n",
" \n",
" 29 | \n",
" 550 | \n",
" 553.0 | \n",
" 548.0 | \n",
" 551.0 | \n",
" 560.0 | \n",
" 574 | \n",
"
\n",
" \n",
" 30 | \n",
" 565 | \n",
" 543.0 | \n",
" 560.0 | \n",
" 586.0 | \n",
" 610.0 | \n",
" 529 | \n",
"
\n",
" \n",
" 31 | \n",
" 531 | \n",
" 570.0 | \n",
" 574.0 | \n",
" 590.0 | \n",
" 580.0 | \n",
" 546 | \n",
"
\n",
" \n",
" 32 | \n",
" 563 | \n",
" 542.0 | \n",
" 537.0 | \n",
" 572.0 | \n",
" 641.0 | \n",
" 564 | \n",
"
\n",
" \n",
" 33 | \n",
" 617 | \n",
" 589.0 | \n",
" 518.0 | \n",
" 592.0 | \n",
" 574.0 | \n",
" 548 | \n",
"
\n",
" \n",
" 34 | \n",
" 594 | \n",
" 553.0 | \n",
" 565.0 | \n",
" 570.0 | \n",
" 619.0 | \n",
" 557 | \n",
"
\n",
" \n",
" 35 | \n",
" 591 | \n",
" 558.0 | \n",
" 511.0 | \n",
" 555.0 | \n",
" 470.0 | \n",
" 603 | \n",
"
\n",
" \n",
" 36 | \n",
" 488 | \n",
" 582.0 | \n",
" 594.0 | \n",
" 542.0 | \n",
" 552.0 | \n",
" 489 | \n",
"
\n",
" \n",
" 37 | \n",
" 578 | \n",
" 567.0 | \n",
" 579.0 | \n",
" 609.0 | \n",
" 576.0 | \n",
" 554 | \n",
"
\n",
" \n",
" 38 | \n",
" 555 | \n",
" 572.0 | \n",
" 598.0 | \n",
" 585.0 | \n",
" 563.0 | \n",
" 555 | \n",
"
\n",
" \n",
" 39 | \n",
" 617 | \n",
" 611.0 | \n",
" 560.0 | \n",
" 593.0 | \n",
" 592.0 | \n",
" 664 | \n",
"
\n",
" \n",
" 40 | \n",
" 671 | \n",
" 597.0 | \n",
" 595.0 | \n",
" 631.0 | \n",
" 562.0 | \n",
" 552 | \n",
"
\n",
" \n",
" 41 | \n",
" 638 | \n",
" 590.0 | \n",
" 606.0 | \n",
" 640.0 | \n",
" 587.0 | \n",
" 652 | \n",
"
\n",
" \n",
" 42 | \n",
" 688 | \n",
" 622.0 | \n",
" 640.0 | \n",
" 650.0 | \n",
" 624.0 | \n",
" 612 | \n",
"
\n",
" \n",
" 43 | \n",
" 661 | \n",
" 670.0 | \n",
" 633.0 | \n",
" 608.0 | \n",
" 624.0 | \n",
" 607 | \n",
"
\n",
" \n",
" 44 | \n",
" 712 | \n",
" 642.0 | \n",
" 604.0 | \n",
" 595.0 | \n",
" 644.0 | \n",
" 593 | \n",
"
\n",
" \n",
" 45 | \n",
" 832 | \n",
" 653.0 | \n",
" 642.0 | \n",
" 598.0 | \n",
" 626.0 | \n",
" 606 | \n",
"
\n",
" \n",
" 46 | \n",
" 742 | \n",
" 674.0 | \n",
" 656.0 | \n",
" 666.0 | \n",
" 681.0 | \n",
" 613 | \n",
"
\n",
" \n",
" 47 | \n",
" 848 | \n",
" 699.0 | \n",
" 657.0 | \n",
" 639.0 | \n",
" 661.0 | \n",
" 611 | \n",
"
\n",
" \n",
" 48 | \n",
" 797 | \n",
" 689.0 | \n",
" 673.0 | \n",
" 636.0 | \n",
" 643.0 | \n",
" 589 | \n",
"
\n",
" \n",
" 49 | \n",
" 836 | \n",
" 737.0 | \n",
" 674.0 | \n",
" 630.0 | \n",
" 693.0 | \n",
" 659 | \n",
"
\n",
" \n",
" 50 | \n",
" 814 | \n",
" 699.0 | \n",
" 708.0 | \n",
" 708.0 | \n",
" 663.0 | \n",
" 688 | \n",
"
\n",
" \n",
" 51 | \n",
" 882 | \n",
" 767.0 | \n",
" 724.0 | \n",
" 762.0 | \n",
" 691.0 | \n",
" 646 | \n",
"
\n",
" \n",
" 52 | \n",
" 825 | \n",
" 496.0 | \n",
" 461.0 | \n",
" 541.0 | \n",
" 558.0 | \n",
" 535 | \n",
"
\n",
" \n",
" 53 | \n",
" 727 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 516 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" total_2020 total_2019 total_2018 total_2017 total_2016 total_2015\n",
"1 787 718.0 783.0 744.0 809.0 725\n",
"2 939 809.0 904.0 825.0 711.0 1031\n",
"3 767 683.0 885.0 835.0 720.0 936\n",
"4 723 734.0 850.0 881.0 717.0 828\n",
"5 727 745.0 815.0 749.0 690.0 801\n",
"6 690 701.0 801.0 723.0 700.0 720\n",
"7 728 748.0 803.0 690.0 657.0 710\n",
"8 679 695.0 789.0 701.0 696.0 739\n",
"9 651 684.0 634.0 715.0 721.0 736\n",
"10 652 622.0 896.0 634.0 734.0 712\n",
"11 675 666.0 918.0 653.0 738.0 661\n",
"12 719 628.0 774.0 635.0 668.0 680\n",
"13 719 654.0 633.0 658.0 712.0 666\n",
"14 920 642.0 730.0 642.0 742.0 580\n",
"15 928 637.0 743.0 577.0 738.0 660\n",
"16 1169 580.0 688.0 654.0 714.0 671\n",
"17 1124 678.0 614.0 727.0 629.0 662\n",
"18 929 688.0 633.0 600.0 569.0 628\n",
"19 692 600.0 530.0 687.0 652.0 589\n",
"20 772 658.0 667.0 615.0 611.0 622\n",
"21 692 627.0 603.0 602.0 624.0 614\n",
"22 587 518.0 538.0 586.0 500.0 588\n",
"23 700 626.0 607.0 584.0 584.0 648\n",
"24 574 598.0 561.0 599.0 578.0 606\n",
"25 617 542.0 562.0 608.0 558.0 595\n",
"26 552 564.0 599.0 546.0 549.0 597\n",
"27 584 534.0 625.0 556.0 524.0 535\n",
"28 572 588.0 583.0 564.0 599.0 554\n",
"29 550 553.0 548.0 551.0 560.0 574\n",
"30 565 543.0 560.0 586.0 610.0 529\n",
"31 531 570.0 574.0 590.0 580.0 546\n",
"32 563 542.0 537.0 572.0 641.0 564\n",
"33 617 589.0 518.0 592.0 574.0 548\n",
"34 594 553.0 565.0 570.0 619.0 557\n",
"35 591 558.0 511.0 555.0 470.0 603\n",
"36 488 582.0 594.0 542.0 552.0 489\n",
"37 578 567.0 579.0 609.0 576.0 554\n",
"38 555 572.0 598.0 585.0 563.0 555\n",
"39 617 611.0 560.0 593.0 592.0 664\n",
"40 671 597.0 595.0 631.0 562.0 552\n",
"41 638 590.0 606.0 640.0 587.0 652\n",
"42 688 622.0 640.0 650.0 624.0 612\n",
"43 661 670.0 633.0 608.0 624.0 607\n",
"44 712 642.0 604.0 595.0 644.0 593\n",
"45 832 653.0 642.0 598.0 626.0 606\n",
"46 742 674.0 656.0 666.0 681.0 613\n",
"47 848 699.0 657.0 639.0 661.0 611\n",
"48 797 689.0 673.0 636.0 643.0 589\n",
"49 836 737.0 674.0 630.0 693.0 659\n",
"50 814 699.0 708.0 708.0 663.0 688\n",
"51 882 767.0 724.0 762.0 691.0 646\n",
"52 825 496.0 461.0 541.0 558.0 535\n",
"53 727 NaN NaN NaN NaN 516"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"deaths_headlines_w = deaths_headlines_w.merge(dh19w['total_2019'], how='outer', left_index=True, right_index=True)\n",
"deaths_headlines_w = deaths_headlines_w.merge(dh18w['total_2018'], how='outer', left_index=True, right_index=True)\n",
"deaths_headlines_w = deaths_headlines_w.merge(dh17w['total_2017'], how='outer', left_index=True, right_index=True)\n",
"deaths_headlines_w = deaths_headlines_w.merge(dh16w['total_2016'], how='outer', left_index=True, right_index=True)\n",
"# deaths_headlines = deaths_headlines.merge(dh15['total_2015'], how='outer', left_index=True, right_index=True)\n",
"deaths_headlines_w = deaths_headlines_w.merge(dh15w['total_2015'], how='left', left_index=True, right_index=True)\n",
"deaths_headlines_w"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" total_2020 | \n",
" total_2019 | \n",
" total_2018 | \n",
" total_2017 | \n",
" total_2016 | \n",
" total_2015 | \n",
"
\n",
" \n",
" \n",
" \n",
" 1 | \n",
" 353 | \n",
" 365.0 | \n",
" 447.0 | \n",
" 416.0 | \n",
" 424.0 | \n",
" 319 | \n",
"
\n",
" \n",
" 2 | \n",
" 395 | \n",
" 371.0 | \n",
" 481.0 | \n",
" 434.0 | \n",
" 348.0 | \n",
" 373 | \n",
"
\n",
" \n",
" 3 | \n",
" 411 | \n",
" 332.0 | \n",
" 470.0 | \n",
" 397.0 | \n",
" 372.0 | \n",
" 383 | \n",
"
\n",
" \n",
" 4 | \n",
" 347 | \n",
" 335.0 | \n",
" 426.0 | \n",
" 387.0 | \n",
" 355.0 | \n",
" 397 | \n",
"
\n",
" \n",
" 5 | \n",
" 323 | \n",
" 296.0 | \n",
" 433.0 | \n",
" 371.0 | \n",
" 314.0 | \n",
" 374 | \n",
"
\n",
" \n",
" 6 | \n",
" 332 | \n",
" 319.0 | \n",
" 351.0 | \n",
" 336.0 | \n",
" 310.0 | \n",
" 347 | \n",
"
\n",
" \n",
" 7 | \n",
" 306 | \n",
" 342.0 | \n",
" 364.0 | \n",
" 337.0 | \n",
" 217.0 | \n",
" 328 | \n",
"
\n",
" \n",
" 8 | \n",
" 297 | \n",
" 337.0 | \n",
" 366.0 | \n",
" 351.0 | \n",
" 423.0 | \n",
" 317 | \n",
"
\n",
" \n",
" 9 | \n",
" 347 | \n",
" 310.0 | \n",
" 314.0 | \n",
" 352.0 | \n",
" 298.0 | \n",
" 401 | \n",
"
\n",
" \n",
" 10 | \n",
" 312 | \n",
" 342.0 | \n",
" 387.0 | \n",
" 357.0 | \n",
" 309.0 | \n",
" 346 | \n",
"
\n",
" \n",
" 11 | \n",
" 324 | \n",
" 343.0 | \n",
" 359.0 | \n",
" 251.0 | \n",
" 292.0 | \n",
" 323 | \n",
"
\n",
" \n",
" 12 | \n",
" 271 | \n",
" 294.0 | \n",
" 326.0 | \n",
" 356.0 | \n",
" 306.0 | \n",
" 310 | \n",
"
\n",
" \n",
" 13 | \n",
" 287 | \n",
" 307.0 | \n",
" 319.0 | \n",
" 314.0 | \n",
" 280.0 | \n",
" 323 | \n",
"
\n",
" \n",
" 14 | \n",
" 434 | \n",
" 287.0 | \n",
" 286.0 | \n",
" 306.0 | \n",
" 295.0 | \n",
" 221 | \n",
"
\n",
" \n",
" 15 | \n",
" 435 | \n",
" 301.0 | \n",
" 350.0 | \n",
" 270.0 | \n",
" 292.0 | \n",
" 294 | \n",
"
\n",
" \n",
" 16 | \n",
" 424 | \n",
" 316.0 | \n",
" 280.0 | \n",
" 245.0 | \n",
" 293.0 | \n",
" 327 | \n",
"
\n",
" \n",
" 17 | \n",
" 470 | \n",
" 272.0 | \n",
" 282.0 | \n",
" 327.0 | \n",
" 306.0 | \n",
" 316 | \n",
"
\n",
" \n",
" 18 | \n",
" 427 | \n",
" 357.0 | \n",
" 292.0 | \n",
" 258.0 | \n",
" 258.0 | \n",
" 335 | \n",
"
\n",
" \n",
" 19 | \n",
" 336 | \n",
" 288.0 | \n",
" 230.0 | \n",
" 302.0 | \n",
" 318.0 | \n",
" 256 | \n",
"
\n",
" \n",
" 20 | \n",
" 396 | \n",
" 330.0 | \n",
" 268.0 | \n",
" 315.0 | \n",
" 259.0 | \n",
" 299 | \n",
"
\n",
" \n",
" 21 | \n",
" 325 | \n",
" 308.0 | \n",
" 268.0 | \n",
" 328.0 | \n",
" 280.0 | \n",
" 290 | \n",
"
\n",
" \n",
" 22 | \n",
" 316 | \n",
" 245.0 | \n",
" 252.0 | \n",
" 256.0 | \n",
" 284.0 | \n",
" 258 | \n",
"
\n",
" \n",
" 23 | \n",
" 304 | \n",
" 279.0 | \n",
" 276.0 | \n",
" 292.0 | \n",
" 275.0 | \n",
" 340 | \n",
"
\n",
" \n",
" 24 | \n",
" 292 | \n",
" 281.0 | \n",
" 277.0 | \n",
" 300.0 | \n",
" 299.0 | \n",
" 272 | \n",
"
\n",
" \n",
" 25 | \n",
" 290 | \n",
" 296.0 | \n",
" 265.0 | \n",
" 271.0 | \n",
" 252.0 | \n",
" 292 | \n",
"
\n",
" \n",
" 26 | \n",
" 295 | \n",
" 262.0 | \n",
" 271.0 | \n",
" 296.0 | \n",
" 291.0 | \n",
" 303 | \n",
"
\n",
" \n",
" 27 | \n",
" 289 | \n",
" 285.0 | \n",
" 266.0 | \n",
" 262.0 | \n",
" 286.0 | \n",
" 300 | \n",
"
\n",
" \n",
" 28 | \n",
" 275 | \n",
" 256.0 | \n",
" 172.0 | \n",
" 253.0 | \n",
" 237.0 | \n",
" 252 | \n",
"
\n",
" \n",
" 29 | \n",
" 240 | \n",
" 280.0 | \n",
" 298.0 | \n",
" 288.0 | \n",
" 281.0 | \n",
" 203 | \n",
"
\n",
" \n",
" 30 | \n",
" 307 | \n",
" 269.0 | \n",
" 282.0 | \n",
" 287.0 | \n",
" 298.0 | \n",
" 274 | \n",
"
\n",
" \n",
" 31 | \n",
" 273 | \n",
" 273.0 | \n",
" 278.0 | \n",
" 287.0 | \n",
" 264.0 | \n",
" 291 | \n",
"
\n",
" \n",
" 32 | \n",
" 280 | \n",
" 266.0 | \n",
" 270.0 | \n",
" 238.0 | \n",
" 270.0 | \n",
" 248 | \n",
"
\n",
" \n",
" 33 | \n",
" 278 | \n",
" 284.0 | \n",
" 283.0 | \n",
" 266.0 | \n",
" 260.0 | \n",
" 235 | \n",
"
\n",
" \n",
" 34 | \n",
" 313 | \n",
" 274.0 | \n",
" 280.0 | \n",
" 271.0 | \n",
" 301.0 | \n",
" 251 | \n",
"
\n",
" \n",
" 35 | \n",
" 303 | \n",
" 223.0 | \n",
" 251.0 | \n",
" 243.0 | \n",
" 264.0 | \n",
" 259 | \n",
"
\n",
" \n",
" 36 | \n",
" 234 | \n",
" 243.0 | \n",
" 265.0 | \n",
" 278.0 | \n",
" 275.0 | \n",
" 237 | \n",
"
\n",
" \n",
" 37 | \n",
" 296 | \n",
" 305.0 | \n",
" 285.0 | \n",
" 266.0 | \n",
" 294.0 | \n",
" 324 | \n",
"
\n",
" \n",
" 38 | \n",
" 322 | \n",
" 281.0 | \n",
" 247.0 | \n",
" 292.0 | \n",
" 272.0 | \n",
" 283 | \n",
"
\n",
" \n",
" 39 | \n",
" 323 | \n",
" 295.0 | \n",
" 298.0 | \n",
" 282.0 | \n",
" 275.0 | \n",
" 287 | \n",
"
\n",
" \n",
" 40 | \n",
" 328 | \n",
" 263.0 | \n",
" 324.0 | \n",
" 307.0 | \n",
" 308.0 | \n",
" 282 | \n",
"
\n",
" \n",
" 41 | \n",
" 348 | \n",
" 287.0 | \n",
" 318.0 | \n",
" 284.0 | \n",
" 288.0 | \n",
" 304 | \n",
"
\n",
" \n",
" 42 | \n",
" 278 | \n",
" 316.0 | \n",
" 282.0 | \n",
" 291.0 | \n",
" 296.0 | \n",
" 299 | \n",
"
\n",
" \n",
" 43 | \n",
" 391 | \n",
" 279.0 | \n",
" 263.0 | \n",
" 318.0 | \n",
" 272.0 | \n",
" 274 | \n",
"
\n",
" \n",
" 44 | \n",
" 368 | \n",
" 302.0 | \n",
" 252.0 | \n",
" 320.0 | \n",
" 279.0 | \n",
" 292 | \n",
"
\n",
" \n",
" 45 | \n",
" 386 | \n",
" 296.0 | \n",
" 293.0 | \n",
" 295.0 | \n",
" 290.0 | \n",
" 290 | \n",
"
\n",
" \n",
" 46 | \n",
" 406 | \n",
" 336.0 | \n",
" 275.0 | \n",
" 323.0 | \n",
" 341.0 | \n",
" 297 | \n",
"
\n",
" \n",
" 47 | \n",
" 396 | \n",
" 361.0 | \n",
" 274.0 | \n",
" 303.0 | \n",
" 329.0 | \n",
" 294 | \n",
"
\n",
" \n",
" 48 | \n",
" 348 | \n",
" 334.0 | \n",
" 297.0 | \n",
" 355.0 | \n",
" 303.0 | \n",
" 279 | \n",
"
\n",
" \n",
" 49 | \n",
" 387 | \n",
" 351.0 | \n",
" 324.0 | \n",
" 324.0 | \n",
" 322.0 | \n",
" 294 | \n",
"
\n",
" \n",
" 50 | \n",
" 366 | \n",
" 353.0 | \n",
" 316.0 | \n",
" 372.0 | \n",
" 324.0 | \n",
" 343 | \n",
"
\n",
" \n",
" 51 | \n",
" 350 | \n",
" 363.0 | \n",
" 317.0 | \n",
" 354.0 | \n",
" 360.0 | \n",
" 301 | \n",
"
\n",
" \n",
" 52 | \n",
" 310 | \n",
" 194.0 | \n",
" 195.0 | \n",
" 249.0 | \n",
" 199.0 | \n",
" 232 | \n",
"
\n",
" \n",
" 53 | \n",
" 333 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 232 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" total_2020 total_2019 total_2018 total_2017 total_2016 total_2015\n",
"1 353 365.0 447.0 416.0 424.0 319\n",
"2 395 371.0 481.0 434.0 348.0 373\n",
"3 411 332.0 470.0 397.0 372.0 383\n",
"4 347 335.0 426.0 387.0 355.0 397\n",
"5 323 296.0 433.0 371.0 314.0 374\n",
"6 332 319.0 351.0 336.0 310.0 347\n",
"7 306 342.0 364.0 337.0 217.0 328\n",
"8 297 337.0 366.0 351.0 423.0 317\n",
"9 347 310.0 314.0 352.0 298.0 401\n",
"10 312 342.0 387.0 357.0 309.0 346\n",
"11 324 343.0 359.0 251.0 292.0 323\n",
"12 271 294.0 326.0 356.0 306.0 310\n",
"13 287 307.0 319.0 314.0 280.0 323\n",
"14 434 287.0 286.0 306.0 295.0 221\n",
"15 435 301.0 350.0 270.0 292.0 294\n",
"16 424 316.0 280.0 245.0 293.0 327\n",
"17 470 272.0 282.0 327.0 306.0 316\n",
"18 427 357.0 292.0 258.0 258.0 335\n",
"19 336 288.0 230.0 302.0 318.0 256\n",
"20 396 330.0 268.0 315.0 259.0 299\n",
"21 325 308.0 268.0 328.0 280.0 290\n",
"22 316 245.0 252.0 256.0 284.0 258\n",
"23 304 279.0 276.0 292.0 275.0 340\n",
"24 292 281.0 277.0 300.0 299.0 272\n",
"25 290 296.0 265.0 271.0 252.0 292\n",
"26 295 262.0 271.0 296.0 291.0 303\n",
"27 289 285.0 266.0 262.0 286.0 300\n",
"28 275 256.0 172.0 253.0 237.0 252\n",
"29 240 280.0 298.0 288.0 281.0 203\n",
"30 307 269.0 282.0 287.0 298.0 274\n",
"31 273 273.0 278.0 287.0 264.0 291\n",
"32 280 266.0 270.0 238.0 270.0 248\n",
"33 278 284.0 283.0 266.0 260.0 235\n",
"34 313 274.0 280.0 271.0 301.0 251\n",
"35 303 223.0 251.0 243.0 264.0 259\n",
"36 234 243.0 265.0 278.0 275.0 237\n",
"37 296 305.0 285.0 266.0 294.0 324\n",
"38 322 281.0 247.0 292.0 272.0 283\n",
"39 323 295.0 298.0 282.0 275.0 287\n",
"40 328 263.0 324.0 307.0 308.0 282\n",
"41 348 287.0 318.0 284.0 288.0 304\n",
"42 278 316.0 282.0 291.0 296.0 299\n",
"43 391 279.0 263.0 318.0 272.0 274\n",
"44 368 302.0 252.0 320.0 279.0 292\n",
"45 386 296.0 293.0 295.0 290.0 290\n",
"46 406 336.0 275.0 323.0 341.0 297\n",
"47 396 361.0 274.0 303.0 329.0 294\n",
"48 348 334.0 297.0 355.0 303.0 279\n",
"49 387 351.0 324.0 324.0 322.0 294\n",
"50 366 353.0 316.0 372.0 324.0 343\n",
"51 350 363.0 317.0 354.0 360.0 301\n",
"52 310 194.0 195.0 249.0 199.0 232\n",
"53 333 NaN NaN NaN NaN 232"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"deaths_headlines_i = deaths_headlines_i.merge(dh19i['total_2019'], how='outer', left_index=True, right_index=True)\n",
"deaths_headlines_i = deaths_headlines_i.merge(dh18i['total_2018'], how='outer', left_index=True, right_index=True)\n",
"deaths_headlines_i = deaths_headlines_i.merge(dh17i['total_2017'], how='outer', left_index=True, right_index=True)\n",
"deaths_headlines_i = deaths_headlines_i.merge(dh16i['total_2016'], how='outer', left_index=True, right_index=True)\n",
"deaths_headlines_i = deaths_headlines_i.merge(dh15i['total_2015'], how='left', left_index=True, right_index=True)\n",
"deaths_headlines_i"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" total_2020 | \n",
" total_2019 | \n",
" total_2018 | \n",
" total_2017 | \n",
" total_2016 | \n",
" total_2015 | \n",
"
\n",
" \n",
" \n",
" \n",
" 1 | \n",
" 1161 | \n",
" 1104.0 | \n",
" 1531.0 | \n",
" 1205.0 | \n",
" 1394.0 | \n",
" 1146 | \n",
"
\n",
" \n",
" 2 | \n",
" 1567 | \n",
" 1507.0 | \n",
" 1899.0 | \n",
" 1379.0 | \n",
" 1305.0 | \n",
" 1708 | \n",
"
\n",
" \n",
" 3 | \n",
" 1322 | \n",
" 1353.0 | \n",
" 1629.0 | \n",
" 1224.0 | \n",
" 1215.0 | \n",
" 1489 | \n",
"
\n",
" \n",
" 4 | \n",
" 1226 | \n",
" 1208.0 | \n",
" 1610.0 | \n",
" 1197.0 | \n",
" 1187.0 | \n",
" 1381 | \n",
"
\n",
" \n",
" 5 | \n",
" 1188 | \n",
" 1206.0 | \n",
" 1369.0 | \n",
" 1332.0 | \n",
" 1205.0 | \n",
" 1286 | \n",
"
\n",
" \n",
" 6 | \n",
" 1216 | \n",
" 1243.0 | \n",
" 1265.0 | \n",
" 1200.0 | \n",
" 1217.0 | \n",
" 1344 | \n",
"
\n",
" \n",
" 7 | \n",
" 1162 | \n",
" 1181.0 | \n",
" 1315.0 | \n",
" 1231.0 | \n",
" 1209.0 | \n",
" 1360 | \n",
"
\n",
" \n",
" 8 | \n",
" 1162 | \n",
" 1245.0 | \n",
" 1245.0 | \n",
" 1185.0 | \n",
" 1239.0 | \n",
" 1320 | \n",
"
\n",
" \n",
" 9 | \n",
" 1171 | \n",
" 1125.0 | \n",
" 1022.0 | \n",
" 1219.0 | \n",
" 1150.0 | \n",
" 1308 | \n",
"
\n",
" \n",
" 10 | \n",
" 1208 | \n",
" 1156.0 | \n",
" 1475.0 | \n",
" 1146.0 | \n",
" 1174.0 | \n",
" 1192 | \n",
"
\n",
" \n",
" 11 | \n",
" 1156 | \n",
" 1108.0 | \n",
" 1220.0 | \n",
" 1141.0 | \n",
" 1175.0 | \n",
" 1201 | \n",
"
\n",
" \n",
" 12 | \n",
" 1196 | \n",
" 1101.0 | \n",
" 1158.0 | \n",
" 1152.0 | \n",
" 1042.0 | \n",
" 1149 | \n",
"
\n",
" \n",
" 13 | \n",
" 1079 | \n",
" 1086.0 | \n",
" 1050.0 | \n",
" 1112.0 | \n",
" 1172.0 | \n",
" 1171 | \n",
"
\n",
" \n",
" 14 | \n",
" 1744 | \n",
" 1032.0 | \n",
" 1192.0 | \n",
" 1060.0 | \n",
" 1166.0 | \n",
" 1042 | \n",
"
\n",
" \n",
" 15 | \n",
" 1978 | \n",
" 1069.0 | \n",
" 1192.0 | \n",
" 998.0 | \n",
" 1048.0 | \n",
" 1192 | \n",
"
\n",
" \n",
" 16 | \n",
" 1916 | \n",
" 902.0 | \n",
" 1136.0 | \n",
" 1111.0 | \n",
" 1092.0 | \n",
" 1095 | \n",
"
\n",
" \n",
" 17 | \n",
" 1836 | \n",
" 1121.0 | \n",
" 1008.0 | \n",
" 1121.0 | \n",
" 1076.0 | \n",
" 1108 | \n",
"
\n",
" \n",
" 18 | \n",
" 1679 | \n",
" 1131.0 | \n",
" 1093.0 | \n",
" 1050.0 | \n",
" 1006.0 | \n",
" 1117 | \n",
"
\n",
" \n",
" 19 | \n",
" 1435 | \n",
" 1018.0 | \n",
" 967.0 | \n",
" 1119.0 | \n",
" 1047.0 | \n",
" 1020 | \n",
"
\n",
" \n",
" 20 | \n",
" 1421 | \n",
" 1115.0 | \n",
" 977.0 | \n",
" 1115.0 | \n",
" 1010.0 | \n",
" 1103 | \n",
"
\n",
" \n",
" 21 | \n",
" 1226 | \n",
" 1061.0 | \n",
" 1070.0 | \n",
" 1063.0 | \n",
" 994.0 | \n",
" 1039 | \n",
"
\n",
" \n",
" 22 | \n",
" 1125 | \n",
" 1029.0 | \n",
" 998.0 | \n",
" 1015.0 | \n",
" 999.0 | \n",
" 1043 | \n",
"
\n",
" \n",
" 23 | \n",
" 1093 | \n",
" 1042.0 | \n",
" 1033.0 | \n",
" 1076.0 | \n",
" 1023.0 | \n",
" 1106 | \n",
"
\n",
" \n",
" 24 | \n",
" 1034 | \n",
" 1028.0 | \n",
" 915.0 | \n",
" 1031.0 | \n",
" 988.0 | \n",
" 1038 | \n",
"
\n",
" \n",
" 25 | \n",
" 1065 | \n",
" 1053.0 | \n",
" 993.0 | \n",
" 1032.0 | \n",
" 994.0 | \n",
" 1025 | \n",
"
\n",
" \n",
" 26 | \n",
" 1008 | \n",
" 1051.0 | \n",
" 1046.0 | \n",
" 994.0 | \n",
" 1007.0 | \n",
" 1032 | \n",
"
\n",
" \n",
" 27 | \n",
" 983 | \n",
" 981.0 | \n",
" 1041.0 | \n",
" 1040.0 | \n",
" 988.0 | \n",
" 1040 | \n",
"
\n",
" \n",
" 28 | \n",
" 976 | \n",
" 1077.0 | \n",
" 1002.0 | \n",
" 1014.0 | \n",
" 1022.0 | \n",
" 1011 | \n",
"
\n",
" \n",
" 29 | \n",
" 1033 | \n",
" 964.0 | \n",
" 928.0 | \n",
" 1025.0 | \n",
" 1041.0 | \n",
" 1023 | \n",
"
\n",
" \n",
" 30 | \n",
" 961 | \n",
" 1041.0 | \n",
" 933.0 | \n",
" 978.0 | \n",
" 979.0 | \n",
" 956 | \n",
"
\n",
" \n",
" 31 | \n",
" 1043 | \n",
" 1020.0 | \n",
" 969.0 | \n",
" 1011.0 | \n",
" 987.0 | \n",
" 985 | \n",
"
\n",
" \n",
" 32 | \n",
" 1011 | \n",
" 1018.0 | \n",
" 953.0 | \n",
" 1002.0 | \n",
" 997.0 | \n",
" 1043 | \n",
"
\n",
" \n",
" 33 | \n",
" 922 | \n",
" 1028.0 | \n",
" 978.0 | \n",
" 1004.0 | \n",
" 982.0 | \n",
" 969 | \n",
"
\n",
" \n",
" 34 | \n",
" 1046 | \n",
" 1011.0 | \n",
" 941.0 | \n",
" 1045.0 | \n",
" 1017.0 | \n",
" 982 | \n",
"
\n",
" \n",
" 35 | \n",
" 1029 | \n",
" 1013.0 | \n",
" 930.0 | \n",
" 980.0 | \n",
" 1039.0 | \n",
" 954 | \n",
"
\n",
" \n",
" 36 | \n",
" 1050 | \n",
" 980.0 | \n",
" 970.0 | \n",
" 1006.0 | \n",
" 1007.0 | \n",
" 977 | \n",
"
\n",
" \n",
" 37 | \n",
" 1069 | \n",
" 1074.0 | \n",
" 1020.0 | \n",
" 972.0 | \n",
" 983.0 | \n",
" 991 | \n",
"
\n",
" \n",
" 38 | \n",
" 952 | \n",
" 1071.0 | \n",
" 946.0 | \n",
" 1049.0 | \n",
" 966.0 | \n",
" 1001 | \n",
"
\n",
" \n",
" 39 | \n",
" 933 | \n",
" 1142.0 | \n",
" 1015.0 | \n",
" 1056.0 | \n",
" 1009.0 | \n",
" 1010 | \n",
"
\n",
" \n",
" 40 | \n",
" 1195 | \n",
" 1051.0 | \n",
" 1042.0 | \n",
" 1016.0 | \n",
" 1072.0 | \n",
" 1008 | \n",
"
\n",
" \n",
" 41 | \n",
" 1071 | \n",
" 1143.0 | \n",
" 1081.0 | \n",
" 1133.0 | \n",
" 1009.0 | \n",
" 1028 | \n",
"
\n",
" \n",
" 42 | \n",
" 1131 | \n",
" 1153.0 | \n",
" 1031.0 | \n",
" 1067.0 | \n",
" 1070.0 | \n",
" 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",
"
\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"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"deaths_headlines_s"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" total_2020 | \n",
" total_2019 | \n",
" total_2018 | \n",
" total_2017 | \n",
" total_2016 | \n",
" total_2015 | \n",
"
\n",
" \n",
" \n",
" \n",
" 1 | \n",
" 13768.0 | \n",
" 12424.0 | \n",
" 14701.0 | \n",
" 13612.0 | \n",
" 14863.0 | \n",
" 13751.0 | \n",
"
\n",
" \n",
" 2 | \n",
" 16020.0 | \n",
" 14487.0 | \n",
" 17430.0 | \n",
" 15528.0 | \n",
" 13154.0 | \n",
" 18318.0 | \n",
"
\n",
" \n",
" 3 | \n",
" 14723.0 | \n",
" 13545.0 | \n",
" 16355.0 | \n",
" 15231.0 | \n",
" 13060.0 | \n",
" 16738.0 | \n",
"
\n",
" \n",
" 4 | \n",
" 13429.0 | \n",
" 13283.0 | \n",
" 15971.0 | \n",
" 14461.0 | \n",
" 12859.0 | \n",
" 15712.0 | \n",
"
\n",
" \n",
" 5 | \n",
" 13123.0 | \n",
" 12799.0 | \n",
" 15087.0 | \n",
" 14188.0 | \n",
" 12571.0 | \n",
" 14560.0 | \n",
"
\n",
" \n",
" 6 | \n",
" 12534.0 | \n",
" 13222.0 | \n",
" 14111.0 | \n",
" 13805.0 | \n",
" 12697.0 | \n",
" 13730.0 | \n",
"
\n",
" \n",
" 7 | \n",
" 12412.0 | \n",
" 13347.0 | \n",
" 13925.0 | \n",
" 13212.0 | \n",
" 12016.0 | \n",
" 13510.0 | \n",
"
\n",
" \n",
" 8 | \n",
" 12300.0 | \n",
" 12877.0 | \n",
" 13753.0 | \n",
" 13330.0 | \n",
" 12718.0 | \n",
" 13071.0 | \n",
"
\n",
" \n",
" 9 | \n",
" 12334.0 | \n",
" 12479.0 | \n",
" 12190.0 | \n",
" 12819.0 | \n",
" 12733.0 | \n",
" 13181.0 | \n",
"
\n",
" \n",
" 10 | \n",
" 12415.0 | \n",
" 12396.0 | \n",
" 14859.0 | \n",
" 12580.0 | \n",
" 12493.0 | \n",
" 13007.0 | \n",
"
\n",
" \n",
" 11 | \n",
" 12499.0 | \n",
" 12018.0 | \n",
" 14367.0 | \n",
" 12089.0 | \n",
" 12489.0 | \n",
" 12475.0 | \n",
"
\n",
" \n",
" 12 | \n",
" 12112.0 | \n",
" 11797.0 | \n",
" 13397.0 | \n",
" 11833.0 | \n",
" 10983.0 | \n",
" 12027.0 | \n",
"
\n",
" \n",
" 13 | \n",
" 12507.0 | \n",
" 11260.0 | \n",
" 11310.0 | \n",
" 11453.0 | \n",
" 11738.0 | \n",
" 11987.0 | \n",
"
\n",
" \n",
" 14 | \n",
" 18565.0 | \n",
" 11445.0 | \n",
" 12272.0 | \n",
" 11305.0 | \n",
" 13060.0 | \n",
" 10325.0 | \n",
"
\n",
" \n",
" 15 | \n",
" 20929.0 | \n",
" 11661.0 | \n",
" 13843.0 | \n",
" 9761.0 | \n",
" 12757.0 | \n",
" 11575.0 | \n",
"
\n",
" \n",
" 16 | \n",
" 24691.0 | \n",
" 10243.0 | \n",
" 12639.0 | \n",
" 11000.0 | \n",
" 12310.0 | \n",
" 13061.0 | \n",
"
\n",
" \n",
" 17 | \n",
" 24303.0 | \n",
" 11452.0 | \n",
" 11596.0 | \n",
" 12356.0 | \n",
" 11795.0 | \n",
" 12023.0 | \n",
"
\n",
" \n",
" 18 | \n",
" 20059.0 | \n",
" 12695.0 | \n",
" 11538.0 | \n",
" 10372.0 | \n",
" 10401.0 | \n",
" 11586.0 | \n",
"
\n",
" \n",
" 19 | \n",
" 14428.0 | \n",
" 10361.0 | \n",
" 9821.0 | \n",
" 12114.0 | \n",
" 12002.0 | \n",
" 10138.0 | \n",
"
\n",
" \n",
" 20 | \n",
" 16390.0 | \n",
" 11717.0 | \n",
" 11386.0 | \n",
" 11718.0 | \n",
" 11222.0 | \n",
" 11692.0 | \n",
"
\n",
" \n",
" 21 | \n",
" 13839.0 | \n",
" 11653.0 | \n",
" 10974.0 | \n",
" 11431.0 | \n",
" 11013.0 | \n",
" 11334.0 | \n",
"
\n",
" \n",
" 22 | \n",
" 11265.0 | \n",
" 9534.0 | \n",
" 9397.0 | \n",
" 9603.0 | \n",
" 9192.0 | \n",
" 9514.0 | \n",
"
\n",
" \n",
" 23 | \n",
" 12106.0 | \n",
" 11461.0 | \n",
" 11259.0 | \n",
" 11134.0 | \n",
" 11171.0 | \n",
" 11603.0 | \n",
"
\n",
" \n",
" 24 | \n",
" 11302.0 | \n",
" 10754.0 | \n",
" 10535.0 | \n",
" 10698.0 | \n",
" 10673.0 | \n",
" 10858.0 | \n",
"
\n",
" \n",
" 25 | \n",
" 10694.0 | \n",
" 10807.0 | \n",
" 10514.0 | \n",
" 10930.0 | \n",
" 10611.0 | \n",
" 10629.0 | \n",
"
\n",
" \n",
" 26 | \n",
" 10282.0 | \n",
" 10824.0 | \n",
" 10529.0 | \n",
" 10624.0 | \n",
" 10526.0 | \n",
" 10525.0 | \n",
"
\n",
" \n",
" 27 | \n",
" 10412.0 | \n",
" 10328.0 | \n",
" 10565.0 | \n",
" 10565.0 | \n",
" 10412.0 | \n",
" 10545.0 | \n",
"
\n",
" \n",
" 28 | \n",
" 9941.0 | \n",
" 10512.0 | \n",
" 10467.0 | \n",
" 10643.0 | \n",
" 10647.0 | \n",
" 10278.0 | \n",
"
\n",
" \n",
" 29 | \n",
" 10096.0 | \n",
" 10324.0 | \n",
" 10353.0 | \n",
" 10426.0 | \n",
" 10672.0 | \n",
" 10028.0 | \n",
"
\n",
" \n",
" 30 | \n",
" 10159.0 | \n",
" 10422.0 | \n",
" 10356.0 | \n",
" 10147.0 | \n",
" 10612.0 | \n",
" 10021.0 | \n",
"
\n",
" \n",
" 31 | \n",
" 10262.0 | \n",
" 10564.0 | \n",
" 10408.0 | \n",
" 10239.0 | \n",
" 10433.0 | \n",
" 9893.0 | \n",
"
\n",
" \n",
" 32 | \n",
" 10236.0 | \n",
" 10406.0 | \n",
" 10542.0 | \n",
" 10278.0 | \n",
" 10439.0 | \n",
" 10153.0 | \n",
"
\n",
" \n",
" 33 | \n",
" 10592.0 | \n",
" 10405.0 | \n",
" 10091.0 | \n",
" 10569.0 | \n",
" 10312.0 | \n",
" 10352.0 | \n",
"
\n",
" \n",
" 34 | \n",
" 10990.0 | \n",
" 10279.0 | \n",
" 10199.0 | \n",
" 10698.0 | \n",
" 10637.0 | \n",
" 10354.0 | \n",
"
\n",
" \n",
" 35 | \n",
" 10364.0 | \n",
" 9478.0 | \n",
" 9046.0 | \n",
" 9372.0 | \n",
" 9226.0 | \n",
" 10239.0 | \n",
"
\n",
" \n",
" 36 | \n",
" 9023.0 | \n",
" 10918.0 | \n",
" 10680.0 | \n",
" 10781.0 | \n",
" 10681.0 | \n",
" 9092.0 | \n",
"
\n",
" \n",
" 37 | \n",
" 11176.0 | \n",
" 10892.0 | \n",
" 10496.0 | \n",
" 10692.0 | \n",
" 10401.0 | \n",
" 10573.0 | \n",
"
\n",
" \n",
" 38 | \n",
" 10797.0 | \n",
" 10792.0 | \n",
" 10498.0 | \n",
" 10875.0 | \n",
" 10183.0 | \n",
" 10381.0 | \n",
"
\n",
" \n",
" 39 | \n",
" 10890.0 | \n",
" 10954.0 | \n",
" 10463.0 | \n",
" 11027.0 | \n",
" 10278.0 | \n",
" 10826.0 | \n",
"
\n",
" \n",
" 40 | \n",
" 11468.0 | \n",
" 11113.0 | \n",
" 10869.0 | \n",
" 11101.0 | \n",
" 10671.0 | \n",
" 10700.0 | \n",
"
\n",
" \n",
" 41 | \n",
" 11373.0 | \n",
" 11403.0 | \n",
" 11048.0 | \n",
" 11357.0 | \n",
" 11016.0 | \n",
" 11108.0 | \n",
"
\n",
" \n",
" 42 | \n",
" 11943.0 | \n",
" 11625.0 | \n",
" 11177.0 | \n",
" 11389.0 | \n",
" 11134.0 | \n",
" 10799.0 | \n",
"
\n",
" \n",
" 43 | \n",
" 12317.0 | \n",
" 11415.0 | \n",
" 10885.0 | \n",
" 11152.0 | \n",
" 11048.0 | \n",
" 10966.0 | \n",
"
\n",
" \n",
" 44 | \n",
" 12517.0 | \n",
" 11567.0 | \n",
" 10866.0 | \n",
" 11366.0 | \n",
" 11463.0 | \n",
" 11026.0 | \n",
"
\n",
" \n",
" 45 | \n",
" 13448.0 | \n",
" 12177.0 | \n",
" 11588.0 | \n",
" 11767.0 | \n",
" 11803.0 | \n",
" 11312.0 | \n",
"
\n",
" \n",
" 46 | \n",
" 13798.0 | \n",
" 12146.0 | \n",
" 11552.0 | \n",
" 11773.0 | \n",
" 12209.0 | \n",
" 11338.0 | \n",
"
\n",
" \n",
" 47 | \n",
" 14291.0 | \n",
" 12472.0 | \n",
" 11289.0 | \n",
" 12102.0 | \n",
" 12064.0 | \n",
" 11178.0 | \n",
"
\n",
" \n",
" 48 | \n",
" 14132.0 | \n",
" 12455.0 | \n",
" 11392.0 | \n",
" 12046.0 | \n",
" 11901.0 | \n",
" 11216.0 | \n",
"
\n",
" \n",
" 49 | \n",
" 13986.0 | \n",
" 12275.0 | \n",
" 11687.0 | \n",
" 12342.0 | \n",
" 12733.0 | \n",
" 11748.0 | \n",
"
\n",
" \n",
" 50 | \n",
" 13942.0 | \n",
" 12853.0 | \n",
" 12078.0 | \n",
" 12924.0 | \n",
" 12076.0 | \n",
" 11713.0 | \n",
"
\n",
" \n",
" 51 | \n",
" 14658.0 | \n",
" 13566.0 | \n",
" 12649.0 | \n",
" 14308.0 | \n",
" 13137.0 | \n",
" 12136.0 | \n",
"
\n",
" \n",
" 52 | \n",
" 13035.0 | \n",
" 8727.0 | \n",
" 8384.0 | \n",
" 9904.0 | \n",
" 9335.0 | \n",
" 9806.0 | \n",
"
\n",
" \n",
" 53 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" total_2020 total_2019 total_2018 total_2017 total_2016 total_2015\n",
"1 13768.0 12424.0 14701.0 13612.0 14863.0 13751.0\n",
"2 16020.0 14487.0 17430.0 15528.0 13154.0 18318.0\n",
"3 14723.0 13545.0 16355.0 15231.0 13060.0 16738.0\n",
"4 13429.0 13283.0 15971.0 14461.0 12859.0 15712.0\n",
"5 13123.0 12799.0 15087.0 14188.0 12571.0 14560.0\n",
"6 12534.0 13222.0 14111.0 13805.0 12697.0 13730.0\n",
"7 12412.0 13347.0 13925.0 13212.0 12016.0 13510.0\n",
"8 12300.0 12877.0 13753.0 13330.0 12718.0 13071.0\n",
"9 12334.0 12479.0 12190.0 12819.0 12733.0 13181.0\n",
"10 12415.0 12396.0 14859.0 12580.0 12493.0 13007.0\n",
"11 12499.0 12018.0 14367.0 12089.0 12489.0 12475.0\n",
"12 12112.0 11797.0 13397.0 11833.0 10983.0 12027.0\n",
"13 12507.0 11260.0 11310.0 11453.0 11738.0 11987.0\n",
"14 18565.0 11445.0 12272.0 11305.0 13060.0 10325.0\n",
"15 20929.0 11661.0 13843.0 9761.0 12757.0 11575.0\n",
"16 24691.0 10243.0 12639.0 11000.0 12310.0 13061.0\n",
"17 24303.0 11452.0 11596.0 12356.0 11795.0 12023.0\n",
"18 20059.0 12695.0 11538.0 10372.0 10401.0 11586.0\n",
"19 14428.0 10361.0 9821.0 12114.0 12002.0 10138.0\n",
"20 16390.0 11717.0 11386.0 11718.0 11222.0 11692.0\n",
"21 13839.0 11653.0 10974.0 11431.0 11013.0 11334.0\n",
"22 11265.0 9534.0 9397.0 9603.0 9192.0 9514.0\n",
"23 12106.0 11461.0 11259.0 11134.0 11171.0 11603.0\n",
"24 11302.0 10754.0 10535.0 10698.0 10673.0 10858.0\n",
"25 10694.0 10807.0 10514.0 10930.0 10611.0 10629.0\n",
"26 10282.0 10824.0 10529.0 10624.0 10526.0 10525.0\n",
"27 10412.0 10328.0 10565.0 10565.0 10412.0 10545.0\n",
"28 9941.0 10512.0 10467.0 10643.0 10647.0 10278.0\n",
"29 10096.0 10324.0 10353.0 10426.0 10672.0 10028.0\n",
"30 10159.0 10422.0 10356.0 10147.0 10612.0 10021.0\n",
"31 10262.0 10564.0 10408.0 10239.0 10433.0 9893.0\n",
"32 10236.0 10406.0 10542.0 10278.0 10439.0 10153.0\n",
"33 10592.0 10405.0 10091.0 10569.0 10312.0 10352.0\n",
"34 10990.0 10279.0 10199.0 10698.0 10637.0 10354.0\n",
"35 10364.0 9478.0 9046.0 9372.0 9226.0 10239.0\n",
"36 9023.0 10918.0 10680.0 10781.0 10681.0 9092.0\n",
"37 11176.0 10892.0 10496.0 10692.0 10401.0 10573.0\n",
"38 10797.0 10792.0 10498.0 10875.0 10183.0 10381.0\n",
"39 10890.0 10954.0 10463.0 11027.0 10278.0 10826.0\n",
"40 11468.0 11113.0 10869.0 11101.0 10671.0 10700.0\n",
"41 11373.0 11403.0 11048.0 11357.0 11016.0 11108.0\n",
"42 11943.0 11625.0 11177.0 11389.0 11134.0 10799.0\n",
"43 12317.0 11415.0 10885.0 11152.0 11048.0 10966.0\n",
"44 12517.0 11567.0 10866.0 11366.0 11463.0 11026.0\n",
"45 13448.0 12177.0 11588.0 11767.0 11803.0 11312.0\n",
"46 13798.0 12146.0 11552.0 11773.0 12209.0 11338.0\n",
"47 14291.0 12472.0 11289.0 12102.0 12064.0 11178.0\n",
"48 14132.0 12455.0 11392.0 12046.0 11901.0 11216.0\n",
"49 13986.0 12275.0 11687.0 12342.0 12733.0 11748.0\n",
"50 13942.0 12853.0 12078.0 12924.0 12076.0 11713.0\n",
"51 14658.0 13566.0 12649.0 14308.0 13137.0 12136.0\n",
"52 13035.0 8727.0 8384.0 9904.0 9335.0 9806.0\n",
"53 NaN NaN NaN NaN NaN NaN"
]
},
"execution_count": 40,
"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": 41,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" total_2020 | \n",
" total_2019 | \n",
" total_2018 | \n",
" total_2017 | \n",
" total_2016 | \n",
" total_2015 | \n",
" previous_mean | \n",
"
\n",
" \n",
" \n",
" \n",
" 1 | \n",
" 13768.0 | \n",
" 12424.0 | \n",
" 14701.0 | \n",
" 13612.0 | \n",
" 14863.0 | \n",
" 13751.0 | \n",
" 13870.2 | \n",
"
\n",
" \n",
" 2 | \n",
" 16020.0 | \n",
" 14487.0 | \n",
" 17430.0 | \n",
" 15528.0 | \n",
" 13154.0 | \n",
" 18318.0 | \n",
" 15783.4 | \n",
"
\n",
" \n",
" 3 | \n",
" 14723.0 | \n",
" 13545.0 | \n",
" 16355.0 | \n",
" 15231.0 | \n",
" 13060.0 | \n",
" 16738.0 | \n",
" 14985.8 | \n",
"
\n",
" \n",
" 4 | \n",
" 13429.0 | \n",
" 13283.0 | \n",
" 15971.0 | \n",
" 14461.0 | \n",
" 12859.0 | \n",
" 15712.0 | \n",
" 14457.2 | \n",
"
\n",
" \n",
" 5 | \n",
" 13123.0 | \n",
" 12799.0 | \n",
" 15087.0 | \n",
" 14188.0 | \n",
" 12571.0 | \n",
" 14560.0 | \n",
" 13841.0 | \n",
"
\n",
" \n",
" 6 | \n",
" 12534.0 | \n",
" 13222.0 | \n",
" 14111.0 | \n",
" 13805.0 | \n",
" 12697.0 | \n",
" 13730.0 | \n",
" 13513.0 | \n",
"
\n",
" \n",
" 7 | \n",
" 12412.0 | \n",
" 13347.0 | \n",
" 13925.0 | \n",
" 13212.0 | \n",
" 12016.0 | \n",
" 13510.0 | \n",
" 13202.0 | \n",
"
\n",
" \n",
" 8 | \n",
" 12300.0 | \n",
" 12877.0 | \n",
" 13753.0 | \n",
" 13330.0 | \n",
" 12718.0 | \n",
" 13071.0 | \n",
" 13149.8 | \n",
"
\n",
" \n",
" 9 | \n",
" 12334.0 | \n",
" 12479.0 | \n",
" 12190.0 | \n",
" 12819.0 | \n",
" 12733.0 | \n",
" 13181.0 | \n",
" 12680.4 | \n",
"
\n",
" \n",
" 10 | \n",
" 12415.0 | \n",
" 12396.0 | \n",
" 14859.0 | \n",
" 12580.0 | \n",
" 12493.0 | \n",
" 13007.0 | \n",
" 13067.0 | \n",
"
\n",
" \n",
" 11 | \n",
" 12499.0 | \n",
" 12018.0 | \n",
" 14367.0 | \n",
" 12089.0 | \n",
" 12489.0 | \n",
" 12475.0 | \n",
" 12687.6 | \n",
"
\n",
" \n",
" 12 | \n",
" 12112.0 | \n",
" 11797.0 | \n",
" 13397.0 | \n",
" 11833.0 | \n",
" 10983.0 | \n",
" 12027.0 | \n",
" 12007.4 | \n",
"
\n",
" \n",
" 13 | \n",
" 12507.0 | \n",
" 11260.0 | \n",
" 11310.0 | \n",
" 11453.0 | \n",
" 11738.0 | \n",
" 11987.0 | \n",
" 11549.6 | \n",
"
\n",
" \n",
" 14 | \n",
" 18565.0 | \n",
" 11445.0 | \n",
" 12272.0 | \n",
" 11305.0 | \n",
" 13060.0 | \n",
" 10325.0 | \n",
" 11681.4 | \n",
"
\n",
" \n",
" 15 | \n",
" 20929.0 | \n",
" 11661.0 | \n",
" 13843.0 | \n",
" 9761.0 | \n",
" 12757.0 | \n",
" 11575.0 | \n",
" 11919.4 | \n",
"
\n",
" \n",
" 16 | \n",
" 24691.0 | \n",
" 10243.0 | \n",
" 12639.0 | \n",
" 11000.0 | \n",
" 12310.0 | \n",
" 13061.0 | \n",
" 11850.6 | \n",
"
\n",
" \n",
" 17 | \n",
" 24303.0 | \n",
" 11452.0 | \n",
" 11596.0 | \n",
" 12356.0 | \n",
" 11795.0 | \n",
" 12023.0 | \n",
" 11844.4 | \n",
"
\n",
" \n",
" 18 | \n",
" 20059.0 | \n",
" 12695.0 | \n",
" 11538.0 | \n",
" 10372.0 | \n",
" 10401.0 | \n",
" 11586.0 | \n",
" 11318.4 | \n",
"
\n",
" \n",
" 19 | \n",
" 14428.0 | \n",
" 10361.0 | \n",
" 9821.0 | \n",
" 12114.0 | \n",
" 12002.0 | \n",
" 10138.0 | \n",
" 10887.2 | \n",
"
\n",
" \n",
" 20 | \n",
" 16390.0 | \n",
" 11717.0 | \n",
" 11386.0 | \n",
" 11718.0 | \n",
" 11222.0 | \n",
" 11692.0 | \n",
" 11547.0 | \n",
"
\n",
" \n",
" 21 | \n",
" 13839.0 | \n",
" 11653.0 | \n",
" 10974.0 | \n",
" 11431.0 | \n",
" 11013.0 | \n",
" 11334.0 | \n",
" 11281.0 | \n",
"
\n",
" \n",
" 22 | \n",
" 11265.0 | \n",
" 9534.0 | \n",
" 9397.0 | \n",
" 9603.0 | \n",
" 9192.0 | \n",
" 9514.0 | \n",
" 9448.0 | \n",
"
\n",
" \n",
" 23 | \n",
" 12106.0 | \n",
" 11461.0 | \n",
" 11259.0 | \n",
" 11134.0 | \n",
" 11171.0 | \n",
" 11603.0 | \n",
" 11325.6 | \n",
"
\n",
" \n",
" 24 | \n",
" 11302.0 | \n",
" 10754.0 | \n",
" 10535.0 | \n",
" 10698.0 | \n",
" 10673.0 | \n",
" 10858.0 | \n",
" 10703.6 | \n",
"
\n",
" \n",
" 25 | \n",
" 10694.0 | \n",
" 10807.0 | \n",
" 10514.0 | \n",
" 10930.0 | \n",
" 10611.0 | \n",
" 10629.0 | \n",
" 10698.2 | \n",
"
\n",
" \n",
" 26 | \n",
" 10282.0 | \n",
" 10824.0 | \n",
" 10529.0 | \n",
" 10624.0 | \n",
" 10526.0 | \n",
" 10525.0 | \n",
" 10605.6 | \n",
"
\n",
" \n",
" 27 | \n",
" 10412.0 | \n",
" 10328.0 | \n",
" 10565.0 | \n",
" 10565.0 | \n",
" 10412.0 | \n",
" 10545.0 | \n",
" 10483.0 | \n",
"
\n",
" \n",
" 28 | \n",
" 9941.0 | \n",
" 10512.0 | \n",
" 10467.0 | \n",
" 10643.0 | \n",
" 10647.0 | \n",
" 10278.0 | \n",
" 10509.4 | \n",
"
\n",
" \n",
" 29 | \n",
" 10096.0 | \n",
" 10324.0 | \n",
" 10353.0 | \n",
" 10426.0 | \n",
" 10672.0 | \n",
" 10028.0 | \n",
" 10360.6 | \n",
"
\n",
" \n",
" 30 | \n",
" 10159.0 | \n",
" 10422.0 | \n",
" 10356.0 | \n",
" 10147.0 | \n",
" 10612.0 | \n",
" 10021.0 | \n",
" 10311.6 | \n",
"
\n",
" \n",
" 31 | \n",
" 10262.0 | \n",
" 10564.0 | \n",
" 10408.0 | \n",
" 10239.0 | \n",
" 10433.0 | \n",
" 9893.0 | \n",
" 10307.4 | \n",
"
\n",
" \n",
" 32 | \n",
" 10236.0 | \n",
" 10406.0 | \n",
" 10542.0 | \n",
" 10278.0 | \n",
" 10439.0 | \n",
" 10153.0 | \n",
" 10363.6 | \n",
"
\n",
" \n",
" 33 | \n",
" 10592.0 | \n",
" 10405.0 | \n",
" 10091.0 | \n",
" 10569.0 | \n",
" 10312.0 | \n",
" 10352.0 | \n",
" 10345.8 | \n",
"
\n",
" \n",
" 34 | \n",
" 10990.0 | \n",
" 10279.0 | \n",
" 10199.0 | \n",
" 10698.0 | \n",
" 10637.0 | \n",
" 10354.0 | \n",
" 10433.4 | \n",
"
\n",
" \n",
" 35 | \n",
" 10364.0 | \n",
" 9478.0 | \n",
" 9046.0 | \n",
" 9372.0 | \n",
" 9226.0 | \n",
" 10239.0 | \n",
" 9472.2 | \n",
"
\n",
" \n",
" 36 | \n",
" 9023.0 | \n",
" 10918.0 | \n",
" 10680.0 | \n",
" 10781.0 | \n",
" 10681.0 | \n",
" 9092.0 | \n",
" 10430.4 | \n",
"
\n",
" \n",
" 37 | \n",
" 11176.0 | \n",
" 10892.0 | \n",
" 10496.0 | \n",
" 10692.0 | \n",
" 10401.0 | \n",
" 10573.0 | \n",
" 10610.8 | \n",
"
\n",
" \n",
" 38 | \n",
" 10797.0 | \n",
" 10792.0 | \n",
" 10498.0 | \n",
" 10875.0 | \n",
" 10183.0 | \n",
" 10381.0 | \n",
" 10545.8 | \n",
"
\n",
" \n",
" 39 | \n",
" 10890.0 | \n",
" 10954.0 | \n",
" 10463.0 | \n",
" 11027.0 | \n",
" 10278.0 | \n",
" 10826.0 | \n",
" 10709.6 | \n",
"
\n",
" \n",
" 40 | \n",
" 11468.0 | \n",
" 11113.0 | \n",
" 10869.0 | \n",
" 11101.0 | \n",
" 10671.0 | \n",
" 10700.0 | \n",
" 10890.8 | \n",
"
\n",
" \n",
" 41 | \n",
" 11373.0 | \n",
" 11403.0 | \n",
" 11048.0 | \n",
" 11357.0 | \n",
" 11016.0 | \n",
" 11108.0 | \n",
" 11186.4 | \n",
"
\n",
" \n",
" 42 | \n",
" 11943.0 | \n",
" 11625.0 | \n",
" 11177.0 | \n",
" 11389.0 | \n",
" 11134.0 | \n",
" 10799.0 | \n",
" 11224.8 | \n",
"
\n",
" \n",
" 43 | \n",
" 12317.0 | \n",
" 11415.0 | \n",
" 10885.0 | \n",
" 11152.0 | \n",
" 11048.0 | \n",
" 10966.0 | \n",
" 11093.2 | \n",
"
\n",
" \n",
" 44 | \n",
" 12517.0 | \n",
" 11567.0 | \n",
" 10866.0 | \n",
" 11366.0 | \n",
" 11463.0 | \n",
" 11026.0 | \n",
" 11257.6 | \n",
"
\n",
" \n",
" 45 | \n",
" 13448.0 | \n",
" 12177.0 | \n",
" 11588.0 | \n",
" 11767.0 | \n",
" 11803.0 | \n",
" 11312.0 | \n",
" 11729.4 | \n",
"
\n",
" \n",
" 46 | \n",
" 13798.0 | \n",
" 12146.0 | \n",
" 11552.0 | \n",
" 11773.0 | \n",
" 12209.0 | \n",
" 11338.0 | \n",
" 11803.6 | \n",
"
\n",
" \n",
" 47 | \n",
" 14291.0 | \n",
" 12472.0 | \n",
" 11289.0 | \n",
" 12102.0 | \n",
" 12064.0 | \n",
" 11178.0 | \n",
" 11821.0 | \n",
"
\n",
" \n",
" 48 | \n",
" 14132.0 | \n",
" 12455.0 | \n",
" 11392.0 | \n",
" 12046.0 | \n",
" 11901.0 | \n",
" 11216.0 | \n",
" 11802.0 | \n",
"
\n",
" \n",
" 49 | \n",
" 13986.0 | \n",
" 12275.0 | \n",
" 11687.0 | \n",
" 12342.0 | \n",
" 12733.0 | \n",
" 11748.0 | \n",
" 12157.0 | \n",
"
\n",
" \n",
" 50 | \n",
" 13942.0 | \n",
" 12853.0 | \n",
" 12078.0 | \n",
" 12924.0 | \n",
" 12076.0 | \n",
" 11713.0 | \n",
" 12328.8 | \n",
"
\n",
" \n",
" 51 | \n",
" 14658.0 | \n",
" 13566.0 | \n",
" 12649.0 | \n",
" 14308.0 | \n",
" 13137.0 | \n",
" 12136.0 | \n",
" 13159.2 | \n",
"
\n",
" \n",
" 52 | \n",
" 13035.0 | \n",
" 8727.0 | \n",
" 8384.0 | \n",
" 9904.0 | \n",
" 9335.0 | \n",
" 9806.0 | \n",
" 9231.2 | \n",
"
\n",
" \n",
" 53 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" total_2020 total_2019 total_2018 total_2017 total_2016 total_2015 \\\n",
"1 13768.0 12424.0 14701.0 13612.0 14863.0 13751.0 \n",
"2 16020.0 14487.0 17430.0 15528.0 13154.0 18318.0 \n",
"3 14723.0 13545.0 16355.0 15231.0 13060.0 16738.0 \n",
"4 13429.0 13283.0 15971.0 14461.0 12859.0 15712.0 \n",
"5 13123.0 12799.0 15087.0 14188.0 12571.0 14560.0 \n",
"6 12534.0 13222.0 14111.0 13805.0 12697.0 13730.0 \n",
"7 12412.0 13347.0 13925.0 13212.0 12016.0 13510.0 \n",
"8 12300.0 12877.0 13753.0 13330.0 12718.0 13071.0 \n",
"9 12334.0 12479.0 12190.0 12819.0 12733.0 13181.0 \n",
"10 12415.0 12396.0 14859.0 12580.0 12493.0 13007.0 \n",
"11 12499.0 12018.0 14367.0 12089.0 12489.0 12475.0 \n",
"12 12112.0 11797.0 13397.0 11833.0 10983.0 12027.0 \n",
"13 12507.0 11260.0 11310.0 11453.0 11738.0 11987.0 \n",
"14 18565.0 11445.0 12272.0 11305.0 13060.0 10325.0 \n",
"15 20929.0 11661.0 13843.0 9761.0 12757.0 11575.0 \n",
"16 24691.0 10243.0 12639.0 11000.0 12310.0 13061.0 \n",
"17 24303.0 11452.0 11596.0 12356.0 11795.0 12023.0 \n",
"18 20059.0 12695.0 11538.0 10372.0 10401.0 11586.0 \n",
"19 14428.0 10361.0 9821.0 12114.0 12002.0 10138.0 \n",
"20 16390.0 11717.0 11386.0 11718.0 11222.0 11692.0 \n",
"21 13839.0 11653.0 10974.0 11431.0 11013.0 11334.0 \n",
"22 11265.0 9534.0 9397.0 9603.0 9192.0 9514.0 \n",
"23 12106.0 11461.0 11259.0 11134.0 11171.0 11603.0 \n",
"24 11302.0 10754.0 10535.0 10698.0 10673.0 10858.0 \n",
"25 10694.0 10807.0 10514.0 10930.0 10611.0 10629.0 \n",
"26 10282.0 10824.0 10529.0 10624.0 10526.0 10525.0 \n",
"27 10412.0 10328.0 10565.0 10565.0 10412.0 10545.0 \n",
"28 9941.0 10512.0 10467.0 10643.0 10647.0 10278.0 \n",
"29 10096.0 10324.0 10353.0 10426.0 10672.0 10028.0 \n",
"30 10159.0 10422.0 10356.0 10147.0 10612.0 10021.0 \n",
"31 10262.0 10564.0 10408.0 10239.0 10433.0 9893.0 \n",
"32 10236.0 10406.0 10542.0 10278.0 10439.0 10153.0 \n",
"33 10592.0 10405.0 10091.0 10569.0 10312.0 10352.0 \n",
"34 10990.0 10279.0 10199.0 10698.0 10637.0 10354.0 \n",
"35 10364.0 9478.0 9046.0 9372.0 9226.0 10239.0 \n",
"36 9023.0 10918.0 10680.0 10781.0 10681.0 9092.0 \n",
"37 11176.0 10892.0 10496.0 10692.0 10401.0 10573.0 \n",
"38 10797.0 10792.0 10498.0 10875.0 10183.0 10381.0 \n",
"39 10890.0 10954.0 10463.0 11027.0 10278.0 10826.0 \n",
"40 11468.0 11113.0 10869.0 11101.0 10671.0 10700.0 \n",
"41 11373.0 11403.0 11048.0 11357.0 11016.0 11108.0 \n",
"42 11943.0 11625.0 11177.0 11389.0 11134.0 10799.0 \n",
"43 12317.0 11415.0 10885.0 11152.0 11048.0 10966.0 \n",
"44 12517.0 11567.0 10866.0 11366.0 11463.0 11026.0 \n",
"45 13448.0 12177.0 11588.0 11767.0 11803.0 11312.0 \n",
"46 13798.0 12146.0 11552.0 11773.0 12209.0 11338.0 \n",
"47 14291.0 12472.0 11289.0 12102.0 12064.0 11178.0 \n",
"48 14132.0 12455.0 11392.0 12046.0 11901.0 11216.0 \n",
"49 13986.0 12275.0 11687.0 12342.0 12733.0 11748.0 \n",
"50 13942.0 12853.0 12078.0 12924.0 12076.0 11713.0 \n",
"51 14658.0 13566.0 12649.0 14308.0 13137.0 12136.0 \n",
"52 13035.0 8727.0 8384.0 9904.0 9335.0 9806.0 \n",
"53 NaN NaN NaN NaN NaN NaN \n",
"\n",
" previous_mean \n",
"1 13870.2 \n",
"2 15783.4 \n",
"3 14985.8 \n",
"4 14457.2 \n",
"5 13841.0 \n",
"6 13513.0 \n",
"7 13202.0 \n",
"8 13149.8 \n",
"9 12680.4 \n",
"10 13067.0 \n",
"11 12687.6 \n",
"12 12007.4 \n",
"13 11549.6 \n",
"14 11681.4 \n",
"15 11919.4 \n",
"16 11850.6 \n",
"17 11844.4 \n",
"18 11318.4 \n",
"19 10887.2 \n",
"20 11547.0 \n",
"21 11281.0 \n",
"22 9448.0 \n",
"23 11325.6 \n",
"24 10703.6 \n",
"25 10698.2 \n",
"26 10605.6 \n",
"27 10483.0 \n",
"28 10509.4 \n",
"29 10360.6 \n",
"30 10311.6 \n",
"31 10307.4 \n",
"32 10363.6 \n",
"33 10345.8 \n",
"34 10433.4 \n",
"35 9472.2 \n",
"36 10430.4 \n",
"37 10610.8 \n",
"38 10545.8 \n",
"39 10709.6 \n",
"40 10890.8 \n",
"41 11186.4 \n",
"42 11224.8 \n",
"43 11093.2 \n",
"44 11257.6 \n",
"45 11729.4 \n",
"46 11803.6 \n",
"47 11821.0 \n",
"48 11802.0 \n",
"49 12157.0 \n",
"50 12328.8 \n",
"51 13159.2 \n",
"52 9231.2 \n",
"53 NaN "
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"deaths_headlines_e['previous_mean'] = deaths_headlines_e['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)\n",
"deaths_headlines_w['previous_mean'] = deaths_headlines_w['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)\n",
"deaths_headlines_s['previous_mean'] = deaths_headlines_s['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)\n",
"deaths_headlines_i['previous_mean'] = deaths_headlines_i['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)\n",
"deaths_headlines['previous_mean'] = deaths_headlines['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)\n",
"deaths_headlines"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"