uk_deaths.md

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  15
  16 Data from:
  17
  18 * [Office of National Statistics](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/weeklyprovisionalfiguresondeathsregisteredinenglandandwales) (Endland and Wales) Weeks start on a Saturday.
  19 * [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.
  20 * [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.
  21
  22
  23 ```python
  24 import itertools
  25 import collections
  26 import json
  27 import pandas as pd
  28 import numpy as np
  29 from scipy.stats import gmean
  30
  31 import matplotlib as mpl
  32 import matplotlib.pyplot as plt
  33 %matplotlib inline
  34 ```
  35
  36 ```python
  37 !ls uk-deaths-data
  38 ```
  39
  40 ```python
  41 raw_data_2015 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2015.csv',
  42                        parse_dates=[1, 2], dayfirst=True,
  43                       index_col=0,
  44                       header=[0, 1]
  45                            )
  46 dh15i = raw_data_2015.iloc[:, [2]]
  47 dh15i.columns = ['total_2015']
  48 # dh15i.head()
  49 ```
  50
  51 ```python
  52 raw_data_2016 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2016.csv',
  53                         parse_dates=[1, 2], dayfirst=True,
  54                       index_col=0,
  55                       header=[0, 1]
  56                            )
  57 dh16i = raw_data_2016.iloc[:, [2]]
  58 dh16i.columns = ['total_2016']
  59 # dh16i.head()
  60 ```
  61
  62 ```python
  63 raw_data_2017 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2017.csv',
  64                         parse_dates=[1, 2], dayfirst=True,
  65                       index_col=0,
  66                       header=[0, 1]
  67                            )
  68 dh17i = raw_data_2017.iloc[:, [2]]
  69 dh17i.columns = ['total_2017']
  70 # dh17i.head()
  71 ```
  72
  73 ```python
  74 raw_data_2018 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2018.csv',
  75                         parse_dates=[1, 2], dayfirst=True,
  76                       index_col=0,
  77                       header=[0, 1]
  78                            )
  79 dh18i = raw_data_2018.iloc[:, [2]]
  80 dh18i.columns = ['total_2018']
  81 # dh18i.head()
  82 ```
  83
  84 ```python
  85 raw_data_2019 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2019.csv',
  86                         parse_dates=[1, 2], dayfirst=True,
  87                       index_col=0,
  88                       header=[0, 1]
  89                            )
  90 dh19i = raw_data_2019.iloc[:, [2]]
  91 dh19i.columns = ['total_2019']
  92 # dh19i.head()
  93 ```
  94
  95 ```python
  96 raw_data_2020_i = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2020.csv',
  97                         parse_dates=[1], dayfirst=True,
  98                       index_col=0,
  99                       header=[0, 1]
 100                            )
 101 deaths_headlines_i = raw_data_2020_i.iloc[:, [1]]
 102 deaths_headlines_i.columns = ['total_2020']
 103 deaths_headlines_i.head()
 104 ```
 105
 106 ```python
 107
 108 ```
 109
 110 ```python
 111
 112 ```
 113
 114 ```python
 115 raw_data_s = pd.read_csv('uk-deaths-data/weekly-deaths-april-20-scotland.csv',
 116                       index_col=0,
 117                       header=0,
 118                         skiprows=2
 119                            )
 120 # raw_data_s
 121 ```
 122
 123 ```python
 124 deaths_headlines_s = raw_data_s[reversed('2015 2016 2017 2018 2019 2020'.split())]
 125 deaths_headlines_s.columns = ['total_' + c for c in deaths_headlines_s.columns]
 126 deaths_headlines_s.reset_index(drop=True, inplace=True)
 127 deaths_headlines_s.index = deaths_headlines_s.index + 1
 128 deaths_headlines_s
 129 ```
 130
 131 ```python
 132
 133 ```
 134
 135 ```python
 136 eng_xls = pd.read_excel('uk-deaths-data/publishedweek312020.xlsx',
 137                         sheet_name="Weekly figures 2020",
 138                         skiprows=[0, 1, 2, 3],
 139                         header=0,
 140                         index_col=[1]
 141                        ).iloc[:91].T
 142 eng_xls
 143 ```
 144
 145 ```python
 146 # eng_xls_columns
 147 ```
 148
 149 ```python
 150 eng_xls_columns = list(eng_xls.columns)
 151
 152 for i, c in enumerate(eng_xls_columns):
 153 #     print(i, c, type(c), isinstance(c, float))
 154     if isinstance(c, float) and np.isnan(c):
 155         if eng_xls.iloc[0].iloc[i] is not pd.NaT:
 156             eng_xls_columns[i] = eng_xls.iloc[0].iloc[i]
 157
 158 # np.isnan(eng_xls_columns[0])
 159 # eng_xls_columns
 160
 161 eng_xls.columns = eng_xls_columns
 162 # eng_xls.columns
 163 ```
 164
 165 ```python
 166 eng_xls['Total deaths, all ages']
 167 ```
 168
 169 ```python
 170 eng_xls['Wales'].iloc[1:]
 171 ```
 172
 173 ```python
 174 # raw_data_2020 = pd.read_csv('uk-deaths-data/publishedweek272020.csv',
 175 #                        parse_dates=[1], dayfirst=True,
 176 #                       index_col=0,
 177 #                       header=[0, 1])
 178 ```
 179
 180 ```python
 181
 182 ```
 183
 184 ```python
 185 # raw_data_2020.head()
 186 ```
 187
 188 ```python
 189 # raw_data_2020['W92000004', 'Wales']
 190 ```
 191
 192 ```python
 193 raw_data_2019 = pd.read_csv('uk-deaths-data/publishedweek522019.csv',
 194                        parse_dates=[1], dayfirst=True,
 195                       index_col=0,
 196                       header=[0, 1])
 197 # raw_data_2019.head()
 198 ```
 199
 200 ```python
 201 raw_data_2018 = pd.read_csv('uk-deaths-data/publishedweek522018.csv',
 202                        parse_dates=[1], dayfirst=True,
 203                       index_col=0,
 204                       header=[0, 1])
 205 # raw_data_2018.head()
 206 ```
 207
 208 ```python
 209 raw_data_2017 = pd.read_csv('uk-deaths-data/publishedweek522017.csv',
 210                        parse_dates=[1], dayfirst=True,
 211                       index_col=0,
 212                       header=[0, 1])
 213 # raw_data_2017.head()
 214 ```
 215
 216 ```python
 217 raw_data_2016 = pd.read_csv('uk-deaths-data/publishedweek522016.csv',
 218                        parse_dates=[1], dayfirst=True,
 219                       index_col=0,
 220                       header=[0, 1])
 221 # raw_data_2016.head()
 222 ```
 223
 224 ```python
 225 raw_data_2015 = pd.read_csv('uk-deaths-data/publishedweek2015.csv',
 226                        parse_dates=[1], dayfirst=True,
 227                       index_col=0,
 228                       header=[0, 1])
 229 # raw_data_2015.head()
 230 ```
 231
 232 ```python
 233 dhw = eng_xls['Wales'].iloc[1:]
 234 dhe = eng_xls['Total deaths, all ages'].iloc[1:] - dhw
 235 deaths_headlines_e = pd.DataFrame({'total_2020': dhe.dropna()})
 236 deaths_headlines_w = pd.DataFrame({'total_2020': dhw.dropna()})
 237 ```
 238
 239 ```python
 240 # deaths_headlines_e = raw_data_2020.iloc[:, [1]].copy()
 241 # deaths_headlines_e.columns = ['total_2020']
 242 # deaths_headlines_w = raw_data_2020['W92000004'].copy()
 243 # deaths_headlines_e.columns = ['total_2020']
 244 # deaths_headlines_w.columns = ['total_2020']
 245 # deaths_headlines_e.total_2020 -= deaths_headlines_w.total_2020
 246 # deaths_headlines_e.head()
 247 # deaths_headlines_e
 248 ```
 249
 250 ```python
 251 dh19e = raw_data_2019.iloc[:, [1]]
 252 dh19w = raw_data_2019['W92000004']
 253 dh19e.columns = ['total_2019']
 254 dh19w.columns = ['total_2019']
 255 dh19e.total_2019 -= dh19w.total_2019
 256 dh19e.tail()
 257 ```
 258
 259 ```python
 260 dh19w.head()
 261 ```
 262
 263 ```python
 264 dh18e = raw_data_2018.iloc[:, [1]]
 265 dh18w = raw_data_2018['W92000004']
 266 dh18e.columns = ['total_2018']
 267 dh18w.columns = ['total_2018']
 268 dh18e.total_2018 -= dh18w.total_2018
 269 # dh18e.head()
 270 ```
 271
 272 ```python
 273 dh17e = raw_data_2017.iloc[:, [1]]
 274 dh17w = raw_data_2017['W92000004']
 275 dh17e.columns = ['total_2017']
 276 dh17w.columns = ['total_2017']
 277 dh17e.total_2017 -= dh17w.total_2017
 278 # dh17e.head()
 279 ```
 280
 281 ```python
 282 dh16e = raw_data_2016.iloc[:, [1]]
 283 dh16w = raw_data_2016['W92000004']
 284 dh16e.columns = ['total_2016']
 285 dh16w.columns = ['total_2016']
 286 dh16e.total_2016 -= dh16w.total_2016
 287 # dh16e.head()
 288 ```
 289
 290 ```python
 291 dh15e = raw_data_2015.iloc[:, [1]]
 292 dh15w = raw_data_2015['W92000004']
 293 dh15e.columns = ['total_2015']
 294 dh15w.columns = ['total_2015']
 295 dh15e.total_2015 -= dh15w.total_2015
 296 # dh15e.head()
 297 ```
 298
 299 ```python
 300 # dh18 = raw_data_2018.iloc[:, [1, 2]]
 301 # dh18.columns = ['total_2018', 'total_previous']
 302 # # dh18.head()
 303 ```
 304
 305 ```python
 306 deaths_headlines_e = deaths_headlines_e.merge(dh19e['total_2019'], how='outer', left_index=True, right_index=True)
 307 deaths_headlines_e = deaths_headlines_e.merge(dh18e['total_2018'], how='outer', left_index=True, right_index=True)
 308 deaths_headlines_e = deaths_headlines_e.merge(dh17e['total_2017'], how='outer', left_index=True, right_index=True)
 309 deaths_headlines_e = deaths_headlines_e.merge(dh16e['total_2016'], how='outer', left_index=True, right_index=True)
 310 # deaths_headlines = deaths_headlines.merge(dh15['total_2015'], how='outer', left_index=True, right_index=True)
 311 deaths_headlines_e = deaths_headlines_e.merge(dh15e['total_2015'], how='left', left_index=True, right_index=True)
 312 deaths_headlines_e
 313 ```
 314
 315 ```python
 316 deaths_headlines_s = raw_data_s[reversed('2015 2016 2017 2018 2019 2020'.split())]
 317 deaths_headlines_s.columns = ['total_' + c for c in deaths_headlines_s.columns]
 318 deaths_headlines_s.reset_index(drop=True, inplace=True)
 319 deaths_headlines_s.index = deaths_headlines_s.index + 1
 320 deaths_headlines_s = deaths_headlines_s.loc[1:52]
 321 deaths_headlines_s
 322 ```
 323
 324 # Correction for missing data
 325
 326 ```python
 327 # deaths_headlines_s.loc[20, 'total_2020'] = 1000
 328 # deaths_headlines_s
 329 ```
 330
 331 ```python
 332 deaths_headlines_w = deaths_headlines_w.merge(dh19w['total_2019'], how='outer', left_index=True, right_index=True)
 333 deaths_headlines_w = deaths_headlines_w.merge(dh18w['total_2018'], how='outer', left_index=True, right_index=True)
 334 deaths_headlines_w = deaths_headlines_w.merge(dh17w['total_2017'], how='outer', left_index=True, right_index=True)
 335 deaths_headlines_w = deaths_headlines_w.merge(dh16w['total_2016'], how='outer', left_index=True, right_index=True)
 336 # deaths_headlines = deaths_headlines.merge(dh15['total_2015'], how='outer', left_index=True, right_index=True)
 337 deaths_headlines_w = deaths_headlines_w.merge(dh15w['total_2015'], how='left', left_index=True, right_index=True)
 338 deaths_headlines_w
 339 ```
 340
 341 ```python
 342 deaths_headlines_i = deaths_headlines_i.merge(dh19i['total_2019'], how='outer', left_index=True, right_index=True)
 343 deaths_headlines_i = deaths_headlines_i.merge(dh18i['total_2018'], how='outer', left_index=True, right_index=True)
 344 deaths_headlines_i = deaths_headlines_i.merge(dh17i['total_2017'], how='outer', left_index=True, right_index=True)
 345 deaths_headlines_i = deaths_headlines_i.merge(dh16i['total_2016'], how='outer', left_index=True, right_index=True)
 346 deaths_headlines_i = deaths_headlines_i.merge(dh15i['total_2015'], how='left', left_index=True, right_index=True)
 347 deaths_headlines_i
 348 ```
 349
 350 ```python
 351 deaths_headlines = deaths_headlines_e + deaths_headlines_w + deaths_headlines_i + deaths_headlines_s
 352 deaths_headlines
 353 ```
 354
 355 ```python
 356 deaths_headlines_e['previous_mean'] = deaths_headlines_e['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)
 357 deaths_headlines_w['previous_mean'] = deaths_headlines_w['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)
 358 deaths_headlines_s['previous_mean'] = deaths_headlines_s['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)
 359 deaths_headlines_i['previous_mean'] = deaths_headlines_i['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)
 360 deaths_headlines['previous_mean'] = deaths_headlines['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)
 361 deaths_headlines
 362 ```
 363
 364 ```python
 365 deaths_headlines['total_2020 total_2019 total_2018 total_2017 total_2016 total_2015'.split()].plot(figsize=(14, 8))
 366 ```
 367
 368 ```python
 369 deaths_headlines[['total_2020', 'previous_mean']].plot(figsize=(10, 8))
 370 ```
 371
 372 ```python
 373 deaths_headlines_i.plot()
 374 ```
 375
 376 ```python
 377 # Radar plot code taken from example at https://stackoverflow.com/questions/42878485/getting-matplotlib-radar-plot-with-pandas#
 378
 379 fig = plt.figure(figsize=(10, 10))
 380 ax = fig.add_subplot(111, projection="polar")
 381
 382 theta = np.roll(
 383     np.flip(
 384         np.arange(len(deaths_headlines))/float(len(deaths_headlines))*2.*np.pi),
 385     14)
 386 # l15, = ax.plot(theta, deaths_headlines['total_2015'], color="#b56363", label="2015") # 0
 387 # l16, = ax.plot(theta, deaths_headlines['total_2016'], color="#a4b563", label="2016") # 72
 388 # l17, = ax.plot(theta, deaths_headlines['total_2017'], color="#63b584", label="2017") # 144
 389 # l18, = ax.plot(theta, deaths_headlines['total_2018'], color="#6384b5", label="2018") # 216
 390 # l19, = ax.plot(theta, deaths_headlines['total_2019'], color="#a4635b", label="2019") # 288
 391 l15, = ax.plot(theta, deaths_headlines['total_2015'], color="#e47d7d", label="2015") # 0
 392 l16, = ax.plot(theta, deaths_headlines['total_2016'], color="#afc169", label="2016") # 72 , d0e47d
 393 l17, = ax.plot(theta, deaths_headlines['total_2017'], color="#7de4a6", label="2017") # 144
 394 l18, = ax.plot(theta, deaths_headlines['total_2018'], color="#7da6e4", label="2018") # 216
 395 l19, = ax.plot(theta, deaths_headlines['total_2019'], color="#d07de4", label="2019") # 288
 396
 397 lmean, = ax.plot(theta, deaths_headlines['previous_mean'], color="black", linestyle='dashed', label="mean")
 398
 399 l20, = ax.plot(theta, deaths_headlines['total_2020'], color="red", label="2020")
 400
 401 # deaths_headlines.total_2019.plot(ax=ax)
 402
 403 def _closeline(line):
 404     x, y = line.get_data()
 405     x = np.concatenate((x, [x[0]]))
 406     y = np.concatenate((y, [y[0]]))
 407     line.set_data(x, y)
 408
 409 [_closeline(l) for l in [l19, l18, l17, l16, l15, lmean]]
 410
 411
 412 ax.set_xticks(theta)
 413 ax.set_xticklabels(deaths_headlines.index)
 414 plt.legend()
 415 plt.title("Deaths by week over years, all UK")
 416 plt.savefig('deaths-radar.png')
 417 plt.show()
 418 ```
 419
 420 # Excess deaths calculation
 421
 422 ```python
 423 # raw_data_2020.loc[12, 'Week ended']
 424 ```
 425
 426 ```python
 427 eng_xls.loc[12, 'Week ended']
 428 ```
 429
 430 ```python
 431 # raw_data_2020.iloc[-1]['Week ended']
 432 ```
 433
 434 ```python
 435 deaths_headlines_e.total_2020.dropna().last_valid_index()
 436 ```
 437
 438 ```python
 439 eng_xls.loc[deaths_headlines_e.total_2020.dropna().last_valid_index(), 'Week ended']
 440 ```
 441
 442 ```python
 443 eng_xls.loc[27, 'Week ended']
 444 ```
 445
 446 ```python
 447 # raw_data_2020.loc[12].droplevel(1)['Week ended']
 448 ```
 449
 450 ```python
 451 # raw_data_2020.iloc[-1].droplevel(1)['Week ended']
 452 ```
 453
 454 ```python
 455 (deaths_headlines.loc[12:].total_2020 - deaths_headlines.loc[12:].previous_mean).sum()
 456 ```
 457
 458 ```python
 459 (deaths_headlines.loc[12:27].total_2020 - deaths_headlines.loc[12:27].previous_mean).sum()
 460 ```
 461
 462 ```python
 463 deaths_headlines.previous_mean.sum()
 464 ```
 465
 466 ```python
 467 # excess_death_data = {
 468 #     'start_date': str(eng_xls.loc[12, 'Week ended']),
 469 #     'end_date': str(eng_xls.loc[deaths_headlines_e.total_2020.dropna().last_valid_index(), 'Week ended']),
 470 #     'excess_deaths': (deaths_headlines.loc[12:].total_2020 - deaths_headlines.loc[12:].previous_mean).sum()
 471 # }
 472
 473 # with open('excess_deaths.json', 'w') as f:
 474 #     json.dump(excess_death_data, f)
 475 ```
 476
 477 ```python
 478 excess_death_data = {
 479     'start_date': str(eng_xls.loc[12, 'Week ended']),
 480     'end_date': str(eng_xls.loc[27, 'Week ended']),
 481     'excess_deaths': (deaths_headlines.loc[12:27].total_2020 - deaths_headlines.loc[12:27].previous_mean).sum()
 482 }
 483
 484 with open('excess_deaths.json', 'w') as f:
 485     json.dump(excess_death_data, f)
 486 ```
 487
 488 ```python
 489 # excess_death_data = {
 490 #     'start_date': str(raw_data_2020.loc[12].droplevel(1)['Week ended']),
 491 #     'end_date': str(raw_data_2020.iloc[-1].droplevel(1)['Week ended']),
 492 #     'excess_deaths': (deaths_headlines.loc[12:].total_2020 - deaths_headlines.loc[12:].previous_mean).sum()
 493 # }
 494
 495 # with open('excess_deaths.json', 'w') as f:
 496 #     json.dump(excess_death_data, f)
 497 ```
 498
 499 ```python
 500 eng_xls['Week ended']
 501 ```
 502
 503 ```python
 504 # raw_data_2020.droplevel(1, axis='columns')['Week ended']
 505 ```
 506
 507 ```python
 508 deaths_by_week = deaths_headlines.merge(eng_xls['Week ended'], left_index=True, right_index=True)
 509 deaths_by_week.rename(columns={'Week ended': 'week_ended'}, inplace=True)
 510 deaths_by_week.to_csv('deaths_by_week.csv', header=True, index=False)
 511 ```
 512
 513 ```python
 514 # deaths_by_week = deaths_headlines.merge(raw_data_2020.droplevel(1, axis='columns')['Week ended'], left_index=True, right_index=True)
 515 # deaths_by_week.rename(columns={'Week ended': 'week_ended'}, inplace=True)
 516 # deaths_by_week.to_csv('deaths_by_week.csv', header=True, index=False)
 517 ```
 518
 519 # Plots for UK nations
 520
 521 ```python
 522 # Radar plot code taken from example at https://stackoverflow.com/questions/42878485/getting-matplotlib-radar-plot-with-pandas#
 523
 524 fig = plt.figure(figsize=(10, 10))
 525 ax = fig.add_subplot(111, projection="polar")
 526
 527 theta = np.roll(
 528     np.flip(
 529         np.arange(len(deaths_headlines_e))/float(len(deaths_headlines_e))*2.*np.pi),
 530     14)
 531 l15, = ax.plot(theta, deaths_headlines_e['total_2015'], color="#e47d7d", label="2015") # 0
 532 l16, = ax.plot(theta, deaths_headlines_e['total_2016'], color="#afc169", label="2016") # 72 , d0e47d
 533 l17, = ax.plot(theta, deaths_headlines_e['total_2017'], color="#7de4a6", label="2017") # 144
 534 l18, = ax.plot(theta, deaths_headlines_e['total_2018'], color="#7da6e4", label="2018") # 216
 535 l19, = ax.plot(theta, deaths_headlines_e['total_2019'], color="#d07de4", label="2019") # 288
 536
 537 lmean, = ax.plot(theta, deaths_headlines_e['previous_mean'], color="black", linestyle='dashed', label="mean")
 538
 539 l20, = ax.plot(theta, deaths_headlines_e['total_2020'], color="red", label="2020")
 540
 541 # deaths_headlines.total_2019.plot(ax=ax)
 542
 543 def _closeline(line):
 544     x, y = line.get_data()
 545     x = np.concatenate((x, [x[0]]))
 546     y = np.concatenate((y, [y[0]]))
 547     line.set_data(x, y)
 548
 549 [_closeline(l) for l in [l19, l18, l17, l16, l15, lmean]]
 550
 551
 552 ax.set_xticks(theta)
 553 ax.set_xticklabels(deaths_headlines_e.index)
 554 plt.legend()
 555 plt.title("Deaths by week over years, England")
 556 plt.savefig('deaths-radar_england.png')
 557 plt.show()
 558 ```
 559
 560 ```python
 561 # Radar plot code taken from example at https://stackoverflow.com/questions/42878485/getting-matplotlib-radar-plot-with-pandas#
 562
 563 fig = plt.figure(figsize=(10, 10))
 564 ax = fig.add_subplot(111, projection="polar")
 565
 566 theta = np.roll(
 567     np.flip(
 568         np.arange(len(deaths_headlines_w))/float(len(deaths_headlines_w))*2.*np.pi),
 569     14)
 570 l15, = ax.plot(theta, deaths_headlines_w['total_2015'], color="#e47d7d", label="2015") # 0
 571 l16, = ax.plot(theta, deaths_headlines_w['total_2016'], color="#afc169", label="2016") # 72 , d0e47d
 572 l17, = ax.plot(theta, deaths_headlines_w['total_2017'], color="#7de4a6", label="2017") # 144
 573 l18, = ax.plot(theta, deaths_headlines_w['total_2018'], color="#7da6e4", label="2018") # 216
 574 l19, = ax.plot(theta, deaths_headlines_w['total_2019'], color="#d07de4", label="2019") # 288
 575
 576 lmean, = ax.plot(theta, deaths_headlines_w['previous_mean'], color="black", linestyle='dashed', label="mean")
 577
 578 l20, = ax.plot(theta, deaths_headlines_w['total_2020'], color="red", label="2020")
 579
 580
 581 def _closeline(line):
 582     x, y = line.get_data()
 583     x = np.concatenate((x, [x[0]]))
 584     y = np.concatenate((y, [y[0]]))
 585     line.set_data(x, y)
 586
 587 [_closeline(l) for l in [l19, l18, l17, l16, l15, lmean]]
 588
 589
 590 ax.set_xticks(theta)
 591 ax.set_xticklabels(deaths_headlines_w.index)
 592 plt.legend()
 593 plt.title("Deaths by week over years, Wales")
 594 plt.savefig('deaths-radar_wales.png')
 595 plt.show()
 596 ```
 597
 598 ```python
 599 # Radar plot code taken from example at https://stackoverflow.com/questions/42878485/getting-matplotlib-radar-plot-with-pandas#
 600
 601 fig = plt.figure(figsize=(10, 10))
 602 ax = fig.add_subplot(111, projection="polar")
 603
 604 theta = np.roll(
 605     np.flip(
 606         np.arange(len(deaths_headlines_s))/float(len(deaths_headlines_s))*2.*np.pi),
 607     14)
 608 l15, = ax.plot(theta, deaths_headlines_s['total_2015'], color="#e47d7d", label="2015") # 0
 609 l16, = ax.plot(theta, deaths_headlines_s['total_2016'], color="#afc169", label="2016") # 72 , d0e47d
 610 l17, = ax.plot(theta, deaths_headlines_s['total_2017'], color="#7de4a6", label="2017") # 144
 611 l18, = ax.plot(theta, deaths_headlines_s['total_2018'], color="#7da6e4", label="2018") # 216
 612 l19, = ax.plot(theta, deaths_headlines_s['total_2019'], color="#d07de4", label="2019") # 288
 613
 614 lmean, = ax.plot(theta, deaths_headlines_s['previous_mean'], color="black", linestyle='dashed', label="mean")
 615
 616 l20, = ax.plot(theta, deaths_headlines_s['total_2020'], color="red", label="2020")
 617
 618
 619 def _closeline(line):
 620     x, y = line.get_data()
 621     x = np.concatenate((x, [x[0]]))
 622     y = np.concatenate((y, [y[0]]))
 623     line.set_data(x, y)
 624
 625 [_closeline(l) for l in [l19, l18, l17, l16, l15, lmean]]
 626
 627
 628 ax.set_xticks(theta)
 629 ax.set_xticklabels(deaths_headlines_s.index)
 630 plt.legend()
 631 plt.title("Deaths by week over years, Scotland")
 632 plt.savefig('deaths-radar_scotland.png')
 633 plt.show()
 634 ```
 635
 636 ```python
 637 # Radar plot code taken from example at https://stackoverflow.com/questions/42878485/getting-matplotlib-radar-plot-with-pandas#
 638
 639 fig = plt.figure(figsize=(10, 10))
 640 ax = fig.add_subplot(111, projection="polar")
 641
 642 theta = np.roll(
 643     np.flip(
 644         np.arange(len(deaths_headlines_i))/float(len(deaths_headlines_i))*2.*np.pi),
 645     14)
 646 l15, = ax.plot(theta, deaths_headlines_i['total_2015'], color="#e47d7d", label="2015") # 0
 647 l16, = ax.plot(theta, deaths_headlines_i['total_2016'], color="#afc169", label="2016") # 72 , d0e47d
 648 l17, = ax.plot(theta, deaths_headlines_i['total_2017'], color="#7de4a6", label="2017") # 144
 649 l18, = ax.plot(theta, deaths_headlines_i['total_2018'], color="#7da6e4", label="2018") # 216
 650 l19, = ax.plot(theta, deaths_headlines_i['total_2019'], color="#d07de4", label="2019") # 288
 651
 652 lmean, = ax.plot(theta, deaths_headlines_i['previous_mean'], color="black", linestyle='dashed', label="mean")
 653
 654 l20, = ax.plot(theta, deaths_headlines_i['total_2020'], color="red", label="2020")
 655
 656
 657 def _closeline(line):
 658     x, y = line.get_data()
 659     x = np.concatenate((x, [x[0]]))
 660     y = np.concatenate((y, [y[0]]))
 661     line.set_data(x, y)
 662
 663 [_closeline(l) for l in [l19, l18, l17, l16, l15, lmean]]
 664
 665
 666 ax.set_xticks(theta)
 667 ax.set_xticklabels(deaths_headlines_i.index)
 668 plt.legend()
 669 plt.title("Deaths by week over years, Northern Ireland")
 670 plt.savefig('deaths-radar_northern_ireland.png')
 671 plt.show()
 672 ```
 673
 674 ```python
 675 # list(raw_data_2020.columns)
 676 ```
 677
 678 ```python
 679 # deaths_headlines_e = raw_data_2020.iloc[:, [1]].copy()
 680 # deaths_headlines_e.columns = ['total_2020']
 681 # deaths_headlines_w = raw_data_2020['W92000004'].copy()
 682 # deaths_headlines_e.columns = ['total_2020']
 683 # deaths_headlines_w.columns = ['total_2020']
 684 # deaths_headlines_e.total_2020 -= deaths_headlines_w.total_2020
 685 # deaths_headlines_e.head()
 686 # deaths_headlines_e
 687 ```
 688
 689 ```python
 690
 691 ```
 692
 693 ```python
 694
 695 ```