uk_deaths.md

   1 ---
   2 jupyter:
   3   jupytext:
   4     formats: ipynb,md
   5     text_representation:
   6       extension: .md
   7       format_name: markdown
   8       format_version: '1.2'
   9       jupytext_version: 1.3.4
  10   kernelspec:
  11     display_name: Python 3
  12     language: python
  13     name: python3
  14 ---
  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 = pd.read_csv('uk-deaths-data/publishedweek192020.csv',
  97                        parse_dates=[1], dayfirst=True,
  98                       index_col=0,
  99                       header=[0, 1])
 100 ```
 101
 102 ```python
 103 raw_data_2020_i = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2020.csv',
 104                         parse_dates=[1], dayfirst=True,
 105                       index_col=0,
 106                       header=[0, 1]
 107                            )
 108 deaths_headlines_i = raw_data_2020_i.iloc[:, [1]]
 109 deaths_headlines_i.columns = ['total_2020']
 110 deaths_headlines_i.head()
 111 ```
 112
 113 ```python
 114
 115 ```
 116
 117 ```python
 118
 119 ```
 120
 121 ```python
 122 raw_data_s = pd.read_csv('uk-deaths-data/weekly-deaths-april-20-scotland.csv',
 123                       index_col=0,
 124                       header=0,
 125                         skiprows=2
 126                            )
 127 # raw_data_s
 128 ```
 129
 130 ```python
 131 deaths_headlines_s = raw_data_s[reversed('2015 2016 2017 2018 2019 2020'.split())]
 132 deaths_headlines_s.columns = ['total_' + c for c in deaths_headlines_s.columns]
 133 deaths_headlines_s.reset_index(drop=True, inplace=True)
 134 deaths_headlines_s.index = deaths_headlines_s.index + 1
 135 deaths_headlines_s
 136 ```
 137
 138 ```python
 139
 140 ```
 141
 142 ```python
 143
 144 ```
 145
 146 ```python
 147
 148 ```
 149
 150 ```python
 151
 152 ```
 153
 154 ```python
 155
 156 ```
 157
 158 ```python
 159 # raw_data_2020.head()
 160 ```
 161
 162 ```python
 163 raw_data_2020['W92000004', 'Wales']
 164 ```
 165
 166 ```python
 167 raw_data_2019 = pd.read_csv('uk-deaths-data/publishedweek522019.csv',
 168                        parse_dates=[1], dayfirst=True,
 169                       index_col=0,
 170                       header=[0, 1])
 171 # raw_data_2019.head()
 172 ```
 173
 174 ```python
 175 raw_data_2018 = pd.read_csv('uk-deaths-data/publishedweek522018.csv',
 176                        parse_dates=[1], dayfirst=True,
 177                       index_col=0,
 178                       header=[0, 1])
 179 # raw_data_2018.head()
 180 ```
 181
 182 ```python
 183 raw_data_2017 = pd.read_csv('uk-deaths-data/publishedweek522017.csv',
 184                        parse_dates=[1], dayfirst=True,
 185                       index_col=0,
 186                       header=[0, 1])
 187 # raw_data_2017.head()
 188 ```
 189
 190 ```python
 191 raw_data_2016 = pd.read_csv('uk-deaths-data/publishedweek522016.csv',
 192                        parse_dates=[1], dayfirst=True,
 193                       index_col=0,
 194                       header=[0, 1])
 195 # raw_data_2016.head()
 196 ```
 197
 198 ```python
 199 raw_data_2015 = pd.read_csv('uk-deaths-data/publishedweek2015.csv',
 200                        parse_dates=[1], dayfirst=True,
 201                       index_col=0,
 202                       header=[0, 1])
 203 # raw_data_2015.head()
 204 ```
 205
 206 ```python
 207 deaths_headlines_e = raw_data_2020.iloc[:, [1]].copy()
 208 deaths_headlines_e.columns = ['total_2020']
 209 deaths_headlines_w = raw_data_2020['W92000004'].copy()
 210 deaths_headlines_e.columns = ['total_2020']
 211 deaths_headlines_w.columns = ['total_2020']
 212 deaths_headlines_e.total_2020 -= deaths_headlines_w.total_2020
 213 deaths_headlines_e.head()
 214 deaths_headlines_e
 215 ```
 216
 217 ```python
 218 dh19e = raw_data_2019.iloc[:, [1]]
 219 dh19w = raw_data_2019['W92000004']
 220 dh19e.columns = ['total_2019']
 221 dh19w.columns = ['total_2019']
 222 dh19e.total_2019 -= dh19w.total_2019
 223 dh19e.head()
 224 ```
 225
 226 ```python
 227 dh19w.head()
 228 ```
 229
 230 ```python
 231 dh18e = raw_data_2018.iloc[:, [1]]
 232 dh18w = raw_data_2018['W92000004']
 233 dh18e.columns = ['total_2018']
 234 dh18w.columns = ['total_2018']
 235 dh18e.total_2018 -= dh18w.total_2018
 236 # dh18e.head()
 237 ```
 238
 239 ```python
 240 dh17e = raw_data_2017.iloc[:, [1]]
 241 dh17w = raw_data_2017['W92000004']
 242 dh17e.columns = ['total_2017']
 243 dh17w.columns = ['total_2017']
 244 dh17e.total_2017 -= dh17w.total_2017
 245 # dh17e.head()
 246 ```
 247
 248 ```python
 249 dh16e = raw_data_2016.iloc[:, [1]]
 250 dh16w = raw_data_2016['W92000004']
 251 dh16e.columns = ['total_2016']
 252 dh16w.columns = ['total_2016']
 253 dh16e.total_2016 -= dh16w.total_2016
 254 # dh16e.head()
 255 ```
 256
 257 ```python
 258 dh15e = raw_data_2015.iloc[:, [1]]
 259 dh15w = raw_data_2015['W92000004']
 260 dh15e.columns = ['total_2015']
 261 dh15w.columns = ['total_2015']
 262 dh15e.total_2015 -= dh15w.total_2015
 263 # dh15e.head()
 264 ```
 265
 266 ```python
 267 # dh18 = raw_data_2018.iloc[:, [1, 2]]
 268 # dh18.columns = ['total_2018', 'total_previous']
 269 # # dh18.head()
 270 ```
 271
 272 ```python
 273 deaths_headlines_e = deaths_headlines_e.merge(dh19e['total_2019'], how='outer', left_index=True, right_index=True)
 274 deaths_headlines_e = deaths_headlines_e.merge(dh18e['total_2018'], how='outer', left_index=True, right_index=True)
 275 deaths_headlines_e = deaths_headlines_e.merge(dh17e['total_2017'], how='outer', left_index=True, right_index=True)
 276 deaths_headlines_e = deaths_headlines_e.merge(dh16e['total_2016'], how='outer', left_index=True, right_index=True)
 277 # deaths_headlines = deaths_headlines.merge(dh15['total_2015'], how='outer', left_index=True, right_index=True)
 278 deaths_headlines_e = deaths_headlines_e.merge(dh15e['total_2015'], how='left', left_index=True, right_index=True)
 279 deaths_headlines_e
 280 ```
 281
 282 ```python
 283 deaths_headlines_s = raw_data_s[reversed('2015 2016 2017 2018 2019 2020'.split())]
 284 deaths_headlines_s.columns = ['total_' + c for c in deaths_headlines_s.columns]
 285 deaths_headlines_s.reset_index(drop=True, inplace=True)
 286 deaths_headlines_s.index = deaths_headlines_s.index + 1
 287 deaths_headlines_s = deaths_headlines_s.loc[1:52]
 288 deaths_headlines_s
 289 ```
 290
 291 ```python
 292 deaths_headlines_w = deaths_headlines_w.merge(dh19w['total_2019'], how='outer', left_index=True, right_index=True)
 293 deaths_headlines_w = deaths_headlines_w.merge(dh18w['total_2018'], how='outer', left_index=True, right_index=True)
 294 deaths_headlines_w = deaths_headlines_w.merge(dh17w['total_2017'], how='outer', left_index=True, right_index=True)
 295 deaths_headlines_w = deaths_headlines_w.merge(dh16w['total_2016'], how='outer', left_index=True, right_index=True)
 296 # deaths_headlines = deaths_headlines.merge(dh15['total_2015'], how='outer', left_index=True, right_index=True)
 297 deaths_headlines_w = deaths_headlines_w.merge(dh15w['total_2015'], how='left', left_index=True, right_index=True)
 298 deaths_headlines_w
 299 ```
 300
 301 ```python
 302 deaths_headlines_i = deaths_headlines_i.merge(dh19i['total_2019'], how='outer', left_index=True, right_index=True)
 303 deaths_headlines_i = deaths_headlines_i.merge(dh18i['total_2018'], how='outer', left_index=True, right_index=True)
 304 deaths_headlines_i = deaths_headlines_i.merge(dh17i['total_2017'], how='outer', left_index=True, right_index=True)
 305 deaths_headlines_i = deaths_headlines_i.merge(dh16i['total_2016'], how='outer', left_index=True, right_index=True)
 306 deaths_headlines_i = deaths_headlines_i.merge(dh15i['total_2015'], how='left', left_index=True, right_index=True)
 307 deaths_headlines_i
 308 ```
 309
 310 ```python
 311 deaths_headlines = deaths_headlines_e + deaths_headlines_w + deaths_headlines_i + deaths_headlines_s
 312 deaths_headlines
 313 ```
 314
 315 ```python
 316 deaths_headlines_e['previous_mean'] = deaths_headlines_e['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)
 317 deaths_headlines_w['previous_mean'] = deaths_headlines_w['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)
 318 deaths_headlines_s['previous_mean'] = deaths_headlines_s['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)
 319 deaths_headlines_i['previous_mean'] = deaths_headlines_i['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)
 320 deaths_headlines['previous_mean'] = deaths_headlines['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)
 321 deaths_headlines
 322 ```
 323
 324 ```python
 325 deaths_headlines['total_2020 total_2019 total_2018 total_2017 total_2016 total_2015'.split()].plot(figsize=(10, 8))
 326 ```
 327
 328 ```python
 329 deaths_headlines_i.plot()
 330 ```
 331
 332 ```python
 333 # Radar plot code taken from example at https://stackoverflow.com/questions/42878485/getting-matplotlib-radar-plot-with-pandas#
 334
 335 fig = plt.figure(figsize=(10, 10))
 336 ax = fig.add_subplot(111, projection="polar")
 337
 338 theta = np.roll(
 339     np.flip(
 340         np.arange(len(deaths_headlines))/float(len(deaths_headlines))*2.*np.pi),
 341     14)
 342 # l15, = ax.plot(theta, deaths_headlines['total_2015'], color="#b56363", label="2015") # 0
 343 # l16, = ax.plot(theta, deaths_headlines['total_2016'], color="#a4b563", label="2016") # 72
 344 # l17, = ax.plot(theta, deaths_headlines['total_2017'], color="#63b584", label="2017") # 144
 345 # l18, = ax.plot(theta, deaths_headlines['total_2018'], color="#6384b5", label="2018") # 216
 346 # l19, = ax.plot(theta, deaths_headlines['total_2019'], color="#a4635b", label="2019") # 288
 347 l15, = ax.plot(theta, deaths_headlines['total_2015'], color="#e47d7d", label="2015") # 0
 348 l16, = ax.plot(theta, deaths_headlines['total_2016'], color="#afc169", label="2016") # 72 , d0e47d
 349 l17, = ax.plot(theta, deaths_headlines['total_2017'], color="#7de4a6", label="2017") # 144
 350 l18, = ax.plot(theta, deaths_headlines['total_2018'], color="#7da6e4", label="2018") # 216
 351 l19, = ax.plot(theta, deaths_headlines['total_2019'], color="#d07de4", label="2019") # 288
 352
 353 lmean, = ax.plot(theta, deaths_headlines['previous_mean'], color="black", linestyle='dashed', label="mean")
 354
 355 l20, = ax.plot(theta, deaths_headlines['total_2020'], color="red", label="2020")
 356
 357 # deaths_headlines.total_2019.plot(ax=ax)
 358
 359 def _closeline(line):
 360     x, y = line.get_data()
 361     x = np.concatenate((x, [x[0]]))
 362     y = np.concatenate((y, [y[0]]))
 363     line.set_data(x, y)
 364
 365 [_closeline(l) for l in [l19, l18, l17, l16, l15, lmean]]
 366
 367
 368 ax.set_xticks(theta)
 369 ax.set_xticklabels(deaths_headlines.index)
 370 plt.legend()
 371 plt.title("Deaths by week over years, all UK")
 372 plt.savefig('deaths-radar.png')
 373 plt.show()
 374 ```
 375
 376 # Excess deaths calculation
 377
 378 ```python
 379 raw_data_2020.loc[12, 'Week ended']
 380 ```
 381
 382 ```python
 383 raw_data_2020.iloc[-1]['Week ended']
 384 ```
 385
 386 ```python
 387 raw_data_2020.loc[12].droplevel(1)['Week ended']
 388 ```
 389
 390 ```python
 391 raw_data_2020.iloc[-1].droplevel(1)['Week ended']
 392 ```
 393
 394 ```python
 395 (deaths_headlines.loc[12:].total_2020 - deaths_headlines.loc[12:].previous_mean).sum()
 396 ```
 397
 398 ```python
 399 deaths_headlines.previous_mean.sum()
 400 ```
 401
 402 ```python
 403 excess_death_data = {
 404     'start_date': str(raw_data_2020.loc[12].droplevel(1)['Week ended']),
 405     'end_date': str(raw_data_2020.iloc[-1].droplevel(1)['Week ended']),
 406     'excess_deaths': (deaths_headlines.loc[12:].total_2020 - deaths_headlines.loc[12:].previous_mean).sum()
 407 }
 408
 409 with open('excess_deaths.json', 'w') as f:
 410     json.dump(excess_death_data, f)
 411 ```
 412
 413 # Plots for UK nations
 414
 415 ```python
 416 # Radar plot code taken from example at https://stackoverflow.com/questions/42878485/getting-matplotlib-radar-plot-with-pandas#
 417
 418 fig = plt.figure(figsize=(10, 10))
 419 ax = fig.add_subplot(111, projection="polar")
 420
 421 theta = np.roll(
 422     np.flip(
 423         np.arange(len(deaths_headlines_e))/float(len(deaths_headlines_e))*2.*np.pi),
 424     14)
 425 l15, = ax.plot(theta, deaths_headlines_e['total_2015'], color="#e47d7d", label="2015") # 0
 426 l16, = ax.plot(theta, deaths_headlines_e['total_2016'], color="#afc169", label="2016") # 72 , d0e47d
 427 l17, = ax.plot(theta, deaths_headlines_e['total_2017'], color="#7de4a6", label="2017") # 144
 428 l18, = ax.plot(theta, deaths_headlines_e['total_2018'], color="#7da6e4", label="2018") # 216
 429 l19, = ax.plot(theta, deaths_headlines_e['total_2019'], color="#d07de4", label="2019") # 288
 430
 431 lmean, = ax.plot(theta, deaths_headlines_e['previous_mean'], color="black", linestyle='dashed', label="mean")
 432
 433 l20, = ax.plot(theta, deaths_headlines_e['total_2020'], color="red", label="2020")
 434
 435 # deaths_headlines.total_2019.plot(ax=ax)
 436
 437 def _closeline(line):
 438     x, y = line.get_data()
 439     x = np.concatenate((x, [x[0]]))
 440     y = np.concatenate((y, [y[0]]))
 441     line.set_data(x, y)
 442
 443 [_closeline(l) for l in [l19, l18, l17, l16, l15, lmean]]
 444
 445
 446 ax.set_xticks(theta)
 447 ax.set_xticklabels(deaths_headlines_e.index)
 448 plt.legend()
 449 plt.title("Deaths by week over years, England")
 450 plt.savefig('deaths-radar_england.png')
 451 plt.show()
 452 ```
 453
 454 ```python
 455 # Radar plot code taken from example at https://stackoverflow.com/questions/42878485/getting-matplotlib-radar-plot-with-pandas#
 456
 457 fig = plt.figure(figsize=(10, 10))
 458 ax = fig.add_subplot(111, projection="polar")
 459
 460 theta = np.roll(
 461     np.flip(
 462         np.arange(len(deaths_headlines_w))/float(len(deaths_headlines_w))*2.*np.pi),
 463     14)
 464 l15, = ax.plot(theta, deaths_headlines_w['total_2015'], color="#e47d7d", label="2015") # 0
 465 l16, = ax.plot(theta, deaths_headlines_w['total_2016'], color="#afc169", label="2016") # 72 , d0e47d
 466 l17, = ax.plot(theta, deaths_headlines_w['total_2017'], color="#7de4a6", label="2017") # 144
 467 l18, = ax.plot(theta, deaths_headlines_w['total_2018'], color="#7da6e4", label="2018") # 216
 468 l19, = ax.plot(theta, deaths_headlines_w['total_2019'], color="#d07de4", label="2019") # 288
 469
 470 lmean, = ax.plot(theta, deaths_headlines_w['previous_mean'], color="black", linestyle='dashed', label="mean")
 471
 472 l20, = ax.plot(theta, deaths_headlines_w['total_2020'], color="red", label="2020")
 473
 474
 475 def _closeline(line):
 476     x, y = line.get_data()
 477     x = np.concatenate((x, [x[0]]))
 478     y = np.concatenate((y, [y[0]]))
 479     line.set_data(x, y)
 480
 481 [_closeline(l) for l in [l19, l18, l17, l16, l15, lmean]]
 482
 483
 484 ax.set_xticks(theta)
 485 ax.set_xticklabels(deaths_headlines_w.index)
 486 plt.legend()
 487 plt.title("Deaths by week over years, Wales")
 488 plt.savefig('deaths-radar_wales.png')
 489 plt.show()
 490 ```
 491
 492 ```python
 493 # Radar plot code taken from example at https://stackoverflow.com/questions/42878485/getting-matplotlib-radar-plot-with-pandas#
 494
 495 fig = plt.figure(figsize=(10, 10))
 496 ax = fig.add_subplot(111, projection="polar")
 497
 498 theta = np.roll(
 499     np.flip(
 500         np.arange(len(deaths_headlines_s))/float(len(deaths_headlines_s))*2.*np.pi),
 501     14)
 502 l15, = ax.plot(theta, deaths_headlines_s['total_2015'], color="#e47d7d", label="2015") # 0
 503 l16, = ax.plot(theta, deaths_headlines_s['total_2016'], color="#afc169", label="2016") # 72 , d0e47d
 504 l17, = ax.plot(theta, deaths_headlines_s['total_2017'], color="#7de4a6", label="2017") # 144
 505 l18, = ax.plot(theta, deaths_headlines_s['total_2018'], color="#7da6e4", label="2018") # 216
 506 l19, = ax.plot(theta, deaths_headlines_s['total_2019'], color="#d07de4", label="2019") # 288
 507
 508 lmean, = ax.plot(theta, deaths_headlines_s['previous_mean'], color="black", linestyle='dashed', label="mean")
 509
 510 l20, = ax.plot(theta, deaths_headlines_s['total_2020'], color="red", label="2020")
 511
 512
 513 def _closeline(line):
 514     x, y = line.get_data()
 515     x = np.concatenate((x, [x[0]]))
 516     y = np.concatenate((y, [y[0]]))
 517     line.set_data(x, y)
 518
 519 [_closeline(l) for l in [l19, l18, l17, l16, l15, lmean]]
 520
 521
 522 ax.set_xticks(theta)
 523 ax.set_xticklabels(deaths_headlines_s.index)
 524 plt.legend()
 525 plt.title("Deaths by week over years, Scotland")
 526 plt.savefig('deaths-radar_scotland.png')
 527 plt.show()
 528 ```
 529
 530 ```python
 531 # Radar plot code taken from example at https://stackoverflow.com/questions/42878485/getting-matplotlib-radar-plot-with-pandas#
 532
 533 fig = plt.figure(figsize=(10, 10))
 534 ax = fig.add_subplot(111, projection="polar")
 535
 536 theta = np.roll(
 537     np.flip(
 538         np.arange(len(deaths_headlines_i))/float(len(deaths_headlines_i))*2.*np.pi),
 539     14)
 540 l15, = ax.plot(theta, deaths_headlines_i['total_2015'], color="#e47d7d", label="2015") # 0
 541 l16, = ax.plot(theta, deaths_headlines_i['total_2016'], color="#afc169", label="2016") # 72 , d0e47d
 542 l17, = ax.plot(theta, deaths_headlines_i['total_2017'], color="#7de4a6", label="2017") # 144
 543 l18, = ax.plot(theta, deaths_headlines_i['total_2018'], color="#7da6e4", label="2018") # 216
 544 l19, = ax.plot(theta, deaths_headlines_i['total_2019'], color="#d07de4", label="2019") # 288
 545
 546 lmean, = ax.plot(theta, deaths_headlines_i['previous_mean'], color="black", linestyle='dashed', label="mean")
 547
 548 l20, = ax.plot(theta, deaths_headlines_i['total_2020'], color="red", label="2020")
 549
 550
 551 def _closeline(line):
 552     x, y = line.get_data()
 553     x = np.concatenate((x, [x[0]]))
 554     y = np.concatenate((y, [y[0]]))
 555     line.set_data(x, y)
 556
 557 [_closeline(l) for l in [l19, l18, l17, l16, l15, lmean]]
 558
 559
 560 ax.set_xticks(theta)
 561 ax.set_xticklabels(deaths_headlines_i.index)
 562 plt.legend()
 563 plt.title("Deaths by week over years, Northern Ireland")
 564 plt.savefig('deaths-radar_northern_ireland.png')
 565 plt.show()
 566 ```
 567
 568 ```python
 569
 570 ```