Hungary: Budapest¶

  • Homepage of project: https://oscovida.github.io
  • Plots are explained at http://oscovida.github.io/plots.html
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In [1]:
import datetime
import time

start = datetime.datetime.now()
print(f"Notebook executed on: {start.strftime('%d/%m/%Y %H:%M:%S%Z')} {time.tzname[time.daylight]}")

Notebook executed on: 07/03/2023 16:41:45 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Hungary", region="Budapest", weeks=5);
2023-03-07T16:41:49.375061 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec 800 800 900 900 1000 1000 1100 1100 7-day incidence rate 1042.0 Budapest, Hungary, last 5 weeks, last data point from 2022-12-21 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec 0 0 250 250 500 500 750 750 1000 1000 daily change Hungary-Budapest new cases (rolling 7d mean) Hungary-Budapest new cases 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec No data available to plot daily change in deaths 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Hungary-Budapest cases daily growth factor Hungary-Budapest cases daily growth factor (rolling mean) Hungary-Budapest estimated R (using cases) 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec No data available to plot R & growth factor (based on deaths) 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec 0 0 1000 1000 2000 2000 3000 3000 cases doubling time [days]
In [4]:
overview(country="Hungary", region="Budapest");
2023-03-07T16:41:54.066161 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 5000 5000 10000 10000 15000 15000 20000 20000 7-day incidence rate 1042.0 Budapest, Hungary, last data point from 2022-12-21 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 2000 2000 4000 4000 6000 6000 8000 8000 daily change Hungary-Budapest new cases (rolling 7d mean) Hungary-Budapest new cases May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 No data available to plot daily change in deaths May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) Hungary-Budapest cases daily growth factor Hungary-Budapest cases daily growth factor (rolling mean) Hungary-Budapest estimated R (using cases) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 No data available to plot R & growth factor (based on deaths) May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 0 0 5000 5000 10000 10000 15000 15000 cases doubling time [days]
In [5]:
compare_plot(country="Hungary", region="Budapest");

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[5], line 1
----> 1 compare_plot(country="Hungary", region="Budapest");

File /tank/oscovida/work/oscovida.github.io/oscovida.github.io/.venv/lib/python3.9/site-packages/oscovida/oscovida.py:1976, in compare_plot(country, region, subregion, savefig, normalise, dates, align)
   1974 if normalise:
   1975     _population = population(country=country, region=region, subregion=subregion)
-> 1976     c *= 100000 / _population
   1977     d *= 100000 / _population
   1979 if not subregion and not region:    # i.e. not a region of Germany

TypeError: unsupported operand type(s) for /: 'int' and 'NoneType'
In [6]:
# load the data
cases, deaths = get_region_hungary(county="Budapest")

# get population of the region for future normalisation:
inhabitants = population(country="Hungary", region="Budapest")
print(f'Population of country="Hungary", region="Budapest": {inhabitants} people')

# compose into one table
table = compose_dataframe_summary(cases, deaths)

# show tables with up to 1000 rows
pd.set_option("display.max_rows", 1000)

# display the table
table

Population of country="Hungary", region="Budapest": None people
Out[6]:
total cases daily new cases
Dátum
2022-12-21 386303 1042
2022-12-14 385261 1171
2022-12-07 384090 1011
2022-11-30 383079 795
2022-11-23 382284 786
2022-11-16 381498 835
2022-11-09 380663 1977
2022-10-26 378686 1437
2022-10-19 377249 1819
2022-10-12 375430 1901
2022-10-05 373529 1799
2022-09-28 371730 1280
2022-09-21 370450 1170
2022-09-14 369280 1158
2022-09-07 368122 1217
2022-08-31 366905 1485
2022-08-24 365420 1917
2022-08-17 363503 2131
2022-08-10 361372 2637
2022-08-03 358735 4783
2022-07-27 353952 2850
2022-07-20 351102 2422
2022-07-13 348680 2129
2022-07-06 346551 1573
2022-06-29 344978 1082
2022-06-22 343896 760
2022-06-15 343136 601
2022-06-08 342535 597
2022-06-01 341938 688
2022-05-25 341250 827
2022-05-18 340423 1138
2022-05-11 339285 1653
2022-05-02 337632 952
2022-04-28 336680 369
2022-04-27 336311 494
2022-04-26 335817 306
2022-04-25 335511 844
2022-04-22 334667 474
2022-04-21 334193 620
2022-04-20 333573 345
2022-04-19 333228 1387
2022-04-14 331841 651
2022-04-13 331190 662
2022-04-12 330528 304
2022-04-11 330224 1245
2022-04-08 328979 1248
2022-04-06 327731 682
2022-04-05 327049 1495
2022-04-01 325554 589
2022-03-31 324965 654
2022-03-30 324311 667
2022-03-29 323644 340
2022-03-28 323304 1367
2022-03-25 321937 588
2022-03-24 321349 621
2022-03-23 320728 627
2022-03-22 320101 294
2022-03-21 319807 1276
2022-03-18 318531 672
2022-03-17 317859 294
2022-03-16 317565 1180
2022-03-11 316385 375
2022-03-10 316010 445
2022-03-09 315565 470
2022-03-08 315095 218
2022-03-07 314877 1035
2022-03-04 313842 469
2022-03-03 313373 689
2022-03-02 312684 581
2022-03-01 312103 269
2022-02-28 311834 1387
2022-02-25 310447 629
2022-02-24 309818 813
2022-02-23 309005 880
2022-02-22 308125 437
2022-02-21 307688 2126
2022-02-18 305562 1015
2022-02-17 304547 1317
2022-02-16 303230 1282
2022-02-15 301948 545
2022-02-14 301403 3093
2022-02-11 298310 1576
2022-02-10 296734 1795
2022-02-09 294939 1884
2022-02-08 293055 1077
2022-02-07 291978 4822
2022-02-04 287156 2299
2022-02-03 284857 2663
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2022-02-01 279433 1507
2022-01-31 277926 7256
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2022-01-07 220179 1937
2022-01-06 218242 1887
2022-01-05 216355 1557
2022-01-04 214798 709
2022-01-03 214089 1581
2021-12-31 212508 859
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2021-12-28 210032 213
2021-12-27 209819 1606
2021-12-23 208213 645
2021-12-22 207568 511
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2020-06-14 1930 3
2020-06-13 1927 5
2020-06-12 1922 5
2020-06-11 1917 7
2020-06-10 1910 4
2020-06-09 1906 0
2020-06-08 1906 3
2020-06-07 1903 5
2020-06-06 1898 12
2020-06-05 1886 6
2020-06-04 1880 12
2020-06-03 1868 2
2020-06-02 1866 18
2020-06-01 1848 7
2020-05-31 1841 3
2020-05-30 1838 9
2020-05-29 1829 11
2020-05-28 1818 13
2020-05-27 1805 16
2020-05-26 1789 5
2020-05-25 1784 4
2020-05-24 1780 15
2020-05-23 1765 11
2020-05-22 1754 28
2020-05-21 1726 16
2020-05-20 1710 21
2020-05-19 1689 8
2020-05-18 1681 9
2020-05-17 1672 17
2020-05-16 1655 19
2020-05-15 1636 17
2020-05-14 1619 10
2020-05-13 1609 14
2020-05-12 1595 8
2020-05-11 1587 8
2020-05-10 1579 17
2020-05-09 1562 10
2020-05-08 1552 8
2020-05-07 1544 16
2020-05-06 1528 25
2020-05-05 1503 19
2020-05-04 1484 11
2020-05-03 1473 25
2020-05-02 1448 40
2020-05-01 1408 40
2020-04-30 1368 21
2020-04-29 1347 42
2020-04-28 1305 19
2020-04-27 1286 12
2020-04-26 1274 27
2020-04-25 1247 26
2020-04-24 1221 48
2020-04-23 1173 60
2020-04-22 1113 31
2020-04-21 1082 80
2020-04-20 1002 31
2020-04-19 971 42
2020-04-18 929 46
2020-04-17 883 78
2020-04-16 805 35
2020-04-15 770 38
2020-04-14 732 29
2020-04-13 703 34
2020-04-12 669 14
2020-04-11 655 75
2020-04-10 580 152
2020-04-09 428 41
2020-04-08 387 42
2020-04-07 345 28
2020-04-06 317 4
2020-04-05 313 22
2020-04-04 291 30
2020-04-03 261 18
2020-04-02 243 11
2020-04-01 232 17

Explore the data in your web browser¶

  • If you want to execute this notebook, click here to use myBinder
  • and wait (~1 to 2 minutes)
  • Then press SHIFT+RETURN to advance code cell to code cell
  • See http://jupyter.org for more details on how to use Jupyter Notebook

Acknowledgements:¶

  • Johns Hopkins University provides data for countries
  • Robert Koch Institute provides data for within Germany
  • Atlo Team for gathering and providing data from Hungary (https://atlo.team/koronamonitor/)
  • Open source and scientific computing community for the data tools
  • Github for hosting repository and html files
  • Project Jupyter for the Notebook and binder service
  • The H2020 project Photon and Neutron Open Science Cloud (PaNOSC)
In [7]:
print(f"Download of data from Johns Hopkins university: cases at {fetch_cases_last_execution()} and "
      f"deaths at {fetch_deaths_last_execution()}.")

Download of data from Johns Hopkins university: cases at 07/03/2023 09:31:22 and deaths at 07/03/2023 09:31:21.
In [8]:
# to force a fresh download of data, run "clear_cache()"
In [9]:
print(f"Notebook execution took: {datetime.datetime.now()-start}")

Notebook execution took: 0:00:10.083796