Hungary: Zala¶

  • 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 16:42:17 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Hungary", region="Zala", weeks=5);
2023-03-07T16:42:20.911156 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec 135 135 140 140 145 145 150 150 155 155 7-day incidence rate Zala, Hungary, last 5 weeks, last data point from 2022-12-21 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec 0 0 50 50 100 100 150 150 daily change Hungary-Zala new cases (rolling 7d mean) Hungary-Zala 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 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on cases) Hungary-Zala cases daily growth factor Hungary-Zala cases daily growth factor (rolling mean) Hungary-Zala 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 cases doubling time [days]
In [4]:
overview(country="Hungary", region="Zala");
2023-03-07T16:42:25.083000 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 1000 1000 2000 2000 3000 3000 7-day incidence rate 143.0 Zala, 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 0 500 500 1000 1000 daily change Hungary-Zala new cases (rolling 7d mean) Hungary-Zala 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 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 R & growth factor (based on cases) Hungary-Zala cases daily growth factor Hungary-Zala cases daily growth factor (rolling mean) Hungary-Zala 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 20000 20000 cases doubling time [days]
In [5]:
compare_plot(country="Hungary", region="Zala");

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

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="Zala")

# get population of the region for future normalisation:
inhabitants = population(country="Hungary", region="Zala")
print(f'Population of country="Hungary", region="Zala": {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="Zala": None people
Out[6]:
total cases daily new cases
Dátum
2022-12-21 58820 143
2022-12-14 58677 148
2022-12-07 58529 154
2022-11-30 58375 134
2022-11-23 58241 155
2022-11-16 58086 152
2022-11-09 57934 551
2022-10-26 57383 407
2022-10-19 56976 550
2022-10-12 56426 531
2022-10-05 55895 503
2022-09-28 55392 383
2022-09-21 55009 439
2022-09-14 54570 406
2022-09-07 54164 330
2022-08-31 53834 396
2022-08-24 53438 394
2022-08-17 53044 413
2022-08-10 52631 511
2022-08-03 52120 608
2022-07-27 51512 445
2022-07-20 51067 368
2022-07-13 50699 232
2022-07-06 50467 109
2022-06-29 50358 60
2022-06-22 50298 37
2022-06-15 50261 34
2022-06-08 50227 35
2022-06-01 50192 68
2022-05-25 50124 122
2022-05-18 50002 161
2022-05-11 49841 210
2022-05-02 49631 95
2022-04-28 49536 47
2022-04-27 49489 53
2022-04-26 49436 61
2022-04-25 49375 74
2022-04-22 49301 55
2022-04-21 49246 68
2022-04-20 49178 36
2022-04-19 49142 62
2022-04-14 49080 43
2022-04-13 49037 42
2022-04-12 48995 37
2022-04-11 48958 62
2022-04-08 48896 94
2022-04-06 48802 63
2022-04-05 48739 108
2022-04-01 48631 37
2022-03-31 48594 67
2022-03-30 48527 71
2022-03-29 48456 38
2022-03-28 48418 92
2022-03-25 48326 41
2022-03-24 48285 38
2022-03-23 48247 66
2022-03-22 48181 41
2022-03-21 48140 106
2022-03-18 48034 67
2022-03-17 47967 40
2022-03-16 47927 43
2022-03-11 47884 38
2022-03-10 47846 28
2022-03-09 47818 41
2022-03-08 47777 40
2022-03-07 47737 103
2022-03-04 47634 53
2022-03-03 47581 51
2022-03-02 47530 56
2022-03-01 47474 58
2022-02-28 47416 113
2022-02-25 47303 94
2022-02-24 47209 107
2022-02-23 47102 129
2022-02-22 46973 110
2022-02-21 46863 242
2022-02-18 46621 139
2022-02-17 46482 207
2022-02-16 46275 250
2022-02-15 46025 178
2022-02-14 45847 408
2022-02-11 45439 296
2022-02-10 45143 345
2022-02-09 44798 429
2022-02-08 44369 212
2022-02-07 44157 877
2022-02-04 43280 365
2022-02-03 42915 509
2022-02-02 42406 498
2022-02-01 41908 273
2022-01-31 41635 1368
2022-01-28 40267 726
2022-01-27 39541 604
2022-01-26 38937 646
2022-01-25 38291 251
2022-01-24 38040 1335
2022-01-21 36705 586
2022-01-20 36119 478
2022-01-19 35641 461
2022-01-18 35180 280
2022-01-17 34900 633
2022-01-14 34267 324
2022-01-13 33943 282
2022-01-12 33661 264
2022-01-11 33397 111
2022-01-10 33286 343
2022-01-07 32943 189
2022-01-06 32754 169
2022-01-05 32585 140
2022-01-04 32445 76
2022-01-03 32369 175
2021-12-31 32194 116
2021-12-30 32078 122
2021-12-29 31956 60
2021-12-28 31896 96
2021-12-27 31800 251
2021-12-23 31549 115
2021-12-22 31434 124
2021-12-21 31310 108
2021-12-20 31202 387
2021-12-17 30815 225
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2021-12-13 30018 628
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2021-12-07 28585 171
2021-12-06 28414 802
2021-12-03 27612 419
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2020-12-31 9221 90
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2020-04-23 42 0
2020-04-22 42 2
2020-04-21 40 0
2020-04-20 40 0
2020-04-19 40 3
2020-04-18 37 0
2020-04-17 37 4
2020-04-16 33 2
2020-04-15 31 1
2020-04-14 30 2
2020-04-13 28 0
2020-04-12 28 2
2020-04-11 26 3
2020-04-10 23 4
2020-04-09 19 8
2020-04-08 11 0
2020-04-07 11 0
2020-04-06 11 1
2020-04-05 10 0
2020-04-04 10 2
2020-04-03 8 1
2020-04-02 7 0
2020-04-01 7 1

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: 0:00:08.976645