Hungary: Tolna¶

  • Homepage of project: https://oscovida.github.io
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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 16:42:18 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Hungary", region="Tolna", weeks=5);
2023-03-07T16:42:21.028138 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec 100 100 120 120 140 140 7-day incidence rate 101.0 Tolna, Hungary, last 5 weeks, last data point from 2022-12-21 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec 0 0 25 25 50 50 75 75 100 100 daily change Hungary-Tolna new cases (rolling 7d mean) Hungary-Tolna 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-Tolna cases daily growth factor Hungary-Tolna cases daily growth factor (rolling mean) Hungary-Tolna 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="Tolna");
2023-03-07T16:42:25.539333 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 7-day incidence rate 101.0 Tolna, 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 0 200 200 400 400 600 600 800 800 daily change Hungary-Tolna new cases (rolling 7d mean) Hungary-Tolna 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-Tolna cases daily growth factor Hungary-Tolna cases daily growth factor (rolling mean) Hungary-Tolna 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="Tolna");
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[5], line 1
----> 1 compare_plot(country="Hungary", region="Tolna");

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="Tolna")

# get population of the region for future normalisation:
inhabitants = population(country="Hungary", region="Tolna")
print(f'Population of country="Hungary", region="Tolna": {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="Tolna": None people
Out[6]:
total cases daily new cases
Dátum
2022-12-21 47760 101
2022-12-14 47659 97
2022-12-07 47562 104
2022-11-30 47458 90
2022-11-23 47368 115
2022-11-16 47253 149
2022-11-09 47104 379
2022-10-26 46725 290
2022-10-19 46435 379
2022-10-12 46056 374
2022-10-05 45682 511
2022-09-28 45171 416
2022-09-21 44755 397
2022-09-14 44358 330
2022-09-07 44028 245
2022-08-31 43783 323
2022-08-24 43460 399
2022-08-17 43061 475
2022-08-10 42586 504
2022-08-03 42082 536
2022-07-27 41546 382
2022-07-20 41164 257
2022-07-13 40907 201
2022-07-06 40706 100
2022-06-29 40606 64
2022-06-22 40542 36
2022-06-15 40506 36
2022-06-08 40470 13
2022-06-01 40457 16
2022-05-25 40441 42
2022-05-18 40399 99
2022-05-11 40300 132
2022-05-02 40168 58
2022-04-28 40110 40
2022-04-27 40070 55
2022-04-26 40015 28
2022-04-25 39987 62
2022-04-22 39925 29
2022-04-21 39896 52
2022-04-20 39844 42
2022-04-19 39802 65
2022-04-14 39737 32
2022-04-13 39705 63
2022-04-12 39642 46
2022-04-11 39596 74
2022-04-08 39522 109
2022-04-06 39413 54
2022-04-05 39359 106
2022-04-01 39253 31
2022-03-31 39222 48
2022-03-30 39174 52
2022-03-29 39122 45
2022-03-28 39077 70
2022-03-25 39007 42
2022-03-24 38965 53
2022-03-23 38912 52
2022-03-22 38860 32
2022-03-21 38828 92
2022-03-18 38736 60
2022-03-17 38676 46
2022-03-16 38630 70
2022-03-11 38560 33
2022-03-10 38527 54
2022-03-09 38473 57
2022-03-08 38416 44
2022-03-07 38372 105
2022-03-04 38267 60
2022-03-03 38207 77
2022-03-02 38130 76
2022-03-01 38054 62
2022-02-28 37992 147
2022-02-25 37845 86
2022-02-24 37759 123
2022-02-23 37636 141
2022-02-22 37495 108
2022-02-21 37387 288
2022-02-18 37099 184
2022-02-17 36915 228
2022-02-16 36687 237
2022-02-15 36450 146
2022-02-14 36304 462
2022-02-11 35842 311
2022-02-10 35531 334
2022-02-09 35197 377
2022-02-08 34820 184
2022-02-07 34636 691
2022-02-04 33945 406
2022-02-03 33539 526
2022-02-02 33013 529
2022-02-01 32484 264
2022-01-31 32220 879
2022-01-28 31341 456
2022-01-27 30885 463
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2022-01-24 29824 623
2022-01-21 29201 291
2022-01-20 28910 271
2022-01-19 28639 244
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2022-01-05 26801 106
2022-01-04 26695 58
2022-01-03 26637 133
2021-12-31 26504 47
2021-12-30 26457 83
2021-12-29 26374 53
2021-12-28 26321 44
2021-12-27 26277 188
2021-12-23 26089 93
2021-12-22 25996 84
2021-12-21 25912 60
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2021-12-07 24276 129
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2021-11-29 22781 659
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2021-11-22 21133 498
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2021-11-15 19871 276
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2020-04-20 14 0
2020-04-19 14 0
2020-04-18 14 0
2020-04-17 14 0
2020-04-16 14 1
2020-04-15 13 0
2020-04-14 13 0
2020-04-13 13 0
2020-04-12 13 0
2020-04-11 13 0
2020-04-10 13 0
2020-04-09 13 0
2020-04-08 13 0
2020-04-07 13 1
2020-04-06 12 2
2020-04-05 10 3
2020-04-04 7 0
2020-04-03 7 0
2020-04-02 7 0
2020-04-01 7 0

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: 0:00:09.594753