Hungary: Bács-Kiskun¶

  • 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="Bács-Kiskun", weeks=5);
2023-03-07T16:41:49.226311 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec 180 180 190 190 200 200 210 210 7-day incidence rate 197.0 Bács-Kiskun, Hungary, last 5 weeks, last data point from 2022-12-21 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec 0 0 50 50 100 100 150 150 200 200 daily change Hungary-Bács-Kiskun new cases (rolling 7d mean) Hungary-Bács-Kiskun 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-Bács-Kiskun cases daily growth factor Hungary-Bács-Kiskun cases daily growth factor (rolling mean) Hungary-Bács-Kiskun 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="Bács-Kiskun");
2023-03-07T16:41:53.835014 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 2000 2000 4000 4000 7-day incidence rate 197.0 Bács-Kiskun, 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 1500 1500 2000 2000 daily change Hungary-Bács-Kiskun new cases (rolling 7d mean) Hungary-Bács-Kiskun 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-Bács-Kiskun cases daily growth factor Hungary-Bács-Kiskun cases daily growth factor (rolling mean) Hungary-Bács-Kiskun 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 10000 10000 20000 20000 30000 30000 40000 40000 cases doubling time [days]
In [5]:
compare_plot(country="Hungary", region="Bács-Kiskun");
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[5], line 1
----> 1 compare_plot(country="Hungary", region="Bács-Kiskun");

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="Bács-Kiskun")

# get population of the region for future normalisation:
inhabitants = population(country="Hungary", region="Bács-Kiskun")
print(f'Population of country="Hungary", region="Bács-Kiskun": {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="Bács-Kiskun": None people
Out[6]:
total cases daily new cases
Dátum
2022-12-21 110352 197
2022-12-14 110155 184
2022-12-07 109971 177
2022-11-30 109794 184
2022-11-23 109610 216
2022-11-16 109394 217
2022-11-09 109177 730
2022-10-26 108447 505
2022-10-19 107942 577
2022-10-12 107365 668
2022-10-05 106697 754
2022-09-28 105943 648
2022-09-21 105295 756
2022-09-14 104539 720
2022-09-07 103819 628
2022-08-31 103191 768
2022-08-24 102423 894
2022-08-17 101529 989
2022-08-10 100540 1041
2022-08-03 99499 1081
2022-07-27 98418 725
2022-07-20 97693 492
2022-07-13 97201 300
2022-07-06 96901 167
2022-06-29 96734 96
2022-06-22 96638 68
2022-06-15 96570 53
2022-06-08 96517 49
2022-06-01 96468 55
2022-05-25 96413 126
2022-05-18 96287 210
2022-05-11 96077 338
2022-05-02 95739 187
2022-04-28 95552 85
2022-04-27 95467 101
2022-04-26 95366 72
2022-04-25 95294 149
2022-04-22 95145 96
2022-04-21 95049 165
2022-04-20 94884 59
2022-04-19 94825 208
2022-04-14 94617 106
2022-04-13 94511 140
2022-04-12 94371 45
2022-04-11 94326 196
2022-04-08 94130 233
2022-04-06 93897 162
2022-04-05 93735 333
2022-04-01 93402 104
2022-03-31 93298 124
2022-03-30 93174 136
2022-03-29 93038 73
2022-03-28 92965 230
2022-03-25 92735 93
2022-03-24 92642 121
2022-03-23 92521 127
2022-03-22 92394 52
2022-03-21 92342 233
2022-03-18 92109 131
2022-03-17 91978 56
2022-03-16 91922 178
2022-03-11 91744 80
2022-03-10 91664 67
2022-03-09 91597 105
2022-03-08 91492 50
2022-03-07 91442 238
2022-03-04 91204 167
2022-03-03 91037 141
2022-03-02 90896 185
2022-03-01 90711 87
2022-02-28 90624 395
2022-02-25 90229 189
2022-02-24 90040 221
2022-02-23 89819 249
2022-02-22 89570 139
2022-02-21 89431 615
2022-02-18 88816 307
2022-02-17 88509 363
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2022-01-04 67622 92
2022-01-03 67530 274
2021-12-31 67256 163
2021-12-30 67093 160
2021-12-29 66933 141
2021-12-28 66792 75
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2021-12-23 66317 161
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2020-05-18 24 0
2020-05-17 24 0
2020-05-16 24 1
2020-05-15 23 0
2020-05-14 23 0
2020-05-13 23 0
2020-05-12 23 0
2020-05-11 23 0
2020-05-10 23 0
2020-05-09 23 0
2020-05-08 23 0
2020-05-07 23 0
2020-05-06 23 0
2020-05-05 23 0
2020-05-04 23 0
2020-05-03 23 0
2020-05-02 23 0
2020-05-01 23 0
2020-04-30 23 0
2020-04-29 23 0
2020-04-28 23 0
2020-04-27 23 0
2020-04-26 23 0
2020-04-25 23 0
2020-04-24 23 0
2020-04-23 23 0
2020-04-22 23 0
2020-04-21 23 0
2020-04-20 23 0
2020-04-19 23 0
2020-04-18 23 1
2020-04-17 22 0
2020-04-16 22 0
2020-04-15 22 0
2020-04-14 22 0
2020-04-13 22 0
2020-04-12 22 0
2020-04-11 22 1
2020-04-10 21 2
2020-04-09 19 0
2020-04-08 19 0
2020-04-07 19 0
2020-04-06 19 0
2020-04-05 19 1
2020-04-04 18 1
2020-04-03 17 2
2020-04-02 15 7
2020-04-01 8 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: 0:00:09.987695