Hungary: Baranya¶

  • 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="Baranya", weeks=5);
2023-03-07T16:41:49.035829 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec 200 200 250 250 300 300 350 350 7-day incidence rate Baranya, Hungary, last 5 weeks, last data point from 2022-12-21 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec 0 0 100 100 200 200 300 300 daily change Hungary-Baranya new cases (rolling 7d mean) Hungary-Baranya 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-Baranya cases daily growth factor Hungary-Baranya cases daily growth factor (rolling mean) Hungary-Baranya 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="Baranya");
2023-03-07T16:41:53.669510 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 6000 6000 7-day incidence rate 354.0 Baranya, 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-Baranya new cases (rolling 7d mean) Hungary-Baranya 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-Baranya cases daily growth factor Hungary-Baranya cases daily growth factor (rolling mean) Hungary-Baranya 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="Baranya");
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[5], line 1
----> 1 compare_plot(country="Hungary", region="Baranya");

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="Baranya")

# get population of the region for future normalisation:
inhabitants = population(country="Hungary", region="Baranya")
print(f'Population of country="Hungary", region="Baranya": {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="Baranya": None people
Out[6]:
total cases daily new cases
Dátum
2022-12-21 95191 354
2022-12-14 94837 232
2022-12-07 94605 193
2022-11-30 94412 182
2022-11-23 94230 204
2022-11-16 94026 257
2022-11-09 93769 661
2022-10-26 93108 537
2022-10-19 92571 658
2022-10-12 91913 847
2022-10-05 91066 972
2022-09-28 90094 717
2022-09-21 89377 801
2022-09-14 88576 849
2022-09-07 87727 760
2022-08-31 86967 796
2022-08-24 86171 841
2022-08-17 85330 849
2022-08-10 84481 850
2022-08-03 83631 903
2022-07-27 82728 666
2022-07-20 82062 434
2022-07-13 81628 322
2022-07-06 81306 157
2022-06-29 81149 92
2022-06-22 81057 60
2022-06-15 80997 58
2022-06-08 80939 64
2022-06-01 80875 79
2022-05-25 80796 118
2022-05-18 80678 204
2022-05-11 80474 306
2022-05-02 80168 177
2022-04-28 79991 103
2022-04-27 79888 152
2022-04-26 79736 83
2022-04-25 79653 158
2022-04-22 79495 119
2022-04-21 79376 198
2022-04-20 79178 81
2022-04-19 79097 223
2022-04-14 78874 111
2022-04-13 78763 160
2022-04-12 78603 82
2022-04-11 78521 201
2022-04-08 78320 226
2022-04-06 78094 184
2022-04-05 77910 246
2022-04-01 77664 112
2022-03-31 77552 136
2022-03-30 77416 167
2022-03-29 77249 122
2022-03-28 77127 277
2022-03-25 76850 141
2022-03-24 76709 175
2022-03-23 76534 210
2022-03-22 76324 115
2022-03-21 76209 394
2022-03-18 75815 243
2022-03-17 75572 101
2022-03-16 75471 247
2022-03-11 75224 132
2022-03-10 75092 173
2022-03-09 74919 211
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2022-03-07 74600 346
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2021-12-31 48012 128
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2020-04-30 34 0
2020-04-29 34 1
2020-04-28 33 0
2020-04-27 33 0
2020-04-26 33 0
2020-04-25 33 0
2020-04-24 33 0
2020-04-23 33 -2
2020-04-22 35 1
2020-04-21 34 1
2020-04-20 33 0
2020-04-19 33 0
2020-04-18 33 0
2020-04-17 33 1
2020-04-16 32 3
2020-04-15 29 0
2020-04-14 29 0
2020-04-13 29 0
2020-04-12 29 0
2020-04-11 29 1
2020-04-10 28 5
2020-04-09 23 0
2020-04-08 23 0
2020-04-07 23 0
2020-04-06 23 0
2020-04-05 23 2
2020-04-04 21 0
2020-04-03 21 2
2020-04-02 19 0
2020-04-01 19 -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: 0:00:09.885172