Hungary: Békés¶

  • 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="Békés", weeks=5);
2023-03-07T16:41:49.556998 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec 200 200 210 210 220 220 230 230 240 240 7-day incidence rate Békés, 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 daily change Hungary-Békés new cases (rolling 7d mean) Hungary-Békés 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-Békés cases daily growth factor Hungary-Békés cases daily growth factor (rolling mean) Hungary-Békés 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 0 500 500 1000 1000 1500 1500 2000 2000 cases doubling time [days]
In [4]:
overview(country="Hungary", region="Békés");
2023-03-07T16:41:54.283725 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 226.0 Békés, 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-Békés new cases (rolling 7d mean) Hungary-Békés 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-Békés cases daily growth factor Hungary-Békés cases daily growth factor (rolling mean) Hungary-Békés 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 cases doubling time [days]
In [5]:
compare_plot(country="Hungary", region="Békés");
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[5], line 1
----> 1 compare_plot(country="Hungary", region="Békés");

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="Békés")

# get population of the region for future normalisation:
inhabitants = population(country="Hungary", region="Békés")
print(f'Population of country="Hungary", region="Békés": {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="Békés": None people
Out[6]:
total cases daily new cases
Dátum
2022-12-21 66973 226
2022-12-14 66747 239
2022-12-07 66508 204
2022-11-30 66304 228
2022-11-23 66076 199
2022-11-16 65877 208
2022-11-09 65669 507
2022-10-26 65162 359
2022-10-19 64803 585
2022-10-12 64218 512
2022-10-05 63706 531
2022-09-28 63175 500
2022-09-21 62675 633
2022-09-14 62042 564
2022-09-07 61478 455
2022-08-31 61023 579
2022-08-24 60444 717
2022-08-17 59727 917
2022-08-10 58810 899
2022-08-03 57911 805
2022-07-27 57106 508
2022-07-20 56598 254
2022-07-13 56344 193
2022-07-06 56151 65
2022-06-29 56086 37
2022-06-22 56049 16
2022-06-15 56033 27
2022-06-08 56006 29
2022-06-01 55977 41
2022-05-25 55936 60
2022-05-18 55876 121
2022-05-11 55755 221
2022-05-02 55534 76
2022-04-28 55458 41
2022-04-27 55417 52
2022-04-26 55365 55
2022-04-25 55310 89
2022-04-22 55221 68
2022-04-21 55153 100
2022-04-20 55053 53
2022-04-19 55000 114
2022-04-14 54886 57
2022-04-13 54829 73
2022-04-12 54756 68
2022-04-11 54688 125
2022-04-08 54563 133
2022-04-06 54430 91
2022-04-05 54339 185
2022-04-01 54154 67
2022-03-31 54087 71
2022-03-30 54016 94
2022-03-29 53922 50
2022-03-28 53872 163
2022-03-25 53709 61
2022-03-24 53648 76
2022-03-23 53572 76
2022-03-22 53496 51
2022-03-21 53445 116
2022-03-18 53329 125
2022-03-17 53204 64
2022-03-16 53140 87
2022-03-11 53053 56
2022-03-10 52997 58
2022-03-09 52939 86
2022-03-08 52853 44
2022-03-07 52809 134
2022-03-04 52675 87
2022-03-03 52588 99
2022-03-02 52489 98
2022-03-01 52391 98
2022-02-28 52293 201
2022-02-25 52092 131
2022-02-24 51961 130
2022-02-23 51831 219
2022-02-22 51612 120
2022-02-21 51492 261
2022-02-18 51231 242
2022-02-17 50989 238
2022-02-16 50751 276
2022-02-15 50475 173
2022-02-14 50302 465
2022-02-11 49837 383
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2021-12-31 38538 76
2021-12-30 38462 51
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2020-04-25 13 0
2020-04-24 13 0
2020-04-23 13 0
2020-04-22 13 0
2020-04-21 13 0
2020-04-20 13 0
2020-04-19 13 0
2020-04-18 13 1
2020-04-17 12 0
2020-04-16 12 0
2020-04-15 12 0
2020-04-14 12 1
2020-04-13 11 0
2020-04-12 11 0
2020-04-11 11 0
2020-04-10 11 0
2020-04-09 11 1
2020-04-08 10 1
2020-04-07 9 1
2020-04-06 8 0
2020-04-05 8 2
2020-04-04 6 2
2020-04-03 4 1
2020-04-02 3 0
2020-04-01 3 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: 0:00:10.835130