Hungary: Borsod-Abaúj-Zemplén¶

  • 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 16:41:45 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Hungary", region="Borsod-Abaúj-Zemplén", weeks=5);
2023-03-07T16:41:49.478596 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec 160 160 180 180 200 200 7-day incidence rate Borsod-Abaúj-Zemplén, 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-Borsod-Abaúj-Zemplén new cases (rolling 7d mean) Hungary-Borsod-Abaúj-Zemplén 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-Borsod-Abaúj-Zemplén cases daily growth factor Hungary-Borsod-Abaúj-Zemplén cases daily growth factor (rolling mean) Hungary-Borsod-Abaúj-Zemplén 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 4000 4000 cases doubling time [days]
In [4]:
overview(country="Hungary", region="Borsod-Abaúj-Zemplén");
2023-03-07T16:41:54.031390 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 205.0 Borsod-Abaúj-Zemplén, 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 −1000 −1000 0 0 1000 1000 2000 2000 daily change Hungary-Borsod-Abaúj-Zemplén new cases (rolling 7d mean) Hungary-Borsod-Abaúj-Zemplén 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-Borsod-Abaúj-Zemplén cases daily growth factor Hungary-Borsod-Abaúj-Zemplén cases daily growth factor (rolling mean) Hungary-Borsod-Abaúj-Zemplén 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="Borsod-Abaúj-Zemplén");
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[5], line 1
----> 1 compare_plot(country="Hungary", region="Borsod-Abaúj-Zemplén");

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="Borsod-Abaúj-Zemplén")

# get population of the region for future normalisation:
inhabitants = population(country="Hungary", region="Borsod-Abaúj-Zemplén")
print(f'Population of country="Hungary", region="Borsod-Abaúj-Zemplén": {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="Borsod-Abaúj-Zemplén": None people
Out[6]:
total cases daily new cases
Dátum
2022-12-21 126146 205
2022-12-14 125941 195
2022-12-07 125746 174
2022-11-30 125572 158
2022-11-23 125414 174
2022-11-16 125240 179
2022-11-09 125061 575
2022-10-26 124486 495
2022-10-19 123991 658
2022-10-12 123333 791
2022-10-05 122542 906
2022-09-28 121636 827
2022-09-21 120809 782
2022-09-14 120027 755
2022-09-07 119272 683
2022-08-31 118589 811
2022-08-24 117778 1040
2022-08-17 116738 1013
2022-08-10 115725 1088
2022-08-03 114637 1118
2022-07-27 113519 777
2022-07-20 112742 451
2022-07-13 112291 307
2022-07-06 111984 138
2022-06-29 111846 75
2022-06-22 111771 73
2022-06-15 111698 65
2022-06-08 111633 56
2022-06-01 111577 92
2022-05-25 111485 142
2022-05-18 111343 198
2022-05-11 111145 287
2022-05-02 110858 142
2022-04-28 110716 76
2022-04-27 110640 108
2022-04-26 110532 68
2022-04-25 110464 131
2022-04-22 110333 140
2022-04-21 110193 154
2022-04-20 110039 48
2022-04-19 109991 220
2022-04-14 109771 114
2022-04-13 109657 138
2022-04-12 109519 62
2022-04-11 109457 215
2022-04-08 109242 249
2022-04-06 108993 140
2022-04-05 108853 289
2022-04-01 108564 124
2022-03-31 108440 151
2022-03-30 108289 177
2022-03-29 108112 83
2022-03-28 108029 279
2022-03-25 107750 115
2022-03-24 107635 158
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2020-05-30 61 0
2020-05-29 61 0
2020-05-28 61 0
2020-05-27 61 0
2020-05-26 61 0
2020-05-25 61 0
2020-05-24 61 0
2020-05-23 61 0
2020-05-22 61 -3
2020-05-21 64 0
2020-05-20 64 0
2020-05-19 64 0
2020-05-18 64 0
2020-05-17 64 0
2020-05-16 64 0
2020-05-15 64 0
2020-05-14 64 0
2020-05-13 64 0
2020-05-12 64 0
2020-05-11 64 1
2020-05-10 63 0
2020-05-09 63 -2
2020-05-08 65 0
2020-05-07 65 0
2020-05-06 65 0
2020-05-05 65 0
2020-05-04 65 0
2020-05-03 65 0
2020-05-02 65 1
2020-05-01 64 1
2020-04-30 63 0
2020-04-29 63 1
2020-04-28 62 0
2020-04-27 62 0
2020-04-26 62 3
2020-04-25 59 5
2020-04-24 54 1
2020-04-23 53 0
2020-04-22 53 0
2020-04-21 53 0
2020-04-20 53 1
2020-04-19 52 0
2020-04-18 52 0
2020-04-17 52 2
2020-04-16 50 2
2020-04-15 48 3
2020-04-14 45 0
2020-04-13 45 1
2020-04-12 44 31
2020-04-11 13 3
2020-04-10 10 0
2020-04-09 10 3
2020-04-08 7 0
2020-04-07 7 0
2020-04-06 7 0
2020-04-05 7 2
2020-04-04 5 -1
2020-04-03 6 0
2020-04-02 6 0
2020-04-01 6 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: 0:00:10.094420