Hungary: Somogy¶

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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 16:42:02 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Hungary", region="Somogy", weeks=5);
2023-03-07T16:42:05.724453 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec 120 120 130 130 140 140 7-day incidence rate Somogy, 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 daily change Hungary-Somogy new cases (rolling 7d mean) Hungary-Somogy 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-Somogy cases daily growth factor Hungary-Somogy cases daily growth factor (rolling mean) Hungary-Somogy 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="Somogy");
2023-03-07T16:42:10.326077 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 146.0 Somogy, 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 daily change Hungary-Somogy new cases (rolling 7d mean) Hungary-Somogy 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-Somogy cases daily growth factor Hungary-Somogy cases daily growth factor (rolling mean) Hungary-Somogy 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="Somogy");
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[5], line 1
----> 1 compare_plot(country="Hungary", region="Somogy");

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="Somogy")

# get population of the region for future normalisation:
inhabitants = population(country="Hungary", region="Somogy")
print(f'Population of country="Hungary", region="Somogy": {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="Somogy": None people
Out[6]:
total cases daily new cases
Dátum
2022-12-21 69461 146
2022-12-14 69315 144
2022-12-07 69171 120
2022-11-30 69051 137
2022-11-23 68914 135
2022-11-16 68779 148
2022-11-09 68631 461
2022-10-26 68170 345
2022-10-19 67825 448
2022-10-12 67377 458
2022-10-05 66919 591
2022-09-28 66328 525
2022-09-21 65803 570
2022-09-14 65233 485
2022-09-07 64748 438
2022-08-31 64310 456
2022-08-24 63854 496
2022-08-17 63358 563
2022-08-10 62795 551
2022-08-03 62244 618
2022-07-27 61626 388
2022-07-20 61238 283
2022-07-13 60955 198
2022-07-06 60757 84
2022-06-29 60673 80
2022-06-22 60593 53
2022-06-15 60540 36
2022-06-08 60504 32
2022-06-01 60472 48
2022-05-25 60424 99
2022-05-18 60325 98
2022-05-11 60227 199
2022-05-02 60028 91
2022-04-28 59937 50
2022-04-27 59887 55
2022-04-26 59832 64
2022-04-25 59768 80
2022-04-22 59688 61
2022-04-21 59627 97
2022-04-20 59530 53
2022-04-19 59477 167
2022-04-14 59310 84
2022-04-13 59226 111
2022-04-12 59115 63
2022-04-11 59052 143
2022-04-08 58909 190
2022-04-06 58719 95
2022-04-05 58624 165
2022-04-01 58459 83
2022-03-31 58376 75
2022-03-30 58301 70
2022-03-29 58231 62
2022-03-28 58169 174
2022-03-25 57995 80
2022-03-24 57915 79
2022-03-23 57836 73
2022-03-22 57763 50
2022-03-21 57713 166
2022-03-18 57547 109
2022-03-17 57438 47
2022-03-16 57391 147
2022-03-11 57244 87
2022-03-10 57157 75
2022-03-09 57082 69
2022-03-08 57013 53
2022-03-07 56960 163
2022-03-04 56797 76
2022-03-03 56721 109
2022-03-02 56612 109
2022-03-01 56503 47
2022-02-28 56456 203
2022-02-25 56253 143
2022-02-24 56110 189
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2021-12-23 39498 102
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2020-04-24 24 0
2020-04-23 24 0
2020-04-22 24 0
2020-04-21 24 0
2020-04-20 24 1
2020-04-19 23 2
2020-04-18 21 0
2020-04-17 21 0
2020-04-16 21 0
2020-04-15 21 0
2020-04-14 21 1
2020-04-13 20 0
2020-04-12 20 2
2020-04-11 18 1
2020-04-10 17 0
2020-04-09 17 0
2020-04-08 17 1
2020-04-07 16 1
2020-04-06 15 0
2020-04-05 15 3
2020-04-04 12 0
2020-04-03 12 1
2020-04-02 11 0
2020-04-01 11 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: 0:00:10.217760