Hungary: Győr-Moson-Sopron¶

  • 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 16:41:46 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Hungary", region="Győr-Moson-Sopron", weeks=5);
2023-03-07T16:41:49.639540 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec 260 260 280 280 300 300 320 320 7-day incidence rate 283.0 Győr-Moson-Sopron, 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-Győr-Moson-Sopron new cases (rolling 7d mean) Hungary-Győr-Moson-Sopron 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-Győr-Moson-Sopron cases daily growth factor Hungary-Győr-Moson-Sopron cases daily growth factor (rolling mean) Hungary-Győr-Moson-Sopron 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 cases doubling time [days]
In [4]:
overview(country="Hungary", region="Győr-Moson-Sopron");
2023-03-07T16:41:54.307398 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 8000 8000 7-day incidence rate 283.0 Győr-Moson-Sopron, 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 1000 1000 2000 2000 3000 3000 daily change Hungary-Győr-Moson-Sopron new cases (rolling 7d mean) Hungary-Győr-Moson-Sopron 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-Győr-Moson-Sopron cases daily growth factor Hungary-Győr-Moson-Sopron cases daily growth factor (rolling mean) Hungary-Győr-Moson-Sopron 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 cases doubling time [days]
In [5]:
compare_plot(country="Hungary", region="Győr-Moson-Sopron");

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[5], line 1
----> 1 compare_plot(country="Hungary", region="Győr-Moson-Sopron");

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="Győr-Moson-Sopron")

# get population of the region for future normalisation:
inhabitants = population(country="Hungary", region="Győr-Moson-Sopron")
print(f'Population of country="Hungary", region="Győr-Moson-Sopron": {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="Győr-Moson-Sopron": None people
Out[6]:
total cases daily new cases
Dátum
2022-12-21 117283 283
2022-12-14 117000 319
2022-12-07 116681 260
2022-11-30 116421 280
2022-11-23 116141 277
2022-11-16 115864 309
2022-11-09 115555 780
2022-10-26 114775 557
2022-10-19 114218 774
2022-10-12 113444 656
2022-10-05 112788 844
2022-09-28 111944 531
2022-09-21 111413 585
2022-09-14 110828 482
2022-09-07 110346 495
2022-08-31 109851 552
2022-08-24 109299 647
2022-08-17 108652 772
2022-08-10 107880 884
2022-08-03 106996 1173
2022-07-27 105823 868
2022-07-20 104955 647
2022-07-13 104308 498
2022-07-06 103810 286
2022-06-29 103524 148
2022-06-22 103376 97
2022-06-15 103279 65
2022-06-08 103214 81
2022-06-01 103133 102
2022-05-25 103031 163
2022-05-18 102868 256
2022-05-11 102612 323
2022-05-02 102289 168
2022-04-28 102121 84
2022-04-27 102037 100
2022-04-26 101937 73
2022-04-25 101864 168
2022-04-22 101696 88
2022-04-21 101608 183
2022-04-20 101425 60
2022-04-19 101365 217
2022-04-14 101148 115
2022-04-13 101033 130
2022-04-12 100903 73
2022-04-11 100830 238
2022-04-08 100592 248
2022-04-06 100344 100
2022-04-05 100244 299
2022-04-01 99945 100
2022-03-31 99845 130
2022-03-30 99715 110
2022-03-29 99605 84
2022-03-28 99521 272
2022-03-25 99249 127
2022-03-24 99122 168
2022-03-23 98954 161
2022-03-22 98793 58
2022-03-21 98735 555
2022-03-18 98180 167
2022-03-17 98013 86
2022-03-16 97927 297
2022-03-11 97630 105
2022-03-10 97525 148
2022-03-09 97377 150
2022-03-08 97227 64
2022-03-07 97163 408
2022-03-04 96755 135
2022-03-03 96620 198
2022-03-02 96422 198
2022-03-01 96224 109
2022-02-28 96115 488
2022-02-25 95627 255
2022-02-24 95372 307
2022-02-23 95065 300
2022-02-22 94765 173
2022-02-21 94592 701
2022-02-18 93891 416
2022-02-17 93475 431
2022-02-16 93044 482
2022-02-15 92562 248
2022-02-14 92314 1226
2022-02-11 91088 641
2022-02-10 90447 737
2022-02-09 89710 792
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2022-01-05 63543 186
2022-01-04 63357 90
2022-01-03 63267 310
2021-12-31 62957 150
2021-12-30 62807 152
2021-12-29 62655 111
2021-12-28 62544 45
2021-12-27 62499 464
2021-12-23 62035 154
2021-12-22 61881 138
2021-12-21 61743 66
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2021-12-17 61118 273
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2020-05-23 84 0
2020-05-22 84 0
2020-05-21 84 0
2020-05-20 84 0
2020-05-19 84 0
2020-05-18 84 1
2020-05-17 83 0
2020-05-16 83 0
2020-05-15 83 0
2020-05-14 83 0
2020-05-13 83 0
2020-05-12 83 0
2020-05-11 83 0
2020-05-10 83 1
2020-05-09 82 0
2020-05-08 82 1
2020-05-07 81 0
2020-05-06 81 0
2020-05-05 81 2
2020-05-04 79 0
2020-05-03 79 0
2020-05-02 79 1
2020-05-01 78 1
2020-04-30 77 0
2020-04-29 77 0
2020-04-28 77 1
2020-04-27 76 1
2020-04-26 75 1
2020-04-25 74 0
2020-04-24 74 2
2020-04-23 72 -3
2020-04-22 75 11
2020-04-21 64 0
2020-04-20 64 1
2020-04-19 63 0
2020-04-18 63 0
2020-04-17 63 1
2020-04-16 62 1
2020-04-15 61 0
2020-04-14 61 0
2020-04-13 61 1
2020-04-12 60 16
2020-04-11 44 1
2020-04-10 43 6
2020-04-09 37 0
2020-04-08 37 1
2020-04-07 36 2
2020-04-06 34 0
2020-04-05 34 1
2020-04-04 33 5
2020-04-03 28 0
2020-04-02 28 2
2020-04-01 26 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: 0:00:12.183536