Hungary: Heves¶

  • 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 16:42:01 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Hungary", region="Heves", weeks=5);
2023-03-07T16:42:05.216346 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec 115 115 120 120 125 125 130 130 135 135 7-day incidence rate 118.0 Heves, 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 daily change Hungary-Heves new cases (rolling 7d mean) Hungary-Heves 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-Heves cases daily growth factor Hungary-Heves cases daily growth factor (rolling mean) Hungary-Heves 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="Heves");
2023-03-07T16:42:09.873640 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 118.0 Heves, 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 0 250 250 500 500 750 750 1000 1000 daily change Hungary-Heves new cases (rolling 7d mean) Hungary-Heves 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-Heves cases daily growth factor Hungary-Heves cases daily growth factor (rolling mean) Hungary-Heves 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="Heves");
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[5], line 1
----> 1 compare_plot(country="Hungary", region="Heves");

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="Heves")

# get population of the region for future normalisation:
inhabitants = population(country="Hungary", region="Heves")
print(f'Population of country="Hungary", region="Heves": {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="Heves": None people
Out[6]:
total cases daily new cases
Dátum
2022-12-21 60557 118
2022-12-14 60439 121
2022-12-07 60318 118
2022-11-30 60200 123
2022-11-23 60077 115
2022-11-16 59962 137
2022-11-09 59825 337
2022-10-26 59488 238
2022-10-19 59250 311
2022-10-12 58939 344
2022-10-05 58595 398
2022-09-28 58197 393
2022-09-21 57804 358
2022-09-14 57446 355
2022-09-07 57091 322
2022-08-31 56769 406
2022-08-24 56363 522
2022-08-17 55841 541
2022-08-10 55300 595
2022-08-03 54705 535
2022-07-27 54170 416
2022-07-20 53754 358
2022-07-13 53396 257
2022-07-06 53139 113
2022-06-29 53026 69
2022-06-22 52957 36
2022-06-15 52921 24
2022-06-08 52897 28
2022-06-01 52869 48
2022-05-25 52821 71
2022-05-18 52750 159
2022-05-11 52591 209
2022-05-02 52382 90
2022-04-28 52292 56
2022-04-27 52236 69
2022-04-26 52167 35
2022-04-25 52132 112
2022-04-22 52020 58
2022-04-21 51962 97
2022-04-20 51865 34
2022-04-19 51831 115
2022-04-14 51716 69
2022-04-13 51647 70
2022-04-12 51577 59
2022-04-11 51518 123
2022-04-08 51395 102
2022-04-06 51293 65
2022-04-05 51228 134
2022-04-01 51094 44
2022-03-31 51050 64
2022-03-30 50986 67
2022-03-29 50919 47
2022-03-28 50872 122
2022-03-25 50750 72
2022-03-24 50678 49
2022-03-23 50629 66
2022-03-22 50563 60
2022-03-21 50503 132
2022-03-18 50371 84
2022-03-17 50287 46
2022-03-16 50241 106
2022-03-11 50135 51
2022-03-10 50084 74
2022-03-09 50010 76
2022-03-08 49934 48
2022-03-07 49886 139
2022-03-04 49747 82
2022-03-03 49665 139
2022-03-02 49526 105
2022-03-01 49421 60
2022-02-28 49361 216
2022-02-25 49145 112
2022-02-24 49033 153
2022-02-23 48880 190
2022-02-22 48690 95
2022-02-21 48595 359
2022-02-18 48236 232
2022-02-17 48004 236
2022-02-16 47768 214
2022-02-15 47554 139
2022-02-14 47415 497
2022-02-11 46918 310
2022-02-10 46608 342
2022-02-09 46266 409
2022-02-08 45857 270
2022-02-07 45587 894
2022-02-04 44693 386
2022-02-03 44307 473
2022-02-02 43834 644
2022-02-01 43190 232
2022-01-31 42958 1074
2022-01-28 41884 528
2022-01-27 41356 494
2022-01-26 40862 630
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2022-01-24 39987 906
2022-01-21 39081 344
2022-01-20 38737 329
2022-01-19 38408 328
2022-01-18 38080 140
2022-01-17 37940 335
2022-01-14 37605 174
2022-01-13 37431 143
2022-01-12 37288 136
2022-01-11 37152 50
2022-01-10 37102 227
2022-01-07 36875 99
2022-01-06 36776 92
2022-01-05 36684 102
2022-01-04 36582 33
2022-01-03 36549 94
2021-12-31 36455 70
2021-12-30 36385 58
2021-12-29 36327 84
2021-12-28 36243 28
2021-12-27 36215 144
2021-12-23 36071 60
2021-12-22 36011 68
2021-12-21 35943 32
2021-12-20 35911 209
2021-12-17 35702 79
2021-12-16 35623 137
2021-12-15 35486 136
2021-12-14 35350 99
2021-12-13 35251 341
2021-12-10 34910 125
2021-12-09 34785 180
2021-12-08 34605 166
2021-12-07 34439 142
2021-12-06 34297 522
2021-12-03 33775 289
2021-12-02 33486 346
2021-12-01 33140 277
2021-11-30 32863 270
2021-11-29 32593 848
2021-11-26 31745 343
2021-11-25 31402 342
2021-11-24 31060 350
2021-11-23 30710 245
2021-11-22 30465 701
2021-11-19 29764 439
2021-11-18 29325 328
2021-11-17 28997 418
2021-11-16 28579 188
2021-11-15 28391 748
2021-11-12 27643 361
2021-11-11 27282 262
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2021-11-09 26654 194
2021-11-08 26460 699
2021-11-05 25761 261
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2021-10-25 23931 289
2021-10-22 23642 99
2021-10-21 23543 120
2021-10-20 23423 74
2021-10-19 23349 31
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2021-10-15 23202 35
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2020-05-20 13 -4
2020-05-19 17 0
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2020-04-23 14 0
2020-04-22 14 0
2020-04-21 14 1
2020-04-20 13 0
2020-04-19 13 0
2020-04-18 13 0
2020-04-17 13 0
2020-04-16 13 1
2020-04-15 12 1
2020-04-14 11 2
2020-04-13 9 0
2020-04-12 9 1
2020-04-11 8 3
2020-04-10 5 1
2020-04-09 4 0
2020-04-08 4 0
2020-04-07 4 0
2020-04-06 4 1
2020-04-05 3 0
2020-04-04 3 0
2020-04-03 3 0
2020-04-02 3 0
2020-04-01 3 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: 0:00:10.240813