Hungary: Hajdú-Bihar¶

  • 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="Hajdú-Bihar", weeks=5);
2023-03-07T16:42:05.221070 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec 180 180 200 200 220 220 240 240 7-day incidence rate 216.0 Hajdú-Bihar, 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-Hajdú-Bihar new cases (rolling 7d mean) Hungary-Hajdú-Bihar 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 1.4 1.4 R & growth factor (based on cases) Hungary-Hajdú-Bihar cases daily growth factor Hungary-Hajdú-Bihar cases daily growth factor (rolling mean) Hungary-Hajdú-Bihar 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="Hajdú-Bihar");
2023-03-07T16:42:10.076961 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 7-day incidence rate 216.0 Hajdú-Bihar, 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 daily change Hungary-Hajdú-Bihar new cases (rolling 7d mean) Hungary-Hajdú-Bihar 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 1.4 1.4 R & growth factor (based on cases) Hungary-Hajdú-Bihar cases daily growth factor Hungary-Hajdú-Bihar cases daily growth factor (rolling mean) Hungary-Hajdú-Bihar 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 40000 40000 cases doubling time [days]
In [5]:
compare_plot(country="Hungary", region="Hajdú-Bihar");

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

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="Hajdú-Bihar")

# get population of the region for future normalisation:
inhabitants = population(country="Hungary", region="Hajdú-Bihar")
print(f'Population of country="Hungary", region="Hajdú-Bihar": {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="Hajdú-Bihar": None people
Out[6]:
total cases daily new cases
Dátum
2022-12-21 112936 216
2022-12-14 112720 217
2022-12-07 112503 239
2022-11-30 112264 223
2022-11-23 112041 167
2022-11-16 111874 214
2022-11-09 111660 562
2022-10-26 111098 405
2022-10-19 110693 563
2022-10-12 110130 570
2022-10-05 109560 695
2022-09-28 108865 598
2022-09-21 108267 694
2022-09-14 107573 605
2022-09-07 106968 455
2022-08-31 106513 570
2022-08-24 105943 745
2022-08-17 105198 914
2022-08-10 104284 979
2022-08-03 103305 1027
2022-07-27 102278 580
2022-07-20 101698 371
2022-07-13 101327 261
2022-07-06 101066 145
2022-06-29 100921 88
2022-06-22 100833 37
2022-06-15 100796 39
2022-06-08 100757 36
2022-06-01 100721 63
2022-05-25 100658 67
2022-05-18 100591 158
2022-05-11 100433 246
2022-05-02 100187 122
2022-04-28 100065 64
2022-04-27 100001 102
2022-04-26 99899 52
2022-04-25 99847 115
2022-04-22 99732 69
2022-04-21 99663 108
2022-04-20 99555 72
2022-04-19 99483 209
2022-04-14 99274 136
2022-04-13 99138 119
2022-04-12 99019 65
2022-04-11 98954 206
2022-04-08 98748 272
2022-04-06 98476 164
2022-04-05 98312 353
2022-04-01 97959 137
2022-03-31 97822 137
2022-03-30 97685 138
2022-03-29 97547 100
2022-03-28 97447 231
2022-03-25 97216 135
2022-03-24 97081 142
2022-03-23 96939 148
2022-03-22 96791 68
2022-03-21 96723 273
2022-03-18 96450 206
2022-03-17 96244 77
2022-03-16 96167 273
2022-03-11 95894 125
2022-03-10 95769 148
2022-03-09 95621 148
2022-03-08 95473 82
2022-03-07 95391 332
2022-03-04 95059 182
2022-03-03 94877 256
2022-03-02 94621 213
2022-03-01 94408 133
2022-02-28 94275 519
2022-02-25 93756 309
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2022-02-18 91802 456
2022-02-17 91346 436
2022-02-16 90910 460
2022-02-15 90450 204
2022-02-14 90246 1361
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2022-01-31 81331 2297
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2022-01-03 69136 249
2021-12-31 68887 166
2021-12-30 68721 159
2021-12-29 68562 129
2021-12-28 68433 57
2021-12-27 68376 350
2021-12-23 68026 204
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2020-05-18 23 0
2020-05-17 23 0
2020-05-16 23 0
2020-05-15 23 0
2020-05-14 23 0
2020-05-13 23 0
2020-05-12 23 0
2020-05-11 23 0
2020-05-10 23 0
2020-05-09 23 0
2020-05-08 23 0
2020-05-07 23 0
2020-05-06 23 0
2020-05-05 23 0
2020-05-04 23 0
2020-05-03 23 0
2020-05-02 23 0
2020-05-01 23 0
2020-04-30 23 0
2020-04-29 23 0
2020-04-28 23 0
2020-04-27 23 0
2020-04-26 23 0
2020-04-25 23 1
2020-04-24 22 1
2020-04-23 21 -1
2020-04-22 22 1
2020-04-21 21 2
2020-04-20 19 0
2020-04-19 19 0
2020-04-18 19 0
2020-04-17 19 0
2020-04-16 19 0
2020-04-15 19 0
2020-04-14 19 1
2020-04-13 18 0
2020-04-12 18 1
2020-04-11 17 0
2020-04-10 17 2
2020-04-09 15 0
2020-04-08 15 0
2020-04-07 15 0
2020-04-06 15 1
2020-04-05 14 1
2020-04-04 13 2
2020-04-03 11 1
2020-04-02 10 1
2020-04-01 9 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: 0:00:10.168324