Hungary: Jász-Nagykun-Szolnok¶

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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="Jász-Nagykun-Szolnok", weeks=5);
2023-03-07T16:42:05.794274 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec 120 120 140 140 160 160 7-day incidence rate Jász-Nagykun-Szolnok, 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-Jász-Nagykun-Szolnok new cases (rolling 7d mean) Hungary-Jász-Nagykun-Szolnok 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-Jász-Nagykun-Szolnok cases daily growth factor Hungary-Jász-Nagykun-Szolnok cases daily growth factor (rolling mean) Hungary-Jász-Nagykun-Szolnok 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="Jász-Nagykun-Szolnok");
2023-03-07T16:42:10.707304 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 4000 4000 7-day incidence rate 167.0 Jász-Nagykun-Szolnok, 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-Jász-Nagykun-Szolnok new cases (rolling 7d mean) Hungary-Jász-Nagykun-Szolnok 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-Jász-Nagykun-Szolnok cases daily growth factor Hungary-Jász-Nagykun-Szolnok cases daily growth factor (rolling mean) Hungary-Jász-Nagykun-Szolnok 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="Jász-Nagykun-Szolnok");
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[5], line 1
----> 1 compare_plot(country="Hungary", region="Jász-Nagykun-Szolnok");

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="Jász-Nagykun-Szolnok")

# get population of the region for future normalisation:
inhabitants = population(country="Hungary", region="Jász-Nagykun-Szolnok")
print(f'Population of country="Hungary", region="Jász-Nagykun-Szolnok": {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="Jász-Nagykun-Szolnok": None people
Out[6]:
total cases daily new cases
Dátum
2022-12-21 76710 167
2022-12-14 76543 151
2022-12-07 76392 144
2022-11-30 76248 143
2022-11-23 76105 110
2022-11-16 75995 150
2022-11-09 75845 347
2022-10-26 75498 241
2022-10-19 75257 365
2022-10-12 74892 468
2022-10-05 74424 473
2022-09-28 73951 358
2022-09-21 73593 435
2022-09-14 73158 481
2022-09-07 72677 394
2022-08-31 72283 502
2022-08-24 71781 608
2022-08-17 71173 725
2022-08-10 70448 813
2022-08-03 69635 758
2022-07-27 68877 470
2022-07-20 68407 286
2022-07-13 68121 204
2022-07-06 67917 108
2022-06-29 67809 61
2022-06-22 67748 38
2022-06-15 67710 40
2022-06-08 67670 45
2022-06-01 67625 46
2022-05-25 67579 93
2022-05-18 67486 130
2022-05-11 67356 195
2022-05-02 67161 113
2022-04-28 67048 61
2022-04-27 66987 69
2022-04-26 66918 43
2022-04-25 66875 95
2022-04-22 66780 57
2022-04-21 66723 85
2022-04-20 66638 38
2022-04-19 66600 147
2022-04-14 66453 71
2022-04-13 66382 92
2022-04-12 66290 51
2022-04-11 66239 136
2022-04-08 66103 122
2022-04-06 65981 75
2022-04-05 65906 178
2022-04-01 65728 66
2022-03-31 65662 70
2022-03-30 65592 94
2022-03-29 65498 56
2022-03-28 65442 130
2022-03-25 65312 71
2022-03-24 65241 59
2022-03-23 65182 84
2022-03-22 65098 48
2022-03-21 65050 144
2022-03-18 64906 107
2022-03-17 64799 41
2022-03-16 64758 128
2022-03-11 64630 50
2022-03-10 64580 69
2022-03-09 64511 110
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2022-03-07 64354 142
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2022-03-03 64128 136
2022-03-02 63992 156
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2022-02-24 63330 171
2022-02-23 63159 189
2022-02-22 62970 117
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2022-02-18 62406 266
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2022-02-16 61862 266
2022-02-15 61596 125
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2022-01-04 50092 33
2022-01-03 50059 134
2021-12-31 49925 78
2021-12-30 49847 74
2021-12-29 49773 58
2021-12-28 49715 35
2021-12-27 49680 227
2021-12-23 49453 88
2021-12-22 49365 84
2021-12-21 49281 59
2021-12-20 49222 328
2021-12-17 48894 164
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2021-12-15 48568 144
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2020-05-21 26 1
2020-05-20 25 0
2020-05-19 25 0
2020-05-18 25 1
2020-05-17 24 0
2020-05-16 24 0
2020-05-15 24 1
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 1
2020-05-04 22 0
2020-05-03 22 0
2020-05-02 22 0
2020-05-01 22 0
2020-04-30 22 0
2020-04-29 22 0
2020-04-28 22 1
2020-04-27 21 0
2020-04-26 21 1
2020-04-25 20 0
2020-04-24 20 1
2020-04-23 19 0
2020-04-22 19 0
2020-04-21 19 0
2020-04-20 19 0
2020-04-19 19 1
2020-04-18 18 1
2020-04-17 17 1
2020-04-16 16 1
2020-04-15 15 0
2020-04-14 15 1
2020-04-13 14 0
2020-04-12 14 1
2020-04-11 13 1
2020-04-10 12 1
2020-04-09 11 1
2020-04-08 10 0
2020-04-07 10 2
2020-04-06 8 0
2020-04-05 8 1
2020-04-04 7 -1
2020-04-03 8 1
2020-04-02 7 2
2020-04-01 5 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: 0:00:11.160934