Hungary: Veszprém¶

  • 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 16:42:18 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Hungary", region="Veszprém", weeks=5);
2023-03-07T16:42:20.886875 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec 140 140 160 160 180 180 7-day incidence rate 147.0 Veszprém, Hungary, last 5 weeks, last data point from 2022-12-21 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec 0 0 50 50 100 100 150 150 200 200 daily change Hungary-Veszprém new cases (rolling 7d mean) Hungary-Veszprém 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-Veszprém cases daily growth factor Hungary-Veszprém cases daily growth factor (rolling mean) Hungary-Veszprém 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="Veszprém");
2023-03-07T16:42:24.990337 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 147.0 Veszprém, 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 2000 2000 daily change Hungary-Veszprém new cases (rolling 7d mean) Hungary-Veszprém 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-Veszprém cases daily growth factor Hungary-Veszprém cases daily growth factor (rolling mean) Hungary-Veszprém 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="Veszprém");

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

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="Veszprém")

# get population of the region for future normalisation:
inhabitants = population(country="Hungary", region="Veszprém")
print(f'Population of country="Hungary", region="Veszprém": {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="Veszprém": None people
Out[6]:
total cases daily new cases
Dátum
2022-12-21 83731 147
2022-12-14 83584 195
2022-12-07 83389 175
2022-11-30 83214 138
2022-11-23 83076 153
2022-11-16 82923 178
2022-11-09 82745 512
2022-10-26 82233 397
2022-10-19 81836 549
2022-10-12 81287 569
2022-10-05 80718 499
2022-09-28 80219 367
2022-09-21 79852 297
2022-09-14 79555 321
2022-09-07 79234 328
2022-08-31 78906 362
2022-08-24 78544 463
2022-08-17 78081 452
2022-08-10 77629 546
2022-08-03 77083 709
2022-07-27 76374 473
2022-07-20 75901 350
2022-07-13 75551 267
2022-07-06 75284 110
2022-06-29 75174 92
2022-06-22 75082 51
2022-06-15 75031 23
2022-06-08 75008 44
2022-06-01 74964 50
2022-05-25 74914 101
2022-05-18 74813 157
2022-05-11 74656 172
2022-05-02 74484 100
2022-04-28 74384 57
2022-04-27 74327 49
2022-04-26 74278 28
2022-04-25 74250 116
2022-04-22 74134 45
2022-04-21 74089 67
2022-04-20 74022 37
2022-04-19 73985 128
2022-04-14 73857 69
2022-04-13 73788 71
2022-04-12 73717 54
2022-04-11 73663 143
2022-04-08 73520 156
2022-04-06 73364 60
2022-04-05 73304 178
2022-04-01 73126 73
2022-03-31 73053 75
2022-03-30 72978 60
2022-03-29 72918 56
2022-03-28 72862 187
2022-03-25 72675 61
2022-03-24 72614 107
2022-03-23 72507 65
2022-03-22 72442 57
2022-03-21 72385 183
2022-03-18 72202 -10
2022-03-17 72212 141
2022-03-16 72071 188
2022-03-11 71883 78
2022-03-10 71805 113
2022-03-09 71692 82
2022-03-08 71610 68
2022-03-07 71542 196
2022-03-04 71346 111
2022-03-03 71235 141
2022-03-02 71094 113
2022-03-01 70981 86
2022-02-28 70895 326
2022-02-25 70569 189
2022-02-24 70380 215
2022-02-23 70165 216
2022-02-22 69949 161
2022-02-21 69788 661
2022-02-18 69127 334
2022-02-17 68793 383
2022-02-16 68410 361
2022-02-15 68049 221
2022-02-14 67828 938
2022-02-11 66890 512
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2022-01-31 60837 2026
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2022-01-07 50261 242
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2022-01-03 49537 233
2021-12-31 49304 128
2021-12-30 49176 110
2021-12-29 49066 103
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2021-12-27 48893 353
2021-12-23 48540 172
2021-12-22 48368 172
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2021-12-17 47597 222
2021-12-16 47375 279
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2020-05-05 58 0
2020-05-04 58 0
2020-05-03 58 0
2020-05-02 58 0
2020-05-01 58 0
2020-04-30 58 2
2020-04-29 56 0
2020-04-28 56 2
2020-04-27 54 0
2020-04-26 54 2
2020-04-25 52 2
2020-04-24 50 0
2020-04-23 50 2
2020-04-22 48 5
2020-04-21 43 0
2020-04-20 43 2
2020-04-19 41 4
2020-04-18 37 1
2020-04-17 36 2
2020-04-16 34 2
2020-04-15 32 0
2020-04-14 32 1
2020-04-13 31 1
2020-04-12 30 2
2020-04-11 28 5
2020-04-10 23 5
2020-04-09 18 4
2020-04-08 14 1
2020-04-07 13 3
2020-04-06 10 0
2020-04-05 10 0
2020-04-04 10 0
2020-04-03 10 0
2020-04-02 10 0
2020-04-01 10 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: 0:00:08.700031