Hungary: Fejér¶

  • 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 16:41:45 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="Hungary", region="Fejér", weeks=5);
2023-03-07T16:41:49.656971 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec 140 140 160 160 180 180 200 200 7-day incidence rate Fejér, 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-Fejér new cases (rolling 7d mean) Hungary-Fejér 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-Fejér cases daily growth factor Hungary-Fejér cases daily growth factor (rolling mean) Hungary-Fejér 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="Fejér");
2023-03-07T16:41:54.423967 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 206.0 Fejér, 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-Fejér new cases (rolling 7d mean) Hungary-Fejér 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-Fejér cases daily growth factor Hungary-Fejér cases daily growth factor (rolling mean) Hungary-Fejér 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="Fejér");

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

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="Fejér")

# get population of the region for future normalisation:
inhabitants = population(country="Hungary", region="Fejér")
print(f'Population of country="Hungary", region="Fejér": {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="Fejér": None people
Out[6]:
total cases daily new cases
Dátum
2022-12-21 88576 206
2022-12-14 88370 205
2022-12-07 88165 180
2022-11-30 87985 167
2022-11-23 87818 178
2022-11-16 87640 142
2022-11-09 87498 425
2022-10-26 87073 317
2022-10-19 86756 396
2022-10-12 86360 436
2022-10-05 85924 419
2022-09-28 85505 358
2022-09-21 85147 404
2022-09-14 84743 357
2022-09-07 84386 328
2022-08-31 84058 421
2022-08-24 83637 546
2022-08-17 83091 636
2022-08-10 82455 763
2022-08-03 81692 771
2022-07-27 80921 596
2022-07-20 80325 325
2022-07-13 80000 263
2022-07-06 79737 140
2022-06-29 79597 101
2022-06-22 79496 60
2022-06-15 79436 52
2022-06-08 79384 50
2022-06-01 79334 84
2022-05-25 79250 119
2022-05-18 79131 202
2022-05-11 78929 256
2022-05-02 78673 120
2022-04-28 78553 49
2022-04-27 78504 88
2022-04-26 78416 46
2022-04-25 78370 109
2022-04-22 78261 65
2022-04-21 78196 110
2022-04-20 78086 42
2022-04-19 78044 200
2022-04-14 77844 113
2022-04-13 77731 63
2022-04-12 77668 56
2022-04-11 77612 191
2022-04-08 77421 170
2022-04-06 77251 124
2022-04-05 77127 205
2022-04-01 76922 95
2022-03-31 76827 79
2022-03-30 76748 80
2022-03-29 76668 57
2022-03-28 76611 170
2022-03-25 76441 106
2022-03-24 76335 76
2022-03-23 76259 87
2022-03-22 76172 57
2022-03-21 76115 178
2022-03-18 75937 118
2022-03-17 75819 49
2022-03-16 75770 181
2022-03-11 75589 84
2022-03-10 75505 82
2022-03-09 75423 85
2022-03-08 75338 38
2022-03-07 75300 267
2022-03-04 75033 116
2022-03-03 74917 131
2022-03-02 74786 136
2022-03-01 74650 65
2022-02-28 74585 350
2022-02-25 74235 171
2022-02-24 74064 278
2022-02-23 73786 228
2022-02-22 73558 184
2022-02-21 73374 538
2022-02-18 72836 332
2022-02-17 72504 332
2022-02-16 72172 371
2022-02-15 71801 156
2022-02-14 71645 1001
2022-02-11 70644 553
2022-02-10 70091 552
2022-02-09 69539 503
2022-02-08 69036 290
2022-02-07 68746 1490
2022-02-04 67256 752
2022-02-03 66504 752
2022-02-02 65752 835
2022-02-01 64917 421
2022-01-31 64496 1792
2022-01-28 62704 807
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2022-01-25 60334 449
2022-01-24 59885 1641
2022-01-21 58244 587
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2022-01-19 57105 551
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2022-01-17 56399 892
2022-01-14 55507 346
2022-01-13 55161 335
2022-01-12 54826 267
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2022-01-10 54445 645
2022-01-07 53800 227
2022-01-06 53573 265
2022-01-05 53308 206
2022-01-04 53102 83
2022-01-03 53019 246
2021-12-31 52773 138
2021-12-30 52635 159
2021-12-29 52476 118
2021-12-28 52358 61
2021-12-27 52297 422
2021-12-23 51875 175
2021-12-22 51700 174
2021-12-21 51526 74
2021-12-20 51452 491
2021-12-17 50961 246
2021-12-16 50715 258
2021-12-15 50457 383
2021-12-14 50074 97
2021-12-13 49977 690
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2021-11-22 43262 1000
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2020-05-05 306 2
2020-05-04 304 4
2020-05-03 300 2
2020-05-02 298 2
2020-05-01 296 25
2020-04-30 271 2
2020-04-29 269 4
2020-04-28 265 2
2020-04-27 263 12
2020-04-26 251 5
2020-04-25 246 6
2020-04-24 240 27
2020-04-23 213 32
2020-04-22 181 6
2020-04-21 175 16
2020-04-20 159 13
2020-04-19 146 5
2020-04-18 141 13
2020-04-17 128 6
2020-04-16 122 8
2020-04-15 114 8
2020-04-14 106 4
2020-04-13 102 9
2020-04-12 93 11
2020-04-11 82 10
2020-04-10 72 17
2020-04-09 55 6
2020-04-08 49 10
2020-04-07 39 15
2020-04-06 24 0
2020-04-05 24 3
2020-04-04 21 0
2020-04-03 21 3
2020-04-02 18 5
2020-04-01 13 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: 0:00:11.002536