Hungary: Komárom-Esztergom¶

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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="Komárom-Esztergom", weeks=5);
2023-03-07T16:42:05.970602 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 21 Nov 28 Nov 05 Dec 12 Dec 19 Dec 120 120 130 130 140 140 150 150 7-day incidence rate Komárom-Esztergom, 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-Komárom-Esztergom new cases (rolling 7d mean) Hungary-Komárom-Esztergom 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-Komárom-Esztergom cases daily growth factor Hungary-Komárom-Esztergom cases daily growth factor (rolling mean) Hungary-Komárom-Esztergom 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="Komárom-Esztergom");
2023-03-07T16:42:10.746937 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 158.0 Komárom-Esztergom, 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-Komárom-Esztergom new cases (rolling 7d mean) Hungary-Komárom-Esztergom 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-Komárom-Esztergom cases daily growth factor Hungary-Komárom-Esztergom cases daily growth factor (rolling mean) Hungary-Komárom-Esztergom 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="Komárom-Esztergom");
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[5], line 1
----> 1 compare_plot(country="Hungary", region="Komárom-Esztergom");

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="Komárom-Esztergom")

# get population of the region for future normalisation:
inhabitants = population(country="Hungary", region="Komárom-Esztergom")
print(f'Population of country="Hungary", region="Komárom-Esztergom": {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="Komárom-Esztergom": None people
Out[6]:
total cases daily new cases
Dátum
2022-12-21 66002 158
2022-12-14 65844 131
2022-12-07 65713 121
2022-11-30 65592 125
2022-11-23 65467 119
2022-11-16 65348 128
2022-11-09 65220 324
2022-10-26 64896 207
2022-10-19 64689 272
2022-10-12 64417 273
2022-10-05 64144 347
2022-09-28 63797 246
2022-09-21 63551 231
2022-09-14 63320 247
2022-09-07 63073 240
2022-08-31 62833 288
2022-08-24 62545 292
2022-08-17 62253 343
2022-08-10 61910 385
2022-08-03 61525 609
2022-07-27 60916 398
2022-07-20 60518 246
2022-07-13 60272 155
2022-07-06 60117 93
2022-06-29 60024 58
2022-06-22 59966 28
2022-06-15 59938 33
2022-06-08 59905 41
2022-06-01 59864 41
2022-05-25 59823 71
2022-05-18 59752 103
2022-05-11 59649 176
2022-05-02 59473 83
2022-04-28 59390 41
2022-04-27 59349 53
2022-04-26 59296 25
2022-04-25 59271 74
2022-04-22 59197 41
2022-04-21 59156 60
2022-04-20 59096 37
2022-04-19 59059 122
2022-04-14 58937 62
2022-04-13 58875 60
2022-04-12 58815 31
2022-04-11 58784 128
2022-04-08 58656 138
2022-04-06 58518 45
2022-04-05 58473 130
2022-04-01 58343 50
2022-03-31 58293 71
2022-03-30 58222 76
2022-03-29 58146 54
2022-03-28 58092 112
2022-03-25 57980 67
2022-03-24 57913 88
2022-03-23 57825 61
2022-03-22 57764 35
2022-03-21 57729 181
2022-03-18 57548 65
2022-03-17 57483 38
2022-03-16 57445 128
2022-03-11 57317 55
2022-03-10 57262 62
2022-03-09 57200 53
2022-03-08 57147 30
2022-03-07 57117 161
2022-03-04 56956 125
2022-03-03 56831 100
2022-03-02 56731 76
2022-03-01 56655 39
2022-02-28 56616 255
2022-02-25 56361 167
2022-02-24 56194 155
2022-02-23 56039 155
2022-02-22 55884 79
2022-02-21 55805 416
2022-02-18 55389 261
2022-02-17 55128 238
2022-02-16 54890 218
2022-02-15 54672 169
2022-02-14 54503 640
2022-02-11 53863 401
2022-02-10 53462 400
2022-02-09 53062 359
2022-02-08 52703 124
2022-02-07 52579 1226
2022-02-04 51353 570
2022-02-03 50783 677
2022-02-02 50106 473
2022-02-01 49633 153
2022-01-31 49480 1857
2022-01-28 47623 678
2022-01-27 46945 836
2022-01-26 46109 694
2022-01-25 45415 320
2022-01-24 45095 1461
2022-01-21 43634 616
2022-01-20 43018 546
2022-01-19 42472 453
2022-01-18 42019 312
2022-01-17 41707 654
2022-01-14 41053 270
2022-01-13 40783 243
2022-01-12 40540 179
2022-01-11 40361 119
2022-01-10 40242 432
2022-01-07 39810 207
2022-01-06 39603 182
2022-01-05 39421 143
2022-01-04 39278 87
2022-01-03 39191 166
2021-12-31 39025 137
2021-12-30 38888 91
2021-12-29 38797 102
2021-12-28 38695 46
2021-12-27 38649 297
2021-12-23 38352 116
2021-12-22 38236 119
2021-12-21 38117 71
2021-12-20 38046 446
2021-12-17 37600 222
2021-12-16 37378 166
2021-12-15 37212 200
2021-12-14 37012 87
2021-12-13 36925 750
2021-12-10 36175 182
2021-12-09 35993 240
2021-12-08 35753 191
2021-12-07 35562 127
2021-12-06 35435 781
2021-12-03 34654 390
2021-12-02 34264 217
2021-12-01 34047 366
2021-11-30 33681 272
2021-11-29 33409 625
2021-11-26 32784 243
2021-11-25 32541 222
2021-11-24 32319 176
2021-11-23 32143 173
2021-11-22 31970 528
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2021-11-16 30962 67
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2021-11-12 30490 135
2021-11-11 30355 95
2021-11-10 30260 102
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2021-11-08 30061 296
2021-11-05 29765 106
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2021-11-03 29558 52
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2021-11-01 29458 177
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2021-10-22 28978 50
2021-10-21 28928 23
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2021-10-18 28877 44
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2020-05-13 176 2
2020-05-12 174 1
2020-05-11 173 5
2020-05-10 168 2
2020-05-09 166 4
2020-05-08 162 5
2020-05-07 157 9
2020-05-06 148 7
2020-05-05 141 4
2020-05-04 137 5
2020-05-03 132 11
2020-05-02 121 11
2020-05-01 110 9
2020-04-30 101 8
2020-04-29 93 1
2020-04-28 92 5
2020-04-27 87 15
2020-04-26 72 2
2020-04-25 70 3
2020-04-24 67 3
2020-04-23 64 16
2020-04-22 48 0
2020-04-21 48 5
2020-04-20 43 11
2020-04-19 32 7
2020-04-18 25 2
2020-04-17 23 2
2020-04-16 21 1
2020-04-15 20 0
2020-04-14 20 0
2020-04-13 20 0
2020-04-12 20 0
2020-04-11 20 3
2020-04-10 17 0
2020-04-09 17 2
2020-04-08 15 0
2020-04-07 15 1
2020-04-06 14 0
2020-04-05 14 0
2020-04-04 14 -1
2020-04-03 15 2
2020-04-02 13 1
2020-04-01 12 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: 0:00:10.755927