India¶

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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 09:35:09 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview("India", weeks=5);
2023-03-07T09:35:13.028903 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar −0.05 −0.05 0.00 0.00 0.05 0.05 0.10 0.10 0.15 0.15 7-day incidence rate (per 100K people) 0.1 India, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.02 0.04 0.06 0.08 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.0000 0.0001 0.0002 0.0003 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 1.0 1.0 1.5 1.5 2.0 2.0 2.5 2.5 3.0 3.0 R & growth factor (based on cases) India cases daily growth factor India cases daily growth factor (rolling mean) India estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on deaths) India deaths daily growth factor India deaths daily growth factor (rolling mean) India estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 100000 200000 300000 400000 cases doubling time [days] India doubling time cases (rolling mean) India doubling time deaths (rolling mean) 0 276 552 828 1104 daily change India new cases (rolling 7d mean) India new cases 0.00 1.38 2.76 4.14 daily change India new deaths (rolling 7d mean) India new deaths 0 130823 261647 392470 523294 deaths doubling time [days]
In [4]:
overview("India");
2023-03-07T09:35:21.573468 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 0 50 50 100 100 150 150 200 200 7-day incidence rate (per 100K people) 0.1 India, last data point from 2023-03-06 Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 10 20 30 daily change normalised per 100K Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0.0 0.2 0.4 daily change normalised per 100K Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 1.0 1.0 1.5 1.5 2.0 2.0 2.5 2.5 3.0 3.0 R & growth factor (based on cases) India cases daily growth factor India cases daily growth factor (rolling mean) India estimated R (using cases) Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0.8 0.8 1.0 1.0 1.2 1.2 1.4 1.4 R & growth factor (based on deaths) India deaths daily growth factor India deaths daily growth factor (rolling mean) India estimated R (using deaths) Jan 20 May 20 Sep 20 Jan 21 May 21 Sep 21 Jan 22 May 22 Sep 22 Jan 23 May 23 0 200000 400000 cases doubling time [days] India doubling time cases (rolling mean) India doubling time deaths (rolling mean) 0 138000 276001 414001 daily change India new cases (rolling 7d mean) India new cases 0 2760 5520 daily change India new deaths (rolling 7d mean) India new deaths 0 218658 437315 deaths doubling time [days]
In [5]:
compare_plot("India", normalise=True);
2023-03-07T09:35:26.053477 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 2020-01 2020-05 2020-09 2021-01 2021-05 2021-09 2022-01 2022-05 2022-09 2023-01 2023-05 0.0001 0.0001 0.01 0.01 1 1 100 100 daily new cases per 100K people (rolling 7-day mean) Daily cases (top) and deaths (below) for India India Germany Australia Poland Korea, South Belarus Switzerland US 2020-01 2020-05 2020-09 2021-01 2021-05 2021-09 2022-01 2022-05 2022-09 2023-01 2023-05 1e-05 1e-05 0.0001 0.0001 0.001 0.001 0.01 0.01 0.1 0.1 1 1 daily new deaths per 100K people (rolling 7-day mean) India Germany Australia Poland Korea, South Belarus Switzerland US
In [6]:
# load the data
cases, deaths = get_country_data("India")

# get population of the region for future normalisation:
inhabitants = population("India")
print(f'Population of "India": {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 "India": 1380004385 people
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 44689593 266 530775 0
2023-03-05 44689327 281 530775 0
2023-03-04 44689046 324 530775 0
2023-03-03 44688722 334 530775 3
2023-03-02 44688388 283 530772 0
... ... ... ... ...
2020-01-27 0 0 0 0
2020-01-26 0 0 0 0
2020-01-25 0 0 0 0
2020-01-24 0 0 0 0
2020-01-23 0 0 0 0

1139 rows × 4 columns

Explore the data in your web browser¶

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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: 0:00:17.562317