United States: North Dakota¶

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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 16:40:23 CEST
In [2]:
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
In [3]:
overview(country="US", region="North Dakota", weeks=5);

/tank/oscovida/work/oscovida.github.io/oscovida.github.io/.venv/lib/python3.9/site-packages/oscovida/oscovida.py:211: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  tmpd = deaths.groupby('Province_State').sum()
/tank/oscovida/work/oscovida.github.io/oscovida.github.io/.venv/lib/python3.9/site-packages/oscovida/oscovida.py:213: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  tmpc = cases.groupby('Province_State').sum()
2023-03-07T16:40:27.512272 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/ 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 40 40 60 60 80 80 100 100 7-day incidence rate (per 100K people) 42.8 North Dakota, US, last 5 weeks, last data point from 2023-03-06 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 10 20 30 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.00 0.25 0.50 0.75 1.00 daily change normalised per 100K 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) United States: North Dakota cases daily growth factor United States: North Dakota cases daily growth factor (rolling mean) United States: North Dakota estimated R (using cases) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) United States: North Dakota deaths daily growth factor United States: North Dakota deaths daily growth factor (rolling mean) United States: North Dakota estimated R (using deaths) 30 Jan 06 Feb 13 Feb 20 Feb 27 Feb 06 Mar 0 2000 4000 6000 cases doubling time [days] United States: North Dakota doubling time cases (rolling mean) United States: North Dakota doubling time deaths (rolling mean) 0.0 76.2 152.4 228.6 daily change United States: North Dakota new cases (rolling 7d mean) United States: North Dakota new cases 0.000 1.905 3.810 5.715 7.621 daily change United States: North Dakota new deaths (rolling 7d mean) United States: North Dakota new deaths 0 1025 2049 3074 deaths doubling time [days]
In [4]:
overview(country="US", region="North Dakota");

/tank/oscovida/work/oscovida.github.io/oscovida.github.io/.venv/lib/python3.9/site-packages/oscovida/oscovida.py:211: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  tmpd = deaths.groupby('Province_State').sum()
/tank/oscovida/work/oscovida.github.io/oscovida.github.io/.venv/lib/python3.9/site-packages/oscovida/oscovida.py:213: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  tmpc = cases.groupby('Province_State').sum()
2023-03-07T16:40:36.874529 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 0 500 500 1000 1000 1500 1500 2000 2000 7-day incidence rate (per 100K people) 42.8 North Dakota, US, 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 0 100 200 300 400 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 5 10 15 20 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 0.6 0.6 0.8 0.8 1.0 1.0 1.2 1.2 R & growth factor (based on cases) United States: North Dakota cases daily growth factor United States: North Dakota cases daily growth factor (rolling mean) United States: North Dakota 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 0 1 1 2 2 3 3 4 4 R & growth factor (based on deaths) United States: North Dakota deaths daily growth factor United States: North Dakota deaths daily growth factor (rolling mean) United States: North Dakota 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 2500 5000 7500 10000 cases doubling time [days] United States: North Dakota doubling time cases (rolling mean) United States: North Dakota doubling time deaths (rolling mean) 0 762 1524 2286 3048 daily change United States: North Dakota new cases (rolling 7d mean) United States: North Dakota new cases 0.0 38.1 76.2 114.3 152.4 daily change United States: North Dakota new deaths (rolling 7d mean) United States: North Dakota new deaths 0 1297 2595 3892 5190 deaths doubling time [days]
In [5]:
compare_plot(country="US", region="North Dakota");

/tank/oscovida/work/oscovida.github.io/oscovida.github.io/.venv/lib/python3.9/site-packages/oscovida/oscovida.py:211: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  tmpd = deaths.groupby('Province_State').sum()
/tank/oscovida/work/oscovida.github.io/oscovida.github.io/.venv/lib/python3.9/site-packages/oscovida/oscovida.py:213: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  tmpc = cases.groupby('Province_State').sum()
In [6]:
# load the data
cases, deaths = get_country_data("US", "North Dakota")

# get population of the region for future normalisation:
inhabitants = population(country="US", region="North Dakota")
print(f'Population of country="US", region="North Dakota": {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

/tank/oscovida/work/oscovida.github.io/oscovida.github.io/.venv/lib/python3.9/site-packages/oscovida/oscovida.py:211: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  tmpd = deaths.groupby('Province_State').sum()

Population of country="US", region="North Dakota": 762062 people

/tank/oscovida/work/oscovida.github.io/oscovida.github.io/.venv/lib/python3.9/site-packages/oscovida/oscovida.py:213: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  tmpc = cases.groupby('Province_State').sum()
Out[6]:
total cases daily new cases total deaths daily new deaths
2023-03-06 286950 0 2465 0
2023-03-05 286950 0 2465 0
2023-03-04 286950 0 2465 0
2023-03-03 286950 0 2465 0
2023-03-02 286950 83 2465 2
... ... ... ... ...
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: 0:00:17.813084