sandy4321 / covid-19-growth

Daily COVID-19 epidemiological data, piped in to friendly Pandas dataframes, functions for dataset construction

covid-19-growth

The Johns Hopkins University Center for Systems Science and Engineering is providing daily COVID-19 CSV files containing the data that are displayed on their ArcGIS dashboard for COVID-19. This repo aims to provide a sensible starting point and some useful functions for ongoing work in Pandas/Python using the JH data.

For VSCode users, available as a self-contained, system-independent environment using Docker Remote with Jupyter Notebook integration.

Installing

Vanilla

Clone the repo with --recursive

git clone --recursive [email protected]:willhaslett/covid-19-growth.git

Set up your Python environmet. For example, with virtualenv

cd covid-19-growth
virtualenv venv
source venv/bin/activate
pip install -r requirements.txt

Verify installation

$ python lib/tests.py

Main dataframe:
  province_state    country      lat      long       date  cases  day
0            NaN   Thailand  15.0000  101.0000 2020-01-22      2  0.0
1            NaN      Japan  36.0000  138.0000 2020-01-22      2  0.0
2            NaN  Singapore   1.2833  103.8333 2020-01-22      0  0.0
3            NaN      Nepal  28.1667   84.2500 2020-01-22      0  0.0
4            NaN   Malaysia   2.5000  112.5000 2020-01-22      0  0.0

Cases for one country:
   index    province_state country      lat     long       date  cases  day
0    159            France  France  46.2276   2.2137 2020-01-22      0  0.0
1    201         St Martin  France  18.0708 -63.0501 2020-01-22      0  0.0
2    208  Saint Barthelemy  France  17.9000 -62.8333 2020-01-22      0  0.0
3    563            France  France  46.2276   2.2137 2020-01-23      0  1.0
4    605         St Martin  France  18.0708 -63.0501 2020-01-23      0  1.0

Cases for one province_state:
   index    province_state country      lat      long       date  cases  day
0      5  British Columbia  Canada  49.2827 -123.1207 2020-01-22      0  0.0
1    409  British Columbia  Canada  49.2827 -123.1207 2020-01-23      0  1.0
2    813  British Columbia  Canada  49.2827 -123.1207 2020-01-24      0  2.0
3   1217  British Columbia  Canada  49.2827 -123.1207 2020-01-25      0  3.0
4   1621  British Columbia  Canada  49.2827 -123.1207 2020-01-26      0  4.0

Tests passed
$

VSCode/Docker

Clone the repo as above (--recursive!)

Have the VSCode extension for Remote Development installed. Here 'remote' means, in a local Docker container (Debian).

In VSCode, Open the project folder in a container

Verify the installation as above.

Usage

To stay in sync with the Johns Hopkins data

./update_data.sh

etl.py currently provides one dataframe and four functions:

• df_all A dataframe with all global cases, long format, dates as Pandas timestamps
• filter(column, vlaue) Generic filter
• for_country(country) Filter by country
• for_province_state(province_state) Filter by province_state
• sum_by_date() Group by date and sum case counts

License

This project is licensed under the MIT License. See the LICENSE.md file for details

Acknowledgments

The Johns Hopkins University Center for Systems Science and Engineering is doing a great public service by sharing these data.