pycovid 0.1.1

Useful tool to access Rami Krispin Novel Corona Dataset

PyCOVID Package

The PyCOVID package provides a Pandas Dataframe of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic based on Rami Krispin's 'coronavirus' package in R. The raw data pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus

Quick Installation

pip install pycovid

Importing

from pycovid import pycovid
pycovid.getCovidCases()

Value Addition

The 'coronavirus' R package gets access to data, but the 'pyCOVID' package builts some additional functionality over it.

1. Wide Format for quicker analysis (Wide by case type - Confirmed/Death/Recovered)
2. Filtering options - By country, timeline, casetype
3. Cumulative Aggregating options - cumsum parameter to look at the cumulative totals of how the Coronavirus has grown over time
4. Time Resampling: Converts dataframe to time-indexed, and resamples at required time level (weekly, monthly, etc)
5. Quick visualization using Plotly: Use the plotCountries() function

Usage

getCovidCasesWide() : Get the wide version of the Coronavirus Dataset Parameters:

1. Countries: List of Countries (Default: All Countries)
2. start_date and end_date: Use these to set the time window you wish to access
3. casetype: Python List of Case Types ('confirmed', 'death' and 'recovered' and Default is all)
4. cumsum: Gets cumulative sums of cases for each country in list (Default: False)

getCovidCases() : Get the Rami Krispin Coronavirus Dataset in the original format Parameters:

1. Countries: List of Countries (Default: All Countries)
2. Provinces: List of Provinces and States (Default: All)
3. start_date and end_date: Use these to set the time window you wish to access
4. casetype: Python List of Case Types ('confirmed', 'death' and 'recovered' and Default is all)

plotCountries(): Plot the country aggregates on world map using Plotly Parameters:

1. df: Pass a wide dataframe to the function with country-wise aggregates on confirmed, death and recovered cases
2. grouped_date: Boolean to indicate whether dataset has been aggregated at country level or not
3. metric: Can be 'confirmed' or 'death' or 'recovered'

plotProvinces(): Plot the values from provinces within a country over time

1. countries - just include one
2. provinces - include names of any states or provinces
3. start_date and end_date: as above
4. casetype: as above

from pycovid import pycovid

pycovid.plot_provinces(contries=['Canada'], 
			provinces=['Alberta', 'Ontario', 'Quebec', 
				'Manitoba', 'British Columbia', 
				'New Brunswick', 'Saskatchewan'], 
			casetype=['confirmed'], start_date="2020-02-20")

getIntervalData(): Get resampled dataset of the Coronavirus based on the date (by default Monthly level)

1. df: Pass a wide dataframe to the function
2. interval: The time interval you wish to resample the dataset to: 1D = Daily, 1W: Weekly, 1M: Monthly

Installation

pip install pycovid

from pycovid import pycovid
pycovid.getCovidCases()

or with virtual environment

# Configure a virtual environment in project directory
python3 -m venv venv 
# Activate the environment (assign paths)
source venv/bin/activate 
# Upgrade Pip and install requirements
pip install --upgrade pip 
pip install pycovid

Requirements

Pandas, Numpy and Plotly

Authors

PyCOVID was written by Sudharshan Ashok [e-mail]

Licence

MIT License