COVID19-Data

Overview

covid19-data is a powerful and easy to use Python client for getting COVID-19 data (see sources below for more information on how data is obtained)

• Uses a fast method of getting data
  • Does not rely on scraping sites, parsing files, or getting (old) data from a repository, so you do not depend on the repository being updated to get up to date data
• Very fast and responsive
  • The client only gets the data once, and parses it into a search friendly format in the backend, so once the data is loaded ( ~ 1 second ), data for the World or any Country/State can be retrieved instantly
• User friendly and simple to implement into your application
• Very flexible and will return the data in multiple forms (read documentation section for more info)
  • Can return data as a "Class Style Object" with attributes (only requires one line of code, and is super easy to read!)
  • Can return an object with the data as attributes
  • Can return a JSON document
• Super simplistic and lightweight and does not rely on any external python packages

Installing

covid19-data can be installed with pip:

$ pip install covid19-data

Alternatively, you can grab the latest source code from GitHub:

$ git clone git://github.com/binarynightowl/covid19_python
$ python setup.py install

Documentation

Usage

There are multiple ways of getting data with covid19-data

1. Object/parameter style retrieval

   • Gets the data by calling the class of the desired information source and the statistics for any location. As of now, only John Hopkins University (JHU) is supported but in a future release, multiple sources will be supported.

     from covid19_data import JHU
     
     # Format: <Data Source>.<Location>.<Statistic>
     # For example to get data from John Hopkins University, review the following example:
     # JHU.China.deaths
       
     print("The number of COVID-19 recoveries in the US: " + str(JHU.US.recovered))
     print("The number of confirmed COVID-19 cases in Texas: " + str(JHU.Texas.confirmed))   # JHU.TX.confirmed also works
     print("The number of COVID-19 deaths in California: " + str(JHU.CA.deaths))     # JHU.California also works
     print("The number of worldwide COVID-19 deaths: " + str(JHU.Total.deaths))
     print("The number of COVID-19 deaths in China: " + str(JHU.China.deaths))
     print("The number of COVID-19 deaths in UK: " + str(JHU.UnitedKingdom.deaths))

     Sample Output:

     The number of COVID-19 recoveries in the US : 685164
     The number of confirmed COVID-19 cases in Texas : 150851
     The number of COVID-19 deaths in California : 5935
     The number of worldwide COVID-19 deaths : 502947
     The number of COVID-19 deaths in China : 4641
     The number of COVID-19 recoveries in the United Kingdom : 1364
     

2. As an object with attributes of COVID data

   • Get the data by name (note: spacing and capitalization do not matter, EX: total = covid19_data.dataByName("New York"), total = covid19_data.dataByName("newyork"), and total = covid19_data.dataByName("NEW YORK") are all interchangable)

     import covid19_data
     
     # example of how to get data by name
     # .dataByName([string of item to find: any state, country, or total amount (spacing and capitalization do not matter)])
     # object has three useful attributes: .deaths, .cases (.confirmed also works), and .recovered
     
     total = covid19_data.dataByName("Total")    # create an object for our total data
     china = covid19_data.dataByName("China")
     US = covid19_data.dataByName("US")
     new_york = covid19_data.dataByName("NewYork")
     print(total.deaths, china.recovered, US.cases)

     Sample Output:

     22184 74181 69246
     

   • Get the data by abbreviation (note: spacing and capitalization do not matter, EX: total = covid19_data.dataByName("New York"), total = covid19_data.dataByName("newyork"), and total = covid19_data.dataByName("NEW YORK") are all interchangable)

     import covid19_data        
     
     # example of how to get data by abbreviated name
     # .dataByNameShort([two letter string of item you want to find, can be any state])
     # object has three useful attributes: .deaths, .cases (.confirmed also works), and .recovered
     
     texas = covid19_data.dataByNameShort("TX")    # create an object for our total data
     california = covid19_data.dataByNameShort("CA")
     new_york = covid19_data.dataByNameShort("NY")
     print(texas.cases, california.deaths, new_york.cases)

     Sample Output:

     1353 67 33033
     

3. As a JSON document

   • Get the json by name (note: spacing and capitalization do not matter, EX: total = covid19_data.dataByName("New York"), total = covid19_data.dataByName("newyork"), and total = covid19_data.dataByName("NEW YORK") are all interchangable)

     import covid19_data
     
     # example of how to get json by name
     # .jsonByName([string of item you want to find, can be any state, country, or total amount (spacing and capitalization do not matter)])
     # object has three useful attributes: .deaths, .cases (.confirmed also works), and .recovered
     
     total = covid19_data.jsonByName("Total")    # create an object for our total data
     china = covid19_data.jsonByName("China")
     US = covid19_data.jsonByName("US")
     new_york = covid19_data.jsonByName("NewYork")
     print(total, china, US, new_york)

     Sample Output::

     {'Confirmed': 492603, 'Deaths': 22184, 'Recovered': 119918}
     {'Confirmed': 81782, 'Deaths': 3291, 'Recovered': 74181, 'Active': 4310}
     {'Confirmed': 69246, 'Deaths': 1046, 'Recovered': 619, 'Active': 0}
     {'Confirmed': 33033, 'Deaths': 366, 'Recovered': 0, 'Active': 0}
     

   • Get the json by abbreviation (note: spacing and capitalization do not matter, EX: total = covid19_data.dataByName("New York"), total = covid19_data.dataByName("newyork"), and total = covid19_data.dataByName("NEW YORK") are all interchangable)

     import covid19_data
     
     # example of how to get json by abbreviated name
     # .jsonByNameShort([two letter string of item you want to find, can be any state])
     # object has three useful attributes: .deaths, .cases (.confirmed also works), and .recovered
     
     texas = covid19_data.jsonByNameShort("TX")    # create an object for our total data
     california = covid19_data.jsonByNameShort("CA")
     new_york = covid19_data.jsonByNameShort("NY")
     print(texas, california, new_york)

     Sample Output::

     {'Confirmed': 1353, 'Deaths': 17, 'Recovered': 0, 'Active': 0}
     {'Confirmed': 3172, 'Deaths': 67, 'Recovered': 0, 'Active': 0}
     {'Confirmed': 33033, 'Deaths': 366, 'Recovered': 0, 'Active': 0}
     

Sources

This package utilizes John Hopkins University's ArcGIS data layer to get its data. Please follow their terms of service and licensing when using their data in your application. The data layer pulls data from the following sources:

• World Health Organization (WHO)
• DXY.cn. Pneumonia. 2020.
• BNO News
• National Health Commission of the People’s Republic of China (NHC)
• China CDC (CCDC)
• Hong Kong Department of Health
• Macau Government
• Taiwan CDC
• US CDC
• Government of Canada
• Australia Government Department of Health
• European Centre for Disease Prevention and Control (ECDC)
• Ministry of Health Singapore (MOH)
• Italy Ministry of Health