Python Interface to COVID-19 Data Hub

Download COVID-19 data across governmental sources at national, regional, and city level, as described in Guidotti and Ardia (2020). Includes the time series of vaccines, tests, cases, deaths, recovered, hospitalizations, intensive therapy, and policy measures by Oxford COVID-19 Government Response Tracker. Please agree to the Terms of Use and cite the following reference when using it:

Reference

Guidotti, E., Ardia, D., (2020).
COVID-19 Data Hub
Journal of Open Source Software, 5(51):2376
https://doi.org/10.21105/joss.02376

Setup and usage

Install from pip with

pip install covid19dh

Importing the main function covid19()

from covid19dh import covid19
x, src = covid19()

Package is regularly updated. Update with

pip install --upgrade covid19dh

Return values

The function covid19() returns 2 pandas dataframes:

the data and
references to the data sources.

Parametrization

Country

List of country names (case-insensitive) or ISO codes (alpha-2, alpha-3 or numeric). The list of ISO codes can be found here.

Fetching data from a particular country:

x, src = covid19("USA") # Unites States

Specify multiple countries at the same time:

x, src = covid19(["ESP","PT","andorra",250])

If country is omitted, the whole dataset is returned:

x, src = covid19()

Raw data

Logical. Skip data cleaning? Default True. If raw=False, the raw data are cleaned by filling missing dates with NaN values. This ensures that all locations share the same grid of dates and no single day is skipped. Then, NaN values are replaced with the previous non-NaN value or 0.

x, src = covid19(raw = False)

Date filter

Date can be specified with datetime.datetime, datetime.date or as a str in format YYYY-mm-dd.

from datetime import datetime
x, src = covid19("SWE", start = datetime(2020,4,1), end = "2020-05-01")

Level

Integer. Granularity level of the data:

Country level
State, region or canton level
City or municipality level

from datetime import date
x, src = covid19("USA", level = 2, start = date(2020,5,1))

Cache

Logical. Memory caching? Significantly improves performance on successive calls. By default, using the cached data is enabled.

Caching can be disabled (e.g. for long running programs) by:

x, src = covid19("FRA", cache = False)

Vintage

Logical. Retrieve the snapshot of the dataset that was generated at the end date instead of using the latest version. Default False.

To fetch e.g. US data that were accessible on 22th April 2020 type

x, src = covid19("USA", end = "2020-04-22", vintage = True)

The vintage data are collected at the end of the day, but published with approximately 48 hour delay, once the day is completed in all the timezones.

Hence if vintage = True, but end is not set, warning is raised and None is returned.

x, src = covid19("USA", vintage = True) # too early to get today's vintage

UserWarning: vintage data not available yet

Data Sources

The data sources are returned as second value.

from covid19dh import covid19
x, src = covid19("USA")
print(src)

Additional information

Find out more at https://covid19datahub.io

Acknowledgements

Developed and maintained by Martin Benes.

Cite as

Guidotti, E., Ardia, D., (2020), "COVID-19 Data Hub", Journal of Open Source Software 5(51):2376, doi: 10.21105/joss.02376.

A BibTeX entry for LaTeX users is

@Article{,
    title = {COVID-19 Data Hub},
    year = {2020},
    doi = {10.21105/joss.02376},
    author = {Emanuele Guidotti and David Ardia},
    journal = {Journal of Open Source Software},
    volume = {5},
    number = {51},
    pages = {2376}
}

covid19datahub/Python