Guidotti, E., Ardia, D., (2020).
COVID-19 Data Hub
Journal of Open Source Software, 5(51):2376
Setup and usage
Install from pip with
Importing the main function
covid19() returns 2 pandas dataframes:
- the data and
- references to the data sources.
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:
Specify multiple countries at the same time:
country is omitted, the whole dataset is returned:
Logical. Skip data cleaning? Default
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
Date can be specified with
datetime.date or as a
str in format
Integer. Granularity level of the data:
- Country level
- State, region or canton level
- City or municipality level
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:
Logical. Retrieve the snapshot of the dataset that was generated at the
end date instead of using the latest version. Default
To fetch e.g. US data that were accessible on 22th April 2020 type
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.
vintage = True, but
end is not set, warning is raised and
None is returned.
UserWarning: vintage data not available yet
COVID-19 Data Hub harmonizes the amount of heterogeneous data that have become available around the pandemic. It represents a first effort towards open public data standards and sharing in light of COVID-19. Publications using COVID-19 Data Hub are available here.