Unified data hub for a better understanding of COVID-19 https://covid19datahub.io
Project description
Python Interface to COVID-19 Data Hub
Python package covid19dh provides access to COVID-19 data from unified data hub.
It is part of COVID-19 Data Hub project.
Setup and usage
Install from pip with
pip install covid19dh
Importing main covid19()
function with
from covid19dh import covid19
x = covid19("ITA") # load data
Package is regularly updated. Update with
pip install --upgrade covid19dh
Parametrization
Country
Country specifies an administrative region, that the data are fetched from. This is connected with source data comes from. It can be given as ISO3, ISO2, numeric ISO or country name (case-insensitively).
Fetching data from a particular country is done with
x = covid19("ESP")
List of ISO codes can be found here.
Filter can also specify multiple countries at the same time
x = covid19(["ESP","PT","andorra",250])
Country can be omitted, then whole world data is used.
x = covid19()
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 = covid19("SWE", start = datetime(2020,4,1), end = "2020-05-01")
Level
Levels work the same way as in all the other our data fetchers.
- Country level
- State, region or canton level
- City or municipality level
from datetime import date
x = covid19("USA", level = 2, start = date(2020,5,1))
Cache
Library keeps downloaded data in simple way during runtime. By default, using the cached data is enabled.
Caching can be disabled (e.g. for long running programs) by
x = covid19("FRA", cache=False)
Citations
Dataset citations are printed by default on stdout
.
from covid19dh import covid19
x = covid19("CZE")
Czech Statistical Office (2018), https://www.czso.cz/csu/czso/demograficka-rocenka-kraju-2009-az-2018
Johns Hopkins Center for Systems Science and Engineering (2020), https://github.com/CSSEGISandData/COVID-19
Ministery of Health of Czech Republic (2020), https://onemocneni-aktualne.mzcr.cz/
Our World in Data (2020), https://github.com/owid/covid-19-data
Hale Thomas, Sam Webster, Anna Petherick, Toby Phillips, and Beatriz Kira (2020). Oxford COVID-19 Government Response Tracker, Blavatnik School of Government.
World Bank Open Data (2018), https://data.worldbank.org/indicator/SP.POP.TOTL
Guidotti, E., Ardia, D., (2020), "COVID-19 Data Hub", Working paper, doi: 10.13140/RG.2.2.11649.81763.
This feature can be turned off by setting verbose
to False
.
from covid19dh import covid19
x = covid19("CZE", verbose = False)
You can separately get the reference data or the string citations as
from covid19dh import covid19,cite
x = covid19("ITA")
refs = cite(x, raw=True)
citations = cite(x)
Pandas dataframe refs
has following structure
title author year institution ... bibtype iso_alpha_3 administrative_area_level data_type
0 Czech Statistical Office 2018 ... 1 1 1
1 Johns Hopkins Center for Systems Science and E... 2020 ... 5 5 5
2 Ministery of Health of Czech Republic 2020 ... 2 2 2
3 Our World in Data 2020 ... 1 1 1
4 Oxford COVID-19 Government Response Tracker Hale Thomas, Sam Webster, Anna Petherick, Toby... 2020 Blavatnik School of Government ... 10 10 10
5 World Bank Open Data 2018 ... 1 1 1
[6 rows x 10 columns]
List citations
is equal to
[
'Czech Statistical Office (2018), https://www.czso.cz/csu/czso/demograficka-rocenka-kraju-2009-az-2018',
'Johns Hopkins Center for Systems Science and Engineering (2020), https://github.com/CSSEGISandData/COVID-19',
'Ministery of Health of Czech Republic (2020), https://onemocneni-aktualne.mzcr.cz/',
'Our World in Data (2020), https://github.com/owid/covid-19-data',
'Hale Thomas, Sam Webster, Anna Petherick, Toby Phillips, and Beatriz Kira (2020). Oxford COVID-19 Government Response Tracker, Blavatnik School of Government.',
'World Bank Open Data (2018), https://data.worldbank.org/indicator/SP.POP.TOTL',
'Guidotti, E., Ardia, D., (2020), "COVID-19 Data Hub", Working paper, doi: 10.13140/RG.2.2.11649.81763.'
]
Contribution
Developed by Martin Benes.
The goal of COVID-19 Data Hub is to provide the research community with a unified data hub by collecting worldwide fine-grained case data, merged with exogenous variables helpful for a better understanding of COVID-19.
Join us on GitHub.
Project details
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