This module contains Penn World Table 10.01. No more than that.
You're looking at a fork of https://github.com/spring-haru/pwtdata.git by Tetsu Haruyama.
The original module is already very useful but I wanted a little more. This fork reads data from the Stata dataset distributed at https://www.rug.nl/ggdc/productivity/pwt/ to be able to use variable labels.
$ pip install git+https://github.com/kenjisato/pwtdata.git
or
git clone https://github.com/kenjisato/pwtdata.git
cd pwtdata
pip install .
Import data and learn what variables are contained.
>>> import pwtdata
>>> print(pwtdata.var_definitions)
Variable name: Variable definition
----- Identifier variables -----
countrycode: 3-letter ISO country code
country: Country name
currency_unit: Currency unit
year: Year
----- Real GDP, employment and population levels -----
rgdpe: Expenditure-side real GDP at chained PPPs (in mil. 2017US$),,,
rgdpo: Output-side real GDP at chained PPPs (in mil. 2017US$),,,\
...
...
Then, select columns you need and convert them into a pandas dataframe.
>>> pwt = pwtdata.load(["country", "year", "rgdpo"])
>>> pwt[pwt.year > 2017]
country year rgdpo
68 Aruba 2018 3444.658691
69 Aruba 2019 3467.299561
138 Angola 2018 233805.062500
139 Angola 2019 227855.718750
208 Anguilla 2018 150.344666
... ... ... ...
12669 South Africa 2019 734094.375000
12738 Zambia 2018 55459.121094
12739 Zambia 2019 56783.714844
12808 Zimbabwe 2018 43420.898438
12809 Zimbabwe 2019 40826.570312
[2196 rows x 3 columns]
If you'd like to refer to labels programatically, pass description=True to load() function.
>>> pwt, desc = pwtdata.load(["country", "year", "rgdpo"], description=True)
>>> desc
Description
country Country name
year Year
rgdpo Output-side real GDP at chained PPPs (in mil. ...
- Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table" American Economic Review, 105(10), 3150-3182, available for download at www.ggdc.net/pwt