Data sets from Introductory Econometrics: A Modern Approach (7th ed, J.M. Wooldridge)
Project description
Wooldridge Meets Python
Data sets from Introductory Econometrics: A Modern Approach (7th ed, J.M. Wooldridge)
Description
A Python package which contains 111 data sets from one of the most famous econometrics textbooks for undergraduates.
It is extensively used in Pythonで学ぶ入門計量経済学 (Japanese). Its Google-translated version (in the language of your choice) is also available in Learning Introductory Econometrics with Python.
It is also used in Using Python for Introductory Econometrics, which is a sister book Using R for Introductory Econometrics.
How to Use
First things first.
import wooldridge
To load a data set named <dataset>
:
wooldridge.data('<dataset>')
It returns pandas DataFrame
. Note that <dataset>
is entered in strings. For example, to load a data set mroz
into df
:
df = wooldridge.data('mroz')
To show the description (e.g. variable definitions and sources) of a data set:
wooldridge.data('mroz', description=True)
To show the list of 111 data sets contained in the package
wooldridge.data()
How to Install
pip install wooldridge
or
git clone https://github.com/spring-haru/wooldridge.git
pip install .
Note
The function dataWoo()
introduced in the previous versions also works:
from wooldridge import *
df = dataWoo('<dataset>')
dataWoo('<dataset>', description=True)
dataWoo()
Reference
J.M. Wooldridge (2019) Introductory Econometrics: A Modern Approach, Cengage Learning, 7th edition.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for wooldridge-0.4.5-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 100de35c9738d66e1a98a93d894ae361d8c86d9548a4bfc665724ade42f3469b |
|
MD5 | 98c2b702064a48bbda36567d82da98e8 |
|
BLAKE2b-256 | e2788d4ca380f46c8a6eebb646b7a09c9ab394dbd11791a579fc7def8e4882bc |