stargazer 0.0.1

Nicely formatted regression reporting

Stargazer

This is a python port of the R stargazer package that can be found on CRAN. I was disappointed that there wasn't equivalent functionality in any python packages I was aware of so I'm re-implementing it here.

There is an experimental function in the statsmodels.regression.linear_model.OLSResults.summary2 that can report single regression model results in HTML/CSV/LaTeX/etc, but it still didn't quite fulfill what I was looking for.

The python package is object oriented now with chained commands to make changes to the rendering parameters, which is hopefully more pythonic and the user doesn't have to put a bunch of arguments in a single function.

I'm a data scientist, not a software engineer so please don't crucify me over my bad code. Thanks in advance :D

Example

Here is an examples of how to quickly get started with the library. More examples can be found in the examples.ipynb file in the github repo. The examples all use the scikit-learn diabetes dataset, but it is not a dependency for the package.

OLS Models Preparation

import pandas as pd
from sklearn import datasets
import statsmodels.api as sm
from stargazer.stargazer import Stargazer

diabetes = datasets.load_diabetes()
df = pd.DataFrame(diabetes.data)
df.columns = ['Age', 'Sex', 'BMI', 'ABP', 'S1', 'S2', 'S3', 'S4', 'S5', 'S6']
df['target'] = diabetes.target

est = sm.OLS(endog=df['target'], exog=sm.add_constant(df[df.columns[0:4]])).fit()
est2 = sm.OLS(endog=df['target'], exog=sm.add_constant(df[df.columns[0:6]])).fit()


stargazer = Stargazer([est, est2])

stargazer.render_html()

	Dependent variable:
	(1)	(2)
ABP	416.674***	397.583***
	(69.495)	(70.87)
Age	37.241	24.704
	(64.117)	(65.411)
BMI	787.179***	789.742***
	(65.424)	(66.887)
S1		197.852
		(143.812)
S2		-169.251
		(142.744)
Sex	-106.578*	-82.862
	(62.125)	(64.851)
const	152.133***	152.133***
	(2.853)	(2.853)
Observations	442.0	442.0
R2	0.4	0.403
Adjusted R2	0.395	0.395
Residual Std. Error	59.976(df = 437.0)	59.982(df = 435.0)
F Statistic	72.913***(df = 4.0; 437.0)	48.915***(df = 6.0; 435.0)
Note:	p<0.1; p<0.05; p<0.01