Regression Table Packages powered by great-tables

[s3alfisc]

Hi all,

We (actually @dsliwka) have just released maketables to PyPi. It spun out of PyFixest and creates regression tables for models from statsmodels, linearmodelsand pyfixest using great-tables.

docs: link
github: link

Here is the sm example from the docs:

import pandas as pd
import maketables as mt

import statsmodels.formula.api as smf
# Load your data (here using a sample Stata dataset with the import_dta function that also stores variable labels)
df = mt.import_dta("https://www.stata-press.com/data/r18/auto.dta")

# Generate a dummy variable and label it
df["foreign_i"] = (df["foreign"] == "Foreign")*1
mt.set_var_labels(df, {"foreign_i": "Foreign (indicator)"})

# Fit your models 
est1 = smf.ols("foreign_i ~ weight + length + price", data=df).fit()
est2 = smf.probit("foreign_i ~ weight + length + price", data=df).fit(disp=0)

# Make the table
mt.ETable([est1, est2], model_stats=["N","r2","pseudo_r2"], 
          model_heads=["OLS","Probit"],
          caption="Regression Results (Statsmodels)")

which produces the following figure:

One core feature of the library is that it can support models from different packages in one table:

import pyfixest as pf 

# Here we just reestimaate the linear probability model using pyfixest instead of statsmodels
est0=pf.feols("foreign_i ~ weight + length + price", data=df)

# Make the table
mt.ETable([est0, est1, est2], model_stats=["N","r2","pseudo_r2"], 
          model_heads=["OLS (PyFixest)","OLS (Statsmodels)","Probit"],
          caption="Regression Results combining different packages")

which produces

Due to a modular design akin to R's modelsummary, it should be fairly easy to add new model classes / estimators.

You can of course install it via pip install maketables.

Any feedback and help (in particular, support for new model classes) is of course more than welcome!

Our ambition is to build a Python equivalent as versatile as R's fabulous modelsummary =)

Best, Alex

[rich-iannone]

This is wonderful and something that was lacking in Python!