maketables 0.1.6

A Python package for creating publication-ready tables from regression results (statsmodels, pyfixest, linearmodels), descriptive statistics, and balance tables with output to LaTeX, Word, and HTML/Great Tables

MakeTables

A Python package for creating publication-ready tables from regression results (statsmodels, pyfixest, linearmodels), descriptive statistics, and balance tables with output to LaTeX, Word, and HTML via Great Tables. To get started, check out the Getting Started Notebook.

Overview

MakeTables provides a unified interface for generating tables such as:

• Regression tables from Statsmodels, PyFixest, Linearmodels and Stata
• Descriptive statistics
• Balance tables

The package supports multiple output formats including:

• Great Tables (HTML)
• LaTeX
• Microsoft Word (docx) documents

Origin

MakeTables originated as the table output functionality within the pyfixest package and has been moved to this standalone package to provide broader table creation capabilities also supporting other statistical packages.

Authors

• Alexander Fischer https://github.com/s3alfischer
• Dirk Sliwka https://dsliwka.github.io/

Installation

From PyPI

pip install maketables

Development Installation

# Clone the repository
git clone https://github.com/yourusername/maketables.git
cd maketables

# Install in development mode
pip install -e .

Quick Start

Descriptive Statistics Table

import pandas as pd
import maketables as mt

# 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")


# Create descriptive statistics table
mt.DTable(df, vars=["mpg","weight","length"], bycol=["foreign"])

Regression Tables

with pyfixest

import pyfixest as pf

# Fit your models here using pyfixest
est1 = pf.feols("mpg ~ weight", data=df)
est2 = pf.feols("mpg ~ weight + length", data=df)

# Make the table
mt.ETable([est1, est2])

with statsmodels

import statsmodels.formula.api as smf

# 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"])

Main Classes

MTable

Base class for all table types with common functionality:

• Multiple output formats (Great Tables, LaTeX, Word)
• Flexible styling and formatting options
• Save and export capabilities
• Can also update tables in existing word documents
• Adapted for use in Jupyter Notebooks and for quarto use (tables automatically rendered as html in notebooks and as latex when rendering to pdf in quarto)

DTable

Extends MTable for descriptive statistics:

• Automatic calculation of summary statistics
• Grouping by categorical variables (rows and columns)
• Customizable statistic labels and formatting

ETable

Extends MTable for econometric model results:

• Support for statsmodels, pyfixest, and (more experimental) linearmodels
• Many layout options (relabelling of variables, keep/drop, choice of reported statistics, column headings,...)

BTable

Extends MTable for simple balance tables.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Built on the excellent pyfixest package for econometric models
• Uses Great Tables for beautiful HTML table output