freetable 1.1.0

Convert statsmodels results to publication-ready LaTeX tables

freetable

Convert statsmodels regression results to publication-ready LaTeX tables. This package is highly opinionated and seeks to do exactly one thing well.

Moreover, unlike many similar packages, this one is completely free to use and imposes no requirements on users (read more here about problems with other packages).

Installation

pip install -U freetable

Features

• Clean, opinionated output using modern LaTeX packages
• Automatic significance stars (default: *** p<0.001; ** p<0.01; * p<0.05)
• Customizable significance thresholds
• Variable renaming for clean presentation
• Standard errors in parentheses below coefficients
• R², adjusted R², and number of observations
• Extra custom rows (e.g., outcome labels, SE types, subsets)
• Grouped column headers with multicolumn support
• Customizable table placement
• Optional automatic resizing for wide tables
• Professional formatting with booktabs, siunitx, and threeparttable

Requirements

Python:

• statsmodels

LaTeX packages:

• threeparttable
• siunitx
• booktabs
• graphicx (only required if using resize=True)

Add these to your LaTeX document preamble:

\usepackage{booktabs}
\usepackage{siunitx}
\usepackage{threeparttable}
\usepackage{graphicx}  % Only needed for resize=True

Or just use paper!

Usage

Basic example

import pandas as pd
import statsmodels.formula.api as smf
from freetable import table

# Fit your models
df = pd.read_csv("data.csv")
m1 = smf.ols("y ~ x1", data=df).fit()
m2 = smf.ols("y ~ x1 + x2", data=df).fit()

# Generate LaTeX table
latex = table(
    [m1, m2],
    model_names=["Model 1", "Model 2"],
    caption="Regression Results",
    label="tab:regression",
    digits=3,
    rename={"x1": "Gender", "x2": "Age"},
    stars=(0.1, 0.05, 0.01),  # Order doesn't matter - automatically sorted
)

print(latex)

Advanced example with all features

# Fit three models
m1 = smf.ols("outcome ~ treatment", data=df).fit(cov_type="HC3")
m2 = smf.ols("outcome ~ treatment + control1", data=df).fit(cov_type="HC3")
m3 = smf.ols("outcome ~ treatment + control1 + control2", data=df).fit(
    cov_type="cluster",
    cov_kwds={"groups": df["cluster_id"]},
)

# Create comprehensive table
latex = table(
    [m1, m2, m3],
    model_names=["Model 1", "Model 2", "Model 3"],
    caption="Treatment Effects on Outcomes",
    label="tab:treatment",
    digits=3,
    rename={"treatment": "Treatment", "control1": "Control 1", "control2": "Control 2"},
    extra_rows={
        "Outcome": ["Y", "Y", "Y"],
        "Subset": ["All", "All", "All"],
        "SE type": ["HC3", "HC3", "Clustered"],
    },
    custom_header=[("Main Effects", 2), ("Full Model", 1)],
    placement="h!",
    resize=True,
)

print(latex)

Development

This package uses the following tools for code quality and consistency:

• black: Code formatting
• ruff: Fast Python linting
• isort: Import sorting
• mypy: Static type checking
• pytest: Testing framework

Running tests

Install development dependencies:

pip install -Ue ".[dev]"

Run tests:

pytest

Run type checking:

mypy src/freetable/

License

Everything in this repository is licensed under CC0 1.0 Universal, or (at your option) any later version. See LICENSE for details. Note that there is absolutely no warranty.