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PyFixest is a Python package for fast high-dimensional fixed effects regression.

The package aims to mimic the syntax and functionality of Laurent Bergé's formidable fixest package as closely as Python allows. If you know fixest well, the goal is that you won't have to read the docs to get started! In particular, this means that all of fixest's defaults are mirrored by PyFixest.

For questions on PyFixest, head over to our GitHub discussions, or join our Discord server.

• Estimation
  • OLS, WLS, IV, and GLMs (Poisson, logit, probit, gaussian) with high-dimensional fixed effects
  • Different demeaning backends (MAP, within, LSMR) on CPU and GPU
  • Fast quantile regression via an interior-point solver
  • Difference-in-differences estimators, including TWFE, Did2s, local projections, and Sun-Abraham event studies
  • Regression decomposition following Gelbach (2016)
  • Multiple estimation syntax
• Inference
  • Several robust, cluster-robust, and HAC variance-covariance estimators
  • Wild cluster bootstrap inference via wildboottest
  • Multiple hypothesis corrections and simultaneous confidence intervals
  • Fast randomization inference
  • The causal cluster variance estimator (CCV)
• Post-Estimation & Reporting
  • Publication-ready tables with Great Tables or LaTeX booktabs via the maketables library

You can install the release version from PyPI by running

# inside an active virtual environment
python -m pip install pyfixest

or the development version from github by running

python -m pip install git+https://github.com/py-econometrics/pyfixest

python -m pip install pyfixest[plots]

pip install cupy-cuda11x
pip install cupy-cuda12x
pip install cupy-cuda13x

pf.feols(
    "Y ~ X1 | f1 + f2",
    data=data,
    demeaner=pf.LsmrDemeaner(backend="cupy", precision="float32"),
)

import pyfixest as pf

data = pf.get_data()
pf.feols("Y ~ X1 | f1 + f2", data=data).summary()

###

Estimation:  OLS
Dep. var.: Y, Fixed effects: f1+f2
Inference:  CRV1
Observations:  997

| Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5% |   97.5% |
|:--------------|-----------:|-------------:|----------:|-----------:|-------:|--------:|
| X1            |     -0.919 |        0.065 |   -14.057 |      0.000 | -1.053 |  -0.786 |
---
RMSE: 1.441   R2: 0.609   R2 Within: 0.2

PyFixest also supports multiple estimation syntax:

fit = pf.feols("Y + Y2 ~ X1 | csw0(f1, f2)", data=data, vcov={"CRV1": "group_id"})
fit.etable()

For more examples, see the quickstart, the formula syntax tutorial, and the Poisson & GLMs tutorial.

The dgps follow the "simple" and "difficult" dgps from the fixest benchmarks.

Simple DGP	Difficult DGP

For the full benchmark suite, see the benchmarks/ directory and the note on difficult fixed effects problems.

• Quickstart
• Function & API Reference
• Difference-in-Differences
• Quantile Regression
• Changelog
• Contributing

First and foremost, we want to acknowledge Laurent Bergé's formidable fixest, which is so good we decided to stick to its API and conventions as closely as Python allows. Without fixest, PyFixest likely wouldn't exist - or at the very least, it would look very different.

For a full list of software packages and papers that have influenced PyFixest, please take a look at the Acknowledgements page.

We thank all institutions that have funded or supported work on PyFixest!

If you want to cite PyFixest, you can use the following BibTeX entry:

@software{pyfixest,
  author  = {{The PyFixest Authors}},
  title   = {{pyfixest: Fast high-dimensional fixed effect estimation in Python}},
  year    = {2025},
  url     = {https://github.com/py-econometrics/pyfixest}
}

If you enjoy using PyFixest, please consider donating to GiveDirectly and dedicating your donation to pyfixest.dev@gmail.com. You can also leave a message through the donation form; your support and encouragement mean a lot to the developers.

Thanks for showing interest in contributing to pyfixest! We appreciate all contributions and constructive feedback, whether that be reporting bugs, requesting new features, or suggesting improvements to documentation.

If you'd like to get involved, but are not yet sure how, please feel free to send us an email. Some familiarity with either Python or econometrics will help, but you really don't need to be a numpy core developer or have published in Econometrica =) We'd be more than happy to invest time to help you get started!

Thanks goes to these wonderful people:

styfenschaer 💻	Niall Keleher 🚇 💻	Wenzhi Ding 💻	Apoorva Lal 💻 🐛	Juan Orduz 🚇 💻	Alexander Fischer 💻 🚇	aeturrell ✅ 📖 📣	leostimpfle 💻 🐛
baggiponte 📖	Sanskriti 🚇	Jaehyung 💻	Alex 📖	Hayden Freedman 💻 📖	Aziz Mamatov 💻	rafimikail 💻	Benjamin Knight 💻
Dirk Sliwka 💻 📖	daltonm-bls 🐛	Marc-André 💻 🐛	Kyle F Butts 🔣	Marco Edward Gorelli 👀	Vincent Arel-Bundock 💻	IshwaraHegde97 💻	Tobias Schmidt 📖
escherpf 🐛 💻	Iván Higuera Mendieta 💻	Ádám Vig 💻	Szymon Sacher 💻	AronNemeth 💻	Dmitri Tchebotarev 💻	FuZhiyu 🐛 💻	Marcelo Ortiz M. 📖
Joseph Stover 📖	JaapCTJ 💻	Matt Shapiro 💻	Kristof Schröder 💻	Wiktor 💻	Daman Dhaliwal 💻	Jaakko Markkanen 🐛	Jonas Skjold Raaschou-Pedersen 💻 📖
Bobby Ho 📖	Erica Ryan 💻	Souhil Abdelmalek Louddad 📖	Demetri Pananos 💻 👀	Patrick Doupe 💻	Ariadna Albors Zumel 💻	Martien Lubberink 💻	jonpcohen 📖
rhstanton 📓 🐛	mdizadi 📖	Tim Mensinger 🚇	Nicholas Junge 💻 🚇	Abel Abate 💻	Jan Teusen (né Boelts) 💻

This project follows the all-contributors specification. Contributions of any kind welcome!