---
doi: "10.21105/joss.01026"
title: "Pingouin: statistics in Python"
source_url: "http://www.theoj.org/joss-papers/joss.01026/10.21105.joss.01026.pdf"
license_spdx: "CC-BY-4.0"
converter: "docling 2.129.0"
retrieved: "2026-09-21"
---
## [DOI: 10.21105/joss.01026](https://doi.org/10.21105/joss.01026)

## Software

- [Review](https://github.com/openjournals/joss-reviews/issues/1026)
- [Repository](https://github.com/raphaelvallat/pingouin)
- [Archive](https://doi.org/10.5281/zenodo.1491783)

Submitted:

05 October 2018

Published:

19 November 2018

## License

Authors of papers retain copyright and release the work under a Creative Commons Attribution 4.0 International License (CC-BY).

## Pingouin: statistics in Python

## Raphael Vallat 1

1 Department of Psychology, University of California, Berkeley.

## Summary

Python is currently the fastest growing programming language in the world, thanks to its ease-of-use, fast learning curve and its numerous high quality packages for data science and machine-learning. Surprisingly however, Python is far behind the R programming language when it comes to general statistics and for this reason many scientists still rely heavily on R to perform their statistical analyses.

In this paper, we present Pingouin , an open-source Python package aimed at partially filling this gap by providing easy-to-use functions for computing some of the main statistical tests that scientists use on an every day basis. This includes basics functions such as ANOVAs, ANCOVAs, post-hoc tests, non-parametric tests, effect sizes, as well as more advanced functions such as Bayesian T-tests (Rouder, Speckman, Sun, Morey, & Iverson, 2009), repeated measures correlations (Bakdash & Marusich, 2017), robust correlations (Pernet, Wilcox, & Rousselet, 2012) and circular statistics (Berens, 2009), to cite but a few. Pingouin is written in Python 3 and is mostly built on top of the Pandas (McKinney, 2010) library, therefore allowing a fluid integration within a data analysis pipeline. Pingouin comes with an extensive documentation and API as well as with several Jupyter notebook examples.

## References

Bakdash, J. Z., & Marusich, L. R. (2017). Repeated measures correlation. Front. Psychol. , 8 , 456.

Berens, P. (2009). CircStat: A MATLAB toolbox for circular statistics. J. Stat. Softw. , 31 (10), 1-21.

McKinney, W. (2010). Data structures for statistical computing in python. In S. van der Walt & J. Millman (Eds.), Proceedings of the 9th python in science conference (pp. 51-56).

Pernet, C. R., Wilcox, R., & Rousselet, G. A. (2012). Robust correlation analyses: False positive and power validation using a new open source matlab toolbox. Front. Psychol. , 3 , 606.

Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychon. Bull. Rev. , 16 (2), 225-237.