raphaelvallat/pingouin
Some data may be outdated — see provenance labels.
Statistical package in Python based on Pandas observed · 2026-09-26
Health v2 · maintenance only
82/100
- Activity 94
- Release rhythm 57
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-10-21. Adoption (stars, forks) is never an input.
- gap_med: 32
- age_days: 3125
- days_rel: 206
- days_push: 41
- n_releases_24m: 2
Adoption not part of the score
1931 stars · 171 forks observed · 2026-09-26
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
Pingouin is an open-source Python 3 statistical package built on Pandas and NumPy that provides simple yet exhaustive statistical tests. It returns rich outputs (effect sizes, confidence intervals, Bayes Factors) from common tests like t-tests and ANOVAs, which low-level libraries like SciPy omit.
Use cases
- run t-tests and anovas in python
- compute effect sizes like cohen's d
- calculate bayes factors for statistical tests
- perform repeated measures anova on a dataframe
- compute partial and robust correlations
- do power analysis for an experiment
- run pairwise post-hoc tests with p-value correction
- compute intraclass correlation for reliability
When to choose
- you want detailed statistical test output (effect size, CI, power, Bayes Factor) from a single function call
- you work with Pandas DataFrames and want stats functions that integrate with them
- you need ANOVAs, post-hoc tests, or repeated-measures correlations in Python
- you find scipy.stats too low-level and statsmodels too complex
When to avoid
- you need advanced modeling like GLMs, time-series analysis, or R-style formula syntax (use statsmodels)
- you only need raw T- and p-values with minimal dependencies (use scipy.stats)
- your project requires a permissive license (Pingouin is GPL-3.0)
Facets
library · maturity active
data-science math data-visualization data-science analytics python cross-platform statistics anova t-test effect-size bayesian-statistics pandas correlation power-analysis circular-statistics
6 sources
- readme: https://github.com/raphaelvallat/pingouin · fetched 2026-09-26 · 11db71d02532
- homepage: https://pingouin-stats.org/ · fetched 2026-08-29 · 22908b117163
- registry_pypi: https://pypi.org/pypi/pingouin/json · fetched 2026-08-29 · 34a73970b9f9
- site_page: https://pingouin-stats.org/faq.html · fetched 2026-08-29 · 5a5738e86770
- site_page: https://pingouin-stats.org/changelog.html · fetched 2026-08-29 · ddc09e6b0f80
- site_page: https://pingouin-stats.org/api.html · fetched 2026-08-29 · f6b2071ebd80
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| raphaelvallat/pingouin | main | 82 |
For agents
markdown · JSON · MCP: product_card(name="raphaelvallat/pingouin")
Data as of 2026-10-19T08:05:51.212177+00:00 · Report a problem