raphaelvallat/pingouin

Health v2 · maintenance only

Adoption not part of the score

1928 stars · 167 forks observed · 2026-08-28

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

Member repositories

For agents

markdown · JSON · MCP: product_card(name="raphaelvallat/pingouin")