pingouin

What it is and what it does

Pingouin is a statistical package built on NumPy, SciPy, and Pandas that provides a high-level interface to common statistical tests and analyses. Unlike lower-level libraries, it returns comprehensive results by default—a t-test includes not just the t-value and p-value but also degrees of freedom, Cohen's d effect size, 95% confidence intervals, statistical power, and Bayes factors. It covers parametric and non-parametric tests, ANOVAs (one-way, repeated measures, mixed, ANCOVA), pairwise post-hoc tests, multiple correlation methods (Pearson, Spearman, robust, partial, distance, repeated measures), linear and logistic regression, mediation analysis, multivariate tests, reliability measures, and circular statistics.

The package is designed for researchers and data analysts who want exhaustive statistical output without writing custom code. It integrates with Pandas DataFrames, supports grouped and long-format data, and includes plotting functions (Bland-Altman, Q-Q, paired plots). Dependencies are all standard scientific Python libraries, making it straightforward to install. The codebase is actively maintained, tested on Python 3.10 through 3.14, and has no known security vulnerabilities.

Use it for:

• Run a t-test and immediately get effect size, confidence intervals, power, and Bayes factor for publication-ready reporting
• Perform repeated-measures ANOVA with post-hoc pairwise comparisons on grouped experimental data in a DataFrame
• Compare correlation robustness by testing Pearson, Spearman, and biweight midcorrelation on the same data to handle outliers
• Test multivariate normality and homogeneity assumptions before choosing parametric or non-parametric tests
• Compute power analysis and effect sizes for study design planning before data collection

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Pingouin provides a comprehensive statistical analysis toolkit for Python, offering t-tests, ANOVAs, correlations, regression, effect sizes, power analysis, and hypothesis testing with detailed output including confidence intervals and Bayes factors.

Yes. Pingouin is well-maintained, actively developed, has no security vulnerabilities, and fills a genuine gap: it wraps SciPy and Statsmodels with sensible defaults and comprehensive output. The GPL-3.0 license is standard for academic software and poses no barrier to research use. Install it if you do statistical testing in Python and want richer output than SciPy alone provides.

Install

pingouin on PyPI

pip install pingouin

uv add pingouin

poetry add pingouin

Installing pingouin

Before you install

Low friction installation with a pure-Python wheel distribution. Active maintenance with a recent release (139 days ago) and steady community engagement. Requires modern Python (3.10+) and depends on established scientific libraries (NumPy, SciPy, Pandas, Statsmodels, Scikit-learn).

License in practice

GPL-3.0 copyleft license means any derivative work or modification must also be released under GPL-3.0. This is permissive for research and internal use but restricts commercial redistribution without source disclosure.

Quickstart

pip install pingouin

import pingouin as pg
import numpy as np

np.random.seed(123)
x = np.random.normal(0, 1, 30)
y = np.random.normal(0, 1, 30)
result = pg.ttest(x, y)
print(result)

Requires Python 3.10 or later. Nine runtime dependencies (NumPy, SciPy, Pandas, Statsmodels, Scikit-learn, Matplotlib, Seaborn, Pandas-flavor, Tabulate) must be installed; pip handles this automatically.

Verify before relying

• Whether all statistical functions work equally well with missing data or require preprocessing
• Performance characteristics on large datasets (memory usage, computation time)
• Availability and completeness of circular statistics functions mentioned in description

Package facts

Evidence: pingouin-0.6.1-py3-none-any.whl

Tags

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