Pingouin is a mature Python library for statistical analysis, providing a comprehensive suite of parametric and non-parametric hypothesis tests, effect sizes, power analysis, Bayes factors, and data visualization. Unlike lower-level libraries like SciPy and statsmodels, Pingouin returns rich, ready-to-interpret DataFrames with all relevant statistics (p-values, confidence intervals, effect sizes, Bayes factors, power) in a single call. The library is designed for researchers and data analysts in the social, biomedical, and behavioral sciences, and its API is tightly integrated with pandas via method chaining (e.g., `df.anova()`, `df.pairwise_corr()`). The codebase is modular, with each module dedicated to a class of statistical methods (parametric, nonparametric, correlation, regression, etc.), and includes bundled example datasets and extensive documentation. Its architecture follows a functional pattern built on top of NumPy, SciPy, pandas, and statsmodels, with optional dependencies for plotting and machine learning. The library is actively maintained, with a recent release (v0.6.1) and CI/CD pipelines ensuring compatibility with Python 3.10–3.14 and the latest versions of its scientific stack.