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.
GPflow is a mature, pip-installable Python library for Gaussian process (GP) modeling built on TensorFlow and TensorFlow Probability. It is not a deployed service or CLI; it is a scientific software library that researchers and practitioners import directly. The library provides exact and approximate GP inference (GPR, SGPR, VGP, SVGP, GPMC, SGPMC, CGLB), a wide range of stationary, non-stationary, multi-output, convolutional, and hierarchical kernels, multiple likelihoods including quadrature and Monte Carlo fallbacks, and optimizers such as Scipy L-BFGS-B, natural gradient, and MCMC sampling helpers. The dominant architecture is modular and dispatch-driven: covariances, conditionals, expectations, KL divergences, and posterior construction are all implemented via a custom multiple-dispatch mechanism, making the library extensible to new kernel/inducing-variable/likelihood combinations. It also includes posterior caching for fast repeated predictions, TensorBoard monitoring, and shape annotations via check_shapes. While the repository contains extensive Jupyter/Sphinx notebooks and a dedicated benchmark harness, these are supporting assets rather than the primary deliverable; the core is clearly a reusable application library. The code is generally high-craftsmanship: clean abstractions, strong test coverage, and several genuinely generic components such as Gauss-Hermite quadrature helpers, a TensorFlow-module traversal utility, and a TF-Scipy optimizer wrapper with compile caching. However, the codebase shows meaningful drift risk: it supports a very wide TensorFlow/TFP version matrix (TF 2.4-2.16, TFP 0.12-0.24), uses private TensorFlow and TFP APIs in places, carries backward-compatibility/deprecation layers (e.g., GPR_deprecated, SVGP_with_posterior), and contains occasional debug leftovers or commented-out code. Maintenance is clearly active, with recent releases, CI matrices across Python 3.7-3.11, and versioned fixes to newly added hierarchical kernels.