MaxHalford/prince

Health v2 · maintenance only

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1479 stars · 194 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Prince is a Python library for multivariate exploratory data analysis, implementing PCA, CA, MCA, MFA, FAMD, GPA, and PGA with a scikit-learn-style API. It is built on pandas and uses Altair for plotting, with correctness tested against scikit-learn and R's FactoMineR.

Use cases

• run principal component analysis on a pandas dataframe
• do multiple correspondence analysis on categorical survey data
• analyze mixed numerical and categorical data with FAMD
• perform multiple factor analysis on grouped columns
• compute correspondence analysis of a contingency table
• compare shapes with generalized Procrustes analysis
• plot PCA component charts in Python

When to choose

• you need FactoMineR-style factor analysis methods in Python
• you want a scikit-learn-like fit/transform API for dimensionality reduction
• you work with pandas dataframes and want built-in Altair visualizations
• you need MCA or FAMD, which scikit-learn lacks

When to avoid

• you need deep learning or neural dimensionality reduction like autoencoders
• you need general-purpose machine learning pipelines beyond exploratory analysis
• you need big-data distributed PCA beyond memory-sized dataframes

Facets

library · maturity stable

data-science machine-learning data-visualization data-science data-visualization python pca correspondence-analysis mca mfa famd svd factor-analysis scikit-learn-api exploratory-data-analysis pandas statistics

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