{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/3"}],"enrichment":{"capability":"Prince implements multivariate exploratory data analysis methods including PCA, CA, MCA, MFA, FAMD, GPA, and PGA with a scikit-learn API for tabular data summarization.","skillfed_tags":["dimensionality-reduction","exploratory-data-analysis","factor-analysis"],"use_cases":["Reduce dimensionality of numerical datasets while preserving variance structure using PCA.","Analyze relationships between categorical variables in contingency tables with CA or MCA.","Explore mixed numerical and categorical data simultaneously using FAMD.","Analyze multiple groups of related columns with MFA.","Compare shapes across multiple datasets using GPA.","Perform dimensionality reduction on data constrained to a manifold using PGA."],"what_it_does":"Prince is a Python library for multivariate exploratory data analysis that implements seven factor analysis methods: PCA, CA, MCA, MFA, FAMD, GPA, and PGA. It follows scikit-learn conventions, making it familiar to users of that ecosystem. The library was originally created in 2016 and underwent a major revamp in 2022 to improve robustness and feature coverage.\n\nThe package works with tabular data and supports advanced features like supplementary rows and columns, as well as row and column weights. It integrates with pandas for data handling and Altair for interactive visualization of results. Prince is tested against scikit-learn and FactoMineR (via rpy2) to ensure correctness, and PGA is validated against geomstats.","worth_installing":"Yes. Prince is actively maintained, has no known vulnerabilities, installs with low friction, and provides a comprehensive suite of factor analysis methods under a permissive MIT license. It is well-tested against established implementations and suitable for exploratory data analysis workflows that need more than standard PCA."},"id":"prince","links":{"html":"https://skillfed.io/packages/prince","md":"https://skillfed.io/packages/prince.md","pypi":"https://pypi.org/project/prince/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-30","license_spdx":"MIT","license_treatment":"permissive","name":"prince","python_support":"supports_current","summary":"Factor analysis in Python: PCA, CA, MCA, MFA, FAMD, GPA, PGA"},"popularity":{"monthly_downloads":139821,"position":11288,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.20.1"}