MaxHalford/prince

:crown: Multivariate exploratory data analysis in Python — PCA, CA, MCA, MFA, FAMD, GPA

What it solves

Prince simplifies the process of performing multivariate exploratory data analysis on tabular data. It provides a set of statistical methods to summarize and visualize complex datasets, helping you uncover patterns and relationships between variables that might not be obvious in raw data.

How it works

Prince implements various statistical techniques—such as Principal Component Analysis (PCA) and Correspondence Analysis (CA)—using an API that mimics the popular scikit-learn library. This allows you to fit models to your data, transform datasets, and generate interactive visualizations using Altair. It is designed to be efficient and is rigorously tested against established tools like scikit-learn and FactoMineR.

Who it’s for

It is for data scientists and researchers who need to perform exploratory analysis on tabular datasets and prefer a familiar, consistent Python interface for statistical modeling.

Highlights

  • Supports a wide range of methods including PCA, MCA, MFA, FAMD, GPA, and PGA.
  • Uses a scikit-learn compatible API for ease of use.
  • Includes support for advanced features like supplementary rows/columns and row/column weighting.
  • Generates interactive charts for data visualization.
  • Thoroughly tested against industry-standard R and Python libraries.

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