pca 2.10.2

pca: A Python Package for Principal Component Analysis.

pca is a Python package for Principal Component Analysis. The core of PCA is built on sklearn functionality to find maximum compatibility when combining with other packages. But this package can do a lot more. Besides the regular PCA, it can also perform SparsePCA, and TruncatedSVD. Depending on your input data, the best approach can be chosen. pca contains the most-wanted analysis and plots. Navigate to API documentations for more detailed information. ⭐️ Star it if you like it ⭐️

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Key Features

Feature	Description	Docs	Medium	Gumroad & Podcast
Fit and Transform	Perform the PCA analysis.	Link	PCA Guide	Link
Biplot and Loadings	Make Biplot with the loadings.	Link	–	–
Explained Variance	Determine the explained variance and plot.	Link	–	–
Best Performing Features	Extract the best performing features.	Link	–	–
Scatterplot	Create scatterplot with loadings.	Link	–	–
Outlier Detection	Detect outliers using Hotelling T2 and/or SPE/Dmodx.	Link	Outlier Detection	Link
Normalize out Variance	Remove any bias from your data.	Link	–	–
Save and load	Save and load models.	Link	–	–
Analyze discrete datasets	Analyze discrete datasets.	Link	–	–

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Resources and Links

• Example Notebooks: Examples
• Medium Blogs Medium
• Gumroad Blogs with podcast: GumRoad
• Documentation: Website
• Bug Reports and Feature Requests: GitHub Issues

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Installation

pip install pca

from pca import pca

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Examples

Quick Start	Make Biplot
Explained Variance Plot	3D Plots
Alpha Transparency	Normalize Out Principal Components
Extract Feature Importance
Make the biplot to visualize the contribution of each feature to the principal components.
Detect Outliers	Show Only Loadings
Detect outliers using Hotelling's T² and Fisher’s method across top components (PC1–PC5).
Select Outliers	Toggle Visibility
Select and filter identified outliers for deeper inspection or removal.	Toggle visibility of samples and components to clean up visualizations.
Map Unseen Datapoints
Project new data into the transformed PCA space. This enables testing new observations without re-fitting the model.

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Contributors

Setting up and maintaining PCA has been possible thanks to users and contributors. Thanks to:

Maintainer

• Erdogan Taskesen, github: erdogant
• Contributions are welcome.
• Yes! This library is entirely free but it runs on coffee! :) Feel free to support with a Coffee.