pca: A Python Package for Principal Component Analysis.
Project description
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 ⭐️
Key Features
Feature | Description |
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Fit and Transform | Perform the PCA analysis. |
Biplot and Loadings | Make Biplot with the loadings. |
Explained Variance | Determine the explained variance and plot. |
Best Performing Features | Extract the best performing features. |
Scatterplot | Create scaterplot with loadings. |
Outlier Detection | Detect outliers using Hotelling T2 and/or SPE/Dmodx. |
Normalize out Variance | Remove any bias from your data. |
Save and load | Save and load models. |
Analyze discrete datasets | Analyze discrete datasets. |
Resources and Links
- Example Notebooks: Examples
- Blog Posts: Medium
- Documentation: Website
- Bug Reports and Feature Requests: GitHub Issues
Installation
pip install pca
from pca import pca
Examples
Quick Start | Make Biplot |
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Explained Variance Plot | 3D Plots |
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Alpha Transparency | Normalize Out Principal Components |
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Extract Feature Importance | |
Make the biplot to visualize the contribution of each feature to the principal components.
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Detect Outliers | Show Only Loadings |
Detect outliers using Hotelling's T² and Fisher’s method across top components (PC1–PC5).
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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. |
Contributors
Setting up and maintaining PCA has been possible thanks to users and contributors. Thanks to:
Maintainer
Project details
Release history Release notifications | RSS feed
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