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 | 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 | – | – |
Resources and Links
- Example Notebooks: Examples
- Medium Blogs Medium
- Gumroad Blogs with podcast: GumRoad
- Documentation: Website
- Bug Reports and Feature Requests: GitHub Issues
Installation
pip install pca
from pca import pca
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. | |
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.
1 maintainer
Metadata
1 maintainer
Release files for pca 2.10.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| pca-2.10.2.tar.gz | 37.5 kB | Without provenance | Details for pca-2.10.2.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| pca-2.10.2-py3-none-any.whl | Python 3 | none | any | Details for pca-2.10.2-py3-none-any.whl |
Total release size: 72.6 kB
Release files / pca-2.10.2.tar.gz
| Download URL | pca-2.10.2.tar.gz |
|---|---|
| Size | 37.5 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
4c5f7bc7e82c02349fc260b1957b5332882a94677b1c822b81038d0f819c5562
|
|
BLAKE2b-256 checksum How to use checksums |
7a0568191cb9929b1f2160efb8b9a2fe6c38c6f9ffc3e78e368d60453739060e
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.12
|
Release files / pca-2.10.2-py3-none-any.whl
| Download URL | pca-2.10.2-py3-none-any.whl |
|---|---|
| Size | 35.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
9cd76e10deb45d4c86cdeb257d45edbb6637926b444018430e9b618ea47028ea
|
|
BLAKE2b-256 checksum How to use checksums |
6640fdc0924e99e772efdb814a013fefbf01f08afa729cae521fa3635d4dce95
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.12
|
Security for pca 2.10.2
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
