ivanachillee / pcalib

PCAlib is a standarized library built in Python 3 for fast and easy-to-use Principal Component Analysis (PCA) data transformation.

PCAlib

PCAlib is a micro-library built in Python 3 for fast and easy-to-use Principal Component Analysis (PCA) data transformation. It will help you to reduce high-dimensional data into a N amount of components with a simplified class function.

Requirements

You will need the following packages to run PCAlib:

- sklearn
- pandas

Usage

PCAlib works with Pandas dataframes, to use it you just have to import the library and create a dpca object to apply the transformations.

from pcalib import dpca
newdf = dpca(df, features, label, 2, ["pc1","pc2"])
newdf = newdf.apply()

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

Apache-2.0