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Sklearo

A versatile Python package featuring scikit-learn like transformers for feature preprocessing, compatible with all kind of DataFrames thanks to narwhals.

Installation

To install the package, use pip:

pip install sklearo

Usage

Here's a basic example of how to use the package with the WOEEncoder:

import pandas as pd
from sklearo.encoding import WOEEncoder


data = {
    "category": ["A", "A", "A", "B", "B", "B", "C", "C", "C"],
    "target": [1, 0, 0, 1, 1, 0, 1, 1, 0],
}
df = pd.DataFrame(data)
encoder = WOEEncoder()
encoder.fit(df[["category"]], df["target"])
encoded = encoder.transform(df[["category"]])
print(encoded)
   category
0 -0.916291
1 -0.916291
2 -0.916291
3  0.470004
4  0.470004
5  0.470004
6  0.470004
7  0.470004
8  0.470004

Features

  • ∫ Easy Integration: built on top of narwhals, meaning it can work with any kind of dataframe supported by narwhals like pandas, polars and much more!
  • 🌸 Scikit-learn Compatibility: Designed to work with scikit-learn pipelines.
  • ✅ tested against pandas and Polars dataframes.

Contributing

We welcome contributions! Please check the development guides for more details.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any questions or suggestions, please open an issue on GitHub.

Why sklearo?

The name sklearo is a combination of sklearn and omni (o), which means all. This package is designed to work with all kinds of dataframes, hence the name sklearo.