/sklɛro/
A versatile Python package featuring scikit-learn like transformers for feature preprocessing, compatible with all kind of DataFrames thanks to narwhals.
To install the package, use pip:
pip install sklearoHere'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- ∫ 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.
We welcome contributions! Please check the development guides for more details.
This project is licensed under the MIT License. See the LICENSE file for details.
For any questions or suggestions, please open an issue on GitHub.
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.