[](https://github.com/ClaudioSalvatoreArcidiacono/sklearo/actions?query=workflow%3A%22Tests%22)
[](#)
[](https://claudiosalvatorearcidiacono.github.io/sklearo/)
# sklearo
> */sklɛro/*
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:
```bash
pip install sklearo
```
## Usage
Here's a basic example of how to use the package with the `WOEEncoder`:
```python
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 -0.223144
1 -0.223144
2 -0.223144
3 1.029619
4 1.029619
5 1.029619
6 1.029619
7 1.029619
8 1.029619
```
## Features
- ∫ **Easy Integration**: built on top of [narwhals](https://narwhals-dev.github.io/narwhals/), meaning it can work with any kind of dataframe supported by [narwhals](https://narwhals-dev.github.io/narwhals/extending/) 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](development_guide.md) for more details.
## License
This project is licensed under the MIT License. See the [LICENSE](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`.
Raw data
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