pybandiger 0.1.7

A data wrangling library

PyBandiger

PyBandiger is a Python library for preprocessing data, including cleaning, encoding, and scaling. It is designed to simplify the data wrangling process for machine learning tasks.

Installation

You can install PyBandiger using pip:

pip install pybandiger

Usage

Importing the Library

from pybandiger import PyBandiger

Creating an Instance

pb = PyBandiger()

Cleaning Data

The clean method fills missing values in categorical columns with 'Missing' and in numerical columns with the mean of the column.

cleaned_data = pb.clean(data)

Encoding and Scaling Data

The EncodeAndScale_fit method encodes categorical columns using LabelEncoder and scales numerical columns using StandardScaler.

encoded_scaled_data = pb.EncodeAndScale_fit(cleaned_data)

The EncodeAndScale_transform method transforms new data using the previously fitted encoders and scaler.

new_encoded_scaled_data = pb.EncodeAndScale_transform(new_data)

Example

import pandas as pd
from pybandiger import PyBandiger

# Sample data
data = pd.DataFrame({
    'category': ['A', 'B', 'A', None],
    'value': [1.0, 2.5, None, 4.0]
})

# Create an instance of PyBandiger
pb = PyBandiger()

# Clean the data
cleaned_data = pb.clean(data)

# Encode and scale the data
encoded_scaled_data = pb.EncodeAndScale_fit(cleaned_data)

print(encoded_scaled_data)

License

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

Contributing Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

Author

Lansari Fedi - [e-mail]