pybandiger 0.1.8

A data wrangling library

PyBandiger

PyBandiger is a Python library for preprocessing data, including cleaning, encoding, scaling, and transforming date columns. 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)

Transforming Date Columns

The time_transform method extracts year, month, day, and day of the week from a date column and drops the original date column.

transformed_data = pb.time_transform(data, date_col='date')

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],
    'date': ['2021-01-01', '2021-02-01', '2021-03-01', '2021-04-01']
})

# 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)

# Transform date columns
transformed_data = pb.time_transform(cleaned_data)

print(encoded_scaled_data)
print(transformed_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]