PreProData: Data Preprocessing Library

" Pre Processing Data: PreProData" a versatile and user-friendly package for data preprocessing, catering to various data analysis and machine learning needs.

PreProData is a Python library designed to simplify data preprocessing tasks, including data cleaning and feature scaling. It provides easy-to-use functions for enhancing the quality of your data before analysis or machine learning tasks.

Installation

You can install PreProData using pip:

pip install PreProData

Usage

You can customize this given sample template/example according to your specific dataset and preprocessing needs. "PreProData" package provides the necessary functions to perform these common data preprocessing tasks efficiently.

import pandas as pd
from preprodata import remove_duplicates, scale_features

try:
    # Load your dataset using the full file path, treating all columns as strings
    data = pd.read_csv('sample.csv', dtype=str)

    # Clean the data by removing duplicate rows
    cleaned_data = remove_duplicates(data)

    # Preprocess the data by scaling numeric features
    # Assuming that your scale_features function handles strings gracefully
    scaled_data = scale_features(cleaned_data)

    # Now you can work with the cleaned and scaled data
    print(scaled_data.head())  # Example: Display the first few rows of the processed data

except FileNotFoundError:
    print("File 'sample.csv' not found. Make sure the file path is correct.")

except Exception as e:
    print(f"An error occurred: {e}")

Testing

You can try testing using this sample.csv in your system.

Features

• Data Cleaning:

  • Remove duplicates.
  • Handle missing values.

• Data Transformation:

  • Scale numeric features.
  • Encode categorical data.

• Feature Selection:

  • Automatic feature selection.
  • Dimensionality reduction.

• Data Splitting:

  • Train-test data splitting.

• Customization:

  • User-configurable preprocessing parameters.
  • Pipeline integration.

• Error Handling:

  • Input validation.
  • Graceful error handling.

• Compatibility:

  • Support for common data formats.
  • Cross-platform compatibility.

• Performance Optimization:

  • Parallel processing.
  • Memory efficiency.

• Integration:

  • Integration with machine learning frameworks.

• Licensing and Distribution:

  • Open-source license.
  • PyPI distribution.

• Security:

  • Data and code security measures.

• Logging and Monitoring:

  • Logging and monitoring capabilities.

Contributing

Contributions to this library are welcome! If you'd like to contribute, please follow these guidelines:

• Fork the repository.
• Create a new branch for your feature or bug fix.
• Make your changes and write tests if applicable.
• Ensure all tests pass.
• Create a pull request with a clear description of your changes.

License

This library is distributed under the MIT License. See LICENSE for more information.

Contact

For questions or feedback, feel free to contact us at whoami_anoint@bugcrowdninja.com.