pydbox 0.1.0

PyDBox - A Python Data Science Toolbox

PyDBox - Python Data Science Toolbox

PyDBox is a comprehensive Python toolbox for data science and machine learning tasks. It provides a collection of tools and utilities to make data analysis and processing easier and more efficient.

🚀 Features

• Modular Design: Easy to use, extend, and integrate with existing workflows
• Production Ready: Thoroughly tested with comprehensive error handling
• Performance Optimized: Efficient implementations for handling large datasets
• Pandas Compatible: Seamless integration with pandas DataFrames
• Type Hints: Full type annotation support for better IDE integration

📦 Installation

pip install pydbox

🧰 Modules

TESI (Time-series Euclidean Space Interpolation)

TESI is a powerful interpolation module that implements various methods for time series data using Euclidean space transformations. It's particularly useful for:

• Financial time series analysis
• Sensor data processing
• Scientific data interpolation
• Any sequential data that requires sophisticated interpolation

Quick Start

import pandas as pd
from pydbox.tesi import TESI

# Create sample data
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

# Create TESI interpolator
interpolator = TESI(
    input_data=df,
    equation='Euc_p1',  # Linear interpolation
    augmentation_factor=5  # Generate 5 points between each pair
)

# Get interpolated data
result = interpolator.pd_frame()
print(result)

Available Interpolation Methods

Method	Description	Best For
Euc_p1	Linear interpolation	Simple, monotonic data
Euc_p2	Quadratic interpolation	Smooth curves with moderate complexity
Euc_p3	Cubic interpolation	Complex curves with multiple inflection points
Euc_Log	Logarithmic interpolation	Data with exponential trends

Advanced Features

• Robust Value Handling:

  • Zero values preservation
  • Near-zero value stability
  • NaN and infinity handling
  • Automatic data type conversion

• Performance Optimizations:

  • Vectorized operations
  • Memory-efficient processing
  • Parallel processing support for large datasets

• Quality Assurance:

  • Input validation
  • Numerical stability checks
  • Result verification

🔧 Configuration

TESI can be configured with various parameters:

interpolator = TESI(
    input_data=df,
    equation='Euc_p1',
    augmentation_factor=5,
    zero_threshold=1e-10,  # Define what constitutes a "zero" value
    precision=1e-6,        # Numerical precision for calculations
    validate_input=True    # Enable/disable input validation
)

🤝 Contributing

We welcome contributions! Here's how you can help:

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Please make sure to update tests as appropriate and adhere to our coding standards.

📝 License

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

👤 Author

• Derraz Radhwane
  • GitHub: @derrazradhwane
  • Email: [e-mail]

📚 Documentation

For detailed documentation and examples, visit our GitHub repository.

✨ Coming Soon

• Additional interpolation methods
• GPU acceleration support
• Interactive visualization tools
• Real-time data processing
• More data science utilities

🙏 Acknowledgments

Special thanks to all contributors and users who have helped improve this package.