Metadata-Version: 2.2
Name: pyturbo-analytics
Version: 0.1.2
Summary: A high-performance Python library for blazing-fast data analysis
Home-page: https://github.com/pyturbo/pyturbo
Author: ghassenTn
Author-email: ghassen.xr@gmail.com
Project-URL: Bug Tracker, https://github.com/pyturbo/pyturbo/issues
Project-URL: Documentation, https://pyturbo.readthedocs.io/
Project-URL: Source Code, https://github.com/pyturbo/pyturbo
Keywords: data analysis,performance optimization,GPU acceleration,parallel processing,pandas,numpy,data science,high performance computing,machine learning
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries :: Python Modules
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License-File: LICENSE
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<<<<<<< HEAD
# pyturbo-analytics
=======
# PyTurbo: Unleashing the Speed of Data Analysis 🚀

PyTurbo is a high-performance Python library designed to dramatically accelerate data analysis tasks by leveraging multiple computing paradigms including multithreading, multiprocessing, GPU acceleration, and compiled code optimization.

## Features

- **Fast DataFrame Operations**: Parallelized Pandas-style operations with GPU acceleration
- **Smart Task Optimization**: Automatic workload distribution across CPU cores and GPUs
- **Performance Profiling**: Built-in analysis tools for code optimization
- **High-Speed Data Loading**: Optimized I/O for CSV, JSON, SQL, and Parquet formats
- **GPU-Accelerated Visualizations**: Real-time plotting of massive datasets
- **Customizable Accelerators**: Easy-to-use APIs for custom optimized operations
- **Distributed Processing**: Seamless scaling with Dask and Ray integration

## Installation

```bash
pip install pyturbo
```

For development installation:
```bash
git clone https://github.com/pyturbo/pyturbo.git
cd pyturbo
pip install -e ".[dev]"
```

## Quick Start

```python
import pyturbo as pt

# Create a TurboFrame (high-performance DataFrame)
tf = pt.TurboFrame.from_csv("large_dataset.csv")

# Perform accelerated operations
result = tf.groupby("category").agg({
    "value": ["mean", "sum", "count"]
}).compute()

# Use GPU acceleration
with pt.use_gpu():
    result = tf.merge(other_tf, on="key")
```

## Requirements

- Python 3.8+
- CUDA-capable GPU (optional, for GPU acceleration)
- CUDA Toolkit 11.x (for GPU features)

## Documentation

Visit our [documentation](https://pyturbo.readthedocs.io/) for:
- Detailed API reference
- Performance optimization guides
- Examples and tutorials
- Best practices

## Contributing

We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.

## License

MIT License - see the [LICENSE](LICENSE) file for details.

## Citation

If you use PyTurbo in your research, please cite:

```bibtex
@software{pyturbo2025,
  author = {PyTurbo Team},
  title = {PyTurbo: High-Performance Data Analysis Library},
  year = {2025},
  url = {https://github.com/pyturbo/pyturbo}
}
```
>>>>>>> 373cfb017 (Initial commit)
