<<<<<<< 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)
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"description": "<<<<<<< HEAD\n# pyturbo-analytics\n=======\n# PyTurbo: Unleashing the Speed of Data Analysis \ud83d\ude80\n\nPyTurbo 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.\n\n## Features\n\n- **Fast DataFrame Operations**: Parallelized Pandas-style operations with GPU acceleration\n- **Smart Task Optimization**: Automatic workload distribution across CPU cores and GPUs\n- **Performance Profiling**: Built-in analysis tools for code optimization\n- **High-Speed Data Loading**: Optimized I/O for CSV, JSON, SQL, and Parquet formats\n- **GPU-Accelerated Visualizations**: Real-time plotting of massive datasets\n- **Customizable Accelerators**: Easy-to-use APIs for custom optimized operations\n- **Distributed Processing**: Seamless scaling with Dask and Ray integration\n\n## Installation\n\n```bash\npip install pyturbo\n```\n\nFor development installation:\n```bash\ngit clone https://github.com/pyturbo/pyturbo.git\ncd pyturbo\npip install -e \".[dev]\"\n```\n\n## Quick Start\n\n```python\nimport pyturbo as pt\n\n# Create a TurboFrame (high-performance DataFrame)\ntf = pt.TurboFrame.from_csv(\"large_dataset.csv\")\n\n# Perform accelerated operations\nresult = tf.groupby(\"category\").agg({\n \"value\": [\"mean\", \"sum\", \"count\"]\n}).compute()\n\n# Use GPU acceleration\nwith pt.use_gpu():\n result = tf.merge(other_tf, on=\"key\")\n```\n\n## Requirements\n\n- Python 3.8+\n- CUDA-capable GPU (optional, for GPU acceleration)\n- CUDA Toolkit 11.x (for GPU features)\n\n## Documentation\n\nVisit our [documentation](https://pyturbo.readthedocs.io/) for:\n- Detailed API reference\n- Performance optimization guides\n- Examples and tutorials\n- Best practices\n\n## Contributing\n\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\n\n## License\n\nMIT License - see the [LICENSE](LICENSE) file for details.\n\n## Citation\n\nIf you use PyTurbo in your research, please cite:\n\n```bibtex\n@software{pyturbo2025,\n author = {PyTurbo Team},\n title = {PyTurbo: High-Performance Data Analysis Library},\n year = {2025},\n url = {https://github.com/pyturbo/pyturbo}\n}\n```\n>>>>>>> 373cfb017 (Initial commit)\n",
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