pyturbo-analytics 0.1.1

A high-performance Python library for blazing-fast data analysis

PyTurbo: High-Performance Data Analysis Library

PyTurbo is a high-performance Python library designed for accelerated data analysis, leveraging both CPU and GPU computing paradigms. It provides significant speedups over traditional pandas operations through vectorized operations and parallel processing.

🚀 Key Features

• High Performance: Up to 120x speedup for complex calculations
• GPU Acceleration: Seamless integration with RAPIDS cuDF for GPU-powered computing
• Automatic Optimization: Smart fallback to CPU when GPU is unavailable
• Pandas Compatible: Familiar pandas-like API for easy adoption
• Memory Efficient: Optimized memory usage for large datasets

📊 Benchmark Results

Operation	PyTurbo	Pandas	Speedup
Complex Scoring	0.16s	19.15s	120x
Rolling Ops	3.49s	36.15s	10x
Filtering	0.06s	0.11s	1.7x

🛠 Installation

Basic Installation (CPU Only)

pip install pyturbo

GPU-Accelerated Installation

For GPU support, you'll need NVIDIA CUDA toolkit and RAPIDS cuDF:

1. Install CUDA Toolkit (11.x recommended):

   # Visit https://developer.nvidia.com/cuda-downloads
   

2. Install RAPIDS cuDF:

   pip install cudf-cuda11x
   

3. Install PyTurbo with GPU support:

   pip install pyturbo[gpu]
   

🎯 Quick Start

import pyturbo as pt
import pandas as pd

# Create a TurboFrame from pandas DataFrame
df = pd.read_csv('large_dataset.csv')
tf = pt.TurboFrame(df)

# Automatic GPU acceleration if available
tf = tf.gpu()  # Falls back to CPU if GPU unavailable

# Complex calculations up to 120x faster
scores = tf['value'].apply(complex_calculation)

# Optimized rolling operations (10x faster)
rolling_stats = tf['value'].rolling(window=1000).apply(lambda x: np.percentile(x, 75))

🔍 Example: Vehicle Analysis

import pyturbo as pt
import numpy as np

# Load data
df = pd.read_csv('vehicle_data.csv')
tf = pt.TurboFrame(df)

# Complex vehicle scoring (120x faster than pandas)
scores = pt.complex_vehicle_score_vectorized(tf)

# Efficient rolling calculations (10x faster)
rolling_scores = tf['score'].rolling(1000).apply(
    lambda x: np.percentile(x, 75), 
    engine='numpy'
)

# Group analysis with automatic optimization
stats = tf.groupby('category').agg({
    'speed': ['mean', 'std'],
    'score': ['mean', 'max']
})

🌟 Advanced Features

GPU Acceleration

# Check GPU availability
tf = pt.TurboFrame(df)
print(f"GPU Available: {tf.gpu_available}")

# Enable GPU processing
tf = tf.gpu()  # Automatic fallback to CPU if needed

Parallel Processing

# Automatic parallel processing for CPU operations
result = tf.parallel_apply(complex_function, num_workers=4)

Memory Optimization

# Efficient chunked processing for large datasets
chunks = tf.chunk_dataframe(num_chunks=4)
results = [chunk.process() for chunk in chunks]

📚 Documentation

For detailed documentation, visit PyTurbo Documentation

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📝 License

PyTurbo is released under the MIT License. See LICENSE for details.

🙏 Acknowledgments

Special thanks to the RAPIDS team for their amazing GPU-accelerated data science tools.