Getting Started

Welcome to arrayops! This guide will help you get started quickly.

What is arrayops?

arrayops is a high-performance library for Python’s built-in array.array type, providing Rust-accelerated operations that are 10-100x faster than pure Python implementations.

Installation

Prerequisites

Python 3.8 or higher
Rust 1.75 or higher (for building from source)
maturin (install with pip install maturin)

Basic Installation

# Install maturin if not already installed
pip install maturin

# Install in development mode
maturin develop

# Or install from source
pip install -e .

Installation with Optional Features

# Install with parallel execution support (recommended for large arrays)
maturin develop --features parallel

# Install with SIMD optimizations (infrastructure in place, full implementation pending)
maturin develop --features simd

# Install with both features
maturin develop --features parallel,simd

# For production wheels
maturin build --release --features parallel

Quick Example

import array
import arrayops as ao

# Create an array
data = array.array('i', [1, 2, 3, 4, 5])

# Fast sum operation
total = ao.sum(data)
print(total)  # 15

# In-place scaling
ao.scale(data, 2.0)
print(list(data))  # [2, 4, 6, 8, 10]

# Map operation (returns new array)
doubled = ao.map(data, lambda x: x * 2)
print(list(doubled))  # [4, 8, 12, 16, 20]

# Filter operation
evens = ao.filter(data, lambda x: x % 2 == 0)
print(list(evens))  # [2, 4, 6, 8, 10]

# Reduce operation
product = ao.reduce(data, lambda acc, x: acc * x, initial=1)
print(product)  # 120

Supported Types

arrayops works with:

array.array - Python’s built-in array type
numpy.ndarray - NumPy arrays (1D, contiguous only)
memoryview - Python memoryview objects

All numeric types are supported: b, B, h, H, i, I, l, L, f, d

Next Steps

Read the API Reference for complete function documentation
Check out Examples for practical usage patterns
Learn about Performance optimization
See Troubleshooting if you encounter issues

Key Features

⚡ High Performance: 10-100x faster than pure Python loops
🔒 Memory Safe: Zero-copy buffer access with Rust’s safety guarantees
📦 Lightweight: No dependencies beyond Rust standard library
🔌 Compatible: Works directly with existing array types - no new types
✅ Fully Tested: 100% code coverage
🎯 Type Safe: Full mypy type checking support

When to Use arrayops

arrayops is ideal for:

Processing large numeric arrays efficiently
Binary protocol parsing and data processing
ETL pipelines with array operations
Performance-critical code using array.array
Scenarios where NumPy’s overhead is too much

arrayops is NOT ideal for:

Multi-dimensional arrays (use NumPy)
Scientific computing with complex operations (use NumPy)
When you need a full array library (use NumPy)

Getting Help

Documentation: Browse the User Guide
Examples: See Examples
Issues: Report bugs on GitHub
Questions: Open a discussion on GitHub