python-performance-optimization

Python Performance Optimization

Comprehensive guide to profiling, analyzing, and optimizing Python code for better performance, including CPU profiling, memory optimization, and implementation best practices.

Core Concepts

1. Profiling Types

• CPU Profiling: Identify time-consuming functions
• Memory Profiling: Track memory allocation and leaks
• Line Profiling: Profile at line-by-line granularity
• Call Graph: Visualize function call relationships

2. Performance Metrics

• Execution Time: How long operations take
• Memory Usage: Peak and average memory consumption
• CPU Utilization: Processor usage patterns
• I/O Wait: Time spent on I/O operations

3. Optimization Strategies

• Algorithmic: Better algorithms and data structures
• Implementation: More efficient code patterns
• Parallelization: Multi-threading/processing
• Caching: Avoid redundant computation
• Native Extensions: C/Rust for critical paths

Quick Start

Basic Timing

import time

def measure_time():
    """Simple timing measurement."""
    start = time.time()

    # Your code here
    result = sum(range(1000000))

    elapsed = time.time() - start
    print(f"Execution time: {elapsed:.4f} seconds")
    return result

# Better: use timeit for accurate measurements
import timeit

execution_time = timeit.timeit(
    "sum(range(1000000))",
    number=100
)
print(f"Average time: {execution_time/100:.6f} seconds")

Detailed patterns and worked examples

Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient. For advanced patterns, see references/advanced-patterns.md.

Best Practices

1. Profile before optimizing - Measure to find real bottlenecks
2. Focus on hot paths - Optimize code that runs most frequently
3. Use appropriate data structures - Dict for lookups, set for membership
4. Avoid premature optimization - Clarity first, then optimize
5. Use built-in functions - They're implemented in C
6. Cache expensive computations - Use lru_cache
7. Batch I/O operations - Reduce system calls
8. Use generators for large datasets
9. Consider NumPy for numerical operations
10. Profile production code - Use py-spy for live systems