Cognition: Skill for Claude Code

Python Performance Optimization

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

When to Use This Skill

• Identifying performance bottlenecks in Python applications
• Reducing application latency and response times
• Optimizing CPU-intensive operations
• Reducing memory consumption and memory leaks
• Improving database query performance
• Optimizing I/O operations
• Speeding up data processing pipelines
• Implementing high-performance algorithms
• Profiling production applications

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")

Profiling Tools

Pattern 1: cProfile - CPU Profiling

import cProfile
import pstats
from pstats import SortKey

def slow_function():
    """Function to profile."""
    total = 0
    for i in range(1000000):
        total += i
    return total

def another_function():
    """Another function."""
    return [i**2 for i in range(100000)]

def main():
    """Main function to profile."""
    result1 = slow_function()
    result2 = another_function()
    return result1, result2

# Profile the code
if __name__ == "__main__":
    profiler = cProfile.Profile()
    profiler.enable()

    main()

    profiler.disable()

    # Print stats
    stats = pstats.Stats(profiler)
    stats.sort_stats(SortKey.CUMULATIVE)
    stats.print_stats(10)  # Top 10 functions

    # Save to file for later analysis
    stats.dump_stats("profile_output.prof")