Python Performance Optimization

When to use this skill

Use Python Performance Optimization when an AI agent needs a reusable SKILL.md workflow for this job: Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.

When not to use it

Skip Python Performance Optimization when the task is outside the coding category, or when a more specific skill in this directory already covers the same workflow with clearer triggers.

How to install

1. Personal install: create ~/.claude/skills/python-performance-optimization/SKILL.md (and any bundled scripts) so Claude Code, Claude Desktop, and compatible agents can load it in every project.
2. Project install: commit the same folder at .claude/skills/python-performance-optimization/ so teammates get the skill with the repo.
3. Restart the agent session after copying files so it re-scans the skills directory, then ask for the task in words that match the skill description.

Full install guide for Claude, Cursor, and Codex

What this skill does

# 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

```python 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.

## 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

## Common Pitfalls

- Optimizing without profiling - Using global variables unnecessarily - Not using appropriate data structures - Creating unnecessary copies of data - Not using connection pooling for databases - Ignoring algorithmic complexity - Over-optimizing rare code paths - Not considering memory usage

Intended uses

• 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