Python Performance

---
name: python-performance
description: '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.'
zh_description: "用于 Python 性能分析、内存优化、热点路径调优和并发模式评审。"
version: "1.0.3"
author: "seaworld008"
source: "skills.sh"
source_url: "https://github.com/wshobson/agents/blob/156b7a5e7a8b93642628a339ee4039c925b34c7f/plugins/python-development/skills/python-performance-optimization/SKILL.md"
license: "MIT"
tags: '["development", "performance", "python"]'
created_at: "2026-03-27"
updated_at: "2026-09-30"
quality: 4
complexity: "intermediate"
---

# 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.perf_counter()

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

    elapsed = time.perf_counter() - 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

## Benchmark Acceptance

Use the same interpreter, dataset, warmup policy and dependency versions for
baseline and candidate runs. Record repeated samples rather than a single run.
Report elapsed-time units, median, spread, input size, and peak memory.
Check result equivalence before accepting a faster implementation.
Separate CPU profiling overhead from wall-clock measurements.
For I/O workloads, include concurrency, service latency and cache state.
Do not infer production gains from a microbenchmark alone.