사용 방법
- 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
- 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
- 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/wshobson/agents --skill python-performance-optimization시작 전 확인
- 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
- 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서
원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.
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
- Profile before optimizing - Measure to find real bottlenecks
- Focus on hot paths - Optimize code that runs most frequently
- Use appropriate data structures - Dict for lookups, set for membership
- Avoid premature optimization - Clarity first, then optimize
- Use built-in functions - They're implemented in C
- Cache expensive computations - Use lru_cache
- Batch I/O operations - Reduce system calls
- Use generators for large datasets
- Consider NumPy for numerical operations
- 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
