How does this skill help with slow Python scripts?

It provides specialized patterns for CPU profiling to identify specific functions causing delays and suggests more efficient alternatives like list comprehensions or vectorization.

Does this require external libraries?

While basic timing uses standard libraries, many advanced profiling features recommend popular tools like line_profiler, memory_profiler, and NumPy for maximum impact.

Is it safe to use these tools in production?

The skill includes patterns for tools like py-spy, which are designed to profile running processes with minimal overhead, making them suitable for production debugging.

Can I profile memory usage with this skill?

Yes, it includes guidance on using memory_profiler to track allocation and identifying leaks using memory-efficient structures like generators and __slots__.

Python Performance Optimization

Name: Python Performance Optimization
Author: HermeticOrmus

byHermeticOrmus

분석 및 모니터링

Profiles, analyzes, and optimizes Python code to eliminate bottlenecks and improve application efficiency.

소개

Python Performance Optimization provides a comprehensive suite of tools and best practices for identifying and resolving performance issues in Python applications. It enables developers to perform deep CPU and memory profiling using industry-standard tools like cProfile and py-spy, while offering actionable optimization patterns for data structures, algorithms, and resource-heavy operations. Whether you are debugging a slow production API or refining a data processing pipeline, this skill helps reduce latency, minimize memory footprint, and implement high-performance patterns like vectorized operations and efficient caching.

주요 기능

0 GitHub stars
Production-safe performance monitoring and flamegraph generation with py-spy
Advanced optimization techniques including lru_cache and NumPy vectorization
Implementation of memory-efficient patterns using generators and __slots__
Benchmarking and comparison of Pythonic vs. high-performance data structures
CPU and memory profiling using cProfile, line_profiler, and memory_profiler

사용 사례

Reducing API response times by optimizing database interactions and function call overhead
Identifying and fixing memory leaks in long-running Python background processes
Accelerating data-intensive pipelines through vectorization and algorithmic improvements

소개

주요 기능

0 GitHub stars
Production-safe performance monitoring and flamegraph generation with py-spy
Advanced optimization techniques including lru_cache and NumPy vectorization
Implementation of memory-efficient patterns using generators and __slots__
Benchmarking and comparison of Pythonic vs. high-performance data structures
CPU and memory profiling using cProfile, line_profiler, and memory_profiler

사용 사례

Reducing API response times by optimizing database interactions and function call overhead
Identifying and fixing memory leaks in long-running Python background processes
Accelerating data-intensive pipelines through vectorization and algorithmic improvements