How do I identify which part of my Python code is slow?

This skill helps you implement cProfile or line_profiler to generate detailed execution time reports, pinpointing exactly which functions or lines of code are causing delays.

Can this skill help with Python memory leaks?

Yes, it provides specific patterns for memory_profiler and suggests memory-efficient alternatives like generators and __slots__ to manage object allocation.

How do I cache expensive function results in Python?

The skill demonstrates how to use the functools.lru_cache decorator to store results of expensive computations and avoid redundant processing.

Is it safe to profile a production Python application?

The skill includes guidance on using py-spy, a sampling profiler that can attach to running processes with extremely low overhead, making it safe for production use.

Does this support numerical optimization?

Yes, it provides patterns for transitioning from pure Python loops to vectorized NumPy operations, which can often lead to 10x-100x speed improvements.

Python Performance Optimization

Name: Python Performance Optimization
Author: as4584

byas4584

分析と監視

Profiles and optimizes Python code to eliminate bottlenecks, reduce memory consumption, and accelerate execution.

概要

The Python Performance Optimization skill provides a comprehensive framework for diagnosing and fixing performance issues in Python applications. It guides users through sophisticated profiling techniques—including CPU, memory, and line-level analysis—and offers proven implementation patterns for high-speed execution. Whether you are troubleshooting slow data pipelines, fixing memory leaks, or reducing API latency, this skill assists in implementing efficient data structures, NumPy vectorization, and advanced caching strategies to ensure your Python code runs at peak efficiency in both development and production environments.

主な機能

Multi-level profiling using cProfile, line_profiler, and memory_profiler
Production-ready monitoring and flamegraph generation with py-spy
Memory management strategies using generator expressions and slotted classes
Advanced optimization via NumPy vectorization and functools LRU caching
Efficient implementation patterns for loops, strings, and dictionary lookups
0 GitHub stars

ユースケース

Reducing RAM footprints in large-scale data processing pipelines
Refactoring legacy Python scripts for improved scalability and speed
Debugging slow application response times and high CPU utilization

概要

主な機能

Multi-level profiling using cProfile, line_profiler, and memory_profiler
Production-ready monitoring and flamegraph generation with py-spy
Memory management strategies using generator expressions and slotted classes
Advanced optimization via NumPy vectorization and functools LRU caching
Efficient implementation patterns for loops, strings, and dictionary lookups
0 GitHub stars

ユースケース

Reducing RAM footprints in large-scale data processing pipelines
Refactoring legacy Python scripts for improved scalability and speed
Debugging slow application response times and high CPU utilization