Does it provide optimization for data science tasks?

Absolutely. It includes advanced patterns for using NumPy vectorization to replace slow Python loops in numerical and data-heavy operations.

How does it help with production application profiling?

It includes patterns for using py-spy, which allows you to profile running Python processes and generate flamegraphs without restarting the application.

Which profiling tools does this skill support?

This skill provides implementation patterns for cProfile (CPU), memory_profiler (RAM), line_profiler (line-by-line), and py-spy (sampling profiler for production).

Can I use this to find memory leaks in Python?

Yes, it includes specific patterns for using memory_profiler and __slots__ to track allocation and reduce the memory footprint of Python objects.

Python Performance Optimization

Name: Python Performance Optimization
Author: acaprino

byacaprino

分析与监控

Profiles and optimizes Python applications to identify bottlenecks, reduce memory usage, and improve execution speed using industry-standard tools and patterns.

关于

The Python Performance Optimization skill empowers developers to transform sluggish Python scripts into high-performance applications. By integrating comprehensive profiling tools like cProfile, memory_profiler, and line_profiler, it helps identify exact lines of code causing latency or memory leaks. Beyond diagnostics, it provides actionable implementation patterns for algorithmic efficiency, caching strategies, and memory-efficient data structures, making it indispensable for scaling production systems and optimizing data-heavy pipelines.

主要功能

High-performance coding patterns for list comprehensions, generators, and string concatenation
Memory footprint reduction techniques using __slots__ and vectorized NumPy operations
Advanced caching implementations using functools.lru_cache to eliminate redundant computations
Line-by-line execution analysis to pinpoint specific code-level bottlenecks
0 GitHub stars
Comprehensive CPU and memory profiling using cProfile, memory_profiler, and py-spy

使用场景

Reducing API response times by optimizing function call overhead and data lookups
Accelerating heavy data processing pipelines through vectorization and efficient memory management
Identifying and resolving memory leaks in long-running Python backend processes

关于

主要功能

High-performance coding patterns for list comprehensions, generators, and string concatenation
Memory footprint reduction techniques using __slots__ and vectorized NumPy operations
Advanced caching implementations using functools.lru_cache to eliminate redundant computations
Line-by-line execution analysis to pinpoint specific code-level bottlenecks
0 GitHub stars
Comprehensive CPU and memory profiling using cProfile, memory_profiler, and py-spy

使用场景

Reducing API response times by optimizing function call overhead and data lookups
Accelerating heavy data processing pipelines through vectorization and efficient memory management
Identifying and resolving memory leaks in long-running Python backend processes