Can I use this skill for production debugging?

Yes, the skill includes patterns for using py-spy, a sampling profiler that can attach to running Python processes without requiring a restart.

Is NumPy really faster than pure Python loops?

Yes, for numerical operations, NumPy uses vectorized C-level implementations that can be 10x to 100x faster than standard Python list iterations.

What is the fastest way to reduce memory usage in Python objects?

Defining __slots__ in your classes prevents the creation of a dynamic __dict__ for every instance, significantly reducing memory overhead for large collections of objects.

How does LRU caching help with Python performance?

Using functools.lru_cache allows you to store the results of expensive function calls and return them when the same inputs occur again, eliminating redundant computation.

How do I profile a specific line of code in Python?

Use the line_profiler pattern included in this skill, which allows you to see the execution time for every individual line within a decorated function.

Python Performance Optimization

Name: Python Performance Optimization
Author: ai-pilo

byai-pilo

Herramientas para Desarrolladores

Optimizes and profiles Python applications to increase execution speed, reduce memory consumption, and resolve performance bottlenecks.

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This skill provides a comprehensive toolkit for identifying and fixing performance issues within Python codebases. It guides developers through essential profiling techniques using tools like cProfile and memory_profiler while offering actionable optimization patterns such as generator expressions, LRU caching, and NumPy vectorization. Whether you are debugging a slow API, reducing the memory footprint of a data pipeline, or tuning high-frequency numerical operations, this skill provides the domain-specific best practices needed to deliver production-grade, high-performance Python software.

Características Principales

Production-safe sampling and flamegraph generation with py-spy
0 GitHub stars
CPU and memory profiling using cProfile, line_profiler, and memory_profiler
Memory efficiency patterns including generators and __slots__ for classes
Numerical optimization through NumPy vectorization and algorithm improvements
Execution speedups via list comprehensions, local variable access, and caching

Casos de Uso

Scaling data processing pipelines by replacing loops with vectorized NumPy operations
Identifying and fixing slow API endpoints by analyzing function call latency
Reducing infrastructure costs by optimizing memory usage in long-running background tasks

Acerca de

Características Principales

Production-safe sampling and flamegraph generation with py-spy
0 GitHub stars
CPU and memory profiling using cProfile, line_profiler, and memory_profiler
Memory efficiency patterns including generators and __slots__ for classes
Numerical optimization through NumPy vectorization and algorithm improvements
Execution speedups via list comprehensions, local variable access, and caching

Casos de Uso

Scaling data processing pipelines by replacing loops with vectorized NumPy operations
Identifying and fixing slow API endpoints by analyzing function call latency
Reducing infrastructure costs by optimizing memory usage in long-running background tasks