How does this skill help identify Python memory leaks?

It provides implementation patterns for memory_profiler and guidance on using tools like py-spy to track allocations and identify objects that aren't being garbage collected.

Does this skill provide algorithmic optimization advice?

Yes, it covers fundamental performance differences between data structures, such as O(1) dictionary lookups versus O(n) list searches, and provides benchmarks for various implementation patterns.

Can I use these optimization patterns for production environments?

Yes, the skill includes non-intrusive profiling techniques like sampling profilers (py-spy) that are designed to attach to running production processes with minimal overhead.

Which Python profiling tool is best for time-consuming functions?

For general function-level timing, cProfile is the standard recommendation included in this skill, while line_profiler is suggested for deep-dives into specific long-running functions.

Python Performance Optimization

Name: Python Performance Optimization
Author: pur3v4d3r

bypur3v4d3r

•

Developer Tools

Optimizes Python applications through comprehensive profiling, memory analysis, and high-performance implementation patterns.

About

This skill provides a comprehensive framework for identifying and resolving performance bottlenecks within Python codebases. It equips developers with specialized patterns for CPU and memory profiling using tools like cProfile and memory_profiler, alongside proven optimization strategies such as generator expressions, efficient string handling, and algorithmic refinements. Whether you are debugging a slow API, reducing the memory footprint of a data pipeline, or implementing high-performance numerical operations with NumPy, this skill provides the domain-specific guidance needed to make Python code run faster and more efficiently.

Key Features

Memory usage tracking and leak detection patterns
Memory-efficient data structures using slots and generators
Line-by-line performance analysis and benchmarking
Advanced optimization via caching and native extensions
Comprehensive CPU profiling with cProfile and py-spy
1 GitHub stars

Use Cases

Scaling data processing pipelines to handle larger datasets efficiently
Reducing backend API latency by identifying and fixing slow functions
Optimizing memory-intensive applications to prevent OOM errors

About

Key Features

Memory usage tracking and leak detection patterns
Memory-efficient data structures using slots and generators
Line-by-line performance analysis and benchmarking
Advanced optimization via caching and native extensions
Comprehensive CPU profiling with cProfile and py-spy
1 GitHub stars

Use Cases

Scaling data processing pipelines to handle larger datasets efficiently
Reducing backend API latency by identifying and fixing slow functions
Optimizing memory-intensive applications to prevent OOM errors