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python-performance-optimization

Profile Python code with cProfile, memory_profiler, and line_profiler to identify bottlenecks.

2|2|Updated Jan 21, 2026
Search Tags:#Python#optimize#profile#line_profiler#cProfile#memory_profiler
One-click install
npx skills add https://github.com/NorkzYT/claude-code-autopilot --skill python-performance-optimization-norkzyt
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: python-performance-optimization
Source: https://github.com/NorkzYT/claude-code-autopilot/tree/main/.claude/skills/python-performance-optimization
Command: npx skills add https://github.com/NorkzYT/claude-code-autopilot --skill python-performance-optimization-norkzyt

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Profiling and optimizing Python code to identify bottlenecks, improve speed, and reduce memory usage across applications.

Core Features & Use Cases

  • CPU profiling and bottleneck identification using cProfile and pstats
  • Memory profiling and leak detection with memory_profiler
  • Line-by-line profiling with line_profiler
  • Practical optimization patterns (generators, caching, vectorization)
  • Real-world scenarios: speeding up a long-running data processing task or a web service

Quick Start

Install the profiling tools: memory_profiler and line_profiler (cProfile is built-in). Start by profiling a script with python -m cProfile -o profile.prof your_script.py and viewing results with python -m pstats profile.prof. For line-by-line profiling, install line_profiler and run kernprof -l your_script.py; for memory usage profiling, run python -m memory_profiler your_script.py. For production profiling, consider py-spy to monitor a running process.

Frequently Asked Questions about python-performance-optimization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I find bottlenecks in a slow Python script?▼

Profile Python code using cProfile to identify CPU bottlenecks. Run your script with python -m cProfile -o profile.prof your_script.py and analyze the output with pstats to pinpoint slow functions.

What's the best way to detect memory leaks in Python data pipelines?▼

Detect memory leaks by profiling Python code with memory_profiler. Execute python -m memory_profiler your_script.py to track memory usage line-by-line and identify where leaks occur in your data pipelines.

How does line_profiler work for line-by-line Python analysis?▼

Line_profiler analyzes Python code execution time line-by-line. Install the tool, run kernprof -l your_script.py to collect metrics, and view detailed results to see exactly which lines consume the most CPU time.

Can I profile a Python web service that is already running in production?▼

Profile running Python web services using py-spy. Monitor an active process without requiring code modifications or restarting the service to capture CPU usage and identify production bottlenecks.

What optimization patterns help accelerate Python code after profiling?▼

Apply practical Python optimization patterns like generators, caching, and vectorization. Implement these improvements after profiling to reduce memory overhead and significantly accelerate code execution.

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