Python-Performance.md

Python Performance Content Moved

This content has been reorganized and moved to a more comprehensive location.

🔄 Redirect Notice

The Python performance content has been consolidated and enhanced in our new dedicated Python section.

➡️ New Location: Python for AI/ML/DL

What's New

The new Python guide includes all the performance content plus much more:

• Performance & Optimization - Profiling, speed optimization, vectorization, parallel processing
• Learning Python - Basics, fundamentals, and advanced topics
• Libraries & Frameworks - Core libraries, web frameworks, ML/AI libraries
• Best Practices - Code quality, static analysis, project templates
• Testing - Testing frameworks and resources
• Tools & Resources - Cheatsheets, development tools, version control

Quick Links to Performance Content

• Performance & Optimization
• Profiling & Analysis
• Speed Optimization
• Vectorization
• Parallel Processing

Key Performance Topics Now Covered

• Profiling Tools: Scalene, pytest-benchmark, VizTracer
• Speed Optimization: Pandas optimization, NumPy techniques, loop optimization
• Vectorization: NumPy vectorization, performance tips
• Parallel Processing: Numba, Dask, Modin, Vaex, Swifter
• High-Performance Python: Ian Ozvald's resources and techniques

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