python-performance-optimization
Python Performance Optimization
Expert guidance for profiling, optimizing, and accelerating Python applications through systematic analysis, algorithmic improvements, efficient data structures, and acceleration techniques.
When to Use This Skill
- Code runs too slowly for production requirements
- High CPU usage or memory consumption issues
- Need to reduce API response times or batch processing duration
- Application fails to scale under load
- Optimizing data processing pipelines or scientific computing
- Reducing cloud infrastructure costs through efficiency gains
- Profile-guided optimization after measuring performance bottlenecks
Core Concepts
The Golden Rule: Never optimize without profiling first. 80% of execution time is spent in 20% of code.
Optimization Hierarchy (in priority order):
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