Python Performance Profiling

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This skill provides a structured workflow for optimizing Python research code by identifying hotspots with profiling tools like cProfile or pyinstrument. It guides users through a repeatable process of measuring slow paths, applying minimal and safe fixes such as vectorization or caching, and verifying improvements with before-and-after metrics. By adhering to strict guardrails, it ensures that optimizations remain local, reversible, and data-driven without introducing unnecessary algorithmic complexity.

Características Principales

• Targeted optimizations including vectorization and caching strategies
• Systematic bottleneck identification using cProfile, pyinstrument, or line_profiler
• Safe, local, and reversible code modifications to maintain stability
• Reproducible performance testing on representative datasets
• 0 GitHub stars
• Quantifiable before-and-after performance measurement and recording

Casos de Uso

• Reducing latency in backend Python services by identifying redundant I/O
• Profiling and caching expensive function calls to improve execution speed
• Optimizing slow data processing loops in Python research scripts