Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.

Python Performance Optimization Comprehensive guide to profiling, analyzing, and optimizing Python code for better performance, including CPU profiling, memory optimization, and implementation best practices. When to Use This Skill Identifying performance bottlenecks in Python applications Reducing application latency and response times Optimizing CPU intensive operations Reducing memory consumption and memory leaks Improving database query performance Optimizing I/O operations Speeding up data processing pipelines Implementing high performance algorithms Profiling production applications Core Concepts 1. Profiling Types CPU Profiling : Identify time consuming functions Memory Profiling : Track memory allocation and leaks Line Profiling : Profile at line by line granularity Call Graph : Visualize function call relationships 2. Performance Metrics Execution Time : How long operations take Memory Usage : Peak and average memory consumption CPU Utilization : Processor usage patterns I/O Wait : Time spent on I/O operations 3. Optimization Strategies Algorithmic : Better algorithms and data structures Implementation : More efficient code patterns Parallelization : Multi threading/processing Caching : Avoid redundant computation Native Extensions : C/Rust for critical paths Quick Start Basic Timing Detailed patterns and worked examples Detailed pattern documentation lives in references/details.md . Read that file when the navigation tier above is insufficient. Best Practices 1. Profile before optimizing Measure to find real bottlenecks 2. Focus on hot paths Optimize code that runs most frequently 3. Use appropriate data structures Dict for lookups, set for membership 4. Avoid premature optimization Clarity first, then optimize 5. Use built in functions They're implemented in C 6. Cache expensive computations Use lru cache 7. Batch I/O operations Reduce system calls 8. Use generators for large datasets 9. Consider NumPy for numerical operations 10. Profile production code Use py spy for live systems Common Pitfalls Optimizing without profiling Using global variables unnecessarily Not using appropriate data structures Creating unnecessary copies of data Not using connection pooling for databases Ignoring algorithmic complexity Over optimizing rare code paths Not considering memory usage

python-performance-optimization