python-performance-optimization

Profile Python scripts to identify CPU bottlenecks using cProfile to measure execution time across function calls and save the output to a .prof file for detailed analysis. This pinpoints exact hotspots for targeted optimization.

Reduce memory consumption in Python by applying patterns like using __slots__ for memory-efficient class instances, leveraging NumPy for numerical operations, and utilizing memory_profiler to detect memory leaks and line-by-line usage.

Profile Python code in production environments without significant overhead using py-spy, a sampling profiler that allows you to monitor running applications and identify performance issues without requiring code modifications or restarting.

Use multiprocessing to optimize CPU-bound Python tasks by bypassing the Global Interpreter Lock through parallelization, and apply asyncio to optimize I/O-bound operations through efficient concurrency and non-blocking execution patterns.

Caching with lru_cache improves Python execution speed by storing function call results, eliminating redundant calculations for repeated inputs. This optimization strategy is highly effective for expensive, deterministic functions.

Detect and fix memory leaks in Python by utilizing memory_profiler to monitor memory usage line-by-line over time. Analyze the generated reports to identify unreferenced objects and implement targeted memory optimization strategies.