python-performance-optimization

Use cProfile to profile Python code by wrapping execution with profiler.enable() and profiler.disable(), then print sorted stats with pstats. For command-line use, run python -m cProfile -o output.prof script.py and inspect results with pstats.

cProfile measures time per function call across the whole program, while line_profiler reports execution time for each individual line inside a function. Use cProfile to find slow functions first, then line_profiler to pinpoint the exact lines causing the bottleneck.

Use tracemalloc to take memory snapshots before and after running code, then compare them to find the largest allocations. The memory_profiler package with its @profile decorator also shows per-line memory consumption when running python -m memory_profiler script.py.

Yes, py-spy attaches to a running Python process by PID without modifying code. Use py-spy top --pid for live monitoring, py-spy record -o profile.svg --pid to generate flamegraphs, or py-spy dump to capture the current call stack.

Use multiprocessing for CPU-bound tasks like heavy calculations, since it bypasses the GIL by running separate processes. Use asyncio with libraries like aiohttp for I/O-bound work such as concurrent HTTP requests, where tasks wait on network or disk.

Repeated string concatenation with += creates a new string object each iteration, causing quadratic behavior. Use str.join() with a list or generator of parts instead, which allocates the final string once and runs significantly faster.