performance-tuning

To profile a Python script and find performance bottlenecks, you measure hot paths first using tools like cProfile, memory_profiler, and line_profiler to identify slow code paths before applying any optimizations.

The best way to optimize slow code paths is applying deterministic optimization patterns like vectorization, hoisting invariant work, caching, and chunking, then validating improvements with before/after benchmarks.

To benchmark Python code and verify optimization improvements, you execute before/after benchmarks to quantify performance gains, ensuring that applied patterns deliver measurable and validated speed increases.

Yes, you can use this performance tuning workflow for a data processing job that slowed down after changes to isolate bottlenecks, optimize the identified hot paths, and quantify the measurable improvements.

A code optimization workflow needs a measure-first approach because it enforces the identification of actual bottlenecks through profiling before making changes, ensuring deterministic optimization steps and measurable performance verification.