python-performance-optimization

To find performance bottlenecks in a slow Python script, use CPU and memory profilers like cProfile, line_profiler, and memory_profiler to measure execution time and locate hot paths within the code.

The best way to profile Python code in production is using py-spy, a sampling profiler that generates flamegraphs to understand runtime behavior without requiring code modifications or restarting the service.

Memory_profiler works by monitoring memory usage line-by-line in Python scripts, helping developers identify memory leaks and optimize memory consumption in data processing and batch jobs.

Yes, you can use cProfile to improve throughput in web services by measuring CPU time spent on function calls, identifying hotspots, and guiding optimization through caching or algorithm improvements.

After profiling Python code, you can apply optimization techniques including algorithm improvements, caching, vectorization, and parallelism for CPU-bound tasks to eliminate identified performance bottlenecks.