⏱️ Is Your Code Slow, or Does It Just Seem That Way?

Optimization without preliminary measurements is not great. But even worse are measurements done "on the fly."

Many people use time.time() for everything and get numbers that have nothing to do with reality.

Read the breakdown of how to properly measure performance in Python.
Inside the article:
▪️ Wall Time vs CPU Time: Why your code might "hang" for 5 seconds while the CPU worked only 0.05s (and what to do about it).
▪️ timeit: Why this is the best way for microbenchmarks and how to use it without embarrassment.
▪️ Jupyter Magic: How to measure speed with one line (%timeit) if you live in notebooks.
▪️ Asyncio: Why standard methods lie in asynchronous code and where to find the correct timer loop.time().

This is the basics without which you simply cannot dive into refactoring.

👉 Profiling and Measuring Code Execution Time in Python: A Complete Guide

#rtfm