Python Benchmark Methodology — ELI5

Imagine you want to know who in your class runs fastest. You wouldn’t have everyone race once on a windy day and call it done. You’d run multiple races, on the same track, at the same time of day, and throw out the race where someone tripped.

Benchmarking Python code works the same way. You run the code many times, keep the conditions the same, and look at the pattern — not just one number.

Why does this matter? Computers aren’t perfectly consistent. Other programs are running. Memory gets shuffled around. The garbage collector kicks in at random moments. A single timing can be wildly off.

A good benchmark has three parts. First, a warmup: run the code a few times before you start measuring so caches fill up and things settle. Second, repetition: run it enough times that one weird result doesn’t ruin your answer. Third, comparison: always measure the old way and the new way in the same session, because your computer’s mood changes.

People often skip these steps and end up believing that a change made things faster when it didn’t — or missing a real improvement because the test was noisy.

The one thing to remember: one timing is an anecdote; many timings under controlled conditions are evidence.