The goal of the article is not to know the exact numbers by heart, duh!

Care about orders of magnitude instead, in combination with the speed of hardware https://gist.github.com/jboner/2841832 you'll have a good understanding of how much overhead is due to the language and the constructs to favor for speed improvements.

Just reading the code should give you a sense of its speed and where it will spend most time. Combined with general timing metrics you can also have a sense of the overhead of 3rd party libraries (pydantic I'm looking at you).

So yeah, I find that list quite useful during the code design, likely reduce time profiling slow code in prod.