That's what I just said. There is zero value to me knowing these numbers. I assume that all python built in methods are pretty much the same speed. I concentrate on IO being slow, minimizing these operations. I think about CPU intensive loops that process large data, and I try to use libraries like numpy, DuckDB, or other tools to do the processing. If I have a more complicated system, I profile its methods, and optimize tight loops based on PROFILING. I don't care what the numbers in the article are, because I PROFILE, and I optimize the procedures that are the slowest, for example, using cython. Which part of what I am saying does not make sense?

As others have pointed out, Python is better used in places where those numbers aren't relevant.

If they start becoming relevant, it's usually a sign that you're using the language in a domain where a duck-typed bytecode scripting-glue language is not well-suited.