Or keep your Python scaffolding, but push the performance-critical bits down into a C or Rust extension, like numpy, pandas, PyTorch and the rest all do.

But I agree with the spirit of what you wrote - these numbers are interesting but aren’t worth memorizing. Instead, instrument your code in production to see where it’s slow in the real world with real user data (premature optimization is the root of all evil etc), profile your code (with pyspy, it’s the best tool for this if you’re looking for cpu-hogging code), and if you find yourself worrying about how long it takes to add something to a list in Python you really shouldn’t be doing that operation in Python at all.

"if you're not measuring, you're not optimizing"