I guess I see this post as more about profiling, discovering unintuitive differences, and exploring python internals than offering 20ns optimizations. I personally appreciated it.

(+20ns x # calls made) can be significant if you’re dealing with billions of executions though.

If nothing else a search/replace for “dict()” to “{}” could yield an aggregate performance bump with basically zero refactoring cost.

Also re Python in general: love it or hate it, it “won” as the language of ML. If you want to use ML libraries you’ll be in python.

I have a book on the shelf called "High Performance Python".

Seriously, though, if every Python program in the world was 0.1% faster, that would probably save a lot of energy.

I agree. If you're benchmarking this then you're using the wrong language.

But I hadn't considered the fact that `dict` can be overridden. That was interesting.

Yeah I would be much more concerned about things which cause exponential time increases, like developers who brute force search lists instead of using dictionaries than how they declare their dictionaries unless I was at the point of profiling and the dict() constructor was somehow near the top of the list.