beartype VS benchmarks

https://github.com/beartype/beartype

I wish more people started using Beartype, it makes Python bearable

I saw this on /r/Python the other day...

As other folks have commented, type hints are now a big deal. For static typing the best checker is pyright. For runtime checking there is typeguard and beartype. These can be integrated with array libraries through jaxtyping. (Which also works for PyTorch/numpy/etc., despite the name.)

Maybe you're looking for https://github.com/beartype/beartype for runtime type enforcement; it's only at function calls, though, but probably a decent solution for codebases that are not completely typed for MyPy or pyright.

It is though, if you add a type checker to your pipeline and use it without any escape hatches such as `Any` or `type: ignore`, you are essentially making the promise that your code is statically typed. But I say it is a matter of perspective because in my opinion runtime type checking should be avoided if we can get away with statically typed code, but there are type checkers that perform runtime type checking via annotations such as [Beartype](https://github.com/beartype/beartype) (with some trickery like assuming homogenous data structures as to not have to check every element of every structure). Anyway the definition of "type safe" is not 100% even in compiled languages.

There is the beartype project.

Personally I find working around mypy's quirks to be more effort than it's worth, so to offer another option: typeguard or beartype can be used to perform run-time type checking.

GitHub - kostya/benchmarks: Some benchmarks of different languages

In all the language comparisons I've found over the years, Python consistently comes out slightly slower, for example:

https://github.com/kostya/benchmarks

Bearing in mind these are probably not even using YJIT, which makes Ruby considerably faster in some scenarios.

Also, here is another benchmark: https://github.com/kostya/benchmarks