[physicsguy]

> The vast majority of reputable Python codebases nowadays use static typing rigorously

As judged by who? And in what field?

I mean, if I look at the big Python libraries I use regularly none of them have types - Django, DRF, NumPy, SciPy, Scikit-learn. That’s not to say there aren’t externally provided stubs but the library authors themselves are often not the ones writing them

[Myrmornis]

Yes fair enough my wording wasn't great. And we can add cpython to your list... But the provided type stubs for those libraries make the resulting user experience the same as if they had types. Does Django ORM have a good typing experience via stubs btw? I know that one's always been a challenge.

Overall though my point was that the article, and most comments here, were completely misrepresenting the situation regarding Python. It's a statically typed language for those that want it to be. There's no need to attempt to run any code that hasn't passed a type checker. And it's an expressive type system; much more so than Go which has been mentioned in comments.

However the fact that the standard library documentation doesn't have types is embarrassing IMO.

[physicsguy]

They do and they don't, there's often mismatches where the library gets updated and the stubs haven't been. It makes adopting difficult to recommend in some cases, especially if the library is under more flux.

Django's stubbing isn't great, there's a lot of polymorphism in the framework (and in DRF). You actually have to change your code rather than just sprinkling annotations in some of places to stop getting 'Any' types.

With the numeric stuff, it's even worse though, for with something like e.g.:

    np.sum(X)

the appropriate type of X can be a python list, a numpy array of any numeric type and dimension, etc.