typeshed VS mypy

"csv.pyi is from VS Code Pylance extension" is misleading. Yes, it's included in the code base of the extension, but it's likely originally from python/typeshed. I diffed csv.pyi in the extension and the repository, and they're exactly the same.

You can check the typeshed library that offers stubs for many packages.

https://github.com/python/typeshed is Python's equivalent of DefinitelyTyped. I'm not 100% sure why it's not more of a popular thing the way DefinitelyTyped is; I think there might, to some extent, be different attitudes around the appropriateness of having third-party typings for packages, when the actual maintainer of the package isn't interested in providing first-party ones.

https://github.com/python/mypy same with typeshed https://github.com/python/typeshed

Here's the tensorflow type stubs on typeshed. https://github.com/python/typeshed/tree/main/stubs/tensorflow

Also, be aware that there is already a central place for stubs files. If you are going to take the time to write one, contributing it there will help everyone if the package owners aren't already including some type hints.

Python's type hints are definitely an improvement and they're getting better all the time, but they're still frustrating to use at anything approaching the edge. I long for something as elegant and functional as TypeScript.

One hurdle I've stumbled over recently is the question "what is a type?", the answer can be surprising. Unions, for example, are types but not `Type`s. A function that takes an argument of type `Type` will not accept a Union. So if you want to write a function that effectively "casts" a parameter to a specified type, you can't. The best you can do is have an overload that accepts `Type` and does an actual cast, and then another that just turns it into `Any`. This is, in fact, how the standard library types its `cast` function [1]. The argument I've seen for the current behavior is that `Type` describes anything that can be passed to isinstance, but that's not a satisfying answer. Even then, `Union` can be passed to isinstance and still does not work with `Type`. Talk currently is to introduce a new kind of type called `TypeForm` or something to address this, which is certainly an improvement over nothing, but still feels like technical debt.

[1]: https://github.com/python/typeshed/blob/main/stdlib/typing.p...

>Ultimately if you care enough about Fody to spend over a hundred dollars worth of your time contributing to it, you probably care enough about Fody to drop them three dollars.

No, I really don't.

https://github.com/keepassxreboot/keepassxc/pull/8500 - I was randomly reading keepassxc's manpage and spotted a curious option, spent some time spelunking through the code and history to discover that it was an outdated option, sent a PR.

https://github.com/python/typeshed/pull/8617 - I converted one of the scripts I use in my DE from shell to Python, saw that VSCode has this new fancy typing support for Python, quickly found a basic bug in the type definitions for the os module, tested a fix locally, sent a PR.

https://gitlab.gnome.org/GNOME/gtk/-/issues/5250 - I found an issue with copy-paste on my phone, investigated it all the way through to the GTK stack, found the commits that introduced the issue, created a distro patch for it while discussing it with GTK upstream.

https://gitlab.alpinelinux.org/alpine/aports/-/merge_request... - I noticed that gnome-passwordsafe crashes some times, debugged it to discover that it was missing a dependency, sent a PR to the distro package to update the dependencies.

etc etc. I've made lots of fixes like these. I have no interest in paying for each and every one of them. The projects are all better off for fixes like mine and gatekeeping them on payment would've been nothing but their loss.

The csv module is one of those standard library modules that doesn't provide its own type hints, but instead gets them through the external typeshed project, and (for compatibility/implementation reasons, I surmise) the name of these types sometimes don't quite align with the objects they correspond to. So, for all intents and purposes, _csv._reader is the correct name of the type that csv.reader() returns, as ugly as it is.

You have to do handling like that in other languages like TypeScript anyway.

Painpoint with type annotations:

- not being able to reuse "shapes" of data: TypedDict, NamedTuple, dataclasses.dataclass, and soon kwargs (PEP 692 [1]) all have named, typed fields now. You have to

- Since there's no generic "shape" structure that works across data types, there isn't a way to load up a JSON / YAML / TOML into a dictionary, upcast it via a `TypedGuard`, and pass it into a TypedDict / NamedTuple / Dataclass. dataclasses.asdict() or dataclasses.astuple() return naive / untyped tuples and dicts. Also the factory functions will not work with TypedDict or NamedTuple, respectively, even if you duplicate the fields by hand. See my post here: https://github.com/python/typeshed/issues/8580

- Standard library doesn't have runtime validation (e.g. pydantic / https://github.com/pydantic/pydantic).

- pytest fixtures are hard.

- Django is hard. PEP 681 may not be a saving grace either. [3]

[1] https://peps.python.org/pep-0692/

It's got type annotations and mypy has a discussion about it here as well: https://github.com/python/mypy/issues/1282

There is already an AOT compiler for Python: Nuitka[0]. But I don't think it's much faster.

And then there is mypyc[1] which uses mypy's static type annotations but is only slightly faster.

And various other compilers like Numba and Cython that work with specialized dialects of Python to achieve better results, but then it's not quite Python anymore.

[0] https://nuitka.net/

[1] https://github.com/python/mypy/tree/master/mypyc

In Python there is MyPy that can help with this. https://www.mypy-lang.org/

It's funny you should say this.

Reading this article prompted me to future-proof a program I maintain for fun that deals with time; it had one use of utcnow, which I fixed.

And then I tripped over a runtime type problem in an unrelated area of the code, despite the code being green under "mypy --strict". (and "100% coverage" from tests, except this particular exception only occured in a "# pragma: no-cover" codepath so it wasn't actually covered)

It turns out that because of some core decisions about how datetime objects work, `datetime.date.today() < datetime.datetime.now()` type-checks but gives a TypeError at runtime. Oops. (cause discussed at length in https://github.com/python/mypy/issues/9015 but without action for 3 years)

One solution is apparently to use `datetype` for type annotations (while continuing to use `datetime` objects at runtime): https://github.com/glyph/DateType

PEP 695 is great. I've been using mypy every day at work in last couple years or so with very strict parameters (no any type etc) and I have experience writing real life programs with Rust, Agda, and some Haskell before, so I'm familiar with strict type systems. I'm sure many will disagree with me but these are my very honest opinions as a professional who uses Python types every day:

* Some types are better than no types. I love Python types, and I consider them required. Even if they're not type-checked they're better than no types. If they're type-checked it's even better. If things are typed properly (no any etc) and type-checked that's even better. And so on...

* Having said this, Python's type system as checked by mypy feels like a toy type system. It's very easy to fool it, and you need to be careful so that type-checking actually fails badly formed programs.

* The biggest issue I face are exceptions. Community discussed this many times [1] [2] and the overall consensus is to not check exceptions. I personally disagree as if you have a Python program that's meticulously typed and type-checked exceptions still cause bad states and since Python code uses exceptions liberally, it's pretty easy to accidentally go to a bad state. E.g. in the linked github issue JukkaL (developer) claims checking things like "KeyError" will create too many false positives, I strongly disagree. If a function can realistically raise a "KeyError" the program should be properly written to accept this at some level otherwise something that returns type T but 0.01% of the time raises "KeyError" should actually be typed "Raises[T, KeyError]".

* PEP 695 will help because typing things particularly is very helpful. Often you want to pass bunch of Ts around but since this is impractical some devs resort to passing "dict[str, Any]"s around and thus things type-check but you still get "KeyError" left and right. It's better to have "SomeStructure[T]" types with "T" as your custom data type (whether dataclass, or pydantic, or traditional class) so that type system has more opportunities to reject bad programs.

* Overall, I'm personally very optimistic about the future of types in Python!

[1] https://github.com/python/mypy/issues/1773

# is fixed: https://github.com/python/mypy/issues/12987.

You probably already know but you can add type hints and then check for consistency with https://github.com/python/mypy in python.

Modern Python with things like https://learnpython.com/blog/python-match-case-statement/ + mypy + Ruff for linting https://github.com/astral-sh/ruff can get pretty good results.

I found typed dataclasses (https://docs.python.org/3/library/dataclasses.html) in python using mypy to give me really high confidence when building data representations.