enforce VS mypy

Every point in this blog post strikes me as either (1) unaware of the tooling around python typing other than mypy, or (2) a criticism of static-typing-bolted-on-to-a-dynamically-typed-language, rather than Python's hints. Regarding (1), my advise to OP is to try out Pyright, Pydantic, and Typeguard. Pyright, especailly, is amazing and makes the process of working with type hints 2 or 3 times smoother IMO. And, I don't think points that fall under (2) are fair criticisms of type *hints*. They are called hints for a reason.

Otherwise, here's a point-by-point response, either recommending OP checks out tooling, or showing that the point being made is not specific to Python.

> type hints are not binding.

There are projects [0][1] that allow you to enforce type hints at runtime if you so choose.

It's worth mentioning that this is very analogous to how Typescript does it, in that type info is erased completely at runtime.

> Type checking is your job after all, ...[and that] requires maintenance.

There are LSPs like Pyright[2] (pyright specifically is the absolute best, IMO) that report type errors as you code. Again, this is very very similar to typescript.

> There is an Any type and it renders everything useless

I have never seen a static-typing tool that was bolted on to a dynamically typed language, without an `Any` type, including typescript.

> Duck type compatibility of int and float

The author admits that they cannot state why this behavior is problematic, except for saying that it's "ambiguous".

> Most projects need third-party type hints

Again, this is a criticism of all cases where static types are bolted on dynamically typed languages, not Python's implementation specifically.

> Sadly, dataclasses ignore type hints as well

Pydantic[3] is an amazing data parsing library that takes advantage of type hints, and it's interface is a superset of that of dataclasses. What's more, it underpins FastAPI[4], an amazing API-backend framework (with 44K Github stars).

> Type inference and lazy programmers

The argument of this section boils down to using `Any` as a generic argument not being an error by default. This is configurable to be an error both in Pyright[5], and mypy[6].

> Exceptions are not covered [like Java]

I can't find the interview/presentation, but Guido Van Rossum specifically calls out Java's implementation of "exception annotations" as a demonstration of why that is a bad idea, and that it would never happen in Python. I'm not saying Guido's opinion is the absolute truth, but just letting you know that this is an explicit decision, not an unwanted shortcoming.

[0] https://github.com/RussBaz/enforce

[1] https://github.com/agronholm/typeguard

[2] https://github.com/microsoft/pyright

[3] https://pydantic-docs.helpmanual.io

[4] https://github.com/tiangolo/fastapi

[5] https://github.com/microsoft/pyright/blob/main/docs/configur...

[6] https://mypy.readthedocs.io/en/stable/config_file.html#confv...

Not by default. But there are libraries to enforce types. https://github.com/RussBaz/enforce or/and https://pydantic-docs.helpmanual.io/

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.