Flake8 VS mypy

Flake8 combines the functionalities of the PyFlakes, pycodestyle, and McCabe libraries. It provides a streamlined approach to code linting by detecting coding errors, enforcing style conventions, and measuring code complexity.

He made yesqa and pyupgrade (among others), and also works on flake8. His main job is for https://sentry.io/.

repos: - repo: https://github.com/psf/black rev: 21.7b0 hooks: - id: black language_version: python3.8 - repo: https://github.com/PyCQA/flake8 rev: 3.9.2 hooks: - id: flake8

https://github.com/psf/black it’s a PEP8 compliant formatter for Python codebases. If you don’t like auto formatting files you can use https://github.com/PyCQA/flake8 it just lists out all of the style issues so you can fix them manually.

I have no stake in that, but my observation is that the actual discussion appears to have both supporters and detractors rather than overwhelming support. Either way, it has nothing to do with whether or not it is realistic to say that Ruff is the "one Python linter to rule them all".

https://github.com/PyCQA/flake8/issues/234

Here are insights about flake8, black, and mypy and the amount of work that went into mypy seems … just massive?!

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.

The in-progress tutorial is here in the official documentation. It sucks that I can never seem to make quick progress on this. Trying to do something clever with Protocols ended up with me making a pull request on the Mypy project for something that likely wasn't critical after all.

I'm also 100% convinced most people who use mypy don't realize the myriad ways it just silently stopps typing things or just silently crashes with a 0 exit code. Even if you configure it to warn untyped functions etc. It will still just not work properly in some of circumstances and you will literally never know until you debug a bug that just happened to trigger it. There are over 1.4k open but tickets it's such a broken piece of software: https://github.com/python/mypy/issues?q=is%3Aissue+is%3Aopen...

The involvement of Guido in mypy is such a tragedy.

The backend service was built using Django with PostgreSQL database, Redis for session storage, and AWS S3 for file storage. The APIs were built without the use of external REST API frameworks such as Django REST framework. Data serialization and JSON responses were manually handled. Most of the views were made asynchronous. For testing, pytest and its ecosystem were heavily used. Mypy, Pylint and others were used for Static analysis. GitHub Actions were used for automated testing, coverage report and static analysis.

Further type checkers like mypy can be used to enable static typing (Python by design is dynamic typing language). Since Python 3.5 typing is a built-in feature. However some additional functionality for typing can be achieved by using typing package (shipped with Python distribution).

Function annotations were added as part of Python 3.0, with PEP 3107, proposed in 2006 [0]. The first public mypy commit was in 2012 [1], and originally it was using C++-style syntax (`list lex(str s):`). The `typing` module and the official use of function annotation for type hints came in 2014 with PEP 484 [2], inspired by mypy. The first pyright commit was in 2019 (with a lot of code in the third commit [3], possibly moved from the VSCode extension).

[0] https://peps.python.org/pep-3107/

[1] https://github.com/python/mypy/commit/6f0826a9c169c4f05bb893...

[2] https://peps.python.org/pep-0484/

[3] https://github.com/microsoft/pyright/commit/1d91744b1f268fd0...