pyenv-installer VS mypy

To install it make sure that all prerequisites are met. Then use the pyenv-installer project like this to get the tool installed:

I tried the uninstaller from https://github.com/pyenv/pyenv-installer and I'm getting this.

After you’ve installed the build dependencies, you’re ready to install pyenv itself. I recommend using the pyenv-installer project:

For python, it's recommended to use pyenv, which allows you to install several versions of python at the same time with simple commands like this: pyenv install 3.9.13

Please don't contribute worthless and irrelevant comments like this. As you doubtless well know, piping from curl into bash is something that a large subset of respected programmers think is reasonable, and another rather tedious subset do not. For example, the entire Rust community clearly has a consensus that it's reasonable: https://rustup.rs/ As does homebrew https://brew.sh/ and pyenv https://github.com/pyenv/pyenv-installer#install to name whatever came to my mind in 30s thought.

Since the debate has such large numbers on both sides, your individual opinion on it is neither interesting nor germane.

name: Test, build and release # whenever a branch or commit is pushed on: [push] jobs: # use pytest test: # used to ensure testing is done right env: DEVELOPMENT: '1' runs-on: ubuntu-latest # to avoid using old sqlite version container: image: debian:latest options: --user root steps: # check out repo - uses: actions/checkout@v2 # prevent from asking user for input - run: export DEBIAN_FRONTEND=noninteractive # install recommended tools for building Python - run: apt -q update - run: apt -q install make build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncursesw5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev git sqlite3 -y - run: apt -q upgrade -y # install pyenv - run: curl -L https://github.com/pyenv/pyenv-installer/raw/master/bin/pyenv-installer | bash - run: exec $SHELL - run: ~/.pyenv/bin/pyenv update # install and set up required Python - run: ~/.pyenv/bin/pyenv install 3.10.2 - run: ~/.pyenv/bin/pyenv virtualenv 3.10.2 npbc - run: ~/.pyenv/bin/pyenv local 3.10.2/envs/npbc # print version info (debugging) - run: ~/.pyenv/shims/python -V - run: ~/.pyenv/shims/python -c "import sqlite3; print(sqlite3.version)" # install pip packages - run: ~/.pyenv/shims/pip install -r requirements.txt pytest # run test - run: ~/.pyenv/shims/pytest -vv

curl -L https://github.com/pyenv/pyenv-installer/raw/master/bin/pyenv-installer | bash

This uses the pyenv-installer project to install pyenv on your system.

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.