ruff
mypy
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ruff | mypy | |
---|---|---|
95 | 112 | |
26,504 | 17,506 | |
7.2% | 1.4% | |
10.0 | 9.7 | |
about 15 hours ago | 8 days ago | |
Rust | Python | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ruff
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Ask HN: High quality Python scripts or small libraries to learn from
I think I mention this all the time when this comes up, but I learned the most 'best practices' through using ruff.
https://docs.astral.sh/ruff/
I just installed and enabled all the rules by setting
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Enhance Your Project Quality with These Top Python Libraries
Ruff is a Python linter that helps to identify and remove code smells. Over 700 built-in rules: Ruff includes native re-implementations of popular Flake8 plugins, like flake8-bugbear. And also built-in caching to avoid re-analyzing unchanged files.
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Ask HN: What interesting project ideas you've got but have no time to work on?
Because the Python's "ast" modules is too slow, and lacks proper "format" feature (it has unparse but it removes comments, and forgets the current style completely). I use "ruff" a lot (https://github.com/astral-sh/ruff) which is in Rust. But I want to be able to implement fast custom linters in Go (linters that ruff / fixit lack, and Python linters lack or are too slow).
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Rye: A Vision Continued
I think it’s interesting that rye uses ruff (https://github.com/astral-sh/ruff) for linting and formatting. That’s the right call, and it’s also correct to bundle that in for an integrated dev experience.
I had to guess, that’s the path that the Astral team would take as well - expand ruff’s capabilities so it can do everything a Python developer needs. So the vision that Armin is describing here might be achieved by ruff eventually. They’d have an advantage that they’re not a single person maintenance team, but the disadvantage of needing to show a return to their investors.
- An fast Python linter and code formatter, written in Rust
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Smooth Packaging: Flowing from Source to PyPi with GitLab Pipelines
Adding more weight to ease of setup and configurability, the choice came down on flake8. It is easy to integrate, since its also available through pip and let’s you configure which standards you want to omit by simply stating them as a list via the --ignore switch. Moving to ruff appears quite smooth, so future updates may do so.
- Show HN: Marimo – an open-source reactive notebook for Python
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AST-grep(sg) is a CLI tool for code structural search, lint, and rewriting
I confess I stole the pip recipe from Charlie :D
https://github.com/astral-sh/ruff/blob/main/.github/workflow...
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Embracing Modern Python for Web Development
Ruff is an emerging tool in the Python ecosystem that describes itself as "an extremely fast Python linter and code formatter, written in Rust".
- Ruff: An fast Python linter and code formatter, written in Rust
mypy
- The GIL can now be disabled in Python's main branch
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Polars – A bird's eye view of Polars
It's got type annotations and mypy has a discussion about it here as well: https://github.com/python/mypy/issues/1282
- Static Typing for Python
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Python 3.13 Gets a JIT
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
- Introducing Flask-Muck: How To Build a Comprehensive Flask REST API in 5 Minutes
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WeveAllBeenThere
In Python there is MyPy that can help with this. https://www.mypy-lang.org/
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It's Time for a Change: Datetime.utcnow() Is Now Deprecated
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
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What's New in Python 3.12
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
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Mypy 1.6 Released
# is fixed: https://github.com/python/mypy/issues/12987.
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Ask HN: Why are all of the best back end web frameworks dynamically typed?
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.
What are some alternatives?
black - The uncompromising Python code formatter
pyright - Static Type Checker for Python
pyre-check - Performant type-checking for python.
Pylint - It's not just a linter that annoys you!
Flake8 - flake8 is a python tool that glues together pycodestyle, pyflakes, mccabe, and third-party plugins to check the style and quality of some python code.
pytype - A static type analyzer for Python code
pre-commit - A framework for managing and maintaining multi-language pre-commit hooks.
pydantic - Data validation using Python type hints