nimpy | mypy | |
---|---|---|
38 | 112 | |
1,420 | 17,541 | |
- | 0.7% | |
5.8 | 9.7 | |
3 months ago | 7 days ago | |
Nim | 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.
nimpy
-
Mojo is now available on Mac
I mean honestly, the closest language to Mojo really is Nim. In the latest Lex Fridman interview [0] when he talks about his ideas behind Mojo it pretty much sounds like he's describing Nim. Ok fair, he wants Mojo to be a full superset of Python, but honestly with nimpy [1] our Python interop is about as seamless as it can really be (without being a superset, which Mojo clearly is not yet). Even the syntax of Mojo looks a damn lot like Nim imo. Anyway, I guess he has the ability to raise enough funds to hire enough people to write his own language within ~2 years so as not have to follow random peoples whim about where to take the language. So I guess I can't blame him. But as someone who's pretty invested in the Nim community it's quite a shame to see such a hyped language receive so much attention by people who should really check out Nim. ¯\_(ツ)_/¯
[0]: https://youtu.be/pdJQ8iVTwj8?si=LfPSNDq8UKKIsJd3
[1]: https://github.com/yglukhov/nimpy
-
Show HN: Pip Imports in Deno
You can also do this in Nim, which basically means you can write any program you could in Python with libraries in Nim. https://github.com/yglukhov/nimpy
-
Nim v2.0 Released
Ones that have not been mentioned so far:
nlvm is an unofficial LLVM backend: https://github.com/arnetheduck/nlvm
npeg lets you write PEGs inline in almost normal PEG notation: https://github.com/zevv/npeg
futhark provides for much more automatic C interop: https://github.com/PMunch/futhark
nimpy allows calling Python code from Nim and vice versa: https://github.com/yglukhov/nimpy
questionable provides a lot of syntax sugar surrounding Option/Result types: https://github.com/codex-storage/questionable
ratel is a framework for embedded programming: https://github.com/PMunch/ratel
cps allows arbitrary procedure rewriting to continuation passing style: https://github.com/nim-works/cps
chronos is an alternative async/await backend: https://github.com/status-im/nim-chronos
zero-functional fixes some inefficiencies when chaining list operations: https://github.com/zero-functional/zero-functional
owlkettle is a declarative macro-oriented library for GTK: https://github.com/can-lehmann/owlkettle
A longer list can be found at https://github.com/ringabout/awesome-nim.
-
Prospects of utilising Nim in scientific computation?
I use Python daily for its massive momentum for scientific stuff, but I also use Nim for everything else. Nim compiles to C, and making Python native modules with Nim is easy with Nimpy.
- Can't run compiled nim code in Python
-
Returning to Nim from Python and Rust
If are a data scientist and come from python take a look at nimpy, a great way to just import python libraries and use them! https://github.com/yglukhov/nimpy Numpy, pandas, pytorch all usable in Nim.
Nim is the ultimate glue language, use libraries from anything: python, c, js, objc.
-
Python's “Disappointing” Superpowers
I've come to really enjoy programming in Nim. Note that Nim is very different language despite sharing a similar syntax. However, I feel it keeps a lot of the "feel" of Python 2 days of being a fairly simple neat language but that lets you do things at compile time (like compile time duck typing).
There's a good Python -> Nim bridge: https://github.com/yglukhov/nimpy
-
Dunder methods in nimpy
See this nimpy issue about it: https://github.com/yglukhov/nimpy/issues/43
-
What language to move to from python to speed up algo?
It has pretty good integration with python, either for having your main code in python and writing small hot functions as nim and importing via nimporter or using python libraries in nim via nimpy.
-
ABI compatibility in Python: How hard could it be?
Related: Nimpy[0] provides an easy way to write Python extensions in Nim, which manages the ABI side very well.
Python 2 is now gone, but until it was, Nimpy was an easy way to write Python extension modules that only needed to be compiled once, and would work with any of your installed Python 2 and Python 3. Magic.
[0] https://github.com/yglukhov/nimpy
mypy
- The GIL can now be disabled in Python's main branch
-
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
-
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
-
WeveAllBeenThere
In Python there is MyPy that can help with this. https://www.mypy-lang.org/
-
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
-
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
-
Mypy 1.6 Released
# is fixed: https://github.com/python/mypy/issues/12987.
-
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?
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).
pyright - Static Type Checker for Python
Box - Python dictionaries with advanced dot notation access
ruff - An extremely fast Python linter and code formatter, written in Rust.
nimporter - Compile Nim Extensions for Python On Import!
pyre-check - Performant type-checking for python.
scinim - The core types and functions of the SciNim ecosystem
black - The uncompromising Python code formatter
nimpylib - Some python standard library functions ported to Nim
pytype - A static type analyzer for Python code
nimskull - An in development statically typed systems programming language; with sustainability at its core. We, the community of users, maintain it.
pydantic - Data validation using Python type hints