pythran
mypy
pythran | mypy | |
---|---|---|
7 | 112 | |
1,966 | 17,569 | |
- | 0.9% | |
8.1 | 9.7 | |
2 days ago | 4 days ago | |
C++ | Python | |
BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
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.
pythran
- Codon: Python Compiler
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How Python virtual environments work
Numpy and Scipy are good reasons. Unfortunately Scipy does not even compile on FreeBSD lately, and I have opened three issues about it against Scipy and Pythran (and the fix was with xsimd).
https://github.com/serge-sans-paille/pythran/issues/2070
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S6: A standalone JIT compiler library for CPython
In someone lands here seeking a maintained compiler for Python, there's a lot, on top of my head:
- Pythran (https://pythran.readthedocs.io) (ahead of time compiler)
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Accelerate Python code 100x by import taichi as ti
Yes, I mean Pythran ( https://github.com/serge-sans-paille/pythran ). Thank you.
Was Nuitka better? Pythran is quite simple to install and use in Jupyter.
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Omyyyy/pycom: A Python compiler, down to native code, using C++
The only project that compares 1:1 is Pythran: https://github.com/serge-sans-paille/pythran
Pythran is fairly nice, and it really does work. I tried it last year and it compiles down to modifiable templated C++. I was able to use it to build Python for a highly specialized environment.
All the others compile down to dynamically linked binaries, and that just puts them in the "other" box.
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OpenAI Codex Python to C++ Code Generator
You might want to contact the author of Pythran [1], maybe something can be learned from what they do.
[1] https://github.com/serge-sans-paille/pythran/commits/master
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PyO3: Rust Bindings for the Python Interpreter
[1] https://github.com/serge-sans-paille/pythran
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?
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
pyright - Static Type Checker for Python
setuptools-rust - Setuptools plugin for Rust support
ruff - An extremely fast Python linter and code formatter, written in Rust.
RustPython - A Python Interpreter written in Rust
pyre-check - Performant type-checking for python.
codex_py2cpp - Converts python code into c++ by using OpenAI CODEX.
black - The uncompromising Python code formatter
shedskin - Shed Skin is a restricted-Python-to-C++ compiler. Read the introduction below to learn about the restrictions.
pytype - A static type analyzer for Python code
Nuitka - Nuitka is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11. You feed it your Python app, it does a lot of clever things, and spits out an executable or extension module.
pydantic - Data validation using Python type hints