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djinni
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Tracking mentions began in Dec 2020.
Cython
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I Use Nim Instead of Python for Data Processing
>Not type safe
That's the point. Look up what duck typing means in Python. Your program is meant to throw exceptions if you pass in data that doesn't look and act how it needs to. This means that in Python you don't need to do defensive programming. It's not like in C where you spend many hundreds of lines safe-guarding buffer lengths, memory allocation, return codes, static type sizes, and so on. That means that Python code can really just look directly like the algorithms they need to implement. That makes Python genuinely shorter and more readable than almost every other language out there.
>Everything is global
Not really. Python passes basic types as a copy. This is 'pass by value.' Objects are passed as a 'reference' to an object. So you can change that named reference without effecting the object or you can use it to manipulate the item it points to. Your own classes, lists, and dicts fall under this category. But I don't think ints, strings, or bytes do.
>virtual environments hassle
Yeah, you're not wrong. pyenv doesn't work that well and installs often break when you try set it up. It's well worth getting it properly installed though. I mostly just test from Python 3.6 (the version that introduced asyncio) and the scattering of major versions between it. I also test on all major operating systems using esxi by vmware.
>very slow.
This is mostly a meme. If you're doing CPU-bound work where each instruction counts then there are ways to tune Python performance. There's https://numba.pydata.org/ and https://cython.org/ 'easily tune readable Python code into plain C performance by adding static type declarations, also in Python syntax.' Python can use multiple processes, multiple interpreters, and soon 'real threads.' I should add that a lot of algorithms aren't parallel so they're not likely to benefit from such improvements. But Python byte code already is quite fast as it is.
As for 'data processing' -- if the work-load is I/O bound its not going to matter if you use Python or C. It will be just as fast.
- Ask HN: C/C++ developer wanting to learn efficient Python
- Ask HN: Is there a way to use Python statically typed or with any type-checking?
- Cython 3.0
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How to make a c++ python extension?
The approach that I favour is to use Cython. The nice thing with this approach is that your code is still written as (almost) Python, but so long as you define all required types correctly it will automatically create the C extension for you. Early versions of Cython required using Cython specific typing (Python didn't have type hints when Cython was created), but it can now use Python's type hints.
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Never again
and again, everything that was released after using an older version of cython.
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Codon: Python Compiler
Just for reference,
* Nuitka[0] "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."
* Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles.
* Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... makes writing C extensions for Python as easy as Python itself."
* Numba[3] "is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code."
* Pyston[4] "is a performance-optimizing JIT for Python, and is drop-in compatible with ... CPython 3.8.12"
[0] https://github.com/Nuitka/Nuitka
[1] https://www.pypy.org/
[2] https://cython.org/
[3] https://numba.pydata.org/
[4] https://github.com/pyston/pyston
- Slow Rust Compiler is a Feature, not a Bug.
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Any faster Python alternatives?
Profile and optimize the hotspots with cython (or whatever the cool kids are using these days... It's been a while.)
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What exactly is 'JIT'?
JIT essentially means generating machine code for the language on the fly, either during loading of the interpreter (method JIT), or by profiling and optimizing hotspots (tracing JIT). The language itself can be statically or dynamically typed. You could also compile a dynamic language ahead of time, for example, cython.
What are some alternatives?
SWIG - SWIG is a software development tool that connects programs written in C and C++ with a variety of high-level programming languages.
libffi - A portable foreign-function interface library.
PyPy
JavaCPP - The missing bridge between Java and native C++
mypyc - Compile type annotated Python to fast C extensions
V8 - The official mirror of the V8 Git repository
Pyston - (No longer maintained) A faster and highly-compatible implementation of the Python programming language.
ChakraCore - ChakraCore is an open source Javascript engine with a C API. [Moved to: https://github.com/chakra-core/ChakraCore]
Pyjion
Wren - The Wren Programming Language. Wren is a small, fast, class-based concurrent scripting language.
Stackless Python