ECMAScript 6 compatibility table
Numba
ECMAScript 6 compatibility table | Numba | |
---|---|---|
33 | 124 | |
4,406 | 9,471 | |
0.1% | 1.3% | |
5.2 | 9.9 | |
14 days ago | 5 days ago | |
HTML | Python | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" 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.
ECMAScript 6 compatibility table
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TypeScript Is Surprisingly OK for Compilers
http://kangax.github.io/compat-table/es6/
This page lists features from es6 (and newer versions linked at the top) along with compliance to the spec. First column is the current browser, second is babel+corejs polyfills.
Overall, babel gets about 70% of the way there.
- Яндекс Браузер не переводит видео про обучение украинских танкистов, хотя другие видео с канала МО Британии переводит нормально
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Brett Slatkin: Why am I building a new functional programming language?
Case in point: Tail Call Optimization has been part of the JS spec since ES6, but remains completely unimplemented in all mainstream browsers/engines besides Safari[1]. For all but the most predictable inputs, you're pretty much forced to use loops where recursion would otherwise be preferable.
Additional case in point: async Iterables cannot be processed as a piped stream. You must use the for await construct, which is a shame considering the FP niceties that the Array type already provides for more traditional lists. Once again, you are forced to use an imperative construct unless you specifically want to defeat the purpose of using an Iterable in the first place by trying to convert it into an Array (... and potentially choking in the process, I might add!).
[1]: https://kangax.github.io/compat-table/es6/
- [AskJS] Is there a detailed comparison chart that shows what's supported in JavaScript ES5 versus ES6?
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A single developer has been maintaining core.js with little recognition or support. Almost all modern single page apps use core.js. Millions of downloads and hardly any compensation
Eventually the browsers started racing to near-full ES6 compatibility. I remember following ES6 progress in realtime with articles and with compatibility tables http://kangax.github.io/compat-table/es6/ . But many people are acting like that either didn't happen, or like it was a one and done thing (despite the ESNext naming shift to avoid the focus on numbers). So we see people just hand-waving away the importance of polyfills like in this gem:
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Tell HN: Firefox Is an awesome browser right now
> https://kangax.github.io/compat-table/es6/
Oh man this was a rough one both for FF and Chrome but Chrome did perform better slightly on cursory glance.
Thanks for providing these links, they're definitely a good rule of thumb benchmarks to test new browsers
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My 1st website "Claw Man" written in javascript
Javascript / CSS language syntax: can see availability for Javascript here - https://kangax.github.io/compat-table/es6/
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Is there any legitimate reasons for the javascript hate?
I say this as a JS user, but there is no singular JavaScript (realistically, it's not even JavaScript but instead ECMAScript). There is no one place to go that lays out all of what the language can or can't do the way PHP and Python do. The ECMAScript board makes recommendations, then the browsers and runtimes implement features of the recommendations. This site does a good job laying out which features are implemented for browsers and runtimes based on the flavor of the ECMAScript standard. This unique experience can be especially frustrating for someone learning JavaScript and coming from another language that does not have this problem.
- JS Polyfills - Part 1
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[AskJS] Is there a JavaScript library that will test all ES features on your browser and tell you which it supports and which it doesn't?
https://kangax.github.io/compat-table/es6/ has a column for "current browser"
Numba
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Mojo🔥: Head -to-Head with Python and Numba
Around the same time, I discovered Numba and was fascinated by how easily it could bring huge performance improvements to Python code.
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Is anyone using PyPy for real work?
Simulations are, at least in my experience, numba’s [0] wheelhouse.
[0]: https://numba.pydata.org/
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Any data folks coding C++ and Java? If so, why did you leave Python?
That's very cool. Numba introduces just-in-time compilation to Python via decorators and its sole reason for being is to turn everything it can into abstract syntax trees.
- Using Matplotlib with Numba to accelerate code
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Python Algotrading with Machine Learning
A super-fast backtesting engine built in NumPy and accelerated with Numba.
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PYTHON vs OCTAVE for Matlab alternative
Regarding speed, I don't agree this is a good argument against Python. For example, it seems no one here has yet mentioned numba, a Python JIT compiler. With a simple decorator you can compile a function to machine code with speeds on par with C. Numba also allows you to easily write cuda kernels for GPU computation. I've never had to drop down to writing C or C++ to write fast and performant Python code that does computationally demanding tasks thanks to numba.
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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
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This new programming language has the potential to make python (the dominant language for AI) run 35,000X faster.
For the benefit of future readers: https://numba.pydata.org/
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Two-tier programming language
Taichi (similar to numba) is a python library that allows you to write high speed code within python. So your program consists of slow python that gets interpreted regularly, and fast python (fully type annotated and restricted to a subset of the language) that gets parallellized and jitted for CPU or GPU. And you can mix the two within the same source file.
- Numba Supports Python 3.11
What are some alternatives?
es6-features - ECMAScript 6: Feature Overview & Comparison
NetworkX - Network Analysis in Python
Babel (Formerly 6to5) - 🐠 Babel is a compiler for writing next generation JavaScript.
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Traceur compiler - Traceur is a JavaScript.next-to-JavaScript-of-today compiler
Dask - Parallel computing with task scheduling
es6-cheatsheet - ES2015 [ES6] cheatsheet containing tips, tricks, best practices and code snippets
cupy - NumPy & SciPy for GPU
es6features - Overview of ECMAScript 6 features
Pyjion - Pyjion - A JIT for Python based upon CoreCLR
Lebab - Turn your ES5 code into readable ES6. Lebab does the opposite of what Babel does.
SymPy - A computer algebra system written in pure Python