brython VS NumPy

Or through an interpreter: https://github.com/brython-dev/brython

I'm a bit sceptical. It's actually not the first time someone wants to include Python in the browser, for example, brython has tried something similar.

This uses Pyodide [0] under the hood [1], which is CPython compiled to WebAssembly. In all my tests of it, loading takes a long time ~5 seconds. Coldbrew [2], another distribution of CPython on Wasm, is another option with similar load times.

And Brython [3] is a completely different option without long load time: a Python interpreter implemented in JavaScript.

If load time is important, Brython is pretty nice. If feature completeness is important, Pyodide and Coldbrew are probably best.

[0] https://github.com/pyscript/pyscript/blob/main/pyscriptjs/sr...

[1] https://github.com/pyodide/pyodide

[2] https://github.com/plasticityai/coldbrew

[3] https://github.com/brython-dev/brython

Brython python implementation that runs in the browser

Yes you can use a tool such as brython. But i think you should learn javascript of you want to be a web developer.

Check out Brython: https://github.com/brython-dev/brython

https://github.com/brython-dev/brython/blob/master/www/console.html is https://brython.info/console.html - a full interpreter running in the browser

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]