pyscript VS NumPy

You are right for the most part. I attended a talk about pyscript[1] (runs python in the browser using wasm which is similar) and there is a 2x performance hit.

[1] https://pyscript.net

PyScript maintainer here - I'd love to hear more about this application! Either here, or over on our Discord (invite link is on the GitHub Page [1]).

[1] https://github.com/pyscript/pyscript?tab=readme-ov-file#summ...

For the first question - why your code previously created an HTML node but now just prints out the literal characters of the HTML - that was a change made in 2022.12.1, in Pull Request 915 to be specific. You can check out the single-line change that caused this, if you want. Essentially, calls to Element.write() or display() have their contents escaped by default, which wasn't the case before.

Note that in the current release (2023.03.1 at time of writing), py-[event] listeners are only hooked up once at page-load time, so changing that attribute after the page loads won't do anything. In the upcoming release, py-[event] handlers are dynamically attached and managed each time a py-[event] attribute on the page is changed. (PR 1435)

PyScript started as a usability layer around Pyodide for those not used to working in JS. It's now working on things like incorporating the Micropython runtime as an alternative, moving the interpreter to a worker thread, adding a plugins ecosystem, easier events API's, and more.

Some answers: https://github.com/pyscript/pyscript/discussions/1414

You can also check out the WebGL demo from the PyScript examples, which uses Thee.JS.

Mainly, the pys-onClick syntax that was present in earlier version in favor of the new py-* syntax, where * is a browser event name. It also takes a string of executable python instead of the name of a Callable. So in your case, you'd use py-click="get_input()". (There's a long-in-progress PR on GitHub with more examples.)

PyScript ? This is still experimental.

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]