generics VS NumPy

Please note there's been some uptick in the intensity of discussion of generics lately [0].

Generics are necessary to bring performant data structures to Fortran, and yet they are nowhere near Go's generics.

For the usual naysayers doubting Fortran's place in a modern world: whenever you are reading or watching a weather forecast, that's decades of Fortran staring at you. Whenever you drive past a nuclear power plant, or an airbase with 'igloos', remember that nuclear safety codes run on Fortran.

[0] https://github.com/j3-fortran/generics

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]