aiofiles VS NumPy

I'm unaware of a library that facilitates any of this, so unless I find one or more suitable libraries with this matter, then I'm looking at using Numba and either aiofiles or aiofile—I'm not yet sure what the difference between the two is—and writing my own encoder(s).

Aiofiles: Makes writing to disk (such as creating and writing bytes to files) a non-blocking task, such that multiple writes can happen on the same thread without blocking one another - even when multiple tasks are bound to the same file.

To solve this, you need an async version of function/library. hopefully, requests has a good async alternative- aiohttp. API structure is nearly identical to requests, so It won't be a big pain to migrate. for doing file I/O, there's aiofiles.

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]