Primes VS NumPy

Take a look at Dave Plummer’s Prime Sieve project.

There's a dedicated and quite cool website where you can browse the benchmark results: https://github.com/PlummersSoftwareLLC/Primes

Should look into Software Drag Racing https://github.com/PlummersSoftwareLLC/Primes

Compiler WTF??? Can't you just use xxd to turn a hexdump into a binary like a real programmer would do?!?

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]