SWIG VS NumPy

How is this any different than a mature tool such as SWIG (https://www.swig.org/)?

I've used SWIG extensively with Python to call C code and import C headers for testing/tooling purposes.

For pure C, CGO. For C++ they are likely creating shims with Swig: https://www.swig.org/

I mostly write in C and C++ so for language bindings I use Swig a lot. Say Im creating a machine learning library in C++, its very easy to create a Python API that can call the C++ classes and methods using Swig. iirc, I am using the same swig interface file to create bindings for Python, OCaml, R and even Fortran. Feel free to DM me if you got any more questions or anything!

Swig can make c++ types and functions available to python.

The likes of SWIG is often used to link C library-like code to 11-ish other widely used languages.

Maybe you could use swig to create a wrapper for Java.

But, you might find these links interesting: * https://github.com/swig/swig/issues/918 * https://github.com/kaby76/swigged.llvm

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]