PyCall.jl VS NumPy

For point 3 you can use https://github.com/cjdoris/PythonCall.jl or https://github.com/JuliaPy/PyCall.jl (and their respective Python sister packages).

PyCall.jl is really handy. Are there any similar projects for calling Chapel code, or Carbon/zig?

Julia and Python interop should not be a problem at all. Actually Julia has one of the best interops I’ve ever seen, so much that swift copied it. https://github.com/JuliaPy/PyCall.jl

One small note, julia also has great python interop via PyCall.jl

It is possible to call Python from Julia using PyCall. Then to install PyCall, run the command in the Julia REPL.

That said, you can run python modules in Julia. So you can just export your code as a module and then use it in Julia via the PyCall package. short description here github here <— you’d just add the pacakge via the really nice package manager built into julia, but for link for more detailed documentation

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]