Pyrsistent VS NumPy

There are python libraries that implement Clojure style functional data types. Have you tried pyrsistent - https://github.com/tobgu/pyrsistent

Hissp is really interesting. Read through the docs and you'll understand Python more deeply. It works well with Toolz and Pyrsistent.

This is normal for some functional languages, since by definition they should prohibit assignment and hence mutation. But you can also achieve a similar (not the same) effect in python, using libraries like pyrsistent (https://github.com/tobgu/pyrsistent/)

It still is, and Cython is great for accelerating critical Python code.

A C extension is far preferable when you want to code in C, either to write a new data type[1], or write a Python frontend to a C library[2] that is too complex to be well supported by simple FFI.

I think people use Cython more internally when they value the maintainability of "mostly Python" over the fact that it's slower than what native C would get them.

[1]: https://github.com/tobgu/pyrsistent

[2]: https://github.com/libgit2/pygit2

There's Pyrsistent[1], which provides persistent data structures.

[1] https://github.com/tobgu/pyrsistent

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]