parsimonious VS NumPy

Oh sorry, I grabbed the wrong PEG parser… I’ve used this one: https://github.com/erikrose/parsimonious

A grammar provides the high level constructs you need to define the "shape" of your data, and it largely takes care of the rest. Grammar libraries exist in other language (eg. lark or Parsimonius in Python) and they weren't created just to make XML parsing easier.

I'm honestly shocked that you are being downvoted heavily for this. I was literally reading a pip module a few days ago that cites stackoverflow in the code. It may not be for code snips but it it's not wild to think that someone would do this for code they pulled from SO.

Parsimonious

Since the input comes in the form of well-formatted text with with variable-width lines, this seems a perfect fit for a PEG parser, as described in Day 04. Using a PEG parser with a node visitor also lets us process each each node as it is being parsed, saving a loop or two. As usual, I will be using the parsimonious library for Python. Importing it with from parsimonious.grammar import Grammar

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]