season-of-docs VS NumPy

Google Season of Docs has a repository where organizations list their interest to participate in the program and the contributors reach out to them for more details.

The interested organisations and technical writers were given a space to find each other in the Season of Docs repository. I did not register myself as a technical writer there as I was unsure whether I was even eligible according to the new format, which stated that this program was primarily to match professional technical writers. Even then, I started checking out organisations in early-mid March and researched a couple that called out to me. Apart from [NumPy],(https://numpy.org/) the ubiquitous Python package in data science, I chose Cuneiform Digital Library Initiative (CDLI). CDLI drew me in because I had an amateur interest in the Cuneiform script through some pretty fun videos by Dr. Irving Finkel. Unfortunately, CDLI didn’t get accepted this year.

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]