awesome-python VS NumPy

8. Vinta/Awesome-python - This repository is a curated list of top Python frameworks, libraries, and tools for a variety of purposes. It's a must-visit for any developer looking to expand their Python skills and discover new resources. https://github.com/vinta/awesome-python

Python: https://github.com/vinta/awesome-python

6) Awesome Python: Embrace the power of Python with this extensive collection of awesome libraries, frameworks, resources, and software. Whether you're a seasoned Pythonista or just starting your journey, this repository is your ultimate guide to maximizing the potential of this versatile language. (https://github.com/vinta/awesome-python)

- https://github.com/vinta/awesome-python

🎇 Repository Link: Awesome Python

Explore here.

I know of curated lists like https://github.com/vinta/awesome-python but they are nowhere close to alternativeto.net in terms of information (relations) and community involvement.

https://github.com/vinta/awesome-python

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]