notebook VS NumPy

For folks asking what the Notebook UX offers that the Lab does not, this github thread may be enlightening: https://github.com/jupyter/notebook/issues/6210

(TLDR: some novice users in educational settings find the lab environment overwhelming.)

For further info refer to the official GitHub Repo.

https://github.com/jupyter/notebook has over 2000 open issues

DisabledFunctionError: cv2.imshow() is disabled in Colab, because it causes Jupyter sessions to crash; see https://github.com/jupyter/notebook/issues/3935. As a substitution, consider using from google.colab.patches import cv2_imshow

As it seems, this is not quite straight forward and manyusers have similar troubles.

cough-cough-cough

Take a look here: https://github.com/jupyter/notebook/issues/3435

https://github.com/jupyter/notebook Jupyter Interactive Notebook

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]