jupyter VS NumPy

6. Jupyter Jupyter is a collection of tools and applications designed for interactive computing and data visualization. At the heart of the Jupyter ecosystem is the Jupyter Notebook, an interactive web-based platform that allows you to create and share documents containing live code, equations, visualizations, and narrative text. It’s an excellent tool for exploratory data analysis, model prototyping, and creating reproducible data science workflows.

How cool. This motivated a quick search - this could be fun:

How to write your own kernel

https://jupyter-client.readthedocs.io/en/stable/kernels.html

All the language kernels (a lot of abandoned ones - the mariaDB one ('binder') will take a while to load but SQL in Jupyter!)

https://github.com/jupyter/jupyter/wiki/Jupyter-kernels

There are Jupyter kernels for Python, Mathics, Wolfram, R, Octave, Matlab, xeus-cling, allthekernels (the polyglot kernel). https://github.com/jupyter/jupyter/wiki/Jupyter-kernels

Not only there is. But it is only a simple Google search away... But to make it simpler... There are 3 😁 https://github.com/jupyter/jupyter/wiki/Jupyter-kernels

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

Community maintained kernels : https://github.com/jupyter/jupyter/wiki/Jupyter-kernels

Derek Banas on YouTube is doing a "Python for Finance" course at ghe moment using Jupyter, and is making the files available. I believe he's done others too.Failing that, there's this Git repo: A gallery of interesting jupyter notebooks

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]