Jupyter notebooks are a very popular development environment for data science and related fields because they provide a great tight feedback loop for experimentation and iteration. Essentially the main difference from a typical dev environment is that there are "cells" of code that you can run individually, and the program "state" is persistent. There's definitely a better way to explain it, because that doesn't sound very big, but it really is a game changer for a lot of workflows. They run python by default, but there are integrations for many other languages. Over the last few years, many alternatives and versions from different companies have been released with various differences and features (i.e. Google Colab, Kaggle, ObservableHQ for a JS version with lots of extra features, and tons of others I'm sure).
So at this point people have come to call this concept in general a "notebook". Basically a development environment with cells where you can add markup and HTML and stuff.
So at this point people have come to call this concept in general a "notebook". Basically a development environment with cells where you can add markup and HTML and stuff.
Hope that helped a bit.