Learn Python by Building Data Science Applications

Notebooks

As we mentioned earlier, Jupyter is designed with a different approach to programming than VS Code. Its central concept is so-called notebooks: files that allow the mixing of actual code, text (including markdown and LaTeX equations), as well as plots, images, videos, and interactive visualizations. In notebooks, you execute code interactively, one cell after another. This way, you can experiment easily—write some code, run it, see the outcomes, and then tweak it again.

The outcomes are shown along with the code so that you can open and read the notebook, even without executing it. Because of that, notebooks are especially useful in scientific/analytical contexts, as on the one hand, they allow us to describe what we're doing with ...