But I've found with jupyter that it's most productive for me to just use jupyterlab in the browser, it has lots of features and for the visualizations that I do I need a good viewer. So vim doesn't work, and executing in ipython is like the console, not the notebook interface anyway.
I recommend using jupytext too, and then you can keep .py files as the canonical notebook files in your repo and code history.
I see the appeal of Jupyter notebooks for someone testing out things or experimenting, but it's a bit like a brain dump that isn't that trivial to navigate around when a second or third person is involved.
(Also, the image-invoking functions are just native functions in the source language, so I get a lot of orthogonality for free. For example, all the eval- and send-expr- style keybindings work automagically -- no need to extend the elisp language mode to special-case images).
1. If there's a really visually intensive workflow, like some sort of data exploration step where I need to be generating and inspecting multiple plots at the same time over and over.
2. If my goal is to make an instructional artifact and interleaving Python and explanatory HTML is helpful.