Notebook provides keyboard shortcuts also accessible and searchable from command palette. There is also ipymd to write notebook from your editor using markdown. Pycharm supports notebooks and maybe you can use IdeaVim? You can export notebook to .py file from file menu. For git, just don't save the output, which can be removed from cells menu. See ipywidgets repo for notebook examples without outputs.
Hey, Tufte also criticizes Excel, which is still most widely used tool for analyzing data and making plots that are not static. Engineers love it. Anyway I will stop here my replies.
Too much thread-nesting here :) I had the same problem even in Mathematica with long-running cells. Fundamentally it is hard problem to solve in notebook, compared to terminal. Here is more explanation: https://mail.scipy.org/pipermail/ipython-user/2013-July/0128...
I use widgets for data exploration, model tuning, and interactive plots. Works great in practice! Have a look at bqplot from Bloomberg used in production.
Ipython terminal is still there, but you can't expect ma$$$ or Excel users to start working in vim or emacs based workflow. Besides browser provides rich output, cross-platform support. The big thing for me are widgets.
Have you tried stopping or killing the kernel? I found jupyter user group on google very responsive when something is not clear or needs to be extended. Google search on jupyter is pretty bad since still points at old ipython docs, although they do have updated docs for jupyter.
That is why it is better to keep a jupyter terminal or qtconsole around for quick and messy experimentation connected to the same kernel before pushing it to notebook
Rodeo is python-only last time I checked. Also it is missing notebooks, widgets, extensions, rich outputs, server architecture, multi-user support. Rodeo is closer to spyder than jupyter.
Notebook is still primary interface for final version of your analysis. But during iteration and experimentation of analysis, the other parts of jupyterlab are really helpful! Before all of these was not so nicely integrated.
Jupyter started from ipython, originally mostly written in python and for python. But the project was so successful that it extended for other languages using cell magic and then kernel system. The team is now mostly using JavaScript, Typescript, except for ipython sub-project. Although it's obvious choice for schools, research, analytical work, and publications, still I really wish for more adoption in corporate environment, that got stuck in proprietary tools.
I asked about lock-free data structures from 2 major open-source libs, but Larry found major issues with them. He may consider some of these data structures in the future. I opened a bounty for this.