I manage a Jupyter instance where we do ~75% of all of our work, and before that used Jupyter daily as a quant. In response to your comments + sibling's: Shari

I manage a Jupyter instance where we do ~75% of all of our work, and before that used Jupyter daily as a quant. In response to your comments + sibling's: Sharing: there hasn't been a good solution, so you have to hack something together. For us (small team) symlinking a "shared_notebooks" network path works fine (but looking forward to the RTC mentioned in the post). It's very important to restart your notebook before sharing. New data: I've never really run into this in a form that "restart + run all cells" didn't really work. Are you trying to keep older versions? Reusability: I highly recommend installing a personal Python package in development mode to your kernel (i.e. pip install -e .). Then just move functions from the notebook to the package and reload. Boundaries: As others have said, I would just make a "user" kernel right away and never touch the Jupyter environment (as TLJH does by default). I am kind of hopeful that Armin Ronacher's rye fixes Python environment hell and we end up with better solutions for many of these things, but there are definitely some issues with version management in Jupyter.