>> marimo notebooks are pure Python and stored as .py files That sounds like a solid improvement. I’m going to give this a test drive. I feel like modularity i

>> marimo notebooks are pure Python and stored as .py files That sounds like a solid improvement. I’m going to give this a test drive. I feel like modularity is one of the hardest aspects of Jupyter notebooks in a team environment. I’d be interested to hear if anyone has cracked a workflow with notebooks for larger teams. Notebooks are easy for solo or very small teams, and the literate programming style benefits still apply in larger teams but there’s a lot of friction: “hey just %run this shared notebook with a bunch of useful utilities in it - oops yeah it tries to write some files because of some stuff unrelated to your use case in there (that’s essential to my use case)” My current best that I know of is to keep “calculation” (pure) code in a .py and just the “action“ (side-effectful) code in the notebook. Then as far as physically possible, keep the data outside of notebook (usually a database or csv’s). That helps avoid the main time sink pitfalls (resolving git conflicts, versioning, testing etc) but it doesn’t solve for example tooling you might want to run - maybe mypy against that action code - sure you can use nbqa but… interested to learn better approaches.