Like everything else in the Python ecosystem, it's half-baked and not composable.
People use it for two reasons: a) because they need to get those graphs on the screen and this is the only way b) running ML code on a remote, beefier server.
People use it for two reasons: a) because they need to get those graphs on the screen and this is the only way b) running ML code on a remote, beefier server.
The real reason is because it’s a much better workflow for data exploration and manipulation because you don’t always know exactly what code to write before you do it. So having the data in memory is really useful.
Or you just use jupyterlab and the problem is fixed.
Jupyter notebook is neither the first nor the only implementation of such literate approach.
If some code is stable enough for reuse, you can make it composable as any other code: put it into the module/create CLI/web API/etc -- whatever is more appropriate in your case.
Do you have a source of this, or is it something you dreamed up? Weird claim as none of those are my use case.
As it is now, you typically wind up “programizing” your notebook once it does what it should so you can run in batch and so on.