When I'm doing some bit of data wrangling or just exploratory work with data I quite like it - at least at first. As you pointed out, it's really easy to extremely quickly iterate on things and start to get an idea of what's in the data, what techniques work, and which don't. It's great.
Until it isn't. I'm probably "using it wrong" but all the suggestions I've seen for doing it "right" start cutting into the iteration time. If I'm in Jupyter it's because I want to flail around real fast and see what happens. And that causes all sorts of problems. Which cells do I need to rerun together? Which cells should I not run again. What the heck is actually in all these variables right now anyway, because I don't remember which order I've run (and rerun) all the cells in. And what was that one approach or parameter that really worked well that one time? I don't remember.
These sorts of things aren't an issue with a non-Jupyter approach. And because I'm taking my time anyway I'm probably being diligent with source control, and all that. Even pulling code out of a notebook into a more stable workflow is a pain, because you have no assurances you can just copy/paste a cell and have it work. In my experience, it never will.
It's one of those things that has a pretty heavy costs and benefits. And unfortunately they aren't really possible to separate.
Not coincidentally, I feel the same about dynamic languages and even less usefully typed static languages. Jupyter takes an already fairly extreme position on the stable(safe)/easy continuum and really ramps it up to a point where it's hard to wrangle time and work done easy into some sort of stability. It's great, and it's awful.