There's a big difference between a line-oriented plain text REPL, and a Mathematica/Jupyter-style notebook REPL, especially when you want to mix and match your charts, image outputs, rich table outputs, interactive JS outputs, and so forth. Also, for experimentation, where you want to go back and change things to see what happens (e.g. very common in data science) I find it much easier and more understandable in a notebook.
I have a video where I show the difference between these styles of working in some detail: https://www.youtube.com/watch?v=9Q6sLbz37gk
Though, I think I get what you mean as I reflect on my own dev experience. As a mainly C# dev which has a very very limited repl experience (I would and should say no repl experience but someone will yell at me about csi.exe or dotnet-script) I have seen people using notebooks for want of a good repl, but I’m curious why anyone writing python would.
I wrote a detailed blog post about the differences between Jupyter and R Notebooks years ago: https://minimaxir.com/2017/06/r-notebooks/
(try "sudo apt-get install ipython && ipython" on your Ubuntu/Debian system to try it out)
Comparing R and Python also could be done better. Python is a general purpose language. R is for statistics and science. Even then, Python is extremely popular for these very domains with Jupyter notebooks.
This is why I shrug looking at "Jupyter killer" notebook alternatives tackling machine learning: in my experience delivering machine learning products to large, paying, clients, the bottleneck never was slicker stylesheets or cool animations. It was the nuts and bolts of things.
I would not call Idle unpopular. But it had a limit for growing for sure.
But Notebooks are mostly coming from the science-corner of python. People there used notebook-like tools and workflows for decades and some brought that over to ipython-project. I remember 15(?) Years ago when the project started focusing more and more on the cluster-aspect of their shell, they brought up many differenct tools for this. One of them was notebook-like and what become later Jupyter. It quickly became popular in certain groups for those reasons.
R illustrates this very well - as an R user you can have your pick between RStudio, Jupyter and Rmarkdown and the overwhelming majority of users pick RStudio and notebooks are reserved only for a niche set of use cases. It also speaks volumes that almost no one writes R in Jupyter even though it's supported very well - R users just have better options available to them.