The main place that I find notebooks useful is for when I want to hack away at some decently sized dataset and poke around at its details. A lot of transformat

The main place that I find notebooks useful is for when I want to hack away at some decently sized dataset and poke around at its details. A lot of transformations can be very time consuming to run, and the ability to cache(for lack of a better word) the computation without having to write to disk, or utilize the python repl (which is very obnoxious to use for anything that extends past a single line of code) really speeds the process up. An example would be: pull a large json from a server. Then break out a new code block to do all of your different manipulations on that json. Lets you prototype around with the object you pulled from the server without having to worry about, among other things - Dealing with how slow reading it from server is - writing it to disk and then reading it from disk to deal with how slow that is - the latency of parsing the file on each run of your script - the list goes on. These arn't things that you want to worry about when your current questions are "what does the structure of this look like" and "what are some of the basic statistical properties of this data". Notebooks are like the python repl with the benefits of having a proper multi-line text input. I've never even heard of people using notebooks for report generation, and honestly I'd agree that sounds like a complete nightmare.