Sure, if you know what the output should look like and if it's possible to e.g. write tests to verify that it's correct, then jupyter notebooks would not be the

Sure, if you know what the output should look like and if it's possible to e.g. write tests to verify that it's correct, then jupyter notebooks would not be the proper tool to use. The intended usecase of these notebooks is in scenarios where the main output is not the code and not a particular set transformed data, but knowledge gained during a 'computational exploration' of that data. With that knowledge in hand, you can then build 'productionized' code with different methodologies (possibly but not necessarily using or adapting large parts of the code in your notebook), if that's needed - and in such data analysis scenarios it often happens that it's not ever needed. Sampling a subset of the data sometimes works. Sometimes it would alter the results substantionally and drive the exploration in a wrong direction; questioning and verifying assumptions is important, and it can be a big difference if all A's are also B or only 99% of them are.