Agreed on the bewildering number of disparate APIs that you need to bash together in a Jupyter notebook. Data wrangling, plotting, machine-learning, logging, wr

Agreed on the bewildering number of disparate APIs that you need to bash together in a Jupyter notebook. Data wrangling, plotting, machine-learning, logging, writing to file/DB... It doesn't help that there are often multiple competing packages that solve basically the same problem in different ways, ensuring that whatever choice you make will be fraught with anxiety about not "doing it right." That being said, I've found the only way I can get interactive programming to stick in my mind is to pick a sample dataset, wonder some open-ended questions about it, and then answer those questions in a Jupyter notebook with the help of Google, documenting your thought process as you go. A great starting point to find real-world sample data is the UCI dataset repository: https://archive.ics.uci.edu/ml/datasets.php https://archive.ics.uci.edu/ml/datasets.php