I think your description is a pretty accurate description of most of our users: they are struggling to write Python in a Jupyter Notebook, and can install some

I think your description is a pretty accurate description of most of our users: they are struggling to write Python in a Jupyter Notebook, and can install some basic packages (albeit it with some struggles -- see our Discord install help channel). The ML code part, you're right, def more rare :) Python code helps these users do a variety of tasks that aren't possible in other analytics tools like PowerQuery/M. Many of these tasks are specific to the company/existing infrastructures, as I mentioned above. A super concrete example: the head of data strategy at a life-sciences company made the transition to Python primarily because the rest of his (2 person) team uses Python. They primarily communicate about new datasets using Mito generated code (e.g. here are the steps to clean this data) - but he's not great at Python - so in practice he uses Mito for 9/10 analyses he does to generate this code he sends to his colleagues! Can give a few more if you'd like -- let me know!