After talking with Excel users for the past year to understand what makes it such a ubiquitous tool, I don't think that its the ability to write complex formulas, but instead the reactive, visual environment (read: IDE). Of course, for some users the ability to nest 100 VLookups or create UDF's is the important part, but for the vast majority of people, their workbooks aren't __that__ complex.

When it comes to data analytics IDEs there is a fundamental tradeoff between staring at the data or staring at the transformations. Excel makes sure you are brutally aware of each edit you make to your data at the expense of reproducibility and auditability of your transformations. Python takes the opposite end of the trade -- obscuring the underlying data, but bringing the transformations to the forefront. For non-programmers trying to learn Python (especially for data analytics), the biggest hurdle is losing touch with their data.

I've been building a Python package, Mito (https://trymito.io/hnc), to try to address this tradeoff for those who want to analyze data with the intuitiveness and data-first-ness of a spreadsheet, but with the power and traceability of Python. Mito is a Jupyter Lab extension which gives users an interactive spreadsheet that automatically converts your spreadsheet analysis to the equivalent pandas code. You can write Excel formulas, merge datasets, create pivot tables, etc.