Sadly I haven't found any good ways to strongly type dataframes, so my solution is mainly cultural: just make people aware that they shouldn't use dataframes un

Sadly I haven't found any good ways to strongly type dataframes, so my solution is mainly cultural: just make people aware that they shouldn't use dataframes unless it makes sense, as opposed to Python codebases where dataframes are the default data type that gets passed around. I've tried to limit dataframe usage only to things like reading data from CSVs, then converting them to typed dataclasses before further processing. It also depends on your use case. pandas is obviously very flexible and easy to use, so it might be worth the tradeoff to keep using it if your main use case is interactive Jupyter notebooks run by data scientists, or dealing with highly variable input data. But most of my Python is backend data pipelines that work on predictable input data, where reliability is important, so I default to dataclasses, only using pandas in rare situations with a lot of validation code surrounding it.