Jean-Francois Zinque

I'm not sure to follow. Can you give a use-case where you want to go from `DataFrameSchema` to `SchemaModel`? Your second paragraph sounds like you want to generate a `SchemaModel`...

I also prefer (1) because you cannot easily tweak a `SchemaModel`. As highlighted by @ericmjl, you usually want to inspect and modify the inferred schema. In summary, if we have...

> although with @jeffzi's work on #369 you could make custom datatypes like this. We could even have a built-in `Number` dype. Coercion would output floats or ints depending on...

I think your proposals are not incompatible: * (1) and (2) are called **after** validation and hand in the failed cases. The later is a desirable feature missing from (3),...

I understand your reasoning about the `parser` name, thanks for clarifying. I completely agree with the flow you described :+1: > Totally agreed on better UX for handling schema errors,...

Hi @andycarter85. Thanks for the reproducible example. Pyarrow supports date types (date32 and date64) but pandas does not. Pandas only supports date **times** with `numpy.datetime64`. However, pandas does allow to...

Hi @roshcagra, interesting question ! > pandera.errors.SchemaError: expected series 'geometry' to have type None, got object It's a little confusing to trace back the source of the error but here...

> I would love to expand on this and contribute. Awesome, thanks ! I think a module inside the core pandera repo should suffice. We have done this before by...

@m-richards Thanks, appreciate you're taking the time to explain in details ! > And then use custom pa.Checks / registering custom checks to implement more specific validation rules. Yes, my...

> I'm using the check_output function to check column & datatypes of the DataFrameSchema If you don't have explicit checks, i.e. just checking column names and types, you could set...