Niels Bantilan

Hi @derinwalters are you still seeing this issue with the latest release?

this issue is resolved by #1593

Looks like PydanticModel doesn't interact well with `strict=True`. This works: ```python class AliasDFSchema(pa.DataFrameModel): """Pandera schema using the pydantic model.""" class Config: """Config with dataframe-level data type.""" dtype = PydanticModel(AliasedRecord) coerce...

@NeerajMalhotra-QB @jaskaransinghsidana FYI

Thanks @vianmixtkz this is an interesting use case: the way pandas handles mixed-type columns is to represent the data in an `object` dtype column. One thing we should clarify in...

Revisiting this issue and thinking about it a little bit, here's another proposal for this issue: ```python from pandera.engines.pandas_engine import Object from typing import Annotated class Model(pa.DataFrameModel): union_column : Union[str,...

Re: this proposal: https://github.com/unionai-oss/pandera/issues/1152#issuecomment-1499660502 Unfortunately `col: Series[TYPE]` and `col: TYPE` in a `DataFrameModel` are equivalent so `Union[Series[str], Series[float]]` and `Series[Union[str,float]]` would effectively be equivalent, and would also introduce more complexity...

@NeerajMalhotra-QB @jaskaransinghsidana any idea on why the check name for custom registered check isn't showing up in the validation report?

The PR description and approach is good! Basically we need to: 1. Add the early return in the pandas API schema/schema components 2. Add tests similar to the ones [here](https://github.com/unionai-oss/pandera/blob/main/tests/pyspark/test_pyspark_config.py#L22)...

I think github codespace should just work out of the box, not sure how it installs the virtual environment tho