Related Issues (20)
- Improve strategies internals: accumulate check statisics instead of filtering
- Nullability for `pl.Float64` in `pl.DataFrame` fails HOT 1
- Try_Pandera edits to be more clear and beginner friendly HOT 2
- Validate on Initialization doesn't work in 3.11.9 and 3.12.3 HOT 6
- Annotated parametrized dtypes error on version >= 0.19.0 HOT 3
- Allow use of generic pa.DataFrameSchema/Model for different supported libraries HOT 2
- Time-agnostic DateTime with pandera-native polars datatype using DataFrameModel not working HOT 2
- Cannot call `get_metadata` on a DataFrameModel if there is a Config without a metadata attribute
- NaNs in boolean column coerced to True, nullable and default parameters are ignored HOT 7
- Pandera is very slow to import when optional dependencies are installed HOT 4
- Missing `reason_code` when using custom checks with PySpark dataframes HOT 1
- Finite values in `pl.DataFrame` HOT 2
- Optional import hypotheses doesn't install hypothesis HOT 3
- Custom Check Methods don't support custom error (any more) HOT 2
- Unexpected behavior when validating date objects. pandera=0.19.1 HOT 1
- Compatibility issues with Pandas HOT 3
- pandera not compatible with numpy 2.0 HOT 2
- `SchemaFieldNotFoundError` with custom check function if no alias is provided. HOT 4
- Adding missing columns with a string default
- Scalar return for check in polars-backed model fails on validation with `lazy=True`
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