Comments (5)
just kidding, here's a PR fix: #1612 🙂
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Hi @evanrasmussen9 are you able to make a PR to fix that line of code?
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from pandera.
no worries, thanks for catching all these bugs!
I'm planning on creating the stable 0.19.0
release in the next few days, I'm sure there's more to fix, but I think it's almost ready for prime time
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Related Issues (20)
- 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
- Pandera is very slow to import when optional dependencies are installed HOT 2
- 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)
- Unexpected behavior when validating date objects. pandera=0.19.1
- 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.
- Adding missing columns with a string default
- Scalar return for check in polars-backed model fails on validation with `lazy=True`
- Setting `coerce` on a column causes the column to be `required` when `required=False` HOT 1
- Support Data synthesis strategies for polars
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