Comments (1)
mastersplinter
commented on May 14, 2024
This is a good idea, I think there's a few pre-reqs to get out of the way, e.g.
- conda availability (important for some teams in my company for example)
- next release on pypi
- tightening up the readme to really explain some of the key differentiators from other schema validation packages (for me, the key differentiator has always been the decorators i.e. easy pipeline integration).
I think Medium/HN would be interesting places to drive interest/commentary. I'll see if there's anything my company would be willing to support.
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Related Issues (20)
- Custom check fails with `pl.DataFrame` HOT 3
- Custom check erroneously passes when validating `pl.LazyFrame` HOT 8
- Remove dependencies on `wrapt`, `packaging`, and potentially `multimethod`
- Make `pydantic` and `typeguard` extras for pandas generic type support
- Is there a way to get a `DataFrameModel` from existing `pandas.DataFrame`? HOT 2
- Hypothesis examples are all the same HOT 7
- Parametrized type annotations are broken for polars DataFrameModels HOT 1
- Piping pandas with pandera schema doesn't raise SchemaError ( python 3.11.9 ) HOT 1
- Lazy schema validation does not raise expected errors with polars dataframes HOT 3
- Update branch name mentioned within bug report template HOT 1
- Custom DTypes With Polars HOT 3
- Error Importing Pandera with Polars extra HOT 2
- Add a polars `Series` type HOT 10
- Allow check type HOT 1
- How to load schema from pyspark struct or avro format from schema registry ? HOT 1
- How to correctly install a release v0.19.0b3 HOT 2
- Support Series generation with serial dependence HOT 1
- Incorrect validation passes pandera=0.19.0b3 HOT 1
- failure_case conversion failed : polars.exceptions.ComputeError - pandera(0.19.0b3) with polars HOT 5
- Incorrect Pandera Polars DataFrameModel Type Coercion Logic HOT 5
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