Polars LazyFrames are validated at the schema-level by default #1534
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Signed-off-by: cosmicBboy <[email protected]>
Signed-off-by: cosmicBboy <[email protected]>
Signed-off-by: cosmicBboy <[email protected]>
cosmicBboy
changed the title
Signed-off-by: cosmicBboy <[email protected]>
Signed-off-by: cosmicBboy <[email protected]>
Signed-off-by: cosmicBboy <[email protected]>
cosmicBboy
changed the title
Signed-off-by: cosmicBboy <[email protected]>
max-raphael
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Jan 24, 2025
…ai-oss#1534) * implement different validation behavior for LazyFrame/DataFrame Signed-off-by: cosmicBboy <[email protected]> * use config context in pandas, pyspark, polars backend Signed-off-by: cosmicBboy <[email protected]> * fix pydantic version-dependent object copying Signed-off-by: cosmicBboy <[email protected]> * LazyFrame coercion does not collect Signed-off-by: cosmicBboy <[email protected]> * add sphinx-design to docs requirements Signed-off-by: cosmicBboy <[email protected]> * add docs, clean up tests Signed-off-by: cosmicBboy <[email protected]> * fix polars validation config tests Signed-off-by: cosmicBboy <[email protected]> --------- Signed-off-by: cosmicBboy <[email protected]>
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Fixes #1528
This PR modifies the default PANDERA_VALIDATION_DEPTH environment setting to
None. This allows for setting default validation behavior for the polars backend. If the environment variable is not set, then LazyFrame doesn't perform any data-level validation. It will only check schema-level properties of the LazyFrame. This behavior can be overriden by explicitly settingexport PANDERA_VALIDATION_DEPTH=SCHEMA_AND_DATA.When doing
pl.DataFramevalidation it will perform both schema- and data-level validations.