Is your feature request related to a problem? Please describe.
Knowing which rows pass and which fail is important. The "RowLevelSchemaValidator" has been in deequ for years; is there plans on creating python bindings?
Describe the solution you'd like
Access to RowLevelSchemaValidator via python.
Describe alternatives you've considered
Using spark to find nulls, out of bound numbers, too large/small strings, etc... is double work. Right now all we can tell is a pass/fail on an entire dataframe. More granular info is needed.
Additional context
Being able to run validations like the unit tests would be wonderful : https://github.com/awslabs/deequ/blob/49e970ce9a8bda5e779602d2981379b65c12ba30/src/test/scala/com/amazon/deequ/schema/RowLevelSchemaValidatorTest.scala
Is your feature request related to a problem? Please describe.
Knowing which rows pass and which fail is important. The "RowLevelSchemaValidator" has been in deequ for years; is there plans on creating python bindings?
Describe the solution you'd like
Access to RowLevelSchemaValidator via python.
Describe alternatives you've considered
Using spark to find nulls, out of bound numbers, too large/small strings, etc... is double work. Right now all we can tell is a pass/fail on an entire dataframe. More granular info is needed.
Additional context
Being able to run validations like the unit tests would be wonderful : https://github.com/awslabs/deequ/blob/49e970ce9a8bda5e779602d2981379b65c12ba30/src/test/scala/com/amazon/deequ/schema/RowLevelSchemaValidatorTest.scala