add support for Row Level of granularity DQ reports

[evoisec]

Hello folks, it seems u forgot to expose in Python PyDeequ, the following Deequ Scala method, which in turn makes it impossible to use PyDeequ to produce row level DQ report - only column/dataset SUMMARY level DQ reports are possible at the moment

def rowLevelResultsAsDataFrame( sparkSession: SparkSession, verificationResult: VerificationResult, data: DataFrame): DataFrame = {

why is that, any plans to rectify and when?

[Cistron]

I would also welcome this functionality to be exposed.

[0xbadidea]

+1 Accessing the scala function from Pyspark fails when assertions are specified using Lambda functions.

[github-actions]

Thank you for raising this — you're correct that rowLevelResultsAsDataFrame from Deequ's Scala API is not currently exposed in PyDeequ. This is a valid feature gap.

The challenge noted by @0xbadidea is also real: our lambda-based assertions use Python callback objects (via ScalaFunction1), which don't serialize cleanly when Deequ tries to evaluate them at the row level.

We'll track this as an enhancement request. A contribution adding a rowLevelResultsAsDataFrame classmethod to VerificationResult in pydeequ/verification.py would be welcome — the implementation would call through to the JVM method similarly to how checkResultsAsDataFrame works today. PRs are appreciated if anyone wants to take a crack at it.

This has also been flagged for our maintainer team to review.

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