support for row level DQ result-report missing #223

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1. github-actions commented on May 18, 2026

   

   github-actionsbot

   on May 18, 2026 – with GitHub Actions

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   Thanks for raising this. You're correct — the rowLevelResultsAsDataFrame method from Deequ's Scala VerificationResult is not currently exposed in PyDeequ. This is a valid feature gap. We'll track this as an enhancement request to add a rowLevelResultsAsDataFrame classmethod to the VerificationResult class in pydeequ/verification.py, similar to how checkResultsAsDataFrame and successMetricsAsDataFrame are already wrapped. Appreciate you flagging it.

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

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2. github-actions

   added

   enhancementNew feature or requestNew feature or request

   on May 18, 2026
3. nikolauspschuetz commented on Jun 28, 2026

   

   nikolauspschuetz

   on Jun 28, 2026

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   rowLevelResultsAsDataFrame has since been exposed in PyDeequ via #262 (merged). It is available as VerificationResult.rowLevelResultsAsDataFrame(spark_session, verificationResult, data) (pydeequ/verification.py), with a usage snippet in the README. That should cover the row-level DQ report use case described here, so I believe this can be closed.
4. github-actions commented on Jun 28, 2026

   

   github-actionsbot

   on Jun 28, 2026 – with GitHub Actions

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   Correct — VerificationResult.rowLevelResultsAsDataFrame() is now available in PyDeequ. You can use it as follows:

   from pydeequ.checks import Check, CheckLevel
   from pydeequ.verification import VerificationSuite, VerificationResult
   
   check = Check(spark, CheckLevel.Warning, "Review Check")
   
   checkResult = VerificationSuite(spark) \
       .onData(df) \
       .addCheck(
           check.isComplete("c") \
           .isContainedIn("a", ["foo", "bar", "baz"])) \
       .run()
   
   rowLevelResult_df = VerificationResult.rowLevelResultsAsDataFrame(spark, checkResult, df)
   rowLevelResult_df.show()

   This returns the original DataFrame with additional Boolean columns (named after the check description) indicating which rows passed or failed. Only checks with row-level-capable constraints (e.g., isComplete, isContainedIn, hasPattern, isUnique) produce output columns. Aggregate-only checks like hasSize are skipped.

   This issue can indeed be closed.

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