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Update semantic-link-validate-data.md #1319

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2 changes: 1 addition & 1 deletion docs/data-science/semantic-link-validate-data.md
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Expand Up @@ -170,7 +170,7 @@ The `plot_dependency_violations` function generates a visualization that can hel

Data quality is crucial for ensuring the reliability and validity of any analysis or model built on a dataset. Enforcement of functional constraints between columns in a dataset can help improve data quality. Functional constraints can help ensure that the relationships between columns have accuracy and consistency, which can lead to more accurate analysis or model results.

The `drop_dependency_violations` function can help enforce functional constraints between columns in a dataset. It rows that violate a given constraint. Given a determinant column and a dependent column, this function removes rows with values that don't conform to the functional constraint between the two columns.
The `drop_dependency_violations` function can help enforce functional constraints between columns in a dataset. It drops rows that violate a given constraint. Given a determinant column and a dependent column, this function removes rows with values that don't conform to the functional constraint between the two columns.

This code snippet shows how to use the `drop_dependency_violations` function:

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