DATASET VERIFY NA

Inspects one or more columns of a dataset for null (NA) values and returns a summary dataset indicating whether nulls were found. Use this worker to gate downstream processing on data completeness or to flag incomplete records before further analysis.

When to use

Classification: process.

Tagged: data_quality, missing_values, na, null, validation, verify.

Inputs

Label ID Type Default Required Description
Dataset dataset dataset   Input dataset to be scanned for null values; must be a tabular dataset object — leave unconnected only if the check is being tested with no data.
Columns To Check columnstocheck scalar   One or more column names from the input dataset to inspect for nulls; supports multi-select driven by the connected dataset — leave blank to check all columns.
Value when Null if found true_value scalar yes   String token emitted in the result when at least one null is found in the checked column(s); defaults to ‘yes’.
Value when no Null if found false_value scalar no   String token emitted in the result when no nulls are found in the checked column(s); defaults to ‘no’.

Outputs

Label ID Type Description
dataset_verify_na_output_1 dataset_verify_na_output_1 dataset Single-row summary dataset with one column per checked column, each cell containing the configured true_value (‘yes’) or false_value (‘no’) to indicate whether nulls were detected.

Disciplines

  • data.dataset.transform
  • data.statistics

Runnable example

A runnable example is registered for this worker. Open the example workflow on the d3VIEW canvas: /api/workflow/example?id=dataset_verify_na


Auto-generated from transformation schema. Worker id: dataset_verify_na. Schema hash: b03fbedcc765. Hand-curated docs in workerexamples/ override this page when present.