DATASET REMOVE NA¶
Scans a dataset and removes any rows or columns that contain null (NA) values. Optionally restrict the null-check to a specific subset of columns. Use this worker to clean incomplete data before downstream analysis or model training.
When to use¶
Classification: process.
Tagged: data_cleaning, dataset, dropna, missing_values, null, process, remove_na.
Inputs¶
| Label | ID | Type | Default | Required | Description |
|---|---|---|---|---|---|
| Dataset | dataset | dataset | — | Input dataset to be scanned for null values; accepts any tabular dataset connected from an upstream worker. | |
| Columns To Check | columnstocheck | scalar | — | One or more column names to restrict the null-check to; if left empty, all columns are evaluated for null values. | |
| Remove Rows Or Columns | remove_rows_columns | scalar | row | Axis to drop when a null value is found — ‘row’ (default) removes the entire row containing the null, ‘column’ removes the entire column. |
Outputs¶
| Label | ID | Type | Description |
|---|---|---|---|
| dataset_remove_na_output_1 | dataset_remove_na_output_1 | dataset | Cleaned dataset with all rows or columns that contained null values removed, ready for downstream processing. |
Disciplines¶
- data.dataset.transform
Runnable example¶
A runnable example is registered for this worker. Open the example workflow on the d3VIEW canvas: /api/workflow/example?id=dataset_remove_na
Auto-generated from transformation schema. Worker id: dataset_remove_na. Schema hash: fbf13f1075b1. Hand-curated docs in workerexamples/ override this page when present.