NULL Function

The NULL function generates null values.

Basic Usage

derive type:single value:NULL()

Output: Generates a column of null values.

derive type:single value: IF((ISNULL(FirstName) || ISNULL(LastName)), null(), 'ok') as:'status'

Output: If there are null values in either the FirstName or LastName column, generate a null value in the status column. Otherwise, the column value is ok.

Syntax

There are no arguments for this function.

Examples

Example - Type check functions

This example illustrates how various type checking functions can be applied to your data.

Source:

Some source values that should match the State and Integer data types:

StateQty
CA10
OR-10
WA2.5
ZZ15
ID
4

Transform:

You can test for invalid values for State using the following:

derive type:single value: ISMISMATCHED (State, 'State')

You can test for valid matches for Qty using the following:

derive type:single value: (ISVALID (Qty, 'Integer') && (Qty > 0)) as:'valid_Qty'

The first transform flags rows 4 and 6 as mismatched.

NOTE: A missing value is not valid for a type, including String type.

The second transform flags as valid all rows where the Qty column is a valid integer that is greater than zero.

The following transform tests for the presence of missing values in either column:

derive type:single value: (ISMISSING(State) || ISMISSING(Qty)) as:'missing_State_Qty'

After re-organizing the columns using the move transform, the dataset should now look like the following:

StateQtymismatched_Statevalid_Qtymissing_State_Qty
CA10falsetruefalse
OR-10falsefalsefalse
WA2.5falsefalsefalse
ZZ15truetruefalse
ID falsefalsetrue
4falsetruetrue

Since the data does not contain null values, the following transform generates null values based on the preceding criteria:

You can then use the ISNULL check to remove the rows that fail the above test:

delete row: ISNULL('status')

Results:

Based on the above tests, the output dataset contains one row:

StateQtymismatched_Statevalid_Qtymissing_State_Qtystatus
CA10falsetruefalseok

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