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Release 6.4.2



The ISMISSING function tests whether a column of values is missing or null. For input column references, this function returns true or false.
  • You can define a conditional test in a single step for valid values. See IFMISSING Function.
  • Missing values are different from null values. To test for the presence of null values exclusively, see ISNULL Function.

Basic Usage

delete row:ISMISSING(Qty)

Output: Deletes any row in which the value in the Qty column is missing.

Syntax and Arguments

delete value:ISMISSING(column_string)

ArgumentRequired?Data TypeDescription
column_stringYstringName of column or string literal to be applied to the function

For more information on syntax standards, see Language Documentation Syntax Notes.


Name of the column or string literal to be tested for missing values.

  • Missing literals or column values generate missing string results.

Multiple columns can be specified as an array, as in the following:

delete value:ISMISSING([col1,col3,col5])

  • Column ranges are not supported.
  • Wildcards are not supported.

Usage Notes:


Required?Data TypeExample Value
YesString literal or column referencemyColumn


Tip: For additional examples, see Common Tasks.

Example - Type check functions

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


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



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:

ID falsefalsetrue

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

derive type:single value: ((mismatched_State == 'true') || (valid_Qty == 'false') || (missing_State_Qty == 'true')) ? NULL() : 'ok' as:'status'

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

delete row: ISNULL('status')


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


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