ISMISSING Function

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.

Wrangle vs. SQL: This function is part of Wrangle, a proprietary data transformation language. Wrangle is not SQL. For more information, see Wrangle Language.

Basic Usage

ismissing(Qty)

Output: Returns true if the value in the Qty column is missing.

Syntax and Arguments

ismissing(column_string)

Argument	Required?	Data Type	Description
column_string	Y	string	Name of column or string literal to be applied to the function

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

column_string

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

Missing literals or column values generate missing string results.
Multiple columns are supported.
Wildcards are not supported.

Usage Notes:

Required?	Data Type	Example Value
Yes	String literal or column reference	myColumn

Examples

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.

ISVALID - Returns true if the input matches the specified data type. See VALID Function.
ISMISMATCHED - Returns true if the input does not match the specified data type. See ISMISMATCHED Function.
ISMISSING - Returns true if the input value is missing. See ISMISSING Function.
ISNULL - Returns true if the input value is null. See ISNULL Function.
NULL - Generates a null value. See NULL Function.

Source:

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

State	Qty
CA	10
OR	-10
WA	2.5
ZZ	15
ID
	4

Transformation:

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

Transformation Name	`New formula`
Parameter: Formula type	Single row formula
Parameter: Formula	ISMISMATCHED (State, 'State')

The above transform flags rows 4 and 6 as mismatched.

Note

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

Invalid Integer values: You can test for valid matches for Qty using the following:

Transformation Name	`New formula`
Parameter: Formula type	Single row formula
Parameter: Formula	(ISVALID (Qty, 'Integer') && (Qty > 0))
Parameter: New column name	'valid_Qty'

The above transform flags as valid all rows where theQtycolumn is a valid integer that is greater than zero.

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

Transformation Name	`New formula`
Parameter: Formula type	Single row formula
Parameter: Formula	(ISMISSING(State) \|\| ISMISSING(Qty))
Parameter: New column name	'missing_State_Qty'

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

State	Qty	mismatched_State	valid_Qty	missing_State_Qty
CA	10	false	true	false
OR	-10	false	false	false
WA	2.5	false	false	false
ZZ	15	true	true	false
ID		false	false	true
	4	false	true	true

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

Transformation Name	`New formula`
Parameter: Formula type	Single row formula
Parameter: Formula	((mismatched_State == 'true') \|\| (valid_Qty == 'false') \|\| (missing_State_Qty == 'true')) ? NULL() : 'ok'
Parameter: New column name	'status'

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

Transformation Name	`Filter rows`
Parameter: Condition	Custom formula
Parameter: Type of formula	Custom single
Parameter: Condition	ISNULL('status')
Parameter: Action	Delete matching rows

Results:

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

State	Qty	mismatched_State	valid_Qty	missing_State_Qty	status
CA	10	false	true	false	ok

In this section: