ISNULL Function

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The ISNULL function tests whether a column of values contains null values. For input column references, this function returns true or false.

The NULL function generates null values. See NULL Function.
Null values are different from missing values.
- To test for missing values, see ISMISSING Function.
For more information on null values, see Manage Null Values.

Basic Usage

delete row:ISNULL(Qty)

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

Syntax and Arguments

delete value:ISNULL(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 null values.

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

Usage Notes:

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

Valid data type strings:

When referencing a data type within a transform, you can use the following strings to identify each type:

NOTE: In Wrangle transforms, these values are case-sensitive.

Data Type	String
String	`'String'`
Integer	`'Integer'`
Decimal	`'Float'`
Boolean	`'Bool'`
Social Security Number	`'SSN'`
Phone Number	`'Phone'`
Email Address	`'Emailaddress'`
Credit Card	`'Creditcard'`
Gender	`'Gender'`
Object	`'Map'`
Array	`'Array'`
IP Address	`'Ipaddress'`
URL	`'Url'`
HTTP Code	`'Httpcodes'`
Zip Code	`'Zipcode'`
State	`'State'`
Date / Time	`'Datetime'`

For custom types, you should reference the name of the type in the string value. For more information, see Create Custom Data Types.

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

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:

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:

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')

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

See Also for ISNULL Function:

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