ISNULL Function

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

Basic Usage

delete row:ISNULL(Qty)

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

Syntax

delete value:ISNULL(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.

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 TypeExample Value
YesString literal or column referencemyColumn

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 TypeString
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'

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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