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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.

Basic Usage

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

Syntax


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

Transformation:

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')

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 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:

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:

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:

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:

StateQtymismatched_Statevalid_Qtymissing_State_Qtystatus
CA10falsetruefalseok

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