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Tests whether a set of values is valid for a specified data type and is not a null value.
  • For a specified data type and set of values, this function returns  true  or  false
  • Inputs can be literal values or column references.

You can use the ISVALID  function keywords interchangeably.

  • You can define a conditional test in a single step for valid values. See IFVALID Function.
  • This function is similar to the ISMISMATCHED function, which tests for mismatches against a specified data type. However, the ISMISMATCHED function also matches against missing values, while the ISVALID function does not. See ISMISMATCHED 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

Column reference example:

(isvalid(Qty, 'Integer') && (Qty > 0))

Output: Returns true when the value in the Qty column contains a valid Integer and the value is greater than zero. 

Column reference example for Datetime:

The Datetime data type requires a special formatting string as part of the evaluation of validity:

(isvalid(myDates, ['Datetime', 'yy-mm-dd hh:mm:ss','yyyy*mm*dd*HH:MM:SSX']))

Output: Returns true when the value in the myDates column conforms to either of the following date format strings:

yy-mm-dd hh:mm:ss

For more information on these and other date format strings:

Numeric literal example:

isvalid('ZZ', 'State')

Output: Returns false, since the value ZZ is not a valid U.S. State code.

Syntax and Arguments


ArgumentRequired?Data TypeDescription
column_stringYstringName of column or string literal to be applied to the function
datatype_literalYstringString literal that identifies the data type against which to validate the source values

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


Name of the column or string literal to be evaluated for validity.

  • Missing literals or column values generate missing string results.
    • Constants must be quoted ('Hello, World').
  • Multiple columns and wildcards are not supported.

Usage Notes:

Required?Data TypeExample Value
YesString literal or column referencemyColumn


Literal value for data type to which to match the source column or string. For more information, see Valid Data Type Strings.

  • Column references are not supported.

Usage Notes:

Required?Data TypeExample Value
YesString literal'Integer'

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.

NOTE: When specifying a data type by name, you must use the String value listed below. The Data Type value is the display name for the type.

Data TypeString
Social Security Number'SSN'
Phone Number'Phone'
Email Address'Emailaddress'
Credit Card'Creditcard'
IP Address'Ipaddress'
HTTP Code'Httpcodes'
Zip Code'Zipcode'
Date / Time'Datetime'


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:



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 the Qty column 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:

ID falsefalsetrue

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


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



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