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

You can define a conditional test in a single step for valid values. See IFMISMATCHED Function.

NOTE: This function is similar to the ISVALID function, which tests for validity against a specified data type. However, unlike the ISVALID function, the ISMISMATCHED function also matches against missing values. See VALID 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:

ismismatched(Qty, 'Integer') || (Qty < 0)

Output: Returns true when the value in the Qty column does not contain a valid Integer and the value is less than zero.

Numeric literal example:

ismismatched('ZZ', 'State')

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

Syntax and Arguments

ismismatched(column_string,datatype_literal)


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.

column_string

Name of the column or string literal to be evaluated for mismatches against the specified type.

  • 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

datatype_literal

Literal value for data type to which to validate the source column or string.

  • 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
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

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.

Source:

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

StateQty
CA10
OR-10
WA2.5
ZZ15
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 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:

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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