VALID Function

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 theISVALID 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, <span>[</span>'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
yyyy*mm*dd*HH:MM:SSX

For more information on these and other date format strings, see DATEFORMAT Function.

Numeric literal example:

isvalid('ZZ', 'State')

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

Syntax and Arguments

isvalid(column_string,datatype_literal)

Argument	Required?	Data Type	Description
column_string	Y	string	Name of column or string literal to be applied to the function
datatype_literal	Y	string	String 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 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 Type	Example Value
Yes	String literal or column reference	myColumn

datatype_literal

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 Type	Example Value
Yes	String literal	'Integer'

Valid data type strings:

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

注記

In Wrangle transforms, these values are case-sensitive.

注記

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 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'

Examples

ヒント

For additional examples, see Common Tasks.

Example - Type check functions

This example illustrates how various type checking functions can be applied to your data.

Functions:

Item	Description
VALID Function	Tests whether a set of values is valid for a specified data type and is not a null value.
ISMISMATCHED Function	Tests whether a set of values is not valid for a specified data type.
ISMISSING Function	The `ISMISSING` function tests whether a column of values is missing or null. For input column references, this function returns `true` or `false`.
ISNULL Function	The `ISNULL` function tests whether a column of values contains null values. For input column references, this function returns `true` or `false`.
NULL Function	The `NULL` function generates null values.

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

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.

注記

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 theQtycolumn 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:

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:

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:

State	Qty	mismatched_State	valid_Qty	missing_State_Qty	status
CA	10	false	true	false	ok

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