NULL Function

The NULL function generates null values.

The ISNULL function tests for the presence of null values. See ISNULL Function.
Null values are different from missing values.
- To test for missing values, see ISMISSING 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

null()

Output: Returns a null value.

if((isnull(FirstName) || isnull(LastName)), null(), 'not null') as:'status'

Output: If there are null values in either the FirstName or LastName column, generate a null value in the status column. Otherwise, the returned value is not null.

Syntax and Arguments

There are no arguments for this function.

Examples

Suggerimento

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.

Nota

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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