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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.

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