ISNULL Function
The ISNULL function tests whether a column of values contains null values. For input column references, this function returns true or false.
The
NULLfunction generates null values. See NULL 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
isnull(Qty)
Output: Returns true if the value in the Qty column is null.
Syntax and Arguments
isnull(column_string)
Argument | Required? | Data Type | Description |
|---|---|---|---|
column_string | Y | string | Name of column or string literal to be applied to the function |
For more information on syntax standards, see Language Documentation Syntax Notes.
column_string
Name of the column or string literal to be tested for null values.
Missing literals or column values generate missing string results.
Multiple columns and wildcards are not supported.
Usage Notes:
Required? | Data Type | Example Value |
|---|---|---|
Yes | String literal or column reference | myColumn |
Valid data type strings:
When referencing a data type within a transform, you can use the following strings to identify each type:
Anmerkung
In Wrangle transforms, these values are case-sensitive.
Anmerkung
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
Tipp
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 |
ISNULL Function | The |
NULL Function | The |
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 | |
|---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | ISMISMATCHED (State, 'State') |
The above transform flags rows 4 and 6 as mismatched.
Anmerkung
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 | |
|---|---|
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 | |
|---|---|
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 | |
|---|---|
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 | |
|---|---|
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 |