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LEN Function

Returns the number of characters in a specified string. String value can be a column reference or string literal.

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:

len(MyName)

Output: Returns the number of characters in the value in column MyName.

String literal example:

len('Hello, World')

Output: Returns the value 12.

Syntax and Arguments

len(column_string)

Argument

Required?

Data Type

Description

column_string

Y

string

Name of the 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 constant to be searched.

  • Missing string or column values generate missing string results.

  • String 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

Examples

提示

For additional examples, see Common Tasks.

Example - Fixed Length Strings

Source:

Your product identifiers follow a specific structure that you'd like to validate in your recipe. In the following example data, the productId column should contain values of length 6.

You can see that there is already a column containing validation errors for the ProductName column. Values in the ProductId column that are not this length should be flagged in a new column. Then, you should merge the two columns together to create a ValidationError column.

ProductName

ProductId

ErrProductName

Chocolate Bunnie

123456

Error-ProductName

Chocolate Squirl

88442286

Error-ProductName

Chocolate Gopher

12345

Transformation:

To validate the length of the values in ProductId, enter the following transform. Note that the as parameter enables you to rename the column as part of the transform.

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

if(len(ProductId) <> 6, 'Error-length-ProductId','')

Parameter: New column name

'ErrProductIdLength'

The dataset now looks like the following:

ProductName

ProductId

ErrProductName

ErrProductIdLength

Chocolate Bunnie

123456

Error-ProductName

Chocolate Squirrel

88442286

Error-ProductName

Error-length-ProductId

Chocolate Gopher

12345

Error-length-ProductId

You can blend the two error columns into a single DataValidationErrors error column using the following merge transform. Note again the use of the as parameter:

Transformation Name

Merge columns

Parameter: Columns

ErrProductName,ErrProductIdlength

Parameter: Separator

''

Parameter: New column name

'DataValidationErrors'

To clean up the data, you might want to do the following, which trims out the whitespace in the DataValidationErrors column and removes the two individual error columns:

Transformation Name

Edit column with formula

Parameter: Columns

DataValidationErrors

Parameter: Formula

trim(DataValidationErrors)

Transformation Name

Delete columns

Parameter: Columns

ErrProductName,ErrProductIdLength

Parameter: Action

Delete selected columns

Results:

The final dataset should look like the following:

ProductName

ProductId

DataValidationErrors

Chocolate Bunnie

123456

Error-ProductName

Chocolate Squirrel

88442286

Error-ProductName Error-length-ProductId

Chocolate Gopher

12345

Error-length-ProductId