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NOTE:  Designer Cloud Powered by Trifacta Educational is a free product with limitations on its features. Some features in the documentation do not apply to this product edition. See Product Limitations.


NOTE: Transforms are a part of the underlying language, which is not directly accessible to users. This content is maintained for reference purposes only. For more information on the user-accessible equivalent to transforms, see Transformation Reference.

Deletes a set of rows in your dataset, based on a condition specified in the row expression. If the conditional expression is true, then the row is deleted. 

The delete transform is the opposite of the keep transform. See Keep Transform.

Basic Usage

delete row:(dateAge >= 90)

Output: For each row in the dataset, if the value in the dateAge column is greater than or equal to 90, the row is deleted. 

Syntax and Parameters

delete row:(expression)

TokenRequired?Data TypeDescription
deleteYtransformName of the transform
rowYstringExpression identifying the row or rows to delete. If expression evaluates to true for a row, the row is removed.  

For more information on syntax standards, see Language Documentation Syntax Notes.


Expression to identify the row or rows on which to perform the transform. Expression must evaluate to true or false.


Score >= 50
true if the value in the Score column is greater than 50.
LEN(LastName) > 8
true if the length of the value in the LastName column is greater than 8.
true if the row value in the Title column is missing.
true if the row value in the Score column is mismatched against the Integer data type.


delete row: (lastContactDate < 01/01/2010 || status == 'Inactive')

Output: Deletes any row in the dataset where the lastContactDate is before January 1, 2010 or the status is Inactive

Usage Notes:

Required?Data Type
YesExpression that evaluates to true or false


Tip: For additional examples, see Common Tasks.

Example - Remove old products and keep new orders

This examples illustrates how you can keep and delete rows from your dataset.


Your dataset includes the following order information. You want to edit your dataset so that:

  • All orders for products that are no longer available are removed. These include the following product IDs: P100, P101, P102, P103.
  • All orders that were placed within the last 90 days are retained.





First, you remove the orders for old products. Since the set of products is relatively small, you can start first by adding the following:

NOTE: Just preview this transformation. Do not add it to your recipe yet.

Transformation Name Filter rows
Parameter: Condition Custom formula
Parameter: Type of formula Custom single
Parameter: Condition (ProdId == 'P100')
Parameter: Action Delete matching rows

When this step is previewed, you should notice that the top row in the above table is highlighted for removal. Notice how the transformation relies on the ProdId value. If you look at the ProductName value, you might notice that there is a misspelling in one of the affected rows, so that column is not a good one for comparison purposes.

You can add the other product IDs to the transformation in the following expansion of the transformation, in which any row that has a matching ProdId value is removed:

Transformation Name Filter rows
Parameter: Condition Custom formula
Parameter: Type of formula Custom single
Parameter: Condition (ProdId == 'P100' || ProdId == 'P101' || ProdId == 'P102' || ProdId == 'P103')
Parameter: Action Delete matching rows

When the above step is added to your recipe, you should see data that looks like the following:


Now, you can filter out of the dataset orders that are older than 90 days. First, add a column with today's date:

Transformation Name New formula
Parameter: Formula type Single row formula
Parameter: Formula '2/25/16'
Parameter: New column name 'today'

Keep the rows that are within 90 days of this date using the following:

Transformation Name Filter rows
Parameter: Condition Custom formula
Parameter: Type of formula Custom single
Parameter: Condition datedif(OrderDate,today,day) <= 90
Parameter: Action Keep matching rows

Don't forget to delete the today column, which is no longer needed:

Transformation Name Delete columns
Parameter: Columns today
Parameter: Action Delete selected columns



See Also for EXAMPLE - Delete and Keep Transforms:


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