Keep Transform

Retains a set of rows in your dataset, which are specified by the conditional in the row expression. All other rows are removed from the dataset. The keep transform is the opposite of the delete transform. See Delete Transform.

Basic Usage

keep row:(customerStatus == 'active')

Output: For each row in the dataset, if the value of the customerStatus column is active, then the row is retained. Otherwise, the row is deleted from the dataset.


keep row:(expression)

TokenRequired?Transform BuilderData TypeDescription
keepYKeep rowstransformName of the transform
rowYConditionstringExpression identifying the row or rows to keep. If expression evaluates to true for a row, the row is retained.

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.

For the keep transform, if the expression for the row parameter evaluates to true for a row, it is kept in the dataset. Otherwise, it is removed.


keep row: (lastOrder >= 10000 && status == 'Active')

Output: Retains all rows in the dataset where the lastOrder value is greater than or equal to 10,000 and the the customer status is Active.

Usage Notes:

Required?Data Type
YesExpression that evaluates to true or false


Example - Remove old products and keep new orders

This examples illustrates how you can keep and delete rows from your dataset using the following transforms:

  • delete - Deletes a set of rows as evaluated by the conditional expression in the row parameter. See Delete Transform.
  • keep - Retains a set of rows as evaluated by the conditional expression in the row parameter. All other rows are deleted from the dataset. See Keep Transform.


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 transform. Do not add it to your recipe yet.

delete row:(ProdId == 'P100')

When this step is previewed, you should notice that the top row in the above table is highlighted for removal. Notice how the transform 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 transform in the following expansion of the transform, in which any row that has a matching ProdId value is removed:

delete row:(ProdId == 'P100' || ProdId == 'P101' || ProdId == 'P102' || ProdId == 'P103')

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:

derive value:'2/25/16' as:'today'

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

keep row:DATEDIF(OrderDate,today,day) <= 90

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

drop col:today



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