Delete Transform

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.

Parameters

delete row:(expression)

TokenRequired?Transform BuilderData TypeDescription
deleteYDelete rowstransformName of the transform
rowYConditionstringExpression 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.

row

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

Examples:

ExpressionDescription
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.
ISMISSING([Title])
true if the row value in the Title column is missing.
ISMISMATCHED(Score,['Integer'])
true if the row value in the Score column is mismatched against the Integer data type.

Example:

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

Examples

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.

Source:

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.
OrderIdOrderDateProdIdProductNameProductColorQtyOrderValue
10016/14/2015P100HatBrown190
10021/15/2016P101HatBlack2180
100311/11/2015P103SweaterBlack3255
10048/6/2015P105CardiganRed4320
10057/29/2015P103SweeterBlack5375
100612/1/2015P102PantsWhite6420
100712/28/2015P107T-shirtWhite7390
10081/15/2016P105CardiganRed8420
10091/31/2016P108CoatNavy9495

Transform:

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:

OrderIdOrderDateProdIdProductNameProductColorQtyOrderValue
10048/6/2015P105CardiganRed4320
100712/28/2015P107T-shirtWhite7390
10081/15/2016P105CardiganRed8420
10091/31/2016P108CoatNavy9495

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

Results:

OrderIdOrderDateProdIdProductNameProductColorQtyOrderValue
100712/28/2015P107T-shirtWhite7390
10081/15/2016P105CardiganRed8420
10091/31/2016P108CoatNavy9495

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