Delete Transform

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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)

Token	Required?	Data Type	Description
delete	Y	transform	Name of the transform
row	Y	string	Expression 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:

Expression	Description
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
Yes	Expression that evaluates to `true` or `false`

Examples

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 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.

OrderId	OrderDate	ProdId	ProductName	ProductColor	Qty	OrderValue
1001	6/14/2015	P100	Hat	Brown	1	90
1002	1/15/2016	P101	Hat	Black	2	180
1003	11/11/2015	P103	Sweater	Black	3	255
1004	8/6/2015	P105	Cardigan	Red	4	320
1005	7/29/2015	P103	Sweeter	Black	5	375
1006	12/1/2015	P102	Pants	White	6	420
1007	12/28/2015	P107	T-shirt	White	7	390
1008	1/15/2016	P105	Cardigan	Red	8	420
1009	1/31/2016	P108	Coat	Navy	9	495

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:

OrderId	OrderDate	ProdId	ProductName	ProductColor	Qty	OrderValue
1004	8/6/2015	P105	Cardigan	Red	4	320
1007	12/28/2015	P107	T-shirt	White	7	390
1008	1/15/2016	P105	Cardigan	Red	8	420
1009	1/31/2016	P108	Coat	Navy	9	495

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:

OrderId	OrderDate	ProdId	ProductName	ProductColor	Qty	OrderValue
1007	12/28/2015	P107	T-shirt	White	7	390
1008	1/15/2016	P105	Cardigan	Red	8	420
1009	1/31/2016	P108	Coat	Navy	9	495

See Also for Delete Transform:

Page:

EXAMPLE - Delete and Keep Transforms
Page:

Settype Transform
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ISNULL Function
Page:

DOMAIN Function
Page:

ISMISMATCHED Function
Page:

MATCHES Function
Page:

EXAMPLE - IF Data Type Validation Functions
Page:

EXAMPLE - Type Functions
Page:

RANGE Function
Page:

ISMISSING Function

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Delete Transform

Basic Usage

Syntax and Parameters

row

Examples

Example - Remove old products and keep new orders