selectne

Remove rows with a value that matches the value you enter.

Introduction

Use the selectne instruction to find and remove rows from a data extract. The rows of the data extract that are an exact match of the entered values are removed. Rows that contains values that are not an exact match are kept. The search is not case-sensitive. This is the opposite of the selecteq transformation.

Creating a custom script transformation using the selectne instruction

To create and configure a custom script using the selectne instruction, follow these steps:

1. Create a custom script transformation.

2. In the Instructions step, select the selectne instruction.

3. To configure the custom script instruction, fill in the following fields. Required fields are marked with an asterisk (*).

Field*

Enter the name of the column that contains the value used to identify which row to remove.

Select a column in one of the following ways:

• Select String and enter the name of the column.

• Select Integer and enter the position of the column in the data extract as an index value. Counting starts at 0. To select the first column, enter 0. To select the second column, enter 1. To select the last column in the data extract, enter -1.

Value*

Enter the value used to identify which row to remove.

Complement

Select this field to reverse the transformation. Any rows that match the search criteria are kept and the rows that do not match the criteria are removed.

Subtable

Enter the name for a subtable that you want to use within this custom script.

A subtable is a temporary table that only exists for this custom script. You can apply additional instructions within the same custom script to the subtable. However, the subtable cannot be used in any other custom scripts.

If a subtable does not exist for the current custom script, the transformation is applied to the data extract, and the enriched data is output into the subtable. If the subtable already exists for the custom script, the subtable is used as the input for the transformation and optionally as the output.

Example

Transformation configuration

Field*

Ad Group

Value

brand awareness

Data table before transformation

Campaign	Ad Group	Clicks
Brand	media	7
Branding	ecommerce	3
Dashboard	brand awareness	18
Brand	ecommerce	4
Dashboard	media|social	5
Branding	media	11

Data table after transformation

Campaign	Ad Group	Clicks
Brand	media	7
Branding	ecommerce	3
Brand	ecommerce	4
Dashboard	media|social	5
Branding	media	11