select

Keep selected rows, using a Python expression to define which rows to keep.

Note

This guide explains how to configure the select instruction. To learn about another instruction, go back to the Custom scripts available in Adverity overview.

Introduction

Use the select instruction to find and keep rows in a data extract for which the Python expression evaluates to true. Only the rows of the data extract that are evaluated as true are kept, all other rows are removed.

Understanding the different select instructions

There are a number of different select custom script instructions that can be used to keep or remove specific rows in your data extract based on the criteria you choose. The different types of select instruction are:

• select - keep selected rows, using a Python expression to define which rows to keep

• selecteq - keep rows with a value that matches the value you enter

• selectne - remove rows with a value that matches the value you enter

• selecttrue - keep rows that contain any value in a selected column

• selectfalse - keep rows that are empty, or contain null or 0, in a selected column

• selectgt - keep rows with a number greater than the number you enter

• selectlt - keep rows with a number less than the number you enter

• selectin - keep rows with a value that matches any of a list of values you enter

• selectnotin - remove rows with a value that matches any of a list of values you enter

• selectnone - keep rows in a selected column that are empty

• selectnotnone - remove rows in a selected column that are empty

• selectinvt - keep rows that match values in a value table

Creating a custom script transformation using the select instruction

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

1. Create a custom script transformation.

2. In the Configuration step, select the select instruction.

3. To configure the custom script instruction, fill in the required fields below, as well as any optional fields you require:

Where (required)

Enter a Python expression to use as search criteria. For example, you can enter the name of a column and a value within the column. Enter the name of the column between braces and the value to search for between quotes. For example, {column_name}=='value to search'.

Complement

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

Missing

Enter a value with which to populate empty fields.

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 1

Transformation configuration

Where

{Campaign} == 'Brand'

Complement

Do not select this field.

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	Sum_Clicks
Brand	media	7
Brand	ecommerce	4

Example 2

Transformation configuration

Where

{Campaign} == 'Brand'

Complement

Select this field.

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	Sum_Clicks
Branding	ecommerce	3
Dashboard	brand awareness	18
Dashboard	media|social	5
Branding	media	11