REGEXP_EXTRACT function: How to use & example

Introduction to REGEXP_EXTRACT Function

In the realm of Google Data Studio, the

REGEXP_EXTRACT function holds a high degree of significance. This tool is responsible for extracting substrings that match a particular pattern from a target value. It proves to be an instrumental utility capable of unearthing the potential in your data and suiting it to your intended output.

How The REGEXP_EXTRACT Function Works

The

REGEXP_EXTRACT command finds the first substring that corresponds to the regular expression given and returns it. The function applies the regular expression pattern to the X parameter, where X is a field or an expression, and returns the first match.

The syntax is as follows:

REGEXP_EXTRACT(X, regular_expression)

In this syntax:-

X is a field or an expression that involves reference to a field.- regular_expression is the pattern that the function uses to extract a portion of the X.

The regular_expression must be a valid extraction pattern and the function only returns textual values.

Sample Usage of REGEXP_EXTRACT Function

Consider the sales metrics of a company. The sales manager has been tracking the sales campaigns and wants a neat list of the campaign types. Now, suppose the campaign names are listed as "SPRING-SUMMER:PROMO", "AUTUMN-WINTER:OFFER", etc. To extract the campaign types, namely "PROMO", "OFFER" etc, we can use this function. Herein we use the parts after ":" to label types, try the following:

REGEXP_EXTRACT(Campaign, ':([a-zA-Z0-9_-]*)')

This will successfully extract the campaign type from the campaign names providing the manager with a clear index to navigate through the data.

Constraints of the REGEXP_EXTRACT Function

The REGEXP_EXTRACT function follows the RE2 regular expression syntax. Expressions containing escape characters like

\ may require additional escape sequences, which can be avoided by using raw string literals.

Handy Tips to Remember

1. Parse your 'X' and 'regular_expression' carefully while using this function. Remember that regular_expression will only succeed if a valid pattern is applied to the existing data in field 'X'.
2. REGEXP_EXTRACT only returns text values even if numerals are involved in the regular_expression. Plan your operations accordingly.

This function stands as a sentinel at the cusp of data interpretation. By mastering REGEXP_EXTRACT usage, one can achieve a high level of data extraction and manipulation that can drive focused insights.