Understanding and Utilizing the PARSE_DATE() Function in Looker Studio for Effective Data Transformation

Understanding and Utilizing the PARSE_DATE() Function in Looker Studio for Effective Data Transformation

In the toolbox of Google Data Studio, there is a function known as

PARSE_DATE()
. Its primary role is to bring about the conversion of text to date. This function is a crucial aspect of data transformation within Google Data Studio, enabling users to work with date data in various formats.

Interestingly, its utility spans numerous applications where date needs to be extracted from textual data or needs conversion from one format to another for the purpose of analysis.

What Does the PARSE_DATE Syntax Look Like?

The typical

PARSE_DATE()
syntax consists of two parameters. Here's how it looks:

PARSE_DATE(format_string, text)

  • format_string : This string provides the format in which date elements are embedded in the text. It signifies how the date is represented in your text. Here, you can refer to Supported Format Elements For DATE for a comprehensive list of supported format elements.

  • text : As the name suggests, it relates to the text representation of a date from which you want to extract the date data.

Let's remember that the primary return data type of

PARSE_DATE()
function is always 'Date'.

How Does the Function Work?

The

PARSE_DATE()
function works by interpreting a text input as a date type, based on the user-specified date format. Once unspecified fields are encountered, they will be initialized from '1970-01-01'. Fascinatingly, format elements like 'Monday', 'February', etc., are case-insensitive.

During the extraction, any set of one or more white spaces in the format string matches zero or more consecutive white spaces in the date string. The function also allows leading and trailing white spaces in the date string irrespective of whether they are in the format string or not.

Let's Discuss Examples!

For the

PARSE_DATE()
function to be better understood, let's consider some examples.

Suppose we have a set of sales data where sales data is recorded as a text like "2020-November-25". To convert this to a regular date format, we would use the function as follows:

```

=PARSE_DATE("%Y-%B-%d", SalesDate)

Here 'SalesDate' is assumed to be a field containing the text "2020-November-25". Post the application of the function, the obtained result will be a date value equivalent to "2020-11-25".

```

Some Limits of The PARSE_DATE() Function

Despite its powerful features, the

PARSE_DATE()
function is not without some limitations. It does not fully support all format elements. Elements like %Q, %a, %A, %g, %G, %j, %u, %U, %V, %w, and %W do not have full functional support for their properties.

Some Tips When Using The PARSE_DATE() Function

Success with the

PARSE_DATE()
function is about understanding its strengths and quirks. Note that when two (or more) format elements contain overlapping information (like %F and %Y affecting the year), the last element generally overrides any earlier ones. Hence, always ensure to order your format elements carefully.

Ensure to use a format_string that matches your text string correctly. Given its complexity, it might take some time to master it. But with patience and practice, you can definitely harness its robust capabilities in Google Data Studio.

More function to use with Looker Studio

NARY_MIN
:
Understanding the Multifaceted NARY_MIN Function in Looker Studio for Efficient Data Analysis
PARSE_DATETIME
:
Transforming Text into Date and Time: Introducing the PARSE_DATETIME Function in Looker Studio
REGEXP_MATCH
:
Unleashing the Power of REGEXP_MATCH in Looker Studio: A Comprehensive Guide to Parsing and Analyzing Text Data
DAY
:
Exploring the DAY Function in Looker Studio: Maximising Insights from Your Date Data
LOWER
:
Mastering the LOWER Function in Looker Studio: Enhancing Data Consistency and Accuracy in Data Analytics