In the toolbox of Google Data Studio, there is a function known as
PARSE_DATE()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.
The typical
PARSE_DATE()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()The
PARSE_DATE()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.
For the
PARSE_DATE()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".
```
Despite its powerful features, the
PARSE_DATE()Success with the
PARSE_DATE()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.
