Google Data Studio is power-packed with a slew of amazing features that make data analysis and representation a breeze. One such feature is the PARSE_DATETIME function. This function, as the name suggests, is useful when there is a need to transform text into a date with time, making it an indispensable tool in data manipulation and conversion.
In simple words, PARSE_DATETIME converts a string-based date and time representation into a proper date and time format suitable for computation, comparison, and further processing.
PARSE_DATETIME adheres to a simple syntax for its execution:
PARSE_DATETIME(format_string, text)The syntax makes use of two parameters: -
format_stringtextFor successful execution of this function, both parameters need to be specified. Not conforming to these ESSENTIAL function arguments would result in an error or incorrect data parsing.
To provide a clearer understanding of the PARSE_DATETIME function usage, let's consider a real-world sales scenario. Suppose we have sales data containing the date and time in text format, and we want to convert it into date and time format.
Example 1:
| SaleDateTimeText | || | 2022-02-24 14:58:30 |
Here, the format string will be
%Y-%m-%d %H:%M:%SPARSE_DATETIME('%Y-%m-%d %H:%M:%S', SaleDateTimeText)While the PARSE_DATETIME function is a boon for data conversion tasks, it's crucial to be aware of its limitations. For instance, unspecified fields during the parse are initialized from 1970-01-01. The function also doesn't fully support certain format elements like
%Q%a%A%g%G%j%u%U%V%w%WKeep in mind that date and time names are case insensitive.
Ensure that the format_string specified matches the data in the text column for successful data conversion.
The format string can include one or more white spaces, and they can match zero or more consecutive white spaces in the date string.
If two or more format elements have overlapping information, the last one generally overrides any earlier ones.
Overall, the PARSE_DATETIME function is a simple yet powerful tool that enriches the capabilities of Google Data Studio and enables users to explore their data in more valuable ways.
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