Power BI Query Magic: Extract Text Between Delimiters to a New Column Effortlessly
In the realm of data analytics and business intelligence, Power BI stands out as a versatile tool that enables users to transform, analyze, and visualize vast amounts of data with ease. However, the journey from raw data to insightful reports often requires meticulous data preparation and transformation steps, which can appear daunting to many. One common task in data preparation involves extracting specific text strings from a column, based on defined delimiters. This article demystifies the process, guiding you through a less complicated method using Power BI Query Editor, illustrated with a practical example.
Understanding the Scenario
Imagine working with a dataset that contains a column with various text entries, and you need to extract a specific part of the text that is enclosed within delimiters (e.g., square brackets). Here's a simple representation of the problem:
Given a column TextColumn
:
qwer
qwer [ABC-1234] asdf
qwer [ABC-1234]
[ABC-1234] asdf
Your objective is to extract the text between [ABC-
and ]
into a new column, resulting in:
None
1234
1234
1234
For rows where the pattern doesn't exist, you'd prefer to have a placeholder value None
.
The Less Complicated Method Explained
Power BI’s Query Editor, built on the M language, provides powerful functions to transform and manipulate data. Let’s break down a less complicated approach to achieve the goal:
-
Initial setup with sample data:
let
tbl = #table({"TextColumn"}, {
{"qwer"},
{"qwer [ABC-1234] asdf"},
{"qwer [ABC-1234]"},
{"[ABC-1234] asdf"}
})
-
Adding a column for the extracted text:
To solve our problem, we add a new column to the table where, for each row, we extract the desired text. This step is achieved using the Table.AddColumn
function, combined with Splitter.SplitTextByEachDelimiter
function to split the text based on our specified delimiters. The key lies in the efficient handling of cases where the delimiters don't exist, using try ... otherwise
.
, AddColumn = Table.AddColumn(tbl, "ExtractedNumber", each try Splitter.SplitTextByEachDelimiter({"[ABC-","]"})([TextColumn]){1} otherwise "None")
-
Final touches and output:
Conclude the query to output the modified table with the newly added column.
in AddColumn
This simple but efficient method demonstrates how to cleanly extract information between delimiters without getting lost in convoluted functions or logic.
Why This Matters
Mastering these techniques in Power BI Query Editor empowers users to preprocess and cleanse data effectively, which is crucial for producing accurate and meaningful analytics. Efficient data preparation not only saves time but also lays the groundwork for insightful analysis, enabling businesses to make informed decisions based on accurate data insights.
Real-World Applications
This method is particularly useful in scenarios such as:
- Extracting codes or identifiers from descriptions or notes in transaction data.
- Isolating keywords from texts in customer feedback or product reviews for sentiment analysis.
- Cleaning and preparing data for creating feature sets in machine learning models.
Final Tips and Further Learning
- Getting comfortable with Power BI’s Query Editor and M language opens up a world of possibilities for data transformation and preparation.
- Practice is key to mastery. Experiment with different data sets and transformation techniques.
- Stay curious and keep exploring official documentation and community forums for new tricks and tips.
Remember, understanding your data and knowing how to manipulate it to fit your analysis needs is crucial in today’s data-driven decision-making processes. Techniques like the one we explored here are essential tools in any data analyst’s toolkit.
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