The Best Power Query UI Features You’re Probably Not Using

Power Query is often seen as a tool for just loading and cleaning data — but it offers far more than the basics. Most users rely on a handful of transformations, while the UI hides powerful features that can make your workflow faster, cleaner, and easier to maintain.

In this post, I’ll walk you through the best Power Query UI functions and hidden gems — no M code required.

I repeat, no M-code, just UI!!!

"Discover the Top 10 Power Query UI Gems: From automatic data type detection to dynamic filtering options, enhance your data management skills with these powerful tools."

1. Column Profiling, Quality, and Distribution

One of the most underrated features in Power Query: Data Profiling.Found under View → Data Preview, you’ll see three powerful toggles:

• Column Quality – shows percentages of valid, error, and empty values.

• Column Distribution – provides histograms of values, helping spot outliers.

• Column Profile – shows detailed statistics like minimum, maximum, distinct count, etc.

Example: Imagine importing a sales dataset. With one click, you can instantly see:

• 15% of “CustomerID” values are missing.

• “Country” has only 8 distinct entries (so maybe it should be a lookup table).

• “SalesAmount” has negative values (potential refunds).

👉 This turns Power Query into a quick data-quality dashboard before even loading your data into Excel or Power BI.

Data quality and distribution overview in a Power Query window, highlighting valid entries across various columns with detailed distribution metrics.

2. Merge and Append with Fuzzy Matching

Everyone uses Merge Queries, but very few try the Fuzzy Match option hidden in the merge dialog.

• Regular Merge: Combines tables by exact key values (e.g., CustomerID).

• Fuzzy Merge: Allows matching on similar, but not exact, text values.

Example: If you have “Jon Smith” in one dataset and “John Smtih” in another, a fuzzy merge can still match them, with adjustable similarity thresholds.

This is a life-saver for messy data like survey results, customer names, or product categories.

Interface for merging tables with an option for fuzzy matching highlighted, allowing for more flexible data merging using specified criteria.

3. Detect Data Types Automatically

Instead of manually converting columns, Power Query can detect data types for you.

• Go to Transform → Detect Data Type.

• It scans the column contents and assigns the most likely type (Date, Number, Text).

Example: Importing CSVs with date columns formatted as text. Instead of fixing one by one, run “Detect Data Type” across all columns in seconds.

Power Query interface showcasing the 'Detect Data Type' feature, with an example of transforming a dataset by mapping the 'CODE' column value 'RETAILER' to 'Correct Channel' as 'Retail'.

4. Group By with Aggregations

Group By isn’t just for counting rows. You can combine it with:

• Sum, Average, Median

• All Rows (to keep entire grouped tables)

Example: In a sales dataset, group by Region and get:

• Sum of sales

• Average transaction size

• Count of customers

The “All Rows” option is a hidden gem — it lets you group data but keep the full detail available for drilling down later.

In the Power Query editor, the "Group By" function is being used to group data in the "Product_Table" by "Product Code" and count the rows, providing a summarized view of the dataset.

5. Conditional Columns – No IF Formula Needed

Instead of writing Excel IF formulas, Power Query’s Conditional Column wizard allows you to build logic step-by-step.

Example:

• If “SalesAmount” > 1000 → mark as “High Value”

• Else → “Regular”

Bonus: You can nest conditions without the formula headaches Excel often brings.

Creating a new conditional column in Power BI by configuring rules and outputs based on existing data.

6. Split by Delimiter, Number of Characters, or Digit to Non-Digit

The Split Column function goes beyond “by delimiter.” You can split by:

• Number of Characters (e.g., first 3 for country code).

• Lower to Upper / Digit to Non-Digit transitions.

Example: “DE12345” → Split into “DE” and “12345” in two clicks.

Power BI Query Editor: Using the "Split Column by Delimiter" feature to separate text based on spaces in the "Product Table" data, allowing for refined data transformation and analysis.

7. Keep/Remove Top/Bottom Rows or Errors

Cleaning data often means removing unwanted rows. The UI offers:

• Remove Top/Bottom Rows

• Remove Alternate Rows

• Keep Errors (to analyze what’s broken)

• Remove Errors (to clean quickly)

Example: Imported log files often have 3 header rows and a summary footer. Instead of manually deleting them, use Remove Top Rows = 3 and Remove Bottom Rows = 1.

Power Query editor interface showcasing options for data transformation, highlighting menu for managing and removing rows including top, bottom, alternate, duplicate, blank, and error rows.

8. Replace Values with Advanced Options

The Replace Values function has an Advanced Options button that few people notice.It lets you:

• Replace entire values

• Replace only within part of a string

• Match case or ignore it

Example: In a product list, replace “Ltd.” with “Limited” only if it occurs at the end of a name, leaving “Ltd. Edition” untouched.

Using Power Query in Microsoft Excel to replace values in the 'Product_Table', highlighting the 'Replace Values' function for efficient data transformation and cleaning.

9. Filter with “Load More” and Dynamic Date Filters

Filtering in Power Query has extra flexibility:

• Load More lets you see additional unique values when the preview is limited.

• Dynamic Date Filters let you pick “This Month,” “Next 7 Days,” or “Year to Date” directly from the filter menu.

Example: Want to see only orders from the current month? Choose “This Month” instead of building a custom condition.

A user navigates the date filter options in a spreadsheet, displaying various date selection criteria and a warning about reaching the limit of 1000 values.

10. Combine Files Wizard (for Folders)

If you load files from a folder, Power Query offers a Combine Files wizard that automatically:

• Applies the same transformation to all files

• Detects headers

• Builds a reusable function behind the scenes (no coding needed)

Example: Weekly CSV reports from accounting can be combined into a single dataset automatically, without reloading each file.

Bonus Tips

✅ Use Choose Columns (Home tab) to keep only the fields you need — it improves performance. ✅ Right-click on steps in the Applied Steps pane → rename them clearly (“Remove Extra Header Rows” instead of “Removed Top Rows”). ✅ Use Close & Load To… in Excel to decide whether to load as Table, Pivot Table, or only create a connection.

Managing data columns in Power Query editor, with options to select or deselect specific fields for analysis in a sales dataset.

Conclusion

Power Query’s UI hides a lot more than meets the eye. From data profiling to fuzzy merges and advanced grouping, you can solve most data cleaning challenges without writing a single line of M code.

The next time you’re in Power Query, explore the View and Home tabs a bit deeper — you’ll be surprised how much time you can save.