Power Query: Column By Example Tips

Key insights

• Column From Examples
  Let users create a new column by typing sample outputs and Power Query infers the transformation automatically. This avoids writing M or DAX formulas and speeds up data prep.
• Power Query Editor
  Open Add Column > Column From Examples > From All Columns, type a few example values, and Power Query detects the pattern for the whole column. The interface shows the suggested result as you type.
• Pattern detection & applied steps
  Power Query converts your examples into recorded transformation steps you can review and edit. This makes the logic transparent, repeatable, and easy to refine.
• Common use cases
  Use it to concatenate fields, extract parts of text (like "Montgomery, AL"), fix formats, or clean messy data without complex formulas. It works well for many repetitive transformation tasks.
• Web and cloud support
  The feature is available in Power BI Desktop, Excel, Power BI Service, and Microsoft Fabric, including in-browser Power Query in the Power BI web service for full cloud-based data prep.
• Benefits and best practices
  Save time, lower the bar for non-coders, and improve consistency; test examples across edge cases, check applied steps, and adjust samples to capture all variations in your data.

Overview of the Video

The YouTube video by Reza Rad (RADACAD) [MVP] introduces viewers to the Column From Examples capability in Power Query, showing how users can prepare and transform data without writing code. It highlights that this feature is available across tools such as Power BI Desktop, the Power Query Editor, Excel, Dataflow in the Power BI service, and Microsoft Fabric. Consequently, Reza frames the tool as a practical option for people who know the desired output but are unsure how to express it in code. Therefore, the video aims to simplify data shaping tasks for a broad audience of analysts and report authors.

How Column From Examples Works

First, Reza demonstrates the workflow by opening the Power Query Editor and choosing Add Column > Column From Examples > From All Columns, then typing a sample output in the new column. The engine detects patterns across rows and builds a series of transformation steps that appear in the query’s Applied Steps pane, which users can inspect and edit. Thus, users gain both immediacy and transparency: they see the generated logic and can refine it if needed. Moreover, this approach reduces reliance on the M language or complex formulas, lowering the barrier for non-developers to perform common transformations.

Then, Reza highlights how pattern detection handles tasks like concatenating fields, extracting parts of strings, or reformatting values by providing a few examples. The tool attempts to generalize the examples into a rule that applies to the whole column and shows the intermediate steps, enabling users to validate the transformation. However, the generalization can sometimes be imperfect, so the video stresses reviewing the Applied Steps and testing on varied data. In practice, this interactive loop of example, inspect, and adjust helps build both accurate results and user confidence quickly.

Real-World Examples and Benefits

Throughout the demo, Reza uses real-world scenarios such as combining city and state fields into a single text value and extracting specific substrings, making it easier to see practical value. These examples show that routine cleaning—like trimming, splitting, or combining columns—can be automated in minutes, which saves analysts time and reduces repetitive manual work. As a result, teams can move faster from raw data to usable models and reports while keeping transformations visible and auditable. Additionally, because the steps are part of the query, they become repeatable when data refreshes, supporting consistent results over time.

The video also points out that recent updates extend these capabilities into the web environment, allowing users to run the same kind of transformations directly in the Power BI service and in cloud-hosted tools like Microsoft Fabric. Consequently, organizations that rely on browser-based workflows can maintain an end-to-end process without switching back to desktop tools. This alignment between desktop and cloud reduces friction for distributed teams and supports a more integrated data pipeline. Therefore, the feature fits well into modern data practices where agility and accessibility matter.

Tradeoffs and Challenges

Despite its clear benefits, Reza addresses important tradeoffs: while Column From Examples speeds up many tasks, it offers less granular control than writing custom M code or DAX expressions. In other words, the convenience can come at the cost of precision when edge cases arise or when transformations need to handle unusually formatted rows. Consequently, users must balance speed against the need for exact logic and consider switching to manual formulas when reliability on every row is essential.

Moreover, the video notes challenges related to pattern inference, such as overfitting to the examples or misinterpreting ambiguous inputs. Performance can also be an issue on very large datasets, where the interactive inference and preview step can slow down. Finally, maintainability matters: automatically generated steps might be less readable to team members who expect explicit code, so labeling and documenting transformations becomes important. Thus, the tool works best as part of a disciplined workflow that includes testing and review.

Practical Tips and Summary

In conclusion, Reza recommends using Column From Examples for rapid prototyping and common cleanup tasks, while reserving manual formulas for complex or mission-critical transformations. He suggests testing sample rules on diverse rows, inspecting the Applied Steps carefully, and naming steps clearly so others can follow the logic. By combining this feature with review and occasional hand-crafted code, teams can enjoy fast development and robust results.

Overall, the video provides a clear, hands-on guide that balances excitement about the feature with realistic advice about its limits. Therefore, analysts who want to work faster with less code will find the demo useful, and teams can adopt the technique while keeping an eye on edge cases and performance. The result is a practical, readable walkthrough that helps both beginners and experienced users evaluate when and how to use this useful part of the Power Query toolbox.

Keywords

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