How to Delete Multiple Columns in Power BI

Nothing slows down your Power BI report like clunky, unnecessary data. Before you start building stunning visuals, it’s crucial to clean up your dataset, and a big part of that cleanup is removing columns you don’t need. This article will show you several ways to delete multiple columns in Power BI, from the simple point-and-click method to a more powerful approach for handling large datasets.

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Why Should You Remove Unnecessary Columns?

It might seem harmless to leave a few extra columns in your data model, but doing so can have a real impact on your report's performance and usability. Taking a moment to tidy up your queries pays off significantly.

Here’s why it's a best practice to remove any columns you won't use:

• Improved Performance: Every column, especially those with many unique values, adds to the size of your data model saved in memory. Smaller models are faster to load, refresh, and calculate. Removing fifty unneeded columns from a table with a million rows can dramatically speed up your report.
• Enhanced Usability: When you’re building visuals or writing DAX measures, fewer columns mean less scrolling and searching. A clean, streamlined Fields pane makes it much easier to find the exact data you need, allowing you to build reports more quickly.
• Reduced Model Complexity: A trimmed-down dataset is simpler to understand and manage. This reduces the cognitive load for you and anyone else who might have to work with your report in the future.
• Data Privacy and Security: Your raw data might contain sensitive information, like email addresses or personal identifiers, that isn't necessary for the final report. Removing these columns at the source is a good practice for data security.

Getting Started: An Introduction to the Power Query Editor

All data transformation and cleanup tasks in Power BI happen within the Power Query Editor. It's your workshop for shaping, cleaning, and preparing data before it ever gets loaded into the data model for your report.

To open the Power Query Editor, go to the Home ribbon in Power BI Desktop and click the Transform data button.

Once inside, you’ll notice three key areas:

1. Queries Pane (Left): A list of all the data tables you’ve connected to.
2. Data Preview Pane (Center): A preview of the data in the selected table.
3. Applied Steps Pane (Right): A list of every transformation step you’ve applied to the data. This is crucial because it tracks your work, allowing you to edit or undo changes easily.

With that context, let's explore the best ways to delete multiple columns.

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Method 1: Manually Selecting Columns to Remove (The Point-and-Click Way)

This is the most straightforward method and is perfect when you only need to remove a handful of columns. It functions just like selecting files in your computer's file explorer.

Step-by-Step Instructions:

1. Open the Power Query Editor by clicking Transform data in the Home ribbon.
2. Select the query (your data table) from the Queries pane on the left.
3. Now, select the columns you want to delete in the data preview pane.
4. Once your columns are selected, you have two options to remove them:

You’ll immediately see a new step named Removed Columns appear in your Applied Steps pane. Power BI has recorded this action, and the columns are now gone from your query.

Best for: Quickly removing 2-10 columns without much fuss. Downsides: Can be tedious and easy to misclick if you're trying to remove dozens of columns from a wide table.

Method 2: Using “Choose Columns” to Select What to Keep

When your table has hundreds of columns and you only need, say, fifteen of them, manually selecting the ones to remove is incredibly inefficient. It's much easier to specify which columns you want to keep. The "Choose Columns" feature is perfect for this.

Step-by-Step Instructions:

1. From within the Power Query Editor, go to the Home ribbon.
2. In the Manage Columns group, click Choose Columns.
3. A dialog box will pop up titled "Choose Columns" that lists every single column in your table with a checkbox next to it.
4. Uncheck the box next to any column you want to remove. For large tables, it's often faster to uncheck the (Select All Columns) box at the top, which deselects everything. Then, go down the list and check only the columns you want to keep.
5. Click OK.

This method accomplishes the same goal but in a much more controlled and organized way. Behind the scenes, the M code generated (Table.SelectColumns) is often cleaner because it explicitly lists the columns you kept, not the ones you removed.

Best for: Narrowing down very wide tables to just a few essential columns. Downsides: Requires you to know exactly which columns you need upfront.

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Method 3: "Remove Other Columns" (The Inverted Option)

This is a quick alternative to the "Choose Columns" method. The logic is the same - you select what you want to keep - but the action feels a bit different. It feels more direct if you find that easier to work with.

Step-by-Step Instructions:

1. Identify and select the columns you want to KEEP in your final dataset using the Ctrl or Shift keys, just as we did in the first method.
2. Once you have highlighted all the columns you want to preserve, right-click any of the selected column headers.
3. From the context menu, select Remove Other Columns.

Power BI will instantly delete every column that was not selected. This single action is incredibly powerful and saves a huge amount of time on wide tables.

Best for: The same scenarios as "Choose Columns" - when you're keeping far fewer columns than you're removing. Downsides: This is a destructive and immediate action. If you forget to select a critical column before clicking, you'll need to undo the step.

Method 4: Manually Editing the M Code (The Power User Approach)

For ultimate control and to handle complex or repetitive scenarios, you can edit the M code that Power Query generates in the background. Every click you make in the user interface is writing a line of this code.

Step-by-Step Instructions:

1. In the Power Query Editor, click the Home tab and then Advanced Editor.
2. The Advanced Editor window will open, showing you all the M code for your current query.
3. Look for the step where you previously removed or selected columns.
4. You can now directly edit the list of column names within the curly braces { }. Just add or remove the column names (in quotes and separated by commas) to modify which columns are affected.

This is extremely powerful if you have a list of column names in a text file or spreadsheet. You can just format the list with quotes and commas and paste it directly into the Advanced Editor. This turns a task that could take 10 minutes of clicking into 10 seconds of copy-pasting.

Best for: Advanced users, making precise bulk changes, and automating data preparation. Downsides: Requires care and basic M language knowledge. A single typo, like a missing comma or quote, can break your entire query.

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Bonus Tip: Renaming Your Applied Steps

As you remove columns, Power Query creates steps like "Removed Columns", "Removed Columns1", etc. This can get confusing quickly.

You can (and should) rename these steps for clarity. Simply right-click a step in the Applied Steps pane, select Rename, and give it a more descriptive name, like "Removed Personal Info Columns" or "Kept Only Sales and Date Columns". Future you will be grateful for this thoughtful documentation.

Final Thoughts

Cleaning and preparing your data is the most important step in building an effective and performant Power BI report. Being able to quickly and efficiently remove multiple unnecessary columns using methods like "Choose Columns" and "Remove Other Columns" will speed up your workflow and improve the quality of your final product. Don't be afraid to try each method to see which one fits your specific situation best.

That initial step of connecting and cleaning data sources is often where the most time is lost. At Graphed, we’ve found that the real bottleneck isn't building charts, but getting the data ready for them. That’s why we help automate the entire process. By connecting your sources, you can use natural language to ask for exactly what you need - like "Show me gross profit and sessions from Google Ads for the last quarter." We automatically pick the right columns and prepare the data, so you can skip the manual cleanup in Power Query and get directly to answering your business questions.