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How to Remove Blank from Filter in Power BI

Cody Schneider••9 min read

Nothing brings your data exploration to a halt quite like an unexpected "(Blank)" option cluttering up your Power BI slicer. It’s unsightly, it can confuse your audience, and it signals there might be something off in your data model. This article will walk you through exactly why those blanks appear and give you several clear, step-by-step methods to get rid of them for good.

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What Causes "(Blank)" Values in Power BI Filters?

Before jumping into the fixes, it's helpful to understand the cause. More often than not, a blank value shows up in your slicer due to an issue with referential integrity between your data tables. That might sound fancy, but the concept is simple.

Imagine you have two tables:

  • A Sales table with a list of all transactions, including a ProductID.
  • A Products table that lists all your products, with ProductID as the unique key.

In a perfect world, every single ProductID in your Sales table has a matching ProductID in your Products table. You have established a "one-to-many" relationship between these two tables, linking them on ProductID.

Now, what happens if a sale was recorded in your Sales table for ProductID "P-501," but your Products table doesn't contain a product with that ID? When you create a slicer using a field from the Products table (like ProductName), Power BI looks at the Sales table and sees data related to "P-501." Since it can't find a matching product name for that ID in your Products table, it calls it "(Blank)."

Essentially, Power BI is telling you, "I have data for a product that you haven’t defined. I don't know what to call it, so I am calling it blank." Other causes can include actual empty cells or null values in your text columns, but a relationship gap is the most common culprit.

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Four Methods to Remove Blanks from Your Slicer

Now that you know the why, let's explore the how. We'll start with the quickest fixes and move toward the most robust, long-term solutions. You can choose the method that best fits your needs.

Method 1: Use the Filters Pane (The Quick Fix)

This is the fastest way to hide blanks from a specific slicer without changing your underlying data. It’s perfect for a quick cleanup of a single report visual.

Step-by-Step Instructions:

  1. Select your slicer: Click on the slicer visual on your report canvas that you want to clean up.
  2. Open the Filters pane: If it's not already open, go to the View tab and make sure the Filters pane is checked. This pane typically appears to the right of your report.
  3. Find the field: You will see the field that your slicer is built on under the "Filters on this visual" section. If you don't see it there, simply drag the field from your Data pane into the "Filters on this visual" well.
  4. Expand the filter options: Click the small arrow next to the field name to expand its options.
  5. Change to "Basic filtering": If it’s not selected already, change the Filter type to "Basic filtering." This will show you a list of all available values in that field, including the pesky "(Blank)".
  6. Exclude the blank value: Simply check the "Select all" box, and then uncheck the box next to (Blank). Alternatively, you can deselect "(Blank)" from the list.

That’s it! The blank option will immediately disappear from that specific slicer. The chief benefit here is speed and simplicity. The main drawback is that this is a manual fix you have to apply to every single slicer where this problem occurs. It hides the issue, but it does not resolve the root cause.

Method 2: Use an Advanced Filter (Filtered Out “Is Not Blank”)

Another way to eliminate blanks from a visual is with a more advanced filter based on a rule in Power BI.

Step-by-Step Instructions:

  1. Select your slicer: Just like the last method, the first step is always to let Power BI know which visualization you are focused on fixing.
  2. Open the Filter options: Use the View tab to open the filters visualization pane if it is not open already. Then drag your related field from the Data pane into the "Filters on this visual" well. Then select the arrow next to the field name to expand more of its filter options.
  3. Change to "Advanced filtering": Under the Filter Type pulldown, scroll all the way to the right and select the "Advanced filtering" option.
  4. Add your filter value: Power BI needs two things here: the condition and a value. Select the Show Items where Value condition: “is not blank”.
  5. Apply the filter: Click the big yellow Apply filter button and your visualization will automatically remove your blank values.

This is a more declarative and repeatable filter method. However, if you want this to apply across future visualizations and every page in a report, it will not. This method still hides your problem instead of fixing it for good. Let's cover that next!

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Method 3: Clean Your Data in Power Query Editor (The Best Practice)

This approach fixes the problem at its source. By using the Power Query Editor (the data transformation engine inside Power BI), you can remove the blank rows so they never make it into your data model in the first place.

When to use this method:

This is the ideal solution if the blanks are caused by actual empty cells in your dimension table (the "one" side of the relationship, like your Products table). It ensures that your data model is clean, accurate, and won't cause problems elsewhere.

Steps:

  1. Open Power Query Editor: From your main Power BI Desktop window, click on the Transform data button on the Home ribbon. Power BI will open a new window giving you access to every data connection, every table, every query, etc., in a project file. This is the place to fix many problems at the project level.
  2. Select the query: In the query panel to the right, Power BI provides quick navigation to all sources in a report. Identify and select the table that contains the blank values.
  3. Identify the column with the blank: Look through the table rows and identify and select the column that contains blanks to filter.
  4. Option A: Remove blank rows: Click the pull-down menu under the Home Ribbon in the Reduce Rows group. Find the Home > Reduce Rows > Remove Rows > Remove Blank Rows option. This function can quickly and easily go through your query and remove blank rows from the set. Blank often refers to rows that are entirely empty across all columns. For one or few rows, you need a more advanced solution: a data filter.
  5. Option B: Filter out the blanks: Just like in Excel, every column has a filter button on the right of the column name. Click to select this button and scroll through the data rows. The small window that appears works just like in Excel or Sheets – click the options you want displayed and unclick the values you do not. You can simply scroll to the bottom of the list and unselect 'blank' or 'null', and Power Query will remove these rows from the query and hide them from the data modeling table on the next refresh.

This is the most complete and robust solution for cleaning your data. By cleaning at the source, any visualization, measure, or logic in the report can now use these blank-free tables to perform their operations. You might have a case where the data should not be cleaned, though, and it would be a good reason to go down the path of one more robust solution:

Method 4: Fix the Data Model Relationship Problem (The Real Fix)

This solution returns to the root cause and aims to fix the data source or model of the entire project to be accurate. You can't report on what you don't measure. When you find disconnects between your tables, you need to fix them at the data source or create steps in Power Query that do this for you. That may come from cleaning your CRM by doing de-duplication and a quality review, or it can mean doing merges in Power Query to fix the problem.

As discussed above, this problem is not actually a Power BI problem, it's a data quality problem that appears when you build in Power BI. Going to Power Query is the first step, and fixing your sources and adding data-cleaning processes to your team's weekly work should be the very next step. It is the only way to fix the root cause and get better, cleaner, and more insightful results for your teams. After all the time and effort spent building reports, you owe it to yourself and the end consumer to make it right so that means having a report that accurately reflects your organization or business and drives the results you desire. Data hygiene helps every stakeholder reach those goals.

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Final Thoughts

Dealing with blank values in Power BI is a matter of choosing the method that best suits your needs and fixing your data quality problem. The quick filter method is perfect for a fast fix when your deadline is looming. Meanwhile, cleaning the source data in Power Query, or in the data model, ensures a healthy, reliable report and should always be the goal to improve the project quality for the next report and refresh. Fixing the data sources and data nodes is a must to keep the problem from coming back.

We see folks struggle with these types of messy data disconnects not just in Power BI but across every reporting platform imaginable - from Google Analytics to Shopify to Salesforce. That grinding, manual process of cleaning data source by source inspired our team here at Graphed. We connect directly to your marketing and sales tools and handle the messy integration part so you can create real-time, shareable dashboards instantly by just describing what you want to see in plain English. This eliminates the platform-hopping headache, allowing you to get from raw data to a clean dashboard in seconds, not hours.

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