How to Remove Rows in Power BI Transform Data

Cleaning up your data often feels like the chore you have to do before you get to the fun part of analysis. If your dataset is riddled with blank rows, summary footers, or just plain incorrect entries, your final report will be inaccurate. The good news is that Power BI’s Power Query Editor (found under the “Transform Data” button) gives you a ton of simple but powerful tools to remove any unwanted rows.

This tutorial will walk you through the most effective ways to remove rows in Power BI. We’ll cover everything from removing the first few rows of a file to dynamically filtering out data based on specific conditions, so you can build your reports with clean, reliable data.

First, What Is the Power Query Editor?

Before you remove any rows, you need to know where to do it. All data cleaning and transformation in Power BI happens in a separate window called the Power Query Editor. Think of it as your workshop where you prepare your raw data (like chopping vegetables and trimming fat) before you start cooking (building visuals).

To access it, once you've loaded your data into Power BI Desktop, look at the Home tab on the ribbon. You’ll see a button labeled Transform Data. Click it.

This will launch the Power Query Editor, a separate window where you can shape and refine your dataset. Every action you see here will be performed on your raw data before it’s loaded into your final Power BI report.

Method 1: Removing a Specific Number of Rows

Sometimes your data export includes extra information at the very top or bottom of the file that isn't part of the actual dataset. A common culprit is a CSV file that has a title in the first row and a file export date in the second row, with the real column headers starting on row three.

Power Query has dedicated buttons just for these situations.

Removing Top Rows

This option is perfect for deleting those introductory header or title rows that aren't the actual column names.

1. In the Power Query Editor, go to the Home tab.
2. Click the Remove Rows dropdown menu.
3. Select Remove Top Rows.
4. A small window will pop up asking you for the Number of rows to remove.
5. Enter the number and click OK.

Just like that, the specified number of rows from the top of your dataset are gone. A new step called "Removed Top Rows" will appear in the Applied Steps pane on the right, keeping a log of your transformation.

Removing Bottom Rows

This works exactly like removing top rows, but it starts from the end of your dataset. This is extremely useful for removing summary rows, footers, or "End of Report" notifications that some systems add to data exports.

1. In the Home tab, click the Remove Rows dropdown.
2. Select Remove Bottom Rows.
3. Enter the number of rows you want to remove from the bottom and click OK.

Removing Alternate Rows

This is a more niche but occasionally useful feature. It allows you to remove rows in a repeating pattern. For instance, if your data was exported with an empty row between every valid data row, this is the tool for you.

1. In the Remove Rows dropdown, select Remove Alternate Rows.
2. A window appears with three fields:
3. Click OK to apply the pattern.

Using the example settings above (remove from row 2, remove 1, keep 1), you would effectively keep rows 1, 3, 5, 7, and so on, while removing rows 2, 4, 6, 8.

Method 2: Removing Rows Based on Data Quality

Clean data goes beyond just extra headers and footers. Power Query can also automatically find and remove entire rows that contain common data issues like duplicates, blanks, or errors.

Remove Duplicates

Duplicate entries are a common headache, potentially leading to inflated totals and skewed analytics. Power BI makes it easy to remove them.

To check for duplicates across all columns (i.e., remove rows that are a perfect match of another row):

1. Select the small table icon in the top-left corner of the data preview.
2. Click Remove Duplicates.

More often, you need to remove duplicates based on a specific column, like a transaction ID or customer email. To do this:

1. Select the column(s) that should contain unique values. To select multiple columns, hold down Ctrl while clicking the column headers.
2. Right-click on one of the selected column headers.
3. From the context menu, choose Remove Duplicates.

This action will only remove a row if the value(s) in the selected column(s) are duplicated elsewhere. The first instance of the value will be kept.

Remove Blank Rows

Blank rows can make your dataset look messy and sometimes cause calculation issues. Use this handy shortcut to get rid of them.

1. On the Home tab, click the Remove Rows dropdown.
2. Select Remove Blank Rows.

Important Note: This option removes only the rows where every single cell in that row is null or empty. If you have a row where only some cells are empty, but others have data, this function will not remove it. For that kind of specific removal, you’ll want to use filters, which we’ll cover next.

Remove Errors

An error in Power Query usually occurs because of a data type mismatch — for example, a cell containing text like "N/A" in a column that has been set to the "Number" data type. These errors can stop your data from loading correctly and break your visuals.

To safely delete any row containing a cell error:

1. Go to the Keep Rows dropdown on the Home tab.
2. Select Remove Errors.

Power Query will scan all columns and remove every row that has at least one cell with an error, ensuring that only clean, convertible data remains.

Method 3: Filtering Rows (The Most Powerful Approach)

While the Remove Rows buttons are great for dedicated tasks, the most flexible and common way to remove rows is by using filters. Filtering works by defining criteria for what data you want to keep, everything else is automatically removed.

Every column header in the Power Query Editor has a small dropdown arrow. Clicking this opens the filter menu for that column, just like in Excel.

Unchecking Values

The simplest method is to uncheck the values you don't want. For example, if you have a Sales Region column and you want to exclude "Internal Testing," you can just click the filter arrow and uncheck the box next to "Internal Testing."

Using Basic and Advanced Filters

For more complex logic, use the filter type menus. Depending on the column’s data type, this will show options like Text Filters, Number Filters, or Date/Time Filters.

Example: Filtering text

• Goal: Remove all rows where the Campaign Name contains the word "Test".
• How: Click the filter arrow on the Campaign Name column → hover over Text Filters → select Does Not Contain → enter "Test" in the text box.

This tells Power BI to keep only the rows where the campaign name does not include "Test".

Example: Filtering numbers

• Goal: Remove any sales transactions that amount to $0.
• How: Click the filter arrow on the Amount column → hover over Number Filters → select Does Not Equal → enter "0". Alternatively, you could use Greater Than and enter "0" to only keep positive sales.

Example: Filtering out nulls in a specific column

Remember when we said Remove Blank Rows only works for totally empty rows? Filtering is how you handle partially empty ones. If you want to remove rows where just a Customer ID is missing:

1. Click the filter arrow on the Customer ID column.
2. In the checklist of values, you'll see (null) or (Blank). Simply uncheck it.

Done. Any row without a Customer ID is gone, regardless of what's in the other columns. This is one of the most common data cleaning actions in Power BI.

Final Thoughts

You now have a complete toolkit for tidying up your datasets by removing unwanted rows directly within Power BI's Transform Data window. From simple top/bottom removals to precision cleaning with data quality tools and dynamic filters, mastering these steps is fundamental to creating accurate and trustworthy reports that drive smart decisions.

Manually performing these types of cleaning steps is a crucial part of data analysis, but it's also where countless hours are lost — clicking through menus, defining filters, and checking your work before you can even build a single chart. For us, we believe this entire process should be faster and more intuitive. That’s why we built Graphed, your AI data analyst that lets you connect data and get insights just by asking questions in plain English. Instead of manually applying five different filters, you can just ask, "Show me my sales by region last quarter, excluding test campaigns and zero-dollar orders," and get a live, interactive dashboard instantly.