How to Select Multiple Columns in Power BI

Selecting multiple columns in Power BI is a fundamental skill you'll use constantly, whether you're cleaning data, reshaping a table, or creating calculations. This guide will walk you through the various ways to select columns, from simple point-and-click methods to more advanced techniques using formulas and code.

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Why Do You Need to Select Multiple Columns?

Before jumping into the "how," let's quickly cover the "why." You're not just selecting columns for fun, you're doing it to perform a specific action on them. Understanding the end goal helps you choose the best method.

Common actions include:

• Data Cleaning: Removing several unnecessary columns at once or changing the data type (e.g., from Text to Number) for a group of columns simultaneously.
• Data Transformation: Merging columns (like combining 'First Name' and 'Last Name') or unpivoting data (transforming multiple columns into rows).
• Calculations: Creating new measures or calculated columns in DAX that rely on values from multiple existing columns (e.g., Profit = Sales - Cost).
• Filtering and Organization: Using the "Choose Columns" feature to quickly keep only the columns you need from a very wide table.

Each of these tasks starts with the same basic action: selecting the right columns.

Method 1: The Classic Ctrl and Shift Click (in Power Query Editor)

The most intuitive and common way to select multiple columns is within the Power Query Editor, which is Power BI’s data transformation engine. If you're coming from an Excel background, this method will feel instantly familiar.

First, open the Power Query Editor by clicking "Transform data" on the Home ribbon in the main Power BI Desktop window.

Selecting Non-Adjacent Columns with Ctrl + Click

Use this when you need to cherry-pick specific columns that aren't next to each other.

1. Click the header of the first column you want to select.
2. Hold down the Ctrl key on your keyboard.
3. While holding Ctrl, click the headers of the other columns you wish to include in your selection.

Example: Imagine a customer table with columns for CustomerID, FirstName, MiddleName, LastName, and Email. If you want to merge FirstName and LastName, you would click on the FirstName header, hold Ctrl, and then click the LastName header. Both will now be highlighted.

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Selecting Adjacent Columns with Shift + Click

Use this when you need to select a continuous block of columns sitting side-by-side.

1. Click the header of the first column in the series.
2. Hold down the Shift key on your keyboard.
3. While holding Shift, click the header of the last column in the series.

All columns between your first and last click will be selected.

Example: You have sales data broken down by month: 'JanSales', 'FebSales', 'MarSales', all the way to 'DecSales'. To change the data type for all of them, click on the 'JanSales' header, hold Shift, and click on the 'DecSales' header. All twelve month columns will be selected in one go.

Once your columns are selected using either method, you can right-click on any of the highlighted headers to see a context menu with options like Remove Columns, Change Type, Merge Columns, and more.

Method 2: Using the "Choose Columns" Feature

When you're dealing with tables that have dozens or even hundreds of columns, scrolling left and right to Ctrl+click becomes inefficient. The "Choose Columns" feature provides a much cleaner way to select what you need.

This feature is essentially an "include" list. Instead of selecting columns to remove, you select the columns you want to keep.

Here’s how to do it:

1. In the Power Query Editor, go to the Home tab on the ribbon.
2. Find the "Manage Columns" group and click "Choose Columns".
3. A dialog box will appear, listing all columns in your table. By default, they are all checked.
4. Uncheck the box next to "(Select All Columns)" to deselect everything first.
5. Now, scroll through the list and check the boxes for only the columns you want to keep. You can also use the search bar to find columns by name.
6. Click OK.

Power BI will remove all unselected columns, leaving you with a clean table. This is incredibly useful for narrowing down wide datasets from sources like Salesforce or Google Analytics to just the essential fields.

Method 3: Selecting Columns with DAX for Calculations

So far, we've focused on the Power Query Editor for data transformation. But you'll also select columns when you're creating calculations back in the main Power BI report view using Data Analysis Expressions (DAX). Here, "selecting" isn't about highlighting but about referencing column names in a formula.

DAX formulas are used to create Calculated Columns and Measures. In both cases, you simply type the table and column name.

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Example: Creating a Calculated Column

Let's say you have a 'Sales' table with Unit Price and Units Sold columns. To create a new column for Total Revenue, you are conceptually selecting both columns for your calculation.

1. In the Data view, select your table.
2. Click on "New Column" from the ribbon.
3. In the formula bar, type your DAX expression:

Total Revenue = Sales[Unit Price] * Sales[Units Sold]

In this formula, Sales[Unit Price] and Sales[Units Sold] are your "selections." Power BI's IntelliSense will help you by autosuggesting column names as you type.

Example: Creating a Measure with Multiple Conditions

Measures are used for aggregations. Here, you often select columns within functions like CALCULATE or FILTER to define your logic.

Imagine you want to calculate sales for only a specific country and product category. Your DAX measure would reference multiple columns in the filter arguments:

USA Electronics Sales = CALCULATE( SUM(Sales[Total Revenue]), FILTER( Sales, Sales[Country] = "USA" && Sales[Category] = "Electronics" ) )

Here, you're effectively selecting four columns - 'Total Revenue' for the aggregation, and 'Country' and 'Category' for the filtering - to arrive at your final value.

Method 4: Programmatic Selection with M Code

For advanced users who want precision and repeatability, you can edit the M code directly in Power Query's Advanced Editor. Every click you make in the UI generates a line of M code in the background. By editing this code, you can change which columns are affected without using your mouse.

To access it, go to the View tab in Power Query Editor and click "Advanced Editor".

Let's look at two common functions:

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Using Table.SelectColumns

This function does exactly what its name implies: it selects specific columns from a table and discards the rest. It's the M code equivalent of the "Choose Columns" button.

An M query step might look like this:

#"Selected Columns" = Table.SelectColumns(Source,{"ProductID", "ProductName", "Total Revenue", "Date"})

Here:

• Source is the name of the previous step in your query.
• {"ProductID", ... "Date"} is a list of the column names you want to keep.

To add or remove a column in this step, you just have to edit this list directly in the Advanced Editor. This is much faster than going back through the UI for wide tables.

Using Table.RemoveColumns

Similarly, this function removes specified columns. When you select a few columns and click "Remove Columns," Power BI writes a step like this:

#"Removed Other Columns" = Table.RemoveColumns(Source,{"MiddleName", "Address2", "FaxNumber"})

You can easily add another column to the removal list by typing its name in the code, like {"MiddleName", "Address2", "FaxNumber", "InternalNote"}.

Working with M code gives you ultimate control and makes your data transformation steps explicit and easy to audit.

Final Thoughts

Mastering how to select multiple columns in Power BI - whether through a simple Ctrl+click in Power Query, building a formula in DAX, or modifying M code - is a gateway to more efficient and powerful reporting. Each method has its place, from quick, interactive data cleanup to complex, repeatable business logic.

Of course, the learning curve for tools like Power BI can be steep, especially when trying to remember the right formulas or navigation steps. We built Graphed to remove this complexity. Instead of learning DAX or clicking through menus, you can simply ask questions in plain English like, "Show me my total revenue by product category for the last quarter." We connect directly to your data sources and build interactive, real-time dashboards for you, turning hours of manual work into a 30-second task.