How to Stack Columns in Power BI

Building a stacked column chart in Power BI is one of the best ways to show how different parts contribute to a whole over time or across categories. Instead of wrangling multiple pie charts or a cluttered line graph, you get a clean, insightful visual that's easy to understand at a glance. This guide will walk you through a few different ways to create and customize stacked columns in Power BI, from the basics to a more advanced technique for tricky data shapes.

What is a Stacked Column Chart?

Before we build one, let's quickly cover what a stacked column chart does. Imagine you want to show your company's total sales per month. A simple column chart works great for that. But what if you also want to show which product categories (e.g., "Electronics," "Apparel," "Home Goods") made up those sales each month?

That's where the stacked column chart comes in. Each column still represents the total for a given month, but it's segmented and color-coded to show the contribution of each product category. You can instantly see both the total sales trend and the shifting mix of sales within that total.

There are two main types:

• Stacked Column Chart: Shows the absolute values of each part. You can see that "Electronics" contributed $5,000 to January's sales, for example. The total height of the column reflects the sum of all segments.
• 100% Stacked Column Chart: Shows the relative percentage of each part. Instead of absolute dollar amounts, you'd see that "Electronics" made up 50% of January's sales. Every column in this chart is the same height (100%), making it ideal for comparing the proportion of categories over time, even if the totals are vastly different.

Method 1: Creating a Basic Stacked Column Chart

Let's start with the most common scenario. You have your data ready, and you just need to turn it into a visual. For this to work best, your data should be in a "long" format - meaning you have separate columns for the categories, the dates (or whatever you'll use on your x-axis), and the values you want to measure.

Imagine a simple dataset of product sales with three columns: SaleDate, ProductCategory, and Revenue.

Step 1: Get Your Data Loaded

First, make sure your data is loaded into your Power BI report. You can use the "Get Data" option on the Home ribbon to connect to your source, whether it's an Excel file, a database, or another connector.

Step 2: Add the Visual to Your Report

In the Visualizations pane on the right-hand side of your report canvas, click on the icon for the "Stacked column chart." An empty visual placeholder will appear on your canvas.

Step 3: Drag and Drop Your Data Fields

With the new blank chart selected, you'll see fields in the Visualizations pane labeled "X-axis," "Y-axis," and "Legend." This is where you tell Power BI how to build the chart.

• X-axis: This is for your categories or time periods. Drag your SaleDate field here. Power BI is smart and will often create a date hierarchy for you (Year, Quarter, Month, Day), allowing you to drill up and down.
• Y-axis: This is for the numerical value you want to measure. Drag your Revenue field here.
• Legend: This is the key to stacking. This field determines how the columns will be segmented. Drag your ProductCategory field here.

As soon as you drag ProductCategory into the Legend field, your standard column chart will instantly transform into a stacked column chart. Each column will represent a time period (like a month or year), the height of the total column will represent the total revenue for that period, and the colored segments inside will show the revenue from each product category.

Method 2: Using the 100% Stacked Column Chart for Proportional Analysis

Sometimes, the total revenue isn't as important as understanding the mix of sales. Are electronics becoming a larger or smaller part of your overall revenue? A 100% stacked chart is perfect for answering this.

The process is nearly identical to the basic stacked column chart, with just one key difference.

Step 1: Choose the 100% Stacked Column Chart Visual

In the Visualizations pane, select the "100% Stacked column chart" icon. It's usually right next to the standard stacked column chart.

Step 2: Drag and Drop the Same Fields

Just like before, populate the fields:

• X-axis: SaleDate
• Y-axis: Revenue
• Legend: ProductCategory

Immediately, you'll see a chart where every single column reaches the 100% mark. Instead of showing actual revenue on the Y-axis, it now shows percentages. This visual makes it incredibly easy to see trends in proportions. For instance, you might spot that your "Apparel" category was only 10% of sales in January but grew to 30% by December, even if total sales fluctuated.

Method 3: Stacking Columns That Aren't in a 'Stack-Friendly' Format

This is a common roadblock that trips up many Power BI users. What if your data doesn't come neatly organized with a single ProductCategory column? Oftentimes, sales data comes in a "wide" format, where each category has its own column.

Imagine your data looks like this:

• A SaleDate column
• An Electronics_Sales column
• An Apparel_Sales column
• A HomeGoods_Sales column

If you try to build a stacked chart with this structure, you'll feel stuck. You can't put multiple columns into the Y-axis to create stacks. The solution isn't in the chart configuration, it's in reshaping your data using Power Query.

The trick is a feature called Unpivot Columns, which transforms your wide data into the "long" format that Power BI visuals love.

Step-by-Step Guide to Unpivot Your Data

Step 1: Open the Power Query Editor

From the Home ribbon in Power BI, click the "Transform data" button. This will launch the Power Query Editor, a separate window where you can clean and reshape your data before it even hits your report canvas.

Step 2: Select the Columns to Unpivot

Find the table with your "wide" data. Now, select the columns you want to stack. In our example, you would click on the Electronics_Sales column header, then hold down the Shift key and click on the HomeGoods_Sales column header. This will select all three sales columns.

Step 3: Use the Unpivot Columns Function

With those columns selected, go to the "Transform" tab in the Power Query ribbon. Click the dropdown arrow on "Unpivot Columns" and choose "Unpivot Columns."

Magically, your selected columns will be gone. In their place, Power Query will have created two new columns:

• Attribute: Contains the headers of the columns you just unpivoted (e.g., "Electronics_Sales", "Apparel_Sales").
• Value: Contains the corresponding sales figures for each of those attributes.

Step 4: Rename the New Columns

"Attribute" and "Value" aren't very descriptive. Double-click on the headers of these new columns and rename them to something intuitive, like ProductCategory and Revenue. Now your data is in the perfect long format for visualization.

Step 5: Close & Apply

In the top-left corner, click the "Close & Apply" button. This will save your data transformation steps and load the newly shaped data back into your Power BI report.

Step 6: Build Your Chart (The Easy Part!)

Now, just follow the steps from Method 1 again. Add a stacked column chart to your report and drag your fields over:

• X-axis: SaleDate
• Y-axis: Revenue (your new value column)
• Legend: ProductCategory (your new attribute column)

And voila! You now have a perfectly stacked column chart, all thanks to a quick data reshape in Power Query.

Customization Tips and Best Practices

Once you've built your chart, it's time to refine it so it communicates your message clearly.

• Data Labels are Your Friend: In the "Format your visual" pane (the paintbrush icon), find the "Data labels" switch and turn it on. This shows the value of each segment directly on the chart, so viewers don't have to guess or hover to see the numbers.
• Choose Meaningful Colors: Power BI picks default colors, but you can override them. Under "Format your visual," go to "Columns" and assign specific colors to each category in your legend. Try to use brand colors or a palette that makes logical sense (e.g., using different shades of blue for related categories).
• Don't Be a Legend Hog: A stacked column chart with more than 5-7 segments per column tends to become a "rainbow," making it very difficult to read. If you have too many categories, consider grouping smaller ones into an "Other" category or choosing a different chart type.
• Clear Titling: Make sure your chart's title is descriptive. Instead of "Revenue by SaleDate and ProductCategory," try something more insightful like "Monthly Revenue Contribution by Product Category."

Final Thoughts

Creating stacked column charts in Power BI is a fundamental skill for building insightful reports. Whether you’re using the standard or 100% version, it provides a powerful way to visualize part-to-whole relationships. And now, you know how to use Power Query to unpivot your data, overcoming one of the most common formatting hurdles.

Manually preparing your data and building visuals in powerful tools like Power BI can be incredibly effective, but it often takes a lot of time - time that you could be spending finding insights. At Graphed, we streamline this entire process for marketing and sales data. We built an AI data analyst that connects directly to sources like Google Analytics, Shopify, and Salesforce. Instead of clicking through menus and reshaping data, you can simply ask in plain English - "show me revenue by product category from Shopify as a stacked column chart for the last quarter" - and watch a live, interactive dashboard get built for you in seconds.