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A Practical Guide to Pie and Donut Charts in Power BI 

September 9, 2025 | BI Consulting Services |

1. Definition: 

Pie and donut charts are circular statistical graphics divided into slices to illustrate numerical proportions. Each slice’s size represents the percentage of a category relative to the whole. A donut chart is a variant with a hollow center. 

2. Key Purpose: 

To visualize part-to-whole relationships for categorical data, showing how individual components contribute to a total sum (e.g., market share, budget allocation, survey responses). 

3. The pie/donut chart visual in Power BI works by representing parts of a whole. Its core mechanics are as follows: 

  • The full circle represents the total (100%). 
  • Each slice’s arc length (central angle and area) is proportional to its quantity. 
  • Colors differentiate categories, and labels/legends identify slices. 
  • Donut charts have a hollow center, which can display a total value or title. 

4. Pie and donut charts are most effective in the following scenarios: 

  • Displaying simple proportions with few categories (ideally 2–5). 
  • Highlighting a dominant category (e.g., >50% share). 
  • Communicating basic percentages to general audiences. 
  • Using the donut center to emphasize a key metric (e.g., total revenue). 

5. Advantages of Pie and Donut Charts: 

Pie and donut charts offer several benefits for data visualization: 

  • Universally recognized and intuitively understood. 
  • Visually appealing for presentations and infographics. 
  • Donut charts reduce visual weight and allow center annotations. 

These strengths make pie and donut charts valuable for communicating simple proportional data to broad audiences.  

6. Limitations of Pie and Donut Charts: 

While useful in specific cases, pie and donut charts have significant drawbacks: 

  • Ineffective for comparing many categories or similar-sized slices. 
  • Accuracy relies on labels; humans misjudge slice sizes. 
  • Become cluttered with too many slices. 
  • Cannot show trends over time or compare multiple datasets 

7. Demo 

This guide uses the ‘Black Friday Sales’ dataset available on kaggle.com. Upload this file into Power BI now to follow along with the steps in the following sections. 

The columns used for this demo are as follows: 

  • Gender- The sex of the buyer  
  • Age – The age category of the buyer 
  • Purchase- The purchase amount of the transaction initiated by the buyer.  

Loading Data in Power BI Desktop  

Follow these steps to load the “train.csv” file into Power BI Desktop:  

  1. Open Power BI Desktop  
  1. Click the “Get Data” option and select “Text/CSV” for data source selection and extraction.  
  1. Navigate to and select the “train.csv” file from your folder. After reviewing the data preview, click Load to import the dataset.  
  1. After loading the data, the main Power BI dashboard opens. This view consists of several key sections:  
  • The Canvas: The central area where you will build your visualizations. 
  • The Visualizations Pane: Where you select and format chart types: Pie Chart and Donut Chart. 
  • The Fields Pane: This pane lists all the tables and columns (variables) from the dataset, which you drag onto the canvas to create visuals. 
  1. Click the pie chart icon in the Visualizations pane. An empty visual will appear on the canvas. 

Case I  

In this example, we will create a pie chart to visualize the total purchase amount by gender. 

  • In the Visualizations pane, click the Pie chart icon to add it to the report canvas. 
  • In the Fields pane, locate the Purchase field and drag it into the Values section. 
  • Next, drag the Gender field into the Legend section. 

The pie chart will automatically update to show the proportion of total purchase for each gender.  

The resulting pie chart will display each gender’s contribution to total sales. Each segment is color-coded by gender and labeled with its corresponding percentage. 

Case II 

To analyze the distribution of purchase amount by both gender and age group, we will add a hierarchy to our existing pie chart for drill-down functionality. 

  • Add the Drill-Down Hierarchy: In the Fields pane, locate the Age field and drag it into the Legend bucket, placing it below the Gender field. This creates a hierarchy where you can drill down from Gender to Age. 
  • Enable Drill Mode: With the pie chart selected, click the Drill Down icon (➘) in the top-right corner of the visual. 
  • Navigate the Data: Click on a segment of the pie chart (e.g., Male) to drill down and view the breakdown of purchase amounts by age group within that category. The result will resemble the figure below. 

Note: The “Drill Up” icon (➚) will appear, allowing you to navigate back to the higher-level view. 

The resulting visualization is a pie chart that displays a hierarchical breakdown of sales: 

  • The primary view shows the distribution of purchase by Gender. 
  • Each gender segment is further divided by Age, creating a detailed, multi-level analysis. 

To view specific metrics, hover your cursor over any segment of the chart. A tooltip will appear, displaying the precise sales figures for that category. 

Ready to create powerful pie and donut charts in Power BI? Put your skills to the test with our step-by-step guide using the ‘Black Friday Sales’ dataset. Download the data and start building your own insightful visuals today. Need something more specific? Our technical team can help develop a custom visual tailored to your unique data story. Reach out for a consultation! 

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