Course: Microsoft Power BI Training
Topic: Using Pie Charts, Donut Charts, and Treemaps
Course Link: Microsoft Power BI Training at JustAcademy
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Microsoft Power BI provides several visualization options for analyzing business data. Pie charts, donut charts, and treemaps are commonly used to show how individual categories contribute to a total. They help users understand proportions, identify major contributors, and compare the distribution of values across categories.
Although these three visuals can communicate part-to-whole relationships, each has different characteristics. Pie charts represent values as slices of a circle, donut charts display values as segments of a ring, and treemaps represent values using nested rectangles whose sizes correspond to the underlying measure.
Choosing the correct visualization depends on the number of categories, the complexity of the data, the presence of a hierarchy, and the question that the report needs to answer.
A pie chart is a circular visualization divided into slices. Each slice represents a category's contribution to the total value. The angle and area of each slice are proportional to its share of the whole.
Example: A company generates ₹10,00,000 in total sales from four product categories. A pie chart can show the percentage of total sales contributed by each category.
A donut chart is similar to a pie chart, but it contains a blank center. It displays categories as segments of a ring and can use the center to display a total or another relevant measure, depending on the visual's supported formatting options.
Example: A donut chart can show the percentage of revenue generated by different sales channels while displaying total revenue in the center.
A treemap represents data using a collection of nested rectangles. The size of each rectangle represents a numerical value, such as sales, revenue, profit, or quantity. Different colors can distinguish categories or communicate an additional measure when the visual supports it.
Example: A treemap can show sales by product category and then break each category into individual products.
After completing this topic, learners should be able to:
Part-to-whole analysis examines how individual components contribute to an overall total. It is useful for understanding the distribution of revenue, expenses, customers, products, orders, and other business metrics.
Suppose an online retailer generates ₹5,00,000 in sales from four categories.
| Category | Sales | Contribution |
|---|---|---|
| Electronics | ₹2,00,000 | 40% |
| Clothing | ₹1,50,000 | 30% |
| Home Appliances | ₹1,00,000 | 20% |
| Accessories | ₹50,000 | 10% |
| Total | ₹5,00,000 | 100% |
This information can be represented using a pie chart or donut chart because each category contributes to a common total. A treemap can also display the relative size of each category through rectangle areas.
The percentage contribution of a category is calculated by dividing its value by the overall total and multiplying the result by 100.
Percentage Contribution =
(Category Value / Total Value) * 100For Electronics:
Percentage Contribution =
(200000 / 500000) * 100
Percentage Contribution = 40%Before creating a pie chart, donut chart, or treemap, ensure that the dataset has meaningful categories and numerical values. Incorrect data types, duplicated records, inappropriate aggregation, or missing categories can lead to misleading results.
| Product Category | Product | Region | Sales Amount |
|---|---|---|---|
| Electronics | Laptop | North | 120000 |
| Electronics | Mobile | South | 80000 |
| Clothing | Shirts | North | 90000 |
| Clothing | Shoes | West | 60000 |
| Home Appliances | Refrigerator | South | 70000 |
| Home Appliances | Washing Machine | West | 30000 |
| Accessories | Headphones | North | 30000 |
| Accessories | Chargers | South | 20000 |
A DAX measure calculates total sales according to the current report filter context. It can be reused across different visuals.
Total Sales =
SUM(Sales[Sales Amount])Replace Sales and Sales Amount with the actual table and column names in your model.
A pie chart is useful when the objective is to show how a small number of categories contribute to a common total. It is most effective when the category shares are easy to distinguish.
| Field | Purpose | Example |
|---|---|---|
| Legend | Identifies the categories represented by slices. | Product Category |
| Values | Determines the numerical size of each slice. | Total Sales |
| Tooltips | Provides additional information when users hover over a slice. | Profit, order count |
Exact field-well names and formatting options may vary slightly by Power BI version. Use the equivalent fields shown in the current visual-building interface.
Each slice represents a category's contribution to the total. A larger slice indicates a larger value, while a smaller slice indicates a smaller value.
For example, if Electronics accounts for 40% of sales and Accessories accounts for 10%, the Electronics slice will occupy a larger portion of the circle.
Select the pie chart and open the Format visual pane. Depending on the current Power BI version, relevant settings may include:
Suppose the pie chart shows Electronics at 40%, Clothing at 30%, Home Appliances at 20%, and Accessories at 10%.
This visualization helps a manager understand category contribution at a glance, although a sorted bar chart may be easier for precise comparisons.
A donut chart represents categories using segments of a ring. It communicates part-to-whole relationships similarly to a pie chart, but its center can provide space for a summary value or label.
Supported Power BI donut chart versions provide a Center value option. This can display a total or a selected measure in the middle of the ring, without requiring a separate overlaid card.
The center value is a donut chart feature, not a standard pie chart feature. Available formatting options can vary by Power BI version.
Suppose a company has total sales of ₹5,00,000. The donut chart shows the category shares as segments, while the center displays Total Sales: ₹5,00,000.
This combination allows the audience to understand both the overall sales figure and the contribution of individual categories.
A treemap displays hierarchical data through nested rectangles. Each rectangle's area represents the value of the associated category or item. Treemaps are especially useful when there are many categories or multiple levels of grouping.
A treemap arranges rectangles according to the values being visualized. Large values generally occupy larger rectangles, while smaller values occupy smaller rectangles. Parent categories can contain smaller rectangles representing child categories.
For example, a treemap can display sales by product category, with each category divided into individual products.
| Field | Purpose | Example |
|---|---|---|
| Category | Defines the main groups or parent categories. | Product Category |
| Details | Splits the categories into smaller items. | Product |
| Values | Determines the size of the rectangles. | Total Sales |
| Tooltips | Displays additional information when hovering over rectangles. | Profit, units sold |
The exact field wells can vary by interface version. Use the corresponding Category, Details, Values, and tooltip options available for the built-in treemap visual.
Consider the following hierarchy:
Total Sales
├── Electronics
│ ├── Laptop
│ └── Mobile
├── Clothing
│ ├── Shirts
│ └── Shoes
├── Home Appliances
│ ├── Refrigerator
│ └── Washing Machine
└── Accessories
├── Headphones
└── ChargersThe main rectangles represent product categories, while smaller rectangles represent individual products. The area of each rectangle reflects its sales value.
A larger rectangle indicates a higher value for the measure being visualized. A smaller rectangle indicates a lower value.
For example, if Laptop sales are ₹1,20,000 and Charger sales are ₹20,000, the Laptop rectangle should occupy a larger area than the Charger rectangle when both are displayed using the same sales measure and comparable hierarchy.
Colors can help distinguish categories or communicate additional information, depending on the visual's available formatting options.
| Feature | Pie Chart | Donut Chart | Treemap |
|---|---|---|---|
| Visual structure | Circle divided into slices | Ring divided into segments | Nested rectangles |
| Main purpose | Show part-to-whole relationships | Show part-to-whole relationships, optionally with a center value | Show proportions and hierarchical categories |
| Best suited for | A small number of categories | A small number of categories with an optional summary value | Many categories or hierarchical data |
| Value encoding | Slice angle and area | Segment angle and area | Rectangle area |
| Center summary value | Not a standard built-in feature | Supported in applicable Power BI versions | Not its primary design purpose |
| Hierarchical display | Limited; hierarchy can be explored through drill features | Limited; hierarchy can be explored through drill features | Well suited to nested categories |
| Large number of categories | Can become cluttered | Can become cluttered | Can accommodate many categories, subject to readability |
| Precise comparisons | Often difficult for similar-sized slices | Often difficult for similar-sized segments | Area comparisons can also be difficult; a bar chart may be clearer |
Percentage contribution is often displayed in pie charts and donut charts. It can also provide useful context when analyzing categories in a treemap.
Total Sales =
SUM(Sales[Sales Amount])To calculate each category's contribution to the total across all product categories while respecting other filters, such as date and region, use a measure such as:
Sales % of All Categories =
DIVIDE(
[Total Sales],
CALCULATE(
[Total Sales],
REMOVEFILTERS(Sales[Product Category])
)
)This example assumes Product Category is a column in the Sales table. If your model uses a separate Product dimension, remove the filter from the corresponding dimension column instead. The denominator should match the intended definition of the total.
The denominator determines what the percentage means. A category may be compared with all categories, all categories in a selected region, or all categories in a selected month.
Using REMOVEFILTERS on the category field removes that category filter while preserving other applicable filters. This is useful when the goal is to show category contribution within the current report context.
When comparing category performance, you may also create measures for average sales, profit, or units sold. Choose the measure that best answers the business question and ensure that it has the correct aggregation.
Total Profit =
SUM(Sales[Profit])
Total Units Sold =
SUM(Sales[Quantity])These measures can be used as values or additional tooltip information, depending on the selected visual and its supported field wells.
Assign distinct colors to categories that users need to distinguish. Keep category colors consistent across report pages when the same categories appear repeatedly.
Display a legend when users need to identify which color represents each category. Place it where it does not obscure the chart or reduce the available viewing area unnecessarily.
Use labels to show useful information such as category name, value, or percentage when supported by the visual's formatting options.
Avoid enabling every possible label when the chart contains numerous small slices. Instead, use tooltips or a supporting bar chart for detailed comparisons.
Use a title such as "Sales Contribution by Product Category" instead of a vague title such as "Sales."
Add measures such as Total Sales, Total Profit, and Percentage Contribution to the tooltip fields when they improve the analysis. Verify that the displayed values reflect the selected category and current filters.
Use the available formatting options to create a balanced ring that leaves enough room for the center value, if one is displayed.
When appropriate, show total revenue, total customers, or another relevant measure in the center. Use a descriptive label so that viewers understand what the number represents.
A donut chart becomes harder to interpret when many categories produce narrow segments. Consider grouping small categories into an appropriate "Other" category, provided the grouping is analytically valid and does not hide important information.
If the center value already displays total sales, consider whether a separate card showing exactly the same total is necessary. Repeated information should have a clear purpose.
Use a title such as "Sales by Product Category and Product" to explain the hierarchy and metric.
Use the Category field for major groups and Details for lower-level items. Avoid adding unnecessary levels if they make the rectangles too small to interpret.
Include useful information such as sales, profit, quantity, and percentage contribution when supported. Tooltips allow users to inspect smaller rectangles without requiring permanent labels for every item.
Use colors to distinguish categories or encode a meaningful additional measure when the visual supports it. If a color scale is used, provide an understandable legend or explanation.
Very small rectangles may not have enough space for labels. Use tooltips or a supporting table for detailed values. If exact ranking is important, add a sorted bar chart.
Pie charts, donut charts, and treemaps can interact with other visuals on a report page. These interactions help users explore category contributions and discover patterns.
Cross-filtering allows a selection in one visual to filter other visuals to the corresponding data.
Example: Selecting Electronics in a pie chart can filter a table to show only Electronics products, if the report's visual interactions are configured accordingly.
Cross-highlighting emphasizes the selected portion of related data while keeping the remaining context visible in supported visuals.
This can be useful when users need to understand a category's contribution while still comparing it with the overall distribution.
Drill-down allows users to move from a higher level of a hierarchy to a lower level.
For example, users may navigate from product category to product. Pie and donut charts can support hierarchical exploration when a suitable hierarchy is configured, while treemaps naturally display nested categories.
Tooltips display additional details when users hover over a category or rectangle. They can provide exact values that are difficult to fit into a small visual.
Project Objective: Build an interactive Power BI report that analyzes how different product categories and products contribute to total sales.
Total Sales =
SUM(Sales[Sales Amount])
Total Profit =
SUM(Sales[Profit])
Total Quantity =
SUM(Sales[Quantity])
Sales % of All Categories =
DIVIDE(
[Total Sales],
CALCULATE(
[Total Sales],
REMOVEFILTERS(Sales[Product Category])
)
)Format Sales % of All Categories as a percentage. Adjust the table and column names to match the actual dataset.
By completing the project, learners should be able to create part-to-whole visualizations, compare category contributions, explore hierarchical data, configure interactive behavior, and explain which products or categories contribute most to the business total.
| Problem | Possible Cause | Solution |
|---|---|---|
| Pie chart shows unexpected totals | Incorrect aggregation, filters, or duplicate records | Validate the dataset and Total Sales measure. |
| Too many pie slices | The category field contains too many distinct values | Group categories appropriately or use a sorted bar chart. |
| Labels overlap | Segments are too small or the visual is too compact | Resize the visual, reduce labels, or use tooltips. |
| Donut center value is missing | The option is unavailable, disabled, or the visual has insufficient room | Check the formatting options and verify the visual size and data. |
| Treemap shows no meaningful hierarchy | Category and detail fields are incorrectly configured | Use a parent category and a meaningful lower-level field. |
| Treemap rectangles are difficult to read | Too many small items or insufficient space | Reduce hierarchy depth and use tooltips or a detail table. |
| Percentages do not add up as expected | The denominator or filter context is not appropriate | Review the percentage formula and the filters removed or retained. |
| Colors are confusing | Too many colors or inconsistent category mapping | Use a consistent palette and distinguish categories clearly. |
| Selecting a slice does not filter another chart | The interaction is disabled or the data model does not support the expected filter path | Check visual interaction settings and model relationships. |
| Small categories are hard to compare | Area or angle encoding makes precise comparison difficult | Use a sorted bar chart or supporting table. |
Answer: A pie chart is a circular visualization divided into slices, where each slice represents a category's contribution to the total value.
Answer: A donut chart is similar to a pie chart but has a hollow center, which can display a summary value in supported Power BI versions.
Answer: A treemap displays hierarchical data through nested rectangles whose areas represent numerical values.
Answer: Both display part-to-whole relationships, but a donut chart has a central space that can display an additional summary value.
Answer: A treemap is useful when users need to compare relative values across many categories or explore hierarchical data in a compact layout.
Answer: Too many slices become difficult to distinguish and compare, reducing readability.
Answer: Divide the category value by the intended total using DAX, and format the measure as a percentage.
Answer: The Legend field identifies the categories represented by different slices or segments.
Answer: Rectangle area is determined by the numerical measure assigned to the visual, such as sales or profit.
Answer: Yes. They can display parent categories and smaller child items as nested rectangles.
Answer: Cross-filtering allows a selection in one visual to filter other visuals to the corresponding data.
Answer: Tooltips provide additional information about a category without requiring every detail to be displayed permanently.
Answer: A sorted bar chart makes it easier to compare values precisely, particularly when category values are similar or numerous.
Answer: Too many hierarchy levels, numerous small rectangles, insufficient space, and unclear colors can reduce readability.
Answer: Validate data aggregation, percentage calculations, labels, colors, filter behavior, tooltips, and the interpretation of the displayed values.
Pie charts, donut charts, and treemaps are useful Power BI visuals for understanding how different categories contribute to a business total. Pie charts are suitable for simple part-to-whole comparisons, donut charts add a central space for a summary value, and treemaps are particularly useful for hierarchical data and larger category sets.
Effective use of these visuals requires clean data, correct aggregation, meaningful categories, readable labels, suitable colors, and a clear understanding of the business question. Percentage contribution measures, tooltips, slicers, and cross-filtering can further improve the analytical experience.
By applying these principles and completing the practical exercises, learners can build informative Power BI reports that help stakeholders understand category performance and make better decisions.
To continue learning, explore the Microsoft Power BI Training course at JustAcademy or register for a course demo.
