Sunburst Charts

When to use sunburst charts

Sunburst charts are for hierarchical breakdowns, where each level nests inside the one before it:

• Region → City → Store
• Category → Subcategory → Product

The center ring is the top of the hierarchy; each ring further out is one level deeper. A segment's angle (arc width) is proportional to its value, so you can see both "how big is this branch" and "how does it split further" in one picture. Use sunbursts when the hierarchy itself is part of the story — not just as a fancier pie chart.

Example

import pandas as pd
import plotly.express as px
 
df = pd.DataFrame({
    "region": ["West", "West", "North", "North"],
    "city": ["Pune", "Mumbai", "Delhi", "Chandigarh"],
    "sales": [120, 90, 180, 60],
})
 
fig = px.sunburst(df, path=["region", "city"], values="sales", title="Sales hierarchy")
fig.show()

path=["region", "city"] tells Plotly the nesting order (outermost group first); values="sales" sizes every arc. Clicking a ring segment in the rendered chart zooms into that branch — a built-in drill-down for free.

Tip

If your hierarchy is large, sunburst can become cluttered — thin outer rings become unreadable slivers. Consider bar charts (or a treemap) for top-level summaries, and reserve the sunburst for cases where the drill-down interaction genuinely helps.

  flowchart TD
  A["DataFrame rows"] --> B["path=['region', 'city']
defines ring order"]
  B --> C["Ring 1 (inner) = region
arc width ∝ sum(sales)"]
  B --> D["Ring 2 (outer) = city
arc width ∝ sales"]
  C --> E["Click a ring segment
to zoom into that branch"]
  D --> E

Check your understanding

Next

Continue to: 3D Scatter Plots for exploring three numeric variables at once.

🧪 Try It Yourself

Exercise 1 – Define the Hierarchy Path

Press Run to see what your code prints.

Exercise 2 – Size Arcs by a Value Column

Press Run to see what your code prints.

Exercise 3 – Three-Level Hierarchy

Press Run to see what your code prints.