Donut Chart
Chart overview
Donut charts are pie charts with a hollow center, which can display summary statistics or labels.
Key points
- The ring format emphasizes the proportions between segments while providing space for central annotations like totals or key metrics.
Example Visualization
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"Create an interactive donut chart showing 'Global Browser Market Share' for 2024. Generate realistic data: Chrome (65.4%), Safari (18.2%), Edge (5.1%), Firefox (3.0%), Samsung Internet (2.5%), Opera (2.3%), Other (3.5%). Use distinct brand colors: Chrome (green/red/yellow), Safari (blue), Edge (blue-green), Firefox (orange), others (grays). Display percentage labels on each segment (only for segments >3%). Add a center annotation showing 'Total: 4.8B Users' and the leading browser's logo/name. Include hover tooltips with exact user counts. Slight separation (explode) for the largest segment. Add a legend on the right. Title: 'Browser Market Share - Q1 2024'. Animate on load with a spin-in effect."
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Python Code Example
# === IMPORTS ===
import pandas as pd
import numpy as np
import plotly.express as px
# === USER-EDITABLE PARAMETERS ===
title = "Global Browser Market Share - Q1 2024 (4.8B Users Total)"
figsize_width = 800
figsize_height = 600
# === EXAMPLE DATASET ===
data = {
'Browser': ['Chrome', 'Safari', 'Edge', 'Firefox', 'Samsung Internet', 'Opera', 'Other'],
'Market_Share': [65.4, 18.2, 5.1, 3.0, 2.5, 2.3, 3.5],
'Users_M': [3139, 874, 245, 144, 120, 110, 168] # Users in millions
}
# Brand colors
colors = {
'Chrome': '#4285F4',
'Safari': '#0066CC',
'Edge': '#0078D7',
'Firefox': '#FF7139',
'Samsung Internet': '#1428A0',
'Opera': '#FF1B2D',
'Other': '#808080'
}
df = pd.DataFrame(data)
df['Color'] = df['Browser'].map(colors)
# Print summary
print("=== Browser Market Share Summary ===")
print(f"\nTotal Users: {df['Users_M'].sum():,}M")
print(f"\nMarket Share by Browser:")
for _, row in df.iterrows():
print(f" {row['Browser']}: {row['Market_Share']:.1f}% ({row['Users_M']:,}M users)")
# === CREATE DONUT CHART ===
fig = px.pie(
df,
values='Market_Share',
names='Browser',
title=title,
hole=0.5,
color='Browser',
color_discrete_map=colors
)
fig.update_traces(
textposition='outside',
textinfo='label+percent',
hovertemplate='<b>%{label}</b><br>%{value:.1f}%<br>~%{customdata[0]:,}M users<extra></extra>',
customdata=df[['Users_M']].values
)
fig.update_layout(
width=figsize_width,
height=figsize_height,
annotations=[
dict(text='4.8B<br>Users', x=0.5, y=0.5, font_size=18, showarrow=False)
],
showlegend=True,
legend=dict(orientation='v', yanchor='middle', y=0.5, xanchor='left', x=1.02)
)
fig.show()
# END-OF-CODE
Opens the Analyze page with this code pre-loaded and ready to execute
Console Output
Leading Browser: Chrome (65.4%) Total Users: 4.8 billion Top 3 Combined: 88.7%
Common Use Cases
- 1Market share display
- 2Budget allocation
- 3Survey responses
- 4Portfolio composition
Pro Tips
Limit to 5-7 segments
Use center for key metric
Sort segments by size
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.