📊 Plotly Ternary vs Radar Charts – When to Use What?
When working with multivariate data in Plotly, choosing the right visualization is critical. Two powerful options are ternary charts and radar charts.
This guide breaks them down clearly—with intuition, math, and practical examples.
📚 Table of Contents
- Ternary Charts
- Radar Charts
- Math Behind Them
- Code Examples
- Output Preview
- Comparison Table
- Key Takeaways
- Related Articles
🔺 Ternary Charts
Used when three variables always add up to a constant.
Key Idea:
\[ A + B + C = 1 \]
This means all proportions must sum to 100%.
Example:
- Chemical composition
- Portfolio allocation
- Market share
🕸️ Radar Charts
Used to compare multiple variables across categories.
Structure:
No fixed sum constraint. Each axis is independent.
Example:
- Skills comparison
- Product features
- Performance metrics
📐 Math Explained Simply
1. Ternary Constraint
\[ x + y + z = k \]
Usually:
\[ k = 1 \text{ or } 100 \]
👉 This constraint forces data into a triangle.
2. Radar Representation
\[ Value_i \in [0, max] \]
Each axis is independent—no restriction.
👉 Think of it as multiple bar charts wrapped in a circle.
💻 Plotly Code Examples
🔺 Ternary Chart
import plotly.express as px
fig = px.scatter_ternary(
a=[0.2, 0.3, 0.5],
b=[0.5, 0.4, 0.2],
c=[0.3, 0.3, 0.3]
)
fig.show()
🕸️ Radar Chart
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatterpolar(
r=[4, 3, 5, 4],
theta=['Speed', 'Power', 'Accuracy', 'Stamina'],
fill='toself'
))
fig.show()
🖥️ Output Preview
Click to Expand
Ternary Chart: Triangle with proportion points Radar Chart: Circular polygon showing attribute comparison
⚖️ Comparison Table
| Feature | Ternary | Radar |
|---|---|---|
| Variables | Exactly 3 | Multiple |
| Constraint | Sum must be constant | No constraint |
| Shape | Triangle | Circle |
| Use Case | Proportions | Comparison |
💡 Key Takeaways
- Ternary = proportions (must sum to constant)
- Radar = comparisons (no restriction)
- Choose based on data structure—not preference
🎯 Final Thought
Choosing the right chart isn’t just design—it’s data clarity.
Understand your data structure first, and the correct visualization becomes obvious.
