Radial Bar Chart
Chart overview
Radial bar charts display categorical data in a circular format, with bars extending outward from the center.
Key points
- They create visually engaging displays for cyclical data like time-of-year patterns or progress metrics, though they can be harder to read precisely than standard bar charts.
Example Visualization
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"Create a radial bar chart showing 'Average Monthly Temperature' for Seattle across all 12 months. Generate realistic Pacific Northwest data in Fahrenheit: Jan (42), Feb (44), Mar (48), Apr (52), May (58), Jun (64), Jul (68), Aug (69), Sep (63), Oct (54), Nov (46), Dec (42). Arrange months clockwise starting from January at top. Color bars using a temperature gradient (blue for cold ≤50°F, yellow for mild 50-60°F, orange/red for warm ≥60°F). Add temperature labels at the end of each bar. Include concentric circular gridlines at 30°, 45°, 60°, 75°F. Add a center annotation showing annual average (54°F). Title: 'Seattle Monthly Temperature Profile'."
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Python Code Example
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.cm import ScalarMappable
from matplotlib.colors import LinearSegmentedColormap
# Data
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
temperatures = [42, 44, 48, 52, 58, 64, 68, 69, 63, 54, 46, 42]
annual_avg = 54
# Color mapping
colors = ['#1E90FF', '#FFD700', '#FF8C00', '#FF4500']
cmap = LinearSegmentedColormap.from_list('temp_gradient', colors, N=100)
norm = plt.Normalize(vmin=30, vmax=75)
sm = ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])
# Create figure
fig, ax = plt.subplots(figsize=(10, 10), subplot_kw={'projection': 'polar'})
# Calculate angles
theta = np.linspace(0, 2*np.pi, len(months), endpoint=False)
width = 2*np.pi / len(months)
# Plot bars
bars = ax.bar(theta, temperatures, width=width, alpha=0.8, edgecolor='white')
for i, (bar, temp) in enumerate(zip(bars, temperatures)):
bar.set_facecolor(cmap(norm(temp)))
angle = theta[i] + width/2
ax.text(angle, temp + 2, f'{temp}°', ha='center', va='center')
# Set month labels
ax.set_xticks(theta + width/2)
ax.set_xticklabels(months)
# Grid lines
ax.set_yticks([30, 45, 60, 75])
ax.set_yticklabels(['30°F', '45°F', '60°F', '75°F'])
ax.grid(True, alpha=0.3)
# Center annotation
ax.text(0, 0, f'Annual Avg:\n{annual_avg}°F', ha='center', va='center',
fontsize=12, bbox=dict(facecolor='white', alpha=0.8))
# Title
plt.title('Seattle Monthly Temperature Profile', pad=20, fontsize=14)
# Colorbar
cbar = plt.colorbar(sm, ax=ax, pad=0.1)
cbar.set_label('Temperature (°F)')
# Adjust layout
plt.tight_layout()
# Remove the radial axis labels for cleaner look
ax.set_rlabel_position(0)
# Add a circle at the center to cover the inner grid lines
center_circle = plt.Circle((0, 0), 30, transform=ax.transData._b,
facecolor='white', edgecolor='none', alpha=0.8)
ax.add_artist(center_circle)
# Add season indicators
season_angles = [np.pi/6, np.pi/2, 5*np.pi/6, 7*np.pi/6, 3*np.pi/2, 11*np.pi/6]
season_labels = ['Winter', 'Spring', 'Summer', 'Fall']
for i, season in enumerate(season_labels):
angle = season_angles[i] + np.pi/4
ax.text(angle, 80, season, ha='center', va='center',
fontsize=10, fontweight='bold')
# Final adjustments
ax.set_ylim(0, 80)
plt.tight_layout()
# Show plot
plt.show()
# END-OF-CODEOpens the Analyze page with this code pre-loaded and ready to execute
Common Use Cases
- 1Seasonal pattern visualization
- 2Progress tracking
- 3Cyclical data display
- 4Dashboard widgets
Pro Tips
Use for cyclical data (months, hours)
Start at 12 o'clock for time-based data
Add value labels for precision
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.