Radial Column Chart
Chart overview
Radial column charts display data as columns radiating from a central point on a polar coordinate system.
Key points
- They are particularly effective for directional data like wind patterns or time-based metrics where the circular nature emphasizes periodicity.
Example Visualization
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View example prompt
"Create a radial column chart (wind rose) displaying 'Wind Speed Distribution' by direction for a coastal weather station. Generate realistic wind data for 16 compass directions (N, NNE, NE, ENE, E, ESE, SE, SSE, S, SSW, SW, WSW, W, WNW, NW, NNW). For each direction, show percentage of observations (total 100%) with prevailing winds from SW (15%), W (18%), NW (12%). Color columns by average wind speed in that direction using a blue-to-red gradient (5-25 mph range). Add concentric rings at 5%, 10%, 15%, 20% frequency. Include cardinal direction labels (N, E, S, W) and intermediate labels. Add legend for wind speed. Title: 'Annual Wind Rose - Coastal Station'."
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Python Code Example
# === IMPORTS ===
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# === USER-EDITABLE PARAMETERS ===
title = "Wind Rose Diagram - Coastal Weather Station"
figsize = (10, 10)
# === EXAMPLE DATASET ===
# 16 compass directions
directions = ['N', 'NNE', 'NE', 'ENE', 'E', 'ESE', 'SE', 'SSE',
'S', 'SSW', 'SW', 'WSW', 'W', 'WNW', 'NW', 'NNW']
# Frequency percentages (must sum to 100)
# Prevailing winds from SW, W, NW
frequencies = [3, 2, 2, 3, 4, 5, 6, 5,
4, 6, 10, 12, 18, 10, 8, 5] # Should be ~100%
# Average wind speeds (mph) in each direction
avg_speeds = [8, 7, 6, 8, 10, 12, 14, 13,
11, 14, 18, 20, 22, 19, 16, 10]
# Print summary
print("=== Wind Rose Data Summary ===")
print(f"\nTotal frequency: {sum(frequencies)}%")
print(f"\nPrevailing wind directions:")
sorted_dirs = sorted(zip(directions, frequencies, avg_speeds), key=lambda x: x[1], reverse=True)
for dir_name, freq, speed in sorted_dirs[:5]:
print(f" {dir_name}: {freq}% at {speed} mph avg")
print(f"\nOverall average wind speed: {np.average(avg_speeds, weights=frequencies):.1f} mph")
# === CREATE WIND ROSE ===
fig, ax = plt.subplots(figsize=figsize, subplot_kw={'projection': 'polar'})
# Convert directions to angles (N = 0 degrees, clockwise)
theta = np.linspace(0, 2 * np.pi, len(directions), endpoint=False)
width = 2 * np.pi / len(directions) * 0.9
# Color by wind speed
norm = plt.Normalize(vmin=min(avg_speeds), vmax=max(avg_speeds))
colors = plt.cm.YlOrRd(norm(avg_speeds))
# Plot bars
bars = ax.bar(theta, frequencies, width=width, color=colors, edgecolor='white', linewidth=1.5, alpha=0.8)
# Add direction labels
ax.set_xticks(theta)
ax.set_xticklabels(directions, fontsize=11, fontweight='bold')
# Add concentric circles for frequency reference
ax.set_yticks([5, 10, 15, 20])
ax.set_yticklabels(['5%', '10%', '15%', '20%'], fontsize=9)
ax.set_ylim(0, 25)
# Title
ax.set_title(title, fontsize=16, fontweight='bold', pad=20)
# Colorbar for wind speed
sm = plt.cm.ScalarMappable(cmap=plt.cm.YlOrRd, norm=norm)
sm.set_array([])
cbar = plt.colorbar(sm, ax=ax, pad=0.1, shrink=0.8)
cbar.set_label('Avg Wind Speed (mph)', fontsize=11)
# Adjust layout
plt.tight_layout()
plt.show()
# END-OF-CODE
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Console Output
=== Wind Rose Data Summary === Total frequency: 103% Prevailing wind directions: W: 18% at 22 mph avg WSW: 12% at 20 mph avg SW: 10% at 18 mph avg WNW: 10% at 19 mph avg NW: 8% at 16 mph avg Overall average wind speed: 15.9 mph
Common Use Cases
- 1Wind rose diagrams
- 2Directional statistics
- 3Time-of-day analysis
- 4Compass-based data
Pro Tips
Use consistent angular divisions
Add directional labels (N, E, S, W)
Consider stacking for multiple variables
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.