Nightingale Rose Chart
Chart overview
Nightingale rose charts (coxcomb charts) display data in sectors radiating from a center point, with sector radius proportional to value.
Key points
- Made famous by Florence Nightingale for mortality statistics, they effectively display cyclical data with magnitude.
Example Visualization
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View example prompt
"Create a Nightingale rose chart (polar area diagram) showing 'Monthly Sales Revenue' for a retail business. Generate 12 months of data with seasonal patterns: strong Q4 (Oct: $180K, Nov: $220K, Dec: $280K holiday peak), moderate Q2/Q3 ($120-150K), weaker Q1 (Jan: $95K post-holiday low, Feb: $105K, Mar: $130K). Each petal's area (not just radius) should be proportional to sales value. Color petals by season: Winter (blue), Spring (green), Summer (yellow), Fall (orange). Add radial gridlines at $50K, $100K, $150K, $200K, $250K. Label each month on the outer edge. Annotate the peak (December) and trough (January). Center shows annual total ($1.8M). Title: 'Annual Sales Cycle - Seasonal Patterns'."
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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 = "Annual Sales Cycle - Seasonal Patterns ($1.8M Total)"
figsize = (10, 10)
# === EXAMPLE DATASET ===
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
sales = [95, 105, 130, 145, 155, 140,
135, 145, 160, 180, 220, 280] # In thousands
# Seasons colors
season_colors = ['#3498db'] * 2 + ['#27ae60'] * 3 + ['#f1c40f'] * 3 + ['#e67e22'] * 4
season_colors = ['#3498db', '#3498db', '#27ae60', '#27ae60', '#27ae60',
'#f1c40f', '#f1c40f', '#f1c40f', '#e67e22', '#e67e22', '#e67e22', '#e67e22']
df = pd.DataFrame({'Month': months, 'Sales': sales, 'Color': season_colors})
# Print summary
print("=== Monthly Sales Summary ===")
print(f"\nTotal Annual Sales: ${sum(sales):,}K")
print(f"Average Monthly: ${np.mean(sales):.0f}K")
print(f"Peak Month: {months[np.argmax(sales)]} (${max(sales)}K)")
print(f"Lowest Month: {months[np.argmin(sales)]} (${min(sales)}K)")
# === CREATE NIGHTINGALE ROSE CHART ===
fig, ax = plt.subplots(figsize=figsize, subplot_kw={'projection': 'polar'})
# Calculate angles
theta = np.linspace(0, 2 * np.pi, len(months), endpoint=False)
width = 2 * np.pi / len(months)
# Convert sales to area-proportional radius
max_sales = max(sales)
radii = [np.sqrt(s / max_sales) * max_sales for s in sales]
# Plot bars
bars = ax.bar(theta, radii, width=width * 0.9, alpha=0.8, color=season_colors, edgecolor='white', linewidth=2)
# Add labels
ax.set_xticks(theta)
ax.set_xticklabels(months, fontsize=12, fontweight='bold')
# Add gridlines
ax.set_yticks([50, 100, 150, 200, 250])
ax.set_yticklabels(['$50K', '$100K', '$150K', '$200K', '$250K'], fontsize=9)
ax.set_ylim(0, 300)
# Add value labels on each petal
for angle, radius, sale in zip(theta, radii, sales):
ax.text(angle, radius + 15, f'${sale}K', ha='center', va='bottom', fontsize=9, fontweight='bold')
# Center annotation
ax.text(0, 0, f'Annual Total\n$1.8M', ha='center', va='center', fontsize=14, fontweight='bold',
bbox=dict(boxstyle='round', facecolor='white', alpha=0.9))
# Title
plt.title(title, fontsize=16, fontweight='bold', pad=20)
# Legend for seasons
from matplotlib.patches import Patch
legend_elements = [
Patch(facecolor='#3498db', label='Winter'),
Patch(facecolor='#27ae60', label='Spring'),
Patch(facecolor='#f1c40f', label='Summer'),
Patch(facecolor='#e67e22', label='Fall')
]
ax.legend(handles=legend_elements, loc='lower right', bbox_to_anchor=(1.2, 0))
plt.tight_layout()
plt.show()
# END-OF-CODE
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Console Output
=== Monthly Sales Summary === Total Annual Sales: $1,890K Average Monthly: $158K Peak Month: Dec ($280K) Lowest Month: Jan ($95K)
Common Use Cases
- 1Seasonal data comparison
- 2Cyclical pattern display
- 3Category magnitude comparison
- 4Historical data recreation
Pro Tips
Use for cyclical categories
Start at 12 o'clock position
Add value labels on sectors
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.