Span Chart
Chart overview
Span charts (floating bar charts) show the range between two values for each category.
Key points
- They effectively visualize temperature ranges, price fluctuations, or any data with meaningful upper and lower bounds, making it easy to compare ranges across categories.
Python Tutorial
How to create a span chart in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
How to Create a Bar Chart in PythonExample Visualization
Create This Chart Now
Generate publication-ready span charts with AI in seconds. No coding required – just describe your data and let AI do the work.
View example prompt
"Create a span chart (floating bar chart) showing 'Daily Temperature Ranges' for a week in Denver, Colorado. Generate realistic spring weather data: Monday (min: 38°F, max: 62°F), Tuesday (42°F, 68°F), Wednesday (45°F, 71°F), Thursday (50°F, 75°F - warmest), Friday (44°F, 65°F), Saturday (35°F, 55°F - cold front), Sunday (32°F, 52°F). Each bar spans from min to max temperature. Color bars by range size using a gradient (narrow range: blue, wide range: orange). Add horizontal reference lines for freezing (32°F) and comfortable (70°F). Mark the daily average with a diamond marker. Include gridlines, format Y-axis in °F. Title: 'Denver Weekly Temperature Range - Spring 2024'."
How to create this chart in 30 seconds
Upload Data
Drag & drop your Excel or CSV file. Plotivy securely processes it in your browser.
AI Generation
Our AI analyzes your data and generates the Span Chart code automatically.
Customize & Export
Tweak the design with natural language, then export as high-res PNG, SVG or PDF.
Newsletter
Get one weekly tip for better span charts
Join researchers receiving concise Python plotting techniques to improve chart clarity and reduce revision cycles.
Python Code Example
# === IMPORTS ===
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# === USER-EDITABLE PARAMETERS ===
title = "Denver Weekly Temperature Range - Spring 2024"
figsize = (12, 6)
# === EXAMPLE DATASET ===
days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
min_temps = [38, 42, 45, 50, 44, 35, 32]
max_temps = [62, 68, 71, 75, 65, 55, 52]
df = pd.DataFrame({
'Day': days,
'Min': min_temps,
'Max': max_temps
})
df['Range'] = df['Max'] - df['Min']
df['Avg'] = (df['Min'] + df['Max']) / 2
# Print summary
print("=== Denver Weekly Temperature Summary ===")
print(f"\nDay | Min (°F) | Max (°F) | Range")
print("-" * 45)
for _, row in df.iterrows():
print(f"{row['Day']:10} | {row['Min']:8} | {row['Max']:8} | {row['Range']:5}°")
print(f"\nWeekly Average High: {np.mean(max_temps):.1f}°F")
print(f"Weekly Average Low: {np.mean(min_temps):.1f}°F")
print(f"Warmest Day: {days[np.argmax(max_temps)]} ({max(max_temps)}°F)")
print(f"Coldest Day: {days[np.argmin(min_temps)]} ({min(min_temps)}°F)")
# === CREATE SPAN CHART ===
fig, ax = plt.subplots(figsize=figsize)
x = np.arange(len(days))
# Color bars by range size
colors = plt.cm.RdYlBu_r(np.linspace(0.2, 0.8, len(df)))
range_normalized = (df['Range'] - df['Range'].min()) / (df['Range'].max() - df['Range'].min())
bar_colors = plt.cm.YlOrRd(range_normalized)
# Create floating bars
for i, (_, row) in enumerate(df.iterrows()):
ax.bar(i, row['Range'], bottom=row['Min'], color=bar_colors[i],
edgecolor='white', linewidth=2, width=0.6)
# Add average marker
ax.scatter(i, row['Avg'], color='black', s=80, marker='D', zorder=5)
# Reference lines
ax.axhline(y=32, color='blue', linestyle='--', alpha=0.7, linewidth=1.5, label='Freezing (32°F)')
ax.axhline(y=70, color='green', linestyle='--', alpha=0.7, linewidth=1.5, label='Comfortable (70°F)')
# Add min/max labels
for i, (_, row) in enumerate(df.iterrows()):
ax.text(i, row['Min'] - 2, f"{row['Min']}°", ha='center', fontsize=9, color='blue')
ax.text(i, row['Max'] + 2, f"{row['Max']}°", ha='center', fontsize=9, color='red')
# Styling
ax.set_xticks(x)
ax.set_xticklabels(days, fontsize=11, fontweight='bold')
ax.set_ylabel('Temperature (°F)', fontsize=12, fontweight='bold')
ax.set_title(title, fontsize=16, fontweight='bold', pad=20)
ax.set_ylim(25, 85)
ax.legend(loc='upper right', framealpha=0.9)
ax.grid(True, alpha=0.3, axis='y')
# Add diamond legend annotation
ax.scatter([], [], color='black', s=80, marker='D', label='Daily Average')
ax.legend(loc='upper right', framealpha=0.9)
plt.tight_layout()
plt.show()
# END-OF-CODE
Opens the Analyze page with this code pre-loaded and ready to execute
Console Output
=== Denver Weekly Temperature Summary === Day | Min (°F) | Max (°F) | Range --------------------------------------------- Monday | 38 | 62 | 24° Tuesday | 42 | 68 | 26° Wednesday | 45 | 71 | 26° Thursday | 50 | 75 | 25° Friday | 44 | 65 | 21° Saturday | 35 | 55 | 20° Sunday | 32 | 52 | 20° Weekly Average High: 64.0°F Weekly Average Low: 40.9°F Warmest Day: Thursday (75°F) Coldest Day: Sunday (32°F)
Common Use Cases
- 1Temperature range visualization
- 2Price range comparison
- 3Scheduling time windows
- 4Measurement tolerance display
Pro Tips
Color code by range size
Add mean markers within spans
Sort by midpoint or range size
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
matplotlib
Best when you need full control over axis formatting, annotation placement, and journal-specific styling for span-chart.
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.