Multi-set Bar Chart
Chart overview
Multi-set bar charts (grouped or clustered bar charts) display multiple data series side by side within each category, allowing direct comparison between groups.
Key points
- They are effective for showing how different categories compare across multiple variables or time periods.
Python Tutorial
How to create a multi-set bar 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
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View example prompt
"Create a grouped bar chart comparing 'Quarterly Sales' (Q1 vs Q2) across 5 departments: Electronics, Clothing, Home & Garden, Sports, and Books. Generate realistic retail data in thousands: Electronics (Q1: $850K, Q2: $920K, +8%), Clothing (Q1: $620K, Q2: $580K, -6%), Home (Q1: $340K, Q2: $410K, +21%), Sports (Q1: $280K, Q2: $350K, +25%), Books (Q1: $180K, Q2: $165K, -8%). Use side-by-side bars with Q1 in blue and Q2 in green. Add value labels above each bar. Include a horizontal reference line at the overall Q1 average. Add percentage change annotations. Group labels on X-axis, Y-axis as 'Sales ($ thousands)'. Add legend and title 'Quarterly Sales Comparison by Department'."
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Python Code Example
# === IMPORTS ===
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# === USER-EDITABLE PARAMETERS ===
title = "Quarterly Sales Comparison by Department"
figsize = (12, 7)
# === EXAMPLE DATASET ===
departments = ['Electronics', 'Clothing', 'Home & Garden', 'Sports', 'Books']
q1_sales = [850, 620, 340, 280, 180] # In thousands
q2_sales = [920, 580, 410, 350, 165]
# Changes
changes = [(q2 - q1) / q1 * 100 for q1, q2 in zip(q1_sales, q2_sales)]
df = pd.DataFrame({
'Department': departments,
'Q1': q1_sales,
'Q2': q2_sales,
'Change': changes
})
# Print summary
print("=== Quarterly Sales Comparison ===")
print(f"\nDepartment | Q1 ($K) | Q2 ($K) | Change")
print("-" * 50)
for _, row in df.iterrows():
print(f"{row['Department']:15} | {row['Q1']:7} | {row['Q2']:7} | {row['Change']:+.1f}%")
print(f"\nQ1 Total: ${sum(q1_sales):,}K")
print(f"Q2 Total: ${sum(q2_sales):,}K")
print(f"Overall Change: {(sum(q2_sales) - sum(q1_sales)) / sum(q1_sales) * 100:+.1f}%")
# === CREATE GROUPED BAR CHART ===
fig, ax = plt.subplots(figsize=figsize)
x = np.arange(len(departments))
width = 0.35
# Create bars
bars1 = ax.bar(x - width/2, q1_sales, width, label='Q1 2024', color='#3498db', edgecolor='white')
bars2 = ax.bar(x + width/2, q2_sales, width, label='Q2 2024', color='#27ae60', edgecolor='white')
# Add value labels on bars
for bar in bars1:
height = bar.get_height()
ax.annotate(f'${height}K',
xy=(bar.get_x() + bar.get_width() / 2, height),
xytext=(0, 3), textcoords="offset points",
ha='center', va='bottom', fontsize=10, fontweight='bold')
for i, bar in enumerate(bars2):
height = bar.get_height()
ax.annotate(f'${height}K',
xy=(bar.get_x() + bar.get_width() / 2, height),
xytext=(0, 3), textcoords="offset points",
ha='center', va='bottom', fontsize=10, fontweight='bold')
# Add change percentage
ax.annotate(f'{changes[i]:+.0f}%',
xy=(bar.get_x() + bar.get_width() / 2, height + 40),
xytext=(0, 3), textcoords="offset points",
ha='center', va='bottom', fontsize=9,
color='green' if changes[i] > 0 else 'red',
fontweight='bold')
# Reference line at Q1 average
q1_avg = np.mean(q1_sales)
ax.axhline(y=q1_avg, color='red', linestyle='--', alpha=0.7,
label=f'Q1 Average (${q1_avg:.0f}K)')
# Styling
ax.set_xlabel('Department', fontsize=12, fontweight='bold')
ax.set_ylabel('Sales ($ thousands)', fontsize=12, fontweight='bold')
ax.set_title(title, fontsize=16, fontweight='bold', pad=20)
ax.set_xticks(x)
ax.set_xticklabels(departments, fontsize=11)
ax.legend(loc='upper right', framealpha=0.9)
ax.set_ylim(0, 1100)
ax.grid(True, alpha=0.3, axis='y')
plt.tight_layout()
plt.show()
# END-OF-CODE
Opens the Analyze page with this code pre-loaded and ready to execute
Console Output
=== Quarterly Sales Comparison === Department | Q1 ($K) | Q2 ($K) | Change -------------------------------------------------- Electronics | 850 | 920 | +8.2% Clothing | 620 | 580 | -6.5% Home & Garden | 340 | 410 | +20.6% Sports | 280 | 350 | +25.0% Books | 180 | 165 | -8.3% Q1 Total: $2,270K Q2 Total: $2,425K Overall Change: +6.8%
Common Use Cases
- 1Year-over-year comparisons
- 2A/B test results
- 3Multi-group performance analysis
- 4Survey results by demographic
Pro Tips
Limit to 3-4 groups for readability
Use distinct colors for each group
Add a legend for clarity
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 multi-set-bar-chart.
seaborn
Fastest path to statistically-aware defaults and tidy-data workflows, especially for grouped and distribution-focused multi-set-bar-chart views.
Scientific Chart Selection Cheat Sheet
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