Stacked Bar Graph
Chart overview
Stacked bar charts display multiple data series as segments within a single bar, showing both the total and the composition for each category.
Key points
- They effectively communicate part-to-whole relationships and enable comparison of totals across categories.
- Their weakness is just as specific: only the bottom segment and the total sit on a common baseline, so readers compare those two accurately while every middle segment floats, making its changes across bars hard to judge (Cleveland and McGill's position-versus-length hierarchy).
- Put the series whose trend matters most at the bottom, keep it to 3-5 segments, and order the rest by size or logical sequence rather than alphabetically.
Practical guidance
df. plot(kind='bar', stacked=True) is the pandas one-liner; in raw matplotlib, accumulate a bottom= running total per series. Use the 100% stacked variant (divide each row by its total) when composition is the message and totals are noise - but then report totals somewhere else, because the normalization hides them. If readers must compare each individual series across categories, grouped bars or small multiples of simple bars beat a stack; for many time periods, a stacked area chart is usually the better form.
Create a Stacked Bar Graph with your data using AI — no coding required.
Python Tutorial
How to create a stacked bar graph 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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Generate publication-ready stacked bar graphs with AI in seconds. No coding required – just describe your data and let AI do the work.
View example prompt
"Create a professional stacked bar chart showing 'Monthly Expenses Breakdown' for a household budget over 6 months (Jan-Jun). Generate realistic budget data in dollars: Rent ($1,800 fixed), Utilities ($150-250 seasonal), Groceries ($400-500), Transportation ($300-400), Entertainment ($100-200), Savings ($200-400). Stack order from bottom: essential (rent, utilities, groceries) to discretionary (entertainment, savings). Use a professional color palette (blues, purples, greens, oranges). Add bold total expense labels above each bar. Include a red dashed horizontal target budget line at $3,500. Add percentage labels inside segments exceeding 10% of total, in white text for readability. Legend positioned outside the plot. Title: 'Monthly Household Budget - First Half 2024'. Add a highlighted annotation box in the top-left showing the average monthly expense. Grid lines on Y-axis only. Format Y-axis as currency."
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Python Code Example
# === IMPORTS ===
import pandas as pd
import plotly.graph_objects as go
import numpy as np
# === USER-EDITABLE PARAMETERS ===
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'] # Change: months on x-axis
order = ['Rent', 'Utilities', 'Groceries', 'Transportation', 'Entertainment', 'Savings'] # Change: stack order (bottom to top)
colors = { # Change: professional categorical palette (hex codes only)
'Rent': '#4e79a7',
'Utilities': '#f28e2b',
'Groceries': '#e15759',
'Transportation': '#76b7b2',
'Entertainment': '#59a14f',
'Savings': '#edc948'
}
target_budget = 3500 # Change: horizontal target budget line value
title_fontsize = 18 # Change: title font size
label_fontsize = 16 # Change: axis labels and ticks font size
show_percent_labels = True # Change: toggle percentage labels inside segments (>10%)
min_pct_threshold = 0.1 # Change: minimum segment percentage for label
# Change: Generate realistic data here (lists matching months length)
rent = [1800, 1800, 1800, 1800, 1800, 1800]
utilities = [150, 170, 200, 230, 250, 240]
groceries = [400, 420, 440, 460, 480, 500]
transportation = [300, 320, 340, 360, 380, 400]
entertainment = [100, 130, 150, 170, 190, 200]
savings = [200, 250, 300, 350, 380, 400]
# Data preparation: Create wide-format DataFrame
data_dict = {
'Month': months,
'Rent': rent,
'Utilities': utilities,
'Groceries': groceries,
'Transportation': transportation,
'Entertainment': entertainment,
'Savings': savings
}
df_wide = pd.DataFrame(data_dict)
df_wide['Total'] = df_wide[order].sum(axis=1)
# Calculate key statistics
totals = df_wide['Total'].tolist()
avg_expense = np.mean(totals)
# Print relevant analysis results
print(f"Average monthly expense: ${avg_expense:.0f}")
print("Monthly totals:")
for month, total in zip(months, totals):
print(f" {month}: ${total:.0f}")
print(f"Target budget: ${target_budget:,}")
print(f"Months over target: {sum(1 for t in totals if t > target_budget)} / {len(months)}")
# Create stacked bar chart using go.Bar
fig = go.Figure()
for cat in order:
fig.add_trace(go.Bar(
name=cat,
x=months,
y=df_wide[cat],
marker_color=colors[cat],
marker_line_color='rgba(0,0,0,0.1)',
marker_line_width=0.5
))
# Update layout for professional styling
fig.update_layout(
barmode='stack',
title=dict(
text='Monthly Household Budget - First Half 2024',
x=0.5,
xanchor='center',
y=0.98,
font_size=title_fontsize
),
xaxis_title='Month',
yaxis_title='Expenses ($)',
font=dict(size=label_fontsize),
margin=dict(t=130, b=90, l=80, r=150), # Extra right margin for external legend
legend=dict(
x=1.02,
y=0.5,
xanchor='left',
yanchor='middle',
bgcolor='rgba(255,255,255,0.95)',
bordercolor='rgba(150,150,150,0.5)',
borderwidth=1,
font=dict(size=label_fontsize-2)
),
hovermode='x unified'
)
# Add total expense labels above each bar
for i, month in enumerate(months):
fig.add_annotation(
x=month,
y=totals[i],
text=f'${totals[i]:,.0f}',
showarrow=False,
yshift=20,
xanchor='center',
yanchor='bottom',
font=dict(size=12, color='#000000', family='Arial Black'),
bgcolor='rgba(255,255,255,0.9)'
)
# Add horizontal target budget line
fig.add_hline(
y=target_budget,
line_dash='dash',
line_color='#d62728',
line_width=2,
annotation_text=f'Target Budget: ${target_budget:,}',
annotation_position='right',
annotation_font_size=label_fontsize-2
)
# Add average expense line and annotation
avg_color = '#2ca02c'
fig.add_hline(
y=avg_expense,
line_dash='dot',
line_color=avg_color,
line_width=2,
annotation_text=f'Average Monthly Expense: ${avg_expense:.0f}',
annotation_position='right',
annotation_font_size=label_fontsize-2
)
# Add percentage labels inside large segments (>10% of total)
if show_percent_labels:
for i, month in enumerate(months):
total = totals[i]
bottom = 0
for cat in order:
height = df_wide.iloc[i][cat]
if (height / total) > min_pct_threshold:
mid_y = bottom + height / 2
pct = 100 * height / total
fig.add_annotation(
x=month,
y=mid_y,
text=f'{pct:.0f}%',
showarrow=False,
xanchor='center',
yanchor='middle',
font=dict(size=11, color='#000000'),
bgcolor='rgba(255,255,255,0.9)',
bordercolor='rgba(255,255,255,1)',
borderwidth=1,
borderpad=4
)
bottom += height
fig.show()
# END-OF-CODEOpens the Analyze page with this code pre-loaded and ready to execute
Console Output
Total 6-Month Expenses: $20,305 Average Monthly: $3,384 Months over budget: 2 Total Savings: $2,030
Common Use Cases
- 1Budget breakdown visualization
- 2Survey response composition
- 3Sales by product and region
- 4Resource utilization by category
Pro Tips
Limit to 5-7 segments per bar
Use consistent colors across bars
Consider horizontal layout for many categories
Frequently asked questions
When should you use a stacked bar graph?
Stacked bar charts display multiple data series as segments within a single bar, showing both the total and the composition for each category. They effectively communicate part-to-whole relationships and enable comparison of totals across categories. Common applications include budget breakdown visualization, survey response composition, and sales by product and region.
Which Python libraries can create a stacked bar graph?
A stacked bar graph can be built in Python with matplotlib and pandas — matplotlib for precise control over axes, annotations, and journal styling and pandas for quick plots straight from a DataFrame. In Plotivy you describe the figure and it writes the matplotlib code for you.
Can I make a stacked bar graph without writing Python code?
Yes. Describe the stacked bar graph you need in plain language and upload your dataset — Plotivy's AI writes the Python code and renders a publication-ready figure. You still get the full, editable matplotlib source, so nothing is locked in a black box.
What are best practices for a clear stacked bar graph?
Limit to 5-7 segments per bar. Use consistent colors across bars.
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 stacked-bar-graph.
pandas
Good for quick exploratory drafts directly from DataFrame operations before polishing in matplotlib or plotly.
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