Stacked bar charts in Matplotlib - Mini Project: Build & Apply

Stacked Bar Charts

📖 Scenario: You work for a small company that sells three types of products: Books, Games, and Electronics. You want to show how many units of each product were sold in the first three months of the year.

🎯 Goal: Create a stacked bar chart using matplotlib to visualize the monthly sales of each product. Each bar will represent a month, and the segments in the bar will show sales of Books, Games, and Electronics stacked on top of each other.

📋 What You'll Learn

Create lists for months and sales of each product

Create a variable for the width of the bars

Use plt.bar to plot stacked bars for Books, Games, and Electronics

Display the final stacked bar chart with labels and legend

💡 Why This Matters

🌍 Real World

Stacked bar charts help businesses compare multiple categories over time or groups, making it easier to see total and individual contributions.

💼 Career

Data analysts and scientists use stacked bar charts to present sales, survey results, or any grouped data clearly to stakeholders.

1

Create sales data lists

Create a list called months with values 'Jan', 'Feb', 'Mar'. Create three lists called books_sales, games_sales, and electronics_sales with the values [120, 150, 170], [80, 90, 100], and [60, 70, 90] respectively.

Matplotlib

# Create lists for months and sales of Books, Games, and Electronics
# Your code here

Hint

Use square brackets [] to create lists with the exact values given.

2

Set bar width

Create a variable called bar_width and set it to 0.5. This will control the width of each bar in the chart.

Matplotlib

months = ['Jan', 'Feb', 'Mar']
books_sales = [120, 150, 170]
games_sales = [80, 90, 100]
electronics_sales = [60, 70, 90]
# Create a variable bar_width and set it to 0.5
# Your code here

Hint

Use a simple assignment to create bar_width with value 0.5.

3

Plot stacked bars

Import matplotlib.pyplot as plt. Create a list positions with values [0, 1, 2] for the x-axis positions. Use plt.bar to plot the books_sales bars at positions with width bar_width. Then plot games_sales bars stacked on top of books_sales by setting the bottom parameter to books_sales. Finally, plot electronics_sales bars stacked on top of the sum of books_sales and games_sales. Use plt.bar with bottom set to the sum of books_sales and games_sales. Use the variable names exactly as given.

Matplotlib

months = ['Jan', 'Feb', 'Mar']
books_sales = [120, 150, 170]
games_sales = [80, 90, 100]
electronics_sales = [60, 70, 90]
bar_width = 0.5
# Import matplotlib.pyplot as plt
# Create positions list [0, 1, 2]
# Plot books_sales bars
# Plot games_sales bars stacked on books_sales
# Plot electronics_sales bars stacked on books_sales + games_sales
# Your code here

Hint

Use zip to add books_sales and games_sales element-wise for the bottom parameter of the last bar.

4

Display the stacked bar chart

Add x-axis labels using plt.xticks with positions and months. Add a legend with plt.legend(). Finally, call plt.show() to display the chart.

Matplotlib

import matplotlib.pyplot as plt

months = ['Jan', 'Feb', 'Mar']
books_sales = [120, 150, 170]
games_sales = [80, 90, 100]
electronics_sales = [60, 70, 90]
bar_width = 0.5

positions = [0, 1, 2]

plt.bar(positions, books_sales, width=bar_width, label='Books')
plt.bar(positions, games_sales, width=bar_width, bottom=books_sales, label='Games')

books_games_sum = [b + g for b, g in zip(books_sales, games_sales)]
plt.bar(positions, electronics_sales, width=bar_width, bottom=books_games_sum, label='Electronics')
# Add x-axis labels with plt.xticks
# Add legend with plt.legend()
# Show the plot with plt.show()
# Your code here

Hint

Use plt.xticks(positions, months) to label the x-axis. Use plt.legend() to show the legend. Use plt.show() to display the chart window.

Practice

1. What is the main purpose of using a stacked bar chart in data visualization?

easy

A. To create a pie chart with multiple slices

B. To display data points as individual dots on a graph

C. To plot a line connecting data points over time

D. To show how different parts contribute to the total within each group

Solution

1. Step 1: Understand the concept of stacked bar charts

   Stacked bar charts combine multiple data parts into one bar to show totals and parts.

2. Step 2: Identify the purpose of stacking

   Stacking helps visualize how each part contributes to the total for each group.

3. Final Answer:

   To show how different parts contribute to the total within each group -> Option D

4. Quick Check:

   Stacked bars = parts + total [OK]

Hint: Stacked bars add parts vertically to show totals [OK]

Common Mistakes:

• Confusing stacked bars with line charts
• Thinking stacked bars show individual points only
• Mixing stacked bars with pie charts

2. Which matplotlib parameter is used to stack bars on top of each other in a bar chart?

easy

A. height

B. bottom

C. width

D. align

Solution

1. Step 1: Recall bar chart parameters

   In matplotlib, height sets bar size, width sets bar thickness, align controls bar alignment.

2. Step 2: Identify stacking parameter

   The bottom parameter sets where the bar starts vertically, allowing stacking on previous bars.

3. Final Answer:

   bottom -> Option B

4. Quick Check:

   Stack bars use bottom parameter [OK]

Hint: Use bottom to place bars on top of others [OK]

Common Mistakes:

• Using height to stack bars (it sets bar size)
• Confusing width with stacking
• Using align which only shifts bars horizontally

3. What will be the output of this code snippet?

import matplotlib.pyplot as plt

labels = ['A', 'B']
values1 = [3, 5]
values2 = [2, 4]

plt.bar(labels, values1)
plt.bar(labels, values2, bottom=values1)
plt.show()

medium

A. Two bars per label, stacked vertically

B. Error because bottom parameter is incorrect

C. Only one bar per label showing values2

D. Two bars per label, side by side

Solution

1. Step 1: Analyze the first bar call

   The first plt.bar draws bars of height 3 and 5 for labels 'A' and 'B'.

2. Step 2: Analyze the second bar call with bottom

   The second plt.bar draws bars of height 2 and 4 stacked on top of the first bars using bottom=values1.

3. Final Answer:

   Two bars per label, stacked vertically -> Option A

4. Quick Check:

   bottom stacks bars vertically [OK]

Hint: bottom stacks bars vertically on previous bars [OK]

Common Mistakes:

• Thinking bars appear side by side without bottom
• Assuming only one bar per label is drawn
• Believing bottom parameter causes error

4. Identify the error in this code for creating a stacked bar chart:

import matplotlib.pyplot as plt

labels = ['X', 'Y']
values1 = [4, 6]
values2 = [3, 5]

plt.bar(labels, values1)
plt.bar(labels, values2, bottom=values2)
plt.show()

medium

A. Labels list is missing an element

B. plt.bar cannot be called twice

C. Using bottom=values2 instead of bottom=values1 causes incorrect stacking

D. values1 and values2 must be the same length

Solution

1. Step 1: Check the bottom parameter usage

   The second bar uses bottom=values2, which stacks bars on themselves, not on the first bars.

2. Step 2: Correct stacking requires bottom=values1

   To stack bars correctly, the second bars should start at the height of the first bars, so bottom=values1 is needed.

3. Final Answer:

   Using bottom=values2 instead of bottom=values1 causes incorrect stacking -> Option C

4. Quick Check:

   bottom must be previous bar heights [OK]

Hint: Stack bars using bottom=previous bar heights [OK]

Common Mistakes:

• Setting bottom to current bar heights instead of previous
• Thinking plt.bar can only be called once
• Ignoring length matching of values

5. You have three categories of sales data for two stores:

store_labels = ['Store1', 'Store2']
sales_A = [10, 15]
sales_B = [5, 7]
sales_C = [3, 4]

How do you create a stacked bar chart showing all three sales categories stacked for each store?

hard

A. Plot sales_A, then sales_B with bottom=sales_A, then sales_C with bottom=sales_A + sales_B

B. Plot sales_A, sales_B, sales_C all with bottom=0

C. Plot sales_A, then sales_B with bottom=sales_A, then sales_C with bottom=sales_B

D. Plot sales_A with bottom=sales_B, sales_B with bottom=sales_C, sales_C with bottom=sales_A

Solution

1. Step 1: Plot the first category normally

   Plot sales_A bars starting at zero (default).

2. Step 2: Stack second category on first

   Plot sales_B bars with bottom=sales_A to stack on top of sales_A.

3. Step 3: Stack third category on sum of first two

   Plot sales_C bars with bottom=[a+b for a,b in zip(sales_A, sales_B)] to stack on top of both.

4. Final Answer:

   Plot sales_A, then sales_B with bottom=sales_A, then sales_C with bottom=sales_A + sales_B -> Option A

5. Quick Check:

   Stack each bar on sum of all below [OK]

Hint: Stack bars using cumulative sums for bottom [OK]

Common Mistakes:

• Stacking third category only on second, ignoring first
• Plotting all bars at bottom=0 causing overlap
• Using wrong bottom values causing incorrect stacking