Grouped bar charts in Matplotlib

Introduction

Grouped bar charts help compare different groups side by side. They make it easy to see differences between categories and groups.

Comparing sales of different products across several months.

Showing test scores of students in different subjects by class.

Visualizing survey results for multiple questions across age groups.

Comparing revenue from different stores over several years.

Syntax

Matplotlib

import matplotlib.pyplot as plt

labels = ['Group1', 'Group2', 'Group3']
values1 = [10, 20, 30]
values2 = [15, 25, 35]

x = range(len(labels))
width = 0.35

plt.bar([p - width/2 for p in x], values1, width=width, label='Set 1')
plt.bar([p + width/2 for p in x], values2, width=width, label='Set 2')

plt.xticks(x, labels)
plt.legend()
plt.show()

Use the width variable to control bar thickness and spacing.

Shift bar positions left and right by half the width to group bars side by side.

Examples

Two groups of bars shown side by side for categories A, B, and C.

Matplotlib

labels = ['A', 'B', 'C']
values1 = [5, 7, 9]
values2 = [6, 8, 10]

x = range(len(labels))
width = 0.4

plt.bar([p - width/2 for p in x], values1, width=width, label='First')
plt.bar([p + width/2 for p in x], values2, width=width, label='Second')

plt.xticks(x, labels)
plt.legend()
plt.show()

Three groups of bars for each category, spaced evenly.

Matplotlib

labels = ['X', 'Y']
values1 = [3, 6]
values2 = [4, 5]
values3 = [2, 7]

x = range(len(labels))
width = 0.25

plt.bar([p - width for p in x], values1, width=width, label='Set 1')
plt.bar(x, values2, width=width, label='Set 2')
plt.bar([p + width for p in x], values3, width=width, label='Set 3')

plt.xticks(x, labels)
plt.legend()
plt.show()

Sample Program

This program creates a grouped bar chart comparing fruit sales in 2023 and 2024 side by side.

Matplotlib

import matplotlib.pyplot as plt

# Categories
labels = ['Apples', 'Bananas', 'Cherries']

# Data for two groups
sales_2023 = [30, 45, 10]
sales_2024 = [40, 35, 15]

x = range(len(labels))
width = 0.35

# Plot bars for 2023 shifted left
plt.bar([p - width/2 for p in x], sales_2023, width=width, label='2023')

# Plot bars for 2024 shifted right
plt.bar([p + width/2 for p in x], sales_2024, width=width, label='2024')

# Label x-axis with category names
plt.xticks(x, labels)

# Add title and legend
plt.title('Fruit Sales Comparison')
plt.legend()

# Show the plot
plt.show()

Important Notes

Make sure the width times number of groups fits well to avoid bars overlapping.

Use plt.xticks() to label the groups clearly.

Adding a legend helps identify each group easily.

Summary

Grouped bar charts show multiple sets of data side by side for easy comparison.

Shift bars left and right by half the bar width to group them.

Label axes and add legends for clarity.

Practice

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

easy

A. To compare multiple categories side by side within groups

B. To show the trend of a single variable over time

C. To display parts of a whole as percentages

D. To plot data points on a continuous scale

Solution

1. Step 1: Understand the chart type

   Grouped bar charts place bars for different categories next to each other for easy comparison.

2. Step 2: Identify the purpose

   This helps compare multiple categories side by side within groups, unlike line or pie charts.

3. Final Answer:

   To compare multiple categories side by side within groups -> Option A

4. Quick Check:

   Grouped bars = side-by-side comparison [OK]

Hint: Grouped bars show categories side by side for easy comparison [OK]

Common Mistakes:

• Confusing grouped bar charts with line charts
• Thinking grouped bars show parts of a whole
• Assuming grouped bars plot continuous data

2. Which of the following code snippets correctly shifts bars to create a grouped bar chart in matplotlib?

easy

A. plt.bar(x - width/2, data1, width); plt.bar(x + width/2, data2, width)

B. plt.bar(x, data1, width); plt.bar(x, data2, width)

C. plt.bar(x + width, data1, width); plt.bar(x + width, data2, width)

D. plt.bar(x - width, data1, width); plt.bar(x - width, data2, width)

Solution

1. Step 1: Understand bar shifting for grouping

   To group bars side by side, shift one set left by half the bar width and the other right by half the bar width.

2. Step 2: Check the code

   plt.bar(x - width/2, data1, width); plt.bar(x + width/2, data2, width) shifts bars by -width/2 and +width/2, correctly grouping them.

3. Final Answer:

   plt.bar(x - width/2, data1, width); plt.bar(x + width/2, data2, width) -> Option A

4. Quick Check:

   Shift bars ±width/2 for grouping [OK]

Hint: Shift bars by ±width/2 to group them side by side [OK]

Common Mistakes:

• Not shifting bars, causing overlap
• Shifting bars by full width, creating gaps
• Using the same x positions for all bars

3. What will be the output of the following code?

import matplotlib.pyplot as plt
import numpy as np

labels = ['A', 'B', 'C']
data1 = [5, 7, 9]
data2 = [6, 8, 10]
x = np.arange(len(labels))
width = 0.4

plt.bar(x - width/2, data1, width, label='Data1')
plt.bar(x + width/2, data2, width, label='Data2')
plt.xticks(x, labels)
plt.legend()
plt.show()

medium

A. A stacked bar chart with Data2 bars on top of Data1 bars

B. A grouped bar chart with bars for Data1 and Data2 side by side for each label

C. A line chart connecting data points for Data1 and Data2

D. A scatter plot with points at positions x and data values

Solution

1. Step 1: Analyze the bar positions

   Bars for data1 are at x - width/2, and for data2 at x + width/2, placing them side by side.

2. Step 2: Understand the plot type

   Using plt.bar twice with shifted x positions creates a grouped bar chart, not stacked or line chart.

3. Final Answer:

   A grouped bar chart with bars for Data1 and Data2 side by side for each label -> Option B

4. Quick Check:

   Shifted bars = grouped bar chart [OK]

Hint: Shift bars left and right to get grouped bars, not stacked [OK]

Common Mistakes:

• Thinking bars are stacked instead of grouped
• Confusing bar chart with line or scatter plot
• Ignoring the x-axis label positions

4. Identify the error in this grouped bar chart code:

import matplotlib.pyplot as plt
import numpy as np

labels = ['X', 'Y', 'Z']
data1 = [3, 5, 7]
data2 = [4, 6, 8]
x = np.arange(len(labels))
width = 0.3

plt.bar(x, data1, width, label='Data1')
plt.bar(x + width/2, data2, width, label='Data2')
plt.xticks(x, labels)
plt.legend()
plt.show()

medium

A. Legend labels are missing

B. Width is too large causing bars to be invisible

C. x positions are incorrect and cause an error

D. Bars overlap because data1 bars are not shifted left

Solution

1. Step 1: Check bar positions

   data1 bars are at x, data2 bars at x + width/2, so data1 bars are not shifted left to avoid overlap.

2. Step 2: Understand grouping logic

   To group bars, data1 should be at x - width/2 and data2 at x + width/2; otherwise, bars overlap.

3. Final Answer:

   Bars overlap because data1 bars are not shifted left -> Option D

4. Quick Check:

   Shift both bars ±width/2 to avoid overlap [OK]

Hint: Shift first bars left by width/2 to prevent overlap [OK]

Common Mistakes:

• Not shifting first bar set left
• Using full width shift causing gaps
• Ignoring bar width when setting positions

5. You have sales data for 3 products over 4 quarters:

products = ['P1', 'P2', 'P3']
quarters = ['Q1', 'Q2', 'Q3', 'Q4']
sales = [[10, 15, 20, 25], [12, 18, 22, 28], [14, 20, 24, 30]]

How would you create a grouped bar chart showing sales per quarter for each product using matplotlib?

hard

A. Use a line plot instead of bars for grouped comparison

B. Plot bars at x positions equal to product indices without shifting

C. Use x = np.arange(len(quarters)), width = 0.25, then plot bars for each product shifted by -width, 0, +width

D. Stack bars for all products at the same x positions for quarters

Solution

1. Step 1: Set x positions for quarters

   Use x = np.arange(len(quarters)) to represent each quarter on the x-axis.

2. Step 2: Define bar width and shifts

   Set width = 0.25 and shift bars for each product by -width, 0, and +width to group them side by side.

3. Step 3: Plot bars for each product

   Plot bars for each product's sales data at shifted x positions to create grouped bars per quarter.

4. Final Answer:

   Use x = np.arange(len(quarters)), width = 0.25, then plot bars for each product shifted by -width, 0, +width -> Option C

5. Quick Check:

   Shift bars by ±width around x for grouping [OK]

Hint: Shift bars by ±width around x for each product group [OK]

Common Mistakes:

• Not shifting bars causing overlap
• Using product indices as x instead of quarters
• Stacking bars instead of grouping