Matplotlib Bar Charts

last modified February 25, 2025

Matplotlib is a powerful Python library for creating static, animated, and interactive visualizations. Bar charts are one of the most common types of charts used to compare categorical data. This tutorial covers how to create various types of bar charts using Matplotlib.

Bar charts are ideal for visualizing discrete data, such as counts or percentages across categories. Matplotlib provides a flexible and easy-to-use interface for creating bar charts with customizations.

Basic Bar Chart

This example demonstrates how to create a basic bar chart.

basic_bar_chart.py

import matplotlib.pyplot as plt

Data

categories = [‘A’, ‘B’, ‘C’, ‘D’] values = [10, 20, 15, 25]

Create a bar chart

plt.bar(categories, values)

Add labels and title

plt.xlabel(“Categories”) plt.ylabel(“Values”) plt.title(“Basic Bar Chart”)

Display the chart

plt.show()

The plt.bar() function is used to create a bar chart. The plt.show() function displays the chart.

Horizontal Bar Chart

This example shows how to create a horizontal bar chart.

horizontal_bar_chart.py

import matplotlib.pyplot as plt

Data

categories = [‘A’, ‘B’, ‘C’, ‘D’] values = [10, 20, 15, 25]

Create a horizontal bar chart

plt.barh(categories, values)

Add labels and title

plt.xlabel(“Values”) plt.ylabel(“Categories”) plt.title(“Horizontal Bar Chart”)

Display the chart

plt.show()

The plt.barh() function is used to create a horizontal bar chart.

Grouped Bar Chart

This example demonstrates how to create a grouped bar chart.

grouped_bar_chart.py

import matplotlib.pyplot as plt import numpy as np

Data

categories = [‘A’, ‘B’, ‘C’, ‘D’] values1 = [10, 20, 15, 25] values2 = [15, 25, 20, 30]

Set the width of the bars

bar_width = 0.35

Create positions for the bars

x = np.arange(len(categories))

Create grouped bars

plt.bar(x - bar_width/2, values1, width=bar_width, label=“Group 1”) plt.bar(x + bar_width/2, values2, width=bar_width, label=“Group 2”)

Add labels, title, and legend

plt.xlabel(“Categories”) plt.ylabel(“Values”) plt.title(“Grouped Bar Chart”) plt.xticks(x, categories) plt.legend()

Display the chart

plt.show()

The np.arange() function is used to create positions for the bars. The width parameter controls the width of the bars.

Stacked Bar Chart

This example shows how to create a stacked bar chart.

stacked_bar_chart.py

import matplotlib.pyplot as plt

Data

categories = [‘A’, ‘B’, ‘C’, ‘D’] values1 = [10, 20, 15, 25] values2 = [15, 25, 20, 30]

Create stacked bars

plt.bar(categories, values1, label=“Group 1”) plt.bar(categories, values2, bottom=values1, label=“Group 2”)

Add labels, title, and legend

plt.xlabel(“Categories”) plt.ylabel(“Values”) plt.title(“Stacked Bar Chart”) plt.legend()

Display the chart

plt.show()

The bottom parameter is used to stack the second group of bars on top of the first group.

Customizing Bar Charts

This example demonstrates how to customize bar charts with colors, edge colors, and patterns.

custom_bar_chart.py

import matplotlib.pyplot as plt

Data

categories = [‘A’, ‘B’, ‘C’, ‘D’] values = [10, 20, 15, 25]

Create a bar chart with custom styles

plt.bar(categories, values, color=“skyblue”, edgecolor=“black”, hatch="/")

Add labels and title

plt.xlabel(“Categories”) plt.ylabel(“Values”) plt.title(“Custom Bar Chart”)

Display the chart

plt.show()

The color, edgecolor, and hatch parameters are used to customize the appearance of the bars.

Best Practices for Bar Charts

• Label Axes Clearly: Always label the X and Y axes to make the chart understandable.

• Use Legends: Add legends when plotting multiple groups to differentiate them.

• Choose Appropriate Colors: Use contrasting colors for multiple groups to improve readability.

• Limit Categories: Avoid cluttering the chart with too many categories.

Source

Matplotlib Bar Chart Documentation

In this article, we have explored various types of bar charts using Matplotlib, including basic, horizontal, grouped, stacked, and custom bar charts.

Author

My name is Jan Bodnar, and I am a passionate programmer with extensive programming experience. I have been writing programming articles since 2007. To date, I have authored over 1,400 articles and 8 e-books. I possess more than ten years of experience in teaching programming.

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