Bar charts are one of the most common and effective types of plots used to visualize categorical data. With Matplotlib, creating and customizing bar charts is straightforward and highly flexible.

This article walks you through creating vertical and horizontal bar charts, customizing colors and labels, plotting grouped bars, and more—with working examples and best practices.

What Is a Bar Chart?

A bar chart represents data with rectangular bars. The length or height of each bar corresponds to the value of the category.

Matplotlib supports two main types:

• plt.bar() for vertical bars

• plt.barh() for horizontal bars

Basic Bar Chart in Matplotlib

Example

import matplotlib.pyplot as plt

categories = ['A', 'B', 'C', 'D']
values = [10, 24, 36, 18]

plt.bar(categories, values)
plt.title("Basic Bar Chart")
plt.xlabel("Categories")
plt.ylabel("Values")
plt.grid(axis='y')
plt.show()

Customizing Bar Appearance

Bar Color

plt.bar(categories, values, color='skyblue')

Edge Color and Line Width

plt.bar(categories, values, edgecolor='black', linewidth=1.2)

Bar Width

plt.bar(categories, values, width=0.5)  # 1.0 is full width

Horizontal Bar Chart

Use barh() instead of bar():

plt.barh(categories, values, color='lightgreen')
plt.xlabel("Values")
plt.ylabel("Categories")
plt.title("Horizontal Bar Chart")
plt.grid(axis='x')
plt.show()

Grouped Bar Chart

Used to compare subcategories side-by-side.

Example:

import numpy as np

labels = ['Q1', 'Q2', 'Q3', 'Q4']
product_A = [10, 15, 20, 25]
product_B = [12, 18, 22, 28]

x = np.arange(len(labels))  # label locations
width = 0.35  # width of the bars

fig, ax = plt.subplots()
ax.bar(x - width/2, product_A, width, label='Product A')
ax.bar(x + width/2, product_B, width, label='Product B')

ax.set_xlabel('Quarter')
ax.set_ylabel('Sales')
ax.set_title('Quarterly Sales Comparison')
ax.set_xticks(x)
ax.set_xticklabels(labels)
ax.legend()
plt.tight_layout()
plt.show()

Adding Labels, Titles & Grid

plt.title("Sales Bar Chart")
plt.xlabel("Products")
plt.ylabel("Units Sold")
plt.grid(axis='y', linestyle='--', alpha=0.7)

Annotate Bars with Data Labels

Example:

bars = plt.bar(categories, values)

for bar in bars:
    yval = bar.get_height()
    plt.text(bar.get_x() + bar.get_width()/2, yval + 0.5, yval, ha='center', va='bottom')

Color Mapping Bars by Value

import matplotlib.cm as cm

colors = cm.viridis([v / max(values) for v in values])
plt.bar(categories, values, color=colors)

Sorting Bars

Sorted bar charts make comparisons easier:

sorted_data = sorted(zip(categories, values), key=lambda x: x[1], reverse=True)
categories, values = zip(*sorted_data)
plt.bar(categories, values)

Full Example: Custom Bar Chart

import matplotlib.pyplot as plt

products = ['Apples', 'Bananas', 'Cherries', 'Dates']
sales = [120, 90, 150, 70]

plt.figure(figsize=(8, 5))
bars = plt.bar(products, sales, color='coral', edgecolor='black')

# Add data labels
for bar in bars:
    yval = bar.get_height()
    plt.text(bar.get_x() + bar.get_width()/2, yval + 3, f'{yval}', ha='center')

plt.title("Fruit Sales")
plt.xlabel("Fruits")
plt.ylabel("Units Sold")
plt.grid(axis='y', linestyle='--')
plt.tight_layout()
plt.show()

Tips for Working with Bar Charts

⚠️ Common Pitfalls

Summary Table

Conclusion

Matplotlib’s bar charts are an excellent tool for comparing categorical values. With built-in options to group, stack, or format bars, you can create professional-looking charts suitable for reports, dashboards, and presentations.

What’s Next?

• Create stacked bar charts

• Plot bar charts from Pandas DataFrames

• Animate bar charts using FuncAnimation

• Create interactive bar charts with Plotly