Labels are essential in any data visualization—they help the viewer understand what the data represents. In Matplotlib, labels include:

• Axis labels (xlabel, ylabel)

• Titles (title)

• Legends (legend)

• Tick labels (xticks, yticks)

• Annotations (annotate)

This guide shows how to use each type, customize them, and avoid common issues.

Setup: Basic Plot Structure

Start by importing Matplotlib and creating a sample plot:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 20, 15, 25, 30]

plt.plot(x, y)

1. Adding Axis Labels

Use plt.xlabel() and plt.ylabel() to name the x and y axes.

plt.xlabel("Time (days)")
plt.ylabel("Sales ($)")

Customizing Axis Labels

You can set font size, color, and font weight:

plt.xlabel("Time (days)", fontsize=14, color='blue', fontweight='bold')
plt.ylabel("Sales ($)", fontsize=14, color='green')

2. Adding a Plot Title

Use plt.title() to give your plot a descriptive heading.

plt.title("Daily Sales Over Time")

Title Customization

plt.title("Daily Sales Over Time", fontsize=16, color='purple', loc='center')

• loc='left', 'center', or 'right' aligns the title.

• You can also use pad to adjust the spacing above the plot.

plt.title("Sales Data", pad=20)

3. Adding a Legend

If you're plotting multiple lines, use label in plot() and then call plt.legend().

plt.plot(x, y, label="Product A", color='blue')
plt.legend()

Legend Customization

plt.legend(loc='upper left', fontsize=12, title='Legend')

Common legend locations:

• 'upper left', 'upper right', 'lower left', 'center', etc.

To place the legend outside the plot:

plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))

4. Customizing Tick Labels

Change Tick Values

plt.xticks([1, 2, 3, 4, 5], ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'])

Rotate Tick Labels

plt.xticks(rotation=45)
plt.yticks(rotation=90)

Change Tick Label Font

plt.xticks(fontsize=12, fontweight='bold')

5. Adding Annotations

Use plt.annotate() to add custom labels at specific data points.

plt.annotate("Peak", xy=(5, 30), xytext=(4, 32),
             arrowprops=dict(facecolor='black', shrink=0.05))

• xy is the data point

• xytext is where the label is displayed

• arrowprops adds an arrow from the label to the point

✅ Full Example: Labeled Line Plot

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 20, 15, 25, 30]

plt.figure(figsize=(8, 5))

# Line with label
plt.plot(x, y, label="Sales", color='blue', marker='o')

# Axis labels and title
plt.xlabel("Day", fontsize=12)
plt.ylabel("Sales ($)", fontsize=12)
plt.title("Sales Over 5 Days", fontsize=14, fontweight='bold')

# Tick customization
plt.xticks([1, 2, 3, 4, 5], ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'], rotation=45)

# Annotation
plt.annotate("High Point", xy=(5, 30), xytext=(3.5, 32),
             arrowprops=dict(facecolor='red', shrink=0.05))

# Legend
plt.legend(loc='upper left')

plt.grid(True)
plt.tight_layout()
plt.show()

Tips for Better Labels

⚠️ Common Pitfalls

Summary

Conclusion

Labels are the voice of your plot—they explain the context, meaning, and structure of your data. With Matplotlib, adding and customizing labels is simple yet powerful. Mastering these elements will help you create clear, compelling, and professional visualizations.

What’s Next?

• Learn about subplots and how to label multiple axes

• Explore interactive labeling with tools like plotly

• Use LaTeX-style labels for scientific formatting: plt.xlabel(r'$x^2 + y^2 = z^2$')