Plot Styling: Text and Labels in Matplotlib

The Story of the Label Artist

Imagine you’re a museum curator. You have beautiful paintings (your data plots), but without labels and descriptions, visitors would be lost! They wouldn’t know what they’re looking at.

Labels and text in Matplotlib work the same way—they’re the helpful signs that guide your viewers through your data story.

X and Y Axis Labels

What Are They?

Axis labels are like street signs for your plot. The X-axis label tells people what’s going along the bottom. The Y-axis label tells people what’s going up the side.

How to Add Them

import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [4, 5, 6])
plt.xlabel('Days')
plt.ylabel('Ice Cream Sales')
plt.show()

Make Them Pretty!

You can change the look:

plt.xlabel('Days',
           fontsize=14,
           color='blue',
           fontweight='bold')

plt.ylabel('Sales',
           fontsize=14,
           color='green')

Think of it like this: You’re writing on a sign, and you get to pick the marker color, size, and thickness!

Plot Titles

The Main Headline

A title is like the name of your painting. It sits at the top and tells everyone: “This is what you’re looking at!”

Adding a Title

plt.plot([1, 2, 3], [10, 20, 15])
plt.title('My First Chart')
plt.show()

Styling Your Title

plt.title('Ice Cream Sales This Week',
          fontsize=18,
          fontweight='bold',
          color='purple',
          loc='center')  # left, center, right

The loc parameter moves the title:

• 'left' → Title sits on the left
• 'center' → Title sits in the middle (default)
• 'right' → Title sits on the right

Figure Suptitle

The “Super” Title

When you have multiple charts in one picture, you need a big boss title that covers them all. That’s suptitle (super title)!

graph TD
    A[Figure Suptitle: My Data Story] --> B[Chart 1: Sales]
    A --> C[Chart 2: Costs]
    A --> D[Chart 3: Profit]

How to Add It

fig, axes = plt.subplots(1, 2)

axes[0].plot([1, 2, 3])
axes[0].set_title('Chart A')

axes[1].plot([3, 2, 1])
axes[1].set_title('Chart B')

fig.suptitle('My Two Charts',
             fontsize=16,
             fontweight='bold')
plt.show()

Key Difference

Subplot Titles

Each Chart Gets a Name

When you have multiple small charts (subplots), each one can have its own title!

fig, axes = plt.subplots(2, 2)

axes[0, 0].plot([1, 2, 3])
axes[0, 0].set_title('Top Left')

axes[0, 1].plot([3, 2, 1])
axes[0, 1].set_title('Top Right')

axes[1, 0].plot([1, 3, 2])
axes[1, 0].set_title('Bottom Left')

axes[1, 1].plot([2, 1, 3])
axes[1, 1].set_title('Bottom Right')

plt.tight_layout()
plt.show()

Pro Tip: Use plt.tight_layout() so titles don’t overlap!

Adding Text to Plots

Write Anywhere!

Sometimes you want to write a note right on your chart. Maybe point out something special!

The text() Function

plt.plot([1, 2, 3, 4], [1, 4, 2, 3])
plt.text(2, 4, 'Peak!')
plt.show()

This puts the word “Peak!” at position (2, 4) on your chart.

Styled Text

plt.text(2, 4, 'Peak!',
         fontsize=12,
         color='red',
         fontweight='bold',
         bbox=dict(boxstyle='round',
                   facecolor='yellow'))

The bbox adds a colored box around your text—like a sticky note!

Text Positioning

Where Should Your Text Go?

There are two ways to position text:

1. Data Coordinates (Default)

Text goes where your data is:

plt.text(2, 4, 'Here!')
# Goes at x=2, y=4 on your chart

2. Axes Coordinates (0 to 1)

Text goes at a percentage of the chart:

plt.text(0.5, 0.5, 'Center!',
         transform=plt.gca().transAxes)
# Goes in the exact middle

graph TD
    A["#40;0,0#41; = Bottom-Left"] --> B["#40;0.5,0.5#41; = Center"]
    B --> C["#40;1,1#41; = Top-Right"]

Quick Reference

Text Font Properties

Make Your Text Beautiful!

You control everything about how text looks.

The Main Properties

plt.text(0.5, 0.5, 'Styled Text',
         fontsize=16,          # How big
         fontweight='bold',    # How thick
         fontstyle='italic',   # Slanted?
         fontfamily='serif',   # Font type
         color='navy',         # What color
         alpha=0.8)            # Transparency

Font Size Options

Font Weight Options

• 'light' → Thin letters
• 'normal' → Regular letters
• 'bold' → Thick letters
• 'heavy' → Extra thick!

Font Style Options

• 'normal' → Regular
• 'italic' → Slanted
• 'oblique' → Also slanted

Font Family Options

• 'serif' → Fancy with little feet (Times)
• 'sans-serif' → Clean and modern (Arial)
• 'monospace' → All letters same width (Code font)
• 'cursive' → Handwriting style

All Together Example

import matplotlib.pyplot as plt

plt.figure(figsize=(8, 6))
plt.plot([1, 2, 3, 4], [1, 4, 2, 3])

# Title with style
plt.title('Sales Report',
          fontsize=20,
          fontweight='bold',
          color='darkblue')

# Axis labels
plt.xlabel('Month',
           fontsize=14,
           fontfamily='sans-serif')
plt.ylabel('Sales ($)',
           fontsize=14,
           fontfamily='sans-serif')

# Text annotation
plt.text(2, 4, 'Best Month!',
         fontsize=12,
         fontweight='bold',
         color='green',
         bbox=dict(boxstyle='round',
                   facecolor='lightyellow',
                   edgecolor='green'))

plt.show()

Quick Summary

graph TD
    A[Text in Matplotlib] --> B[Axis Labels]
    A --> C[Titles]
    A --> D[Annotations]

    B --> B1[xlabel]
    B --> B2[ylabel]

    C --> C1[title - one chart]
    C --> C2[suptitle - whole figure]
    C --> C3[set_title - subplots]

    D --> D1[text - add anywhere]
    D --> D2[Position - data or axes]
    D --> D3[Style - size, color, font]

Remember These!

1. xlabel/ylabel = Street signs for your axes
2. title = Name of one chart
3. suptitle = Name of the whole picture
4. set_title = Name for each subplot
5. text = Notes anywhere on your chart
6. transform = Switch between data and axes positions
7. fontsize, fontweight, color = Make it pretty!

You’re now a Label Master! Your plots will never be confusing again.