The Figure & Axes

Reviewed & published by Brayan K

Matplotlib is a Python library for creating charts and visualizations — and every chart you draw lives inside two objects: a Figure (the whole canvas) and one or more Axes (the individual plot areas).

Part of the free Matplotlib course at LearnCodingFast — hands-on lessons with worked examples and the output they print, plus practice exercises and a quick quiz.

In this lesson you'll meet fig, ax = plt.subplots() and learn the difference between the quick pyplot interface and the explicit object-oriented interface.

What You'll Learn in This Lesson

• What a Figure is and what an Axes is
• How fig, ax = plt.subplots() creates both
• The pyplot state-machine interface (plt.plot)
• The object-oriented interface (ax.plot)
• Setting figure size with figsize
• Adding a figure-wide title with fig.suptitle

1 The Figure and the Axes

Think of the Figure as a blank sheet of paper and an Axes as a single chart drawn on that paper. A Figure can hold one Axes or a whole grid of them, but each Axes has its own data, x-axis, and y-axis.

📊 The two core objects

• Figure — the overall container (size, background, super-title)
• Axes — one plot region with its data and ticks
• One Figure → many Axes (subplots)
• fig.suptitle() titles the whole Figure
• ax.set_title() titles a single Axes

The cleanest way to get both at once is plt.subplots(), which returns a Figure and an Axes together so you always know exactly which object you're working with.

import matplotlib.pyplot as plt

# Create a Figure and a single Axes together
fig, ax = plt.subplots()

x = [1, 2, 3, 4, 5]
y = [2, 5, 3, 8, 7]

# Draw on the Axes (object-oriented interface)
ax.plot(x, y)

plt.show()

What you'll see: a single chart with a blue line that rises, dips, peaks at x=4, then eases down. It looks just like a normal plot — but now you hold explicit references to the Figure (fig) and the Axes (ax).

2 pyplot vs. Object-Oriented Interface

Matplotlib offers two ways to build the same chart. The pyplot interface (plt.plot) quietly tracks a "current" Axes for you. The object-oriented interface (ax.plot) makes that Axes explicit.

import matplotlib.pyplot as plt

x = [0, 1, 2, 3, 4]
y = [0, 1, 4, 9, 16]

# --- pyplot (state-machine) style ---
plt.plot(x, y)
plt.title("pyplot style")
plt.show()

# --- object-oriented style: exactly the same chart ---
fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_title("object-oriented style")
plt.show()

What you'll see: two windows, one after the other, each showing the same upward parabola. The only difference is the code style — plt.title versus ax.set_title — proving the two interfaces produce identical results.

3 Figure Size and Super-Titles

You control how big the canvas is with figsize=(width, height) in inches, and you can give the whole Figure a heading with fig.suptitle().

import matplotlib.pyplot as plt

# A wide, short canvas: 8 inches by 4 inches
fig, ax = plt.subplots(figsize=(8, 4))

x = [1, 2, 3, 4, 5, 6]
y = [10, 12, 9, 14, 13, 17]

ax.plot(x, y)

# A title for the entire Figure
fig.suptitle("Monthly Visitors")

plt.show()

What you'll see: a noticeably wider-than-tall chart with a bold "Monthly Visitors" heading across the very top of the Figure, sitting above the plotted line.

🎯 Your Turn: Fill in the Blanks

Replace each ___ to build a Figure and Axes and draw on it.

import matplotlib.pyplot as plt

# Create a Figure and Axes that is 6 by 4 inches
fig, ax = plt.___(figsize=(6, 4))

x = [1, 2, 3, 4]
y = [5, 9, 7, 12]

# Draw on the Axes (not plt)
ax.___(x, y)

# Title the whole Figure
fig.___("My Chart")

plt.show()

📋 Common Errors & Quick Reference

❌ AttributeError: 'Axes' object has no attribute 'title'

On an Axes use ax.set_title("..."), not ax.title("..."). The plain title form belongs to plt.

❌ Only one chart shows when I expected two

Each plt.show() displays and then clears the current figure. Build a fresh fig, ax for the next chart.

🏆 Mini Challenge: A Titled Figure

Build a wide Figure with one Axes, plot some data, and add a figure-wide title.

import matplotlib.pyplot as plt

# 1. Create a 9x4 inch Figure and Axes
fig, ax = plt.subplots(figsize=(9, 4))

weeks = [1, 2, 3, 4, 5, 6, 7, 8]
signups = [12, 18, 25, 22, 30, 35, 33, 40]

# 2. Plot on the Axes
ax.plot(weeks, signups)

# 3. Add a figure-wide title
fig.suptitle("Weekly Sign-ups")

plt.show()

import matplotlib.pyplot as plt

fig, axes = plt.subplots(2, 2, figsize=(8, 6))

axes[0][0].plot([1, 2, 3], [1, 4, 9])
axes[0][1].bar(["a", "b"], [3, 5])
axes[1][0].scatter([1, 2, 3], [3, 2, 1])
axes[1][1].axis("off")

fig.suptitle("Four panels")

print("Shape of the axes grid:", axes.shape)
print("Axes on the figure:", len(fig.axes))
print("Figure size:", fig.get_size_inches().tolist())
print("Super title:", fig.get_suptitle())
print("Top-left has a line:", len(axes[0][0].lines) == 1)
print("Top-right has bars:", len(axes[0][1].patches) == 2)
print("Bottom-right axis is hidden:", not axes[1][1].axison)

# ✅ Expected output:
# Shape of the axes grid: (2, 2)
# Axes on the figure: 4
# Figure size: [8.0, 6.0]
# Super title: Four panels
# Top-left has a line: True
# Top-right has bars: True
# Bottom-right axis is hidden: True

❓ Frequently Asked Questions

Lesson complete — you understand the canvas!

You learned that a Figure holds Axes, met plt.subplots(), and saw the difference between the pyplot and object-oriented interfaces.

Practice quiz

Continue this course

• Previous: Introduction to Matplotlib
• Next: Your First Line Plot — Draw a line chart from lists of x and y data with plt.plot
• Quick reference: Matplotlib cheat sheet › Basics

Frequently asked questions

What is the difference between a Figure and an Axes?

The Figure is the whole window or page that holds everything. An Axes is a single plot area inside that Figure — with its own x-axis, y-axis, and data. One Figure can contain many Axes.

What does fig, ax = plt.subplots() do?

It creates a Figure and a single Axes at the same time and returns both. You then draw on the Axes with ax.plot(...) and adjust the Figure with fig methods like fig.suptitle(...).

Should I use plt.plot or ax.plot?

Both work. plt.plot uses the implicit 'current axes' (the pyplot state-machine interface), which is quick for one-off charts. ax.plot uses the object-oriented interface, which is explicit and scales better to complex, multi-plot figures.

How do I control the figure size?

Pass figsize=(width, height) in inches to plt.figure() or plt.subplots(), for example plt.subplots(figsize=(8, 4)). Width comes first, height second.