Figure and Axes mental model in Matplotlib

Introduction

We use the Figure and Axes model to organize and control plots clearly. It helps us put one or many charts in a single window.

When you want to create a single plot in a window.

When you want to put multiple plots side by side or in a grid.

When you want to customize the size or layout of your plots.

When you want to add titles or labels to the whole window or individual plots.

When you want to save your plots as images with specific sizes.

Syntax

Matplotlib

fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(width, height))

fig is the whole window or figure that holds one or more plots.

ax is the actual plot area where data is drawn.

Examples

Create one plot and draw a simple line chart.

Matplotlib

fig, ax = plt.subplots()
ax.plot([1, 2, 3], [4, 5, 6])

Create a 2x2 grid of plots and draw lines on two of them.

Matplotlib

fig, axs = plt.subplots(2, 2)
axs[0, 0].plot([1, 2], [3, 4])
axs[1, 1].plot([5, 6], [7, 8])

Create a wider plot and draw a bar chart.

Matplotlib

fig, ax = plt.subplots(figsize=(8, 4))
ax.bar(['A', 'B'], [10, 20])

Sample Program

This code creates one window (figure) with two plots (axes) side by side. The first plot shows a line chart, and the second shows a scatter chart. We add titles to each plot and a main title for the whole window.

Matplotlib

import matplotlib.pyplot as plt

# Create a figure with 2 plots side by side
fig, axs = plt.subplots(1, 2, figsize=(10, 4))

# First plot: line chart
axs[0].plot([1, 2, 3], [1, 4, 9])
axs[0].set_title('Line Chart')

# Second plot: scatter chart
axs[1].scatter([1, 2, 3], [9, 4, 1])
axs[1].set_title('Scatter Chart')

# Add a main title for the whole figure
fig.suptitle('Figure with Two Axes')

# Show the plot window
plt.show()

Important Notes

Think of Figure as a picture frame and Axes as the canvas inside where you paint your data.

You can have many Axes inside one Figure to compare different charts easily.

Use figsize to control the size of the whole Figure window.

Summary

The Figure is the whole window or image that holds your plots.

The Axes is the area where the actual data is drawn.

You can create multiple Axes inside one Figure to show many plots together.

Practice

1. What does the Figure represent in matplotlib's Figure and Axes model?

easy

A. A single data point on the plot

B. The area where data points are drawn

C. The entire image or window that contains all plots

D. The color scheme of the plot

Solution

1. Step 1: Understand the role of Figure

   The Figure is the whole container or window that holds one or more plots.

2. Step 2: Differentiate Figure from Axes

   Axes is the part inside the Figure where data is drawn, not the entire window.

3. Final Answer:

   The entire image or window that contains all plots -> Option C

4. Quick Check:

   Figure = whole window [OK]

Hint: Figure is the whole plot window, not just the data area [OK]

Common Mistakes:

• Confusing Figure with Axes
• Thinking Figure is a single plot line
• Mixing Figure with plot colors

2. Which of the following is the correct way to create a Figure and Axes in matplotlib?

easy

A. fig, ax = plt.subplots()

B. fig = plt.figure(), ax = plt.plot()

C. ax = plt.figure(), fig = plt.axes()

D. plt.create_figure(), plt.create_axes()

Solution

1. Step 1: Recall matplotlib syntax

   The standard way to create Figure and Axes together is using plt.subplots().

2. Step 2: Check each option

   fig, ax = plt.subplots() correctly assigns Figure and Axes from plt.subplots(). Others misuse functions or assign incorrectly.

3. Final Answer:

   fig, ax = plt.subplots() -> Option A

4. Quick Check:

   Use plt.subplots() to get Figure and Axes [OK]

Hint: Use plt.subplots() to get both Figure and Axes [OK]

Common Mistakes:

• Using plt.plot() to create Axes
• Confusing plt.figure() with plt.subplots()
• Trying non-existent functions like plt.create_figure()

3. What will be the output type of the variable ax after running this code?

import matplotlib.pyplot as plt
fig, ax = plt.subplots()

medium

A. A matplotlib Figure object

B. A matplotlib Axes object

C. A list of Axes objects

D. A NumPy array

Solution

1. Step 1: Understand plt.subplots() return values

   It returns a tuple: (Figure object, Axes object or array of Axes).

2. Step 2: Check variable assignment

   Here, ax is assigned the second element, which is a single Axes object.

3. Final Answer:

   A matplotlib Axes object -> Option B

4. Quick Check:

   ax = Axes object from plt.subplots() [OK]

Hint: plt.subplots() returns (Figure, Axes) tuple [OK]

Common Mistakes:

• Thinking ax is a Figure
• Assuming ax is a list without multiple plots
• Confusing Axes with NumPy arrays

4. Identify the error in this code snippet:

import matplotlib.pyplot as plt
fig = plt.figure()
ax = plt.axes()
ax.plot([1, 2, 3], [4, 5, 6])
plt.show()

medium

A. plt.axes() should be called on the Figure object

B. plt.axes() creates a new Figure, not Axes

C. plt.figure() must be called after plt.axes()

D. No error, code runs fine

Solution

1. Step 1: Understand plt.figure() and plt.axes()

   plt.figure() creates a Figure and sets it as current. plt.axes() creates Axes in the current Figure using default positioning.

2. Step 2: Verify code execution

   The plot is correctly added to ax and plt.show() displays the figure. The code runs without errors.

3. Final Answer:

   No error, code runs fine -> Option D

4. Quick Check:

   plt.axes() adds to current Figure [OK]

Hint: plt.figure() sets current figure for plt.axes() [OK]

Common Mistakes:

• Calling plt.axes() without linking to Figure
• Assuming plt.axes() auto-links to last Figure
• Calling plt.figure() after plt.axes()

5. You want to create a figure with two side-by-side plots sharing the same y-axis. Which code correctly uses the Figure and Axes model to achieve this?

hard

A. fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.plot([1,2,3], [4,5,6])
ax2.plot([1,2,3], [6,5,4])

B. fig = plt.figure()
ax1 = fig.add_subplot(1, 2, 1)
ax2 = fig.add_subplot(1, 2, 2)
ax1.sharey(ax2)

C. fig, ax = plt.subplots(2, 1)
ax[0].plot([1,2,3], [4,5,6])
ax[1].plot([1,2,3], [6,5,4])

D. fig, ax = plt.subplots(1, 2)
ax[0].plot([1,2,3], [4,5,6])
ax[1].plot([1,2,3], [6,5,4])

Solution

1. Step 1: Understand subplots with shared axes

   Using plt.subplots(1, 2, sharey=True) creates one row, two columns of Axes sharing the y-axis.

2. Step 2: Check code correctness

   fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True) ax1.plot([1,2,3], [4,5,6]) ax2.plot([1,2,3], [6,5,4]): fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True) ax1.plot([1,2,3], [4,5,6]) ax2.plot([1,2,3], [6,5,4]) correctly unpacks two Axes and plots data with shared y-axis. fig, ax = plt.subplots(1, 2) ax[0].plot([1,2,3], [4,5,6]) ax[1].plot([1,2,3], [6,5,4]) omits sharey=True. fig = plt.figure() ax1 = fig.add_subplot(1, 2, 1) ax2 = fig.add_subplot(1, 2, 2) ax1.sharey(ax2) calls non-existent sharey() method. fig, ax = plt.subplots(2, 1) ax[0].plot([1,2,3], [4,5,6]) ax[1].plot([1,2,3], [6,5,4]) creates vertical subplots.

3. Final Answer:

   fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True) ax1.plot([1,2,3], [4,5,6]) ax2.plot([1,2,3], [6,5,4]) -> Option A

4. Quick Check:

   Use sharey=True in plt.subplots() for shared y-axis [OK]

Hint: Use sharey=True in plt.subplots() for shared y-axis [OK]

Common Mistakes:

• Trying to call sharey() on Axes objects
• Creating vertical subplots instead of horizontal
• Not unpacking multiple Axes correctly