Resizing Axes with Constrained Layout

Introduction

In data visualization, it is crucial to have clear and readable plots. However, creating multiple subplots can make it difficult to avoid overlaps between axes objects and labels. In such cases, we can use a feature called constrained layout in Matplotlib, which automatically resizes subplots to prevent overlaps between axes objects and labels.

VM Tips

After the VM startup is done, click the top left corner to switch to the Notebook tab to access Jupyter Notebook for practice.

Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.

If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.

Importing Required Libraries

We start by importing the necessary libraries for creating subplots and plotting data.

import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec

Defining Example Plot

We define a function that creates a simple line plot with x and y labels and a title.

def example_plot(ax):
    ax.plot([1, 2])
    ax.set_xlabel('x-label', fontsize=12)
    ax.set_ylabel('y-label', fontsize=12)
    ax.set_title('Title', fontsize=14)

Creating Subplots Without Constrained Layout

We create a figure with 2x2 subplots without using constrained layout. This results in overlapping labels on the axes.

fig, axs = plt.subplots(nrows=2, ncols=2, layout=None)

for ax in axs.flat:
    example_plot(ax)

Creating Subplots With Constrained Layout

We create the same 2x2 subplots, but this time we use constrained layout. This automatically adjusts the subplots to prevent overlaps between axes objects and labels.

fig, axs = plt.subplots(nrows=2, ncols=2, layout='constrained')

for ax in axs.flat:
    example_plot(ax)

Creating Nested Gridspecs with Constrained Layout

We create a more complicated example by using nested gridspecs with constrained layout. This allows us to have more control over the layout of the subplots.

fig = plt.figure(layout='constrained')

gs0 = gridspec.GridSpec(1, 2, figure=fig)

gs1 = gridspec.GridSpecFromSubplotSpec(3, 1, subplot_spec=gs0[0])
for n in range(3):
    ax = fig.add_subplot(gs1[n])
    example_plot(ax)


gs2 = gridspec.GridSpecFromSubplotSpec(2, 1, subplot_spec=gs0[1])
for n in range(2):
    ax = fig.add_subplot(gs2[n])
    example_plot(ax)

plt.show()

Summary

Constrained layout is a useful feature in Matplotlib that automatically resizes subplots to prevent overlaps between axes objects and labels. It is particularly useful when creating multiple subplots in a figure. By following the steps outlined in this tutorial, you can create clear and readable plots without having to worry about overlapping labels.

Summary

Congratulations! You have completed the Resizing Axes With Constrained Layout lab. You can practice more labs in LabEx to improve your skills.