Matplotlib | Python Visualization | Subplot Adjustment

Introduction

Matplotlib is a powerful library for creating visualizations in Python. Sometimes, when creating plots, you may need to adjust the subplot parameters manually. This lab will show you how to programmatically adjust subplot parameters based on the size of the 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.

Skills Graph

%%%%{init: {'theme':'neutral'}}%%%% flowchart RL python(("`Python`")) -.-> python/BasicConceptsGroup(["`Basic Concepts`"]) matplotlib(("`Matplotlib`")) -.-> matplotlib/BasicConceptsGroup(["`Basic Concepts`"]) matplotlib(("`Matplotlib`")) -.-> matplotlib/PlottingDataGroup(["`Plotting Data`"]) matplotlib(("`Matplotlib`")) -.-> matplotlib/AdvancedTopicsGroup(["`Advanced Topics`"]) python(("`Python`")) -.-> python/ControlFlowGroup(["`Control Flow`"]) python(("`Python`")) -.-> python/DataStructuresGroup(["`Data Structures`"]) python(("`Python`")) -.-> python/FunctionsGroup(["`Functions`"]) python(("`Python`")) -.-> python/ModulesandPackagesGroup(["`Modules and Packages`"]) python(("`Python`")) -.-> python/DataScienceandMachineLearningGroup(["`Data Science and Machine Learning`"]) python/BasicConceptsGroup -.-> python/comments("`Comments`") matplotlib/BasicConceptsGroup -.-> matplotlib/importing_matplotlib("`Importing Matplotlib`") matplotlib/BasicConceptsGroup -.-> matplotlib/figures_axes("`Understanding Figures and Axes`") matplotlib/PlottingDataGroup -.-> matplotlib/line_plots("`Line Plots`") matplotlib/AdvancedTopicsGroup -.-> matplotlib/event_handling("`Event Handling`") python/ControlFlowGroup -.-> python/conditional_statements("`Conditional Statements`") python/ControlFlowGroup -.-> python/for_loops("`For Loops`") python/DataStructuresGroup -.-> python/lists("`Lists`") python/DataStructuresGroup -.-> python/tuples("`Tuples`") python/FunctionsGroup -.-> python/function_definition("`Function Definition`") python/ModulesandPackagesGroup -.-> python/importing_modules("`Importing Modules`") python/DataScienceandMachineLearningGroup -.-> python/data_visualization("`Data Visualization`") python/FunctionsGroup -.-> python/build_in_functions("`Build-in Functions`") subgraph Lab Skills python/comments -.-> lab-48558{{"`Programmatically Controlling Subplot Adjustment`"}} matplotlib/importing_matplotlib -.-> lab-48558{{"`Programmatically Controlling Subplot Adjustment`"}} matplotlib/figures_axes -.-> lab-48558{{"`Programmatically Controlling Subplot Adjustment`"}} matplotlib/line_plots -.-> lab-48558{{"`Programmatically Controlling Subplot Adjustment`"}} matplotlib/event_handling -.-> lab-48558{{"`Programmatically Controlling Subplot Adjustment`"}} python/conditional_statements -.-> lab-48558{{"`Programmatically Controlling Subplot Adjustment`"}} python/for_loops -.-> lab-48558{{"`Programmatically Controlling Subplot Adjustment`"}} python/lists -.-> lab-48558{{"`Programmatically Controlling Subplot Adjustment`"}} python/tuples -.-> lab-48558{{"`Programmatically Controlling Subplot Adjustment`"}} python/function_definition -.-> lab-48558{{"`Programmatically Controlling Subplot Adjustment`"}} python/importing_modules -.-> lab-48558{{"`Programmatically Controlling Subplot Adjustment`"}} python/data_visualization -.-> lab-48558{{"`Programmatically Controlling Subplot Adjustment`"}} python/build_in_functions -.-> lab-48558{{"`Programmatically Controlling Subplot Adjustment`"}} end

Import the necessary libraries

We will need matplotlib.pyplot and matplotlib.transforms to create the plot and manipulate the subplot parameters.

import matplotlib.pyplot as plt
import matplotlib.transforms as mtransforms

Create the plot

Let's create a simple line plot with some long y-labels.

fig, ax = plt.subplots()
ax.plot(range(10))
ax.set_yticks([2, 5, 7], labels=['really, really, really', 'long', 'labels'])

Define the draw callback function

We will define a function that will be called every time the plot is drawn. This function will calculate the bounding boxes of the y-labels, determine if the subplot leaves enough room for the labels, and adjust the subplot parameters if necessary.

def on_draw(event):
    bboxes = []
    for label in ax.get_yticklabels():
        ## Bounding box in pixels
        bbox_px = label.get_window_extent()
        ## Transform to relative figure coordinates. This is the inverse of
        ## transFigure.
        bbox_fig = bbox_px.transformed(fig.transFigure.inverted())
        bboxes.append(bbox_fig)
    ## the bbox that bounds all the bboxes, again in relative figure coords
    bbox = mtransforms.Bbox.union(bboxes)
    if fig.subplotpars.left < bbox.width:
        ## Move the subplot left edge more to the right
        fig.subplots_adjust(left=1.1*bbox.width)  ## pad a little
        fig.canvas.draw()

Connect the draw event to the callback function

We need to connect the draw_event to our on_draw function.

fig.canvas.mpl_connect('draw_event', on_draw)

Show the plot

Finally, we will show the plot.

plt.show()

Summary

In this lab, we learned how to programmatically adjust subplot parameters based on the size of the labels. We used the matplotlib.transforms module to calculate the bounding boxes of the labels and the draw_event to call our on_draw function.

Programmatically Controlling Subplot Adjustment