Matplotlib: Text Commands

Introduction

Matplotlib is a Python library used to create visualizations such as line plots, scatter plots, bar plots, and more. In this lab, we will learn how to use text commands to add text to our plots. We will explore different ways to add text and annotations to our plots.

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

First, we need to import the matplotlib library and its pyplot module. We will use the pyplot module to create and customize our plots.

import matplotlib.pyplot as plt

Creating a Figure and Subplot

We will create a figure and subplot using the plt.subplots() function. This function returns a tuple that contains a figure and a subplot. We will use the subplot to add text and annotations to our plot.

fig, ax = plt.subplots()

Adding a Title to the Figure

We can add a title to the figure using the fig.suptitle() function. This function takes a string as an argument and sets the title of the figure.

fig.suptitle('bold figure suptitle', fontsize=14, fontweight='bold')

Adding a Title to the Subplot

We can add a title to the subplot using the ax.set_title() function. This function takes a string as an argument and sets the title of the subplot.

ax.set_title('axes title')

Adding Labels to the Axes

We can add labels to the x and y axes using the ax.set_xlabel() and ax.set_ylabel() functions, respectively. These functions take a string as an argument and set the label of the corresponding axis.

ax.set_xlabel('xlabel')
ax.set_ylabel('ylabel')

Adding Text to the Plot

We can add text to the plot using the ax.text() function. This function takes three arguments: the x-coordinate, the y-coordinate, and the text string. We can customize the text style using the style, bbox, and fontsize arguments.

ax.text(3, 8, 'boxed italics text in data coords', style='italic',
        bbox={'facecolor': 'red', 'alpha': 0.5, 'pad': 10})

ax.text(2, 6, r'an equation: $E=mc^2$', fontsize=15)

ax.text(3, 2, 'Unicode: Institut f\374r Festk\366rperphysik')

ax.text(0.95, 0.01, 'colored text in axes coords',
        verticalalignment='bottom', horizontalalignment='right',
        transform=ax.transAxes,
        color='green', fontsize=15)

Adding Annotations to the Plot

We can add annotations to the plot using the ax.annotate() function. This function takes three arguments: the annotation text, the xy-coordinate of the point to annotate, and the xy-coordinate of the text position. We can customize the annotation style using the arrowprops argument.

ax.annotate('annotate', xy=(2, 1), xytext=(3, 4),
            arrowprops=dict(facecolor='black', shrink=0.05))

Setting the Plot Limits

We can set the x and y limits of the plot using the ax.set() function. This function takes two arguments: the x and y limits as tuples.

ax.set(xlim=(0, 10), ylim=(0, 10))

Displaying the Plot

Finally, we can display the plot using the plt.show() function. This function shows the plot in a separate window.

plt.show()

Summary

In this lab, we learned how to use text commands to add text and annotations to our plots. We explored different ways to add text and annotations to our plots using the ax.text() and ax.annotate() functions. We also learned how to set the limits of the plot using the ax.set() function. By using these text commands, we can make our plots more informative and easier to understand.