Annotate Plots with Matplotlib

Introduction

In this lab, you will learn how to use Matplotlib to annotate plots. Annotation refers to the process of adding text, arrows, and shapes to plots to provide additional context or highlight specific points of interest.

VM Tips

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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.

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Import Matplotlib

Before we can start annotating plots with Matplotlib, we must first import the library. In this step, we will import Matplotlib and create a simple plot to use for annotation.

import matplotlib.pyplot as plt

## Create a simple plot
fig, ax = plt.subplots()
ax.plot([0, 1, 2, 3, 4], [0, 1, 4, 9, 16])
plt.show()

Add Text Annotation

The simplest form of annotation is adding text to a plot. In this step, we will add text to the plot we created in the previous step.

## Add text annotation
ax.text(2, 10, "Important Point", fontsize=12, color='red')
plt.show()

Add Arrow Annotation

Arrows can be used to point out specific features or trends in a plot. In this step, we will add an arrow to the plot that points to the maximum value.

## Find the maximum value
y = [0, 1, 4, 9, 16]
max_index = y.index(max(y))
xmax = max_index
ymax = y[max_index]

## Add arrow annotation
ax.annotate('Maximum Value', xy=(xmax, ymax), xytext=(xmax, ymax + 5),
            arrowprops=dict(facecolor='black', shrink=0.05))
plt.show()

Add Shape Annotation

Shapes can be used to draw attention to specific regions of a plot. In this step, we will add a rectangle to highlight the area between x=1 and x=3.

## Add shape annotation
ax.axvspan(1, 3, facecolor='gray', alpha=0.2)
plt.show()

Summary

In this lab, you learned how to annotate plots using Matplotlib. You learned how to add text, arrows, and shapes to provide additional context or highlight specific points of interest. With these tools, you can create more informative and visually appealing plots to share with others.