Matplotlib Annotation Tutorial | Python Visualization

Introduction

This tutorial will guide you through the process of annotating a plot using Matplotlib. Annotating a plot is a useful way to highlight specific features or data points on a graph. In this tutorial, we will demonstrate how to annotate a plot with an arrow pointing to provided coordinates.

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Skills Graph

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Import Libraries

Before we begin, we need to import the necessary libraries. In this tutorial, we will be using Matplotlib and Numpy.

import matplotlib.pyplot as plt
import numpy as np

Create a Plot

Next, we will create a plot using Matplotlib. In this example, we will plot the cosine function over a range of values.

fig, ax = plt.subplots()

t = np.arange(0.0, 5.0, 0.01)
s = np.cos(2*np.pi*t)
line, = ax.plot(t, s, lw=2)

Annotate the Plot

Now, we will annotate the plot by adding an arrow pointing to a specific coordinate. In this example, we will add an arrow pointing to the local maximum of the cosine function.

ax.annotate('local max', xy=(2, 1), xytext=(3, 1.5),
            arrowprops=dict(facecolor='black', shrink=0.05),
            )

The ax.annotate() function takes several arguments. The first argument is the text that will be displayed on the plot. The xy argument specifies the coordinates of the point that we want to annotate. The xytext argument specifies the coordinates of the text. The arrowprops argument is a dictionary that specifies the properties of the arrow.

Set the Plot Limits

Finally, we will set the limits of the plot to ensure that the annotated point is visible.

ax.set_ylim(-2, 2)
plt.show()

Summary

In this tutorial, we have learned how to annotate a plot using Matplotlib. We started by importing the necessary libraries and creating a plot. Then, we annotated the plot by adding an arrow pointing to a specific coordinate. Finally, we set the limits of the plot to ensure that the annotated point is visible.

Annotating a Plot Using Matplotlib