Customizing plots with labels,titles, legends and annotations

Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. The library provides an interface for plotting various types of plots and charts, including line plots, scatter plots, bar plots, histograms, pie charts, box plots, and more. Matplotlib also provides a high-level interface for customizing plots with labels, titles, legends, and annotations. In this tutorial, we will show you how to customize matplotlib.pyplot plots using these features.

Before we start, let's import the required libraries:

import numpy as np

import matplotlib.pyplot as plt

Labels, Titles, and Legends

Labels, titles, and legends are essential components of a well-designed plot. Labels provide descriptive information about the x and y axes of a plot, titles provide a title for the plot, and legends provide an explanation of the data being plotted.

Axes Labels

To add labels to the x and y axes of a plot, we use the xlabel and ylabel functions, respectively. The following code demonstrates how to add labels to a simple line plot:

x = np.linspace(0, 10, 100)

y = np.sin(x)

plt.plot(x, y)

plt.xlabel("Time (s)")

plt.ylabel("Amplitude (m)")

plt.show()

Plot Title

To add a title to a plot, we use the title function. The following code demonstrates how to add a title to the same line plot:

x = np.linspace(0, 10, 100)

y = np.sin(x)

plt.plot(x, y)

plt.xlabel("Time (s)")

plt.ylabel("Amplitude (m)")

plt.title("Sine Wave")

plt.show()

Legends

Legends are used to explain the data being plotted. In matplotlib, legends are automatically generated when multiple lines are plotted in the same figure. To add a legend to a plot, we use the legend function. The following code demonstrates how to add a legend to a plot with multiple lines:

x = np.linspace(0, 10, 100)

y1 = np.sin(x)

y2 = np.cos(x)

plt.plot(x, y1, label="Sine")

plt.plot(x, y2, label="Cosine")

plt.xlabel("Time (s)")

plt.ylabel("Amplitude (m)")

plt.title("Sine and Cosine Waves")

plt.legend()

plt.show()

Annotations

Annotations are a way to add text annotations to your plots, pointing to specific points or regions in the data. To add annotations to a plot, you can use the annotate function from matplotlib.pyplot.

Here's an example of adding an annotation to a sine wave plot:

import numpy as np

import matplotlib.pyplot as plt

x = np.linspace(0, 2 * np.pi, 100)

y = np.sin(x)

plt.plot(x, y)

plt.xlabel("Angle (radians)")

plt.ylabel("Amplitude (m)")

plt.title("Sine Wave")

max_y = max(y)

max_x = x[np.argmax(y)]

plt.annotate(f"Max: {max_y:.2f}", (max_x, max_y), textcoords="offset points", xytext=(-15,10), ha="center", fontsize=12, arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=0.2"))

plt.show()

In the above example, the annotate function takes the following arguments:

• The first argument is the text to be displayed, which includes the maximum value of the sine wave.
• The second argument is the xy coordinate of the point to be annotated.
• The textcoords argument specifies the coordinate system for the text. In this case, we use the offset points system, which means the text will be offset by a certain number of points from the annotated point.
• The xytext argument specifies the position of the text relative to the annotated point.
• The ha argument stands for "horizontal alignment" and determines the alignment of the text relative to the annotated point. In this case, we use "center" to align the text in the center of the annotated point.
• The fontsize argument specifies the font size of the text.
• The arrowprops argument specifies the properties of the arrow connecting the annotated point to the text. In this case, we use the arrowstyle argument to specify the style of the arrow, and the connectionstyle argument to specify the connection style between the arrow and the annotated point.

Annotations can also be added to other types of plots, such as scatter plots and bar plots, in a similar fashion.

In this tutorial, I have shown you how to customize matplotlib.pyplot plots with labels, titles, legends, and annotations. By using these features, you can create well-designed, informative plots that clearly convey your data and insights.

 

Amelioration

This article was researched and written with the help of ChatGPT, a language model developed by OpenAI.

Special thanks to ChatGPT for providing valuable information and examples used in this article.