legend font size python matplotlib with code examples

Introduction:

Matplotlib is a data visualization library in Python that is essential for creating charts, graphs, and plots. It allows you to create professional visualizations with ease and provides multiple options to customize the look and feel of your plot. In this article, we will take a closer look at how to change the font size of the legend in Matplotlib using Python programming.

What is Legend in Matplotlib?

The legend in Matplotlib is a box containing a patch (colored rectangle) representing the data series and its label. It allows you to identify different data series on the plot and is helpful in creating an informative plot. By default, the legend text is small and may appear blurry, especially when the plot is resized or viewed from a distance. Therefore, changing the font size of the legend text is essential.

Changing the Legend Font Size:

In Matplotlib, changing the font size of the legend text is quite simple. You can use the fontsize attribute of the legend object to set the font size of the legend text. Here's the code to do it:

import matplotlib.pyplot as plt

# plot the data
x = [1, 2, 3, 4, 5]
y = [10, 20, 30, 40, 50]
plt.plot(x, y, label='Data series')

# set the font size of the legend text
plt.legend(fontsize=14)

# show the plot
plt.show()

In the above code, we first plot the data using plt.plot() and label the data series using the label attribute. Next, we set the font size of the legend text to 14 using the fontsize attribute of the legend object. Finally, we show the plot using plt.show().

You can also change the font size of the legend text using the rcParams dictionary. Here's the code to do it:

import matplotlib.pyplot as plt
import matplotlib as mpl

# update the font size of the legend text
mpl.rcParams['legend.fontsize'] = 14

# plot the data
x = [1, 2, 3, 4, 5]
y = [10, 20, 30, 40, 50]
plt.plot(x, y, label='Data series')

# show the plot
plt.show()

In the above code, we first update the font size of the legend text to 14 using the rcParams dictionary. Next, we plot the data using plt.plot() and label the data series using the label attribute. Finally, we show the plot using plt.show().

Conclusion:

Changing the legend font size in Matplotlib can enhance the readability and visual appeal of the plot. You can use either the fontsize attribute of the legend object or the rcParams dictionary to change the font size of the legend text. In this article, we have provided examples of both approaches. With these techniques, you can create professional-grade visualizations with ease.

In addition to changing the font size of the legend text, Matplotlib provides various other properties that can be customized to create a high-quality plot. Here are some of the essential properties of the legend object:

  1. Location: You can set the location of the legend box using the loc attribute of the legend object. The value of loc can be a string or an integer representing the location of the legend box on the plot. Example: plt.legend(loc='upper right')

  2. Title: You can set the title of the legend box using the title attribute of the legend object. The title string can be enclosed in quotes. Example: plt.legend(title='Legend')

  3. Border and Background: You can customize the border and background of the legend box using the frameon and facecolor attributes of the legend object. Example: plt.legend(frameon=False, facecolor='white')

  4. Shadow: You can add a shadow effect to the legend box using the shadow attribute of the legend object. Example: plt.legend(shadow=True)

  5. Labels: You can use the labels attribute of the legend object to change the labels of the data series in the legend box. Example: plt.legend(labels=['Series 1', 'Series 2'])

The above properties can be used in combination with each other to create an informative and visually appealing legend box.

Apart from legends, Matplotlib allows you to customize various other properties of the plot, such as title, axes, colors, and markers. Here are some examples:

  1. Title: You can set the title of the plot using the title function of the plt object. Example: plt.title('Plot Title')

  2. Axes Labels: You can set the labels of the x and y axes using the xlabel and ylabel functions of the plt object. Example: plt.xlabel('X-axis Label')

  3. Colors: You can customize the colors of the data series using the color attribute of the plot function. Example: plt.plot(x, y, color='red')

  4. Markers: You can add markers to the data points using the marker attribute of the plot function. Example: plt.plot(x, y, marker='o')

These are just a few examples of the many properties that can be customized in Matplotlib. With its vast collection of options, Matplotlib provides a powerful tool to create high-quality visualizations with ease.

Popular questions

  1. What is the default font size of the legend text in Matplotlib?
    Answer: By default, the font size of the legend text in Matplotlib is small, which may appear blurry when the plot is resized or viewed from a distance.

  2. How can you change the font size of the legend text in Matplotlib using Python programming?
    Answer: You can change the font size of the legend text in Matplotlib using the fontsize attribute of the legend object or the rcParams dictionary.

  3. Can you customize other properties of the legend box in Matplotlib?
    Answer: Yes, you can customize other properties of the legend box in Matplotlib, such as its location, title, border, background, shadow, and labels.

  4. How can you set the location of the legend box in Matplotlib?
    Answer: You can set the location of the legend box using the loc attribute of the legend object, and the value can be a string or an integer representing the location of the legend box on the plot.

  5. What are some other properties of the plot that can be customized in Matplotlib?
    Answer: Apart from legends, Matplotlib allows you to customize various other properties of the plot, such as title, axes, colors, and markers, among others.

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As a developer, I have experience in full-stack web application development, and I'm passionate about utilizing innovative design strategies and cutting-edge technologies to develop distributed web applications and services. My areas of interest extend to IoT, Blockchain, Cloud, and Virtualization technologies, and I have a proficiency in building efficient Cloud Native Big Data applications. Throughout my academic projects and industry experiences, I have worked with various programming languages such as Go, Python, Ruby, and Elixir/Erlang. My diverse skillset allows me to approach problems from different angles and implement effective solutions. Above all, I value the opportunity to learn and grow in a dynamic environment. I believe that the eagerness to learn is crucial in developing oneself, and I strive to work with the best in order to bring out the best in myself.
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