How to set plot background color in matplotlib?

How to Set Plot Background Color in Matplotlib

Matplotlib is a powerful Python library for creating static, animated, and interactive visualizations. One of the key aspects of customizing your plots is setting the background color. This not only enhances the aesthetic appeal but also helps in emphasizing the data. In this guide, we'll explore various methods to set the plot background color in Matplotlib.

Why Set Plot Background Color?

By default, Matplotlib plots have a white background. However, changing the background color can:

• Improve contrast and readability
• Align with branding or presentation themes
• Highlight specific data points or trends

Whether you're preparing plots for a dark-themed presentation or aiming for a minimalist design, adjusting the background color is a fundamental customization.

Setting the Axes Background Color

The most direct way to change the background color is by modifying the axes properties. Here's how you can set the background color of the plot area:

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(0, 10, 0.1)
y = np.sin(x)

plt.plot(x, y)
plt.xlabel("X-axis")
plt.ylabel("Y-axis")

ax = plt.gca()  # Get current axes
ax.set_facecolor("lightblue")  # Set background color

plt.show()

In this example, the plot's background is set to light blue using the set_facecolor() method on the axes object.

Setting the Figure Background Color

To change the background color of the entire figure, including the area outside the plot, you can use the figure() function:

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(0, 10, 0.1)
y = np.sin(x)

plt.figure(facecolor="lightgray")  # Set figure background color
plt.plot(x, y)
plt.xlabel("X-axis")
plt.ylabel("Y-axis")

plt.show()

Here, the facecolor parameter in plt.figure() sets the background color of the entire figure.

Using Hexadecimal Color Codes

Matplotlib allows you to use hexadecimal color codes for precise color control. For instance:

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(0, 10, 0.1)
y = np.sin(x)

plt.figure(facecolor="#f0e68c")  # Set figure background using hex code
plt.plot(x, y)
plt.xlabel("X-axis")
plt.ylabel("Y-axis")

plt.show()

In this example, #f0e68c is the hex code for a khaki color, providing a soft background for the plot.

Applying Built-in Styles

Matplotlib offers several built-in styles that can quickly change the appearance of your plots, including background colors:

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(0, 10, 0.1)
y = np.sin(x)

plt.style.use('dark_background')  # Apply dark background style
plt.plot(x, y)
plt.xlabel("X-axis")
plt.ylabel("Y-axis")

plt.show()

By using plt.style.use('dark_background'), the plot adopts a dark theme, which is particularly useful for presentations in low-light environments.

Conclusion

Customizing the background color of your plots in Matplotlib is a simple yet effective way to enhance the visual appeal and clarity of your data visualizations. Whether you're adjusting the axes or figure background, using hexadecimal color codes, or applying built-in styles, Matplotlib provides flexible options to suit your design preferences. Experiment with different colors and styles to find the combination that best represents your data.