WikiGalaxy
Creating Subplots with plt.subplots()
The plt.subplots() function in Matplotlib is a versatile tool for creating multiple plots within a single figure. It provides a convenient way to generate subplots, allowing you to organize and display multiple graphs simultaneously. This function returns a tuple containing a figure object and an array of axes objects, which can be used to plot data on individual subplots.
Basic Subplots
In this example, we'll create a basic 2x2 grid of subplots using plt.subplots(). Each subplot will display a simple line plot.
import matplotlib.pyplot as plt
fig, axs = plt.subplots(2, 2)
axs[0, 0].plot([1, 2, 3], [1, 4, 9])
axs[0, 1].plot([1, 2, 3], [1, 2, 3])
axs[1, 0].plot([1, 2, 3], [3, 2, 1])
axs[1, 1].plot([1, 2, 3], [9, 4, 1])
plt.show()
This code creates a 2x2 grid of subplots, each displaying a different line plot. The axs array allows access to individual subplots for plotting.
Sharing Axes
Sharing axes between subplots can be useful for comparing data. Here, we'll create subplots that share the x-axis.
import matplotlib.pyplot as plt
fig, axs = plt.subplots(2, 1, sharex=True)
axs[0].plot([1, 2, 3], [1, 4, 9])
axs[1].plot([1, 2, 3], [1, 2, 3])
plt.show()
By setting sharex=True, the subplots share the same x-axis, making it easier to compare data across different plots.
Adjusting Spacing
Adjusting the spacing between subplots can improve the layout of your figure. Here, we'll use plt.subplots_adjust() to modify the spacing.
import matplotlib.pyplot as plt
fig, axs = plt.subplots(2, 2)
fig.subplots_adjust(hspace=0.5, wspace=0.3)
axs[0, 0].plot([1, 2, 3], [1, 4, 9])
axs[0, 1].plot([1, 2, 3], [1, 2, 3])
axs[1, 0].plot([1, 2, 3], [3, 2, 1])
axs[1, 1].plot([1, 2, 3], [9, 4, 1])
plt.show()
Using plt.subplots_adjust(), we can control the horizontal (wspace) and vertical (hspace) spacing between subplots for better visualization.
Different Plot Types
You can use different plot types within subplots to compare various data representations. Here, we'll mix line plots and scatter plots.
import matplotlib.pyplot as plt
fig, axs = plt.subplots(2, 2)
axs[0, 0].plot([1, 2, 3], [1, 4, 9])
axs[0, 1].scatter([1, 2, 3], [1, 2, 3])
axs[1, 0].plot([1, 2, 3], [3, 2, 1])
axs[1, 1].scatter([1, 2, 3], [9, 4, 1])
plt.show()
This example demonstrates how to use different plot types within subplots, providing a diverse view of data.
Customizing Subplot Appearance
Customization options allow you to personalize the appearance of subplots. Here, we'll adjust titles, labels, and colors.
import matplotlib.pyplot as plt
fig, axs = plt.subplots(2, 2)
axs[0, 0].plot([1, 2, 3], [1, 4, 9], color='red')
axs[0, 0].set_title('Quadratic')
axs[0, 1].plot([1, 2, 3], [1, 2, 3], color='green')
axs[0, 1].set_title('Linear')
axs[1, 0].plot([1, 2, 3], [3, 2, 1], color='blue')
axs[1, 0].set_title('Inverse')
axs[1, 1].plot([1, 2, 3], [9, 4, 1], color='purple')
axs[1, 1].set_title('Cubic')
plt.show()
This example illustrates how to customize subplots by setting titles, labels, and colors to enhance the presentation of data.
