Creating Multiple Plots - ( Matplotlib Subplots and Figures )

Heading h2

Syntax

import matplotlib.pyplot as plt

fig, axs = plt.subplots(nrows, ncols)

Example

import matplotlib.pyplot as plt
import numpy as np

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

fig, axs = plt.subplots(2, 2)

axs[0, 0].plot(x, np.sin(x))
axs[0, 1].plot(x, np.cos(x))
axs[1, 0].plot(x, np.tan(x))
axs[1, 1].plot(x, np.exp(x))

plt.show()

Output

The output of the example code is a figure containing four subplots, each with a different plot.

Explanation

Matplotlib is a widely used Python library for data visualization. It provides various functions for creating different types of plots. plt.subplots() is a function provided by Matplotlib for creating multiple plots in a single figure.

plt.subplots() takes in two arguments, nrows and ncols, which specify the number of rows and columns of the subplot grid, respectively. The function returns two objects, fig and axs. These objects are used to specify properties of the figure and the subplots, respectively.

In the above example, we create a figure with four subplots organized in a 2x2 grid. We plot sine, cosine, tangent, and exponential functions in each of the four subplots.

Use

Creating multiple plots in a single figure is a common requirement in data visualization. Matplotlib provides the functionality to achieve this using plt.subplots().

Important Points

• plt.subplots() creates multiple plots in a single figure
• The function takes two arguments nrows and ncols to specify the number of rows and columns of the subplot grid
• plt.subplots() returns two objects fig and axs which define the figure and subplots
• The axs object is used to specify properties of the subplots

Summary

In conclusion, creating multiple plots in a single figure is an important requirement in data visualization. Matplotlib provides the plt.subplots() function for creating subplots in a grid arrangement. The function returns fig and axs objects that are used to specify properties of the figure and subplots.