How to Create Multiple Subplots in Matplotlib in Python?

To create multiple plots use matplotlib.pyplot.subplots method which returns the figure along with the objects Axes object or array of Axes object. nrows, ncols attributes of subplots() method determine the number of rows and columns of the subplot grid.

By default, it returns a figure with a single plot. For each axes object i.e. plot we can set a title (set via set_title()), an x-label (set via set_xlabel()), and a y-label set via set_ylabel()).

Let's see how this works:

1. When we call the subplots() method by stacking only in one direction it returns a 1D array of axes objects i.e. subplots.
2. We can access these axes objects using indices just like we access elements of the array. To create specific subplots, call matplotlib.pyplot.plot() on the corresponding index of the axes. Refer to the following figure for a better understanding

Create Multiple Subplots in Matplotlib in Python

Here, we have various Ways to Matplotlib create subplots in Python. Here, we are using some generally used Ways to Matplotlib create subplots in Python those are following.

• 1-D Array of Subplots
• Using Stacking in Two Directions
• Using plt.subplots with 2D Array of Subplots

Subplots in Matplotlib Using 1-D Array of Subplots

In this example code utilizes Matplotlib to generate a figure with two subplots. The data, represented by arrays 'x,' 'y,' and 'z,' is plotted on separate axes within the figure. The resulting visualization displays distinct plots for the datasets 'y' and 'z' in the designated subplots.

# importing library
import matplotlib.pyplot as plt

# Some data to display
x = [1, 2, 3]
y = [0, 1, 0]
z = [1, 0, 1]

# Creating 2 subplots
fig, ax = plt.subplots(2)

# Accessing each axes object to plot the data through returned array
ax[0].plot(x, y)
ax[1].plot(x, z)

Output

Subplots in Matplotlib Using Stacking in Two Directions

In this Python script, the Matplotlib library is imported, and NumPy is used for numerical operations. Data for plotting sine waves is generated. The script then creates a 3x2 grid of subplots, each plotting the same sine wave with different colors (orange, green, blue, magenta, black, and red).

# importing library
import matplotlib.pyplot as plt
import numpy as np

# Data for plotting
x = np.arange(0.0, 2.0, 0.01)
y = 1 + np.sin(2 * np.pi * x)

# Creating 6 subplots and unpacking the output array immediately
fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(3, 2)
ax1.plot(x, y, color="orange")
ax2.plot(x, y, color="green")
ax3.plot(x, y, color="blue")
ax4.plot(x, y, color="magenta")
ax5.plot(x, y, color="black")
ax6.plot(x, y, color="red")

Output

Matplotlib Multiple Plots Same Figure

In this example Python script utilizes Matplotlib to create a 2x2 grid of subplots. Each subplot showcases a different type of plot: line plot, scatter plot, bar plot, and histogram. The Axes objects are accessed through the 2D array 'axs,' and specific data is visualized in each subplot, demonstrating the flexibility of Matplotlib for diverse plotting needs.

import matplotlib.pyplot as plt

# Create a 2x2 grid of subplots
fig, axs = plt.subplots(2, 2)

# Now axs is a 2D array of Axes objects
axs[0, 0].plot([1, 2, 3], [4, 5, 6])
axs[0, 1].scatter([1, 2, 3], [4, 5, 6])
axs[1, 0].bar([1, 2, 3], [4, 5, 6])
axs[1, 1].hist([1, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4, 5])

plt.show()

Output