Matplotlib Subplots

Last Updated : 23 Dec, 2024

In Matplotlib, subplots() function simplifies the creation of multiple plots within a single figure for organized visualization of various datasets.

Before diving into subplots, let’s start with a simple plot using matplotlib.pyplot.plot():

import matplotlib.pyplot as plt 
  
plt.plot([1, 2, 3, 4], [16, 4, 1, 8]) 
plt.show()

Output:

Plot using Python matplotlib

What is matplotlib.pyplot.subplots()?

The subplots() function in matplotlib.pyplot creates a figure with a set of subplots arranged in a grid. It allows you to easily plot multiple graphs in a single figure, making your visualizations more organized and efficient.

Syntax: matplotlib.pyplot.subplots(nrows=1, ncols=1)

This syntax creates a figure with nrows rows and ncols columns of subplots.

Creating Multiple Plots with subplots()

In this example, we will use subplots() to create two plots in a single figure.

import matplotlib.pyplot as plt
import numpy as np

# Plot 1:
x1 = np.array([1, 2, 3, 4])
y1 = np.array([10, 20, 25, 30])

plt.subplot(1, 2, 1)
plt.plot(x1, y1)

# Plot 2:
x2 = np.array([1, 2, 3, 4])
y2 = np.array([30, 25, 20, 10])

plt.subplot(1, 2, 2)
plt.plot(x2, y2)

plt.show()

Output:

Two side-by-side plots displaying different datasets.

The subplots() function in Matplotlib allows plotting multiple plots using the same data or axes. For example, setting nrows=1 and ncols=2 creates two subplots that share the y-axis.

import matplotlib.pyplot as plt
import numpy as np

x = np.array([0, 1, 2, 3])
y = np.array([3, 8, 1, 10])

fig, ax = plt.subplots(1, 2)

ax[0].plot(x, y)
ax[0].set_title('Plot 1')

ax[1].plot(x, y, 'r')
ax[1].set_title('Plot 2')

plt.show()

Output:

Two side-by-side plots displaying different datasets.

Stacking Subplots in Two Directions

You can stack subplots vertically and horizontally by adjusting the nrows and ncols parameters in subplots(). This example demonstrates a 2×2 grid layout.

# Implementation of matplotlib function
import numpy as np
import matplotlib.pyplot as plt

# First create some toy data:
x = np.linspace(0, 2 * np.pi, 400)
y1 = np.sin(x)
y2 = np.sin(x**2)
y3 = y1**2
y4 = y2**2

fig, ax = plt.subplots(nrows=2, ncols=2)
ax[0, 0].plot(x, y1, c='red')
ax[0, 1].plot(x, y2, c='red')
ax[1, 0].plot(x, y3, c='blue')
ax[1, 1].plot(x, y3, c='blue')

ax[0, 0].set_title('Simple plot with sin(x)')
ax[0, 1].set_title('Simple plot with sin(x**2)')
ax[1, 0].set_title('Simple plot with sin(x)**2')
ax[1, 1].set_title('Simple plot with sin(x**2)**2')

fig.suptitle('Stacked subplots in two direction')
plt.show()

Output:

A 2×2 grid of plots, each displaying different mathematical functions.

Sharing Axes Between Subplots

In some cases, you may want your subplots to share axes. This is useful when comparing datasets with similar ranges. By setting the sharex or sharey parameter to True, the subplots will share their x or y axis.

Example Code for Shared Axis:

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 2 * np.pi, 400)
y1 = np.sin(x)
y2 = np.sin(x**2)

fig, (ax1, ax2) = plt.subplots(2, sharex=True)
ax1.plot(x, y1, c='red')
ax2.plot(x, y2, c='red')

ax1.set_ylabel('Simple plot with sin(x)')
ax2.set_ylabel('Simple plot with sin(x**2)')

fig.suptitle('Subplots with shared axis')
plt.show()

Output:

Subplots with shared axis

Creating Polar Plots

Matplotlib also supports polar plots. You can create them by passing a dictionary with projection=’polar’ to the subplot_kw parameter in subplots().

Example Code for Polar Axis:

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 1.5 * np.pi, 100)
y = np.sin(x**2)+np.cos(x**2)

fig, axs = plt.subplots(nrows=2, ncols=2,
                        subplot_kw = dict(polar = True))
axs[0, 0].plot(x, y)
axs[1, 1].scatter(x, y)

fig.suptitle('matplotlib.pyplot.subplots() Example')
plt.show()