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Matplotlib.pyplot.axes() in Python - GeeksforGeeks

Last Updated : 12 Jul, 2025

axes() method in Matplotlib is used to create a new Axes instance (i.e., a plot area) within a figure. This allows you to specify the location and size of the plot within the figure, providing more control over subplot layout compared to plt.subplot(). It's key features include:

• Creates a new Axes at a specified position inside the figure.
• Allows precise control of the Axes position using normalized figure coordinates.
• Returns an Axes object for further customization and plotting.
• Supports multiple Axes in a single figure for complex layouts.

Example:

import matplotlib.pyplot as plt
import numpy as np

fig = plt.figure()
ax = plt.axes([0.1, 0.1, 0.8, 0.8])

x = np.linspace(0, 10, 100)
y = np.sin(x)
ax.plot(x, y)
plt.show()

Output

Explanation:

• plt.figure() creates a blank figure and plt.axes([0.1, 0.1, 0.8, 0.8]) adds a subplot positioned within it.
• np.linspace(0, 10, 100) creates 100 points and np.sin(x) computes their sine values.
• ax.plot(x, y) plots the sine wave.

matplotlib.pyplot.axes(*args, **kwargs)

Parameters: The parameters can be passed in different ways:

1. Positional Argument rect: A list or tuple of 4 floats [left, bottom, width, height] and defines the position and size of the axes as fractions of the figure width and height, all values between 0 and 1.

• left: The distance from the left side of the figure to the left side of the axes.
• bottom: The distance from the bottom of the figure to the bottom of the axes.
• width: The width of the axes.
• height: The height of the axes.

2. Keyword Arguments (kwargs): A dictionary of additional keyword arguments to customize the Axes object. Some common keyword arguments include:

• polar: A boolean value (True or False). If True, creates a polar plot.
• facecolor: A color value to set the background color of the axes.
• projection: A string specifying the projection type (e.g., 'polar').
• Other Axes properties can also be passed as keyword arguments for further customization.

Returns: (Axes) An instance of matplotlib.axes.Axes (or a subclass like Axes3D) .The newly created Axes object which can be used to plot data and customize the plot.

Example 1: In this example we are creating multiple axes in one figure.

import matplotlib.pyplot as plt
import numpy as np

fig = plt.figure()
ax1 = plt.axes([0.1, 0.1, 0.35, 0.8])  # Left small axes
ax2 = plt.axes([0.55, 0.1, 0.35, 0.8]) # Right small axes

x = np.linspace(0, 10, 100)
ax1.plot(x, np.sin(x))
ax2.plot(x, np.cos(x))

plt.show()

Output

Explanation:

• plt.figure() initializes a blank figure and plt.axes() adds two Axes objects (ax1 and ax2) with specified positions using normalized figure coordinates [left, bottom, width, height].
• ax1 (left side) is 10% from left, 10% from bottom, 35% width, 80% height, and ax2 (right side) is 55% from left, 10% from bottom, 35% width, 80% height.
• np.linspace(0, 10, 100) generates 100 evenly spaced x-values between 0 and 10.

Example 2: In this example, we are creating 3d axes

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np

fig = plt.figure()
ax = plt.axes([0.1, 0.1, 0.8, 0.8], projection='3d')

x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))

ax.plot_surface(X, Y, Z, cmap='viridis')
plt.show()

Output

Explanation:

• plt.axes() creates a 3D Axes at position [0.1, 0.1, 0.8, 0.8] with projection='3d'.
• np.linspace(-5, 5, 100) generates 100 points for both x and y and np.meshgrid(x, y) creates a 2D grid.
• np.sin(np.sqrt(X**2 + Y**2)) calculates height values as the sine of the distance from the origin.
• ax.plot_surface() plots the 3D surface using the viridis colormap.

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