In the context of Matplotlib, axes does not refer to the plural form of an axis. Instead, it represents the entire plotting area on a figure or canvas. Which includes the x-axis, y-axis, plotting data, ticks, ticks labels, and more.

Refer to the image below −

Consider the figure where two Axes objects are created using the ax = fig.subplots() method. The first axes display exponential data, while the second axes show a sine wave. Each Axes (subplot) has its own set of labels, ticks, and legends, providing a distinct representation within the same figure.

Axes class in matplotlib

The Axes() class serves as the gateway to creating data visualizations. Once an Axes object is instantiated on a figure, a variety of methods become available to add and manipulate data within that plotting area.

This class is part of the matplotlib.axes module, providing fundamental functionalities to work with the Matplotlib object-oriented programming (OOP) interface. Most of the essential plotting methods are defined on the Axes class, making it a central component for customizing and enhancing visualizations.

Creating an Axes

Creating an Axes object is typically the first step in Matplotlib plotting. This can be done through methods on Figure object like Figure.subplots(), Figure.add_axes(), or via the pyplot interface function, pyplot.subplots(). These methods provides the creation of one or more Axes objects in a figure.

Example

The following example uses the pyplot.subplot() method to creat two axes on a figure. The subplots() method is useful for generating the axes instance.

import matplotlib.pyplot as plt
import numpy as np

# Creating a 1x2 subplot layout
fig, (axes1, axes2)  = plt.subplots(1, 2, figsize=(7, 4),
   layout="constrained")

# Adding labels to each subplot
axes1.annotate(''axes1'', (0.5, 0.5),transform=axes1.transAxes,
   ha=''center'', va=''center'', fontsize=18,
   color=''darkgrey'')

axes2.annotate(''axes2'', (0.5, 0.5),transform=axes2.transAxes,
   ha=''center'', va=''center'', fontsize=18,
   color=''darkgrey'')

fig.suptitle(''Creating Two Axes on a Figure'')

# Displaying the plot
plt.show()

Output

On executing the above code we will get the following output −

Changing an Axes properties

To set the properties of an axes, you have to access the axes object, after that you can use various `set_*` methods to modify its properties.

Example

import matplotlib.pyplot as plt
import numpy as np

# Creating a 1x2 subplot layout
fig, (axes1, axes2) = plt.subplots(1, 2, figsize=(7, 4),
   constrained_layout=True)

# Changing the properties of the first axes
axes1.set_xlabel("X-axis")        # Set label for X-axis
axes1.set_ylabel("Y-axis")        # Set label for Y-axis
axes1.set_facecolor(''lightgreen'') # Setting background color
axes1.annotate(''axes1'', (0.5, 0.5), transform=axes1.transAxes,
   ha=''center'', va=''center'', fontsize=18,
   color=''darkgrey'')
axes2.set_title(''Second axes'')
axes2.annotate(''axes2'', (0.5, 0.5), transform=axes2.transAxes,
   ha=''center'', va=''center'', fontsize=18,
   color=''darkgrey'')

# Adding a title to the figure
fig.suptitle(''Changing Axes Properties'')

# Displaying the plot
plt.show()

Output

On executing the above code you will get the following output −

Plotting on an Axes

This class offers several high-level plotting methods to create different plot on axes.

Example

Here is an example uses the Axes.plot() method to create a line plot representing the sin(x).

import matplotlib.pyplot as plt
import numpy as np

# Sample Data
x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x**2)

# Create subplots
fig, axs = plt.subplots(figsize=(7,4))

# Draw the plot
axs.plot(x, y)

# Show the plot
plt.show()

Output

On executing the above code we will get the following output −

Customizing the axes Data

The Axes object contains most of the figure elements such as Axis, Tick, Line2D, Text, Polygon, etc., and sets the coordinate system. These elements can be customized by adding labels, titles, legends, and annotations to the Axes enhances the clarity of visualizations.

Example

Here is a simple example that adds the labels, titles, legends to an Axes.

import matplotlib.pyplot as plt
import numpy as np

# Sample Data
x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x**2)

# Create subplots
fig, axs = plt.subplots(figsize=(7,4))

# Draw the plot
axs.plot(x, y, label=''Sin(x)'')

# Add titles
axs.set_title(''Sin Plot'')

# Add X and Y labels
axs.set_xlabel(''X-axis'')
axs.set_ylabel(''Y-axis'')

# Add legend
axs.legend()

# Show the plot
plt.show()

Output

On executing the above code we will get the following output −

Clearing the Axes

To clear the contents of an axes, you can use the axes.cla() or axes.clear() method.

Example

Here is an example that show how you can clear the first axes in a subplot.

import matplotlib.pyplot as plt
import numpy as np

# Sample data
x = np.linspace(-1, 1, 10)
y = np.exp(x)

# Creating subplots
fig, (axes1, axes2) = plt.subplots(1, 2, figsize=(7, 4),
   constrained_layout=True)

# Plotting on the first axes
axes1.plot(x, y, c=''red'')
axes1.set_xlabel("X-axis")        # Set label for X-axis
axes1.set_ylabel("Y-axis")        # Set label for Y-axis
axes1.set_facecolor(''lightgreen'') # Setting background color
axes1.annotate(''axes1'', (0.5, 0.5), transform=axes1.transAxes,
   ha=''center'', va=''center'', fontsize=18,
   color=''darkgrey'')

# Adding a title to the second axes
axes2.set_title(''Second axes'')
axes2.annotate(''axes2'', (0.5, 0.5), transform=axes2.transAxes,
   ha=''center'', va=''center'', fontsize=18,
   color=''darkgrey'')

# Clearing the first axes
axes1.cla()

# Adding a title to the figure
fig.suptitle(''Clearing the Axes'')

# Displaying the plot
plt.show()

Output

On executing the above code you will get the following output −