Matplotlib.axes.Axes.secondary_xaxis() in Python

Last Updated : 28 Apr, 2025

Matplotlib is a library in Python and it is numerical - mathematical extension for NumPy library. The Axes Class contains most of the figure elements: Axis, Tick, Line2D, Text, Polygon, etc., and sets the coordinate system. And the instances of Axes supports callbacks through a callbacks attribute.

matplotlib.axes.Axes.secondary_xaxis() Function

The Axes.secondary_xaxis() function in axes module of matplotlib library is also used to add a second x-axis to this axes.

Syntax: Axes.secondary_xaxis(self, location, *, functions=None, **kwargs)

Parameters: This method accept the following parameters that are described below:

location : This parameter is the position to put the secondary axis.
functions : This parameter is used to specify the transform function and its inverse.

Returns: This method returns the following:

ax : This return the axes._secondary_axes.SecondaryAxis.

Note: This function works in Matplotlib version >= 3.1

Below examples illustrate the matplotlib.axes.Axes.secondary_xaxis() function in matplotlib.axes:

Example 1:

Python3

# Implementation of matplotlib function
import matplotlib.pyplot as plt
import numpy as np
   
fig, ax = plt.subplots()
ax.plot([1, 2, 3])

ax.set_xlabel('X-Axis')
ax.set_ylabel('Y-Axis')

secax = ax.secondary_xaxis('top')
secax.set_xlabel('Secondary-X-Axis')
ax.set_title('matplotlib.axes.Axes.secondary_xaxis() Example',
             fontsize = 14, fontweight ='bold')
plt.show()

Output:

Example 2:

Python3

# Implementation of matplotlib function
import matplotlib.pyplot as plt
import numpy as np
import datetime
import matplotlib.dates as mdates
from matplotlib.transforms import Transform
from matplotlib.ticker import (
    AutoLocator, AutoMinorLocator)

fig, ax = plt.subplots(constrained_layout = True)
x = np.arange(0, 500, 2)
y = np.sin(3 * x * np.pi / 180)
ax.plot(x, y)
ax.set_xlabel('Degree')
ax.set_ylabel('Frequency')


def val1(x):
    return x * np.pi / 180


def val2(x):
    return x * 180 / np.pi

secax = ax.secondary_xaxis('top', functions =(val1, val2))
secax.set_xlabel('Radian')
ax.set_title('matplotlib.axes.Axes.secondary_xaxis() Example',
             fontsize = 14, fontweight ='bold')
plt.show()