matplotlib.axes.Axes.twiny() in Python

Last Updated : 21 Apr, 2020

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.twiny() Function

The Axes.twiny() function in axes module of matplotlib library is used to create a twin Axes sharing the yaxis.

Syntax: Axes.twiny(self) Return value: This method is used to returns the following.

ax_twin : This returns the newly created Axes instance.

Below examples illustrate the matplotlib.axes.Axes.twiny() function in matplotlib.axes: Example 1:

Python3

# Implementation of matplotlib function
# Implementation of matplotlib function
import matplotlib.pyplot as plt
import numpy as np
 
 
def GFG1(temp):
    return (5. / 9.) * (temp - 32)
 
def GFG2(ax1):
    y1, y2 = ax1.get_ylim()
    ax_twin .set_ylim(GFG1(y1), GFG1(y2))
    ax_twin .figure.canvas.draw()
 
fig, ax1 = plt.subplots()
ax_twin = ax1.twiny()
 
ax1.callbacks.connect("ylim_changed", GFG2)
ax1.plot(np.linspace(-40, 120, 100))
ax1.set_ylim(0, 100)
 
ax1.set_xlabel('Fahrenheit')
ax_twin .set_xlabel('Celsius')

fig.suptitle('matplotlib.axes.Axes.twiny()\
 function Example\n\n', fontweight ="bold")
plt.show()

Output:

Example 2:

Python3

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

# Create some mock data
t = np.arange(0.01, 10.0, 0.001)
data1 = np.exp(t)
data2 = np.sin(0.4 * np.pi * t)

fig, ax1 = plt.subplots()

color = 'tab:blue'
ax1.set_ylabel('time (s)')
ax1.set_xlabel('exp', color = color)
ax1.plot(data1, t, color = color)
ax1.tick_params(axis ='x', labelcolor = color)

ax2 = ax1.twiny()

color = 'tab:green'
ax2.set_xlabel('sin', color = color)
ax2.plot(data2, t, color = color)
ax2.tick_params(axis ='x', labelcolor = color)

fig.suptitle('matplotlib.axes.Axes.twiny()\
 function Example\n\n', fontweight ="bold")
plt.show()