Matplotlib.axes.Axes.set_yticklabels() in Python

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.set_yticklabels() Function

The Axes.set_yticklabels() function in axes module of matplotlib library is used to Set the y-tick labels with list of string labels.

Syntax: Axes.set_yticklabels(self, labels, fontdict=None, minor=False, **kwargs) 

Parameters: This method accepts the following parameters.

• labels : This parameter is the list of string labels.
• fontdict : This parameter is the dictionary controlling the appearance of the ticklabels.
• minor : This parameter is used whether set major ticks or to set minor ticks

Return value: This method returns a list of Text instances.

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

Example 1:

Python3

# Implementation of matplotlib function
import matplotlib.pyplot as plt
import matplotlib.transforms as mtransforms

fig, ax = plt.subplots()
ax.plot(range(12, 24), range(12))
ax.set_yticks((2, 5, 7, 10))
ax.set_yticklabels(("Label-1", "Label-2",
                    "Label-3", "Label-4"))

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

Output:

Example 2:

Python3

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

# Fixing random state for reproducibility
np.random.seed(19680801)

x = np.linspace(0, 2 * np.pi, 100)
y = np.sin(x)
y2 = y + 0.2 * np.random.normal(size = x.shape)

fig, ax = plt.subplots()
ax.plot(x, y)
ax.plot(x, y2)

ax.set_yticks([-1, 0, 1])

ax.spines['left'].set_bounds(-1, 1)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)

ax.set_yticklabels(("Val-1", "Val-2", "Val-3"))

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

Output: