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.
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: