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_label() Function
The Axes.set_label() function in axes module of matplotlib library is used to set the label that will be displayed in the legend.
Syntax: Axes.set_label(self, s)
Parameters: This method accepts only one parameters.
- s: This parameter is converted to a string by calling str.
Returns: This method does not return any value.
Below examples illustrate the matplotlib.axes.Axes.set_label() function in matplotlib.axes:
Example 1:
# Implementation of matplotlib function import matplotlib.pyplot as plt import numpy as np from matplotlib.collections import EllipseCollection x = np.arange( 10 ) y = np.arange( 15 ) X, Y = np.meshgrid(x, y) XY = np.column_stack((X.ravel(), Y.ravel())) fig, ax = plt.subplots() ec = EllipseCollection( 10 , 10 , 5 , units = 'y' , offsets = XY * 0.5 , transOffset = ax.transData, cmap = "inferno" ) ec.set_array((X * Y + X * X).ravel()) ax.add_collection(ec) ax.autoscale_view() ax.set_xlabel( 'X' ) ax.set_ylabel( 'y' ) cbar = plt.colorbar(ec) cbar.set_label( 'X + Y' ) fig.suptitle('matplotlib.axes.Axes.set_label() function \ Example\n', fontweight = "bold" ) fig.canvas.draw() plt.show() |
Output:
Example 2:
# Implementation of matplotlib function import matplotlib.pyplot as plt import numpy as np np.random.seed( 19680801 ) n = 100000 x = np.random.standard_normal(n) y = 2 * np.random.standard_normal(n) z = [ 1 , 2 , 3 , 4 ] xmin = x. min () xmax = x. max () ymin = y. min () ymax = y. max () fig, ax = plt.subplots() hb = ax.hexbin(x, y, gridsize = 50 , bins = 'log' , cmap = 'BuGn' ) ax. set (xlim = (xmin, xmax), ylim = (ymin, ymax)) cb = fig.colorbar(hb, ax = ax) cb.set_label( 'log' ) fig.suptitle('matplotlib.axes.Axes.set_label() function\ Example\n', fontweight = "bold" ) fig.canvas.draw() plt.show() |
Output: