Matplotlib.axes.Axes.set_label() in Python
Last Updated :
30 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.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:
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()
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Output:
Example 2:
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()
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Output:
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