Matplotlib.axis.Axis.get_majorticklocs() function in Python
Last Updated :
03 Jun, 2020
Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. It is an amazing visualization library in Python for 2D plots of arrays and used for working with the broader SciPy stack.
matplotlib.axis.Axis.get_majorticklocs() Function
The Axis.get_majorticklocs() function in axis module of matplotlib library is used to get the array of major tick locations in data coordinates.
Syntax: Axis.get_majorticklocs(self)
Parameters: This method does not accepts any parameter.
Return value: This method returns the array of major tick locations in data coordinates.
Below examples illustrate the matplotlib.axis.Axis.get_majorticklocs() function in matplotlib.axis:
Example 1:
Python3
from matplotlib.axis import Axis
from matplotlib.artist import Artist
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
def tellme(s):
ax.set_title(s, fontsize = 16 )
fig.canvas.draw()
renderer = fig.canvas.renderer
Artist.draw(ax, renderer)
tellme("Matplotlib.axis.Axis.get_majorticklocs()\n\
Function Example")
ax.grid()
print ( "Value of get_majorticklocs() :" )
for i in ax.xaxis.get_majorticklocs():
print (i)
plt.show()
|
Output:
Value of get_majorticklocs() :
0.0
0.2
0.4
0.6000000000000001
0.8
1.0
Example 2:
Python3
from matplotlib.axis import Axis
from matplotlib.artist import Artist
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot( 111 , projection = '3d' )
X, Y, Z = axes3d.get_test_data( 0.1 )
ax.plot_wireframe(X, Y, Z, rstride = 5 ,
cstride = 5 )
ax.view_init( 30 , 50 )
fig.canvas.draw()
renderer = fig.canvas.renderer
Artist.draw(ax, renderer)
fig.suptitle('Matplotlib.axis.Axis.get_majorticklocs()\n\
Function Example')
ax.grid()
print ( "Value of get_majorticklocs() :" )
for i in ax.xaxis.get_majorticklocs():
print (i)
plt.show()
|
Output:
Value of get_majorticklocs() :
-40.0
-30.0
-20.0
-10.0
0.0
10.0
20.0
30.0
40.0
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