Matplotlib.pyplot.yticks() in Python
Last Updated :
12 Apr, 2020
Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. Pyplot is a state-based interface to a Matplotlib module which provides a MATLAB-like interface.
Matplotlib.pyplot.yticks() Function
The annotate() function in pyplot module of matplotlib library is used to get and set the current tick locations and labels of the y-axis.
Syntax: matplotlib.pyplot.yticks(ticks=None, labels=None, **kwargs)
Parameters: This method accept the following parameters that are described below:
- ticks: This parameter is the list of xtick locations. and an optional parameter. If an empty list is passed as an argument then it will removes all xticks
- labels: This parameter contains labels to place at the given ticks locations. And it is an optional parameter.
- **kwargs: This parameter is Text properties that is used to control the appearance of the labels.
Returns: This returns the following:
- locs :This returns the list of ytick locations.
- labels :This returns the list of ylabel Text objects.
The resultant is (locs, labels)
Below examples illustrate the matplotlib.pyplot.yticks() function in matplotlib.pyplot:
Example #1:
import numpy as np
import matplotlib.pyplot as plt
valx = [ 30 , 35 , 50 , 5 , 10 , 40 , 45 , 15 , 20 , 25 ]
valy = [ 1 , 4 , 3 , 2 , 7 , 6 , 9 , 8 , 10 , 5 ]
plt.plot(valx, valy)
plt.xlabel( 'X-axis' )
plt.ylabel( 'Y-axis' )
plt.xticks(np.arange( 0 , 60 , 5 ))
plt.yticks(np.arange( 0 , 15 , 1 ))
plt.show()
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Output:
Example #2:
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes, zoomed_inset_axes
def get_demo_image():
from matplotlib.cbook import get_sample_data
import numpy as np
f = get_sample_data( "axes_grid/bivariate_normal.npy" ,
asfileobj = False )
z = np.load(f)
return z, ( 3 , 19 , 4 , 13 )
fig, ax = plt.subplots(figsize = [ 5 , 4 ])
Z, extent = get_demo_image()
ax. set (aspect = 1 ,
xlim = ( 0 , 65 ),
ylim = ( 0 , 50 ))
axins = zoomed_inset_axes(ax, zoom = 2 , loc = 'upper right' )
im = axins.imshow(Z, extent = extent, interpolation = "nearest" ,
origin = "upper" )
plt.xlabel( 'X-axis' )
plt.ylabel( 'Y-axis' )
plt.yticks(visible = False )
plt.show()
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Output:
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