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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:




# Implementation of matplotlib.pyplot.yticks()
# function
  
import numpy as np
import matplotlib.pyplot as plt
    
# values of x and y axes 
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() 

Output:

Example #2:




#Implementation of matplotlib.pyplot.yticks() 
# function
   
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)
    # z is a numpy array of 15x15
    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() 

Output:


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