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()  