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Matplotlib.axis.Tick.set_picker() function in Python

Last Updated : 10 Jun, 2020
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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.Tick.set_picker() Function

The Tick.set_picker() function in axis module of matplotlib library is used to define the picking behavior of the artist. 
 

Syntax: Tick.set_picker(self, picker) 
Parameters: This method accepts the following parameters. 

  • picker: This parameter is used to set picking behavior. This can be None or bool or float or function.


Return value: This method does not return any value. 


Below examples illustrate the matplotlib.axis.Tick.set_picker() function in matplotlib.axis:
Example 1:

Python3
# Implementation of matplotlib function
from matplotlib.axis import Tick
import numpy as np  
import matplotlib.pyplot as plt  
        
np.random.seed(19680801)  
   
volume = np.random.rayleigh(27, size = 40)  
amount = np.random.poisson(7, size = 40)  
ranking = np.random.normal(size = 40)  
price = np.random.uniform(1, 7, size = 40)  
        
fig, ax = plt.subplots()  
        
scatter = ax.scatter(volume * 3,   
                     amount**4,  
                     c = ranking**4,  
                     s = price**4,  
                     vmin = -3,  
                     vmax = 3,  
                     cmap ="Spectral")  
     
Tick.set_picker(ax, picker = 4) 

fig.suptitle('matplotlib.axis.Tick.set_picker() \
function Example', fontweight ="bold")  
   
plt.show() 

Output: 
 


Example 2:

Python3
# Implementation of matplotlib function
from matplotlib.axis import Tick
import numpy as np  
import matplotlib.pyplot as plt  
   
     
X = np.random.rand(10, 200)  
xs = np.mean(X, axis = 1)  
ys = np.std(X, axis = 1)  
     
fig = plt.figure()  
ax = fig.add_subplot(111)  
line, = ax.plot(xs, ys, 'go-')   
     
Tick.set_picker(ax, picker = True) 

fig.suptitle('matplotlib.axis.Tick.set_picker() \
function Example', fontweight ="bold")  
   
plt.show() 

Output: 
 


 


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