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

Last Updated : 10 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.Tick.pickable() Function

The Tick.pickable() function in axis module of matplotlib library is used to return whether the artist is pickable or not.

Syntax: Tick.pickable(self)

Parameters: This method does not accept any parameters.

Return value: This method return whether the artist is pickable.

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

Python3

# Implementation of matplotlib function 
from matplotlib.axis import Tick 
import numpy as np   
np.random.seed(19680801)   
import matplotlib.pyplot as plt   
        
     
volume = np.random.rayleigh(27, size = 100)   
amount = np.random.poisson(10, size = 100)   
ranking = np.random.normal(size = 100)   
price = np.random.uniform(1, 10, size = 100)   
        
fig, ax = plt.subplots()   
        
scatter = ax.scatter(volume * 2, amount * 3,   
                     c = ranking ** 3,    
                     s = (price * 5)**2,   
                     vmin = -4, vmax = 4,    
                     cmap = "Spectral")   
    
       
ax.text(60, 30, "Value return : "
        + str(Tick.pickable(ax)),    
        fontweight ="bold",    
        fontsize = 16) 
  
fig.suptitle('matplotlib.axis.Tick.pickable() \ 
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   
import matplotlib.cbook as cbook   
        
     
np.random.seed(10**7)   
data = np.random.lognormal(size =(10, 4),   
                           mean = 4.5,   
                           sigma = 4.75)   
       
labels = ['G1', 'G2', 'G3', 'G4']   
        
result = cbook.boxplot_stats(data,    
                             labels = labels,    
                             bootstrap = 1000)   
  
fig, axes1 = plt.subplots()   
axes1.bxp(result)   
       
axes1.text(2, 30000,   
           "Value return : "
           + str(Tick.pickable(axes1)),    
           fontweight ="bold") 
  
fig.suptitle('matplotlib.axis.Tick.pickable() \ 
function Example', fontweight ="bold")   
     
plt.show()  