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

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() 


Output: 
 

 

Dominic
Dominichttp://wardslaus.com
infosec,malicious & dos attacks generator, boot rom exploit philanthropist , wild hacker , game developer,
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