Python | Pandas Panel.shape

In Pandas, Panel is a very important container for three-dimensional data. The names for the 3 axes are intended to give some semantic meaning to describing operations involving panel data and, in particular, econometric analysis of panel data.

In Pandas Panel.shape can be used to get a tuple of axis dimensions.

Syntax: Panel.shape

Parameters: None

Returns: Return a tuple of axis dimensions

Code #1:

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# importing pandas module  
import pandas as pd  
import numpy as np 
    
df1 = pd.DataFrame({'a': ['Geeks', 'For', 'geeks', 'for', 'real'],  
                    'b': [11, 1.025, 333, 114.48, 1333]}) 
                        
data = {'item1':df1, 'item2':df1} 
    
# creating Panel  
panel = pd.Panel.from_dict(data, orient ='minor'
    
print("panel['b'] is - \n\n", panel['b'], '\n'
    
print("\nSize of panel['b'] is - ", panel['b'].shape)   

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Output:

 

Code #2:

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# importing pandas module  
import pandas as pd  
import numpy as np 
    
df1 = pd.DataFrame({'a': ['Geeks', 'For', 'geeks', 'for', 'real'],  
                    'b': [11, 1.025, 333, 114.48, 1333]}) 
  
# Create a 5 * 5 dataframe 
df2 = pd.DataFrame(np.random.rand(10, 2), columns =['a', 'b']) 
    
data = {'item1':df1, 'item2':df2} 
    
# creating Panel  
panel = pd.Panel.from_dict(data, orient ='minor'
print("panel['b'] is - \n\n", panel['b'], '\n'
  
  
print("\nShape of Panel is - ", panel['b'].shape) 

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Output:

 

Code #3:

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# importing pandas module 
import pandas as pd 
import numpy as np 
  
df1 = pd.DataFrame({'a': ['Geeks', 'For', 'geeks', 'real'], 
                    'b': [-11, +1.025, -114.48, 1333]}) 
  
df2 = pd.DataFrame({'a': ['I', 'am', 'dataframe', 'two'], 
                    'b': [100, 100, 100, 100]}) 
                      
data = {'item1':df1, 'item2':df2}
  
# creating Panel 
panel = pd.Panel.from_dict(data, orient ='minor'
print("panel['b'] is - \n\n", panel['b']) 
  
print("\nShape of panel['b'] is - ",  panel['b'].shape) 

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Output:

 



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