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:
# 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) |
Output:
Code #2:
# 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) |
Output:
Code #3:
# 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) |
Output:
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