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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