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.
Panel.sum() function is used to return the sum of the values for the requested axis.
Syntax: Panel.sum(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)
Parameters:
axis : {items (0), major_axis (1), minor_axis (2)}
skipna : Exclude NA/null values when computing the result.
level : If the axis is a MultiIndex, count along a particular level, collapsing into a DataFrame
numeric_only : Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data.
min_count : The required number of valid values to perform the operation.Returns: DataFrame or Panel
Code #1:
# importing pandas module import pandas as pd import numpy as np   df1 = pd.DataFrame({'a': ['Geeks', 'For', 'neveropen', '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'], '\n')   print("\n", panel['b'].sum(axis = 0)) |
Output:
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Code #2:
# importing pandas module import pandas as pd import numpy as np   df1 = pd.DataFrame({'a': ['Geeks', 'For', 'neveropen', 'for', 'real'],                     'b': [33.0, -152.140, 3.0133, 114.48, 13.033]})                       data = {'item1':df1, 'item2':df1}   # creating Panel panel = pd.Panel.from_dict(data, orient ='minor')   print(panel['b'], '\n')   print("\n", panel['b'].sum(axis = 1)) |
Output:
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Code #3:
# importing pandas module import pandas as pd import numpy as np   df1 = pd.DataFrame({'a': ['Geeks', 'For', 'neveropen'],                     'b': np.random.randn(3)})                       data = {'item1':df1, 'item2':df1}   # creating Panel panel = pd.Panel.from_dict(data, orient ='minor')   print(panel['b'], '\n')     print("\n", panel['b'].sum(axis = 1)) |
Output:
