PANDAS PIVOT

 PANDAS PIVOT

pandas.pivot_table(data, values=None, index=None, columns=None, aggfunc=’mean’, fill_value=None, margins=False, dropna=True, margins_name=’All’) create a spreadsheet-style pivot table as a DataFrame.

Levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame.

Parameters:

data : DataFrame
values : column to aggregate, optional
index: column, Grouper, array, or list of the previous
columns: column, Grouper, array, or list of the previous

aggfunc: function, list of functions, dict, default numpy.mean
-> If list of functions passed, the resulting pivot table will have hierarchical columns whose top level are the function names.
-> If dict is passed, the key is column to aggregate and value is function or list of functions

fill_value[scalar, default None] : Value to replace missing values with
margins[boolean, default False] : Add all row / columns (e.g. for subtotal / grand totals)
dropna[boolean, default True] : Do not include columns whose entries are all NaN
margins_name[string, default ‘All’] : Name of the row / column that will contain the totals when margins is True.

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👉EXAMPLE PROGRAMS ON PANDAS PIVOT TABLE👈