Python | Pandas.pivot_table ()

The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) in the index and columns of the resulting 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 (eg 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.

Returns: DataFrame

Code:

# Create a simple data frame   # pandas import as pd import pandas as pd import numpy as np   # create data frame df = pd.DataFrame ({ < code class = "string"> ’A’ : [ ’ John’ , ’Boby’ , ’ Mina’ , ’Peter’ , ’ Nicky ’ ],   ’ B’ : [ ’Masters’ , ’Graduate’ , ’ Graduate’ , ’Masters’ , ’ Graduate’ ],   ’ C’ : [ 27 , 23 , 21 , 23 , 24 ]})   df

# A simple pivot table should have a data frame # and an index / index list. table = pd.pivot_table (df, index = [ ’A’ , ’B’ ])    table

# Creates a pivot table data frame table = pd.pivot_table (df, values ​​ = ’ A’ , index = [ ’B’ , ’C’ ],   columns = [ ’B’ ], aggfunc =  np. sum )   table