Python | Pandas Series.agg ()

NumPy | Python Methods and Functions

Pandas Series.agg() is used to pass a function or list of functions to be applied to a series or even to each element of a series individually. In the case of a list of functions, multiple results are returned by the agg () method.

Syntax: Series.agg (func, axis = 0)
Parameters:
func: Function, list of function or string of function name to be called on Series.
axis: 0 or `index` for row wise operation and 1 or `columns` for column wise operation.

Return Type: The return type depends on return type of function passed as parameter.

Example # 1:
This example passes a lambda function that simply adds 2 to each value in the series. Since the function will be applied to each series value, the return type is also series. A random row of 10 elements is generated by passing an array using a random Numpy method.

# pandas module import

import pandas as pd

# numpy module import

import numpy as np

   
  # create a random array of 10 elements  
   arr   =   np.random.randn (  10  ) 
    
  # create series from array  
   series   =   pd.Series (arr) 
    
  # calling the .agg () method  
   result   =   series.agg (  lambda   num: num   +   2  ) 
    
  # display  
   print   (  `Array before operation:`  , series, 
    `Array after operation:`  , result)

Output:

As shown in the output, f an function has been applied to each value, and 2 — to each value in the series.

Example # 2: Passing a list of functions

This example passes a list of some default Python functions and the agg method () returns multiple results in multiple variables.

# pandas module import

import pandas as pd

# numpy module import

import numpy as np

# create a random array of 10 elements

arr = np.random.randn ( 10 )

# create series from array

series = pd.Series (arr)

# create a list of function names

func_list = [ min , max , sorted ]

   
  # calling the .agg () method  
  # passing the list functions  
   result1, result2, result3   =   series.agg (func_list) 
   
  # display   
  print   (  `Series before operation:`  , series) 
   print   (` Min   =   {} Max   =   {},  
    Sorted Series: {} `.   format   (result1, result2, result3))

Output:

As shown in the output, several results were returned. Min, Max and Sorted Array were returned in different variables result1, result2, result3 respectively.

Python | Pandas Series.agg ()

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