Python | Pandas Series.agg()

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.

Pandas Series.agg() is used to pass a function or list of function to be applied on a series or even each element of series separately. In case of list of function, multiple results are returned by 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:
In this example, a lambda function is passed which simply adds 2 to each value of series. Since the function will be applied to each value of series, the return type is also series. A random series of 10 elements is generated by passing array generated using Numpy random method.

filter_none

edit
close

play_arrow

link
brightness_4
code

# importing pandas module
import pandas as pd
   
# importing numpy module
import numpy as np
   
# creating random arr of 10 elements
arr=np.random.randn(10)
   
# creating series from array
series=pd.Series(arr)
   
# calling .agg() method
result=series.agg(lambda num : num + 2) 
   
# display
print('Array before operation: \n', series,
      '\n\nArray after operation: \n',result)

chevron_right






                        filter_none










Output:



As shown in output, the function was applied to each value and 2 was added to each value of series.


 

Example #2: Passing List of functions


In this example, a list of some Python’s default function is passed and multiple results are returned by agg() method into multiple variables.













                                    filter_none




                                    edit

                                    close




                                    play_arrow




                                    link

                                    brightness_4

                                    code
                                




















# importing pandas module 


import pandas as pd 


   


# importing numpy module 


import numpy as np 


   


# creating random arr of 10 elements 


arr=np.random.randn(10) 


   


# creating series from array 


series=pd.Series(arr) 


   


# creating list of function names 


func_list=[min, max, sorted] 


   


# calling .agg() method 


# passing list of functions 


result1, result2, result3= series.agg(func_list)  


   


# display 


print('Series before operation: \n', series) 


print('\nMin = {}\n\nMax = {},\ 


      \n\nSorted Series:\n{}'.format(result1,result2,result3)) 



















                        chevron_right







                        filter_none










Output:



As shown in output, multiple results were returned. Min, Max and Sorted array were returned into different variables result1, result2, result3 respectively.



          
          
          
          

            

    

        My Personal Notes
        arrow_drop_up
    

    

        
        

            
            
        

    


Recommended Posts:
Qubot
Check out this Author's contributed articles.
If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please Improve this article if you find anything incorrect by clicking on  the "Improve Article" button below.