Python | Pandas Series.iloc

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 is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.

Pandas Series.iloc attribute enables purely integer-location based indexing for selection by position over the given Series object.

Syntax:Series.iloc

Parameter : None



Returns : Series

Example #1: Use Series.iloc attribute to perform indexing over the given Series object.

filter_none

edit
close

play_arrow

link
brightness_4
code

# importing pandas as pd
import pandas as pd
  
# Creating the Series
sr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon'])
  
# Creating the row axis labels
sr.index = ['City 1', 'City 2', 'City 3', 'City 4'] 
  
# Print the series
print(sr)

chevron_right






                        filter_none








Output : 




Now we will use Series.iloc attribute to perform indexing over the given Series object.













                                    filter_none




                                    edit

                                    close




                                    play_arrow




                                    link

                                    brightness_4

                                    code
                                




















# slice the object element in the  


# passed range 


sr.iloc[0:2] 



















                        chevron_right







                        filter_none










Output : 




As we can see in the output, the Series.iloc attribute has returned a series object containing the sliced element from the original Series object.

 

Example #2 :  Use Series.iloc attribute to perform indexing over the given Series object.











                                    filter_none




                                    edit

                                    close




                                    play_arrow




                                    link

                                    brightness_4

                                    code
                                




















# importing pandas as pd 


import pandas as pd 


  


# Creating the Series 


sr = pd.Series(['1/1/2018', '2/1/2018', '3/1/2018', '4/1/2018']) 


  


# Creating the row axis labels 


sr.index = ['Day 1', 'Day 2', 'Day 3', 'Day 4'] 


  


# Print the series 


print(sr) 



















                        chevron_right







                        filter_none








Output : 




Now we will use Series.iloc attribute to perform indexing over the given Series object.













                                    filter_none




                                    edit

                                    close




                                    play_arrow




                                    link

                                    brightness_4

                                    code
                                




















# slice the object element in the  


# passed range 


sr.iloc[1:3] 



















                        chevron_right







                        filter_none










Output : 



As we can see in the output, the Series.iloc attribute has returned a series object containing the sliced element from the original Series object.





    

        My Personal Notes
        arrow_drop_up
    

    

        
        

            
            
        

    


Recommended Posts:
Shubham__Ranjan
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.