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.
import pandas as pd
sr = pd.Series([ 'New York' , 'Chicago' , 'Toronto' , 'Lisbon' ])
sr.index = [ 'City 1' , 'City 2' , 'City 3' , 'City 4' ]
print (sr)
|
Output :

Now we will use Series.iloc
attribute to perform indexing over the given Series object.
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.
import pandas as pd
sr = pd.Series([ '1/1/2018' , '2/1/2018' , '3/1/2018' , '4/1/2018' ])
sr.index = [ 'Day 1' , 'Day 2' , 'Day 3' , 'Day 4' ]
print (sr)
|
Output :

Now we will use Series.iloc
attribute to perform indexing over the given Series object.
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.
Whether you're preparing for your first job interview or aiming to upskill in this ever-evolving tech landscape,
GeeksforGeeks Courses are your key to success. We provide top-quality content at affordable prices, all geared towards accelerating your growth in a time-bound manner. Join the millions we've already empowered, and we're here to do the same for you. Don't miss out -
check it out now!
Last Updated :
28 Jan, 2019
Like Article
Save Article