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.
# 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) |
Output :
Now we will use Series.iloc
attribute to perform indexing over the given Series object.
# slice the object element in the # passed range sr.iloc[ 0 : 2 ] |
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.
# 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) |
Output :
Now we will use Series.iloc
attribute to perform indexing over the given Series object.
# slice the object element in the # passed range sr.iloc[ 1 : 3 ] |
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.
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