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.

# 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.