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 pdimport pandas as pd # Creating the Seriessr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon']) # Creating the row axis labelssr.index = ['City 1', 'City 2', 'City 3', 'City 4'] # Print the seriesprint(sr) |
Output :
Now we will use Series.iloc attribute to perform indexing over the given Series object.
# slice the object element in the # passed rangesr.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 pdimport pandas as pd # Creating the Seriessr = pd.Series(['1/1/2018', '2/1/2018', '3/1/2018', '4/1/2018']) # Creating the row axis labelssr.index = ['Day 1', 'Day 2', 'Day 3', 'Day 4'] # Print the seriesprint(sr) |
Output :
Now we will use Series.iloc attribute to perform indexing over the given Series object.
# slice the object element in the # passed rangesr.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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