Python | Pandas Series.is_unique
Last Updated :
28 Jan, 2019
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.is_unique
attribute return a boolean value. It returns True
if the data in the given Series object is unique else it return False
.
Syntax:Series.is_unique
Parameter : None
Returns : boolean
Example #1: Use Series.is_unique
attribute to check if the underlying data in the given Series object is unique or not.
import pandas as pd
sr = pd.Series([ 'New York' , 'Chicago' , 'Toronto' , 'Lisbon' , 'Chicago' ])
sr.index = [ 'City 1' , 'City 2' , 'City 3' , 'City 4' , 'City 5' ]
print (sr)
|
Output :
Now we will use Series.is_unique
attribute to check if the underlying data in the given Series object is unique or it contains some duplicated value.
Output :
As we can see in the output, the Series.is_unique
attribute has returned False
indicating the underlying in the given series object is not unique.
Example #2 : Use Series.is_unique
attribute to check if the underlying data in the given Series object is unique or not.
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.is_unique
attribute to check if the underlying data in the given Series object is unique or it contains some duplicated value.
Output :
As we can see in the output, the Series.is_unique
attribute has returned True
indicating the underlying in the given series object is unique.
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