Python | Pandas Series.duplicated()
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.duplicated() function indicate duplicate Series values. The duplicated values are indicated as True values in the resulting Series. Either all duplicates, all except the first or all except the last occurrence of duplicates can be indicated.
Syntax: Series.duplicated(keep=’first’)
Parameter :
keep : {‘first’, ‘last’, False}, default ‘first’Returns : pandas.core.series.Series
Example #1: Use Series.duplicated() function to find the duplicate values in the given series object.
# importing pandas as pdimport pandas as pd # Creating the Seriessr = pd.Series([80, 25, 3, 25, 24, 6]) # Create the Indexindex_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp'] # set the indexsr.index = index_ # Print the seriesprint(sr) |
Output :
Now we will use Series.duplicated() function to find the duplicate values in the underlying data of the given series object.
# detect duplicatesresult = sr.duplicated() # Print the resultprint(result) |
Output :
As we can see in the output, the Series.duplicated() function has successfully detected the duplicated values in the given series object. False indicates that the corresponding value is unique whereas, True indicates that the corresponding value was a duplicated value in the given series object.
Example #2 : Use Series.duplicated() function to find the duplicate values in the given series object.
# importing pandas as pdimport pandas as pd # Creating the Seriessr = pd.Series([11, 11, 8, 18, 65, 18, 32, 10, 5, 32, 32]) # Create the Indexindex_ = pd.date_range('2010-10-09', periods = 11, freq ='M') # set the indexsr.index = index_ # Print the seriesprint(sr) |
Output :
Now we will use Series.duplicated() function to find the duplicate values in the underlying data of the given series object.
# detect duplicatesresult = sr.duplicated() # Print the resultprint(result) |
Output :
As we can see in the output, the Series.duplicated() function has successfully detected the duplicated values in the given series object. False indicates that the corresponding value is unique whereas, True indicates that the corresponding value was a duplicated value in the given series object.
Last Updated on October 24, 2021 by admin