Python | Pandas Series.drop_duplicates()
Pandas Series.drop_duplicates()
function returns a series object with duplicate values removed from the given series object.
Syntax: Series.drop_duplicates(keep=’first’, inplace=False)
Parameter :
keep : {‘first’, ‘last’, False}, default ‘first’
inplace : If True, performs operation inplace and returns None.Returns : deduplicated : Series
Example #1: Use Series.drop_duplicates()
function to drop the duplicate values from the given series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([ 80 , 25 , 3 , 25 , 24 , 6 ]) # Create the Index index_ = [ 'Coca Cola' , 'Sprite' , 'Coke' , 'Fanta' , 'Dew' , 'ThumbsUp' ] # set the index sr.index = index_ # Print the series print (sr) |
Output :
Now we will use Series.drop_duplicates()
function to drop the duplicate values in the underlying data of the given series object.
# drop duplicates result = sr.drop_duplicates() # Print the result print (result) |
Output :
As we can see in the output, the Series.drop_duplicates()
function has successfully dropped the duplicate entries from the given series object.
Example #2 : Use Series.drop_duplicates()
function to drop the duplicate values from the given series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([ 11 , 11 , 8 , 18 , 65 , 18 , 32 , 10 , 5 , 32 , 32 ]) # Create the Index index_ = pd.date_range( '2010-10-09' , periods = 11 , freq = 'M' ) # set the index sr.index = index_ # Print the series print (sr) |
Output :
Now we will use Series.drop_duplicates()
function to drop the duplicate values in the underlying data of the given series object.
# drop duplicates result = sr.drop_duplicates() # Print the result print (result) |
Output :
As we can see in the output, the Series.drop_duplicates()
function has successfully dropped the duplicate entries from the given series object.