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. Python3

# 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. Python3 1==

# 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. Python3

# 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. Python3 1==

# 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.