Pandas DataFrame duplicated() Method - Python

Last Updated : 07 Jun, 2025

Pandas is widely used library in Python used for tasks like cleaning, analyzing and transforming data. One important part of cleaning data is identifying and handling duplicate rows which can lead to incorrect results if left unchecked.

The duplicated() method in Pandas helps us to find these duplicates in our data quickly and returns True for duplicates and False for unique rows. It's a simple way to clean up our dataset before going into analysis. In this article, we'll see how the duplicated() method works with easy examples.

Lets see an example:

Python

import pandas as pd df = pd.DataFrame({     'Name': ['Alice', 'Bob', 'Alice', 'Charlie'],     'Age': [25, 32, 25, 37] }) duplicates = df[df.duplicated()] print(duplicates)

Output:

Name Age
2 Alice 25

Syntax

DataFrame.duplicated(subset=None, keep='first')

Parameters:

1. subset: (Optional) Specifies which columns to check for duplicates. By default, it checks all columns.

2. keep: Finds which duplicates to mark as True:

'first' (default): Marks duplicates after the first occurrence as True.
'last': Marks duplicates after the last occurrence as True.
False: Marks all occurrences of duplicates as True.

Returns: A Boolean series where each value corresponds to whether the row is a duplicate (True) or unique (False).

Let's look at some examples of the duplicated method in Pandas library used to identify duplicated rows in a DataFrame. Here we will be using custom dataset you can download it by clicking Here.

Example 1: Returning a Boolean Series

In this example we will identify duplicate values in the First Name column using the default keep='first' parameter.. This keeps the first occurrence of each duplicate and marks the rest as duplicates.

Python

import pandas as pd data = pd.read_csv("/content/employees.csv") data.sort_values("First Name", inplace = True) bool_series = data["First Name"].duplicated() data.head() data[bool_series]

Output:

Example 2: Removing duplicates

In this example we'll remove all duplicates from the DataFrame. By setting keep=False we remove every instance of a duplicate.

Python

import pandas as pd data = pd.read_csv("/content/employees.csv") data.sort_values("First Name", inplace = True) bool_series = data["First Name"].duplicated(keep = False) bool_series data = data[~bool_series] data.info() data

Output:

Example 3: Keeping the Last Occurrence of Duplicates

In this example, we will keep the last occurrence of each duplicate and mark the rest as duplicates. This is done using the keep='last' arguments.

Python

import pandas as pd data = pd.read_csv("/content/employees.csv") data.sort_values("First Name", inplace=True) bool_series_last = data["First Name"].duplicated(keep='last') data_last = data[~bool_series_last] data_last.info() print(data_last)

Output:

Mastering the use of the duplicated() method in Pandas helps in effective data cleaning, helping us manage duplicate entries and retain only the unique, meaningful data for analysis.