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In this post you will learn how to find duplicate rows in Pandas dataframe.

How to find duplicate rows

First, I’ll check if there are any duplicate rows in my dataframe. For this purpose, I will add a column in which to display information whether the row is a duplicate. I’ll use the duplicated function to check if the rows are duplicates.

import pandas as pd

my_df = pd.DataFrame({'id':['id1','id2','id3', 'id2'],
                          'Column1': ['2', '7', '6', '7'],
                           'Column2': ['2', '5', '8', '5'],
                           'Column3': ['4', '1', '9', '1']})

dup_df = my_df.copy()

dup_df['Dup_Column'] = my_df.duplicated()

print(f'Looking for duplicates in my dataframe: \n{dup_df}')

How to list duplicate rows

You will also learn how to easily display only duplicate rows in Pandas.

In order to display the redundant rows, I will create a copy of the dataframe. Will use duplicated function.

import pandas as pd

my_df = pd.DataFrame({'id':['id1','id2','id3', 'id2'],
                          'Column1': ['2', '7', '6', '7'],
                           'Column2': ['2', '5', '8', '5'],
                           'Column3': ['4', '1', '9', '1']})

dup_list = my_df[my_df.duplicated()]

print(f'Duplicate rows: \n{dup_list}')

Pandas displays only one row which is duplicated. This is the same line we found in the first example.

These techniques are useful for identifying and handling duplicate data in your Pandas DataFrame, which is an essential step in data cleaning and analysis.

See also:
Duplicated function documentation.