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Python Pandas DataFrame.drop_duplicates() function DataFrameremoves all duplicate rows from .

pandas.DataFrame.drop_duplicates()Syntax

DataFrame.drop_duplicates(subset: Union[Hashable, Sequence[Hashable], NoneType]=None,
                          keep: Union[str, bool]='first',
                          inplace: bool=False,
                          ignore_index: bool=False)

parameter

Return Value

If inplaceis True, then DataFrameremove all duplicate rows from , otherwise is None.

Example Code: DataFrame.set_index()Remove Duplicate Rows Using Pandas Methods

import pandas as pd
fruit_list = [ ('Orange', 34, 'Yes' ,'ABC') ,
             ('Mango', 24, 'No','XYZ' ) ,
             ('banana', 14, 'No','BCD' ) ,
            ('Orange', 34, 'Yes' ,'ABC') ]

df = pd.DataFrame(fruit_list, 
                  columns = ['Name',
                             'Price',
                             'In_Stock',
                             'Supplier'])

print("DataFrame:")
print(df)

df_unique=df.drop_duplicates() 

print("DataFrame with Unique Rows:")
print(df_unique)

Output:

DataFrame:
     Name  Price In_Stock Supplier
0  Orange     34      Yes      ABC
1   Mango     24       No      XYZ
2  banana     14       No      BCD
3  Orange     34      Yes      ABC
DataFrame with Unique Rows:
     Name  Price In_Stock Supplier
0  Orange     34      Yes      ABC
1   Mango     24       No      XYZ
2  banana     14       No      BCD

DataFrameRows 1 and 4 of the original are identical.

You can drop_duplicates()remove all duplicate rows from a DataFrame by using the method.

Example code Pandas method to set subsetparametersDataFrame.set_index()

import pandas as pd
fruit_list = [ ('Orange', 34, 'Yes' ,'ABC') ,
             ('Mango', 24, 'No','XYZ' ) ,
             ('banana', 14, 'No','ABC' ) ,
            ('Orange', 34, 'Yes' ,'ABC') ]

df = pd.DataFrame(fruit_list, 
                  columns = ['Name',
                             'Price',
                             'In_Stock',
                             'Supplier'])

print("DataFrame:")
print(df)

df_unique=df.drop_duplicates(subset ="Supplier") 

print("DataFrame with Unique vales of Supplier Column:")
print(df_unique)

Output:

DataFrame:
     Name  Price In_Stock Supplier
0  Orange     34      Yes      ABC
1   Mango     24       No      XYZ
2  banana     14       No      ABC
3  Orange     34      Yes      ABC
DataFrame with Unique vales of Supplier Column:
     Name  Price In_Stock Supplier
0  Orange     34      Yes      ABC
1   Mango     24       No      XYZ

This method removes all Supplierrows in the DataFrame that do not have unique values ​​for the column.

Here, Supplierthe column has a common value for rows 1, 3, and 4. Therefore, rows 3 and 4 will be DataFramedeleted from ; by default, the first duplicate row will not be deleted.

Example Code: Setting keepParameters Pandas DataFrame.set_index()Method

import pandas as pd
fruit_list = [ ('Orange', 34, 'Yes' ,'ABC') ,
             ('Mango', 24, 'No','XYZ' ) ,
             ('banana', 14, 'No','ABC' ) ,
            ('Orange', 34, 'Yes' ,'ABC') ]

df = pd.DataFrame(fruit_list, 
                  columns = ['Name',
                             'Price',
                             'In_Stock',
                             'Supplier'])

print("DataFrame:")
print(df)

df_unique=df.drop_duplicates(subset ="Supplier",keep="last") 

print("DataFrame with Unique vales of Supplier Column:")
print(df_unique)

Output:

DataFrame:
     Name  Price In_Stock Supplier
0  Orange     34      Yes      ABC
1   Mango     24       No      XYZ
2  banana     14       No      ABC
3  Orange     34      Yes      ABC
DataFrame with Unique vales of Supplier Column:
     Name  Price In_Stock Supplier
1   Mango     24       No      XYZ
3  Orange     34      Yes      ABC

This method removes DataFrameall Supplierrows in that do not have a unique value in the column, keeping only the last duplicate row.

Here, Supplierthe column of rows 1, 3, and 4 has a common value. So rows 1 and 3 will DataFramebe deleted from .

Example Code: Pandas Method for Setting ignore_indexParametersDataFrame.set_index()

import pandas as pd
fruit_list = [ ('Orange', 34, 'Yes' ,'ABC') ,
             ('Mango', 24, 'No','XYZ' ) ,
             ('banana', 14, 'No','ABC' ) ,
            ('Orange', 34, 'Yes' ,'ABC') ]

df = pd.DataFrame(fruit_list, 
                  columns = ['Name',
                             'Price',
                             'In_Stock',
                             'Supplier'])

print("DataFrame:")
print(df)

df.drop_duplicates(subset ="Supplier",keep="last",inplace=True,ignore_index=True) 

print("DataFrame with Unique vales of Supplier Column:")
print(df)

Output:

DataFrame:
     Name  Price In_Stock Supplier
0  Orange     34      Yes      ABC
1   Mango     24       No      XYZ
2  banana     14       No      ABC
3  Orange     34      Yes      ABC
DataFrame with Unique vales of Supplier Column:
     Name  Price In_Stock Supplier
0   Mango     24       No      XYZ
1  Orange     34      Yes      ABC

Here, because ignore_indexis set to True, DataFramethe index in the original is ignored and a new index is set for the row.

Due to the effect of the function, the original is modified after inplace=Truecalling the function.ignore_index()DataFrame