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Python | Pandas dataframe.drop_duplicates()

Last Updated : 12 Mar, 2024
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Pandas drop_duplicates() method helps in removing duplicates from the Pandas Dataframe In Python.

dataframe.drop_duplicates() Syntax in Python

Syntax: DataFrame.drop_duplicates(subset=None, keep=’first’, inplace=False)


  • subset: Subset takes a column or list of column label. It’s default value is none. After passing columns, it will consider them only for duplicates. 
  • keep: keep is to control how to consider duplicate value. It has only three distinct value and default is ‘first’. 
    • If ‘first‘, it considers first value as unique and rest of the same values as duplicate.
    • If ‘last‘, it considers last value as unique and rest of the same values as duplicate.
    • If False, it consider all of the same values as duplicates
  • inplace: Boolean values, removes rows with duplicates if True.

Return type: DataFrame with removed duplicate rows depending on Arguments passed. 

Python dataframe.drop_duplicates() Example

Below, we are discussing example of dataframe.drop_duplicates() method as its also use for Removing duplicates with Pandas in Python, example are :

Pandas DataFrame Drop Duplicates

As we can see one of the TeamA and team has been dropped due to duplicate value.

import pandas as pd

data = {
    "A": ["TeamA", "TeamB", "TeamB", "TeamC", "TeamA"],
    "B": [50, 40, 40, 30, 50],
    "C": [True, False, False, False, True]

df = pd.DataFrame(data)



    A        B    C
0 TeamA 50 True
1 TeamB 40 False
3 TeamC 30 False

Managing Duplicate Data Using dataframe.drop_duplicates()

In this example , we manages student data, showcasing techniques to removing duplicates with Pandas in Python, removing all duplicates, and deleting duplicates based on specific columns then the last part demonstrates making names case-insensitive while preserving the first occurrence.

import pandas as pd

# Sample data for student names and ages
students_data = {
    'Name': ["Alice", "Bob", "Charlie", "David", "Alice", "Eva", "Bob"],
    'Age': [20, 22, 21, 23, 20, 19, 22],
    'Grade': [85, 90, 78, 92, 85, 88, 90],
    'Attendance': [95, 92, 88, 93, 95, 96, 92]

# Create a DataFrame
students_df = pd.DataFrame(students_data)

# Remove all duplicate rows
students_df_no_duplicates = students_df.drop_duplicates(keep=False)

# Delete duplicate rows based on specific columns (Name and Age)
students_df_specific_columns = students_df.drop_duplicates(subset=["Name", "Age"], keep=False)

# Using DataFrame.apply() and lambda function to make names case-insensitive and keep the first occurrence
students_df_case_insensitive = students_df.apply(lambda x: x.astype(str).str.lower()).drop_duplicates(subset=['Name', 'Age'], keep='first')


     Name  Age  Grade  Attendance
0 Alice 20 85 95
1 Bob 22 90 92
2 Charlie 21 78 88
3 David 23 92 93
4 Alice 20 85 95
5 Eva 19 88 96
6 Bob 22 90 92

Name Age Grade Attendance
2 Charlie 21 78 88
3 David 23 92 93
5 Eva 19 88 96

Name Age Grade Attendance
5 Eva 19 88 96

Name Age Grade Attendance
5 Eva 19 88 96

To download the CSV file used, Click Here. 

Removing Rows with the Same First Name 

In the following example, rows having the same First Name are removed and a new data frame is returned.

# importing pandas package
import pandas as pd

# making data frame from csv file
data = pd.read_csv("employees.csv")

# sorting by first name
data.sort_values("First Name", inplace=True)

# dropping ALL duplicate values
data.drop_duplicates(subset="First Name",
                     keep=False, inplace=True)

# displaying data


As shown in the image, the rows with the same names were removed from a data frame. 

Removing Rows with all Duplicate Values

In this example, rows having all values will be removed. Since the CSV file isn’t having such a row, a random row is duplicated and inserted into the data frame first.

# length before adding row
length1 = len(data)

# manually inserting duplicate of a row of row 440
data.loc[1001] = [data["First Name"][440],
                  data["Start Date"][440],
                  data["Last Login Time"][440],
                  data["Bonus %"][440],
                  data["Senior Management"][440],

# length after adding row
length2 = len(data)

# sorting by first name
data.sort_values("First Name", inplace=True)

# dropping duplicate values
data.drop_duplicates(keep=False, inplace=True)

# length after removing duplicates
length3 = len(data)

# printing all data frame lengths
print(length1, length2, length3)


As shown in the output image, the length after removing duplicates is 999. Since the keep parameter was set to False, all of the duplicate rows were removed.

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