drop_duplicates() for removal in Pandas

Introduction

We use drop_duplicates() to remove repeated rows from data. This helps keep data clean and easier to understand.

When you have a list of customer orders and want to see each order only once.

When you collect survey answers and want to remove repeated responses.

When you combine data from different sources and want to avoid counting the same item twice.

When you want to prepare data for analysis and need unique records.

When cleaning data before making charts or reports.

Syntax

Pandas

DataFrame.drop_duplicates(subset=None, keep='first', inplace=False)

subset lets you choose columns to check for duplicates. If None, all columns are checked.

keep decides which duplicate to keep: 'first' keeps the first, 'last' keeps the last, and False drops all duplicates.

Examples

Remove duplicate rows considering all columns, keep the first occurrence.

Pandas

df.drop_duplicates()

Remove duplicates based only on 'Name' and 'Age' columns.

Pandas

df.drop_duplicates(subset=['Name', 'Age'])

Keep the last occurrence of each duplicate row.

Pandas

df.drop_duplicates(keep='last')

Remove duplicates and change the original DataFrame directly.

Pandas

df.drop_duplicates(inplace=True)

Sample Program

This code creates a small table with repeated rows. Then it removes duplicates and shows the cleaned table.

Pandas

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Alice', 'David', 'Bob'],
        'Age': [25, 30, 25, 40, 30],
        'City': ['NY', 'LA', 'NY', 'Chicago', 'LA']}

df = pd.DataFrame(data)

print('Original DataFrame:')
print(df)

# Remove duplicate rows keeping the first occurrence
unique_df = df.drop_duplicates()

print('\nDataFrame after drop_duplicates():')
print(unique_df)

Important Notes

Using inplace=True changes the original DataFrame without needing to assign it again.

If you want to remove duplicates based on some columns but keep others, use the subset parameter.

Remember that drop_duplicates() keeps the first occurrence by default.

Summary

drop_duplicates() removes repeated rows from data.

You can choose which columns to check and which duplicates to keep.

It helps clean data before analysis or reporting.

Practice

1. What does the drop_duplicates() function do in pandas?

easy

A. Removes repeated rows from a DataFrame

B. Adds new rows to a DataFrame

C. Sorts the DataFrame by column values

D. Changes the data type of columns

Solution

1. Step 1: Understand the purpose of drop_duplicates()

   This function is designed to remove rows that appear more than once in a DataFrame.

2. Step 2: Compare options with function behavior

   Only Removes repeated rows from a DataFrame correctly describes removing repeated rows. Other options describe different functions.

3. Final Answer:

   Removes repeated rows from a DataFrame -> Option A

4. Quick Check:

   drop_duplicates() removes repeats [OK]

Hint: Remember: drop_duplicates() cleans repeated rows fast [OK]

Common Mistakes:

• Confusing drop_duplicates() with sorting functions
• Thinking it adds or modifies data instead of removing duplicates
• Assuming it changes data types

2. Which of the following is the correct syntax to remove duplicate rows from a DataFrame df?

easy

A. df.drop_duplicate_rows()

B. df.remove_duplicates()

C. df.delete_duplicates()

D. df.drop_duplicates()

Solution

1. Step 1: Recall the exact pandas method name

   The correct method to remove duplicates is drop_duplicates().

2. Step 2: Check each option for correct syntax

   Only df.drop_duplicates() uses the correct method name and syntax. Others are invalid pandas methods.

3. Final Answer:

   df.drop_duplicates() -> Option D

4. Quick Check:

   Correct method name is drop_duplicates() [OK]

Hint: Use exact method name: drop_duplicates() [OK]

Common Mistakes:

• Using incorrect method names like remove_duplicates()
• Adding extra words like delete or rows
• Confusing with other DataFrame methods

3. Given the DataFrame df below:

import pandas as pd
data = {'A': [1, 2, 2, 3], 'B': ['x', 'y', 'y', 'z']}
df = pd.DataFrame(data)
print(df.drop_duplicates())

What will be the output?

medium

A. Rows with index 1, 2, and 3

B. Rows with index 0, 1, and 3

C. Rows with index 0, 2, and 3

D. All rows remain unchanged

Solution

1. Step 1: Identify duplicate rows in the DataFrame

   Rows 2 and 3 have the same values (A=2, B='y'). So one will be removed.

2. Step 2: Understand default behavior of drop_duplicates()

   By default, it keeps the first occurrence and removes later duplicates. So row 1 stays, row 2 is removed.

3. Final Answer:

   Rows with index 0, 1, and 3 -> Option B

4. Quick Check:

   First duplicates kept, later removed [OK]

Hint: drop_duplicates() keeps first by default [OK]

Common Mistakes:

• Assuming last duplicate is kept instead of first
• Thinking all duplicates are removed including first
• Confusing row indices after removal

4. What is wrong with this code snippet to remove duplicates from DataFrame df?

df.drop_duplicates(inplace=True)
print(df.drop_duplicates())

medium

A. Calling drop_duplicates() twice is redundant

B. Using inplace=True and then printing drop_duplicates() returns original data

C. drop_duplicates() does not have inplace parameter

D. print statement syntax is incorrect

Solution

1. Step 1: Understand inplace=True effect

   Using inplace=True modifies df directly by removing duplicates.

2. Step 2: Analyze print statement

   After the first call, df is already deduplicated. The second call to df.drop_duplicates() is redundant as it returns the same DataFrame since there are no duplicates left.

3. Final Answer:

   Calling drop_duplicates() twice is redundant -> Option A

4. Quick Check:

   Second call redundant after inplace [OK]

Hint: inplace=True changes df; extra drop_duplicates() returns copy [OK]

Common Mistakes:

• Thinking drop_duplicates() lacks inplace parameter
• Assuming print statement is wrong syntax
• Believing calling twice causes error

5. You have a DataFrame df with columns ['Name', 'Age', 'City']. You want to remove duplicates only based on Name and City, keeping the last occurrence. Which code achieves this?

hard

A. df.drop_duplicates(columns=['Name', 'City'], keep='last')

B. df.drop_duplicates(subset=['Name', 'City'], keep='first')

C. df.drop_duplicates(subset=['Name', 'City'], keep='last')

D. df.drop_duplicates(subset=['Age'], keep='last')

Solution

1. Step 1: Use subset parameter to specify columns

   To check duplicates only on 'Name' and 'City', use subset=['Name', 'City'].

2. Step 2: Set keep='last' to keep last occurrence

   keep='last' keeps the last duplicate row, removing earlier ones.

3. Final Answer:

   df.drop_duplicates(subset=['Name', 'City'], keep='last') -> Option C

4. Quick Check:

   subset selects columns; keep='last' keeps last duplicate [OK]

Hint: Use subset for columns, keep='last' to keep last duplicate [OK]

Common Mistakes:

• Using 'columns' instead of 'subset' parameter
• Forgetting to set keep='last' to keep last occurrence
• Selecting wrong columns for subset