drop_duplicates() for removal in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of drop_duplicates() with subset and keep parameters

What is the output DataFrame after running the following code?

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 2, 3, 3, 3],
    'B': ['x', 'y', 'y', 'z', 'z', 'z'],
    'C': [10, 20, 20, 30, 30, 30]
})

result = df.drop_duplicates(subset=['A'], keep='last')
print(result)

A

   A  B   C
0  1  x  10
2  2  y  20
5  3  z  30

B

   A  B   C
0  1  x  10
1  2  y  20
3  3  z  30

C

   A  B   C
0  1  x  10
1  2  y  20
2  2  y  20
3  3  z  30
4  3  z  30
5  3  z  30

D

   A  B   C
0  1  x  10
3  3  z  30

💡 Hint

Remember that subset=['A'] means duplicates are checked only on column 'A'. The keep='last' parameter keeps the last occurrence.

The drop_duplicates() method removes duplicate rows based on column 'A'. Since keep='last', it keeps the last row for each unique value in 'A'. For 'A' = 2, the last row is index 2, and for 'A' = 3, the last row is index 5. The row at index 0 (A=1) is also kept as it is unique.

❓ data_output

intermediate

Number of rows after drop_duplicates() with keep=False

Given the DataFrame below, how many rows remain after removing all duplicates (no rows kept) based on columns 'A' and 'B'?

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 2, 3, 3, 3],
    'B': ['x', 'y', 'y', 'z', 'z', 'w'],
    'C': [10, 20, 20, 30, 30, 40]
})

result = df.drop_duplicates(subset=['A', 'B'], keep=False)
print(len(result))

A 2

B 3

C 4

D 5

💡 Hint

Using keep=False removes all rows that have duplicates in the specified subset.

Rows with duplicates in columns 'A' and 'B' are removed completely. The pairs (2, 'y') and (3, 'z') appear more than once, so all those rows are removed. Remaining unique rows are (1, 'x') and (3, 'w'), so 2 rows remain.

🔧 Debug

advanced

Identify the error in drop_duplicates() usage

What error will this code raise?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

result = df.drop_duplicates(subset='A,B')
print(result)

A TypeError: unhashable type: 'list'

B No error, prints the original DataFrame

C ValueError: subset must be a list-like of column labels

D KeyError: 'A,B'

💡 Hint

Check the type and format of the subset argument.

The subset parameter expects a list or array of column names, not a single string with commas. Passing 'A,B' as a string looks for a column named 'A,B' which does not exist, causing a KeyError.

🚀 Application

advanced

Remove duplicates keeping first occurrence per group

You have a DataFrame with sales data. You want to keep only the first sale per customer. Which code snippet achieves this?

Pandas

import pandas as pd

df = pd.DataFrame({
    'customer_id': [101, 102, 101, 103, 102],
    'sale_amount': [200, 150, 300, 400, 100],
    'date': ['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04', '2023-01-05']
})

A df.drop_duplicates(subset=['customer_id', 'sale_amount'], keep='last')

B df.drop_duplicates(subset=['sale_amount'], keep='last')

C df.drop_duplicates(subset=['customer_id'], keep='first')

D df.drop_duplicates(subset=['date'], keep=False)

💡 Hint

Think about which column identifies customers and which occurrence to keep.

To keep the first sale per customer, remove duplicates based on 'customer_id' and keep the first occurrence. Option C does exactly this.

🧠 Conceptual

expert

Effect of drop_duplicates() on DataFrame index

After using drop_duplicates() on a DataFrame, what happens to the index of the resulting DataFrame?

A The index is converted to a MultiIndex based on duplicate columns.

B The original index values are preserved, including gaps from dropped rows.

C The index is reset to a continuous range starting at 0 automatically.

D The index is dropped and replaced with a default integer index only if <code>reset_index()</code> is called.

💡 Hint

Consider what happens to row labels when rows are removed but no explicit index reset is done.

drop_duplicates() removes rows but keeps the original index values. This means the resulting DataFrame may have non-continuous index values with gaps where rows were dropped. To get a continuous index, you must call reset_index() explicitly.

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