Removing duplicates (drop_duplicates) in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of drop_duplicates with subset

What is the output DataFrame after running this code?

Data Analysis Python

import pandas as pd

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

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

A

   A  B   C
0  1  x  10
1  2  y  20
2  2  y  20
3  3  z  30
4  1  x  40

B

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

C

   A  B   C
1  2  y  20
2  2  y  20
3  3  z  30

D

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

💡 Hint

Look at the first occurrence of each unique value in column 'A'.

drop_duplicates with subset=['A'] keeps the first row for each unique value in column 'A'. Rows with duplicate 'A' values after the first are removed.

❓ data_output

intermediate

Number of rows after drop_duplicates

How many rows remain after removing duplicates based on columns 'A' and 'B'?

Data Analysis Python

import pandas as pd

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

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

A 3

B 4

C 5

D 2

💡 Hint

Check unique pairs of (A, B) in the DataFrame.

The unique pairs are (1,x), (2,y), (3,z). The pair (1,x) appears twice but only first is kept. So total unique pairs are 3, but since duplicates are removed, rows with indices 0,1,3 remain. The row with index 4 is duplicate of (1,x) so removed. So total rows after drop_duplicates is 3.

🔧 Debug

advanced

Error when using drop_duplicates with inplace=True

What error will this code raise?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'A': [1, 1, 2], 'B': [3, 3, 4]})
df.drop_duplicates(inplace=True, subset=['A', 'B'])
print(df)

A NameError: name 'df' is not defined

B No error, prints the DataFrame with duplicates removed

C AttributeError: 'NoneType' object has no attribute 'print'

D TypeError: drop_duplicates() got an unexpected keyword argument 'inplace'

💡 Hint

Check what drop_duplicates returns when inplace=True.

drop_duplicates with inplace=True modifies the DataFrame in place and returns None. The variable df remains defined and prints correctly.

🚀 Application

advanced

Removing duplicates but keeping last occurrence

Which option correctly removes duplicates from DataFrame df based on column 'A' but keeps the last occurrence?

Data Analysis Python

import pandas as pd

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

A df.drop_duplicates(subset=['A'], keep='none')

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

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

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

💡 Hint

The keep parameter controls which duplicate to keep.

keep='last' keeps the last occurrence of each duplicate, removing earlier ones.

🧠 Conceptual

expert

Effect of drop_duplicates on index

After using drop_duplicates on a DataFrame, what happens to the index by default?

A The original index values are preserved, including gaps from removed rows

B The index is reset to a new continuous range starting from 0

C The index is dropped and replaced with a default integer index without gaps

D The index is converted to a MultiIndex based on duplicate columns

💡 Hint

Think about whether drop_duplicates changes the index or just drops rows.

drop_duplicates removes duplicate rows but keeps the original index values, so gaps remain unless reset_index() is called.