Dropping columns and rows in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output after dropping rows by index

What is the output DataFrame after dropping rows with index 1 and 3?

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': [10, 20, 30, 40, 50],
    'B': [5, 4, 3, 2, 1]
})
result = df.drop([1, 3])
print(result)

A

   A  B
1  20  4
3  40  2

B

   A  B
0  10  5
2  30  3
4  50  1

C

   A  B
0  10  5
1  20  4
2  30  3
3  40  2
4  50  1

D

   A  B
0  10  5
2  30  3
3  40  2
4  50  1

💡 Hint

Use df.drop() with a list of row indices to remove those rows.

The drop method removes rows with index 1 and 3. Remaining rows are 0, 2, and 4.

❓ data_output

intermediate

Resulting DataFrame after dropping columns

What is the resulting DataFrame after dropping columns 'X' and 'Z'?

Pandas

import pandas as pd

df = pd.DataFrame({
    'X': [1, 2, 3],
    'Y': [4, 5, 6],
    'Z': [7, 8, 9]
})
result = df.drop(columns=['X', 'Z'])
print(result)

A

   Y
0  4
1  5
2  6

B

   X  Y
0  1  4
1  2  5
2  3  6

C

   X  Z
0  1  7
1  2  8
2  3  9

D

   X  Y  Z
0  1  4  7
1  2  5  8
2  3  6  9

💡 Hint

Use df.drop() with columns parameter to remove columns.

Dropping columns 'X' and 'Z' leaves only column 'Y'.

🔧 Debug

advanced

Identify the error when dropping rows

What error will this code raise? import pandas as pd df = pd.DataFrame({'A': [1, 2, 3]}) result = df.drop(5) print(result)

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3]})
result = df.drop(5)
print(result)

A KeyError: '[5] not found in axis'

B IndexError: index 5 is out of bounds

C TypeError: drop() missing required argument 'axis'

D No error, prints original DataFrame

💡 Hint

Check if the index 5 exists in the DataFrame before dropping.

Index 5 does not exist in the DataFrame index, so drop raises a KeyError.

🚀 Application

advanced

Drop rows with missing values

Given this DataFrame, which code correctly drops all rows that have any missing (NaN) values?

Pandas

import pandas as pd
import numpy as np

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

A df.drop(columns=['A', 'B'])

B df.dropna(axis=1)

C df.dropna()

D df.drop([0, 1])

💡 Hint

Use dropna() to remove rows with missing values.

dropna() removes rows with any NaN values. axis=1 drops columns, not rows.

🧠 Conceptual

expert

Effect of inplace parameter in drop method

What is the effect of using inplace=True in the drop method on a DataFrame?

A The drop method ignores inplace=True and always returns a new DataFrame.

B A new DataFrame is returned with rows/columns dropped, original unchanged.

C The method raises an error if inplace=True is used.

D The original DataFrame is modified and the method returns None.

💡 Hint

inplace=True changes the original DataFrame directly.

When inplace=True, drop modifies the original DataFrame and returns None.