When to use apply vs vectorized operations in Pandas - Practice Questions

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of vectorized operation vs apply

Given the DataFrame below, what is the output of the code snippet?

Pandas

import pandas as pd

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

result = df['A'] + df['B']
print(result.tolist())

A [5, 7, 9]

B [4, 5, 6]

C [1, 2, 3]

D [1, 6, 9]

💡 Hint

Adding two columns in pandas adds their values element-wise.

The vectorized operation adds each element of column 'A' to the corresponding element in column 'B'. So 1+4=5, 2+5=7, 3+6=9.

❓ Predict Output

intermediate

Output of apply with a custom function

What is the output of this code using apply with a custom function on a DataFrame column?

Pandas

import pandas as pd

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

result = df['A'].apply(lambda x: x * 2)
print(result.tolist())

A [2, 4, 6]

B [1, 2, 3]

C [3, 6, 9]

D [0, 2, 4]

💡 Hint

The lambda function doubles each element.

The apply method applies the lambda function to each element, doubling the values.

❓ data_output

advanced

Performance difference between apply and vectorized operation

Which option correctly describes the performance difference when using apply vs vectorized operations on large DataFrames?

A Apply is faster because it processes rows one by one in Python.

B Vectorized operations are faster because they use optimized C code under the hood.

C Both have the same speed for large DataFrames.

D Apply is faster because it uses parallel processing automatically.

💡 Hint

Think about how pandas is built to handle operations.

Vectorized operations use optimized low-level code and avoid Python loops, making them faster than apply which runs Python functions row-wise.

🚀 Application

advanced

Choosing apply vs vectorized operation for complex logic

You want to create a new column in a DataFrame based on complex logic that depends on multiple columns and conditions. Which approach is best?

A Use vectorized operations only, even if logic is complex.

B Use a for loop over DataFrame rows instead of apply or vectorized operations.

C Use apply with a custom function to handle the complex logic row-wise.

D Use apply but only on a single column ignoring other columns.

💡 Hint

Complex logic often needs row-wise processing.

Apply lets you run a custom function on each row, which is useful for complex logic involving multiple columns.

🧠 Conceptual

expert

When to prefer vectorized operations over apply

Which scenario best explains when you should prefer vectorized operations instead of apply in pandas?

A When you want to process data row by row with different logic for each row.

B When the operation requires checking multiple columns with complex conditions per row.

C When you want to apply a Python function that is not supported by pandas natively.

D When the operation can be expressed as simple arithmetic or built-in pandas functions applied to entire columns.

💡 Hint

Vectorized operations are best for simple, column-wise calculations.

Vectorized operations are optimized for simple, column-wise calculations and are faster than apply for these cases.