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.

Practice

1. Which of the following best describes when to use apply in pandas?

easy

A. When you want to merge two DataFrames.

B. When you want to perform simple arithmetic on entire columns quickly.

C. When you want to sort a DataFrame by a column.

D. When you need to run a custom function on each row or column.

Solution

1. Step 1: Understand the purpose of apply

   apply is used to apply custom or complex functions to rows or columns in a DataFrame.

2. Step 2: Compare with vectorized operations

   Vectorized operations are for simple, fast math on entire columns, not custom row-wise functions.

3. Final Answer:

   When you need to run a custom function on each row or column. -> Option D

4. Quick Check:

   Custom function use = apply = A [OK]

Hint: Use apply for custom row/column functions, vectorized for simple math [OK]

Common Mistakes:

• Confusing apply with vectorized operations for simple math
• Using apply for sorting or merging
• Thinking apply is always faster

2. Which of these is the correct syntax to use apply on a DataFrame column df['col'] with a function func?

easy

A. apply(df['col'], func)

B. df['col'].apply(func)

C. df.apply['col'](func)

D. df['col'].apply = func

Solution

1. Step 1: Recall the method call syntax

   In pandas, apply is a method called on a Series or DataFrame with the function as argument.

2. Step 2: Check each option

   df['col'].apply(func) uses correct syntax: df['col'].apply(func). Others have syntax errors or wrong usage.

3. Final Answer:

   df['col'].apply(func) -> Option B

4. Quick Check:

   Method call with function argument = C [OK]

Hint: Use dot apply with parentheses and function inside [OK]

Common Mistakes:

• Using square brackets instead of parentheses for apply
• Trying to assign function to apply attribute
• Calling apply as a standalone function

3. Given the DataFrame:

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

What is the output of:

df['C'] = df['A'] + df['B']
df['D'] = df['A'].apply(lambda x: x * 2)
df[['C', 'D']]

medium

A. C D
0 5 2
1 7 4
2 9 6

B. C D
0 5 1
1 7 2
2 9 3

C. C D
0 4 2
1 5 4
2 6 6

D. C D
0 5 4
1 7 6
2 9 8

Solution

1. Step 1: Calculate column C using vectorized addition

   Column C = A + B = [1+4, 2+5, 3+6] = [5, 7, 9]

2. Step 2: Calculate column D using apply with lambda

   Column D = A * 2 = [1*2, 2*2, 3*2] = [2, 4, 6]

3. Final Answer:

   C D 0 5 2 1 7 4 2 9 6 -> Option A

4. Quick Check:

   Vectorized + apply results = B [OK]

Hint: Add columns directly, use apply for custom row-wise ops [OK]

Common Mistakes:

• Mixing up which columns are added
• Forgetting to multiply by 2 in apply
• Confusing apply output with original values

4. You want to double the values in column 'A' of DataFrame df. Which code has an error?

medium

A. df['A'] = df['A'] * 2

B. df['A'] = df['A'].apply(lambda x: x * 2)

C. df['A'] = df.apply(lambda x: x['A'] * 2)

D. df['A'] = df['A'].map(lambda x: x * 2)

Solution

1. Step 1: Analyze df['A'] = df.apply(lambda x: x['A'] * 2)'s use of apply

   Using df.apply without specifying axis defaults to axis=0 (columns), so x['A'] fails because x is a column name string, not a row.

2. Step 2: Compare with other options

   Options B, C, and D correctly double values using apply on Series, vectorized, or map on Series.

3. Final Answer:

   df['A'] = df.apply(lambda x: x['A'] * 2) -> Option C

4. Quick Check:

   apply on DataFrame needs axis=1 for row-wise, else error = A [OK]

Hint: Use axis=1 with apply on DataFrame rows to avoid errors [OK]

Common Mistakes:

• Forgetting axis=1 in DataFrame apply
• Confusing apply on DataFrame vs Series
• Using map on DataFrame instead of Series

5. You have a DataFrame df with columns 'X' and 'Y'. You want to create a new column 'Z' that is 'X' squared if 'Y' is even, else 'X' cubed. Which is the best approach?

hard

A. Use vectorized operations with np.where and powers.

B. Use apply with a custom function checking Y and computing Z.

C. Use a for loop to iterate rows and assign Z values.

D. Use map on column 'X' only.

Solution

1. Step 1: Understand the condition and operations

   We want Z = X^2 if Y is even, else Z = X^3.

2. Step 2: Choose efficient method

   Using np.where with vectorized power operations is fast and clear. apply works but is slower. For loops are slow and verbose. map on X alone can't check Y.

3. Final Answer:

   Use vectorized operations with np.where and powers. -> Option A

4. Quick Check:

   Conditional vectorized ops > apply or loops = D [OK]

Hint: Use np.where for conditional vectorized operations [OK]

Common Mistakes:

• Using apply when vectorized is faster
• Using loops instead of vectorized code
• Trying to map without condition on another column