apply() function for custom logic in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the apply() function do in pandas?

The apply() function lets you run a custom function on each row or column of a DataFrame or on each element of a Series.

beginner

How do you apply a function to each row in a DataFrame?

Use df.apply(your_function, axis=1). The axis=1 means the function runs on each row.

beginner

What is the difference between axis=0 and axis=1 in apply()?

axis=0 applies the function to each column, while axis=1 applies it to each row.

beginner

Can apply() be used with lambda functions?

Yes! You can pass a lambda function directly to apply() for quick custom logic without defining a separate function.

intermediate

Why use apply() instead of a for loop?

apply() is often faster and cleaner than a for loop because it uses optimized internal code and works well with pandas data structures.

What does df.apply(func, axis=1) do?

A Applies func to each column

B Applies func to each element individually

C Applies func to the entire DataFrame at once

D Applies func to each row

axis=1 means the function runs on each row.

Which of these is a valid way to use apply() with a lambda function?

A All of the above

B df.apply(lambda x: x + 1, axis=1)

C df.apply(lambda x: x + 1, axis=0)

D df.apply(lambda x: x + 1)

You can use apply() with lambda functions and specify axis as needed.

What happens if you use apply() without specifying axis on a DataFrame?

A It applies the function to each row

B It applies the function to each column

C It throws an error

D It applies the function to the entire DataFrame

The default axis is 0, so it applies to each column.

Which is NOT a benefit of using apply()?

A Ability to use custom functions easily

B Cleaner code than loops

C Faster execution than vectorized operations

D Works well with pandas data structures

Vectorized operations are usually faster than apply().

If you want to apply a function to each element of a Series, which method is best?

A apply()

B map()

C applymap()

D groupby()

apply() works on Series elements; applymap() is for DataFrames.

Explain how to use the apply() function to run a custom calculation on each row of a DataFrame.

Think about how to tell pandas to work row by row.

Describe the difference between apply() with axis=0 and axis=1.

Consider what each axis means in a table.

Practice

1. What does the apply() function do in a pandas DataFrame?

easy

A. It merges two DataFrames together.

B. It sorts the DataFrame by a column.

C. It filters rows based on a condition.

D. It applies a custom function to each row or column.

Solution

1. Step 1: Understand the purpose of apply()

   The apply() function is used to run a custom function on each row or column of a DataFrame.

2. Step 2: Compare with other DataFrame methods

   Sorting, filtering, and merging are different operations and not done by apply().

3. Final Answer:

   It applies a custom function to each row or column. -> Option D

4. Quick Check:

   apply() = custom function application [OK]

Hint: Remember: apply() runs your function on rows or columns [OK]

Common Mistakes:

• Confusing apply() with sort or filter methods
• Thinking apply() merges DataFrames
• Assuming apply() only works on columns

2. Which of the following is the correct syntax to apply a function f row-wise on a DataFrame df?

easy

A. df.apply(f, axis=0)

B. df.apply(f)

C. df.apply(f, axis=1)

D. df.apply(f, axis=2)

Solution

1. Step 1: Recall axis parameter meaning

   In pandas, axis=1 means apply function row-wise, while axis=0 means column-wise.

2. Step 2: Identify correct axis for row-wise

   Since the question asks for row-wise application, axis=1 is correct.

3. Final Answer:

   df.apply(f, axis=1) -> Option C

4. Quick Check:

   Row-wise apply = axis=1 [OK]

Hint: Use axis=1 for rows, axis=0 for columns [OK]

Common Mistakes:

• Using axis=0 for row-wise apply
• Omitting axis parameter and expecting row-wise
• Using invalid axis values like 2

3. Given the DataFrame:

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

What is the output of:

df['Sum'] = df.apply(lambda row: row['A'] + row['B'], axis=1)
print(df)

medium

A. A B Sum
0 1 3 3
1 2 4 4

B. A B Sum
0 1 3 4
1 2 4 6

C. A B Sum
0 1 3 1
1 2 4 2

D. Error: axis parameter missing

Solution

1. Step 1: Understand the lambda function

   The lambda adds values from columns 'A' and 'B' for each row.

2. Step 2: Apply row-wise addition

   Row 0: 1 + 3 = 4; Row 1: 2 + 4 = 6.

3. Final Answer:

   A B Sum 0 1 3 4 1 2 4 6 -> Option B

4. Quick Check:

   Sum column = A + B per row [OK]

Hint: Sum columns row-wise with apply and axis=1 [OK]

Common Mistakes:

• Forgetting axis=1 causes wrong operation
• Adding columns incorrectly (e.g., column-wise)
• Expecting apply to work without lambda

4. What is wrong with this code snippet?

df = pd.DataFrame({'X': [10, 20], 'Y': [30, 40]})
df['Result'] = df.apply(lambda x: x['X'] * 2 if x['Y'] > 35 else x['X'], axis=0)

medium

A. Using axis=0 applies function column-wise, causing a KeyError.

B. Lambda function syntax is incorrect.

C. DataFrame is missing required columns.

D. No error; code runs correctly.

Solution

1. Step 1: Check axis parameter usage

   axis=0 applies function to each column (Series), not rows.

2. Step 2: Understand lambda function context

   Lambda expects row with keys 'X' and 'Y', but column Series only has index labels, causing KeyError.

3. Final Answer:

   Using axis=0 applies function column-wise, causing a KeyError. -> Option A

4. Quick Check:

   axis=0 = columns, lambda expects rows [OK]

Hint: Use axis=1 for row-wise logic to avoid key errors [OK]

Common Mistakes:

• Confusing axis=0 and axis=1
• Expecting row keys when applying column-wise
• Ignoring error messages about missing keys

5. You have a DataFrame df with columns "score" and "passed" (boolean). You want to create a new column "grade" where:
- If passed is True and score >= 90, grade is 'A'
- If passed is True and score >= 75 but < 90, grade is 'B'
- Otherwise, grade is 'F'
Which apply() function correctly implements this logic?

hard

A. df['grade'] = df.apply(lambda r: 'A' if r['passed'] and r['score'] >= 90 else ('B' if r['passed'] and r['score'] >= 75 else 'F'), axis=1)

B. df['grade'] = df.apply(lambda r: 'A' if r['score'] >= 90 else 'B' if r['score'] >= 75 else 'F', axis=0)

C. df['grade'] = df.apply(lambda r: 'A' if r['passed'] or r['score'] >= 90 else 'F', axis=1)

D. df['grade'] = df.apply(lambda r: 'A' if r['passed'] and r['score'] > 90 else 'B', axis=1)

Solution

1. Step 1: Check axis parameter for row-wise logic

   axis=1 is needed to apply function on each row.

2. Step 2: Verify conditional logic matches requirements

   df['grade'] = df.apply(lambda r: 'A' if r['passed'] and r['score'] >= 90 else ('B' if r['passed'] and r['score'] >= 75 else 'F'), axis=1) correctly checks if passed is True and score thresholds for 'A' and 'B', else 'F'.

3. Step 3: Identify errors in other options

   df['grade'] = df.apply(lambda r: 'A' if r['score'] >= 90 else 'B' if r['score'] >= 75 else 'F', axis=0) uses axis=0 (wrong axis). df['grade'] = df.apply(lambda r: 'A' if r['passed'] or r['score'] >= 90 else 'F', axis=1) uses 'or' instead of 'and'. df['grade'] = df.apply(lambda r: 'A' if r['passed'] and r['score'] > 90 else 'B', axis=1) misses 'F' grade and uses wrong comparison for 90.

4. Final Answer:

   df['grade'] = df.apply(lambda r: 'A' if r['passed'] and r['score'] >= 90 else ('B' if r['passed'] and r['score'] >= 75 else 'F'), axis=1) -> Option A

5. Quick Check:

   Correct logic and axis=1 for row-wise apply [OK]

Hint: Use axis=1 and nested if-else for multi-condition grading [OK]

Common Mistakes:

• Using axis=0 instead of axis=1
• Using 'or' instead of 'and' in conditions
• Missing else condition for failing grade