apply() on rows (axis=1) in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the apply() function do when used with axis=1 in pandas?

It applies a function to each row of the DataFrame, allowing you to perform row-wise operations.

beginner

How do you specify that apply() should work on rows instead of columns?

By setting the parameter axis=1 inside the apply() function.

beginner

Example: What will this code do?

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

It will add the values in columns 'A' and 'B' for each row and return a Series with these sums.

intermediate

Why might you use apply() with axis=1 instead of vectorized operations?

To perform custom or complex operations that involve multiple columns in each row, which are not easily done with simple vectorized functions.

beginner

What type of object does the function inside apply(axis=1) receive as input?

It receives a pandas Series representing a single row of the DataFrame.

What does axis=1 mean when used with apply() in pandas?

A Apply function only to the first row

B Apply function to each column

C Apply function to each row

D Apply function to the entire DataFrame at once

Setting axis=1 tells pandas to apply the function to each row.

What type of object is passed to the function inside apply(axis=1)?

A A Series representing a row

B A DataFrame

C A list

D A scalar value

The function receives a Series object representing one row of the DataFrame.

Which of these is a correct way to sum columns 'A' and 'B' for each row using apply()?

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

B df.apply(lambda x: x['A'] + x['B'], axis=0)

C df.apply(sum, axis=1)

D df.sum(axis=1)

Using apply() with axis=1 and a lambda function sums columns 'A' and 'B' row-wise.

Why might apply(axis=1) be slower than vectorized operations?

A Because it does not support lambda functions

B Because it processes columns instead of rows

C Because it only works on small DataFrames

D Because it uses loops internally

apply(axis=1) often uses Python-level loops, which are slower than vectorized operations.

If you want to create a new column based on row-wise calculations, which method is suitable?

A df['new'] = df.apply(func, axis=0)

B df['new'] = df.apply(func, axis=1)

C df['new'] = df.sum(axis=0)

D df['new'] = df.sum(axis=1)

Using apply() with axis=1 lets you compute values row-wise for a new column.

Explain how apply() with axis=1 works in pandas and give a simple example.

Think about how you would add two columns for each row.

Describe a situation where using apply() with axis=1 is better than vectorized operations.

Consider when you need custom calculations per row.

Practice

1.

What does apply() with axis=1 do in a pandas DataFrame?

easy

A. It applies a function to each column of the DataFrame.

B. It applies a function to each row of the DataFrame.

C. It applies a function to the entire DataFrame at once.

D. It applies a function only to the first row.

Solution

1. Step 1: Understand the axis parameter

   In pandas, axis=1 means the function is applied across columns for each row.

2. Step 2: Apply function on rows

   Using apply() with axis=1 runs the function on each row individually.

3. Final Answer:

   It applies a function to each row of the DataFrame. -> Option B

4. Quick Check:

   apply(axis=1) = row-wise function [OK]

Hint: Remember axis=1 means row-wise operation [OK]

Common Mistakes:

• Confusing axis=1 with column-wise operation
• Thinking apply(axis=1) applies to the whole DataFrame at once
• Assuming it only applies to the first row

2.

Which of the following is the correct syntax to apply a function f to each row of a DataFrame df?

def f(row):
    return row['A'] + row['B']

easy

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

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

C. df.apply(f)

D. df.apply(f, axis='rows')

Solution

1. Step 1: Identify axis for row-wise apply

   Row-wise apply requires axis=1.

2. Step 2: Check syntax correctness

   df.apply(f, axis=1) uses axis=1, which is correct. The other options use incorrect axis parameters.

3. Final Answer:

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

4. Quick Check:

   Use axis=1 for rows [OK]

Hint: Use axis=1 to apply function row-wise [OK]

Common Mistakes:

• Using axis=0 applies function column-wise
• Omitting axis defaults to axis=0
• Using incorrect axis string values

3.

What is the output of the following code?

import pandas as pd
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
def sum_row(row):
    return row['A'] + row['B']
df['Sum'] = df.apply(sum_row, axis=1)
print(df)

medium

A. Error: KeyError

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

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

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

Solution

1. Step 1: Understand the function sum_row

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

2. Step 2: Apply function row-wise and add new column

   Applying with axis=1 sums each row's 'A' and 'B'. For 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 D

4. Quick Check:

   Row sums = 4, 6 [OK]

Hint: Sum columns per row with apply(axis=1) [OK]

Common Mistakes:

• Using axis=0 sums columns, not rows
• Forgetting to assign result back to DataFrame
• Mixing up row and column indices

4.

Identify the error in the following code snippet:

import pandas as pd
df = pd.DataFrame({'X': [10, 20], 'Y': [30, 40]})
def multiply(row):
    return row['x'] * row['Y']
df['Product'] = df.apply(multiply, axis=1)
print(df)

medium

A. KeyError because 'x' column does not exist (case sensitive).

B. TypeError due to wrong axis parameter.

C. No error, output is correct.

D. SyntaxError in function definition.

Solution

1. Step 1: Check column names in DataFrame

   Columns are 'X' and 'Y' with uppercase letters.

2. Step 2: Check function usage of column names

   Function uses 'x' (lowercase), which does not match 'X' (uppercase), causing KeyError.

3. Final Answer:

   KeyError because 'x' column does not exist (case sensitive). -> Option A

4. Quick Check:

   Column names are case sensitive [OK]

Hint: Check exact column names and case sensitivity [OK]

Common Mistakes:

• Ignoring case sensitivity in column names
• Using wrong axis parameter
• Assuming no error without testing

5.

You have a DataFrame df with columns 'Name', 'Math', and 'English'. You want to create a new column 'Result' that shows 'Pass' if the average of 'Math' and 'English' is 50 or more, otherwise 'Fail'. Which code correctly uses apply() with axis=1 to do this?

hard

A. df['Result'] = df.apply(lambda row: 'Pass' if row['Math'] + row['English'] >= 50 else 'Fail', axis=1)

B. df['Result'] = df.apply(lambda col: 'Pass' if (col['Math'] + col['English']) / 2 >= 50 else 'Fail', axis=0)

C. df['Result'] = df.apply(lambda row: 'Pass' if (row['Math'] + row['English']) / 2 >= 50 else 'Fail', axis=1)

D. df['Result'] = df.apply(lambda row: 'Pass' if row['Math'] >= 50 and row['English'] >= 50 else 'Fail')

Solution

1. Step 1: Understand the condition for passing

   Pass if average of 'Math' and 'English' >= 50, else Fail.

2. Step 2: Check each option's logic and axis

   df['Result'] = df.apply(lambda row: 'Pass' if (row['Math'] + row['English']) / 2 >= 50 else 'Fail', axis=1) correctly uses axis=1 for row-wise apply and calculates average properly. df['Result'] = df.apply(lambda col: 'Pass' if (col['Math'] + col['English']) / 2 >= 50 else 'Fail', axis=0) uses axis=0 (column-wise), which is wrong. df['Result'] = df.apply(lambda row: 'Pass' if row['Math'] >= 50 and row['English'] >= 50 else 'Fail') misses axis=1 and uses wrong condition. df['Result'] = df.apply(lambda row: 'Pass' if row['Math'] + row['English'] >= 50 else 'Fail', axis=1) checks sum >= 50, which is incorrect for average >= 50.

3. Final Answer:

   df['Result'] = df.apply(lambda row: 'Pass' if (row['Math'] + row['English']) / 2 >= 50 else 'Fail', axis=1) -> Option C

4. Quick Check:

   Use axis=1 and correct average condition [OK]

Hint: Use axis=1 and check average inside lambda [OK]

Common Mistakes:

• Using axis=0 applies function column-wise
• Forgetting to divide sum by 2 for average
• Using wrong logical conditions