apply() on rows (axis=1) in Pandas - Mini Project: Build & Apply

Using apply() on rows with axis=1 in pandas

📖 Scenario: You work in a small bookstore. You have a table of books with their prices and quantities. You want to find the total value of each book's stock.

🎯 Goal: Create a pandas DataFrame with book data, then use apply() on rows (axis=1) to calculate the total value for each book.

📋 What You'll Learn

Create a pandas DataFrame called books with columns Title, Price, and Quantity using the exact data provided.

Create a function called calculate_value that takes a row and returns the product of Price and Quantity.

Use apply() on books with axis=1 and the calculate_value function to create a new column TotalValue.

Print the books DataFrame to show the new column.

💡 Why This Matters

🌍 Real World

Calculating total stock value helps bookstores and shops understand their inventory worth quickly.

💼 Career

Data analysts and data scientists often use apply() on rows to create new features or calculate values based on multiple columns.

1

Create the books DataFrame

Create a pandas DataFrame called books with these exact columns and data: Title with values 'Book A', 'Book B', 'Book C'; Price with values 10, 15, 7; and Quantity with values 5, 3, 10.

Pandas

import pandas as pd
# Create the books DataFrame with Title, Price, and Quantity columns
# Your code here

Hint

Use pd.DataFrame with a dictionary where keys are column names and values are lists of data.

2

Create the calculate_value function

Create a function called calculate_value that takes one argument row and returns the product of row['Price'] and row['Quantity'].

Pandas

import pandas as pd

books = pd.DataFrame({
    'Title': ['Book A', 'Book B', 'Book C'],
    'Price': [10, 15, 7],
    'Quantity': [5, 3, 10]
})

# Define the calculate_value function
# Your code here

Hint

Define a function with def that multiplies row['Price'] and row['Quantity'].

3

Apply the function to each row

Use apply() on the books DataFrame with axis=1 and the calculate_value function to create a new column called TotalValue.

Pandas

import pandas as pd

books = pd.DataFrame({
    'Title': ['Book A', 'Book B', 'Book C'],
    'Price': [10, 15, 7],
    'Quantity': [5, 3, 10]
})

def calculate_value(row):
    return row['Price'] * row['Quantity']

# Use apply() with axis=1 to create TotalValue column
# Your code here

Hint

Use books['TotalValue'] = books.apply(calculate_value, axis=1) to add the new column.

4

Print the books DataFrame

Print the books DataFrame to show the new TotalValue column.

Pandas

import pandas as pd

books = pd.DataFrame({
    'Title': ['Book A', 'Book B', 'Book C'],
    'Price': [10, 15, 7],
    'Quantity': [5, 3, 10]
})

def calculate_value(row):
    return row['Price'] * row['Quantity']

books['TotalValue'] = books.apply(calculate_value, axis=1)

# Print the books DataFrame
# Your code here

Hint

Use print(books) to display the DataFrame with the new column.

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