Arithmetic operations on columns in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to add two columns 'A' and 'B' in the DataFrame and store the result in a new column 'C'.

Pandas

df['C'] = df['A'] [1] df['B']

Drag options to blanks, or click blank then click option'

A-

B/

C+

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using '*' or '/' instead of '+' for addition.

Forgetting to use brackets around column names.

To add two columns in pandas, use the '+' operator between the columns.

2fill in blank

medium

Complete the code to subtract column 'B' from column 'A' and save it in column 'D'.

Pandas

df['D'] = df['A'] [1] df['B']

Drag options to blanks, or click blank then click option'

A-

B+

C*

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using '+' instead of '-' for subtraction.

Mixing up the order of columns.

The '-' operator subtracts values element-wise between two columns.

3fill in blank

hard

Fix the error in the code to multiply columns 'A' and 'B' correctly and store in 'E'.

Pandas

df['E'] = df['A'] [1] df['B']

Drag options to blanks, or click blank then click option'

A+

B-

C/

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using '+' or '-' instead of '*' for multiplication.

Using '/' which divides instead of multiplies.

The '*' operator multiplies values element-wise between two columns.

4fill in blank

hard

Fill both blanks to divide column 'A' by column 'B' and store the result in column 'F'.

Pandas

df['F'] = df[[1]] [2] df['B']

Drag options to blanks, or click blank then click option'

A'A'

B'B'

C/

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using '*' instead of '/' for division.

Using wrong column names in the code.

To divide column 'A' by column 'B', use df['A'] / df['B'].

5fill in blank

hard

Fill all three blanks to create a new column 'G' with the sum of columns 'A' and 'B' multiplied by column 'C'.

Pandas

df['G'] = (df[[1]] [2] df[[3]]) * df['C']

Drag options to blanks, or click blank then click option'

A'A'

B'B'

C+

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using '*' instead of '+' for addition.

Mixing up the order of columns.

The expression adds columns 'A' and 'B' first, then multiplies by column 'C'. Here, the blanks fill the addition part.

Practice

1. What does the expression df['total'] = df['price'] + df['tax'] do in pandas?

easy

A. Multiplies the 'price' and 'tax' columns and stores in 'total'.

B. Deletes the 'price' and 'tax' columns from the DataFrame.

C. Filters rows where 'price' plus 'tax' is greater than zero.

D. Creates a new column 'total' by adding 'price' and 'tax' columns element-wise.

Solution

1. Step 1: Understand the operation on columns

   The expression adds values from 'price' and 'tax' columns for each row.

2. Step 2: Assign result to new column

   The sum is stored in a new column named 'total' in the DataFrame.

3. Final Answer:

   Creates a new column 'total' by adding 'price' and 'tax' columns element-wise. -> Option D

4. Quick Check:

   Adding columns creates new column with sums [OK]

Hint: Adding columns creates a new column with their sums [OK]

Common Mistakes:

• Thinking it multiplies instead of adds
• Assuming it deletes columns
• Confusing assignment with filtering

2. Which of the following is the correct syntax to subtract column 'B' from column 'A' in a pandas DataFrame df and store the result in a new column 'C'?

easy

A. df['C'] = df['A'] - df['B']

B. df['C'] = df['A'] + df['B']

C. df['C'] = df['A'] * df['B']

D. df['C'] = df['A'] / df['B']

Solution

1. Step 1: Identify the subtraction operation

   We want to subtract column 'B' from 'A', so the operator is '-'.

2. Step 2: Assign the result to new column 'C'

   Use df['C'] = df['A'] - df['B'] to store the difference.

3. Final Answer:

   df['C'] = df['A'] - df['B'] -> Option A

4. Quick Check:

   Subtraction uses '-' operator between columns [OK]

Hint: Use '-' operator between columns to subtract [OK]

Common Mistakes:

• Using '+' instead of '-'
• Using '*' or '/' operators incorrectly
• Confusing column names or assignment

3. Given the DataFrame:

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

What is the output?

medium

A. [6, 20]

B. [5, 9]

C. [2, 4]

D. [3, 5]

Solution

1. Step 1: Multiply columns 'A' and 'B' element-wise

   Row 0: 2 * 3 = 6, Row 1: 4 * 5 = 20.

2. Step 2: Print the new column 'C'

   The output shows the values [6, 20] for the two rows.

3. Final Answer:

   [6, 20] -> Option A

4. Quick Check:

   Element-wise multiplication = [6, 20] [OK]

Hint: Multiply columns element-wise to get new column values [OK]

Common Mistakes:

• Adding instead of multiplying
• Printing wrong column
• Confusing list output format

4. What is wrong with this code snippet?

df['D'] = df['A'] + '5'

Assuming 'A' is a numeric column.

medium

A. It will create a new column 'D' with all values as '5'.

B. It will add 5 to each value in column 'A' correctly.

C. It will raise a TypeError because you cannot add a string to a numeric column.

D. It will concatenate the string '5' to each numeric value in 'A'.

Solution

1. Step 1: Check data types involved in addition

   Column 'A' is numeric, but '5' is a string, so adding them causes a type mismatch.

2. Step 2: Understand pandas behavior on mixed types

   Pandas cannot add string to numeric directly, so it raises a TypeError.

3. Final Answer:

   It will raise a TypeError because you cannot add a string to a numeric column. -> Option C

4. Quick Check:

   Adding string to number causes TypeError [OK]

Hint: Check data types before arithmetic to avoid errors [OK]

Common Mistakes:

• Assuming string '5' is converted automatically
• Expecting concatenation with numbers
• Ignoring error messages

5. You have a DataFrame df with columns 'cost' and 'discount'. You want to create a new column 'final_price' that subtracts the discount from cost and then applies a 10% tax on the result. Which code correctly does this?

hard

A. df['final_price'] = df['cost'] * 1.10 - df['discount']

B. df['final_price'] = (df['cost'] - df['discount']) * 1.10

C. df['final_price'] = df['cost'] - (df['discount'] * 1.10)

D. df['final_price'] = df['cost'] - df['discount'] * 1.10

Solution

1. Step 1: Subtract discount from cost

   Calculate the price after discount: df['cost'] - df['discount'].

2. Step 2: Apply 10% tax on discounted price

   Multiply the result by 1.10 to add 10% tax.

3. Step 3: Assign to 'final_price'

   Store the final value in the new column 'final_price'.

4. Final Answer:

   df['final_price'] = (df['cost'] - df['discount']) * 1.10 -> Option B

5. Quick Check:

   Subtract then multiply for tax = correct formula [OK]

Hint: Use parentheses to control order: subtract first, then multiply [OK]

Common Mistakes:

• Missing parentheses causing wrong order
• Multiplying discount instead of discounted price
• Applying tax before subtracting discount