Arithmetic operations on columns in Pandas - Mini Project: Build & Apply

Arithmetic operations on columns

📖 Scenario: You work in a small store and keep track of product prices and quantities sold in a table. You want to calculate the total sales amount for each product.

🎯 Goal: Create a pandas DataFrame with product data, set a tax rate, calculate total sales including tax for each product, and display the results.

📋 What You'll Learn

Create a pandas DataFrame with product names, prices, and quantities

Create a variable for the tax rate

Calculate total sales including tax for each product using arithmetic operations on columns

Print the resulting DataFrame with the new total sales column

💡 Why This Matters

🌍 Real World

Stores and businesses often calculate total sales including taxes to understand revenue.

💼 Career

Data analysts and data scientists use pandas to manipulate sales data and perform calculations for reports.

1

Create the product DataFrame

Import pandas as pd and create a DataFrame called products with these exact columns and values: 'Product' with ['Pen', 'Notebook', 'Eraser'], 'Price' with [1.5, 3.0, 0.5], and 'Quantity' with [10, 5, 20].

Pandas

# Import pandas and create the products DataFrame
# Your code here

Hint

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

2

Set the tax rate

Create a variable called tax_rate and set it to 0.1 to represent 10% tax.

Pandas

import pandas as pd
products = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Eraser'],
    'Price': [1.5, 3.0, 0.5],
    'Quantity': [10, 5, 20]
})
# Create the tax_rate variable
# Your code here

Hint

Just assign 0.1 to the variable tax_rate.

3

Calculate total sales including tax

Create a new column in products called Total_Sales that calculates the total sales amount including tax for each product. Use the formula: Price * Quantity * (1 + tax_rate).

Pandas

import pandas as pd
products = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Eraser'],
    'Price': [1.5, 3.0, 0.5],
    'Quantity': [10, 5, 20]
})
tax_rate = 0.1
# Calculate Total_Sales column
# Your code here

Hint

Use arithmetic operations on the DataFrame columns and assign the result to a new column.

4

Display the updated DataFrame

Print the products DataFrame to display the product details along with the new Total_Sales column.

Pandas

import pandas as pd
products = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Eraser'],
    'Price': [1.5, 3.0, 0.5],
    'Quantity': [10, 5, 20]
})
tax_rate = 0.1
products['Total_Sales'] = products['Price'] * products['Quantity'] * (1 + tax_rate)
# Print the products DataFrame
# Your code here

Hint

Use print(products) to show the DataFrame.

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