Problem Set - Python Applications

• 🏆 60 points available
• ✏️ Last updated on 9/16/2025

▶️ First, run the code cell below to import unittest, the module used for the 🧭 Check Your Work boxes and the autograder.

🎯 Problem 1: Compare Nike and McDonald’s Advertising Spending 📊¶

👇 Tasks¶

You are given two list variables named nike_spending and mcdonalds_spending. Each list contains advertising spending (in millions) for fiscal years 2015–2019.

Create the following three variables using one or more for loops:

1. nike_total: The sum of Nike’s ad spending for FY 2015–2019 (in millions).
2. mcdonalds_total: The sum of McDonald’s ad spending for FY 2015–2019 (in millions).
3. nike_higher_spending_years: The number of years Nike’s ad spending surpassed McDonald’s.

💡 Expected Output¶

Nike Total: 7240, McDonald's Total: 7560, Nike spent more on advertising 0 time(s)!

🧭 Check Your Work¶

Run the code cell below to test your solution.

• ✔️ If the code cell runs without errors, you’re good to move on.
• ❌ If the code cell produces an error, review your code and fix any mistakes.

🎯 Problem 2: McDonald’s Employees-to-Locations Ratio 🍔¶

👇 Tasks¶

You are given a list variable named mcdonalds, which contains a list of dictionaries.
Each dictionary represents a fiscal year and contains the following keys:

• "year": The fiscal year
• "num_employees": The number of employees in that year
• "num_locations": The number of locations in that year

👉 Create a new list variable named loc_emp_ratios. Use a for loop to calculate the ratio of employees to locations for fiscal years 2015–2019. During each iteration in the for loop, append the new ratio to the loc_emp_ratios list.

🧭 Example¶

If num_employees is 11500 and num_locations is 1000 for a given year,
the ratio is 11.5 (11500 / 1000).

💡 Sample Output¶

Your printed values will be different — this only demonstrates the format.

[11.0, 10.2, 8.7, 5.6, 5.4]

🧭 Check Your Work¶

Run the code cell below to test your solution.

• ✔️ If the code cell runs without errors, you’re good to move on.
• ❌ If the code cell produces an error, review your code and fix any mistakes.

🎯 Problem 3: Is McDonald’s a fast-food company? 🏬¶

👇 Tasks¶

Is McDonald’s a fast-food company? Well duh, right? McDonald’s is also a brilliant real estate company. We will examine the franchise revenue percentages from real estate.

You are given a list of dictionaries named franchise_revenues. Each dictionary contains the amount of franchise revenues from rents and royalties each year in millions (from fiscal years 2015-2019).

👉 Using a for loop, add a new key-value pair to each dictionary in the franchise_revenues list.

1. Key should be named rents_percent.
2. Value should be the percent of rents out of the total franchise revenues. As an example, if rents is 3000 and royalties is 2000, rents_percent will be 60 (3000 / (3000 + 2000) * 100).

🧭 Example¶

As an example, the first dictionary in franchise_revenues should be {'rents': 5860.6, 'royalties': 2980.7, 'rents_percent': 66.2866...} after you run your code.

💡 Expected Output¶

FY 2015: 66.3% of franchise revenues came from real estate.
FY 2016: 66.1% of franchise revenues came from real estate.
FY 2017: 64.9% of franchise revenues came from real estate.
FY 2018: 64.6% of franchise revenues came from real estate.
FY 2019: 64.6% of franchise revenues came from real estate.

🧭 Check Your Work¶

Run the code cell below to test your solution.

• ✔️ If the code cell runs without errors, you’re good to move on.
• ❌ If the code cell produces an error, review your code and fix any mistakes.

🎯 Problem 4: Find the Company with Highest Revenue per Employee 🛢️¶

👇 Tasks¶

You are given the following three lists containing information about five companies in Oman.

1. company: Names of the company
2. revenue: Revenue of each company (in millions)
3. num_employees: Number of employees

Revenue per Employee is calculated by revenue / num_employees.

👉 Use a for loop to find the company with the highest revenue per employee and update the following two keys in the highest dictionary.

1. company: Name of the company with the highest revenue per employee. (highest["company"])
2. revenue_per_employee: Revenue per employee (highest["revenue_per_employee"])

💡 Expected Output¶

Shell Oman's revenue per employee is the highest at 5.25!

🧭 Check Your Work¶

Run the code cell below to test your solution.

• ✔️ If the code cell runs without errors, you’re good to move on.
• ❌ If the code cell produces an error, review your code and fix any mistakes.

🎯 Problem 5: Sales Tax Calculation 💵¶

👇 Tasks¶

👉 Create a function that calculates the after-tax price given a before-tax price and the sales tax percentage. The function, calculate_total(), should take the following two parameters.

1. price: before-tax price of an item
2. tax: the sales tax percentage in decimal

• 0.08 indicates 8%
• 0.2 indicates 20%

🧭 Example¶

# The code below should print 108
# This after-tax price can be calculated by 100 * (1 + 0.08)
print(calculate_total(100, 0.08))

💡 Expected Output¶

108.0
24.0
10.5

🧭 Check Your Work¶

Run the code cell below to test your solution.

• ✔️ If the code cell runs without errors, you’re good to move on.
• ❌ If the code cell produces an error, review your code and fix any mistakes.

🎯 Problem 6: Mean population of cities in Texas 🏠¶

👇 Tasks¶

You are given a list variable named cities that contains a list of dictionaries. Each dictionary contains the name, state, and population of a city.

👉 Use a for loop to find the mean population of cities in Texas (TX). Store the result in a new variable named texas_mean_pop.

🧭 Hint¶

Use the following two variables with a for loop (this is only a suggestion, we only check your texas_mean_pop result):

1. pop_sum: Increment pop_sum by population if a city’s state is "TX".
2. num_cities Increment num_cities by 1 if a city’s state is "TX".

Then, texas_mean_pop would be pop_sum / num_cities.

💡 Expected Output¶

Mean city population of Texas is 1560856.6666666667.

🧭 Check Your Work¶

Run the code cell below to test your solution.

• ✔️ If the code cell runs without errors, you’re good to move on.
• ❌ If the code cell produces an error, review your code and fix any mistakes.

🎯 Problem 7: Mean population of cities in a given state 🔮¶

👇 Tasks¶

Let’s make your logic from the previous question reusable by creating a function.

👉 Create a function named get_mean_pop that takes the following two inputs (parameters):

1. cities - a list of dictionaries. Each dictionary in the list contains the name, state, and population of a city.
2. state - a string value containing the 2-letter state code (e.g., "CA", "CO").

• The get_mean_pop() should return the mean population of cities in a given state inside the given cities.

🧭 Example 1¶

get_mean_pop(cities_group1, "CA") should return the mean population of cities in California ("Los Angeles" and "San Diego") present in cities_group1. The exact value is 2705630.0.

🧭 Example 2¶

get_mean_pop(cities_group2, "CO") should return the mean population of cities in Colorado ("Colorado Springs" and "Denver") present in cities_group2. The exact value is 597552.5.

💡 Expected Output¶

1560856.6666666667
2705630.0
695506.0
597552.5

🧭 Check Your Work¶

Run the code cell below to test your solution.

• ✔️ If the code cell runs without errors, you’re good to move on.
• ❌ If the code cell produces an error, review your code and fix any mistakes.