← Computer Programming I

Problem 1: The Ingredient Checker

Problem Statement: You are writing a helper function for a cooking app. Users have a list of ingredients currently in their fridge (“pantry”), and they want to cook a specific recipe that requires a list of items (“recipe”).

Write a function check_ingredients(pantry_list, recipe_list) that compares the two lists and returns two specific lists:

  1. missing_items: Ingredients the user needs to buy (items in the recipe but NOT in the pantry).
  2. available_items: Ingredients the user already has for this recipe (items in both the recipe AND the pantry).

Requirements:

  • The input lists might contain duplicates (e.g., user added “eggs” twice) - handle this using Sets.
  • The input lists might have inconsistent casing (e.g., “Eggs” vs “eggs”). Convert everything to lowercase before comparing.
  • The returned lists must be sorted alphabetically.

Test Case:

pantry = ["Eggs", "flour", "Milk", "eggs", "salt"]
recipe = ["flour", "milk", "Sugar", "Eggs", "butter"]

missing, available = check_ingredients(pantry, recipe)

print(f"Need to buy: {missing}")
print(f"Already have: {available}")

Expected Output:

Need to buy: ['butter', 'sugar']
Already have: ['eggs', 'flour', 'milk']
Problem 2: The Receipt Printer

Problem Statement: You are building a point-of-sale system. You have three lists representing a customer’s shopping cart. The lists are parallel (index 0 in all lists refers to the first item).

  1. items: Names of products.
  2. prices: Base price of each product (float).
  3. quantities: How many of each product the customer is buying (int).

Write a script that iterates through these three lists simultaneously to generate a printed receipt.

Requirements:

  1. Use zip() to group the data.
  2. Use enumerate() to generate line numbers starting at 1.
  3. Calculate the total cost for each line (price * quantity).
  4. Print each line in the format: Line #X: [Item Name] x[Qty] = $[Total]
  5. At the end, print the Grand Total of the entire receipt.

Test Case:

items = ["Apple", "Banana", "Cherry"]
prices = [0.50, 0.30, 0.10]
quantities = [4, 2, 10]

# Write your loop here

Expected Output:

Line #1: Apple x4 = $2.0
Line #2: Banana x2 = $0.6
Line #3: Cherry x10 = $1.0
--------------------
Grand Total: $3.6
Problem 3: The Scholarship Committee

Problem Statement: You are writing a script to automatically select candidates for a university scholarship. You have three lists of data corresponding to the same students:

  1. names: Student names (string).
  2. gpas: Grade Point Averages (float, 0.0 - 4.0).
  3. volunteering_hours: Total hours volunteered (int).

Rules for Eligibility: A student is eligible if they meet EITHER of these criteria:

  • Academic Excellence: GPA is 3.8 or higher (regardless of volunteering).
  • Well-Rounded: GPA is 3.5 or higher AND they have volunteered for more than 50 hours.

Your Task: Write a script that:

  1. Combines the lists into a single structure.
  2. Filters out the ineligible students based on the rules above.
  3. Sorts the eligible students by GPA in descending order. (If two students have the same GPA, sort them by volunteering hours descending).
  4. Returns/Prints a list of strings in the format: "[Name]: [GPA] / [Hours]h".

Test Case:

names = ["Alice", "Bob", "Charlie", "David", "Eve"]
gpas = [3.9, 3.2, 3.6, 3.7, 3.5]
hours = [10, 100, 60, 20, 40]

# Alice: 3.9 (Eligible - Academic)
# Bob: 3.2 (Ineligible)
# Charlie: 3.6 + 60h (Eligible - Well Rounded)
# David: 3.7 + 20h (Ineligible - Needs 3.8 for Academic, or >50h for Well Rounded)
# Eve: 3.5 + 40h (Ineligible - Needs >50h)

Expected Output:

['Alice: 3.9 / 10h', 'Charlie: 3.6 / 60h']
Problem 4: The Hashtag Trend Analyzer

Problem Statement: You are analyzing social media data to find trending topics. You have a list of raw tweet strings (tweets) and a set of “banned” hashtags (banned_tags) that should be ignored (e.g., generic tags like #fyp or #viral).

Your Task: Write a function analyze_trends(tweets, banned_tags) that performs the following pipeline:

  1. Extract & Clean: Parse the tweets to find words starting with #. Convert them to lowercase.
  2. Filter: Remove any hashtags that are in the banned_tags set.
  3. Count: Calculate the frequency of each remaining hashtag.
  4. Sort: Sort the hashtags by count (Highest to Lowest). If counts are equal, sort alphabetically (A-Z).
  5. Output: Return the top 3 trending hashtags as a list of tuples (tag, count).

Constraints:

  • Assume hashtags are separated by spaces in the strings.
  • You must not count the banned tags.

Test Case:

tweets = [
    "I love coding! #Python #coding #Life",
    "Just learned #coding and lists in #PYTHON",
    "#life is good but #coding is better",
    "Ignore this #viral #FYP post"
]
banned = {"#viral", "#fyp"}

# Expected Logic:
# #python: 2
# #coding: 3
# #life: 2
# #viral, #fyp: Ignored

Expected Output:

[('#coding', 3), ('#life', 2), ('#python', 2)]

(Note: ‘#life’ and ‘#python’ both have 2, so ‘#life’ comes first alphabetically)

Problem 5: The Team Builder

Problem Statement: You are organizing a Hackathon. You have a dictionary where the keys are team names and the values are lists of student IDs in that team. teams = {"TeamA": [1, 2, 3], "TeamB": [3, 4, 5], "TeamC": [6, 7]}

However, there is a rule violation: A student cannot belong to more than one team.

Write a script that identifies:

  1. Disqualified Students: A set of IDs of students who are registered in more than one team.
  2. Valid Teams: A list of team names that have zero disqualified students.
  3. Total Unique Participants: The total headcount of distinct student IDs participating.

Requirements:

  • You must find the students appearing in multiple lists dynamically (do not hardcode for just 3 teams).
  • Use sets to efficiently track seen students and duplicates.

Test Case:

teams = {
    "Alpha": [101, 102, 103],
    "Beta":  [103, 104, 105], # 103 is a duplicate (Alpha & Beta)
    "Gamma": [106, 107],      # Clean
    "Delta": [102, 108]       # 102 is a duplicate (Alpha & Delta)
}

Expected Output:

Disqualified Students: {102, 103}
Valid Teams: ['Gamma']
Total Unique Participants: 8
Problem 6: The Inventory Merger

Problem Statement: You own two warehouses, warehouse_A and warehouse_B. Each is represented by a dictionary mapping Product_ID (string) to Quantity (int).

You need to merge these into a single total_inventory dictionary.

  • If a product is in both warehouses, sum the quantities.
  • If a product is only in one, keep its quantity.
  • After merging, create a list of “Critical Stock” items: products with a total quantity less than 10.

Constraint: Try to iterate through the Union of keys from both dictionaries to solve this cleanly.

Test Case:

warehouse_A = {"apple": 5, "banana": 20, "orange": 3}
warehouse_B = {"apple": 2, "grape": 15, "orange": 4}

# Apple: 5 + 2 = 7 (Critical)
# Banana: 20 (OK)
# Orange: 3 + 4 = 7 (Critical)
# Grape: 15 (OK)

Expected Output:

Total Inventory: {'apple': 7, 'banana': 20, 'orange': 7, 'grape': 15}
Critical Stock: ['apple', 'orange']