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# Q8. Which call correctly creates a modern, reproducible Nu…

# Q8. Which call correctly creates a modern, reproducible NumPy Generator seeded with 404?# A) rng = np.random.RandomState(404)# B) rng = np.random.seed(404)# C) rng = np.random.default_rng(404)# D) rng = np.random.Generator(404)

# Q9. Which produces a frequency count of values in Series `…

# Q9. Which produces a frequency count of values in Series `s`?# A) s.counts()# B) s.value_counts()# C) s.hist()# D) s.describe()

# B4. Given `s = “ABcd”`, assign a SINGLE expression that re…

# B4. Given `s = “ABcd”`, assign a SINGLE expression that returns the lowercase version.s = “ABcd”B4 = … # your answer here

# Q10. Create the date October 31, 2025:# A) datetime.da…

# Q10. Create the date October 31, 2025:# A) datetime.date(2025, 10, 31)# B) datetime(2025, 10, 31)# C) date(10, 31, 2025)# D) datetime.date(“2025-10-31”)

# Q3. Which expression correctly filters rows where df[‘unit…

# Q3. Which expression correctly filters rows where df[‘units’] > 10?# A) df[df[‘units’] > 10]# B) df.where(df[‘units’] > 10)# C) df.loc[:, df[‘units’] > 10]# D) df.filter(df[‘units’] > 10)

“””Task (30 pts total)——————-Given a mock sales…

“””Task (30 pts total)——————-Given a mock sales DataFrame `SALES`, complete the CSV read/load task with all column labels retained.””” SALES = pd.DataFrame( { “date”: [“2025-09-28”, “2025-09-28”, “2025-09-29”, “2025-09-30”, “2025-10-01”, “2025-10-01”, “2025-10-02”, “2025-10-02”], “customer”: [“Alice”, “Bob”, “Alice”, “Dana”, “Eli”, “Bob”, “Faye”, “Charlie”], “item”: [“coffee”, “tea”, “cake”, “coffee”, “sandwich”, “coffee”, “tea”, “cake”], “price”: [3.50, 2.25, 4.00, 3.50, 6.75, 3.50, 2.25, 4.00], “quantity”: [2, 3, 1, 4, 2, 1, 5, 2], “store”: [“S1”, “S1”, “S2”, “S1”, “S2”, “S1”, “S2”, “S2”], “region”: [“West”, “West”, “West”, “West”, “East”, “West”, “East”, “East”], }) CSV_PATH = “CIS_404_Exam_Exam3_sales.csv” # (1) Save the DataFrame to CSV: 10pts”””Save the given DataFrame `SALES` to a CSV file located at `CSV_PATH`.Your output file must: – ONLY include all column labels, – use semicolon (;) as the field separator, – and otherwise follow the default argument options.””” # TODO: implementraise NotImplementedError # (2) Read the DataFrame back from CSV: 10pts”””Read the CSV file located at `CSV_PATH` into a new DataFrame named `SALES_readback`.Your read statement must: – correctly read the file that was just saved, – use semicolon (;) as the field separator, – and otherwise follow the default argument options.After reading, you should ensure that the columns in `SALES_readback`match the columns in the original `SALES` DataFrame.””” # TODO: implementraise NotImplementedError # testing: # (please comment in)# print(“Read back from CIS_404_Exam_Exam3_sales.csv:\n”, SALES_readback, “\n”) # assert list(SALES_readback.columns) == list(SALES.columns), “Column labels differ.” # (3) Single .loc / .iloc expression: 10pts”””Use a single expression with .loc or .iloc to SELECT FROM `SALES_readback`: – rows 2 through 5 (inclusive of 2 and exclusive of 6) – the columns ‘customer’, ‘item’, ‘price’ in that order.Assign the result to a variable named `SALES_subset`.””” # TODO: implementraise NotImplementedError # testing: # (please comment in)# print(“Subset:\n”, SALES_subset, “\n”)

“””Task (30 pts total)——————-Create a simple bus…

“””Task (30 pts total)——————-Create a simple business object that computes total revenue for a customer order. Part 1 (18pts): Constructor (__init__): Write a class Order with: – __init__(self, order_id: str, customer: str, item_tuples: list) * Store the arguments `order_id` and `customer` as instance attributes. * item_tuples is provided as a list of tuples, each in the form: (prod_code, price, quantity) where `prod_code` is a string, `price` is a float, and `quantity` is an integer. Here “prod_code” is a short product identifier (e.g., “P1001”). * Inside __init__, convert this list of tuples into a list of dictionaries, {“prod_code”: …, “price”: …, “quantity”: …}, and store it in self.items. Part 2 (6pts): Method (total_revenue): Implement total_revenue(self) -> float * Calls safe_line_total(item, 0.15) for each item to apply a 15% discount. * Returns the total after discount. Part 3 (6pts): Function (safe_line_total): Define safe_line_total(item: dict, discount: float) -> float * Computes: price × quantity × (1 – discount) * discount is a decimal reduction (e.g., 0.10 means 10% off)””” class Order: “””Simple business object representing a customer’s order.””” # (1) Constructor: 5pt + 5pts + 8pts = 18pts def __init__(self, order_id: str, customer: str, item_tuples: list): self.order_id = … # TODO: implement self.customer = … # TODO: implement # convert list of tuples to list of dicts (use tuple unpacking for clarity) self.items = … # TODO: implement raise NotImplementedError # (2) Method: 6pts def total_revenue(self) -> float: “””Return total revenue after 15% discount on all items.””” # TODO: implement raise NotImplementedError # (3) Helper Function: 6ptsdef safe_line_total(item: dict, discount: float) -> float: “””Compute line revenue for one item with a given discount rate.””” # TODO: implement raise NotImplementedError # testing:# sample input dataitem_tuples = [ (“P1001”, 10.0, 2), (“P1002”, 5.0, 3), (“P1003″, 2.5, 4),] # instantiate the Orderorder = Order(order_id=”O123″, customer=”Yankun”, item_tuples=item_tuples) # print testing infoprint(“testing:”)print(” order_id =”, order.order_id) # O123print(” customer =”, order.customer) # Yankunprint(” items =”, order.items) # [{‘prod_code’: ‘P1001’, ‘price’: 10.0, ‘quantity’: 2}, # {‘prod_code’: ‘P1002’, ‘price’: 5.0, ‘quantity’: 3}, # {‘prod_code’: ‘P1003’, ‘price’: 2.5, ‘quantity’: 4}] total = order.total_revenue()print(“\ncomputed total revenue (after 15% discount):”, total)# expected:# items: (10*2) + (5*3) + (2.5*4) = 20 + 15 + 10 = 45# after 15% discount → 45 * 0.85 = 38.25# computed total revenue (after 15% discount): 38.25