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# B4. Assign a SINGLE expression that creates a tuple of the squares of even numbers #     from `data = [1, 2, 3, 4, 5, 6]`.data = [1, 2, 3, 4, 5, 6]B4 = … # your answer here

# Shared data for all SECTION B questionss = pd.Series([10.8, 20.5, 30.2, 40.4], index=[“a”, “b”, “c”, “d”])t = pd.Series([20.5, 5.4, 10.8, 15.6], index=[“a”, “b”, “c”, “d”])df = pd.DataFrame({    “product”: [“A”, “B”, “A”, “C”],    “units”:   [10,   3,   8,   5],    “price”:   [2.5,  5.0, 3.0, 4.5],    “region”:  [“West”,”West”,”East”,”East”]}, index=[0,1,2,3]) # B7. Assign a SINGLE expression that returns a DataFrame containing only#     the columns ‘product’ and ‘price’ using .iloc[] integer-based indexing.B7 = … # your answer here

# Shared data for all SECTION B questionss = pd.Series([10.8, 20.5, 30.2, 40.4], index=[“a”, “b”, “c”, “d”])t = pd.Series([20.5, 5.4, 10.8, 15.6], index=[“a”, “b”, “c”, “d”])df = pd.DataFrame({    “product”: [“A”, “B”, “A”, “C”],    “units”:   [10,   3,   8,   5],    “price”:   [2.5,  5.0, 3.0, 4.5],    “region”:  [“West”,”West”,”East”,”East”]}, index=[0,1,2,3]) # B8. Assign a SINGLE expression that returns the total number of units across all rows (a single integer).B8 = … # your answer here  

# B7. Using NumPy, assign a SINGLE expression that creates a 1D float array [1.0, 2.0, 3.0].B7 = … # your answer here

# Q2. What does this dict comprehension produce?#     `src = [(“a”,1), (“b”,2)]; d = {k:v for (k,v) in src}`#     A) [(“a”,1), (“b”,2)]#     B) {“a”:1, “b”:2}#     C) {(“a”,1), (“b”,2)}#     D) (“a”:1, “b”:2)

# 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 `s`?#     A) s.counts()#     B) s.value_counts()#     C) s.hist()#     D) s.describe()

# 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.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[‘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)