Homework

3.4.3. Homework#

  • Use this assignment to practice the chapter’s concepts and coding skills in runnable Python code.

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  • Write and run your code directly in the coding cells.

  • Multiple submissions are allowed. The highest score is kept.

1. Array Shape

A store manager tracks sales for three departments across four weeks. Treating those values as a 3 by 4 array is a reasonable way to describe the data shape.

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### Solution
# True
# Rows and columns describe the dimensions of an array-like dataset.

2. Elementwise Operations

A pricing analyst wants to add the same fee to every item price. This is the kind of repeated numeric work that vectorized array operations are designed to express.

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### Solution
# True
# Vectorized operations apply one calculation across many values.

3. Index Positions

In Python-style array indexing, position 1 always refers to the first value in a sequence.

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### Solution
# False
# Python indexing starts at 0, so position 1 is the second value.

4. Boolean Masks

A Boolean mask can represent which stores met a sales target before selecting only those stores.

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### Solution
# True
# Masks mark which values satisfy a condition.

5. Random Seeds

Setting a random seed guarantees that every possible future simulation will produce different values each time it runs.

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### Solution
# False
# A seed is used for reproducibility, not guaranteed difference.

### 6. Revenue Array Totals
# Treat sales as a two-row array stored with nested lists.
# Print exactly:
# Week 1 total: 42
# Week 2 total: 47
sales = [[20, 22], [21, 26]]

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### Solution
sales = [[20, 22], [21, 26]]

week_1_total = sum(sales[0])
week_2_total = sum(sales[1])

print("Week 1 total:", week_1_total)
print("Week 2 total:", week_2_total)

Week 1 total: 42
Week 2 total: 47

### 7. Elementwise Discounts
# Subtract each discount from the matching price.
# Store the result in final_prices.
# Print exactly:
# Final prices: [90, 135, 180]
prices = [100, 150, 200]
discounts = [10, 15, 20]

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### Solution
prices = [100, 150, 200]
discounts = [10, 15, 20]

final_prices = []
index = 0
for price in prices:
    final_prices.append(price - discounts[index])
    index = index + 1

print("Final prices:", final_prices)

Final prices: [90, 135, 180]

### 8. Slice the Last Values
# Select the last three demand values.
# Print exactly:
# Recent demand: [32, 35, 37]
demand = [28, 30, 32, 35, 37]

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### Solution
demand = [28, 30, 32, 35, 37]

recent_demand = demand[2:]
print("Recent demand:", recent_demand)

Recent demand: [32, 35, 37]

### 9. Compare to Target
# Build a list showing whether each store met the target.
# Print exactly:
# Met target: [False, True, True, False]
sales = [80, 105, 110, 95]
target = 100

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### Solution
sales = [80, 105, 110, 95]
target = 100

met_target = []
for sale in sales:
    met_target.append(sale >= target)

print("Met target:", met_target)

Met target: [False, True, True, False]

### 10. Simulation Average
# Treat the list as saved simulation results and calculate its average.
# Print exactly:
# Average simulated demand: 51.0
simulated_demand = [48, 52, 55, 49]

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### Solution
simulated_demand = [48, 52, 55, 49]

average_simulated_demand = sum(simulated_demand) / len(simulated_demand)
print("Average simulated demand:", average_simulated_demand)

Average simulated demand: 51.0