Lab

3.4.2. Lab#

  • 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.

### Question 1: Array Shape and Total
# Represent the weekly sales table as a nested list.
# Calculate rows, columns, and the total number of values.
# Print exactly:
# Rows: 3
# Columns: 4
# Values: 12
sales = [[18, 21, 19, 24], [15, 17, 16, 20], [22, 25, 23, 28]]
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### Solution
sales = [[18, 21, 19, 24], [15, 17, 16, 20], [22, 25, 23, 28]]

rows = len(sales)
columns = len(sales[0])
values = rows * columns

print("Rows:", rows)
print("Columns:", columns)
print("Values:", values)

Rows: 3
Columns: 4
Values: 12

### Question 2: Vectorized Price Change
# Add the same price increase to each base price.
# Store the adjusted prices in a list named adjusted_prices.
# Print exactly:
# Adjusted prices: [13, 17, 21, 25]
base_prices = [10, 14, 18, 22]
increase = 3
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### Solution
base_prices = [10, 14, 18, 22]
increase = 3

adjusted_prices = []
for price in base_prices:
    adjusted_prices.append(price + increase)

print("Adjusted prices:", adjusted_prices)

Adjusted prices: [13, 17, 21, 25]

### Question 3: Boolean Mask Selection
# Build a list of sales values that meet or exceed the target.
# Store the result in selected_sales.
# Print exactly:
# Selected sales: [120, 135, 150]
sales = [95, 120, 88, 135, 150]
target = 100
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### Solution
sales = [95, 120, 88, 135, 150]
target = 100

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

print("Selected sales:", selected_sales)

Selected sales: [120, 135, 150]

### Question 4: Universal Function Style
# Square each demand error and total the squared errors.
# Store the squared values in squared_errors.
# Print exactly:
# Squared errors: [4, 1, 9, 16]
# Total squared error: 30
errors = [2, -1, 3, -4]
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### Solution
errors = [2, -1, 3, -4]

squared_errors = []
for error in errors:
    squared_errors.append(error ** 2)

total_squared_error = sum(squared_errors)
print("Squared errors:", squared_errors)
print("Total squared error:", total_squared_error)

Squared errors: [4, 1, 9, 16]
Total squared error: 30

### Question 5: Reproducible Sample Summary
# Treat the list below as a saved random sample.
# Calculate the sample size and sample mean.
# Print exactly:
# Sample size: 5
# Sample mean: 42.0
sample = [38, 41, 45, 39, 47]
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### Solution
sample = [38, 41, 45, 39, 47]

sample_size = len(sample)
sample_mean = sum(sample) / sample_size

print("Sample size:", sample_size)
print("Sample mean:", sample_mean)

Sample size: 5
Sample mean: 42.0