6.4.2. 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: Revenue Trend
# A store manager wants to see how revenue moved across the year.
#   1. Create a figure and axes with plt.subplots().
#   2. Plot quarters vs revenue as a line with circle markers (marker='o').
#   3. Set the title "Quarterly Revenue", the x-label "Quarter",
#      and the y-label "Revenue ($K)".
import matplotlib.pyplot as plt

quarters = ["Q1", "Q2", "Q3", "Q4"]
revenue = [120, 135, 128, 150]
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### Solution
import matplotlib.pyplot as plt

quarters = ["Q1", "Q2", "Q3", "Q4"]
revenue = [120, 135, 128, 150]

fig, ax = plt.subplots()
ax.plot(quarters, revenue, marker='o')
ax.set_title("Quarterly Revenue")
ax.set_xlabel("Quarter")
ax.set_ylabel("Revenue ($K)")
plt.show()

### Question 2: Actual vs Forecast
# Finance wants actual revenue compared with the forecast on one chart.
#   1. Create a figure and axes with plt.subplots().
#   2. Plot actual as a solid line with the label "Actual".
#   3. Plot forecast as a dashed gray line (ls='--', color='gray')
#      with the label "Forecast".
#   4. Set the title "Actual vs Forecast" and show the legend.
import matplotlib.pyplot as plt

quarters = ["Q1", "Q2", "Q3", "Q4"]
actual = [120, 135, 128, 150]
forecast = [118, 130, 140, 145]
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### Solution
import matplotlib.pyplot as plt

quarters = ["Q1", "Q2", "Q3", "Q4"]
actual = [120, 135, 128, 150]
forecast = [118, 130, 140, 145]

fig, ax = plt.subplots()
ax.plot(quarters, actual, label="Actual")
ax.plot(quarters, forecast, ls='--', color='gray', label="Forecast")
ax.set_title("Actual vs Forecast")
ax.legend()
plt.show()

### Question 3: Regional Dashboard
# A regional director wants two views side by side.
#   1. Create one row of two axes: fig, axes = plt.subplots(1, 2, figsize=(10, 4)).
#   2. On axes[0], draw a bar chart of regions vs region_revenue
#      with the title "Revenue by Region".
#   3. On axes[1], draw a histogram of order_values with bins=5
#      and the title "Order Values".
#   4. Add the figure title "Regional Dashboard" with fig.suptitle().
import matplotlib.pyplot as plt

regions = ["North", "South", "East", "West"]
region_revenue = [150, 120, 180, 90]
order_values = [45, 52, 38, 61, 75, 49, 58, 66, 40, 55, 70, 48]
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### Solution
import matplotlib.pyplot as plt

regions = ["North", "South", "East", "West"]
region_revenue = [150, 120, 180, 90]
order_values = [45, 52, 38, 61, 75, 49, 58, 66, 40, 55, 70, 48]

fig, axes = plt.subplots(1, 2, figsize=(10, 4))
axes[0].bar(regions, region_revenue)
axes[0].set_title("Revenue by Region")
axes[1].hist(order_values, bins=5)
axes[1].set_title("Order Values")
fig.suptitle("Regional Dashboard")
plt.show()

### Question 4: Ad Spend and Sales
# Marketing asks whether higher ad spend goes with higher sales.
#   1. Create a figure and axes with plt.subplots().
#   2. Draw a scatter plot of ad_spend (x) vs sales (y).
#   3. Label the axes "Ad Spend ($K)" and "Sales ($K)".
#   4. Start both axes at zero: x from 0 to 50 and y from 0 to 250
#      using set_xlim() and set_ylim().
import matplotlib.pyplot as plt

ad_spend = [5, 10, 15, 20, 25, 30, 35, 40]
sales = [60, 85, 110, 120, 150, 165, 190, 205]
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### Solution
import matplotlib.pyplot as plt

ad_spend = [5, 10, 15, 20, 25, 30, 35, 40]
sales = [60, 85, 110, 120, 150, 165, 190, 205]

fig, ax = plt.subplots()
ax.scatter(ad_spend, sales)
ax.set_xlabel("Ad Spend ($K)")
ax.set_ylabel("Sales ($K)")
ax.set_xlim(0, 50)
ax.set_ylim(0, 250)
plt.show()

### Question 5: Report-Ready Figure
# The margin chart goes into a printed quarterly report.
#   1. Create a figure and axes with plt.subplots(figsize=(8, 4)).
#   2. Draw a bar chart of regions vs margin_pct.
#   3. Set the title "Q2 Margin by Region" and the y-label "Margin (%)".
#   4. Save the figure as "regional_margin_q2.png" with dpi=200.
import matplotlib.pyplot as plt

regions = ["North", "South", "East", "West"]
margin_pct = [12.5, 9.8, 14.2, 7.6]
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### Solution
import matplotlib.pyplot as plt

regions = ["North", "South", "East", "West"]
margin_pct = [12.5, 9.8, 14.2, 7.6]

fig, ax = plt.subplots(figsize=(8, 4))
ax.bar(regions, margin_pct)
ax.set_title("Q2 Margin by Region")
ax.set_ylabel("Margin (%)")
fig.savefig("regional_margin_q2.png", dpi=200)
plt.show()

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