Module 2 · Section 12 of 12

Mini-Project 2: Sector Energy Demand Summary Report

Objective: Load the sector electricity-demand dataset, clean it, and produce a summary table showing peak demand by sector and by year - formatted as an engineering report table.

Brief

Using the sector_demand DataFrame built in Lesson 2.7 (sector, year, month, avg_demand_kw), produce a clean summary table showing peak monthly demand per sector per year, sorted by sector then year, values rounded to one decimal place and columns clearly labeled for a non-technical reader.

Starter Code

# --- Rebuild sector_demand so this cell runs standalone ---
# (identical to the generator in Lesson 2.7)
np.random.seed(7)
sectors = ["Chemical", "Semiconductor", "Data Center", "Manufacturing", "Logistics"]
years = [2021, 2022, 2023]
months = range(1, 13)
sector_base = {"Chemical": 2600, "Semiconductor": 4200, "Data Center": 5200,
               "Manufacturing": 3100, "Logistics": 1400}

demand_rows = []
for sector in sectors:
    base = sector_base[sector]
    for year in years:
        growth = 1 + 0.04 * (year - 2021)
        for month in months:
            seasonal = 1.15 if month in (4, 5, 6) else (0.92 if month in (12, 1) else 1.0)
            demand_kw = base * growth * seasonal * (1 + np.random.normal(0, 0.03))
            demand_rows.append((sector, year, month, round(demand_kw, 1)))

sector_demand = pd.DataFrame(demand_rows, columns=["sector", "year", "month", "avg_demand_kw"])

# --- Starter summary table: peak monthly demand per (sector, year) ---
peak_summary = (
    sector_demand
    .groupby(["sector", "year"])["avg_demand_kw"]   # one group per sector-year
    .max()                                           # highest single month in each group
    .reset_index()                                   # sector, year back to columns
    .rename(columns={"avg_demand_kw": "peak_demand_kw"})
    .sort_values(["sector", "year"])                 # tidy ordering for a report
)
peak_summary["peak_demand_kw"] = peak_summary["peak_demand_kw"].round(1)
peak_summary

Expected shape: 5 sectors x 3 years = 15 rows, 3 columns (sector, year, peak_demand_kw).

Deliverable Checklist

sector_demand is inspected for missing values and duplicates before summarizing (even though this synthetic version is clean, show the check)
Peak demand is correctly computed per sector and year (not collapsed across years)
Table is sorted logically (by sector, then year) and values rounded for presentation
A pivot-table version (sector x year) is also produced for year-over-year comparison
A short written note (2-3 sentences) identifying which sector shows the fastest-growing peak demand

Grading Rubric

Criterion	Points
Data inspected for quality before summarizing	15
Correct groupby/aggregation logic	35
Table formatted clearly (sorted, rounded, labeled)	25
Pivot-table comparison view included	10
Written interpretation	15
Total	100

# Your Mini-Project 2 submission

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