Lab 03: E-Commerce Economic Impact Report¶

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick

df = pd.read_csv("ecommerce_data.csv")

print("=== E-Commerce Economic Impact Analysis ===\n")

# ── 1. Growth metrics ─────────────────────────────────────────────
df["yoy_growth_pct"] = df["us_ecommerce_sales_bn"].pct_change() * 100
df["ecommerce_vs_retail_ratio"] = df["us_ecommerce_sales_bn"] / df["total_retail_sales_bn"]

print("Year-over-Year E-Commerce Growth:")
print(df[["year","us_ecommerce_sales_bn","yoy_growth_pct","ecommerce_share_pct"]].to_string(index=False))

# ── 2. CAGR calculation ───────────────────────────────────────────
years = df["year"].max() - df["year"].min()
start_sales = df.loc[df["year"]==df["year"].min(), "us_ecommerce_sales_bn"].values[0]
end_sales   = df.loc[df["year"]==df["year"].max(), "us_ecommerce_sales_bn"].values[0]
cagr = ((end_sales / start_sales) ** (1/years) - 1) * 100
print(f"\n10-Year CAGR: {cagr:.1f}%")
print(f"Total growth: ${start_sales:.1f}B → ${end_sales:.1f}B (+{(end_sales/start_sales-1)*100:.0f}%)")

# ── 3. Labor market correlation ───────────────────────────────────
corr_retail = df["us_ecommerce_sales_bn"].corr(df["retail_jobs_thousands"])
corr_warehouse = df["us_ecommerce_sales_bn"].corr(df["warehouse_jobs_thousands"])
print(f"\nCorrelation: E-commerce growth vs retail jobs:    {corr_retail:.3f}")
print(f"Correlation: E-commerce growth vs warehouse jobs: {corr_warehouse:.3f}")

# ── 4. Charts ─────────────────────────────────────────────────────
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
fig.suptitle("U.S. E-Commerce Economic Impact Analysis", fontsize=14, fontweight="bold")

# Chart 1: E-commerce sales vs total retail
ax1 = axes[0, 0]
ax1.bar(df["year"], df["total_retail_sales_bn"], label="Total Retail", color="#c8c8c8")
ax1.bar(df["year"], df["us_ecommerce_sales_bn"], label="E-Commerce", color="#0f3460")
ax1.set_title("E-Commerce vs Total Retail Sales ($B)")
ax1.set_xlabel("Year"); ax1.set_ylabel("Sales ($B)")
ax1.legend()

# Chart 2: E-commerce market share
ax2 = axes[0, 1]
ax2.plot(df["year"], df["ecommerce_share_pct"], marker="o", color="#0f3460", linewidth=2)
ax2.fill_between(df["year"], df["ecommerce_share_pct"], alpha=0.2, color="#0f3460")
ax2.set_title("E-Commerce Share of Total Retail (%)")
ax2.set_xlabel("Year"); ax2.set_ylabel("Share (%)")
ax2.yaxis.set_major_formatter(mtick.PercentFormatter())

# Chart 3: Job displacement vs creation
ax3 = axes[1, 0]
ax3.plot(df["year"], df["retail_jobs_thousands"], marker="s", label="Retail Jobs", color="#c62828")
ax3.plot(df["year"], df["warehouse_jobs_thousands"], marker="^", label="Warehouse Jobs", color="#2e7d32")
ax3.set_title("Retail vs Warehouse Employment (Thousands)")
ax3.set_xlabel("Year"); ax3.set_ylabel("Jobs (Thousands)")
ax3.legend()

# Chart 4: Online seller growth
ax4 = axes[1, 1]
ax4.bar(df["year"], df["online_sellers_millions"], color="#c8a951")
ax4.set_title("Online Sellers (Millions)")
ax4.set_xlabel("Year"); ax4.set_ylabel("Sellers (Millions)")

plt.tight_layout()
plt.savefig("ecommerce_impact_charts.png", dpi=150)
print("\nCharts saved: ecommerce_impact_charts.png")