Python for Data Analysis (Preview)

# Data analysis without external libraries
# (Previewing what pandas/numpy do)

# Sample dataset
sales_data = [
    {"month": "Jan", "revenue": 12000, "expenses": 8000},
    {"month": "Feb", "revenue": 15000, "expenses": 9000},
    {"month": "Mar", "revenue": 18000, "expenses": 10000},
    {"month": "Apr", "revenue": 14000, "expenses": 8500},
    {"month": "May", "revenue": 20000, "expenses": 11000},
    {"month": "Jun", "revenue": 22000, "expenses": 12000},
]

# Analysis functions
def analyze_column(data, key):
    values = [row[key] for row in data]
    return {
        "mean": sum(values) / len(values),
        "min": min(values),
        "max": max(values),
        "sum": sum(values),
    }

# Revenue analysis
print("=== Revenue Analysis ===")
rev_stats = analyze_column(sales_data, "revenue")
for stat, val in rev_stats.items():
    print(f"  {stat}: ${val:,.0f}")

# Profit calculation
print("\n=== Profit by Month ===")
for row in sales_data:
    profit = row["revenue"] - row["expenses"]
    margin = profit / row["revenue"] * 100
    bar = "█" * int(margin / 5)
    print(f"  {row['month']}: ${profit:>6,} ({margin:.0f}%) {bar}")

# Growth rate
print("\n=== Month-over-Month Growth ===")
for i in range(1, len(sales_data)):
    prev = sales_data[i-1]["revenue"]
    curr = sales_data[i]["revenue"]
    growth = (curr - prev) / prev * 100
    arrow = "📈" if growth > 0 else "📉"
    print(f"  {sales_data[i]['month']}: {growth:+.1f}% {arrow}")

# With pandas (the real way):
print("\n=== With pandas ===")
print("import pandas as pd")
print("df = pd.DataFrame(sales_data)")
print("print(df.describe())")
print("df['profit'] = df['revenue'] - df['expenses']")
print("df.plot(x='month', y='profit', kind='bar')")