# Step 1: Sort
df = df.sort_values(['region', 'order_date'])

# Step 2: Rolling average per region
df['rolling_avg'] = (
    df.groupby('region')['sales']
    .transform(lambda x: x.rolling(window=7).mean())
)

# Step 3: Flag high-value orders
df['high_value'] = df['sales'] > df['rolling_avg'] * 1.5

# Step 4: Monthly aggregation
df['month'] = pd.to_datetime(df['order_date']).dt.to_period('M')
monthly_summary = df.groupby(['region', 'month'])['sales'].sum()