result = (
    pd.read_csv("sales.csv")  # Load data
    
    .assign(
        # Create revenue
        revenue=lambda df: df["quantity"] * df["price"],
        
        # Convert and extract time features
        order_date=lambda df: pd.to_datetime(df["order_date"]),
        month=lambda df: df["order_date"].dt.to_period("M")
    )
    
    # Apply custom transformation
    .pipe(filter_high_revenue)
    
    # Filter by date
    .loc[lambda df: df["order_date"] >= "2023-01-01"]
    
    # Aggregate results
    .groupby(["category", "month"], as_index=False)["revenue"]
    .sum()
    
    # Sort output
    .sort_values(by="revenue", ascending=False)
)