# Create a new column for revenue
df["revenue"] = df["quantity"] * df["price"]

# Filter for orders from 2023 onwards
df_filtered = df[df["order_date"] >= "2023-01-01"]

# Convert order_date to datetime and extract month
df_filtered["month"] = pd.to_datetime(df_filtered["order_date"]).dt.to_period("M")

# Group by category and month, then sum revenue
grouped = df_filtered.groupby(["category", "month"])["revenue"].sum()

# Convert Series back to DataFrame
result = grouped.reset_index()

# Sort by revenue descending
result = result.sort_values(by="revenue", ascending=False)
print(result)