Pivot monthly revenue by city for the leadership deck

Question

Leadership wants a wide table: one row per city, one column per month, revenue in each cell and zero where a city had no orders that month.

Write `monthly_revenue(df)` that returns a DataFrame indexed by `city`, with month numbers (1, 2, 3, 4) as columns and the summed `amount` as values. Missing c

Accepted Answer

Step 1: `pivot_table` is the right tool here — unlike `pivot`, it aggregates duplicates instead of raising on them, and a city can absolutely have several orders in the same month. Step 2: Passing `df['order_date'].dt.month` directly as `columns` derives the month without polluting the source DataFrame with a helper column. Step 3: `aggfunc='sum'` totals the revenue and `fill_value=0.0` turns absent city/month pairs into zeros rather than `NaN`, which is what a finance deck expects. Step 4: `sort_index()` on both axes makes the output deterministic, so the same data always renders in the same order.

Pivot monthly revenue by city for the leadership deck

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