Paid revenue per customer
Given `orders` with columns `order_id`, `customer`, `amount`, `status` and `placed`, sum `amount` per `customer` counting only rows whose `status` is `paid`. Return `customer` and `total`, sorted by `customer` ascending. Customers with no paid orders do not appear.
Implement
paid_per_customer(orders: dataframe) → dataframeExamples
in
[{"__df__":[{"amount":120.5,"placed":"2024-01-05","status":"paid","customer":"ada","order_id":1},{"amount":80,"placed":"2024-01-07","status":"refunded","customer":"bo","order_id":2},{"amount":45.25,"placed":"2024-02-11","status":"paid","customer":"ada","order_id":3},{"amount":200,"placed":"2024-02-14","status":"paid","customer":"cy","order_id":4},{"amount":15.75,"placed":"2024-03-02","status":"pending","customer":"bo","order_id":5},{"amount":60,"placed":"2024-03-19","status":"paid","customer":"ada","order_id":6}]}]out[{"total":225.75,"customer":"ada"},{"total":200,"customer":"cy"}]What a strong answer looks like
State your approach and its time/space complexity out loud before you optimize. Handle the edge cases (empty input, duplicates, overflow), and say why you chose this over the brute force. Green tests are the floor, not the grade.
0:00 of about 9 min
solution.py
InputExpectedGot
[{"__df__":[{"amount":120.5,"placed":"2024-01-05","status":"paid","customer":"ada","order_id":1},{"amount":80,"placed":"2024-01-07","status":"refunded","customer":"bo","order_id":2},{"amount":45.25,"placed":"2024-02-11","status":"paid","customer":"ada","order_id":3},{"amount":200,"placed":"2024-02-14","status":"paid","customer":"cy","order_id":4},{"amount":15.75,"placed":"2024-03-02","status":"pending","customer":"bo","order_id":5},{"amount":60,"placed":"2024-03-19","status":"paid","customer":"ada","order_id":6}]}][{"total":225.75,"customer":"ada"},{"total":200,"customer":"cy"}]not run yetsample