Orders placed in one month
Given `orders` with columns `order_id`, `customer`, `amount`, `status` and `placed`, where `placed` is an ISO date string, return the orders placed in the given year and month. Return `order_id` and `placed`, sorted by `order_id` ascending.
Implement
orders_in_month(orders: dataframe, year: int, month: int) → 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}]},2024,2]out[{"placed":"2024-02-11","order_id":3},{"placed":"2024-02-14","order_id":4}]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 10 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}]},2024,2][{"placed":"2024-02-11","order_id":3},{"placed":"2024-02-14","order_id":4}]not run yetsample