Split SKU into letter number
Given `products` with columns `sku`, `category`, `price` and `stock`, where every `sku` looks like `A-1`, split it on the hyphen into `letter` and `number`, keeping `number` as a string. Return `sku`, `letter` and `number`, sorted by `sku` ascending.
Implement
split_sku(products: dataframe) → dataframeExamples
in
[{"__df__":[{"sku":"A-1","price":9.99,"stock":12,"category":"tools"},{"sku":"B-2","price":24.5,"stock":0,"category":"tools"},{"sku":"C-3","price":15,"stock":7,"category":"garden"},{"sku":"D-4","price":15,"stock":3,"category":"garden"},{"sku":"E-5","price":42,"stock":0,"category":"kitchen"}]}]out[{"sku":"A-1","letter":"A","number":"1"},{"sku":"B-2","letter":"B","number":"2"},{"sku":"C-3","letter":"C","number":"3"},{"sku":"D-4","letter":"D","number":"4"},{"sku":"E-5","letter":"E","number":"5"}]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__":[{"sku":"A-1","price":9.99,"stock":12,"category":"tools"},{"sku":"B-2","price":24.5,"stock":0,"category":"tools"},{"sku":"C-3","price":15,"stock":7,"category":"garden"},{"sku":"D-4","price":15,"stock":3,"category":"garden"},{"sku":"E-5","price":42,"stock":0,"category":"kitchen"}]}][{"sku":"A-1","letter":"A","number":"1"},{"sku":"B-2","letter":"B","number":"2"},{"sku":"C-3","letter":"C","number":"3"},{"sku":"D-4","letter":"D","number":"4"},{"sku":"E-5","letter":"E","number":"5"}]not run yetsample