Rank pay within each team
Given `staff` with columns `name`, `team`, `salary`, `hired` and `manager`, rank each person by `salary` within their `team`, highest first, using dense ranking so ties share a rank. Return `name`, `team` and `rank` as an integer, sorted by `team` ascending and then `rank` ascending and then `name` ascending.
Implement
pay_rank(staff: dataframe) → dataframeExamples
in
[{"__df__":[{"name":"Ada","team":"eng","hired":"2021-03-01","salary":120,"manager":"Grace"},{"name":"Grace","team":"eng","hired":"2019-06-15","salary":150,"manager":null},{"name":"Linus","team":"eng","hired":"2022-01-10","salary":95,"manager":"Grace"},{"name":"Mel","team":"sales","hired":"2020-11-05","salary":80,"manager":"Kay"},{"name":"Kay","team":"sales","hired":"2018-02-20","salary":110,"manager":null},{"name":"Ivan","team":"sales","hired":"2023-07-01","salary":80,"manager":"Kay"},{"name":"Jean","team":"design","hired":"2021-09-30","salary":105,"manager":null},{"name":"Nia","team":"design","hired":"2022-05-04","salary":140,"manager":"Jean"},{"name":"Omar","team":"sales","hired":"2021-01-08","salary":125,"manager":"Kay"}]}]out[{"name":"Nia","rank":1,"team":"design"},{"name":"Jean","rank":2,"team":"design"},{"name":"Grace","rank":1,"team":"eng"},{"name":"Ada","rank":2,"team":"eng"},{"name":"Linus","rank":3,"team":"eng"},{"name":"Omar","rank":1,"team":"sales"},{"name":"Kay","rank":2,"team":"sales"},{"name":"Ivan","rank":3,"team":"sales"},{"name":"Mel","rank":3,"team":"sales"}]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 11 min
solution.py
InputExpectedGot
[{"__df__":[{"name":"Ada","team":"eng","hired":"2021-03-01","salary":120,"manager":"Grace"},{"name":"Grace","team":"eng","hired":"2019-06-15","salary":150,"manager":null},{"name":"Linus","team":"eng","hired":"2022-01-10","salary":95,"manager":"Grace"},{"name":"Mel","team":"sales","hired":"2020-11-05","salary":80,"manager":"Kay"},{"name":"Kay","team":"sales","hired":"2018-02-20","salary":110,"manager":null},{"name":"Ivan","team":"sales","hired":"2023-07-01","salary":80,"manager":"Kay"},{"name":"Jean","team":"design","hired":"2021-09-30","salary":105,"manager":null},{"name":"Nia","team":"design","hired":"2022-05-04","salary":140,"manager":"Jean"},{"name":"Omar","team":"sales","hired":"2021-01-08","salary":125,"manager":"Kay"}]}][{"name":"Nia","rank":1,"team":"design"},{"name":"Jean","rank":2,"team":"design"},{"name":"Grace","rank":1,"team":"eng"},{"name":"Ada","rank":2,"team":"eng"},{"name":"Linus","rank":3,"team":"eng"},{"name":"Omar","rank":1,"team":"sales"},{"name":"Kay","rank":2,"team":"sales"},{"name":"Ivan","rank":3,"team":"sales"},{"name":"Mel","rank":3,"team":"sales"}]not run yetsample