Multi-key sort
Given `staff` with columns `name`, `team`, `salary`, `hired` and `manager`, return every row sorted by `team` ascending, then `salary` descending, then `name` ascending. Return `name`, `team` and `salary`.
Implement
by_team_then_pay(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","team":"design","salary":140},{"name":"Jean","team":"design","salary":105},{"name":"Grace","team":"eng","salary":150},{"name":"Ada","team":"eng","salary":120},{"name":"Linus","team":"eng","salary":95},{"name":"Omar","team":"sales","salary":125},{"name":"Kay","team":"sales","salary":110},{"name":"Ivan","team":"sales","salary":80},{"name":"Mel","team":"sales","salary":80}]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 8 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","team":"design","salary":140},{"name":"Jean","team":"design","salary":105},{"name":"Grace","team":"eng","salary":150},{"name":"Ada","team":"eng","salary":120},{"name":"Linus","team":"eng","salary":95},{"name":"Omar","team":"sales","salary":125},{"name":"Kay","team":"sales","salary":110},{"name":"Ivan","team":"sales","salary":80},{"name":"Mel","team":"sales","salary":80}]not run yetsample