Teams with multiple members
Given `staff` with columns `name`, `team`, `salary`, `hired` and `manager`, return the `team` values that have more than one member, sorted ascending. Return a list of strings, not a frame.
Implement
crowded_teams(staff: dataframe) → list[str]Examples
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["design","eng","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 9 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"}]}]["design","eng","sales"]not run yetsample