Distance from the team average
Given `staff` with columns `name`, `team`, `salary`, `hired` and `manager`, return how far each person `salary` sits from their own team mean as `diff`, rounded to two decimal places. Return `name` and `diff`, sorted by `name` ascending.
Implement
pay_vs_team(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[{"diff":-1.67,"name":"Ada"},{"diff":28.33,"name":"Grace"},{"diff":-18.75,"name":"Ivan"},{"diff":-17.5,"name":"Jean"},{"diff":11.25,"name":"Kay"},{"diff":-26.67,"name":"Linus"},{"diff":-18.75,"name":"Mel"},{"diff":17.5,"name":"Nia"},{"diff":26.25,"name":"Omar"}]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 12 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"}]}][{"diff":-1.67,"name":"Ada"},{"diff":28.33,"name":"Grace"},{"diff":-18.75,"name":"Ivan"},{"diff":-17.5,"name":"Jean"},{"diff":11.25,"name":"Kay"},{"diff":-26.67,"name":"Linus"},{"diff":-18.75,"name":"Mel"},{"diff":17.5,"name":"Nia"},{"diff":26.25,"name":"Omar"}]not run yetsample