Staff with team budgets
Given `staff` with columns `name`, `team`, `salary`, `hired` and `manager`, and `budgets` with columns `team` and `budget`, return every employee with their `budget`, using null where the team has no budget row. Return `name` and `budget`, sorted by `name` ascending.
Implement
budget_or_null(staff: dataframe, budgets: 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"}]},{"__df__":[{"team":"eng","budget":900},{"team":"sales","budget":400},{"team":"ops","budget":250}]}]out[{"name":"Ada","budget":900},{"name":"Grace","budget":900},{"name":"Ivan","budget":400},{"name":"Jean","budget":null},{"name":"Kay","budget":400},{"name":"Linus","budget":900},{"name":"Mel","budget":400},{"name":"Nia","budget":null},{"name":"Omar","budget":400}]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"}]},{"__df__":[{"team":"eng","budget":900},{"team":"sales","budget":400},{"team":"ops","budget":250}]}][{"name":"Ada","budget":900},{"name":"Grace","budget":900},{"name":"Ivan","budget":400},{"name":"Jean","budget":null},{"name":"Kay","budget":400},{"name":"Linus","budget":900},{"name":"Mel","budget":400},{"name":"Nia","budget":null},{"name":"Omar","budget":400}]not run yetsample