Pivot with gaps filled
Given `marks` with columns `student`, `subject` and `score`, build a table of the MEAN score for every student and subject pair, using 0 where a pair is missing. Return `student`, `math` and `science`, sorted by `student` ascending.
Implement
mean_grid(marks: dataframe) → dataframeExamples
in
[{"__df__":[{"score":88,"student":"ana","subject":"math"},{"score":92,"student":"ana","subject":"science"},{"score":75,"student":"ben","subject":"math"},{"score":64,"student":"ben","subject":"science"},{"score":95,"student":"cara","subject":"math"},{"score":81,"student":"cara","subject":"science"},{"score":100,"student":"ana","subject":"math"}]}]out[{"math":94,"science":92,"student":"ana"},{"math":75,"science":64,"student":"ben"},{"math":95,"science":81,"student":"cara"}]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__":[{"score":88,"student":"ana","subject":"math"},{"score":92,"student":"ana","subject":"science"},{"score":75,"student":"ben","subject":"math"},{"score":64,"student":"ben","subject":"science"},{"score":95,"student":"cara","subject":"math"},{"score":81,"student":"cara","subject":"science"},{"score":100,"student":"ana","subject":"math"}]}][{"math":94,"science":92,"student":"ana"},{"math":75,"science":64,"student":"ben"},{"math":95,"science":81,"student":"cara"}]not run yetsample