My first instinct was to zip the two lists together and compute averages, which worked fine.
First, clarify the data structure: are the lists parallel (same index corresponds to same company) or is there a mapping? Then, compute the average price per company by summing daily prices and dividing by the number of days, and finally select the top 3 using a min-heap or sorting. Discuss time and space complexity, and consider edge cases like ties or fewer than 3 companies.
Pro tip: Mention that if the lists are large and you only need the top 3, a min-heap of size 3 gives O(n) time instead of O(n log n) sorting, showing you optimize for the specific constraint.
Ask whether the two lists are parallel (index-aligned) or if there's a mapping. Confirm that each company has the same number of daily prices and that prices are numeric.
Iterate through the lists, summing prices per company and counting days. Then divide sum by count to get the average. Use a hash map to store company -> (sum, count).
Use a min-heap of size 3 to track the top 3 averages efficiently, or sort the averages if simplicity is preferred. Handle ties by any consistent rule.
State time complexity: O(n) with heap, O(n log n) with sorting. Space: O(k) for heap or O(n) for map. Discuss edge cases: fewer than 3 companies, empty lists, negative prices, ties.
Implement the solution with clear variable names and modular functions. Walk through a small example to verify correctness.
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