The core counting part is straightforward, just a frequency map over the first element of each ballot.
Clarify the input format and edge cases, then propose a single-pass hash map solution that counts first choices and tracks the best candidate using a tie-breaking rule. Walk through the algorithm, analyze time and space complexity, and test with examples including ties and empty ballots.
Pro tip: Explicitly handle edge cases like empty ballot list or ballots with no candidates, and mention that the tie-breaking rule can be integrated into the comparison logic to avoid a separate sorting step.
Ask about input format, empty ballots, and whether candidate names are case-sensitive. Confirm that ties are broken by lexicographical order.
Use a hash map to count first-choice votes. Iterate through ballots, skip empty ones, and update counts. Track the current winner, updating when a candidate has more votes or equal votes but a lexicographically smaller name.
State that time complexity is O(N) where N is the number of ballots, and space complexity is O(C) where C is the number of unique candidates.
Walk through a simple case, a tie case, and an edge case like empty ballots or all ballots empty. Verify the output matches expectations.
Mention that if ballots are large, we only need the first element, so no need to process the rest. Also note that the tie-breaking could be done by sorting candidates at the end, but the integrated approach is more efficient.
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