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This approach uses a hash map to count the frequency of each element. We then use a min-heap to keep track of the top k elements.
Time Complexity: O(n log n) due to sorting.
Space Complexity: O(n) for storing frequencies.
1from collections import Counter
2import heapq
3
4def topKFrequent(nums, k):
5 count = Counter(nums)
6 return heapq.nlargest(k, count.keys(), key=count.get)
7
8if __name__ == '__main__':
9 nums = [1,1,1,2,2,3]
10 k = 2
11 print(topKFrequent(nums, k))
12We use Python's collections.Counter and heapq.nlargest functions to efficiently get the top k frequent elements.
We manually record each element's frequency and sort the list based on counts into a frequency bucket. Then, we retrieve the top k elements.