347. Top K Frequent Elements

Problem

Given an integer array nums and an integer k, return the k most frequent elements. You may return the answer in any order.

https://leetcode.com/problems/top-k-frequent-elements/

Example 1:

Input: nums = [1,1,1,2,2,3], k = 2
Output: [1,2]

Example 2:

Input: nums = [1], k = 1
Output: [1]

Constraints:

1 <= nums.length <= 10⁵
-10⁴ <= nums[i] <= 10⁴
k is in the range [1, the number of unique elements in the array].
It is guaranteed that the answer is unique

Follow up: Your algorithm’s time complexity must be better than O(n log n), where n is the array’s size.

Test Cases

python
class Solution:
    def topKFrequent(self, nums: List[int], k: int) -> List[int]:

solution_test.py下载完整源码
py
import pytest

from solution import Solution


@pytest.mark.parametrize('nums, k, expected', [
    ([1,1,1,2,2,3], 2, [1,2]),
    ([1], 1, [1]),
])
class Test:
    def test_solution(self, nums, k, expected):
        sol = Solution()
        result = sol.topKFrequent(nums, k)
        assert sorted(result) == sorted(expected)

Thoughts

用哈希表统计每个数字的出现次数。

然后用大小为 k 的最小堆，可以找到刚好第 k 大的词频（以及对应的数字），

设 nums 有 n 个元素，其中各不相同的数字有 d 个。

时间复杂度 O(n + d log k)，空间复杂度 O(d)。

小优化点：堆内元素少于 k 个时不用维护堆的结构，第 k 个元素入堆时做一次堆的构建即可。

Code

solution.py下载完整源码
py
class Solution:
    def topKFrequent(self, nums: list[int], k: int) -> list[int]:
        counting: dict[int, int] = {} # counting[i]: freq of number i.
        for v in nums:
            counting[v] = counting.get(v, 0) + 1

        min_heap = [None] * k
        leaf = k >> 1

        def shift_down(pos: int):
            while pos < leaf:
                left = (pos << 1) + 1
                right = left + 1
                child = right if right < k and min_heap[right][1] < min_heap[left][1] else left
                if min_heap[pos][1] > min_heap[child][1]:
                    min_heap[pos], min_heap[child] = min_heap[child], min_heap[pos]
                    pos = child
                else:
                    return

        for i, item in enumerate(counting.items()):
            if i < k:
                min_heap[i] = item
                if i == k - 1:
                    # Build heap.
                    for j in range(leaf - 1, -1, -1):
                        shift_down(j)
            elif item[1] > min_heap[0][1]:
                # Replace heap top with v.
                min_heap[0] = item
                shift_down(0)

        return [v for v, _ in min_heap]

#Problem

#Test Cases

#Thoughts

#Code

Problem

Test Cases

Thoughts

Code