> For the complete documentation index, see [llms.txt](https://zedive.gitbook.io/project-l/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://zedive.gitbook.io/project-l/part-1/basic_data_structure/heap/top-k-frequent-elements.md).

# Top K Frequent Elements

## Question ([LC.347](https://leetcode.com/problems/top-k-frequent-elements/description/))

> Given a list of integers, return the top k most frequent values. 

## Example

```
I: [1, 1, 1, 2, 2, 3], k = 2 
O: [1, 2] 

I: [3, 2, 1], k = 1 
O: the answer is not unique 
```

## Simple Heap

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

    # use a hash map to reduce this problem to a common top k problem

    if len(nums) == 0 or k == 0:
        return []

    num_dict: Dict[int, int] = {}

    for num in nums:
        if num in num_dict:
            num_dict[num] = num_dict[num] + 1
        else:
            num_dict[num] = 1

    # use a min heap of k elements

    min_heap = []

    for key in num_dict:
        if len(min_heap) < k:
            heapq.heappush(min_heap, (num_dict[key], key))
        elif num_dict[key] > min_heap[0][0]:
            heapq.heappop(min_heap)
            heapq.heappush(min_heap, (num_dict[key], key))

    return [k for (v, k) in min_heap]
```

Worst case O(nlogk) time and O(n) + O(k) space&#x20;

## Quick Select

## Bucket Sort


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