> ## Documentation Index
> Fetch the complete documentation index at: https://leetcode-py.wisl.dev/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> leetcode-py is a Python LeetCode practice environment generator with one CLI: lcpy. It is not a service or platform.
> Each problem is a directory under leetcode/ with README.md, solution.py, test_solution.py, helpers.py, and playground.ipynb. lcpy gen creates them from JSON templates bundled with the package.
> Examples are backed by tests; copy them verbatim.

# Top K Frequent Elements Python Solution

> Tested Python solution for LeetCode 347 with 13 pytest cases. Generate a practice environment with lcpy.

LeetCode 347, Medium. Topics: Array, Hash Table, Divide and Conquer, Sorting, Heap (Priority Queue), Bucket Sort, Counting, Quickselect. [View on LeetCode](https://leetcode.com/problems/top-k-frequent-elements/description/).

Generate this problem as a practice environment: tested reference solution, 13 [parametrized pytest cases](/practice/testing), and a playground notebook:

```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
lcpy gen -n 347   # by problem number
lcpy gen -s top_k_frequent_elements   # by problem name
```

## Problem

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

### Examples

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

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

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

### Constraints

* 1 \<= nums.length \<= 10^5
* -10^4 \<= nums\[i] \<= 10^4
* 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.

## Solution

Reference implementation from [solution.py on GitHub](https://github.com/wislertt/leetcode-py/blob/main/leetcode/top_k_frequent_elements/solution.py), full suite in [test\_solution.py](https://github.com/wislertt/leetcode-py/blob/main/leetcode/top_k_frequent_elements/test_solution.py):

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
import heapq
from collections import Counter


class Solution:
    def top_k_frequent(self, nums: list[int], k: int) -> list[int]:
        """
        Optimized version using heap for O(n log k) time complexity.

        Time: O(n log k) - heap operations
        Space: O(n) - for counter and heap
        """
        counter = Counter(nums)

        # Use min heap of size k - keep the k most frequent elements
        heap: list[tuple[int, int]] = []
        for num, count in counter.items():
            if len(heap) < k:
                heapq.heappush(heap, (count, num))
            elif count > heap[0][0]:
                heapq.heapreplace(heap, (count, num))

        # Extract numbers from heap (order doesn't matter for this problem)
        return [num for _, num in heap]
```

## Complexity

| Time | Space |
| ---- | ----- |
| -    | -     |

## Tags

[Blind 75](/catalog/blind-75), [NeetCode 150](/catalog/neetcode-150), [NeetCode 250](/catalog/neetcode-250), [NeetCode All](/catalog/neetcode), [AlgoMaster 75](/catalog/algo-master-75).