Top K Frequent Elements — Interview Guide

• Count frequencies with a hash map in O(n).
• A min-heap of size k keeps the top k by frequency in O(n log k).
• Bucket sort by frequency achieves O(n) when values are bounded.

import heapq, collections
def top_k(nums, k):
    freq = collections.Counter(nums)
    return heapq.nlargest(k, freq.keys(), key=freq.get)

1. Build a frequency map.
2. Create buckets indexed by frequency (0..n).
3. Walk the buckets from high to low, collecting k elements.