Top K Frequent Elements - Leetcode 347 - Heaps (Python)

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[GregHogg]

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[johnboamah]

I hardly ever comment on YouTube, but I had to. Your second solution is unique compared to others I have seen online. Great work!

[Banana-Joanne]

very grateful for your solution. admittedly, I'm not too familiar with heaps and bucket sort, so they both really went over my head. but I will keep on researching. Thanks for the good work.

[alibaba]

This is the best explanation I came across, thank you!

[rahuldwivedi]

Great work. But if you switch context in the middle, please make sure to complete the context. At 4:32 you were talking about max_heap and in that process it seemed you were explaining the time complexity would be O(Nlogk) but it actually was for min_heap.

[JoeTan-nqfq]

Since we are already using Counter class, we can use most_common() method.
counter = Counter(nums)
top_k = counter.most_common(k)
return list(map(lambda x: x[0], top_k))

[hatemlamine]

at 9:22, why not use another hashmap instead of an array? we're linking particular key value pairs just like in the first hashmap we used to get the number of occurences no?

[christianjt]

The second solution is very creative, thanks for sharing!

[jiasubaowenke]

Thank you so much! I have a question that why don't we just use couter.most_common in the heap solution, is it slower than the heap sort?

[n.h.son]

2:12, wait, it should've been O(klogn) if we only consider the work of heap

[samspeaks-hkvp]

please help, how come the second solution is not o(n2)

[venzdrop]

from collections import Counter
class Solution:
def topKFrequent(self, nums: List[int], k: int) -> List[int]:
f = Counter(nums)
return [x[0] for x in f.most_common(k)]