Kth LARGEST ELEMENT IN AN ARRAY - SOLUTION EXPLAINED [PYTHON]

In this video we're going to be solving a popular interview question that can be solved using a heap data structure. It's a popular question at pretty much all the FAANG companies and will be going forward as it's a very commonly used topic in most algorithms interviews.

[@dnm9931]

You would probably see me in all your videos at this point but I appreciate you man! Thank you! I pray I get my faang job soon with the effor Tim putting in!

[@user-js9rn4qx5x]

It really clear explanation, and also shows the time and space complexity.

[@mit8392]

Great video what does it mean heapq and why you didn't import it to use it

[@crackfaang]

heapq is the Python standard library for heaps. In Leetcode it's basically globally imported for you so you don't have to write `import heapq`

[@suri4Musiq]

Can you tell me why not do this instead?
def findKthLargest(self, nums: List[int], k: int) -> int:
#max heap creation
negatedNums = [-x for x in nums]
heapq.heapify(negatedNums)
returnElement = -1
#remove k elements
while k>0:
returnElement = heapq.heappop(negatedNums)
k-=1
return -1*returnElement
Runtime to create the heap is O(N) and runtime to pop an element is O(logN) and we do it K times = O(N + kLogN) = O(kLogN) is that not enough? How do justify O(NLogK) is better than this?

[@cemgurhan1370]

I believe when you heapify nums, you need to perform a heap operation for N items, resulting in O(NlogN) every time?

[@BlueSkyVL158]

Why you don't implement heap data structure instead of using heapq

[@crackfaang]

Why would I do that, it's complete overkill.