Kth Largest Integer In An Array Leetcode Problem 1985 [Python]

If you’ve ever wondered how to find the kth largest integer in an array without sorting, you’re onto the right problem; Kth Largest Integer In an Array.

In this blog post, we’ll explore the Kth Largest Integer In an Array problem from LeetCode, which is categorized as a medium-difficulty problem in the Heap/Priority Queue category.

We’ll provide a Python solution, including an explanation of the code and its time and space complexity.

By the end of this post, you’ll have a clear understanding of how to tackle this problem efficiently.

Problem Overview

The problem statement is as follows:

Question: Given an integer array nums and an integer k, return the kth largest element in the array.

Note that it is the kth largest element in the sorted order, not the kth distinct element.

Can you solve it without sorting?

Let’s break down the problem step by step.

Example 1:

Input: nums = [3, 2, 1, 5, 6, 4], k = 2
Output: 5

Example 2:

Input: nums = [3, 2, 3, 1, 2, 4, 5, 5, 6], k = 4
Output: 4

Constraints:

• 1 <= k <= nums.length <= 105
• -104 <= nums[i] <= 104

Now that we have a clear understanding of the problem, let’s dive into the solution.

Efficient Python Code Solution

def findKthLargest(self, nums: List[int], k: int) -> int:
        maxHeap = [-int(n) for n in nums]
        heapq.heapify(maxHeap)
        while k > 1:
            heapq.heappop(maxHeap)
            k -= 1
        return -maxHeap[0]

Here’s a step-by-step explanation of the code:

1. We define a function called kthLargestNumber that takes two parameters: nums (a list of strings representing integers) and k (the kth largest element to find).
2. We create a maxHeap by negating the values in nums.

This is because Python’s heapq library provides a min-heap, but we want a max-heap.

By negating the values, we simulate a max-heap where the largest elements will appear at the top when popped.

3. We use the heapify function from the heapq library to turn the maxHeap list into a valid max-heap.

This operation takes linear time.

4. We enter a loop that continues as long as k is greater than 1. In each iteration, we pop the smallest element from the max-heap using heapq.heappop.

This operation ensures that the kth largest element will be at the top of the heap when we finish.

5. Once we’ve popped the kth largest element, we negate it back to its original positive value and convert it to a string.
6. Finally, we return the kth largest element as a string.

The core of this solution lies in the efficient use of a max-heap.

Popping elements from a max-heap is a logarithmic time operation, which makes this solution much more efficient than sorting the entire array.

The time complexity of this solution is O(k * log n), where n is the number of elements in the input array nums.

Time and Space Complexity

• Time Complexity: The time complexity of this solution is O(k * log n), where n is the number of elements in the input array nums.

We iterate k times, and in each iteration, we perform a log(n) operation when popping from the max-heap.

• Space Complexity: The space complexity is O(n) because we create a max-heap with n elements.

The extra space required is proportional to the size of the input.

Reasoning Behind Our Approach

The approach we’ve taken is based on the efficient use of a max-heap.

By converting the elements in the input array to negative values and creating a max-heap, we can easily find the kth largest element in O(k * log n) time complexity.

This approach avoids sorting the entire array, making it more efficient, especially when k is much smaller than the length of the array.

Related Interview Questions By Company:

• Combination Sum LeetCode
• Wiggle Sort LeetCode
• Meeting Rooms II LeetCode

Related Interview Questions By Difficulty:

• Kth Largest Element In A Stream LeetCode
• Find The Kth Largest Integer In The Array LeetCode
• Last Stone Weight LeetCode

Conclusion

In this blog post, we’ve tackled the Find The Kth Largest Integer In The Array problem on LeetCode.

We provided an efficient Python solution using a max-heap to find the kth largest element without sorting the entire array.

This solution has a time complexity of O(k * log n), which is much better than the O(n^2) worst-case time complexity of some other methods.

We hope this post has been helpful, and if you have any questions, comments, or suggestions, please feel free to share them in the comments section.

Solving problems like these is a great way to sharpen your coding skills, and we encourage you to explore more LeetCode problems to enhance your programming abilities.

Question Link

If you found this content useful, don’t forget to like and engage to support the our platform.

Thank you for watching, and we’ll see you in the next coding adventure!