Learn and practice the Top K Elements and Heaps patterns to solve coding interview problems.

Speedrun the Oracle Coding Interview: Top Problems in Python

This module will teach you the underlying concepts and equip you with the techniques needed to use heaps to efficiently solve a diverse range of problems. In many problems, we need to find the k most/least frequent or k largest/smallest elements in a given set of elements. Such problems, common in data analysis, natural language processing, online algorithms, as well as in recommendation systems, may be solved using the Top K Elements pattern. The fast insertion and deletion operations possible with heaps make them ideal to implement efficient algorithms for such problems. Further, we can use heaps when we need to simultaneously keep track of the k largest elements in a set, as well as the k smallest elements in the same set. Extending this idea, each heap may be based on a separate dataset.

Heaps



**Constraints:**

* $1 \leq$ `k` $\leq$ `nums.length` $\leq 10^3$

* $-10^4 \leq$ `nums[i]` $\leq 10^4$

# Examples


Try to solve the Kth Largest Element in an Array problem.

Kth Largest Element in an Array

Statement