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

Educative-99 in Python: Accelerate Your Coding Interview Prep

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 two 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. These techniques are used to solve problems categorized under the Heaps pattern.

Heaps

# Statement

Given an array of integers, `arr`, and an integer, `k`, return the $k$ most frequent elements.

> **Note:** You can return the answer in any order.

**Constraints:**
* $1$ $\leq$ `arr.length` $\leq$ $10^{3}$
* $-10^{-4}$ $\leq$ `arr[i]` $\leq$ $10^{4}$
* 1 $\leq$ `k` $\leq$ number of unique elements in an array.
* It is guaranteed that the answer is unique.

# Solution

So far, you've probably brainstormed some approaches and have an idea of how to solve this problem. Let’s explore some of these approaches and figure out which one to follow based on considerations such as time complexity and any implementation constraints.


## Naive approach

Let's solve the Top K Frequent Elements problem using the Top K Elements pattern.

Top K Elements

Heaps

Conclusion

Solution: Top K Frequent Elements

Statement