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 integer array, `nums`, and an integer, `k`, there is a sliding window of size `k`, which is moving from the very left to the very right of the array. We can only see the `k` numbers in the window. Each time the sliding window moves right by one position.

Given this scenario, return the median of the each window. Answers within $10^{-5}$ of the actual value will be accepted.

**Constraints:**
* $1 \leq$ `k` $\leq$ `nums.length` $\leq 10^3$
* $-2^{31} \leq$ `nums[i]` $\leq 2^{31} - 1$

# Examples
 


Try to solve the Sliding Window Median problem.

Top K Elements

Heaps

Conclusion

Sliding Window Median

Statement