Quickselect Algorithm in Python

In the following tutorial, we will learn the implementation of the Quickselect Algorithm in Python.

But before we get to that, let us discuss what Quickselect Algorithm is.

What is Quickselect Algorithm?

A selection procedure called Quickselect is used to identify the k-th order statistic, or the smallest data element in an unordered list. Quickselect Algorithm utilizes the overall similar approach as Quicksort Algorithm.

Examples:

The algorithm is similar to QuickSort. The sole distinction is that, after locating the pivot, it only recurs for the portion that includes the k-th smallest element, as opposed to reoccurring for both sides. The idea is straightforward: we repeat for the left portion if the index of the partitioned element is greater than k. We return after determining the k-th lowest element if the index equals k. If the index is smaller than k, the right portion is repeated. From O(n log n) to O(n), with O(n^2) as the worst-case scenario, the projected complexity reduces.

Pseudocode:

Implementation of the Quickselect Algorithm in Python

Explanation:

The algorithm divides the array around a pivot element and uses the Quick Select technique to determine the k-th smallest element in an unsorted array by determining if the pivot index corresponds to the required k-th position.

Output:

 
The k-th smallest element is: 6   

Important Points:

• Similar to quicksort, it has terrible worst-case performance while being quick in practice. It's employed in
• Only the recursive code is different from QuickSort's partitioning procedure.
• In the worst situation, there is an algorithm that finds the k-th smallest element in O(n), although QuickSelect often works better.