Mastering Selection Sort: A Step-by-Step Guide with Code Examples and a LeetCode Challenge

Ayesha IrshadAyesha Irshad
2 min read

Sorting algorithms are the backbone of data organization and retrieval in computer science. Among these algorithms, Selection Sort stands out as a simple yet informative approach. In this blog, we'll dive into the mechanics of Selection Sort, explore its intricacies, analyze its efficiency, provide Python code examples, and even tackle a LeetCode challenge using this sorting technique.

Selection Sort Unveiled

Selection Sort is a comparison-based sorting algorithm known for its simplicity. It divides the array into a sorted part on the left and an unsorted part on the right. The algorithm repeatedly selects the smallest (or largest) element from the unsorted part and swaps it with the first element of the unsorted part.

Selection Sort in Action:

  1. Find the smallest element in the unsorted part.

  2. Swap it with the first element of the unsorted part.

  3. Expand the sorted part to include the newly sorted element.

  4. Repeat the process until the entire array is sorted.

Selection Sort Code Example

Let's take a closer look at the Python implementation of Selection Sort:

def selection_sort(arr):
    n = len(arr)
    for i in range(n):
        min_index = i
        for j in range(i + 1, n):
            if arr[j] < arr[min_index]:
                min_index = j
        arr[i], arr[min_index] = arr[min_index], arr[i]
    return arr

Solving a LeetCode Problem with Selection Sort

Problem: LeetCode 215 - Kth Largest Element in an Array

Given an array of integers, find the kth largest element.

Solution using Selection Sort: Sort the array in descending order using Selection Sort and return the kth element.

def findKthLargest(nums, k):
    sorted_nums = selection_sort(nums)
    return sorted_nums[k - 1]

Analyzing Time Complexity

Selection Sort's time complexity is O(n^2), making it less suitable for large datasets. The algorithm's nested loop structure results in n*(n-1)/2 comparisons in the worst case.

Conclusion

Selection Sort might not be the most efficient algorithm for larger datasets, but it offers valuable insights into sorting techniques. By understanding its logic, implementation, and analyzing its time complexity, you build a strong foundation for more advanced sorting algorithms. Whether you're a newcomer to sorting algorithms or an experienced coder revisiting the basics, Selection Sort provides a window into the core principles of sorting.

Happy coding and sorting! ๐Ÿš€๐Ÿ”๐Ÿง 

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Written by

Ayesha Irshad
Ayesha Irshad

I am a Developer Program Member at GitHub, where I collaborate with a global community of developers and contribute to open source projects that advance the field of Artificial Intelligence (AI). I am passionate about learning new skills and technologies, and I have completed multiple certifications in Data Science, Python, and Java from DataCamp and Udemy. I am also pursuing my Bachelor's degree in AI at National University of Computer and Emerging Sciences (FAST NUCES), where I have gained theoretical and practical knowledge of Machine Learning, Neural Networks, and Data Analysis. Additionally, I have worked as an AI Trainee at Scale AI, where I reviewed and labeled data for various AI applications. Through these experiences, I have developed competencies in Supervised Learning, Data Science, and Artificial Neural Networks. My goal is to apply my skills and knowledge to solve real-world problems and create positive impact with AI.