Today, we will discuss one of the effective sorting algorithms using Python, known as Quick Sort<\/strong>. Quick Sort has an average time complexity of O(n log n) and is, in practice, a very fast and efficient sorting algorithm. In this process, we will examine the working principle of Quick Sort and learn how to implement it in Python.<\/p>\n Quick Sort is a type of divide-and-conquer algorithm that divides an array into two sub-arrays and recursively sorts each sub-array to sort the entire array. The key to this process is to choose a reference value called ‘pivot’ and partition the array based on that value.<\/p>\n There are several methods for selecting the pivot, and generally, one of the first element, last element, or middle element is chosen. Additionally, there is a method to select the pivot randomly, which helps maintain an O(n log n) performance even in the worst-case scenario.<\/p>\n Write a function that takes an input integer array and returns a sorted array. You must use the Quick Sort algorithm.<\/p>\n Let’s examine the process of implementing the Quick Sort algorithm in Python step by step.<\/p>\n First, we will write a function to select the pivot and partition the array based on the pivot. Below is the code that performs this task.<\/p>\n Now we will implement a function that recursively sorts the partitioned array.<\/p>\n Using the functions we have written above, the complete Quick Sort code is as follows.<\/p>\n2. What is Quick Sort?<\/h2>\n
2.1 Steps of Quick Sort<\/h3>\n
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2.2 Pivot Selection<\/h3>\n
3. Problem Solving<\/h2>\n
3.1 Problem Description<\/h3>\n
3.2 Input and Output Format<\/h3>\n
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arr = [3, 6, 8, 10, 1, 2, 1]<\/code><\/li>\n[1, 1, 2, 3, 6, 8, 10]<\/code><\/li>\n<\/ul>\n3.3 Solution Process<\/h3>\n
3.3.1 Pivot Selection and Array Partitioning<\/h4>\n
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def partition(arr, low, high):\n pivot = arr[high]\n i = low - 1\n \n for j in range(low, high):\n if arr[j] <= pivot:\n i += 1\n arr[i], arr[j] = arr[j], arr[i]\n \n arr[i + 1], arr[high] = arr[high], arr[i + 1]\n return i + 1<\/code>\n <\/pre>\n3.3.2 Implementing the Quick Sort Function<\/h4>\n
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def quick_sort(arr, low, high):\n if low < high:\n pi = partition(arr, low, high)\n quick_sort(arr, low, pi - 1)\n quick_sort(arr, pi + 1, high)<\/code>\n <\/pre>\n3.3.3 Complete Code<\/h4>\n
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def partition(arr, low, high):\n pivot = arr[high]\n i = low - 1\n \n for j in range(low, high):\n if arr[j] <= pivot:\n i += 1\n arr[i], arr[j] = arr[j], arr[i]\n \n arr[i + 1], arr[high] = arr[high], arr[i + 1]\n return i + 1\n\n def quick_sort(arr, low, high):\n if low < high:\n pi = partition(arr, low, high)\n quick_sort(arr, low, pi - 1)\n quick_sort(arr, pi + 1, high)\n\n arr = [3, 6, 8, 10, 1, 2, 1]\n quick_sort(arr, 0, len(arr) - 1)\n print(\"Sorted array:\", arr)<\/code>\n <\/pre>\n4. Advantages and Disadvantages of Quick Sort<\/h2>\n
4.1 Advantages<\/h3>\n
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4.2 Disadvantages<\/h3>\n