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Python sorting methods include bubble sort, selection sort, insertion sort, quick sort, merge sort, heap sort, radix sort, etc. Detailed introduction: 1. Bubble sorting, sorting by comparing adjacent elements and exchanging their positions; 2. Selection sorting, sorting by finding the smallest element in the list and placing it at the end of the sorted part ; 3. Insertion sort, sorting by inserting each element into the appropriate position of the sorted part; 4. Quick sort, using the divide and conquer method to divide the list into smaller sublists, etc.
Operating system for this tutorial: Windows 10 system, Python version 3.11.4, Dell G3 computer.
Python is a powerful programming language that provides a variety of sorting methods to sort data. In this article, we'll cover at least 7 different sorting methods, with detailed code examples.
1. Bubble Sort:
Bubble sort is a simple sorting algorithm that sorts by comparing adjacent elements and exchanging their positions. It iterates through the list repeatedly until no swaps occur.
def bubble_sort(arr): n = len(arr) for i in range(n-1): for j in range(0, n-i-1): if arr[j] > arr[j+1]: arr[j], arr[j+1] = arr[j+1], arr[j] return arr
2. Selection Sort:
Selection sort is a simple sorting algorithm that finds the smallest element in the list and places it in the sorted part. Sort at the end.
def selection_sort(arr): n = len(arr) for i in range(n): min_idx = i for j in range(i+1, n): if arr[j] < arr[min_idx]: min_idx = j arr[i], arr[min_idx] = arr[min_idx], arr[i] return arr
3. Insertion Sort:
Insertion sort is a simple sorting algorithm that sorts by inserting each element into the appropriate position of the sorted part.
def insertion_sort(arr): n = len(arr) for i in range(1, n): key = arr[i] j = i-1 while j >= 0 and arr[j] > key: arr[j+1] = arr[j] j -= 1 arr[j+1] = key return arr
4. Quick Sort:
Quick sort is an efficient sorting algorithm that uses the divide-and-conquer method to divide the list into smaller sublists and then recursively Sort a sublist.
def quick_sort(arr): if len(arr) <= 1: return arr pivot = arr[len(arr)//2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quick_sort(left) + middle + quick_sort(right)
5. Merge Sort:
Merge sort is an efficient sorting algorithm that uses the divide-and-conquer method to divide the list into smaller sublists and then recursively Sort the sublists and finally merge them into an ordered list.
def merge_sort(arr): if len(arr) <= 1: return arr mid = len(arr) // 2 left = arr[:mid] right = arr[mid:] left = merge_sort(left) right = merge_sort(right) return merge(left, right) def merge(left, right): result = [] i = j = 0 while i < len(left) and j < len(right): if left[i] < right[j]: result.append(left[i]) i += 1 else: result.append(right[j]) j += 1 result.extend(left[i:]) result.extend(right[j:]) return result
6. Heap Sort:
Heap sort is an efficient sorting algorithm that uses a binary heap data structure for sorting.
def heapify(arr, n, i): largest = i l = 2 * i + 1 r = 2 * i + 2 if l < n and arr[i] < arr[l]: largest = l if r < n and arr[largest] < arr[r]: largest = r if largest != i: arr[i], arr[largest] = arr[largest], arr[i] heapify(arr, n, largest) def heap_sort(arr): n = len(arr) for i in range(n//2 - 1, -1, -1): heapify(arr, n, i) for i in range(n-1, 0, -1): arr[i], arr[0] = arr[0], arr[i] heapify(arr, i, 0) return arr
7. Radix Sort:
Radix sort is a non-comparative sorting algorithm that sorts elements based on the number of digits.
def counting_sort(arr, exp): n = len(arr) output = [0] * n count = [0] * 10 for i in range(n): index = arr[i] // exp count[index % 10] += 1 for i in range(1, 10): count[i] += count[i-1] i = n - 1 while i >= 0: index = arr[i] // exp output[count[index % 10] - 1] = arr[i] count[index % 10] -= 1 i -= 1 for i in range(n): arr[i] = output[i] def radix_sort(arr): max_val = max(arr) exp = 1 while max_val // exp > 0: counting_sort(arr, exp) exp *= 10 return arr
This is a detailed code example of 7 different sorting methods. According to different data sets and performance requirements, choosing a suitable sorting algorithm can improve the efficiency and performance of the code
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