\n This lecture explains the process of solving basic algorithm problems using Python. \n Bubble Sort is an algorithm that sorts by comparing two adjacent elements. \n The basic working process of Bubble Sort is as follows:\n<\/p>\n \n Now, let’s implement Bubble Sort in Python code. Here is the basic code for the Bubble Sort algorithm.\n<\/p>\n \n The above code defines a function called \n Let’s actually use Bubble Sort to see it in action with an example.\n<\/p>\n \n Let’s sort the following list: \n 1. First Pass:<\/p>\n The largest number, 90, has bubbled up to the end.\n<\/p>\n \n 2. Second Pass:<\/p>\n The second largest number, 64, has moved to the second last position.\n<\/p>\n \n By repeating this process, we eventually obtain the sorted list \n The time complexity of Bubble Sort is O(n\u00b2) in the worst case. This means that the time taken grows proportionally to the square of the length \\( n \\) of the list. \n While Bubble Sort has the advantage of being simple to implement, it has the drawback of being inefficient.
\n In this session, we will take a closer look at one of the most fundamental sorting algorithms, Bubble Sort<\/strong>.
\n Bubble Sort is a simple yet easy-to-understand sorting algorithm that helps in understanding the basics of algorithms.\n<\/p>\n1. What is Bubble Sort?<\/h2>\n
\n It goes through each element of the list sequentially, swapping them when two adjacent elements are in the wrong order.
\n This process is repeated, which is why it is named Bubble Sort, as the largest element “bubbles” up to the end of the list.\n<\/p>\n1.1. The working process of Bubble Sort<\/h3>\n
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2. Implementing the Bubble Sort Algorithm<\/h2>\n
def bubble_sort(arr):\n n = len(arr)\n # Repeat for the length of the list\n for i in range(n):\n # In each pass, the last i elements are already sorted, so repeat until n-i-1\n for j in range(0, n-i-1):\n # Compare and swap adjacent elements\n if arr[j] > arr[j+1]:\n arr[j], arr[j+1] = arr[j+1], arr[j]\n return arr<\/code><\/pre>\n2.1. Code Explanation<\/h3>\n
bubble_sort<\/code>, which takes a list to be sorted as an argument. <\/p>\n\n
n = len(arr)<\/code>: Stores the length of the given list.<\/li>\nfor<\/code> loop repeats for all elements of the list.<\/li>\nfor<\/code> loop compares adjacent elements in the currently unsorted part and swaps them if necessary.<\/li>\n3. Bubble Sort with an Example<\/h2>\n
3.1. Example List<\/h3>\n
[64, 34, 25, 12, 22, 11, 90]<\/code>\n<\/p>\n3.2. Step-by-Step Explanation of the Sorting Process<\/h3>\n
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[34, 64, 25, 12, 22, 11, 90]<\/code><\/li>\n[34, 25, 64, 12, 22, 11, 90]<\/code><\/li>\n[34, 25, 12, 64, 22, 11, 90]<\/code><\/li>\n[34, 25, 12, 22, 64, 11, 90]<\/code><\/li>\n[34, 25, 12, 22, 11, 64, 90]<\/code><\/li>\n[34, 25, 12, 22, 11, 64, 90]<\/code><\/li>\n<\/ul>\n\n
[25, 34, 12, 22, 11, 64, 90]<\/code><\/li>\n[25, 12, 34, 22, 11, 64, 90]<\/code><\/li>\n[25, 12, 22, 34, 11, 64, 90]<\/code><\/li>\n[25, 12, 22, 11, 34, 64, 90]<\/code><\/li>\n[25, 12, 22, 11, 34, 64, 90]<\/code><\/li>\n<\/ul>\n[11, 12, 22, 25, 34, 64, 90]<\/code>.\n<\/p>\n4. Time Complexity<\/h2>\n
\n However, in the best case scenario of dealing with an already sorted list, it can be reduced to O(n).
\n This occurs because no swaps happen during the first pass.\n<\/p>\n5. Improvements on Bubble Sort<\/h2>\n
\n In practical use, the following improvements can be applied:\n<\/p>\n