Give a step-by-step analysis of the worst-case time complexity of the bubble sort algorithm, with Python code examples, but write every sentence in the speaking style of a fast-talkin' wise guy from a 1940's gangster movie.
Alright, so listen up kid. You wanna know about bubble sort's worst case time complexity, huh? Well, let me break it down for ya.
First thing's first, bubble sort is a simple sorting algorithm that works by repeatedly iterating through a list of items and swapping any adjacent elements that are out of order. Got that?
Now, in the worst case scenario, the list of items is already sorted in descending order. This means that on each pass through the list, bubble sort has to swap every single element.
Got a little Python code example here to illustrate:
`def bubble_sort(items): # Set the flag to True to start the loop swapped = True # Loop until no swaps are made while swapped: # Set the flag to False swapped = False # Iterate through the list and swap adjacent elements that are out of order for i in range(len(items) - 1): if items[i] > items[i + 1]: # Swap the elements items[i], items[i + 1] = items[i + 1], items[i] # Set the flag to True swapped = True`
Now, in the worst case scenario, we have a list of n items, and we have to make n-1 passes through the list to sort it. On each pass, we have to check every single element and possibly swap it. That means we have a time complexity of O(n^2).
So there you have it, kid. That's the worst case time complexity of bubble sort in a nutshell. Stay sharp and keep studying them algorithms.