Python 3: Project-based Python, Algorithms, Data Structures - Implement merge function - part 1

The video tutorial explains the bubble sort algorithm, demonstrating how it sorts an unsorted list of numbers by comparing and swapping elements. The process is illustrated through multiple iterations until the list is sorted in ascending order. The tutorial also analyzes the algorithm's performance using Big O notation, highlighting its complexity as O(N^2).

What is the primary goal of the bubble sort algorithm?

• To count the number of elements in a list

• To reverse the order of a list

• To find the largest number in a list

• To sort a list of numbers in ascending order

During the first iteration, what happens when the current element is larger than the next element?

• The next element is duplicated

• The current element is removed

• The elements are added together

• The elements are swapped

What indicates the completion of an iteration in bubble sort?

• All elements are equal

• No elements are left to compare

• The smallest element is at the end of the list

• The largest unsorted element is in its correct position

How does bubble sort determine that the list is fully sorted?

• The first element is the smallest

• No swaps occur during a complete pass

• All elements are zero

• The last element is the largest

What is the time complexity of the bubble sort algorithm in the worst-case scenario?

• O(n log n)

• O(n^2)

• O(log n)

• O(n)

In terms of Big O notation, how is the complexity of bubble sort expressed?

• O(log n)

• O(n^2)

• O(n)

• O(n^3)

Why is bubble sort considered inefficient for large datasets?

• It cannot handle duplicate values

• It requires additional memory

• It has a high time complexity of O(n^2)

• It only works with numbers