Python 3: Project-based Python, Algorithms, Data Structures - Project phase 2: Get input from user for size and range

The video tutorial introduces the insertion sort algorithm, explaining its complexity as O(n^2) and comparing it to bubble and selection sorts. The instructor walks through the insertion sort process using a list of numbers, detailing how the key index is used to compare and swap elements to achieve a sorted list. Implementation notes emphasize the importance of tracking the key index and using nested loops. An assignment is given to implement the algorithm in Python, with tips for testing and debugging provided.

What is the time complexity of insertion sort?

• O(n)

• O(n^2)

• O(log n)

• O(n log n)

In the insertion sort algorithm, what is the initial position of the key?

• Index 0

• Index 1

• Index 2

• Index 3

What happens when the item at the key index is smaller than the item before it?

• The algorithm stops

• The items are swapped

• The key moves to the next index

• The list is reversed

Why is it important to keep track of the key index during insertion sort?

• To ensure the list is sorted in descending order

• To prevent the key from moving backwards

• To increase the speed of sorting

• To reduce the number of swaps

What is a key characteristic of the insertion sort algorithm?

• It involves a nested loop structure

• It requires a sorted list to start

• It uses a single loop

• It sorts in descending order by default

What should you do if you get stuck while implementing the insertion sort algorithm?

• Start a new project

• Skip the implementation

• Use print statements to debug

• Ignore the errors

What is the purpose of testing the insertion sort algorithm with lists of various sizes?

• To ensure it works only for large lists

• To verify its performance on different list sizes

• To make it compatible with other algorithms

• To reduce its time complexity