In this two day project, you will be implementing many different solutions to the same problem: sort a list of integers in ascending order. You will also be using your newfound knowledge of complexity analysis to evaluate each implementation for efficiency.

Many times in your Lambda career, we encourage you to scour the internet anytime you are stumped by a problem. This is NOT one of those cases. Yes, it is possible to Google "quicksort in Python" and find a solution in about 10 seconds but that is not the point of these exercises. Your task is to take a simple problem (sort an list of ints) and a pre-defined plan (we give you an algorithm description) and turn that into code. These steps should sound familiar, as they are 1-3 of Polya's Problem Solving techniques. Soon, you will be coming up with your own plans for more complex problems so don't cheat yourself out of valuable coding practice.

That being said, please still use the 20 minute rule 🙂

[x] - Open up the iterative_sorting directory

[x] - Read through the descriptions of the bubble_sort and selection_sort algorithms [x] - Implement bubble_sort and selection_sort in iterative_sorting.py [x] - Test your implementation by running test_iterative.py

[x] - Open up the recursive_sorting directory [x] - Read through the descriptions of the merge_sort algorithm

[x] - Implement merge_sort in recursive_sorting.py [x] - Test your implementation by running test_recursive.py

[x] - Implement all the methods in the searching.py file in the searching directory.

• Implement the count_sort algorithm in the iterative_sorting directory.
• Implement an in-place version of merge_sort that does not allocate any additional memory. In other words, the space complexity for this function should be O(1).
• Implement the timsort algorithm, which is a real-world sorting algorithm. In fact, it is the sorting algorithm that is used when you run Python's built-in sort method.