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 🙂
- Open up the iterative_sorting directory
- Read through the descriptions of the
bubble_sort
andselection_sort
algorithms - Implement
bubble_sort
andselection_sort
in iterative_sorting.py - Test your implementation by running
test_iterative.py
- Open up the recursive_sorting directory
- Read through the descriptions of the
merge_sort
algorithm - Implement
merge_sort
in recursive_sorting.py - Test your implementation by running
test_recursive.py
- Implement all the methods in the
searching.py
file in thesearching
directory. - Implement the
count_sort
algorithm in theiterative_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-insort
method.