What are the performance differences between sorting algorithms in Python?

What are the performance differences between sorting algorithms in Python?

The performance of sorting algorithms can vary significantly based on their time complexity. For example, Quick Sort has an average time complexity of O(n log n), while Bubble Sort has O(n^2). Would you like to know more about specific algorithms?

Yes, can you explain how Quick Sort works?

Quick Sort is a divide-and-conquer algorithm. It selects a 'pivot' element and partitions the array into two sub-arrays: elements less than the pivot and elements greater than the pivot. It then recursively sorts the sub-arrays.

What about Merge Sort? How does it compare?

Merge Sort also uses a divide-and-conquer approach. It divides the array into halves, sorts each half, and then merges them back together. It has a consistent time complexity of O(n log n), making it stable and efficient for large datasets.

Which one should I use in my project?

It depends on your specific needs. If you need a stable sort and are working with large datasets, Merge Sort is a good choice. If you want faster performance on average and can handle the worst-case scenario, Quick Sort is often preferred.