Master the Quicksort algorithm in Python with our detailed guide. Dive deep into each step, from choosing the pivot to recursive sorting, complemented by simple code examples. Elevate your programming toolkit with this efficient sorting method.
Quicksort is a divide-and-conquer algorithm. This means it breaks down a problem into smaller sub-problems, solves each sub-problem, and combines the solutions to solve the original problem. In the world of sorting algorithms, Quicksort is known for its impressive average-case performance.
To truly appreciate the simplicity and elegance of Quicksort, let’s understand its primary steps:
The primary objective of this step is to choose a ‘pivot’ element from the array and partition the other elements into two sub-arrays, according to whether they are less than or greater than the pivot.
Steps involved in partitioning:
- Choosing a Pivot: The choice of the pivot element can be random, fixed (like the first element, the last element, or the middle one), or based on certain heuristics.
- Reordering the Array: Once the pivot is selected, other elements are reordered. Those less than the pivot come before it and those greater than the pivot come after it. This operation effectively places the pivot in its correct position in the sorted array.
Post-partitioning, the pivot is in its rightful position. Now, the elements to the left and right of the pivot haven’t been sorted. They form the sub-arrays which are then sorted recursively using the same approach.
Here’s a simple implementation of Quicksort in Python:
def quicksort(arr):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2] # Using middle element as pivot
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)…
