# A memo that I wrote a quicksort in Python

I wrote a note for myself about Quicksort.

# What is quicksort?

--A high-speed sorting algorithm based on the divide-and-conquer method that can handle large data sizes. --Computational complexity is O (nlogn) --Because there are logn layers and the amount of calculation for each layer is O (n). --Unstable sorting that changes the order of the same values when sorting --No need for memory to temporarily store data

# code

#### `quick_sort.py`

``````
def partition(A, p, r):
x = A[r-1]
i = p-1
for j in range(p, r-1):
if A[j] <= x:
i += 1
A[i], A[j] = A[j], A[i]
A[i+1], A[r-1] = A[r-1], A[i+1]

return i+1

n = int(input())
A = list(map(int, input().split()))

def quick_sort(A, p, r):
if p < r:
q = partition(A, p, r)
quick_sort(A, p, q)
quick_sort(A, q+1, r)
return A

print(quick_sort([3, 1, 10, 2.5, 11, 3, 21,4, -1], 0, 9))
# [-1, 1, 2.5, 3, 3, 4, 10, 11, 21]
``````

The above algorithm has a constant selection of criteria (the last value), so it may be inefficient depending on the data sequence. Computational complexity may be O (n ^ 2) The recursion may be too deep and an error may occur Therefore, it is necessary to devise ways to randomly select the criteria, select the median, and so on.

# reference

Sort algorithm and implementation in Python