Heap sorting algorithm-python implementation

1. Definition

Heap sorting needs to store n data in the heap at the beginning, and the time required is O(nlogn). During the queuing process, the heap starts from an empty heap and is gradually filled with data. Since the height of the heap is less than log2n, the time required to insert a piece of data is O(logn)

2: Time complexity: O(nlogn) is faster

3. Space complexity: more complicated

4. Python implementation:

from collections import deque

def swap_param(L, i, j):
    L[i], L[j] = L[j], L[i]
    return L

def heap_adjust(L, start, end):
    temp = L[start]
    i = start
    j = 2 * i

    while j <= end:
        if (j < end) and (L[j] < L[j + 1]):
            j += 1
        if temp < L[j]:
            L[i] = L[j]
            i = j
            j = 2 * i
        else:
            break
    L[i] = temp

def heap_sort(L):
    L_length = len(L) - 1

    first_sort_count = L_length / 2
    for i in range(first_sort_count):
        heap_adjust(L, first_sort_count - i, L_length)

    for i in range(L_length - 1):
        L = swap_param(L, 1, L_length - i)
        heap_adjust(L, 1, L_length - i - 1)

    return [L[i] for i in range(1, len(L))]

def main():
    L = deque([50, 16, 30, 10, 60,  90,  2, 80, 70])
    L.appendleft(0)
    print heap_sort(L)

main()