Python 的堆与优先队列

Python 中内置的 heapq 库和 queue 分别提供了堆和优先队列结构,其中优先队列 queue.PriorityQueue 本身也是基于 heapq 实现的,因此我们这次重点看一下 heapq

堆(Heap)是一种特殊形式的完全二叉树,其中父节点的值总是大于子节点,根据其性质,Python 中可以用一个满足 heap[k] <= heap[2*k+1] and heap[k] <= heap[2*k+2] 的列表来实现(heapq 也确实是这么做的)。堆可以用于实现调度器(例见:Python 3.5 之协程),更常用的是优先队列(例如:ImageColorTheme)。

heapq 提供了下面这些方法:


In [1]:
import heapq
print(heapq.__all__)


['heappush', 'heappop', 'heapify', 'heapreplace', 'merge', 'nlargest', 'nsmallest', 'heappushpop']

由于 Heap 是通过列表实现的,我们可以直接用列表创建:


In [2]:
from heapq import *
heap = []
heappush(heap, 3)
heappush(heap, 2)
heappush(heap, 1)
print(heap)


[1, 3, 2]

pop 或 sort 前要确保 heapify

或者通过 heapify 将普通列表转化为 Heap:


In [3]:
heap = list(reversed(range(5)))
print("List: ", heap)
heapify(heap)
print("Heap: ", heap)


List:  [4, 3, 2, 1, 0]
Heap:  [0, 1, 2, 4, 3]

每次从 Heap 中 pop 出来的元素都是最小的(因而可以据此实现堆排序):


In [4]:
heap = [5,4,3,2,1]
heapify(heap)
print(heappop(heap))
print(heappop(heap))
print(heappop(heap))


1
2
3

优先队列

queue.PriorityQueue 实际上只是对 heapq 的简单封装,直接使用其 heappush/heappop 方法:


In [5]:
from queue import PriorityQueue as PQueue
pq = PQueue()
pq.put((5 * -1, 'Python'))
pq.put((4 * -1, 'C'))
pq.put((3 * -1, 'Js'))
print("Inside PriorityQueue: ", pq.queue) # 内部存储
while not pq.empty():
    print(pq.get()[1])


Inside PriorityQueue:  [(-5, 'Python'), (-4, 'C'), (-3, 'Js')]
Python
C
Js

由于 heapq 是最小堆,而通常 PriorityQueue 用在较大有限制的排前面,所以需要给 priority * -1

sorted 一定是 Heap,反之未必

需要注意的是,虽然 Heap 通过 List 实习,但未经过 heapify() 处理的仍然是一个普通的 List,而 heappushheappop 操作每次都会对 Heap 进行重新整理。此外,一个 Heap 列表不一定是正确排序的,但是经过 list.sort() 的列表一定是 Heap:


In [6]:
import random
lst = [random.randrange(1, 100) for _ in range(5)]
lst.sort()
print("List: ", lst)
print("Poped: ", heappop(lst))
heappush(lst, 4)
print("Heap: ", lst)


List:  [24, 55, 81, 83, 87]
Poped:  24
Heap:  [4, 55, 81, 87, 83]

最大/最小的 N 个数

Heap 还提供了 nsmallestnlargest 方法用于取出前 n 个最大/最小数:


In [7]:
heap = [random.randrange(1, 1000) for _ in range(1000)]
heapify(heap)
print("N largest: ", nlargest(10, heap))
print("N smallest: ", nsmallest(10, heap))
print(len(heap))  # 不原地修改


N largest:  [999, 999, 998, 994, 992, 991, 990, 988, 985, 982]
N smallest:  [1, 1, 1, 2, 4, 5, 5, 6, 6, 9]
1000

合并(排序)

merge 方法用于将两个 Heap 进行合并:


In [8]:
heapA = sorted([random.randrange(1, 100) for _ in range(3)])
heapB = sorted([random.randrange(1, 100) for _ in range(3)])

merged = []
for i in merge(heapA, heapB):
    merged.append(i)
print(merged)


[5, 29, 66, 66, 70, 99]

最后两个方法 heapreplaceheappushpop 分别相当于:


In [9]:
lstA = [1,2,3,4,5]
lstB = [1,2,3,4,5]

poped = heapreplace(lstA, 0)
print("lstA: ", lstA, "poped: ", poped)

# is equal to...
poped = heappop(lstB)
heappush(lstB, 0)
print("lstB: ", lstA, "poped: ", poped)

print("*"*30)

poped = heappushpop(lstA, 9)
print("lstA: ", lstA, "poped: ", poped)

# is equal to...
heappush(lstB, 9)
poped = heappop(lstB)
print("lstB: ", lstB, "poped: ", poped)


lstA:  [0, 2, 3, 4, 5] poped:  1
lstB:  [0, 2, 3, 4, 5] poped:  1
******************************
lstA:  [2, 4, 3, 9, 5] poped:  0
lstB:  [2, 4, 3, 5, 9] poped:  0

这两个方法的执行效率要比分开写的方法高,但要注意 heapreplace 要取代的值是否比 heap[0] 大,如果不是,可以用更有效的方法:


In [10]:
item = 0
lstA = [1,2,3,4,5]
if item < lstA[0]:
    # replace
    poped = lstA[0]
    lstA[0] = item
    print("lstA: ", lstA, "poped: ", poped)


lstA:  [0, 2, 3, 4, 5] poped:  1