Python heapq模块

这个模块(build-in)实现了一个堆的数据结构，完美的解决了Top-K问题，以后解决Top-K问题的时候，直接把这个模块拿来用就可以了

注意，默认的heap是一个小顶堆！

 

heapq模块提供了如下几个函数：

 

heapq.heappush(heap, item) 把item添加到heap中（heap是一个列表）

heapq.heappop(heap) 把堆顶元素弹出，返回的就是堆顶

heapq.heappushpop(heap, item) 先把item加入到堆中，然后再pop，比heappush()再heappop()要快得多

heapq.heapreplace(heap, item) 先pop，然后再把item加入到堆中，比heappop()再heappush()要快得多

heapq.heapify(x) 将列表x进行堆调整，默认的是小顶堆

heapq.merge(*iterables) 将多个列表合并，并进行堆调整，返回的是合并后的列表的迭代器

heapq.nlargest(n, iterable, key=None) 返回最大的n个元素（Top-K问题）

heapq.nsmallest(n, iterable, key=None) 返回最小的n个元素（Top-K问题）

 

下面看下示例：

 

import heapq
import random
 
# Top-K
mylist = list(random.sample(range(100), 10))
k = 3
largest = heapq.nlargest(k, mylist)
smallest = heapq.nsmallest(k, mylist)
print('original list is', mylist)
print('largest-'+str(k), '  is ', largest)
print('smallest-'+str(k), ' is ', smallest)
 
# heapify
print('original list is', mylist)
heapq.heapify(mylist)
print('heapify  list is', mylist)
 
# heappush & heappop
heapq.heappush(mylist, 105)
print('pushed heap is', mylist)
heapq.heappop(mylist)
print('popped heap is', mylist)
 
# heappushpop & heapreplace
heapq.heappushpop(mylist, 130)    # heappush -> heappop
print('heappushpop', mylist)
heapq.heapreplace(mylist, 2)    # heappop -> heappush
print('heapreplace', mylist)
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输出结果为：

original list is [18, 6, 10, 24, 48, 2, 9, 25, 16, 89]
largest-3   is  [89, 48, 25]
smallest-3  is  [2, 6, 9]
original list is [18, 6, 10, 24, 48, 2, 9, 25, 16, 89]
heapify  list is [2, 6, 9, 16, 48, 10, 18, 25, 24, 89]
pushed heap is [2, 6, 9, 16, 48, 10, 18, 25, 24, 89, 105]
popped heap is [6, 16, 9, 24, 48, 10, 18, 25, 105, 89]
heappushpop [9, 16, 10, 24, 48, 130, 18, 25, 105, 89]
heapreplace [2, 16, 10, 24, 48, 130, 18, 25, 105, 89]
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希望能对你有所帮助！