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)
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]
希望能对你有所帮助！