heapq.heapreplace(heap,item): python3中heappushpop的更高效版。heapq.heappushpop(heap, item):向 heap 中加入 item 元素,并返回 heap 中最小元素。heapq.heapify(x):Transform list into a heap, in-place, in O(len(x)) timeheapq.merge(*iterables, key=None, reverse=False)heapq.nlargest(n, ...
参考书籍:《Python3 标准库》 # heap queue,联想到的就是C++ STL的优先队列 import heapq # 创建堆,默认时最小堆 data = [1, -10, 19, 5, 30] heap = [] for item in data: heapq.heappush(heap, item) print(heap) # 也可以利用原有的迭代容器作为参数 heapq.heapify(data) print(data) '''...
1、heappush(heap,n)数据堆入 In[1]:import heapq as hq In[2]:import numpy as np In[3]:data=np.arange(10)#将生成的数据随机打乱顺序In[4]:np.random.shuffle(data)In[5]:data Out[5]:array([5,8,6,3,4,7,0,1,2,9])#定义heap列表In[6]:heap=[]#使用heapq库的heappush函数将数据堆...
print(heapq.nlargest(3,li1)) # using nsmallest to print 3 smallest numbers # prints 1, 3 and 4 print("The 3 smallest numbers in list are : ",end="") print(heapq.nsmallest(3,li1)) 输出: The3largest numbersinlist are:[10,9,8] The3smallest numbersinlist are:[1,3,4] 注:本文...
heapq是python自带的堆+优先级队列 官网docs.python.org/3.7/lib 要点1:heapq实现的是小顶堆 要点2:大顶堆的做法是: [-i for i in heap] 下面是它的类函数 heapq.heappush(heap,item) 放入堆 Push the value item onto the heap, maintaining the heap invariant. heapq.heappop(heap) 取出最小一个元素...
Python 中的堆队列(或 heapq) 原文:https://www . geesforgeks . org/heap-queue-or-heap q-in-python/ 堆数据结构主要用来表示一个优先级队列。在 Python 中,它可以使用“ heapq ”模块获得。Python 中这种数据结构的属性是每次弹出最小的堆元素(最小堆)。每当元素被推
Pythonheapq模块还包括heappush()用于将元素推送到堆,同时保留堆属性。 以下示例显示将值推送到堆: >>> >>>importheapq>>>a=[2,5,3,7,6,8]>>>heapq.heappush(a,4)>>>a[2,5,3,7,6,8,4]>>>heapq.heappop(a)2>>>heapq.heappop(a)3>>>heapq.heappop(a)4 ...
Python3实现 importheapq jobs=[(1,'eat'),(3,'code'),(2,'sleep')] heapq.heapify(jobs) for_inrange(len(jobs)): popped_item=heapq.heappop(jobs) print(popped_item) 输出: (1,'eat') (2,'sleep') (3,'code') 示例2:使用 PriorityQueue ...
def TopK(self): return [x for x in reversed([heapq.heappop(self.data) for x in xrange(len(self.data))])] if __name__ == "__main__": print "Hello" list_rand = random.sample(xrange(1000000), 100) th = TopkHeap(3) for i in list_rand: th.Push(i) print th.TopK() ...
参见下面的示例（适用于Python 3.7）：heapq documentation建议堆元素可以是元组，其中第一个元素是优先级...