Python data structure heap __ data structure

Heap definition: The heap is a special kind of tree data structure, each node has a value, usually we say the heap of data structure refers to the two-fork tree. The heap is characterized by the maximum (or minimum) value of the root node, and the two children of the root node can also form a subtree with the child's node, which is called a heap.
The heap is divided into two, the Dagen and the small Gan is a tree where each node's key value is not less than (greater than) the key value of its child node. Whether it's a Dagen or a small Gan (the premise is a two-fork heap) can be considered as a complete binary tree. Here is a picture of the form of intuitive experience:

>>> Import HEAPQ #heap和random是python的基础包
>>> Import Random
>>> data=list (range) #注意range对象并不是一个列表, want data to be a list need to convert it to list type >>> data
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> random.choice (data) #从列表中随机选择一个数显示出来, the elements of the original list are not changed at this time
3
>>> random.shuffle (data) #random基础库中的shuffle能打乱列表元素的顺序, the Python official document says it will return an upset list, #实际上只是对原来的列表进行了原地修改
>>> data
[1, 4, 8, 0, 3, 2, 9, 7, 6, 5]
>>> heap=[] #新建一个空堆
>>> for N in data: #用for循环把列表中的元素放在空堆中, the order of the elements in the heap is not in the order of the list, it seems to have been disrupted
Heapq.heappush (heap,n) >>> heap
[0, 1, 2, 4, 3, 8, 9, 7, 6, 5]
>>> Heapq.heappush (heap,0.5) #把0.5 inserted into the heap, automatically rebuilding the heap
>>> Heap
[0, 0.5, 2, 4, 1, 8, 9, 7, 6, 5, 3]
>>> Heapq.heappush (heap,5.1) #堆中插入元素的顺序好像并不是有规律
>>> Heap
[0, 0.5, 2, 4, 1, 5.1, 9, 7, 6, 5, 3, 8]
>>> Heapq.heappush (heap,5.2)
>>> Heap
[0, 0.5, 2, 4, 1, 5.1, 9, 7, 6, 5, 3, 8, 5.2]
>>> Heapq.heappop (Heap)
0
>>> Heapq.heappop (Heap)
0.5
>>> heapq.heappop (Heap) #弹出堆中的元素, the returned values are not sequential
1
>>> myheap=[1,2,3,5,7,8,9,4,10,333] >>> heapq.heapify (myheap) # Converts a list to a heap at linear time (previously built with A For loop)
>>> Myheap
[1, 2, 3, 4, 7, 8, 9, 5, 333] >>> item = myheap[0] #查看堆中最小的值, do not eject
>>> Item
2
>>> heapq.heapreplace (myheap,6) #用一个新的元素替代掉原堆中最小的元素
1
>>> Myheap
[2, 4, 3, 5, 7, 8, 9, 6, 10, 333]
>>> heapq.nlargest (3, Myheap) #返回堆中前3个最大的元素
[333, 10, 9]
>>> Myheap
[2, 4, 3, 5, 7, 8, 9, 6, 10, 333]
>>> heapq.nsmallest (3, Myheap) #返回堆中前3个最小的元素, this does not change the elements in the original heap
[2, 3, 4]
>>> Myheap
[2, 4, 3, 5, 7, 8, 9, 6, 10, 333]