python数据结构和算法

Posted 2020-12-13 时间管理者

栈-先进后出

class Stack():
    def __init__(self):
        self.items = []
    def push(self,item):
        self.items.append(item)
    def pop(self):
        return self.items.pop()
    def peek(self):
        return len(self.items)-1
    def isEmpty(self):
        return self.items == []
    def size(self):
        return len(self.items)
stack = Stack()
stack.push(1)
stack.push(2)
stack.push(3)

print(\'栈顶元素下标:\',stack.peek())
print(stack.isEmpty())
print(stack.pop())
print(stack.pop())
print(stack.pop())

 

 队列-先进先出

class Queue():
    def __init__(self):
        self.items = []
    def enqueue(self,item):
        self.items.insert(0,item)
    def dequeue(self):
        return self.items.pop()
    def isEmpty(self):
        return self.items == []
    def size(self):
        return len(self.items)

q = Queue()
q.enqueue(1)
q.enqueue(2)
q.enqueue(3)
print(q.dequeue())
print(q.dequeue())
print(q.dequeue())

 

 双向队列-头尾都能进出-实现判断回文

 

 

 

 

 

 单链表-从头部插入节点

 

该数据结构主要是依靠head的不停改变，

 

 

class Node():
    def __init__(self,item):
        self.item = item
        self.next = None

class Link():
    def __init__(self):
        #构造出一个空的链表
        self._head = None
    def add(self,item):
        node = Node(item) #先创建一个节点，在内存中，再考虑怎样操作
        node.next =self. _head
        self._head = node
    def travel(self):
        cur = self._head
        while cur:
            print(cur.item)
            cur = cur.next
    
link = Link()
link.add(3)
link.add(4)
link.travel()

 

 两个队列实现栈

队列是先进先出，栈是先进后出，所以只要把数据一个一个先进的后出即可

主要思想就是利用另一个队列来放主要数据队列的n-1个，然后主要数据队列就只剩下最后一个，取出即可，然后重复，就把最后的先出了

 

 链表倒置

 

 

 

 

 二叉树

 

 

class Node():
    def __init__(self,item):
        self.item = item
        self.left = None
        self.right = None

class Tree():
    def __init__(self):
        self.root = None

    def addNode(self,item):
        node = Node(item)
        if self.root == None:
            self.root = node
            return
        cur  = self.root
        q = [cur] #用列表来进行遍历节点
        while q:
            nd = q.pop(0) #先把根结点取出
            if nd.left == None:
                nd.left = node
                return
            else:
                q.append(nd.left)
            if nd.right == None:
                nd.right = node
                return
            else:
                q.append(nd.right)

    def travel(self):
        cur = self.root
        q = [cur]
        while q:
            nd = q.pop(0)
            print(nd.item)
            if nd.left:
                q.append(nd.left)
            if nd.right:
                q.append(nd.right)

tree = Tree()
tree.addNode(1)
tree.addNode(2)
tree.addNode(3)
tree.addNode(4)
tree.addNode(5)
tree.addNode(6)
tree.addNode(7)
tree.travel()

 

 排序二叉树

 

 

class Node():
    def __init__(self,item):
        self.item = item
        self.left = None
        self.right = None

class SortTree():
    def __init__(self):
        self.root = None

    def addNode(self,item):
        node = Node(item)
        cur = self.root
        if self.root == None:
            self.root = node
            return
        while cur:
            #向右插入
            if item>cur.item:
                if cur.right == None:
                    cur.right = node
                    break
                else:
                    cur=cur.right
            #向左插入
            else:
                if cur.left == None:
                    cur.left = node
                    break
                else:
                    cur =cur.left
    
tree = SortTree()
alist = [3,8,5,7,6,2,9,4,1]
for i in alist:
    tree.addNode(i)

        

 二分查找

 

排序 

1 冒泡排序

 

 2 快速排序

def sort(alist):
    #基数
    mid = alist[0]
    low = 0
    high = len(alist)
    while low < high:
        #偏移high
        while low<high:
            if alist[high] > mid:
                high-=1
            else:
                alist[low]=alist[high]
                break
        #偏移low
        while low<high:
            if alist[low]<mid:
                low+=1
            else:
                alist[high]=alist[low]
                break
        if low==high:
            alist[low]=mid
            break
    return alist

alist=[6,1,24,5,3,7,4]
print(sort(alist))

BFS（广度优先搜索）

 

 由谁先展开就需要把它的子节点先遍历出来，然后接子节点的遍历，当然节点也是不唯一的，如第一个的CB当然可以换，不过换了后面也要需要注意前面的子节点遍历

 

 

 

DFS（深度优先搜索）

一条路走到底，从头到尾的探索，结果也是不唯一的