[笔记]python数据结构之线性表：linkedlist链表,stack栈,queue队列

python数据结构之线性表

python内置了很多高级数据结构，list，dict，tuple，string，set等，在使用的时候十分舒心。但是，如果从一个初学者的角度利用python学习数据结构时，这些高级的数据结构可能给我们以迷惑。

比如，使用list实现queue的时候，入队操作append()时间复杂度可以认为是O(1),但是，出队操作pop(0)的时间复杂度就是O(n)。

如果是想利用python学学数据结构的话，我觉得还是自己实现一遍基本的数据结构为好。

1.链表

在这里，我想使用类似于c语言链式存储的形式，借助于class，分别构成无序链表以及有序链表。

我们先看看链表节点的定义：

class ListNode(object):    def __init__(self, data):        self.data = data        self.next = None    def getData(self):        return self.data    def setData(self, newData):        self.data = newData    def getNext(self):        return self.next    def setNext(self, nextNode):        self.next = nextNode

利用链表节点，组成无序链表类：

class UnorderedList(object):    def __init__(self):        self.head = None    def getHead(self):        return self.head    def isEmpty(self):        return self.head is None    def add(self, item):        node = ListNode(item)        node.next = self.head        self.head = node   # the head is the most recently added node    def size(self):        current = self.head        count = 0        while current is not None:            count += 1            current = current.getNext()        return count    def search(self, item):        current = self.head        found = False        while current is not None and not found:            if current.getData() == item:                found = True            else:                current = current.getNext()        return found    def append(self, item):        node = ListNode(item)        if self.isEmpty():            self.head = node        else:            current = self.head            while current.getNext() is not None:                current = current.getNext()            current.setNext(node)    def remove(self, item):        current = self.head        previous = None        found = False        while not found:            if current.getData() == item:                found = True            else:                previous = current                current = current.getNext()        if previous is None:            self.head = current.getNext()        else:            previous.setNext(current.getNext())

在上面的链表中，每次添加元素都直接添加在链表头部，add()的时间复杂度为O(1)，而append()操作在队尾，其时间复杂度为O(n)。有没有前后加入操作的时间复杂度都为O(1)的链表呢，当然是有的：

class UnorderedList(object):    def __init__(self):        self.head = None        self.tail = None    def getHead(self):        return self.head    def isEmpty(self):        return self.head is None and self.tail is None    def add(self, item):        node = ListNode(item)        if self.isEmpty():            self.head = self.tail = node        else:            node.next = self.head            self.head = node   # the head is the most recently added node    def size(self):        current = self.head        count = 0        while current is not None:            count += 1            current = current.getNext()        return count    def search(self, item):        current = self.head        found = False        while current is not None and not found:            if current.getData() == item:                found = True            else:                current = current.getNext()        return found    def append(self, item):        node = ListNode(item)        self.tail.setNext(node)        self.tail = node    def remove(self, item):        current = self.head        previous = None        found = False        while not found:            if current.getData() == item:                found = True            else:                previous = current                current = current.getNext()        if current.getNext() is None:            self.tail = previous        if previous is None:            self.head = current.getNext()        else:            previous.setNext(current.getNext())

对无序链表类加入一个属性，引用链表末尾节点，即可。做出了这样的改变，在add和remove操作也应作出相应变化。

下面再看看有序链表。有序链表在插入节点的时候便寻找适合节点的位置。

class OrderedList(object):    def __init__(self):        self.head = None    def isEmpty(self):        return self.head is None    def search(self, item):        stop = False        found = False        current = self.head        while current is not None and not found and not stop:            if current.getData() > item:                stop = True            elif current.getData() == item:                found = True            else:                current = current.getNext()        return found    def add(self, item):        previous = None        current = self.head        stop = False        while current is not None and not stop:            if current.getData() >item:                stop = True            else:                previous = current                current = current.getNext()        node = ListNode(item)        if previous is None:            node.getNext(current)            self.head = node        else:            previous.setNext(node)            node.setNext(current)

2.栈stack

对于栈来说，python内置的列表已经可以满足栈的要求。

入栈操作为append(),出栈操作为pop()。它们的时间复杂度都为O(1).

class Stack(object):    def __init__(self):        self._items = []    def is_empty(self):        return self._items == []    def push(self, item):        self._items.append(item)    def pop(self):        return self._items.pop()    def peek(self):        return self._items[-1]

当然了，我们也可以自己实现链栈，跟链表的实现类似。

class StackNode(object):    """docstring for StackNode"""    def __init__(self, value):        self.value = value        self.next = Noneclass Stack(object):    """docstring for Stack"""    def __init__(self, top=None):        self.top = top        def get_top(self):        return self.top    def is_empty(self):        return self.top is None    def push(self, val):        if self.is_empty():            self.top = StackNode(val)            return        else:            node = StackNode(val)            node.next = self.top.next            self.top = node            return    def pop(self):        if self.is_empty():            print("Stack is Empty, cannot pop anymore.\n")            return        node = self.top        self.top = self.top.next        return node

3.队列queue

队列如果利用链表实现的话会，出现文章开头提及的问题。

所以队列可以用链表实现。

class QueueNode(object):    def __init__(self, value):        self.value = value        self.next = Noneclass Queue(object):    def __init__(self):        self.front = None        self.rear = None    def is_empty(self):        return self.front is None and self.rear is None    def enqueue(self, num):        node = QueueNode(num)        if self.is_empty():            self.front = node            self.rear = node        else:            self.rear.next = node            self.rear = node    def dequeue(self):        if self.front is self.rear:            node = self.front            self.front = None            self.rear = None            return node.value        else:            node = self.front            self.front = node.next            return node.value

在python的库中，比如collections以及Queue中都有deque模块。

deque模块顾名思义，可以做双端队列。所以，deque模块也可以做队列，和栈。

dq = deque([1,2,3,4,5,6,7,8,9])

dq.pop() # pop 9

dq.popleft() #pop 1

dq.apend(9) # append 9

dq.appendleft(1) #insert 1 in index 0

在多线程，多进程编程时，经常使用Queue模块的Queue类。

其实：假设q=Queue.Queue() 

那么 q.queue就是一个deque。

这个以后再谈。