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 = None


class Stack(object):
    """docstring for Stack"""
    def __init__(self, top=StackNode(None)):
        self.top = top
    
    def get_top(self):
        return self.top

    def is_empty(self):
        return self.top.value is None

    def push(self, val):
        node = StackNode(val)
        node.next = self.top
        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 = None


class 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。