Compulsory content data structure as a computer-based, but also many large Internet companies will interview questions. One can imagine its importance in the computer field.
Many computer science students, however, are only theories to understand the data structure, but can not achieve a variety of data structures in code.
Stack
class Stack(object):
def __init__(self, limit=10):
self.stack = [] #存放元素
self.limit = limit #栈容量极限
def push(self, data): #判断栈是否溢出
if len(self.stack) >= self.limit:
print('StackOverflowError')
pass
self.stack.append(data)
def pop(self):
if self.stack:
return self.stack.pop()
else:
raise IndexError('pop from an empty stack') #空栈不能被弹出
def peek(self): #查看堆栈的最上面的元素
if self.stack:
return self.stack[-1]
def is_empty(self): #判断栈是否为空
return not bool(self.stack)
def size(self): #返回栈的大小
return len(self.stack)Single list
class Node:
def __init__(self, data):
self.data = data
self.next = None
class Linked_List:
def __init__(self):
self.head = None
def initlist(self,data_list): #链表初始化函数
self.head=Node(data_list[0]) #创建头结点
temp=self.head
for i in data_list[1:]: #逐个为 data 内的数据创建结点, 建立链表
node=Node(i)
temp.next=node
temp=temp.next
def is_empty(self): #判断链表是否为空
if self.head.next==None:
print("Linked_list is empty")
return True
else:
return False
def get_length(self): #获取链表的长度
temp=self.head #临时变量指向队列头部
length=0 #计算链表的长度变量
while temp!=None:
length=length+1
temp=temp.next
return length #返回链表的长度
def insert(self,key,value): #链表插入数据函数
if key<0 or key>self.get_length()-1:
print("insert error")
temp=self.head
i=0
while i<=key: #遍历找到索引值为 key 的结点后, 在其后面插入结点
pre=temp
temp=temp.next
i=i+1
node=Node(value)
pre.next=node
node.next=temp
def print_list(self): #遍历链表，并将元素依次打印出来
print("linked_list:")
temp=self.head
new_list=[]
while temp is not None:
new_list.append(temp.data)
temp=temp.next
print(new_list)
def remove(self,key): #链表删除数据函数
if key<0 or key>self.get_length()-1:
print("insert error")
i=0
temp=self.head
while temp !=None: #遍历找到索引值为 key 的结点
pre=temp
temp=temp.next
i=i+1
if i==key:
pre.next=temp.next
temp=None
return True
pre.next=None
def reverse(self): #将链表反转
prev = None
current = self.head
while current:
next_node = current.next
current.next = prev
prev = current
current = next_node
self.head = prevDoubly linked list
class Node(object):
#Python学习交流QQ群：857662006
# 双向链表节点
def __init__(self, item):
self.item = item
self.next = None
self.prev = None
class DLinkList(object):
# 双向链表
def __init__(self):
self._head = None
def is_empty(self):
# 判断链表是否为空
return self._head == None
def get_length(self):
# 返回链表的长度
cur = self._head
count = 0
while cur != None:
count=count+1
cur = cur.next
return count
def travel(self):
# 遍历链表
cur = self._head
while cur != None:
print(cur.item)
cur = cur.next
print("")
def add(self, item):
# 头部插入元素
node = Node(item)
if self.is_empty():
# 如果是空链表，将_head指向node
self._head = node
else:
# 将node的next指向_head的头节点
node.next = self._head
# 将_head的头节点的prev指向node
self._head.prev = node
# 将_head 指向node
self._head = node
def append(self, item):
# 尾部插入元素
node = Node(item)
if self.is_empty():
# 如果是空链表，将_head指向node
self._head = node
else:
# 移动到链表尾部
cur = self._head
while cur.next != None:
cur = cur.next
# 将尾节点cur的next指向node
cur.next = node
# 将node的prev指向cur
node.prev = cur
def search(self, item):
# 查找元素是否存在
cur = self._head
while cur != None:
if cur.item == item:
return True
cur = cur.next
return False
def insert(self, pos, item):
# 在指定位置添加节点
if pos <= 0:
self.add(item)
elif pos > (self.length()-1):
self.append(item)
else:
node = Node(item)
cur = self._head
count = 0
# 移动到指定位置的前一个位置
while count < (pos-1):
count += 1
cur = cur.next
# 将node的prev指向cur
node.prev = cur
# 将node的next指向cur的下一个节点
node.next = cur.next
# 将cur的下一个节点的prev指向node
cur.next.prev = node
# 将cur的next指向node
cur.next = node
def remove(self, item):
# 删除元素
if self.is_empty():
return
else:
cur = self._head
if cur.item == item:
# 如果首节点的元素即是要删除的元素
if cur.next == None:
# 如果链表只有这一个节点
self._head = None
else:
# 将第二个节点的prev设置为None
cur.next.prev = None
# 将_head指向第二个节点
self._head = cur.next
return
while cur != None:
if cur.item == item:
# 将cur的前一个节点的next指向cur的后一个节点
cur.prev.next = cur.next
# 将cur的后一个节点的prev指向cur的前一个节点
cur.next.prev = cur.prev
break
cur = cur.nextQueue (implemented as a linked list)
class Node(object):
def __init__(self,elem,next=None):
self.elem = elem #表示对应的元素值
self.next=next #表示下一个链接的链点
class Queue(object):
def __init__(self):
self.head = None #头部链点为 None
self.rear = None #尾部链点为 None
def is_empty(self):
return self.head is None #判断队列是否为空
def enqueue(self, elem):
p = Node(elem) #初始化一个新的点
if self.is_empty():
self.head = p #队列头部为新的链点
self.rear = p #队列尾部为新的链点
else:
self.rear.next = p #队列尾部的后继是这个新的点
self.rear =p #然后让队列尾部指针指向这个新的点
def dequeue(self):
if self.is_empty(): #判断队列是否为空
print('Queue_is_empty') #若队列为空，则退出 dequeue 操作
else:
result = self.head.elem #result为队列头部元素
self.head = self.head.next #改变队列头部指针位置
return result #返回队列头部元素
def peek(self):
if self.is_empty(): #判断队列是否为空
print('NOT_FOUND') #为空则返回 NOT_FOUND
else:
return self.head.elem #返回队列头部元素
def print_queue(self):
print("queue:")
temp=self.head
myqueue=[] #暂时存放队列数据
while temp is not None:
myqueue.append(temp.elem)
temp=temp.next
print(myqueue)Queue (implemented in the form of an array)
class Queue():
def __init__(self):
self.entries = [] #表示队列内的参数
self.length = 0 #表示队列的长度
self.front=0 #表示队列头部位置
def enqueue(self, item):
self.entries.append(item) #添加元素到队列里面
self.length = self.length + 1 #队列长度增加 1
def dequeue(self):
self.length = self.length - 1 #队列的长度减少 1
dequeued = self.entries[self.front] #队首元素为dequeued
self.front-=1 #队首的位置减少1
self.entries = self.entries[self.front:] #队列的元素更新为退队之后的队列
return dequeued
def peek(self):
return self.entries[0] #直接返回队列的队首元素Binary Tree
class Node(object):
def __init__(self,item):
self.item=item #表示对应的元素
self.left=None #表示左节点
self.right=None #表示右节点
def __str__(self):
return str(self.item) #print 一个 Node 类时会打印 __str__ 的返回值
class Tree(object):
def __init__(self):
self.root=Node('root') #根节点定义为 root 永不删除，作为哨兵使用。
def add(self,item):
node = Node(item)
if self.root is None: #如果二叉树为空，那么生成的二叉树最终为新插入树的点
self.root = node
else:
q = [self.root] # 将q列表，添加二叉树的根节点
while True:
pop_node = q.pop(0)
if pop_node.left is None: #左子树为空则将点添加到左子树
pop_node.left = node
return
elif pop_node.right is None: #右子树为空则将点添加到右子树
pop_node.right = node
return
else:
q.append(pop_node.left)
q.append(pop_node.right)
def get_parent(self, item):
if self.root.item == item:
return None # 根节点没有父节点
tmp = [self.root] # 将tmp列表，添加二叉树的根节点
while tmp:
pop_node = tmp.pop(0)
if pop_node.left and pop_node.left.item == item: #某点的左子树为寻找的点
return pop_node #返回某点，即为寻找点的父节点
if pop_node.right and pop_node.right.item == item: #某点的右子树为寻找的点
return pop_node #返回某点，即为寻找点的父节点
if pop_node.left is not None: #添加tmp 元素
tmp.append(pop_node.left)
if pop_node.right is not None:
tmp.append(pop_node.right)
return None
def delete(self, item):
if self.root is None: # 如果根为空，就什么也不做
return False
parent = self.get_parent(item)
if parent:
del_node = parent.left if parent.left.item == item else parent.right # 待删除节点
if del_node.left is None:
if parent.left.item == item:
parent.left = del_node.right
else:
parent.right = del_node.right
del del_node
return True
elif del_node.right is None:
if parent.left.item == item:
parent.left = del_node.left
else:
parent.right = del_node.left
del del_node
return True
else: # 左右子树都不为空
tmp_pre = del_node
tmp_next = del_node.right
if tmp_next.left is None:
# 替代
tmp_pre.right = tmp_next.right
tmp_next.left = del_node.left
tmp_next.right = del_node.right
else:
while tmp_next.left: # 让tmp指向右子树的最后一个叶子
tmp_pre = tmp_next
tmp_next = tmp_next.left
# 替代
tmp_pre.left = tmp_next.right
tmp_next.left = del_node.left
tmp_next.right = del_node.right
if parent.left.item == item:
parent.left = tmp_next
else:
parent.right = tmp_next
del del_node
return True
else:
return FalseTrie
class TrieNode:
def __init__(self):
self.nodes = dict() # 构建字典
self.is_leaf = False
def insert(self, word: str):
curr = self
for char in word:
if char not in curr.nodes:
curr.nodes[char] = TrieNode()
curr = curr.nodes[char]
curr.is_leaf = True
def insert_many(self, words: [str]):
for word in words:
self.insert(word)
def search(self, word: str):
curr = self
for char in word:
if char not in curr.nodes:
return False
curr = curr.nodes[char]
return curr.is_leafstack
class heap(object):
def __init__(self):
#初始化一个空堆，使用数组来在存放堆元素，节省存储
self.data_list = []
def get_parent_index(self,index):
#返回父节点的下标
if index == 0 or index > len(self.data_list) -1:
return None
else:
return (index -1) >> 1
def swap(self,index_a,index_b):
#交换数组中的两个元素
self.data_list[index_a],self.data_list[index_b] = self.data_list[index_b],self.data_list[index_a]
def insert(self,data):
#先把元素放在最后，然后从后往前依次堆化
#这里以大顶堆为例，如果插入元素比父节点大，则交换，直到最后
self.data_list.append(data)
index = len(self.data_list) -1
parent = self.get_parent_index(index)
#循环，直到该元素成为堆顶，或小于父节点（对于大顶堆)
while parent is not None and self.data_list[parent] < self.data_list[index]:
#交换操作
self.swap(parent,index)
index = parent
parent = self.get_parent_index(parent)
def removeMax(self):
#删除堆顶元素，然后将最后一个元素放在堆顶，再从上往下依次堆化
remove_data = self.data_list[0]
self.data_list[0] = self.data_list[-1]
del self.data_list[-1]
#堆化
self.heapify(0)
return remove_data
def heapify(self,index):
#从上往下堆化，从index 开始堆化操作 (大顶堆)
total_index = len(self.data_list) -1
while True:
maxvalue_index = index
if 2*index +1 <= total_index and self.data_list[2*index +1] > self.data_list[maxvalue_index]:
maxvalue_index = 2*index +1
if 2*index +2 <= total_index and self.data_list[2*index +2] > self.data_list[maxvalue_index]:
maxvalue_index = 2*index +2
if maxvalue_index == index:
break
self.swap(index,maxvalue_index)
index = maxvalue_index