python实现各基本数据结构(1)——离散和线性
离散
集合(set)
Operation | Average case | Worst Case | notes |
---|---|---|---|
x in s | O(1) | O(n) | |
Union s | t | O(len(s)+len(t)) | |
Intersection s&t | O(min(len(s), len(t)) | O(len(s) * len(t)) | replace “min” with “max” if t is not a set |
Multiple intersection s1&s2&..&sn | (n-1)*O(l) where l is max(len(s1),..,len(sn)) | ||
Difference s-t | O(len(s)) | ||
s.difference_update(t) | O(len(t)) | ||
Symmetric Difference s^t | O(len(s)) | O(len(s) * len(t)) | |
s.symmetric_difference_update(t) | O(len(t)) | O(len(t) * len(s)) |
dict
Operation | Average case | Worst Case |
---|---|---|
Copy | O(n) | O(n) |
Get Item | O(1) | O(n) |
Set Item | O(1) | O(n) |
Delete Item | O(1) | O(n) |
Iteration | O(n) | O(n) |
defaultdict(collections.defaultdict)
defaultdict在处理不存在的key时和dict不同:若某key不存在则用调用default_factory并将其值作为该key的value。例如
from collections import defaultdict
defdict = defaultdict(list)
defdict['boo'].append(1)
线性数据结构
队列
栈(list)
Operation | Average Case | Amortized Worst Case |
---|---|---|
Copy | O(n) | O(n) |
Append | O(1) | O(1) |
Insert | O(n) | O(n) |
Get Item | O(1) | O(1) |
Set Item | O(1) | O(1) |
Delete Item | O(n) | O(n) |
Iteration | O(n) | O(n) |
Get Slice | O(k) | O(k) |
Del Slice | O(n) | O(n) |
Set Slice | O(k+n) | O(k+n) |
Extend | O(k) | O(k) |
Sort | O(n log n) | O(n log n) |
Multiply | O(nk) | O(nk) |
x in s | O(n) | |
min(s), max(s) | O(n) | |
Get Length | O(1) | O(1) |
array
相对list它固定了数据类型,可以节约内存。
队列 (Queue,LifoQueue)
Queue为先进先出(fifo) LifoQueue为后进先出(lifo)
q = Queue.LifoQueue()
q.put(1)
q.put(2)
q.put(3)
while not q.empty():
print q.get(),
q = Queue.Queue()
q.put(1)
q.put(2)
q.put(3)
while not q.empty():
print q.get(),
output:
3 2 1 1 2 3
双向队列 double-ended(collections.deque)
线程安全,且deque的popleft()
, appendleft(item)
比list的pop(0)
和insert(0, v)
要快的多。
Operation | Average Case | Amortized Worst Case |
---|---|---|
Copy | O(n) | O(n) |
append | O(1) | O(1) |
appendleft | O(1) | O(1) |
pop | O(1) | O(1) |
popleft | O(1) | O(1) |
extend | O(k) | O(k) |
extendleft | O(k) | O(k) |
rotate | O(k) | O(k) |
remove | O(n) | O(n) |
priority queue(Queue.PriorityQueue)
优先队列是利用 heepq实现的
from Queue import PriorityQueue
q = PriorityQueue()
q.put(2)
q.put(1)
q.put(3)
while not q.empty():
print q.get(),
q.put((2,'b'))
q.put((1,'c'))
q.put((3,'a'))
while not q.empty():
print q.get(),
class A(object):
def __init__(self, priority, v):
self.priority = priority
self.value = v
def __cmp__(self, other):
return cmp(self.priority, other.priority)
q.put(A(2, 'B'))
q.put(A(1, 'C'))
q.put(A(3, 'A'))
while not q.empty():
next_item = q.get()
print next_item.value,
output:
1 2 3 (1, 'c') (2, 'b') (3, 'a') C B A