python常用的数据结构运行效率分析

1.while循环和for循环相同条件下的运行效率比较：

如下代码：

 import time as tm
 import timeit as tt
 import random as rm

 def while_time():
     i = 0
     while i < 100000001:
         i += 1
         if i > 100000:
             break
     return i

 def for_time():
     j = 0
     for i in range(100000001):
         j += 1
         if j > 100000:
             break
     return j

 if __name__ == "__main__":
     t1 = tt.Timer("for_time()", "from __main__ import for_time")
         print(t1.timeit(number=10))
         t2 = tt.Timer("while_time()", "from __main__ import while_time")
         print(t2.timeit(number=10))

结果：

说明：如上结果，for循环和while循环的时间复杂度都是T（n）=5*n+1，但是for循环的效率比while循环稍高。

2.遍历列表、元组、集合的效率比较：
如下代码：

 def list_time():
     for i in list1:
     i += 1

 def tuple_time():
     for i in tp1:
     i += 1

 def set_time():
     for i in set1:
     i += 1

 def main():
     list1 = list(range(30000000))
     tp1 = tuple(list1)
     set1 = set(list1)
     t1 = tt.Timer("list_time()", "from __main__ import list_time")
     print(t1.timeit(number=10))
     t2 = tt.Timer("tuple_time()", "from __main__ import tuple_time")
     print(t2.timeit(number=10))
     t3 = tt.Timer("set_time()", "from __main__ import set_time")
     print(t3.timeit(number=10))
 if __name__ == "__main__":
     main()

结果：

说明：三个函数的查找的时间复杂度都为O（n），列表和元组的遍历效率差不多，集合遍历的效率稍低。