Python数据结构与算法-Day1-引入

引入

枚举法

for a in range(0,1001):
    for b in range(0, 1001):
        for c in range(0, 1001):
            if a+b+c==1000 and a**2 + b**2 == c**2:
                print("a,b,c:%d,%d,%d" % (a,b,c))


a,b,c:0,500,500
a,b,c:200,375,425
a,b,c:375,200,425
a,b,c:500,0,500

244s
改进

import time
start_time = time.time()
for a in range(0,1001):
    for b in range(0, 1001):
        c = 1000 - a - b
        if a**2 + b**2 == c**2:
            print("a,b,c:%d,%d,%d" % (a,b,c))
end_time = time.time()
print("times:%d" % (end_time-start_time))

a,b,c:0,500,500
a,b,c:200,375,425
a,b,c:375,200,425
a,b,c:500,0,500
times:1

算法的概念

算法的五大特性

算法效率衡量

时间复杂度与“大O记法”

最坏时间复杂度

时间复杂度的基本计算原则

常见时间复杂度

Python内置类型性能分析

timeit模块

list的操作测试

def test1():
   l = []
   for i in range(1000):
      l = l + [i]
def test2():
   l = []
   for i in range(1000):
      l.append(i)
def test3():
   l = [i for i in range(1000)]
def test4():
   l = list(range(1000))

from timeit import Timer

t1 = Timer("test1()", "from __main__ import test1")
print("concat ",t1.timeit(number=1000), "seconds")
t2 = Timer("test2()", "from __main__ import test2")
print("append ",t2.timeit(number=1000), "seconds")
t3 = Timer("test3()", "from __main__ import test3")
print("comprehension ",t3.timeit(number=1000), "seconds")
t4 = Timer("test4()", "from __main__ import test4")
print("list range ",t4.timeit(number=1000), "seconds")

# ('concat ', 1.7890608310699463, 'seconds')
# ('append ', 0.13796091079711914, 'seconds')
# ('comprehension ', 0.05671119689941406, 'seconds')
# ('list range ', 0.014147043228149414, 'seconds')

list内置操作的时间复杂度

dict内置操作的时间复杂度

数据结构

概念

算法与数据结构的区别

抽象数据类型(Abstract Data Type)