# 生成[[1,2,3],[4,5,6],[1,2,3],[4,5,6].......]
lst = [[1,2,3],[4,5,6]]
c = [lst.extend(lst) for j in range(3)] # 2**n
d = [[1,2,3] if not i&1 else [4,5,6] for i in range(6)] #/2
print(c)
print(d)
##生成 n * m 矩阵 matrix
m,n = 5,5
print([[0]*m]*n) # 陷阱
tm = [[0]*m for j in range(n)]
print(tm)
# 封装和解构,加方括号,相当于后面加了一个逗号
[*a] = [[1,2,3],[4,5,6]] # a = [[1,2,3],[4,5,6]]
*a, = [1,2,3],[4,5,6] # a = [[1,2,3],[4,5,6]]
*a, = ([1,2,3],[4,5,6]) # a = [[1,2,3],[4,5,6]]
[x,*y] = [1,2] # x,y = (1,[2])
# 反转矩阵效率 matrix
import datetime
square = [[4,5,6] for i in range(30)]
n = 100000
start = datetime.datetime.now()
for i in range(n):
# tm = [[0 for col in range(len(square))] for row in range(len(square[0]))]
tm = [0] * len(square[0]) # 构建大列表空间 乘法效率更高,列表解析式和append相当
for i in range(len(tm)):
tm[i] = [0] * len(square)
for i in range(len(tm)):
for j in range(len(tm[0])):
tm[i][j] = square[j][i]
delta = (datetime.datetime.now() - start).total_seconds()
print(delta)
print(tm)
start = datetime.datetime.now()
for i in range(n):
chsquare = []
for i in range(len(square[0])):
chsquare.append([])
for j in range(len(square)):
chsquare[i].append(square[j][i])
delta = (datetime.datetime.now()-start).total_seconds()
print(delta)
print(chsquare)
# 效率低
start = datetime.datetime.now()
for i in range(n):
chsquare = []
for row in square:
for i,val in enumerate(row):
if len(chsquare) < i+1:
chsquare.append([])
chsquare[i].append(val)
delta = (datetime.datetime.now()-start).total_seconds()
print(delta)
print(chsquare)