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列表推导式和seed（）的理解
对seed()的理解
某些场合为了得到两个一模一样的随机数系列，可以使用seed（）来实现
即同一个种子可以得到的随机序列必定相同
import random
random.seed(0)
lst=[random.random() for i in range(5)]
random.seed(0)
lst1=[random.random() for i in range(5)]
print(lst==lst1)
#True
#lst必定等于lst1列表推导式
List Comprehensions is fast， readable and use less code
第一种用法
out_list = [out_express for out_express in input_list if out_express_condition]
out_list = [out_express for out_express in input_list if out_express_condition]
#其中的 if 条件判断根据需要可有可无。
animal_doctor = [animal for animal in animal_park]
nimal_doctor = [animal for animal in animal_park if animal != 'Dog'and animal != 'Cat']
digit=[chr(i) for i in range(48,57)]
letters=[chr(i) for i in range(ord('A'),ord('z'))]+
[chr(i) for i in range(ord('a'),ord('z'))]第二种用法
列表推导式的项可以是任意的，包括列表
matrix = [
[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
]
matrix2=[[row[i] for row in matrix] for i in range(4)]
'''
transposed = []
for i in range(4):
transposed.append([row[i] for row in matrix])
#细看 [row[0] for row in matrix]
transposed = []
for i in range(4):
# the following 3 lines implement the nested listcomp
transposed_row = []
for row in matrix:
transposed_row.append(row[i])
transposed.append(transposed_row)
transposed
'''
flattened = []
for row in matrix:
for i in row:
flattened.append(i)
flattened = [i for row in matrix for i in row]