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]

python官方文档

巴特西

列表推导式和seed（）的理解

列表推导式和seed（）的理解

对seed()的理解

列表推导式

第一种用法

第二种用法

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