[摘要]Python 最佳實踐指南

wklken發表於2015-12-08

這份文件

目標物件：入門後，有一定基礎的Pythonista
關鍵詞：最佳實踐，Pythonic，各類工具介紹

1 2	目標物件：入門後，有一定基礎的Pythonista 關鍵詞：最佳實踐，Pythonic，各類工具介紹

粗粗粗略地過了一遍，大體撈了一些東西出來，大段大段英文太費眼了，回頭細讀在更新進來

濃縮版，20分鐘可大體過完，然後根據自己需要去看詳細的吧

整體內容還是很不錯的，建議細讀英文

PS:文件含有巨量的TODO(沒寫空白著待補充的)，不過但從目錄上來看還是很強大滴，相信完善後，會成為一份很牛逼的指南(難度比官方指南高一點點)

第零部分 Getting Started

連結

不解釋，不翻譯，自個看….真的沒啥(每本入門書籍第一章…)

第一部分 Writing Great Code

Structuring Your Project

連結

import 最佳實踐

Very bad

[...]
from modu import *
[...]
x = sqrt(4)  # Is sqrt part of modu? A builtin? Defined above?

[...]

from modu import *

[...]

x = sqrt(4) # Is sqrt part of modu? A builtin? Defined above?

Better

from modu import sqrt
[...]
x = sqrt(4)  # sqrt may be part of modu, if not redefined in between

from modu import sqrt

[...]

x = sqrt(4) # sqrt may be part of modu, if not redefined in between

Best

import modu
[...]
x = modu.sqrt(4)  # sqrt is visibly part of modu's namespace

import modu

[...]

x = modu.sqrt(4) # sqrt is visibly part of modu's namespace

Python中關於OOP的觀點

Decorators

def foo():
    # do something

def decorator(func):
    # manipulate func
    return func

foo = decorator(foo)  # Manually decorate

@decorator
def bar():
    # Do something
# bar() is decorated

def foo():

# do something

def decorator(func):

# manipulate func

return func

foo = decorator(foo) # Manually decorate

@decorator

def bar():

# Do something

# bar() is decorated

動態型別(Dynamic typing)

Avoid using the same variable name for different things.

Bad

a = 1
a = 'a string'
def a():
    pass  # Do something

a = 1

a = 'a string'

def a():

pass # Do something

Good

count = 1
msg = 'a string'
def func():
    pass  # Do something

count = 1

msg = 'a string'

def func():

pass # Do something

It is better to use different names even for things that are related, when they have a different type:

Bad

items = 'a b c d'  # This is a string...
items = items.split(' ')  # ...becoming a list
items = set(items)  # ...and then a set

Bad

items = 'a b c d' # This is a string...

items = items.split(' ') # ...becoming a list

items = set(items) # ...and then a set

可變和不可變型別(Mutable and immutable types)

字串拼接最佳實踐

Bad

# create a concatenated string from 0 to 19 (e.g. "012..1819")
nums = ""
for n in range(20):
  nums += str(n)   # slow and inefficient
print nums

# create a concatenated string from 0 to 19 (e.g. "012..1819")

nums = ""

for n in range(20):

nums += str(n) # slow and inefficient

print nums

Good

# create a concatenated string from 0 to 19 (e.g. "012..1819")
nums = []
for n in range(20):
  nums.append(str(n))
print "".join(nums)  # much more efficient

# create a concatenated string from 0 to 19 (e.g. "012..1819")

nums = []

for n in range(20):

nums.append(str(n))

print "".join(nums) # much more efficient

Best

# create a concatenated string from 0 to 19 (e.g. "012..1819")
nums = [str(n) for n in range(20)]
print "".join(nums)

# create a concatenated string from 0 to 19 (e.g. "012..1819")

nums = [str(n) for n in range(20)]

print "".join(nums)

join() is not always best
建立新字串和修改原有字串

foo = 'foo'
bar = 'bar'

foobar = foo + bar  # This is good
foo += 'ooo'  # This is bad, instead you should do:
foo = ''.join([foo, 'ooo'])

foo = 'foo'

bar = 'bar'

foobar = foo + bar # This is good

foo += 'ooo' # This is bad, instead you should do:

foo = ''.join([foo, 'ooo'])

字串格式化

foo = 'foo'
bar = 'bar'

foobar = '%s%s' % (foo, bar) # It is OK
foobar = '{0}{1}'.format(foo, bar) # It is better
foobar = '{foo}{bar}'.format(foo=foo, bar=bar) # It is best

foo = 'foo'

bar = 'bar'

foobar = '%s%s' % (foo, bar) # It is OK

foobar = '{0}{1}'.format(foo, bar) # It is better

foobar = '{foo}{bar}'.format(foo=foo, bar=bar) # It is best

Code Style

連結

一般概念(General concepts)

明確的程式碼

Bad

def make_complex(*args):
    x, y = args
    return dict(**locals())

def make_complex(*args):

x, y = args

return dict(**locals())

Good

def make_complex(x, y):
    return {'x': x, 'y': y}

1 2	def make_complex(x, y): return {'x': x, 'y': y}

每行一個宣告

Bad

print 'one'; print 'two'

if x == 1: print 'one'

if  and :
    # do something

print 'one'; print 'two'

if x == 1: print 'one'

if and :

# do something

Good

print 'one'
print 'two'

if x == 1:
    print 'one'

cond1 = 
cond2 = 
if cond1 and cond2:
    # do something

print 'one'

print 'two'

if x == 1:

print 'one'

cond1 =

cond2 =

if cond1 and cond2:

# do something

函式引數

#不解釋了
位置引數，預設引數，*args, **args

1 2	#不解釋了位置引數，預設引數，args, *args

Avoid the magical wand(這個腫麼翻…)

原因：
Python comes with a very rich set of hooks and tools allowing to do almost any kind of tricky tricks

建議：
it is always better to use the most straightforward way to achieve your goal

感受一下:
We consider that a Python developer should know about these nearly infinite possibilities,
because it grows the confidence that no hard-wall will be on the way.
However, knowing how to use them and particularly when not to use them is the most important.

Like a Kungfu master, a Pythonista knows how to kill with a single finger, and never to actually do it.

其實就是告訴你，騷年，這玩意你要去學習去了解去掌握，目的是增強實力保持自信，但是不要去用啊
(說的原子彈吧....)

原因：

Python comes with a very rich set of hooks and tools allowing to do almost any kind of tricky tricks

建議：

it is always better to use the most straightforward way to achieve your goal

感受一下:

We consider that a Python developer should know about these nearly infinite possibilities,

because it grows the confidence that no hard-wall will be on the way.

However, knowing how to use them and particularly when not to use them is the most important.

Like a Kungfu master, a Pythonista knows how to kill with a single finger, and never to actually do it.

其實就是告訴你，騷年，這玩意你要去學習去了解去掌握，目的是增強實力保持自信，但是不要去用啊

(說的原子彈吧....)

方言(Idioms)

Idiomatic Python code is often referred to as being Pythonic.

列舉了一些：

Unpacking

for index, item in enumerate(some_list):
    # do something with index and item

a, b = b, a

a, (b, c) = 1, (2, 3)

for index, item in enumerate(some_list):

# do something with index and item

a, b = b, a

a, (b, c) = 1, (2, 3)

忽略接收變數,這裡用的是兩個下劃線，原因
http://docs.python-guide.org/en/latest/writing/style/#create-an-ignored-variable

filename = 'foobar.txt'
basename, __, ext = filename.rpartition('.')

1 2	filename = 'foobar.txt' basename, __, ext = filename.rpartition('.')

同一個元素建立一個長度為N的列表

four_nones = [None] * 4

1	four_nones = [None] * 4

建立一個長度N的巢狀列表

four_lists = [[] for __ in xrange(4)]

1	four_lists = [[] for __ in xrange(4)]

由列表拼接字串

letters = ['s', 'p', 'a', 'm']
word = ''.join(letters)

1 2	letters = ['s', 'p', 'a', 'm'] word = ''.join(letters)

快速查詢

d = {'s': [], 'p': [], 'a': [], 'm': []}
l = ['s', 'p', 'a', 'm']

def lookup_dict(d): #O(1)
    return 's' in d

def lookup_list(l): #O(n)
    return 's' in l

d = {'s': [], 'p': [], 'a': [], 'm': []}

l = ['s', 'p', 'a', 'm']

def lookup_dict(d): #O(1)

return 's' in d

def lookup_list(l): #O(n)

return 's' in l

Zen of Python

import this

1	import this

PEP8

$ pip install pep8
$ pep8 optparse.py
optparse.py:69:11: E401 multiple imports on one line
optparse.py:77:1: E302 expected 2 blank lines, found 1

$ pip install pep8

$ pep8 optparse.py

optparse.py:69:11: E401 multiple imports on one line

optparse.py:77:1: E302 expected 2 blank lines, found 1

慣例(Conventions)

判斷值是否等於常量

Bad:

if attr == True:
    print 'True!'

if attr == None:
    print 'attr is None!'

if attr == True:

print 'True!'

if attr == None:

print 'attr is None!'

Good:

# Just check the value
if attr:
    print 'attr is truthy!'

# or check for the opposite
if not attr:
    print 'attr is falsey!'

# or, since None is considered false, explicitly check for it
if attr is None:
    print 'attr is None!'

# Just check the value

if attr:

print 'attr is truthy!'

# or check for the opposite

if not attr:

print 'attr is falsey!'

# or, since None is considered false, explicitly check for it

if attr is None:

print 'attr is None!'

獲取字典元素

Bad:

d = {'hello': 'world'}
if d.has_key('hello'):
    print d['hello']    # prints 'world'
else:
    print 'default_value'

d = {'hello': 'world'}

if d.has_key('hello'):

print d['hello'] # prints 'world'

else:

print 'default_value'

Good:

d = {'hello': 'world'}

print d.get('hello', 'default_value') # prints 'world'
print d.get('thingy', 'default_value') # prints 'default_value'

# Or:
if 'hello' in d:
    print d['hello']

d = {'hello': 'world'}

print d.get('hello', 'default_value') # prints 'world'

print d.get('thingy', 'default_value') # prints 'default_value'

# Or:

if 'hello' in d:

print d['hello']

快捷列表操作

Bad:

# Filter elements greater than 4
a = [3, 4, 5]
b = []
for i in a:
    if i > 4:
        b.append(i)

# Filter elements greater than 4

a = [3, 4, 5]

b = []

for i in a:

if i > 4:

b.append(i)

Good:

a = [3, 4, 5]
b = [i for i in a if i > 4]
b = filter(lambda x: x > 4, a)

a = [3, 4, 5]

b = [i for i in a if i > 4]

b = filter(lambda x: x > 4, a)

Bad:

# Add three to all list members.
a = [3, 4, 5]
for i in range(len(a)):
    a[i] += 3

# Add three to all list members.

a = [3, 4, 5]

for i in range(len(a)):

a[i] += 3

Good:

a = [3, 4, 5]
a = [i + 3 for i in a]
# Or:
a = map(lambda i: i + 3, a)

a = [3, 4, 5]

a = [i + 3 for i in a]

# Or:

a = map(lambda i: i + 3, a)

使用enumerate

for i, item in enumerate(a):
    print i, item
# prints
# 0 3
# 1 4
# 2 5

for i, item in enumerate(a):

print i, item

# prints

# 0 3

# 1 4

# 2 5

讀檔案

Bad:

f = open('file.txt')
a = f.read()
print a
f.close()

f = open('file.txt')

a = f.read()

print a

f.close()

Good:

with open('file.txt') as f:
    for line in f:
        print line

with open('file.txt') as f:

for line in f:

print line

超長的行

Bad:

my_very_big_string = """For a long time I used to go to bed early. Sometimes, 
    when I had put out my candle, my eyes would close so quickly that I had not even 
    time to say “I’m going to sleep.”"""

from some.deep.module.inside.a.module import a_nice_function, another_nice_function, 
    yet_another_nice_function

my_very_big_string = """For a long time I used to go to bed early. Sometimes,

when I had put out my candle, my eyes would close so quickly that I had not even

time to say “I’m going to sleep.”"""

from some.deep.module.inside.a.module import a_nice_function, another_nice_function,

yet_another_nice_function

Good:

#受教了....
my_very_big_string = (
    "For a long time I used to go to bed early. Sometimes, "
    "when I had put out my candle, my eyes would close so quickly "
    "that I had not even time to say “I’m going to sleep.”"
)

from some.deep.module.inside.a.module import (
    a_nice_function, another_nice_function, yet_another_nice_function)

#受教了....

my_very_big_string = (

"For a long time I used to go to bed early. Sometimes, "

"when I had put out my candle, my eyes would close so quickly "

"that I had not even time to say “I’m going to sleep.”"

)

from some.deep.module.inside.a.module import (

a_nice_function, another_nice_function, yet_another_nice_function)

Reading Great Code

連結

感受下：The number one thing that Python programmers do is read code.

再感受一把：One of the secrets of becoming a great Python programmer is to read, understand, and comprehend excellent code.

幾個推薦閱讀原始碼專案

文件(Documentation)

連結

感受一下：Readability is a primary focus for Python developers, in both project and code documentation.

具體還是讀原文吧

專案文件組成

1.A README file
  at the root directory should give general information to the users and the maintainers.

  reStructuredText 或 Markdown
2.An INSTALL file
  is less necessary with python

  setup.py

3.A LICENSE file
  should always be present and specify the license under which the software is made available to the public
4.A TODO file or a TODO section in README
  should list the planned development for the code.
5.A CHANGELOG file or section in README
  should compile a short overview of the changes in the code base for the latest versions.

1.A README file

at the root directory should give general information to the users and the maintainers.

reStructuredText 或 Markdown

2.An INSTALL file

is less necessary with python

setup.py

3.A LICENSE file

should always be present and specify the license under which the software is made available to the public

4.A TODO file or a TODO section in README

should list the planned development for the code.

5.A CHANGELOG file or section in README

should compile a short overview of the changes in the code base for the latest versions.

幾種文件工具

Sphinx(聽說最強大....)
reStructuredText
Markdown(俺的最愛...)

Sphinx(聽說最強大....)

reStructuredText

Markdown(俺的最愛...)

程式碼文件建議

Comments clarify code and begin with a hash (#).

In Python, docstrings describe modules, classes, and functions:

def square_and_rooter(x):
    """Returns the square root of self times self."""

1 2	def square_and_rooter(x): """Returns the square root of self times self."""

註解程式碼塊

Do not use triple-quote strings to comment code.

This is not a good practice, because line-oriented command-line tools such as grep will not be aware that the commented code is inactive.

It is better to add hashes at the proper indentation level for every commented line.

最佳實踐:

不用三引號註解程式碼塊
每一行加#來註釋

1 2	不用三引號註解程式碼塊每一行加#來註釋

測試你的程式碼(Testing Your Code)

連結

測試一些通用原則

1.A testing unit should focus on one tiny bit of functionality and prove it correct.
2.Each test unit must be fully independent
3.Try hard to make tests that run fast
4.Learn your tools and learn how to run a single test or a test case
5.Always run the full test suite before a coding session, and run it again after.
6.It is a good idea to implement a hook that runs all tests before pushing code to a shared repository.
7.If you are in the middle of a development session and have to interrupt your work,
  it is a good idea to write a broken unit test about what you want to develop next.
8.The first step when you are debugging your code is to write a new test pinpointing the bug.
9.Use long and descriptive names for testing functions
10.When something goes wrong or has to be changed, and if your code has a good set of tests,
  you or other maintainers will rely largely on the testing suite to fix the problem or modify a given behavior.
11.Another use of the testing code is as an introduction to new developers.

1.A testing unit should focus on one tiny bit of functionality and prove it correct.

2.Each test unit must be fully independent

3.Try hard to make tests that run fast

4.Learn your tools and learn how to run a single test or a test case

5.Always run the full test suite before a coding session, and run it again after.

6.It is a good idea to implement a hook that runs all tests before pushing code to a shared repository.

7.If you are in the middle of a development session and have to interrupt your work,

it is a good idea to write a broken unit test about what you want to develop next.

8.The first step when you are debugging your code is to write a new test pinpointing the bug.

9.Use long and descriptive names for testing functions

10.When something goes wrong or has to be changed, and if your code has a good set of tests,

you or other maintainers will rely largely on the testing suite to fix the problem or modify a given behavior.

11.Another use of the testing code is as an introduction to new developers.

單元測試(Unittest)

Python內建模組, 文件

import unittest

def fun(x):
    return x + 1

class MyTest(unittest.TestCase):
    def test(self):
        self.assertEqual(fun(3), 4)

import unittest

def fun(x):

return x + 1

class MyTest(unittest.TestCase):

def test(self):

self.assertEqual(fun(3), 4)

文件測試(Doctest)

非精細case，只驗證主體功能可用

def square(x):
    """Squares x.

    >>> square(2)
    4
    >>> square(-2)
    4
    """

    return x * x

if __name__ == '__main__':
    import doctest
    doctest.testmod()

def square(x):

"""Squares x.

>>> square(2)

>>> square(-2)

"""

return x * x

if __name__ == '__main__':

import doctest

doctest.testmod()

相關工具

py.text
$ pip install pytest

Nose unittest的擴充套件
$ pip install nose

tox
$ pip install tox

Unittest2
$ pip install unittest2

mock
$ pip install mock

Common Gotchas(不懂怎麼翻…╮(╯▽╰)╭ )

一些新手可能疑惑的例子

兩個例子

1.可變預設引數

What You Wrote

def append_to(element, to=[]):
    to.append(element)
    return to

def append_to(element, to=[]):

to.append(element)

return to

What You Might Have Expected to Happen

my_list = append_to(12)
print my_list

my_other_list = append_to(42)
print my_other_list

[12]
[42]

my_list = append_to(12)

print my_list

my_other_list = append_to(42)

print my_other_list

[12]

[42]

What Does Happen

[12]
[12, 42]

1 2	[12] [12, 42]

What You Should Do Instead

def append_to(element, to=None):
    if to is None:
        to = []
    to.append(element)
    return to

def append_to(element, to=None):

if to is None:

to = []

to.append(element)

return to

Python預設引數在函式定義處執行一次，而不是每次函式呼叫時執行。

2.Late Binding Closures(又一個，延遲繫結閉包?)

What You Wrote

def create_multipliers():
    return [lambda x : i * x for i in range(5)] #

1 2	def create_multipliers(): return [lambda x : i * x for i in range(5)] #

What You Might Have Expected to Happen

for multiplier in create_multipliers():
    print multiplier(2) # 任意一個返回的函式被呼叫時，內部迴圈i=4

0
2
4
6
8

for multiplier in create_multipliers():

print multiplier(2) # 任意一個返回的函式被呼叫時，內部迴圈i=4

What Does Happen

What You Should Do Instead

from functools import partial
from operator import mul

def create_multipliers():
    return [partial(mul, i) for i in range(5)]

from functools import partial

from operator import mul

def create_multipliers():

return [partial(mul, i) for i in range(5)]

Python的閉包是延時繫結

選擇證照(Choosing a License)

連結

開源證照列表

證照選擇器入口

第二部分 Scenario Guide

都是介紹性質的，類似工具目錄,而且大部分是空的，目前沒詳細資訊

要了解具體，goole相關關鍵詞吧

具體自己翻吧位置

Network Applications

Http:

Requests

Requests

Distributed Systems

ZeroMQ
RabbitMQ

1 2	ZeroMQ RabbitMQ

Web Applications

Context

WSGI

WSGI

Frameworks

Django
Flask
Werkzeug
Tornado
Pyramid

Django

Flask

Werkzeug

Tornado

Pyramid

Web Servers

Nginx

Nginx

WSGI Servers

Gunicorn

Gunicorn

Hosting

PasS (Platform as a service)
Heroku
DotCloud
Gondor

PasS (Platform as a service)

Heroku

DotCloud

Gondor

Templating

Jinja2

Jinja2

HTML Scraping

lxml
Requests

1 2	lxml Requests

Command Line Applications

Clint
docopt

1 2	Clint docopt

GUI Applications

Qt
Cocoa
wxPython
GTk
Tk
Kivy
PyjamasDesktop (pyjs Desktop)
Camelot

Cocoa

wxPython

GTk

Kivy

PyjamasDesktop (pyjs Desktop)

Camelot

Databases

DB-API
SQLAlchemy
Django ORM

DB-API

SQLAlchemy

Django ORM

Networking

Twisted
PyZMQ
gevent

Twisted

PyZMQ

gevent

Systems Administration

Fabric
Salt
Psutil
Chef
Puppet
Blueprint
Buildout

Fabric

Salt

Psutil

Chef

Puppet

Blueprint

Buildout

Continuous Integration

Jenkins
Buildbot
Mule?
Tox
Travis-CI

Jenkins

Buildbot

Mule?

Tox

Travis-CI

Speed

C Extensions

GIL
Cython
Pyrex
Shedskin
Numba

GIL

Cython

Pyrex

Shedskin

Numba

Threading

Threading
Multiprocessing

1 2	Threading Multiprocessing

Scientific Applications

Tools

IPython

IPython

Libraries

NumPy
Numba
SciPy
Matplotlib
Pandas
Rpy2
PsychoPy

NumPy

Numba

SciPy

Matplotlib

Pandas

Rpy2

PsychoPy

Image Manipulation

Python Imaging Library(PIL)

1	Python Imaging Library(PIL)

XML parsing

untangle
xmltodict

1 2	untangle xmltodict

有需要自取…..

第三部分 Shipping Great Code

http://docs.python-guide.org/en/latest/#shipping-great-code

第四部分 Development Environment

http://docs.python-guide.org/en/latest/#development-environment

第五部分 Additional Notes

http://docs.python-guide.org/en/latest/#additional-notes

The end!

2013-11-25

wklken

打賞支援我寫出更多好文章，謝謝！
打賞作者

打賞支援我寫出更多好文章，謝謝！

任選一種支付方式

[摘要]Python 最佳實踐指南

Python實踐：提取文章摘要
2015-12-27
Python
Python開發指南：最佳實踐精選
2015-11-16
Python
JavaScript 專案最佳實踐指南
2017-07-20
JavaScript
Kubernetes 最佳安全實踐指南
2020-12-15
RESTful API 設計指南——最佳實踐
2017-03-03
RESTAPI
AngularJS最佳編碼實踐指南
2014-12-11
AngularJS
資料庫安全最佳實踐：基本指南
2021-10-31
資料庫
MongoDB 最佳實踐和場景避坑指南
2019-03-24
MongoDB
我理解的 Python 最佳實踐
2015-11-18
Python
敏捷最佳實踐：設計衝刺完整指南 -Useberry
2020-05-09
敏捷
Google 官方應用架構的最佳實踐指南
2017-06-19
Go應用架構
企業Facebook營銷最佳實踐指南（官方版）
2011-05-12
製作 Python Docker 映象的最佳實踐
2022-12-15
PythonDocker
Python將字串轉為字典最佳實踐
2018-06-28
Python字串
python編碼最佳實踐之總結
2016-01-12
Python
Python程式設計規範+最佳實踐
2024-03-18
Python程式設計
容器映象加速指南：探索 Kubernetes 快取最佳實踐
2024-03-25
快取
Pika最佳實踐
2019-02-20
Flutter 最佳實踐
2021-07-19
Flutter
MongoDB 最佳實踐
2021-03-02
MongoDB
《.NET最佳實踐》
2019-01-08
Django 最佳實踐
2018-11-25
Django
metaq最佳實踐
2018-10-09
Iptables 最佳實踐！
2020-11-21
Java最佳實踐
2022-11-08
Java
Kafka最佳實踐
2023-03-13
Kafka
Log最佳實踐
2016-12-08
SnapKit 最佳實踐
2018-04-05
APK
MacBook 最佳實踐
2017-01-02
Mac
viewport 最佳實踐
2017-02-16
View
ViewPager最佳實踐
2017-12-26
Viewpager
OpenResty最佳實踐
2015-12-08
REST
PHP最佳實踐
2010-12-27
PHP
MongoDB最佳實踐
2012-09-19
MongoDB
機器學習實踐指南
2017-09-30
機器學習
Mahout實踐指南
2014-06-12
Kotlin 變數詳解：宣告、賦值與最佳實踐指南
2024-06-20
Kotlin變數賦值
restful api最佳實踐
2020-08-24
RESTAPI

[摘要]Python 最佳實踐指南

第零部分 Getting Started

第一部分 Writing Great Code

Structuring Your Project

Code Style

一般概念(General concepts)

方言(Idioms)

慣例(Conventions)

Reading Great Code

文件(Documentation)

測試你的程式碼(Testing Your Code)

Common Gotchas(不懂怎麼翻…╮(╯▽╰)╭ )

選擇證照(Choosing a License)

第二部分 Scenario Guide

Network Applications

Web Applications

HTML Scraping

Command Line Applications

GUI Applications

Databases

Networking

Systems Administration

Continuous Integration

Speed

Scientific Applications

Image Manipulation

XML parsing

第三部分 Shipping Great Code

第四部分 Development Environment

第五部分 Additional Notes

打賞支援我寫出更多好文章，謝謝！

相關文章