http://lovesoo.org/pythonic-python-programming.html

Python最大的优点之一就是语法简洁，好的代码就像伪代码一样，干净、整洁、一目了然。要写出 Pythonic（优雅的、地道的、整洁的）代码，需要多看多学大牛们写的代码，github 上有很多非常优秀的源代码值得阅读，比如：requests、flask、tornado，下面列举一些常见的Pythonic写法。

0、程序必须先让人读懂，然后才能让计算机执行。

“Programs must be written for people to read, and only incidentally for machines to execute.”

1、交换赋值

##不推荐temp = aa = bb = a  ##推荐a, b = b, a  #  先生成一个元组(tuple)对象，然后unpack

2、Unpacking

##不推荐l = [\\\\\\\'David\\\\\\\', \\\\\\\'Pythonista\\\\\\\', \\\\\\\'+1-514-555-1234\\\\\\\']first_name = l[0]last_name = l[1]phone_number = l[2]  

##推荐l = [\\\\\\\'David\\\\\\\', \\\\\\\'Pythonista\\\\\\\', \\\\\\\'+1-514-555-1234\\\\\\\']first_name, last_name, phone_number = l# Python 3 Onlyfirst, *middle, last = another_list

3、使用操作符in

##不推荐if fruit == "apple" or fruit == "orange" or fruit == "berry":    # 多次判断  

##推荐if fruit in ["apple", "orange", "berry"]:    # 使用 in 更加简洁

4、字符串操作

##不推荐colors = [\\\\\\\'red\\\\\\\', \\\\\\\'blue\\\\\\\', \\\\\\\'green\\\\\\\', \\\\\\\'yellow\\\\\\\']

result = \\\\\\\'\\\\\\\'for s in colors:    result += s  #  每次赋值都丢弃以前的字符串对象, 生成一个新对象  

##推荐colors = [\\\\\\\'red\\\\\\\', \\\\\\\'blue\\\\\\\', \\\\\\\'green\\\\\\\', \\\\\\\'yellow\\\\\\\']result = \\\\\\\'\\\\\\\'.join(colors)  #  没有额外的内存分配

5、字典键值列表

##不推荐for key in my_dict.keys():    #  my_dict[key] ...  ##推荐for key in my_dict:    #  my_dict[key] ...# 只有当循环中需要更改key值的情况下，我们需要使用 my_dict.keys()# 生成静态的键值列表。

6、字典键值判断

##不推荐if my_dict.has_key(key):    # ...do something with d[key]  

##推荐if key in my_dict:    # ...do something with d[key]

7、字典 get 和 setdefault 方法

##不推荐navs = {}for (portfolio, equity, position) in data:    if portfolio not in navs:            navs[portfolio] = 0    navs[portfolio] += position * prices[equity]##推荐navs = {}for (portfolio, equity, position) in data:    # 使用 get 方法    navs[portfolio] = navs.get(portfolio, 0) + position * prices[equity]    # 或者使用 setdefault 方法    navs.setdefault(portfolio, 0)    navs[portfolio] += position * prices[equity]

8、判断真伪

##不推荐if x == True:    # ....if len(items) != 0:    # ...if items != []:    # ...  

##推荐if x:    # ....if items:    # ...

9、遍历列表以及索引

##不推荐items = \\\\\\\'zero one two three\\\\\\\'.split()# method 1i = 0for item in items:    print i, item    i += 1# method 2for i in range(len(items)):    print i, items[i]

##推荐items = \\\\\\\'zero one two three\\\\\\\'.split()for i, item in enumerate(items):    print i, item

10、列表推导

##不推荐new_list = []for item in a_list:    if condition(item):        new_list.append(fn(item))  

##推荐new_list = [fn(item) for item in a_list if condition(item)]

11、列表推导-嵌套

##不推荐for sub_list in nested_list:    if list_condition(sub_list):        for item in sub_list:            if item_condition(item):                # do something...  ##推荐gen = (item for sl in nested_list if list_condition(sl)             for item in sl if item_condition(item))for item in gen:    # do something...

12、循环嵌套

##不推荐for x in x_list:    for y in y_list:        for z in z_list:            # do something for x & y  

##推荐from itertools import productfor x, y, z in product(x_list, y_list, z_list):    # do something for x, y, z

13、尽量使用生成器代替列表

##不推荐def my_range(n):    i = 0    result = []    while i < n:        result.append(fn(i))        i += 1    return result  #  返回列表

##推荐def my_range(n):    i = 0    result = []    while i < n:        yield fn(i)  #  使用生成器代替列表        i += 1*尽量用生成器代替列表，除非必须用到列表特有的函数。

14、中间结果尽量使用imap/ifilter代替map/filter

##不推荐reduce(rf, filter(ff, map(mf, a_list)))

##推荐from itertools import ifilter, imapreduce(rf, ifilter(ff, imap(mf, a_list)))*lazy evaluation 会带来更高的内存使用效率，特别是当处理大数据操作的时候。

15、使用any/all函数

##不推荐found = Falsefor item in a_list:    if condition(item):        found = True        breakif found:    # do something if found...  

##推荐if any(condition(item) for item in a_list):    # do something if found...

16、属性(property)

##不推荐class Clock(object):    def __init__(self):        self.__hour = 1    def setHour(self, hour):        if 25 > hour > 0: self.__hour = hour        else: raise BadHourException    def getHour(self):        return self.__hour

##推荐class Clock(object):    def __init__(self):        self.__hour = 1    def __setHour(self, hour):        if 25 > hour > 0: self.__hour = hour        else: raise BadHourException    def __getHour(self):        return self.__hour    hour = property(__getHour, __setHour)

17、使用 with 处理文件打开

##不推荐f = open("some_file.txt")try:    data = f.read()    # 其他文件操作..finally:    f.close()

##推荐with open("some_file.txt") as f:    data = f.read()    # 其他文件操作...

18、使用 with 忽视异常(仅限Python 3)

##不推荐try:    os.remove("somefile.txt")except OSError:    pass

##推荐from contextlib import ignored  # Python 3 only

with ignored(OSError):    os.remove("somefile.txt")

19、使用 with 处理加锁

##不推荐import threadinglock = threading.Lock()

lock.acquire()try:    # 互斥操作...finally:    lock.release()

##推荐import threadinglock = threading.Lock()

with lock:    # 互斥操作...

参考

1) Idiomatic Python:

http://python.net/~goodger/projects/pycon/2007/idiomatic/handout.html

2) PEP 8: Style Guide for Python Code:

http://www.python.org/dev/peps/pep-0008/