符合语言习惯的Python编程技巧

##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

##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

##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

##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

##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。

##不推荐if my_dict.has_key(key):    # ...do something with d[key]      #python2支持，python3已经不支持此写法  ##推荐if key in my_dict:    # ...do something with d[key]

##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]

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

##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

##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)]

##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...

##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

##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。

##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。

##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...

##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)

##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...

##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")

##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...