##不推薦temp = aa = bb = a ##推薦a, b = b, a # 先生成一個元組(tuple)對象，然后unpack

##不推薦l = ['David', 'Pythonista', '+1-514-555-1234']first_name = l[0]last_name = l[1]phone_number = l[2] ##推薦l = ['David', 'Pythonista', '+1-514-555-1234']first_name, last_name, phone_number = l# Python 3 Onlyfirst, *middle, last = another_list

##不推薦if fruit == "apple" or fruit == "orange" or fruit == "berry":  # 多次判斷 ##推薦if fruit in ["apple", "orange", "berry"]:  # 使用 in 更加簡潔

##不推薦colors = ['red', 'blue', 'green', 'yellow']result = ''for s in colors:  result += s # 每次賦值都丟棄以前的字符串對象, 生成一個新對象 ##推薦colors = ['red', 'blue', 'green', 'yellow']result = ''.join(colors) # 沒有額外的內存分配

##不推薦for key in my_dict.keys():  # my_dict[key] ... ##推薦for key in my_dict:  # my_dict[key] ...# 只有當循環中需要更改key值的情況下，我們需要使用 my_dict.keys()# 生成靜態的鍵值列表。

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

##不推薦navs = {}for (portfolio, equity, position) in data:  if portfolio not in navs:      navs[portfolio] = 0  navs[portfolio] += position * prices[equity]##推薦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]

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

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

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

##不推薦for sub_list in nested_list:  if list_condition(sub_list):    for item in sub_list:      if item_condition(item):        # do something... ##推薦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...

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

##不推薦def my_range(n):  i = 0  result = []  while i < n:    result.append(fn(i))    i += 1  return result # 返回列表##推薦def my_range(n):  i = 0  result = []  while i < n:    yield fn(i) # 使用生成器代替列表    i += 1*盡量用生成器代替列表，除非必須用到列表特有的函數。

##不推薦reduce(rf, filter(ff, map(mf, a_list)))##推薦from itertools import ifilter, imapreduce(rf, ifilter(ff, imap(mf, a_list)))*lazy evaluation 會帶來更高的內存使用效率，特別是當處理大數據操作的時候。

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

=##不推薦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##推薦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)

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

##不推薦try:  os.remove("somefile.txt")except OSError:  pass##推薦from contextlib import ignored # Python 3 onlywith ignored(OSError):  os.remove("somefile.txt")

##不推薦import threadinglock = threading.Lock()lock.acquire()try:  # 互斥操作...finally:  lock.release()##推薦import threadinglock = threading.Lock()with lock:  # 互斥操作...