##不推荐
temp = a
a = b
b = 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
# Python 3 Only
first, *middle, last = another_list

##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]
##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] = 0navs[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 1
i = 0
for item in items:print i, itemi += 1
# method 2
for 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 product
for 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 = 0result = []while i < n:result.append(fn(i))i += 1return result # 返回列表
##推荐
def my_range(n):i = 0result = []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, imap
reduce(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 = False
for item in a_list:if condition(item):found = Truebreak
if 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 = 1def setHour(self, hour):if 25 > hour > 0: self.__hour = hourelse: raise BadHourExceptiondef getHour(self):return self.__hour
##推荐
class Clock(object):def __init__(self):self.__hour = 1def __setHour(self, hour):if 25 > hour > 0: self.__hour = hourelse: raise BadHourExceptiondef __getHour(self):return self.__hourhour = 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 threading
lock = threading.Lock()
lock.acquire()
try:# 互斥操作...
finally:lock.release()
##推荐
import threading
lock = threading.Lock()
with lock:# 互斥操作...