1.交换赋值
#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，然后在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 only
first, *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:
5 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 1
i = 0
for item in items:
print i , item
i += 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
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 s1 in nested_list if list_condition(s1) \
for item in s1 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 product
for 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 #返回值 #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, filiter(ff, map(my, 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】
15.使用 any/all 函数
#不推荐
found = False
for item in a_list:
if condition(item):
found = True
break
if 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 BadHour Exception
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 BadHour Exception
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("something.txt')
19.使用 with 处理加锁
#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 ...