符合语言习惯的 Python 优雅编程技巧

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 = a
 a = b
 b = 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 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:
 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 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 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, 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 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 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...

20. 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/