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
temp = a
a = b
b = a
a, b = b, a
2、Unpacking
l = ['David', 'Pythonista', '+1-514-555-1234']
first_name = l[0]
last_name = l[1]
phone_number = l[2]
l = ['David', 'Pythonista', '+1-514-555-1234']
first_name, last_name, phone_number = l
first, *middle, last = another_list
3、使用操作符in
if fruit == "apple" or fruit == "orange" or fruit == "berry":
if fruit in ["apple", "orange", "berry"]:
4、字符串操作
colors = ['red', 'blue', 'green', 'yellow']
result = ''
for s in colors:
result += s
colors = ['red', 'blue', 'green', 'yellow']
result = ''.join(colors)
5、字典键值列表
for key in my_dict.keys():
for key in my_dict:
6、字典键值判断
if my_dict.has_key(key):
if key in my_dict:
7、字典 get 和 setdefault 方法
navs = {}
for (portfolio, equity, position) in data:
if portfolio not in navs:
navs[portfolio] = 0
navs[portfolio] += position * prices[equity]
navs = {}
for (portfolio, equity, position) in data:
navs[portfolio] = navs.get(portfolio, 0) + position * prices[equity]
navs.setdefault(portfolio, 0)
navs[portfolio] += position * prices[equity]
8、判断真伪
if x == True:
if len(items) != 0:
if items != []:
if x:
if items:
9、遍历列表以及索引
items = 'zero one two three'.split()
i = 0
for item in items:
print i, item
i += 1
for i in range(len(items)):
print i, items[i]
items = 'zero one two three'.split()
for i, item in enumerate(items):
print i, item
10、列表推导
new_list = []
for item in a_list:
if condition(item):
new_list.append(fn(item))
new_list = [fn(item) for item in a_list if condition(item)]
11、列表推导-嵌套
for sub_list in nested_list:
if list_condition(sub_list):
for item in sub_list:
if item_condition(item):
gen = (item for sl in nested_list if list_condition(sl) \
for item in sl if item_condition(item))
for item in gen:
12、循环嵌套
for
x in x_list:
for y in y_list:
for z in z_list:
from itertools import product
for x, y, z in product(x_list, y_list, z_list):
13、尽量使用生成器代替列表
def my_range(n):
i = 0
result = []
while i < n:
result.append(fn(i))
i += 1
return result
def my_range(n):
i = 0
result = []
while i < n:
yield fn(i)
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
reduce(rf, filter(ff, map(mf, a_list)))
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
found = False
for item in a_list:
if condition(item):
found = True
break
if found:
if any(condition(item) for item in a_list):
16、属性(property)
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
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
f = open("some_file.txt")
try:
data = f.read()
finally:
f.close()
with open("some_file.txt") as f:
data = f.read()
18、使用 with 忽视异常(仅限Python 3)
try:
os.remove("somefile.txt")
except OSError:
pass
from contextlib import ignored
with ignored(OSError):
os.remove("somefile.txt")
19、使用 with 处理加锁
import threading
lock = threading.Lock()
lock.acquire()
try:
finally:
lock.release()
import threading
lock = threading.Lock()
with lock:
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/
原文:http://lovesoo.org/pythonic-python-programming.html
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