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...来源：微信