给大家整理了19个pythonic的编程习惯(小结)

##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] = 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]

##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, 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

##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 = 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。

##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 = True
    break
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 = 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)

##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...