python编码最佳实践之总结

#使用profile stats工具进行性能分析

>>> from pbp.scripts.profiler import profile, stats
>>> s = [('yellow', 1), ('blue', 2), ('yellow', 3),
... ('blue', 4), ('red', 1)]
>>> @profile('defaultdict')
... def faster():
... d = defaultdict(list)
... for k, v in s:
... d[k].append(v)
...
>>> @profile('dict')
... def slower():
... d = {}
... for k, v in s:
... d.setdefault(k, []).append(v)
...
>>> slower(); faster()
Optimization: Solutions
[ 306 ]
>>> stats['dict']
{'stones': 16.587882671716077, 'memory': 396,
'time': 0.35166311264038086}
>>> stats['defaultdict']
{'stones': 6.5733464259021686, 'memory': 552,
'time': 0.13935494422912598}

可见性能提升了快3倍。defaultdict用一个list工厂作为参数，同样可用于内建类型，比如long等。

除了实现的算法、架构之外，python提倡简单、优雅。所以正确的语法实践又很有必要，这样才会写出优雅易于阅读的代码。

二、语法最佳实践
字符串操作：优于python字符串对象是不可改变的，因此对任何字符串的操作如拼接，修改等都将产生一个新的字符串对象，而不是基于原字符串，因此这种持续的 copy会在一定程度上影响Python的性能：
(1)用join代替 '+' 操作符，后者有copy开销；

(2)同时当对字符串可以使用正则表达式或者内置函数来处理的时候，选择内置函数。如str.isalpha()，str.isdigit()，str.startswith((‘x', ‘yz'))，str.endswith((‘x', ‘yz'))

(3)字符格式化操作优于直接串联读取：

str = "%s%s%s%s" % (a, b, c, d) # efficient
str = "" + a + b + c + d + "" # slow

2. 善用list comprehension(列表解析) & generator（生成器） & decorators（装饰器），熟悉itertools等模块：

(1) 列表解析，我觉得是python2中最让我印象深刻的特性，举例1：

   >>> # the following is not so Pythonic 
   >>> numbers = range(10)
   >>> i = 0 
   >>> evens = [] 
   >>> while i < len(numbers): 
   >>>  if i %2 == 0: evens.append(i) 
   >>>  i += 1 
   >>> [0, 2, 4, 6, 8] 

   >>> # the good way to iterate a range, elegant and efficient
   >>> evens = [ i for i in range(10) if i%2 == 0] 
   >>> [0, 2, 4, 6, 8]  

def _treament(pos, element):
  return '%d: %s' % (pos, element)
f = open('test.txt', 'r')
if __name__ == '__main__':
  #list comps 1
  print sum(len(word) for line in f for word in line.split())
  #list comps 2
  print [(x + 1, y + 1) for x in range(3) for y in range(4)]
  #func
  print filter(lambda x: x % 2 == 0, range(10))
  #list comps3
  print [i for i in range(10) if i % 2 == 0]
  #list comps4 pythonic
  print [_treament(i, el) for i, el in enumerate(range(10))]

output：
24
[(1, 1), (1, 2), (1, 3), (1, 4), (2, 1), (2, 2), (2, 3), (2, 4), (3, 1), (3, 2), (3, 3), (3, 4)]
[0, 2, 4, 6, 8]
[0, 2, 4, 6, 8]
['0: 0', '1: 1', '2: 2', '3: 3', '4: 4', '5: 5', '6: 6', '7: 7', '8: 8', '9: 9']

f = open('/etc/motd, 'r')
longest = max(len(x.strip()) for x in f)
f.close()
return longest

import time
import hashlib
import pickle
from itertools import chain
cache = {}
def is_obsolete(entry, duration):
  return time.time() - entry['time'] > duration

def compute_key(function, args, kw):
  #序列化/反序列化一个对象,这里是用pickle模块对函数和参数对象进行序列化为一个hash值
  key = pickle.dumps((function.func_name, args, kw))
  #hashlib是一个提供MD5和sh1的一个库，该结果保存在一个全局字典中
  return hashlib.sha1(key).hexdigest()

def memoize(duration=10):
  def _memoize(function):
    def __memoize(*args, **kw):
      key = compute_key(function, args, kw)

      # do we have it already
      if (key in cache and
        not is_obsolete(cache[key], duration)):
        print 'we got a winner'
        return cache[key]['value']

      # computing
      result = function(*args, **kw)
      # storing the result
      cache[key] = {'value': result,-
              'time': time.time()}
      return result
    return __memoize
  return _memoize

@memoize()
def very_very_complex_stuff(a, b, c):
  return a + b + c

print very_very_complex_stuff(2, 2, 2)
print very_very_complex_stuff(2, 2, 2)


@memoize(1)
def very_very_complex_stuff(a, b):
  return a + b

print very_very_complex_stuff(2, 2)
time.sleep(2)
print very_very_complex_stuff(2, 2)

6

we got a winner

6

4

4

switch (var)
{
  case v1: func1();
  case v2: func2();
  ...
  case vN: funcN();
  default: default_func();
}
dictionary实现：

values = {
      v1: func1,
      v2: func2,
      ...
      vN: funcN,
     }
values.get(var, default_func)()
lambda实现：

{
 '1': lambda: func1,
 '2': lambda: func2,
 '3': lambda: func3
}[value]()