Python代码编写中的性能优化点

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...

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