1. 交換賦值 ##不推薦 temp = a a = b b = a ##推薦 a, b = b, a # 先生成一個元組(tuple)對象,而後unpack 2. Unpacking ##不推薦 l = ['David', 'Pythonista', '+1-514-555-1234'] first_name = l[0] last_name = l[1] phone_number = l[2] ##推薦 l = ['David', 'Pythonista', '+1-514-555-1234'] first_name, last_name, phone_number = l # Python 3 Only first, *middle, last = another_list 3. 使用操做符in ##不推薦 if fruit == "apple" or fruit == "orange" or fruit == "berry": # 屢次判斷 ##推薦 if fruit in ["apple", "orange", "berry"]: # 使用 in 更加簡潔 4. 字符串操做 ##不推薦 colors = ['red', 'blue', 'green', 'yellow'] result = '' for s in colors: result += s # 每次賦值都丟棄之前的字符串對象, 生成一個新對象 ##推薦 colors = ['red', 'blue', 'green', 'yellow'] result = ''.join(colors) # 沒有額外的內存分配 5. 字典鍵值列表 ##不推薦 for key in my_dict.keys(): # my_dict[key] ... ##推薦 for key in my_dict: # my_dict[key] ... # 只有當循環中須要更改key值的狀況下,咱們須要使用 my_dict.keys() # 生成靜態的鍵值列表。 6. 字典鍵值判斷 ##不推薦 if my_dict.has_key(key): # ...do something with d[key] ##推薦 if key in my_dict: # ...do something with d[key] 7. 字典 get 和 setdefault 方法 ##不推薦 navs = {} for (portfolio, equity, position) in data: if portfolio not in navs: navs[portfolio] = 0 navs[portfolio] += position * prices[equity] ##推薦 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. 判斷真僞 ##不推薦 if x == True: # .... if len(items) != 0: # ... if items != []: # ... ##推薦 if x: # .... if items: # ... 9. 遍歷列表以及索引 ##不推薦 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] ##推薦 items = 'zero one two three'.split() for i, item in enumerate(items): print i, item 10. 列表推導 ##不推薦 new_list = [] for item in a_list: if condition(item): new_list.append(fn(item)) ##推薦 new_list = [fn(item) for item in a_list if condition(item)] 11. 列表推導-嵌套 ##不推薦 for sub_list in nested_list: if list_condition(sub_list): for item in sub_list: if item_condition(item): # do something... ##推薦 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. 循環嵌套 ##不推薦 for x in x_list: for y in y_list: for z in z_list: # do something for x & y ##推薦 from itertools import product for x, y, z in product(x_list, y_list, z_list): # do something for x, y, z 13. 儘可能使用生成器代替列表 ##不推薦 def my_range(n): i = 0 result = [] while i < n: result.append(fn(i)) i += 1 return result # 返回列表 ##推薦 def my_range(n): i = 0 result = [] while i < n: yield fn(i) # 使用生成器代替列表 i += 1 *儘可能用生成器代替列表,除非必須用到列表特有的函數。 14. 中間結果儘可能使用imap/ifilter代替map/filter ##不推薦 reduce(rf, filter(ff, map(mf, a_list))) ##推薦 from itertools import ifilter, imap reduce(rf, ifilter(ff, imap(mf, a_list))) *lazy evaluation 會帶來更高的內存使用效率,特別是當處理大數據操做的時候。 15. 使用any/all函數 ##不推薦 found = False for item in a_list: if condition(item): found = True break if found: # do something if found... ##推薦 if any(condition(item) for item in a_list): # do something if found... 16. 屬性(property) = ##不推薦 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 ##推薦 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 處理文件打開 ##不推薦 f = open("some_file.txt") try: data = f.read() # 其餘文件操做.. finally: f.close() ##推薦 with open("some_file.txt") as f: data = f.read() # 其餘文件操做... 18. 使用 with 忽視異常(僅限Python 3) ##不推薦 try: os.remove("somefile.txt") except OSError: pass ##推薦 from contextlib import ignored # Python 3 only with ignored(OSError): os.remove("somefile.txt") 19. 使用 with 處理加鎖 ##不推薦 import threading lock = threading.Lock() lock.acquire() try: # 互斥操做... finally: lock.release() ##推薦 import threading lock = threading.Lock() with lock: # 互斥操做...
來源:微信微信