一、概念描述
可迭代对象就是可以迭代的对象，我们可以通过内置的iter函数获取其迭代器，可迭代对象内部需要实现__iter__函数来返回其关联的迭代器；
迭代器是负责具体数据的逐个遍历的，其通过实现__next__函数得以逐个的访问关联的数据元素；同时通过实现__iter__来实现对可迭代对象的兼容；
生成器是一种迭代器模式，其实现了数据的惰性生成，即只有使用的时候才会生成对应的元素；
二、序列的可迭代性
python内置的序列可以通过for进行迭代，解释器会调用iter函数获取序列的迭代器，由于iter函数兼容序列实现的__getitem__，会自动创建一个迭代器；
迭代器的
import refrom dis import disclass WordAnalyzer: reg_word = re.compile('\w+') def __init__(self, text): self.words = self.__class__.reg_word.findall(text) def __getitem__(self, index): return self.words[index]def iter_word_analyzer(): wa = WordAnalyzer('this is mango word analyzer') print('start for wa') for w in wa: print(w) print('start while wa_iter') wa_iter = iter(wa) while True: try: print(next(wa_iter)) except StopIteration as e: break;iter_word_analyzer()dis(iter_word_analyzer)# start for wa# this# is# mango# word# analyzer# start while wa_iter# this# is# mango# word# analyzer# 15 0 LOAD_GLOBAL 0 (WordAnalyzer)# 2 LOAD_CONST 1 ('this is mango word analyzer')# 4 CALL_FUNCTION 1# 6 STORE_FAST 0 (wa)# # 16 8 LOAD_GLOBAL 1 (print)# 10 LOAD_CONST 2 ('start for wa')# 12 CALL_FUNCTION 1# 14 POP_TOP# # 17 16 LOAD_FAST 0 (wa)# 18 GET_ITER# >> 20 FOR_ITER 12 (to 34)# 22 STORE_FAST 1 (w)# # 18 24 LOAD_GLOBAL 1 (print)# 26 LOAD_FAST 1 (w)# 28 CALL_FUNCTION 1# 30 POP_TOP# 32 JUMP_ABSOLUTE 20# # 20 >> 34 LOAD_GLOBAL 1 (print)# 36 LOAD_CONST 3 ('start while wa_iter')# 38 CALL_FUNCTION 1# 40 POP_TOP# # 21 42 LOAD_GLOBAL 2 (iter)# 44 LOAD_FAST 0 (wa)# 46 CALL_FUNCTION 1# 48 STORE_FAST 2 (wa_iter)# # 23 >> 50 SETUP_FINALLY 16 (to 68)# # 24 52 LOAD_GLOBAL 1 (print)# 54 LOAD_GLOBAL 3 (next)# 56 LOAD_FAST 2 (wa_iter)# 58 CALL_FUNCTION 1# 60 CALL_FUNCTION 1# 62 POP_TOP# 64 POP_BLOCK# 66 JUMP_ABSOLUTE 50# # 25 >> 68 DUP_TOP# 70 LOAD_GLOBAL 4 (StopIteration)# 72 JUMP_IF_NOT_EXC_MATCH 114# 74 POP_TOP# 76 STORE_FAST 3 (e)# 78 POP_TOP# 80 SETUP_FINALLY 24 (to 106)# # 26 82 POP_BLOCK# 84 POP_EXCEPT# 86 LOAD_CONST 0 (None)# 88 STORE_FAST 3 (e)# 90 DELETE_FAST 3 (e)# 92 JUMP_ABSOLUTE 118# 94 POP_BLOCK# 96 POP_EXCEPT# 98 LOAD_CONST 0 (None)# 100 STORE_FAST 3 (e)# 102 DELETE_FAST 3 (e)# 104 JUMP_ABSOLUTE 50# >> 106 LOAD_CONST 0 (None)# 108 STORE_FAST 3 (e)# 110 DELETE_FAST 3 (e)# 112 RERAISE# >> 114 RERAISE# 116 JUMP_ABSOLUTE 50# >> 118 LOAD_CONST 0 (None)# 120 RETURN_VALUE三、经典的迭代器模式
标准的迭代器需要实现两个接口方法，一个可以获取下一个元素的__next__方法和直接返回self的__iter__方法；
迭代器迭代完所有的元素的时候会抛出StopIteration异常，但是python内置的for、列表推到、元组拆包等会自动处理这个异常；
实现__iter__主要为了方便使用迭代器，这样就可以最大限度的方便使用迭代器；
迭代器只能迭代一次，如果需要再次迭代就需要再次调用iter方法获取新的迭代器，这就要求每个迭代器维护自己的内部状态，即一个对象不能既是可迭代对象同时也是迭代器；
从经典的面向对象设计模式来看，可迭代对象可以随时生成自己关联的迭代器，而迭代器负责具体的元素的迭代处理；
import refrom dis import disclass WordAnalyzer: reg_word = re.compile('\w+') def __init__(self, text): self.words = self.__class__.reg_word.findall(text) def __iter__(self): return WordAnalyzerIterator(self.words)class WordAnalyzerIterator: def __init__(self, words): self.words = words self.index = 0 def __iter__(self): return self; def __next__(self): try: word = self.words[self.index] except IndexError: raise StopIteration() self.index +=1 return worddef iter_word_analyzer(): wa = WordAnalyzer('this is mango word analyzer') print('start for wa') for w in wa: print(w) print('start while wa_iter') wa_iter = iter(wa) while True: try: print(next(wa_iter)) except StopIteration as e: break;iter_word_analyzer()# start for wa# this# is# mango# word# analyzer# start while wa_iter# this# is# mango# word# analyzer四、生成器也是迭代器
生成器是调用生成器函数生成的，生成器函数是含有yield的工厂函数；
生成器本身就是迭代器，其支持使用next函数遍历生成器，同时遍历完也会抛出StopIteration异常；
生成器执行的时候会在yield语句的地方暂停，并返回yield右边的表达式的值；
def gen_func(): print('first yield') yield 'first' print('second yield') yield 'second'print(gen_func)g = gen_func()print(g)for val in g: print(val)g = gen_func()print(next(g))print(next(g))print(next(g))# <function gen_func at 0x7f1198175040># <generator object gen_func at 0x7f1197fb6cf0># first yield# first# second yield# second# first yield# first# second yield# second# StopIteration我们可以将__iter__作为生成器函数
import refrom dis import disclass WordAnalyzer: reg_word = re.compile('\w+') def __init__(self, text): self.words = self.__class__.reg_word.findall(text) def __iter__(self): for word in self.words: yield worddef iter_word_analyzer(): wa = WordAnalyzer('this is mango word analyzer') print('start for wa') for w in wa: print(w) print('start while wa_iter') wa_iter = iter(wa) while True: try: print(next(wa_iter)) except StopIteration as e: break;iter_word_analyzer()# start for wa# this# is# mango# word# analyzer# start while wa_iter# this# is# mango# word# analyzer五、实现惰性迭代器
迭代器的一大亮点就是通过__next__来实现逐个元素的遍历，这个大数据容器的遍历带来了可能性；
我们以前的实现在初始化的时候，直接调用re.findall得到了所有的序列元素，并不是一个很好的实现；我们可以通过re.finditer来在遍历的时候得到数据；
import refrom dis import disclass WordAnalyzer: reg_word = re.compile('\w+') def __init__(self, text): # self.words = self.__class__.reg_word.findall(text) self.text = text def __iter__(self): g = self.__class__.reg_word.finditer(self.text) print(g) for match in g: yield match.group()def iter_word_analyzer(): wa = WordAnalyzer('this is mango word analyzer') print('start for wa') for w in wa: print(w) print('start while wa_iter') wa_iter = iter(wa) wa_iter1= iter(wa) while True: try: print(next(wa_iter)) except StopIteration as e: break;iter_word_analyzer()# start for wa# <callable_iterator object at 0x7feed103e040># this# is# mango# word# analyzer# start while wa_iter# <callable_iterator object at 0x7feed103e040># this# is# mango# word# analyzer六、使用生成器表达式简化惰性迭代器
生成器表达式是生成器的声明性定义，与列表推到的语法类似，只是生成元素是惰性的；
def gen_func(): print('first yield') yield 'first' print('second yield') yield 'second'l = [x for x in gen_func()]for x in l: print(x)print()ge = (x for x in gen_func())print(ge)for x in ge: print(x)# first yield# second yield# first# second## <generator object <genexpr> at 0x7f78ff5dfd60># first yield# first# second yield# second使用生成器表达式实现word analyzer
import refrom dis import disclass WordAnalyzer: reg_word = re.compile('\w+') def __init__(self, text): # self.words = self.__class__.reg_word.findall(text) self.text = text def __iter__(self): # g = self.__class__.reg_word.finditer(self.text) # print(g) # for match in g: # yield match.group() ge = (match.group() for match in self.__class__.reg_word.finditer(self.text)) print(ge) return gedef iter_word_analyzer(): wa = WordAnalyzer('this is mango word analyzer') print('start for wa') for w in wa: print(w) print('start while wa_iter') wa_iter = iter(wa) while True: try: print(next(wa_iter)) except StopIteration as e: break;iter_word_analyzer()# start for wa# <generator object WordAnalyzer.__iter__.<locals>.<genexpr> at 0x7f4178189200># this# is# mango# word# analyzer# start while wa_iter# <generator object WordAnalyzer.__iter__.<locals>.<genexpr> at 0x7f4178189200># this# is# mango# word# analyzer
