Python之可迭代对象、迭代器、生成器

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;

image

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 re from dis import dis class 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 re from dis import dis class 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 word 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() # 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 re from dis import dis class 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 word 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() # 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 re from dis import dis class 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 re from dis import dis class 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 ge 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() # 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 
本网页由快兔兔AI采集器生成,目的为演示采集效果,若侵权请及时联系删除。

原文链接:https://www.cnblogs.com/wufengtinghai/p/15696252.html

更多内容