详解Python之可迭代对象,迭代器和生成器

目录

• 一、概念描述
• 二、序列的可迭代性
• 三、经典的迭代器模式
• 四、生成器也是迭代器
• 五、实现惰性迭代器
• 六、使用生成器表达式简化惰性迭代器
• 总结

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 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
 

总结

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