CP学习笔记(9) - 迭代

Python中自定义迭代类型，需要包含自定义的

__next__

方法。

__next__

方法能够返回迭代类型的下一项，并在迭代结束时提示编译器：

StopIteration

。

# define a new iteration class
>>> class LetterIter:
"""An iterator over letters of the alphabet in ASCII order."""
def __init__(self, start='a', end='e'):
self.next_letter = start
self.end = end
def __next__(self):
if self.next_letter == self.end:
raise StopIteration
letter = self.next_letter
self.next_letter = chr(ord(letter)+1)
return letter

# init a object of defined iteration class
>>> letter_iter = LetterIter()
>>> letter_iter.__next__()
'a'
>>> letter_iter.__next__()
'b'
>>> next(letter_iter)
'c'
>>> letter_iter.__next__()
'd'
>>> letter_iter.__next__()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 12, in next
StopIteration

如果一个对象的

__iter__

方法被调用后能够返回一个迭代对象，那么这个对象就是可迭代(iterable)。

>>> class Letters:
def __init__(self, start='a', end='e'):
self.start = start
self.end = end
def __iter__(self):
return LetterIter(self.start, self.end)

>>> b_to_k = Letters('b', 'k')
>>> first_iterator = b_to_k.__iter__()
>>> next(first_iterator)
'b'
>>> next(first_iterator)
'c'
>>> second_iterator = iter(b_to_k)
>>> second_iterator.__next__()
'b'
>>> first_iterator.__next__()
'd'
>>> first_iterator.__next__()
'e'
>>> second_iterator.__next__()
'c'
>>> second_iterator.__next__()
'd'

for语句也可以用于列举。

for <name> in <expression>:
<suite>

编译器首先会检查

<expression>

是否是可迭代对象，然后调用

__iter__

方法。编译器会反复调用

__next__

方法直至遇到

StopIteration

。每次调用

__next__

方法，编译器都会把得到的值绑定在

<name>

上，然后执行

<suite>

语句。下面两个示例是等价的。

>>> counts = [1, 2, 3]
>>> for item in counts:
print(item)
1
2
3

>>> items = counts.__iter__()
>>> try:
while True:
item = items.__next__()
print(item)
except StopIteration:
pass
1
2
3

Python官网文档Iterator types一章建议迭代对象的

__iter__

方法最好返回迭代对象本身，这样所有迭代对象都是可迭代的。

__next__

方法只能用于列举简单的迭代对象，对于复杂的迭代对象需要用到

generator

迭代类型。不同的是，

generator

迭代类型不使用

return

返回值，而是用

yield

语句。

如何理解Python关键字yield:

>>> def letters_generator():
current = 'a'
while current <= 'd':
yield current
current = chr(ord(current)+1)
>>> for letter in letters_generator():
print(letter)
a
b
c
d

>>> letters = letters_generator()
>>> type(letters)
<class 'generator'>
>>> letters.__next__()
'a'
>>> letters.__next__()
'b'
>>> letters.__next__()
'c'
>>> letters.__next__()
'd'
>>> letters.__next__()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
StopIteration

Python中还有

Streams

类可以返回数据序列。

Streams

实例是懒惰计算(Lazyily Computed)的。

>>> class Stream:
"""A lazily computed linked list."""
class empty:
def __repr__(self):
return 'Stream.empty'
empty = empty()
def __init__(self, first, compute_rest=lambda: empty):
assert callable(compute_rest), 'compute_rest must be callable.'
self.first = first
self._compute_rest = compute_rest
@property
def rest(self):
"""Return the rest of the stream, computing it if necessary."""
if self._compute_rest is not None:
self._rest = self._compute_rest()
self._compute_rest = None
return self._rest
def __repr__(self):
return 'Stream({0}, <...>)'.format(repr(self.first))

Streams

实例返回两项：first 和 rest。每次返回值时只计算first，不计算rest。

>>> r = Link(1, Link(2+3, Link(9)))

>>> s = Stream(1, lambda: Stream(2+3, lambda: Stream(9)))

>>> r.first
1
>>> s.first
1
>>> r.rest.first
5
>>> s.rest.first
5
>>> r.rest
Link(5, Link(9))
>>> s.rest
Stream(5, <...>) # rest项为None，并没有被计算出来