天真的方法
def transpose_finite_iterable(iterable):
return zip(*iterable) # `itertools.izip` for Python 2 users
适用于(可能是无限的)可迭代对象的有限可迭代对象(例如,list/tuple/str 之类的序列)
| |a_00| |a_10| ... |a_n0| |
| |a_01| |a_11| ... |a_n1| |
| |... | |... | ... |... | |
| |a_0i| |a_1i| ... |a_ni| |
| |... | |... | ... |... | |
在哪里
-
n in ℕ,
-
a_ij 对应于i-th 可迭代的j-th 元素,
在应用transpose_finite_iterable 之后,我们得到
| |a_00| |a_01| ... |a_0i| ... |
| |a_10| |a_11| ... |a_1i| ... |
| |... | |... | ... |... | ... |
| |a_n0| |a_n1| ... |a_ni| ... |
a_ij == j, n == 2n == 2 的 Python 示例
>>> from itertools import count
>>> iterable = [count(), count()]
>>> result = transpose_finite_iterable(iterable)
>>> next(result)
(0, 0)
>>> next(result)
(1, 1)
但是我们不能再次使用transpose_finite_iterable 来返回原始iterable 的结构,因为result 是有限迭代的无限迭代(在我们的例子中是tuples):
>>> transpose_finite_iterable(result)
... hangs ...
Traceback (most recent call last):
File "...", line 1, in ...
File "...", line 2, in transpose_finite_iterable
MemoryError
那么我们该如何处理这种情况呢?
...这里是deque
在我们查看itertools.tee function 的文档之后,有一个 Python 配方,经过一些修改可以帮助我们的案例
def transpose_finite_iterables(iterable):
iterator = iter(iterable)
try:
first_elements = next(iterator)
except StopIteration:
return ()
queues = [deque([element])
for element in first_elements]
def coordinate(queue):
while True:
if not queue:
try:
elements = next(iterator)
except StopIteration:
return
for sub_queue, element in zip(queues, elements):
sub_queue.append(element)
yield queue.popleft()
return tuple(map(coordinate, queues))
我们来看看
>>> from itertools import count
>>> iterable = [count(), count()]
>>> result = transpose_finite_iterables(transpose_finite_iterable(iterable))
>>> result
(<generator object transpose_finite_iterables.<locals>.coordinate at ...>, <generator object transpose_finite_iterables.<locals>.coordinate at ...>)
>>> next(result[0])
0
>>> next(result[0])
1
合成
现在我们可以使用functools.singledispatch decorator like 定义通用函数来处理可迭代的迭代,其中一个是有限的,另一个可能是无限的
from collections import (abc,
deque)
from functools import singledispatch
@singledispatch
def transpose(object_):
"""
Transposes given object.
"""
raise TypeError('Unsupported object type: {type}.'
.format(type=type))
@transpose.register(abc.Iterable)
def transpose_finite_iterables(object_):
"""
Transposes given iterable of finite iterables.
"""
iterator = iter(object_)
try:
first_elements = next(iterator)
except StopIteration:
return ()
queues = [deque([element])
for element in first_elements]
def coordinate(queue):
while True:
if not queue:
try:
elements = next(iterator)
except StopIteration:
return
for sub_queue, element in zip(queues, elements):
sub_queue.append(element)
yield queue.popleft()
return tuple(map(coordinate, queues))
def transpose_finite_iterable(object_):
"""
Transposes given finite iterable of iterables.
"""
yield from zip(*object_)
try:
transpose.register(abc.Collection, transpose_finite_iterable)
except AttributeError:
# Python3.5-
transpose.register(abc.Mapping, transpose_finite_iterable)
transpose.register(abc.Sequence, transpose_finite_iterable)
transpose.register(abc.Set, transpose_finite_iterable)
在有限非空可迭代的二元运算符类中,它可以被认为是它自己的逆(数学家称这种函数"involutions")。
作为singledispatching 的奖励,我们可以处理numpy 之类的数组
import numpy as np
...
transpose.register(np.ndarray, np.transpose)
然后像这样使用它
>>> array = np.arange(4).reshape((2,2))
>>> array
array([[0, 1],
[2, 3]])
>>> transpose(array)
array([[0, 2],
[1, 3]])
注意
由于transpose 返回迭代器,如果有人想在 OP 中拥有 lists 中的 tuple - 这可以另外使用 map built-in function like 进行
>>> original = [('a', 1), ('b', 2), ('c', 3), ('d', 4)]
>>> tuple(map(list, transpose(original)))
(['a', 'b', 'c', 'd'], [1, 2, 3, 4])
广告
我已经从0.5.0 版本向lz package 添加了通用解决方案,可以像这样使用
>>> from lz.transposition import transpose
>>> list(map(tuple, transpose(zip(range(10), range(10, 20)))))
[(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), (10, 11, 12, 13, 14, 15, 16, 17, 18, 19)]
附言
没有解决方案(至少显而易见)来处理潜在无限迭代的潜在无限迭代,但这种情况不太常见。