将单个维度添加到NumPy向量的有效方法,以便切片分配起作用

Question

在NumPy中,如何有效地将1-D对象制作成2-D对象,其中单个维度是从当前对象推断出来的(即列表应该是1xlength还是lengthx1向量)？

 # This comes from some other, unchangeable code that reads data files.
 my_list = [1,2,3,4]

 # What I want to do:
 my_numpy_array[some_index,:] = numpy.asarray(my_list)

 # The above doesn't work because of a broadcast error, so:
 my_numpy_array[some_index,:] = numpy.reshape(numpy.asarray(my_list),(1,len(my_list)))

 # How to do the above without the call to reshape?
 # Is there a way to directly convert a list, or vector, that doesn't have a
 # second dimension, into a 1 by length "array" (but really it's still a vector)?

Answer 1

在最常见的情况下,向数组添加额外维度的最简单方法是None在位置处建立索引时使用关键字来添加额外维度.例如

my_array = numpy.array([1,2,3,4])

my_array[None, :] # shape 1x4

my_array[:, None] # shape 4x1

Answer 2

为什么不简单地添加方括号？

>> my_list
[1, 2, 3, 4]
>>> numpy.asarray([my_list])
array([[1, 2, 3, 4]])
>>> numpy.asarray([my_list]).shape
(1, 4)

.. 等等，再想一想，为什么你的切片分配失败了？它不应该：

>>> my_list = [1,2,3,4]
>>> d = numpy.ones((3,4))
>>> d
array([[ 1.,  1.,  1.,  1.],
       [ 1.,  1.,  1.,  1.],
       [ 1.,  1.,  1.,  1.]])
>>> d[0,:] = my_list
>>> d[1,:] = numpy.asarray(my_list)
>>> d[2,:] = numpy.asarray([my_list])
>>> d
array([[ 1.,  2.,  3.,  4.],
       [ 1.,  2.,  3.,  4.],
       [ 1.,  2.,  3.,  4.]])

甚至：

>>> d[1,:] = (3*numpy.asarray(my_list)).T
>>> d
array([[  1.,   2.,   3.,   4.],
       [  3.,   6.,   9.,  12.],
       [  1.,   2.,   3.,   4.]])