python - Numpy: use reshape or newaxis to add dimensions

Question

Either ndarray.reshape or numpy.newaxis can be used to add a new dimension to an array. They both seem to create a view, is there any reason or advantage to use one instead of the other?

>>> b
array([ 1.,  1.,  1.,  1.])
>>> c = b.reshape((1,4))
>>> c *= 2
>>> c
array([[ 2.,  2.,  2.,  2.]])
>>> c.shape
(1, 4)
>>> b
array([ 2.,  2.,  2.,  2.])
>>> d = b[np.newaxis,...]
>>> d
array([[ 2.,  2.,  2.,  2.]])
>>> d.shape
(1, 4)
>>> d *= 2
>>> b
array([ 4.,  4.,  4.,  4.])
>>> c
array([[ 4.,  4.,  4.,  4.]])
>>> d
array([[ 4.,  4.,  4.,  4.]])
>>> 

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Answer 1

One reason to use numpy.newaxis over ndarray.reshape is when you have more than one "unknown" dimension to operate with. So, for example, for the following array:

>>> arr.shape
(10, 5)

This works:

>>> arr[:, np.newaxis, :].shape
(10, 1, 5)

But this does not:

>>> arr.reshape(-1, 1, -1)
...
ValueError: can only specify one unknown dimension