How to convert (5,) numpy array to (5,1)?

Question

And how to convert backwards from (5,1) to (5,)?

What is the purpose of (5,) array, why is one dimension omitted? I mean why we didn't always use (5,1) form?

Does this happen only with 1D and 2D arrays or does it happen across 3D arrays, like can (2,3,) array exist?

UPDATE:

I managed to convert from (5,) to (5,1) by

a= np.reshape(a, (a.shape[0], 1))

but suggested variant looks simpler:

a = a[:, None] or a = a[:, np.newaxis]

To convert from (5,1) to (5,) np.ravel can be used

a= np.ravel(a)

There's no need for "placeholder" comments, and it can often take more than two minutes to find a good dup target anyway. — DSM
– DSM, Commented Oct 26, 2015 at 16:04
Why should there be a 2nd dimension? Are you a MATLAB refugee? :) — hpaulj
– hpaulj, Commented Oct 26, 2015 at 16:06
@hpaulj Sometimes sklearn requires a 2 dimensional array of features — P. Camilleri
– P. Camilleri, Commented Oct 26, 2015 at 16:08
I don't think the duplicate suggestion is good, because none of the answers it suggests will work in this case. — P. Camilleri
– P. Camilleri, Commented Oct 26, 2015 at 16:24

musically_ut · Accepted Answer · 2017-10-24 09:27:53Z

A numpy array with shape (5,) is a 1 dimensional array while one with shape (5,1) is a 2 dimensional array. The difference is subtle, but can alter some computations in a major way. One has to be specially careful since these changes can be bull-dozes over by operations which flatten all dimensions, like np.mean or np.sum.

In addition to @m-massias's answer, consider the following as an example:

17:00:25 [2]: import numpy as np
17:00:31 [3]: a = np.array([1,2])
17:00:34 [4]: b = np.array([[1,2], [3,4]])
17:00:45 [6]: b * a
      Out[6]: 
array([[1, 4],
       [3, 8]])
17:00:50 [7]: b * a[:,None] # Different result!
      Out[7]: 
array([[1, 2],
       [6, 8]])

a has shape (2,) and it is broadcast over the second dimension. So the result you get is that each row (the first dimension) is multiplied by the vector:

17:02:44 [10]: b * np.array([[1, 2], [1, 2]])
      Out[10]: 
array([[1, 4],
       [3, 8]])

On the other hand, a[:,None] has the shape (2,1) and so the orientation of the vector is known to be a column. Hence, the result you get is from the following operation (where each column is multiplied by a):

17:03:39 [11]: b * np.array([[1, 1], [2, 2]])
      Out[11]: 
array([[1, 2],
       [6, 8]])

I hope that sheds some light on how the two arrays will behave differently.

P. Camilleri · Accepted Answer · 2015-10-26 15:57:34Z

4

You can add a new axis to an array a by doing a = a[:, None] or a = a[:, np.newaxis]

As far as "one dimension omitted", I don't really understand your question, because it has no end : the array could be (5, 1, 1), etc.

answered Oct 26, 2015 at 15:57

P. Camilleri

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Himanshu Singh · Accepted Answer · 2018-07-16 01:08:14Z

4

Use reshape() function e.g. open python terminal and type following:

    >>> import numpy as np
    >>> a = np.random.random(5)
    >>> a
    array([0.85694461, 0.37774476, 0.56348081, 0.02972139, 0.23453958])
    >>> a.shape
    (5,)
    >>> b = a.reshape(5, 1)
    >>> b.shape
    (5, 1)

answered Jul 16, 2018 at 1:08

Himanshu Singh

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