Multidimensional array in numpy

Question

I have an array of shape (5,2) which each row consist of an array of shape (4,3,2) and a float number.

After I slice that array[:,0], I get an array of shape (5,) which each element has shape of (4,3,2), instead of an array of shape (5,4,3,2) (even if I'd use np.array()).

Why?

Edited

Example:

a1 = np.arange(50).reshape(5, 5, 2)
a2 = np.arange(50).reshape(5, 5, 2)

b1 = 15.0
b2 = 25.0

h = []
h.append(np.array([a1, b1]))
h.append(np.array([a2, b2]))

h = np.array(h)[:,0]
np.shape(h) # (2,)
np.shape(h[0]) # (5, 5, 2)
np.shape(h[1]) # (5, 5, 2)
h = np.array(h)
np.shape(h) # (2,) Why not (2, 5, 5, 2)?

What do you mean with "each row consists of an array of shape (4,3,2)"? Please given an example. — willeM_ Van Onsem
– willeM_ Van Onsem, Commented Aug 5, 2017 at 18:39
You asked for the first column of each row. The first column is an array of shape (4, 3, 2), the second column is a float. Why are you surprised that you got an array with just (4, 3, 2) elements? — Barmar
– Barmar, Commented Aug 5, 2017 at 18:41

akuiper · Accepted Answer · 2017-08-05 18:55:54Z

2

You have an array of objects; You can use np.stack to convert it to the shape you need if you are sure all the sub elements have the same shape:

np.stack(a[:,0])

a = np.array([[np.arange(24).reshape(4,3,2), 1.]]*5)

a.shape
# (5, 2)

a[:,0].shape
# (5,)

a[:,0][0].shape
# (4, 3, 2)

np.stack(a[:,0]).shape
# (5, 4, 3, 2)

answered Aug 5, 2017 at 18:55

akuiper

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hpaulj · Accepted Answer · 2017-08-05 19:21:49Z

In [121]: a1.dtype, a1.shape
Out[121]: (dtype('int32'), (5, 5, 2))
In [122]: c1 = np.array([a1,b1])
In [123]: c1.dtype, c1.shape
Out[123]: (dtype('O'), (2,))

Because a1 and b1 are different shaped objects (b1 isn't even an array), an array made from them will have dtype object. And the h made from several continues to be object dtype.

In [124]: h = np.array(h)
In [125]: h.dtype, h.shape
Out[125]: (dtype('O'), (2, 2))
In [126]: h[:,1]
Out[126]: array([15.0, 25.0], dtype=object)
In [127]: h[:,0].dtype
Out[127]: dtype('O')

After the appends, h (as an array) is object dtype. The 2nd column is the b1 and b2 values, the 1st column the a1 and a2.

Some form of concatenate is required to combine those a1 a2 arrays into one. stack does it on a new axis.

In [128]: h[0,0].shape
Out[128]: (5, 5, 2)
In [129]: np.array(h[:,0]).shape     # np.array doesn't cross the object boundary
Out[129]: (2,)
In [130]: np.stack(h[:,0]).shape
Out[130]: (2, 5, 5, 2)
In [131]: np.concatenate(h[:,0],0).shape
Out[131]: (10, 5, 2)

Turning the (2,) array into a list, does allow np.array to recombine the elements into a higher dimensional array, just as np.stack does:

In [133]: np.array(list(h[:,0])).shape
Out[133]: (2, 5, 5, 2)

holdenweb · Accepted Answer · 2017-08-05 18:51:11Z

0

You appear to believe that Numpy can magically divine your intent. As @Barmar explains in the comments, when you slice a shape(5,2) array with [:, 0] you get all rows of the first column of that array. Each element of that slice is a shape(4,3,2) array. Numpy is giving you exactly what you asked for.

If you want to convert that into a shape(5,4,3,2) array you'll need to perform further processing to extract the elements from the shape(4,3,2) arrays.

answered Aug 5, 2017 at 18:51

holdenweb

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