I have a numpy array of shape (1429,1) where each row itself is a numpy array of shape (3,100) where l may vary from row to row.
How can I reshape this array by flattening each row such that the resulting numpy array will have the shape (1429, 300)?
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Show us your minimal and verifiable exampleChiel– Chiel2017-10-15 13:32:16 +00:00Commented Oct 15, 2017 at 13:32
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3 Answers
I guess your initial array's shape is (1429, 3, 100), if that's true, you can change it's shape as below:
import numpy as np
a = a.flatten().reshape((1429, 300)) #a is the initial numpy array
Comments
The type of your embedding structure is probably object. It's just a collection of references on 1429 numpy.ndarrays.
As an exemple :
a=np.empty((1429,1),object)
for x in a :
x[0]=np.random.rand(3,100)
In [19]: a.shape,a.dtype
Out[19]: ((1429, 1), dtype('O'))
In [20]: a[0,0].shape
Out[20]: (3, 100)
The structure is probably not contiguous. To obtain a block containing all your data, you must reconstruct it to obtain the good layout :
b=np.array([x.ravel() for x in a.ravel()])
In [21]: b.shape
Out[21]: (1429, 300)
ravel discard unwanted dimensions.
Comments
Assuming it is an object dtype array with shape (1429,1), and all elements are 2d of shape (3,100), a good way to 'flatten' is to use concatenate or stack.
np.stack(arr.ravel()).reshape(-1,300)
I use arr.ravel() so the array looks like a (1429) element list to stack. stack then concatenates the elements, creating a (1429, 3, 100) array. The reshape then converts that to (1429, 300).
In [939]: arr = np.empty((5,1),object)
In [940]: arr[:,0] = [np.arange(6).reshape(2,3) for _ in range(5)]
In [941]: arr
Out[941]:
array([[array([[0, 1, 2],
[3, 4, 5]])],
[array([[0, 1, 2],
[3, 4, 5]])],
[array([[0, 1, 2],
[3, 4, 5]])],
[array([[0, 1, 2],
[3, 4, 5]])],
[array([[0, 1, 2],
[3, 4, 5]])]], dtype=object)
In [942]: np.stack(arr.ravel())
Out[942]:
array([[[0, 1, 2],
[3, 4, 5]],
[[0, 1, 2],
[3, 4, 5]],
[[0, 1, 2],
[3, 4, 5]],
[[0, 1, 2],
[3, 4, 5]],
[[0, 1, 2],
[3, 4, 5]]])
In [943]: np.stack(arr.ravel()).reshape(-1,6)
Out[943]:
array([[0, 1, 2, 3, 4, 5],
[0, 1, 2, 3, 4, 5],
[0, 1, 2, 3, 4, 5],
[0, 1, 2, 3, 4, 5],
[0, 1, 2, 3, 4, 5]])
np.stack with the default axis=0 is the same as np.array(...).
Or with concatenate
In [950]: np.concatenate(arr.ravel(),axis=0)
Out[950]:
array([[0, 1, 2],
[3, 4, 5],
[0, 1, 2],
[3, 4, 5],
[0, 1, 2],
[3, 4, 5],
[0, 1, 2],
[3, 4, 5],
[0, 1, 2],
[3, 4, 5]])
In [951]: np.concatenate(arr.ravel(),axis=0).reshape(5,6)
Out[951]:
array([[0, 1, 2, 3, 4, 5],
[0, 1, 2, 3, 4, 5],
[0, 1, 2, 3, 4, 5],
[0, 1, 2, 3, 4, 5],
[0, 1, 2, 3, 4, 5]])
