I have a numpy array of shape 28 x 1875. Each element is a 3-element list (only floats). I need to split each of these elements to individual ones, to obtain an array of shape 28x5625(1875*3). I've tried np.split, however it only separates each element, but no each sub-element. Is there a fast way to do this?
1 Answer
Making a 2d array of lists:
In [522]: arr = np.empty(6,object)
In [523]: arr[:] = [list(range(i,i+3)) for i in range(6)]
In [524]: arr = arr.reshape(2,3)
In [525]: arr
Out[525]:
array([[list([0, 1, 2]), list([1, 2, 3]), list([2, 3, 4])],
[list([3, 4, 5]), list([4, 5, 6]), list([5, 6, 7])]], dtype=object)
It's easier to fill such an array if it is 1d, which is why I start with (6,) and reshape after.
Paul Panzer's suggestion:
In [526]: np.array(arr.tolist())
Out[526]:
array([[[0, 1, 2],
[1, 2, 3],
[2, 3, 4]],
[[3, 4, 5],
[4, 5, 6],
[5, 6, 7]]])
In [527]: _.reshape(2,-1)
Out[527]:
array([[0, 1, 2, 1, 2, 3, 2, 3, 4],
[3, 4, 5, 4, 5, 6, 5, 6, 7]])
You can also use np.stack (a version of np.concatenate) to create a nd array. It does though, require a 1d object array - hence the ravel:
In [536]: np.stack(arr.ravel())
Out[536]:
array([[0, 1, 2],
[1, 2, 3],
[2, 3, 4],
[3, 4, 5],
[4, 5, 6],
[5, 6, 7]])
That can be reshaped as needed:
In [537]: np.stack(arr.ravel()).reshape(2,-1)
Out[537]:
array([[0, 1, 2, 1, 2, 3, 2, 3, 4],
[3, 4, 5, 4, 5, 6, 5, 6, 7]])
In some cases we need to transpose axes to get the desired order.
list, orarray? If object, why is it that way, instead of being 3d?np.reshapewill probably not work directly but it should if you first convert to list:np.reshape(arr.tolist(), (*arr.shape[:-1], -1))