If I create an array X = np.random.rand(D, 1) it has shape (3,1):
[[ 0.31215124]
[ 0.84270715]
[ 0.41846041]]
If I create my own array A = np.array([0,1,2]) then it has shape (1,3) and looks like
[0 1 2]
How can I force the shape (3, 1) on my array A?
You can assign a shape tuple directly to numpy.ndarray.shape.
A.shape = (3,1)
As of 2022, the docs state:
Setting arr.shape is discouraged and may be deprecated in the future. Using ndarray.reshape is the preferred approach.
The current best solution would be
A = np.reshape(A, (3,1))
np.reshape is a function, A.reshape a method, and A.shape= a functionality? They all do the same job.
shape will sometimes raise an error, it's not guaranteed to work in all cases. Just adding info, I'm not the down vote.np.reshape docs says: If you want an error to be raise if the data is copied, you should assign the new shape to the shape attribute of the array. The fact that a.shape= does not always work may be a good thing. Do you have any other cases in mind?.shape= would be wrong.A=np.array([0,1,2])
A.shape=(3,1)
or
A=np.array([0,1,2]).reshape((3,1)) #reshape takes the tuple shape as input
The numpy module has a reshape function and the ndarray has a reshape method, either of these should work to create an array with the shape you want:
import numpy as np
A = np.reshape([1, 2, 3, 4], (4, 1))
# Now change the shape to (2, 2)
A = A.reshape(2, 2)
Numpy will check that the size of the array does not change, ie prod(old_shape) == prod(new_shape). Because of this relation, you're allowed to replace one of the values in shape with -1 and numpy will figure it out for you:
A = A.reshape([1, 2, 3, 4], (-1, 1))
You can set the shape directy i.e.
A.shape = (3L, 1L)
or you can use the resize function:
A.resize((3L, 1L))
or during creation with reshape
A = np.array([0,1,2]).reshape((3L, 1L))
shape and resize should be avoided unless you know what you're doing because they both have behaviour that will likely surprise new users.Your 1-D array has the shape (3,):
>>>A = np.array([0,1,2]) # create 1-D array
>>>print(A.shape) # print array shape
(3,)
If you create an array with shape (1,3), you can use the numpy.reshape mentioned in other answers or numpy.swapaxes:
>>>A = np.array([[0,1,2]]) # create 2-D array
>>>print(A.shape) # print array shape
>>>A = np.swapaxes(A,0,1) # swap 0th and 1st axes
>>>A # display array with swapped axes
(1, 3)
array([[0],
[1],
[2]])
A.reshape([3,1])?