CODEWITHSUNDEEP
pythonpython-2.7python-3.xnumpy
Mohamad
Mohamad

Reputation: 123

Why isn't the shape of the array correct?

I'm having some array operation issues. Here's an example: 

A = np.ones((5,2))
B = np.ones((5,2)) * 2
X = np.zeros((5,1))

C = A[:,0] + B[:,0]
D = C + X

The shapes I'm getting are:

shape(A[:,0]) = (5,)
shape(B[:,0]) = (5,)
shape(X) = (5,1)
shape(C) = (5,)
shape(D) = (5,5)

When I extract a column from an array, the output is from shape (5,), not (5,1). Is there any way to correct that without having to reshape arrays all the time?

When I add D = C + X, the result is an (5,5) array, but should be (5,1).

Upvotes: 2

Views: 134

Answers (2)

hpaulj
hpaulj

Reputation: 231385

When broadcasting an array like C with (5,) with a 2d array, numpy adds dimensions at the start as needed, (1,5). So a (1,5) + (5,1) => (5,5).

To get a (5,1) result, you need, in one way or other, make C a (5,1) array.

C[:,None] + X   # None or np.newaxis is an easy way
C.reshape(5,1) + X  # equivalent

or index A with a list or slice

C = A[:,[0]] + B[:,[0]]

A[:,0] removes a dimension, producing a (5,) array.

Note, MATLAB adds the default dimensions to the end; numpy because it has a default C order, does so at the start. Adding dimensions like that requires minimal change, just changing the shape.

Functions like np.sum have a keepdimensions parameter to avoid this sort of dimension reduction.

Upvotes: 2

Jose Ricardo Bustos M.
Jose Ricardo Bustos M.

Reputation: 8164

Solution 1

D = X + C.reshape(shape(X))
shape(D)                        
#(5, 1)
print(D)
#[[ 3.]
# [ 3.]
# [ 3.]
# [ 3.]
# [ 3.]]

Solution 2 (better) numpy-convert-row-vector-to-column-vector

C = A[:,0:1] + B[:,0:1]

Why,

C and X have different shapes, and you sum row with number, geting a matrix with shape (5,5)

print(C)
#[ 3.  3.  3.  3.  3.]

print(X)
#[[ 0.]
# [ 0.]
# [ 0.]
# [ 0.]
# [ 0.]]

Upvotes: 3

Related Questions