define a 3d numpy array with a range in column-wise format

Question

I want to define a 3d numpy array with shape = [3, 3, 5] and also with values as a range starting with 11 and step = 3 in column-wise manner. I mean:

B[:,:,0] = [[ 11,  20,  29],
            [ 14,  23,  32],
            [17,   26,  35]]

B[:,:,1] = [[ 38,  ...],
            [ 41,  ...],
            [ 44,  ...]]
...

I am new to numpy and I doubt doing it with np.arange or np.mgrid maybe. but I don't how to do.

How can this be done with minimal lines of code?

Mark · Accepted Answer · 2020-03-12 19:55:39Z

1

You can calculate the end of the range by multiplying the shape by the step and adding the start. Then it's just reshape and transpose to move the column around:

start = 11
step = 3
shape = [5, 3, 3]

end = np.prod(shape) * step + start

B = np.arange(start, end, step).reshape([5, 3, 3]).transpose(2, 1, 0)
B[:, :, 0]

# array([[11, 20, 29],
#        [14, 23, 32],
#        [17, 26, 35]])

answered Mar 12, 2020 at 19:55

Mark

92.6k8 gold badges116 silver badges156 bronze badges

Sign up to request clarification or add additional context in comments.

Collectives™ on Stack Overflow

define a 3d numpy array with a range in column-wise format

1 Answer 1

Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

Comments

Your Answer

Sign up or log in

Post as a guest

Related