I have a 3D list ll which can be of size 100 K * 10 * 3
ll = [
[[1, 2, 3], [4, 5, 6], [7, 8, 9], [10,11,12]], [[6, 7, 8],[12, 13, 14]], [[10, 20, 30], [40, 50, 60], [70, 80, 90]]
]
I want it to be
ll = [[[1, 2, 3], [4, 5, 6], [7, 8, 9], [10,11,12]], [[6, 7, 8],[12, 13, 14], [0, 0, 0], [0, 0, 0]], [[10, 20, 30], [40, 50, 60], [70, 80, 90], [0,0,0]]]
so that I can create a1 = np.array(l1)
a1
array([
[[1,2,3], [4,5,6], [7,8,9], [10,11,12]]
[[6,7,8], [12,13,14], [0,0,0], [0,0,0]]
[[10, 20, 30], [40, 50, 60], [70, 80, 90], [0,0,0]]
])
I have read the following but they are for 2D, i am not able to do it for 3D.
Here's a way that allocates the NumPy array up front then copies the data over. Assuming you don't actually need the expanded ll, this should use less memory than appending the 0-triples to ll before creating a1:
a1 = np.zeros((len(ll), max([len(k) for k in ll]), 3))
for ctr,k in enumerate(ll):
a1[ctr,:len(k),:] = k
a1
array([[[ 1., 2., 3.],
[ 4., 5., 6.],
[ 7., 8., 9.],
[10., 11., 12.]],
[[ 6., 7., 8.],
[12., 13., 14.],
[ 0., 0., 0.],
[ 0., 0., 0.]],
[[10., 20., 30.],
[40., 50., 60.],
[70., 80., 90.],
[ 0., 0., 0.]]])
max([len(k) for k in ll]) tells us the maximum number of triples in any member of ll. We allocate a 0-initialized NumPy array of the desired size. Then in the loop, smart indexing tells us where in a1 to copy each member of ll.