Is it possible to create a numpy array from data pointing to same object in memory

Question

suppose we have two scalars with type of numpy.float32:

a
 >>>1.0
type(a)
 >>><class 'numpy.float32'>
b
 >>>2.0
type(b)
 >>><class 'numpy.float32'>

I am trying to create a numpy array from list([a,b,a,a,b,b,b,a,b,b,a,a,a,b,b,a,a]).

My question is, can we make this array point its scalars to same object in memory? instead of copying them per stride of its shape?

I have a very long array eg shape(1,30000), which consist of a few scalars (around 30) sequenced in different orders eg 1,2,3,2,1,3,2,1,2,1,3,3,2,2,3,1,2,1,1,3...

Creating this array take a huge amount of memory, but since they are actualy same scalars repeated, I thought there might be a way to load them once in memory and point array members to them.

Answer 1

One way that might help you reduce half memory if your array size is smaller than 32767 (which is maximum int16 in numpy, try: np.iinfo(np.int16).max ) is to store array of indices of your values as int16 instead of array of values itself, with a cost of calling the value from another list. Although this will be almost useless in a sense that you cannot leverage array calculations without making the array of values:

values = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32)
indices = np.array([1,2,3,2,1,3,2,1,2,1,3,3,2,2,3,1,2,1,1,3], dtype=np.int16)