Split A Large Numpy Array Into Separate Arrays With A List Of Grouped Indices

Given 2 arrays: One for a master dataset, and the second as list of grouped indices that reference the master dataset. I'm looking for the fastest to generate new arrays from the g

Solution 1:

You just have to reshape the index array:

>>> result = POINT_CLOUD[INDICES.T]
>>> np.allclose(result[0], LIST1)
True
>>> np.allclose(result[1], LIST2)
True

If you know the number of sub-arrays you can also unpack the list

>>> result.shape
(2, 1000000, 3)
>>> L1, L2 = result
>>> np.allclose(L1, LIST1)
True
>>> # etc

This works for larger index groups. For the second example in your question:

>>> INDICES = (np.random.rand(COUNT,4)*COUNT).astype(int)
>>> SPLIT = POINT_CLOUD[INDICES.T]
>>> SPLIT.shape
(4, 1000000, 3)
>>>