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It's called fancy indexing in numpy.

You can image the first list and the second list as x-axis and y-axis. So a[[0,1,2],[0,1,0]] is like getting three elements which their coordinates are (0,0), (1,1), (2,0) from a.

The problem is in the index you identify.

In your example, D.size == len(S), so the difference is due to the difference between D.itemsize (8) and sys.getsizeof(5) (28).

D.dtype shows you that NumPy used int64 as the data type, which uses (unsurprisingly) 64 bits == 8 bytes per item. This is really only the raw numerical data, similar to a data type in C (under the hood it pretty much is exactly that).

In contrast, Python uses an int for storing the items, which (as pointed out the question linked to by FlyingTeller) is more than just the raw numerical data.

I think the error is in your line:

return quicksort_np(left) + middle + quicksort_np(right)

The '+' operator is a concatenation operator for python lists, but a sum operator for numpy arrays.

You can probably solve the problem by using np.concatenate