How much memory is used by the underlying buffer of a broadcasted numpy array?

Question

Note that nbytes doesn't provide the correct value. For instance:

>>> n = 1000
>>> x = np.arange(n)
>>> bx = np.broadcast_to(x, (int(1e15), n))
>>> bx.nbytes
8e18

....which probably requires more RAM than exists on Earth.

EDIT: More specifically, is there a way to obtain the size of the buffer that bx refers to? Something along the lines of:

>>> x.nbytes
8000
>>> bx.underlying_buffer_size()
8000

Answer 1

Note that as you can see in the docs, broadcast_to returns a view, where the broadcasted array may refer to a single memory location , from the docs :

broadcast : array A readonly view on the original array with the given shape. It is typically not contiguous. Furthermore, more than one element of a broadcasted array may refer to a single memory location.

Hence, in this case all new rows are pointing to the same memory location.

In order to see the actual of size of the object in bytes you can use sys.getsizeof :

from sys import getsizeof

getsizeof(bx)
112

---

This can be seen by checking which is the actual identity of the inner arrays:

id(bx[0])
# 1434315204368

id(bx[1])
# 1434315203968