Why a single Numpy array element is not a Python scalar?

Question

Can someone explain the Numpy design decision to keep single elements of arrays as distinct from Python scalars?

The following code works without errors

import numpy as np
a = np.array([1, 2, 3])
b = a[0]
print(b.size)

This illustrates that b is not a simple Python scalar, and in fact type(b) gives numpy.int32 instead of int.

Of course, if one defines b = 1, the command b.size throws an error because

AttributeError: 'int' object has no attribute 'size'

I find this difference of behaviour confusing and I am wondering what is its motivation.

Answer 1

There is a difference between elements of an array and the object you get when indexing one.

The array has a data buffer. It is a block of bytes the numpy manages with its own compiled code. Individual elements may be represented by 1 byte, 4, 8, 16, etc.

In [478]: A=np.array([1,2,3])

In [479]: A.__array_interface__
Out[479]: 
{'data': (167487856, False),
 'descr': [('', '<i4')],
 'shape': (3,),
 'strides': None,
 'typestr': '<i4',
 'version': 3}

view the data as a list of bytes (displayed as characters):

In [480]: A.view('S1')
Out[480]: 
array(['\x01', '', '', '', '\x02', '', '', '', '\x03', '', '', ''], 
      dtype='|S1')

When you select an element of A you get back a one element array (or something like it):

In [491]: b=A[0]

In [492]: b.shape
Out[492]: ()

In [493]: b.__array_interface__
Out[493]: 
{'__ref': array(1),
 'data': (167480104, False),
 'descr': [('', '<i4')],
 'shape': (),
 'strides': None,
 'typestr': '<i4',
 'version': 3}

The type is different, but b has most of the same attributes as A, shape, strides, mean, etc.

You have to use .item to access the underlying 'scalar':

In [496]: b.item()
Out[496]: 1

In [497]: type(b.item())
Out[497]: int

So you can think of b as a scalar with a numpy wrapper. The __array_interface__ for b looks very much like that of np.array(1).