A NumPy scalar is any object which is an instance of np.generic or whose type is in np.ScalarType:

In [12]: np.ScalarType
Out[13]:
(int,
 float,
 complex,
 long,
 bool,
 str,
 unicode,
 buffer,
 numpy.int16,
 numpy.float16,
 numpy.int8,
 numpy.uint64,
 numpy.complex192,
 numpy.void,
 numpy.uint32,
 numpy.complex128,
 numpy.unicode_,
 numpy.uint32,
 numpy.complex64,
 numpy.string_,
 numpy.uint16,
 numpy.timedelta64,
 numpy.bool_,
 numpy.uint8,
 numpy.datetime64,
 numpy.object_,
 numpy.int64,
 numpy.float96,
 numpy.int32,
 numpy.float64,
 numpy.int32,
 numpy.float32)

This definition comes from looking at the source code for np.isscalar:

def isscalar(num):
    if isinstance(num, generic):
        return True
    else:
        return type(num) in ScalarType

Note that you can test if something is a scalar by using np.isscalar:

>>> np.isscalar(3.1)
True

>>> np.isscalar([3.1])
False

>>> np.isscalar(False)
True

How do we know what we know?

I like learning how people know what they know—more than the answers themselves. So let me try to explain where the above answer comes from.

Having the right tools can help you figure out things like this for yourself.

I found this out by using IPython. Using its TAB-completion feature, typing

In [19]: import numpy as np
In [20]: np.[TAB]

causes IPython to display all variables in the np module namespace. A search for the string "scalar" will lead you to np.ScalarType and np.isscalar. Typing

In [20]: np.isscalar?

(note the question mark at the end) prompts IPython to show you where np.isscalar is defined:

File:  /data1/unutbu/.virtualenvs/dev/lib/python2.7/site-packages/numpy/core/numeric.py

which is how I got to the definition of isscalar. Alternatively, the NumPy documentation for isscalar has a link to the source code as well.

You can use the item() function:

import numpy as np

matrix = np.array([[[[7]]]])
print(matrix.item())

Output

7