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.