numpy.isscalar#

numpy.isscalar(element)[source]#

Returns True if the type of element is a scalar type.

Parameters

elementany: Input argument, can be of any type and shape.

Returns

valbool: True if element is a scalar type, False if it is not.

See also

ndim: Get the number of dimensions of an array

Notes

If you need a stricter way to identify a numerical scalar, use isinstance(x, numbers.Number), as that returns False for most non-numerical elements such as strings.

In most cases np.ndim(x) == 0 should be used instead of this function, as that will also return true for 0d arrays. This is how numpy overloads functions in the style of the dx arguments to gradient and the bins argument to histogram. Some key differences:

x	`isscalar(x)`	`np.ndim(x) == 0`
PEP 3141 numeric objects (including builtins)	`True`	`True`
builtin string and buffer objects	`True`	`True`
other builtin objects, like `pathlib.Path`, Exception, the result of `re.compile`	`False`	`True`
third-party objects like `matplotlib.figure.Figure`	`False`	`True`
zero-dimensional numpy arrays	`False`	`True`
other numpy arrays	`False`	`False`
list, tuple, and other sequence objects	`False`	`False`

Examples

>>> np.isscalar(3.1)
True
>>> np.isscalar(np.array(3.1))
False
>>> np.isscalar([3.1])
False
>>> np.isscalar(False)
True
>>> np.isscalar('numpy')
True

NumPy supports PEP 3141 numbers:

>>> from fractions import Fraction
>>> np.isscalar(Fraction(5, 17))
True
>>> from numbers import Number
>>> np.isscalar(Number())
True

numpy.isrealobj

numpy.logical_and