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isnan#

ivy.isnan(x, /, *, out=None)[source]#

Test each element x_i of the input array x to determine whether the element is NaN.

Parameters:

  • x (Union[Array, NativeArray]) – input array. Should have a numeric data type.

  • out (Optional[Array], default: None) – optional output array, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing test results. An element out_i is True if x_i is NaN and False otherwise. The returned array should have a data type of bool.

Special Cases

For real-valued floating-point operands,

  • If x_i is NaN, the result is True.

  • In the remaining cases, the result is False.

For complex floating-point operands, let a = real(x_i), b = imag(x_i), and

  • If a or b is NaN, the result is True.

  • In the remaining cases, the result is False.

This function conforms to the Array API Standard. This docstring is an extension of the docstring in the standard.

Both the description and the type hints above assumes an array input for simplicity, but this function is nestable, and therefore also accepts ivy.Container instances in place of any of the arguments

Examples

With ivy.Array inputs:

>>> x = ivy.array([1, 2, 3])
>>> z = ivy.isnan(x)
>>> print(z)
ivy.array([False, False, False])

>>> x = ivy.array([[1.1, 2.3, -3.6]])
>>> z = ivy.isnan(x)
>>> print(z)
ivy.array([[False, False, False]])

>>> x = ivy.array([[[1.1], [float('inf')], [-6.3]]])
>>> z = ivy.isnan(x)
>>> print(z)
ivy.array([[[False],
            [False],
            [False]]])

>>> x = ivy.array([[-float('nan'), float('nan'), 0.0]])
>>> z = ivy.isnan(x)
>>> print(z)
ivy.array([[ True,  True, False]])

>>> x = ivy.array([[-float('nan'), float('inf'), float('nan'), 0.0]])
>>> z = ivy.isnan(x)
>>> print(z)
ivy.array([[ True, False,  True, False]])

>>> x = ivy.zeros((3, 3))
>>> z = ivy.isnan(x)
>>> print(z)
ivy.array([[False, False, False],
   [False, False, False],
   [False, False, False]])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([-1, -float('nan'), 1.23]),
...                   b=ivy.array([float('nan'), 3.3, -4.2]))
>>> z = ivy.isnan(x)
>>> print(z)
{
    a: ivy.array([False, True, False]),
    b: ivy.array([True, False, False])
}
Array.isnan(self, *, out=None)[source]#

ivy.Array instance method variant of ivy.isnan. This method simply wraps the function, and so the docstring for ivy.isnan also applies to this method with minimal changes.

Parameters:

  • self (Array) – input array. Should have a real-valued data type.

  • out (Optional[Array], default: None) – optional output array, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing test results. An element out_i is True if self_i is NaN and False otherwise. The returned array should have a data type of bool.

Examples

With ivy.Array inputs:

>>> x = ivy.array([1, 2, 3])
>>> x.isnan()
ivy.array([False, False, False])

>>> x = ivy.array([[1.1, 2.3, -3.6]])
>>> x.isnan()
ivy.array([[False, False, False]])

>>> x = ivy.array([[[1.1], [float('inf')], [-6.3]]])
>>> x.isnan()
ivy.array([[[False],
        [False],
        [False]]])

>>> x = ivy.array([[-float('nan'), float('nan'), 0.0]])
>>> x.isnan()
ivy.array([[ True, True, False]])

>>> x = ivy.array([[-float('nan'), float('inf'), float('nan'), 0.0]])
>>> x.isnan()
ivy.array([[ True, False,  True, False]])

>>> x = ivy.zeros((3, 3))
>>> x.isnan()
ivy.array([[False, False, False],
    [False, False, False],
    [False, False, False]])
Container.isnan(self, *, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)[source]#

ivy.Container instance method variant of ivy.isnan. This method simply wraps the function, and so the docstring for ivy.isnan also applies to this method with minimal changes.

Parameters:

  • self (Container) – input container. Should have a real-valued data type.

  • key_chains (Optional[Union[List[str], Dict[str, str], Container]], default: None) – The key-chains to apply or not apply the method to. Default is None.

  • to_apply (Union[bool, Container], default: True) – If True, the method will be applied to key_chains, otherwise key_chains will be skipped. Default is True.

  • prune_unapplied (Union[bool, Container], default: False) – Whether to prune key_chains for which the function was not applied. Default is False.

  • map_sequences (Union[bool, Container], default: False) – Whether to also map method to sequences (lists, tuples). Default is False.

  • out (Optional[Container], default: None) – optional output container, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

Container

Returns:

ret – a container containing the test result. An element out_i is True if self_i is NaN and False otherwise. The returned array should have a data type of bool.

Examples

>>> x = ivy.Container(a=ivy.array([-1, -float('nan'), 1.23]),
...                   b=ivy.array([float('nan'), 3.3, -4.2]))
>>> y = x.isnan()
>>> print(y)
{
    a: ivy.array([False, True, False]),
    b: ivy.array([True, False, False])
}
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