value_is_nan#
- ivy.value_is_nan(x, /, *, include_infs=True)[source]#
Determine whether the single valued array or scalar is of nan type.
- Parameters:
x (
Union
[Array
,NativeArray
,Number
]) – The input to check Input array.include_infs (
bool
, default:True
) – Whether to include infs and -infs in the check. Default isTrue
.
- Return type:
bool
- Returns:
ret – Boolean as to whether the input value is a nan or not.
Examples
>>> x = ivy.array([451]) >>> y = ivy.value_is_nan(x) >>> print(y) False
>>> x = ivy.array([float('inf')]) >>> y = ivy.value_is_nan(x) >>> print(y) True
>>> x = ivy.array([float('inf')]) >>> y = ivy.value_is_nan(x, include_infs=False) >>> print(y) False
>>> x = ivy.array([float('nan')]) >>> y = ivy.value_is_nan(x, include_infs=False) >>> print(y) True
>>> x = ivy.array([0]) >>> y = ivy.value_is_nan(x) >>> print(y) False
- Array.value_is_nan(self, /, *, include_infs=True)[source]#
ivy.Array instance method variant of ivy.value_is_nan. This method simply wraps the function, and so the docstring for ivy.value_is_nan also applies to this method with minimal changes.
- Parameters:
self (
Array
) – input arrayinclude_infs (
bool
, default:True
) – Whether to include infs and -infs in the check. Default isTrue
.
- Return type:
bool
- Returns:
ret – Boolean as to whether the input value is a nan or not.
Examples
With one
ivy.Array
instance method:>>> x = ivy.array([92]) >>> y = x.value_is_nan() >>> print(y) False
>>> x = ivy.array([float('inf')]) >>> y = x.value_is_nan() >>> print(y) True
>>> x = ivy.array([float('nan')]) >>> y = x.value_is_nan() >>> print(y) True
>>> x = ivy.array([float('inf')]) >>> y = x.value_is_nan(include_infs=False) >>> print(y) False
- Container.value_is_nan(self, /, *, include_infs=True, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#
ivy.Container instance method variant of ivy.value_is_nan. This method simply wraps the function, and so the docstring for ivy.value_is_nan also applies to this method with minimal changes.
- Parameters:
self (
Container
) – input container.include_infs (
Union
[bool
,Container
], default:True
) – Whether to include infs and -infs in the check. Default isTrue
.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 isTrue
.prune_unapplied (
Union
[bool
,Container
], default:False
) – Whether to prune key_chains for which the function was not applied. Default isFalse
.map_sequences (
Union
[bool
,Container
], default:False
) – Whether to also map method to sequences (lists, tuples). Default isFalse
.
- Return type:
Container
- Returns:
ret – Boolean as to whether the input value is a nan or not.
Examples
>>> x = ivy.Container(a=ivy.array([425]), b=ivy.array([float('nan')])) >>> y = x.value_is_nan() >>> print(y) { a: False, b: True }
>>> x = ivy.Container(a=ivy.array([float('inf')]), b=ivy.array([0])) >>> y = x.value_is_nan() >>> print(y) { a: True, b: False }
>>> x = ivy.Container(a=ivy.array([float('inf')]), b=ivy.array([22])) >>> y = x.value_is_nan(include_infs=False) >>> print(y) { a: False, b: False }