xorbits.numpy.isnan#

xorbits.numpy.isnan(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])[source]#

Test element-wise for NaN and return result as a boolean array.

Parameters

  • x (array_like) – Input array.

  • out (ndarray, None, or tuple of ndarray and None, optional) – A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

  • where (array_like, optional) – This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized.

  • **kwargs – For other keyword-only arguments, see the ufunc docs.

Returns

y – True where x is NaN, false otherwise. This is a scalar if x is a scalar.

Return type

ndarray or bool

See also

isinf, isneginf, isposinf, isfinite, isnat

Notes

NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity.

Examples

>>> np.isnan(np.nan)  
True
>>> np.isnan(np.inf)  
False
>>> np.isnan([np.log(-1.),1.,np.log(0)])  
array([ True, False, False])

This docstring was copied from numpy.