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# numpy.isnan

`numpy.``isnan`(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'isnan'>

  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 Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone. **kwargs For other keyword-only arguments, see the ufunc docs.
  Returns: | y : ndarray or bool For scalar input, the result is a new boolean with value True if the input is NaN; otherwise the value is False. For array input, the result is a boolean array of the same dimensions as the input and the values are True if the corresponding element of the input is NaN; otherwise the values are False.

  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], dtype=bool)