# numpy.isfinite

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

  Test element-wise for finiteness (not infinity and not Not a Number).

  The result is returned as a boolean array.

  Parameters
    x array_like

      Input values.

    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 ndarray, bool

      True where `x` is not positive infinity, negative infinity, or NaN; false otherwise. This is a scalar if x is a scalar.

  See also

  `isinf`, `isneginf`, `isposinf`, `isnan`

  Notes

  Not a Number, positive infinity and negative infinity are considered to be non-finite.

  NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Also that positive infinity is not equivalent to negative infinity. But infinity is equivalent to positive infinity. Errors result if the second argument is also supplied when x is a scalar input, or if first and second arguments have different shapes.

  Examples

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

      >>> x = np.array([-np.inf, 0., np.inf])
      >>> y = np.array([2, 2, 2])
      >>> np.isfinite(x, y)
      array([0, 1, 0])
      >>> y
      array([0, 1, 0])

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