#### Previous topic

numpy.may_share_memory

#### Next topic

numpy.lib.NumpyVersion

# numpy.lib.mixins.NDArrayOperatorsMixin

class `numpy.lib.mixins.``NDArrayOperatorsMixin`

  Mixin defining all operator special methods using __array_ufunc__.

  This class implements the special methods for almost all of Python’s builtin operators defined in the `operator` module, including comparisons (`==`, `>`, etc.) and arithmetic (`+`, `*`, `-`, etc.), by deferring to the `__array_ufunc__` method, which subclasses must implement.

  This class does not yet implement the special operators corresponding to `matmul` (`@`), because `np.matmul` is not yet a NumPy ufunc.

  It is useful for writing classes that do not inherit from `numpy.ndarray`, but that should support arithmetic and numpy universal functions like arrays as described in A Mechanism for Overriding Ufuncs.

  As an trivial example, consider this implementation of an `ArrayLike` class that simply wraps a NumPy array and ensures that the result of any arithmetic operation is also an `ArrayLike` object:

      class ArrayLike(np.lib.mixins.NDArrayOperatorsMixin):
          def __init__(self, value):
              self.value = np.asarray(value)

          # One might also consider adding the built-in list type to this
          # list, to support operations like np.add(array_like, list)
          _HANDLED_TYPES = (np.ndarray, numbers.Number)

          def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
              out = kwargs.get('out', ())
              for x in inputs + out:
                  # Only support operations with instances of _HANDLED_TYPES.
                  # Use ArrayLike instead of type(self) for isinstance to
                  # allow subclasses that don't override __array_ufunc__ to
                  # handle ArrayLike objects.
                  if not isinstance(x, self._HANDLED_TYPES + (ArrayLike,)):
                      return NotImplemented

              # Defer to the implementation of the ufunc on unwrapped values.
              inputs = tuple(x.value if isinstance(x, ArrayLike) else x
                             for x in inputs)
              if out:
                  kwargs['out'] = tuple(
                      x.value if isinstance(x, ArrayLike) else x
                      for x in out)
              result = getattr(ufunc, method)(*inputs, **kwargs)

              if type(result) is tuple:
                  # multiple return values
                  return tuple(type(self)(x) for x in result)
              elif method == 'at':
                  # no return value
                  return None
              else:
                  # one return value
                  return type(self)(result)

          def __repr__(self):
              return '%s(%r)' % (type(self).__name__, self.value)

  In interactions between `ArrayLike` objects and numbers or numpy arrays, the result is always another `ArrayLike`:

      >>> x = ArrayLike([1, 2, 3])
      >>> x - 1
      ArrayLike(array([0, 1, 2]))
      >>> 1 - x
      ArrayLike(array([ 0, -1, -2]))
      >>> np.arange(3) - x
      ArrayLike(array([-1, -1, -1]))
      >>> x - np.arange(3)
      ArrayLike(array([1, 1, 1]))

  Note that unlike `numpy.ndarray`, `ArrayLike` does not allow operations with arbitrary, unrecognized types. This ensures that interactions with ArrayLike preserve a well-defined casting hierarchy.