Skip to main content
`Ctrl`+`K`

# 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.

  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 NEP 13 — A mechanism for overriding Ufuncs.

  As a 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:

      >>> import numbers
      >>> 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.