DevTut

# Overloading

# Operator overloading

Below are the operators that can be overloaded in classes, along with the method definitions that are required, and an example of the operator in use within an expression.

N.B. The use of other as a variable name is not mandatory, but is considered the norm.

Operator Method Expression
+ Addition __add__(self, other) a1 + a2
- Subtraction __sub__(self, other) a1 - a2
* Multiplication __mul__(self, other) a1 * a2
@ Matrix Multiplication __matmul__(self, other) a1 @ a2 (Python 3.5)
/ Division __div__(self, other) a1 / a2 (Python 2 only)
/ Division __truediv__(self, other) a1 / a2 (Python 3)
// Floor Division __floordiv__(self, other) a1 // a2
% Modulo/Remainder __mod__(self, other) a1 % a2
** Power __pow__(self, other[, modulo]) a1 ** a2
<< Bitwise Left Shift __lshift__(self, other) a1 << a2
>> Bitwise Right Shift __rshift__(self, other) a1 >> a2
& Bitwise AND __and__(self, other) a1 & a2
^ Bitwise XOR __xor__(self, other) a1 ^ a2
| (Bitwise OR) __or__(self, other) a1 | a2
- Negation (Arithmetic) __neg__(self) -a1
+ Positive __pos__(self) +a1
~ Bitwise NOT __invert__(self) ~a1
< Less than __lt__(self, other) a1 < a2
<= Less than or Equal to __le__(self, other) a1 <= a2
== Equal to __eq__(self, other) a1 == a2
!= Not Equal to __ne__(self, other) a1 != a2
> Greater than __gt__(self, other) a1 > a2
>= Greater than or Equal to __ge__(self, other) a1 >= a2
[index] Index operator __getitem__(self, index) a1[index]
in In operator __contains__(self, other) a2 in a1
(*args, ...) Calling __call__(self, *args, **kwargs) a1(*args, **kwargs)

The optional parameter modulo for __pow__ is only used by the pow built-in function.

Each of the methods corresponding to a binary operator has a corresponding "right" method which start with __r, for example __radd__:

class A:
    def __init__(self, a):
        self.a = a
    def __add__(self, other):
        return self.a + other
    def __radd__(self, other):
        print("radd")
        return other + self.a

A(1) + 2  # Out:  3
2 + A(1)  # prints radd. Out: 3

as well as a corresponding inplace version, starting with __i:

class B:
    def __init__(self, b):
        self.b = b
    def __iadd__(self, other):
        self.b += other
        print("iadd")
        return self

b = B(2)
b.b       # Out: 2
b += 1    # prints iadd
b.b       # Out: 3

Since there's nothing special about these methods, many other parts of the language, parts of the standard library, and even third-party modules add magic methods on their own, like methods to cast an object to a type or checking properties of the object. For example, the builtin str() function calls the object's __str__ method, if it exists. Some of these uses are listed below.

Function Method Expression
Casting to int __int__(self) int(a1)
Absolute function __abs__(self) abs(a1)
Casting to str __str__(self) str(a1)
Casting to unicode __unicode__(self) unicode(a1) (Python 2 only)
String representation __repr__(self) repr(a1)
Casting to bool __nonzero__(self) bool(a1)
String formatting __format__(self, formatstr) "Hi {:abc}".format(a1)
Hashing __hash__(self) hash(a1)
Length __len__(self) len(a1)
Reversed __reversed__(self) reversed(a1)
Floor __floor__(self) math.floor(a1)
Ceiling __ceil__(self) math.ceil(a1)

There are also the special methods __enter__ and __exit__ for context managers, and many more.

# Magic/Dunder Methods

Magic (also called dunder as an abbreviation for double-underscore) methods in Python serve a similar purpose to operator overloading in other languages. They allow a class to define its behavior when it is used as an operand in unary or binary operator expressions. They also serve as implementations called by some built-in functions.

Consider this implementation of two-dimensional vectors.

import math

class Vector(object):
    # instantiation
    def __init__(self, x, y):
        self.x = x
        self.y = y

    # unary negation (-v)
    def __neg__(self):
        return Vector(-self.x, -self.y)

    # addition (v + u)
    def __add__(self, other):
        return Vector(self.x + other.x, self.y + other.y)

    # subtraction (v - u)
    def __sub__(self, other):
        return self + (-other)

    # equality (v == u)
    def __eq__(self, other):
        return self.x == other.x and self.y == other.y

    # abs(v)
    def __abs__(self):
        return math.hypot(self.x, self.y)

    # str(v)
    def __str__(self):
        return '<{0.x}, {0.y}>'.format(self)

    # repr(v)
    def __repr__(self):
        return 'Vector({0.x}, {0.y})'.format(self)

Now it is possible to naturally use instances of the Vector class in various expressions.

v = Vector(1, 4)
u = Vector(2, 0)

u + v           # Vector(3, 4)
print(u + v)    # "<3, 4>" (implicit string conversion)
u - v           # Vector(1, -4)
u == v          # False
u + v == v + u  # True
abs(u + v)      # 5.0

# Container and sequence types

It is possible to emulate container types, which support accessing values by key or index.

Consider this naive implementation of a sparse list, which stores only its non-zero elements to conserve memory.

class sparselist(object):
    def __init__(self, size):
        self.size = size
        self.data = {}
    
    # l[index]
    def __getitem__(self, index):
        if index < 0:
            index += self.size
        if index >= self.size:
            raise IndexError(index)
        try:
            return self.data[index]
        except KeyError:
            return 0.0

    # l[index] = value
    def __setitem__(self, index, value):
        self.data[index] = value

    # del l[index]
    def __delitem__(self, index):
        if index in self.data:
            del self.data[index]

    # value in l
    def __contains__(self, value):
        return value == 0.0 or value in self.data.values()

    # len(l)
    def __len__(self):
        return self.size

    # for value in l: ...
    def __iter__(self):
        return (self[i] for i in range(self.size)) # use xrange for python2

Then, we can use a sparselist much like a regular list.

l = sparselist(10 ** 6)  # list with 1 million elements
0 in l                   # True
10 in l                  # False

l[12345] = 10            
10 in l                  # True
l[12345]                 # 10

for v in l:
    pass  # 0, 0, 0, ... 10, 0, 0 ... 0

# Callable types

class adder(object):
    def __init__(self, first):
        self.first = first

    # a(...)
    def __call__(self, second):
        return self.first + second

add2 = adder(2)
add2(1)  # 3
add2(2)  # 4

# Handling unimplemented behaviour

If your class doesn't implement a specific overloaded operator for the argument types provided, it should return NotImplemented (note that this is a special constant (opens new window), not the same as NotImplementedError). This will allow Python to fall back to trying other methods to make the operation work:

When `NotImplemented` is returned, the interpreter will then try the reflected operation on the other type, or some other fallback, depending on the operator. If all attempted operations return `NotImplemented`, the interpreter will raise an appropriate exception.

For example, given x + y, if x.__add__(y) returns unimplemented, y.__radd__(x) is attempted instead.

class NotAddable(object):

    def __init__(self, value):
        self.value = value

    def __add__(self, other):
        return NotImplemented


class Addable(NotAddable):

    def __add__(self, other):
        return Addable(self.value + other.value)

    __radd__ = __add__

As this is the reflected method we have to implement __add__ and __radd__ to get the expected behaviour in all cases; fortunately, as they are both doing the same thing in this simple example, we can take a shortcut.

In use:

>>> x = NotAddable(1)
>>> y = Addable(2)
>>> x + x
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unsupported operand type(s) for +: 'NotAddable' and 'NotAddable'
>>> y + y
<so.Addable object at 0x1095974d0>
>>> z = x + y
>>> z
<so.Addable object at 0x109597510>
>>> z.value
3

← Polymorphism →