I know that Python does not support method overloading, but I’ve run into a problem that I can’t seem to solve in a nice Pythonic way.

I am making a game where a character needs to shoot a variety of bullets, but how do I write different functions for creating these bullets? For example suppose I have a function that creates a bullet travelling from point A to B with a given speed. I would write a function like this:

    def add_bullet(sprite, start, headto, speed):
        ... Code ...

But I want to write other functions for creating bullets like:

    def add_bullet(sprite, start, direction, speed):
    def add_bullet(sprite, start, headto, spead, acceleration):
    def add_bullet(sprite, script): # For bullets that are controlled by a script
    def add_bullet(sprite, curve, speed): # for bullets with curved paths
    ... And so on ...

And so on with many variations. Is there a better way to do it without using so many keyword arguments cause its getting kinda ugly fast. Renaming each function is pretty bad too because you get either add_bullet1, add_bullet2, or add_bullet_with_really_long_name.

To address some answers:

1. No I can’t create a Bullet class hierarchy because thats too slow. The actual code for managing bullets is in C and my functions are wrappers around C API.

2. I know about the keyword arguments but checking for all sorts of combinations of parameters is getting annoying, but default arguments help allot like acceleration=0

What you are asking for is called multiple dispatch. See Julia language examples which demonstrates different types of dispatches.

However, before looking at that, we’ll first tackle why overloading is not really what you want in python.

Why Not Overloading?

First, one needs to understand the concept of overloading and why it’s not applicable to python.

When working with languages that can discriminate data types at compile-time, selecting among the alternatives can occur at compile-time. The act of creating such alternative functions for compile-time selection is usually referred to as overloading a function. (Wikipedia)

Python is a dynamically typed language, so the concept of overloading simply does not apply to it. However, all is not lost, since we can create such alternative functions at run-time:

In programming languages that defer data type identification until run-time the selection among alternative functions must occur at run-time, based on the dynamically determined types of function arguments. Functions whose alternative implementations are selected in this manner are referred to most generally as multimethods. (Wikipedia)

So we should be able to do multimethods in python—or, as it is alternatively called: multiple dispatch.

Multiple dispatch

The multimethods are also called multiple dispatch:

Multiple dispatch or multimethods is the feature of some object-oriented programming languages in which a function or method can be dynamically dispatched based on the run time (dynamic) type of more than one of its arguments. (Wikipedia)

Python does not support this out of the box¹, but, as it happens, there is an excellent python package called multipledispatch that does exactly that.

Solution

Here is how we might use multipledispatch² package to implement your methods:

>>> from multipledispatch import dispatch
>>> from collections import namedtuple  
>>> from types import *  # we can test for lambda type, e.g.:
>>> type(lambda a: 1) == LambdaType
True

>>> Sprite = namedtuple('Sprite', ['name'])
>>> Point = namedtuple('Point', ['x', 'y'])
>>> Curve = namedtuple('Curve', ['x', 'y', 'z'])
>>> Vector = namedtuple('Vector', ['x','y','z'])

>>> @dispatch(Sprite, Point, Vector, int)
... def add_bullet(sprite, start, direction, speed):
...     print("Called Version 1")
...
>>> @dispatch(Sprite, Point, Point, int, float)
... def add_bullet(sprite, start, headto, speed, acceleration):
...     print("Called version 2")
...
>>> @dispatch(Sprite, LambdaType)
... def add_bullet(sprite, script):
...     print("Called version 3")
...
>>> @dispatch(Sprite, Curve, int)
... def add_bullet(sprite, curve, speed):
...     print("Called version 4")
...

>>> sprite = Sprite('Turtle')
>>> start = Point(1,2)
>>> direction = Vector(1,1,1)
>>> speed = 100 #km/h
>>> acceleration = 5.0 #m/s
>>> script = lambda sprite: sprite.x * 2
>>> curve = Curve(3, 1, 4)
>>> headto = Point(100, 100) # somewhere far away

>>> add_bullet(sprite, start, direction, speed)
Called Version 1

>>> add_bullet(sprite, start, headto, speed, acceleration)
Called version 2

>>> add_bullet(sprite, script)
Called version 3

>>> add_bullet(sprite, curve, speed)
Called version 4

<sub>1. Python 3 currently supports single dispatch
2. Take care not to use multipledispatch in a multi-threaded environment or you will get weird behavior.</sub>

Python does support “method overloading” as you present it. In fact, what you just describe is trivial to implement in Python, in so many different ways, but I would go with:

class Character(object):
    # your character __init__ and other methods go here

    def add_bullet(self, sprite=default, start=default, 
                 direction=default, speed=default, accel=default, 
                  curve=default):
        # do stuff with your arguments

In the above code, default is a plausible default value for those arguments, or None. You can then call the method with only the arguments you are interested in, and Python will use the default values.

You could also do something like this:

class Character(object):
    # your character __init__ and other methods go here

    def add_bullet(self, **kwargs):
        # here you can unpack kwargs as (key, values) and
        # do stuff with them, and use some global dictionary
        # to provide default values and ensure that ``key``
        # is a valid argument...

        # do stuff with your arguments

Another alternative is to directly hook the desired function directly to the class or instance:

def some_implementation(self, arg1, arg2, arg3):
  # implementation
my_class.add_bullet = some_implementation_of_add_bullet

Yet another way is to use an abstract factory pattern:

class Character(object):
   def __init__(self, bfactory, *args, **kwargs):
       self.bfactory = bfactory
   def add_bullet(self):
       sprite = self.bfactory.sprite()
       speed = self.bfactory.speed()
       # do stuff with your sprite and speed

class pretty_and_fast_factory(object):
    def sprite(self):
       return pretty_sprite
    def speed(self):
       return 10000000000.0

my_character = Character(pretty_and_fast_factory(), a1, a2, kw1=v1, kw2=v2)
my_character.add_bullet() # uses pretty_and_fast_factory

# now, if you have another factory called "ugly_and_slow_factory" 
# you can change it at runtime in python by issuing
my_character.bfactory = ugly_and_slow_factory()

# In the last example you can see abstract factory and "method
# overloading" (as you call it) in action 

You can use “roll-your-own” solution for function overloading. This one is copied from Guido van Rossum’s article about multimethods (because there is little difference between mm and overloading in python):

registry = {}

class MultiMethod(object):
    def __init__(self, name):
        self.name = name
        self.typemap = {}
    def __call__(self, *args):
        types = tuple(arg.__class__ for arg in args) # a generator expression!
        function = self.typemap.get(types)
        if function is None:
            raise TypeError("no match")
        return function(*args)
    def register(self, types, function):
        if types in self.typemap:
            raise TypeError("duplicate registration")
        self.typemap[types] = function


def multimethod(*types):
    def register(function):
        name = function.__name__
        mm = registry.get(name)
        if mm is None:
            mm = registry[name] = MultiMethod(name)
        mm.register(types, function)
        return mm
    return register

The usage would be

from multimethods import multimethod
import unittest

# 'overload' makes more sense in this case
overload = multimethod

class Sprite(object):
    pass

class Point(object):
    pass

class Curve(object):
    pass

@overload(Sprite, Point, Direction, int)
def add_bullet(sprite, start, direction, speed):
    # ...

@overload(Sprite, Point, Point, int, int)
def add_bullet(sprite, start, headto, speed, acceleration):
    # ...

@overload(Sprite, str)
def add_bullet(sprite, script):
    # ...

@overload(Sprite, Curve, speed)
def add_bullet(sprite, curve, speed):
    # ...

Most restrictive limitations at the moment are:

• methods are not supported, only functions that are not class members;
• inheritance is not handled;
• kwargs are not supported;
• registering new functions should be done at import time thing is not thread-safe

A possible option is to use the multipledispatch module as detailed here: http://matthewrocklin.com/blog/work/2014/02/25/Multiple-Dispatch

Instead of doing this:

def add(self, other):
    if isinstance(other, Foo):
        ...
    elif isinstance(other, Bar):
        ...
    else:
        raise NotImplementedError()

You can do this:

from multipledispatch import dispatch
@dispatch(int, int)
def add(x, y):
    return x + y    

@dispatch(object, object)
def add(x, y):
    return "%s + %s" % (x, y)

With the resulting usage:

>>> add(1, 2)
3

>>> add(1, 'hello')
'1 + hello'

In Python 3.4 was added PEP-0443. Single-dispatch generic functions.

Here is short API description from PEP.

To define a generic function, decorate it with the @singledispatch decorator. Note that the dispatch happens on the type of the first argument. Create your function accordingly:

from functools import singledispatch
@singledispatch
def fun(arg, verbose=False):
    if verbose:
        print("Let me just say,", end=" ")
    print(arg)

To add overloaded implementations to the function, use the register() attribute of the generic function. This is a decorator, taking a type parameter and decorating a function implementing the operation for that type:

@fun.register(int)
def _(arg, verbose=False):
    if verbose:
        print("Strength in numbers, eh?", end=" ")
    print(arg)

@fun.register(list)
def _(arg, verbose=False):
    if verbose:
        print("Enumerate this:")
    for i, elem in enumerate(arg):
        print(i, elem)

This type of behaviour is typically solved (in OOP languages) using Polymorphism. Each type of bullet would be responsible for knowing how it travels. For instance:

class Bullet(object):
    def __init__(self):
        self.curve = None
        self.speed = None
        self.acceleration = None
        self.sprite_image = None

class RegularBullet(Bullet):
    def __init__(self):
        super(RegularBullet, self).__init__()
        self.speed = 10

class Grenade(Bullet):
    def __init__(self):
        super(Grenade, self).__init__()
        self.speed = 4
        self.curve = 3.5

add_bullet(Grendade())

def add_bullet(bullet):
    c_function(bullet.speed, bullet.curve, bullet.acceleration, bullet.sprite, bullet.x, bullet.y) 


void c_function(double speed, double curve, double accel, char[] sprite, ...) {
    if (speed != null && ...) regular_bullet(...)
    else if (...) curved_bullet(...)
    //..etc..
}

Pass as many arguments to the c_function that exist, then do the job of determining which c function to call based on the values in the initial c function. So, python should only ever be calling the one c function. That one c function looks at the arguments, and then can delegate to other c functions appropriately.

You’re essentially just using each subclass as a different data container, but by defining all the potential arguments on the base class, the subclasses are free to ignore the ones they do nothing with.

When a new type of bullet comes along, you can simply define one more property on the base, change the one python function so that it passes the extra property, and the one c_function that examines the arguments and delegates appropriately. Doesn’t sound too bad I guess.

By passing keyword args.

def add_bullet(**kwargs):
    #check for the arguments listed above and do the proper things

Either use multiple keyword arguments in the definition, or create a Bullet hierarchy whose instances are passed to the function.

I think your basic requirement is to have a C/C++ like syntax in python with the least headache possible. Although I liked Alexander Poluektov’s answer it doesn’t work for classes.

The following should work for classes. It works by distinguishing by the number of non keyword arguments (but doesn’t support distinguishing by type):

class TestOverloading(object):
    def overloaded_function(self, *args, **kwargs):
        # Call the function that has the same number of non-keyword arguments.  
        getattr(self, "_overloaded_function_impl_" + str(len(args)))(*args, **kwargs)
    
    def _overloaded_function_impl_3(self, sprite, start, direction, **kwargs):
        print "This is overload 3"
        print "Sprite: %s" % str(sprite)
        print "Start: %s" % str(start)
        print "Direction: %s" % str(direction)
        
    def _overloaded_function_impl_2(self, sprite, script):
        print "This is overload 2"
        print "Sprite: %s" % str(sprite)
        print "Script: "
        print script

And it can be used simply like this:

test = TestOverloading()

test.overloaded_function("I'm a Sprite", 0, "Right")
print
test.overloaded_function("I'm another Sprite", "while x == True: print 'hi'")

Output:

This is overload 3
Sprite: I’m a Sprite
Start: 0
Direction: Right

This is overload 2
Sprite: I’m another Sprite
Script:
while x == True: print ‘hi’

The @overload decorator was added with type hints (PEP 484). While this doesn’t change the behaviour of python, it does make it easier to understand what is going on, and for mypy to detect errors.
See: Type hints and PEP 484

I think a Bullet class hierarchy with the associated polymorphism is the way to go. You can effectively overload the base class constructor by using a metaclass so that calling the base class results in the creation of the appropriate subclass object. Below is some sample code to illustrate the essence of what I mean.

Updated

The code has been modified to run under both Python 2 and 3 to keep it relevant. This was done in a way that avoids the use Python’s explicit metaclass syntax, which varies between the two versions.

To accomplish that objective, a BulletMetaBase instance of the BulletMeta class is created by explicitly calling the metaclass when creating the Bullet baseclass (rather than using the __metaclass__= class attribute or via a metaclass keyword argument depending on the Python version).

class BulletMeta(type):
    def __new__(cls, classname, bases, classdict):
        """ Create Bullet class or a subclass of it. """
        classobj = type.__new__(cls, classname, bases, classdict)
        if classname != 'BulletMetaBase':
            if classname == 'Bullet':  # Base class definition?
                classobj.registry = {}  # Initialize subclass registry.
            else:
                try:
                    alias = classdict['alias']
                except KeyError:
                    raise TypeError("Bullet subclass %s has no 'alias'" %
                                    classname)
                if alias in Bullet.registry: # unique?
                    raise TypeError("Bullet subclass %s's alias attribute "
                                    "%r already in use" % (classname, alias))
                # Register subclass under the specified alias.
                classobj.registry[alias] = classobj

        return classobj

    def __call__(cls, alias, *args, **kwargs):
        """ Bullet subclasses instance factory.

            Subclasses should only be instantiated by calls to the base
            class with their subclass' alias as the first arg.
        """
        if cls != Bullet:
            raise TypeError("Bullet subclass %r objects should not to "
                            "be explicitly constructed." % cls.__name__)
        elif alias not in cls.registry: # Bullet subclass?
            raise NotImplementedError("Unknown Bullet subclass %r" %
                                      str(alias))
        # Create designated subclass object (call its __init__ method).
        subclass = cls.registry[alias]
        return type.__call__(subclass, *args, **kwargs)


class Bullet(BulletMeta('BulletMetaBase', (object,), {})):
    # Presumably you'd define some abstract methods that all here
    # that would be supported by all subclasses.
    # These definitions could just raise NotImplementedError() or
    # implement the functionality is some sub-optimal generic way.
    # For example:
    def fire(self, *args, **kwargs):
        raise NotImplementedError(self.__class__.__name__ + ".fire() method")

    # Abstract base class's __init__ should never be called.
    # If subclasses need to call super class's __init__() for some
    # reason then it would need to be implemented.
    def __init__(self, *args, **kwargs):
        raise NotImplementedError("Bullet is an abstract base class")


# Subclass definitions.
class Bullet1(Bullet):
    alias = 'B1'
    def __init__(self, sprite, start, direction, speed):
        print('creating %s object' % self.__class__.__name__)
    def fire(self, trajectory):
        print('Bullet1 object fired with %s trajectory' % trajectory)


class Bullet2(Bullet):
    alias = 'B2'
    def __init__(self, sprite, start, headto, spead, acceleration):
        print('creating %s object' % self.__class__.__name__)


class Bullet3(Bullet):
    alias = 'B3'
    def __init__(self, sprite, script): # script controlled bullets
        print('creating %s object' % self.__class__.__name__)


class Bullet4(Bullet):
    alias = 'B4'
    def __init__(self, sprite, curve, speed): # for bullets with curved paths
        print('creating %s object' % self.__class__.__name__)


class Sprite: pass
class Curve: pass

b1 = Bullet('B1', Sprite(), (10,20,30), 90, 600)
b2 = Bullet('B2', Sprite(), (-30,17,94), (1,-1,-1), 600, 10)
b3 = Bullet('B3', Sprite(), 'bullet42.script')
b4 = Bullet('B4', Sprite(), Curve(), 720)
b1.fire('uniform gravity')
b2.fire('uniform gravity')

Output:

creating Bullet1 object
creating Bullet2 object
creating Bullet3 object
creating Bullet4 object
Bullet1 object fired with uniform gravity trajectory
Traceback (most recent call last):
  File "python-function-overloading.py", line 93, in <module>
    b2.fire('uniform gravity') # NotImplementedError: Bullet2.fire() method
  File "python-function-overloading.py", line 49, in fire
    raise NotImplementedError(self.__class__.__name__ + ".fire() method")
NotImplementedError: Bullet2.fire() method

Python 3.8 added functools.singledispatchmethod

Transform a method into a single-dispatch generic function.

To define a generic method, decorate it with the @singledispatchmethod decorator. Note that the dispatch happens on the type of the first non-self or non-cls argument, create your function accordingly:

from functools import singledispatchmethod


class Negator:
    @singledispatchmethod
    def neg(self, arg):
        raise NotImplementedError("Cannot negate a")

    @neg.register
    def _(self, arg: int):
        return -arg

    @neg.register
    def _(self, arg: bool):
        return not arg


negator = Negator()
for v in [42, True, "Overloading"]:
    neg = negator.neg(v)
    print(f"{v=}, {neg=}")

Output

v=42, neg=-42
v=True, neg=False
NotImplementedError: Cannot negate a

@singledispatchmethod supports nesting with other decorators such as @classmethod. Note that to allow for dispatcher.register, singledispatchmethod must be the outer most decorator. Here is the Negator class with the neg methods being class bound:

from functools import singledispatchmethod


class Negator:
    @singledispatchmethod
    @staticmethod
    def neg(arg):
        raise NotImplementedError("Cannot negate a")

    @neg.register
    def _(arg: int) -> int:
        return -arg

    @neg.register
    def _(arg: bool) -> bool:
        return not arg


for v in [42, True, "Overloading"]:
    neg = Negator.neg(v)
    print(f"{v=}, {neg=}")

Output:

v=42, neg=-42
v=True, neg=False
NotImplementedError: Cannot negate a

The same pattern can be used for other similar decorators: staticmethod, abstractmethod, and others.

Use keyword arguments with defaults. E.g.

def add_bullet(sprite, start=default, direction=default, script=default, speed=default):

In the case of a straight bullet versus a curved bullet, I’d add two functions: add_bullet_straight and add_bullet_curved.

overloading methods is tricky in python. However, there could be usage of passing the dict, list or primitive variables.

I have tried something for my use cases, this could help here to understand people to overload the methods.

Let’s take your example:

a class overload method with call the methods from different class.

def add_bullet(sprite=None, start=None, headto=None, spead=None, acceleration=None):

pass the arguments from remote class:

add_bullet(sprite = 'test', start=Yes,headto={'lat':10.6666,'long':10.6666},accelaration=10.6}

OR

add_bullet(sprite = 'test', start=Yes, headto={'lat':10.6666,'long':10.6666},speed=['10','20,'30']}

So, handling is being achieved for list, Dictionary or primitive variables from method overloading.

try it out for your codes.

Just a simple decorator

class overload:
    def __init__(self, f):
        self.cases = {}

    def args(self, *args):
        def store_function(f):
            self.cases[tuple(args)] = f
            return self
        return store_function

    def __call__(self, *args):
        function = self.cases[tuple(type(arg) for arg in args)]
        return function(*args)

You can use it like this

@overload
def f():
    pass

@f.args(int, int)
def f(x, y):
    print('two integers')

@f.args(float)
def f(x):
    print('one float')


f(5.5)
f(1, 2)

Modify it to adapt it to your use case.

A clarification of concepts

• function dispatch: there are multiple functions with the same name. Which one should be called? two strategies
• static/compile-time dispatch (aka. “overloading”). decide which function to call based on the compile-time type of the arguments. In all dynamic languages, there is no compile-time type, so overloading is impossible by definition
• dynamic/run-time dispatch: decide which function to call based on the runtime type of the arguments. This is what all OOP languages do: multiple classes have the same methods, and the language decides which one to call based on the type of self/this argument. However, most languages only do it for the this argument only. The above decorator extends the idea to multiple parameters.

To clear up, assume a static language, and define the functions

void f(Integer x):
    print('integer called')

void f(Float x):
    print('float called')

void f(Number x):
    print('number called')


Number x = new Integer('5')
f(x)
x = new Number('3.14')
f(x)

With static dispatch (overloading) you will see “number called” twice, because x has been declared as Number, and that’s all overloading cares about. With dynamic dispatch you will see “integer called, float called”, because those are the actual types of x at the time the function is called.