Decorator for overloading in Python

Question

I know it\'s not Pythonic to write functions that care about the type of the arguments, but there are cases when it\'s simply impossible to ignore types because they are han

Answer 1

This doesn't directly answer your question, but if you really want to have something that behaves like an overloaded function for different types and (quite rightly) don't want to use isinstance then I'd suggest something like:

def func(int_val=None, str_val=None):
    if sum(x != None for x in (int_val, str_val)) != 1:
        #raise exception - exactly one value should be passed in
    if int_val is not None:
        print('This is an int')
    if str_val is not None:
        print('This is a string')

In use the intent is obvious, and it doesn't even require the different options to have different types:

func(int_val=3)
func(str_val="squirrel")

Answer 2

Since Python 3.4 the functools module now supports a @singledispatch decorator. In your case this would look like:

from functools import singledispatch


@singledispatch
def func(val):
    raise NotImplementedError


@func.register
def _(val: str):
    print('This is a string')


@func.register
def _(val: int):
    print('This is an int')

Usage

func("test") --> "This is a string"
func(1) --> "This is an int"
func(None) --> NotImplementedError

Answer 3

Quick answer: there is an overload package on PyPI which implements this more robustly than what I describe below, although using a slightly different syntax. It's declared to work only with Python 3 but it looks like only slight modifications (if any, I haven't tried) would be needed to make it work with Python 2.

---

Long answer: In languages where you can overload functions, the name of a function is (either literally or effectively) augmented by information about its type signature, both when the function is defined and when it is called. When a compiler or interpreter looks up the function definition, then, it uses both the declared name and the types of the parameters to resolve which function to access. So the logical way to implement overloading in Python is to implement a wrapper that uses both the declared name and the parameter types to resolve the function.

Here's a simple implementation:

from collections import defaultdict

def determine_types(args, kwargs):
    return tuple([type(a) for a in args]), \
           tuple([(k, type(v)) for k,v in kwargs.iteritems()])

function_table = defaultdict(dict)
def overload(arg_types=(), kwarg_types=()):
    def wrap(func):
        named_func = function_table[func.__name__]
        named_func[arg_types, kwarg_types] = func
        def call_function_by_signature(*args, **kwargs):
            return named_func[determine_types(args, kwargs)](*args, **kwargs)
        return call_function_by_signature
    return wrap

overload should be called with two optional arguments, a tuple representing the types of all positional arguments and a tuple of tuples representing the name-type mappings of all keyword arguments. Here's a usage example:

>>> @overload((str, int))
... def f(a, b):
...     return a * b

>>> @overload((int, int))
... def f(a, b):
...     return a + b

>>> print f('a', 2)
aa
>>> print f(4, 2)
6

>>> @overload((str,), (('foo', int), ('bar', float)))
... def g(a, foo, bar):
...     return foo*a + str(bar)

>>> @overload((str,), (('foo', float), ('bar', float)))
... def g(a, foo, bar):
...     return a + str(foo*bar)

>>> print g('a', foo=7, bar=4.4)
aaaaaaa4.4
>>> print g('b', foo=7., bar=4.4)
b30.8

Shortcomings of this include

• It doesn't actually check that the function the decorator is applied to is even compatible with the arguments given to the decorator. You could write

  @overload((str, int))
  def h():
      return 0
  

  and you'd get an error when the function was called.

• It doesn't gracefully handle the case where no overloaded version exists corresponding to the types of the arguments passed (it would help to raise a more descriptive error)

• It distinguishes between named and positional arguments, so something like

  g('a', 7, bar=4.4)
  

  doesn't work.

• There are a lot of nested parentheses involved in using this, as in the definitions for g.
• As mentioned in the comments, this doesn't deal with functions having the same name in different modules.

All of these could be remedied with enough fiddling, I think. In particular, the issue of name collisions is easily resolved by storing the dispatch table as an attribute of the function returned from the decorator. But as I said, this is just a simple example to demonstrate the basics of how to do it.