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How to create a class with multiple constructors that take different number of parameters in Python? [duplicate]

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:
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.
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**2
>>> 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
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.

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 multimethods 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 PEP-0443. Single-dispatch generic functions was added.
Here is a 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)

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

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, and 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. It doesn't sound too bad I guess.

It is impossible by definition to overload a function in python (read on for details), but you can achieve something similar with 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 that we define, in a hypothetical static language, 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.

By passing keyword args.
def add_bullet(**kwargs):
#check for the arguments listed above and do the proper things

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.

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 it 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'

You can achieve this with the following Python code:
#overload
def test(message: str):
return message
#overload
def test(number: int):
return number + 1

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

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

You can easily implement function overloading in Python. Here is an example using floats and integers:
class OverloadedFunction:
def __init__(self):
self.router = {int : self.f_int ,
float: self.f_float}
def __call__(self, x):
return self.router[type(x)](x)
def f_int(self, x):
print('Integer Function')
return x**2
def f_float(self, x):
print('Float Function (Overloaded)')
return x**3
# f is our overloaded function
f = OverloadedFunction()
print(f(3 ))
print(f(3.))
# Output:
# Integer Function
# 9
# Float Function (Overloaded)
# 27.0
The main idea behind the code is that a class holds the different (overloaded) functions that you would like to implement, and a Dictionary works as a router, directing your code towards the right function depending on the input type(x).
PS1. In case of custom classes, like Bullet1, you can initialize the internal dictionary following a similar pattern, such as self.D = {Bullet1: self.f_Bullet1, ...}. The rest of the code is the same.
PS2. The time/space complexity of the proposed solution is fairly good as well, with an average cost of O(1) per operation.

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, and 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 the 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 code.

Plum supports it in a straightforward pythonic way. Copying an example from the README below.
from plum import dispatch
#dispatch
def f(x: str):
return "This is a string!"
#dispatch
def f(x: int):
return "This is an integer!"
>>> f("1")
'This is a string!'
>>> f(1)
'This is an integer!'

You can also try this code. We can try any number of arguments
# Finding the average of given number of arguments
def avg(*args): # args is the argument name we give
sum = 0
for i in args:
sum += i
average = sum/len(args) # Will find length of arguments we given
print("Avg: ", average)
# call function with different number of arguments
avg(1,2)
avg(5,6,4,7)
avg(11,23,54,111,76)

What's the best way to define a function (not a method) inside a class? [duplicate]

Can I define a static method which I can call directly on the class instance? e.g.,
MyClass.the_static_method()

Yep, using the staticmethod decorator:
class MyClass(object):
#staticmethod
def the_static_method(x):
print(x)
MyClass.the_static_method(2) # outputs 2
Note that some code might use the old method of defining a static method, using staticmethod as a function rather than a decorator. This should only be used if you have to support ancient versions of Python (2.2 and 2.3):
class MyClass(object):
def the_static_method(x):
print(x)
the_static_method = staticmethod(the_static_method)
MyClass.the_static_method(2) # outputs 2
This is entirely identical to the first example (using #staticmethod), just not using the nice decorator syntax.
Finally, use staticmethod sparingly! There are very few situations where static-methods are necessary in Python, and I've seen them used many times where a separate "top-level" function would have been clearer.
The following is verbatim from the documentation::
A static method does not receive an implicit first argument. To declare a static method, use this idiom:
class C:
#staticmethod
def f(arg1, arg2, ...): ...
The #staticmethod form is a function decorator – see the description of function definitions in Function definitions for details.
It can be called either on the class (such as C.f()) or on an instance (such as C().f()). The instance is ignored except for its class.
Static methods in Python are similar to those found in Java or C++. For a more advanced concept, see classmethod().
For more information on static methods, consult the documentation on the standard type hierarchy in The standard type hierarchy.
New in version 2.2.
Changed in version 2.4: Function decorator syntax added.

I think that Steven is actually right. To answer the original question, then, in order to set up a class method, simply assume that the first argument is not going to be a calling instance, and then make sure that you only call the method from the class.
(Note that this answer refers to Python 3.x. In Python 2.x you'll get a TypeError for calling the method on the class itself.)
For example:
class Dog:
count = 0 # this is a class variable
dogs = [] # this is a class variable
def __init__(self, name):
self.name = name #self.name is an instance variable
Dog.count += 1
Dog.dogs.append(name)
def bark(self, n): # this is an instance method
print("{} says: {}".format(self.name, "woof! " * n))
def rollCall(n): #this is implicitly a class method (see comments below)
print("There are {} dogs.".format(Dog.count))
if n >= len(Dog.dogs) or n < 0:
print("They are:")
for dog in Dog.dogs:
print(" {}".format(dog))
else:
print("The dog indexed at {} is {}.".format(n, Dog.dogs[n]))
fido = Dog("Fido")
fido.bark(3)
Dog.rollCall(-1)
rex = Dog("Rex")
Dog.rollCall(0)
In this code, the "rollCall" method assumes that the first argument is not an instance (as it would be if it were called by an instance instead of a class). As long as "rollCall" is called from the class rather than an instance, the code will work fine. If we try to call "rollCall" from an instance, e.g.:
rex.rollCall(-1)
however, it would cause an exception to be raised because it would send two arguments: itself and -1, and "rollCall" is only defined to accept one argument.
Incidentally, rex.rollCall() would send the correct number of arguments, but would also cause an exception to be raised because now n would be representing a Dog instance (i.e., rex) when the function expects n to be numerical.
This is where the decoration comes in:
If we precede the "rollCall" method with
#staticmethod
then, by explicitly stating that the method is static, we can even call it from an instance. Now,
rex.rollCall(-1)
would work. The insertion of #staticmethod before a method definition, then, stops an instance from sending itself as an argument.
You can verify this by trying the following code with and without the #staticmethod line commented out.
class Dog:
count = 0 # this is a class variable
dogs = [] # this is a class variable
def __init__(self, name):
self.name = name #self.name is an instance variable
Dog.count += 1
Dog.dogs.append(name)
def bark(self, n): # this is an instance method
print("{} says: {}".format(self.name, "woof! " * n))
#staticmethod
def rollCall(n):
print("There are {} dogs.".format(Dog.count))
if n >= len(Dog.dogs) or n < 0:
print("They are:")
for dog in Dog.dogs:
print(" {}".format(dog))
else:
print("The dog indexed at {} is {}.".format(n, Dog.dogs[n]))
fido = Dog("Fido")
fido.bark(3)
Dog.rollCall(-1)
rex = Dog("Rex")
Dog.rollCall(0)
rex.rollCall(-1)

Yes, check out the staticmethod decorator:
>>> class C:
... #staticmethod
... def hello():
... print "Hello World"
...
>>> C.hello()
Hello World

You don't really need to use the #staticmethod decorator. Just declaring a method (that doesn't expect the self parameter) and call it from the class. The decorator is only there in case you want to be able to call it from an instance as well (which was not what you wanted to do)
Mostly, you just use functions though...

Static methods in Python?
Is it possible to have static methods in Python so I can call them
without initializing a class, like:
ClassName.StaticMethod()
Yes, static methods can be created like this (although it's a bit more Pythonic to use underscores instead of CamelCase for methods):
class ClassName(object):
#staticmethod
def static_method(kwarg1=None):
'''return a value that is a function of kwarg1'''
The above uses the decorator syntax. This syntax is equivalent to
class ClassName(object):
def static_method(kwarg1=None):
'''return a value that is a function of kwarg1'''
static_method = staticmethod(static_method)
This can be used just as you described:
ClassName.static_method()
A builtin example of a static method is str.maketrans() in Python 3, which was a function in the string module in Python 2.
Another option that can be used as you describe is the classmethod, the difference is that the classmethod gets the class as an implicit first argument, and if subclassed, then it gets the subclass as the implicit first argument.
class ClassName(object):
#classmethod
def class_method(cls, kwarg1=None):
'''return a value that is a function of the class and kwarg1'''
Note that cls is not a required name for the first argument, but most experienced Python coders will consider it badly done if you use anything else.
These are typically used as alternative constructors.
new_instance = ClassName.class_method()
A builtin example is dict.fromkeys():
new_dict = dict.fromkeys(['key1', 'key2'])

Perhaps the simplest option is just to put those functions outside of the class:
class Dog(object):
def __init__(self, name):
self.name = name
def bark(self):
if self.name == "Doggy":
return barking_sound()
else:
return "yip yip"
def barking_sound():
return "woof woof"
Using this method, functions which modify or use internal object state (have side effects) can be kept in the class, and the reusable utility functions can be moved outside.
Let's say this file is called dogs.py. To use these, you'd call dogs.barking_sound() instead of dogs.Dog.barking_sound.
If you really need a static method to be part of the class, you can use the staticmethod decorator.

Aside from the particularities of how static method objects behave, there is a certain kind of beauty you can strike with them when it comes to organizing your module-level code.
# garden.py
def trim(a):
pass
def strip(a):
pass
def bunch(a, b):
pass
def _foo(foo):
pass
class powertools(object):
"""
Provides much regarded gardening power tools.
"""
#staticmethod
def answer_to_the_ultimate_question_of_life_the_universe_and_everything():
return 42
#staticmethod
def random():
return 13
#staticmethod
def promise():
return True
def _bar(baz, quux):
pass
class _Dice(object):
pass
class _6d(_Dice):
pass
class _12d(_Dice):
pass
class _Smarter:
pass
class _MagicalPonies:
pass
class _Samurai:
pass
class Foo(_6d, _Samurai):
pass
class Bar(_12d, _Smarter, _MagicalPonies):
pass
...
# tests.py
import unittest
import garden
class GardenTests(unittest.TestCase):
pass
class PowertoolsTests(unittest.TestCase):
pass
class FooTests(unittest.TestCase):
pass
class BarTests(unittest.TestCase):
pass
...
# interactive.py
from garden import trim, bunch, Foo
f = trim(Foo())
bunch(f, Foo())
...
# my_garden.py
import garden
from garden import powertools
class _Cowboy(garden._Samurai):
def hit():
return powertools.promise() and powertools.random() or 0
class Foo(_Cowboy, garden.Foo):
pass
It now becomes a bit more intuitive and self-documenting in which context certain components are meant to be used and it pans out ideally for naming distinct test cases as well as having a straightforward approach to how test modules map to actual modules under tests for purists.
I frequently find it viable to apply this approach to organizing a project's utility code. Quite often, people immediately rush and create a utils package and end up with 9 modules of which one has 120 LOC and the rest are two dozen LOC at best. I prefer to start with this and convert it to a package and create modules only for the beasts that truly deserve them:
# utils.py
class socket(object):
#staticmethod
def check_if_port_available(port):
pass
#staticmethod
def get_free_port(port)
pass
class image(object):
#staticmethod
def to_rgb(image):
pass
#staticmethod
def to_cmyk(image):
pass

So, static methods are the methods which can be called without creating the object of a class.
For Example :-
#staticmethod
def add(a, b):
return a + b
b = A.add(12,12)
print b
In the above example method add is called by the class name A not the object name.

Summarizing others' answers and adding, there are many ways to declare Static Methods or Variables in python.
Using staticmethod() as a decorator:
One can simply put a decorator above a method(function) declared to make it a static method. For eg.
class Calculator:
#staticmethod
def multiply(n1, n2, *args):
Res = 1
for num in args: Res *= num
return n1 * n2 * Res
print(Calculator.multiply(1, 2, 3, 4)) # 24
Using staticmethod() as a parameter function:
This method can receive an argument which is of function type, and it returns a static version of the function passed. For eg.
class Calculator:
def add(n1, n2, *args):
return n1 + n2 + sum(args)
Calculator.add = staticmethod(Calculator.add)
print(Calculator.add(1, 2, 3, 4)) # 10
Using classmethod() as a decorator:
#classmethod has similar effect on a function as #staticmethod has, but
this time, an additional argument is needed to be accepted in the function (similar to self parameter for instance variables). For eg.
class Calculator:
num = 0
def __init__(self, digits) -> None:
Calculator.num = int(''.join(digits))
#classmethod
def get_digits(cls, num):
digits = list(str(num))
calc = cls(digits)
return calc.num
print(Calculator.get_digits(314159)) # 314159
Using classmethod() as a parameter function:
#classmethod can also be used as a parameter function, in case one doesn't want to modify class definition. For eg.
class Calculator:
def divide(cls, n1, n2, *args):
Res = 1
for num in args: Res *= num
return n1 / n2 / Res
Calculator.divide = classmethod(Calculator.divide)
print(Calculator.divide(15, 3, 5)) # 1.0
Direct declaration
A method/variable declared outside all other methods, but inside a class is automatically static.
class Calculator:
def subtract(n1, n2, *args):
return n1 - n2 - sum(args)
print(Calculator.subtract(10, 2, 3, 4)) # 1
The whole program
class Calculator:
num = 0
def __init__(self, digits) -> None:
Calculator.num = int(''.join(digits))
#staticmethod
def multiply(n1, n2, *args):
Res = 1
for num in args: Res *= num
return n1 * n2 * Res
def add(n1, n2, *args):
return n1 + n2 + sum(args)
#classmethod
def get_digits(cls, num):
digits = list(str(num))
calc = cls(digits)
return calc.num
def divide(cls, n1, n2, *args):
Res = 1
for num in args: Res *= num
return n1 / n2 / Res
def subtract(n1, n2, *args):
return n1 - n2 - sum(args)
Calculator.add = staticmethod(Calculator.add)
Calculator.divide = classmethod(Calculator.divide)
print(Calculator.multiply(1, 2, 3, 4)) # 24
print(Calculator.add(1, 2, 3, 4)) # 10
print(Calculator.get_digits(314159)) # 314159
print(Calculator.divide(15, 3, 5)) # 1.0
print(Calculator.subtract(10, 2, 3, 4)) # 1
Refer to Python Documentation for mastering OOP in python.

Python Static methods can be created in two ways.
Using staticmethod()
class Arithmetic:
def add(x, y):
return x + y
# create add static method
Arithmetic.add = staticmethod(Arithmetic.add)
print('Result:', Arithmetic.add(15, 10))
Output:
Result: 25
Using #staticmethod
class Arithmetic:
# create add static method
#staticmethod
def add(x, y):
return x + y
print('Result:', Arithmetic.add(15, 10))
Output:
Result: 25

You can use #staticmethod decorator to define a static method as shown below. In detail, I explain about #staticmethod decorator and #classmethod decorator in my answer for #classmethod vs #staticmethod in Python and also explain about instance method in my answer for What is an "instance method" in Python?:
class Person:
#staticmethod # Here
def test():
print("Test")

I encounter this question from time to time. The use case and example that I am fond of is:
jeffs#jeffs-desktop:/home/jeffs $ python36
Python 3.6.1 (default, Sep 7 2017, 16:36:03)
[GCC 6.3.0 20170406] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import cmath
>>> print(cmath.sqrt(-4))
2j
>>>
>>> dir(cmath)
['__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atanh', 'cos', 'cosh', 'e', 'exp', 'inf', 'infj', 'isclose', 'isfinite', 'isinf', 'isnan', 'log', 'log10', 'nan', 'nanj', 'phase', 'pi', 'polar', 'rect', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'tau']
>>>
It does not make sense to create an object of class cmath, because there is no state in a cmath object. However, cmath is a collection of methods that are all related in some way. In my example above, all of the functions in cmath act on complex numbers in some way.

what does it means that method should be static? [duplicate]

Can I define a static method which I can call directly on the class instance? e.g.,
MyClass.the_static_method()

Yep, using the staticmethod decorator:
class MyClass(object):
#staticmethod
def the_static_method(x):
print(x)
MyClass.the_static_method(2) # outputs 2
Note that some code might use the old method of defining a static method, using staticmethod as a function rather than a decorator. This should only be used if you have to support ancient versions of Python (2.2 and 2.3):
class MyClass(object):
def the_static_method(x):
print(x)
the_static_method = staticmethod(the_static_method)
MyClass.the_static_method(2) # outputs 2
This is entirely identical to the first example (using #staticmethod), just not using the nice decorator syntax.
Finally, use staticmethod sparingly! There are very few situations where static-methods are necessary in Python, and I've seen them used many times where a separate "top-level" function would have been clearer.
The following is verbatim from the documentation::
A static method does not receive an implicit first argument. To declare a static method, use this idiom:
class C:
#staticmethod
def f(arg1, arg2, ...): ...
The #staticmethod form is a function decorator – see the description of function definitions in Function definitions for details.
It can be called either on the class (such as C.f()) or on an instance (such as C().f()). The instance is ignored except for its class.
Static methods in Python are similar to those found in Java or C++. For a more advanced concept, see classmethod().
For more information on static methods, consult the documentation on the standard type hierarchy in The standard type hierarchy.
New in version 2.2.
Changed in version 2.4: Function decorator syntax added.

I think that Steven is actually right. To answer the original question, then, in order to set up a class method, simply assume that the first argument is not going to be a calling instance, and then make sure that you only call the method from the class.
(Note that this answer refers to Python 3.x. In Python 2.x you'll get a TypeError for calling the method on the class itself.)
For example:
class Dog:
count = 0 # this is a class variable
dogs = [] # this is a class variable
def __init__(self, name):
self.name = name #self.name is an instance variable
Dog.count += 1
Dog.dogs.append(name)
def bark(self, n): # this is an instance method
print("{} says: {}".format(self.name, "woof! " * n))
def rollCall(n): #this is implicitly a class method (see comments below)
print("There are {} dogs.".format(Dog.count))
if n >= len(Dog.dogs) or n < 0:
print("They are:")
for dog in Dog.dogs:
print(" {}".format(dog))
else:
print("The dog indexed at {} is {}.".format(n, Dog.dogs[n]))
fido = Dog("Fido")
fido.bark(3)
Dog.rollCall(-1)
rex = Dog("Rex")
Dog.rollCall(0)
In this code, the "rollCall" method assumes that the first argument is not an instance (as it would be if it were called by an instance instead of a class). As long as "rollCall" is called from the class rather than an instance, the code will work fine. If we try to call "rollCall" from an instance, e.g.:
rex.rollCall(-1)
however, it would cause an exception to be raised because it would send two arguments: itself and -1, and "rollCall" is only defined to accept one argument.
Incidentally, rex.rollCall() would send the correct number of arguments, but would also cause an exception to be raised because now n would be representing a Dog instance (i.e., rex) when the function expects n to be numerical.
This is where the decoration comes in:
If we precede the "rollCall" method with
#staticmethod
then, by explicitly stating that the method is static, we can even call it from an instance. Now,
rex.rollCall(-1)
would work. The insertion of #staticmethod before a method definition, then, stops an instance from sending itself as an argument.
You can verify this by trying the following code with and without the #staticmethod line commented out.
class Dog:
count = 0 # this is a class variable
dogs = [] # this is a class variable
def __init__(self, name):
self.name = name #self.name is an instance variable
Dog.count += 1
Dog.dogs.append(name)
def bark(self, n): # this is an instance method
print("{} says: {}".format(self.name, "woof! " * n))
#staticmethod
def rollCall(n):
print("There are {} dogs.".format(Dog.count))
if n >= len(Dog.dogs) or n < 0:
print("They are:")
for dog in Dog.dogs:
print(" {}".format(dog))
else:
print("The dog indexed at {} is {}.".format(n, Dog.dogs[n]))
fido = Dog("Fido")
fido.bark(3)
Dog.rollCall(-1)
rex = Dog("Rex")
Dog.rollCall(0)
rex.rollCall(-1)

Yes, check out the staticmethod decorator:
>>> class C:
... #staticmethod
... def hello():
... print "Hello World"
...
>>> C.hello()
Hello World

You don't really need to use the #staticmethod decorator. Just declaring a method (that doesn't expect the self parameter) and call it from the class. The decorator is only there in case you want to be able to call it from an instance as well (which was not what you wanted to do)
Mostly, you just use functions though...

Static methods in Python?
Is it possible to have static methods in Python so I can call them
without initializing a class, like:
ClassName.StaticMethod()
Yes, static methods can be created like this (although it's a bit more Pythonic to use underscores instead of CamelCase for methods):
class ClassName(object):
#staticmethod
def static_method(kwarg1=None):
'''return a value that is a function of kwarg1'''
The above uses the decorator syntax. This syntax is equivalent to
class ClassName(object):
def static_method(kwarg1=None):
'''return a value that is a function of kwarg1'''
static_method = staticmethod(static_method)
This can be used just as you described:
ClassName.static_method()
A builtin example of a static method is str.maketrans() in Python 3, which was a function in the string module in Python 2.
Another option that can be used as you describe is the classmethod, the difference is that the classmethod gets the class as an implicit first argument, and if subclassed, then it gets the subclass as the implicit first argument.
class ClassName(object):
#classmethod
def class_method(cls, kwarg1=None):
'''return a value that is a function of the class and kwarg1'''
Note that cls is not a required name for the first argument, but most experienced Python coders will consider it badly done if you use anything else.
These are typically used as alternative constructors.
new_instance = ClassName.class_method()
A builtin example is dict.fromkeys():
new_dict = dict.fromkeys(['key1', 'key2'])

Perhaps the simplest option is just to put those functions outside of the class:
class Dog(object):
def __init__(self, name):
self.name = name
def bark(self):
if self.name == "Doggy":
return barking_sound()
else:
return "yip yip"
def barking_sound():
return "woof woof"
Using this method, functions which modify or use internal object state (have side effects) can be kept in the class, and the reusable utility functions can be moved outside.
Let's say this file is called dogs.py. To use these, you'd call dogs.barking_sound() instead of dogs.Dog.barking_sound.
If you really need a static method to be part of the class, you can use the staticmethod decorator.

Aside from the particularities of how static method objects behave, there is a certain kind of beauty you can strike with them when it comes to organizing your module-level code.
# garden.py
def trim(a):
pass
def strip(a):
pass
def bunch(a, b):
pass
def _foo(foo):
pass
class powertools(object):
"""
Provides much regarded gardening power tools.
"""
#staticmethod
def answer_to_the_ultimate_question_of_life_the_universe_and_everything():
return 42
#staticmethod
def random():
return 13
#staticmethod
def promise():
return True
def _bar(baz, quux):
pass
class _Dice(object):
pass
class _6d(_Dice):
pass
class _12d(_Dice):
pass
class _Smarter:
pass
class _MagicalPonies:
pass
class _Samurai:
pass
class Foo(_6d, _Samurai):
pass
class Bar(_12d, _Smarter, _MagicalPonies):
pass
...
# tests.py
import unittest
import garden
class GardenTests(unittest.TestCase):
pass
class PowertoolsTests(unittest.TestCase):
pass
class FooTests(unittest.TestCase):
pass
class BarTests(unittest.TestCase):
pass
...
# interactive.py
from garden import trim, bunch, Foo
f = trim(Foo())
bunch(f, Foo())
...
# my_garden.py
import garden
from garden import powertools
class _Cowboy(garden._Samurai):
def hit():
return powertools.promise() and powertools.random() or 0
class Foo(_Cowboy, garden.Foo):
pass
It now becomes a bit more intuitive and self-documenting in which context certain components are meant to be used and it pans out ideally for naming distinct test cases as well as having a straightforward approach to how test modules map to actual modules under tests for purists.
I frequently find it viable to apply this approach to organizing a project's utility code. Quite often, people immediately rush and create a utils package and end up with 9 modules of which one has 120 LOC and the rest are two dozen LOC at best. I prefer to start with this and convert it to a package and create modules only for the beasts that truly deserve them:
# utils.py
class socket(object):
#staticmethod
def check_if_port_available(port):
pass
#staticmethod
def get_free_port(port)
pass
class image(object):
#staticmethod
def to_rgb(image):
pass
#staticmethod
def to_cmyk(image):
pass

So, static methods are the methods which can be called without creating the object of a class.
For Example :-
#staticmethod
def add(a, b):
return a + b
b = A.add(12,12)
print b
In the above example method add is called by the class name A not the object name.

Summarizing others' answers and adding, there are many ways to declare Static Methods or Variables in python.
Using staticmethod() as a decorator:
One can simply put a decorator above a method(function) declared to make it a static method. For eg.
class Calculator:
#staticmethod
def multiply(n1, n2, *args):
Res = 1
for num in args: Res *= num
return n1 * n2 * Res
print(Calculator.multiply(1, 2, 3, 4)) # 24
Using staticmethod() as a parameter function:
This method can receive an argument which is of function type, and it returns a static version of the function passed. For eg.
class Calculator:
def add(n1, n2, *args):
return n1 + n2 + sum(args)
Calculator.add = staticmethod(Calculator.add)
print(Calculator.add(1, 2, 3, 4)) # 10
Using classmethod() as a decorator:
#classmethod has similar effect on a function as #staticmethod has, but
this time, an additional argument is needed to be accepted in the function (similar to self parameter for instance variables). For eg.
class Calculator:
num = 0
def __init__(self, digits) -> None:
Calculator.num = int(''.join(digits))
#classmethod
def get_digits(cls, num):
digits = list(str(num))
calc = cls(digits)
return calc.num
print(Calculator.get_digits(314159)) # 314159
Using classmethod() as a parameter function:
#classmethod can also be used as a parameter function, in case one doesn't want to modify class definition. For eg.
class Calculator:
def divide(cls, n1, n2, *args):
Res = 1
for num in args: Res *= num
return n1 / n2 / Res
Calculator.divide = classmethod(Calculator.divide)
print(Calculator.divide(15, 3, 5)) # 1.0
Direct declaration
A method/variable declared outside all other methods, but inside a class is automatically static.
class Calculator:
def subtract(n1, n2, *args):
return n1 - n2 - sum(args)
print(Calculator.subtract(10, 2, 3, 4)) # 1
The whole program
class Calculator:
num = 0
def __init__(self, digits) -> None:
Calculator.num = int(''.join(digits))
#staticmethod
def multiply(n1, n2, *args):
Res = 1
for num in args: Res *= num
return n1 * n2 * Res
def add(n1, n2, *args):
return n1 + n2 + sum(args)
#classmethod
def get_digits(cls, num):
digits = list(str(num))
calc = cls(digits)
return calc.num
def divide(cls, n1, n2, *args):
Res = 1
for num in args: Res *= num
return n1 / n2 / Res
def subtract(n1, n2, *args):
return n1 - n2 - sum(args)
Calculator.add = staticmethod(Calculator.add)
Calculator.divide = classmethod(Calculator.divide)
print(Calculator.multiply(1, 2, 3, 4)) # 24
print(Calculator.add(1, 2, 3, 4)) # 10
print(Calculator.get_digits(314159)) # 314159
print(Calculator.divide(15, 3, 5)) # 1.0
print(Calculator.subtract(10, 2, 3, 4)) # 1
Refer to Python Documentation for mastering OOP in python.

Python Static methods can be created in two ways.
Using staticmethod()
class Arithmetic:
def add(x, y):
return x + y
# create add static method
Arithmetic.add = staticmethod(Arithmetic.add)
print('Result:', Arithmetic.add(15, 10))
Output:
Result: 25
Using #staticmethod
class Arithmetic:
# create add static method
#staticmethod
def add(x, y):
return x + y
print('Result:', Arithmetic.add(15, 10))
Output:
Result: 25

You can use #staticmethod decorator to define a static method as shown below. In detail, I explain about #staticmethod decorator and #classmethod decorator in my answer for #classmethod vs #staticmethod in Python and also explain about instance method in my answer for What is an "instance method" in Python?:
class Person:
#staticmethod # Here
def test():
print("Test")

I encounter this question from time to time. The use case and example that I am fond of is:
jeffs#jeffs-desktop:/home/jeffs $ python36
Python 3.6.1 (default, Sep 7 2017, 16:36:03)
[GCC 6.3.0 20170406] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import cmath
>>> print(cmath.sqrt(-4))
2j
>>>
>>> dir(cmath)
['__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atanh', 'cos', 'cosh', 'e', 'exp', 'inf', 'infj', 'isclose', 'isfinite', 'isinf', 'isnan', 'log', 'log10', 'nan', 'nanj', 'phase', 'pi', 'polar', 'rect', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'tau']
>>>
It does not make sense to create an object of class cmath, because there is no state in a cmath object. However, cmath is a collection of methods that are all related in some way. In my example above, all of the functions in cmath act on complex numbers in some way.

How to find out the arity of a method in Python

I'd like to find out the arity of a method in Python (the number of parameters that it receives).
Right now I'm doing this:
def arity(obj, method):
return getattr(obj.__class__, method).func_code.co_argcount - 1 # remove self
class Foo:
def bar(self, bla):
pass
arity(Foo(), "bar") # => 1
I'd like to be able to achieve this:
Foo().bar.arity() # => 1
Update: Right now the above function fails with built-in types, any help on this would also be appreciated:
# Traceback (most recent call last):
# File "bla.py", line 10, in <module>
# print arity('foo', 'split') # =>
# File "bla.py", line 3, in arity
# return getattr(obj.__class__, method).func_code.co_argcount - 1 # remove self
# AttributeError: 'method_descriptor' object has no attribute 'func_co

Module inspect from Python's standard library is your friend -- see the online docs! inspect.getargspec(func) returns a tuple with four items, args, varargs, varkw, defaults: len(args) is the "primary arity", but arity can be anything from that to infinity if you have varargs and/or varkw not None, and some arguments may be omitted (and defaulted) if defaults is not None. How you turn that into a single number, beats me, but presumably you have your ideas in the matter!-)
This applies to Python-coded functions, but not to C-coded ones. Nothing in the Python C API lets C-coded functions (including built-ins) expose their signature for introspection, except via their docstring (or optionally via annotations in Python 3); so, you will need to fall back to docstring parsing as a last ditch if other approaches fail (of course, the docstring might be missing too, in which case the function will remain a mystery).

Use a decorator to decorate methods e.g.
def arity(method):
def _arity():
return method.func_code.co_argcount - 1 # remove self
method.arity = _arity
return method
class Foo:
#arity
def bar(self, bla):
pass
print Foo().bar.arity()
Now implement _arity function to calculate arg count based on your needs

This is the only way that I can think of that should be 100% effective (at least with regard to whether the function is user-defined or written in C) at determining a function's (minimum) arity. However, you should be sure that this function won't cause any side-effects and that it won't throw a TypeError:
from functools import partial
def arity(func):
pfunc = func
i = 0
while True:
try:
pfunc()
except TypeError:
pfunc = partial(pfunc, '')
i += 1
else:
return i
def foo(x, y, z):
pass
def varfoo(*args):
pass
class klass(object):
def klassfoo(self):
pass
print arity(foo)
print arity(varfoo)
x = klass()
print arity(x.klassfoo)
# output
# 3
# 0
# 0
As you can see, this will determine the minimum arity if a function takes a variable amount of arguments. It also won't take into account the self or cls argument of a class or instance method.
To be totally honest though, I wouldn't use this function in a production environment unless I knew exactly which functions would be called though as there is a lot of room for stupid errors. This may defeat the purpose.

Ideally, you'd want to monkey-patch the arity function as a method on Python functors. Here's how:
def arity(self, method):
return getattr(self.__class__, method).func_code.co_argcount - 1
functor = arity.__class__
functor.arity = arity
arity.__class__.arity = arity
But, CPython implements functors in C, you can't actually modify them. This may work in PyPy, though.
That's all assuming your arity() function is correct. What about variadic functions? Do you even want an answer then?

here is another attempt using metaclass, as i use python 2.5, but with 2.6 you could easily decorate the class
metaclass can also be defined at module level, so it works for all classes
from types import FunctionType
def arity(unboundmethod):
def _arity():
return unboundmethod.func_code.co_argcount - 1 # remove self
unboundmethod.arity = _arity
return unboundmethod
class AirtyMetaclass(type):
def __new__(meta, name, bases, attrs):
newAttrs = {}
for attributeName, attribute in attrs.items():
if type(attribute) == FunctionType:
attribute = arity(attribute)
newAttrs[attributeName] = attribute
klass = type.__new__(meta, name, bases, newAttrs)
return klass
class Foo:
__metaclass__ = AirtyMetaclass
def bar(self, bla):
pass
print Foo().bar.arity()

What is a clean "pythonic" way to implement multiple constructors?

I can't find a definitive answer for this. As far as I know, you can't have multiple __init__ functions in a Python class. So how do I solve this problem?
Suppose I have a class called Cheese with the number_of_holes property. How can I have two ways of creating cheese objects...
One that takes a number of holes like this: parmesan = Cheese(num_holes = 15).
And one that takes no arguments and just randomizes the number_of_holes property: gouda = Cheese().
I can think of only one way to do this, but this seems clunky:
class Cheese():
def __init__(self, num_holes = 0):
if (num_holes == 0):
# Randomize number_of_holes
else:
number_of_holes = num_holes
What do you say? Is there another way?

Actually None is much better for "magic" values:
class Cheese():
def __init__(self, num_holes = None):
if num_holes is None:
...
Now if you want complete freedom of adding more parameters:
class Cheese():
def __init__(self, *args, **kwargs):
#args -- tuple of anonymous arguments
#kwargs -- dictionary of named arguments
self.num_holes = kwargs.get('num_holes',random_holes())
To better explain the concept of *args and **kwargs (you can actually change these names):
def f(*args, **kwargs):
print 'args: ', args, ' kwargs: ', kwargs
>>> f('a')
args: ('a',) kwargs: {}
>>> f(ar='a')
args: () kwargs: {'ar': 'a'}
>>> f(1,2,param=3)
args: (1, 2) kwargs: {'param': 3}
http://docs.python.org/reference/expressions.html#calls

Using num_holes=None as the default is fine if you are going to have just __init__.
If you want multiple, independent "constructors", you can provide these as class methods. These are usually called factory methods. In this case you could have the default for num_holes be 0.
class Cheese(object):
def __init__(self, num_holes=0):
"defaults to a solid cheese"
self.number_of_holes = num_holes
#classmethod
def random(cls):
return cls(randint(0, 100))
#classmethod
def slightly_holey(cls):
return cls(randint(0, 33))
#classmethod
def very_holey(cls):
return cls(randint(66, 100))
Now create object like this:
gouda = Cheese()
emmentaler = Cheese.random()
leerdammer = Cheese.slightly_holey()

One should definitely prefer the solutions already posted, but since no one mentioned this solution yet, I think it is worth mentioning for completeness.
The #classmethod approach can be modified to provide an alternative constructor which does not invoke the default constructor (__init__). Instead, an instance is created using __new__.
This could be used if the type of initialization cannot be selected based on the type of the constructor argument, and the constructors do not share code.
Example:
class MyClass(set):
def __init__(self, filename):
self._value = load_from_file(filename)
#classmethod
def from_somewhere(cls, somename):
obj = cls.__new__(cls) # Does not call __init__
super(MyClass, obj).__init__() # Don't forget to call any polymorphic base class initializers
obj._value = load_from_somewhere(somename)
return obj

All of these answers are excellent if you want to use optional parameters, but another Pythonic possibility is to use a classmethod to generate a factory-style pseudo-constructor:
def __init__(self, num_holes):
# do stuff with the number
#classmethod
def fromRandom(cls):
return cls( # some-random-number )

Why do you think your solution is "clunky"? Personally I would prefer one constructor with default values over multiple overloaded constructors in situations like yours (Python does not support method overloading anyway):
def __init__(self, num_holes=None):
if num_holes is None:
# Construct a gouda
else:
# custom cheese
# common initialization
For really complex cases with lots of different constructors, it might be cleaner to use different factory functions instead:
#classmethod
def create_gouda(cls):
c = Cheese()
# ...
return c
#classmethod
def create_cheddar(cls):
# ...
In your cheese example you might want to use a Gouda subclass of Cheese though...

Those are good ideas for your implementation, but if you are presenting a cheese making interface to a user. They don't care how many holes the cheese has or what internals go into making cheese. The user of your code just wants "gouda" or "parmesean" right?
So why not do this:
# cheese_user.py
from cheeses import make_gouda, make_parmesean
gouda = make_gouda()
paremesean = make_parmesean()
And then you can use any of the methods above to actually implement the functions:
# cheeses.py
class Cheese(object):
def __init__(self, *args, **kwargs):
#args -- tuple of anonymous arguments
#kwargs -- dictionary of named arguments
self.num_holes = kwargs.get('num_holes',random_holes())
def make_gouda():
return Cheese()
def make_paremesean():
return Cheese(num_holes=15)
This is a good encapsulation technique, and I think it is more Pythonic. To me this way of doing things fits more in line more with duck typing. You are simply asking for a gouda object and you don't really care what class it is.

Overview
For the specific cheese example, I agree with many of the other answers about using default values to signal random initialization or to use a static factory method. However, there may also be related scenarios that you had in mind where there is value in having alternative, concise ways of calling the constructor without hurting the quality of parameter names or type information.
Since Python 3.8 and functools.singledispatchmethod can help accomplish this in many cases (and the more flexible multimethod can apply in even more scenarios). (This related post describes how one could accomplish the same in Python 3.4 without a library.) I haven't seen examples in the documentation for either of these that specifically shows overloading __init__ as you ask about, but it appears that the same principles for overloading any member method apply (as shown below).
"Single dispatch" (available in the standard library) requires that there be at least one positional parameter and that the type of the first argument be sufficient to distinguish among the possible overloaded options. For the specific Cheese example, this doesn't hold since you wanted random holes when no parameters were given, but multidispatch does support the very same syntax and can be used as long as each method version can be distinguish based on the number and type of all arguments together.
Example
Here is an example of how to use either method (some of the details are in order to please mypy which was my goal when I first put this together):
from functools import singledispatchmethod as overload
# or the following more flexible method after `pip install multimethod`
# from multimethod import multidispatch as overload
class MyClass:
#overload # type: ignore[misc]
def __init__(self, a: int = 0, b: str = 'default'):
self.a = a
self.b = b
#__init__.register
def _from_str(self, b: str, a: int = 0):
self.__init__(a, b) # type: ignore[misc]
def __repr__(self) -> str:
return f"({self.a}, {self.b})"
print([
MyClass(1, "test"),
MyClass("test", 1),
MyClass("test"),
MyClass(1, b="test"),
MyClass("test", a=1),
MyClass("test"),
MyClass(1),
# MyClass(), # `multidispatch` version handles these 3, too.
# MyClass(a=1, b="test"),
# MyClass(b="test", a=1),
])
Output:
[(1, test), (1, test), (0, test), (1, test), (1, test), (0, test), (1, default)]
Notes:
I wouldn't usually make the alias called overload, but it helped make the diff between using the two methods just a matter of which import you use.
The # type: ignore[misc] comments are not necessary to run, but I put them in there to please mypy which doesn't like decorating __init__ nor calling __init__ directly.
If you are new to the decorator syntax, realize that putting #overload before the definition of __init__ is just sugar for __init__ = overload(the original definition of __init__). In this case, overload is a class so the resulting __init__ is an object that has a __call__ method so that it looks like a function but that also has a .register method which is being called later to add another overloaded version of __init__. This is a bit messy, but it please mypy becuase there are no method names being defined twice. If you don't care about mypy and are planning to use the external library anyway, multimethod also has simpler alternative ways of specifying overloaded versions.
Defining __repr__ is simply there to make the printed output meaningful (you don't need it in general).
Notice that multidispatch is able to handle three additional input combinations that don't have any positional parameters.

Use num_holes=None as a default, instead. Then check for whether num_holes is None, and if so, randomize. That's what I generally see, anyway.
More radically different construction methods may warrant a classmethod that returns an instance of cls.

The best answer is the one above about default arguments, but I had fun writing this, and it certainly does fit the bill for "multiple constructors". Use at your own risk.
What about the new method.
"Typical implementations create a new instance of the class by invoking the superclass’s new() method using super(currentclass, cls).new(cls[, ...]) with appropriate arguments and then modifying the newly-created instance as necessary before returning it."
So you can have the new method modify your class definition by attaching the appropriate constructor method.
class Cheese(object):
def __new__(cls, *args, **kwargs):
obj = super(Cheese, cls).__new__(cls)
num_holes = kwargs.get('num_holes', random_holes())
if num_holes == 0:
cls.__init__ = cls.foomethod
else:
cls.__init__ = cls.barmethod
return obj
def foomethod(self, *args, **kwargs):
print "foomethod called as __init__ for Cheese"
def barmethod(self, *args, **kwargs):
print "barmethod called as __init__ for Cheese"
if __name__ == "__main__":
parm = Cheese(num_holes=5)

I'd use inheritance. Especially if there are going to be more differences than number of holes. Especially if Gouda will need to have different set of members then Parmesan.
class Gouda(Cheese):
def __init__(self):
super(Gouda).__init__(num_holes=10)
class Parmesan(Cheese):
def __init__(self):
super(Parmesan).__init__(num_holes=15)

Since my initial answer was criticised on the basis that my special-purpose constructors did not call the (unique) default constructor, I post here a modified version that honours the wishes that all constructors shall call the default one:
class Cheese:
def __init__(self, *args, _initialiser="_default_init", **kwargs):
"""A multi-initialiser.
"""
getattr(self, _initialiser)(*args, **kwargs)
def _default_init(self, ...):
"""A user-friendly smart or general-purpose initialiser.
"""
...
def _init_parmesan(self, ...):
"""A special initialiser for Parmesan cheese.
"""
...
def _init_gouda(self, ...):
"""A special initialiser for Gouda cheese.
"""
...
#classmethod
def make_parmesan(cls, *args, **kwargs):
return cls(*args, **kwargs, _initialiser="_init_parmesan")
#classmethod
def make_gouda(cls, *args, **kwargs):
return cls(*args, **kwargs, _initialiser="_init_gouda")

This is how I solved it for a YearQuarter class I had to create. I created an __init__ which is very tolerant to a wide variety of input.
You use it like this:
>>> from datetime import date
>>> temp1 = YearQuarter(year=2017, month=12)
>>> print temp1
2017-Q4
>>> temp2 = YearQuarter(temp1)
>>> print temp2
2017-Q4
>>> temp3 = YearQuarter((2017, 6))
>>> print temp3
2017-Q2
>>> temp4 = YearQuarter(date(2017, 1, 18))
>>> print temp4
2017-Q1
>>> temp5 = YearQuarter(year=2017, quarter = 3)
>>> print temp5
2017-Q3
And this is how the __init__ and the rest of the class looks like:
import datetime
class YearQuarter:
def __init__(self, *args, **kwargs):
if len(args) == 1:
[x] = args
if isinstance(x, datetime.date):
self._year = int(x.year)
self._quarter = (int(x.month) + 2) / 3
elif isinstance(x, tuple):
year, month = x
self._year = int(year)
month = int(month)
if 1 <= month <= 12:
self._quarter = (month + 2) / 3
else:
raise ValueError
elif isinstance(x, YearQuarter):
self._year = x._year
self._quarter = x._quarter
elif len(args) == 2:
year, month = args
self._year = int(year)
month = int(month)
if 1 <= month <= 12:
self._quarter = (month + 2) / 3
else:
raise ValueError
elif kwargs:
self._year = int(kwargs["year"])
if "quarter" in kwargs:
quarter = int(kwargs["quarter"])
if 1 <= quarter <= 4:
self._quarter = quarter
else:
raise ValueError
elif "month" in kwargs:
month = int(kwargs["month"])
if 1 <= month <= 12:
self._quarter = (month + 2) / 3
else:
raise ValueError
def __str__(self):
return '{0}-Q{1}'.format(self._year, self._quarter)

class Cheese:
def __init__(self, *args, **kwargs):
"""A user-friendly initialiser for the general-purpose constructor.
"""
...
def _init_parmesan(self, *args, **kwargs):
"""A special initialiser for Parmesan cheese.
"""
...
def _init_gauda(self, *args, **kwargs):
"""A special initialiser for Gauda cheese.
"""
...
#classmethod
def make_parmesan(cls, *args, **kwargs):
new = cls.__new__(cls)
new._init_parmesan(*args, **kwargs)
return new
#classmethod
def make_gauda(cls, *args, **kwargs):
new = cls.__new__(cls)
new._init_gauda(*args, **kwargs)
return new

I do not see a straightforward answer with an example yet. The idea is simple:
use __init__ as the "basic" constructor as python only allows one __init__ method
use #classmethod to create any other constructors and call the basic constructor
Here is a new try.
class Person:
def __init__(self, name, age):
self.name = name
self.age = age
#classmethod
def fromBirthYear(cls, name, birthYear):
return cls(name, date.today().year - birthYear)
Usage:
p = Person('tim', age=18)
p = Person.fromBirthYear('tim', birthYear=2004)

Here (drawing on this earlier answer, the pure Python version of classmethod in the docs, and as suggested by this comment) is a decorator that can be used to create multiple constructors.
from types import MethodType
from functools import wraps
class constructor:
def __init__(self, func):
#wraps(func)
def wrapped(cls, *args, **kwargs):
obj = cls.__new__(cls) # Create new instance but don't init
super(cls, obj).__init__() # Init any classes it inherits from
func(obj, *args, **kwargs) # Run the constructor with obj as self
return obj
self.wrapped = wrapped
def __get__(self, _, cls):
return MethodType(self.wrapped, cls) # Bind this constructor to the class
class Test:
def __init__(self, data_sequence):
""" Default constructor, initiates with data sequence """
self.data = [item ** 2 for item in data_sequence]
#constructor
def zeros(self, size):
""" Initiates with zeros """
self.data = [0 for _ in range(size)]
a = Test([1,2,3])
b = Test.zeros(100)
This seems the cleanest way in some cases (see e.g. multiple dataframe constructors in Pandas), where providing multiple optional arguments to a single constructor would be inconvenient: for example cases where it would require too many parameters, be unreadable, be slower or use more memory than needed. However, as earlier comments have argued, in most cases it is probably more Pythonic to route through a single constructor with optional parameters, adding class methods where needed.