Related
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.
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.
I've read What are Class methods in Python for? but the examples in that post are complex. I am looking for a clear, simple, bare-bones example of a particular use case for classmethods in Python.
Can you name a small, specific example use case where a Python classmethod would be the right tool for the job?
Helper methods for initialization:
class MyStream(object):
#classmethod
def from_file(cls, filepath, ignore_comments=False):
with open(filepath, 'r') as fileobj:
for obj in cls(fileobj, ignore_comments):
yield obj
#classmethod
def from_socket(cls, socket, ignore_comments=False):
raise NotImplemented # Placeholder until implemented
def __init__(self, iterable, ignore_comments=False):
...
Well __new__ is a pretty important classmethod. It's where instances usually come from
so dict() calls dict.__new__ of course, but there is another handy way to make dicts sometimes which is the classmethod dict.fromkeys()
eg.
>>> dict.fromkeys("12345")
{'1': None, '3': None, '2': None, '5': None, '4': None}
I don't know, something like named constructor methods?
class UniqueIdentifier(object):
value = 0
def __init__(self, name):
self.name = name
#classmethod
def produce(cls):
instance = cls(cls.value)
cls.value += 1
return instance
class FunkyUniqueIdentifier(UniqueIdentifier):
#classmethod
def produce(cls):
instance = super(FunkyUniqueIdentifier, cls).produce()
instance.name = "Funky %s" % instance.name
return instance
Usage:
>>> x = UniqueIdentifier.produce()
>>> y = FunkyUniqueIdentifier.produce()
>>> x.name
0
>>> y.name
Funky 1
The biggest reason for using a #classmethod is in an alternate constructor that is intended to be inherited. This can be very useful in polymorphism. An example:
class Shape(object):
# this is an abstract class that is primarily used for inheritance defaults
# here is where you would define classmethods that can be overridden by inherited classes
#classmethod
def from_square(cls, square):
# return a default instance of cls
return cls()
Notice that Shape is an abstract class that defines a classmethod from_square, since Shape is not really defined, it does not really know how to derive itself from a Square so it simply returns a default instance of the class.
Inherited classes are then allowed to define their own versions of this method:
class Square(Shape):
def __init__(self, side=10):
self.side = side
#classmethod
def from_square(cls, square):
return cls(side=square.side)
class Rectangle(Shape):
def __init__(self, length=10, width=10):
self.length = length
self.width = width
#classmethod
def from_square(cls, square):
return cls(length=square.side, width=square.side)
class RightTriangle(Shape):
def __init__(self, a=10, b=10):
self.a = a
self.b = b
self.c = ((a*a) + (b*b))**(.5)
#classmethod
def from_square(cls, square):
return cls(a=square.length, b=square.width)
class Circle(Shape):
def __init__(self, radius=10):
self.radius = radius
#classmethod
def from_square(cls, square):
return cls(radius=square.length/2)
The usage allows you to treat all of these uninstantiated classes polymorphically
square = Square(3)
for polymorphic_class in (Square, Rectangle, RightTriangle, Circle):
this_shape = polymorphic_class.from_square(square)
This is all fine and dandy you might say, but why couldn't I just use as #staticmethod to accomplish this same polymorphic behavior:
class Circle(Shape):
def __init__(self, radius=10):
self.radius = radius
#staticmethod
def from_square(square):
return Circle(radius=square.length/2)
The answer is that you could, but you do not get the benefits of inheritance because Circle has to be called out explicitly in the method. Meaning if I call it from an inherited class without overriding, I would still get Circle every time.
Notice what is gained when I define another shape class that does not really have any custom from_square logic:
class Hexagon(Shape):
def __init__(self, side=10):
self.side = side
# note the absence of classmethod here, this will use from_square it inherits from shape
Here you can leave the #classmethod undefined and it will use the logic from Shape.from_square while retaining who cls is and return the appropriate shape.
square = Square(3)
for polymorphic_class in (Square, Rectangle, RightTriangle, Circle, Hexagon):
this_shape = polymorphic_class.from_square(square)
I find that I most often use #classmethod to associate a piece of code with a class, to avoid creating a global function, for cases where I don't require an instance of the class to use the code.
For example, I might have a data structure which only considers a key valid if it conforms to some pattern. I may want to use this from inside and outside of the class. However, I don't want to create yet another global function:
def foo_key_is_valid(key):
# code for determining validity here
return valid
I'd much rather group this code with the class it's associated with:
class Foo(object):
#classmethod
def is_valid(cls, key):
# code for determining validity here
return valid
def add_key(self, key, val):
if not Foo.is_valid(key):
raise ValueError()
..
# lets me reuse that method without an instance, and signals that
# the code is closely-associated with the Foo class
Foo.is_valid('my key')
Another useful example of classmethod is in extending enumerated types. A classic Enum provides symbolic names which can be used later in the code for readability, grouping, type-safety, etc. This can be extended to add useful features using a classmethod. In the example below, Weekday is an enuerated type for the days of the week. It has been extended using classmethod so that instead of keeping track of the weekday ourselves, the enumerated type can extract the date and return the related enum member.
from enum import Enum
from datetime import date
class Weekday(Enum):
MONDAY = 1
TUESDAY = 2
WEDNESDAY = 3
THURSDAY = 4
FRIDAY = 5
SATURDAY = 6
SUNDAY = 7
#
#classmethod
def from_date(cls, date):
return cls(date.isoweekday())
Weekday.from_date(date.today())
<Weekday.TUESDAY: 2>
Source: https://docs.python.org/3/howto/enum.html
in class MyClass(object):
'''
classdocs
'''
obj=0
x=classmethod
def __init__(self):
'''
Constructor
'''
self.nom='lamaizi'
self.prenom='anas'
self.age=21
self.ville='Casablanca'
if __name__:
ob=MyClass()
print(ob.nom)
print(ob.prenom)
print(ob.age)
print(ob.ville)
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.
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.