Meaning of @classmethod and @staticmethod for beginner? [duplicate]

Though classmethod and staticmethod are quite similar, there's a slight difference in usage for both entities: classmethod must have a reference to a class object as the first parameter, whereas staticmethod can have no parameters at all.

Example

class Date(object):

    def __init__(self, day=0, month=0, year=0):
        self.day = day
        self.month = month
        self.year = year

    @classmethod
    def from_string(cls, date_as_string):
        day, month, year = map(int, date_as_string.split('-'))
        date1 = cls(day, month, year)
        return date1

    @staticmethod
    def is_date_valid(date_as_string):
        day, month, year = map(int, date_as_string.split('-'))
        return day <= 31 and month <= 12 and year <= 3999

date2 = Date.from_string('11-09-2012')
is_date = Date.is_date_valid('11-09-2012')

Explanation

Let's assume an example of a class, dealing with date information (this will be our boilerplate):

class Date(object):

    def __init__(self, day=0, month=0, year=0):
        self.day = day
        self.month = month
        self.year = year

This class obviously could be used to store information about certain dates (without timezone information; let's assume all dates are presented in UTC).

Here we have __init__, a typical initializer of Python class instances, which receives arguments as a typical instancemethod, having the first non-optional argument (self) that holds a reference to a newly created instance.

Class Method

We have some tasks that can be nicely done using classmethods.

Let's assume that we want to create a lot of Date class instances having date information coming from an outer source encoded as a string with format 'dd-mm-yyyy'. Suppose we have to do this in different places in the source code of our project.

So what we must do here is:

Parse a string to receive day, month and year as three integer variables or a 3-item tuple consisting of that variable.
Instantiate Date by passing those values to the initialization call.

This will look like:

day, month, year = map(int, string_date.split('-'))
date1 = Date(day, month, year)

For this purpose, C++ can implement such a feature with overloading, but Python lacks this overloading. Instead, we can use classmethod. Let's create another "constructor".

    @classmethod
    def from_string(cls, date_as_string):
        day, month, year = map(int, date_as_string.split('-'))
        date1 = cls(day, month, year)
        return date1

date2 = Date.from_string('11-09-2012')

Let's look more carefully at the above implementation, and review what advantages we have here:

We've implemented date string parsing in one place and it's reusable now.
Encapsulation works fine here (if you think that you could implement string parsing as a single function elsewhere, this solution fits the OOP paradigm far better).
cls is an object that holds the class itself, not an instance of the class. It's pretty cool because if we inherit our Date class, all children will have from_string defined also.

Static method

What about staticmethod? It's pretty similar to classmethod but doesn't take any obligatory parameters (like a class method or instance method does).

Let's look at the next use case.

We have a date string that we want to validate somehow. This task is also logically bound to the Date class we've used so far, but doesn't require instantiation of it.

Here is where staticmethod can be useful. Let's look at the next piece of code:

    @staticmethod
    def is_date_valid(date_as_string):
        day, month, year = map(int, date_as_string.split('-'))
        return day <= 31 and month <= 12 and year <= 3999

    # usage:
    is_date = Date.is_date_valid('11-09-2012')

So, as we can see from usage of staticmethod, we don't have any access to what the class is---it's basically just a function, called syntactically like a method, but without access to the object and its internals (fields and another methods), while classmethod does.

Rostyslav Dzinko's answer is very appropriate. I thought I could highlight one other reason you should choose @classmethod over @staticmethod when you are creating an additional constructor.

In the example above, Rostyslav used the @classmethod from_string as a Factory to create Date objects from otherwise unacceptable parameters. The same can be done with @staticmethod as is shown in the code below:

class Date:
  def __init__(self, month, day, year):
    self.month = month
    self.day   = day
    self.year  = year


  def display(self):
    return "{0}-{1}-{2}".format(self.month, self.day, self.year)


  @staticmethod
  def millenium(month, day):
    return Date(month, day, 2000)

new_year = Date(1, 1, 2013)               # Creates a new Date object
millenium_new_year = Date.millenium(1, 1) # also creates a Date object. 

# Proof:
new_year.display()           # "1-1-2013"
millenium_new_year.display() # "1-1-2000"

isinstance(new_year, Date) # True
isinstance(millenium_new_year, Date) # True

Thus both new_year and millenium_new_year are instances of the Date class.

But, if you observe closely, the Factory process is hard-coded to create Date objects no matter what. What this means is that even if the Date class is subclassed, the subclasses will still create plain Date objects (without any properties of the subclass). See that in the example below:

class DateTime(Date):
  def display(self):
      return "{0}-{1}-{2} - 00:00:00PM".format(self.month, self.day, self.year)


datetime1 = DateTime(10, 10, 1990)
datetime2 = DateTime.millenium(10, 10)

isinstance(datetime1, DateTime) # True
isinstance(datetime2, DateTime) # False

datetime1.display() # returns "10-10-1990 - 00:00:00PM"
datetime2.display() # returns "10-10-2000" because it's not a DateTime object but a Date object. Check the implementation of the millenium method on the Date class for more details.

datetime2 is not an instance of DateTime? WTF? Well, that's because of the @staticmethod decorator used.

In most cases, this is undesired. If what you want is a Factory method that is aware of the class that called it, then @classmethod is what you need.

Rewriting Date.millenium as (that's the only part of the above code that changes):

@classmethod
def millenium(cls, month, day):
    return cls(month, day, 2000)

ensures that the class is not hard-coded but rather learnt. cls can be any subclass. The resulting object will rightly be an instance of cls.
Let's test that out:

datetime1 = DateTime(10, 10, 1990)
datetime2 = DateTime.millenium(10, 10)

isinstance(datetime1, DateTime) # True
isinstance(datetime2, DateTime) # True


datetime1.display() # "10-10-1990 - 00:00:00PM"
datetime2.display() # "10-10-2000 - 00:00:00PM"

The reason is, as you know by now, that @classmethod was used instead of @staticmethod

@classmethod means: when this method is called, we pass the class as the first argument instead of the instance of that class (as we normally do with methods). This means you can use the class and its properties inside that method rather than a particular instance.

@staticmethod means: when this method is called, we don't pass an instance of the class to it (as we normally do with methods). This means you can put a function inside a class but you can't access the instance of that class (this is useful when your method does not use the instance).

When to use each

@staticmethod function is nothing more than a function defined inside a class. It is callable without instantiating the class first. It’s definition is immutable via inheritance.

Python does not have to instantiate a bound-method for object.
It eases the readability of the code: seeing @staticmethod, we know that the method does not depend on the state of object itself;

@classmethod function also callable without instantiating the class, but its definition follows Sub class, not Parent class, via inheritance, can be overridden by subclass. That’s because the first argument for @classmethod function must always be cls (class).

Factory methods, that are used to create an instance for a class using for example some sort of pre-processing.
Static methods calling static methods: if you split a static methods in several static methods, you shouldn't hard-code the class name but use class methods

here is good link to this topic.

Meaning of @classmethod and @staticmethod?

A method is a function in an object's namespace, accessible as an attribute.
A regular (i.e. instance) method gets the instance (we usually call it self) as the implicit first argument.
A class method gets the class (we usually call it cls) as the implicit first argument.
A static method gets no implicit first argument (like a regular function).

when should I use them, why should I use them, and how should I use them?

You don't need either decorator. But on the principle that you should minimize the number of arguments to functions (see Clean Coder), they are useful for doing just that.

class Example(object):

    def regular_instance_method(self):
        """A function of an instance has access to every attribute of that 
        instance, including its class (and its attributes.)
        Not accepting at least one argument is a TypeError.
        Not understanding the semantics of that argument is a user error.
        """
        return some_function_f(self)

    @classmethod
    def a_class_method(cls):
        """A function of a class has access to every attribute of the class.
        Not accepting at least one argument is a TypeError.
        Not understanding the semantics of that argument is a user error.
        """
        return some_function_g(cls)

    @staticmethod
    def a_static_method():
        """A static method has no information about instances or classes
        unless explicitly given. It just lives in the class (and thus its 
        instances') namespace.
        """
        return some_function_h()

For both instance methods and class methods, not accepting at least one argument is a TypeError, but not understanding the semantics of that argument is a user error.

(Define some_function's, e.g.:

some_function_h = some_function_g = some_function_f = lambda x=None: x

and this will work.)

dotted lookups on instances and classes:

A dotted lookup on an instance is performed in this order - we look for:

a data descriptor in the class namespace (like a property)
data in the instance __dict__
a non-data descriptor in the class namespace (methods).

Note, a dotted lookup on an instance is invoked like this:

instance = Example()
instance.regular_instance_method

and methods are callable attributes:

instance.regular_instance_method()

instance methods

The argument, self, is implicitly given via the dotted lookup.

You must access instance methods from instances of the class.

>>> instance = Example()
>>> instance.regular_instance_method()
<__main__.Example object at 0x00000000399524E0>

class methods

The argument, cls, is implicitly given via dotted lookup.

You can access this method via an instance or the class (or subclasses).

>>> instance.a_class_method()
<class '__main__.Example'>
>>> Example.a_class_method()
<class '__main__.Example'>

static methods

No arguments are implicitly given. This method works like any function defined (for example) on a modules' namespace, except it can be looked up

>>> print(instance.a_static_method())
None

Again, when should I use them, why should I use them?

Each of these are progressively more restrictive in the information they pass the method versus instance methods.

Use them when you don't need the information.

This makes your functions and methods easier to reason about and to unittest.

Which is easier to reason about?

def function(x, y, z): ...

def function(y, z): ...

def function(z): ...

The functions with fewer arguments are easier to reason about. They are also easier to unittest.

These are akin to instance, class, and static methods. Keeping in mind that when we have an instance, we also have its class, again, ask yourself, which is easier to reason about?:

def an_instance_method(self, arg, kwarg=None):
    cls = type(self)             # Also has the class of instance!
    ...

@classmethod
def a_class_method(cls, arg, kwarg=None):
    ...

@staticmethod
def a_static_method(arg, kwarg=None):
    ...

Builtin examples

Here are a couple of my favorite builtin examples:

The str.maketrans static method was a function in the string module, but it is much more convenient for it to be accessible from the str namespace.

>>> 'abc'.translate(str.maketrans({'a': 'b'}))
'bbc'

The dict.fromkeys class method returns a new dictionary instantiated from an iterable of keys:

>>> dict.fromkeys('abc')
{'a': None, 'c': None, 'b': None}

When subclassed, we see that it gets the class information as a class method, which is very useful:

>>> class MyDict(dict): pass
>>> type(MyDict.fromkeys('abc'))
<class '__main__.MyDict'>

My advice - Conclusion

Use static methods when you don't need the class or instance arguments, but the function is related to the use of the object, and it is convenient for the function to be in the object's namespace.

Use class methods when you don't need instance information, but need the class information perhaps for its other class or static methods, or perhaps itself as a constructor. (You wouldn't hardcode the class so that subclasses could be used here.)

Meaning of @classmethod and @staticmethod for beginner? [duplicate]

Example

Explanation

When to use each