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How does the @property decorator work in Python?

I would like to understand how the built-in function property works. What confuses me is that property can also be used as a decorator, but it only takes arguments when used as a built-in function and not when used as a decorator.

This example is from the documentation:

class C:
    def __init__(self):
        self._x = None

    def getx(self):
        return self._x
    def setx(self, value):
        self._x = value
    def delx(self):
        del self._x
    x = property(getx, setx, delx, "I'm the 'x' property.")

property's arguments are getx, setx, delx and a doc string.

In the code below property is used as a decorator. The object of it is the x function, but in the code above there is no place for an object function in the arguments.

class C:
    def __init__(self):
        self._x = None

    @property
    def x(self):
        """I'm the 'x' property."""
        return self._x

    @x.setter
    def x(self, value):
        self._x = value

    @x.deleter
    def x(self):
        del self._x

How are the x.setter and x.deleter decorators created in this case?

property is actually a class (not a function), although it does probably does call the __init__() method when you make an object, of course. Using help(property) from the terminal is insightful. help is also a class for some reason.
I think this link provides a good example: [property] (journaldev.com/14893/python-property-decorator)
@Shule 2-year-old thread, but still: Everything is a class. Even classes.
This was confusing to me too. I finally found an article that was able to break it down for me. I hope this helps someone else. programiz.com/python-programming/property I'm not affiliated in any way with the site.

B
Boris Verkhovskiy

The property() function returns a special descriptor object:

>>> property()
<property object at 0x10ff07940>

It is this object that has extra methods:

>>> property().getter
<built-in method getter of property object at 0x10ff07998>
>>> property().setter
<built-in method setter of property object at 0x10ff07940>
>>> property().deleter
<built-in method deleter of property object at 0x10ff07998>

These act as decorators too. They return a new property object:

>>> property().getter(None)
<property object at 0x10ff079f0>

that is a copy of the old object, but with one of the functions replaced.

Remember, that the @decorator syntax is just syntactic sugar; the syntax:

@property
def foo(self): return self._foo

really means the same thing as

def foo(self): return self._foo
foo = property(foo)

so foo the function is replaced by property(foo), which we saw above is a special object. Then when you use @foo.setter(), what you are doing is call that property().setter method I showed you above, which returns a new copy of the property, but this time with the setter function replaced with the decorated method.

The following sequence also creates a full-on property, by using those decorator methods.

First we create some functions and a property object with just a getter:

>>> def getter(self): print('Get!')
... 
>>> def setter(self, value): print('Set to {!r}!'.format(value))
... 
>>> def deleter(self): print('Delete!')
... 
>>> prop = property(getter)
>>> prop.fget is getter
True
>>> prop.fset is None
True
>>> prop.fdel is None
True

Next we use the .setter() method to add a setter:

>>> prop = prop.setter(setter)
>>> prop.fget is getter
True
>>> prop.fset is setter
True
>>> prop.fdel is None
True

Last we add a deleter with the .deleter() method:

>>> prop = prop.deleter(deleter)
>>> prop.fget is getter
True
>>> prop.fset is setter
True
>>> prop.fdel is deleter
True

Last but not least, the property object acts as a descriptor object, so it has .__get__(), .__set__() and .__delete__() methods to hook into instance attribute getting, setting and deleting:

>>> class Foo: pass
... 
>>> prop.__get__(Foo(), Foo)
Get!
>>> prop.__set__(Foo(), 'bar')
Set to 'bar'!
>>> prop.__delete__(Foo())
Delete!

The Descriptor Howto includes a pure Python sample implementation of the property() type:

class Property: "Emulate PyProperty_Type() in Objects/descrobject.c" def __init__(self, fget=None, fset=None, fdel=None, doc=None): self.fget = fget self.fset = fset self.fdel = fdel if doc is None and fget is not None: doc = fget.__doc__ self.__doc__ = doc def __get__(self, obj, objtype=None): if obj is None: return self if self.fget is None: raise AttributeError("unreadable attribute") return self.fget(obj) def __set__(self, obj, value): if self.fset is None: raise AttributeError("can't set attribute") self.fset(obj, value) def __delete__(self, obj): if self.fdel is None: raise AttributeError("can't delete attribute") self.fdel(obj) def getter(self, fget): return type(self)(fget, self.fset, self.fdel, self.__doc__) def setter(self, fset): return type(self)(self.fget, fset, self.fdel, self.__doc__) def deleter(self, fdel): return type(self)(self.fget, self.fset, fdel, self.__doc__)


Very good. You could add the fact that after Foo.prop = prop you can do Foo().prop = 5; pront Foo().prop; del Foo().prop with the desired outcome.
Method objects are created on the fly and can reuse the same memory location if available.
@MarkusMeskanen: I rather use type() as accessing dunder attributes and methods are meant to be used as extension points by the standard functions and operators.
@MarkusMeskanen: because the object is immutable, and if you mutated it in place you could not specialise it in a subclass.
@MarkusMeskanen: see Python overriding getter without setter; if @human.name.getter altered the property object in-place rather than return a new, the human.name attribute would be altered, changing the behaviour of that superclass.
M
Mateen Ulhaq

The documentation says it's just a shortcut for creating read-only properties. So

@property
def x(self):
    return self._x

is equivalent to

def getx(self):
    return self._x
x = property(getx)

The full context (most-upvoted answer) is good, but this answer was practically useful for figuring out why someone else had used @property as a decorator in their class.
"...shortcut for creating readonly properties.". The million dolah answer!
It does not create a read-only property. It creates a "standard" getter method. This statement will still work as expected: obj.x = 5
A
Alex

Here is a minimal example of how @property can be implemented:

class Thing:
    def __init__(self, my_word):
        self._word = my_word 
    @property
    def word(self):
        return self._word

>>> print( Thing('ok').word )
'ok'

Otherwise word remains a method instead of a property.

class Thing:
    def __init__(self, my_word):
        self._word = my_word
    def word(self):
        return self._word

>>> print( Thing('ok').word() )
'ok'

How would this example look if the word() function/property needed to be defined in init ?
Can someone please explain why I would create a property decorator here, instead of just having self.word = my_word -- which would then work the same way print( Thing('ok').word ) = 'ok'
@SilverSlash This is just a simple example, a real use-case would involve a more complicated method
can you please explain me, how printing Thing('ok').word calls the function internally at runtime?
@property hides the fact that it's a function? Without it you would just need parenthesis to call it?
g
glglgl

The first part is simple:

@property
def x(self): ...

is the same as

def x(self): ...
x = property(x)

which, in turn, is the simplified syntax for creating a property with just a getter.

The next step would be to extend this property with a setter and a deleter. And this happens with the appropriate methods:

@x.setter
def x(self, value): ...

returns a new property which inherits everything from the old x plus the given setter.

x.deleter works the same way.


C
Cleb

Below is another example on how @property can help when one has to refactor code which is taken from here (I only summarize it below):

Imagine you created a class Money like this:

class Money:
    def __init__(self, dollars, cents):
        self.dollars = dollars
        self.cents = cents

and an user creates a library depending on this class where he/she uses e.g.

money = Money(27, 12)

print("I have {} dollar and {} cents.".format(money.dollars, money.cents))
# prints I have 27 dollar and 12 cents.

Now let's suppose you decide to change your Money class and get rid of the dollars and cents attributes but instead decide to only track the total amount of cents:

class Money:
    def __init__(self, dollars, cents):
        self.total_cents = dollars * 100 + cents

If the above mentioned user now tries to run his/her library as before

money = Money(27, 12)

print("I have {} dollar and {} cents.".format(money.dollars, money.cents))

it will result in an error

AttributeError: 'Money' object has no attribute 'dollars'

That means that now everyone who relies on your original Money class would have to change all lines of code where dollars and cents are used which can be very painful... So, how could this be avoided? By using @property!

That is how:

class Money:
    def __init__(self, dollars, cents):
        self.total_cents = dollars * 100 + cents

    # Getter and setter for dollars...
    @property
    def dollars(self):
        return self.total_cents // 100

    @dollars.setter
    def dollars(self, new_dollars):
        self.total_cents = 100 * new_dollars + self.cents

    # And the getter and setter for cents.
    @property
    def cents(self):
        return self.total_cents % 100

    @cents.setter
    def cents(self, new_cents):
        self.total_cents = 100 * self.dollars + new_cents

when we now call from our library

money = Money(27, 12)

print("I have {} dollar and {} cents.".format(money.dollars, money.cents))
# prints I have 27 dollar and 12 cents.

it will work as expected and we did not have to change a single line of code in our library! In fact, we would not even have to know that the library we depend on changed.

Also the setter works fine:

money.dollars += 2
print("I have {} dollar and {} cents.".format(money.dollars, money.cents))
# prints I have 29 dollar and 12 cents.

money.cents += 10
print("I have {} dollar and {} cents.".format(money.dollars, money.cents))
# prints I have 29 dollar and 22 cents.

You can use @property also in abstract classes; I give a minimal example here.


your summary is very good, the example that website takes is a little bit strange .. A beginner would ask .. why can't we just stick to the self.dollar = dollars? we have done so much with @property, but it seems no extract functionality is added.
@ShengBi: Do not focus that much on the actual example but more on the underlying principle: If - for what ever reason - you have to refactor code, you can do so without affecting anyone other's code.
@cleb you da real mvp. Everybody else uses that getter setter example like this one, programiz.com/python-programming/property. But you are the only one that actually explains why we want property. It's because when we writes something upon which a lot of people are going to build, we want to be able to modify the base classes without no real impact on how the successors use or build upon our work, implementation wise.
@Cleb I sure did! :)
B
Bimo

This following:

class C(object):
    def __init__(self):
        self._x = None

    @property
    def x(self):
        """I'm the 'x' property."""
        return self._x

    @x.setter
    def x(self, value):
        self._x = value

    @x.deleter
    def x(self):
        del self._x

Is the same as:

class C(object):
    def __init__(self):
        self._x = None

    def _x_get(self):
        return self._x

    def _x_set(self, value):
        self._x = value

    def _x_del(self):
        del self._x

    x = property(_x_get, _x_set, _x_del, 
                    "I'm the 'x' property.")

Is the same as:

class C(object):
    def __init__(self):
        self._x = None

    def _x_get(self):
        return self._x

    def _x_set(self, value):
        self._x = value

    def _x_del(self):
        del self._x

    x = property(_x_get, doc="I'm the 'x' property.")
    x = x.setter(_x_set)
    x = x.deleter(_x_del)

Is the same as:

class C(object):
    def __init__(self):
        self._x = None

    def _x_get(self):
        return self._x
    x = property(_x_get, doc="I'm the 'x' property.")

    def _x_set(self, value):
        self._x = value
    x = x.setter(_x_set)

    def _x_del(self):
        del self._x
    x = x.deleter(_x_del)

Which is the same as :

class C(object):
    def __init__(self):
        self._x = None

    @property
    def x(self):
        """I'm the 'x' property."""
        return self._x

    @x.setter
    def x(self, value):
        self._x = value

    @x.deleter
    def x(self):
        del self._x

The first and last code examples are the same (verbatim).
I think it's intentional. Either way, this was the most useful example to me because I can grok meaning from these examples. Thanks @Bill Moore
U
Useless

Let's start with Python decorators.

A Python decorator is a function that helps to add some additional functionalities to an already defined function.

In Python, everything is an object. Functions in Python are first-class objects which means that they can be referenced by a variable, added in the lists, passed as arguments to another function, etc.

Consider the following code snippet.

def decorator_func(fun):
    def wrapper_func():
        print("Wrapper function started")
        fun()
        print("Given function decorated")
        # Wrapper function add something to the passed function and decorator 
        # returns the wrapper function
    return wrapper_func

def say_bye():
    print("bye!!")

say_bye = decorator_func(say_bye)
say_bye()

# Output:
#  Wrapper function started
#  bye!!
#  Given function decorated
 

Here, we can say that the decorator function modified our say_bye function and added some extra lines of code to it.

Python syntax for decorator

def decorator_func(fun):
    def wrapper_func():
        print("Wrapper function started")
        fun()
        print("Given function decorated")
        # Wrapper function add something to the passed function and decorator 
        # returns the wrapper function
    return wrapper_func

@decorator_func
def say_bye():
    print("bye!!")

say_bye()

Let's go through everything with a case scenario. But before that, let's talk about some OOP principles.

Getters and setters are used in many object-oriented programming languages to ensure the principle of data encapsulation(which is seen as the bundling of data with the methods that operate on these data.)

These methods are, of course, the getter for retrieving the data and the setter for changing the data.

According to this principle, the attributes of a class are made private to hide and protect them from other code.

Yup, @property is basically a pythonic way to use getters and setters.

Python has a great concept called property which makes the life of an object-oriented programmer much simpler.

Let us assume that you decide to make a class that could store the temperature in degrees Celsius.

class Celsius:
def __init__(self, temperature = 0):
    self.set_temperature(temperature)

def to_fahrenheit(self):
    return (self.get_temperature() * 1.8) + 32

def get_temperature(self):
    return self._temperature

def set_temperature(self, value):
    if value < -273:
        raise ValueError("Temperature below -273 is not possible")
    self._temperature = value

Refactored Code, Here is how we could have achieved it with 'property.'

In Python, property() is a built-in function that creates and returns a property object.

A property object has three methods, getter(), setter(), and delete().

class Celsius:
def __init__(self, temperature = 0):
    self.temperature = temperature

def to_fahrenheit(self):
    return (self.temperature * 1.8) + 32

def get_temperature(self):
    print("Getting value")
    return self.temperature

def set_temperature(self, value):
    if value < -273:
        raise ValueError("Temperature below -273 is not possible")
    print("Setting value")
    self.temperature = value

temperature = property(get_temperature,set_temperature)

Here,

temperature = property(get_temperature,set_temperature)

could have been broken down as,

# make empty property
temperature = property()
# assign fget
temperature = temperature.getter(get_temperature)
# assign fset
temperature = temperature.setter(set_temperature)

Point To Note:

get_temperature remains a property instead of a method.

Now you can access the value of temperature by writing.

C = Celsius()
C.temperature
# instead of writing C.get_temperature()

We can go on further and not define names get_temperature and set_temperature as they are unnecessary and pollute the class namespace.

The pythonic way to deal with the above problem is to use @property.

class Celsius:
    def __init__(self, temperature = 0):
        self.temperature = temperature

    def to_fahrenheit(self):
        return (self.temperature * 1.8) + 32

    @property
    def temperature(self):
        print("Getting value")
        return self.temperature

    @temperature.setter
    def temperature(self, value):
        if value < -273:
            raise ValueError("Temperature below -273 is not possible")
        print("Setting value")
        self.temperature = value

Points to Note -

A method that is used for getting a value is decorated with "@property". The method which has to function as the setter is decorated with "@temperature.setter", If the function had been called "x", we would have to decorate it with "@x.setter". We wrote "two" methods with the same name and a different number of parameters, "def temperature(self)" and "def temperature(self,x)".

As you can see, the code is definitely less elegant.

Now, let's talk about one real-life practical scenario.

Let's say you have designed a class as follows:

class OurClass:

    def __init__(self, a):
        self.x = a


y = OurClass(10)
print(y.x)

Now, let's further assume that our class got popular among clients and they started using it in their programs, They did all kinds of assignments to the object.

And one fateful day, a trusted client came to us and suggested that "x" has to be a value between 0 and 1000; this is really a horrible scenario!

Due to properties, it's easy: We create a property version of "x".

class OurClass:

    def __init__(self,x):
        self.x = x

    @property
    def x(self):
        return self.__x

    @x.setter
    def x(self, x):
        if x < 0:
            self.__x = 0
        elif x > 1000:
            self.__x = 1000
        else:
            self.__x = x

This is great, isn't it: You can start with the simplest implementation imaginable, and you are free to later migrate to a property version without having to change the interface! So properties are not just a replacement for getters and setters!

You can check this Implementation here


Your Celsius class is going to infinitely recurse when setting (which means upon instantiation).
@Ted Petrou I Didn't get you? How it will infinitely recurse when setting?
People are asking how does it work not why it works? @ShengBi
It is just a comment, my personal opinion. Your answer could be really good. so leave it.
compared to the top voted answers, this one is designed for humans; thanks.
L
Leo Skhrnkv

I read all the posts here and realized that we may need a real life example. Why, actually, we have @property? So, consider a Flask app where you use authentication system. You declare a model User in models.py:

class User(UserMixin, db.Model):
    __tablename__ = 'users'
    id = db.Column(db.Integer, primary_key=True)
    email = db.Column(db.String(64), unique=True, index=True)
    username = db.Column(db.String(64), unique=True, index=True)
    password_hash = db.Column(db.String(128))

    ...

    @property
    def password(self):
        raise AttributeError('password is not a readable attribute')

    @password.setter
    def password(self, password):
        self.password_hash = generate_password_hash(password)

    def verify_password(self, password):
        return check_password_hash(self.password_hash, password)

In this code we've "hidden" attribute password by using @property which triggers AttributeError assertion when you try to access it directly, while we used @property.setter to set the actual instance variable password_hash.

Now in auth/views.py we can instantiate a User with:

...
@auth.route('/register', methods=['GET', 'POST'])
def register():
    form = RegisterForm()
    if form.validate_on_submit():
        user = User(email=form.email.data,
                    username=form.username.data,
                    password=form.password.data)
        db.session.add(user)
        db.session.commit()
...

Notice attribute password that comes from a registration form when a user fills the form. Password confirmation happens on the front end with EqualTo('password', message='Passwords must match') (in case if you are wondering, but it's a different topic related Flask forms).

I hope this example will be useful


D
Devendra Bhat

This point is been cleared by many people up there but here is a direct point which I was searching. This is what I feel is important to start with the @property decorator. eg:-

class UtilityMixin():
    @property
    def get_config(self):
        return "This is property"

The calling of function "get_config()" will work like this.

util = UtilityMixin()
print(util.get_config)

If you notice I have not used "()" brackets for calling the function. This is the basic thing which I was searching for the @property decorator. So that you can use your function just like a variable.


V
Victor Wang

The best explanation can be found here: Python @Property Explained – How to Use and When? (Full Examples) by Selva Prabhakaran | Posted on November 5, 2018

It helped me understand WHY not only HOW.

https://www.machinelearningplus.com/python/python-property/


This is the best source as of now
p
prosti

property is a class behind @property decorator.

You can always check this:

print(property) #<class 'property'>

I rewrote the example from help(property) to show that the @property syntax

class C:
    def __init__(self):
        self._x=None

    @property 
    def x(self):
        return self._x

    @x.setter 
    def x(self, value):
        self._x = value

    @x.deleter
    def x(self):
        del self._x

c = C()
c.x="a"
print(c.x)

is functionally identical to property() syntax:

class C:
    def __init__(self):
        self._x=None

    def g(self):
        return self._x

    def s(self, v):
        self._x = v

    def d(self):
        del self._x

    prop = property(g,s,d)

c = C()
c.x="a"
print(c.x)

There is no difference how we use the property as you can see.

To answer the question @property decorator is implemented via property class.

So, the question is to explain the property class a bit. This line:

prop = property(g,s,d)

Was the initialization. We can rewrite it like this:

prop = property(fget=g,fset=s,fdel=d)

The meaning of fget, fset and fdel:

 |    fget
 |      function to be used for getting an attribute value
 |    fset
 |      function to be used for setting an attribute value
 |    fdel
 |      function to be used for del'ing an attribute
 |    doc
 |      docstring

The next image shows the triplets we have, from the class property:

https://i.stack.imgur.com/FmVdx.png

__get__, __set__, and __delete__ are there to be overridden. This is the implementation of the descriptor pattern in Python.

In general, a descriptor is an object attribute with “binding behavior”, one whose attribute access has been overridden by methods in the descriptor protocol.

We can also use property setter, getter and deleter methods to bind the function to property. Check the next example. The method s2 of the class C will set the property doubled.

class C:
    def __init__(self):
        self._x=None

    def g(self):
        return self._x

    def s(self, x):
        self._x = x

    def d(self):
        del self._x

    def s2(self,x):
        self._x=x+x


    x=property(g)
    x=x.setter(s)
    x=x.deleter(d)      


c = C()
c.x="a"
print(c.x) # outputs "a"

C.x=property(C.g, C.s2)
C.x=C.x.deleter(C.d)
c2 = C()
c2.x="a"
print(c2.x) # outputs "aa"

Y
Yilmaz

A decorator is a function that takes a function as an argument and returns a closure. The closure is a set of inner functions and free variables. The inner function is closing over the free variable and that is why it is called 'closure'. A free variable is a variable that is outside the inner function and passed into the inner via docorator.

As the name says, decorator is decorating the received function.

function decorator(undecorated_func):
    print("calling decorator func")
    inner():
       print("I am inside inner")
       return undecorated_func
    return inner

this is a simple decorator function. It received "undecorated_func" and passed it to inner() as a free variable, inner() printed "I am inside inner" and returned undecorated_func. When we call decorator(undecorated_func), it is returning the inner. Here is the key, in decorators we are naming the inner function as the name of the function that we passed.

   undecorated_function= decorator(undecorated_func) 

now inner function is called "undecorated_func". Since inner is now named as "undecorated_func", we passed "undecorated_func" to the decorator and we returned "undecorated_func" plus printed out "I am inside inner". so this print statement decorated our "undecorated_func".

now let's define a class with a property decorator:

class Person:
    def __init__(self,name):
        self._name=name
    @property
    def name(self):
        return self._name
    @name.setter
    def name(self.value):
        self._name=value

when we decorated name() with @property(), this is what happened:

name=property(name) # Person.__dict__ you ll see name 

first argument of property() is getter. this is what happened in the second decoration:

   name=name.setter(name) 

As I mentioned above, the decorator returns the inner function, and we name the inner function with the name of the function that we passed.

Here is an important thing to be aware of. "name" is immutable. in the first decoration we got this:

  name=property(name)

in the second one we got this

  name=name.setter(name)

We are not modifying name obj. In the second decoration, python sees that this is property object and it already had getter. So python creates a new "name" object, adds the "fget" from the first obj and then sets the "fset".


Your answer has a lot of typos and sytax mistakes that prevented me from reading it.
@thanos.a I am so sorry about it :) I did edited some typos but I do not see any syntax error
n
nvd

A property can be declared in two ways.

Creating the getter, setter methods for an attribute and then passing these as argument to property function

Using the @property decorator.

You can have a look at few examples I have written about properties in python.


can you updated your answer saying that property is a class so I can upvote.
A
Acecool

Here is another example:

##
## Python Properties Example
##
class GetterSetterExample( object ):
    ## Set the default value for x ( we reference it using self.x, set a value using self.x = value )
    __x = None


##
## On Class Initialization - do something... if we want..
##
def __init__( self ):
    ## Set a value to __x through the getter / setter... Since __x is defined above, this doesn't need to be set...
    self.x = 1234

    return None


##
## Define x as a property, ie a getter - All getters should have a default value arg, so I added it - it will not be passed in when setting a value, so you need to set the default here so it will be used..
##
@property
def x( self, _default = None ):
    ## I added an optional default value argument as all getters should have this - set it to the default value you want to return...
    _value = ( self.__x, _default )[ self.__x == None ]

    ## Debugging - so you can see the order the calls are made...
    print( '[ Test Class ] Get x = ' + str( _value ) )

    ## Return the value - we are a getter afterall...
    return _value


##
## Define the setter function for x...
##
@x.setter
def x( self, _value = None ):
    ## Debugging - so you can see the order the calls are made...
    print( '[ Test Class ] Set x = ' + str( _value ) )

    ## This is to show the setter function works.... If the value is above 0, set it to a negative value... otherwise keep it as is ( 0 is the only non-negative number, it can't be negative or positive anyway )
    if ( _value > 0 ):
        self.__x = -_value
    else:
        self.__x = _value


##
## Define the deleter function for x...
##
@x.deleter
def x( self ):
    ## Unload the assignment / data for x
    if ( self.__x != None ):
        del self.__x


##
## To String / Output Function for the class - this will show the property value for each property we add...
##
def __str__( self ):
    ## Output the x property data...
    print( '[ x ] ' + str( self.x ) )


    ## Return a new line - technically we should return a string so it can be printed where we want it, instead of printed early if _data = str( C( ) ) is used....
    return '\n'

##
##
##
_test = GetterSetterExample( )
print( _test )

## For some reason the deleter isn't being called...
del _test.x

Basically, the same as the C( object ) example except I'm using x instead... I also don't initialize in __init - ... well.. I do, but it can be removed because __x is defined as part of the class....

The output is:

[ Test Class ] Set x = 1234
[ Test Class ] Get x = -1234
[ x ] -1234

and if I comment out the self.x = 1234 in init then the output is:

[ Test Class ] Get x = None
[ x ] None

and if I set the _default = None to _default = 0 in the getter function ( as all getters should have a default value but it isn't passed in by the property values from what I've seen so you can define it here, and it actually isn't bad because you can define the default once and use it everywhere ) ie: def x( self, _default = 0 ):

[ Test Class ] Get x = 0
[ x ] 0

Note: The getter logic is there just to have the value be manipulated by it to ensure it is manipulated by it - the same for the print statements...

Note: I'm used to Lua and being able to dynamically create 10+ helpers when I call a single function and I made something similar for Python without using properties and it works to a degree, but, even though the functions are being created before being used, there are still issues at times with them being called prior to being created which is strange as it isn't coded that way... I prefer the flexibility of Lua meta-tables and the fact I can use actual setters / getters instead of essentially directly accessing a variable... I do like how quickly some things can be built with Python though - for instance gui programs. although one I am designing may not be possible without a lot of additional libraries - if I code it in AutoHotkey I can directly access the dll calls I need, and the same can be done in Java, C#, C++, and more - maybe I haven't found the right thing yet but for that project I may switch from Python..

Note: The code output in this forum is broken - I had to add spaces to the first part of the code for it to work - when copy / pasting ensure you convert all spaces to tabs.... I use tabs for Python because in a file which is 10,000 lines the filesize can be 512KB to 1MB with spaces and 100 to 200KB with tabs which equates to a massive difference for file size, and reduction in processing time...

Tabs can also be adjusted per user - so if you prefer 2 spaces width, 4, 8 or whatever you can do it meaning it is thoughtful for developers with eye-sight deficits.

Note: All of the functions defined in the class aren't indented properly because of a bug in the forum software - ensure you indent it if you copy / paste