Related
I'm doing it like:
def set_property(property,value):
def get_property(property):
or
object.property = value
value = object.property
What's the pythonic way to use getters and setters?
Try this: Python Property
The sample code is:
class C(object):
def __init__(self):
self._x = None
#property
def x(self):
"""I'm the 'x' property."""
print("getter of x called")
return self._x
#x.setter
def x(self, value):
print("setter of x called")
self._x = value
#x.deleter
def x(self):
print("deleter of x called")
del self._x
c = C()
c.x = 'foo' # setter called
foo = c.x # getter called
del c.x # deleter called
What's the pythonic way to use getters and setters?
The "Pythonic" way is not to use "getters" and "setters", but to use plain attributes, like the question demonstrates, and del for deleting (but the names are changed to protect the innocent... builtins):
value = 'something'
obj.attribute = value
value = obj.attribute
del obj.attribute
If later, you want to modify the setting and getting, you can do so without having to alter user code, by using the property decorator:
class Obj:
"""property demo"""
#
#property # first decorate the getter method
def attribute(self): # This getter method name is *the* name
return self._attribute
#
#attribute.setter # the property decorates with `.setter` now
def attribute(self, value): # name, e.g. "attribute", is the same
self._attribute = value # the "value" name isn't special
#
#attribute.deleter # decorate with `.deleter`
def attribute(self): # again, the method name is the same
del self._attribute
(Each decorator usage copies and updates the prior property object, so note that you should use the same name for each set, get, and delete function/method.)
After defining the above, the original setting, getting, and deleting code is the same:
obj = Obj()
obj.attribute = value
the_value = obj.attribute
del obj.attribute
You should avoid this:
def set_property(property,value):
def get_property(property):
Firstly, the above doesn't work, because you don't provide an argument for the instance that the property would be set to (usually self), which would be:
class Obj:
def set_property(self, property, value): # don't do this
...
def get_property(self, property): # don't do this either
...
Secondly, this duplicates the purpose of two special methods, __setattr__ and __getattr__.
Thirdly, we also have the setattr and getattr builtin functions.
setattr(object, 'property_name', value)
getattr(object, 'property_name', default_value) # default is optional
The #property decorator is for creating getters and setters.
For example, we could modify the setting behavior to place restrictions the value being set:
class Protective(object):
#property
def protected_value(self):
return self._protected_value
#protected_value.setter
def protected_value(self, value):
if acceptable(value): # e.g. type or range check
self._protected_value = value
In general, we want to avoid using property and just use direct attributes.
This is what is expected by users of Python. Following the rule of least-surprise, you should try to give your users what they expect unless you have a very compelling reason to the contrary.
Demonstration
For example, say we needed our object's protected attribute to be an integer between 0 and 100 inclusive, and prevent its deletion, with appropriate messages to inform the user of its proper usage:
class Protective(object):
"""protected property demo"""
#
def __init__(self, start_protected_value=0):
self.protected_value = start_protected_value
#
#property
def protected_value(self):
return self._protected_value
#
#protected_value.setter
def protected_value(self, value):
if value != int(value):
raise TypeError("protected_value must be an integer")
if 0 <= value <= 100:
self._protected_value = int(value)
else:
raise ValueError("protected_value must be " +
"between 0 and 100 inclusive")
#
#protected_value.deleter
def protected_value(self):
raise AttributeError("do not delete, protected_value can be set to 0")
(Note that __init__ refers to self.protected_value but the property methods refer to self._protected_value. This is so that __init__ uses the property through the public API, ensuring it is "protected".)
And usage:
>>> p1 = Protective(3)
>>> p1.protected_value
3
>>> p1 = Protective(5.0)
>>> p1.protected_value
5
>>> p2 = Protective(-5)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 3, in __init__
File "<stdin>", line 15, in protected_value
ValueError: protectected_value must be between 0 and 100 inclusive
>>> p1.protected_value = 7.3
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 17, in protected_value
TypeError: protected_value must be an integer
>>> p1.protected_value = 101
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 15, in protected_value
ValueError: protectected_value must be between 0 and 100 inclusive
>>> del p1.protected_value
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 18, in protected_value
AttributeError: do not delete, protected_value can be set to 0
Do the names matter?
Yes they do. .setter and .deleter make copies of the original property. This allows subclasses to properly modify behavior without altering the behavior in the parent.
class Obj:
"""property demo"""
#
#property
def get_only(self):
return self._attribute
#
#get_only.setter
def get_or_set(self, value):
self._attribute = value
#
#get_or_set.deleter
def get_set_or_delete(self):
del self._attribute
Now for this to work, you have to use the respective names:
obj = Obj()
# obj.get_only = 'value' # would error
obj.get_or_set = 'value'
obj.get_set_or_delete = 'new value'
the_value = obj.get_only
del obj.get_set_or_delete
# del obj.get_or_set # would error
I'm not sure where this would be useful, but the use-case is if you want a get, set, and/or delete-only property. Probably best to stick to semantically same property having the same name.
Conclusion
Start with simple attributes.
If you later need functionality around the setting, getting, and deleting, you can add it with the property decorator.
Avoid functions named set_... and get_... - that's what properties are for.
In [1]: class test(object):
def __init__(self):
self.pants = 'pants'
#property
def p(self):
return self.pants
#p.setter
def p(self, value):
self.pants = value * 2
....:
In [2]: t = test()
In [3]: t.p
Out[3]: 'pants'
In [4]: t.p = 10
In [5]: t.p
Out[5]: 20
Using #property and #attribute.setter helps you to not only use the "pythonic" way but also to check the validity of attributes both while creating the object and when altering it.
class Person(object):
def __init__(self, p_name=None):
self.name = p_name
#property
def name(self):
return self._name
#name.setter
def name(self, new_name):
if type(new_name) == str: #type checking for name property
self._name = new_name
else:
raise Exception("Invalid value for name")
By this, you actually 'hide' _name attribute from client developers and also perform checks on name property type. Note that by following this approach even during the initiation the setter gets called. So:
p = Person(12)
Will lead to:
Exception: Invalid value for name
But:
>>>p = person('Mike')
>>>print(p.name)
Mike
>>>p.name = 'George'
>>>print(p.name)
George
>>>p.name = 2.3 # Causes an exception
This is an old question but the topic is very important and always current. In case anyone wants to go beyond simple getters/setters i have wrote an article about superpowered properties in python with support for slots, observability and reduced boilerplate code.
from objects import properties, self_properties
class Car:
with properties(locals(), 'meta') as meta:
#meta.prop(read_only=True)
def brand(self) -> str:
"""Brand"""
#meta.prop(read_only=True)
def max_speed(self) -> float:
"""Maximum car speed"""
#meta.prop(listener='_on_acceleration')
def speed(self) -> float:
"""Speed of the car"""
return 0 # Default stopped
#meta.prop(listener='_on_off_listener')
def on(self) -> bool:
"""Engine state"""
return False
def __init__(self, brand: str, max_speed: float = 200):
self_properties(self, locals())
def _on_off_listener(self, prop, old, on):
if on:
print(f"{self.brand} Turned on, Runnnnnn")
else:
self._speed = 0
print(f"{self.brand} Turned off.")
def _on_acceleration(self, prop, old, speed):
if self.on:
if speed > self.max_speed:
print(f"{self.brand} {speed}km/h Bang! Engine exploded!")
self.on = False
else:
print(f"{self.brand} New speed: {speed}km/h")
else:
print(f"{self.brand} Car is off, no speed change")
This class can be used like this:
mycar = Car('Ford')
# Car is turned off
for speed in range(0, 300, 50):
mycar.speed = speed
# Car is turned on
mycar.on = True
for speed in range(0, 350, 50):
mycar.speed = speed
This code will produce the following output:
Ford Car is off, no speed change
Ford Car is off, no speed change
Ford Car is off, no speed change
Ford Car is off, no speed change
Ford Car is off, no speed change
Ford Car is off, no speed change
Ford Turned on, Runnnnnn
Ford New speed: 0km/h
Ford New speed: 50km/h
Ford New speed: 100km/h
Ford New speed: 150km/h
Ford New speed: 200km/h
Ford 250km/h Bang! Engine exploded!
Ford Turned off.
Ford Car is off, no speed change
More info about how and why here: https://mnesarco.github.io/blog/2020/07/23/python-metaprogramming-properties-on-steroids
Properties are pretty useful since you can use them with assignment but then can include validation as well. You can see this code where you use the decorator #property and also #<property_name>.setter to create the methods:
# Python program displaying the use of #property
class AgeSet:
def __init__(self):
self._age = 0
# using property decorator a getter function
#property
def age(self):
print("getter method called")
return self._age
# a setter function
#age.setter
def age(self, a):
if(a < 18):
raise ValueError("Sorry your age is below eligibility criteria")
print("setter method called")
self._age = a
pkj = AgeSet()
pkj.age = int(input("set the age using setter: "))
print(pkj.age)
There are more details in this post I wrote about this as well: https://pythonhowtoprogram.com/how-to-create-getter-setter-class-properties-in-python-3/
You can use accessors/mutators (i.e. #attr.setter and #property) or not, but the most important thing is to be consistent!
If you're using #property to simply access an attribute, e.g.
class myClass:
def __init__(a):
self._a = a
#property
def a(self):
return self._a
use it to access every* attribute! It would be a bad practice to access some attributes using #property and leave some other properties public (i.e. name without an underscore) without an accessor, e.g. do not do
class myClass:
def __init__(a, b):
self.a = a
self.b = b
#property
def a(self):
return self.a
Note that self.b does not have an explicit accessor here even though it's public.
Similarly with setters (or mutators), feel free to use #attribute.setter but be consistent! When you do e.g.
class myClass:
def __init__(a, b):
self.a = a
self.b = b
#a.setter
def a(self, value):
return self.a = value
It's hard for me to guess your intention. On one hand you're saying that both a and b are public (no leading underscore in their names) so I should theoretically be allowed to access/mutate (get/set) both. But then you specify an explicit mutator only for a, which tells me that maybe I should not be able to set b. Since you've provided an explicit mutator I am not sure if the lack of explicit accessor (#property) means I should not be able to access either of those variables or you were simply being frugal in using #property.
*The exception is when you explicitly want to make some variables accessible or mutable but not both or you want to perform some additional logic when accessing or mutating an attribute. This is when I am personally using #property and #attribute.setter (otherwise no explicit acessors/mutators for public attributes).
Lastly, PEP8 and Google Style Guide suggestions:
PEP8, Designing for Inheritance says:
For simple public data attributes, it is best to expose just the attribute name, without complicated accessor/mutator methods. Keep in mind that Python provides an easy path to future enhancement, should you find that a simple data attribute needs to grow functional behavior. In that case, use properties to hide functional implementation behind simple data attribute access syntax.
On the other hand, according to Google Style Guide Python Language Rules/Properties the recommendation is to:
Use properties in new code to access or set data where you would normally have used simple, lightweight accessor or setter methods. Properties should be created with the #property decorator.
The pros of this approach:
Readability is increased by eliminating explicit get and set method calls for simple attribute access. Allows calculations to be lazy. Considered the Pythonic way to maintain the interface of a class. In terms of performance, allowing properties bypasses needing trivial accessor methods when a direct variable access is reasonable. This also allows accessor methods to be added in the future without breaking the interface.
and cons:
Must inherit from object in Python 2. Can hide side-effects much like operator overloading. Can be confusing for subclasses.
You can use the magic methods __getattribute__ and __setattr__.
class MyClass:
def __init__(self, attrvalue):
self.myattr = attrvalue
def __getattribute__(self, attr):
if attr == "myattr":
#Getter for myattr
def __setattr__(self, attr):
if attr == "myattr":
#Setter for myattr
Be aware that __getattr__ and __getattribute__ are not the same. __getattr__ is only invoked when the attribute is not found.
Is it possible to create a class of integer where an instance of a certain class (say AutomaticCounter) will increase itself by some number (say 1) each time it is called?
>>> n = AutomaticCounter(start=0)
>>> print(n)
1
>>> print(n)
2
This is what I have tried so far:
class AutomaticCounter(int):
def __init__(self):
self = 0
def __str__(self):
self = self + 1
return self
If you really, really, really need to mangle an immutable and built-in type, then you can create a kind-of "pointer" to it:
class AutomaticCounter(int):
def __new__(cls, *args, **kwargs):
# create a new instance of int()
self = super().__new__(cls, *args, **kwargs)
# create a member "ptr" and storing a ref to the instance
self.ptr = self
# return the normal instance
return self
def __str__(self):
# first, create a copy via int()
# which "decays" from your subclass to an ordinary int()
# then stringify it to obtain the normal __str__() value
value = str(int(self.ptr))
# afterwards, store a new instance of your subclass
# that is incremented by 1
self.ptr = AutomaticCounter(self.ptr + 1)
return value
n = AutomaticCounter(0)
print(n) # 0
print(n) # 1
print(n) # 2
# to increment first and print later, use this __str__() instead:
def __str__(self):
self.ptr = AutomaticCounter(self.ptr + 1)
return str(int(self.ptr))
This, however, doesn't make the type immutable per se. If you do print(f"{self=}") at the beginning of __str__() you'll see the instance is unchanged, so you effectively have a size of 2x int() (+ some trash) for your object and you access the real instance via self.ptr.
It wouldn't work with self alone as self is merely a read-only reference (created via __new__()) passed to instance's methods as the first argument, so something like this:
def func(instance, ...):
instance = <something else>
and you doing the assignment would, as mentioned by Daniel, simply assign a new value to the local variable named instance (self is just a quasi-standard name for the reference) which doesn't really change the instance. Therefore the next solution which looks similar would be a pointer and as you'd like to manipulate it the way you described, I "hid" it to a custom member called ptr.
As pointed out by user2357112, there is a desynchronization caused by the instance being immutable, therefore if you choose the self.ptr hack, you'll need to update the magic methods (__*__()), for example this is updating the __add__(). Notice the int() calls, it converts it to int() to prevent recursions.
class AutomaticCounter(int):
def __new__(cls, *args, **kwargs):
self = super().__new__(cls, *args, **kwargs)
self.ptr = self
return self
def __str__(self):
value = int(self.ptr)
self.ptr = AutomaticCounter(int(self.ptr) + 1)
return str(value)
def __add__(self, other):
value = other
if hasattr(other, "ptr"):
value = int(other.ptr)
self.ptr = AutomaticCounter(int(self.ptr) + value)
return int(self.ptr)
def __rmul__(self, other):
# [1, 2, 3] * your_object
return other * int(self.ptr)
n = AutomaticCounter(0)
print(n) # 0
print(n) # 1
print(n) # 2
print(n+n) # 6
However, anything that attempts to pull the raw value or tries to access it with C API will most likely fail, namely reverse operations e.g. with immutable built-ins should be the case as for those you can't edit the magic methods reliably so it's corrected in all modules and all scopes.
Example:
# will work fine because it's your class
a <operator> b -> a.__operator__(b)
vs
# will break everything because it's using the raw value, not self.ptr hack
b <operator> a -> b.__operator__(a)
with exception of list.__mul__() for some reason. When I find the code line in CPython, I'll add it here.
Or, a more sane solution would be to create a custom and mutable object, create a member in it and manipulate that. Then return it, stringified, in __str__:
class AutomaticCounter(int):
def __init__(self, start=0):
self.item = start
def __str__(self):
self.item += 1
return str(self.item)
There are two issues here. First, self isn't actually the object but rather a variable reference to the object. When you reassign self, you're not changing the object but merely causing the self variable, which only has local scope, to now reference some other object. The original object remains unchanged.
Second, unless you really know what you're doing (and I don't), it is, in my opinion, unadvisable to subclass immutable built-ins. What you can do is have the object have an integer attribute and then define the __getattr__ method to pass any attribute calls on to the integer.
class AutomaticCounter:
def __init__(self, start=0):
self.item = start
def __str__(self):
self.item += 1
return str(self.item)
def __getattr__(self, attr):
return getattr(self.item, attr)
I'm doing it like:
def set_property(property,value):
def get_property(property):
or
object.property = value
value = object.property
What's the pythonic way to use getters and setters?
Try this: Python Property
The sample code is:
class C(object):
def __init__(self):
self._x = None
#property
def x(self):
"""I'm the 'x' property."""
print("getter of x called")
return self._x
#x.setter
def x(self, value):
print("setter of x called")
self._x = value
#x.deleter
def x(self):
print("deleter of x called")
del self._x
c = C()
c.x = 'foo' # setter called
foo = c.x # getter called
del c.x # deleter called
What's the pythonic way to use getters and setters?
The "Pythonic" way is not to use "getters" and "setters", but to use plain attributes, like the question demonstrates, and del for deleting (but the names are changed to protect the innocent... builtins):
value = 'something'
obj.attribute = value
value = obj.attribute
del obj.attribute
If later, you want to modify the setting and getting, you can do so without having to alter user code, by using the property decorator:
class Obj:
"""property demo"""
#
#property # first decorate the getter method
def attribute(self): # This getter method name is *the* name
return self._attribute
#
#attribute.setter # the property decorates with `.setter` now
def attribute(self, value): # name, e.g. "attribute", is the same
self._attribute = value # the "value" name isn't special
#
#attribute.deleter # decorate with `.deleter`
def attribute(self): # again, the method name is the same
del self._attribute
(Each decorator usage copies and updates the prior property object, so note that you should use the same name for each set, get, and delete function/method.)
After defining the above, the original setting, getting, and deleting code is the same:
obj = Obj()
obj.attribute = value
the_value = obj.attribute
del obj.attribute
You should avoid this:
def set_property(property,value):
def get_property(property):
Firstly, the above doesn't work, because you don't provide an argument for the instance that the property would be set to (usually self), which would be:
class Obj:
def set_property(self, property, value): # don't do this
...
def get_property(self, property): # don't do this either
...
Secondly, this duplicates the purpose of two special methods, __setattr__ and __getattr__.
Thirdly, we also have the setattr and getattr builtin functions.
setattr(object, 'property_name', value)
getattr(object, 'property_name', default_value) # default is optional
The #property decorator is for creating getters and setters.
For example, we could modify the setting behavior to place restrictions the value being set:
class Protective(object):
#property
def protected_value(self):
return self._protected_value
#protected_value.setter
def protected_value(self, value):
if acceptable(value): # e.g. type or range check
self._protected_value = value
In general, we want to avoid using property and just use direct attributes.
This is what is expected by users of Python. Following the rule of least-surprise, you should try to give your users what they expect unless you have a very compelling reason to the contrary.
Demonstration
For example, say we needed our object's protected attribute to be an integer between 0 and 100 inclusive, and prevent its deletion, with appropriate messages to inform the user of its proper usage:
class Protective(object):
"""protected property demo"""
#
def __init__(self, start_protected_value=0):
self.protected_value = start_protected_value
#
#property
def protected_value(self):
return self._protected_value
#
#protected_value.setter
def protected_value(self, value):
if value != int(value):
raise TypeError("protected_value must be an integer")
if 0 <= value <= 100:
self._protected_value = int(value)
else:
raise ValueError("protected_value must be " +
"between 0 and 100 inclusive")
#
#protected_value.deleter
def protected_value(self):
raise AttributeError("do not delete, protected_value can be set to 0")
(Note that __init__ refers to self.protected_value but the property methods refer to self._protected_value. This is so that __init__ uses the property through the public API, ensuring it is "protected".)
And usage:
>>> p1 = Protective(3)
>>> p1.protected_value
3
>>> p1 = Protective(5.0)
>>> p1.protected_value
5
>>> p2 = Protective(-5)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 3, in __init__
File "<stdin>", line 15, in protected_value
ValueError: protectected_value must be between 0 and 100 inclusive
>>> p1.protected_value = 7.3
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 17, in protected_value
TypeError: protected_value must be an integer
>>> p1.protected_value = 101
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 15, in protected_value
ValueError: protectected_value must be between 0 and 100 inclusive
>>> del p1.protected_value
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 18, in protected_value
AttributeError: do not delete, protected_value can be set to 0
Do the names matter?
Yes they do. .setter and .deleter make copies of the original property. This allows subclasses to properly modify behavior without altering the behavior in the parent.
class Obj:
"""property demo"""
#
#property
def get_only(self):
return self._attribute
#
#get_only.setter
def get_or_set(self, value):
self._attribute = value
#
#get_or_set.deleter
def get_set_or_delete(self):
del self._attribute
Now for this to work, you have to use the respective names:
obj = Obj()
# obj.get_only = 'value' # would error
obj.get_or_set = 'value'
obj.get_set_or_delete = 'new value'
the_value = obj.get_only
del obj.get_set_or_delete
# del obj.get_or_set # would error
I'm not sure where this would be useful, but the use-case is if you want a get, set, and/or delete-only property. Probably best to stick to semantically same property having the same name.
Conclusion
Start with simple attributes.
If you later need functionality around the setting, getting, and deleting, you can add it with the property decorator.
Avoid functions named set_... and get_... - that's what properties are for.
In [1]: class test(object):
def __init__(self):
self.pants = 'pants'
#property
def p(self):
return self.pants
#p.setter
def p(self, value):
self.pants = value * 2
....:
In [2]: t = test()
In [3]: t.p
Out[3]: 'pants'
In [4]: t.p = 10
In [5]: t.p
Out[5]: 20
Using #property and #attribute.setter helps you to not only use the "pythonic" way but also to check the validity of attributes both while creating the object and when altering it.
class Person(object):
def __init__(self, p_name=None):
self.name = p_name
#property
def name(self):
return self._name
#name.setter
def name(self, new_name):
if type(new_name) == str: #type checking for name property
self._name = new_name
else:
raise Exception("Invalid value for name")
By this, you actually 'hide' _name attribute from client developers and also perform checks on name property type. Note that by following this approach even during the initiation the setter gets called. So:
p = Person(12)
Will lead to:
Exception: Invalid value for name
But:
>>>p = person('Mike')
>>>print(p.name)
Mike
>>>p.name = 'George'
>>>print(p.name)
George
>>>p.name = 2.3 # Causes an exception
This is an old question but the topic is very important and always current. In case anyone wants to go beyond simple getters/setters i have wrote an article about superpowered properties in python with support for slots, observability and reduced boilerplate code.
from objects import properties, self_properties
class Car:
with properties(locals(), 'meta') as meta:
#meta.prop(read_only=True)
def brand(self) -> str:
"""Brand"""
#meta.prop(read_only=True)
def max_speed(self) -> float:
"""Maximum car speed"""
#meta.prop(listener='_on_acceleration')
def speed(self) -> float:
"""Speed of the car"""
return 0 # Default stopped
#meta.prop(listener='_on_off_listener')
def on(self) -> bool:
"""Engine state"""
return False
def __init__(self, brand: str, max_speed: float = 200):
self_properties(self, locals())
def _on_off_listener(self, prop, old, on):
if on:
print(f"{self.brand} Turned on, Runnnnnn")
else:
self._speed = 0
print(f"{self.brand} Turned off.")
def _on_acceleration(self, prop, old, speed):
if self.on:
if speed > self.max_speed:
print(f"{self.brand} {speed}km/h Bang! Engine exploded!")
self.on = False
else:
print(f"{self.brand} New speed: {speed}km/h")
else:
print(f"{self.brand} Car is off, no speed change")
This class can be used like this:
mycar = Car('Ford')
# Car is turned off
for speed in range(0, 300, 50):
mycar.speed = speed
# Car is turned on
mycar.on = True
for speed in range(0, 350, 50):
mycar.speed = speed
This code will produce the following output:
Ford Car is off, no speed change
Ford Car is off, no speed change
Ford Car is off, no speed change
Ford Car is off, no speed change
Ford Car is off, no speed change
Ford Car is off, no speed change
Ford Turned on, Runnnnnn
Ford New speed: 0km/h
Ford New speed: 50km/h
Ford New speed: 100km/h
Ford New speed: 150km/h
Ford New speed: 200km/h
Ford 250km/h Bang! Engine exploded!
Ford Turned off.
Ford Car is off, no speed change
More info about how and why here: https://mnesarco.github.io/blog/2020/07/23/python-metaprogramming-properties-on-steroids
Properties are pretty useful since you can use them with assignment but then can include validation as well. You can see this code where you use the decorator #property and also #<property_name>.setter to create the methods:
# Python program displaying the use of #property
class AgeSet:
def __init__(self):
self._age = 0
# using property decorator a getter function
#property
def age(self):
print("getter method called")
return self._age
# a setter function
#age.setter
def age(self, a):
if(a < 18):
raise ValueError("Sorry your age is below eligibility criteria")
print("setter method called")
self._age = a
pkj = AgeSet()
pkj.age = int(input("set the age using setter: "))
print(pkj.age)
There are more details in this post I wrote about this as well: https://pythonhowtoprogram.com/how-to-create-getter-setter-class-properties-in-python-3/
You can use accessors/mutators (i.e. #attr.setter and #property) or not, but the most important thing is to be consistent!
If you're using #property to simply access an attribute, e.g.
class myClass:
def __init__(a):
self._a = a
#property
def a(self):
return self._a
use it to access every* attribute! It would be a bad practice to access some attributes using #property and leave some other properties public (i.e. name without an underscore) without an accessor, e.g. do not do
class myClass:
def __init__(a, b):
self.a = a
self.b = b
#property
def a(self):
return self.a
Note that self.b does not have an explicit accessor here even though it's public.
Similarly with setters (or mutators), feel free to use #attribute.setter but be consistent! When you do e.g.
class myClass:
def __init__(a, b):
self.a = a
self.b = b
#a.setter
def a(self, value):
return self.a = value
It's hard for me to guess your intention. On one hand you're saying that both a and b are public (no leading underscore in their names) so I should theoretically be allowed to access/mutate (get/set) both. But then you specify an explicit mutator only for a, which tells me that maybe I should not be able to set b. Since you've provided an explicit mutator I am not sure if the lack of explicit accessor (#property) means I should not be able to access either of those variables or you were simply being frugal in using #property.
*The exception is when you explicitly want to make some variables accessible or mutable but not both or you want to perform some additional logic when accessing or mutating an attribute. This is when I am personally using #property and #attribute.setter (otherwise no explicit acessors/mutators for public attributes).
Lastly, PEP8 and Google Style Guide suggestions:
PEP8, Designing for Inheritance says:
For simple public data attributes, it is best to expose just the attribute name, without complicated accessor/mutator methods. Keep in mind that Python provides an easy path to future enhancement, should you find that a simple data attribute needs to grow functional behavior. In that case, use properties to hide functional implementation behind simple data attribute access syntax.
On the other hand, according to Google Style Guide Python Language Rules/Properties the recommendation is to:
Use properties in new code to access or set data where you would normally have used simple, lightweight accessor or setter methods. Properties should be created with the #property decorator.
The pros of this approach:
Readability is increased by eliminating explicit get and set method calls for simple attribute access. Allows calculations to be lazy. Considered the Pythonic way to maintain the interface of a class. In terms of performance, allowing properties bypasses needing trivial accessor methods when a direct variable access is reasonable. This also allows accessor methods to be added in the future without breaking the interface.
and cons:
Must inherit from object in Python 2. Can hide side-effects much like operator overloading. Can be confusing for subclasses.
You can use the magic methods __getattribute__ and __setattr__.
class MyClass:
def __init__(self, attrvalue):
self.myattr = attrvalue
def __getattribute__(self, attr):
if attr == "myattr":
#Getter for myattr
def __setattr__(self, attr):
if attr == "myattr":
#Setter for myattr
Be aware that __getattr__ and __getattribute__ are not the same. __getattr__ is only invoked when the attribute is not found.
I am generally confused about the difference between a "property" and an "attribute", and can't find a great resource to concisely detail the differences.
Properties are a special kind of attribute. Basically, when Python encounters the following code:
spam = SomeObject()
print(spam.eggs)
it looks up eggs in spam, and then examines eggs to see if it has a __get__, __set__, or __delete__ method — if it does, it's a property. If it is a property, instead of just returning the eggs object (as it would for any other attribute) it will call the __get__ method (since we were doing lookup) and return whatever that method returns.
More information about Python's data model and descriptors.
With a property you have complete control on its getter, setter and deleter methods, which you don't have (if not using caveats) with an attribute.
class A(object):
_x = 0
'''A._x is an attribute'''
#property
def x(self):
'''
A.x is a property
This is the getter method
'''
return self._x
#x.setter
def x(self, value):
"""
This is the setter method
where I can check it's not assigned a value < 0
"""
if value < 0:
raise ValueError("Must be >= 0")
self._x = value
>>> a = A()
>>> a._x = -1
>>> a.x = -1
Traceback (most recent call last):
File "ex.py", line 15, in <module>
a.x = -1
File "ex.py", line 9, in x
raise ValueError("Must be >= 0")
ValueError: Must be >= 0
In general speaking terms a property and an attribute are the same thing. However, there is a property decorator in Python which provides getter/setter access to an attribute (or other data).
class MyObject(object):
# This is a normal attribute
foo = 1
#property
def bar(self):
return self.foo
#bar.setter
def bar(self, value):
self.foo = value
obj = MyObject()
assert obj.foo == 1
assert obj.bar == obj.foo
obj.bar = 2
assert obj.foo == 2
assert obj.bar == obj.foo
The property allows you to get and set values like you would normal attributes, but underneath there is a method being called translating it into a getter and setter for you. It's really just a convenience to cut down on the boilerplate of calling getters and setters.
Lets say for example, you had a class that held some x and y coordinates for something you needed. To set them you might want to do something like:
myObj.x = 5
myObj.y = 10
That is much easier to look at and think about than writing:
myObj.setX(5)
myObj.setY(10)
The problem is, what if one day your class changes such that you need to offset your x and y by some value? Now you would need to go in and change your class definition and all of the code that calls it, which could be really time consuming and error prone. The property allows you to use the former syntax while giving you the flexibility of change of the latter.
In Python, you can define getters, setters, and delete methods with the property function. If you just want the read property, there is also a #property decorator you can add above your method.
http://docs.python.org/library/functions.html#property
I learnt 2 differences from site of Bernd Klein, in summary:
1. A property is a more convenient way to achieve data encapsulation
For example, let's say you have a public attribute length. Later on, your project requires you to encapsulate it, i.e. to change it to private and provide a getter and setter => you have to change the the code you wrote before:
# Old code
obj1.length = obj1.length + obj2.length
# New code (using private attributes and getter and setter)
obj1.set_length(obj1.get_length() + obj2.get_length()) # => this is ugly
If you use #property and #length.setter => you don't need to change that old code.
2. A property can encapsulate multiple attributes
class Person:
def __init__(self, name, physic_health, mental_health):
self.name = name
self.__physic_health = physic_health
self.__mental_health = mental_health
#property
def condition(self):
health = self.__physic_health + self.__mental_health
if(health < 5.0):
return "I feel bad!"
elif health < 8.0:
return "I am ok!"
else:
return "Great!"
In this example, __physic_health and __mental_health are private and cannot be accessed directly from outside.
There is also one not obvious difference that i use to cache or refresh data , often we have a function connected to class attribute. For instance i need to read file once and keep content assigned to the attribute so the value is cached:
class Misc():
def __init__(self):
self.test = self.test_func()
def test_func(self):
print 'func running'
return 'func value'
cl = Misc()
print cl.test
print cl.test
Output:
func running
func value
func value
We accessed the attribute twice but our function was fired only once. Changing the above example to use property will cause attribute's value refresh each time you access it:
class Misc():
#property
def test(self):
print 'func running'
return 'func value'
cl = Misc()
print cl.test
print cl.test
Output:
func running
func value
func running
func value
I like to think that, if you want to set a restriction for an attribute, use a property.
Although all attributes are public, generally programmers differentiate public and private attributes with an underscore(_). Consider the following class,
class A:
def __init__(self):
self.b = 3 # To show public
self._c = 4 # To show private
Here, b attribute is intended to be accessed from outside class A. But, readers of this class might wonder, can b attribute be set from outside class A?
If we intend to not set b from outside, we can show this intention with #property.
class A:
def __init__(self):
self._c = 4 # To show private
#property
def b(self):
return 3
Now, b can't be set.
a = A()
print(a.b) # prints 3
a.b = 7 # Raises AttributeError
Or, if you wish to set only certain values,
class A:
#property
def b(self):
return self._b
#b.setter
def b(self, val):
if val < 0:
raise ValueError("b can't be negative")
self._b = val
a = A()
a.b = 6 # OK
a.b = -5 # Raises ValueError
I am generally confused about the difference between a "property" and an "attribute", and can't find a great resource to concisely detail the differences.
Properties are a special kind of attribute. Basically, when Python encounters the following code:
spam = SomeObject()
print(spam.eggs)
it looks up eggs in spam, and then examines eggs to see if it has a __get__, __set__, or __delete__ method — if it does, it's a property. If it is a property, instead of just returning the eggs object (as it would for any other attribute) it will call the __get__ method (since we were doing lookup) and return whatever that method returns.
More information about Python's data model and descriptors.
With a property you have complete control on its getter, setter and deleter methods, which you don't have (if not using caveats) with an attribute.
class A(object):
_x = 0
'''A._x is an attribute'''
#property
def x(self):
'''
A.x is a property
This is the getter method
'''
return self._x
#x.setter
def x(self, value):
"""
This is the setter method
where I can check it's not assigned a value < 0
"""
if value < 0:
raise ValueError("Must be >= 0")
self._x = value
>>> a = A()
>>> a._x = -1
>>> a.x = -1
Traceback (most recent call last):
File "ex.py", line 15, in <module>
a.x = -1
File "ex.py", line 9, in x
raise ValueError("Must be >= 0")
ValueError: Must be >= 0
In general speaking terms a property and an attribute are the same thing. However, there is a property decorator in Python which provides getter/setter access to an attribute (or other data).
class MyObject(object):
# This is a normal attribute
foo = 1
#property
def bar(self):
return self.foo
#bar.setter
def bar(self, value):
self.foo = value
obj = MyObject()
assert obj.foo == 1
assert obj.bar == obj.foo
obj.bar = 2
assert obj.foo == 2
assert obj.bar == obj.foo
The property allows you to get and set values like you would normal attributes, but underneath there is a method being called translating it into a getter and setter for you. It's really just a convenience to cut down on the boilerplate of calling getters and setters.
Lets say for example, you had a class that held some x and y coordinates for something you needed. To set them you might want to do something like:
myObj.x = 5
myObj.y = 10
That is much easier to look at and think about than writing:
myObj.setX(5)
myObj.setY(10)
The problem is, what if one day your class changes such that you need to offset your x and y by some value? Now you would need to go in and change your class definition and all of the code that calls it, which could be really time consuming and error prone. The property allows you to use the former syntax while giving you the flexibility of change of the latter.
In Python, you can define getters, setters, and delete methods with the property function. If you just want the read property, there is also a #property decorator you can add above your method.
http://docs.python.org/library/functions.html#property
I learnt 2 differences from site of Bernd Klein, in summary:
1. A property is a more convenient way to achieve data encapsulation
For example, let's say you have a public attribute length. Later on, your project requires you to encapsulate it, i.e. to change it to private and provide a getter and setter => you have to change the the code you wrote before:
# Old code
obj1.length = obj1.length + obj2.length
# New code (using private attributes and getter and setter)
obj1.set_length(obj1.get_length() + obj2.get_length()) # => this is ugly
If you use #property and #length.setter => you don't need to change that old code.
2. A property can encapsulate multiple attributes
class Person:
def __init__(self, name, physic_health, mental_health):
self.name = name
self.__physic_health = physic_health
self.__mental_health = mental_health
#property
def condition(self):
health = self.__physic_health + self.__mental_health
if(health < 5.0):
return "I feel bad!"
elif health < 8.0:
return "I am ok!"
else:
return "Great!"
In this example, __physic_health and __mental_health are private and cannot be accessed directly from outside.
There is also one not obvious difference that i use to cache or refresh data , often we have a function connected to class attribute. For instance i need to read file once and keep content assigned to the attribute so the value is cached:
class Misc():
def __init__(self):
self.test = self.test_func()
def test_func(self):
print 'func running'
return 'func value'
cl = Misc()
print cl.test
print cl.test
Output:
func running
func value
func value
We accessed the attribute twice but our function was fired only once. Changing the above example to use property will cause attribute's value refresh each time you access it:
class Misc():
#property
def test(self):
print 'func running'
return 'func value'
cl = Misc()
print cl.test
print cl.test
Output:
func running
func value
func running
func value
I like to think that, if you want to set a restriction for an attribute, use a property.
Although all attributes are public, generally programmers differentiate public and private attributes with an underscore(_). Consider the following class,
class A:
def __init__(self):
self.b = 3 # To show public
self._c = 4 # To show private
Here, b attribute is intended to be accessed from outside class A. But, readers of this class might wonder, can b attribute be set from outside class A?
If we intend to not set b from outside, we can show this intention with #property.
class A:
def __init__(self):
self._c = 4 # To show private
#property
def b(self):
return 3
Now, b can't be set.
a = A()
print(a.b) # prints 3
a.b = 7 # Raises AttributeError
Or, if you wish to set only certain values,
class A:
#property
def b(self):
return self._b
#b.setter
def b(self, val):
if val < 0:
raise ValueError("b can't be negative")
self._b = val
a = A()
a.b = 6 # OK
a.b = -5 # Raises ValueError