I have a class Step, which I want to derive by many sub-classes. I want every class deriving from Step to be "registered" by a name I choose for it (not the class's name), so I can later call Step.getStepTypeByName().
Something like this, only working :):
class Step(object):
_STEPS_BY_NAME = {}
#staticmethod
def REGISTER(cls, name):
_STEPS_BY_NAME[name] = cls
class Derive1(Step):
REGISTER(Derive1, "CustomDerive1Name")
...
class Derive2(Step):
REGISTER(Derive2, "CustomDerive2Name")
...
Your solution do not work for three reasons.
The first one is that _STEPS_BY_NAME only exists as an attribute of the Step class, so Step.REGISTER cannot access _STEPS_BY_NAME without a reference to the Step class. IOW you have to make it a classmethod (cf below)
The second one is that you need to explicitely use Step.REGISTER(cls) - the name REGISTER does not exist outside the Step class.
The third reason is that within a class statement's body, the class object has not yet been created not bound to it's name, so you cannot not reference the class itself at this point.
IOW, you'd want this instead:
class Step(object):
_STEPS_BY_NAME = {}
# NB : by convention, "ALL_UPPER" names denote pseudo-constants
#classmethod
def register(cls, name):
# here `cls` is the current class
cls._STEPS_BY_NAME[name] = stepclass
class Derive1(Step):
...
Step.register(Derive1, "CustomDerive1Name")
class Derive2(Step):
...
Step.register(Derive2, "CustomDerive2Name")
Now with a minor modification to Step.register you could use it as a class decorator, making things much clearer:
class Step(object):
_STEPS_BY_NAME = {}
#classmethod
def register(cls, name):
def _register(stepclass):
cls._STEPS_BY_NAME[name] = stepclass
return stepclass
return _register
#Step.register("CustomDerive1Name")
class Derive1(Step):
...
#Step.register("CustomDerive2Name")
class Derive2(Step):
...
As a last note: unless you have a compelling reason to register your subclasses in the base class itself, it might be better to use module-level variables and functions (a Python module is actually a kind of singleton):
# steps.py
class Step(object):
#....
_STEPS_BY_NAME = {}
def register(name):
def _register(cls):
_STEPS_BY_NAME[name] = cls
return cls
return _register
def get_step_class(name):
return _STEPS_BY_NAME[name]
And in your other modules
import steps
#steps.register("CustomDerive1Name")
class Derive1(steps.Step):
# ...
The point here is to avoid giving too many responsabilies to your Step class. I don't know your concrete use case so I can't tell which design best fits your need, but I've been using this last one on quite a few projects and it always worked fine so far.
You are close. Use this
class Step(object):
pass
class Derive1(Step):
pass
class Derive2(Step):
pass
_STEPS_BY_NAME = {
'foo': Step,
'bar': Derive1,
'bar': Derive2
}
def get_step_by_name(name):
return _STEPS_BY_NAME[name]
Warning: there might be better approaches depending on what you are trying to achieve. Such a mapping from strings to methods is a maintenance nightmare. If you want to change the name of a method, you would have to remember to change it in multiple place. You won't get any autocomplete help from your IDE either.
I am reading up on how we ensure data encapsulation in python.One of the blog says
"Data Encapsulation means, that we should only be able to access private attributes via getters and setters"
Consider the following snippets from the blog:
class Robot:
def __init__(self, name=None, build_year=None):
self.name = name
self.build_year = build_year
Now, if i create the object of the class as below:
obj1=Robot()
obj1.name('Robo1")
obj1.build_year("1978")
Currently, i can access the attributes directly as i have defined them public(without the __notation)
Now to ensure data encapsulation, i need to define the attributes as privates
using the __ notation and access private attributes via getters and setters.
So the new class definition is as follows:
class Robot:
def __init__(self, name=None, build_year=2000):
self.__name = name
self.__build_year = build_year
def set_name(self, name):
self.__name = name
def get_name(self):
return self.__name
def set_build_year(self, by):
self.__build_year = by
def get_build_year(self):
return self.__build_year
Now i instantiate the class as below:
x = Robot("Marvin", 1979)
x.set_build_year(1993)
This way, i achive data encapsulation as private data members are no longer accessed directly and they can only be accessed via the class methods.
Q1:Why are we doing this? Who are we protecting the code from? Who is outside world?Anyone who has the source code can tweak it as per their requirement, so why at all do we add extra methods(get/set) to modify/tweak the attributes?
Q2:Is the above example considered data encapsulation?
Data encapsulation is slightly more general than access protection. name and build_year are encapsulated by the class Robot regardless of how you define the attributes. Python takes the position that getters and setters that do nothing more than access or assign to the underlying attribute are unnecessary.
Even using the double-underscore prefix is just advisory, and is more concerned with preventing name collisions in subclasses. If you really wanted to get to the __build_year attribute directly, you still could with
# Prefix attribute name with _Robot
x._Robot__build_year = 1993
A better design in Python is to use a property, which causes Python to invoke a defined getter and/or setter whenever an attribute is defined directly. For example:
class Robot(object):
def __init__(self, name, by):
self.name = name
self.build_year = by
#property
def name(self):
return self._name
#name.setter
def name(self, newname):
self._name = newname
#property
def build_year(self):
return self._build_year
#build_year.setter
def build_year(self, newby):
self._build_year = newby
You wouldn't actually define these property functions so simply, but a big benefit is that you can start by allowing direct access to a name attribute, and if you decide later that there should be more logic involved in getting/setting the value and you want to switch to properties, you can do so without affecting existing code. Code like
x = Robot("bob", 1993)
x.build_year = 1993
will work the same whether or not x.build_year = 1993 assigns to build_year directly or if it really triggers a call to the property setter.
About source code: sometimes you supply others with compiled python files that does not present the source, and you don't want people to get in mess with direct attribute assignments.
Now, consider data encapsulation as safe guards, last point before assigning or supplying values:
You may want to validate or process assignments using the sets, to make sure the assignment is valid for your needs or enters to the variable in the right format, (e.g. you want to check that attribute __build_year is higher than 1800, or that the name is a string). Very important in dynamic languages like python where a variable is not declared with a specific type.
Same goes for gets. You might want to return the year as a decimal, but use it as an integer in the class.
Yes, your example is a basic data encapsulation.
So I have some god forsaken legacy code that uses the reserved word property, um wrong. In a base class that gets inherited they have basically implemented.
class TestClass(object):
def __init__(self, property):
self._property = property
#property
def property(self):
return self._property
test = TestClass('test property')
print(test.property)
Which runs without error. If you add another method below that you get,
class TestClass2(object):
def __init__(self, property):
self._property = property
#property
def property(self):
return self._property
#property
def other_property(self):
return 'test other property'
test = TestClass2('test property')
print(test.property)
print(test.other_property)
Which throws:
---> 10 #property
11 def other_property(self):
12 print('test other property')
TypeError: 'property' object is not callable
Because you know you have overwritten property in the local namespace.
class TestClass3(object):
def __init__(self, property):
self._property = property
#property
def other_property(self):
return 'test other property'
#property
def property(self):
return self._property
test = TestClass3('test property')
print(test.property)
print(test.other_property)
You can work around this if you always define your property overwrite at the bottom of your class. If the property method is only defined on the base class you inherit from things also work out, because namespaces.
class TestClass4(TestClass):
def __init__(self, property):
super(TestClass4, self).__init__(property)
#property
def other_property(self):
return 'test other property'
test = TestClass4('test property')
print(test.property)
print(test.other_property)
My righteous indignation says that we MUST update this variable name in the huge amount of legacy code, because GAAAAH, but other than having to remember to add new methods above the definition of property definition in the rarely modified base class, this doesn't actually break anything right?
dont shadow builtins... with almost no refactoring at all you can avoid shadowing the builtin entirely
use __getattr__ instead of #property to return your _property member ...
class TestClass(object):
def __init__(self):
self._property = 12
def __getattr__(self,item):
if item == "property":
#do your original getter code for `property` here ...
# now you have not overwritten the property keyword at all
return getattr(self,"_property") # just return the variable
class TestClass2(TestClass):
def __init__(self):
self._property = 67
print TestClass2().property
class MySubClass(TestClass):
#property
def a_property(self):
return 5
print MySubClass().property
print MySubClass().a_property
really, as an aside, theres not any good reason imho to use #property in python. all it does is end up confusing other programmers later and obscuring the fact that you are actually calling a function. I used to do it regularly ... I now avoid it unless I have a very very compelling reason not to
Yes, if you always add new methods above the definition of the property method nothing will break. So put a nice big comment to that effect in the code. Hopefully, anyone wanting to override property in a derived class will look at the base class first and see your comment...
BTW, the property arg to the __init__ method also shadows property, but I guess that's not an issue.
Ideally, someone should clean this mess up, but I understand that it may not be cost-effective to do that.
Also, I'm somewhat baffled why the original coder made property an #property in the first place. That construct should only be used when the value of the attribute has to be calculated dynamically, not for simply returning a static attribute. Perhaps they were new to Python and they were told that's the way to do getters in Python...
You could always remap property to another name. So long as you choose a name that doesn't match your other class attributes, and it won't be exposed in the external interface for the class, so it doesn't really matter what you name it.
tproperty = property
class Test(...)
#tproperty
def property(self):
....
This question already has answers here:
What's the pythonic way to use getters and setters?
(8 answers)
Closed 4 months ago.
What advantages does the #property notation hold over the classic getter+setter? In which specific cases/situations should a programmer choose to use one over the other?
With properties:
class MyClass(object):
#property
def my_attr(self):
return self._my_attr
#my_attr.setter
def my_attr(self, value):
self._my_attr = value
Without properties:
class MyClass(object):
def get_my_attr(self):
return self._my_attr
def set_my_attr(self, value):
self._my_attr = value
Prefer properties. It's what they're there for.
The reason is that all attributes are public in Python. Starting names with an underscore or two is just a warning that the given attribute is an implementation detail that may not stay the same in future versions of the code. It doesn't prevent you from actually getting or setting that attribute. Therefore, standard attribute access is the normal, Pythonic way of, well, accessing attributes.
The advantage of properties is that they are syntactically identical to attribute access, so you can change from one to another without any changes to client code. You could even have one version of a class that uses properties (say, for code-by-contract or debugging) and one that doesn't for production, without changing the code that uses it. At the same time, you don't have to write getters and setters for everything just in case you might need to better control access later.
In Python you don't use getters or setters or properties just for the fun of it. You first just use attributes and then later, only if needed, eventually migrate to a property without having to change the code using your classes.
There is indeed a lot of code with extension .py that uses getters and setters and inheritance and pointless classes everywhere where e.g. a simple tuple would do, but it's code from people writing in C++ or Java using Python.
That's not Python code.
Using properties lets you begin with normal attribute accesses and then back them up with getters and setters afterwards as necessary.
The short answer is: properties wins hands down. Always.
There is sometimes a need for getters and setters, but even then, I would "hide" them to the outside world. There are plenty of ways to do this in Python (getattr, setattr, __getattribute__, etc..., but a very concise and clean one is:
def set_email(self, value):
if '#' not in value:
raise Exception("This doesn't look like an email address.")
self._email = value
def get_email(self):
return self._email
email = property(get_email, set_email)
Here's a brief article that introduces the topic of getters and setters in Python.
[TL;DR? You can skip to the end for a code example.]
I actually prefer to use a different idiom, which is a little involved for using as a one off, but is nice if you have a more complex use case.
A bit of background first.
Properties are useful in that they allow us to handle both setting and getting values in a programmatic way but still allow attributes to be accessed as attributes. We can turn 'gets' into 'computations' (essentially) and we can turn 'sets' into 'events'. So let's say we have the following class, which I've coded with Java-like getters and setters.
class Example(object):
def __init__(self, x=None, y=None):
self.x = x
self.y = y
def getX(self):
return self.x or self.defaultX()
def getY(self):
return self.y or self.defaultY()
def setX(self, x):
self.x = x
def setY(self, y):
self.y = y
def defaultX(self):
return someDefaultComputationForX()
def defaultY(self):
return someDefaultComputationForY()
You may be wondering why I didn't call defaultX and defaultY in the object's __init__ method. The reason is that for our case I want to assume that the someDefaultComputation methods return values that vary over time, say a timestamp, and whenever x (or y) is not set (where, for the purpose of this example, "not set" means "set to None") I want the value of x's (or y's) default computation.
So this is lame for a number of reasons describe above. I'll rewrite it using properties:
class Example(object):
def __init__(self, x=None, y=None):
self._x = x
self._y = y
#property
def x(self):
return self.x or self.defaultX()
#x.setter
def x(self, value):
self._x = value
#property
def y(self):
return self.y or self.defaultY()
#y.setter
def y(self, value):
self._y = value
# default{XY} as before.
What have we gained? We've gained the ability to refer to these attributes as attributes even though, behind the scenes, we end up running methods.
Of course the real power of properties is that we generally want these methods to do something in addition to just getting and setting values (otherwise there is no point in using properties). I did this in my getter example. We are basically running a function body to pick up a default whenever the value isn't set. This is a very common pattern.
But what are we losing, and what can't we do?
The main annoyance, in my view, is that if you define a getter (as we do here) you also have to define a setter.[1] That's extra noise that clutters the code.
Another annoyance is that we still have to initialize the x and y values in __init__. (Well, of course we could add them using setattr() but that is more extra code.)
Third, unlike in the Java-like example, getters cannot accept other parameters. Now I can hear you saying already, well, if it's taking parameters it's not a getter! In an official sense, that is true. But in a practical sense there is no reason we shouldn't be able to parameterize an named attribute -- like x -- and set its value for some specific parameters.
It'd be nice if we could do something like:
e.x[a,b,c] = 10
e.x[d,e,f] = 20
for example. The closest we can get is to override the assignment to imply some special semantics:
e.x = [a,b,c,10]
e.x = [d,e,f,30]
and of course ensure that our setter knows how to extract the first three values as a key to a dictionary and set its value to a number or something.
But even if we did that we still couldn't support it with properties because there is no way to get the value because we can't pass parameters at all to the getter. So we've had to return everything, introducing an asymmetry.
The Java-style getter/setter does let us handle this, but we're back to needing getter/setters.
In my mind what we really want is something that capture the following requirements:
Users define just one method for a given attribute and can indicate there
whether the attribute is read-only or read-write. Properties fail this test
if the attribute writable.
There is no need for the user to define an extra variable underlying the function, so we don't need the __init__ or setattr in the code. The variable just exists by the fact we've created this new-style attribute.
Any default code for the attribute executes in the method body itself.
We can set the attribute as an attribute and reference it as an attribute.
We can parameterize the attribute.
In terms of code, we want a way to write:
def x(self, *args):
return defaultX()
and be able to then do:
print e.x -> The default at time T0
e.x = 1
print e.x -> 1
e.x = None
print e.x -> The default at time T1
and so forth.
We also want a way to do this for the special case of a parameterizable attribute, but still allow the default assign case to work. You'll see how I tackled this below.
Now to the point (yay! the point!). The solution I came up for for this is as follows.
We create a new object to replace the notion of a property. The object is intended to store the value of a variable set to it, but also maintains a handle on code that knows how to calculate a default. Its job is to store the set value or to run the method if that value is not set.
Let's call it an UberProperty.
class UberProperty(object):
def __init__(self, method):
self.method = method
self.value = None
self.isSet = False
def setValue(self, value):
self.value = value
self.isSet = True
def clearValue(self):
self.value = None
self.isSet = False
I assume method here is a class method, value is the value of the UberProperty, and I have added isSet because None may be a real value and this allows us a clean way to declare there really is "no value". Another way is a sentinel of some sort.
This basically gives us an object that can do what we want, but how do we actually put it on our class? Well, properties use decorators; why can't we? Let's see how it might look (from here on I'm going to stick to using just a single 'attribute', x).
class Example(object):
#uberProperty
def x(self):
return defaultX()
This doesn't actually work yet, of course. We have to implement uberProperty and
make sure it handles both gets and sets.
Let's start with gets.
My first attempt was to simply create a new UberProperty object and return it:
def uberProperty(f):
return UberProperty(f)
I quickly discovered, of course, that this doens't work: Python never binds the callable to the object and I need the object in order to call the function. Even creating the decorator in the class doesn't work, as although now we have the class, we still don't have an object to work with.
So we're going to need to be able to do more here. We do know that a method need only be represented the one time, so let's go ahead and keep our decorator, but modify UberProperty to only store the method reference:
class UberProperty(object):
def __init__(self, method):
self.method = method
It is also not callable, so at the moment nothing is working.
How do we complete the picture? Well, what do we end up with when we create the example class using our new decorator:
class Example(object):
#uberProperty
def x(self):
return defaultX()
print Example.x <__main__.UberProperty object at 0x10e1fb8d0>
print Example().x <__main__.UberProperty object at 0x10e1fb8d0>
in both cases we get back the UberProperty which of course is not a callable, so this isn't of much use.
What we need is some way to dynamically bind the UberProperty instance created by the decorator after the class has been created to an object of the class before that object has been returned to that user for use. Um, yeah, that's an __init__ call, dude.
Let's write up what we want our find result to be first. We're binding an UberProperty to an instance, so an obvious thing to return would be a BoundUberProperty. This is where we'll actually maintain state for the x attribute.
class BoundUberProperty(object):
def __init__(self, obj, uberProperty):
self.obj = obj
self.uberProperty = uberProperty
self.isSet = False
def setValue(self, value):
self.value = value
self.isSet = True
def getValue(self):
return self.value if self.isSet else self.uberProperty.method(self.obj)
def clearValue(self):
del self.value
self.isSet = False
Now we the representation; how do get these on to an object? There are a few approaches, but the easiest one to explain just uses the __init__ method to do that mapping. By the time __init__ is called our decorators have run, so just need to look through the object's __dict__ and update any attributes where the value of the attribute is of type UberProperty.
Now, uber-properties are cool and we'll probably want to use them a lot, so it makes sense to just create a base class that does this for all subclasses. I think you know what the base class is going to be called.
class UberObject(object):
def __init__(self):
for k in dir(self):
v = getattr(self, k)
if isinstance(v, UberProperty):
v = BoundUberProperty(self, v)
setattr(self, k, v)
We add this, change our example to inherit from UberObject, and ...
e = Example()
print e.x -> <__main__.BoundUberProperty object at 0x104604c90>
After modifying x to be:
#uberProperty
def x(self):
return *datetime.datetime.now()*
We can run a simple test:
print e.x.getValue()
print e.x.getValue()
e.x.setValue(datetime.date(2013, 5, 31))
print e.x.getValue()
e.x.clearValue()
print e.x.getValue()
And we get the output we wanted:
2013-05-31 00:05:13.985813
2013-05-31 00:05:13.986290
2013-05-31
2013-05-31 00:05:13.986310
(Gee, I'm working late.)
Note that I have used getValue, setValue, and clearValue here. This is because I haven't yet linked in the means to have these automatically returned.
But I think this is a good place to stop for now, because I'm getting tired. You can also see that the core functionality we wanted is in place; the rest is window dressing. Important usability window dressing, but that can wait until I have a change to update the post.
I'll finish up the example in the next posting by addressing these things:
We need to make sure UberObject's __init__ is always called by subclasses.
So we either force it be called somewhere or we prevent it from being implemented.
We'll see how to do this with a metaclass.
We need to make sure we handle the common case where someone 'aliases'
a function to something else, such as:
class Example(object):
#uberProperty
def x(self):
...
y = x
We need e.x to return e.x.getValue() by default.
What we'll actually see is this is one area where the model fails.
It turns out we'll always need to use a function call to get the value.
But we can make it look like a regular function call and avoid having to use e.x.getValue(). (Doing this one is obvious, if you haven't already fixed it out.)
We need to support setting e.x directly, as in e.x = <newvalue>. We can do this in the parent class too, but we'll need to update our __init__ code to handle it.
Finally, we'll add parameterized attributes. It should be pretty obvious how we'll do this, too.
Here's the code as it exists up to now:
import datetime
class UberObject(object):
def uberSetter(self, value):
print 'setting'
def uberGetter(self):
return self
def __init__(self):
for k in dir(self):
v = getattr(self, k)
if isinstance(v, UberProperty):
v = BoundUberProperty(self, v)
setattr(self, k, v)
class UberProperty(object):
def __init__(self, method):
self.method = method
class BoundUberProperty(object):
def __init__(self, obj, uberProperty):
self.obj = obj
self.uberProperty = uberProperty
self.isSet = False
def setValue(self, value):
self.value = value
self.isSet = True
def getValue(self):
return self.value if self.isSet else self.uberProperty.method(self.obj)
def clearValue(self):
del self.value
self.isSet = False
def uberProperty(f):
return UberProperty(f)
class Example(UberObject):
#uberProperty
def x(self):
return datetime.datetime.now()
[1] I may be behind on whether this is still the case.
I think both have their place. One issue with using #property is that it is hard to extend the behaviour of getters or setters in subclasses using standard class mechanisms. The problem is that the actual getter/setter functions are hidden in the property.
You can actually get hold of the functions, e.g. with
class C(object):
_p = 1
#property
def p(self):
return self._p
#p.setter
def p(self, val):
self._p = val
you can access the getter and setter functions as C.p.fget and C.p.fset, but you can't easily use the normal method inheritance (e.g. super) facilities to extend them. After some digging into the intricacies of super, you can indeed use super in this way:
# Using super():
class D(C):
# Cannot use super(D,D) here to define the property
# since D is not yet defined in this scope.
#property
def p(self):
return super(D,D).p.fget(self)
#p.setter
def p(self, val):
print 'Implement extra functionality here for D'
super(D,D).p.fset(self, val)
# Using a direct reference to C
class E(C):
p = C.p
#p.setter
def p(self, val):
print 'Implement extra functionality here for E'
C.p.fset(self, val)
Using super() is, however, quite clunky, since the property has to be redefined, and you have to use the slightly counter-intuitive super(cls,cls) mechanism to get an unbound copy of p.
Using properties is to me more intuitive and fits better into most code.
Comparing
o.x = 5
ox = o.x
vs.
o.setX(5)
ox = o.getX()
is to me quite obvious which is easier to read. Also properties allows for private variables much easier.
I feel like properties are about letting you get the overhead of writing getters and setters only when you actually need them.
Java Programming culture strongly advise to never give access to properties, and instead, go through getters and setters, and only those which are actually needed.
It's a bit verbose to always write these obvious pieces of code, and notice that 70% of the time they are never replaced by some non-trivial logic.
In Python, people actually care for that kind of overhead, so that you can embrace the following practice :
Do not use getters and setters at first, when if they not needed
Use #property to implement them without changing the syntax of the rest of your code.
I would prefer to use neither in most cases. The problem with properties is that they make the class less transparent. Especially, this is an issue if you were to raise an exception from a setter. For example, if you have an Account.email property:
class Account(object):
#property
def email(self):
return self._email
#email.setter
def email(self, value):
if '#' not in value:
raise ValueError('Invalid email address.')
self._email = value
then the user of the class does not expect that assigning a value to the property could cause an exception:
a = Account()
a.email = 'badaddress'
--> ValueError: Invalid email address.
As a result, the exception may go unhandled, and either propagate too high in the call chain to be handled properly, or result in a very unhelpful traceback being presented to the program user (which is sadly too common in the world of python and java).
I would also avoid using getters and setters:
because defining them for all properties in advance is very time consuming,
makes the amount of code unnecessarily longer, which makes understanding and maintaining the code more difficult,
if you were define them for properties only as needed, the interface of the class would change, hurting all users of the class
Instead of properties and getters/setters I prefer doing the complex logic in well defined places such as in a validation method:
class Account(object):
...
def validate(self):
if '#' not in self.email:
raise ValueError('Invalid email address.')
or a similiar Account.save method.
Note that I am not trying to say that there are no cases when properties are useful, only that you may be better off if you can make your classes simple and transparent enough that you don't need them.
I am surprised that nobody has mentioned that properties are bound methods of a descriptor class, Adam Donohue and NeilenMarais get at exactly this idea in their posts -- that getters and setters are functions and can be used to:
validate
alter data
duck type (coerce type to another type)
This presents a smart way to hide implementation details and code cruft like regular expression, type casts, try .. except blocks, assertions or computed values.
In general doing CRUD on an object may often be fairly mundane but consider the example of data that will be persisted to a relational database. ORM's can hide implementation details of particular SQL vernaculars in the methods bound to fget, fset, fdel defined in a property class that will manage the awful if .. elif .. else ladders that are so ugly in OO code -- exposing the simple and elegant self.variable = something and obviate the details for the developer using the ORM.
If one thinks of properties only as some dreary vestige of a Bondage and Discipline language (i.e. Java) they are missing the point of descriptors.
In complex projects I prefer using read-only properties (or getters) with explicit setter function:
class MyClass(object):
...
#property
def my_attr(self):
...
def set_my_attr(self, value):
...
In long living projects debugging and refactoring takes more time than writing the code itself. There are several downsides for using #property.setter that makes debugging even harder:
1) python allows creating new attributes for an existing object. This makes a following misprint very hard to track:
my_object.my_atttr = 4.
If your object is a complicated algorithm then you will spend quite some time trying to find out why it doesn't converge (notice an extra 't' in the line above)
2) setter sometimes might evolve to a complicated and slow method (e.g. hitting a database). It would be quite hard for another developer to figure out why the following function is very slow. He might spend a lot of time on profiling do_something() method, while my_object.my_attr = 4. is actually the cause of slowdown:
def slow_function(my_object):
my_object.my_attr = 4.
my_object.do_something()
Both #property and traditional getters and setters have their advantages. It depends on your use case.
Advantages of #property
You don't have to change the interface while changing the implementation of data access. When your project is small, you probably want to use direct attribute access to access a class member. For example, let's say you have an object foo of type Foo, which has a member num. Then you can simply get this member with num = foo.num. As your project grows, you may feel like there needs to be some checks or debugs on the simple attribute access. Then you can do that with a #property within the class. The data access interface remains the same so that there is no need to modify client code.
Cited from PEP-8:
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.
Using #property for data access in Python is regarded as Pythonic:
It can strengthen your self-identification as a Python (not Java) programmer.
It can help your job interview if your interviewer thinks Java-style getters and setters are anti-patterns.
Advantages of traditional getters and setters
Traditional getters and setters allow for more complicated data access than simple attribute access. For example, when you are setting a class member, sometimes you need a flag indicating where you would like to force this operation even if something doesn't look perfect. While it is not obvious how to augment a direct member access like foo.num = num, You can easily augment your traditional setter with an additional force parameter:
def Foo:
def set_num(self, num, force=False):
...
Traditional getters and setters make it explicit that a class member access is through a method. This means:
What you get as the result may not be the same as what is exactly stored within that class.
Even if the access looks like a simple attribute access, the performance can vary greatly from that.
Unless your class users expect a #property hiding behind every attribute access statement, making such things explicit can help minimize your class users surprises.
As mentioned by #NeilenMarais and in this post, extending traditional getters and setters in subclasses is easier than extending properties.
Traditional getters and setters have been widely used for a long time in different languages. If you have people from different backgrounds in your team, they look more familiar than #property. Also, as your project grows, if you may need to migrate from Python to another language that doesn't have #property, using traditional getters and setters would make the migration smoother.
Caveats
Neither #property nor traditional getters and setters makes the class member private, even if you use double underscore before its name:
class Foo:
def __init__(self):
self.__num = 0
#property
def num(self):
return self.__num
#num.setter
def num(self, num):
self.__num = num
def get_num(self):
return self.__num
def set_num(self, num):
self.__num = num
foo = Foo()
print(foo.num) # output: 0
print(foo.get_num()) # output: 0
print(foo._Foo__num) # output: 0
Here is an excerpts from "Effective Python: 90 Specific Ways to Write Better Python" (Amazing book. I highly recommend it).
Things to Remember
✦ Define new class interfaces using simple public attributes and avoid
defining setter and getter methods.
✦ Use #property to define special behavior when attributes are
accessed on your objects, if necessary.
✦ Follow the rule of least surprise and avoid odd side effects in your
#property methods.
✦ Ensure that #property methods are fast; for slow or complex
work—especially involving I/O or causing side effects—use normal
methods instead.
One advanced but common use of #property is transitioning what was
once a simple numerical attribute into an on-the-fly calculation. This
is extremely helpful because it lets you migrate all existing usage of
a class to have new behaviors without requiring any of the call sites
to be rewritten (which is especially important if there’s calling code
that you don’t control). #property also provides an important stopgap
for improving interfaces over time.
I especially like #property because it lets you make incremental
progress toward a better data model over time.
#property is a tool to
help you address problems you’ll come across in real-world code. Don’t
overuse it. When you find yourself repeatedly extending #property
methods, it’s probably time to refactor your class instead of further
paving over your code’s poor design.
✦ Use #property to give existing instance attributes
new functionality.
✦ Make incremental progress toward better data
models by using #property.
✦ Consider refactoring a class and all call
sites when you find yourself using #property too heavily.
I have many different small classes which have a few fields each, e.g. this:
class Article:
def __init__(self, name, available):
self.name = name
self.available = available
What's the easiest and/or most idiomatic way to make the name field read only, so that
a = Article("Pineapple", True)
a.name = "Banana" # <-- should not be possible
is not possible anymore?
Here's what I considered so far:
Use a getter (ugh!).
class Article:
def __init__(self, name, available):
self._name = name
self.available = available
def name(self):
return self._name
Ugly, non-pythonic - and a lot of boilerplate code to write (especially if I have multiple fields to make read-only). However, it does the job and it's easy to see why that is.
Use __setattr__:
class Article:
def __init__(self, name, available):
self.name = name
self.available = available
def __setattr__(self, name, value):
if name == "name":
raise Exception("%s property is read-only" % name)
self.__dict__[name] = value
Looks pretty on the caller side, seems to be the idiomatic way to do the job - but unfortunately I have many classes with only a few fields to make read only each. So I'd need to add a __setattr__ implementation to all of them. Or use some sort of mixin maybe? In any case, I'd need to make up my mind how to behave in case a client attempts to assign a value to a read-only field. Yield some exception, I guess - but which?
Use a utility function to define properties (and optionally getters) automatically. This is basically the same idea as (1) except that I don't write the getters explicitely but rather do something like
class Article:
def __init__(self, name, available):
# This function would somehow give a '_name' field to self
# and a 'name()' getter to the 'Article' class object (if
# necessary); the getter simply returns self._name
defineField(self, "name")
self.available = available
The downside of this is that I don't even know if this is possible (or how to implement it) since I'm not familiar with runtime code generation in Python. :-)
So far, (2) appears to be most promising to me except for the fact that I'll need __setattr__ definitions to all my classes. I wish there was a way to 'annotate' fields so that this happens automatically. Does anybody have a better idea?
For what it's worth, I'mu sing Python 2.6.
UPDATE:
Thanks for all the interesting responses! By now, I have this:
def ro_property(o, name, value):
setattr(o.__class__, name, property(lambda o: o.__dict__["_" + name]))
setattr(o, "_" + name, value)
class Article(object):
def __init__(self, name, available):
ro_property(self, "name", name)
self.available = available
This seems to work quite nicely. The only changes needed to the original class are
I need to inherit object (which is not such a stupid thing anyway, I guess)
I need to change self._name = name to ro_property(self, "name", name).
This looks quite neat to me - can anybody see a downside with it?
I would use property as a decorator to manage your getter for name (see the example for the class Parrot in the documentation). Use, for example, something like:
class Article(object):
def __init__(self, name, available):
self._name = name
self.available = available
#property
def name(self):
return self._name
If you do not define the setter for the name property (using the decorator x.setter around a function) this throws an AttributeError when you try and reset name.
Note: You have to use Python's new-style classes (i.e. in Python 2.6 you have to inherit from object) for properties to work correctly. This is not the case according to #SvenMarnach.
As pointed out in other answers, using a property is the way to go for read-only attributes. The solution in Chris' answer is the cleanest one: It uses the property() built-in in a straight-forward, simple way. Everyone familiar with Python will recognize this pattern, and there's no domain-specific voodoo happening.
If you don't like that every property needs three lines to define, here's another straight-forward way:
from operator import attrgetter
class Article(object):
def __init__(self, name, available):
self._name = name
self.available = available
name = property(attrgetter("_name"))
Generally, I don't like defining domain-specific functions to do something that can be done easily enough with standard tools. Reading code is so much easier if you don't have to get used to all the project-specific stuff first.
Based in the Chris answer, but arguably more pythonic:
def ro_property(field):
return property(lambda self : self.__dict__[field])
class Article(object):
name = ro_property('_name')
def __init__(self):
self._name = "banana"
If trying to modify the property it will raise an AttributeError.
a = Article()
print a.name # -> 'banana'
a.name = 'apple' # -> AttributeError: can't set attribute
UPDATE: About your updated answer, the (little) problem I see is that you are modifying the definition of the property in the class every time you create an instance. And I don't think that is such a good idea. That's why I put the ro_property call outside of the __init__ function
What about?:
def ro_property(name):
def ro_property_decorator(c):
setattr(c, name, property(lambda o: o.__dict__["_" + name]))
return c
return ro_property_decorator
#ro_property('name')
#ro_property('other')
class Article(object):
def __init__(self, name):
self._name = name
self._other = "foo"
a = Article("banana")
print a.name # -> 'banana'
a.name = 'apple' # -> AttributeError: can't set attribute
Class decorators are fancy!
It should be noted that it's always possible to modify attributes of an object in Python - there are no truly private variables in Python. It's just that some approaches make it a bit harder. But a determined coder can always lookup and modify the value of an attribute. For example, I can always modify your __setattr__ if I want to...
For more information, see Section 9.6 of The Python Tutorial. Python uses name mangling when attributes are prefixed with __ so the actual name at runtime is different but you could still derive what that name at runtime is (and thus modify the attribute).
I would stick with your option 1 but refined it to use Python property:
class Article
def get_name(self):
return self.__name
name = property(get_name)