In other languages, a general guideline that helps produce better code is always make everything as hidden as possible. If in doubt about whether a variable should be private or protected, it's better to go with private.
Does the same hold true for Python? Should I use two leading underscores on everything at first, and only make them less hidden (only one underscore) as I need them?
If the convention is to use only one underscore, I'd also like to know the rationale.
Here's a comment I left on JBernardo's answer. It explains why I asked this question and also why I'd like to know why Python is different from the other languages:
I come from languages that train you to think everything should be only as public as needed and no more. The reasoning is that this will reduce dependencies and make the code safer to alter. The Python way of doing things in reverse -- starting from public and going towards hidden -- is odd to me.
When in doubt, leave it "public" - I mean, do not add anything to obscure the name of your attribute. If you have a class with some internal value, do not bother about it. Instead of writing:
class Stack(object):
def __init__(self):
self.__storage = [] # Too uptight
def push(self, value):
self.__storage.append(value)
write this by default:
class Stack(object):
def __init__(self):
self.storage = [] # No mangling
def push(self, value):
self.storage.append(value)
This is for sure a controversial way of doing things. Python newbies hate it, and even some old Python guys despise this default - but it is the default anyway, so I recommend you to follow it, even if you feel uncomfortable.
If you really want to send the message "Can't touch this!" to your users, the usual way is to precede the variable with one underscore. This is just a convention, but people understand it and take double care when dealing with such stuff:
class Stack(object):
def __init__(self):
self._storage = [] # This is ok, but Pythonistas use it to be relaxed about it
def push(self, value):
self._storage.append(value)
This can be useful, too, for avoiding conflict between property names and attribute names:
class Person(object):
def __init__(self, name, age):
self.name = name
self._age = age if age >= 0 else 0
#property
def age(self):
return self._age
#age.setter
def age(self, age):
if age >= 0:
self._age = age
else:
self._age = 0
What about the double underscore? Well, we use the double underscore magic mainly to avoid accidental overloading of methods and name conflicts with superclasses' attributes. It can be pretty valuable if you write a class to be extended many times.
If you want to use it for other purposes, you can, but it is neither usual nor recommended.
EDIT: Why is this so? Well, the usual Python style does not emphasize making things private - on the contrary! There are many reasons for that - most of them controversial... Let us see some of them.
Python has properties
Today, most OO languages use the opposite approach: what should not be used should not be visible, so attributes should be private. Theoretically, this would yield more manageable, less coupled classes because no one would change the objects' values recklessly.
However, it is not so simple. For example, Java classes have many getters that only get the values and setters that only set the values. You need, let us say, seven lines of code to declare a single attribute - which a Python programmer would say is needlessly complex. Also, you write a lot of code to get one public field since you can change its value using the getters and setters in practice.
So why follow this private-by-default policy? Just make your attributes public by default. Of course, this is problematic in Java because if you decide to add some validation to your attribute, it would require you to change all:
person.age = age;
in your code to, let us say,
person.setAge(age);
setAge() being:
public void setAge(int age) {
if (age >= 0) {
this.age = age;
} else {
this.age = 0;
}
}
So in Java (and other languages), the default is to use getters and setters anyway because they can be annoying to write but can spare you much time if you find yourself in the situation I've described.
However, you do not need to do it in Python since Python has properties. If you have this class:
class Person(object):
def __init__(self, name, age):
self.name = name
self.age = age
...and then you decide to validate ages, you do not need to change the person.age = age pieces of your code. Just add a property (as shown below)
class Person(object):
def __init__(self, name, age):
self.name = name
self._age = age if age >= 0 else 0
#property
def age(self):
return self._age
#age.setter
def age(self, age):
if age >= 0:
self._age = age
else:
self._age = 0
Suppose you can do it and still use person.age = age, why would you add private fields and getters and setters?
(Also, see Python is not Java and this article about the harms of using getters and setters.).
Everything is visible anyway - and trying to hide complicates your work
Even in languages with private attributes, you can access them through some reflection/introspection library. And people do it a lot, in frameworks and for solving urgent needs. The problem is that introspection libraries are just a complicated way of doing what you could do with public attributes.
Since Python is a very dynamic language, adding this burden to your classes is counterproductive.
The problem is not being possible to see - it is being required to see
For a Pythonista, encapsulation is not the inability to see the internals of classes but the possibility of avoiding looking at it. Encapsulation is the property of a component that the user can use without concerning about the internal details. If you can use a component without bothering yourself about its implementation, then it is encapsulated (in the opinion of a Python programmer).
Now, if you wrote a class you can use it without thinking about implementation details, there is no problem if you want to look inside the class for some reason. The point is: your API should be good, and the rest is details.
Guido said so
Well, this is not controversial: he said so, actually. (Look for "open kimono.")
This is culture
Yes, there are some reasons, but no critical reason. This is primarily a cultural aspect of programming in Python. Frankly, it could be the other way, too - but it is not. Also, you could just as easily ask the other way around: why do some languages use private attributes by default? For the same main reason as for the Python practice: because it is the culture of these languages, and each choice has advantages and disadvantages.
Since there already is this culture, you are well-advised to follow it. Otherwise, you will get annoyed by Python programmers telling you to remove the __ from your code when you ask a question in Stack Overflow :)
First - What is name mangling?
Name mangling is invoked when you are in a class definition and use __any_name or __any_name_, that is, two (or more) leading underscores and at most one trailing underscore.
class Demo:
__any_name = "__any_name"
__any_other_name_ = "__any_other_name_"
And now:
>>> [n for n in dir(Demo) if 'any' in n]
['_Demo__any_name', '_Demo__any_other_name_']
>>> Demo._Demo__any_name
'__any_name'
>>> Demo._Demo__any_other_name_
'__any_other_name_'
When in doubt, do what?
The ostensible use is to prevent subclassers from using an attribute that the class uses.
A potential value is in avoiding name collisions with subclassers who want to override behavior, so that the parent class functionality keeps working as expected. However, the example in the Python documentation is not Liskov substitutable, and no examples come to mind where I have found this useful.
The downsides are that it increases cognitive load for reading and understanding a code base, and especially so when debugging where you see the double underscore name in the source and a mangled name in the debugger.
My personal approach is to intentionally avoid it. I work on a very large code base. The rare uses of it stick out like a sore thumb and do not seem justified.
You do need to be aware of it so you know it when you see it.
PEP 8
PEP 8, the Python standard library style guide, currently says (abridged):
There is some controversy about the use of __names.
If your class is intended to be subclassed, and you have attributes that you do not want subclasses to use, consider naming them with double leading underscores and no trailing underscores.
Note that only the simple class name is used in the mangled name, so if a subclass chooses both the same class name and attribute name,
you can still get name collisions.
Name mangling can make certain uses, such as debugging and __getattr__() , less convenient. However the name mangling algorithm is well documented and easy to perform manually.
Not everyone likes name mangling. Try to balance the need to avoid accidental name clashes with potential use by advanced callers.
How does it work?
If you prepend two underscores (without ending double-underscores) in a class definition, the name will be mangled, and an underscore followed by the class name will be prepended on the object:
>>> class Foo(object):
... __foobar = None
... _foobaz = None
... __fooquux__ = None
...
>>> [name for name in dir(Foo) if 'foo' in name]
['_Foo__foobar', '__fooquux__', '_foobaz']
Note that names will only get mangled when the class definition is parsed:
>>> Foo.__test = None
>>> Foo.__test
>>> Foo._Foo__test
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: type object 'Foo' has no attribute '_Foo__test'
Also, those new to Python sometimes have trouble understanding what's going on when they can't manually access a name they see defined in a class definition. This is not a strong reason against it, but it's something to consider if you have a learning audience.
One Underscore?
If the convention is to use only one underscore, I'd also like to know the rationale.
When my intention is for users to keep their hands off an attribute, I tend to only use the one underscore, but that's because in my mental model, subclassers would have access to the name (which they always have, as they can easily spot the mangled name anyways).
If I were reviewing code that uses the __ prefix, I would ask why they're invoking name mangling, and if they couldn't do just as well with a single underscore, keeping in mind that if subclassers choose the same names for the class and class attribute there will be a name collision in spite of this.
I wouldn't say that practice produces better code. Visibility modifiers only distract you from the task at hand, and as a side effect force your interface to be used as you intended. Generally speaking, enforcing visibility prevents programmers from messing things up if they haven't read the documentation properly.
A far better solution is the route that Python encourages: Your classes and variables should be well documented, and their behaviour clear. The source should be available. This is far more extensible and reliable way to write code.
My strategy in Python is this:
Just write the damn thing, make no assumptions about how your data should be protected. This assumes that you write to create the ideal interfaces for your problems.
Use a leading underscore for stuff that probably won't be used externally, and isn't part of the normal "client code" interface.
Use double underscore only for things that are purely convenience inside the class, or will cause considerable damage if accidentally exposed.
Above all, it should be clear what everything does. Document it if someone else will be using it. Document it if you want it to be useful in a year's time.
As a side note, you should actually be going with protected in those other languages: You never know your class might be inherited later and for what it might be used. Best to only protect those variables that you are certain cannot or should not be used by foreign code.
You shouldn't start with private data and make it public as necessary. Rather, you should start by figuring out the interface of your object. I.e. you should start by figuring out what the world sees (the public stuff) and then figure out what private stuff is necessary for that to happen.
Other language make difficult to make private that which once was public. I.e. I'll break lots of code if I make my variable private or protected. But with properties in python this isn't the case. Rather, I can maintain the same interface even with rearranging the internal data.
The difference between _ and __ is that python actually makes an attempt to enforce the latter. Of course, it doesn't try really hard but it does make it difficult. Having _ merely tells other programmers what the intention is, they are free to ignore at their peril. But ignoring that rule is sometimes helpful. Examples include debugging, temporary hacks, and working with third party code that wasn't intended to be used the way you use it.
There are already a lot of good answers to this, but I'm going to offer another one. This is also partially a response to people who keep saying that double underscore isn't private (it really is).
If you look at Java/C#, both of them have private/protected/public. All of these are compile-time constructs. They are only enforced at the time of compilation. If you were to use reflection in Java/C#, you could easily access private method.
Now every time you call a function in Python, you are inherently using reflection. These pieces of code are the same in Python.
lst = []
lst.append(1)
getattr(lst, 'append')(1)
The "dot" syntax is only syntactic sugar for the latter piece of code. Mostly because using getattr is already ugly with only one function call. It just gets worse from there.
So with that, there can't be a Java/C# version of private, as Python doesn't compile the code. Java and C# can't check if a function is private or public at runtime, as that information is gone (and it has no knowledge of where the function is being called from).
Now with that information, the name mangling of the double underscore makes the most sense for achieving "private-ness". Now when a function is called from the 'self' instance and it notices that it starts with '__', it just performs the name mangling right there. It's just more syntactic sugar. That syntactic sugar allows the equivalent of 'private' in a language that only uses reflection for data member access.
Disclaimer: I have never heard anybody from the Python development say anything like this. The real reason for the lack of "private" is cultural, but you'll also notice that most scripting/interpreted languages have no private. A strictly enforceable private is not practical at anything except for compile time.
First: Why do you want to hide your data? Why is that so important?
Most of the time you don't really want to do it but you do because others are doing.
If you really really really don't want people using something, add one underscore in front of it. That's it... Pythonistas know that things with one underscore is not guaranteed to work every time and may change without you knowing.
That's the way we live and we're okay with that.
Using two underscores will make your class so bad to subclass that even you will not want to work that way.
The chosen answer does a good job of explaining how properties remove the need for private attributes, but I would also add that functions at the module level remove the need for private methods.
If you turn a method into a function at the module level, you remove the opportunity for subclasses to override it. Moving some functionality to the module level is more Pythonic than trying to hide methods with name mangling.
Following code snippet will explain all different cases :
two leading underscores (__a)
single leading underscore (_a)
no underscore (a)
class Test:
def __init__(self):
self.__a = 'test1'
self._a = 'test2'
self.a = 'test3'
def change_value(self,value):
self.__a = value
return self.__a
printing all valid attributes of Test Object
testObj1 = Test()
valid_attributes = dir(testObj1)
print valid_attributes
['_Test__a', '__doc__', '__init__', '__module__', '_a', 'a',
'change_value']
Here, you can see that name of __a has been changed to _Test__a to prevent this variable to be overridden by any of the subclass. This concept is known as "Name Mangling" in python.
You can access this like this :
testObj2 = Test()
print testObj2._Test__a
test1
Similarly, in case of _a, the variable is just to notify the developer that it should be used as internal variable of that class, the python interpreter won't do anything even if you access it, but it is not a good practise.
testObj3 = Test()
print testObj3._a
test2
a variable can be accesses from anywhere it's like a public class variable.
testObj4 = Test()
print testObj4.a
test3
Hope the answer helped you :)
At first glance it should be the same as for other languages (under "other" I mean Java or C++), but it isn't.
In Java you made private all variables that shouldn't be accessible outside. In the same time in Python you can't achieve this since there is no "privateness" (as one of Python principles says - "We're all adults"). So double underscore means only "Guys, do not use this field directly". The same meaning has singe underscore, which in the same time doesn't cause any headache when you have to inherit from considered class (just an example of possible problem caused by double underscore).
So, I'd recommend you to use single underscore by default for "private" members.
"If in doubt about whether a variable should be private or protected, it's better to go with private." - yes, same holds in Python.
Some answers here say about 'conventions', but don't give the links to those conventions. The authoritative guide for Python, PEP 8 states explicitly:
If in doubt, choose non-public; it's easier to make it public later than to make a public attribute non-public.
The distinction between public and private, and name mangling in Python have been considered in other answers. From the same link,
We don't use the term "private" here, since no attribute is really private in Python (without a generally unnecessary amount of work).
#EXAMPLE PROGRAM FOR Python name mangling
class Demo:
__any_name = "__any_name"
__any_other_name_ = "__any_other_name_"
[n for n in dir(Demo) if 'any' in n] # GIVES OUTPUT AS ['_Demo__any_name',
# '_Demo__any_other_name_']
I'm writing a class named "MyObject".
one of the class methods is:
addTo: aCodeString assertType: aTypeCollection
when the method is called with aCodeString, I want to add (in runtime) a new method to "MyObject" class which aCodeString is it's source code and inject type checking code into the source code.
for example, if I call addTo: assertType: like that:
a := MyObject new.
a addTo: 'foo: a boo:b baz: c
^(a*b+c)'
assertType: #(SmallInteger SmallInteger SmallInteger).
I expect that I could write later:
answer := (a foo: 2 boo: 5 baz: 10).
and get 20 in answer.
and if I write:
a foo: 'someString' boo: 5 baz: 10.
I get the proper message because 'someString' is not a SmallInteger.
I know how to write the type checking code, and I know that to add the method to the class in runtime I can use 'compile' method from Behavior class.
the problem is that I want to add the type checking code inside the source code.
I'm not really familiar with all of squeak classes so I'm not sure if I rather edit the aCodeString as a string inside addTo: assertType: and then use compile: (and I don't know how to do so), or that there is a way to inject code to an existing method in Behavior class or other squeak class.
so basically, what I'm asking is how can I inject string into an existing string or to inject code into an existing method.
There are many ways you could achieve such type checking...
The one you propose is to modify the source code (a String) so as to insert additional pre-condition type checks.
The key point with this approach is that you will have to insert the type checking at the right place. That means somehow parsing the original source (or at least the selector and arguments) so as to find its exact span (and the argument names).
See method initPattern:return: in Parser and its senders. You will find quite low level (not most beautiful) code that feed the block (passed thru return: keyword) with sap an Array of 3 objects: the method selector, the method arguments and the method precedence (a code telling if the method is connected to unary, binary or keyword message). From there, you'll get enough material for achieving source code manipulation (insert a string into another with copyReplace:from:to:with:).
Do not hesitate to write small snippets of code and execute in the Debugger (select code to debug, then use debug it menu or ALT+Shift+D). Also use the inspectors extensively to gain more insight on how things work!
Another solution is to parse the whole Abstract Syntax Tree (AST) of the source code, and manipulate that AST to insert the type checks. Normally, the Parser builds the AST, so observe how it works. From the modified AST, you can then generate new CompiledMethod (the bytecode instructions) and install it in methodDictionary - see the source code of compile: and follow the message sent until you discover generateMethodFromNode:trailer:. This is a bit more involved, and has a bad side effect that the source code is now not in phase with generated code, which might become a problem once you want to debug the method (fortunately, Squeak can used decompiled code in place of source code!).
Last, you can also arrange to have an alternate compiler and parser for some of your classes (see compilerClass and/or parserClass). The alternate TypeHintParser would accept modified syntax with the type hints in source code (once upon a time, it was implemented with type hints following the args inside angle brackets foo: x <Integer> bar: y <Number>). And the alternate TypeHintCompiler would arrange to compile preconditions automatically given those type hints. Since you will then be very advanced in Squeak, you will also create special mapping between source code index and bytecodes so as to have sane debugger and even special Decompiler class that could recognize the precondition type checks and transform them back to type hints just in case.
My advice would be to start with the first approach that you are proposing.
EDIT
I forgot to say, there is yet another way, but it is currently available in Pharo rather than Squeak: Pharo compiler (named OpalCompiler) does reify the bytecode instructions as objects (class names beginning with IR) in the generation phase. So it is also possible to directly manipulate the bytecode instructions by proper hacking at this stage... I'm pretty sure that we can find examples of usage. Probably the most advanced technic.
MyClass is derived from "list": MyClass(list)
I would like to document MyClass nicely.
Unfortunately, when trying help(MyClass),
I get my own documentation, but I also get a lot of stuff about "list".
Would there be a simple way to control that?
I read something about metaclasses, but I was unable to do something.
Thanks for your suggestions,
Michel
Well, that is what help does. It introspects into your class and show the name and the associated __doc__ for each callable attribute in the class, and that is not customizable.
Attributes of the superclass are considered attributes of the class, and are reached in the introspection Python's help do.
Metaclasses could even be used to customize the output one gets when he does "dir" on your class - but they do not change the output of the help text. To change "dir" output, create a metaclass implementing a __dir__ method, and return a list of what you want visible as dir's output.
class M(type):
def __dir__(self):
return [] # blank dir contents
class MyList(list, metaclass=M):
...
On the other hand, the help contents displayed for list attributes are not that verbose, and can actually be helpful - if you override any methods to do something different than described, the incorrect text won't show anyway. So you might just live with it.
Another tip is that instead of subclassing list you might prefer to subclass collections.abc.MutableSequence instead, and use an inner agregated (normal) list to keep your data: that will require you to implement a lot less methods to have your class working properly as a sequence and is preferable in most cases to subclass list. That won't change help's verbosity though.
I'm using PHPStorm EAP version PS-138.940.
I have code as follows:
Config(__NAMESPACE__."\ObjectsToIdentifiers")->oldTables = array('Modules\Old\Model\DeviceStock','Modules\Old\Model\ProductPack','Modules\Old\Model\SpareStock','Modules\Old\Model\ConsumStock');
The functionality is irrelevant in this case. Important are the entries in the array. These are fully qualified class names - the leading / is omitted, but adding it doesn't solve my problem. I want to be able to click inside one of the string literals, press Ctrl+B and be redirected to the class definition.
Note that this works in ExtJS (javascript framework) where a string literal like
"MyApp.namespace.view.MyComponent"
will take me there.
Is there any way to manually configure this or do I have to submit a feature request. If so, how can I do that?
Update 1:
I created a feature request on JetBrains Youtrack: http://youtrack.jetbrains.com/issue/WI-24262
Came across this question looking for something in Storm that would let me quickly convert "\Some\ClassName" to Some\ClassName::class like an Intention or plugin, but so far have found nothing.
i'm surprised the ::class static property hasn't been mentioned. It produces the FQN of the Class, and resolves from import aliases.
The following should be a legit rewrite that the IDE can resolve:
// Same __NAMESPACE__, so no prefix needed
Config(ObjectsToIdentifiers::class)
->oldTables = [
Modules\Old\Model\DeviceStock::class,
Modules\Old\Model\ProductPack::class,
Modules\Old\Model\SpareStock::class,
Modules\Old\Model\ConsumStock::class
];
// or, if you import the classes:
use Modules\Old\Model\DeviceStock as OldDeviceStock;
// you can refer to
OldDeviceStock::class;
Unfortunately, right now if you jump-to-definition of the alias, Storm won't take you to the actual class, but where the alias is defined. Not terribly helpful, but gets you halfway since the actual class is right there.
(All in ActivePython 3.1.2)
I tried to change the class (rather than instance) attributes. The __dict__ of the metaclass seemed like the perfect solution. But when I tried to modify, I got:
TypeError: 'dict_proxy' object does
not support item assignment
Why, and what can I do about it?
EDIT
I'm adding attributes inside the class definition.
setattr doesn't work because the class is not yet built, and hence I can't refer to it yet (or at least I don't know how).
The traditional assignment doesn't work because I'm adding a large number of attributes, whose names are determined by a certain rule (so I can't just type them out).
In other words, suppose I want class A to have attributes A.a001 through A.a999; and all of them have to be defined before it's fully built (since otherwise SQLAlchemy won't instrument it properly).
Note also that I made a typo in the original title: it's __dict__ of a regular class, not a metaclass, that I wanted to modify.
The creation of a large number of attributes following some rule smells like something is seriously wrong. I'd go back and see if there isn't a better way of doing that.
Having said there here is "Evil Code" (but it'll work, I think)
class A:
locals()['alpha'] = 1
print A.alpha
This works because while the class is being defined there is a dictionary that tracks the local variables you are definining. These local variables eventually become the class attributes. Be careful with locals as it won't necessarily act "correctly." You aren't really supposed to be modifying locals, but it does seem to work when I tried it.
Instead of using the declarative syntax, build the table seperately and then use mapper on it. see http://www.sqlalchemy.org/docs/05/ormtutorial.html# I think there is just no good way to add computed attributes to class while defining it.
Alternatively, I don't know whether this will work but:
class A(object):
pass
A.all_my_attributes = values
class B(declarative_base, A):
pass
might possibly work.
I'm not too familiar with how 3 treats dict but you might be able to circumvent this problem by simply inheriting the dictionary class like so:
class A(dict):
def __init__(self,dict_of_args):
self['key'] = 'myvalue'
self.update(dict_of_args)
# whatever else you need to do goes here...
A() can be referenced like so:
d = {1:2,3:4}
obj = A(mydict)
print obj['test'],obj[3] # this will print myvalue and 4
Hope this helps.