Does Python have “private” variables in classes?

Python has limited support for private identifiers, through a feature that automatically prepends the class name to any identifiers starting with two underscores. This is transparent to the programmer, for the most part, but the net effect is that any variables named this way can be used as private variables.

See here for more on that.

In general, Python’s implementation of object orientation is a bit primitive compared to other languages. But I enjoy this, actually. It’s a very conceptually simple implementation and fits well with the dynamic style of the language.

“In java, we have been taught about public/private/protected variables”

“Why is that not required in python?”

For the same reason, it’s not required in Java.

You’re free to use — or not use private and protected.

As a Python and Java programmer, I’ve found that private and protected are very, very important design concepts. But as a practical matter, in tens of thousands of lines of Java and Python, I’ve never actually used private or protected.

Why not?

Here’s my question “protected from whom?”

Other programmers on my team? They have the source. What does protected mean when they can change it?

Other programmers on other teams? They work for the same company. They can — with a phone call — get the source.

Clients? It’s work-for-hire programming (generally). The clients (generally) own the code.

So, who — precisely — am I protecting it from?

Private and protected concepts are very important. But Python is just a tool for prototyping and rapid development with restricted resources available for development, and that is why some of the protection levels are not so strictly followed in Python. You can use "__" in a class member. It works properly, but it does not look good enough. Each access to such field contains these characters.

Also, you can notice that the Python OOP concept is not perfect. Smalltalk or Ruby are much closer to a pure OOP concept. Even C# or Java are closer.

Python is a very good tool. But it is a simplified OOP language. Syntactically and conceptually simplified. The main goal of Python’s existence is to bring to developers the possibility to write easy readable code with a high abstraction level in a very fast manner.

The only time I ever use private variables is when I need to do other things when writing to or reading from the variable and as such I need to force the use of a setter and/or getter.

Again this goes to culture, as already stated. I’ve been working on projects where reading and writing other classes variables was free-for-all. When one implementation became deprecated it took a lot longer to identify all code paths that used that function. When use of setters and getters was forced, a debug statement could easily be written to identify that the deprecated method had been called and the code path that calls it.

When you are on a project where anyone can write an extension, notifying users about deprecated methods that are to disappear in a few releases hence is vital to keep module breakage at a minimum upon upgrades.

So my answer is; if you and your colleagues maintain a simple code set then protecting class variables is not always necessary. If you are writing an extensible system then it becomes imperative when changes to the core is made that needs to be caught by all extensions using the code.

There is a variation of private variables in the underscore convention.

In [5]: class Test(object):
   ...:     def __private_method(self):
   ...:         return "Boo"
   ...:     def public_method(self):
   ...:         return self.__private_method()
   ...:

In [6]: x = Test()

In [7]: x.public_method()
Out[7]: 'Boo'

In [8]: x.__private_method()
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-8-fa17ce05d8bc> in <module>()
----> 1 x.__private_method()

AttributeError: 'Test' object has no attribute '__private_method'

There are some subtle differences, but for the sake of programming pattern ideological purity, it’s good enough.

There are examples out there of @private decorators that more closely implement the concept, but your mileage may vary. Arguably, one could also write a class definition that uses meta.

As correctly mentioned by many of the comments above, let’s not forget the main goal of Access Modifiers: To help users of code understand what is supposed to change and what is supposed not to. When you see a private field you don’t mess around with it. So it’s mostly syntactic sugar which is easily achieved in Python by the _ and __.

Private variables in Python is more or less a hack: the interpreter intentionally renames the variable.

class A:
    def __init__(self):
        self.__var = 123
    def printVar(self):
        print self.__var

Now, if you try to access __var outside the class definition, it will fail:

>>> x = A()
>>> x.__var # this will return error: "A has no attribute __var"

>>> x.printVar() # this gives back 123

But you can easily get away with this:

>>> x.__dict__ # this will show everything that is contained in object x
               # which in this case is something like {'_A__var' : 123}

>>> x._A__var = 456 # you now know the masked name of private variables
>>> x.printVar() # this gives back 456

You probably know that methods in OOP are invoked like this: x.printVar() => A.printVar(x). If A.printVar() can access some field in x, this field can also be accessed outside A.printVar()… After all, functions are created for reusability, and there isn’t any special power given to the statements inside.

As mentioned earlier, you can indicate that a variable or method is private by prefixing it with an underscore. If you don’t feel like this is enough, you can always use the property decorator. Here’s an example:

class Foo:

    def __init__(self, bar):
        self._bar = bar

    @property
    def bar(self):
        """Getter for '_bar'."""
        return self._bar

This way, someone or something that references bar is actually referencing the return value of the bar function rather than the variable itself, and therefore it can be accessed but not changed. However, if someone really wanted to, they could simply use _bar and assign a new value to it. There is no surefire way to prevent someone from accessing variables and methods that you wish to hide, as has been said repeatedly. However, using property is the clearest message you can send that a variable is not to be edited. property can also be used for more complex getter/setter/deleter access paths, as explained here: https://docs.python.org/3/library/functions.html#property

In Python 3, if you just want to "encapsulate" the class attributes, like in Java, you can just do the same thing like this:

class Simple:
    def __init__(self, str):
        print("inside the simple constructor")
        self.__s = str

    def show(self):
        print(self.__s)

    def showMsg(self, msg):
        print(msg + ':', self.show())

To instantiate this do:

ss = Simple("lol")
ss.show()

Note that: print(ss.__s) will throw an error.

In practice, Python 3 will obfuscate the global attribute name. It is turning this like a "private" attribute, like in Java. The attribute’s name is still global, but in an inaccessible way, like a private attribute in other languages.

But don’t be afraid of it. It doesn’t matter. It does the job too. 😉

Python does not have any private variables like C++ or Java does. You could access any member variable at any time if wanted, too. However, you don’t need private variables in Python, because in Python it is not bad to expose your classes’ member variables. If you have the need to encapsulate a member variable, you can do this by using "@property" later on without breaking existing client code.

In Python, the single underscore "_" is used to indicate that a method or variable is not considered as part of the public API of a class and that this part of the API could change between different versions. You can use these methods and variables, but your code could break, if you use a newer version of this class.

The double underscore "__" does not mean a "private variable". You use it to define variables which are "class local" and which can not be easily overridden by subclasses. It mangles the variables name.

For example:

class A(object):
    def __init__(self):
        self.__foobar = None # Will be automatically mangled to self._A__foobar

class B(A):
    def __init__(self):
        self.__foobar = 1 # Will be automatically mangled to self._B__foobar

self.__foobar’s name is automatically mangled to self._A__foobar in class A. In class B it is mangled to self._B__foobar. So every subclass can define its own variable __foobar without overriding its parents variable(s). But nothing prevents you from accessing variables beginning with double underscores. However, name mangling prevents you from calling this variables /methods incidentally.

I strongly recommend you watch Raymond Hettinger’s Python’s class development toolkit from PyCon 2013, which gives a good example why and how you should use @property and "__"-instance variables.

If you have exposed public variables and you have the need to encapsulate them, then you can use @property. Therefore you can start with the simplest solution possible. You can leave member variables public unless you have a concrete reason to not do so. Here is an example:

class Distance:
    def __init__(self, meter):
        self.meter = meter


d = Distance(1.0)
print(d.meter)
# prints 1.0

class Distance:
    def __init__(self, meter):
        # Customer request: Distances must be stored in millimeters.
        # Public available internals must be changed.
        # This would break client code in C++.
        # This is why you never expose public variables in C++ or Java.
        # However, this is Python.
        self.millimeter = meter * 1000

    # In Python we have @property to the rescue.
    @property
    def meter(self):
        return self.millimeter *0.001

    @meter.setter
    def meter(self, value):
        self.millimeter = value * 1000

d = Distance(1.0)
print(d.meter)
# prints 1.0

About sources (to change the access rights and thus bypass language encapsulation like Java or C++):

You don’t always have the sources and even if you do, the sources are managed by a system that only allows certain programmers to access a source (in a professional context). Often, every programmer is responsible for certain classes and therefore knows what he can and cannot do. The source manager also locks the sources being modified and of course, manages the access rights of programmers.

So I trust more in software than in human, by experience. So convention is good, but multiple protections are better, like access management (real private variable) + sources management.

So I’m new to Python but I have a background in C# and JavaScript. Python feels like a mix of the two in terms of features. JavaScript also struggles in this area and the way around it here, is to create a closure. This prevents access to data you don’t want to expose by returning a different object.

def print_msg(msg):
    # This is the outer enclosing function

    def printer():
        # This is the nested function
        print(msg)

    return printer  # returns the nested function


# Now let's try calling this function.
# Output: Hello
another = print_msg("Hello")
another()

https://www.programiz.com/python-programming/closure

https://developer.mozilla.org/en-US/docs/Web/JavaScript/Closures#emulating_private_methods_with_closures

Here’s how I handle Python 3 class fields:

class MyClass:
    def __init__(self, public_read_variable, private_variable):
        self.public_read_variable_ = public_read_variable
        self.__private_variable = private_variable

I access the __private_variable with two underscores only inside MyClass methods.

I do read access of the public_read_variable_ with one underscore
outside the class, but never modify the variable:

my_class = MyClass("public", "private")
print(my_class.public_read_variable_) # OK
my_class.public_read_variable_ = 'another value' # NOT OK, don't do that.

I have been thinking about private class attributes and methods (named members in further reading) since I have started to develop a package that I want to publish. The thought behind it were never to make it impossible to overwrite these members, but to have a warning for those who touch them. I came up with a few solutions that might help. The first solution is used in one of my favorite Python books, Fluent Python.

Upsides of technique 1:

• It is unlikely to be overwritten by accident.
• It is easily understood and implemented.
• Its easier to handle than leading double underscore for instance attributes.

*In the book the hash-symbol was used, but you could use integer converted to strings as well. In Python it is forbidden to use klass.1

class Technique1:

    def __init__(self, name, value):
        setattr(self, f'private#{name}', value)
        setattr(self, f'1{name}', value)

Downsides of technique 1:

• Methods are not easily protected with this technique though. It is possible.
• Attribute lookups are just possible via getattr
• Still no warning to the user

Another solution I came across was to write __setattr__. Pros:

• It is easily implemented and understood
• It works with methods
• Lookup is not affected
• The user gets a warning or error

class Demonstration:

    def __init__(self):
        self.a = 1

    def method(self):
        return None

    def __setattr__(self, name, value):
        if not getattr(self, name, None):
            super().__setattr__(name, value)
        else:
            raise ValueError(f'Already reserved name: {name}')

d = Demonstration()
#d.a = 2
d.method = None

Cons:

• You can still overwrite the class
• To have variables not just constants, you need to map allowed input.
• Subclasses can still overwrite methods

To prevent subclasses from overwriting methods you can use __init_subclass__:

class Demonstration:
    __protected = ['method']

    def method(self):
        return None

    def __init_subclass__(cls):
        protected_methods = Demonstration.__protected
        subclass_methods = dir(cls)
        for i in protected_methods:
            p = getattr(Demonstration,i)
            j = getattr(cls, i)
            if not p is j:
                raise ValueError(f'Protected method "{i}" was touched')

You see, there are ways to protect your class members, but it isn’t any guarantee that users don’t overwrite them anyway. This should just give you some ideas. In the end, you could also use a meta class, but this might open up new dangers to encounter. The techniques used here are also very simple minded and you should definitely take a look at the documentation, you can find useful feature to this technique and customize them to your need.