I'm coming from the Java world and reading Bruce Eckels' Python 3 Patterns, Recipes and Idioms.
While reading about classes, it goes on to say that in Python there is no need to declare instance variables. You just use them in the constructor, and boom, they are there.
So for example:
class Simple:
def __init__(self, s):
print("inside the simple constructor")
self.s = s
def show(self):
print(self.s)
def showMsg(self, msg):
print(msg + ':', self.show())
If that’s true, then any object of class Simple can just change the value of variable s outside of the class.
For example:
if __name__ == "__main__":
x = Simple("constructor argument")
x.s = "test15" # this changes the value
x.show()
x.showMsg("A message")
In Java, we have been taught about public/private/protected variables. Those keywords make sense because at times you want variables in a class to which no one outside the class has access to.
Why is that not required in Python?
It's cultural. In Python, you don't write to other classes' instance or class variables. In Java, nothing prevents you from doing the same if you really want to - after all, you can always edit the source of the class itself to achieve the same effect. Python drops that pretence of security and encourages programmers to be responsible. In practice, this works very nicely.
If you want to emulate private variables for some reason, you can always use the __ prefix from PEP 8. Python mangles the names of variables like __foo so that they're not easily visible to code outside the namespace that contains them (although you can get around it if you're determined enough, just like you can get around Java's protections if you work at it).
By the same convention, the _ prefix means _variable should be used internally in the class (or module) only, even if you're not technically prevented from accessing it from somewhere else. You don't play around with another class's variables that look like __foo or _bar.
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 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 __.
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
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 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
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.
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.
"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?
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. ;)
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.
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.
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
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.
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.
I have a library with one parent and a dozen of children:
# mylib1.py:
#
class Foo(object):
def __init__(self, a):
self.a = a
class FooChild(Foo):
def __init__(self, a, b):
super(FooChild, self).__init__(a)
self.b = b
# more children here...
Now I want to extend that library with a simple (but a bit spesific, for use in another approach) method. So I would like to change parent class and use it's children.
# mylib2.py:
#
import mylib1
def fooMethod(self):
print 'a={}, b={}'.format(self.a, self.b)
setattr(mylib1.Foo, 'fooMethod', fooMethod)
And now I can use it like this:
# way ONE:
import mylib2
fc = mylib2.mylib1.FooChild(3, 4)
fc.fooMethod()
or like this:
# way TWO:
# order doesn't matter here:
import mylib1
import mylib2
fc = mylib1.FooChild(3, 4)
fc.fooMethod()
So, my questions are:
Is this good thing?
How this should be done in a better way?
A common approach is to use mixin
If you want, you could add dynamically How do I dynamically add mixins as base classes without getting MRO errors?.
There is a general rule in programming, that you should avoid dependence on global state. Which in other words means that your globals should be if possible constant. Classes are (mostly) globals.
Your approach is called monkey patching. And if you don't have a really really good reason to explain it, you should avoid it. This is because monkey patching violates the above rule.
Imagine you have two separate modules and both of them use this approach. One of them sets Foo.fooMethod to some method. The other - to another. Then you somehow switch control between these modules. The result would be, that it would be hard to determine what fooMethod is used where. This means hard to debug problems.
There are people (e.g. Brandon Craig-Rhodes), who believe that patching is bad even in tests.
What I would suggest is to use some attribute that you would set when instantiating instances of your Foo() class (and its children), that would control the behaviour of your fooMethod. Then the behaviour of this method would depend on how you instantiated the object, not on global state.
I have the sense that this must be kind of a dumb question—nub here. So I'm open to an answer of the sort "This is ass-backwards, don't do it, please try this: [proper way]".
I'm using Python 2.7.5.
General Form of the Problem
This causes an infinite loop unless Thesaurus (an app-wide singleton) does not call Baseclass.__init__()
class Baseclass():
def __init__(self):
thes = Thesaurus()
#do stuff
class Thesaurus(Baseclass):
def __init__(self):
Baseclass.__init__(self)
#do stuff
My Specific Case
I have a base class that virtually every other class in my app extends (just some basic conventions for functionality within the app; perhaps should just be an interface). This base class is meant to house a singleton of a Thesaurus class that grants some flexibility with user input by inferring some synonyms (ie. {'yes':'yep', 'ok'}).
But since the subclass calls the superclass's __init__(), which in turn creates another subclass, loops ensue. Not calling the superclass's __init__() works just fine, but I'm concerned that's merely a lucky coincidence, and that my Thesaurus class may eventually be modified to require it's parent __init__().
Advice?
Well, I'm stopping to look at your code, and I'll just base my answer on what you say:
I have a base class that virtually every other class in my app extends (just some basic conventions for functionality within the app; perhaps should just be an interface).
this would be ThesaurusBase in the code below
This base class is meant to house a singleton of a Thesaurus class that grants some flexibility with user input by inferring some synonyms (ie. {'yes':'yep', 'ok'}).
That would be ThesaurusSingleton, that you can call with a better name and make it actually useful.
class ThesaurusBase():
def __init__(self, singleton=None):
self.singleton = singleton
def mymethod1(self):
raise NotImplementedError
def mymethod2(self):
raise NotImplementedError
class ThesaurusSingleton(ThesaurusBase):
def mymethod1(self):
return "meaw!"
class Thesaurus(TheraususBase):
def __init__(self, singleton=None):
TheraususBase.__init__(self, singleton)
def mymethod1(self):
return "quack!"
def mymethod2(self):
return "\\_o<"
now you can create your objects as follows:
singleton = ThesaurusSingleton()
thesaurus = Thesaurus(singleton)
edit:
Basically, what I've done here is build a "Base" class that is just an interface defining an expected behavior for all its children classes. The class ThesaurusSingleton (I know that's a terrible name) is also implementing that interface, because you said it had too and I did not want to discuss your design, you may always have good reasons for weird constraints.
And finally, do you really need to instantiate your singleton inside the class that is defining the singleton object? Though there may be some hackish way to do so, there's often a better design that avoids the "hackish" part.
What I think is that however you create your singleton, you should better do it explicitly. That's in the "Zen of python": explicit is better than implicit. Why? because then people reading your code (and that might be you in six months) will be able to understand what's happening and what you were thinking when you wrote that code. If you try to make things more implicit (like using sophisticated meta classes and weird self-inheritance) you may wonder what this code does in less than three weeks!
I'm not telling to avoid that kind of options, but to only use sophisticated stuff when you're out of simple ones!
Based on what you said I think the solution I gave can be a starting point. But as you focus on some obscure, yet not very useful hackish stuff instead of talking about your design, I can't be sure if my example is that appropriate, and hint you on the design.
edit2:
There's an another way to achieve what you say you want (but be sure that's really the design you want). You may want to use a class method that will act on the class itself (instead of the instances) and thus enable you to store a class-wide instance of itself:
>>> class ThesaurusBase:
... #classmethod
... def initClassWide(cls):
... cls._shared = cls()
...
>>> class T(ThesaurusBase):
... def foo(self):
... print self._shared
...
>>> ThesaurusBase.initClassWide()
>>> t = T()
>>> t.foo()
<__main__.ThesaurusBase instance at 0x7ff299a7def0>
and you can call the initClassWide method at the module level of where you declare ThesaurusBase, so whenever you import that module, it will have the singleton loaded (the import mechanism ensuring that python modules are run only once).
the short answer is:
do not instantiate an instance of a sub class from the super class constructor
longer answer:
if the motive you have to try to do this is the fact the Thesaurus is a singleton then you'll be better off exposing the singleton using a static method in the class (Thesaurus) and calling this method when you need the singleton
My Situation
I'm currently writing on a project in python which I want to use to learn a bit more about software architecture. I've read a few texts and watched a couple of talks about dependency injection and learned to love how clear constructor injection shows the dependencies of an object.
However, I'm kind of struggling how to get a dependency passed to an object. I decided NOT to use a DI framework since:
I don't have enough knowledge of DI to specify my requirements and thus cannot choose a framework.
I want to keep the code free of more "magical" stuff since I have the feeling that introducing a seldom used framework drastically decreases readability. (More code to read of which only a small part is used).
Thus, I'm using custom factory functions to create objects and explicitly pass their dependencies:
# Business and Data Objects
class Foo:
def __init__(self,bar):
self.bar = bar
def do_stuff(self):
print(self.bar)
class Bar:
def __init__(self,prefix):
self.prefix = prefix
def __str__(self):
return str(self.prefix)+"Hello"
# Wiring up dependencies
def create_bar():
return Bar("Bar says: ")
def create_foo():
return Foo(create_bar())
# Starting the application
f = create_foo()
f.do_stuff()
Alternatively, if Foo has to create a number of Bars itself, it gets the creator function passed through its constructor:
# Business and Data Objects
class Foo:
def __init__(self,create_bar):
self.create_bar = create_bar
def do_stuff(self,times):
for _ in range(times):
bar = self.create_bar()
print(bar)
class Bar:
def __init__(self,greeting):
self.greeting = greeting
def __str__(self):
return self.greeting
# Wiring up dependencies
def create_bar():
return Bar("Hello World")
def create_foo():
return Foo(create_bar)
# Starting the application
f = create_foo()
f.do_stuff(3)
While I'd love to hear improvement suggestions on the code, this is not really the point of this post. However, I feel that this introduction is required to understand
My Question
While the above looks rather clear, readable and understandable to me, I run into a problem when the prefix dependency of Bar is required to be identical in the context of each Foo object and thus is coupled to the Foo object lifetime. As an example consider a prefix which implements a counter (See code examples below for implementation details).
I have two Ideas how to realize this, however, none of them seems perfect to me:
1) Pass Prefix through Foo
The first idea is to add a constructor parameter to Foo and make it store the prefix in each Foo instance.
The obvious drawback is, that it mixes up the responsibilities of Foo. It controls the business logic AND provides one of the dependencies to Bar. Once Bar does not require the dependency any more, Foo has to be modified. Seems like a no-go for me. Since I don't really think this should be a solution, I did not post the code here, but provided it on pastebin for the very interested reader ;)
2) Use Functions with State
Instead of placing the Prefix object inside Foo this approach is trying to encapsulate it inside the create_foo function. By creating one Prefix for each Foo object and referencing it in a nameless function using lambda, I keep the details (a.k.a there-is-a-prefix-object) away from Foo and inside my wiring-logic. Of course a named function would work, too (but lambda is shorter).
# Business and Data Objects
class Foo:
def __init__(self,create_bar):
self.create_bar = create_bar
def do_stuff(self,times):
for _ in range(times):
bar = self.create_bar()
print(bar)
class Bar:
def __init__(self,prefix):
self.prefix = prefix
def __str__(self):
return str(self.prefix)+"Hello"
class Prefix:
def __init__(self,name):
self.name = name
self.count = 0
def __str__(self):
self.count +=1
return self.name+" "+str(self.count)+": "
# Wiring up dependencies
def create_bar(prefix):
return Bar(prefix)
def create_prefix(name):
return Prefix(name)
def create_foo(name):
prefix = create_prefix(name)
return Foo(lambda : create_bar(prefix))
# Starting the application
f1 = create_foo("foo1")
f2 = create_foo("foo2")
f1.do_stuff(3)
f2.do_stuff(2)
f1.do_stuff(2)
This approach seems much more useful to me. However, I'm not sure about common practices and thus fear that having state inside functions is not really recommended. Coming from a java/C++ background, I'd expect a function to be dependent on its parameters, its class members (if it's a method) or some global state. Thus, a parameterless function that does not use global state would have to return exactly the same value every time it is called. This is not the case here. Once the returned object is modified (which means that counter in prefix has been increased), the function returns an object which has a different state than it had when beeing returned the first time.
Is this assumption just caused by my restricted experience in python and do I have to change my mindset, i.e. don't think of functions but of something callable? Or is supplying functions with state an unintended misuse of lambda?
3) Using a Callable Class
To overcome my doubts on stateful functions I could use callable classes where the create_foo function of approach 2 would be replaced by this:
class BarCreator:
def __init__(self, prefix):
self.prefix = prefix
def __call__(self):
return create_bar(self.prefix)
def create_foo(name):
return Foo(BarCreator(create_prefix(name)))
While this seems a usable solution for me, it is sooo much more verbose.
Summary
I'm not absolutely sure how to handle the situation. Although I prefer number 2 I still have my doubts. Furthermore, I'm still hope that anyone comes up with a more elegant way.
Please comment, if there is anything you think is too vague or can be possibly misunderstood. I will improve the question as far as my abilities allow me to do :)
All examples should run under python2.7 and python3 - if you experience any problems, please report them in the comments and I'll try to fix my code.
If you want to inject a callable object but don't want it to have a complex setup -- if, as in your example, it's really just binding to a single input value -- you could try using functools.partial to provide a function <> value pair:
def factory_function(arg):
#processing here
return configurted_object_base_on_arg
class Consumer(object):
def __init__(self, injection):
self._injected = injection
def use_injected_value():
print self._injected()
injectable = functools.partial(factory_function, 'this is the configuration argument')
example = Consumer(injectable)
example.use_injected_value() # should return the result of your factory function and argument
As an aside, if you're creating a dependency injection setup like your option 3, you probably want to put the knwledge about how to do the configuration into a factory class rather than doing it inline as you're doing here. That way you can swap out factories if you want to choose between strategies. It's not functionally very different (unless the creation is more complex than this example and involves persistent state) but it's more flexible down the road if the code looks like
factory = FooBarFactory()
bar1 = factory.create_bar()
alt_factory = FooBlahFactory(extra_info)
bar2 = alt_factory.create_bar()
I have inherited code in which there are standalone functions, one per country code. E.g.
def validate_fr(param):
pass
def validate_uk(param):
pass
My idea is to create a class to group them together and consolidate the code into one method. Unfortunately that breaks cohesion. Another option is to dispatch to instance methods ?
class Validator(object):
def validate(param, country_code):
# dispatch
Alas, python does not have a switch statement.
UPDATE: I am still not convinced why I should leave them as global functions in my module. Lumping them as class methods seems cleaner.
I would keep the functions at module level -- no need for a class if you don't want to instantiate it anyway. The switch statement can easily be simulated using a dicitonary:
def validate_fr(param):
pass
def validate_uk(param)
pass
validators = {"fr": validate_fr,
"uk": validate_uk}
def validate(country_code, param):
return validators[country_code](param)
Given the naming scheme, you could also do it without the dictionary:
def validate(country_code, param):
return gloabls()["validate_" + country_code](param)
You do not need a switch statement for this.
validators = {
'fr': Validator(...),
'uk': Validator(...),
...
}
...
validators['uk'](foo)
Classes are not meant to group functions together, modules are. Functions in a class should be either methods that operate on the object itself (changing it's state, emitting information about the state, etc.) or class methods that do the same, but for the class itself (classes in Python are also objects). There's not even a need for static methods in Python, since you can always have functions at module level. As they say: Flat is better than nested.
If you want to have a set of functions place them in separate module.