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I want to clarify how variables are declared in Python.
I have seen variable declaration as
class writer:
path = ""
sometimes, there is no explicit declaration but just initialization using __init__:
def __init__(self, name):
self.name = name
I understand the purpose of __init__, but is it advisable to declare variable in any other functions?
How can I create a variable to hold a custom type?
class writer:
path = "" # string value
customObj = ??
Okay, first things first.
There is no such thing as "variable declaration" or "variable initialization" in Python.
There is simply what we call "assignment", but should probably just call "naming".
Assignment means "this name on the left-hand side now refers to the result of evaluating the right-hand side, regardless of what it referred to before (if anything)".
foo = 'bar' # the name 'foo' is now a name for the string 'bar'
foo = 2 * 3 # the name 'foo' stops being a name for the string 'bar',
# and starts being a name for the integer 6, resulting from the multiplication
As such, Python's names (a better term than "variables", arguably) don't have associated types; the values do. You can re-apply the same name to anything regardless of its type, but the thing still has behaviour that's dependent upon its type. The name is simply a way to refer to the value (object). This answers your second question: You don't create variables to hold a custom type. You don't create variables to hold any particular type. You don't "create" variables at all. You give names to objects.
Second point: Python follows a very simple rule when it comes to classes, that is actually much more consistent than what languages like Java, C++ and C# do: everything declared inside the class block is part of the class. So, functions (def) written here are methods, i.e. part of the class object (not stored on a per-instance basis), just like in Java, C++ and C#; but other names here are also part of the class. Again, the names are just names, and they don't have associated types, and functions are objects too in Python. Thus:
class Example:
data = 42
def method(self): pass
Classes are objects too, in Python.
So now we have created an object named Example, which represents the class of all things that are Examples. This object has two user-supplied attributes (In C++, "members"; in C#, "fields or properties or methods"; in Java, "fields or methods"). One of them is named data, and it stores the integer value 42. The other is named method, and it stores a function object. (There are several more attributes that Python adds automatically.)
These attributes still aren't really part of the object, though. Fundamentally, an object is just a bundle of more names (the attribute names), until you get down to things that can't be divided up any more. Thus, values can be shared between different instances of a class, or even between objects of different classes, if you deliberately set that up.
Let's create an instance:
x = Example()
Now we have a separate object named x, which is an instance of Example. The data and method are not actually part of the object, but we can still look them up via x because of some magic that Python does behind the scenes. When we look up method, in particular, we will instead get a "bound method" (when we call it, x gets passed automatically as the self parameter, which cannot happen if we look up Example.method directly).
What happens when we try to use x.data?
When we examine it, it's looked up in the object first. If it's not found in the object, Python looks in the class.
However, when we assign to x.data, Python will create an attribute on the object. It will not replace the class' attribute.
This allows us to do object initialization. Python will automatically call the class' __init__ method on new instances when they are created, if present. In this method, we can simply assign to attributes to set initial values for that attribute on each object:
class Example:
name = "Ignored"
def __init__(self, name):
self.name = name
# rest as before
Now we must specify a name when we create an Example, and each instance has its own name. Python will ignore the class attribute Example.name whenever we look up the .name of an instance, because the instance's attribute will be found first.
One last caveat: modification (mutation) and assignment are different things!
In Python, strings are immutable. They cannot be modified. When you do:
a = 'hi '
b = a
a += 'mom'
You do not change the original 'hi ' string. That is impossible in Python. Instead, you create a new string 'hi mom', and cause a to stop being a name for 'hi ', and start being a name for 'hi mom' instead. We made b a name for 'hi ' as well, and after re-applying the a name, b is still a name for 'hi ', because 'hi ' still exists and has not been changed.
But lists can be changed:
a = [1, 2, 3]
b = a
a += [4]
Now b is [1, 2, 3, 4] as well, because we made b a name for the same thing that a named, and then we changed that thing. We did not create a new list for a to name, because Python simply treats += differently for lists.
This matters for objects because if you had a list as a class attribute, and used an instance to modify the list, then the change would be "seen" in all other instances. This is because (a) the data is actually part of the class object, and not any instance object; (b) because you were modifying the list and not doing a simple assignment, you did not create a new instance attribute hiding the class attribute.
This might be 6 years late, but in Python 3.5 and above, you can give a hint about a variable type like this:
variable_name: type_name
or this:
variable_name # type: shinyType
This hint has no effect in the core Python interpreter, but many tools will use it to aid the programmer in writing correct code.
So in your case(if you have a CustomObject class defined), you can do:
customObj: CustomObject
See this or that for more info.
There's no need to declare new variables in Python. If we're talking about variables in functions or modules, no declaration is needed. Just assign a value to a name where you need it: mymagic = "Magic". Variables in Python can hold values of any type, and you can't restrict that.
Your question specifically asks about classes, objects and instance variables though. The idiomatic way to create instance variables is in the __init__ method and nowhere else — while you could create new instance variables in other methods, or even in unrelated code, it's just a bad idea. It'll make your code hard to reason about or to maintain.
So for example:
class Thing(object):
def __init__(self, magic):
self.magic = magic
Easy. Now instances of this class have a magic attribute:
thingo = Thing("More magic")
# thingo.magic is now "More magic"
Creating variables in the namespace of the class itself leads to different behaviour altogether. It is functionally different, and you should only do it if you have a specific reason to. For example:
class Thing(object):
magic = "Magic"
def __init__(self):
pass
Now try:
thingo = Thing()
Thing.magic = 1
# thingo.magic is now 1
Or:
class Thing(object):
magic = ["More", "magic"]
def __init__(self):
pass
thing1 = Thing()
thing2 = Thing()
thing1.magic.append("here")
# thing1.magic AND thing2.magic is now ["More", "magic", "here"]
This is because the namespace of the class itself is different to the namespace of the objects created from it. I'll leave it to you to research that a bit more.
The take-home message is that idiomatic Python is to (a) initialise object attributes in your __init__ method, and (b) document the behaviour of your class as needed. You don't need to go to the trouble of full-blown Sphinx-level documentation for everything you ever write, but at least some comments about whatever details you or someone else might need to pick it up.
For scoping purpose, I use:
custom_object = None
Variables have scope, so yes it is appropriate to have variables that are specific to your function. You don't always have to be explicit about their definition; usually you can just use them. Only if you want to do something specific to the type of the variable, like append for a list, do you need to define them before you start using them. Typical example of this.
list = []
for i in stuff:
list.append(i)
By the way, this is not really a good way to setup the list. It would be better to say:
list = [i for i in stuff] # list comprehension
...but I digress.
Your other question.
The custom object should be a class itself.
class CustomObject(): # always capitalize the class name...this is not syntax, just style.
pass
customObj = CustomObject()
As of Python 3, you can explicitly declare variables by type.
For instance, to declare an integer one can do it as follows:
x: int = 3
or:
def f(x: int):
return x
see this question for more detailed info about it:
Explicitly declaring a variable type in Python
I want to clarify how variables are declared in Python.
I have seen variable declaration as
class writer:
path = ""
sometimes, there is no explicit declaration but just initialization using __init__:
def __init__(self, name):
self.name = name
I understand the purpose of __init__, but is it advisable to declare variable in any other functions?
How can I create a variable to hold a custom type?
class writer:
path = "" # string value
customObj = ??
Okay, first things first.
There is no such thing as "variable declaration" or "variable initialization" in Python.
There is simply what we call "assignment", but should probably just call "naming".
Assignment means "this name on the left-hand side now refers to the result of evaluating the right-hand side, regardless of what it referred to before (if anything)".
foo = 'bar' # the name 'foo' is now a name for the string 'bar'
foo = 2 * 3 # the name 'foo' stops being a name for the string 'bar',
# and starts being a name for the integer 6, resulting from the multiplication
As such, Python's names (a better term than "variables", arguably) don't have associated types; the values do. You can re-apply the same name to anything regardless of its type, but the thing still has behaviour that's dependent upon its type. The name is simply a way to refer to the value (object). This answers your second question: You don't create variables to hold a custom type. You don't create variables to hold any particular type. You don't "create" variables at all. You give names to objects.
Second point: Python follows a very simple rule when it comes to classes, that is actually much more consistent than what languages like Java, C++ and C# do: everything declared inside the class block is part of the class. So, functions (def) written here are methods, i.e. part of the class object (not stored on a per-instance basis), just like in Java, C++ and C#; but other names here are also part of the class. Again, the names are just names, and they don't have associated types, and functions are objects too in Python. Thus:
class Example:
data = 42
def method(self): pass
Classes are objects too, in Python.
So now we have created an object named Example, which represents the class of all things that are Examples. This object has two user-supplied attributes (In C++, "members"; in C#, "fields or properties or methods"; in Java, "fields or methods"). One of them is named data, and it stores the integer value 42. The other is named method, and it stores a function object. (There are several more attributes that Python adds automatically.)
These attributes still aren't really part of the object, though. Fundamentally, an object is just a bundle of more names (the attribute names), until you get down to things that can't be divided up any more. Thus, values can be shared between different instances of a class, or even between objects of different classes, if you deliberately set that up.
Let's create an instance:
x = Example()
Now we have a separate object named x, which is an instance of Example. The data and method are not actually part of the object, but we can still look them up via x because of some magic that Python does behind the scenes. When we look up method, in particular, we will instead get a "bound method" (when we call it, x gets passed automatically as the self parameter, which cannot happen if we look up Example.method directly).
What happens when we try to use x.data?
When we examine it, it's looked up in the object first. If it's not found in the object, Python looks in the class.
However, when we assign to x.data, Python will create an attribute on the object. It will not replace the class' attribute.
This allows us to do object initialization. Python will automatically call the class' __init__ method on new instances when they are created, if present. In this method, we can simply assign to attributes to set initial values for that attribute on each object:
class Example:
name = "Ignored"
def __init__(self, name):
self.name = name
# rest as before
Now we must specify a name when we create an Example, and each instance has its own name. Python will ignore the class attribute Example.name whenever we look up the .name of an instance, because the instance's attribute will be found first.
One last caveat: modification (mutation) and assignment are different things!
In Python, strings are immutable. They cannot be modified. When you do:
a = 'hi '
b = a
a += 'mom'
You do not change the original 'hi ' string. That is impossible in Python. Instead, you create a new string 'hi mom', and cause a to stop being a name for 'hi ', and start being a name for 'hi mom' instead. We made b a name for 'hi ' as well, and after re-applying the a name, b is still a name for 'hi ', because 'hi ' still exists and has not been changed.
But lists can be changed:
a = [1, 2, 3]
b = a
a += [4]
Now b is [1, 2, 3, 4] as well, because we made b a name for the same thing that a named, and then we changed that thing. We did not create a new list for a to name, because Python simply treats += differently for lists.
This matters for objects because if you had a list as a class attribute, and used an instance to modify the list, then the change would be "seen" in all other instances. This is because (a) the data is actually part of the class object, and not any instance object; (b) because you were modifying the list and not doing a simple assignment, you did not create a new instance attribute hiding the class attribute.
This might be 6 years late, but in Python 3.5 and above, you can give a hint about a variable type like this:
variable_name: type_name
or this:
variable_name # type: shinyType
This hint has no effect in the core Python interpreter, but many tools will use it to aid the programmer in writing correct code.
So in your case(if you have a CustomObject class defined), you can do:
customObj: CustomObject
See this or that for more info.
There's no need to declare new variables in Python. If we're talking about variables in functions or modules, no declaration is needed. Just assign a value to a name where you need it: mymagic = "Magic". Variables in Python can hold values of any type, and you can't restrict that.
Your question specifically asks about classes, objects and instance variables though. The idiomatic way to create instance variables is in the __init__ method and nowhere else — while you could create new instance variables in other methods, or even in unrelated code, it's just a bad idea. It'll make your code hard to reason about or to maintain.
So for example:
class Thing(object):
def __init__(self, magic):
self.magic = magic
Easy. Now instances of this class have a magic attribute:
thingo = Thing("More magic")
# thingo.magic is now "More magic"
Creating variables in the namespace of the class itself leads to different behaviour altogether. It is functionally different, and you should only do it if you have a specific reason to. For example:
class Thing(object):
magic = "Magic"
def __init__(self):
pass
Now try:
thingo = Thing()
Thing.magic = 1
# thingo.magic is now 1
Or:
class Thing(object):
magic = ["More", "magic"]
def __init__(self):
pass
thing1 = Thing()
thing2 = Thing()
thing1.magic.append("here")
# thing1.magic AND thing2.magic is now ["More", "magic", "here"]
This is because the namespace of the class itself is different to the namespace of the objects created from it. I'll leave it to you to research that a bit more.
The take-home message is that idiomatic Python is to (a) initialise object attributes in your __init__ method, and (b) document the behaviour of your class as needed. You don't need to go to the trouble of full-blown Sphinx-level documentation for everything you ever write, but at least some comments about whatever details you or someone else might need to pick it up.
For scoping purpose, I use:
custom_object = None
Variables have scope, so yes it is appropriate to have variables that are specific to your function. You don't always have to be explicit about their definition; usually you can just use them. Only if you want to do something specific to the type of the variable, like append for a list, do you need to define them before you start using them. Typical example of this.
list = []
for i in stuff:
list.append(i)
By the way, this is not really a good way to setup the list. It would be better to say:
list = [i for i in stuff] # list comprehension
...but I digress.
Your other question.
The custom object should be a class itself.
class CustomObject(): # always capitalize the class name...this is not syntax, just style.
pass
customObj = CustomObject()
As of Python 3, you can explicitly declare variables by type.
For instance, to declare an integer one can do it as follows:
x: int = 3
or:
def f(x: int):
return x
see this question for more detailed info about it:
Explicitly declaring a variable type in Python
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.
Basically I have a process class that takes in 3 variables. process as in the name, time and io.
I put that process into a ready queue then in another class I have put the first object from readyqueue into self.__ current. Usually when I put the object into a variable eg. current = process('hi', 6, False). I can say current._process.__time and it returns >>> 6.
Can anyone explain how i can do this with self.__current.
class Process:
def __init__(self,process,time,io = False):
self.__process = process
self.__time = time
self.__io = io
class cpu(Queue):
def __init__(self):
self.timequantum = 1
self.__current = []
The issue you have is because you're using attribute names that start (and don't end) with two underscores. When you do that in a method, it invokes Python's "name mangling" feature, which transforms a name like __process into _Process__process. That makes it awkward to access from other code.
The purpose of the name mangling system is to allow mixin classes to assign attributes on objects that may have any number of other attributes. Since the mangling adds the name of the class where the attribute is being used (not the name of class of the object), the mixin-author can be fairly confident that no other class will accidentally use the same attribute name.
You probably don't want name mangling, so you should not use __ at the start of your attribute names. If you want the attributes to be "private", use a single underscore, which indicates that the attribute is not part of the public API of the class. This is a convention, so for debugging or testing you can still access the _ prefixed attributes if you need to.
Or if you want other code to use the attributes, just use bare names. In Python, it's perfectly acceptable to have public attributes as part of an object's API. (Other programming languages discourage that, but in Python you can reimplement an attribute-API with getter and setter functions using a property later if you need to.)
In Python, I have a class that I've built.
However, there is one method where I apply a rather specific type of substring-search procedure. This procedure could be a standalone function by itself (it just requires a needle a haystack string), but it feels odd to have the function outside the class, because my class depends on it.
What is the typical design paradigm for this? Is it typical to just have myClassName.py with the main class, as well as all the support functions outside the class itself, in the same file? Or is it better to have the support function embedded within the class at the expense of modularity?
You can create a staticmethod, like so:
class yo:
#staticmethod
def say_hi():
print "Hi there!"
Then, you can do this:
>>> yo.say_hi()
Hi there!
>>> a = yo()
>>> a.say_hi()
Hi there!
They can be used non-statically, and statically (if that makes sense).
About where to put your functions...
If a method is required by a class, and it is appropriate for the method to perform data that is specific to the class, then make it a method. This is what you would want:
class yo:
self.message = "Hello there!"
def say_message(self):
print self.message
My say_message relies on the data that is particular to the instance of a class.
If you feel the need to have a function, in addition to the class method, by all means go ahead. Use whichever one is more appropriate in your script. There are many examples of this, including in the python built-ins. Take generator objects for example:
a = my_new_generator()
a.next()
Can also be done as:
a = my_new_generator()
next(a)
Use whichever is more appropriate, and obviously whichever one is more readable. :)
If you can think or any reason to override this function one day, make it a staticmethod, else a plain function is just ok - FWIW, your class probably depends on much more than this simple function. And if you cannot think of any reason for anyone else to ever use this function, keep it in the same module as your class.
As a side note: "myClassName.py" is definitly unpythonic. First because module names should be all_lower, then because the one-module-per-class stuff is a nonsense in Python - we group related classes and functions (and exceptions and whatnots) together.
If the search method you are talking about is really so specific and you will never need to reuse it somewhere else, I do not see any reason to make it static. The fact that it doesn't require access to instance variables doesn't make it static by definition.
If there is a possibility, that this method is going to be reused, refactor it into a helper/utility class (no static again).
ADDED:
Just wanted to add, that when you consider something being static or not, think about how method name relates to the class name. Does this method name makes more sense when used in class context or object context?