9. Classes

[class_computer_programming|Classes]] provide a means of bundling data and functionality together. Creating a new class creates a new type of object, allowing new instances of that type to be made. Each class instance can have attributes attached to it for maintaining its state. Class instances can also have methods (defined by its class) for modifying its state.

Python classes provide all the standard features of OOP: the class inheritance mechanism allows multiple base classes, a derived class can override any methods of its base class or classes, and a method can call the method of a base class with the same name. Objects can contain arbitrary amounts and kinds of data. As is true for modules, classes partake of the dynamic nature of Python: they are created at ==runtime==, and can be modified further after creation.

In C++ terminology, normally class members (including the data members) are ==public== (except see below Private Variables), and all member functions are virtual. As in Modula-3, there are no shorthands for referencing the object’s members from its methods: the method function is declared with an explicit first argument representing the object, which is provided implicitly by the call. As in Smalltalk, classes themselves are objects. This provides semantics for importing and renaming. Unlike C++ and Modula-3, built-in types can be used as base classes for extension by the user. Also, like in C++, most built-in operators with special syntax (arithmetic operators, subscripting etc.) can be redefined for class instances.

(Lacking universally accepted terminology to talk about classes, I will make occasional use of Smalltalk and C++ terms. I would use Modula-3 terms, since its object-oriented semantics are closer to those of Python than C++, but I expect that few readers have heard of it.)

Objects have individuality, and multiple names (in multiple scopes) can be bound to the same object. This is known as aliasing in other languages. This is usually not appreciated on a first glance at Python, and can be safely ignored when dealing with immutable basic types (numbers, strings, tuples). However, aliasing has a possibly surprising effect on the semantics of Python code involving mutable objects such as lists, dictionaries, and most other types. This is usually used to the benefit of the program, since aliases behave like pointers in some respects. For example, passing an object is cheap since only a pointer is passed by the implementation; and if a function modifies an object passed as an argument, the caller will see the change — this eliminates the need for two different argument passing mechanisms as in Pascal.

A namespace is a mapping from names to objects. Most namespaces are currently implemented as Python dictionaries, but that’s normally not noticeable in any way (except for performance), and it may change in the future. Examples of namespaces are: the set of built-in names (containing functions such as “abs”, and built-in exception names); the global names in a module; and the local names in a function invocation. In a sense the set of attributes of an object also form a namespace.

The important thing to know about namespaces is that there is absolutely no relation between names in different namespaces; for instance, two different modules may both define a function maximize without confusion — users of the modules must prefix it with the module name.

In the expression z.real, real is an attribute of the object z.

In the expression modname.funcname, modname is a module object and funcname is an attribute of it. In this case there happens to be a straightforward mapping between the module’s attributes and the global names defined in the module: they share the same namespace! Except for one thing. Module objects have a secret read-only attribute called __dict__, which returns the dictionary used to implement the module’s namespace; the name __dict__ is an attribute but not a global name. Obviously, using this violates the abstraction of namespace implementation, and should be restricted to things like post-mortem debuggers.

Can we read/write attributes of an object?
Attributes may be read-only or writable. In the latter case, assignment to attributes is possible. Module attributes are writable: you can write modname.the_answer = 42.

How to delete an attribute of an object?
Writable attributes may also be deleted with the del statement. For example, del modname.the_answer will remove the attribute the_answer from the object named by modname.

When namespace with built-in names is created/deleted?
The namespace containing the built-in names is created when the Python interpreter starts up, and is never deleted.

The global namespace for a module is created when the module definition is read in; normally, module namespaces also last until the interpreter quits. The statements executed by the top-level invocation of the interpreter, either read from a script file or interactively, are considered part of a module called __main__: The environment where top-level code is run. Covers command-line interfaces, import-time behavior, and __name__ == '__main__'., so they have their own global namespace.

The built-in names actually also live in a module; this is called ==__builtins__, dir(__builtins__)==: The module that provides the built-in namespace.

How local namespace for a function is created/deleted?
The local namespace for a function is created when the function is called, and deleted when the function returns or raises an exception that is not handled within the function. (Actually, forgetting would be a better way to describe what actually happens.) Of course, recursive invocations each have their own local namespace.

A scope is a textual region of a Python program where a namespace is directly accessible. “Directly accessible” here means that an unqualified reference to a name attempts to find the name in the namespace.

Although scopes are determined statically, they are used dynamically. At any time during execution, there are 3 or 4 nested scopes whose namespaces are directly accessible:

1. the innermost scope, which is searched first, contains the local names
2. the scopes of any enclosing functions, which are searched starting with the nearest enclosing scope, contain non-local, but also non-global names
3. the next-to-last scope contains the current module’s global names
4. the outermost scope (searched last) is the namespace containing built-in names

What’s the scope of a variable initialized in an if statement in Python?
Python variables are scoped to the innermost function, class, or module in which they’re assigned. Control blocks like if and while blocks don’t count, so a variable assigned inside an if is still scoped to a function, class, or module.

If a name is declared global, then all references and assignments go directly to the next-to-last scope containing the module’s global names. To rebind variables found outside of the innermost scope, the nonlocal statement can be used; if not declared nonlocal, those variables are read-only (an attempt to write to such a variable will simply create a new local variable in the innermost scope, leaving the identically named outer variable unchanged).

Usually, the local scope references the local names of the (textually) current function. Outside functions, the local scope references the same namespace as the global scope: the module’s namespace. Class definitions place yet another namespace in the local scope.

What this code will print?

a = "global"
class C:
    a = "class"
    def f():
        print(a)
    f()

The code will print global. The a in the print statement is not qualified by a namespace, so it refers to the global a rather than the class a. If you remove a from the global scope, the code will raise a NameError.

It is important to realize that scopes are determined textually: the global scope of a function defined in a module is that module’s namespace, no matter from where or by what alias the function is called.

On the other hand, the actual search for names is done dynamically, at run time — however, the language definition is evolving towards static name resolution, at “compile” time, so don’t rely on it! (In fact, local variables are already determined statically.)

If no global or nonlocal statement is in effect – assignments to names always go into the innermost scope.

Assignments do not copy data — they just bind names to objects. The same is true for deletions: the statement del x removes the binding of x from the namespace referenced by the local scope. In fact, all operations that introduce new names use the local scope: in particular, import statements and function definitions bind the module or function name in the local scope.

The global statement can be used to indicate that particular variables live in the global scope and should be rebound there; the nonlocal statement indicates that particular variables live in an enclosing scope and should be rebound there.

This is an example demonstrating how to reference the different scopes and namespaces, and how global and nonlocal affect variable binding.

def scope_test():
    def do_local():
        spam = "local spam"
 
    def do_nonlocal():
        nonlocal spam
        spam = "nonlocal spam"
 
    def do_global():
        global spam  # module-level binding
        spam = "global spam"
 
    spam = "test spam" # scope_test binding
 
    # Step 1.
    do_local()
    print("After local assignment:", spam)
 
    # Step 2.
    do_nonlocal()
    print("After nonlocal assignment:", spam)
 
    # Step 3.
    do_global()
    print("After global assignment:", spam)
 
# Step 4.
# `spam` not exists in module-level binding
scope_test()
# now `spam` exists in module-level binding
print("In global scope:", spam)

The output of the example code is:

After local assignment: test spam
After nonlocal assignment: nonlocal spam
After global assignment: nonlocal spam
In global scope: global spam

Note how the local assignment (which is default) didn’t change scope_test’s binding of spam. The nonlocal assignment changed scope_test’s binding of spam, and the global assignment changed the module-level binding.
You can also see that there was no previous binding for spam before the global assignment.

The simplest form of class definition looks like this:

class ClassName:
    <statement-1>
    <statement-N>

Class definitions, like function definitions (def statements) must be executed before they have any effect. You could conceivably place a class definition in a branch of an if statement, or inside a function.

In practice, the statements inside a class definition will usually be function definitions, but other statements are allowed, and sometimes useful. The function definitions inside a class normally have a peculiar form of argument list, dictated by the calling conventions for methods (how we name functions in class).

When a class definition is entered, a new namespace is created, and used as the local scope — thus, all assignments to local variables go into this new namespace. In particular, function definitions bind the name of the new function here.

When a class definition is left normally (via the end), a ==class object== is created. This is basically a wrapper around the contents of the namespace created by the class definition; The original local scope (the one in effect just before the class definition was entered) is reinstated, and the class object is bound here to the class name given in the class definition header (ClassName in the example).

Class objects support two kinds of operations: attribute references and instantiation.

Attribute references use the standard syntax used for all attribute references in Python: ==obj.name==. Valid attribute names are all the names that were in the class’s namespace when the class object was created. So, if the class definition looked like this:

class MyClass:
    """A simple example class"""
    i = 12345
 
    def f(self):
        return 'hello world'

then MyClass.i and MyClass.f are valid attribute references, returning an integer and a function object, respectively. Class attributes can also be assigned to, so you can change the value of MyClass.i by assignment. __doc__ is also a valid attribute, returning the docstring belonging to the class: "A simple example class".

How to create new instance of class?
Class instantiation uses function notation. Just pretend that the class object is a parameterless function that returns a new instance of the class. For example (assuming the above class):

x = MyClass()

creates a new instance of the class and assigns this object to the local variable x.

The instantiation operation (“calling” a class object) creates an empty object. Many classes like to create objects with instances customized to a specific initial state. Therefore a class may define a special method named ==__init__, __init__(self, ...)== (automatically invokes on class instantiation).

Now what can we do with instance objects? The only operations understood by instance objects are attribute references.

There are two kinds of valid attribute names: data attributes and methods.

Do we need to declare data attributes in Python?
data attributes correspond to “instance variables” in Smalltalk, and to “data members” in C++. Data attributes need not be declared; like local variables, they spring into existence when they are first assigned to. For example, if x is the instance of MyClass created above, the following piece of code will print the value 16, without leaving a trace:

x.counter = 1
while x.counter < 10:
    x.counter = x.counter * 2
print(x.counter)
del x.counter

A method (kind of instance attribute reference) is a function that “belongs to” an object.
Valid method names of an instance object depend on its class. By definition, all attributes of a class that are function objects define corresponding methods of its instances. So in our example, x.f is a valid method reference, since MyClass.f is a function, but x.i is not, since MyClass.i is not. But x.f is not the same thing as MyClass.f — it is a method object, not a function object.

class MyClass:
    """A simple example class"""
    i = 12345
 
    def f(self):
        return 'hello world'
 
# Usually, a method is called right after it is bound:
x = MyClass()
print(x.f())  # returns 'hello world', x here is an instance of MyClass, and
              # we pass here x as the first argument to the method `f`
 
# However, it is not necessary to call a method right away: `x.f` is a method
# object, and can be stored away and called at a later time.
#
# This will continue to print `hello world` until the end of time.
xf = x.f
while True:
    print(xf())
    break  # avoid infinite loop, remove this line to see the effect

In general, calling a method with a list of n arguments is equivalent to calling the corresponding function with an argument list that is created by inserting the method’s instance object before the first argument.

How Python class methods are working if we are reference non-data attribute of a class instance with some arguments?
When a non-data attribute of an instance is referenced, the instance’s class is searched.
If the name denotes a valid class attribute that is a function object, references to both the instance object and the function object are packed into a method object.
When the method object is called with an argument list, a new argument list is constructed from the instance object and the argument list, and the function object is called with this new argument list.

Generally speaking, instance variables are for data unique to each instance and class variables are for attributes and methods shared by all instances of the class:

class Dog:
    kind = 'canine'         # class variable shared by all instances
 
    def __init__(self, name):
        self.name = name    # instance variable unique to each instance
 
d = Dog('Fido')
e = Dog('Buddy')
d.kind                  # 'canine', shared by all dogs
e.kind                  # 'canine', shared by all dogs
d.name                  # 'Fido', unique to d
e.name                  # 'Buddy', unique to e

Possible surprising effect of this code? Can it be improved?

class Dog:
    tricks = []             # mistaken use of a class variable
 
    def __init__(self, name):
        self.name = name
 
    def add_trick(self, trick):
        self.tricks.append(trick)
 
d = Dog('Fido')
e = Dog('Buddy')
d.add_trick('roll over')
e.add_trick('play dead')

Shared data can have possibly surprising effects with involving mutable objects such as lists and dictionaries. For example, the tricks list in the following code should not be used as a class variable because just a single list would be shared by all Dog instances:

class Dog:
    tricks = []             # mistaken use of a class variable
 
    def __init__(self, name):
        self.name = name
 
    def add_trick(self, trick):
        self.tricks.append(trick)
 
d = Dog('Fido')
e = Dog('Buddy')
d.add_trick('roll over')
e.add_trick('play dead')
d.tricks  # unexpectedly shared by all dogs ['roll over', 'play dead']

Correct design of the class should use an instance variable instead:

class Dog:
    def __init__(self, name):
        self.name = name
        self.tricks = []    # creates a new empty list for each dog
 
    def add_trick(self, trick):
        self.tricks.append(trick)
 
d = Dog('Fido')
e = Dog('Buddy')
d.add_trick('roll over')
e.add_trick('play dead')
d.tricks  # ['roll over']
e.tricks  # ['play dead']

If the same attribute name occurs in both an instance and in a class, then attribute lookup prioritizes the instance:

class Warehouse:
   purpose = 'storage'
   region = 'west'
 
w1 = Warehouse()
print(w1.purpose, w1.region) # storage west
 
w2 = Warehouse()
w2.region = 'east'  # override the class attribute for current instance
print(w2.purpose, w2.region) # storage east
 
print(Warehouse.purpose, Warehouse.region) # storage west

Data attributes may be referenced by methods as well as by ordinary users (“clients”) of an object. In other words, classes are not usable to implement pure abstract data types. In fact, nothing in Python makes it possible to enforce data hiding — it is all based upon convention. (On the other hand, the Python implementation, written in C, can completely hide implementation details and control access to an object if necessary; this can be used by extensions to Python written in C.)

Clients should use data attributes with care — clients may mess up invariants maintained by the methods by stamping on their data attributes. Note that clients may add data attributes of their own to an instance object without affecting the validity of the methods, as long as name conflicts are avoided — again, a naming convention can save a lot of headaches here.

There is no shorthand for referencing data attributes (or other methods!) from within methods (need to use ==self or cls==). I find that this actually increases the readability of methods: there is no chance of confusing local variables and instance variables when glancing through a method.

Often, the first argument of a method is called self. This is nothing more than a convention: the name self has absolutely no special meaning to Python. Note, however, that by not following the convention your code may be less readable to other Python programmers, and it is also conceivable that a class browser program might be written that relies upon such a convention.

Any function object that is a class attribute defines a method for instances of that class. It is not necessary that the function definition is textually enclosed in the class definition: assigning a function object to a local variable in the class is also ok. For example:

# Function defined outside the class
def f1(self, x, y):
    return min(x, x+y)
 
class C:
    f = f1
 
    def g(self):
        return 'hello world'
 
    h = g
 
# Now `f`, `g` and `h` are all attributes of class `C` that refer to function
# objects, and consequently they are all methods of instances of `C` — `h` being
# exactly equivalent to `g`. Note that this practice usually only serves to
# confuse the reader of a program.

Methods may call other methods by using method attributes of the ==self== argument:

class Bag:
    def __init__(self):
        self.data = []
 
    def add(self, x):
        self.data.append(x)
 
    def addtwice(self, x):
        self.add(x)
        self.add(x)

Methods may reference global names in the same way as ordinary functions. The global scope associated with a method is the module containing its definition.

A class is never used as a global scope. While one rarely encounters a good reason for using global data in a method, there are many legitimate uses of the global scope: for one thing, functions and modules imported into the global scope can be used by methods, as well as functions and classes defined in it. Usually, the class containing the method is itself defined in this global scope.

Each value is an object, and therefore has a class (also called its type). It is stored as object.==__class__==.

To get parent class you can use this dunder variable: ==object.__class__.__bases__==.

Of course, a language feature would not be worthy of the name “class” without supporting inheritance. The syntax for a derived class definition looks like this:

class DerivedClassName(BaseClassName):
    <statement-1>
    <statement-N>

The name BaseClassName must be defined in a namespace accessible from the scope containing the derived class definition. In place of a base class name, other arbitrary expressions are also allowed. This can be useful, for example, when the base class is defined in another module:

class DerivedClassName(modname.BaseClassName):

Execution of a derived class definition proceeds the same as for a base class. When the class object is constructed, the base class is remembered. This is used for resolving attribute references: if a requested attribute is not found in the class, the search proceeds to look in the base class. This rule is applied recursively if the base class itself is derived from some other class.

There’s nothing special about instantiation of derived classes: DerivedClassName() creates a new instance of the class. Method references are resolved as follows: the corresponding class attribute is searched, descending down the chain of base classes if necessary, and the method reference is valid if this yields a function object.

Derived classes may override methods of their base classes. Because methods have no special privileges when calling other methods of the same object, a method of a base class that calls another method defined in the same base class may end up calling a method of a derived class that overrides it. (For C++ programmers: all methods in Python are effectively virtual.)

An overriding method in a derived class may in fact want to extend rather than simply replace the base class method of the same name. There is a simple way to call the base class method directly: just call ==BaseClassName.methodname(self, arguments)==. This is occasionally useful to clients as well. Note: that this only works if the base class is accessible as BaseClassName in the global scope.

Python has two built-in functions that work with inheritance (check type of an object):

• Use isinstance() to check an instance’s type: isinstance(obj, int) will be True only if obj.__class__ is int or some class derived from int
• Use issubclass() to check class inheritance: issubclass(bool, int) is True since bool is a subclass of int. However, issubclass(float, int) is False since float is not a subclass of int.

Python supports a form of multiple inheritance as well. A class definition with multiple base classes looks like this:

class DerivedClassName(Base1, Base2, Base3):
    <statement-1>
    <statement-N>

For most purposes, in the simplest cases, you can think of the search for attributes inherited from a parent class as depth-first, left-to-right, not searching twice in the same class where there is an overlap in the hierarchy. Thus, if an attribute is not found in DerivedClassName, it is searched for in Base1, then (recursively) in the base classes of Base1, and if it was not found there, it was searched for in Base2, and so on (in fact more complex logic is used here).

In fact, search for attributes from derived classes more complex than depth-first, left-to-right; the method resolution order changes dynamically to support cooperative calls to ==super()==. This approach is known in some other multiple-inheritance languages as call-next-method and is more powerful than the super call found in single-inheritance languages.

Dynamic ordering is necessary because all cases of multiple inheritance exhibit one or more diamond relationships (where at least one of the parent classes can be accessed through multiple paths from the bottommost class). For example, all classes inherit from object, so any case of multiple inheritance provides more than one path to reach object. To keep the base classes from being accessed more than once, the dynamic algorithm linearizes the search order in a way that preserves the left-to-right ordering specified in each class, that calls each parent only once, and that is monotonic (meaning that a class can be subclassed without affecting the precedence order of its parents). Taken together, these properties make it possible to design reliable and extensible classes with multiple inheritance. For more detail, see The Python 2.3 Method Resolution Order.

Do “private” instance variables exist in Python?
“Private” instance variables that cannot be accessed except from inside an object don’t exist in Python. However, there is a convention that is followed by most Python code: a name prefixed with an underscore (e.g. _spam) should be treated as a non-public part of the API (whether it is a function, a method or a data member). It should be considered an implementation detail and subject to change without notice.

Since there is a valid use-case for class-private members (namely to avoid name clashes of names with names defined by subclasses), there is limited support for such a mechanism, called ==name mangling==. Any identifier of the form __spam (at least two leading underscores, at most one trailing underscore) is textually replaced with _classname__spam, where classname is the current class name with leading underscore(s) stripped. This mangling is done without regard to the syntactic position of the identifier, as long as it occurs within the definition of a class.

Name mangling is helpful for letting subclasses override methods without breaking intraclass method calls. For example:

class Mapping:
    def __init__(self, iterable):
        self.items_list = []
        self.__update(iterable)
 
    def update(self, iterable):
        for item in iterable:
            self.items_list.append(item)
 
    __update = update   # private copy of original update() method
 
class MappingSubclass(Mapping):
 
    def update(self, keys, values):
        # provides new signature for update()
        # but does not break __init__()
        for item in zip(keys, values):
            self.items_list.append(item)

Will this example work if MappingSubclass introduces a __update identifier?
Yes. The above example would work even if MappingSubclass were to introduce a __update identifier since it is replaced with _Mapping__update in the Mapping class and _MappingSubclass__update in the MappingSubclass class respectively. Example:

class Mapping:
    __update = "__update"
 
class MappingSubclass(Mapping):
    __update = "__update"
 
print(dir(Mapping))          # ['_Mapping__update', '__doc__', '__module__']
print(dir(MappingSubclass))  # ['_MappingSubclass__update', '__doc__', '__module__']

Note that the mangling rules are designed mostly to avoid accidents; it still is possible to access or modify a variable that is considered private. This can even be useful in special circumstances, such as in the debugger.

Notice that code passed to exec() or eval() does not consider the classname of the invoking class to be the current class; this is similar to the effect of the global statement, the effect of which is likewise restricted to code that is byte-compiled together. The same restriction applies to getattr(), setattr() and delattr(), as well as when referencing __dict__ directly.
In other words “magic” of double-underscores will not work with exec or eval, so consider the following example:

class Foo:
    def __init__(self):
        self.__bar = 42
    def method0(self):
        return self.__bar * 2
    def method1(self):
        return eval('self.__bar * 2')
 
f = Foo()
f.method0()  # 84
 
f.method1()
# Traceback (most recent call last):
#   File "<stdin>", line 1, in <module>
#   File "<stdin>", line 7, in method1
#   File "<string>", line 1, in <module>
# AttributeError: 'Foo' object has no attribute '__bar'
# Similarly, for getattr etc:
 
getattr(f, '__bar')
# Traceback (most recent call last):
#   File "<stdin>", line 1, in <module>
# AttributeError: 'Foo' object has no attribute '__bar'

Sometimes it is useful to have a data type similar to the Pascal “record” or C “struct”, bundling together a few named data items. The idiomatic approach is to use dataclasses. (generate special methods on user-defined classes) for this purpose, can you provide simple example?

from dataclasses import dataclass
 
@dataclass
class Employee:
    name: str
    dept: str
    salary: int
 
john = Employee('john', 'computer lab', 1000)
print(john.dept)    # 'computer lab'
print(john.salary)  # 1000

A piece of Python code that expects a particular abstract data type can often be passed a class that emulates the methods of that data type instead. For instance, if you have a function that formats some data from a file object, you can define a class with methods read() and readline() that get the data from a string buffer instead, and pass it as an argument.

Instance method objects have attributes, too: m.__self__ is the instance object with the method m(), and ==m.__func__== is the function object corresponding to the method.

By now you have probably noticed that most container objects can be looped over using a for statement: \

for element in [1, 2, 3]:
    print(element)
for element in (1, 2, 3):
    print(element)
for key in {'one':1, 'two':2}:
    print(key)
for char in "123":
    print(char)
for line in open("myfile.txt"):
    print(line, end='')

This style of access is clear, concise, and convenient. The use of iterators pervades and unifies Python. Behind the scenes, the for statement calls ==iter()== on the container object. The function returns an iterator object that defines the method __next__() which accesses elements in the container one at a time. When there are no more elements, __next__() raises a StopIteration exception which tells the for loop to terminate. You can call the __next__() method using the next() built-in function; this example shows how it all works: \

s = 'abc'
it = iter(s)
print(it)  # <str_iterator object at 0x10c90e650>
print(next(it))  # 'a'
print(next(it))  # 'b'
print(next(it))  # 'c'
print(next(it))
# Traceback (most recent call last):
#   File "<stdin>", line 1, in <module>
#     next(it)
# StopIteration

Can you provide an example of how to create an iterator class?
Having seen the mechanics behind the iterator protocol, it is easy to add iterator behavior to your classes. Define an __iter__() method which returns an object with a __next__() method. If the class defines __next__(), then __iter__() can just return self: \

class Reverse:
    """Iterator for looping over a sequence backwards."""
    def __init__(self, data):
        self.data = data
        self.index = len(data)
 
    def __iter__(self):
        return self  # Object with __next__ method
 
    def __next__(self):
        if self.index == 0:
            raise StopIteration  # Signal the end of iteration
        self.index = self.index - 1  # Offset by -1
        return self.data[self.index]
 
rev = Reverse('spam')
iter(rev)  # <__main__.Reverse object at 0x00A1DB50>
for char in rev:
    print(char)
 
# m
# a
# p
# s

What is a generator in Python?
Generators are a simple and powerful tool for creating iterators. They are written like regular functions but use the yield statement whenever they want to return data. Each time next() is called on it, the generator resumes where it left off (it remembers all the data values and which statement was last executed). An example shows that generators can be trivially easy to create:

def reverse(data):  # Generator function
    for index in range(len(data)-1, -1, -1):
        yield data[index]  # Pause iterations here and return the value to func.
        # Point of execution is saved here, we resume when external code calls
        # the next() method
 
generator_object = reverse('golf')
print(generator_object)  # <generator object reverse at 0x00A1DB50>
for char in generator_object:
    print(char)
 
# f
# l
# o
# g

Anything that can be done with generators can also be done with class-based iterators. What makes generators so compact is that the __iter__() and __next__() methods are created automatically.
Another key feature is that the local variables and execution state are automatically saved between calls. This made the function easier to write and much more clear than an approach using instance variables like self.index and self.data.
In addition to automatic method creation and saving program state, when generators terminate, they automatically raise StopIteration. In combination, these features make it easy to create iterators with no more effort than writing a regular function.

How to create Generator Expressions? Can you provide some examples?
Some simple generators can be coded succinctly as expressions using a syntax similar to list comprehensions but with parentheses instead of square brackets. These expressions are designed for situations where the generator is used right away by an enclosing function. Generator expressions are more compact but less versatile than full generator definitions and tend to be more memory friendly than equivalent list comprehensions:

sum(i*i for i in range(10))                 # 285, sum of squares
 
xvec = [10, 20, 30]
yvec = [7, 5, 3]
sum(x*y for x,y in zip(xvec, yvec))         # 260,  dot product
 
page = "lorem ipsum dolor"
unique_words = set(word for line in page  for word in line.split())
 
valedictorian = max((student.gpa, student.name) for student in graduates)
 
data = 'golf'
list(data[i] for i in range(len(data)-1, -1, -1))
# ['f', 'l', 'o', 'g']