9. Classes
**********

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.

Compared with other programming languages, Python's class mechanism
adds classes with a minimum of new syntax and semantics.  It is a
mixture of the class mechanisms found in C++ and Modula-3.  Python
classes provide all the standard features of Object Oriented
Programming: 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.)


9.1. A Word About Names and Objects
===================================

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.


9.2. Python Scopes and Namespaces
=================================

Before introducing classes, I first have to tell you something about
Python's scope rules.  Class definitions play some neat tricks with
namespaces, and you need to know how scopes and namespaces work to
fully understand what's going on. Incidentally, knowledge about this
subject is useful for any advanced Python programmer.

Let's begin with some definitions.

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.

By the way, I use the word *attribute* for any name following a dot
--- for example, in the expression "z.real", "real" is an attribute of
the object "z".  Strictly speaking, references to names in modules are
attribute references: 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!  [1]

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".  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".

Namespaces are created at different moments and have different
lifetimes.  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__", so they have their own
global namespace.  (The built-in names actually also live in a module;
this is called "builtins".)

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:

* the innermost scope, which is searched first, contains the local
  names

* the scopes of any enclosing functions, which are searched starting
  with the nearest enclosing scope, contain non-local, but also non-
  global names

* the next-to-last scope contains the current module's global names

* the outermost scope (searched last) is the namespace containing
  built-in names

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.

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 dynamic name resolution!  (In fact, local variables are
already determined statically.)

A special quirk of Python is that -- 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.


9.2.1. Scopes and Namespaces Example
------------------------------------

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
           spam = "global spam"

       spam = "test spam"
       do_local()
       print("After local assignment:", spam)
       do_nonlocal()
       print("After nonlocal assignment:", spam)
       do_global()
       print("After global assignment:", spam)

   scope_test()
   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.


9.3. A First Look at Classes
============================

Classes introduce a little bit of new syntax, three new object types,
and some new semantics.


9.3.1. Class Definition Syntax
------------------------------

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 --- we'll come back to this later.  The function definitions
inside a class normally have a peculiar form of argument list,
dictated by the calling conventions for methods --- again, this is
explained later.

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; we'll learn more
about class objects in the next section.  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).


9.3.2. Class Objects
--------------------

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"".

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__()", like this:

   def __init__(self):
       self.data = []

When a class defines an "__init__()" method, class instantiation
automatically invokes "__init__()" for the newly created class
instance.  So in this example, a new, initialized instance can be
obtained by:

   x = MyClass()

Of course, the "__init__()" method may have arguments for greater
flexibility.  In that case, arguments given to the class instantiation
operator are passed on to "__init__()".  For example,

   >>> class Complex:
   ...     def __init__(self, realpart, imagpart):
   ...         self.r = realpart
   ...         self.i = imagpart
   ...
   >>> x = Complex(3.0, -4.5)
   >>> x.r, x.i
   (3.0, -4.5)


9.3.3. Instance Objects
-----------------------

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.

*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

The other kind of instance attribute reference is a *method*. A method
is a function that "belongs to" an object.  (In Python, the term
method is not unique to class instances: other object types can have
methods as well.  For example, list objects have methods called
append, insert, remove, sort, and so on. However, in the following
discussion, we'll use the term method exclusively to mean methods of
class instance objects, unless explicitly stated otherwise.)

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.


9.3.4. Method Objects
---------------------

Usually, a method is called right after it is bound:

   x.f()

In the "MyClass" example, this will return the string "'hello world'".
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.  For
example:

   xf = x.f
   while True:
       print(xf())

will continue to print "hello world" until the end of time.

What exactly happens when a method is called?  You may have noticed
that "x.f()" was called without an argument above, even though the
function definition for "f()" specified an argument.  What happened to
the argument? Surely Python raises an exception when a function that
requires an argument is called without any --- even if the argument
isn't actually used...

Actually, you may have guessed the answer: the special thing about
methods is that the instance object is passed as the first argument of
the function.  In our example, the call "x.f()" is exactly equivalent
to "MyClass.f(x)".  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.

If you still don't understand how methods work, a look at the
implementation can perhaps clarify matters.  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, a
method object is created by packing (pointers to) the instance object
and the function object just found together in an abstract object:
this is the 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.


9.3.5. Class and Instance Variables
-----------------------------------

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                  # shared by all dogs
   'canine'
   >>> e.kind                  # shared by all dogs
   'canine'
   >>> d.name                  # unique to d
   'Fido'
   >>> e.name                  # unique to e
   'Buddy'

As discussed in A Word About Names and Objects, 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']


9.4. Random Remarks
===================

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'
   >>> print(w2.purpose, w2.region)
   storage east

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.  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, and in the next section we'll find some good reasons why
a method would want to reference its own class.

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


9.5. Inheritance
================

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:

* 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".


9.5.1. Multiple Inheritance
---------------------------

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, it is slightly more complex than that; 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 https://www.python.org/download/releases/2.3/mro/.


9.6. Private Variables
======================

"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)

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.

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.


9.7. Odds and Ends
==================

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" for this purpose:

   from dataclasses import dataclass

   @dataclass
   class Employee:
       name: str
       dept: str
       salary: int

   >>> john = Employee('john', 'computer lab', 1000)
   >>> john.dept
   'computer lab'
   >>> 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.


9.8. Iterators
==============

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

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

       def __next__(self):
           if self.index == 0:
               raise StopIteration
           self.index = self.index - 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


9.9. Generators
===============

*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):
       for index in range(len(data)-1, -1, -1):
           yield data[index]

   >>> for char in reverse('golf'):
   ...     print(char)
   ...
   f
   l
   o
   g

Anything that can be done with generators can also be done with class-
based iterators as described in the previous section.  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.


9.10. Generator Expressions
===========================

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.

Examples:

   >>> sum(i*i for i in range(10))                 # sum of squares
   285

   >>> xvec = [10, 20, 30]
   >>> yvec = [7, 5, 3]
   >>> sum(x*y for x,y in zip(xvec, yvec))         # dot product
   260

   >>> 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']

-[ Footnotes ]-

[1] 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.
