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.

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)

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

9.3. A First Look at Classes¶

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

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

class MyClass:
    """A simple example class"""
    i = 12345

    def f(self):
        return 'hello world'

x = MyClass()

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

x = MyClass()

>>> 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)

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

x.f()

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

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'

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']

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 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 .

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

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.