Chap 3 Python Object Oriented Programming - Copy.ppt

Object Oriented Programming language
Python Object Oriented Programming Concept of class, object and instances, method call
Constructor, class attributes and destructors
Real time use of class in live projects
Inheritance, super class and overloading operators,
Static and class methods
Adding and retrieving dynamic attributes of classes
Programming using OOPS Deligation and container
Extra Readings: Integrating GUI framework with OOP
By Dr. Sharada Patil

• .
Concept of class, object and instances, method call

• 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 definitions, like function definitions must be executed before they have
any effect.
class ClassName:
<statement-1>
.
.
.
<statement-N>

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

• 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):.
• 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:
• When a class defines an _init_() method, class instantiation automatically
invokes it for the newly-created class instance. So in this example, a new,
initialized instance can be obtained by:
x = MyClass()
def __init__(self):
self.data = []
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)

• 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

• 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:
printf(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…

• 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.

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

• .
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__.

• .
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 clas
that calls another method defined in the same base class may end up calling a method of
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.

• .
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

• .
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 (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.

• .
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. An empty class definition will do nicely:
class Employee:
pass
john = Employee() # Create an empty employee record
# Fill the fields of the record
john.name = 'John Doe'
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.

• .
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, 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

• .
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

• .
Generator
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.

• 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.
Generator Expressions

• .
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']

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