Object Oriented Programming in Python

Alexey Ulashchick

EPAM Systems

March 2014

Agenda

1. Classes and Instances

2. Attribute accessing

3. Descriptors

4. OOP Principles

5. Desing Patterns

A user-defined prototype for an object that defines a set of attributes that characterize any object of the class

CLASS

OBJECT

A unique instance of a data structure that's defined by its class

Each class statement generates a new class object.
Each time a class is called, it generates a new instance object.
Instances are automatically linked to the classes from which they are created.
Classes are automatically linked to their superclasses according to the way we list them in parentheses in a class header line; the left-to-right order there gives the order in the tree.

Object & Class init cycle

Class has a documentation string, which can be accessed via ClassName.__doc__.

Class body consists of all the component statements defining class members, data attributes and functions.

Class Creating

class ClassName:
	'Optional class documentation string'
	pass

The variable empCount is a class variable whose value would be shared among all instances of a this class. This can be accessed as Employee.empCount from inside the class or outside the class.
The first method __init__() is a special method, which is called class constructor or initialization method that Python calls when you create a new instance of this class.
You declare other class methods like normal functions with the exception that the first argument to each method is self. Python adds the self argument to the list for you; you don't need to include it when you call the methods.

Class Example

class Employee:
   'Common base class for all employees'
   empCount = 0

   def __init__(self, name, salary):
      self.name = name
      self.salary = salary
      Employee.empCount += 1

   def displayCount(self):
     print "Total Employee %d" % \
           Employee.empCount

   def displayEmployee(self):
      print "Name : ", self.name,  \
          ", Salary: ", self.salary

Instance Methods

class Inst:

    def __init__(self, name):
        self.name = name

    def introduce(self):
        print "Hello, my name is " + self.name

When creating an instance method, the first parameter is always self. You can name it anything you want, but the meaning will always be the same, and you should use self since it's the naming convention. self is (usually) passed hiddenly when calling an instance method; method has access to other instance variables and methods using self

Class Methods

class Cls:
    name = 'World'

    @classmethod
    def introduce(cls):
        print "Hello, I am %s!" % cls.name

In class methods we pass the class itself as a first parameter.

Class method doesn't have access to instance

Should be called using class name:

Cls.introduce()

Static Methods

class Cls:

    @staticmethod
    def introduce():
        print "Hello, I am static method"

Static method mounted to the class but doesn't have access neither to class, no instance.

Cls.introduce()

__dict__ : Dictionary containing the class's namespace.

__doc__ : Class documentation string or None if undefined.

__name__: Class name.

__module__: Module name in which the class is defined. This attribute is "__main__" in interactive mode.

__bases__: A possibly empty tuple containing the base classes, in the order of their occurrence in the base class list

Built-In Class Attributes

Instance Creation & Attributes accessing

"This would create first object of Employee class"
emp1 = Employee("Zara", 2000)

"This would create second object of Employee class"
emp2 = Employee("Manni", 5000)

emp1.displayEmployee()
emp2.displayEmployee()

print "Total Employee %d" % Employee.empCount

getattr(obj, name[, default]) : to access the attribute of object.
hasattr(obj,name) : to check if an attribute exists or not.
setattr(obj,name,value) : to set an attribute. If attribute does not exist, then it would be created.
delattr(obj, name) : to delete an attribute.

Alternative element accessing

hasattr(emp1, 'salary')         # Returns true if 'salary' attribute exists
getattr(emp1, 'salary')         # Returns value of 'salary' attribute
getattr(emp1, 'salary', 0)      # Returns value of 'salary' attribute when exists or 0
setattr(emp1, 'salary', 8)      # Set attribute 'salary' at 8
delattr(emp1, 'salary')         # Delete attribute 'salary'

getattr(emp1, 'empCount')       # Search attribute in instance. When fail, 
                                # continue search in class

Attributes searching

instance

__dict__

instance class

__dict__

class ancestor

__dict__

Attribute accessing pitfall

class Car:
    wheels = 4

    def __init__(self, color):
        self.color = color


car1 = Car('blue')
car2 = Car('red')

print car1.wheels       # Prints 4
print car2.wheels       # Prints 4


car1.wheels = 10        # Set wheels count to 10
print car1.wheels       # Prints 10


print car2.wheels       # 4
print Car.wheels        # 4

Object-Oriented Approach

OOP

Encapsulation

Polymorphism

Inheritance

Object-oriented programming (OOP) is a programming language model organized around objects rather than "actions" and data rather than logic

Encapsulation

class Person:

    def __init__(self, name):
        self._name = name

    def set_name(self, name):
        self._name = 'Mr. '+ name

    def get_name(self, name):
        return ['<', self._name, '>'].join(' ')

Control attributes via @property

class Deck(object):
    def __init__(self):
        self._cards = []

    @property
    def card( self ):
        return self._cards
       
    @card.setter
    def card( self, aCard ):
        self._cards.append( aCard )
       
    @card.deleter
    def card( self ):
        self._cards.pop(-1)

Since setter (and deleter) properties are created from the getter property, we must always define a getter property first

Descriptors

A descriptor is a class that mediates attribute access.
The descriptor class can be used to get, set, or delete attribute values.
Descriptor objects are built inside a class at class definition time.

To be recognized as a descriptor, a class must implement any combination of the following three methods.

__get__(self, instance, owner) → object

__set__(self, instance, value)

__delete__(self, instance)

Descriptors usage example

class Descriptor(object):

    def __init__(self):
        self._name = ''

    def __get__(self, instance, owner):
        print "Getting: %s" % self._name
        return self._name

    def __set__(self, instance, name):
        print "Setting: %s" % name
        self._name = name.title()

    def __delete__(self, instance):
        print "Deleting: %s" %self._name
        del self._name

class Person(object):
    name = Descriptor()

Inheritance

Inheritance is when an object or class is based on another object or class, using the same implementation (inheriting from a class) specifying implementation to maintain the same behavior

Inheritance is used to indicate that one class will get most or all of its features from a parent class.

When you are doing this kind of specialization, there are three ways that the parent and child classes can interact:

Actions on the child imply an action on the parent.
Actions on the child override the action on the parent.
Actions on the child alter the action on the parent.

Implicit Inheritance

Method defined in parent, but not in child

class Parent(object):

    def implicit(self):
        print "PARENT implicit()"

class Child(Parent):
    pass

dad = Parent()
son = Child()

dad.implicit()		# print PARENT implicit()
son.implicit()		# print PARENT implicit()

Child will just extend Parent and add some behavior

(Methods, Properties)

Override Explicitly - Polymorphism

class Parent(object):

    def override(self):
        print "PARENT"

class Child(Parent):

    def override(self):
        print "CHILD"

dad = Parent()
son = Child()

dad.override()		#print PARENT
son.override()		#print CHILD

The problem with having functions called implicitly is sometimes you want the child to behave differently. In this case you want to override the function in the child, effectively replacing the functionality. To do this just define a function with the same name in Child.

Alter Before or After

class Parent(object):

    def altered(self):
        print "PARENT"

class Child(Parent):

    def altered(self):
        print "BEFORE ",
        super(Child, self).altered()
        print " AFTER",

dad = Parent()
son = Child()

dad.altered()	#print PARENT
son.altered()	#print BEFORE PARENT AFTER

The third way to use inheritance is a special case of overriding where you want to alter the behavior before or after the Parent class's version runs. You first override the function just like in the last example, but then you use a Python built-in function named super to get the Parent version to call

Multiple inheritance

class A:

    def show_message(self):
	print "I’m class A"

class B:

    def show_message(self):
	print "I’m class B"

class C(A,B):
    pass

instance = C()
instance.show_message()

Class C that inherits from classes A and B at the same time.

In this case, whenever you have implicit actions on instance, Python has to look-up the possible function in the class hierarchy for both A and B subclasses, but it needs to do this in a consistent order. To do this Python uses "method resolution order" (MRO) and an algorithm called C3 to get it straight.

Method Resolution Order

class A:  x = 'a'
class B(A): pass
class C(A): x = 'c'
class D(B, C): pass

print D.x	# print 'a'

class A(object):  x = 'a'
class B(A): pass
class C(A): x = 'c'
class D(B, C): pass

print D.x	# print 'a’

MRO: D - B - A - C – A

MRO: D - B – C – A

So when looking up D.x, A is the first base in resolution order to solve it, thereby hiding the definition in C

A forced to come in resolution order only once and after all of its subclasses

You able to use __class__.mro() in new-style classes to observe resolution order

Patterns

Singleton

class Singleton(object):
    _instance = None
    
    def __new__(cls, *args, **kwargs):
        if not cls._instance:
            cls._instance = super(Singleton, cls).__new__(cls, *args, **kwargs)
        return cls._instance



if __name__ == '__main__':
    s1=Singleton()
    s2=Singleton()
    if s1 == s2:
        print "Same"
    else:
        print "Different"

Singleton pattern is a design pattern that restricts the instantiation of a class to one object. This is useful when exactly one object is needed to coordinate actions across the system.

Chaining method

class Person:

    def change_name(self, value):
        self.name = value
        return self
 
    def set_age(self, value):
        self.age = value
        return self
 
    def introduce(self):
        print "Hello, my name is", self.name, "and I am", self.age, "years old."
 
person = Person()
person.change_name("Peter").set_age(21).introduce()

Each method returns an object, allowing the calls to be chained together in a single statement.

Chaining is syntactic sugar which eliminates the need for intermediate variables.

Factory: Encapsulating Object Creation

class HugeBusinessObject(object):
    
    def __init__(self, name, surname, job_title):
        self.name, self.surname, self.job_title = name, surname, job_title
    
    def transform(self, condition):
        # some transformation logic
        pass
    

class Factory(object):
    
    @staticmethod
    def get_simple_person():
        return HugeBusinessObject('Mike', 'Petrov', 'cleaner')
    
    @staticmethod
    def get_complex_person():
        obj = HugeBusinessObject('Vasya', 'Ivanov', 'manager')
        obj.transform()
        return obj

Pattern strive to aggregate object creation and initialization in separated factory classes.

Decorator

def make_bold(fn):
    def wrapped(*args, **kwargs):
        return ''.join(['<b>', fn(*args, **kwargs), '</b>'])
    return wrapped

class Dom:
    def __init__(self, text):
        self.text = text

    @make_bold
    def capitalized(self):
        return self.text.upper()

print Dom('hello').capitalized()    # <b>HELLO</b>

Decorator pattern (also known as Wrapper) is a design pattern that allows behavior to be added to an object

Memoization decorator

def memoize(f):

    def wrapped(*args, **kwargs):
        if hasattr(wrapped, '_cached_val'):
            return wrapped._cached_val
        result = f(*args, **kwargs)
        wrapped._cached_val = result
        return result

    return wrapped


@memoize
def expensive_function():
    print "Computing expensive function..."

Memoization is an optimization technique used primarily to speed up computer programs by storing the results of expensive function calls and returning the cached result when the same inputs occur again.

Strategy

class StrategyExample :

    def __init__(self, func=None) :
        if func :
             self.execute = func

    def execute(self) :
        print "Original execution"


def executeReplacement1() :
        print "Strategy 1"


def executeReplacement2() :
         print "Strategy 2"

if __name__ == "__main__" :

    strat0 = StrategyExample()
    strat1 = StrategyExample(executeReplacement1)
    strat2 = StrategyExample(executeReplacement2)

    strat0.execute()
    strat1.execute()
    strat2.execute()

Strategy pattern (also known as the policy pattern) is a software design pattern that enables an algorithm's behavior to be selected at runtime. The strategy pattern

defines a family of algorithms,
encapsulates each algorithm, and
makes the algorithms interchangeable within that family.

Best practices

test inheritance and data-objects inheritance
just 2 line test steps
multi-layer architecture
build a service layer
separate things a much as possible

Test inheritance

class BaseTest(object):

    def before(self):
        # Initialization (open connection, crete huge instances, prepare data)
        pass

    def after(self):
        # Finalization (clos connections, destory instances)
        pass


class Test1(BaseTest):
    def test_check_1(self):
        pass

    def test_check_2(self):
        pass

    def test_check_3(self):
        pass

"2 line" tests

class Test1(BaseTest):
    
    def test_check_1(self):
        result = make_action()
        assert.check_data(expected, result)   

    def test_check_2(self):
        result = make_action_2()
        assert.check_data(expected, result)

Multi-layer structure

Test

Service

API

Testing object

Object Oriented Programming in Python

Agenda

Object & Class init cycle

Class Creating

Class Example

Instance Methods

Class Methods

Static Methods

Built-In Class Attributes

Instance Creation & Attributes accessing

Alternative element accessing

Attributes searching

Attribute accessing pitfall

Object-Oriented Approach

OOP

Encapsulation

Control attributes via @property

Descriptors

Descriptors usage example

Inheritance

Implicit Inheritance

Override Explicitly - Polymorphism

Alter Before or After

Multiple inheritance

Method Resolution Order

Patterns

Singleton

Chaining method

Factory: Encapsulating Object Creation

Decorator

Memoization decorator

Strategy

Best practices

Test inheritance

"2 line" tests

Multi-layer structure

Questions?

Thank you!