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Python OOP

Q: What is Python OOP?

Python OOP is a Python Tutorial lesson that explains python oop in Python. Learn Object-Oriented Programming (OOP) concepts in Python and how to create and use classes and objects. Copy the samples and run them in the Python editor. It is written for beginners who want a clear definition and working examples.

Learn Object-Oriented Programming (OOP) concepts in Python and how to create and use classes and objects.

Python Classes and Objects

Python is an object oriented programming language.

Almost everything in Python is an object, with its properties and methods.

A Class is like an object constructor, or a "blueprint" for creating objects.

Create a Class

To create a class, use the keyword class:

Example - Create a class named MyClass, with a property named x:

class MyClass:
    x = 5

Create Object

Now we can use the class named MyClass to create objects:

Example - Create an object named p1, and print the value of x:

class MyClass:
    x = 5

p1 = MyClass()
print(p1.x)

The init() Function

The examples above are classes and objects in their simplest form, and are not really useful in real life applications.

To understand the meaning of classes we have to understand the built-in __init__() function.

All classes have a function called __init__(), which is always executed when the class is being initiated.

Use the __init__() function to assign values to object properties, or other operations that are necessary to do when the object is being created:

Example - Create a class named Person, use the init() function to assign values for name and age:

class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age

p1 = Person("John", 36)

print(p1.name)
print(p1.age)

Note: The __init__() function is called automatically every time the class is being used to create a new object.

The str() Function

The __str__() function controls what should be returned when the class object is represented as a string.

If the __str__() function is not set, the string representation of the object will be returned:

Example - The string representation of an object WITHOUT the str() function:

class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age

p1 = Person("John", 36)

print(p1)

Example - The string representation of an object WITH the str() function:

class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age

    def __str__(self):
        return f"{self.name}({self.age})"

p1 = Person("John", 36)

print(p1)

Object Methods

Objects can also contain methods. Methods in objects are functions that belong to the object.

Let us create a method in the Person class:

Example - Insert a function that prints a greeting, and execute it on the p1 object:

class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age

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

p1 = Person("John", 36)
p1.myfunc()

Note: The self parameter is a reference to the current instance of the class, and is used to access variables that belong to the class.

The self Parameter

The self parameter is a reference to the current instance of the class, and is used to access variables that belong to the class.

It does not have to be named self, you can call it whatever you like, but it has to be the first parameter of any function in the class:

Example - Use the words mysillyobject and abc instead of self:

class Person:
    def __init__(mysillyobject, name, age):
        mysillyobject.name = name
        mysillyobject.age = age

    def myfunc(abc):
        print("Hello my name is " + abc.name)

p1 = Person("John", 36)
p1.myfunc()

Modify Object Properties

You can modify properties on objects like this:

Example - Set the age of p1 to 40:

class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age

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

p1 = Person("John", 36)

p1.age = 40

print(p1.age)

Delete Object Properties

You can delete properties on objects by using the del keyword:

Example - Delete the age property from the p1 object:

class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age

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

p1 = Person("John", 36)

del p1.age

print(p1.age)

Delete Objects

You can delete objects by using the del keyword:

Example - Delete the p1 object:

class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age

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

p1 = Person("John", 36)

del p1

print(p1)

The pass Statement

class definitions cannot be empty, but if you for some reason have a class definition with no content, put in the pass statement to avoid getting an error.

Example

class Person:
    pass

OOP Principles

1. Encapsulation

Encapsulation is the bundling of data and methods that work on that data within one unit (class).

Example - Bank Account with encapsulation:

class BankAccount:
    def __init__(self, account_number, initial_balance=0):
        self.account_number = account_number
        self.__balance = initial_balance  # Private attribute
    
    def deposit(self, amount):
        if amount > 0:
            self.__balance += amount
            return True
        return False
    
    def withdraw(self, amount):
        if 0 < amount <= self.__balance:
            self.__balance -= amount
            return True
        return False
    
    def get_balance(self):
        return self.__balance

# Usage
account = BankAccount("12345", 1000)
account.deposit(500)
print(account.get_balance())  # 1500

2. Abstraction

Abstraction hides complex implementation details and shows only essential features.

Example - Abstract base class:

from abc import ABC, abstractmethod

class Animal(ABC):
    @abstractmethod
    def make_sound(self):
        pass
    
    @abstractmethod
    def move(self):
        pass

class Dog(Animal):
    def make_sound(self):
        return "Woof!"
    
    def move(self):
        return "Running"

class Bird(Animal):
    def make_sound(self):
        return "Tweet!"
    
    def move(self):
        return "Flying"

# Usage
dog = Dog()
print(dog.make_sound())  # Woof!
print(dog.move())        # Running

Class Variables vs Instance Variables

Example

class Student:
    # Class variable (shared by all instances)
    school_name = "Python High School"
    student_count = 0
    
    def __init__(self, name, grade):
        # Instance variables (unique to each instance)
        self.name = name
        self.grade = grade
        Student.student_count += 1
    
    def get_info(self):
        return f"{self.name} - Grade {self.grade}"
    
    @classmethod
    def get_student_count(cls):
        return cls.student_count

# Usage
student1 = Student("Alice", 10)
student2 = Student("Bob", 11)

print(Student.school_name)        # Python High School
print(Student.get_student_count()) # 2
print(student1.get_info())        # Alice - Grade 10

Property Decorators

Python provides property decorators to create getter, setter, and deleter methods:

Example

class Temperature:
    def __init__(self, celsius=0):
        self._celsius = celsius
    
    @property
    def celsius(self):
        return self._celsius
    
    @celsius.setter
    def celsius(self, value):
        if value < -273.15:
            raise ValueError("Temperature cannot be below absolute zero")
        self._celsius = value
    
    @property
    def fahrenheit(self):
        return (self._celsius * 9/5) + 32
    
    @fahrenheit.setter
    def fahrenheit(self, value):
        self._celsius = (value - 32) * 5/9

# Usage
temp = Temperature(25)
print(temp.celsius)     # 25
print(temp.fahrenheit)  # 77.0

temp.fahrenheit = 86
print(temp.celsius)     # 30.0

The Four Pillars of OOP

Pillar	Meaning
Encapsulation	Bundle data and the methods that act on it; hide internals
Abstraction	Expose a simple interface, hide complexity
Inheritance	Build new classes on existing ones to reuse code
Polymorphism	Same method name, different behaviour per class

A Complete Class

class BankAccount:
    def __init__(self, owner, balance=0):
        self.owner = owner
        self._balance = balance      # _name = "internal, please don't touch"

    def deposit(self, amount):
        self._balance += amount
        return self._balance

    def __repr__(self):              # developer-friendly display
        return f"BankAccount({self.owner!r}, {self._balance})"

acc = BankAccount("Ada")
acc.deposit(100)
print(acc)              # BankAccount('Ada', 100)

A leading underscore (_balance) signals "internal by convention". A double underscore (__balance) triggers name-mangling for stronger, though not absolute, privacy.

Try It Yourself

Exercise 1: Create a Dog class with a name and a bark() method that returns "Woof!".

Show solution

class Dog:
    def __init__(self, name):
        self.name = name
    def bark(self):
        return "Woof!"

d = Dog("Rex")
print(d.name, d.bark())   # Rex Woof!

Exercise 2: Add a __str__ to Dog so print(d) shows Dog: Rex.

Show solution

class Dog:
    def __init__(self, name):
        self.name = name
    def __str__(self):
        return f"Dog: {self.name}"

print(Dog("Rex"))   # Dog: Rex

Key Takeaways

A class is a blueprint; an object is an instance of it.
__init__ initializes each instance; self refers to it.
OOP rests on encapsulation, abstraction, inheritance, polymorphism.
Add __repr__/__str__ for readable objects.

📘 Real-World Deep Dive

Object-oriented programming in Python is "duck typing plus dataclasses". The four big ideas — encapsulation, abstraction, inheritance, polymorphism — are useful but lean: use composition, write small classes, prefer data over behaviour where possible.

Real-Life Scenario

A small "Pipeline" pattern: a base <code>Step</code> with two concrete subclasses, each wrapping one pure function and feeding the next.

Real-Life Example

from dataclasses import dataclass
from typing import Callable

@dataclass
class Step:
    name: str
    fn: Callable[[dict], dict]

@dataclass
class FilterStep(Step):
    def __call__(self, ctx: dict) -> dict:
        print(f"[filter:{self.name}] before: {len(ctx.get('rows', []))}")
        ctx["rows"] = [r for r in ctx["rows"] if self.fn(r)]
        print(f"[filter:{self.name}] after:  {len(ctx['rows'])}")
        return ctx

@dataclass
class MapStep(Step):
    def __call__(self, ctx: dict) -> dict:
        print(f"[map:{self.name}] mutating fields")
        ctx["rows"] = [self.fn(r) for r in ctx["rows"]]
        return ctx

class Pipeline:
    def __init__(self, steps: list[Step]):
        self.steps = steps
    def run(self, ctx: dict) -> dict:
        for step in self.steps:
            ctx = step(ctx)
        return ctx

data = {"rows": [
    {"id": 1, "n": 4, "ok": True},
    {"id": 2, "n": 7, "ok": False},
    {"id": 3, "n": 9, "ok": True},
]}
pipe = Pipeline([
    FilterStep("active",  lambda r: r["ok"]),
    MapStep("double",     lambda r: {**r, "n": r["n"] * 2}),
])
print(pipe.run(data))

Expected Output

[filter:active] before: 3
[filter:active] after:  2
[map:double] mutating fields
[filter:active] before: 2
...
{'rows': [{'id': 1, 'n': 8, 'ok': True}, {'id': 3, 'n': 18, 'ok': True}]}

Common mistakes

Heavy inheritance hierarchies are an anti-pattern in Python — flatten into small focused classes.
Mutable default values on classes are shared instances — use field(default_factory=...) on dataclasses.
Implicit __init__ from dataclasses does NOT inherit unless you opt-in with @dataclass on subclasses too.

🚀 Performance & Best Practices

Frozen dataclasses (frozen=True) hash for free — great for keys in caches.
__slots__ on a manually written class saves memory; on dataclasses use slots=True (3.10+).
Function-style pipelines beat hand-rolled class hierarchies for one-shot data transforms.

🧪 Try It Yourself

Add a ReduceStep that calls functools.reduce across rows.
Refactor Pipeline to a context-manager that auto-logs input/output sizes.
Add a parallel AsyncPipeline using asyncio.gather.

Python Classes and Objects

Create a Class

Example - Create a class named MyClass, with a property named x:

Create Object

Example - Create an object named p1, and print the value of x:

The __init__() Function

Example - Create a class named Person, use the __init__() function to assign values for name and age:

The __str__() Function

Example - The string representation of an object WITHOUT the __str__() function:

Example - The string representation of an object WITH the __str__() function:

Object Methods

Example - Insert a function that prints a greeting, and execute it on the p1 object:

The self Parameter

Example - Use the words mysillyobject and abc instead of self:

Modify Object Properties

Example - Set the age of p1 to 40:

Delete Object Properties

Example - Delete the age property from the p1 object:

Delete Objects

Example - Delete the p1 object:

The pass Statement

Example

OOP Principles

1. Encapsulation

Example - Bank Account with encapsulation:

2. Abstraction

Example - Abstract base class:

Class Variables vs Instance Variables

Example

Property Decorators

Example

The Four Pillars of OOP

A Complete Class

Try It Yourself

Key Takeaways

📘 Real-World Deep Dive

Real-Life Scenario

Real-Life Example

Expected Output

Common mistakes

🚀 Performance & Best Practices

🧪 Try It Yourself

FAQ: Python OOP

The init() Function

Example - Create a class named Person, use the init() function to assign values for name and age:

The str() Function

Example - The string representation of an object WITHOUT the str() function:

Example - The string representation of an object WITH the str() function: