Thursday 23 March 2023

Data abstraction in Python


Data Abstraction is another fundamental concept of object-oriented programming (OOP). It refers to the practice of hiding the implementation details of a class from the outside world and presenting only the essential features of the class to the user. Data Abstraction allows us to separate the interface of a class from its implementation, which makes the code more robust, secure, and maintainable.

Data Abstraction has two main benefits:


Data Abstraction provides security by preventing direct access to the data of a class. This means that the data is protected from accidental modification or tampering, which can cause errors or crashes in the program.


Data Abstraction simplifies the use of a class by presenting only the essential features of the class to the user. This means that the user of the class doesn't need to know how the class works internally, but only needs to know how to use its interface.

In Python, we can achieve Data Abstraction using abstract classes and interfaces. An abstract class is a class that cannot be instantiated, but can only be subclassed. An abstract class can define abstract methods, which are methods that have no implementation in the abstract class but must be implemented in its subclasses. An interface is a collection of abstract methods, which define the interface that a class must implement.

Let's take an example to understand Data Abstraction in Python:

from abc import ABC, abstractmethod

class Shape(ABC):


    def area(self):




    def perimeter(self):



class Circle(Shape):

    def __init__(self, radius):

        self.radius = radius


    def area(self):

        return 3.14 * self.radius ** 2


    def perimeter(self):

        return 2 * 3.14 * self.radius


class Square(Shape):

    def __init__(self, length):

        self.length = length


    def area(self):

        return self.length ** 2


    def perimeter(self):

        return 4 * self.length

In the above example, we have defined an abstract class named "Shape" that defines two abstract methods: "area" and "perimeter". We have then defined two concrete classes named "Circle" and "Square" that inherit from the Shape class and implement the "area" and "perimeter" methods.

The Circle class takes a radius as a parameter in its constructor and calculates its area and perimeter using the formulas for a circle. The Square class takes a length as a parameter in its constructor and calculates its area and perimeter using the formulas for a square.

We can create objects of the Circle and Square classes and use their methods as follows:


my_circle = Circle(5)

print("Area of circle:", my_circle.area())  # Output: Area of circle: 78.5

print("Perimeter of circle:", my_circle.perimeter())  # Output: Perimeter of circle: 31.4

my_square = Square(4)

print("Area of square:", my_square.area())  # Output: Area of square: 16

print("Perimeter of square:", my_square.perimeter())  # Output: Perimeter of square: 16

In the above example, we have created objects of the Circle and Square classes named "my_circle" and "my_square" and called their "area" and "perimeter" methods to calculate their area and perimeter. Since the Shape class is an abstract class, we cannot create objects of it directly, but only use its methods through its subclasses.


Data abstraction is a powerful technique for building modular, maintainable, and extensible software systems in Python. By hiding implementation details behind well-defined interfaces, we can create higher-level abstractions that simplify complex tasks and promote code reuse. In this blog post, we have explored the fundamentals of data abstraction in Python, including abstract classes, interfaces, and inheritance. We have also seen how data abstraction can be applied to real-world problems, such as database access and GUI development. By mastering data abstraction, you can become a more efficient and effective Python developer, capable of creating elegant and robust solutions to any problem.

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