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Understanding Abstraction: How Does Abstraction Work in Python?

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8th Apr, 2023
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Understanding Abstraction: How Does Abstraction Work in Python?

Python is one of the most extensively used programming languages. Python has made it simple for users to program more efficiently with the help of abstraction. This concept keeps users focused on only the essential information and keeps the superfluous details hidden.

What is Abstraction in Python?

Abstraction is defined as a core concept of object-oriented programming. It assists you in encapsulating the implementation data of a class from its users. The abstraction in Python helps you interact with a class without understanding its internal working. It is achieved with the use of abstract interfaces and classes.

The abstraction in Python further refers to managing complexity by concealing unneeded information from the user. Hence, the coding is simplified. The user merely interacts with the fundamental implementation of classes and functions; the internal working stays hidden.

Essentially, the user only knows the purpose of the function; they don’t know how it accomplishes. Note that an abstract can’t be instantiated, i.e., we can’t create objects for the abstract class. It makes your code more extensible, readable, and maintainable. Moreover, it avoids duplication. Focusing only on the main concept can help improve your work efficiency.

How does Abstraction work in Python?

The key role of abstraction is to conceal the internal working of the code. The interactions are accomplished with the users via the basic implementation. The abstraction concept in Python works by using abstract classes and methods.

The abstract class is defined as a class mentioned in the code that has abstract methods. The abstract methods don’t have any implementation; all the implementations are performed within the sub-classes.

Python uses self-variable method definitions while initialising a variable. A class method provides information about the class. This method must declare self as a parameter to define an instant method.

You can easily understand the working of data abstraction in Python through a real-world scenario. When you shop online, you purchase or add products to the cart and then pay for them. However, when you click on the available options, they perform several functions internally whose implementation is unavailable to the user. The user can only see the required view. This is where an abstraction operates by seeking the advantage of Hierarchical classification, i.e., complex modules are fragmented into manageable pieces.

Importance of Abstraction in Python

Here’s how abstraction asserts its significance across different functions in Python to simplify programming runs with the relevant language.

  • The abstraction in Python helps to conceal the irrelevant class/data/process and thus decreases the coding complexity.
  • It improves the application efficiency.
  • Abstraction in Python enables users to create scalable and flexible functions which are easy to adapt.

Abstraction Base Classes

In Python, an abstract base class refers to the common application program of the interface for multiple subclasses. In simpler terms, it works as a blueprint for other classes. The third-party can use it to benefit from the implementations like with plugins.

By defining abstract base classes, you can develop a common Application Programming Interface (API) for various subclasses. It is beneficial when working in huge teams such that all the classes need not be remembered. Moreover, third parties don’t have to provide all the classes at the library.

You can use this feature of data abstraction in Python if you are working with a huge code base and find it difficult to remember all the classes.

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The Functionality of Abstract Classes

Unlike other high-level programming languages, Python doesn’t provide an abstract class of its own. You can use Python’s ABC method, which offers the base and essential tools for defining the Abstract Base Classes (ABC). Essentially, ABCs provides the feature of virtual subclasses. They are classes that don’t inherit property from a class and can still be identified by

isinstance() and issubclass().

The following module helps you to develop your own ABCs.

from abc import ABC

class MyABC(ABC):

         pass

As mentioned above, the ABC module offers the metaclass, i.e., ABCMeta, that defines defining ABCs, and a helper class ABC to define ABCs using inheritance. Furthermore, the ABC module offers the @abstractmethod decorator for representing abstract methods. ABC is defined such that the @abstractmethod keyword decorates the abstract methods within the base class when they are created. Moreover, the concrete methods are represented as implementations of the base class.

The following abstraction in Python example helps you to better understand the functionality of abstract classes.

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from abc import ABC, abstractmethod

class Subject(ABC):

         def score(self):

                     pass

class English(Subject):

         def score(self):

                     print(“The score is 80”)

class Maths(Subject):

         def score(self):

                     print(“The score is 75”)

class Science(Subject):

         def score (self):

                       print(“The score is 85”)

class Literature(Subject):

def score(self):

                     print(“The score is 70”) 

e= English()

e.score()

m = Maths()

m.score()

s = Science()

s.score()

l = Literature()

l.score()

Output:

The score is 80

The score is 75

The score is 85

The score is 70

Let’s understand the implementation of the above code. 

We’ve imported the ABC module to make the abstract base class. The Subject class inherits the ABC class, and an abstract method, “score()” is defined. Next, we inherited the base class from various subclasses and implemented the abstract method uniquely. The objects (here, the subject names) are created to call the abstract method. Finally, the score of each subject is provided as output.

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Abstraction in Python Example: Real-world Applications

1. Web scraping

One of the best real-world applications of Python is in web scraping. It indicates the scraping of a massive amount of data by companies to retrieve customer information and make cost-effective decisions. The abstraction concept only displays the required information and thus helps companies to access customer data easily. Moreover, tools like Selenium, PythonRequest, and MechanicalSoup are used in Python programming to develop web scraping applications.

2. Machine Learning and AI

We can consider the abstraction in Python example in the Machine learning and AI domain. The ease of coding and ease of understanding of the abstraction concept helps Python to handle the complex computations required to develop machine learning models. Python libraries like Pandas, Keras, NumPy, and others are helpful for machine learning applications. Moreover, Python’s abstraction concept is used in various AI solutions like image recognition, advanced computing, data processing, etc.

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3. Desktop GUI

Python is an open-source, easy-to-learn, platform-independent, and stable programming language. These aspects make Python suitable for creating desktop GUI. The tools like PyGUI, PyQt, and WxPython are extensively used for efficiently developing high-quality GUIs.

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4. Cooking

If you want to heat your meal in a microwave, you might want to press certain buttons to set the timer and other features. Consequently, you get a warm and tasty meal. The microwave’s internal working and details are hidden from you. You can only access the external functionality to customise the cooking time and process.

Takeaway

The abstraction in Python simplifies the process of handling complexity by distributing them into smaller parts. You can use the smaller parts in the software programs or projects without understanding how they function. This concept helps programmers cut down complex processes, enhancing efficiency across their workings. 

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Rohit Sharma

Blog Author
Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.

Frequently Asked Questions (FAQs)

1Q. What is the difference between abstraction and encapsulation in Python?

The abstraction in Python is used to conceal internal details and display only functionalities. The process provides names to things to ensure the name captures the fundamental concept of what a function or an entire program does. On the other hand, encapsulation restricts access to variables and methods. It wraps data and programmes together in a single unit to avoid them being accidentally amended.

2Q. How does abstraction in Python help you to build reusable code?

In Python, the abstraction process hides implementation details from the user. Its ability to hide the details of how a program function makes your code more reusable. It implies you can use the same code in a more generalised manner across various situations without worrying about how it functions under the same hood. Moreover, abstraction in Python makes your code simpler to read and understand.

3Q. What are the methods supported in the abstract classes in Python?

Abstract classes in Python can support both abstract and concrete methods in Python. The abstract methods should be implemented in the derived classes. The concrete methods are implemented in the base class.

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