Tutorial Playlist
200 Lessons1. Introduction to Python
2. Features of Python
3. How to install python in windows
4. How to Install Python on macOS
5. Install Python on Linux
6. Hello World Program in Python
7. Python Variables
8. Global Variable in Python
9. Python Keywords and Identifiers
10. Assert Keyword in Python
11. Comments in Python
12. Escape Sequence in Python
13. Print In Python
14. Python-if-else-statement
15. Python for Loop
16. Nested for loop in Python
17. While Loop in Python
18. Python’s do-while Loop
19. Break in Python
20. Break Pass and Continue Statement in Python
21. Python Try Except
22. Data Types in Python
23. Float in Python
24. String Methods Python
25. List in Python
26. List Methods in Python
27. Tuples in Python
28. Dictionary in Python
29. Set in Python
30. Operators in Python
31. Boolean Operators in Python
32. Arithmetic Operators in Python
33. Assignment Operator in Python
34. Bitwise operators in Python
35. Identity Operator in Python
36. Operator Precedence in Python
37. Functions in Python
38. Lambda and Anonymous Function in Python
39. Range Function in Python
40. len() Function in Python
41. How to Use Lambda Functions in Python?
42. Random Function in Python
43. Python __init__() Function
44. String Split function in Python
45. Round function in Python
46. Find Function in Python
47. How to Call a Function in Python?
48. Python Functions Scope
49. Method Overloading in Python
50. Method Overriding in Python
51. Static Method in Python
52. Python List Index Method
53. Python Modules
54. Math Module in Python
55. Module and Package in Python
56. OS module in Python
57. Python Packages
58. OOPs Concepts in Python
59. Class in Python
60. Abstract Class in Python
61. Object in Python
62. Constructor in Python
63. Inheritance in Python
64. Multiple Inheritance in Python
65. Encapsulation in Python
66. Data Abstraction in Python
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67. Opening and closing files in Python
68. How to open JSON file in Python
69. Read CSV Files in Python
70. How to Read a File in Python
71. How to Open a File in Python?
72. Python Write to File
73. JSON Python
74. Python JSON – How to Convert a String to JSON
75. Python JSON Encoding and Decoding
76. Exception Handling in Python
77. Recursion in Python
78. Python Decorators
79. Python Threading
80. Multithreading in Python
81. Multiprocеssing in Python
82. Python Regular Expressions
83. Enumerate() in Python
84. Map in Python
85. Filter in Python
86. Eval in Python
87. Difference Between List, Tuple, Set, and Dictionary in Python
88. List to String in Python
89. Linked List in Python
90. Length of list in Python
91. Reverse a List in Python
92. Python List remove() Method
93. How to Add Elements in a List in Python
94. How to Reverse a List in Python?
95. Difference Between List and Tuple in Python
96. List Slicing in Python
97. Sort in Python
98. Merge Sort in Python
99. Selection Sort in Python
100. Sort Array in Python
101. Sort Dictionary by Value in Python
102. Datetime Python
103. Random Number in Python
104. 2D Array in Python
105. Abs in Python
106. Advantages of Python
107. Anagram Program in Python
108. Append in Python
109. Applications of Python
110. Armstrong Number in Python
111. Assert in Python
112. Binary Search in Python
113. Binary to Decimal in Python
114. Bool in Python
115. Calculator Program in Python
116. chr in Python
117. Control Flow Statements in Python
118. Convert String to Datetime Python
119. Count in python
120. Counter in Python
121. Data Visualization in Python
122. Datetime in Python
123. Extend in Python
124. F-string in Python
125. Fibonacci Series in Python
126. Format in Python
127. GCD of Two Numbers in Python
128. How to Become a Python Developer
129. How to Run Python Program
130. In Which Year Was the Python Language Developed?
131. Indentation in Python
132. Index in Python
133. Interface in Python
134. Is Python Case Sensitive?
135. Isalpha in Python
136. Isinstance() in Python
137. Iterator in Python
138. Join in Python
139. Leap Year Program in Python
140. Lexicographical Order in Python
141. Literals in Python
142. Matplotlib
143. Matrix Multiplication in Python
144. Memory Management in Python
145. Modulus in Python
146. Mutable and Immutable in Python
147. Namespace and Scope in Python
148. OpenCV Python
149. Operator Overloading in Python
150. ord in Python
151. Palindrome in Python
152. Pass in Python
153. Pattern Program in Python
154. Perfect Number in Python
155. Permutation and Combination in Python
156. Prime Number Program in Python
157. Python Arrays
158. Python Automation Projects Ideas
159. Python Frameworks
160. Python Graphical User Interface GUI
161. Python IDE
162. Python input and output
163. Python Installation on Windows
164. Python Object-Oriented Programming
165. Python PIP
166. Python Seaborn
167. Python Slicing
168. type() function in Python
169. Queue in Python
170. Replace in Python
171. Reverse a Number in Python
172. Reverse a string in Python
173. Reverse String in Python
174. Stack in Python
175. scikit-learn
176. Selenium with Python
177. Self in Python
178. Sleep in Python
179. Speech Recognition in Python
180. Split in Python
181. Square Root in Python
182. String Comparison in Python
183. String Formatting in Python
184. String Slicing in Python
185. Strip in Python
186. Subprocess in Python
187. Substring in Python
188. Sum of Digits of a Number in Python
189. Sum of n Natural Numbers in Python
190. Sum of Prime Numbers in Python
191. Switch Case in Python
192. Python Program to Transpose a Matrix
193. Type Casting in Python
194. What are Lists in Python?
195. Ways to Define a Block of Code
196. What is Pygame
197. Why Python is Interpreted Language?
198. XOR in Python
199. Yield in Python
200. Zip in Python
Data abstraction in Python is a crucial aspect of object-oriented programming, acting as a beacon for developers to simplify complex architectures. This tutorial speaks about the nuances of this concept, exploring the symbiosis between abstract classes and efficient code. As we journey through, you'll uncover how abstraction not only streamlines development but also paves the way for a more intuitive and maintainable codebase.
This tutorial delves deep into data abstraction in Python, unraveling its intricacies, and understanding its indispensable role within object-oriented programming. From the foundational abstract classes to the reasons that underscore their importance, we'll embark on a journey to grasp the essence of abstraction and how it contributes to scalable and maintainable code.
In Python programming, abstract classes emerge as one of the paramount pillars, underpinning the architecture of object-oriented programming. Understanding these classes paves the way for better software design, ensuring robustness and scalability. They, in essence, serve as foundational structures, emphasizing more "what" an object does, rather than "how".
By laying down a blueprint for other classes, they facilitate a structure that other classes can build upon, ensuring a standard set of methods that must be implemented in the derived classes. This creates a scenario where the abstract class dictates a contract of sorts, and the inheriting classes fulfill this contract by implementing the methods. Some key components include:
In the vast expanse of software engineering, abstraction stands as a cornerstone. Its strength lies in taming the complexities of code, shaping it into a form that’s both palatable and scalable. But, why exactly does abstraction hold such a pivotal position? Let's unravel this.
Abstraction is the art of discernment, it is about sieving through layers of complexity to present only what's needed. Think of it like an iceberg, where the visible tip signifies the presented features, while the submerged, larger portion represents the hidden complexities. Through abstraction, software developers provide a simplified view of the intricate mechanisms working beneath, shielding end-users and even other developers from the daunting intricacies.
At the heart of object-oriented programming (OOP), two concepts often get mingled, encapsulation, and abstraction. Both abstraction and encapsulation in Python play pivotal roles, yet they have distinct purposes.
Abstraction isn't just a theoretical construct; its real-world applications underscore its vitality in software design.
An abstract class is declared using the abstract keyword or a similar construct depending on the programming language (ABC in Python). Abstract classes cannot be instantiated on their own; they serve as a template for other classes
Abstract classes may contain abstract methods, which are methods declared without any implementation. Subclasses of the abstract class must provide concrete implementations for all the abstract methods. Abstract methods are used to define a common interface that subclasses must adhere to.
Concrete subclasses inherit from abstract classes to provide specific implementations for the abstract methods. The concrete subclasses are responsible for providing meaningful code for the abstract methods.
Abstract classes enforce a contract or interface that subclasses must follow.
Subclasses must provide concrete implementations for all abstract methods defined in the abstract class. If a subclass fails to implement any of the abstract methods, it is considered abstract and cannot be instantiated.
Abstract classes enable polymorphism, which allows objects of different concrete subclasses to be treated uniformly through the common interface defined by the abstract class.
This simplifies code by allowing you to work with objects of different types without needing to know their specific implementations.
Abstract classes promote code reusability by providing a common structure for subclasses to build upon. Shared functionality and attributes can be defined in the abstract class, reducing redundancy in code.
Abstract classes are commonly used in scenarios where you want to define a set of methods that must be implemented by related classes. For example, in geometric shapes, you might have an abstract class "Shape" with abstract methods like "area" and "perimeter," and concrete subclasses like "Circle" and "Rectangle" that implement these methods.
Here is an example of abstract classes in Python:
Code:
from abc import ABC, abstractmethod
class AbstractClass(ABC):
@abstractmethod
def abstract_method(self):
pass
class ConcreteClass(AbstractClass):
def abstract_method(self):
print("ConcreteClass's implementation of abstract_method")
obj = ConcreteClass()
obj.abstract_method()
In the above example, AbstractClass is an abstract class with an abstract method abstract_method. ConcreteClass inherits from AbstractClass and provides a concrete implementation of abstract_method. An instance of ConcreteClass can be created and used.
Abstract Base Classes (ABCs) in Python are a way to define a blueprint for a class, specifying a set of methods that must be implemented by any concrete (derived) class. They are part of the abc module in Python's standard library and provide a way to enforce a common interface among a group of related classes.
Here's how they work:
To use abstract base classes, you need to import the abc module:
from abc import ABC, abstractmethod
To define an abstract base class, you inherit from the ABC class provided by the abc module. Additionally, you can use the @abstractmethod decorator to indicate which methods must be implemented by concrete subclasses. Here's an example:
from abc import ABC, abstractmethod
class Shape(ABC):
@abstractmethod
def area(self):
pass
@abstractmethod
def perimeter(self):
pass
In this example, the Shape class is an abstract base class with two abstract methods: area() and perimeter(). Any concrete subclass of Shape must implement these methods.
Concrete subclasses are classes that inherit from an abstract base class and provide implementations for its abstract methods. For example:
class Circle(Shape):
def __init__(self, radius):
self.radius = radius
def area(self):
return 3.14 * self.radius * self.radius
def perimeter(self):
return 2 * 3.14 * self.radius
Here, Circle is a concrete subclass of the Shape abstract base class. It implements the area() and perimeter() methods, which are required by the Shape base class.
With abstract base classes, you can use polymorphism to work with objects of different concrete subclasses through a common interface. For example:
def print_shape_info(shape):
print(f"Area: {shape.area()}")
print(f"Perimeter: {shape.perimeter()}")
circle = Circle(5)
print_shape_info(circle)
The print_shape_info() function can accept any object that is a subclass of Shape, allowing you to work with different shapes without knowing their specific types.
If a concrete subclass of an abstract base class doesn't implement all the required abstract methods, Python will raise a TypeError at runtime, indicating that the subclass is not "concrete" and must implement all abstract methods.
Here is another working abstract class in python example where we'll create an abstract base class representing a data storage interface and then create concrete subclasses for different types of data storage, such as a database and a file system:
Code:
from abc import ABC, abstractmethod
# Abstract base class for data storage
class DataStorage(ABC):
@abstractmethod
def read(self, key):
pass
@abstractmethod
def write(self, key, value):
pass
# Concrete subclass for database storage
class DatabaseStorage(DataStorage):
def __init__(self):
# Simulate a database connection
self.database = {}
def read(self, key):
if key in self.database:
return self.database[key]
else:
return None
def write(self, key, value):
self.database[key] = value
print(f"Writing to the database: {key} -> {value}")
# Concrete subclass for file system storage
class FileSystemStorage(DataStorage):
def __init__(self):
# Simulate a file system
self.files = {}
def read(self, key):
if key in self.files:
return self.files[key]
else:
return None
def write(self, key, value):
self.files[key] = value
print(f"Writing to the file system: {key} -> {value}")
# Client code
if __name__ == "__main__":
# Create instances of data storage types
db_storage = DatabaseStorage()
fs_storage = FileSystemStorage()
# Perform data storage operations
db_storage.write("user123", "Alice")
fs_storage.write("order456", "Product: XYZ")
# Read data
user_data = db_storage.read("user123")
order_data = fs_storage.read("order456")
# Display retrieved data
print("User data:", user_data)
print("Order data:", order_data)
In the above example, DataStorage is the abstract base class representing a data storage interface. It defines two abstract methods, read and write, which must be implemented by its concrete subclasses. DatabaseStorage and FileSystemStorage are concrete subclasses of DataStorage. They provide specific implementations of the read and write methods for database and file system storage, respectively.
In the client code, we create instances of DatabaseStorage and FileSystemStorage, perform data storage operations, and then read and display the retrieved data.
The use of the abc module ensures that all concrete subclasses adhere to the common interface defined by the DataStorage abstract base class, achieving data abstraction and allowing us to work with different types of data storage using a consistent API.
Mastering data abstraction in Python provides a developer with the tools to craft organized, efficient, and scalable code. As we journey through Python's vast landscapes, the role of concepts like these becomes undeniable. For professionals who seek to further upskill, upGrad offers an array of courses tailored for excellence.
1. What is encapsulation in Python?
It's a mechanism of wrapping data (variables) and code (methods) together as a single unit.
2. How is abstraction different from polymorphism in Python?
Abstraction hides complexity, while polymorphism lets a single interface represent different data types.
3. Are there distinct types of abstraction in Python?
Two primary types exist: data abstraction and control abstraction.
4. Can an abstract class in Python contain regular methods?
An abstract class can mix abstract and concrete methods.
5. What's the core role of an abstract method in Python?
It sets a framework for derived classes, mandating a specific implementation for the method.
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Director of Engineering
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upGrad does not grant credit; credits are granted, accepted or transferred at the sole discretion of the relevant educational institution offering the diploma or degree. We advise you to enquire further regarding the suitability of this program for your academic, professional requirements and job prospects before enr...