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
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
Now Reading
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
Python Object-oriented programming represents a programming paradigm, a collection of concepts establishing a framework for prescribed practices.
At its core, OOP revolves around system modeling through the utilization of objects. Within this context, an object serves as a constituent within a focal system, typically characterized by a particular function and conduct.
Essential to each object are functions known as methods and associated data. Methods, in essence, function as processes for executing operations on data, often necessitating specific parameters as input arguments.
Python OOP offers a structured approach to software design. It revolves around objects representing real-world entities, each encapsulating data and behavior. Some of the key concepts are class, object, method, inheritance, encapsulation and polymorphism.
A class in Python is a blueprint for constructing objects. It defines a set of attributes and methods that objects derived from the class will have.
The importance of classes lies in their ability to structure and organize code. They allow you to create reusable and maintainable code and model real-world entities in a program.
Classes include data and behavior, which helps in achieving modularity and abstraction. The role of a class is to define the attributes (data) and methods (functions) that objects of that class will have. It acts as a template or a design for creating objects.
An object can be considered a real-world thing or concept in your program. It's like a copy of a blueprint created from a class. These objects are beneficial because they bring structure and order to your code. They let you deal with data and actions related to those things organizationally.
Imagine objects as individual entities. Each has unique characteristics (like its name, age, color, etc.), which we call attributes.
An object in Python can also perform specific tasks or actions, which we call methods. These actions are defined by the class they belong to.
In Python, methods are like specialized functions within a class that enable objects to perform specific tasks. They serve as the tools that define what an object can do and how it manages its data.
Methods play a crucial role in code organization by neatly covering a class's behaviors. For example, in a "Car" class, methods such as "start_engine" or "turn_off_lights" define the actions a car can take.
What makes methods important is their efficiency and reusability. You can use the same methods with various objects, such as starting the engines of different cars, which simplifies your code.
Additionally, methods ensure that an object's state remains consistent and functions as intended.
Inheritance holds a position of fundamental significance in the context of OOP. It includes the ability of a class to acquire attributes and methods from another class.
This process involves two primary participants: the subclass, also known as the child class, which gains access to the inherited elements, and the superclass, identified as the parent class, serving as the source of these shared methods and attributes.
Polymorphism in Python is a core concept that enhances code flexibility and extensibility.
It enables you to interact with objects from different classes as if they all belong to a shared base class. This simplifies coding by eliminating the need to worry about the specific identities of individual objects, making your code more versatile and adaptable.
Think of polymorphism as a way for objects to wear disguises. They might look different on the outside (different classes), but they can all respond to the same basic commands (methods). It makes your code more versatile and adaptable.
Polymorphism has two main roles:
Method Overriding: It lets subclasses provide their unique implementation of a method inherited from a base class.
Method Overloading: It allows you to use the same method name in related classes but customize how each class handles it.
Data abstraction is a crucial programming concept that conceals complex details within a class while providing a user-friendly interface. It is crucial for effective software management.
Data abstraction lets us define vital attributes and methods without overwhelming users with technical complexities. It's like offering a simple dashboard for a complex machine, sparing users from understanding the inner workings.
The main goal of data abstraction is to create a clear separation between what users interact with (the interface) and the behind-the-scenes workings. This clarity enhances code maintainability, reduces errors, and promotes smoother collaboration among programmers.
Encapsulation in Python bundles data and methods into a class, playing a vital role in software design.
Primarily, it safeguards data by limiting direct access and controlling it through designated methods, akin to placing a protective barrier around your data.
Secondly, it organizes code by grouping related data and functions into classes, enhancing clarity and structure in your codebase.
Lastly, encapsulation safeguards the integrity of data by effectively reducing the chances of unintentional data manipulation.
class ClassName:
# Statement-1
.
.
.
# Statement-N
class MyClass:
pass
In the example above, MyClass is an empty class. It doesn't have any attributes or methods defined within it. It's often used as a starting point for creating more complex classes by adding attributes and methods later.
Here's a breakdown of the code:
In Python, an object is an instance of a class. Here are some points:
This syntax create an object named obj of the class Cat:
obj = Cat()
obj = MyClass()
In Python, __init__() is a special method that is called when an object is created from a class. It is used to initialize the attributes of the object. The __init__() method takes at least one argument, which is usually named self. This argument refers to the object being created.
Here’s an example of how to define a class with an __init__() method:
class MyClass:
def __init__(self, arg1, arg2):
self.arg1 = arg1
self.arg2 = arg2
In this example, MyClass has two attributes, arg1 and arg2, which are initialized in the __init__() method.
Code:
class Car:
# Class attribute
manufacturer = "Unknown"
def __init__(self, model, year):
# Instance attributes
self.model = model
self.year = year
def display_info(self):
print(f"This car is a {self.year} {self.manufacturer} {self.model}")
# Creating objects of the Car class
car1 = Car("Civic", 2022)
car2 = Car("Accord", 2023)
# Accessing instance attributes
print(f"Car 1: {car1.year} {car1.manufacturer} {car1.model}")
print(f"Car 2: {car2.year} {car2.manufacturer} {car2.model}")
# Accessing class attribute
print(f"All cars are manufactured by {Car.manufacturer}")
# Modifying class attribute (applies to all instances)
Car.manufacturer = "Honda"
# Updated class attribute
print(f"Now, all cars are manufactured by {Car.manufacturer}")
# Calling instance method
car1.display_info()
car2.display_info()
Code:
class Cat:
attr1 = "mammal"
def __init__(self, name):
self.name = name
def speak(self):
print("My name is {}".format(self.name))
abc = Cat("abc")
xyz = Cat("xyz")
abc.speak()
xyz.speak()
In Python, you define inheritance by including the name of the superclass in parentheses when defining the subclass. For example:
class Subclass(Superclass):
# Subclass attributes and methods
Code:
class P(object):
def __init__(self, name, idnumber):
self.name = name
self.idnumber = idnumber
def display(self):
print(self.name)
print(self.idnumber)
def details(self):
print("My name is {}".format(self.name))
print("IdNumber: {}".format(self.idnumber))
class Emp(P):
def __init__(self, name, idnumber, salary, post):
self.salary = salary
self.post = post
P.__init__(self, name, idnumber)
def details(self):
print("My name is {}".format(self.name))
print("IdNumber: {}".format(self.idnumber))
print("Post: {}".format(self.post))
x = Emp('Rakesh', 786013, 300000, "Employee")
x.display()
x.details()
Code:
class Bird:
def introduction(self):
print("There are many types of birds.")
def fly(self):
print("Most of the birds can fly but some cannot.")
class Pigeon(Bird):
def fly(self):
print("Pigeon can fly.")
class Emu(Bird):
def fly(self):
print("Emu cannot fly.")
obj_bird = Bird()
obj_spr = Pigeon()
obj_ost = Emu()
obj_bird.introduction()
obj_bird.fly()
obj_spr.fly()
obj_ost.fly()
Code:
class Student:
def __init__(self, name, age):
self.__name = name # Private attribute
self.__age = age # Private attribute
# Getter methods to access private attributes
def get_name(self):
return self.__name
def get_age(self):
return self.__age
# Setter methods to modify private attributes
def set_name(self, name):
self.__name = name
def set_age(self, age):
if 0 <= age <= 120: # Valid age range
self.__age = age
else:
print("Invalid age")
def display_student_info(self):
print(f"Name: {self.__name}, Age: {self.__age}")
# Create a Student object
student1 = Student("Alice", 20)
# Access private attributes using getter methods
print("Student name:", student1.get_name())
print("Student age:", student1.get_age())
# Modify private attributes using setter methods
student1.set_name("Bob")
student1.set_age(22)
# Display student information
student1.display_student_info()
Abstraction is another fundamental concept in object-oriented programming (OOP) and plays a significant role in Python. Here are some key points on Python abstraction:
from abc import ABC, abstractmethod
class Shape(ABC): # Abstract base class
@abstractmethod
def area(self):
pass
class Circle(Shape):
def __init__(self, radius):
self.radius = radius
def area(self):
return 3.14 * self.radius * self.radius
class Rectangle(Shape):
def __init__(self, length, width):
self.length = length
self.width = width
def area(self):
return self.length * self.width
In this example, the Shape class is an abstract base class with an abstract method area. Concrete subclasses like Circle and Rectangle must implement the area method, enforcing abstraction.
Python's OOP empowers developers with a structured approach to software design, ensuring efficient code organization and flexibility. By mastering these principles, developers unlock the potential to craft sophisticated, modular, and adaptable software solutions.
1. Why is OOP beneficial in Python?
OOP enhances code reusability and simplifies working with larger projects. Using classes, developers can avoid redundant code and define structures once for multiple uses.
2. What is the difference between class and object in Python?
A class is a blueprint defining attributes and methods, while an object is an instance of that class with specific data values.
3. What are the fundamental principles of OOP in Python?
The fundamental principles of OOP in Python are Inheritance, Polymorphism, Encapsulation, and Abstraction.
PAVAN VADAPALLI
Director of Engineering
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working …Read More
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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...