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Python is an interpreted, object-oriented programming language. Thanks to its high-level built-in data structures and dynamic typing, it has been popular for fast development of new applications and also scripting code to combine existing components written in different languages.
Python's simple, easy to learn syntax emphasizes readability and thus reducing the cost and complication of long-term program maintenance. It supports various packages for containing code, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available at no cost for all major platforms.
Every programming language was originally designed to solve a specific problem or shortcoming. Python was developed because Guido van Rossum and his team found it exhausting to develop in C and Unix Shell scripts. Development in these languages was slow, and it took time even for experienced engineers to understand code that they had not seen before.
Learning Python allows you to build different kinds of programs and it also means that its user has a new set of tools and features available. Python can do many things including but not limited to:
Web-based
Non Web-based
References:
About Python
The Early Years of Python (Guido van Rossum)
Object-oriented programming (OOP) is a programming paradigm that is based on the concept of objects, which can contain data, in the form of fields, which are called attributes or properties, and code, in the form of procedures, which are called functions or methods. OOP emphasizes data structure and for the user to be able to structure code so that its functionality can be shared throughout the application. This is opposed to procedural programming, in which programs are built in a sequential order and procedures are called or invoked when a specific sequence of statements is to be shared and reused within the program.
References:
Object-Oriented Programming in Python
Differences Between Object-Oriented and Procedural Programming
Here are some key terms that relevant to OOP and will be illustrated by examples later in this article.
Classes and Instances:
A class is a blueprint for creating instances a.k.a. objects that share similar characteristics and behaviors. It defines a set of attributes and methods a.k.a. functions that the objects can have and perform.
A class acts as a template or a structure that allows you to create multiple instances of objects with the same properties and behaviors. Therefore, it encapsulates data and functions into a single unit, promoting code reusability and organization.
Here is an example for the class Pet:
class Pet:
def __init__(self, name, species):
self.name = name
self.species = species
def introduce(self):
print(f"Hi, my name is {self.name} and I am a {self.species}.")
def eat(self, food):
print(f"{self.name} is eating {food}.")
Instance Methods
In the above example, the Pet class has three methods:
my_pet = Pet("Max", "dog")
my_pet.introduce() # Output: Hi, my name is Max and I am a dog.
my_pet.eat("bones") # Output: Max is eating bones.
The init()-method is a special method called a constructor. It is executed automatically when a new instance of the Pet class is created. It initializes the name and species attributes for each instance.
The introduce()-method prints out a message introducing the pet with its name and species.
The eat()-method takes a parameter, food, and prints out a message indicating that the pet is eating the specified food.
Note that multiple instances of the Pet class can be created and each instance will have its own name and species attributes.
Attributes
The table below shows some potential attributes a pet of class Pet may have.
class Pet:
| id | name | age | species |
|---|---|---|---|
| 1 | Colleen | 5 | Dog |
| 2 | Rowdy | 2 | Dog |
| 3 | Whiskers | 11 | Cat |
The different columns correspond to different attributes or properties i.e. pieces of data that all Pets have but may be different among each individual pet. Here is an example for the class Pet with id, name, age and species as attributes.
class Pet:
def __init__(self, id, name, age, species):
self.id = id
self.name = name
self.age = age
self.species = species
Calling or instantiating the different pets can be done as follows.
# Creating instances of Pet class dog1 = Pet(1, “Colleen", 5, "dog”) dog2 = Pet(2, “Rowdy", 2, “dog”) cat3 = Pet(3, “Whiskers”, 11, “cat")
Some key benefits of OOP are:
Modularity and Reusability: OOP allows you to break down your code into smaller, modular objects. These objects can be reused in different parts of your program or in other programs, promoting code reusability and reducing duplication.
Encapsulation: OOP encapsulates data and functions into objects, which helps to organize and manage complex codebases. It allows the developer to hide the internal implementation details of an object and only expose a clean interface for interacting with it.
Maintainability: OOP promotes a clear and organized code structure. Objects and their interactions can be easily understood and modified, making it easier to maintain and debug your code.
Inheritance and Polymorphism: Inheritance allows you to create new classes based on existing classes, inheriting their attributes and behaviors. This promotes code reuse and helps to create a hierarchical structure of classes. Polymorphism allows objects of different classes to be used interchangeably, providing flexibility and extensibility.
Flexibility and Scalability: OOP provides a flexible and scalable approach to programming. You can easily add new features by creating new classes or modifying existing ones, without affecting other parts of your code.
Collaboration: OOP promotes collaboration among developers by providing a common structure and terminology for designing and implementing software. It allows multiple developers to work on different parts of a program simultaneously, using a shared understanding of objects and their interactions.
Testing and Debugging: OOP makes testing and debugging easier. Objects can be tested individually, making it easier to isolate and fix issues. Additionally, OOP encourages the use of modular and loosely coupled code, which makes it easier to write unit tests.
Given all the benefits of OOP in Python in the previous section that contributes to writing more organized, maintainable, and scalable code, which can improve productivity and code quality.
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