Introduction
Python is a high-level, interpreted programming language known for its readability and versatile capabilities. It has grown to become one of the most popular programming languages in the world.
Timeline
- Late 1980s: Guido van Rossum began working on Python as a successor to the ABC language at Centrum Wiskunde & Informatica (CWI) in the Netherlands.
- February 1991: First public release of Python (version
0.9.0
) was posted to the alt.sources newsgroup. - 1994:
Python 1.0
was released. - 2000:
Python 2.0
introduced list comprehensions and garbage collection. - 2008:
Python 3.0
(akaPy3k
) was released. It was not backward compatible. - 2020:
Python 2
reached end of life. - 2025:
Python 3.13
brought enhanced performance and AI/ML improvements.
Why It Matters
Python's simplicity and powerful libraries make it a top choice for web development, data science, AI, and automation. Its open-source nature and strong community continue to drive adoption.
Motivation behind Python Development
Python was developed to address the shortcomings of the ABC programming language, particularly in terms of extensibility and ease of use. Guido van Rossum aimed to create a language that was easy to read and write, while also being powerful enough for complex tasks. The language's design philosophy emphasizes code readability, simplicity, and explicitness, making it accessible to both beginners and experienced programmers.
Python is a pure object oriented language
Python is considered a pure object-oriented language because everything in Python is an object, including functions and classes. This allows for a consistent and flexible programming model, enabling developers to create complex applications with ease. Python's object-oriented features, such as inheritance, encapsulation, and polymorphism, allow for code reuse and modular design. This makes it easier to maintain and extend codebases over time. Additionally, Python's dynamic typing and duck typing principles further enhance its object-oriented capabilities, allowing for more flexible and reusable code. This means that Python allows for a more natural and intuitive way of programming, as developers can focus on the behavior of objects rather than their specific types. Overall, Python's pure object-oriented nature makes it a powerful and versatile language for a wide range of applications, from web development to data analysis and machine learning.
- Everything is an object, including functions and classes.
- Supports inheritance, encapsulation, and polymorphism.
- Dynamic typing and duck typing principles enhance flexibility.
- Encourages code reuse and modular design.
Python's object-oriented features include:
- Classes and objects: Python allows you to define classes and create objects, encapsulating data and behavior.
- Inheritance: You can create new classes based on existing ones, promoting code reuse.
- Encapsulation: Data and methods are bundled together, allowing for better organization and control over access.
- Polymorphism: Different classes can be treated as instances of the same class through a common interface.
- Dynamic typing: Variables can hold objects of different types, allowing for flexibility in programming.
- Duck typing: The type of an object is determined by its behavior (methods and properties) rather than its explicit type.
- Method overriding: Subclasses can provide specific implementations of methods defined in their parent classes.
- Operator overloading: You can define how operators behave with user-defined classes.
- Magic methods: Special methods (like
__init__
,__str__
, etc.) that allow you to define custom behavior for your classes. - Composition: You can create complex objects by combining simpler ones, promoting code reuse and modularity.
- Interfaces: Python supports the concept of interfaces, allowing you to define a contract for classes to implement.
- Abstract base classes: You can define abstract classes that cannot be instantiated, serving as a blueprint for other classes.
- Mixins: You can create classes that provide additional functionality to other classes through inheritance.
- Class methods and static methods: You can define methods that operate on the class itself rather than instances.
- Properties: You can define getter and setter methods for class attributes, allowing for controlled access.
- Class variables: Variables that are shared among all instances of a class, allowing for shared state.
- Instance variables: Variables that are unique to each instance of a class, allowing for individual state.
- Class decorators: You can use decorators to modify the behavior of classes and methods.
- Type hints: Python supports type annotations, allowing you to specify the expected types of function arguments and return values.
- Type checking: You can use tools like mypy to perform static type checking on your Python code.
- Data classes: Python 3.7 introduced data classes, which automatically generate special methods for classes used to store data.
- Named tuples: A subclass of tuples that allows you to define named fields for better readability.
- Enums: Python supports enumerations, allowing you to define a set of named values.