Python OOP is an intermediate course in object-oriented Python, from object thinking and class design to encapsulation, inheritance, polymorphism, dataclasses, protocols and type hints checked with mypy, through to SOLID principles and Pythonic design patterns. It suits developers who have written Python for a while and team leads who set coding standards.
Object-oriented programming (OOP) keeps growing Python code readable, easy to change and reusable, because it groups data and the behaviour that belongs to it into objects with clear responsibilities. Widely used frameworks such as Django, FastAPI and pytest are designed this way, so people who understand OOP can read and extend framework code much faster.
This course takes people who can already write Python through object-oriented thinking and design, from identifying objects in a problem and writing classes and objects to applying abstraction, encapsulation, inheritance and polymorphism. Learners use dataclasses, abstract classes and protocols, check types with type hints, and move on to the SOLID principles and commonly used design patterns written the Python way, finishing by refactoring an existing script into object-oriented code with tests. (3 days, 6 hours per day, 18 hours in total, Intermediate level.)
Course Details
This course is the 2026 edition of Python OOP. It runs for 3 days, 6 hours per day (18 hours in total, 09:00-16:00), as lectures with labs. Intermediate level. This edition moves to Python 3.14 with projects managed by uv, adds dataclasses, typing.Protocol, the new generic syntax, the @override decorator and type checking with mypy, adds the SOLID principles and composition, and teaches design patterns the way they suit Python rather
than translating code from other languages line by line. It keeps the original core of object-oriented thinking, classes and objects, abstraction, encapsulation, inheritance, polymorphism, abstract classes and design patterns such as Singleton, Composite, Factory Method, Observer and Strategy, and finishes with a workshop that refactors an existing script into object-oriented code with tests in pytest. Every lab uses open source or free tools. Learners take home a lab guide and the source code.
Section 1: Lab: Setting Up and Why OOP
Section 2: Object Thinking and Class Design
Section 3: Lab: Classes and Objects in Python
Section 4: Lab: Encapsulation and Properties
Section 5: Lab: Dunder Methods and Dataclasses
Section 6: Lab: Inheritance and the MRO
Section 7: Polymorphism and Duck Typing
Section 8: Lab: Abstract Classes and Protocols
Section 9: Composition and the SOLID Principles
Section 10: Lab: Type Hints for Object-Oriented Code
Section 11: Design Patterns and Creational Patterns
Section 12: Lab: Structural Patterns
Section 13: Lab: Observer and Strategy
Section 14: Other Common Behavioural Patterns
Section 15: Workshop: Refactoring to OOP with Tests
No public rounds are open right now. Join the waiting list and we will contact you first when the next round opens, or ask us on LINE. Or call 02-570-8449 or 088-807-9770
Declare the class with the class keyword and its name, then write an __init__ method to set the object's initial values, with self as the first parameter, meaning the object itself. Store data as attributes on self and create an object by calling the class like a function. When a class mainly holds data, @dataclass lets Python generate __init__, __repr__ and __eq__ automatically from the type hints of each field.
Python has no interface or abstract keyword, so you use the abc module: inherit from ABC and mark the methods subclasses must implement with @abstractmethod. A subclass that does not implement them all cannot be instantiated. When you only need to state which methods an object must have without forcing inheritance, use typing.Protocol with a type checker such as mypy, which fits Python's duck typing approach.
A singleton guarantees that a class has only one instance in the whole program, such as a configuration object. A common approach is to override __new__ so it returns the existing instance if there is one, but a simpler, more Pythonic option is to create the object in a module and import it, since a module is loaded only once. Use singletons sparingly, as they make testing harder; passing the object in through the constructor is often more flexible.
It suits developers who have written Python for a while, people who have finished a basic Python course and want to move on to web applications, IT managers and team leads who review code, and students and teachers. You should already write Python, including variables, conditions, loops, functions, lists and dictionaries, and be able to use the command line and VS Code at a basic level.
You will be able to design object-oriented programs in Python, write classes and objects, apply abstraction, encapsulation, inheritance and polymorphism, use dataclasses, abstract classes and protocols, check types with mypy, structure code with the SOLID principles and design patterns such as Singleton, Factory Method, Composite, Observer and Strategy, and refactor existing code with tests in place. Participants who complete the training receive a Certificate of Completion with its own number, which can be verified online.
Yes. It can be run in-house for your team, with examples adapted to your own work, such as restructuring Python scripts or systems your team maintains into an object-oriented design with tests. You can request a quotation from this page, or contact us on LINE @itgenius or by phone on 02-570-8449.
Articles are published in Thai.
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