OOP for Data Science
Object-oriented programming (OOP) is how Python organizes complex systems. Libraries like pandas, scikit-learn, and PyTorch are built on OOP. Understanding OOP helps you use these libraries effectively and build your own reusable components.
Classes and Objects
A class is a blueprint: . An object is an instance: .
Inheritance: means Child inherits Parent's attributes and methods.
Instance Variables and Methods
Class Variables vs Instance Variables
Class Hierarchy in Data Science
Inheritance
Inheritance lets you extend existing classes without modifying them.
Multiple Inheritance
Dunder (Magic) Methods
Dunder methods define operator behavior: calls __add__, len(obj) calls __len__, repr(obj) calls __repr__.
Property pattern: triggers @property, obj.attr = val triggers @attr.setter.
Common Dunder Methods
Properties and Encapsulation
How OOP Is Used in Data Science Libraries
scikit-learn Pattern
Custom DataFrame Extension
Design Patterns for Data Science
Strategy Pattern
Key Takeaways
- Classes encapsulate data and behavior together.
- Inheritance lets you extend functionality without rewriting code.
- Dunder methods make your objects work naturally with Python's syntax.
- Properties provide controlled access to internal state.
- Data science libraries follow consistent OOP patterns (fit/transform/predict).
- Mixins and composition are often more flexible than deep inheritance.