Understanding the differences between normal classes and data classes in Python

Python offers two popular approaches for defining classes: normal classes and data classes. Understanding the strengths and weaknesses of each helps you choose the right tool for the job.

Normal classes: The classic choice

Normal classes are the workhorses of object-oriented programming in Python. They provide a flexible blueprint for creating objects with custom attributes and methods. 

Key features

1. Customisation: Define any methods and attributes needed.
2. Flexibility: Modify attributes after object creation (mutable).
3. Control: Granular control over object behaviour and interactions.

Ideal use cases:

1. Complex objects with unique behaviours.
2. Situations requiring custom logic or state management.
3. Classes intended for inheritance and extension.

Data Classes: Streamlined data structures

Data classes are a recent addition specifically designed for storing data. They offer a concise and clean way to define data models.

Key features:

1. Conciseness: Fewer code thanks to automatic boilerplate generation.
2. Immutability (Optional): Attributes remain unchanged after creation (default).
3. Type Hints: Encourage type safety and improve code readability.
4. Equality and Hashing: Default implementations based on attribute values.

Ideal use cases:

1. Simple data structures with well-defined attributes.
2. Situations where immutability is desirable.
3. Efficient data transfer and serialisation.
4. Integrating with data-driven frameworks.

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Choosing the right tool

When to choose a data class:

1. Need a simple data holder with minimal behaviour.
2. Value code conciseness and readability.
3. Prefer immutability for data integrity.

When to choose a normal class:

1. Custom methods or logic are required for object behaviour.
2. Need to modify attributes after object creation.
3. Plan to extend functionality through inheritance.

By understanding these distinctions, you can leverage the strengths of both normal and data classes to create robust and well-structured Python custom applications.