2.10 Dataclasses, properties and clean design
You write classes that stay readable after six months.
Before:00. Orientation & SetupUnlocks:03. Data Handling & Analysis04. Classical AI — Agents, Search & Knowledge Representation
Dataclasses remove the boilerplate from classes that mainly hold data, and properties add validation without changing an interface — together they keep experiment configs and results readable months later. It closes the class sequence with design judgement rather than new mechanics. The trap it targets is the plain dict adopted just for now; three weeks later nobody remembers which keys exist, and the dataclass that would have documented them costs three lines.
Work through these
@dataclass, frozen, field defaults
A decorator that writes the tedious parts of a data-holding class for you, with options for immutability and default values. It removes a great deal of boilerplate from ordinary code.
@property, setters and validation
Turning attribute access into a method call so values can be validated or computed on demand. It lets an interface stay simple while the implementation changes.
Enums and NamedTuple
Named constant sets and lightweight immutable records, both of which make code more readable than passing bare strings and positions around.
SOLID principles applied to Python, briefly
Five design principles, applied to Python rather than recited. Treated briefly here, they are a vocabulary for explaining why one arrangement of code is better than another.
The Python Tutorial · Docs
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