Lesson 166 of 1596
Python Classes and OOP With AI
Classes group state and behavior. Dataclasses cut boilerplate. Let AI scaffold while you understand what's under the hood.
Creators · AI-Assisted Coding · ~27 min read
When to Reach for a Class
Reach for a class when you have state that changes over time plus behavior that acts on it. A bank account has a balance and methods that mutate it. That is a class.
@dataclass generates __init__, __repr__, and __eq__ for free. Modern Python almost never writes raw classes.
from dataclasses import dataclass, field @dataclass class Account: owner: str balance: float = 0.0 history: list[str] = field(default_factory=list) def deposit(self, amount: float) -> None: if amount <= 0: raise ValueError("amount must be positive") self.balance += amount self.history.append(f"+{amount:.2f}") def withdraw(self, amount: float) -> None: if amount > self.balance: raise ValueError("insufficient funds") self.balance -= amount self.history.append(f"-{amount:.2f}") acc = Account("Ada") acc.deposit(100) acc.withdraw(30) print(acc.balance, acc.history)When inheritance helps
Inheritance shines when the subclass genuinely is-a parent. Do not inherit just to share code.
@dataclass class SavingsAccount(Account): rate: float = 0.02 def accrue(self) -> None: interest = self.balance * self.rate self.deposit(interest) s = SavingsAccount("Bo", balance=1000.0) s.accrue() print(s.balance) # 1020.0Key terms in this lesson
The big idea: classes tie state to behavior, dataclasses remove boilerplate, and inheritance is a last resort, not a first move.
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