Lesson 190 of 2116
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.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1When to Reach for a Class
- 2class
- 3dataclass
- 4method
Concept cluster
Terms to connect while reading
Section 1
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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