python-data-classes

# Python Data Classes

Master Python data modeling using dataclasses, attrs, and Pydantic for
creating clean, type-safe data structures with validation and serialization.

## dataclasses Module

**Basic dataclass usage:**

```python
from dataclasses import dataclass

@dataclass
class User:
    id: int
    name: str
    email: str
    is_active: bool = True  # Default value

# Create instance
user = User(
    id=1,
    name="Alice",
    email="alice@example.com"
)

print(user)
# User(id=1, name='Alice', email='alice@example.com', is_active=True)

print(user.name)  # Alice
```

**dataclass with methods:**

```python
from dataclasses import dataclass

@dataclass
class Point:
    x: float
    y: float

    def distance_from_origin(self) -> float:
        return (self.x ** 2 + self.y ** 2) ** 0.5

    def move(self, dx: float, dy: float) -> "Point":
        return Point(self.x + dx, self.y + dy)

point = Point(3.0, 4.0)
print(point.distance_from_origin())  # 5.0
new_point = point.move(1.0, 1.0)
print(new_point)  # Point(x=4.0, y=5.0)
```

## dataclass Parameters

**Controlling dataclass behavior:**

```python
from dataclasses import dataclass, field

# frozen=True makes it immutable
@dataclass(frozen=True)
class ImmutableUser:
    id: int
    name: str

# order=True enables comparison operators
@dataclass(order=True)
class Person:
    age: int
    name: str

p1 = Person(30, "Alice")
p2 = Person(25, "Bob")
print(p1 > p2)  # True (compares by age first)

# slots=True uses __slots__ for memory efficiency
@dataclass(slots=True)
class Coordinate:
    x: float
    y: float

# kw_only=True requires keyword arguments
@dataclass(kw_only=True)
class Config:
    host: str
    port: int

config = Config(host="localhost", port=8080)
```

## Field Configuration

**Using field() for advanced configuration:**

```python
from dataclasses import dataclass, field
from typing import List

@dataclass
class Product:
    name: str
    price: float

    # Exclude from __init__
    id: int = fie