2-Minute Python Guide: dataclasses

Python's @dataclass decorator is a powerful tool for creating classes that mainly hold data. But how does it compare to a regular class? Let's take a look.

Regular Class

class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age

    def __repr__(self):
        return f"Person(name={self.name}, age={self.age})"

person = Person("John", 30)
print(person)  # Person(name=John, age=30)

Now, let's create the same class using @dataclass:

from dataclasses import dataclass, field

@dataclass
class Person:
    name: str
    age: int
    occupation: str = field(default="Unknown")

person = Person("John", 30)
print(person)  # Person(name='John', age=30, occupation='Unknown')

Notice how @dataclass automatically generates __init__ and __repr__ methods for us. We can also use the field function to specify default values.

If we want to make our class immutable, we can use the frozen=True parameter:

@dataclass(frozen=True)
class Person:
    name: str
    age: int

person = Person("John", 30)
try:
    person.name = "Jane"
except FrozenInstanceError:
    print("Cannot modify a frozen instance")

Takeaway: The @dataclass decorator is a convenient way to create classes that mainly hold data, with features like automatic method generation and immutability. Use it to simplify your code and make your data classes more readable and maintainable.

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