Code & Development
Python Decorators: Understanding @property, @classmethod, and @staticmethod
Confused by Python decorators? This guide demystifies @property, @classmethod, and @staticmethod, showing you how to write cleaner, modular code.
Have you ever seen @property, @staticmethod and felt that look of confusion? Before understanding these three, we need to understand what a decorator is for real.
First: functions are first-class citizens
In Python, functions are objects like any other. This means you can pass functions as arguments, return functions and store functions in variables.
def saudacao(): print("Olá, mundo!")# Guardando a função numa variável (sem os parênteses!)minha_fn = saudacaominha_fn() # Olá, mundo!# Passando função como argumentodef executar(fn): print("Antes...") fn() print("Depois.")executar(saudacao)# Antes...# Olá, mundo!# Depois.💡 Why does this matter? A decorator is nothing more than a function that receives another function, adds some behavior, and returns a new function. That is exactly it.
Building a decorator from scratch
Let's create a decorator that measures the execution time of any function. Useful for debugging and optimization.
import timedef medir_tempo(funcao): # funcao é a função que vamos "decorar" def wrapper(*args, **kwargs): inicio = time.time() resultado = funcao(*args, **kwargs) # executa a função original fim = time.time() print(f"⏱️ {funcao.__name__} levou {fim - inicio:.4f}s") return resultado return wrapper # retorna a função "embrulhada"# Sem a sintaxe @, seria assim:def processar_dados(): time.sleep(0.5) print("Dados processados!")processar_dados = medir_tempo(processar_dados) # manual e feioprocessar_dados()🧠 Mental model: the decorator is a wrapper. The original function goes in, the decorator puts it inside a box with extra behaviors, and returns the box.
Syntactic sugar: the @
The @ is just a prettier way to write exactly what we did above. Both versions are identical:
# Com @ — a forma que você vai usar sempre@medir_tempodef processar_dados(): time.sleep(0.5) print("Dados processados!")processar_dados()# Dados processados!# ⏱️ processar_dados levou 0.5003s# Funciona em qualquer função!@medir_tempodef buscar_usuario(id): time.sleep(0.1) return {"id": id, "nome": "Genildo"}usuario = buscar_usuario(42)# ⏱️ buscar_usuario levou 0.1001s✨ Got the magic? You wrote the logic to measure time only once and applied it to any function with a
@. This is the DRY (Don't Repeat Yourself) principle in action.
Now for real: OOP decorators
Python has three native decorators for classes that you will see everywhere:
Python Native Class Decorators
@property
Attribute-like Method
Allows methods to be accessed like attributes for controlled access.
@classmethod
Class Method
Defines methods bound to the class, not instances.
@staticmethod
Static Utility
Creates independent functions inside the class without access to instance or class.
Key decorators that shape class behavior and usage
Decorator | What it does |
|---|---|
| Method that looks like an attribute |
| Class method, not object method |
| Independent function inside the class |
@property — controlled access with elegance
Remember encapsulation with __saldo in Part 1? The @property allows you to access protected data as if they were simple attributes, but with logic underneath.
class Produto: def __init__(self, nome, preco): self.nome = nome self.__preco = preco # privado @property def preco(self): # getter: chamado ao ler produto.preco return f"R$ {self.__preco:.2f}" @preco.setter def preco(self, valor): # setter: chamado ao atribuir produto.preco = X if valor < 0: raise ValueError("Preço não pode ser negativo! 🚫") self.__preco = valorcamiseta = Produto("Camiseta Blueprint", 89.90)print(camiseta.preco) # R$ 89.90 — parece atributo, mas é método!camiseta.preco = 79.90 # usa o setter automaticamenteprint(camiseta.preco) # R$ 79.90camiseta.preco = -10 # ValueError: Preço não pode ser negativo! 🚫🔶 When to use @property? When you want access to data to have built-in logic — formatting, validation, derived calculation — but the external API remains clean, without
get_preco()andset_preco().
@classmethod — building objects in alternative ways
A @classmethod receives the class as the first argument (conventionally cls), not the object. It is widely used to create factory methods — alternative ways to instantiate a class.
class Artigo: def __init__(self, titulo, autor, categoria): self.titulo = titulo self.autor = autor self.categoria = categoria @classmethod def de_dicionario(cls, dados: dict): # cria um Artigo a partir de um dict (ex: resposta de API) return cls( titulo=dados["title"], autor=dados["author"], categoria=dados["category"] ) @classmethod def rascunho(cls, titulo): # cria um artigo rascunho com defaults return cls(titulo=titulo, autor="Desconhecido", categoria="Rascunho") def __repr__(self): return f"[{self.categoria}] {self.titulo} — {self.autor}"# Forma normala1 = Artigo("OOP em Python", "Genildo", "Python")# Via dicionário (vindo de uma API, por exemplo)payload = {"title": "Decorators", "author": "Genildo", "category": "Python"}a2 = Artigo.de_dicionario(payload)# Rascunho rápidoa3 = Artigo.rascunho("Ideia de artigo novo")print(a2) # [Python] Decorators — Genildoprint(a3) # [Rascunho] Ideia de artigo novo — Desconhecido@staticmethod — stateless utility
A @staticmethod does not receive self or cls. It is a common function that lives inside the class for logical organization, not for coupling.
class ValidadorSenha: @staticmethod def tem_tamanho_minimo(senha: str) -> bool: return len(senha) >= 8 @staticmethod def tem_numero(senha: str) -> bool: return any(c.isdigit() for c in senha) @staticmethod def validar(senha: str) -> bool: return ( ValidadorSenha.tem_tamanho_minimo(senha) and ValidadorSenha.tem_numero(senha) )print(ValidadorSenha.validar("abc")) # False — curta e sem númeroprint(ValidadorSenha.validar("blueprint1")) # True ✅⚠️ Quick rule to avoid confusion:
Need to access
self(object data)? → normal methodNeed to access
cls(the class itself)? →@classmethodDon't need either? →
@staticmethod
Custom decorator + OOP together
To wrap it up: a decorator that automatically logs every method call in a class. This pattern appears in real-world frameworks.
def logar_chamada(funcao): def wrapper(*args, **kwargs): print(f"📋 Chamando: {funcao.__name__}()") resultado = funcao(*args, **kwargs) print(f"✅ Concluído: {funcao.__name__}()") return resultado return wrapperclass ServicoDePost: def __init__(self): self.__posts = [] @logar_chamada def criar_post(self, titulo): self.__posts.append(titulo) return titulo @logar_chamada def publicar_todos(self): for post in self.__posts: print(f" 🚀 Publicando: {post}") @property def total(self): return len(self.__posts) @staticmethod def slug(titulo: str) -> str: return titulo.lower().replace(" ", "-")servico = ServicoDePost()servico.criar_post("OOP em Python")servico.criar_post("Decorators em Python")servico.publicar_todos()print(f"Total: {servico.total}")print(ServicoDePost.slug("Decorators em Python"))# decorators-em-pythonWhat you learned today
✅ Functions are objects in Python — they can be passed and returned.
✅ A decorator is a function that wraps another function.
✅ The
@is syntactic sugar — no magic, just elegance.✅
@propertycreates controlled access to private attributes.✅
@classmethodoperates on the class, not the object — great for factory methods.✅
@staticmethodis a utility function that lives in the class for organization.✅ Custom decorators + OOP = clean and reusable code.
The elite dev's arsenal.
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