Python __init__ Mutable Defaults — The Shared State Bug
- __init__ is an initialiser, not a constructor — the object already exists when it runs. The real constructor is __new__, which you almost never need to touch.
- Use class methods as alternative constructors when your class can be built from multiple input formats — it keeps __init__ clean and gives callers a readable API (e.g.
Product.from_csv_row()). - The @property decorator is Python's way of adding getters and setters without breaking existing code — it enforces valid state while keeping attribute-style access that feels natural to callers.
- A class is a blueprint; an object is the live instance — each object holds its own data
- __init__ initializes the existing object (not construct it) — __new__ allocates memory
- Three method types: instance (self), class (@classmethod), static (@staticmethod) — one does all but pick the right one
- @property exposes computed or validated attributes without breaking callers' code
- Worst mistake: mutable default arguments in __init__ share state across all instances — use None defaults
Python OOP Debugging Cheat Sheet
Mutable default argument sharing
python -c "import inspect; print(inspect.signature(YourClass.__init__))"grep -rn "def __init__(self.*=\[\|={}" your_code/*.pyProperty setter not called — value unchanged after assignment
python -c "print(type(YourClass.name))" # should be <class 'property'>Get attribute access trace: `python -m trace --trace your_script.py 2>&1 | grep 'property'`Missing super().__init__() in child class
python -c "import inspect; mro = [c.__name__ for c in YourClass.__mro__]; print(mro)"Inspect class attributes: `python -c "from your_module import YourClass; print([a for a in dir(YourClass) if not a.startswith('_')])"`Production Incident
def __init__(self, items=[]) — the list literal is evaluated once at class definition time, not on each __init__ call. All instances share the same list object.None and create a new list inside __init__: self.items = items if items is not None else [].None as sentinel for mutable defaults and create the actual mutable inside the method body.Add a unit test that verifies instance independence: obj1.add(1); assert len(obj2.items) == 0.Production Debug GuideSymptom → Action quick reference for the three most common class-related failures
None and initialize inside the body. Also check that class attributes aren't being mutated via self.__init__ exists and assigns self.something. If the attribute is set in a method called after init, ensure that method is invoked before access. Use hasattr(obj, 'something') to check.instance.some_static() works but acts on the class instead of the instance→Check the decorator: @staticmethod methods receive no self or cls. If you need instance state, remove @staticmethod. If you need class state, use @classmethod.@property for the getter and @<property_name>.setter for the setter. The setter is not called if you assign to the underscore-named backing attribute directly (e.g., obj._salary = 50000 bypasses validation).Every serious Python codebase — from Django web apps to machine learning pipelines — is built around classes and objects. Without them, your code grows into one long, tangled script that becomes impossible to maintain past a few hundred lines. Classes let you model the real world in code: a User, a BankAccount, a Product. They bundle data and behaviour together so tightly that you can reason about one thing at a time instead of juggling dozens of loose variables and functions.
The problem OOP solves isn't a technical one — it's a human one. Our brains think in terms of things and their behaviors. A dog barks, a bank account accrues interest, a shopping cart holds items. Procedural code fights that instinct by scattering related data across functions and global state. Classes align your code with how you already think, which means fewer bugs, easier testing, and teammates who can actually read what you wrote.
By the end of this article, you'll know exactly how to define a class with meaningful attributes and methods, understand what __init__ is really doing under the hood, recognise when a class is the right tool versus a plain function or dictionary, and avoid the three most common mistakes that trip up developers making the jump to OOP in Python.
What a Class Actually Is (and Why __init__ Isn't a Constructor)
A class is a blueprint that combines state (data) and behaviour (functions) into one named unit. The moment you write class BankAccount:, Python creates a new type — just like int or str are types. When you call BankAccount(), Python creates a new instance of that type and hands it back to you.
Here's the part that trips people up: __init__ is NOT a constructor. The object already exists by the time __init__ runs. Python's actual constructor is __new__, which allocates memory and creates the instance. __init__ is an initialiser — it receives the already-created object (self) and sets its starting values. This distinction matters when you start working with inheritance and metaclasses.
self is just a reference to the specific instance being initialised. When you call account.deposit(100), Python silently rewrites it as BankAccount.deposit(account, 100). There's no magic — self is just the first positional argument, and the name self is a strong convention, not a keyword. Knowing this makes error messages like missing 1 required positional argument: 'self' instantly readable.
class BankAccount: # Class attribute — shared across ALL instances of BankAccount interest_rate = 0.03 def __init__(self, owner_name: str, opening_balance: float = 0.0): # Instance attributes — unique to each BankAccount object self.owner_name = owner_name self.balance = opening_balance self._transaction_history = [] # Underscore signals 'treat this as private' def deposit(self, amount: float) -> None: if amount <= 0: raise ValueError(f"Deposit amount must be positive, got {amount}") self.balance += amount self._transaction_history.append(f"Deposited £{amount:.2f}") def withdraw(self, amount: float) -> None: if amount > self.balance: raise ValueError("Insufficient funds") self.balance -= amount self._transaction_history.append(f"Withdrew £{amount:.2f}") def apply_interest(self) -> None: # Accessing the class attribute via self — works, but be aware of the lookup order earned = self.balance * BankAccount.interest_rate self.balance += earned self._transaction_history.append(f"Interest applied: £{earned:.2f}") def get_statement(self) -> str: lines = [f"Account owner: {self.owner_name}", f"Balance: £{self.balance:.2f}", "Transactions:"] lines.extend(f" - {entry}" for entry in self._transaction_history) return "\n".join(lines) # Creating two INDEPENDENT objects from the same class alices_account = BankAccount(owner_name="Alice", opening_balance=500.0) bobs_account = BankAccount(owner_name="Bob", opening_balance=100.0) alices_account.deposit(250.0) alices_account.apply_interest() bobs_account.deposit(50.0) bobs_account.withdraw(30.0) print(alices_account.get_statement()) print() print(bobs_account.get_statement()) # Prove they are independent — changing Bob's balance doesn't touch Alice's print(f"\nAre they the same object? {alices_account is bobs_account}")
Balance: £773.00
Transactions:
- Deposited £250.00
- Interest applied: £23.00
Account owner: Bob
Balance: £120.00
Transactions:
- Deposited £50.00
- Withdrew £30.00
Are they the same object? False
def __init__(this, name) works fine. But never do it. The Python community reads self the same way drivers read road signs — instantly and without thinking. Breaking that convention makes your code feel foreign to every Python developer who opens it.BankAccount.deposit(100) instead of account.deposit(100) produces the infamous 'missing 1 required positional argument: self'.Instance vs Class vs Static Methods — Choosing the Right Tool
Python gives you three kinds of methods, and picking the wrong one is one of the most common intermediate-level mistakes. The difference isn't just syntactic — each one signals intent to the reader.
An instance method receives self and can read and write the instance's state. Use it whenever the behaviour depends on, or changes, a specific object's data. This is 90% of your methods.
A class method receives cls (the class itself) instead of an instance. Use it for alternative constructors — ways to build an object from different inputs. The canonical example is parsing from a string or a file. You've seen this in the wild: datetime.fromisoformat('2024-01-15') is a class method.
A static method receives neither self nor cls. It's just a regular function that lives inside the class namespace because it logically belongs there. Use it for pure utility functions that relate to the class concept but don't need access to any state. If you find yourself writing a static method that accesses class data, it should probably be a class method.
class Product: # Class attribute tracking how many Product objects exist _product_count = 0 TAX_RATE = 0.20 # 20% VAT — a constant belonging to the Product concept def __init__(self, name: str, price_ex_tax: float, category: str): self.name = name self.price_ex_tax = price_ex_tax self.category = category Product._product_count += 1 # Increment shared counter on every new instance # --- INSTANCE METHOD: needs to read this specific product's price --- def price_inc_tax(self) -> float: return self.price_ex_tax * (1 + Product.TAX_RATE) def describe(self) -> str: return (f"{self.name} ({self.category}) — " f"£{self.price_ex_tax:.2f} ex VAT / " f"£{self.price_inc_tax():.2f} inc VAT") # --- CLASS METHOD: alternative constructor — build from a CSV string --- @classmethod def from_csv_row(cls, csv_string: str) -> "Product": # csv_string format: "name,price,category" name, price, category = csv_string.strip().split(",") return cls(name=name, price_ex_tax=float(price), category=category) # --- CLASS METHOD: factory that accesses shared class state --- @classmethod def total_products_created(cls) -> int: return cls._product_count # --- STATIC METHOD: pure utility — belongs here logically but needs no state --- @staticmethod def is_valid_price(price: float) -> bool: # A price check doesn't need any Product instance or class data return isinstance(price, (int, float)) and price >= 0 # Standard construction headphones = Product(name="Wireless Headphones", price_ex_tax=79.99, category="Electronics") # Alternative construction via class method — clean API, no manual parsing by the caller shirt = Product.from_csv_row("Cotton Shirt,24.99,Clothing") novel = Product.from_csv_row("The Midnight Library,8.99,Books") print(headphones.describe()) print(shirt.describe()) print(novel.describe()) print(f"\nTotal products created: {Product.total_products_created()}") # Static method — call on the class, no instance needed print(f"\nIs -5.00 a valid price? {Product.is_valid_price(-5.00)}") print(f"Is 19.99 a valid price? {Product.is_valid_price(19.99)}")
Cotton Shirt (Clothing) — £24.99 ex VAT / £29.99 inc VAT
The Midnight Library (Books) — £8.99 ex VAT / £10.79 inc VAT
Total products created: 3
Is -5.00 a valid price? False
Is 19.99 a valid price? True
Model.objects.get(), Model.objects.create() are all class-level entry points. It keeps __init__ clean and gives callers a readable, intention-revealing API.self is missing.Encapsulation with Properties — Protect State Without Sacrificing Readability
Encapsulation is about controlling how the outside world reads and writes your object's internal data. In Java, you'd write explicit getAge() and setAge() methods. Python's @property decorator gives you the same control with attribute-style access — so callers write employee.salary instead of employee.get_salary(), but you still control what happens when they do.
This matters more than it sounds. Imagine you store a temperature in Celsius internally but need to expose Fahrenheit. Or you store a user's birth date but want .age to compute dynamically. Properties let you add that logic later without breaking any code that already uses your class — that's the Open/Closed principle in action.
The underscore convention (_salary, __password) is Python's way of signalling access intent. Single underscore: 'I'd prefer you didn't touch this directly, but I trust you.' Double underscore: name mangling kicks in — Python renames it to _ClassName__attribute to prevent accidental overrides in subclasses. Neither is truly private, because Python respects adult developers. They're social contracts, not padlocks.
class Employee: def __init__(self, full_name: str, salary: float, department: str): self.full_name = full_name self.department = department self._salary = None # Will be set via the property setter below self.salary = salary # Triggers the @salary.setter validation immediately # @property turns this method into a readable attribute: employee.salary @property def salary(self) -> float: return self._salary # @salary.setter is called when someone writes: employee.salary = 50000 @salary.setter def salary(self, new_salary: float) -> None: if not isinstance(new_salary, (int, float)): raise TypeError(f"Salary must be numeric, got {type(new_salary).__name__}") if new_salary < 0: raise ValueError(f"Salary cannot be negative: {new_salary}") self._salary = float(new_salary) # A computed property — no setter needed, this is read-only @property def annual_bonus(self) -> float: # Senior staff (salary > 60k) get 15%, everyone else gets 8% rate = 0.15 if self._salary > 60_000 else 0.08 return round(self._salary * rate, 2) @property def display_name(self) -> str: # Derive first name from full name — computed on demand, not stored return self.full_name.split()[0] def __repr__(self) -> str: # __repr__ is for developers — shown in the REPL, logs, and debugging return (f"Employee(full_name={self.full_name!r}, " f"salary={self._salary}, department={self.department!r})") def __str__(self) -> str: # __str__ is for end users — shown by print() return (f"{self.display_name} | {self.department} | " f"£{self._salary:,.2f} salary | £{self.annual_bonus:,.2f} bonus") # Property setter validates on creation — no separate validate() call needed junior_dev = Employee(full_name="Maria Santos", salary=45_000, department="Engineering") senior_dev = Employee(full_name="James Okafor", salary=85_000, department="Engineering") print(junior_dev) print(senior_dev) # Property setter validates on update too junior_dev.salary = 52_000 # Promotion — triggers setter validation print(f"\nAfter promotion: {junior_dev}") # This is blocked by our setter try: junior_dev.salary = -1000 except ValueError as e: print(f"\nCaught invalid salary update: {e}") # repr() is what you see in a REPL or when printing a list of objects print(f"\nrepr: {repr(junior_dev)}")
James | Engineering | £85,000.00 salary | £12,750.00 bonus
After promotion: Maria | Engineering | £52,000.00 salary | £4,160.00 bonus
Caught invalid salary update: Salary cannot be negative: -1000
repr: Employee(full_name='Maria Santos', salary=52000.0, department='Engineering')
obj._salary = x) will bypass the setter silently._salary from outside the class, even in tests. If you must, use the property.Inheritance and Method Resolution Order — Supercharge Without Breaking
Inheritance lets a child class reuse and extend a parent's behaviour. Python supports single and multiple inheritance, and its method resolution order (MRO) determines which method is called when there's ambiguity. The MRO uses the C3 linearization algorithm — it's deterministic, but can produce surprising results if you don't understand it.
The golden rule: always call in the child's super().__init__()__init__. If you skip it, the parent's constructor never runs, and instance attributes defined there won't exist. This is the most common inheritance bug in production.
Multiple inheritance works via cooperative multiple dispatch: each class in the MRO gets a chance to run its __init__ via the chain. The MRO respects the order of base classes and ensures each class is visited exactly once. Use the super()__mro__ attribute to inspect the order.
class Employee: def __init__(self, name: str, salary: float): self.name = name self.salary = salary print(f"Employee.__init__ called for {self.name}") def work(self) -> str: return f"{self.name} is working." class Manager(Employee): def __init__(self, name: str, salary: float, team_size: int): super().__init__(name, salary) self.team_size = team_size print(f"Manager.__init__ called for {self.name}") def work(self) -> str: return f"{self.name} is managing {self.team_size} people." class Developer(Employee): def __init__(self, name: str, salary: float, tech_stack: list): super().__init__(name, salary) self.tech_stack = tech_stack print(f"Developer.__init__ called for {self.name}") def work(self) -> str: return f"{self.name} is coding with {', '.join(self.tech_stack)}." class TechLead(Manager, Developer): def __init__(self, name: str, salary: float, team_size: int, tech_stack: list): # super() follows the MRO: TechLead -> Manager -> Developer -> Employee -> object super().__init__(name, salary, team_size, tech_stack) print(f"TechLead.__init__ called for {self.name}") def work(self) -> str: return f"{self.name} is leading the team and coding." # Check MRO print("MRO:", [c.__name__ for c in TechLead.__mro__]) print() tl = TechLead("Alice", 120_000, 5, ["Python", "Kubernetes"]) print(tl.work()) print() # Demonstrate that single inheritance still works mgr = Manager("Bob", 90_000, 3) print(mgr.work())
Employee.__init__ called for Alice
Developer.__init__ called for Alice
Manager.__init__ called for Alice
TechLead.__init__ called for Alice
Alice is leading the team and coding.
Employee.__init__ called for Bob
Manager.__init__ called for Bob
Bob is managing 3 people.
super().__init__() doesn't just call the parent's __init__ — it calls the next class in the MRO. That's why the order matters. In the TechLead example, super() in Developer's __init__ calls Employee's __init__, not Manager's. The MRO ensures each class in the chain is called exactly once.super().__init__() in a subclass is the top cause of missing attribute errors in production. The parent's attributes are never initialized, so self.name raises AttributeError.super() in any class breaks the entire chain, leaving some parent attributes uninitialized.super().__init__() in every __init__ — even if you think the parent doesn't need it. Consistency prevents bugs.super().__init__() in child class __init__ methods.super() call follows the MRO, not just the 'first' parent.Magic Methods — Customize Object Behaviour for Production Code
Magic methods (dunder methods) let you define how your objects behave with Python's built-in operations: , print()==, , len(), iteration, and more. They're the difference between a class that feels like a Python native and one that feels clunky.str()
__repr__: unambiguous developer-facing representation__str__: user-facing string (falls back to__repr__if missing)__eq__and__hash__: equality and hashability (must be paired for use in sets/dicts)__len__: support forlen(obj)__getitem__: subscription (obj[key])__call__: make an object callable ()obj()
Critical pairing: if you define __eq__, you should either define __hash__ or set it to None. Mutable objects should set __hash__ = None to prevent them from being used in sets or dict keys — mutating an object that's in a set breaks the data structure.
class Vector: def __init__(self, x: float, y: float): self.x = x self.y = y def __repr__(self) -> str: return f"Vector({self.x}, {self.y})" def __str__(self) -> str: return f"({self.x}, {self.y})" def __eq__(self, other: object) -> bool: if not isinstance(other, Vector): return NotImplemented return self.x == other.x and self.y == other.y def __hash__(self) -> int: # Since Vector is mutable in theory, but we treat as immutable, we provide hash return hash((self.x, self.y)) def __add__(self, other: 'Vector') -> 'Vector': return Vector(self.x + other.x, self.y + other.y) def __len__(self) -> int: # Manhatten length as a silly example return int(abs(self.x) + abs(self.y)) def __getitem__(self, index: int) -> float: if index == 0: return self.x elif index == 1: return self.y raise IndexError("Vector index out of range") def __call__(self) -> float: # Return magnitude return (self.x ** 2 + self.y ** 2) ** 0.5 # Using magic methods v1 = Vector(3, 4) v2 = Vector(3, 4) v3 = Vector(1, 2) print(repr(v1)) # __repr__ print(str(v1)) # __str__ print(v1 == v2) # __eq__ print(v1 == v3) # __eq__ print(hash(v1)) # __hash__ (used in sets/dicts) s = {v1, v2} # __hash__ + __eq__ print(f"Set size: {len(s)}") # Because v1 == v2, only one element print(v1 + v3) # __add__ print(len(v1)) # __len__ print(v1[0], v1[1]) # __getitem__ print(v1()) # __call__ as magnitude
(3, 4)
True
False
1076899327
Set size: 1
Vector(4, 6)
7
3 4
5.0
__eq__ but not __hash__. Instances become unhashable — you can't use them in sets or as dict keys. If you try, you get a TypeError.__eq__ and __hash__ on a class that's actually mutable, then mutate an instance while it's in a set. The set gets corrupted — you can't find the object anymore.__hash__ = None to avoid the hazard entirely.| Feature | Instance Method | Class Method | Static Method |
|---|---|---|---|
| First parameter | self (instance) | cls (the class) | None |
| Access instance state? | Yes | No | No |
| Access class state? | Yes (via self or ClassName) | Yes (via cls) | No |
| Decorator needed? | None | @classmethod | @staticmethod |
| Primary use case | Object behaviour & mutation | Alternative constructors | Utility functions related to the concept |
| Call on instance? | Yes | Yes (but unusual) | Yes (but unusual) |
| Call on class? | No (needs an instance) | Yes — preferred | Yes — preferred |
| Real-world example | account.deposit(100) | datetime.fromisoformat() | str.maketrans() |
🎯 Key Takeaways
- __init__ is an initialiser, not a constructor — the object already exists when it runs. The real constructor is __new__, which you almost never need to touch.
- Use class methods as alternative constructors when your class can be built from multiple input formats — it keeps __init__ clean and gives callers a readable API (e.g.
Product.from_csv_row()). - The @property decorator is Python's way of adding getters and setters without breaking existing code — it enforces valid state while keeping attribute-style access that feels natural to callers.
- Always implement __repr__ for any class you'll use in production. It's what appears in logs, debuggers, and the REPL — making it useful saves you hours of print-statement debugging.
- Inheritance requires consistent
super().calls in every subclass. Skipping it breaks the chain and leaves parent attributes uninitialised.__init__() - If you define __eq__, also define __hash__ — or explicitly set __hash__ = None for mutable classes to prevent corrupted sets/dicts.
⚠ Common Mistakes to Avoid
Interview Questions on This Topic
- QWhat's the difference between a class attribute and an instance attribute, and can you describe a bug that arises from confusing the two?JuniorReveal
- QWhen would you choose a @classmethod over a @staticmethod, and vice versa? Give a concrete example for each.Mid-levelReveal
- QWhat does Python's @property decorator actually do under the hood, and how does it let you add validation to an attribute without changing the class's public API?SeniorReveal
- QExplain Python's method resolution order (MRO) and how it resolves the diamond problem in multiple inheritance.SeniorReveal
Frequently Asked Questions
What is the difference between a class and an object in Python?
A class is the blueprint — it defines the structure and behaviour but holds no data itself. An object is a live instance created from that blueprint, with its own copy of the instance attributes. You can create thousands of independent objects from one class, just like stamping cookies from one cutter.
Why does Python use 'self' in class methods?
When you call a method on an instance, Python automatically passes that instance as the first argument. 'self' is just the conventional name for that parameter — it's how the method knows which object's data to read or change. Technically you can name it anything, but the convention is universal and you should always follow it.
When should I use a class instead of a plain dictionary or function in Python?
Use a class when you have both data AND behaviour that belong together and will be used repeatedly. A plain dictionary is fine for passive data bags. A function is fine for a single transformation. But if you find yourself writing functions that all take the same dictionary as their first argument, that's a strong signal to reach for a class instead.
What is the purpose of __repr__ vs __str__?
__repr__ should be an unambiguous representation of the object, ideally valid Python code that could recreate it (e.g., Vector(3, 4)). It's used by the REPL, logging, and debugging tools. __str__ should be a human-readable representation (e.g., (3, 4)). It's used by and print(). Python falls back to __repr__ if __str__ is missing, but not the other way around. Always implement at least __repr__ for every class you use in production.str()
How does Python's name mangling work with double underscores?
When you write self.__attribute, Python automatically rewrites it to self._ClassName__attribute. This prevents accidental overrides in subclasses. For example, a parent class with self.__private and a child that also defines self.__private will have distinct attributes: _Parent__private and _Child__private. It's not true privacy (you can still access the mangled name), but it avoids name collisions. Use double underscores only when you specifically need to prevent subclass interference; use single underscore for most internal attributes.
Developer and founder of TheCodeForge. I built this site because I was tired of tutorials that explain what to type without explaining why it works. Every article here is written to make concepts actually click.