When a Python object keeps old values, another instance changes unexpectedly, or an override calls the wrong method, start by asking who owns the state or behavior: one function call, one instance, the class, or a place in the inheritance hierarchy. The fixes below connect each symptom to the Python rule behind it and show when to use a fresh value, deliberate sharing, or composition.
Why does my Python default list keep its old values?
Python evaluates a function’s default arguments once, when it defines the function. If that default is a list, calls that omit the argument reuse the same list.
def add_item(item, items=[]):
items.append(item)
return itemsCalling add_item("a") and then add_item("b") returns lists that contain both values. The list is not recreated for each call.
Create per-call state inside the function
Use a sentinel such as None, then create the list when the caller has not supplied one:
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def add_item(item, items=None):
if items is None:
items = []
items.append(item)
return itemsEach call that omits items now gets a fresh list. If a caller does pass a list, this function still mutates that caller-owned list. If the function should not change the supplied list, copy it before appending. The Python Programming FAQ recommends avoiding mutable objects as defaults: Mutable default values.
Why is my list shared between Python objects?
A mutable class attribute belongs to the class. Instances that look up that attribute find the same list unless they have an instance attribute of that name.
class Dog:
tricks = []
def add_trick(self, trick):
self.tricks.append(trick)Here, self.tricks resolves to the class’s list until an instance attribute shadows it. Appending through one dog therefore changes the list another dog sees.
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Put per-object data on each instance
class Dog:
def __init__(self, name):
self.name = name
self.tricks = []
def add_trick(self, trick):
self.tricks.append(trick)Now each Dog receives its own list in __init__. The distinction is ownership: use an instance attribute for state that varies by object, and a class attribute when sharing is intentional, such as a constant or a shared registry. Assigning a value to self.attribute creates or updates an instance attribute; it does not change the class attribute. The Python Tutorial explains class and instance variables: Class and instance variables.
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For a mutable data-class field that should belong to each instance, use field(default_factory=...). The factory is a zero-argument callable invoked when a default is needed.
from dataclasses import dataclass, field
@dataclass
class Cart:
items: list[str] = field(default_factory=list)Each Cart instance that uses the default gets a new list. In current Python, the data-class decorator rejects unhashable defaults as a partial safeguard against mutable defaults. This diagnostic changed in Python 3.11: earlier versions checked a narrower set of built-in mutable types, while Python 3.11 and later use unhashability as the proxy. That check does not decide whether data conceptually belongs to an instance or is meant to be shared. See the dataclasses reference on mutable defaults.
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Should this class attribute be shared or belong to each instance?
Choose based on intended ownership, not merely on whether a value is mutable. A mutable class attribute is not inherently wrong: a shared registry may be intentional. The risk is treating class-owned state as if it were private to each object.
| Choice | Ownership | Mutation and assignment | Typical use |
|---|---|---|---|
| Class attribute | Shared by instances that look it up through the class | Mutating the object is visible through those lookups. Assigning self.name shadows the class attribute for that instance. |
Constants or deliberately shared registries |
| Instance attribute | Belongs to one object | Each instance can hold and mutate its own value independently. | Names, per-object lists, configuration or other object-specific state |
When behavior is surprising, inspect where an attribute is assigned and whether it is an instance attribute or inherited from the class. The Python Tutorial’s discussion of class and instance variables describes this lookup distinction.
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Why does my subclass method not call the method I expected?
Overriding a method changes what callers reach through that method name, so an override can surprise code that relies on the base method’s assumptions. It can also fail if it does not initialize state the rest of the class expects. Before adding or keeping a subclass, ask whether code written for the base class can use the subtype without special cases. Substitutability is a design expectation, not a rule Python enforces.
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Use composition when the base interface does not fit
If the new type only needs a capability from another object, store that helper and delegate the needed operation instead of inheriting an interface the new type cannot honor. Inheritance remains useful for genuine subtype relationships and well-designed extension points; composition is a better fit when the inherited behavior or contract creates special cases.
| Decision factor | Inheritance | Composition |
|---|---|---|
| Relationship | Use when the subtype can honor the expectations of the base type. | Use when one object needs another object’s capability without being that object’s subtype. |
| Interface coupling | The subclass is coupled to the base interface and its behavior. | The containing class exposes or delegates only the operations it needs. |
| Dispatch | Overrides and the method resolution order affect which implementation runs. | Calls go through the stored helper object, making the delegation explicit. |
| Independent testing | Tests must account for inherited behavior and base-class assumptions. | The helper can be tested or replaced independently of the containing class. |
How does super() work with multiple inheritance?
In multiple inheritance, Python follows a method resolution order (MRO). super() means continue lookup after the current class in the receiver’s MRO; it does not simply mean “call my direct parent.” This lets cooperating methods pass control through a hierarchy, including a diamond-shaped one.
Make cooperative methods consistent
For methods designed to cooperate, each participant should use super() and have a compatible signature. A direct-parent call can bypass another class in the MRO or cause work to happen twice.
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class Root:
def run(self):
print("Root")
class Left(Root):
def run(self):
print("Left")
super().run()
class Right(Root):
def run(self):
print("Right")
super().run()
class Combined(Left, Right):
def run(self):
print("Combined")
super().run()
print(Combined.__mro__)
Combined().run()The MRO for Combined is Combined, Left, Right, Root, object. The calls proceed in that order because each implementation continues lookup with super(). To see the actual order for a class, inspect Class.__mro__, or use type(instance).__mro__ for an object. The Python Tutorial’s explanation of multiple inheritance and cooperative super() is in its multiple-inheritance section.
Does Python have private instance variables?
Python does not provide strictly inaccessible private instance variables. A leading underscore, as in _cache, signals that an attribute is non-public by convention. Double-leading underscores, as in __cache, trigger name mangling: Python changes the stored name to reduce accidental clashes with names in subclasses. Name mangling is not access control and does not make the value impossible to reach. Use it mainly when avoiding some subclass name conflicts; use a single leading underscore to mark internal details.
The Python Tutorial covers these privacy conventions in its private variables section.
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