When an attribute name is computed at runtime, use Python’s getattr(obj, name) to read it, setattr(obj, name, value) to assign it, and delattr(obj, name) to delete it. For names already known in your code, ordinary syntax such as obj.name is usually clearer. If you need custom lookup, assignment rules, validation, or a runtime-defined schema, Python offers narrower tools for each job.
Read, set, or delete an attribute by a runtime name
The built-in functions take an object and a string attribute name. The name can come from configuration, user input, or another runtime value.
name = "theme"
value = getattr(settings, name)
setattr(settings, name, "dark")
delattr(settings, name)getattr also accepts a default value for a missing attribute:
value = getattr(settings, "theme", "light")With no default, an unavailable attribute raises AttributeError. The default applies to that missing-attribute case; it does not make arbitrary errors inside custom lookup disappear. If the attribute name is fixed in the source, prefer settings.theme because it is easier to read and inspect.
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Provide a fallback for missing attributes
Define __getattr__(self, name) when normal lookup should happen first and only missing attributes need special treatment. A common pattern is to expose values stored in a mapping through attribute syntax:
class Settings:
def __init__(self, values):
self._values = values
def __getattr__(self, name):
try:
return self._values[name]
except KeyError:
raise AttributeError(name) from NoneRaising AttributeError for an absent name preserves Python’s expected meaning of “this attribute is unavailable.” Avoid catching broad exceptions here: an unrelated bug should not be disguised as a missing attribute.
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Intercept every read only when necessary
__getattribute__(self, name) is called for every instance attribute read, not just failed lookups. That makes it appropriate only when every read needs interception. When implementing it, delegate ordinary lookup to object.__getattribute__(self, name); using self.some_attribute inside the method can call __getattribute__ again and recurse.
For writes and deletes, the corresponding hooks are __setattr__(self, name, value) and __delattr__(self, name). Keep normal behavior for names that your customization does not intend to change. Use these hooks when policy applies broadly to an object’s assignments or deletions; for reusable field-level rules, a descriptor is often a better fit.
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Use descriptors for reusable field behavior
A descriptor is an object that implements one or more of __get__, __set__, and __delete__. Descriptors mediate attribute access and are the protocol behind properties, methods, static methods, class methods, and super(). Use one when the same conversion, validation, lazy computation, or storage rule should apply consistently to multiple fields or classes. A property is a convenient managed attribute for a particular class; a custom descriptor packages reusable behavior.
Default lookup is not simply a search of an object’s __dict__. For a typical instance read, Python checks in this order:
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- A data descriptor on the class: one that defines
__set__or__delete__. - A value in the instance dictionary.
- A non-data descriptor on the class, which defines
__get__but not__set__or__delete__. - A class variable.
__getattr__, if normal lookup did not find the attribute.
Consequently, an instance dictionary value cannot override a same-named data descriptor, but it can override a non-data descriptor. Likewise, assigning obj.x does not necessarily write directly to obj.__dict__: a descriptor or custom __setattr__ can control the operation.
Choose a class, mapping, or runtime model for the data shape
Declared fields: ordinary classes and dataclasses
If the fields are known when you define the class, declare them on the class. A regular class or dataclass makes the object’s interface visible to readers and development tools. Dataclasses use annotated class variables to identify fields and generate methods on that class. A descriptor used as a field default remains active and continues to receive get and set calls.
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@dataclass(frozen=True) generates assignment and deletion methods that raise FrozenInstanceError. This emulates protection against ordinary reassignment; it is not absolute immutability.
Runtime-defined fields: Pydantic models
When field definitions themselves arrive at runtime, Pydantic provides create_model() for generating a model from those definitions. Pydantic models ignore extra input fields by default; model configuration can instead allow or forbid extra fields. These are Pydantic’s model policies, not rules imposed by Python’s attribute system.
Open-ended keys: a dictionary
If callers routinely add, remove, and enumerate arbitrary keys, a dictionary usually communicates that shape more clearly than dynamically manufacturing object attributes. Attribute syntax is most useful when names form a reasonably stable object interface; unbounded or user-controlled attribute names can be harder to inspect, validate, type-check, and document.
Pick the narrowest mechanism that fits
| Need | Good fit | Why |
|---|---|---|
| Name is a string computed at runtime | getattr, setattr, or delattr |
Built-ins directly operate on a named attribute. |
| Only missing reads need a fallback | __getattr__ |
Normal lookup runs first; the hook handles what remains missing. |
| Every instance read needs interception | __getattribute__ |
It runs for each instance read, so it can apply a universal policy. |
| Assignment or deletion needs object-wide control | __setattr__ or __delattr__ |
These hooks control assignment or deletion operations. |
| Reusable per-field conversion, validation, or storage | Descriptor or property |
Behavior is attached to a managed attribute and can be reused. |
| Fields are known when the class is written | Ordinary class or dataclass |
The declared schema is explicit. |
| Fields are defined at runtime and need a model | Pydantic create_model() |
The model can be built from runtime field definitions. |
| Arbitrary keys are the central data structure | Dictionary | Mapping operations make an open-ended key/value shape explicit. |
Why Python has no expression-based attribute syntax
Python does not support syntax such as obj.(expression). PEP 363 proposed that form, but the proposal was rejected. Use getattr, setattr, or delattr when the name is an expression instead.
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