Pydantic Computed Fields: Serialize Derived Values
A Pydantic computed field makes a derived value, like an area from width and length, part of your model's serialized output. Use it to include calculated attributes when calling .model_dump().
Why it exists
Pydantic models define a data shape. But often, you need to expose values that are derived from that data, not stored directly. Before computed fields, you would have to manually add these derived values after serialization, which is clumsy and error-prone.
The mental model
Think of @computed_field as a promotion. It takes a regular Python property—a method that acts like an attribute—and promotes it to be a full-fledged member of the Pydantic model's exportable data. It is the bridge between your object's internal logic and its external JSON representation.
How it works
You apply the @computed_field decorator to a method on your Pydantic BaseModel. For best results and IDE support, you should stack it on top of a @property or @cached_property decorator. When you call .model_dump() or .model_dump_json(), Pydantic executes that method and includes its return value in the output. You can also pass arguments to @computed_field to control its behavior, like setting an alias for serialization or using repr=False to hide it from the model's string representation.
When to use it
Use computed fields when you need to include a value in your serialized output that is calculated from other fields, like a full_name from first_name and last_name. It's also perfect for expensive operations when combined with @cached_property, ensuring a costly database lookup or calculation runs only once per instance.
When not to use it
Avoid computed fields for values that are fundamental inputs to your model. If a value is required to create the object and isn't derived from other inputs, it should be a regular Pydantic field. Also, be wary of creating complex, hidden dependencies inside computed fields that make the model's behavior hard to predict.
One canonical example
A Rectangle model has width and length as integer fields. A method area(self) which returns self.width * self.length is decorated first with @property and then with @computed_field. When you create an instance like Rectangle(width=5, length=10) and call .model_dump(), the output dictionary is {'width': 5, 'length': 10, 'area': 50}, automatically including the calculated area.
Interview question
What is the primary purpose of using a Pydantic @computed_field?
- a.To define a field that must be explicitly passed as an argument during model instantiation.
- b.To automatically include a value derived from other fields in the model's serialized output.Correct
- c.To enable runtime validation of complex business logic within a model's methods.
- d.To prevent a model's internal properties from being exposed in its external representation.
Why? this is the answer
A computed field promotes a derived value to be part of the model's exportable data, automatically including it in the serialized output. It is not for defining fundamental inputs, which should be regular Pydantic fields.
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