Introduction
Pydantic is a Python library for data verification and setting management. It provides powerful data verification capabilities through type hints. This article will discuss in-depth in PydanticOptionalandUnionThe use of types, both of which are particularly important when dealing with optional fields and multi-type fields.
Optional Type
OptionalType is used to indicate that a field can be a specified type or None. This is very useful when optional fields are needed.
Define optional fields
passOptional fields can be defined. In actual use,Optional[X]Equivalent toUnion[X, None]。
from typing import Optional
from pydantic import BaseModel
class User(BaseModel):
id: int
name: str
age: Optional[int] = None # age can be int or None
user1 = User(id=1, name='Alice', age=30)
user2 = User(id=2, name='Bob') # age omitted, default to None
print(user1)
print(user2)
Verify optional fields
Pydantic automatically handles verification of optional fields. If the field value isNoneor not provide a value, it will not throw a validation error.
from pydantic import ValidationError
try:
user = User(id=3, name='Charlie', age='thirty')
except ValidationError as e:
print(e)
The above code will be becauseageCannot convert to integers and raise verification errors.
Union Type
The Union type is used to represent that a field can be one of multiple types. It allows for more flexible data input.
Define multi-type fields
passIt is possible to define fields that can accept multiple types.
from typing import Union
from pydantic import BaseModel
class Item(BaseModel):
id: int
value: Union[str, int] # value can be str or int
item1 = Item(id=1, value='a string')
item2 = Item(id=2, value=100)
print(item1)
print(item2)
Verify multi-type fields
Pydantic tries to match the field values to each type listed in the Union type until successful.
from pydantic import ValidationError
try:
item = Item(id=3, value=[1, 2, 3]) # list is not a legal typeexcept ValidationError as e:
print(e)
The above code will be becausevaluenostrorintThis triggers a verification error.
A combination of Optional and Union
In practical applications, we often need to use it in combinationOptionalandUnionTo handle more complex scenarios.
Optional multi-type fields
We can useOptional[Union[X, Y]]The field can beXType orYType, or None.
class Product(BaseModel):
id: int
discount: Optional[Union[int, float]] = None # discount can be int, float, or None
product1 = Product(id=1, discount=20)
product2 = Product(id=2, discount=15.5)
product3 = Product(id=3) # discount omitted, default is None
print(product1)
print(product2)
print(product3)
Verify optional multi-type fields
Pydantic will verify in orderUnionEach type listed in and allow fields to beNone。
from pydantic import ValidationError
try:
product = Product(id=4, discount='50%') # str is not a legal typeexcept ValidationError as e:
print(e)
The above code will be becausediscountnointorfloatThis triggers a verification error.
Summarize
Pydantic'sOptionalandUnionTypes provide a flexible data verification mechanism, allowing us to handle complex optional and multi-type fields. When defining data models, using these two types reasonably can significantly improve the robustness and readability of the code.
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