今天來介紹 pydantic, pydantic 完全支援 IDE,
你可能會說已經有內建的 PEP 484 Type Hints 介紹 和 dataclasses_tutorial.py 了, 為什麼還需要這個 ❓
原因是 pydantic 在處理 json 以及資料驗證上面比較強大 😀
我建議大家還是可以花個時間稍微看一下 😆
安裝
pip install pydantic==2.9.1官網文件 寫的非常豐富,
這邊就寫一些官方的範例給大家看
from pydantic import BaseModel
class User(BaseModel):
id: int
name: str = 'Jane Doe'
# 正確的初始化
user = User(id=3)
# 錯誤的初始化 (因為定義的是 int)
user = User(id='abc')
# 可以使用 model_dump 來完成 serialized
user.model_dump() # {'id': 3, 'name': 'Jane Doe'}from typing import List, Optional
from pydantic import BaseModel
class Foo(BaseModel):
count: int
size: Optional[float] = None
class Bar(BaseModel):
apple: str = 'x'
banana: str = 'y'
class Spam(BaseModel):
foo: Foo
bars: List[Bar]
# 以下兩種方法都可以
m = Spam(foo={'count': 4}, bars=[{'apple': 'x1'}, {'apple': 'x2'}])
m1 = Spam(foo=Foo(count=4), bars=[Bar(apple='x1'), Bar(apple='x2')])
print(m)
print(m1)from pydantic import BaseModel, ValidationError
class User(BaseModel):
id: int
name: str = 'Jane Doe'
data = dict(
id=['1', 2, 'bad'],
)
try:
User(**data)
except ValidationError as e:
print(e)from typing import Generic, List, Optional, TypeVar
from pydantic import BaseModel, ValidationError
DataT = TypeVar('DataT')
class DataModel(BaseModel):
numbers: List[int]
people: List[str]
class Response(BaseModel, Generic[DataT]):
data: Optional[DataT] = None
print(Response[int](data=1))
#> data=1
print(Response[str](data='value'))
#> data='value'
print(Response[str](data='value').model_dump())
#> {'data': 'value'}
data = DataModel(numbers=[1, 2, 3], people=[])
print(Response[DataModel](data=data).model_dump())
#> {'data': {'numbers': [1, 2, 3], 'people': []}}
try:
Response[int](data='value') # 型態錯誤
except ValidationError as e:
print(e)parametrized generic
from typing import Generic, TypeVar
from pydantic import BaseModel, ValidationError
T = TypeVar('T')
class ResponseModel(BaseModel, Generic[T]):
content: T
class Product(BaseModel):
name: str
price: float
class Order(BaseModel):
id: int
product: ResponseModel[Product]
product = Product(name='Apple', price=0.5)
response = ResponseModel[Product](content=product)
order = Order(id=1, product=response)
print(repr(order))
try:
response = ResponseModel[int](content=product)
except ValidationError as e:
print(e)Validation of unparametrized type variables
from typing import Generic
from typing_extensions import TypeVar
from pydantic import BaseModel, ValidationError
T = TypeVar('T')
U = TypeVar('U', bound=int)
V = TypeVar('V', default=str)
class Model(BaseModel, Generic[T, U, V]):
t: T
u: U
v: V
print(Model(t='t', u=1, v='v'))
#> t='t' u=1 v='v'
try:
Model(t='t', u='u', v=1)
except ValidationError as exc:
print(exc)如果 frozen 設定為 False, 是可以動態修改變數的屬性的.
from pydantic import BaseModel, ConfigDict, ValidationError
class FooBarModel(BaseModel):
model_config = ConfigDict(frozen=True)
a: str
b: dict
foobar = FooBarModel(a='hello', b={'apple': 'pear'})
try:
foobar.a = 'different'
except ValidationError as e:
print(e)
"""
1 validation error for FooBarModel
a
Instance is frozen [type=frozen_instance, input_value='different', input_type=str]
"""以下三種方法都代表必填
from pydantic import BaseModel, Field
class Model(BaseModel):
a: int
b: int = ... # emphasize that a field is required
c: int = Field(..., alias='C') # emphasize that a field is required
model = Model(a=1, b=2, C=3) # 使用 C 是因為設定了 alias
# model = Model(a=1, b=2, c=3) # ValidationError
print(model)Fields with non-hashable default values
from typing import Dict, List
from pydantic import BaseModel
class Model(BaseModel):
item_counts: List[Dict[str, int]] = [{}]
m1 = Model()
m1.item_counts[0]['a'] = 1
print(m1.item_counts)
#> [{'a': 1}]
m2 = Model()
print(m2.item_counts)
#> [{}]Fields with dynamic default values
使用 default_factory 建立 default
from datetime import datetime, timezone
from uuid import UUID, uuid4
from pydantic import BaseModel, Field
def datetime_now() -> datetime:
return datetime.now(timezone.utc)
class Model(BaseModel):
uid: UUID = Field(default_factory=uuid4)
updated: datetime = Field(default_factory=datetime_now)
print(Model())
print(Model())支援 match_case, 可參考 match_case_tutorial.py
from pydantic import BaseModel
class Pet(BaseModel):
name: str
species: str
a = Pet(name='Bones', species='dog') # Bones is a dog
# a = Pet(name='Bones', species='dog1') # No dog matched
print(a)
match a:
# match `species` to 'dog', declare and initialize `dog_name`
case Pet(species='dog', name=dog_name):
print(f'{dog_name} is a dog')
# default case
case _:
print('No dog matched')正常是可以加入額外的 fields
from pydantic import BaseModel
class Model(BaseModel):
x: int
m = Model(x=1, y='a')如果你不想要可以加入額外的 fields, 可以這樣設定
from pydantic import BaseModel, ConfigDict, ValidationError
class Model(BaseModel):
x: int
model_config = ConfigDict(extra='forbid')
try:
Model(x=1, y='a')
except ValidationError as exc:
print(exc)from pydantic import BaseModel, Field
class User(BaseModel):
name: str = Field(default='John Doe')
user = User()
print(user)或是使用前面介紹的 default_factory 都可以
from uuid import uuid4
from pydantic import BaseModel, Field
class User(BaseModel):
id: str = Field(default_factory=lambda: uuid4().hex)from uuid import uuid4
from typing_extensions import Annotated
from pydantic import BaseModel, Field
class User(BaseModel):
id: Annotated[str, Field(default_factory=lambda: uuid4().hex)]from pydantic import BaseModel, Field
class User(BaseModel):
name: str = Field(..., alias='username')
user = User(username='johndoe')
# user = User(name='johndoe') # ValidationError
print(user) # name='johndoe'
print(user.model_dump(by_alias=True)) # {'username': 'johndoe'}from pydantic import BaseModel, Field
class Foo(BaseModel):
positive: int = Field(gt=0) # greater than
non_negative: int = Field(ge=0) # greater than or equal to
negative: int = Field(lt=0) # less than
non_positive: int = Field(le=0) # less than or equal to
even: int = Field(multiple_of=2) # a multiple of the given number
love_for_pydantic: float = Field(allow_inf_nan=True) # allow 'inf', '-inf', 'nan' values
foo = Foo(
positive=1,
non_negative=0,
negative=-1,
non_positive=0,
even=2,
love_for_pydantic=float('inf'),
)
print(foo)from pydantic import BaseModel, Field
class Foo(BaseModel):
short: str = Field(min_length=3)
long: str = Field(max_length=10)
regex: str = Field(pattern=r'^\d*$')
foo = Foo(short='foo', long='foobarbaz', regex='123')
print(foo)from decimal import Decimal
from pydantic import BaseModel, Field
class Foo(BaseModel):
precise: Decimal = Field(max_digits=5, decimal_places=2)
foo = Foo(precise=Decimal('123.45'))
print(foo)from pydantic import BaseModel, Field
class User(BaseModel):
name: str = Field(strict=True)
age: int = Field(strict=False)
c: int = Field(strict=False, default=1)
user = User(name='John', age='42')
user = User(name='John', age='42', c='3')from pydantic import BaseModel, computed_field
class Box(BaseModel):
width: float
height: float
depth: float
@computed_field
@property # 可以不加, 但建議還是加進去
def volume(self) -> float:
return self.width * self.height * self.depth
b = Box(width=1, height=2, depth=3)
print(b.model_dump())
#> {'width': 1.0, 'height': 2.0, 'depth': 3.0, 'volume': 6.0}import json
from enum import Enum
from typing import Annotated
from pydantic import BaseModel, Field
from pydantic.config import ConfigDict
class FooBar(BaseModel):
count: int
size: float | None = None
class Gender(str, Enum):
male = 'male'
female = 'female'
other = 'other'
not_given = 'not_given'
class MainModel(BaseModel):
"""
This is the description of the main model
"""
model_config = ConfigDict(title='Main')
foo_bar: FooBar
gender: Annotated[Gender | None, Field(alias='Gender')] = None
snap: int = Field(
42,
title='The Snap',
description='this is the value of snap',
gt=30,
lt=50,
)
main_model_schema = MainModel.model_json_schema() # (1)!
print(json.dumps(main_model_schema, indent=2)) # (2)!from typing import Any, List
from typing_extensions import Annotated
from pydantic import BaseModel, ValidationError
from pydantic.functional_validators import AfterValidator
def check_squares(v: int) -> int:
assert v**0.5 % 1 == 0, f'{v} is not a square number'
return v
def double(v: Any) -> Any:
return v * 2
MyNumber = Annotated[int, AfterValidator(double), AfterValidator(check_squares)]
class DemoModel(BaseModel):
number: List[MyNumber]
print(DemoModel(number=[2, 8])) # number=[4, 16]
try:
DemoModel(number=[2, 4])
except ValidationError as e:
print(e)from pydantic import (
BaseModel,
ValidationError,
ValidationInfo,
field_validator,
)
class UserModel(BaseModel):
name: str
id: int
@field_validator('name')
@classmethod
def name_must_contain_space(cls, v: str) -> str:
if ' ' not in v:
raise ValueError('must contain a space')
return v.title()
# you can select multiple fields, or use '*' to select all fields
@field_validator('id', 'name')
@classmethod
def check_alphanumeric(cls, v: str, info: ValidationInfo) -> str:
if isinstance(v, str):
# info.field_name is the name of the field being validated
is_alphanumeric = v.replace(' ', '').isalnum()
assert is_alphanumeric, f'{info.field_name} must be alphanumeric'
return v
print(UserModel(name='John Doe', id=1)) # name='John Doe' id=1
try:
UserModel(name='samuel', id=1)
except ValidationError as e:
print(e)
try:
UserModel(name='John Doe', id='abc')
except ValidationError as e:
print(e)
try:
UserModel(name='John Doe!', id=1)
except ValidationError as e:
print(e)from typing import Any
from typing_extensions import Self
from pydantic import BaseModel, ValidationError, model_validator
class UserModel(BaseModel):
username: str
password1: str
password2: str
@model_validator(mode='before')
@classmethod
def check_card_number_omitted(cls, data: Any) -> Any:
if isinstance(data, dict):
assert (
'card_number' not in data
), 'card_number should not be included'
return data
@model_validator(mode='after')
def check_passwords_match(self) -> Self:
pw1 = self.password1
pw2 = self.password2
if pw1 is not None and pw2 is not None and pw1 != pw2:
raise ValueError('passwords do not match')
return self
print(UserModel(username='scolvin', password1='zxcvbn', password2='zxcvbn'))
#> username='scolvin' password1='zxcvbn' password2='zxcvbn'
try:
UserModel(username='scolvin', password1='zxcvbn', password2='zxcvbn2')
except ValidationError as e:
print(e)
try:
UserModel(
username='scolvin',
password1='zxcvbn',
password2='zxcvbn',
card_number='1234',
)
except ValidationError as e:
print(e)from typing import List
from typing_extensions import TypedDict
from pydantic import TypeAdapter, ValidationError
class User(TypedDict):
name: str
id: int
user_list_adapter = TypeAdapter(List[User])
user_list = user_list_adapter.validate_python([{'name': 'Fred', 'id': '3'}])
print(repr(user_list))
#> [{'name': 'Fred', 'id': 3}]
try:
user_list_adapter.validate_python(
[{'name': 'Fred', 'id': 'wrong', 'other': 'no'}]
)
except ValidationError as e:
print(e)
print(repr(user_list_adapter.dump_json(user_list)))
#> b'[{"name":"Fred","id":3}]'在 Performance 中, nested 的情境底下, 使用 Type Adapter 會比 model 好,
可參考 Use TypedDict over nested models
可參考 Performance tips