在看这个之前，请先看
插件
在 pycharm 上，安装 pydantic 插件，可以检测你的语法对不对。
使用
简单的来说，Pydantic 就是结合注解的一种解析行库。
首先，我们看一个简单的例子
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| from pydantic import BaseModel
from typing import Optional, List
from datetime import datetime
class Score(BaseModel):
score: int = 100
class User(BaseModel):
id: int
name: str = "xxx"
score: Score = Score()
sign_ts: Optional[datetime] = None
friends: List[int] = []
external_data = {
"id": 123,
"name": "8765",
"sign_ts": "2022-12-22 12:22",
"friends": [123, "3"],
"score": {
"score": 90
}
}
user = User(**external_data)
print(user)
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这个 user 的结构如下
和 python | @dataclass 进行对比，其可以通过 dict 还原对象。
这种用法在 Pydantic 叫做递归模性。
捕获异常 ValidationError
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| from pydantic import BaseModel, ValidationError
from typing import Optional, List
from datetime import datetime
class Score(BaseModel):
score: int = 100
class User(BaseModel):
id: int
name: str = "xxx"
score: Score = Score()
sign_ts: Optional[datetime] = None
friends: List[int] = []
try:
user = User(id=123, friends=[1, 2, "not number"])
except ValidationError as e:
print(e.json())
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则输出
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| [
{
"loc": [
"friends",
2
],
"msg": "value is not a valid integer",
"type": "type_error.integer"
}
]
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这里面说的非常清楚，即 friends 中下标 2 的值出了问题。
相关 API
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| from pydantic import BaseModel, ValidationError
from typing import Optional, List
from datetime import datetime
from pathlib import Path
class Score(BaseModel):
score: int = 100
class User(BaseModel):
id: int
name: str = "xxx"
score: Score = Score()
sign_ts: Optional[datetime] = None
friends: List[int] = []
user = User(id=123, friends=[1, 2])
print(user.dict())
print(user.json())
print(user.copy()) # 浅层 copy
exter_data = {"id": 123, "friends": [1, 2, 3]}
print(User.parse_obj(obj=exter_data)) # 解析
print(User.parse_raw('{"id": 123, "friends": [1, 2, 3]}')) # 解析原生字符串
path = Path("./t.txt")
path.write_text('{"id": 123, "friends": [1, 2, 3]}')
print(User.parse_file('./t.txt'))
data = {"id": "123", "friends": [1, 2, 3]}
print(User.construct(**data))
print(User.__fields__.keys()) # 定义模型类的时候，所有字段都注明类型，字段顺序就不会乱
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