Python編程pydantic觸發(fā)及訪問錯(cuò)誤處理

更新時(shí)間：2021年09月29日 09:00:48 作者：小菠蘿測(cè)試筆記

這篇文章主要為大家介紹了Python編程中pydantic會(huì)觸發(fā)及發(fā)生訪問錯(cuò)誤的處理，有需要的朋友可以借鑒參考下，希望能夠有所幫助，祝大家多多進(jìn)步

常見觸發(fā)錯(cuò)誤的情況

如果傳入的字段多了會(huì)自動(dòng)過濾
如果傳入的少了會(huì)報(bào)錯(cuò)，必填字段
如果傳入的字段名稱對(duì)不上也會(huì)報(bào)錯(cuò)
如果傳入的類型不對(duì)會(huì)自動(dòng)轉(zhuǎn)換
如果不能轉(zhuǎn)換則會(huì)報(bào)錯(cuò)

錯(cuò)誤的觸發(fā)

pydantic 會(huì)在它正在驗(yàn)證的數(shù)據(jù)中發(fā)現(xiàn)錯(cuò)誤時(shí)引發(fā) ValidationError

注意

驗(yàn)證代碼不應(yīng)該拋出 ValidationError 本身

而是應(yīng)該拋出 ValueError、TypeError、AssertionError 或他們的子類

ValidationError 會(huì)包含所有錯(cuò)誤及其發(fā)生方式的信息

訪問錯(cuò)誤的方式

e.errors() ：返回輸入數(shù)據(jù)中發(fā)現(xiàn)的錯(cuò)誤的列表

e.json() ：以 JSON 格式返回錯(cuò)誤(推薦)

str(e) ：以人類可讀的方式返回錯(cuò)誤

簡(jiǎn)單栗子

# 一定要導(dǎo)入 ValidationError
from pydantic import BaseModel, ValidationError 
class Person(BaseModel):
    id: int
    name: str
 try:
    # id是個(gè)int類型，如果不是int或者不能轉(zhuǎn)換int會(huì)報(bào)錯(cuò)
    p = Person(id="ss", name="hallen")  
except ValidationError as e:
　　# 打印異常消息
    print(e.errors())

e.errors() 的輸出結(jié)果

[{'loc': ('id',), 'msg': 'value is not a valid integer', 'type': 'type_error.integer'}]

e.json() 的輸出結(jié)果

[
  {
    "loc": [
      "id"
    ],
    "msg": "value is not a valid integer",
    "type": "type_error.integer"
  }
]

str(e) 的輸出結(jié)果

1 validation error for Person
id
  value is not a valid integer (type=type_error.integer)

復(fù)雜栗子

class Location(BaseModel):
    lat = 0.1
    lng = 10.1
class Model(BaseModel):
    is_required: float
    gt_int: conint(gt=42)
    list_of_ints: List[int] = None
    a_float: float = None
    recursive_model: Location = None  
data = dict(
    list_of_ints=['1', 2, 'bad'],
    a_float='not a float',
    recursive_model={'lat': 4.2, 'lng': 'New York'},
    gt_int=21
) 
try:
    Model(**data)
except ValidationError as e:
    print(e.json(indent=4))

輸出結(jié)果

[
    {
        "loc": [
            "is_required"
        ],
        "msg": "field required",
        "type": "value_error.missing"
    },
    {
        "loc": [
            "gt_int"
        ],
        "msg": "ensure this value is greater than 42",
        "type": "value_error.number.not_gt",
        "ctx": {
            "limit_value": 42
        }
    },
    {
        "loc": [
            "list_of_ints",
            2
        ],
        "msg": "value is not a valid integer",
        "type": "type_error.integer"
    },
    {
        "loc": [
            "a_float"
        ],
        "msg": "value is not a valid float",
        "type": "type_error.float"
    },
    {
        "loc": [
            "recursive_model",
            "lng"
        ],
        "msg": "value is not a valid float",
        "type": "type_error.float"
    }
]

value_error.missing：必傳字段缺失

value_error.number.not_gt：字段值沒有大于 42

type_error.integer：字段類型錯(cuò)誤，不是 integer

自定義錯(cuò)誤

# 導(dǎo)入 validator
from pydantic import BaseModel, ValidationError, validator 
class Model(BaseModel):
    foo: str
 
    # 驗(yàn)證器
    @validator('foo')
    def name_must_contain_space(cls, v):
        if v != 'bar':
            # 自定義錯(cuò)誤信息
            raise ValueError('value must be bar')
        # 返回傳進(jìn)來的值
        return v 
try:
    Model(foo="ber")
except ValidationError as e:
    print(e.json())

輸出結(jié)果

[
  {
    "loc": [
      "foo"
    ],
    "msg": "value must be bar",
    "type": "value_error"
  }
]

自定義錯(cuò)誤模板類

from pydantic import BaseModel, PydanticValueError, ValidationError, validator 
class NotABarError(PydanticValueError):
    code = 'not_a_bar'
    msg_template = 'value is not "bar", got "{wrong_value}"' 
class Model(BaseModel):
    foo: str
 
    @validator('foo')
    def name_must_contain_space(cls, v):
        if v != 'bar':
            raise NotABarError(wrong_value=v)
        return v
  try:
    Model(foo='ber')
except ValidationError as e:
    print(e.json())

輸出結(jié)果

[
  {
    "loc": [
      "foo"
    ],
    "msg": "value is not \"bar\", got \"ber\"",
    "type": "value_error.not_a_bar",
    "ctx": {
      "wrong_value": "ber"
    }
  }
]