11

我有一个工作模型来接收json使用pydantic. 模型数据集如下所示：

data = {'thing_number': 123, 
        'thing_description': 'duck',
        'thing_amount': 4.56}

我想做的是将json文件列表作为数据集并能够验证它们。最终，列表将被转换为记录以pandas供进一步处理。我的目标是验证一个任意长的json条目列表，看起来像这样：

bigger_data = [{'thing_number': 123, 
                'thing_description': 'duck',
                'thing_amount': 4.56}, 
               {'thing_number': 456, 
                'thing_description': 'cow',
                'thing_amount': 7.89}]

我现在的基本设置如下。请注意，添加class ItemList是尝试使任意长度起作用的一部分。

from typing import List
from pydantic import BaseModel
from pydantic.schema import schema
import json

class Item(BaseModel):
    thing_number: int
    thing_description: str
    thing_amount: float

class ItemList(BaseModel):
    each_item: List[Item]

然后，基本代码将产生我认为我在一个将获取Item对象的数组对象中寻找的内容。

item_schema = schema([ItemList])
print(json.dumps(item_schema, indent=2)) 

    {
      "definitions": {
        "Item": {
          "title": "Item",
          "type": "object",
          "properties": {
            "thing_number": {
              "title": "Thing_Number",
              "type": "integer"
            },
            "thing_description": {
              "title": "Thing_Description",
              "type": "string"
            },
            "thing_amount": {
              "title": "Thing_Amount",
              "type": "number"
            }
          },
          "required": [
            "thing_number",
            "thing_description",
            "thing_amount"
          ]
        },
        "ItemList": {
          "title": "ItemList",
          "type": "object",
          "properties": {
            "each_item": {
              "title": "Each_Item",
              "type": "array",
              "items": {
                "$ref": "#/definitions/Item"
              }
            }
          },
          "required": [
            "each_item"
          ]
        }
      }
    }

该设置适用于正在传递的单个 json 项目：

item = Item(**data)                                                      

print(item)

Item thing_number=123 thing_description='duck' thing_amount=4.56

但是当我尝试将单个项目传递给ItemList模型时，它会返回一个错误：

item_list = ItemList(**data)

---------------------------------------------------------------------------
ValidationError                           Traceback (most recent call last)
<ipython-input-94-48efd56e7b6c> in <module>
----> 1 item_list = ItemList(**data)

/opt/conda/lib/python3.7/site-packages/pydantic/main.cpython-37m-x86_64-linux-gnu.so in pydantic.main.BaseModel.__init__()

/opt/conda/lib/python3.7/site-packages/pydantic/main.cpython-37m-x86_64-linux-gnu.so in pydantic.main.validate_model()

ValidationError: 1 validation error for ItemList
each_item
  field required (type=value_error.missing)

我也尝试过bigger_data传入数组，认为它需要以列表的形式开始。这也会返回一个错误 - - 虽然，我至少对字典错误有了更好的理解，但我不知道如何解决。

item_list2 = ItemList(**data_big)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-100-8fe9a5414bd6> in <module>
----> 1 item_list2 = ItemList(**data_big)

TypeError: MetaModel object argument after ** must be a mapping, not list

谢谢。

我尝试过的其他事情

我已经尝试将数据传递到特定的键中，运气好一点（也许？）。

item_list2 = ItemList(each_item=data_big)

---------------------------------------------------------------------------
ValidationError                           Traceback (most recent call last)
<ipython-input-111-07e5c12bf8b4> in <module>
----> 1 item_list2 = ItemList(each_item=data_big)

/opt/conda/lib/python3.7/site-packages/pydantic/main.cpython-37m-x86_64-linux-gnu.so in pydantic.main.BaseModel.__init__()

/opt/conda/lib/python3.7/site-packages/pydantic/main.cpython-37m-x86_64-linux-gnu.so in pydantic.main.validate_model()

ValidationError: 6 validation errors for ItemList
each_item -> 0 -> thing_number
  field required (type=value_error.missing)
each_item -> 0 -> thing_description
  field required (type=value_error.missing)
each_item -> 0 -> thing_amount
  field required (type=value_error.missing)
each_item -> 1 -> thing_number
  field required (type=value_error.missing)
each_item -> 1 -> thing_description
  field required (type=value_error.missing)
each_item -> 1 -> thing_amount
  field required (type=value_error.missing)
4

3 回答 3

12

为避免出现"each_item"在 中ItemList，您可以使用__root__Pydantic 关键字：

from typing import List
from pydantic import BaseModel

class Item(BaseModel):
    thing_number: int
    thing_description: str
    thing_amount: float

class ItemList(BaseModel):
    __root__: List[Item]    # ⯇-- __root__

要构建item_list：

just_data = [
    {"thing_number": 123, "thing_description": "duck", "thing_amount": 4.56},
    {"thing_number": 456, "thing_description": "cow", "thing_amount": 7.89},
]
item_list = ItemList(__root__=just_data)

a_json_duck = {"thing_number": 123, "thing_description": "duck", "thing_amount": 4.56}
item_list.__root__.append(a_json_duck)

支持 Pydantic 的 web 框架经常 jsonify，例如ItemList没有中间__root__关键字的 JSON 数组。

于 2019-10-31T03:55:22.647 回答
9 
from typing import List
from pydantic import BaseModel
import json


class Item(BaseModel):
    thing_number: int
    thing_description: str
    thing_amount: float


class ItemList(BaseModel):
    each_item: List[Item]

基于您的代码，将 each_item 作为项目列表

a_duck = Item(thing_number=123, thing_description="duck", thing_amount=4.56)
print(a_duck.json())

a_list = ItemList(each_item=[a_duck])

print(a_list.json())

生成以下输出：

{"thing_number": 123, "thing_description": "duck", "thing_amount": 4.56}
{"each_item": [{"thing_number": 123, "thing_description": "duck", "thing_amount": 4.56}]}

使用这些作为“入口json”：

a_json_duck = {"thing_number": 123, "thing_description": "duck", "thing_amount": 4.56}
a_json_list = {
    "each_item": [
        {"thing_number": 123, "thing_description": "duck", "thing_amount": 4.56}
    ]
}

print(Item(**a_json_duck))
print(ItemList(**a_json_list))

工作得很好并生成：

Item thing_number=123 thing_description='duck' thing_amount=4.56
ItemList each_item=[<Item thing_number=123 thing_description='duck' thing_amount=4.56>]

我们只剩下唯一的数据：

just_datas = [
    {"thing_number": 123, "thing_description": "duck", "thing_amount": 4.56},
    {"thing_number": 456, "thing_description": "cow", "thing_amount": 7.89},
]
item_list = ItemList(each_item=just_datas)
print(item_list)
print(type(item_list.each_item[1]))
print(item_list.each_item[1])

那些按预期工作：

ItemList each_item=[<Item thing_number=123 thing_description='duck'thing_amount=4.56>,<Item thin…
<class '__main__.Item'>
Item thing_number=456 thing_description='cow' thing_amount=7.89

因此，如果我遗漏了某些东西，pydantic 库会按预期工作。

我的 pydantic 版本：0.30 python 3.7.4

从相似文件中读取：

json_data_file = """[
{"thing_number": 123, "thing_description": "duck", "thing_amount": 4.56},
{"thing_number": 456, "thing_description": "cow", "thing_amount": 7.89}]"""

from io import StringIO
item_list2 = ItemList(each_item=json.load(StringIO(json_data_file)))

工作也很好。

于 2019-09-24T13:09:08.967 回答
4

以下也适用，并且不需要根类型。

从 a 转换List[dict]为 a List[Item]：

items = parse_obj_as(List[Item], bigger_data)

从 JSON 转换str为List[Item]：

items = parse_raw_as(List[Item], bigger_data_json)

从 a 转换List[Item]为 JSON str：

bigger_data_json = json.dumps(items, default=pydantic_encoder)

或使用自定义编码器：

def custom_encoder(**kwargs):
    def base_encoder(obj):
        if isinstance(obj, BaseModel):
            return obj.dict(**kwargs)
        else:
            return pydantic_encoder(obj)
    return base_encoder


bigger_data_json = json.dumps(items, default=custom_encoder(by_alias=True))
于 2021-10-08T01:35:29.083 回答