Question 109468 in Json Question posted in
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How can I parse a dict/json object using pydantic when the keys are also data?

Using pydantic in Python how can I parse data where I want the key of a mapping to be placed into one attribute and the value of a mapping placed into anothe rattribute?

For example, imagine my data is represented as

data = {
    "pets": [
        {"felix": "cat"},
        {"rover": "dog"},
        {"snuffles": "dog"},
    ]
}

And my pydantic models are

from typing import Literal
from pydantic import BaseModel


class Pet(BaseModel):
    name: str
    species: Literal["dog", "cat"]


class Household(BaseModel):
    pets: list[Pet]

Obviously Household(**data) doesn’t work to parse the data into the class. How can I adjust the class so this does work (efficiently).

Ideally the data would be in this format

data_transformed = {
    "pets": [
        {"name": "felix", "species": "cat"},
        {"name": "rover", "species": "dog"},
        {"name": "snuffles", "species": "dog"},
    ]
}

And this would then work.

Household(**data_transformed)

But if the data is in the original format (data), how can I write a pydantic class to parse it?

Tags:

2

Answers


  1. Chosen as BEST ANSWER
    0

    I have solved this by adding a pydantic.root_validator to the Pet class like so:

    from typing import Literal
from pydantic import BaseModel, root_validator


class Pet(BaseModel):
    name: str
    species: Literal["dog", "cat"]

    @root_validator(pre=True)
    def reorient_kv(cls, input):
        match input:
            case {"name": _, "species": _}:
                return input
            case input if len(input.keys()) == 1:
                name, species = next(iter(input.items()))
                return {"name": name, "species": species}
            case input:
                raise ValueError(f"Cannot interpret input as a pet: {input}")


class Household(BaseModel):
    pets: list[Pet]


data = {
    "pets": [
        {"felix": "cat"},
        {"rover": "dog"},
        {"snuffles": "dog"},
    ]
}

Household(**data)

# Household(
#     pets=[
#         Pet(
#             name='felix',
#             species='cat',
#         ),
#         Pet(
#             name='rover',
#             species='dog',
#         ),
#         Pet(
#             name='snuffles',
#             species='dog',
#         ),
#     ],
# )

  2. 0 
    from typing import Any, Literal
from pydantic import BaseModel, validator


class Pet(BaseModel):
    name: str
    species: Literal["dog", "cat"]


class Household(BaseModel):
    pets: list[Pet]

    @validator("pets", pre=True, each_item=True)
    def dict_to_pet(cls, v: Any) -> Any:
        if not isinstance(v, dict) or len(v) != 1:
            return v  # let the default validator handle it
        name, species = v.popitem()
        return Pet(name=name, species=species)


obj = Household.parse_obj({
    "pets": [
        {"felix": "cat"},
        {"rover": "dog"},
        {"snuffles": "dog"},
    ]
})
print(obj.json(indent=4))

    Output:

    {
    "pets": [
        {
            "name": "felix",
            "species": "cat"
        },
        {
            "name": "rover",
            "species": "dog"
        },
        {
            "name": "snuffles",
            "species": "dog"
        }
    ]
}

    This may be a matter of personal preference, but I think this is best solved by a regular validator on the Household model and not by a root validator on the Pet model.

    This seems to be a rather obscure edge-case, where the data you want to parse as a Household has weirdly-formatted pets. I would still assume that a single instance of Pet should not expect this format to ever occur, which is why I find it semantically more appropriate to perform this transformation on the Household model.

    More importantly however, I think a simple each_item=True validator makes the intent much clearer and is more readable.

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