If I use GET (given an id) I get a JSON like:

{
    "data": {
        "id": "81",
        "ks": {
            "k1": 25,
            "k2": 5
        },
        "items": [
            {
                "id": 1,
                "name": "John",
                "surname": "Smith"
            },
            {
                "id": 2,
                "name": "Jane",
                "surname": "Doe"
            }
        ]
    },
    "server-time": "2021-12-09 14:18:40"
}

with the particular case (if id does not exist):

{
    "data": {
        "id": -1,
        "ks": "",
        "items": []
    },
    "server-time": "2021-12-10 09:35:22"
}

I would like to create a Pydantic model for managing this data structure (I mean to formally define these objects). What is the smartest way to manage this data structure by creating classes (possibly nested)?

CodePudding user response：

I see that you have taged fastapi and pydantic so i would sugest you follow the official Tutorial to learn how fastapi work. You have a whole part explaining the usage of pydantic with fastapi here.

to respond more precisely to your question pydantic models are well explain in the doc.

simple exemple:

from typing import List
from pydantic import BaseModel

class Data(BaseModel):
    id: int
    ks: str
    items: List[str]

class Something(BaseModel):
    data: Data
    # you can replace it by a pydantic time type that fit your need
    server_time: str = Field(alias="server-time")

CodePudding user response：

If you don't need data validation that pydantic offers, you can use data classes along with the dataclass-wizard for this same task. It's slightly easier as you don't need to define a mapping for lisp-cased keys such as server-time.

Simple example below:

from __future__ import annotations

from dataclasses import dataclass
from datetime import datetime

from dataclass_wizard import fromdict


@dataclass
class Something:
    data: Data
    # or simply:
    #   server_time: str
    server_time: datetime


@dataclass
class Data:
    id: int
    ks: dict[str, int]
    items: list[Person]


@dataclass
class Person:
    id: int
    name: str
    surname: str


# note: data is defined in the OP above
input_data = ...

print(fromdict(Something, input_data))

Output:

Something(data=Data(id=81, ks={'k1': 25, 'k2': 5}, items=[Person(id=1, name='John', surname='Smith'), Person(id=2, name='Jane', surname='Doe')]), server_time=datetime.datetime(2021, 12, 9, 14, 18, 40))

CodePudding user response：

from pydantic import BaseModel

class User(BaseModel): id: int name = 'Jane Doe'