We Are Going To Discuss About Define a Pydantic (nested) model. So lets Start this Python Article.
Define a Pydantic (nested) model
- How to solve Define a Pydantic (nested) model
If you don't need data validation that
pydanticoffers, you can use data classes along with thedataclass-wizardfor this same task. It's slightly easier as you don't need to define a mapping for lisp-cased keys such asserver-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)) - Define a Pydantic (nested) model
If you don't need data validation that
pydanticoffers, you can use data classes along with thedataclass-wizardfor this same task. It's slightly easier as you don't need to define a mapping for lisp-cased keys such asserver-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))
Solution 1
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))
Original Author rv.kvetch Of This Content
Solution 2
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")
Original Author Bastien B Of This Content
Solution 3
from pydantic import BaseModel
class User(BaseModel):
id: int
name = "Jane Doe"
Original Author Divyanshu Lohar Of This Content
Conclusion
So This is all About This Tutorial. Hope This Tutorial Helped You. Thank You.
