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* Derive concrete subclasses for parameterised generics * Resolve type issues * Add negative assertions to generic subclass tests * Remove incorrect subclassing of partial. The type was incorrectly being picked up for this style of subclassing, and it can be regardless inferred through cls. * Apply feedback: * Improve parameterisation explanation * fix typos * Alias Parameterisation type * Apply suggestions from code review * start docstring with newline. * Use None as default over empty tuple. Co-authored-by: Samuel Colvin <samcolvin@gmail.com> * Combine _assigned_parameters cases in __paramaterized_bases__ of generics * Add description for the `_assigned_parameters` variable. Co-authored-by: Samuel Colvin <samcolvin@gmail.com> Co-authored-by: Samuel Colvin <s@muelcolvin.com>
pydantic
Data validation and settings management using Python type hinting.
Fast and extensible, pydantic plays nicely with your linters/IDE/brain. Define how data should be in pure, canonical Python 3.6+; validate it with pydantic.
Help
See documentation for more details.
Installation
Install using pip install -U pydantic or conda install pydantic -c conda-forge.
For more installation options to make pydantic even faster,
see the Install section in the documentation.
A Simple Example
from datetime import datetime
from typing import List, Optional
from pydantic import BaseModel
class User(BaseModel):
id: int
name = 'John Doe'
signup_ts: Optional[datetime] = None
friends: List[int] = []
external_data = {'id': '123', 'signup_ts': '2017-06-01 12:22', 'friends': [1, '2', b'3']}
user = User(**external_data)
print(user)
#> User id=123 name='John Doe' signup_ts=datetime.datetime(2017, 6, 1, 12, 22) friends=[1, 2, 3]
print(user.id)
#> 123
Contributing
For guidance on setting up a development environment and how to make a contribution to pydantic, see Contributing to Pydantic.
Reporting a Security Vulnerability
See our security policy.
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