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* Add exclude/include as field parameters - Add "exclude" / "include" as a field parameter so that it can be configured using model config (or fields) instead of purely at `.dict` / `.json` export time. - Unify merging logic of advanced include/exclude fields - Add tests for merging logic and field/config exclude/include params - Closes #660 * Precompute include/exclude fields for class * Increase test coverage * Remove (now) redundant type checks in Model._iter: New exclusion/inclusion algorithms guarantee that no sets are passed further down. * Add docs for advanced field level exclude/include settings * Minimal optimization for simple exclude/include export Running benchmarks this vs. master is at: this: pydantic best=33.225μs/iter avg=33.940μs/iter stdev=1.120μs/iter version=1.7.3 master: pydantic best=32.901μs/iter avg=33.276μs/iter stdev=0.242μs/iter version=1.7.3 * Apply review comments on exclude/enclude field arguments * Fix/simplify type annotations * Allow both ``True`` and ``Ellipsis`` to be used to indicate full field exclusion * Reenable hypothesis plugin (removed by mistake) * Update advanced include/include docs to use ``True`` instead of ``...`` * Move field info exclude/include updates into FieldInfo class This way, the model field object does not need to concern itself with dealing with field into specific fields. (Same was done for alias in a previous commit). * remove double back tick in markdown. Co-authored-by: Samuel Colvin <samcolvin@gmail.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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