Skip to main content

Adds some YAML functionality to the excellent `pydantic` library.

Project description


PyPI version Documentation Status Unit Tests

Pydantic-YAML adds YAML capabilities to Pydantic, which is an excellent Python library for data validation and settings management. If you aren't familiar with Pydantic, I would suggest you first check out their docs.

Documentation on

Basic Usage

from enum import Enum
from pydantic import BaseModel, validator
from pydantic_yaml import parse_yaml_raw_as, to_yaml_str

class MyEnum(str, Enum):
    """A custom enumeration that is YAML-safe."""

    a = "a"
    b = "b"

class InnerModel(BaseModel):
    """A normal pydantic model that can be used as an inner class."""

    fld: float = 1.0

class MyModel(BaseModel):
    """Our custom Pydantic model."""

    x: int = 1
    e: MyEnum = MyEnum.a
    m: InnerModel = InnerModel()

    def _chk_x(cls, v: int) -> int:  # noqa
        """You can add your normal pydantic validators, like this one."""
        assert v > 0
        return v

m1 = MyModel(x=2, e="b", m=InnerModel(fld=1.5))

# This dumps to YAML and JSON respectively
yml = to_yaml_str(m1)
jsn = m1.json()

# This parses YAML as the MyModel type
m2 = parse_yaml_raw_as(MyModel, yml)
assert m1 == m2

# JSON is also valid YAML, so this works too
m3 = parse_yaml_raw_as(MyModel, jsn)
assert m1 == m3

With Pydantic v2, you can also dump dataclasses:

from pydantic import RootModel
from pydantic.dataclasses import dataclass
from pydantic.version import VERSION as PYDANTIC_VERSION
from pydantic_yaml import to_yaml_str

assert PYDANTIC_VERSION >= "2"

class YourType:
    foo: str = "bar"

obj = YourType(foo="wuz")
assert to_yaml_str(RootModel[YourType](obj)) == 'foo: wuz\n'


Currently we use the JSON dumping of Pydantic to perform most of the magic.

This uses the Config inner class, as in Pydantic:

class MyModel(BaseModel):
    # ...
    class Config:
        # You can override these fields, which affect JSON and YAML:
        json_dumps = my_custom_dumper
        json_loads = lambda x: MyModel()
        # As well as other Pydantic configuration:
        allow_mutation = False

You can control some YAML-specfic options via the keyword options:

to_yaml_str(model, indent=4)  # Makes it wider
to_yaml_str(model, map_indent=9, sequence_indent=7)  # ... you monster.

You can additionally pass your own YAML instance:

from ruamel.yaml import YAML
my_writer = YAML(typ="safe")
my_writer.default_flow_style = True
to_yaml_file("foo.yaml", model, custom_yaml_writer=my_writer)

A separate configuration for YAML specifically will be added later, likely in v2.

Breaking Changes for pydantic-yaml V1

The API for pydantic-yaml version 1.0.0 has been greatly simplified!

Mixin Class

This functionality has currently been removed! YamlModel and YamlModelMixin base classes are no longer needed. The plan is to re-add it before v1 fully releases, to allow the .yaml() or .parse_*() methods. However, this will be availble only for pydantic<2.

Versioned Models

This functionality has been removed, as it's questionably useful for most users. There is an example in the docs that's available.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pydantic_yaml-1.3.0.tar.gz (21.3 kB view hashes)

Uploaded Source

Built Distribution

pydantic_yaml-1.3.0-py3-none-any.whl (18.0 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page