Booleans in `pyfake`

Simple usage

pyfake can generate values for bool fields in your Pydantic models. By default it samples True or False uniformly.

from pyfake import fake
from pydantic import BaseModel

class Feature(BaseModel):
  enabled: bool

result = fake(Feature)
print(result)

{'enabled': True}

No setup required — pyfake inspects the model schema and generates valid boolean values automatically.

Returning multiple values

Generate more than one instance by passing num:

from pyfake import fake
from pydantic import BaseModel

class Feature(BaseModel):
  enabled: bool

results = fake(Feature, num=3)
print(results)

[
  {'enabled': True},
  {'enabled': False},
  {'enabled': True}
]

Receiving model instances

By default pyfake returns dictionaries. To receive Pydantic model instances set as_dict=False:

from pyfake import fake
from pydantic import BaseModel

class Feature(BaseModel):
  enabled: bool

results = fake(Feature, num=2, as_dict=False)
print(results)

[
  Feature(enabled=True),
  Feature(enabled=False)
]

Nullable / Optional fields

If a field is declared as Optional[bool] or using the | None shorthand (e.g. bool | None), pyfake treats the field as a union that may be None.

The registry currently returns None roughly 20% of the time for nullable unions; otherwise it generates True or False as usual.

from typing import Optional
from pyfake import fake
from pydantic import BaseModel

class Feature(BaseModel):
  enabled: Optional[bool]

results = fake(Feature, num=5)
print(results)

[
  {'enabled': True},
  {'enabled': None},
  {'enabled': False},
  {'enabled': True},
  {'enabled': False}
]

Default values

If you provide an explicit (non-None) default for a field, pyfake will return that default instead of generating a random value. For example:

from pydantic import BaseModel, Field
from pyfake import fake

class User(BaseModel):
  active: bool = Field(default=True)

result = fake(User)
print(result)

{'active': True}

Implementation notes

The boolean generator is pyfake.generators.primitives.generate_bool and simply samples from [True, False] using the Context RNG.
Nullability is handled by the resolver/registry: union/optional fields are eligible to be None (about 20% probability in current implementation).
If a non-None default is present in the field metadata, the registry returns that value before attempting generation.

Unsupported / Partial support

There is no built-in way to bias the generator (e.g., 70% True / 30% False). Use a custom generator if you need weighted booleans.
There are no field-level boolean constraints (min/max, pattern, etc.) — booleans are binary values.

Custom generators

If you need more control (for example, a weighted probability), write a small custom generator that accepts a prob_true argument and registers it with the registry.

Booleans in pyfake