bool_field()`function`

Create a boolean column specification.

USAGE

bool_field(
    p_true=0.5,
    nullable=False,
    null_probability=0.0,
    unique=False,
    generator=None,
)

Parameters

p_true : float = 0.5: Probability of generating True. Default is 0.5 (equal probability). Must be between 0.0 and 1.0.
nullable : bool = False: Whether the column can contain null values. Default is False.
null_probability : float = 0.0: Probability of generating null when nullable=True. Default is 0.0.
unique : bool = False: Whether all values must be unique. Default is False. Note: Boolean can only have 2 unique non-null values.
generator : Callable[[], Any] | None = None: Custom callable that generates values. Overrides other settings.

Returns

BoolField: A boolean field specification.

Examples

Define a schema with boolean fields and generate test data:

import pointblank as pb

# Define a schema with boolean field specifications
schema = pb.Schema(
    is_active=pb.bool_field(p_true=0.8),      # 80% True
    is_premium=pb.bool_field(p_true=0.2),     # 20% True
    is_verified=pb.bool_field(),              # 50% True (default)
)

# Generate 100 rows of test data
pb.preview(pb.generate_dataset(schema, n=100, seed=23))

	is_active Boolean	is_premium Boolean	is_verified Boolean
PolarsRows100Columns3
1	False	False	False
2	False	False	False
3	False	False	False
4	True	True	True
5	True	False	False
96	True	False	True
97	True	False	True
98	False	False	False
99	False	False	False
100	False	False	False

The p_true= parameter controls the probability of generating True values, which is helpful for simulating real-world distributions.