## bool_field() Create a boolean column specification for use in a schema. Usage ``` python bool_field( p_true=0.5, nullable=False, null_probability=0.0, unique=False, generator=None ) ``` The [bool_field()](bool_field.md#pointblank.bool_field) function defines the constraints and behavior for a boolean column when generating synthetic data with [generate_dataset()](generate_dataset.md#pointblank.generate_dataset). The `p_true=` parameter controls the probability of generating `True` values, which is useful for simulating real-world distributions where events may be rare or common (e.g., 5% fraud rate, 80% active users). By default, `True` and `False` are equally likely (`p_true=0.5`). Setting `p_true=0.0` produces all `False` values, and `p_true=1.0` produces all `True` values. ## 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 a null value for each row when `nullable=True`. Must be between `0.0` and `1.0`. Default is `0.0`. `unique: bool = ``False` Whether all values must be unique. Default is `False`. Note that boolean columns can only have 2 unique non-null values, so [n](Validate.n.md#pointblank.Validate.n) must be `<= 2` when `unique=True` (or `<= 3` with `nullable=True`). `generator: Callable[[], Any] | None = None` Custom callable that generates values. When provided, this overrides all other constraints. The callable should take no arguments and return a single boolean value. ## Returns `BoolField` A boolean field specification that can be passed to [Schema()](Schema.md#pointblank.Schema). ## Raises `ValueError` If `p_true` is not between `0.0` and `1.0`, or if `null_probability` is not between `0.0` and `1.0`. ## Examples The `p_true=` parameter controls the distribution of `True`/`False` values, allowing you to simulate different probabilities: ``` python import pointblank as pb schema = pb.Schema( is_active=pb.bool_field(p_true=0.8), is_premium=pb.bool_field(p_true=0.2), is_verified=pb.bool_field(), ) pb.preview(pb.generate_dataset(schema, n=100, seed=23)) ```
PolarsRows100Columns3
is_active Boolean
is_premium Boolean
is_verified Boolean
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
Optional boolean flags can be simulated by combining `nullable=True` with `null_probability=`: ``` python schema = pb.Schema( opted_in=pb.bool_field(p_true=0.6), has_referral=pb.bool_field( p_true=0.3, nullable=True, null_probability=0.25, ), ) pb.preview(pb.generate_dataset(schema, n=50, seed=23)) ```
PolarsRows50Columns2
opted_in Boolean
has_referral Boolean
1 False False
2 False False
3 False None
4 True True
5 True None
46 True True
47 True None
48 False None
49 True False
50 True False
Boolean fields can be combined with other field types in a realistic schema: ``` python schema = pb.Schema( user_id=pb.int_field(min_val=1, unique=True), name=pb.string_field(preset="name"), email_verified=pb.bool_field(p_true=0.9), is_admin=pb.bool_field(p_true=0.05), ) pb.preview(pb.generate_dataset(schema, n=30, seed=10)) ```
PolarsRows30Columns4
user_id Int64
name String
email_verified Boolean
is_admin Boolean
1 300544187282452692 Joshua Robinson True False
2 4450845842995915857 Marie Young True False
3 136805169105849017 Melissa Atkins True False
4 4266552081017657985 Paisley Jackson True False
5 4531409703019074138 Martha White True False
26 4055035012371305120 Zachary Ramirez False False
27 3461005733469027473 Sarah Gallagher True False
28 5376594645785815587 Kimberly Tran True True
29 2173523867229340168 John Marshall True True
30 1798126872665261570 Linda Garner True False