int_field()`function`

Create an integer column specification.

USAGE

int_field(
    min_val=None,
    max_val=None,
    allowed=None,
    nullable=False,
    null_probability=0.0,
    unique=False,
    generator=None,
    dtype='Int64',
)

Parameters

min_val : int | None = None: Minimum value (inclusive). Default is None (no minimum).
max_val : int | None = None: Maximum value (inclusive). Default is None (no maximum).
allowed : list[int] | None = None: List of allowed values (categorical constraint). When provided, values are sampled from this list.
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.
generator : Callable[[], Any] | None = None: Custom callable that generates values. Overrides other settings.
dtype : str = 'Int64': Integer dtype. Default is "Int64". Options: "Int8", "Int16", "Int32", "Int64", "UInt8", "UInt16", "UInt32", "UInt64".

Returns

IntField: An integer field specification.

Examples

Define a schema with integer fields and generate test data:

import pointblank as pb

# Define a schema with integer field specifications
schema = pb.Schema(
    user_id=pb.int_field(min_val=1, unique=True),
    age=pb.int_field(min_val=0, max_val=120),
    rating=pb.int_field(allowed=[1, 2, 3, 4, 5]),
)

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

	user_id Int64	age Int64	rating Int64
PolarsRows100Columns3
1	7188536481533917197	118	3
2	2674009078779859984	99	1
3	7652102777077138151	37	1
4	157503859921753049	114	5
5	2829213282471975080	106	3
96	7027508096731143831	36	2
97	6055996548456656575	69	1
98	3822709996092631588	39	2
99	1522653102058131295	114	1
100	5690877051669225499	99	5

The generated data will have unique user IDs starting from 1, ages between 0-120, and ratings sampled from the allowed values.