date_field()`function`

Create a date column specification.

USAGE

date_field(
    min_date=None,
    max_date=None,
    nullable=False,
    null_probability=0.0,
    unique=False,
    generator=None,
)

Parameters

min_date : str | date | None = None: Minimum date (inclusive). Can be ISO string or date object.
max_date : str | date | None = None: Maximum date (inclusive). Can be ISO string or date object.
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.

Returns

DateField: A date field specification.

Examples

Define a schema with date fields and generate test data:

import pointblank as pb
from datetime import date

# Define a schema with date field specifications
schema = pb.Schema(
    birth_date=pb.date_field(
        min_date=date(1960, 1, 1),
        max_date=date(2005, 12, 31)
    ),
    hire_date=pb.date_field(
        min_date=date(2020, 1, 1),
        max_date=date(2024, 12, 31)
    ),
)

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

	birth_date Date	hire_date Date
PolarsRows100Columns2
1	1986-01-03	2024-05-15
2	1967-06-30	2021-08-16
3	1961-07-13	2024-08-26
4	1987-07-09	2020-06-20
5	1998-01-06	2020-02-04
96	1969-04-14	2023-01-29
97	1975-03-23	2021-03-23
98	1981-05-29	2021-06-13
99	1982-09-14	2020-11-02
100	1968-12-21	2020-08-07

Date values are uniformly distributed within the specified range.