time_field()`function`

Create a time column specification for use in a schema.

USAGE

time_field(
    min_time=None,
    max_time=None,
    nullable=False,
    null_probability=0.0,
    unique=False,
    generator=None,
)

The time_field() function defines the constraints and behavior for a time-of-day column when generating synthetic data with generate_dataset(). You can control the time range with min_time= and max_time=, enforce uniqueness with unique=True, and introduce null values with nullable=True and null_probability=.

Time values are generated uniformly (at second-level resolution) within the specified range. If no range is provided, the default range is 00:00:00 to 23:59:59. Both min_time= and max_time= accept datetime.time objects or ISO format time strings (e.g., "09:30:00").

Parameters

min_time : str | time | None = None: Minimum time (inclusive). Can be an ISO format string (e.g., "08:00:00") or a datetime.time object. Default is None (defaults to 00:00:00).
max_time : str | time | None = None: Maximum time (inclusive). Can be an ISO format string (e.g., "17:30:00") or a datetime.time object. Default is None (defaults to 23:59:59).
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. With second-level resolution within a time range, uniqueness is feasible for moderate dataset sizes.
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 value.

Returns

TimeField: A time field specification that can be passed to Schema().

Raises

: ValueError: If min_time is later than max_time, or if a time string cannot be parsed.

Examples

The min_time= and max_time= parameters accept datetime.time objects, making it easy to define business-hours ranges:

import pointblank as pb
from datetime import time

schema = pb.Schema(
    start_time=pb.time_field(
        min_time=time(9, 0, 0),
        max_time=time(12, 0, 0),
    ),
    end_time=pb.time_field(
        min_time=time(13, 0, 0),
        max_time=time(17, 0, 0),
    ),
)

pb.preview(pb.generate_dataset(schema, n=100, seed=23))

	start_time String	end_time String
PolarsRows100Columns2
1	10:19:09	16:32:49
2	09:22:48	14:19:09
3	09:04:39	16:46:32
4	11:41:39	13:22:48
5	10:23:45	13:04:39
96	10:53:39	15:29:54
97	10:47:25	13:59:36
98	09:06:22	14:10:33
99	09:44:23	13:40:49
100	10:01:36	13:29:15

ISO format strings can also be used for convenience:

schema = pb.Schema(
    login_time=pb.time_field(min_time="06:00:00", max_time="23:59:59"),
    alarm_time=pb.time_field(min_time="05:00:00", max_time="09:00:00"),
)

pb.preview(pb.generate_dataset(schema, n=30, seed=42))

	login_time String	alarm_time String
PolarsRows30Columns2
1	17:38:25	07:54:36
2	08:01:36	05:30:24
3	06:27:19	05:06:49
4	19:29:58	08:22:29
5	11:00:24	06:15:06
26	16:13:01	05:54:17
27	09:37:11	08:15:31
28	19:02:05	07:57:27
29	17:49:50	08:11:30
30	18:46:02	07:28:48

It’s possible to introduce optional time values with nullable=True and combine them with other field types:

schema = pb.Schema(
    employee_id=pb.int_field(min_val=100, max_val=999, unique=True),
    check_in=pb.time_field(min_time="07:00:00", max_time="10:00:00"),
    check_out=pb.time_field(
        min_time="16:00:00", max_time="20:00:00",
        nullable=True, null_probability=0.15,
    ),
)

pb.preview(pb.generate_dataset(schema, n=30, seed=7))

	employee_id Int64	check_in String	check_out String
PolarsRows30Columns3
1	431	08:28:25	17:28:25
2	254	07:41:11	16:41:11
3	504	08:47:48	None
4	766	09:57:44	18:57:44
5	149	07:13:11	None
26	679	08:00:57	18:34:24
27	226	09:52:12	16:33:48
28	328	09:51:19	17:00:57
29	745	09:39:11	18:52:12
30	742	07:16:53	18:51:19