Examples: Date and Datetime Validations

Date and Datetime Validations

pointblank provides comprehensive support for validating date and datetime values, including timezone-aware comparisons. This ensures temporal data quality in applications that handle time-sensitive information.

		STEP	COLUMNS	VALUES	EVAL	UNITS	PASS	FAIL	W	E	C	EXT
Pointblank Validation
2025-10-29\|23:17:41 Polars
#4CA64C	1	col_vals_ge() Orders are from 2023 or later	order_date	2023-01-01	✓	4	4 1.00	0 0.00	—	—	—	—
#4CA64C	2	col_vals_between() Creation timestamps within expected range	created_at	[2023-01-01 00:00:00, 2024-12-31 23:59:59]	✓	4	4 1.00	0 0.00	—	—	—	—
#4CA64C	3	col_vals_ge() Timezone-aware events after 8 AM Eastern	event_time_tz	2023-01-01 08:00:00-04:56	✓	4	4 1.00	0 0.00	—	—	—	—
#4CA64C	4	col_schema_match() Schema includes proper date/datetime types	—	SCHEMA	✓	1	1 1.00	0 0.00	—	—	—	—
2025-10-29 23:17:41 UTC< 1 s2025-10-29 23:17:41 UTC

import pointblank as pb
import polars as pl
from datetime import date, datetime
import pytz

# Create sample data with various temporal data types
temporal_data = pl.DataFrame({
    "order_date": [
        date(2023, 1, 15),
        date(2023, 6, 10),
        date(2023, 12, 5),
        date(2024, 3, 20)
    ],
    "created_at": [
        datetime(2023, 1, 15, 9, 30, 0),
        datetime(2023, 6, 10, 14, 45, 30),
        datetime(2023, 12, 5, 8, 15, 0),
        datetime(2024, 3, 20, 17, 22, 45)
    ],
    "event_time_tz": [
        datetime(2023, 1, 15, 9, 0, tzinfo=pytz.timezone("America/New_York")),
        datetime(2023, 6, 10, 12, 30, tzinfo=pytz.timezone("America/New_York")),
        datetime(2023, 12, 5, 15, 45, tzinfo=pytz.timezone("America/New_York")),
        datetime(2024, 3, 20, 18, 15, tzinfo=pytz.timezone("America/New_York"))
    ],
    "order_id": [1001, 1002, 1003, 1004],
    "amount": [150.0, 275.5, 89.99, 420.00]
})

validation = (
    pb.Validate(temporal_data)
    .col_vals_ge(
        columns="order_date",
        value=date(2023, 1, 1),
        brief="Orders are from 2023 or later"
    )
    .col_vals_between(
        columns="created_at",
        left=datetime(2023, 1, 1, 0, 0, 0),
        right=datetime(2024, 12, 31, 23, 59, 59),
        brief="Creation timestamps within expected range"
    )
    .col_vals_ge(
        columns="event_time_tz",
        value=datetime(2023, 1, 1, 8, 0, tzinfo=pytz.timezone("America/New_York")),
        brief="Timezone-aware events after 8 AM Eastern"
    )
    .col_schema_match(
        pb.Schema(
            columns=[
                ("order_date", "Date"),
                ("created_at", "Datetime(time_unit='us', time_zone=None)"),
                ("event_time_tz", "Datetime(time_unit='us', time_zone='America/New_York')"),
                ("order_id", "Int64"),
                ("amount", "Float64")
            ]
        ),
        brief="Schema includes proper date/datetime types"
    )
    .interrogate()
)

validation

Preview of Input Table

	order_date Date	created_at Datetime	event_time_tz Datetime	order_id Int64	amount Float64
PolarsRows4Columns5
1	2023-01-15	2023-01-15 09:30:00	2023-01-15 08:56:00-05:00	1001	150.0
2	2023-06-10	2023-06-10 14:45:30	2023-06-10 13:26:00-04:00	1002	275.5
3	2023-12-05	2023-12-05 08:15:00	2023-12-05 15:41:00-05:00	1003	89.99
4	2024-03-20	2024-03-20 17:22:45	2024-03-20 19:11:00-04:00	1004	420.0