# Date and Datetime Validations
Pointblank Validation
2026-10-15|18:25:54 Polars
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#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 08:00:00-05:00 ✓ 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		 -- -- -- --
2026-10-15 18:25:54 UTC< 1 s2026-10-15 18:25:54 UTC
Notes

Step 4 (schema_check) ✓ Schema validation passed.

Schema Comparison
TARGET EXPECTED
COLUMN DATA TYPE COLUMN DATA TYPE
1 order_date Date 1 order_date ✓ Date ✓
2 created_at Datetime(time_unit='us', time_zone=None) 2 created_at ✓ Datetime(time_unit='us', time_zone=None) ✓
3 event_time_tz Datetime(time_unit='us', time_zone='America/New_York') 3 event_time_tz ✓ Datetime(time_unit='us', time_zone='America/New_York') ✓
4 order_id Int64 4 order_id ✓ Int64 ✓
5 amount Float64 5 amount ✓ Float64 ✓
Supplied Column Schema: [('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')]
Schema Match Settings COMPLETE IN ORDER COLUMN ≠ column DTYPE ≠ dtype float ≠ float64
``` python import pointblank as pb import polars as pl from datetime import date, datetime from zoneinfo import ZoneInfo # 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=ZoneInfo("America/New_York")), datetime(2023, 6, 10, 12, 30, tzinfo=ZoneInfo("America/New_York")), datetime(2023, 12, 5, 15, 45, tzinfo=ZoneInfo("America/New_York")), datetime(2024, 3, 20, 18, 15, tzinfo=ZoneInfo("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=ZoneInfo("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
PolarsRows4Columns5
order_date Date
created_at Datetime
event_time_tz Datetime
order_id Int64
amount Float64
1 2023-01-15 2023-01-15 09:30:00 2023-01-15 09:00:00-05:00 1001 150.0
2 2023-06-10 2023-06-10 14:45:30 2023-06-10 12:30:00-04:00 1002 275.5
3 2023-12-05 2023-12-05 08:15:00 2023-12-05 15:45:00-05:00 1003 89.99
4 2024-03-20 2024-03-20 17:22:45 2024-03-20 18:15:00-04:00 1004 420.0