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Support strftime format expectation gallery metadata #12005

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150 changes: 139 additions & 11 deletions great_expectations/expectations/core/expect_column_values_to_match_strftime_format.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
from __future__ import annotations

from datetime import datetime, timezone
from typing import TYPE_CHECKING, Optional, Union
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Type, Union

from great_expectations.compatibility import pydantic
from great_expectations.core.suite_parameters import (
Expand All @@ -12,6 +12,12 @@
_style_row_condition,
render_suite_parameter_string,
)
from great_expectations.expectations.metadata_types import DataQualityIssues, SupportedDataSources
from great_expectations.expectations.model_field_descriptions import (
COLUMN_DESCRIPTION,
FAILURE_SEVERITY_DESCRIPTION,
MOSTLY_DESCRIPTION,
)
from great_expectations.render import LegacyRendererType, RenderedStringTemplateContent
from great_expectations.render.renderer.renderer import renderer
from great_expectations.render.renderer_configuration import (
Expand All @@ -33,25 +39,39 @@
)
from great_expectations.render.renderer_configuration import AddParamArgs

EXPECTATION_SHORT_DESCRIPTION = (
"Expect the column entries to be strings representing a date or time with a given format."
)
STRFTIME_FORMAT_DESCRIPTION = "A strftime format string to use for matching."
DATA_QUALITY_ISSUES = [DataQualityIssues.VALIDITY.value]
SUPPORTED_DATA_SOURCES = [
SupportedDataSources.PANDAS.value,
SupportedDataSources.SPARK.value,
]


class ExpectColumnValuesToMatchStrftimeFormat(ColumnMapExpectation):
"""Expect the column entries to be strings representing a date or time with a given format.
__doc__ = f"""{EXPECTATION_SHORT_DESCRIPTION}

ExpectColumnValuesToMatchStrftimeFormat is a \
Column Map Expectation.

Column Map Expectations are one of the most common types of Expectation.
They are evaluated for a single column and ask a yes/no question for every row in that column.
Based on the result, they then calculate the percentage of rows that gave a positive answer. If the percentage is high enough, the Expectation considers that data valid.

This Expectation supports Pandas and Spark Data Sources. It does not currently support SQL Data Sources because Python strftime format tokens do not map consistently to SQL dialect date parsing functions.

Args:
column (str): \
The column name.
{COLUMN_DESCRIPTION}
strftime_format (str or SuiteParameterDict): \
A strftime format string to use for matching

Keyword Args:
mostly (None or a float between 0 and 1): \
Successful if at least mostly fraction of values match the expectation. \
For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).
{STRFTIME_FORMAT_DESCRIPTION}

Other Parameters:
mostly (None or a float between 0 and 1): \
{MOSTLY_DESCRIPTION} \
For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly). Default 1.
result_format (str or None): \
Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. \
For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).
Expand All @@ -69,9 +89,82 @@ class ExpectColumnValuesToMatchStrftimeFormat(ColumnMapExpectation):
An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)

Exact fields vary depending on the values passed to result_format, catch_exceptions, and meta.

Supported Data Sources:
[{SUPPORTED_DATA_SOURCES[0]}](https://docs.greatexpectations.io/docs/application_integration_support/)
[{SUPPORTED_DATA_SOURCES[1]}](https://docs.greatexpectations.io/docs/application_integration_support/)
Data Quality Issues:
{DATA_QUALITY_ISSUES[0]}

Example Data:
event_date logged_at
0 "2026-01-15" "2026-01-15T10:30:00+0000"
1 "2026-06-20" "2026-06-20T14:45:00+0000"
2 "not a date" "2026-12-31T23:59:59+0000"

Code Examples:
Passing Case:
Input:
ExpectColumnValuesToMatchStrftimeFormat(
column="logged_at",
strftime_format="%Y-%m-%dT%H:%M:%S%z",
)

Output:
{{
"exception_info": {{
"raised_exception": false,
"exception_traceback": null,
"exception_message": null
}},
"result": {{
"element_count": 3,
"unexpected_count": 0,
"unexpected_percent": 0.0,
"partial_unexpected_list": [],
"missing_count": 0,
"missing_percent": 0.0,
"unexpected_percent_total": 0.0,
"unexpected_percent_nonmissing": 0.0
}},
"meta": {{}},
"success": true
}}

Failing Case:
Input:
ExpectColumnValuesToMatchStrftimeFormat(
column="event_date",
strftime_format="%Y-%m-%d",
)

Output:
{{
"exception_info": {{
"raised_exception": false,
"exception_traceback": null,
"exception_message": null
}},
"result": {{
"element_count": 3,
"unexpected_count": 1,
"unexpected_percent": 33.33333333333333,
"partial_unexpected_list": [
"not a date"
],
"missing_count": 0,
"missing_percent": 0.0,
"unexpected_percent_total": 33.33333333333333,
"unexpected_percent_nonmissing": 33.33333333333333
}},
"meta": {{}},
"success": false
}}
""" # noqa: E501 # FIXME CoP

strftime_format: Union[str, SuiteParameterDict]
strftime_format: Union[str, SuiteParameterDict] = pydantic.Field(
description=STRFTIME_FORMAT_DESCRIPTION
)

@pydantic.validator("strftime_format")
def validate_strftime_format(
Expand All @@ -88,7 +181,7 @@ def validate_strftime_format(

return strftime_format

library_metadata = {
library_metadata: ClassVar[Dict[str, Union[str, list, bool]]] = {
"maturity": "production",
"tags": ["core expectation", "column map expectation"],
"contributors": [
Expand All @@ -98,6 +191,7 @@ def validate_strftime_format(
"has_full_test_suite": True,
"manually_reviewed_code": True,
}
_library_metadata = library_metadata

map_metric = "column_values.match_strftime_format"
success_keys = (
Expand All @@ -109,6 +203,40 @@ def validate_strftime_format(
"strftime_format",
)

class Config:
title = "Expect column values to match strftime format"

@staticmethod
def schema_extra(
schema: Dict[str, Any],
model: Type[ExpectColumnValuesToMatchStrftimeFormat],
) -> None:
ColumnMapExpectation.Config.schema_extra(schema, model)
schema["properties"]["metadata"]["properties"].update(
{
"data_quality_issues": {
"title": "Data Quality Issues",
"type": "array",
"const": DATA_QUALITY_ISSUES,
},
"library_metadata": {
"title": "Library Metadata",
"type": "object",
"const": model._library_metadata,
},
"short_description": {
"title": "Short Description",
"type": "string",
"const": EXPECTATION_SHORT_DESCRIPTION,
},
"supported_data_sources": {
"title": "Supported Data Sources",
"type": "array",
"const": SUPPORTED_DATA_SOURCES,
},
}
)

@classmethod
def _prescriptive_template(
cls,
Expand Down
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