Expect column pair a to be smaller or equal than b

Expectation to check if column 'a' is lower or equal than column 'b'.

ColumnPairCustom

Bases: ColumnPairMapMetricProvider

Asserts that column 'A' is lower or equal than column 'B'.

Additionally, the 'margin' parameter can be used to add a margin to the check between column 'A' and 'B': 'A' <= 'B' + 'margin'.

Source code in mkdocs/lakehouse_engine/packages/dq_processors/custom_expectations/expect_column_pair_a_to_be_smaller_or_equal_than_b.py

class ColumnPairCustom(ColumnPairMapMetricProvider):
    """Asserts that column 'A' is lower or equal than column 'B'.

    Additionally, the 'margin' parameter can be used to add a margin to the
    check between column 'A' and 'B': 'A' <= 'B' + 'margin'.
    """

    condition_metric_name = "column_pair_values.a_smaller_or_equal_than_b"
    condition_domain_keys = (
        "batch_id",
        "table",
        "column_A",
        "column_B",
        "ignore_row_if",
    )
    condition_value_keys = ("margin",)

    @column_pair_condition_partial(engine=SparkDFExecutionEngine)
    def _spark(
        self: ColumnPairMapMetricProvider,
        column_A: Any,
        column_B: Any,
        **kwargs: dict,
    ) -> Any:
        """Implementation of the expectation's logic.

        Args:
            column_A: Value of the row of column_A.
            column_B: Value of the row of column_B.
            kwargs: dict with additional parameters.

        Returns:
            If the condition is met.
        """
        margin = kwargs.get("margin") or None
        if margin is None:
            approx = 0
        elif not isinstance(margin, (int, float, complex)):
            raise TypeError(
                f"margin must be one of int, float, complex."
                f" Found: {margin} as {type(margin)}"
            )
        else:
            approx = margin  # type: ignore

        return column_A <= column_B + approx  # type: ignore

ExpectColumnPairAToBeSmallerOrEqualThanB

Bases: ColumnPairMapExpectation

Expect values in column A to be lower or equal than column B.

Parameters:

Name	Type	Description	Default
column_A		The first column name.	required
column_B		The second column name.	required
margin		additional approximation to column B value.	required

Other Parameters:

Name	Type	Description
allow_cross_type_comparisons		If True, allow comparisons between types (e.g. integer and string). Otherwise, attempting such comparisons will raise an exception.
ignore_row_if		"both_values_are_missing", "either_value_is_missing", "neither" (default).
result_format		Which output mode to use: BOOLEAN_ONLY, BASIC (default), COMPLETE, or SUMMARY.
include_config		If True (default), then include the expectation config as part of the result object.
catch_exceptions		If True, then catch exceptions and include them as part of the result object. Default: False.
meta		A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification.

Returns:

Type	Description
	An ExpectationSuiteValidationResult.

Source code in mkdocs/lakehouse_engine/packages/dq_processors/custom_expectations/expect_column_pair_a_to_be_smaller_or_equal_than_b.py

class ExpectColumnPairAToBeSmallerOrEqualThanB(ColumnPairMapExpectation):
    """Expect values in column A to be lower or equal than column B.

    Args:
        column_A: The first column name.
        column_B: The second column name.
        margin: additional approximation to column B value.

    Keyword Args:
        allow_cross_type_comparisons: If True, allow
            comparisons between types (e.g. integer and string).
            Otherwise, attempting such comparisons will raise an exception.
        ignore_row_if: "both_values_are_missing",
            "either_value_is_missing", "neither" (default).
        result_format: Which output mode to use:
            `BOOLEAN_ONLY`, `BASIC` (default), `COMPLETE`, or `SUMMARY`.
        include_config: If True (default), then include the expectation config
            as part of the result object.
        catch_exceptions: If True, then catch exceptions and
            include them as part of the result object. Default: False.
        meta: A JSON-serializable dictionary (nesting allowed)
            that will be included in the output without modification.

    Returns:
        An ExpectationSuiteValidationResult.
    """

    mostly: float = 1.0
    ignore_row_if: str = "neither"
    result_format: dict = {"result_format": "BASIC"}
    include_config: bool = True
    catch_exceptions: bool = False
    margin: Any = None
    column_A: Any = None
    column_B: Any = None

    examples = [
        {
            "dataset_name": "Test Dataset",
            "data": [
                {
                    "data": {
                        "a": [11, 22, 50],
                        "b": [10, 21, 100],
                        "c": [9, 21, 30],
                    },
                    "schemas": {
                        "spark": {
                            "a": "IntegerType",
                            "b": "IntegerType",
                            "c": "IntegerType",
                        }
                    },
                }
            ],
            "tests": [
                {
                    "title": "negative_test",
                    "exact_match_out": False,
                    "include_in_gallery": True,
                    "in": {
                        "column_A": "a",
                        "column_B": "c",
                        "result_format": {
                            "result_format": "COMPLETE",
                            "unexpected_index_column_names": ["c"],
                        },
                    },
                    "out": {
                        "success": False,
                        "unexpected_index_list": [
                            {"c": 9, "a": 11},
                            {"c": 21, "a": 22},
                            {"c": 30, "a": 50},
                        ],
                    },
                },
                {
                    "title": "positive_test",
                    "exact_match_out": False,
                    "include_in_gallery": True,
                    "in": {
                        "column_A": "a",
                        "column_B": "b",
                        "margin": 1,
                        "result_format": {
                            "result_format": "COMPLETE",
                            "unexpected_index_column_names": ["a"],
                        },
                    },
                    "out": {
                        "success": True,
                        "unexpected_index_list": [],
                    },
                },
            ],
        },
    ]

    map_metric = "column_pair_values.a_smaller_or_equal_than_b"
    success_keys = (
        "column_A",
        "column_B",
        "ignore_row_if",
        "margin",
        "mostly",
    )

    def _validate(
        self,
        metrics: Dict,
        runtime_configuration: Optional[dict] = None,
        execution_engine: Optional[ExecutionEngine] = None,
    ) -> Any:
        """Custom implementation of the GE _validate method.

        This method is used on the tests to validate both the result
        of the tests themselves and if the unexpected index list
        is correctly generated.
        The GE test logic does not do this validation, and thus
        we need to make it manually.

        Args:
            metrics: Test result metrics.
            runtime_configuration: Configuration used when running the expectation.
            execution_engine: Execution Engine where the expectation was run.

        Returns:
            Dictionary with the result of the validation.
        """
        validate_result(
            self,
            metrics,
        )

        return super()._validate(metrics, runtime_configuration, execution_engine)