DataQualityExpectation

laktory.models.dataquality.DataQualityExpectation

Bases: BaseModel, PipelineChild

Data Quality Expectation for a given DataFrame expressed as a row-specific condition (type="ROW") or as an aggregated metric (type="AGGREGATE").

The expression may be defined as a SQL statement or a DataFrame expression.

Examples:

from laktory import models

dqe = models.DataQualityExpectation(
    name="price higher than 10",
    action="WARN",
    expr="close > 127",
    tolerance={"rel": 0.05},
)
print(dqe)
'''
dataframe_backend_=None dataframe_api_=None variables={} action='WARN' type='ROW' name='price higher than 10' expr=DataFrameColumnExpr(dataframe_backend_=None, dataframe_api_=None, variables={}, expr='close > 127', type='SQL', dataframe_backend=<DataFrameBackends.PYSPARK: 'PYSPARK'>, dataframe_api='NARWHALS') tolerance=ExpectationTolerance(variables={}, abs=None, rel=0.05) dataframe_backend=<DataFrameBackends.PYSPARK: 'PYSPARK'> dataframe_api='NARWHALS'
'''

dqe = models.DataQualityExpectation(
    name="rows count",
    expr="COUNT(*) > 50",
    type="AGGREGATE",
)
print(dqe)
'''
dataframe_backend_=None dataframe_api_=None variables={} action='WARN' type='AGGREGATE' name='rows count' expr=DataFrameColumnExpr(dataframe_backend_=None, dataframe_api_=None, variables={}, expr='COUNT(*) > 50', type='SQL', dataframe_backend=<DataFrameBackends.PYSPARK: 'PYSPARK'>, dataframe_api='NARWHALS') tolerance=ExpectationTolerance(variables={}, abs=0, rel=None) dataframe_backend=<DataFrameBackends.PYSPARK: 'PYSPARK'> dataframe_api='NARWHALS'
'''

References

• Data Quality
• DLT Table Expectations

PARAMETER	DESCRIPTION
action	Action to take when expectation is not met. - WARN: Write invalid records to the output DataFrame, but log exception. - DROP: Drop Invalid records to the output DataFrame and log exception. - QUARANTINE: Forward invalid data for quarantine. - FAIL: Raise exception when invalid records are found. TYPE: Literal['WARN', 'DROP', 'QUARANTINE', 'FAIL'] | VariableType DEFAULT: 'WARN'
expr	SQL or DataFrame expression representing a row-specific condition or an aggregated metric. TYPE: str | DataFrameColumnExpr | VariableType DEFAULT: None
name	Name of the expectation TYPE: str | VariableType
tolerance	Tolerance for non-matching rows before resulting in failure. Only available for 'ROW' type expectation. TYPE: ExpectationTolerance | VariableType DEFAULT: ExpectationTolerance(variables={}, abs=0, rel=None)
type	Type of expectation: - "ROW": Row-specific condition. Must be a boolean expression. - "AGGREGATE": Global condition. Must be a boolean expression. TYPE: Literal['AGGREGATE', 'ROW'] | VariableType DEFAULT: 'ROW'

METHOD	DESCRIPTION
raise_or_warn	Raise exception or issue warning if expectation is not met.
run_check	Check if expectation is met save result.

ATTRIBUTE	DESCRIPTION
fail_filter	Expression representing all rows not meeting the expectation. TYPE: Expr | None
is_dlt_compatible	Expectation is supported by DLT TYPE: bool
is_dlt_managed	Expectation is DLT-compatible and pipeline node is executed by DLT TYPE: bool
keep_filter	Expression representing all rows to keep, considering both the TYPE: Expr | None
pass_filter	Expression representing all rows meeting the expectation. TYPE: Expr | None
quarantine_filter	Expression representing all rows to quarantine, considering both the TYPE: Expr | None

fail_filter property

Expression representing all rows not meeting the expectation.

is_dlt_compatible property

Expectation is supported by DLT

is_dlt_managed property

Expectation is DLT-compatible and pipeline node is executed by DLT

keep_filter property

Expression representing all rows to keep, considering both the expectation and the selected action.

pass_filter property

Expression representing all rows meeting the expectation.

quarantine_filter property

Expression representing all rows to quarantine, considering both the expectation and the selected action.

raise_or_warn(node=None)

Raise exception or issue warning if expectation is not met.

Source code in laktory/models/dataquality/expectation.py

def raise_or_warn(self, node=None) -> None:
    """
    Raise exception or issue warning if expectation is not met.
    """

    # Failure Message
    msg = f"Expectation '{self.name}'"
    if node:
        msg += f" for node '{node.name}'"
    msg += f" FAILED | {self.log_msg}"

    if self.check.status != "FAIL":
        return

    # Raise Exception
    if self.action == "FAIL":
        raise DataQualityCheckFailedError(self, node)
    else:
        # actions: WARN, DROP, QUARANTINE
        warnings.warn(msg)

run_check(df, raise_or_warn=False, node=None)

Check if expectation is met save result.

PARAMETER	DESCRIPTION
df	Input DataFrame for checking the expectation. TYPE: AnyFrame
raise_or_warn	Raise exception or issue warning if expectation is not met. TYPE: bool DEFAULT: False
node	Pipeline Node DEFAULT: None

RETURNS	DESCRIPTION
output	Check result. TYPE: DataQualityCheck

Source code in laktory/models/dataquality/expectation.py

def run_check(
    self,
    df: AnyFrame,
    raise_or_warn: bool = False,
    node=None,
) -> DataQualityCheck:
    """
    Check if expectation is met save result.

    Parameters
    ----------
    df:
        Input DataFrame for checking the expectation.
    raise_or_warn:
        Raise exception or issue warning if expectation is not met.
    node:
        Pipeline Node

    Returns
    -------
    output: DataQualityCheck
        Check result.
    """

    logger.info(
        f"Checking expectation '{self.name}' | {self.expr.expr} (type: {self.type})"
    )

    # Run Check
    self._check = self._check_df(df)

    if raise_or_warn:
        self.raise_or_warn(node)

    return self._check

---

laktory.models.dataquality.expectation.ExpectationTolerance

Bases: BaseModel

Tolerance values for data quality expectations with support for either absolute or relative tolerances.

PARAMETER	DESCRIPTION
abs	Maximum number of rows with failure for a PASS status TYPE: int | VariableType DEFAULT: None
rel	Relative number of rows with failure for a PASS status TYPE: float | VariableType DEFAULT: None