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DataQualityCheck

`laktory.models.dataquality.DataQualityCheck` ¤

Bases: BaseModel

A Data Quality Check is the result of an expectation compared to a dataset.

Examples:

from laktory import models

check = models.DataQualityCheck(
    rows_count=10,
    fails_count=2,
    status="FAIL",
)
print(check)
# > variables={} fails_count=2 rows_count=10 status='FAIL'

check = models.DataQualityCheck(
    rows_count=10,
    fails_count=2,
    status="PASS",
)
print(check)
# > variables={} fails_count=2 rows_count=10 status='PASS'

References

Data Quality

PARAMETER	DESCRIPTION
`fails_count`	Number of rows not meeting the expectation. TYPE: `int \| VariableType` DEFAULT: `None`
`rows_count`	Total number of rows in dataset. TYPE: `int \| VariableType` DEFAULT: `None`
`status`	Result of comparison, considering the expectation criteria and tolerances. TYPE: `Literal['PASS', 'FAIL'] \| VariableType`

ATTRIBUTE	DESCRIPTION
`failure_rate`	Ratio of the number of rows not meeting the expectation to the total number of rows in the dataframe. Returns

`failure_rate` `property` ¤

Ratio of the number of rows not meeting the expectation to the total number of rows in the dataframe. Returns None when the type is "AGGREGATE".