great_expectations.expectations.core.expect_column_quantile_values_to_be_between

Module Contents

Classes

ExpectColumnQuantileValuesToBeBetween(configuration: Optional[ExpectationConfiguration] = None)

Expect the specific provided column quantiles to be between a minimum value and a maximum value.

class great_expectations.expectations.core.expect_column_quantile_values_to_be_between.ExpectColumnQuantileValuesToBeBetween(configuration: Optional[ExpectationConfiguration] = None)

Bases: great_expectations.expectations.expectation.ColumnExpectation

Expect the specific provided column quantiles to be between a minimum value and a maximum value.

expect_column_quantile_values_to_be_between is a [Column Aggregate Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_aggregate_expectations).

For example:

# my_df.my_col = [1,2,2,3,3,3,4]
>>> my_df.expect_column_quantile_values_to_be_between(
    "my_col",
    {
        "quantiles": [0., 0.333, 0.6667, 1.],
        "value_ranges": [[0,1], [2,3], [3,4], [4,5]]
    }
)
{
  "success": True,
    "result": {
      "observed_value": {
        "quantiles: [0., 0.333, 0.6667, 1.],
        "values": [1, 2, 3, 4],
      }
      "element_count": 7,
      "missing_count": 0,
      "missing_percent": 0.0,
      "details": {
        "success_details": [true, true, true, true]
      }
    }
  }
}

expect_column_quantile_values_to_be_between can be computationally intensive for large datasets.

Parameters
  • column (str) – The column name.

  • quantile_ranges (dictionary with keys 'quantiles' and 'value_ranges') – Key ‘quantiles’ is an increasingly ordered list of desired quantile values (floats). Key ‘value_ranges’ is a list of 2-value lists that specify a lower and upper bound (inclusive) for the corresponding quantile (with [min, max] ordering). The length of the ‘quantiles’ list and the ‘value_ranges’ list must be equal.

  • allow_relative_error (boolean or string) – Whether to allow relative error in quantile communications on backends that support or require it.

Other Parameters
Returns

//docs.greatexpectations.io/docs/terms/validation_result)

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

Return type

An [ExpectationSuiteValidationResult](https

Notes

  • min_value and max_value are both inclusive.

  • If min_value is None, then max_value is treated as an upper bound only

  • If max_value is None, then min_value is treated as a lower bound only

  • details.success_details field in the result object is customized for this expectation

library_metadata
metric_dependencies = ['column.quantile_values']
success_keys = ['quantile_ranges', 'allow_relative_error', 'auto', 'profiler_config']
quantile_value_ranges_estimator_parameter_builder_config
validation_parameter_builder_configs :List[ParameterBuilderConfig]
default_profiler_config
default_kwarg_values
args_keys = ['column', 'quantile_ranges', 'allow_relative_error']
validate_configuration(self, configuration: Optional[ExpectationConfiguration])
classmethod _atomic_prescriptive_template(cls, configuration: Optional[ExpectationConfiguration] = None, result: Optional[ExpectationValidationResult] = None, runtime_configuration: Optional[dict] = None, **kwargs)

Template function that contains the logic that is shared by AtomicPrescriptiveRendererType.SUMMARY and LegacyRendererType.PRESCRIPTIVE.

classmethod _prescriptive_summary(cls, configuration: Optional[ExpectationConfiguration] = None, result: Optional[ExpectationValidationResult] = None, runtime_configuration: Optional[dict] = None, **kwargs)
classmethod _prescriptive_renderer(cls, configuration: Optional[ExpectationConfiguration] = None, result: Optional[ExpectationValidationResult] = None, runtime_configuration: Optional[dict] = None, **kwargs)
classmethod _diagnostic_observed_value_renderer(cls, configuration: Optional[ExpectationConfiguration] = None, result: Optional[ExpectationValidationResult] = None, runtime_configuration: Optional[dict] = None, **kwargs)
classmethod _atomic_diagnostic_observed_value_template(cls, configuration: Optional[ExpectationConfiguration] = None, result: Optional[ExpectationValidationResult] = None, runtime_configuration: Optional[dict] = None, **kwargs)
classmethod _atomic_diagnostic_observed_value(cls, configuration: Optional[ExpectationConfiguration] = None, result: Optional[ExpectationValidationResult] = None, runtime_configuration: Optional[dict] = None, **kwargs)
classmethod _descriptive_quantile_table_renderer(cls, configuration: Optional[ExpectationConfiguration] = None, result: Optional[ExpectationValidationResult] = None, runtime_configuration: Optional[dict] = None, **kwargs)
get_validation_dependencies(self, configuration: Optional[ExpectationConfiguration] = None, execution_engine: Optional[ExecutionEngine] = None, runtime_configuration: Optional[dict] = None)

Returns the result format and metrics required to validate this Expectation using the provided result format.

_validate(self, configuration: ExpectationConfiguration, metrics: Dict, runtime_configuration: Optional[dict] = None, execution_engine: Optional[ExecutionEngine] = None)