great_expectations.expectations.core.expect_column_mean_to_be_between

Module Contents

Classes

ExpectColumnMeanToBeBetween(configuration: Optional[ExpectationConfiguration] = None)

Expect the column mean to be between a minimum value and a maximum value (inclusive).
class great_expectations.expectations.core.expect_column_mean_to_be_between.ExpectColumnMeanToBeBetween(configuration: Optional[ExpectationConfiguration] = None)

Bases: great_expectations.expectations.expectation.ColumnExpectation

Expect the column mean to be between a minimum value and a maximum value (inclusive).

expect_column_mean_to_be_between is a column_aggregate_expectation.

Parameters

  • column (str) – The column name.

  • min_value (float or None) – The minimum value for the column mean.

  • max_value (float or None) – The maximum value for the column mean.

  • strict_min (boolean) – If True, the column mean must be strictly larger than min_value, default=False

  • strict_max (boolean) – If True, the column mean must be strictly smaller than max_value, default=False

Other Parameters

  • result_format (str or None) – Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see result_format.

  • include_config (boolean) – If True, then include the expectation config as part of the result object. For more detail, see include_config.

  • catch_exceptions (boolean or None) – If True, then catch exceptions and include them as part of the result object. For more detail, see catch_exceptions.

  • meta (dict or None) – A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see meta.

Returns

An ExpectationSuiteValidationResult

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

Notes

These fields in the result object are customized for this expectation:

{
    "observed_value": (float) The true mean for the column
}

  • min_value and max_value are both inclusive unless strict_min or strict_max are set to True.

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

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

See also

expect_column_median_to_be_between

expect_column_stdev_to_be_between

library_metadata
metric_dependencies = ['column.mean']
success_keys = ['min_value', 'strict_min', 'max_value', 'strict_max']
default_kwarg_values
args_keys = ['column', 'min_value', 'max_value', 'strict_min', 'strict_max']
kwargs_json_schema_base_properties
kwargs_json_schema
validate_configuration(self, configuration: Optional[ExpectationConfiguration])

Validates that a configuration has been set, and sets a configuration if it has yet to be set. Ensures that necessary configuration arguments have been provided for the validation of the expectation.

Parameters

configuration (OPTIONAL[ExpectationConfiguration]) – An optional Expectation Configuration entry that will be used to configure the expectation

Returns

True if the configuration has been validated successfully. Otherwise, raises an exception

classmethod _atomic_prescriptive_template(cls, configuration=None, result=None, language=None, runtime_configuration=None, **kwargs)

Template function that contains the logic that is shared by atomic.prescriptive.summary (GE Cloud) and renderer.prescriptive (OSS GE)

classmethod _prescriptive_renderer(cls, configuration=None, result=None, language=None, runtime_configuration=None, **kwargs)
classmethod _descriptive_stats_table_mean_row_renderer(cls, configuration=None, result=None, language=None, runtime_configuration=None, **kwargs)
_validate(self, configuration: ExpectationConfiguration, metrics: Dict, runtime_configuration: dict = None, execution_engine: ExecutionEngine = None)