great_expectations.expectations.core.expect_column_quantile_values_to_be_between
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Module Contents¶
Classes¶
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Expect the specific provided column quantiles to be between a minimum value and a maximum value. |
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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
result_format (str or None) – Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).
include_config (boolean) – If True, then include the expectation config as part of the result object.
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](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#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](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).
- 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
See also
[expect_column_min_to_be_between](https://greatexpectations.io/expectations/expect_column_min_to_be_between) [expect_column_max_to_be_between](https://greatexpectations.io/expectations/expect_column_max_to_be_between) [expect_column_median_to_be_between](https://greatexpectations.io/expectations/expect_column_median_to_be_between)
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library_metadata
¶
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metric_dependencies
= ['column.quantile_values']¶
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success_keys
= ['quantile_ranges', 'allow_relative_error', 'auto', 'profiler_config']¶
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quantile_value_ranges_estimator_parameter_builder_config
¶
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validation_parameter_builder_configs
:List[ParameterBuilderConfig]¶
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default_profiler_config
¶
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default_kwarg_values
¶
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args_keys
= ['column', 'quantile_ranges', 'allow_relative_error']¶
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validate_configuration
(self, configuration: Optional[ExpectationConfiguration])¶
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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.
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classmethod
_prescriptive_summary
(cls, configuration: Optional[ExpectationConfiguration] = None, result: Optional[ExpectationValidationResult] = None, runtime_configuration: Optional[dict] = None, **kwargs)¶
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classmethod
_prescriptive_renderer
(cls, configuration: Optional[ExpectationConfiguration] = None, result: Optional[ExpectationValidationResult] = None, runtime_configuration: Optional[dict] = None, **kwargs)¶
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classmethod
_diagnostic_observed_value_renderer
(cls, configuration: Optional[ExpectationConfiguration] = None, result: Optional[ExpectationValidationResult] = None, runtime_configuration: Optional[dict] = None, **kwargs)¶
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classmethod
_atomic_diagnostic_observed_value_template
(cls, configuration: Optional[ExpectationConfiguration] = None, result: Optional[ExpectationValidationResult] = None, runtime_configuration: Optional[dict] = None, **kwargs)¶
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classmethod
_atomic_diagnostic_observed_value
(cls, configuration: Optional[ExpectationConfiguration] = None, result: Optional[ExpectationValidationResult] = None, runtime_configuration: Optional[dict] = None, **kwargs)¶
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classmethod
_descriptive_quantile_table_renderer
(cls, configuration: Optional[ExpectationConfiguration] = None, result: Optional[ExpectationValidationResult] = None, runtime_configuration: Optional[dict] = None, **kwargs)¶
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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.
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_validate
(self, configuration: ExpectationConfiguration, metrics: Dict, runtime_configuration: Optional[dict] = None, execution_engine: Optional[ExecutionEngine] = None)¶