great_expectations.expectations.core.expect_column_proportion_of_unique_values_to_be_between

Module Contents

Classes

ExpectColumnProportionOfUniqueValuesToBeBetween(configuration: Optional[ExpectationConfiguration] = None)

Expect the proportion of unique values to be between a minimum value and a maximum value.

class great_expectations.expectations.core.expect_column_proportion_of_unique_values_to_be_between.ExpectColumnProportionOfUniqueValuesToBeBetween(configuration: Optional[ExpectationConfiguration] = None)

Bases: great_expectations.expectations.expectation.ColumnExpectation

Expect the proportion of unique values to be between a minimum value and a maximum value.

For example, in a column containing [1, 2, 2, 3, 3, 3, 4, 4, 4, 4], there are 4 unique values and 10 total values for a proportion of 0.4.

expect_column_proportion_of_unique_values_to_be_between is a column_aggregate_expectation.

Parameters
  • column (str) – The column name.

  • min_value (float or None) – The minimum proportion of unique values. (Proportions are on the range 0 to 1)

  • max_value (float or None) – The maximum proportion of unique values. (Proportions are on the range 0 to 1)

  • strict_min (boolean) – If True, the minimum proportion of unique values must be strictly larger than min_value, default=False

  • strict_max (boolean) – If True, the maximum proportion of unique values 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 proportion of unique values in 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_unique_value_count_to_be_between

library_metadata
metric_dependencies = ['column.unique_proportion']
success_keys = ['min_value', 'strict_min', 'max_value', 'strict_max']
default_kwarg_values

A Column Aggregate MetricProvider Decorator for the Unique Proportion

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 neccessary 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 _prescriptive_renderer(cls, configuration=None, result=None, language=None, runtime_configuration=None, **kwargs)
classmethod _descriptive_column_properties_table_distinct_percent_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)