great_expectations.expectations.metrics
¶
Subpackages¶
great_expectations.expectations.metrics.column_aggregate_metrics
great_expectations.expectations.metrics.column_aggregate_metrics.column_bootstrapped_ks_test_p_value
great_expectations.expectations.metrics.column_aggregate_metrics.column_distinct_values
great_expectations.expectations.metrics.column_aggregate_metrics.column_histogram
great_expectations.expectations.metrics.column_aggregate_metrics.column_max
great_expectations.expectations.metrics.column_aggregate_metrics.column_mean
great_expectations.expectations.metrics.column_aggregate_metrics.column_median
great_expectations.expectations.metrics.column_aggregate_metrics.column_min
great_expectations.expectations.metrics.column_aggregate_metrics.column_most_common_value
great_expectations.expectations.metrics.column_aggregate_metrics.column_parameterized_distribution_ks_test_p_value
great_expectations.expectations.metrics.column_aggregate_metrics.column_partition
great_expectations.expectations.metrics.column_aggregate_metrics.column_proportion_of_unique_values
great_expectations.expectations.metrics.column_aggregate_metrics.column_quantile_values
great_expectations.expectations.metrics.column_aggregate_metrics.column_standard_deviation
great_expectations.expectations.metrics.column_aggregate_metrics.column_sum
great_expectations.expectations.metrics.column_aggregate_metrics.column_value_counts
great_expectations.expectations.metrics.column_aggregate_metrics.column_values_between_count
great_expectations.expectations.metrics.column_map_metrics
great_expectations.expectations.metrics.column_map_metrics.column_value_lengths
great_expectations.expectations.metrics.column_map_metrics.column_values_between
great_expectations.expectations.metrics.column_map_metrics.column_values_dateutil_parseable
great_expectations.expectations.metrics.column_map_metrics.column_values_decreasing
great_expectations.expectations.metrics.column_map_metrics.column_values_in_set
great_expectations.expectations.metrics.column_map_metrics.column_values_in_type_list
great_expectations.expectations.metrics.column_map_metrics.column_values_increasing
great_expectations.expectations.metrics.column_map_metrics.column_values_json_parseable
great_expectations.expectations.metrics.column_map_metrics.column_values_match_json_schema
great_expectations.expectations.metrics.column_map_metrics.column_values_match_like_pattern
great_expectations.expectations.metrics.column_map_metrics.column_values_match_like_pattern_list
great_expectations.expectations.metrics.column_map_metrics.column_values_match_regex
great_expectations.expectations.metrics.column_map_metrics.column_values_match_regex_list
great_expectations.expectations.metrics.column_map_metrics.column_values_match_strftime_format
great_expectations.expectations.metrics.column_map_metrics.column_values_non_null
great_expectations.expectations.metrics.column_map_metrics.column_values_not_in_set
great_expectations.expectations.metrics.column_map_metrics.column_values_not_match_like_pattern
great_expectations.expectations.metrics.column_map_metrics.column_values_not_match_like_pattern_list
great_expectations.expectations.metrics.column_map_metrics.column_values_not_match_regex
great_expectations.expectations.metrics.column_map_metrics.column_values_not_match_regex_list
great_expectations.expectations.metrics.column_map_metrics.column_values_null
great_expectations.expectations.metrics.column_map_metrics.column_values_of_type
great_expectations.expectations.metrics.column_map_metrics.column_values_unique
great_expectations.expectations.metrics.column_map_metrics.column_values_z_score
great_expectations.expectations.metrics.column_pair_map_metrics
great_expectations.expectations.metrics.multicolumn_map_metrics
great_expectations.expectations.metrics.table_metrics
great_expectations.expectations.metrics.table_metrics.table_column_count
great_expectations.expectations.metrics.table_metrics.table_column_types
great_expectations.expectations.metrics.table_metrics.table_columns
great_expectations.expectations.metrics.table_metrics.table_head
great_expectations.expectations.metrics.table_metrics.table_row_count
Submodules¶
great_expectations.expectations.metrics.column_aggregate_metric
great_expectations.expectations.metrics.column_aggregate_metric_provider
great_expectations.expectations.metrics.import_manager
great_expectations.expectations.metrics.map_metric
great_expectations.expectations.metrics.map_metric_provider
great_expectations.expectations.metrics.meta_metric_provider
great_expectations.expectations.metrics.metric_provider
great_expectations.expectations.metrics.table_metric
great_expectations.expectations.metrics.table_metric_provider
great_expectations.expectations.metrics.util
Package Contents¶
Classes¶
MetaMetricProvider registers metrics as they are defined. |
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Goals: |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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MetricProvider Class for Aggregate Mean MetricProvider |
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MetricProvider Class for Aggregate Mean MetricProvider |
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Base class for all metric providers. |
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Base class for all metric providers. |
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MetricProvider Class for Aggregate Standard Deviation metric |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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MetricProvider Class for Aggregate Standard Deviation metric |
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Base class for all metric providers. |
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Base class for all metric providers. |
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This metric is an aggregate helper for rare cases. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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While the support for “PandasExecutionEngine” and “SparkDFExecutionEngine” is accomplished using a compact |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
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Base class for all metric providers. |
Functions¶
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Return the column aggregate metric decorator for the specified engine. |
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Return the column aggregate metric decorator for the specified engine. |
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Provides engine-specific support for authoring a metric_fn with a simplified signature. |
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Provides engine-specific support for authoring a metric_fn with a simplified signature. |
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class
great_expectations.expectations.metrics.
MetaMetricProvider
¶ Bases:
type
MetaMetricProvider registers metrics as they are defined.
-
class
great_expectations.expectations.metrics.
DeprecatedMetaMetricProvider
¶ Bases:
great_expectations.expectations.metrics.meta_metric_provider.MetaMetricProvider
Goals: Instantiation of a deprecated class should raise a warning; Subclassing of a deprecated class should raise a warning; Support isinstance and issubclass checks.
-
__instancecheck__
(cls, instance)¶ Check if an object is an instance.
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__subclasscheck__
(cls, subclass)¶ Check if a class is a subclass.
-
-
class
great_expectations.expectations.metrics.
ColumnMetricProvider
¶ -
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
_DeprecatedMetaMetricProvider__alias
¶
-
class
great_expectations.expectations.metrics.
ColumnAggregateMetricProvider
¶ Bases:
great_expectations.expectations.metrics.table_metric_provider.TableMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
domain_keys
= ['batch_id', 'table', 'column', 'row_condition', 'condition_parser']¶
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filter_column_isnull
= False¶
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classmethod
_get_evaluation_dependencies
(cls, metric: MetricConfiguration, configuration: Optional[ExpectationConfiguration] = None, execution_engine: Optional[ExecutionEngine] = None, runtime_configuration: Optional[dict] = None)¶
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great_expectations.expectations.metrics.
column_aggregate_partial
(engine: Type[ExecutionEngine], **kwargs)¶ Return the column aggregate metric decorator for the specified engine.
- Parameters
engine –
**kwargs –
Returns:
-
great_expectations.expectations.metrics.
column_aggregate_value
(engine: Type[ExecutionEngine], metric_fn_type='value', domain_type='column', **kwargs)¶ Return the column aggregate metric decorator for the specified engine.
- Parameters
engine –
**kwargs –
Returns:
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class
great_expectations.expectations.metrics.
ColumnDistinctValues
¶ -
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
metric_name
= column.distinct_values¶
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_pandas
(cls, column, **kwargs)¶
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_sqlalchemy
(cls, execution_engine: SqlAlchemyExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
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_spark
(cls, execution_engine: SqlAlchemyExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
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classmethod
_get_evaluation_dependencies
(cls, metric: MetricConfiguration, configuration: Optional[ExpectationConfiguration] = None, execution_engine: Optional[ExecutionEngine] = None, runtime_configuration: Optional[Dict] = None)¶ Returns a dictionary of given metric names and their corresponding configuration, specifying the metric types and their respective domains
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class
great_expectations.expectations.metrics.
ColumnDistinctValuesCount
¶ -
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
metric_name
= column.distinct_values.count¶
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_pandas
(cls, column, **kwargs)¶
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_sqlalchemy
(cls, execution_engine: SqlAlchemyExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
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_spark
(cls, execution_engine: SqlAlchemyExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
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classmethod
_get_evaluation_dependencies
(cls, metric: MetricConfiguration, configuration: Optional[ExpectationConfiguration] = None, execution_engine: Optional[ExecutionEngine] = None, runtime_configuration: Optional[Dict] = None)¶ Returns a dictionary of given metric names and their corresponding configuration, specifying the metric types and their respective domains
-
class
great_expectations.expectations.metrics.
ColumnHistogram
¶ -
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
metric_name
= column.histogram¶
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value_keys
= ['bins']¶
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_pandas
(cls, execution_engine: PandasExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
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_sqlalchemy
(cls, execution_engine: SqlAlchemyExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶ return a list of counts corresponding to bins
- Parameters
column – the name of the column for which to get the histogram
bins – tuple of bin edges for which to get histogram values; must be tuple to support caching
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_spark
(cls, execution_engine: SparkDFExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
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class
great_expectations.expectations.metrics.
ColumnMax
¶ -
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
metric_name
= column.max¶
-
value_keys
= ['parse_strings_as_datetimes']¶
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_pandas
(cls, column, **kwargs)¶
-
_sqlalchemy
(cls, column, **kwargs)¶
-
_spark
(cls, column, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnMean
¶ -
MetricProvider Class for Aggregate Mean MetricProvider
-
metric_name
= column.mean¶
-
_pandas
(cls, column, **kwargs)¶ Pandas Mean Implementation
-
_sqlalchemy
(cls, column, **kwargs)¶ SqlAlchemy Mean Implementation
-
_spark
(cls, column, _table, _column_name, **kwargs)¶ Spark Mean Implementation
-
-
class
great_expectations.expectations.metrics.
ColumnMedian
¶ -
MetricProvider Class for Aggregate Mean MetricProvider
-
metric_name
= column.median¶
-
_pandas
(cls, column, **kwargs)¶ Pandas Median Implementation
-
_sqlalchemy
(cls, execution_engine: SqlAlchemyExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
_spark
(cls, execution_engine: SqlAlchemyExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
classmethod
_get_evaluation_dependencies
(cls, metric: MetricConfiguration, configuration: Optional[ExpectationConfiguration] = None, execution_engine: Optional[ExecutionEngine] = None, runtime_configuration: Optional[dict] = None)¶ This should return a dictionary: {
“dependency_name”: MetricConfiguration, …
}
-
-
class
great_expectations.expectations.metrics.
ColumnMin
¶ -
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
metric_name
= column.min¶
-
value_keys
= ['parse_strings_as_datetimes']¶
-
_pandas
(cls, column, **kwargs)¶
-
_sqlalchemy
(cls, column, **kwargs)¶
-
_spark
(cls, column, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnMostCommonValue
¶ -
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
metric_name
= column.most_common_value¶
-
_pandas
(cls, column, **kwargs)¶
-
_spark
(cls, execution_engine: SqlAlchemyExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
_sqlalchemy
(cls, execution_engine: SqlAlchemyExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
classmethod
_get_evaluation_dependencies
(cls, metric: MetricConfiguration, configuration: Optional[ExpectationConfiguration] = None, execution_engine: Optional[ExecutionEngine] = None, runtime_configuration: Optional[Dict] = None)¶ Returns a dictionary of given metric names and their corresponding configuration, specifying the metric types and their respective domains
-
class
great_expectations.expectations.metrics.
ColumnParameterizedDistributionKSTestPValue
¶ -
MetricProvider Class for Aggregate Standard Deviation metric
-
metric_name
= column.parameterized_distribution_ks_test_p_value¶
-
value_keys
= ['distribution', 'p_value', 'params']¶
-
_pandas
(cls, column, distribution, p_value=0.05, params=None, **kwargs)¶
-
-
class
great_expectations.expectations.metrics.
ColumnPartition
¶ -
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
metric_name
= column.partition¶
-
value_keys
= ['bins', 'n_bins', 'allow_relative_error']¶
-
default_kwarg_values
¶
-
_pandas
(cls, execution_engine: PandasExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
_sqlalchemy
(cls, execution_engine: PandasExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
_spark
(cls, execution_engine: PandasExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
classmethod
_get_evaluation_dependencies
(cls, metric: MetricConfiguration, configuration: Optional[ExpectationConfiguration] = None, execution_engine: Optional[ExecutionEngine] = None, runtime_configuration: Optional[dict] = None)¶
-
class
great_expectations.expectations.metrics.
ColumnUniqueProportion
¶ -
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
metric_name
= column.unique_proportion¶
-
_pandas
(*args, metrics, **kwargs)¶
-
_sqlalchemy
(*args, metrics, **kwargs)¶
-
_spark
(*args, metrics, **kwargs)¶
-
classmethod
_get_evaluation_dependencies
(cls, metric: MetricConfiguration, configuration: Optional[ExpectationConfiguration] = None, execution_engine: Optional[ExecutionEngine] = None, runtime_configuration: Optional[dict] = None)¶
-
class
great_expectations.expectations.metrics.
ColumnQuantileValues
¶ -
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
metric_name
= column.quantile_values¶
-
value_keys
= ['quantiles', 'allow_relative_error']¶
-
_pandas
(cls, column, quantiles, allow_relative_error, **kwargs)¶ Quantile Function
-
_sqlalchemy
(cls, execution_engine: SqlAlchemyExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
_spark
(cls, execution_engine: SqlAlchemyExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
class
great_expectations.expectations.metrics.
ColumnStandardDeviation
¶ -
MetricProvider Class for Aggregate Standard Deviation metric
-
metric_name
= column.standard_deviation¶
-
_pandas
(cls, column, **kwargs)¶ Pandas Standard Deviation implementation
-
_sqlalchemy
(cls, column, _dialect, **kwargs)¶ SqlAlchemy Standard Deviation implementation
-
_spark
(cls, column, **kwargs)¶ Spark Standard Deviation implementation
-
-
class
great_expectations.expectations.metrics.
ColumnSum
¶ -
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
metric_name
= column.sum¶
-
_pandas
(cls, column, **kwargs)¶
-
_sqlalchemy
(cls, column, **kwargs)¶
-
_spark
(cls, column, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnValueCounts
¶ -
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
metric_name
= column.value_counts¶
-
value_keys
= ['sort', 'collate']¶
-
default_kwarg_values
¶
-
_pandas
(cls, execution_engine: PandasExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
_sqlalchemy
(cls, execution_engine: SqlAlchemyExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
_spark
(cls, execution_engine: SparkDFExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesBetweenCount
¶ Bases:
great_expectations.expectations.metrics.metric_provider.MetricProvider
This metric is an aggregate helper for rare cases.
-
metric_name
= column_values.between.count¶
-
value_keys
= ['min_value', 'max_value', 'strict_min', 'strict_max']¶
-
_pandas
(cls, execution_engine: PandasExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
_sqlalchemy
(cls, execution_engine: SqlAlchemyExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
_spark
(cls, execution_engine: SparkDFExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
-
class
great_expectations.expectations.metrics.
ColumnValuesValueLength
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.value_length.between¶
-
function_metric_name
= column_values.value_length¶
-
condition_value_keys
= ['min_value', 'max_value', 'strict_min', 'strict_max']¶
-
_pandas_function
(cls, column, **kwargs)¶
-
_sqlalchemy_function
(cls, column, **kwargs)¶
-
_spark_function
(cls, column, **kwargs)¶
-
_pandas
(cls, column, _metrics, min_value=None, max_value=None, strict_min=None, strict_max=None, **kwargs)¶
-
_sqlalchemy
(cls, column, _metrics, min_value=None, max_value=None, strict_min=None, strict_max=None, **kwargs)¶
-
_spark
(cls, column, _metrics, min_value=None, max_value=None, strict_min=None, strict_max=None, **kwargs)¶
-
classmethod
_get_evaluation_dependencies
(cls, metric: MetricConfiguration, configuration: Optional[ExpectationConfiguration] = None, execution_engine: Optional[ExecutionEngine] = None, runtime_configuration: Optional[dict] = None)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesValueLengthEquals
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.value_length.equals¶
-
condition_value_keys
= ['value']¶
-
_pandas
(cls, column, value, _metrics, **kwargs)¶
-
_sqlalchemy
(cls, column, value, _metrics, **kwargs)¶
-
_spark
(cls, column, value, _metrics, **kwargs)¶
-
classmethod
_get_evaluation_dependencies
(cls, metric: MetricConfiguration, configuration: Optional[ExpectationConfiguration] = None, execution_engine: Optional[ExecutionEngine] = None, runtime_configuration: Optional[dict] = None)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesBetween
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.between¶
-
condition_value_keys
= ['min_value', 'max_value', 'strict_min', 'strict_max', 'parse_strings_as_datetimes', 'allow_cross_type_comparisons']¶
-
_pandas
(cls, column, min_value=None, max_value=None, strict_min=None, strict_max=None, parse_strings_as_datetimes: bool = False, allow_cross_type_comparisons=None, **kwargs)¶
-
_sqlalchemy
(cls, column, min_value=None, max_value=None, strict_min=None, strict_max=None, parse_strings_as_datetimes: bool = False, **kwargs)¶
-
_spark
(cls, column, min_value=None, max_value=None, strict_min=None, strict_max=None, parse_strings_as_datetimes: bool = False, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesDateutilParseable
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.dateutil_parseable¶
-
_pandas
(cls, column, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesDecreasing
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.decreasing¶
-
condition_value_keys
= ['strictly', 'parse_strings_as_datetimes']¶
-
default_kwarg_values
¶
-
_pandas
(cls, column, **kwargs)¶
-
_spark
(cls, execution_engine: SparkDFExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesInSet
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.in_set¶
-
condition_value_keys
= ['value_set', 'parse_strings_as_datetimes']¶
-
_pandas
(cls, column, value_set, **kwargs)¶
-
_sqlalchemy
(cls, column, value_set, **kwargs)¶
-
_spark
(cls, column, value_set, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesInTypeList
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.in_type_list¶
-
condition_value_keys
= ['type_list']¶
-
_pandas
(cls, column, type_list, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesIncreasing
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.increasing¶
-
condition_value_keys
= ['strictly', 'parse_strings_as_datetimes']¶
-
default_kwarg_values
¶
-
_pandas
(cls, column, **kwargs)¶
-
_spark
(cls, execution_engine: SparkDFExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesJsonParseable
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.json_parseable¶
-
_pandas
(cls, column, **kwargs)¶
-
_spark
(cls, column, json_schema, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesMatchJsonSchema
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.match_json_schema¶
-
condition_value_keys
= ['json_schema']¶
-
_pandas
(cls, column, json_schema, **kwargs)¶
-
_spark
(cls, column, json_schema, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesMatchLikePattern
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.match_like_pattern¶
-
condition_value_keys
= ['like_pattern']¶
-
_sqlalchemy
(cls, column, like_pattern, _dialect, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesMatchLikePatternList
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.match_like_pattern_list¶
-
condition_value_keys
= ['like_pattern_list', 'match_on']¶
-
_sqlalchemy
(cls, column, like_pattern_list, match_on, _dialect, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesMatchRegex
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.match_regex¶
-
condition_value_keys
= ['regex']¶
-
_pandas
(cls, column, regex, **kwargs)¶
-
_sqlalchemy
(cls, column, regex, _dialect, **kwargs)¶
-
_spark
(cls, column, regex, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesMatchRegexList
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.match_regex_list¶
-
condition_value_keys
= ['regex_list', 'match_on']¶
-
default_kwarg_values
¶
-
_pandas
(cls, column, regex_list, match_on, **kwargs)¶
-
_sqlalchemy
(cls, column, regex_list, match_on, _dialect, **kwargs)¶
-
_spark
(cls, column, regex_list, match_on, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesMatchStrftimeFormat
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.match_strftime_format¶
-
condition_value_keys
= ['strftime_format']¶
-
_pandas
(cls, column, strftime_format, **kwargs)¶
-
_spark
(cls, column, strftime_format, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesNonNull
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.nonnull¶
-
filter_column_isnull
= False¶
-
_pandas
(cls, column, **kwargs)¶
-
_sqlalchemy
(cls, column, **kwargs)¶
-
_spark
(cls, column, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesNotInSet
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.not_in_set¶
-
condition_value_keys
= ['value_set', 'parse_strings_as_datetimes']¶
-
_pandas
(cls, column, value_set, **kwargs)¶
-
_sqlalchemy
(cls, column, value_set, **kwargs)¶
-
_spark
(cls, column, value_set, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesNotMatchLikePattern
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.not_match_like_pattern¶
-
condition_value_keys
= ['like_pattern']¶
-
_sqlalchemy
(cls, column, like_pattern, _dialect, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesNotMatchLikePatternList
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.not_match_like_pattern_list¶
-
condition_value_keys
= ['like_pattern_list', 'match_on']¶
-
_sqlalchemy
(cls, column, like_pattern_list, _dialect, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesNotMatchRegex
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.not_match_regex¶
-
condition_value_keys
= ['regex']¶
-
_pandas
(cls, column, regex, **kwargs)¶
-
_sqlalchemy
(cls, column, regex, _dialect, **kwargs)¶
-
_spark
(cls, column, regex, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesNotMatchRegexList
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.not_match_regex_list¶
-
condition_value_keys
= ['regex_list']¶
-
_pandas
(cls, column, regex_list, **kwargs)¶
-
_sqlalchemy
(cls, column, regex_list, _dialect, **kwargs)¶
-
_spark
(cls, column, regex_list, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesNull
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.null¶
-
filter_column_isnull
= False¶
-
_pandas
(cls, column, **kwargs)¶
-
_sqlalchemy
(cls, column, **kwargs)¶
-
_spark
(cls, column, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesOfType
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.of_type¶
-
condition_value_keys
= ['type_']¶
-
_pandas
(cls, column, type_, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesUnique
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.unique¶
-
_pandas
(cls, column, **kwargs)¶
-
_sqlalchemy_window
(cls, column, _table, **kwargs)¶
-
_spark
(cls, column, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnValuesZScore
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_values.z_score.under_threshold¶
-
condition_value_keys
= ['double_sided', 'threshold']¶
-
default_kwarg_values
¶
-
function_metric_name
= column_values.z_score¶
-
function_value_keys
¶
-
_pandas_function
(self, column, _metrics, **kwargs)¶
-
_pandas_condition
(cls, column, _metrics, threshold, double_sided, **kwargs)¶
-
_sqlalchemy_function
(cls, column, _metrics, _dialect, **kwargs)¶
-
_sqlalchemy_condition
(cls, column, _metrics, threshold, double_sided, **kwargs)¶
-
_spark_function
(cls, column, _metrics, **kwargs)¶
-
_spark_condition
(cls, column, _metrics, threshold, double_sided, **kwargs)¶
-
classmethod
_get_evaluation_dependencies
(cls, metric: MetricConfiguration, configuration: Optional[ExpectationConfiguration] = None, execution_engine: Optional[ExecutionEngine] = None, runtime_configuration: Optional[dict] = None)¶ Returns a dictionary of given metric names and their corresponding configuration, specifying the metric types and their respective domains
-
class
great_expectations.expectations.metrics.
ColumnPairValuesEqual
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnPairMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_pair_values.equal¶
-
condition_domain_keys
= ['batch_id', 'table', 'column_A', 'column_B', 'row_condition', 'condition_parser', 'ignore_row_if']¶
-
condition_value_keys
= []¶
-
_pandas
(cls, column_A, column_B, **kwargs)¶
-
_sqlalchemy
(cls, column_A, column_B, **kwargs)¶
-
_spark
(cls, column_A, column_B, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnPairValuesAGreaterThanB
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnPairMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_pair_values.a_greater_than_b¶
-
condition_domain_keys
= ['batch_id', 'table', 'column_A', 'column_B', 'row_condition', 'condition_parser', 'ignore_row_if']¶
-
condition_value_keys
= ['or_equal', 'parse_strings_as_datetimes', 'allow_cross_type_comparisons']¶
-
_pandas
(cls, column_A, column_B, **kwargs)¶
-
_sqlalchemy
(cls, column_A, column_B, **kwargs)¶
-
_spark
(cls, column_A, column_B, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnPairValuesInSet
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.ColumnPairMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= column_pair_values.in_set¶
-
condition_value_keys
= ['value_pairs_set']¶
-
condition_domain_keys
= ['batch_id', 'table', 'column_A', 'column_B', 'ignore_row_if']¶
-
_pandas
(cls, column_A, column_B, **kwargs)¶
-
_sqlalchemy
(cls, column_A, column_B, **kwargs)¶
-
_spark
(cls, column_A, column_B, **kwargs)¶
-
class
great_expectations.expectations.metrics.
ColumnMapMetricProvider
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.MapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_domain_keys
= ['batch_id', 'table', 'column', 'row_condition', 'condition_parser']¶
-
function_domain_keys
= ['batch_id', 'table', 'column', 'row_condition', 'condition_parser']¶
-
condition_value_keys
¶
-
function_value_keys
¶
-
classmethod
_get_evaluation_dependencies
(cls, metric: MetricConfiguration, configuration: Optional[ExpectationConfiguration] = None, execution_engine: Optional[ExecutionEngine] = None, runtime_configuration: Optional[dict] = None)¶
-
class
great_expectations.expectations.metrics.
MapMetricProvider
¶ Bases:
great_expectations.expectations.metrics.metric_provider.MetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_domain_keys
= ['batch_id', 'table', 'row_condition', 'condition_parser']¶
-
function_domain_keys
= ['batch_id', 'table', 'row_condition', 'condition_parser']¶
-
condition_value_keys
¶
-
function_value_keys
¶
-
filter_column_isnull
= True¶
-
SQLALCHEMY_SELECTABLE_METRICS
¶
-
classmethod
_register_metric_functions
(cls)¶
-
classmethod
_get_evaluation_dependencies
(cls, metric: MetricConfiguration, configuration: Optional[ExpectationConfiguration] = None, execution_engine: Optional[ExecutionEngine] = None, runtime_configuration: Optional[dict] = None)¶
-
static
is_sqlalchemy_metric_selectable
(map_metric_provider: MetaMetricProvider)¶ - Parameters
map_metric_provider – object of type “MapMetricProvider”, whose SQLAlchemy implementation is inspected
- Returns
boolean indicating whether or not the returned value of a method implementing the metric resolves all
columns – hence the caller must not use “select_from” clause as part of its own SQLAlchemy query; otherwise an unwanted selectable (e.g., table) will be added to “FROM”, leading to duplicated and/or erroneous results.
-
great_expectations.expectations.metrics.
column_condition_partial
(engine: Type[ExecutionEngine], partial_fn_type: Optional[Union[str, MetricPartialFunctionTypes]] = None, **kwargs)¶ Provides engine-specific support for authoring a metric_fn with a simplified signature.
A column_condition_partial must provide a map function that evaluates to a boolean value; it will be used to provide supplemental metrics, such as the unexpected_value count, unexpected_values, and unexpected_rows.
A metric function that is decorated as a column_condition_partial will be called with the engine-specific column type and any value_kwargs associated with the Metric for which the provider function is being declared.
- Parameters
engine –
partial_fn_type –
**kwargs –
- Returns
An annotated metric_function which will be called with a simplified signature.
-
great_expectations.expectations.metrics.
column_function_partial
(engine: Type[ExecutionEngine], partial_fn_type: str = None, **kwargs)¶ Provides engine-specific support for authoring a metric_fn with a simplified signature.
A metric function that is decorated as a column_function_partial will be called with the engine-specific column type and any value_kwargs associated with the Metric for which the provider function is being declared.
- Parameters
engine –
partial_fn_type –
**kwargs –
- Returns
An annotated metric_function which will be called with a simplified signature.
-
class
great_expectations.expectations.metrics.
CompoundColumnsUnique
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.MulticolumnMapMetricProvider
While the support for “PandasExecutionEngine” and “SparkDFExecutionEngine” is accomplished using a compact implementation, which combines the “map” and “condition” parts in a single step, the support for “SqlAlchemyExecutionEngine” is more detailed. Thus, the “map” and “condition” parts for “SqlAlchemyExecutionEngine” are handled separately, with the “condition” part relying on the “map” part as a metric dependency.
-
function_metric_name
= compound_columns.count¶
-
condition_metric_name
= compound_columns.unique¶
-
condition_domain_keys
= ['batch_id', 'table', 'column_list', 'row_condition', 'condition_parser', 'ignore_row_if']¶
-
_pandas
(cls, column_list, **kwargs)¶
-
_sqlalchemy_function
(self, column_list, **kwargs)¶ Computes the “map” between the specified “column_list” (treated as a group so as to model the “compound” aspect) and the number of occurrences of every permutation of the values of “column_list” as the grouped subset of all rows of the table. In the present context, the term “compound” refers to having to treat the specified columns as unique together (e.g., as a multi-column primary key). For example, suppose that in the example below, all three columns (“A”, “B”, and “C”) of the table are included as part of the “compound” columns list (i.e., column_list = [“A”, “B”, “C”]):
A B C _num_rows 1 1 2 2 1 2 3 1 1 1 2 2 2 2 2 1 3 2 3 1
The fourth column, “_num_rows”, holds the value of the “map” function – the number of rows the group occurs in.
-
_sqlalchemy_condition
(cls, column_list, **kwargs)¶ Retrieve the specified “map” metric dependency value as the “FromClause” “compound_columns_count_query” object and extract from it – using the supported SQLAlchemy column access method – the “_num_rows” columns. The uniqueness of “compound” columns (as a group) is expressed by the “BinaryExpression” “row_wise_cond” returned.
Importantly, since the “compound_columns_count_query” is the “FromClause” object that incorporates all columns of the original table, no additional “FromClause” objects (“select_from”) must augment this “condition” metric. Other than boolean operations, column access, argument of filtering, and limiting the size of the result set, this “row_wise_cond”, serving as the main component of the unexpected condition logic, carries along with it the entire object hierarchy, making any encapsulating query ready for execution against the database engine.
-
_spark
(cls, column_list, **kwargs)¶
-
classmethod
_get_evaluation_dependencies
(cls, metric: MetricConfiguration, configuration: Optional[ExpectationConfiguration] = None, execution_engine: Optional[ExecutionEngine] = None, runtime_configuration: Optional[dict] = None)¶ Returns a dictionary of given metric names and their corresponding configuration, specifying the metric types and their respective domains.
-
-
class
great_expectations.expectations.metrics.
MulticolumnSumEqual
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.MulticolumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= multicolumn_sum.equal¶
-
condition_domain_keys
= ['batch_id', 'table', 'column_list', 'row_condition', 'condition_parser', 'ignore_row_if']¶
-
condition_value_keys
= ['sum_total']¶
-
_pandas
(cls, column_list, **kwargs)¶
-
_sqlalchemy
(cls, column_list, **kwargs)¶
-
_spark
(cls, column_list, **kwargs)¶
-
class
great_expectations.expectations.metrics.
SelectColumnValuesUniqueWithinRecord
¶ Bases:
great_expectations.expectations.metrics.map_metric_provider.MulticolumnMapMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
condition_metric_name
= select_column_values.unique.within_record¶
-
condition_domain_keys
= ['batch_id', 'table', 'column_list', 'row_condition', 'condition_parser', 'ignore_row_if']¶
-
_pandas
(cls, column_list, **kwargs)¶
-
_sqlalchemy
(cls, column_list, **kwargs)¶ The present approach relies on an inefficient query condition construction implementation, whose computational cost is O(num_columns^2). However, until a more efficient implementation compatible with SQLAlchemy is available, this is the only feasible mechanism under the current architecture, where map metric providers must return a condition. Nevertheless, SQL query length limit is 1GB (sufficient for most practical scenarios).
-
_spark
(cls, column_list, **kwargs)¶
-
class
great_expectations.expectations.metrics.
TableColumnCount
¶ Bases:
great_expectations.expectations.metrics.table_metric_provider.TableMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
metric_name
= table.column_count¶
-
_pandas
(cls, execution_engine: ExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
_sqlalchemy
(cls, execution_engine: ExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
_spark
(cls, execution_engine: ExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
classmethod
_get_evaluation_dependencies
(cls, metric: MetricConfiguration, configuration: Optional[ExpectationConfiguration] = None, execution_engine: Optional[ExecutionEngine] = None, runtime_configuration: Optional[dict] = None)¶
-
class
great_expectations.expectations.metrics.
ColumnTypes
¶ Bases:
great_expectations.expectations.metrics.table_metric_provider.TableMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
metric_name
= table.column_types¶
-
value_keys
= ['include_nested']¶
-
default_kwarg_values
¶
-
_pandas
(cls, execution_engine: PandasExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
_sqlalchemy
(cls, execution_engine: SqlAlchemyExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
_spark
(cls, execution_engine: SparkDFExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
class
great_expectations.expectations.metrics.
TableColumns
¶ Bases:
great_expectations.expectations.metrics.table_metric_provider.TableMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
metric_name
= table.columns¶
-
_pandas
(cls, execution_engine: PandasExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
_sqlalchemy
(cls, execution_engine: SqlAlchemyExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
_spark
(cls, execution_engine: SparkDFExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
classmethod
_get_evaluation_dependencies
(cls, metric: MetricConfiguration, configuration: Optional[ExpectationConfiguration] = None, execution_engine: Optional[ExecutionEngine] = None, runtime_configuration: Optional[dict] = None)¶
-
class
great_expectations.expectations.metrics.
TableHead
¶ Bases:
great_expectations.expectations.metrics.table_metric_provider.TableMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
metric_name
= table.head¶
-
value_keys
= ['n_rows', 'fetch_all']¶
-
default_kwarg_values
¶
-
_pandas
(cls, execution_engine: PandasExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
_sqlalchemy
(cls, execution_engine: SqlAlchemyExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
_spark
(cls, execution_engine: SparkDFExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
class
great_expectations.expectations.metrics.
TableRowCount
¶ Bases:
great_expectations.expectations.metrics.table_metric_provider.TableMetricProvider
Base class for all metric providers.
- MetricProvider classes must have the following attributes set:
metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.
domain_keys: a tuple of the keys used to determine the domain of the metric
value_keys: a tuple of the keys used to determine the value of the metric.
In some cases, subclasses of Expectation, such as TableMetricProvider will already have correct values that may simply be inherited.
They may optionally override the default_kwarg_values attribute.
- MetricProvider classes must implement the following:
1. _get_evaluation_dependencies. Note that often, _get_evaluation_dependencies should augment dependencies provided by a parent class; consider calling super()._get_evaluation_dependencies
In some cases, subclasses of Expectation, such as MapMetricProvider will already have correct implementations that may simply be inherited.
- Additionally, they may provide implementations of:
1. Data Docs rendering methods decorated with the @renderer decorator. See the guide “How to create renderers for custom expectations” for more information.
-
metric_name
= table.row_count¶
-
_pandas
(cls, execution_engine: PandasExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
_sqlalchemy
(cls, execution_engine: SqlAlchemyExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶
-
_spark
(cls, execution_engine: SqlAlchemyExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[str, Any], runtime_configuration: Dict)¶