great_expectations.expectations.metrics.column_aggregate_metrics
¶
Submodules¶
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
Package Contents¶
Classes¶
Base class for all metric providers. |
|
Base class for all metric providers. |
|
Base class for all metric providers. |
|
Base class for all metric providers. |
|
MetricProvider Class for Aggregate Mean MetricProvider |
|
MetricProvider Class for Aggregate Mean MetricProvider |
|
Base class for all metric providers. |
|
Base class for all metric providers. |
|
MetricProvider Class for Aggregate Standard Deviation metric |
|
Base class for all metric providers. |
|
Base class for all metric providers. |
|
Base class for all metric providers. |
|
MetricProvider Class for Aggregate Standard Deviation metric |
|
Base class for all metric providers. |
|
Base class for all metric providers. |
|
This metric is an aggregate helper for rare cases. |
-
class
great_expectations.expectations.metrics.column_aggregate_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¶
-
_pandas
(cls, column, **kwargs)¶
-
_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)¶ Returns a dictionary of given metric names and their corresponding configuration, specifying the metric types and their respective domains
-
class
great_expectations.expectations.metrics.column_aggregate_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¶
-
_pandas
(cls, column, **kwargs)¶
-
_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)¶ Returns a dictionary of given metric names and their corresponding configuration, specifying the metric types and their respective domains
-
class
great_expectations.expectations.metrics.column_aggregate_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¶
-
value_keys
= ['bins']¶
-
_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)¶ 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
-
_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.column_aggregate_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']¶
-
_pandas
(cls, column, **kwargs)¶
-
_sqlalchemy
(cls, column, **kwargs)¶
-
_spark
(cls, column, **kwargs)¶
-
class
great_expectations.expectations.metrics.column_aggregate_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.column_aggregate_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.column_aggregate_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.column_aggregate_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.column_aggregate_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.column_aggregate_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.column_aggregate_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.column_aggregate_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.column_aggregate_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.column_aggregate_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.column_aggregate_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.column_aggregate_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)¶
-