great_expectations.expectations.metrics.table_metrics

Package Contents

Classes

TableColumnCount()

Base class for all metric providers.

ColumnTypes()

Base class for all metric providers.

TableColumns()

Base class for all metric providers.

TableHead()

Base class for all metric providers.

TableRowCount()

Base class for all metric providers.

class great_expectations.expectations.metrics.table_metrics.TableColumnCount

Bases: great_expectations.expectations.metrics.table_metric.TableMetricProvider

Base class for all metric providers.

MetricProvider classes must have the following attributes set:
  1. metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.

  1. domain_keys: a tuple of the keys used to determine the domain of the metric

  2. 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[Tuple, Any], runtime_configuration: Dict)
_sqlalchemy(cls, execution_engine: ExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[Tuple, Any], runtime_configuration: Dict)
_spark(cls, execution_engine: ExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[Tuple, 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.table_metrics.ColumnTypes

Bases: great_expectations.expectations.metrics.table_metric.TableMetricProvider

Base class for all metric providers.

MetricProvider classes must have the following attributes set:
  1. metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.

  1. domain_keys: a tuple of the keys used to determine the domain of the metric

  2. 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[Tuple, Any], runtime_configuration: Dict)
_sqlalchemy(cls, execution_engine: SqlAlchemyExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[Tuple, Any], runtime_configuration: Dict)
_spark(cls, execution_engine: SparkDFExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[Tuple, Any], runtime_configuration: Dict)
class great_expectations.expectations.metrics.table_metrics.TableColumns

Bases: great_expectations.expectations.metrics.table_metric.TableMetricProvider

Base class for all metric providers.

MetricProvider classes must have the following attributes set:
  1. metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.

  1. domain_keys: a tuple of the keys used to determine the domain of the metric

  2. 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[Tuple, Any], runtime_configuration: Dict)
_sqlalchemy(cls, execution_engine: SqlAlchemyExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[Tuple, Any], runtime_configuration: Dict)
_spark(cls, execution_engine: SparkDFExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[Tuple, 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.table_metrics.TableHead

Bases: great_expectations.expectations.metrics.table_metric.TableMetricProvider

Base class for all metric providers.

MetricProvider classes must have the following attributes set:
  1. metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.

  1. domain_keys: a tuple of the keys used to determine the domain of the metric

  2. 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[Tuple, Any], runtime_configuration: Dict)
_sqlalchemy(cls, execution_engine: SqlAlchemyExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[Tuple, Any], runtime_configuration: Dict)
_spark(cls, execution_engine: SparkDFExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[Tuple, Any], runtime_configuration: Dict)
class great_expectations.expectations.metrics.table_metrics.TableRowCount

Bases: great_expectations.expectations.metrics.table_metric.TableMetricProvider

Base class for all metric providers.

MetricProvider classes must have the following attributes set:
  1. metric_name: the name to use. Metric Name must be globally unique in a great_expectations installation.

  1. domain_keys: a tuple of the keys used to determine the domain of the metric

  2. 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[Tuple, Any], runtime_configuration: Dict)
_sqlalchemy(cls, execution_engine: SqlAlchemyExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[Tuple, Any], runtime_configuration: Dict)
_spark(cls, execution_engine: SqlAlchemyExecutionEngine, metric_domain_kwargs: Dict, metric_value_kwargs: Dict, metrics: Dict[Tuple, Any], runtime_configuration: Dict)