great_expectations.expectations.metrics.column_map_metrics

Submodules

Package Contents

Classes

ColumnValuesValueLength()

Base class for all metric providers.

ColumnValuesValueLengthEquals()

Base class for all metric providers.

ColumnValuesBetween()

Base class for all metric providers.

ColumnValuesDateutilParseable()

Base class for all metric providers.

ColumnValuesDecreasing()

Base class for all metric providers.

ColumnValuesInSet()

Base class for all metric providers.

ColumnValuesInTypeList()

Base class for all metric providers.

ColumnValuesIncreasing()

Base class for all metric providers.

ColumnValuesJsonParseable()

Base class for all metric providers.

ColumnValuesMatchJsonSchema()

Base class for all metric providers.

ColumnValuesMatchLikePattern()

Base class for all metric providers.

ColumnValuesMatchLikePatternList()

Base class for all metric providers.

ColumnValuesMatchRegex()

Base class for all metric providers.

ColumnValuesMatchRegexList()

Base class for all metric providers.

ColumnValuesMatchStrftimeFormat()

Base class for all metric providers.

ColumnValuesNonNull()

Base class for all metric providers.

ColumnValuesNotInSet()

Base class for all metric providers.

ColumnValuesNotMatchLikePattern()

Base class for all metric providers.

ColumnValuesNotMatchLikePatternList()

Base class for all metric providers.

ColumnValuesNotMatchRegex()

Base class for all metric providers.

ColumnValuesNotMatchRegexList()

Base class for all metric providers.

ColumnValuesNull()

Base class for all metric providers.

ColumnValuesOfType()

Base class for all metric providers.

ColumnValuesUnique()

Base class for all metric providers.

ColumnValuesZScore()

Base class for all metric providers.

class great_expectations.expectations.metrics.column_map_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:
  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.

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.column_map_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:
  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.

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.column_map_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:
  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.

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: Optional[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: Optional[bool] = False, **kwargs)
_spark(cls, column, min_value=None, max_value=None, strict_min=None, strict_max=None, parse_strings_as_datetimes: Optional[bool] = False, **kwargs)
class great_expectations.expectations.metrics.column_map_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:
  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.

condition_metric_name = column_values.dateutil_parseable
_pandas(cls, column, **kwargs)
class great_expectations.expectations.metrics.column_map_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:
  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.

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)
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_map_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:
  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.

condition_metric_name = column_values.in_set
condition_value_keys = ['value_set']
_pandas(cls, column, value_set, **kwargs)
_sqlalchemy(cls, column, value_set, **kwargs)
_spark(cls, column, value_set, **kwargs)
class great_expectations.expectations.metrics.column_map_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:
  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.

condition_metric_name = column_values.in_type_list
condition_value_keys = ['type_list']
_pandas(cls, column, type_list, **kwargs)
class great_expectations.expectations.metrics.column_map_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:
  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.

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)
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_map_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:
  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.

condition_metric_name = column_values.json_parseable
_pandas(cls, column, **kwargs)
_spark(cls, column, json_schema, **kwargs)
class great_expectations.expectations.metrics.column_map_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:
  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.

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.column_map_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:
  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.

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.column_map_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:
  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.

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.column_map_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:
  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.

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.column_map_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:
  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.

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.column_map_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:
  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.

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.column_map_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:
  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.

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.column_map_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:
  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.

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.column_map_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:
  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.

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.column_map_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:
  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.

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.column_map_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:
  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.

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.column_map_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:
  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.

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.column_map_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:
  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.

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.column_map_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:
  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.

condition_metric_name = column_values.of_type
condition_value_keys = ['type_']
_pandas(cls, column, type_, **kwargs)
class great_expectations.expectations.metrics.column_map_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:
  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.

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.column_map_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:
  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.

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