great_expectations.expectations.metrics.column_map_metrics.column_values_null

Module Contents

Classes

ColumnValuesNull()

Base class for all metric providers.

ColumnValuesNullCount()

A convenience class to provide an alias for easier access to the null count in a column.

class great_expectations.expectations.metrics.column_map_metrics.column_values_null.ColumnValuesNull

Bases: great_expectations.expectations.metrics.map_metric.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.column_values_null.ColumnValuesNullCount

Bases: great_expectations.expectations.metrics.metric_provider.MetricProvider

A convenience class to provide an alias for easier access to the null count in a column.

metric_name = column_values.null.count
_pandas(*, metrics, **kwargs)
_sqlalchemy(*, metrics, **kwargs)
_spark(*, metrics, **kwargs)
classmethod _get_evaluation_dependencies(cls, metric: MetricConfiguration, configuration: Optional[ExpectationConfiguration] = None, execution_engine: Optional[ExecutionEngine] = None, runtime_configuration: Optional[dict] = None)