great_expectations.expectations.core.expect_column_values_to_be_of_type

Module Contents

Classes

ExpectColumnValuesToBeOfType(configuration: Optional[ExpectationConfiguration] = None)

Expect a column to contain values of a specified data type.

Functions

_get_dialect_type_module(execution_engine)

_native_type_type_map(type_)

great_expectations.expectations.core.expect_column_values_to_be_of_type.logger
great_expectations.expectations.core.expect_column_values_to_be_of_type.sa
great_expectations.expectations.core.expect_column_values_to_be_of_type.sqlalchemy_redshift
great_expectations.expectations.core.expect_column_values_to_be_of_type._BIGQUERY_MODULE_NAME = sqlalchemy_bigquery
great_expectations.expectations.core.expect_column_values_to_be_of_type.BIGQUERY_GEO_SUPPORT = False
great_expectations.expectations.core.expect_column_values_to_be_of_type.bigquery_types_tuple
great_expectations.expectations.core.expect_column_values_to_be_of_type.teradatasqlalchemy
class great_expectations.expectations.core.expect_column_values_to_be_of_type.ExpectColumnValuesToBeOfType(configuration: Optional[ExpectationConfiguration] = None)

Bases: great_expectations.expectations.expectation.ColumnMapExpectation

Expect a column to contain values of a specified data type.

expect_column_values_to_be_of_type is a column_aggregate_expectation for typed-column backends, and also for PandasDataset where the column dtype and provided type_ are unambiguous constraints (any dtype except ‘object’ or dtype of ‘object’ with type_ specified as ‘object’).

For PandasDataset columns with dtype of ‘object’ expect_column_values_to_be_of_type is a column_map_expectation and will independently check each row’s type.

Parameters

  • column (str) – The column name.

  • type\_ (str) – A string representing the data type that each column should have as entries. Valid types are defined by the current backend implementation and are dynamically loaded. For example, valid types for PandasDataset include any numpy dtype values (such as ‘int64’) or native python types (such as ‘int’), whereas valid types for a SqlAlchemyDataset include types named by the current driver such as ‘INTEGER’ in most SQL dialects and ‘TEXT’ in dialects such as postgresql. Valid types for SparkDFDataset include ‘StringType’, ‘BooleanType’ and other pyspark-defined type names.

Keyword Arguments

mostly (None or a float between 0 and 1) – Return “success”: True if at least mostly fraction of values match the expectation. For more detail, see mostly.

Other Parameters

  • result_format (str or None) – Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see result_format.

  • include_config (boolean) – If True, then include the expectation config as part of the result object. For more detail, see include_config.

  • catch_exceptions (boolean or None) – If True, then catch exceptions and include them as part of the result object. For more detail, see catch_exceptions.

  • meta (dict or None) – A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see meta.

Returns

An ExpectationSuiteValidationResult

Exact fields vary depending on the values passed to result_format and include_config, catch_exceptions, and meta.

See also

expect_column_values_to_be_in_type_list

library_metadata
map_metric = column_values.of_type
success_keys = ['type_', 'mostly']
default_kwarg_values
args_keys = ['column', 'type_']
validate_configuration(self, configuration: Optional[ExpectationConfiguration])
classmethod _atomic_prescriptive_template(cls, configuration=None, result=None, language=None, runtime_configuration=None, **kwargs)

Template function that contains the logic that is shared by atomic.prescriptive.summary (GE Cloud) and renderer.prescriptive (OSS GE)

classmethod _prescriptive_renderer(cls, configuration=None, result=None, language=None, runtime_configuration=None, **kwargs)
_validate_pandas(self, actual_column_type, expected_type)
_validate_sqlalchemy(self, actual_column_type, expected_type, execution_engine)
_validate_spark(self, actual_column_type, expected_type)
get_validation_dependencies(self, configuration: Optional[ExpectationConfiguration] = None, execution_engine: Optional[ExecutionEngine] = None, runtime_configuration: Optional[dict] = None)

Returns the result format and metrics required to validate this Expectation using the provided result format.

_validate(self, configuration: ExpectationConfiguration, metrics: Dict, runtime_configuration: dict = None, execution_engine: ExecutionEngine = None)
great_expectations.expectations.core.expect_column_values_to_be_of_type._get_dialect_type_module(execution_engine)
great_expectations.expectations.core.expect_column_values_to_be_of_type._native_type_type_map(type_)