`great_expectations.rule_based_profiler.types.domain`¶

Module Contents¶

Classes¶

`SemanticDomainTypes`()	Generic enumeration.
`InferredSemanticDomainType`()	A convenience class for migrating away from untyped dictionaries to stronger typed objects.
`DomainKwargs`()	Analogously to the way “SerializableDictDot” extends “DictDot” to provide JSON serialization, the present class,
`Domain`(domain_type: Union[str, MetricDomainTypes], domain_kwargs: Optional[Union[Dict[str, Any], DomainKwargs]] = None, details: Optional[Dict[str, Any]] = None)	Analogously to the way “SerializableDictDot” extends “DictDot” to provide JSON serialization, the present class,

Functions¶

build_domains_from_column_names(column_names: List[str])

Build column type domains from column names.

class great_expectations.rule_based_profiler.types.domain.SemanticDomainTypes¶

Bases: enum.Enum

Generic enumeration.

Derive from this class to define new enumerations.

NUMERIC = numeric¶

TEXT = text¶

LOGIC = logic¶

DATETIME = datetime¶

BINARY = binary¶

CURRENCY = currency¶

VALUE_SET = value_set¶

IDENTIFIER = identifier¶

MISCELLANEOUS = miscellaneous¶

UNKNOWN = unknown¶

class great_expectations.rule_based_profiler.types.domain.InferredSemanticDomainType¶

Bases: great_expectations.types.SerializableDictDot

A convenience class for migrating away from untyped dictionaries to stronger typed objects.

Can be instantiated with arguments:

my_A = MyClassA(

foo=”a string”, bar=1,

)

Can be instantiated from a dictionary:

my_A = MyClassA(

**{
“foo”: “a string”, “bar”: 1,

}

)

Can be accessed using both dictionary and dot notation

my_A.foo == “a string” my_A.bar == 1

my_A[“foo”] == “a string” my_A[“bar”] == 1

Pairs nicely with @dataclass:

@dataclass() class MyClassA(DictDot):

foo: str bar: int

Can be made immutable:

@dataclass(frozen=True) class MyClassA(DictDot):

foo: str bar: int

For more examples of usage, please see test_dataclass_serializable_dot_dict_pattern.py in the tests folder.

semantic_domain_type :Optional[Union[str, SemanticDomainTypes]]¶

details :Optional[Dict[str, Any]]¶

to_dict(self)¶

to_json_dict(self)¶: # TODO: <Alex>2/4/2022</Alex> A reference implementation can be provided, once circular import dependencies, caused by relative locations of the “great_expectations/types/__init__.py” and “great_expectations/core/util.py” modules are resolved.

class great_expectations.rule_based_profiler.types.domain.DomainKwargs¶

Bases: great_expectations.types.SerializableDotDict

Analogously to the way “SerializableDictDot” extends “DictDot” to provide JSON serialization, the present class, “SerializableDotDict” extends “DotDict” to provide JSON-serializable version of the “DotDict” class as well. Since “DotDict” is already YAML-serializable, “SerializableDotDict” is both YAML-serializable and JSON-serializable.

to_dict(self)¶

to_json_dict(self)¶

class great_expectations.rule_based_profiler.types.domain.Domain(domain_type: Union[str, MetricDomainTypes], domain_kwargs: Optional[Union[Dict[str, Any], DomainKwargs]] = None, details: Optional[Dict[str, Any]] = None)¶