great_expectations.rule_based_profiler.types.domain
¶
Module Contents¶
Classes¶
Generic enumeration. |
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A convenience class for migrating away from untyped dictionaries to stronger typed objects. |
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Analogously to the way “SerializableDictDot” extends “DictDot” to provide JSON serialization, the present class, |
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Analogously to the way “SerializableDictDot” extends “DictDot” to provide JSON serialization, the present class, |
Functions¶
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Build column type domains from column names. |
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class
great_expectations.rule_based_profiler.types.domain.
SemanticDomainTypes
¶ Bases:
enum.Enum
Generic enumeration.
Derive from this class to define new enumerations.
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NUMERIC
= numeric¶
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TEXT
= text¶
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LOGIC
= logic¶
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DATETIME
= datetime¶
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BINARY
= binary¶
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CURRENCY
= currency¶
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VALUE_SET
= value_set¶
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IDENTIFIER
= identifier¶
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MISCELLANEOUS
= miscellaneous¶
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UNKNOWN
= unknown¶
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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.
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semantic_domain_type
:Optional[Union[str, SemanticDomainTypes]]¶
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details
:Optional[Dict[str, Any]]¶
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to_dict
(self)¶
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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.
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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.
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to_dict
(self)¶
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to_json_dict
(self)¶
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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)¶ 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.
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__repr__
(self)¶ Return repr(self).
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__str__
(self)¶ Return str(self).
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__eq__
(self, other)¶ Return self==value.
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__ne__
(self, other)¶ Return self!=value.
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property
id
(self)¶
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to_json_dict
(self)¶
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_convert_dictionaries_to_domain_kwargs
(self, source: Optional[Any] = None)¶
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great_expectations.rule_based_profiler.types.domain.
build_domains_from_column_names
(column_names: List[str]) → List[Domain]¶ Build column type domains from column names.
- Parameters
column_names – List of columns to convert.
- Returns
A list of column type Domain objects built from column names.