great_expectations.profile.base

Module Contents

Classes

ProfilerDataType()

Useful data types for building profilers.

ProfilerCardinality()

Useful cardinality categories for building profilers.

ProfilerTypeMapping()

Useful backend type mapping for building profilers.

Profiler(configuration: dict = None)

Profilers creates suites from various sources of truth.

DataAssetProfiler()

DatasetProfiler()

great_expectations.profile.base.logger
class great_expectations.profile.base.ProfilerDataType

Bases: enum.Enum

Useful data types for building profilers.

INT = int
FLOAT = float
STRING = string
BOOLEAN = boolean
DATETIME = datetime
UNKNOWN = unknown
class great_expectations.profile.base.ProfilerCardinality

Bases: enum.Enum

Useful cardinality categories for building profilers.

NONE = none
ONE = one
TWO = two
FEW = few
VERY_FEW = very few
MANY = many
VERY_MANY = very many
UNIQUE = unique
class great_expectations.profile.base.ProfilerTypeMapping

Useful backend type mapping for building profilers.

INT_TYPE_NAMES = ['INTEGER', 'integer', 'int', 'int_', 'int8', 'int16', 'int32', 'int64', 'uint8', 'uint16', 'uint32', 'uint64', 'INT', 'TINYINT', 'BYTEINT', 'SMALLINT', 'BIGINT', 'IntegerType', 'LongType', 'DECIMAL']
FLOAT_TYPE_NAMES = ['FLOAT', 'DOUBLE', 'FLOAT4', 'FLOAT8', 'DOUBLE_PRECISION', 'NUMERIC', 'FloatType', 'DoubleType', 'float_', 'float16', 'float32', 'float64', 'number']
STRING_TYPE_NAMES = ['CHAR', 'VARCHAR', 'NVARCHAR', 'TEXT', 'STRING', 'StringType', 'string', 'str']
BOOLEAN_TYPE_NAMES = ['BOOLEAN', 'boolean', 'BOOL', 'TINYINT', 'BIT', 'bool', 'BooleanType']
DATETIME_TYPE_NAMES = ['DATETIME', 'DATE', 'TIME', 'TIMESTAMP', 'DateType', 'TimestampType', 'datetime64', 'Timestamp']
class great_expectations.profile.base.Profiler(configuration: dict = None)

Profilers creates suites from various sources of truth.

These sources of truth can be data or non-data sources such as DDLs.

When implementing a Profiler ensure that you: - Implement a . _profile() method - Optionally implement .validate() method that verifies you are running on the right

kind of object. You should raise an appropriate Exception if the object is not valid.

validate(self, item_to_validate: Any)
profile(self, item_to_profile: Any, suite_name: str = None)
abstract _profile(self, item_to_profile: Any, suite_name: str = None)
class great_expectations.profile.base.DataAssetProfiler
classmethod validate(cls, data_asset)
class great_expectations.profile.base.DatasetProfiler

Bases: great_expectations.profile.base.DataAssetProfiler

classmethod validate(cls, dataset)
classmethod add_expectation_meta(cls, expectation)
classmethod add_meta(cls, expectation_suite, batch_kwargs=None)
classmethod profile(cls, data_asset, run_id=None, profiler_configuration=None, run_name=None, run_time=None)
abstract classmethod _profile(cls, dataset, configuration=None)