great_expectations.core

Package Contents

Classes

DataContextKey()

DataContextKey objects are used to uniquely identify resources used by the DataContext.

IDDict()

dict() -> new empty dictionary

DictDot()

RunIdentifier(run_name=None, run_time=None)

A RunIdentifier identifies a run (collection of validations) by run_name and run_time.

RunIdentifierSchema(*, only: types.StrSequenceOrSet = None, exclude: types.StrSequenceOrSet = (), many: bool = False, context: typing.Dict = None, load_only: types.StrSequenceOrSet = (), dump_only: types.StrSequenceOrSet = (), partial: typing.Union[bool, types.StrSequenceOrSet] = False, unknown: str = None)

Base schema class with which to define custom schemas.

ExpectationConfiguration(expectation_type, kwargs, meta=None, success_on_last_run=None)

ExpectationConfiguration defines the parameters and name of a specific expectation.

ExpectationConfigurationSchema(*, only: types.StrSequenceOrSet = None, exclude: types.StrSequenceOrSet = (), many: bool = False, context: typing.Dict = None, load_only: types.StrSequenceOrSet = (), dump_only: types.StrSequenceOrSet = (), partial: typing.Union[bool, types.StrSequenceOrSet] = False, unknown: str = None)

Base schema class with which to define custom schemas.

ExpectationSuite(expectation_suite_name, expectations=None, evaluation_parameters=None, data_asset_type=None, meta=None)

This ExpectationSuite object has create, read, update, and delete functionality for its expectations:

ExpectationSuiteSchema(*, only: types.StrSequenceOrSet = None, exclude: types.StrSequenceOrSet = (), many: bool = False, context: typing.Dict = None, load_only: types.StrSequenceOrSet = (), dump_only: types.StrSequenceOrSet = (), partial: typing.Union[bool, types.StrSequenceOrSet] = False, unknown: str = None)

Base schema class with which to define custom schemas.

ExpectationValidationResult(success=None, expectation_config=None, result=None, meta=None, exception_info=None)

ExpectationValidationResultSchema(*, only: types.StrSequenceOrSet = None, exclude: types.StrSequenceOrSet = (), many: bool = False, context: typing.Dict = None, load_only: types.StrSequenceOrSet = (), dump_only: types.StrSequenceOrSet = (), partial: typing.Union[bool, types.StrSequenceOrSet] = False, unknown: str = None)

Base schema class with which to define custom schemas.

ExpectationSuiteValidationResult(success=None, results=None, evaluation_parameters=None, statistics=None, meta=None)

ExpectationSuiteValidationResultSchema(*, only: types.StrSequenceOrSet = None, exclude: types.StrSequenceOrSet = (), many: bool = False, context: typing.Dict = None, load_only: types.StrSequenceOrSet = (), dump_only: types.StrSequenceOrSet = (), partial: typing.Union[bool, types.StrSequenceOrSet] = False, unknown: str = None)

Base schema class with which to define custom schemas.

Functions

find_evaluation_parameter_dependencies(parameter_expression)

Parse a parameter expression to identify dependencies including GE URNs.

nested_update(d, u)

in_jupyter_notebook()

get_metric_kwargs_id(metric_name, metric_kwargs)

convert_to_json_serializable(data)

Helper function to convert an object to one that is json serializable

ensure_json_serializable(data)

Helper function to convert an object to one that is json serializable

_deduplicate_evaluation_parameter_dependencies(dependencies)

great_expectations.core.ge_version
class great_expectations.core.DataContextKey

Bases: object

DataContextKey objects are used to uniquely identify resources used by the DataContext.

A DataContextKey is designed to support clear naming with multiple representations including a hashable version making it suitable for use as the key in a dictionary.

abstract to_tuple(self)
classmethod from_tuple(cls, tuple_)
abstract to_fixed_length_tuple(self)
abstract classmethod from_fixed_length_tuple(cls, tuple_)
__eq__(self, other)

Return self==value.

__ne__(self, other)

Return self!=value.

__hash__(self)

Return hash(self).

__repr__(self)

Return repr(self).

great_expectations.core.find_evaluation_parameter_dependencies(parameter_expression)

Parse a parameter expression to identify dependencies including GE URNs.

Parameters

parameter_expression – the parameter to parse

Returns

  • “urns”: set of strings that are valid GE URN objects

  • ”other”: set of non-GE URN strings that are required to evaluate the parameter expression

Return type

a dictionary including

class great_expectations.core.IDDict

Bases: dict

dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object’s

(key, value) pairs

dict(iterable) -> new dictionary initialized as if via:

d = {} for k, v in iterable:

d[k] = v

dict(**kwargs) -> new dictionary initialized with the name=value pairs

in the keyword argument list. For example: dict(one=1, two=2)

_id_ignore_keys
to_id(self, id_keys=None, id_ignore_keys=None)
great_expectations.core.ge_urn
great_expectations.core.nested_update(d, u)
exception great_expectations.core.DataContextError(message)

Bases: great_expectations.exceptions.GreatExpectationsError

Common base class for all non-exit exceptions.

exception great_expectations.core.InvalidCacheValueError(result_dict)

Bases: great_expectations.exceptions.GreatExpectationsError

Common base class for all non-exit exceptions.

exception great_expectations.core.InvalidExpectationConfigurationError(message)

Bases: great_expectations.exceptions.GreatExpectationsError

Common base class for all non-exit exceptions.

exception great_expectations.core.InvalidExpectationKwargsError(message)

Bases: great_expectations.exceptions.GreatExpectationsError

Common base class for all non-exit exceptions.

exception great_expectations.core.ParserError(message)

Bases: great_expectations.exceptions.GreatExpectationsError

Common base class for all non-exit exceptions.

exception great_expectations.core.UnavailableMetricError(message)

Bases: great_expectations.exceptions.GreatExpectationsError

Common base class for all non-exit exceptions.

class great_expectations.core.DictDot

Bases: object

__getitem__(self, item)
__setitem__(self, key, value)
__delitem__(self, key)
great_expectations.core.logger
great_expectations.core.RESULT_FORMATS = ['BOOLEAN_ONLY', 'BASIC', 'COMPLETE', 'SUMMARY']
great_expectations.core.EvaluationParameterIdentifier
great_expectations.core.in_jupyter_notebook()
great_expectations.core.get_metric_kwargs_id(metric_name, metric_kwargs)
great_expectations.core.convert_to_json_serializable(data)

Helper function to convert an object to one that is json serializable

Parameters

data – an object to attempt to convert a corresponding json-serializable object

Returns

(dict) A converted test_object

Warning

test_obj may also be converted in place.

great_expectations.core.ensure_json_serializable(data)

Helper function to convert an object to one that is json serializable

Parameters

data – an object to attempt to convert a corresponding json-serializable object

Returns

(dict) A converted test_object

Warning

test_obj may also be converted in place.

class great_expectations.core.RunIdentifier(run_name=None, run_time=None)

Bases: great_expectations.core.data_context_key.DataContextKey

A RunIdentifier identifies a run (collection of validations) by run_name and run_time.

property run_name(self)
property run_time(self)
to_tuple(self)
to_fixed_length_tuple(self)
__repr__(self)

Return repr(self).

__str__(self)

Return str(self).

to_json_dict(self)
classmethod from_tuple(cls, tuple_)
classmethod from_fixed_length_tuple(cls, tuple_)
class great_expectations.core.RunIdentifierSchema(*, only: types.StrSequenceOrSet = None, exclude: types.StrSequenceOrSet = (), many: bool = False, context: typing.Dict = None, load_only: types.StrSequenceOrSet = (), dump_only: types.StrSequenceOrSet = (), partial: typing.Union[bool, types.StrSequenceOrSet] = False, unknown: str = None)

Bases: marshmallow.Schema

Base schema class with which to define custom schemas.

Example usage:

import datetime as dt
from dataclasses import dataclass

from marshmallow import Schema, fields


@dataclass
class Album:
    title: str
    release_date: dt.date


class AlbumSchema(Schema):
    title = fields.Str()
    release_date = fields.Date()


album = Album("Beggars Banquet", dt.date(1968, 12, 6))
schema = AlbumSchema()
data = schema.dump(album)
data  # {'release_date': '1968-12-06', 'title': 'Beggars Banquet'}
Parameters

  • only – Whitelist of the declared fields to select when instantiating the Schema. If None, all fields are used. Nested fields can be represented with dot delimiters.

  • exclude – Blacklist of the declared fields to exclude when instantiating the Schema. If a field appears in both only and exclude, it is not used. Nested fields can be represented with dot delimiters.

  • many – Should be set to True if obj is a collection so that the object will be serialized to a list.

  • context – Optional context passed to fields.Method and fields.Function fields.

  • load_only – Fields to skip during serialization (write-only fields)

  • dump_only – Fields to skip during deserialization (read-only fields)

  • partial – Whether to ignore missing fields and not require any fields declared. Propagates down to Nested fields as well. If its value is an iterable, only missing fields listed in that iterable will be ignored. Use dot delimiters to specify nested fields.

  • unknown – Whether to exclude, include, or raise an error for unknown fields in the data. Use EXCLUDE, INCLUDE or RAISE.

Changed in version 3.0.0: prefix parameter removed.

Changed in version 2.0.0: __validators__, __preprocessors__, and __data_handlers__ are removed in favor of marshmallow.decorators.validates_schema, marshmallow.decorators.pre_load and marshmallow.decorators.post_dump. __accessor__ and __error_handler__ are deprecated. Implement the handle_error and get_attribute methods instead.

run_name
run_time
make_run_identifier(self, data, **kwargs)
great_expectations.core._deduplicate_evaluation_parameter_dependencies(dependencies)
class great_expectations.core.ExpectationConfiguration(expectation_type, kwargs, meta=None, success_on_last_run=None)

Bases: great_expectations.types.DictDot

ExpectationConfiguration defines the parameters and name of a specific expectation.

kwarg_lookup_dict
runtime_kwargs = ['result_format', 'include_config', 'catch_exceptions']
patch(self, op: str, path: str, value: Any)
Parameters

  • op – A jsonpatch operation. One of ‘add’, ‘replace’, or ‘remove’

  • path – A jsonpatch path for the patch operation

  • value – The value to patch

Returns

The patched ExpectationConfiguration object

property expectation_type(self)
property kwargs(self)
_get_default_custom_kwargs(self)
get_domain_kwargs(self)
get_success_kwargs(self)
get_runtime_kwargs(self)
applies_to_same_domain(self, other_expectation_configuration)
isEquivalentTo(self, other, match_type='success')

ExpectationConfiguration equivalence does not include meta, and relies on equivalence of kwargs.

__eq__(self, other)

ExpectationConfiguration equality does include meta, but ignores instance identity.

__ne__(self, other)

Return self!=value.

__repr__(self)

Return repr(self).

__str__(self)

Return str(self).

to_json_dict(self)
get_evaluation_parameter_dependencies(self)
class great_expectations.core.ExpectationConfigurationSchema(*, only: types.StrSequenceOrSet = None, exclude: types.StrSequenceOrSet = (), many: bool = False, context: typing.Dict = None, load_only: types.StrSequenceOrSet = (), dump_only: types.StrSequenceOrSet = (), partial: typing.Union[bool, types.StrSequenceOrSet] = False, unknown: str = None)

Bases: marshmallow.Schema

Base schema class with which to define custom schemas.

Example usage:

import datetime as dt
from dataclasses import dataclass

from marshmallow import Schema, fields


@dataclass
class Album:
    title: str
    release_date: dt.date


class AlbumSchema(Schema):
    title = fields.Str()
    release_date = fields.Date()


album = Album("Beggars Banquet", dt.date(1968, 12, 6))
schema = AlbumSchema()
data = schema.dump(album)
data  # {'release_date': '1968-12-06', 'title': 'Beggars Banquet'}
Parameters

  • only – Whitelist of the declared fields to select when instantiating the Schema. If None, all fields are used. Nested fields can be represented with dot delimiters.

  • exclude – Blacklist of the declared fields to exclude when instantiating the Schema. If a field appears in both only and exclude, it is not used. Nested fields can be represented with dot delimiters.

  • many – Should be set to True if obj is a collection so that the object will be serialized to a list.

  • context – Optional context passed to fields.Method and fields.Function fields.

  • load_only – Fields to skip during serialization (write-only fields)

  • dump_only – Fields to skip during deserialization (read-only fields)

  • partial – Whether to ignore missing fields and not require any fields declared. Propagates down to Nested fields as well. If its value is an iterable, only missing fields listed in that iterable will be ignored. Use dot delimiters to specify nested fields.

  • unknown – Whether to exclude, include, or raise an error for unknown fields in the data. Use EXCLUDE, INCLUDE or RAISE.

Changed in version 3.0.0: prefix parameter removed.

Changed in version 2.0.0: __validators__, __preprocessors__, and __data_handlers__ are removed in favor of marshmallow.decorators.validates_schema, marshmallow.decorators.pre_load and marshmallow.decorators.post_dump. __accessor__ and __error_handler__ are deprecated. Implement the handle_error and get_attribute methods instead.

expectation_type
kwargs
meta
make_expectation_configuration(self, data, **kwargs)
class great_expectations.core.ExpectationSuite(expectation_suite_name, expectations=None, evaluation_parameters=None, data_asset_type=None, meta=None)

Bases: object

This ExpectationSuite object has create, read, update, and delete functionality for its expectations: -create: self.add_expectation() -read: self.find_expectation_indexes() -update: self.add_expectation() or self.patch_expectation() -delete: self.remove_expectation()

add_citation(self, comment, batch_kwargs=None, batch_markers=None, batch_parameters=None, citation_date=None)
isEquivalentTo(self, other)

ExpectationSuite equivalence relies only on expectations and evaluation parameters. It does not include: - data_asset_name - expectation_suite_name - meta - data_asset_type

__eq__(self, other)

ExpectationSuite equality ignores instance identity, relying only on properties.

__ne__(self, other)

Return self!=value.

__repr__(self)

Return repr(self).

__str__(self)

Return str(self).

to_json_dict(self)
get_evaluation_parameter_dependencies(self)
get_citations(self, sort=True, require_batch_kwargs=False)
get_table_expectations(self)

Return a list of table expectations.

get_column_expectations(self)

Return a list of column map expectations.

static _filter_citations(citations, filter_key)
static _sort_citations(citations)
append_expectation(self, expectation_config)

Appends an expectation.

Parameters

expectation_config (ExpectationConfiguration) – The expectation to be added to the list.

Notes

May want to add type-checking in the future.

remove_expectation(self, expectation_configuration: ExpectationConfiguration, match_type: str = 'domain', remove_multiple_matches: bool = False)
Parameters

  • expectation_configuration – A potentially incomplete (partial) Expectation Configuration to match against for for the removal of expectations.

  • match_type –

    This determines what kwargs to use when matching. Options are ‘domain’ to match based on the data evaluated by that expectation, ‘success’ to match based on all configuration parameters

    that influence whether an expectation succeeds based on a given batch of data, and ‘runtime’ to match based on all configuration parameters

  • remove_multiple_matches – If True, will remove multiple matching expectations. If False, will raise a ValueError.

Returns: The list of deleted ExpectationConfigurations

Raises

  • No match –

  • More than 1 match, if remove_multiple_matches = False –

remove_all_expectations_of_type(self, expectation_types: Union[List[str], str])
find_expectation_indexes(self, expectation_configuration: ExpectationConfiguration, match_type: str = 'domain')
Parameters

  • expectation_configuration – A potentially incomplete (partial) Expectation Configuration to match against to find the index of any matching Expectation Configurations on the suite.

  • match_type –

    This determines what kwargs to use when matching. Options are ‘domain’ to match based on the data evaluated by that expectation, ‘success’ to match based on all configuration parameters

    that influence whether an expectation succeeds based on a given batch of data, and ‘runtime’ to match based on all configuration parameters

Returns: A list of indexes of matching ExpectationConfiguration

Raises

InvalidExpectationConfigurationError –

find_expectations(self, expectation_configuration: ExpectationConfiguration, match_type: str = 'domain')
patch_expectation(self, expectation_configuration: ExpectationConfiguration, op: str, path: str, value: Any, match_type: str)
Parameters

  • expectation_configuration – A potentially incomplete (partial) Expectation Configuration to match against to find the expectation to patch.

  • op – A jsonpatch operation (one of ‘add’,’update’, or ‘remove’) (see http://jsonpatch.com/)

  • path – A jsonpatch path for the patch operation (see http://jsonpatch.com/)

  • value – The value to patch (see http://jsonpatch.com/)

  • match_type – The match type to use for find_expectation_index()

Returns: The patched ExpectationConfiguration

Raises

  • No match –

  • More than 1 match –

add_expectation(self, expectation_configuration: ExpectationConfiguration, match_type: str = 'domain', overwrite_existing: bool = True)
Parameters

  • expectation_configuration – The ExpectationConfiguration to add or update

  • match_type – The criteria used to determine whether the Suite already has an ExpectationConfiguration and so whether we should add or replace.

  • overwrite_existing – If the expectation already exists, this will overwrite if True and raise an error if False.

Returns

The ExpectationConfiguration to add or replace.

Raises

  • More than one match –

  • One match if overwrite_existing = False –

class great_expectations.core.ExpectationSuiteSchema(*, only: types.StrSequenceOrSet = None, exclude: types.StrSequenceOrSet = (), many: bool = False, context: typing.Dict = None, load_only: types.StrSequenceOrSet = (), dump_only: types.StrSequenceOrSet = (), partial: typing.Union[bool, types.StrSequenceOrSet] = False, unknown: str = None)

Bases: marshmallow.Schema

Base schema class with which to define custom schemas.

Example usage:

import datetime as dt
from dataclasses import dataclass

from marshmallow import Schema, fields


@dataclass
class Album:
    title: str
    release_date: dt.date


class AlbumSchema(Schema):
    title = fields.Str()
    release_date = fields.Date()


album = Album("Beggars Banquet", dt.date(1968, 12, 6))
schema = AlbumSchema()
data = schema.dump(album)
data  # {'release_date': '1968-12-06', 'title': 'Beggars Banquet'}
Parameters

  • only – Whitelist of the declared fields to select when instantiating the Schema. If None, all fields are used. Nested fields can be represented with dot delimiters.

  • exclude – Blacklist of the declared fields to exclude when instantiating the Schema. If a field appears in both only and exclude, it is not used. Nested fields can be represented with dot delimiters.

  • many – Should be set to True if obj is a collection so that the object will be serialized to a list.

  • context – Optional context passed to fields.Method and fields.Function fields.

  • load_only – Fields to skip during serialization (write-only fields)

  • dump_only – Fields to skip during deserialization (read-only fields)

  • partial – Whether to ignore missing fields and not require any fields declared. Propagates down to Nested fields as well. If its value is an iterable, only missing fields listed in that iterable will be ignored. Use dot delimiters to specify nested fields.

  • unknown – Whether to exclude, include, or raise an error for unknown fields in the data. Use EXCLUDE, INCLUDE or RAISE.

Changed in version 3.0.0: prefix parameter removed.

Changed in version 2.0.0: __validators__, __preprocessors__, and __data_handlers__ are removed in favor of marshmallow.decorators.validates_schema, marshmallow.decorators.pre_load and marshmallow.decorators.post_dump. __accessor__ and __error_handler__ are deprecated. Implement the handle_error and get_attribute methods instead.

expectation_suite_name
expectations
evaluation_parameters
data_asset_type
meta
clean_empty(self, data)
prepare_dump(self, data, **kwargs)
make_expectation_suite(self, data, **kwargs)
class great_expectations.core.ExpectationValidationResult(success=None, expectation_config=None, result=None, meta=None, exception_info=None)

Bases: object

__eq__(self, other)

ExpectationValidationResult equality ignores instance identity, relying only on properties.

__ne__(self, other)

Return self!=value.

__repr__(self)

Return repr(self).

__str__(self)

Return str(self).

validate_result_dict(self, result)
to_json_dict(self)
get_metric(self, metric_name, **kwargs)
class great_expectations.core.ExpectationValidationResultSchema(*, only: types.StrSequenceOrSet = None, exclude: types.StrSequenceOrSet = (), many: bool = False, context: typing.Dict = None, load_only: types.StrSequenceOrSet = (), dump_only: types.StrSequenceOrSet = (), partial: typing.Union[bool, types.StrSequenceOrSet] = False, unknown: str = None)

Bases: marshmallow.Schema

Base schema class with which to define custom schemas.

Example usage:

import datetime as dt
from dataclasses import dataclass

from marshmallow import Schema, fields


@dataclass
class Album:
    title: str
    release_date: dt.date


class AlbumSchema(Schema):
    title = fields.Str()
    release_date = fields.Date()


album = Album("Beggars Banquet", dt.date(1968, 12, 6))
schema = AlbumSchema()
data = schema.dump(album)
data  # {'release_date': '1968-12-06', 'title': 'Beggars Banquet'}
Parameters

  • only – Whitelist of the declared fields to select when instantiating the Schema. If None, all fields are used. Nested fields can be represented with dot delimiters.

  • exclude – Blacklist of the declared fields to exclude when instantiating the Schema. If a field appears in both only and exclude, it is not used. Nested fields can be represented with dot delimiters.

  • many – Should be set to True if obj is a collection so that the object will be serialized to a list.

  • context – Optional context passed to fields.Method and fields.Function fields.

  • load_only – Fields to skip during serialization (write-only fields)

  • dump_only – Fields to skip during deserialization (read-only fields)

  • partial – Whether to ignore missing fields and not require any fields declared. Propagates down to Nested fields as well. If its value is an iterable, only missing fields listed in that iterable will be ignored. Use dot delimiters to specify nested fields.

  • unknown – Whether to exclude, include, or raise an error for unknown fields in the data. Use EXCLUDE, INCLUDE or RAISE.

Changed in version 3.0.0: prefix parameter removed.

Changed in version 2.0.0: __validators__, __preprocessors__, and __data_handlers__ are removed in favor of marshmallow.decorators.validates_schema, marshmallow.decorators.pre_load and marshmallow.decorators.post_dump. __accessor__ and __error_handler__ are deprecated. Implement the handle_error and get_attribute methods instead.

success
expectation_config
result
meta
exception_info
convert_result_to_serializable(self, data, **kwargs)
make_expectation_validation_result(self, data, **kwargs)
class great_expectations.core.ExpectationSuiteValidationResult(success=None, results=None, evaluation_parameters=None, statistics=None, meta=None)

Bases: great_expectations.types.DictDot

__eq__(self, other)

ExpectationSuiteValidationResult equality ignores instance identity, relying only on properties.

__repr__(self)

Return repr(self).

__str__(self)

Return str(self).

to_json_dict(self)
get_metric(self, metric_name, **kwargs)
class great_expectations.core.ExpectationSuiteValidationResultSchema(*, only: types.StrSequenceOrSet = None, exclude: types.StrSequenceOrSet = (), many: bool = False, context: typing.Dict = None, load_only: types.StrSequenceOrSet = (), dump_only: types.StrSequenceOrSet = (), partial: typing.Union[bool, types.StrSequenceOrSet] = False, unknown: str = None)

Bases: marshmallow.Schema

Base schema class with which to define custom schemas.

Example usage:

import datetime as dt
from dataclasses import dataclass

from marshmallow import Schema, fields


@dataclass
class Album:
    title: str
    release_date: dt.date


class AlbumSchema(Schema):
    title = fields.Str()
    release_date = fields.Date()


album = Album("Beggars Banquet", dt.date(1968, 12, 6))
schema = AlbumSchema()
data = schema.dump(album)
data  # {'release_date': '1968-12-06', 'title': 'Beggars Banquet'}
Parameters

  • only – Whitelist of the declared fields to select when instantiating the Schema. If None, all fields are used. Nested fields can be represented with dot delimiters.

  • exclude – Blacklist of the declared fields to exclude when instantiating the Schema. If a field appears in both only and exclude, it is not used. Nested fields can be represented with dot delimiters.

  • many – Should be set to True if obj is a collection so that the object will be serialized to a list.

  • context – Optional context passed to fields.Method and fields.Function fields.

  • load_only – Fields to skip during serialization (write-only fields)

  • dump_only – Fields to skip during deserialization (read-only fields)

  • partial – Whether to ignore missing fields and not require any fields declared. Propagates down to Nested fields as well. If its value is an iterable, only missing fields listed in that iterable will be ignored. Use dot delimiters to specify nested fields.

  • unknown – Whether to exclude, include, or raise an error for unknown fields in the data. Use EXCLUDE, INCLUDE or RAISE.

Changed in version 3.0.0: prefix parameter removed.

Changed in version 2.0.0: __validators__, __preprocessors__, and __data_handlers__ are removed in favor of marshmallow.decorators.validates_schema, marshmallow.decorators.pre_load and marshmallow.decorators.post_dump. __accessor__ and __error_handler__ are deprecated. Implement the handle_error and get_attribute methods instead.

success
results
evaluation_parameters
statistics
meta
prepare_dump(self, data, **kwargs)
make_expectation_suite_validation_result(self, data, **kwargs)
great_expectations.core.expectationConfigurationSchema
great_expectations.core.expectationSuiteSchema
great_expectations.core.expectationValidationResultSchema
great_expectations.core.expectationSuiteValidationResultSchema
great_expectations.core.runIdentifierSchema