Format the value or raise a :exc:`ValidationError` if an error occurs.

To handle window "\" path. "s3://bucket\\prefix" => "s3://bucket/prefix" >>> path = os.path.join("s3://bucket", "prefix") >>>

Util function for turning an action list into pretty string

Add a BatchKwargGenerator to the datasource. Args: name (str): the name of the new BatchKwargGenerator to add class_n

Add data_asset to DataConnector using data_asset name as key, and data_asset configuration as value.

Interactive flow for adding a datasource to an existing context. :param context: :param choose_one_data_asset: optional - if True, this

Appends an expectation. Args: expectation_config (ExpectationConfiguration): \ The expectation to be add

This method is a thin wrapper for ExpectationSuite.append_expectation

Assert presence of message in logging output messages. :param my_caplog: the caplog pytest fixutre :param message: message to be searche

Assert no logging output messages. :param my_caplog: the caplog pytest fixutre

Build a full configuration object for a datasource, potentially including generators with defaults. Args: data_asset_typ

Build a full configuration object for a datasource, potentially including generators with defaults. Args: data_asset_typ

Build a full configuration object for a datasource, potentially including batch kwargs generators with defaults. Args: c

Build a full configuration object for a datasource, potentially including generators with defaults. Args: data_asset_typ

Build the gallery object by running diagnostics for each Expectation and returning the resulting reports. Args: include_core: if tru

Adds information that may be available only after full data context construction, but is useful to calculate only one time (for example, anony

Welcome to the great_expectations CLI! Most commands follow this format: great_expectations <NOUN> <VERB> The nouns are: datasource, do

Constructs an expectation using column-aggregate semantics. The column_aggregate_expectation decorator handles boilerplate issues surrounding

Constructs an expectation using column-map semantics. The column_map_expectation decorator handles boilerplate issues surrounding the common

Check if all config_variables.yml exists.

Convenience method for building a partition object on continuous data Args: data (list-like): The data from which to construct the estima

Copies static assets, using a special "static_assets" backend store that accepts variable-length tuples as keys, with no filepath_tem

Creates an identical expectation for each of the given columns with the specified arguments, if any. Args: df (great_expectations.dataset

Create Temporary table based on sql query. This will be used as a basis for executing expectations. WARNING: this feature is new in v

Main VCS-independent setup function for installing Versioneer.

Git-specific installation logic for Versioneer. For Git, this means creating/changing .gitattributes to mark _version.py for export-subst key

This method will evaluate the result of a test build using the Great Expectations json test format. NOTE: Tests can be suppressed for certai

This method will evaluate the result of a test build using the Great Expectations json test format. NOTE: Tests can be suppressed for certai

This method will evaluate the result of a test build using the Great Expectations json test format. NOTE: Tests can be suppressed for certai

Expect the column standard deviation to be between a minimum value and a maximum value. Uses sample standard deviation (normalized by N-1).

Expect column values to be null. expect_column_values_to_be_null is a \ :func:`column_map_expectation <great_expectations.dataset.dat

The pandas implementation of this expectation takes kwargs mostly, result_format, include_config, catch_exceptions, and meta as other