This function is useful when one needs to load a file that is relative to the position of the current file. (Such as when you encode a co

Deserialize a data structure to an object defined by this Schema's fields. :param data: The data to deserialize. :param many: Whether

Use this assertion in all CLI tests unless you have a very good reason. Without this assertion, it is easy to let errors and tracebacks bubb

Returns _expectation_config as a JSON object, and perform some cleaning along the way. Args: discard_failed_expectations (boolean

Same as :meth:`dump`, except return a JSON-encoded string. :param obj: The object to serialize. :param many: Whether to serialize `ob

Build a GE class from configuration dictionaries.

Build a new expectation suite and save it into the data_context expectation store. Args: expectation_suite_name: The name of the

resolve_metrics is the main entrypoint for an execution engine. The execution engine will compute the value of the provided metrics.

Convert the given float to a string, centralizing standards for precision and decisions about scientific notation. Adds an approximately equal sig

get_compute_domain computes the optimal domain_kwargs for computing metrics based on the given domain_kwargs and specific engine semantics.

Filter the entries of the source dictionary according to directives concerning the existing keys and values. Args: properties: source dic

send a usage statistics message.

Get a batch of data from the datasource. Args: batch_kwargs: the BatchKwargs to use to construct the batch batch_para

List currently-configured datasources on this context. Masks passwords. Returns: List(dict): each dictionary includes "name", "cl

Utility function to plug Nones in when optional parameters are not specified in expectation kwargs. Example: Input: kwargs={"

Convenience method for testing yaml configs test_yaml_config is a convenience method for configuring the moving parts of a Great Expe

Expect the column values to be distributed similarly to a scipy distribution. \ This expectation compares the provided column to the

Interprets batch_data and returns the appropriate data. This method is primarily useful for utility cases (e.g. testing) where data i

Writes ``_expectation_config`` to a JSON file. Writes the DataAsset's expectation config to the specified JSON ``filepath``. Failing expec

Set a default expectation argument for this data_asset Args: argument (string): The argument to be replaced

Returns a dictionary of data_asset_names that the specified batch kwarg generator can provide. Note that some batch kwargs generators

Lists the available expectation suite names

Same as :meth:`load`, except it takes a JSON string as input. :param json_data: A JSON string of the data to deserialize. :param many

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

Args: op: A jsonpatch operation. One of 'add', 'replace', or 'remove' path: A jsonpatch path for the patch operation

Helper function to convert an object to one that is json serializable Args: data: an object to attempt to convert a corresponding jso

Print the structure of directory as a tree: Ex: project_dir0/ AAA/ BBB/ aaa.txt bbb.txt #Note: f

Return a list of available expectation suite names.

Mask password in database url. Uses sqlalchemy engine parsing if sqlalchemy is installed, otherwise defaults to using urlparse from t

Build a new great_expectations directory and DataContext object in the provided project_root_dir. `create` will not create a new "gr

Helper method to make a `ValidationError` with an error message from ``self.error_messages``.

This method is a thin wrapper for ExpectationSuite.remove()

:param resource_identifiers: a list of resource identifiers (ExpectationSuiteIdentifier, ValidationResul

Produce a diagnostic report about this expectation. The current uses for this method's output are using the JSON structure to

This method applies only to the new (V3) Datasource schema.

Get the named datasource Args: datasource_name (str): the name of the datasource from the configuration Returns:

Get URLs for a resource for all data docs sites. This function will return URLs for any configured site even if the sites ha

Returns: List[str], list of column names

Run a validation operator to validate data assets and to perform the business logic around validation that the operator implements.

Replace all `$` characters with the DOLLAR_SIGN_ESCAPE_STRING Args: value: config variable value dollar_sign

Expect column entries to be between a minimum value and a maximum value (inclusive). expect_column_values_to_be_between is a \ :func:

Profile the named datasource using the named profiler. Args: datasource_name: the name of the datasource for which to profile dat

Helper function to construct expectation result objects for map_expectations (such as column_map_expectation and file_lines_map_expectation).

update d with items from u, recursively and joining elements

Expect the column mean to be between a minimum value and a maximum value (inclusive). expect_column_mean_to_be_between is a \ :func:`

Utility to create datasets for json-formatted tests.

:param module_name: a possibly-relative name of a module :param package_name: the name of a package, to which the given module belongs

Calculate success and percent_success for column_map_expectations Args: success_count (int): \ The number of succ

Expect a column to contain values from a specified type list. expect_column_values_to_be_in_type_list is a :func:`column_aggregate_expectatio

Loads the specified batch_data into the execution engine

This method takes a string, and if it contains a pattern ${SOME_VARIABLE} or $SOME_VARIABLE, returns a string where the pattern is replaced w

Expect each column value to be in a given set. For example: :: # my_df.my_col = [1,2,2,3,3,3] >>> my_df.expe

r"""Save config variable value Escapes $ unless they are used in substitution variables e.g. the $ characters in ${SOME_VAR} or $SOME_VAR are