Deserialize ``value``. :param value: The value to deserialize. :param attr: The attribute/key in `data` to deserialize. :para

Return True if the great_expectations.yml exists on disk.

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

Expect computed file hash to equal some given value. Args: value: A string to compare with the computed hash value

Args: schema : string optional JSON schema file on which JSON data file is validated against result_

Expect the number of rows to be between two values. expect_table_row_count_to_be_between is a :func:`expectation \ <great_expectation

Takes the number and percentage of unique values in a column and returns the column cardinality. If you are unexpectedly returning a

This method manages the interaction with user necessary to obtain batch_kwargs for a batch of a data asset. In order to get batch_kwargs thi

return the value of the requested metric.

Returns: int, table row count

Tests whether a given object is a valid continuous partition object. See :ref:`partition_object`. :param partition_object: The partition_object t

Lint strings of code passed in. Optional dependency "black" must be installed.

Adds styling information suitable for an html tag. Example styling block:: styling = { "classes": ["alert", "ale

Call the given command(s).

Generates a JSON-formatted report describing the outcome of all expectations. Use the default expectation_suite=None to validate the expectat

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

Static helper for parsing reader types. If reader_method is not provided, path will be used to guess the correct reader_method. Args:

This test requires that a library is NOT installed. It tests that: - a helpful error message is returned to install the missing library -

Builds batch kwargs using the provided datasource, batch kwargs generator, and batch_parameters. Args: datasource (str): the name

Convenience method for building a partition object on categorical data from a dataset and column Args: dataset (GE Dataset): the dataset

Convenience method for building a partition object on continuous data from a dataset and column Args: dataset (GE Dataset): the dataset f

Build a dictionary of parameters to evaluate, using the provided evaluation_parameters, AND mutate expectation_args by removing any parameter valu

Simple util function for displaying simple lists in cli

Emit a message.

Expect the Kulback-Leibler (KL) divergence (relative entropy) of the specified column with respect to the \ partition object to be lower than

Expect the values in column A to be the same as column B. Args: column_A (str): The first column name column

Expect the proportion of unique values to be between a minimum value and a maximum value. For example, in a column containing [1, 2, 2, 3, 3,

Expect the values for each record to be unique across the columns listed. Note that records can be duplicated. For example::

Args: expectation_configuration: A potentially incomplete (partial) Expectation Configuration to match against to

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

Uses batch_definition to retrieve batch_data and batch_markers by building a batch_spec from batch_definition, then using execution_e

Get the list of zero or more batches, based on a variety of flexible input types. This method applies only to the new (V3) Datasource schema.

Returns: Any

Get the values in column closest to the requested quantiles Args: column (string): name of column quantiles (tuple of

Fetch default expectation arguments for this data_asset Returns: A dictionary containing all the current default expectation argu

Get an evaluation parameter value that has been stored in meta. Args: parameter_name (string): The name of the parameter