Expect the specified column to exist. expect_column_to_exist is a :func:`expectation \ <great_expectations.data_asset.data_asset.Data

Returns: int

Get a series containing the frequency counts of unique values from the named column. Args: column: the column for which to obtain

:param module_name: a fully-qualified name of a module (e.g., "great_expectations.dataset.sqlalchemy_dataset") :return: raw source code of th

Use the provided evaluation_parameters dict to parse a given parameter expression. Args: parameter_expression (str): A string, potentiall

Save the current project to disk.

EXPERIMENTAL Add a row condition for handling null filter. Args: domain_kwargs: the domain kwargs to use as the base and

Add the given markdown as a new markdown cell.

Expect that the columns are unique together, e.g. a multi-column primary key Note that all instances of any duplicates are considered

Setter for config value

Provide a value to be stored in the data_asset evaluation_parameters object and used to evaluate parameterized expectations.

Adds a citation to an existing Expectation Suite within the validator

Expect the column max to be between an min and max value expect_column_max_to_be_between is a \ :func:`column_aggregate_expectation <

Expect the number of times a regular expression appears on each line of a file to be between a maximum and minimum value. Ar

Utility function for the at least and at most conditions based on strictness. Args: params: dictionary containing "strict_min" and "

Convert a (pandas) dataframe to a great_expectations dataset, with (optional) expectation_suite Args: df: the DataFrame object to co

Get all config variables from the default location.

Assert no tracebacks. :param click_result: the Result object returned from click runner.invoke()

Construct a DataContextConfig fixture given the modifications in the input parameters Returns: Dictionary representation of a DataCon

Expect the number of times a regular expression appears on each line of a file to be between a maximum and minimum value. Ar

Get the (named) BatchKwargGenerator from a datasource) Args: name (str): name of BatchKwargGenerator (default value is 'default')

This should return a dictionary: { "dependency_name": MetricConfiguration, ... }

Get validation results from a configured store. Args: data_asset_name: name of data asset for which to get validation result

Parse and check whether the string contains a substitution variable of the case insensitive form ${SOME_VAR} or $SOME_VAR Args: subst

Build Data Docs for your project. These make it simple to visualize data quality in your project. These include Expectations

User-facing expectation-suite building function. Works with an instantiated UserConfigurableProfiler object. Args: Returns:

Expect each column value to be unique. This expectation detects duplicates. All duplicated values are counted as exceptions. For exa

Processes batch_request and returns the (possibly empty) list of batch objects. Args: :batch_request encapsulation of re

Uses a given batch dictionary and domain kwargs (which include a row condition and a condition parser) to obtain and/or query a batch. Returns

Uses a given batch dictionary and domain kwargs (which include a row condition and a condition parser) to obtain and/or query a batch. Returns

Returns the result format and metrics required to validate this Expectation using the provided result format.

A stdlib cross-platform way to open a file in a browser. Args: resource_identifier: ExpectationSuiteIdentifier,

This is a simple helper utility that can be used to parse a string result_format into the dict format used internally by great_expectations. It is

Validate against a pre-defined checkpoint. (Experimental) Args: checkpoint_name: The name of a checkpoint defined via the

Expect the column minimum to be between an min and max value expect_column_min_to_be_between is a \ :func:`column_aggregate_expectati

Expect a column to contain values of a specified data type. expect_column_values_to_be_of_type is a :func:`column_aggregate_expectation \

Bridges between Execution Engines in providing access to the batch data. Validates that Dataset classes contain proper type of data (

Return True if the project is initialized. To be considered initialized, all of the following must be true: - all project di

Use datasource-specific configuration to translate any batch parameters into batch kwargs at the datasource level. Args:

Save the provided expectation suite into the DataContext. Args: expectation_suite: the suite to save expectation_suit

:returns a JSON-serialiable dict containing the project configuration

arg1: the first positional argument (can take on various types) **kwargs: variable arguments First check: Returns "

Instantiates `_expectation_suite` as empty by default or with a specified expectation `config`. In addition, this always sets the `default_exp

Create a string template based on a regex and corresponding list of group names. For example: filepath_template = _invert_regex_to_data_

Add the given code as a new code cell.

Removes any expectations from the validator where the validation has failed

Expect the set of distinct column values to be contained by a given set. The success value for this expectation will match that of expect_col

Expect file size to be between a user specified maxsize and minsize. Args: minsize(integer): minimum expected file size

This method is a thin wrapper for ExpectationSuite.find_expectation_indexes

This method is a thin wrapper for ExpectationSuite.find_expectation_indexes

class-private implementation of get_data_asset

Args: suite: An expectation suite Returns: A tuple containing a set of columns and a set of expectations found in a suite