`great_expectations.util`¶

Module Contents¶

Functions¶

`pluralize`(singular_ge_noun)	Pluralizes a Great Expectations singular noun
`singularize`(plural_ge_noun)	Singularizes a Great Expectations plural noun
`underscore`(word: str)	Borrowed from inflection.underscore
`hyphen`(input: str)
`profile`(func: Callable = None)
`measure_execution_time`(func: Callable = None)
`get_project_distribution`()
`get_currently_executing_function`()
`get_currently_executing_function_call_arguments`(include_module_name: bool = False, include_caller_names: bool = False, **kwargs)	param include_module_name bool If True, module name will be determined and included in output dictionary (default is False)
`verify_dynamic_loading_support`(module_name: str, package_name: str = None)	param module_name a possibly-relative name of a module
`import_library_module`(module_name: str)	param module_name a fully-qualified name of a module (e.g., “great_expectations.dataset.sqlalchemy_dataset”)
`is_library_loadable`(library_name: str)
`load_class`(class_name: str, module_name: str)
`_convert_to_dataset_class`(df, dataset_class, expectation_suite=None, profiler=None)	Convert a (pandas) dataframe to a great_expectations dataset, with (optional) expectation_suite
`_load_and_convert_to_dataset_class`(df, class_name, module_name, expectation_suite=None, profiler=None)	Convert a (pandas) dataframe to a great_expectations dataset, with (optional) expectation_suite
`read_csv`(filename, class_name=’PandasDataset’, module_name=’great_expectations.dataset’, dataset_class=None, expectation_suite=None, profiler=None, args, *kwargs)	Read a file using Pandas read_csv and return a great_expectations dataset.
`read_json`(filename, class_name=’PandasDataset’, module_name=’great_expectations.dataset’, dataset_class=None, expectation_suite=None, accessor_func=None, profiler=None, args, *kwargs)	Read a file using Pandas read_json and return a great_expectations dataset.
`read_excel`(filename, class_name=’PandasDataset’, module_name=’great_expectations.dataset’, dataset_class=None, expectation_suite=None, profiler=None, args, *kwargs)	Read a file using Pandas read_excel and return a great_expectations dataset.
`read_table`(filename, class_name=’PandasDataset’, module_name=’great_expectations.dataset’, dataset_class=None, expectation_suite=None, profiler=None, args, *kwargs)	Read a file using Pandas read_table and return a great_expectations dataset.
`read_feather`(filename, class_name=’PandasDataset’, module_name=’great_expectations.dataset’, dataset_class=None, expectation_suite=None, profiler=None, args, *kwargs)	Read a file using Pandas read_feather and return a great_expectations dataset.
`read_parquet`(filename, class_name=’PandasDataset’, module_name=’great_expectations.dataset’, dataset_class=None, expectation_suite=None, profiler=None, args, *kwargs)	Read a file using Pandas read_parquet and return a great_expectations dataset.
`from_pandas`(pandas_df, class_name=’PandasDataset’, module_name=’great_expectations.dataset’, dataset_class=None, expectation_suite=None, profiler=None)	Read a Pandas data frame and return a great_expectations dataset.
`read_pickle`(filename, class_name=’PandasDataset’, module_name=’great_expectations.dataset’, dataset_class=None, expectation_suite=None, profiler=None, args, *kwargs)	Read a file using Pandas read_pickle and return a great_expectations dataset.
`validate`(data_asset, expectation_suite=None, data_asset_name=None, expectation_suite_name=None, data_context=None, data_asset_class_name=None, data_asset_module_name=’great_expectations.dataset’, data_asset_class=None, args, *kwargs)	Validate the provided data asset. Validate can accept an optional data_asset_name to apply, data_context to use
`gen_directory_tree_str`(startpath)	Print the structure of directory as a tree:
`lint_code`(code: str)	Lint strings of code passed in. Optional dependency “black” must be installed.
`filter_properties_dict`(properties: dict, keep_fields: Optional[list] = None, delete_fields: Optional[list] = None, clean_nulls: Optional[bool] = True, clean_falsy: Optional[bool] = False, keep_falsy_numerics: Optional[bool] = True, inplace: Optional[bool] = False)	Filter the entries of the source dictionary according to directives concerning the existing keys and values.
`is_numeric`(value: Any)
`is_int`(value: Any)
`is_float`(value: Any)
`is_parseable_date`(value: Any, fuzzy: bool = False)
`get_context`()
`is_sane_slack_webhook`(url: str)	Really basic sanity checking.
`is_list_of_strings`(_list)
`generate_library_json_from_registered_expectations`()	Generate the JSON object used to populate the public gallery
`delete_blank_lines`(text: str)

great_expectations.util.logger¶

great_expectations.util.SINGULAR_TO_PLURAL_LOOKUP_DICT¶

great_expectations.util.PLURAL_TO_SINGULAR_LOOKUP_DICT¶

great_expectations.util.pluralize(singular_ge_noun)¶: Pluralizes a Great Expectations singular noun

great_expectations.util.singularize(plural_ge_noun)¶: Singularizes a Great Expectations plural noun

great_expectations.util.underscore(word: str) → str¶

Borrowed from inflection.underscore Make an underscored, lowercase form from the expression in the string.

Example:

>>> underscore("DeviceType")
'device_type'

As a rule of thumb you can think of underscore() as the inverse of camelize(), though there are cases where that does not hold:

>>> camelize(underscore("IOError"))
'IoError'

great_expectations.util.hyphen(input: str)¶

great_expectations.util.profile(func: Callable = None) → Callable¶

great_expectations.util.measure_execution_time(func: Callable = None) → Callable¶

great_expectations.util.get_project_distribution() → Optional[Distribution]¶

great_expectations.util.get_currently_executing_function() → Callable¶

great_expectations.util.get_currently_executing_function_call_arguments(include_module_name: bool = False, include_caller_names: bool = False, **kwargs) → dict¶

Parameters

include_module_name – bool If True, module name will be determined and included in output dictionary (default is False)
include_caller_names – bool If True, arguments, such as “self” and “cls”, if present, will be included in output dictionary (default is False)
kwargs –

Returns

dict Output dictionary, consisting of call arguments as attribute “name: value” pairs.

Example usage: # Gather the call arguments of the present function (include the “module_name” and add the “class_name”), filter # out the Falsy values, and set the instance “_config” variable equal to the resulting dictionary. self._config = get_currently_executing_function_call_arguments(

include_module_name=True, **{

“class_name”: self.__class__.__name__,

},

) filter_properties_dict(properties=self._config, clean_falsy=True, inplace=True)

great_expectations.util.verify_dynamic_loading_support(module_name: str, package_name: str = None) → None¶

Parameters

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

great_expectations.util.import_library_module(module_name: str) → Optional[ModuleType]¶

Parameters: module_name – a fully-qualified name of a module (e.g., “great_expectations.dataset.sqlalchemy_dataset”)
Returns: raw source code of the module (if can be retrieved)

great_expectations.util.is_library_loadable(library_name: str) → bool¶

great_expectations.util.load_class(class_name: str, module_name: str)¶

great_expectations.util._convert_to_dataset_class(df, dataset_class, expectation_suite=None, profiler=None)¶

Convert a (pandas) dataframe to a great_expectations dataset, with (optional) expectation_suite

Parameters

df – the DataFrame object to convert
dataset_class – the class to which to convert the existing DataFrame
expectation_suite – the expectation suite that should be attached to the resulting dataset
profiler – the profiler to use to generate baseline expectations, if any

Returns

A new Dataset object

great_expectations.util._load_and_convert_to_dataset_class(df, class_name, module_name, expectation_suite=None, profiler=None)¶

Convert a (pandas) dataframe to a great_expectations dataset, with (optional) expectation_suite

Parameters

df – the DataFrame object to convert
class_name (str) – class to which to convert resulting Pandas df
module_name (str) – dataset module from which to try to dynamically load the relevant module
expectation_suite – the expectation suite that should be attached to the resulting dataset
profiler – the profiler to use to generate baseline expectations, if any

Returns

A new Dataset object

great_expectations.util.read_csv(filename, class_name='PandasDataset', module_name='great_expectations.dataset', dataset_class=None, expectation_suite=None, profiler=None, *args, **kwargs)¶

Read a file using Pandas read_csv and return a great_expectations dataset.

Parameters

filename (string) – path to file to read
class_name (str) – class to which to convert resulting Pandas df
module_name (str) – dataset module from which to try to dynamically load the relevant module
dataset_class (Dataset) – If specified, the class to which to convert the resulting Dataset object; if not specified, try to load the class named via the class_name and module_name parameters
expectation_suite (string) – path to great_expectations expectation suite file
profiler (Profiler class) – profiler to use when creating the dataset (default is None)

Returns

great_expectations dataset

great_expectations.util.read_json(filename, class_name='PandasDataset', module_name='great_expectations.dataset', dataset_class=None, expectation_suite=None, accessor_func=None, profiler=None, *args, **kwargs)¶

Read a file using Pandas read_json and return a great_expectations dataset.

Parameters

filename (string) – path to file to read
class_name (str) – class to which to convert resulting Pandas df
module_name (str) – dataset module from which to try to dynamically load the relevant module
dataset_class (Dataset) – If specified, the class to which to convert the resulting Dataset object; if not specified, try to load the class named via the class_name and module_name parameters
expectation_suite (string) – path to great_expectations expectation suite file
accessor_func (Callable) – functions to transform the json object in the file
profiler (Profiler class) – profiler to use when creating the dataset (default is None)

Returns

great_expectations dataset

great_expectations.util.read_excel(filename, class_name='PandasDataset', module_name='great_expectations.dataset', dataset_class=None, expectation_suite=None, profiler=None, *args, **kwargs)¶

Read a file using Pandas read_excel and return a great_expectations dataset.

Parameters

filename (string) – path to file to read
class_name (str) – class to which to convert resulting Pandas df
module_name (str) – dataset module from which to try to dynamically load the relevant module
dataset_class (Dataset) – If specified, the class to which to convert the resulting Dataset object; if not specified, try to load the class named via the class_name and module_name parameters
expectation_suite (string) – path to great_expectations expectation suite file
profiler (Profiler class) – profiler to use when creating the dataset (default is None)

Returns

great_expectations dataset or ordered dict of great_expectations datasets, if multiple worksheets are imported

great_expectations.util.read_table(filename, class_name='PandasDataset', module_name='great_expectations.dataset', dataset_class=None, expectation_suite=None, profiler=None, *args, **kwargs)¶

Read a file using Pandas read_table and return a great_expectations dataset.

Parameters

filename (string) – path to file to read
class_name (str) – class to which to convert resulting Pandas df
module_name (str) – dataset module from which to try to dynamically load the relevant module
dataset_class (Dataset) – If specified, the class to which to convert the resulting Dataset object; if not specified, try to load the class named via the class_name and module_name parameters
expectation_suite (string) – path to great_expectations expectation suite file
profiler (Profiler class) – profiler to use when creating the dataset (default is None)

Returns

great_expectations dataset

great_expectations.util.read_feather(filename, class_name='PandasDataset', module_name='great_expectations.dataset', dataset_class=None, expectation_suite=None, profiler=None, *args, **kwargs)¶

Read a file using Pandas read_feather and return a great_expectations dataset.

Parameters

filename (string) – path to file to read
class_name (str) – class to which to convert resulting Pandas df
module_name (str) – dataset module from which to try to dynamically load the relevant module
dataset_class (Dataset) – If specified, the class to which to convert the resulting Dataset object; if not specified, try to load the class named via the class_name and module_name parameters
expectation_suite (string) – path to great_expectations expectation suite file
profiler (Profiler class) – profiler to use when creating the dataset (default is None)

Returns

great_expectations dataset

great_expectations.util.read_parquet(filename, class_name='PandasDataset', module_name='great_expectations.dataset', dataset_class=None, expectation_suite=None, profiler=None, *args, **kwargs)¶

Read a file using Pandas read_parquet and return a great_expectations dataset.

Parameters

filename (string) – path to file to read
class_name (str) – class to which to convert resulting Pandas df
module_name (str) – dataset module from which to try to dynamically load the relevant module
dataset_class (Dataset) – If specified, the class to which to convert the resulting Dataset object; if not specified, try to load the class named via the class_name and module_name parameters
expectation_suite (string) – path to great_expectations expectation suite file
profiler (Profiler class) – profiler to use when creating the dataset (default is None)

Returns

great_expectations dataset

great_expectations.util.from_pandas(pandas_df, class_name='PandasDataset', module_name='great_expectations.dataset', dataset_class=None, expectation_suite=None, profiler=None)¶

Read a Pandas data frame and return a great_expectations dataset.

Parameters

pandas_df (Pandas df) – Pandas data frame
class_name (str) – class to which to convert resulting Pandas df
module_name (str) – dataset module from which to try to dynamically load the relevant module
dataset_class (Dataset) – If specified, the class to which to convert the resulting Dataset object; if not specified, try to load the class named via the class_name and module_name parameters
expectation_suite (string) – path to great_expectations expectation suite file
profiler (profiler class) – The profiler that should be run on the dataset to establish a baseline expectation suite.

Returns

great_expectations dataset

great_expectations.util.read_pickle(filename, class_name='PandasDataset', module_name='great_expectations.dataset', dataset_class=None, expectation_suite=None, profiler=None, *args, **kwargs)¶

Read a file using Pandas read_pickle and return a great_expectations dataset.

Parameters

filename (string) – path to file to read
class_name (str) – class to which to convert resulting Pandas df
module_name (str) – dataset module from which to try to dynamically load the relevant module
dataset_class (Dataset) – If specified, the class to which to convert the resulting Dataset object; if not specified, try to load the class named via the class_name and module_name parameters
expectation_suite (string) – path to great_expectations expectation suite file
profiler (Profiler class) – profiler to use when creating the dataset (default is None)

Returns

great_expectations dataset

great_expectations.util.validate(data_asset, expectation_suite=None, data_asset_name=None, expectation_suite_name=None, data_context=None, data_asset_class_name=None, data_asset_module_name='great_expectations.dataset', data_asset_class=None, *args, **kwargs)¶

Validate the provided data asset. Validate can accept an optional data_asset_name to apply, data_context to use to fetch an expectation_suite if one is not provided, and data_asset_class_name/data_asset_module_name or data_asset_class to use to provide custom expectations.

Parameters

data_asset – the asset to validate
expectation_suite – the suite to use, or None to fetch one using a DataContext
data_asset_name – the name of the data asset to use
expectation_suite_name – the name of the expectation_suite to use
data_context – data context to use to fetch an an expectation suite, or the path from which to obtain one
data_asset_class_name – the name of a class to dynamically load a DataAsset class
data_asset_module_name – the name of the module to dynamically load a DataAsset class
data_asset_class – a class to use. overrides data_asset_class_name/ data_asset_module_name if provided
*args –
**kwargs –

Returns:

great_expectations.util.gen_directory_tree_str(startpath)¶

Print the structure of directory as a tree:

Ex: project_dir0/

AAA/ BBB/

aaa.txt bbb.txt

#Note: files and directories are sorted alphabetically, so that this method can be used for testing.

great_expectations.util.lint_code(code: str) → str¶: Lint strings of code passed in. Optional dependency “black” must be installed.

great_expectations.util.filter_properties_dict(properties: dict, keep_fields: Optional[list] = None, delete_fields: Optional[list] = None, clean_nulls: Optional[bool] = True, clean_falsy: Optional[bool] = False, keep_falsy_numerics: Optional[bool] = True, inplace: Optional[bool] = False) → Optional[dict]¶

Filter the entries of the source dictionary according to directives concerning the existing keys and values.

Parameters

properties – source dictionary to be filtered according to the supplied filtering directives
keep_fields – list of keys that must be retained, with the understanding that all other entries will be deleted
delete_fields – list of keys that must be deleted, with the understanding that all other entries will be retained
clean_nulls – If True, then in addition to other filtering directives, delete entries, whose values are None
clean_falsy – If True, then in addition to other filtering directives, delete entries, whose values are Falsy
the "clean_falsy" argument is specified at "True", then "clean_nulls" is assumed to be "True" as well.) ((If) –
inplace – If True, then modify the source properties dictionary; otherwise, make a copy for filtering purposes
keep_falsy_numerics – If True, then in addition to other filtering directives, do not delete zero-valued numerics

Returns

The (possibly) filtered properties dictionary (or None if no entries remain after filtering is performed)

great_expectations.util.is_numeric(value: Any) → bool¶

great_expectations.util.is_int(value: Any) → bool¶

great_expectations.util.is_float(value: Any) → bool¶

great_expectations.util.is_parseable_date(value: Any, fuzzy: bool = False) → bool¶

great_expectations.util.get_context()¶

great_expectations.util.is_sane_slack_webhook(url: str) → bool¶: Really basic sanity checking.

great_expectations.util.is_list_of_strings(_list) → bool¶

great_expectations.util.generate_library_json_from_registered_expectations()¶: Generate the JSON object used to populate the public gallery

great_expectations.util.delete_blank_lines(text: str) → str¶

great_expectations.util¶

Module Contents¶

Functions¶

`great_expectations.util`¶