great_expectations.util

Module Contents

Functions

measure_execution_time(func)

get_project_distribution()

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, module_name)

_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)

Lint strings of code passed in.
great_expectations.util.logger
great_expectations.util.measure_execution_time(func) → Callable
great_expectations.util.get_project_distribution() → Union[Distribution, None]
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) → Union[ModuleType, None]
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, module_name)
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)

Lint strings of code passed in.